TY - CONF TI - Progress in the Implementation of Genomic Selection and Marker-Assisted Breeding in Sweetpotato AU - Oloka, B.M. AU - Pereira, G. da S. AU - Mollinari, M. AU - Gesteira, G.S. AU - Fraher, S. AU - Pecota, K. AU - Olukolu, B.A. AU - Yada, B. AU - Anyanga, M.O. AU - Chelangat, D. AU - Musana, P. AU - Alajo, A. AU - Ssali, R. AU - Campos, H. AU - Zeng, Z.-B. AU - Yencho, C. T2 - International Plant & Animal Genome 30 Conference C2 - 2019/// C3 - International Plant & Animal Genome 30 Conference CY - San Diego, CA DA - 2019/// PY - 2019/1/12/ ER - TY - JOUR TI - Toward epidemic thresholds on temporal networks: a review and open questions AU - Leitch, Jack AU - Alexander, Kathleen A. AU - Sengupta, Srijan T2 - Applied Network Science AB - Abstract Epidemiological contact network models have emerged as an important tool in understanding and predicting spread of infectious disease, due to their capacity to engage individual heterogeneity that may underlie essential dynamics of a particular host-pathogen system. Just as fundamental are the changes that real-world contact networks undergo over time, both independently of and in response to pathogen spreading. These dynamics play a central role in determining whether a disease will die out or become epidemic within a population, known as the epidemic threshold. In this paper, we provide an overview of methods to predict the epidemic threshold for temporal contact network models, and discuss areas that remain unexplored. DA - 2019/11/14/ PY - 2019/11/14/ DO - 10.1007/s41109-019-0230-4 VL - 4 IS - 1 SP - 1-21 J2 - Appl Netw Sci LA - en OP - SN - 2364-8228 UR - http://dx.doi.org/10.1007/s41109-019-0230-4 DB - Crossref ER - TY - JOUR TI - Statistical evaluation of spectral methods for anomaly detection in static networks AU - Komolafe, Tomilayo AU - Quevedo, A Valeria AU - Sengupta, Srijan AU - Woodall, William H T2 - Network Science DA - 2019/// PY - 2019/// VL - 7 IS - 3 SP - 319-352 ER - TY - THES TI - Parameter estimation from retarding potential analyzers in the presence of realistic noise AU - Debchoudhury, Shantanab DA - 2019/// PY - 2019/// PB - Virginia Tech ER - TY - JOUR TI - A Bootstrap-based Inference Framework for Testing Similarity of Paired Networks AU - Bhadra, Somnath AU - Chakraborty, Kaustav AU - Sengupta, Srijan AU - Lahiri, Soumendra T2 - arXiv preprint arXiv:1911.06869 DA - 2019/// PY - 2019/// ER - TY - JOUR TI - Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen AU - Li, Meng M AU - Sengupta, Srijan AU - Hanigan, Mark D T2 - Journal of dairy science DA - 2019/// PY - 2019/// VL - 102 IS - 10 SP - 8850-8861 ER - TY - JOUR TI - A Bootstrap-Based Approach for Improving Measurements by Retarding Potential Analyzers AU - Debchoudhury, Shantanab AU - Sengupta, Srijan AU - Earle, Gregory AU - Coley, William T2 - Journal of Geophysical Research: Space Physics DA - 2019/// PY - 2019/// VL - 124 IS - 6 SP - 4569-4584 ER - TY - JOUR TI - Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets AU - Stanley, Emily H. AU - Rojas‐Salazar, Shirley AU - Lottig, Noah R. AU - Schliep, Erin M. AU - Filstrup, Christopher T. AU - Collins, Sarah M. T2 - Limnology and Oceanography: Methods AB - Abstract There are multiple protocols for determining total nitrogen (TN) in water, but most can be grouped into direct approaches (TN‐d) that convert N forms to nitrogen‐oxides (NO x ) and combined approaches (TN‐c) that combine Kjeldahl N (organic N +NH 3 ) and nitrite+nitrate (NO 2 +NO 3 ‐N). TN concentrations from these two approaches are routinely treated as equal in studies that use data derived from multiple sources (i.e., integrated data sets) despite the distinct chemistries of the two methods. We used two integrated data sets to determine if TN‐c and TN‐d results were interchangeable. Accuracy, determined as the difference between reported concentrations and the most probable value (MPV) of reference samples, was high and similar in magnitude (within 3.5–4.5% of the MPV) for both methods, although the bias was significantly smaller at low concentrations for TN‐d. Detection limits and data flagged as below detection suggested greater sensitivity for TN‐d for one data set, while patterns from the other data set were ambiguous. TN‐c results were more variable (less precise) by many measures, although TN‐d data included a small fraction of notably inaccurate results. Precision of TN‐c was further compromised by propagated error, which may not be acknowledged or detectable in integrated data sets unless complete metadata are available and inspected. Finally, concurrent measures of TN‐c and TN‐d in lake samples were extremely similar. Overall, TN‐d tended to be slightly more accurate and precise, but similarities in accuracy and the near 1 : 1 relationship for concurrent TN‐d and TN‐c measurements support careful use of data interchangeably in analyses of heterogeneous, integrated data sets. DA - 2019/10/25/ PY - 2019/10/25/ DO - 10.1002/lom3.10338 VL - 17 IS - 12 SP - 639-649 J2 - Limnol Oceanogr Methods LA - en OP - SN - 1541-5856 1541-5856 UR - http://dx.doi.org/10.1002/lom3.10338 DB - Crossref ER - TY - JOUR TI - Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data AU - Wagner, Tyler AU - Lottig, Noah R. AU - Bartley, Meridith L. AU - Hanks, Ephraim M. AU - Schliep, Erin M. AU - Wikle, Nathan B. AU - King, Katelyn B. S. AU - McCullough, Ian AU - Stachelek, Jemma AU - Cheruvelil, Kendra S. AU - Filstrup, Christopher T. AU - Lapierre, Jean Francois AU - Liu, Boyang AU - Soranno, Patricia A. AU - Tan, Pang‐Ning AU - Wang, Qi AU - Webster, Katherine AU - Zhou, Jiayu T2 - Limnology and Oceanography Letters AB - Abstract Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty. DA - 2019/12/27/ PY - 2019/12/27/ DO - 10.1002/lol2.10134 VL - 5 IS - 2 SP - 228-235 J2 - Limnol Oceanogr Letters LA - en OP - SN - 2378-2242 2378-2242 UR - http://dx.doi.org/10.1002/lol2.10134 DB - Crossref ER - TY - JOUR TI - Velocities for spatio-temporal point patterns AU - Schliep, Erin M. AU - Gelfand, Alan E. T2 - Spatial Statistics AB - Point patterns gathered over space and time are receiving increasing attention in the literature. Examples include incidence of disease events, incidence of insurgent activity events, or incidence of crime events. Point pattern models can attempt to explain these events. Here, a log Gaussian Cox process specification is used to learn about the behavior of the intensity over space and time. Our contribution is to expand inference by introducing the notion of the velocity of a point pattern. We develop a velocity at any location and time within the region and period of study. These velocities are associated with the evolution of the intensity driving the spatio-temporal point pattern, where this intensity is a realization of a stochastic process. Working with directional derivative processes, we are able to develop derivatives in arbitrary directions in space as well as derivatives in time. The ratio of the latter to the former provides a velocity in that direction at that location and time, i.e., speed of change in intensity in that direction. This velocity can be interpreted in terms of speed of change in chance for an event. The magnitude and direction of the minimum velocity provides the slowest speed and direction of change in chance for an event. We use a sparse Gaussian process model approximation to expedite the demanding computation for model fitting and gradient calculation. We illustrate our methodology with a simulation for proof of concept and with a spatio-temporal point pattern of theft events in San Francisco, California in 2012. DA - 2019/3// PY - 2019/3// DO - 10.1016/j.spasta.2018.12.007 VL - 29 SP - 204-225 J2 - Spatial Statistics LA - en OP - SN - 2211-6753 UR - http://dx.doi.org/10.1016/j.spasta.2018.12.007 DB - Crossref KW - Crime data KW - Directional derivative processes KW - Intensity surface KW - Log Gaussian Cox process KW - Markov chain Monte Carlo KW - Nearest neighbor Gaussian process ER - TY - JOUR TI - Identifying and characterizing extrapolation in multivariate response data AU - Bartley, Meridith L. AU - Hanks, Ephraim M. AU - Schliep, Erin M. AU - Soranno, Patricia A. AU - Wagner, Tyler T2 - PLOS ONE AB - Extrapolation is defined as making predictions beyond the range of the data used to estimate a statistical model. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are more and more common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we illustrate novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. DA - 2019/12/5/ PY - 2019/12/5/ DO - 10.1371/journal.pone.0225715 VL - 14 IS - 12 SP - e0225715 J2 - PLoS ONE LA - en OP - SN - 1932-6203 UR - http://dx.doi.org/10.1371/journal.pone.0225715 DB - Crossref ER - TY - JOUR TI - Is body appreciation a mechanism of depression and anxiety? An investigation of the 3-Dimensional Body Appreciation Mapping (3D-BAM) intervention AU - Ramseyer Winter, Virginia AU - Landor, Antoinette M. AU - Teti, Michelle AU - Morris, Kristen AU - Schliep, Erin M. AU - Pevehouse-Pfeiffer, Danielle AU - Pekarek, Emily T2 - Mental Health & Prevention AB - Body appreciation is related to numerous mental health outcomes, including depression and anxiety. This pilot study investigated the effects of an intervention, 3-Dimensional Body Appreciation Mapping (3D-BAM), developed to improve body image, depression, and anxiety by using 3D scanning technology to train participants to focus on ways they appreciate their bodies. Eighty-nine emerging adult women (Mage = 20.64) participated in the intervention and completed body image and mental health measures at baseline, pre/post-intervention, and 3-month follow up. For the intervention, participants digitally “painted” body parts of their personalized 3D avatar that they believed lived up to the cultural image of women, and that they appreciated for their appearance, utility, and role in interpersonal relationships. Following the intervention, participants reported increased body appreciation over time. Depression and anxiety decreased, but the reduction cannot be attributed to the intervention. However, body appreciation had a significant negative effect on depression and anxiety. These preliminary findings illustrate how utilizing 3D scanning technology to focus on body appreciation can improve body image among emerging adult women and reduce pathology. DA - 2019/6// PY - 2019/6// DO - 10.1016/j.mph.2019.200158 VL - 14 SP - 200158 J2 - Mental Health & Prevention LA - en OP - SN - 2212-6570 UR - http://dx.doi.org/10.1016/j.mph.2019.200158 DB - Crossref ER - TY - BOOK TI - Dynamic Treatment Regimes AU - Tsiatis, Anastasios A. AU - Davidian, Marie AU - Holloway, Shannon T. AU - Laber, Eric B. DA - 2019/12/19/ PY - 2019/12/19/ DO - 10.1201/9780429192692 OP - PB - Chapman and Hall/CRC SN - 9780429192692 UR - http://dx.doi.org/10.1201/9780429192692 DB - Crossref ER - TY - CONF TI - Dynamic Correlation Multivariate Stochastic Volatility with Latent Factors AU - Ghosh, Sujit T2 - International Conference on Computer Age Statistics C2 - 2019/// C3 - Proceedings of the International Conference on Computer Age Statistics in the Era of Big and High Dimensional Data CY - Savitribai Phule Pune University, Pune, India DA - 2019/// PY - 2019/1/3/ PB - Savitribai Phule Pune University ER - TY - SOUND TI - Does Knowledge of Shapes Matter in Statistics? AU - Ghosh, Sujit DA - 2019/3/22/ PY - 2019/3/22/ ER - TY - SOUND TI - Dynamic Correlation Multivariate Stochastic Volatility with Latent Factors AU - Ghosh, Sujit DA - 2019/4/11/ PY - 2019/4/11/ ER - TY - CONF TI - On the Probability Distributions of Duration of Heatwaves AU - Ghosh, Sujit T2 - ICSA 2019 Applied Statistics Symposium C2 - 2019/// C3 - Proceedings of the ICSA 2019 Applied Statistics Symposium CY - Raleigh, NC DA - 2019/// PY - 2019/6/9/ PB - International Chinese Statistical Association ER - TY - SOUND TI - When are PH, AFT and PO Models not Adequate for Health Risk Assessment? AU - Ghosh, Sujit DA - 2019/8/6/ PY - 2019/8/6/ ER - TY - CONF TI - Spatial Models for the Duration and Frequency of Heatwaves Based on Stationary Processes AU - Ghosh, Sujit K. T2 - 13th International Conference of IMBIC on Mathematical Science for Advancement of Science and Technology (MSAST 2019) A2 - Adhikari, Avishek A2 - Adhikari, M.R. C2 - 2019/// C3 - Proceedings of the IMBIC CY - Kolkata, India DA - 2019/// PY - 2019/12/21/ VL - 8 SP - 209 PB - Institute for Mathematics, Bio-informatics, Information-Technology and Computer Science SN - 9788192583273 ER - TY - RPRT TI - An Unified Semiparametric Approach to Model Lifetime Data with Crossing Survival Curves AU - Demarqui, F.N. AU - Mayrink, V.D. AU - Ghosh, S.K. DA - 2019/// PY - 2019/// M1 - 1910.04475 M3 - arXiv SN - 1910.04475 ER - TY - JOUR TI - How High the Hedge: Relationships Between Prices and Yields in the Federal Crop Insurance Program AU - Ramsey, A.F. AU - Goodwin, B.K. AU - Ghosh, S.K. T2 - Journal of Agricultural and Resource Economics AB - The theory of the natural hedge states that agricultural yields and prices are inversely related. Actuarial rules for U.S. crop revenue insurance assume that dependence between yield and price is constant across all counties within a state and that dependence can be adequately described by the Gaussian copula. We use nonlinear measures of association and a selection of bivariate copulas to empirically characterize spatially-varying dependence between prices and yields and examine premium rate sensitivity for all corn producing counties in the United States. A simulation analysis across copula types and parameter values exposes hypothetical impacts of actuarial changes. DA - 2019/// PY - 2019/// DO - 10.22004/ag.econ.287967 VL - 44 IS - 2 SP - 227–245 ER - TY - BOOK TI - Bayesian Statistical Methods AU - Reich, Brian J. AU - Ghosh, Sujit K. AB - Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute. DA - 2019/4/12/ PY - 2019/4/12/ DO - 10.1201/9780429202292 OP - PB - Chapman and Hall/CRC SN - 9780429202292 UR - http://dx.doi.org/10.1201/9780429202292 DB - Crossref ER - TY - JOUR TI - Effects of Proportional Hazard Assumption on Variable Selection Methods for Censored Data AU - Sheng, Alvin AU - Ghosh, Sujit K. T2 - STATISTICS IN BIOPHARMACEUTICAL RESEARCH AB - The Cox proportional hazard (PH) model is widely used to determine the effects of risk factors and treatments (covariates) on survival time of subjects that might be right censored. The selection of covariates depends crucially on the specific form of the conditional hazard model, which is often assumed to be PH, accelerated failure time (AFT), or proportional odds (PO). However, we show that none of these semiparametric models allow for the crossing of the survival functions and hence such strong assumptions may adversely affect the selection of variables. Moreover, the most commonly used PH assumption may also be violated when there is a delayed effect of the risk factors. Taking into account all of these modeling assumptions, this study examines the effect of the PH assumption on covariate selection when the data generating model may have non-PH. In particular, variable selection under two alternative models are explored: (i) the penalized PH model (using the elastic-net penalty) and (ii) the linear spline based hazard regression model. We apply the aforementioned models to the ACTG-175 dataset and simulated datasets with survival times generated from the Weibull and log-normal distributions. We also examine the effect on covariate selection of stratifying the analysis on the off-treatment indicator. DA - 2019/// PY - 2019/// DO - 10.1080/19466315.2019.1694578 VL - 12 IS - 2 SP - 199–209 KW - AIDS trials KW - Crossing survival curves KW - Hazard regression KW - Penalized regression ER - TY - JOUR TI - Determining the Number of Latent Factors in Statistical Multi-Relational Learning AU - Shi, C. AU - Lu, W. AU - Song, R. T2 - Journal of Machine Learning Research DA - 2019/// PY - 2019/// VL - 20 IS - 23 SP - 1-38 ER - TY - RPRT TI - Matrix completion for survey data prediction with multivariate missingness AU - Mao, X. AU - Wang, Z. AU - Yang, S. DA - 2019/8/2/ PY - 2019/8/2/ UR - https://arxiv.org/pdf/1907.08360 ER - TY - RPRT TI - Nonparametric mass imputation for data integration AU - Chen, S. AU - Yang, S. AU - Kim, J.K. DA - 2019/// PY - 2019/// M1 - #301796 M3 - Joint Statistical Meetings Report SN - #301796 ER - TY - RPRT TI - Muti-cause causal inference with unmeasured confounding and binary outcome AU - Kong, D. AU - Yang, S. AU - Wang, L. DA - 2019/7/31/ PY - 2019/7/31/ UR - https://arxiv.org/pdf/1907.13323 ER - TY - JOUR TI - MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments AU - Grantham, Neal S. AU - Guan, Yawen AU - Reich, Brian AU - Borer, Elizabeth T. AU - Gross, Kevin T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of Andropogon gerardii, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. DA - 2019/// PY - 2019/// DO - 10.1080/01621459.2019.1626242 KW - Continuous shrinkage prior KW - Factor analysis KW - Microbiome KW - Mixed model KW - Nutrient Network KW - OTU abundance data ER - TY - JOUR TI - Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles AU - Guan, Yawen AU - Johnson, Margaret C. AU - Katzfuss, Matthias AU - Mannshardt, Elizabeth AU - Messier, Kyle P. AU - Reich, Brian AU - Song, Joon J. T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. To make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally sized fixed-location network. This modeling framework has important real-world implications in understanding citizens’ personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. DA - 2019/// PY - 2019/// DO - 10.1080/01621459.2019.1665526 KW - Google Street View Air Quality Data KW - Kriging KW - Mobile sensors KW - Spatiotemporal models KW - Vecchia approximation ER - TY - JOUR TI - Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals AU - Guan, Qian AU - Reich, Brian AU - Laber, Eric B. AU - Bandyopadhyay, Dipankar T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine nonparametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. DA - 2019/// PY - 2019/// DO - 10.1080/01621459.2019.1660169 KW - Dirichlet process prior KW - Dynamic treatment regimes KW - Observational data KW - Periodontal disease KW - Practice-based setting KW - Precision medicine KW - Sequential optimization ER - TY - JOUR TI - The use of Bayesian inference in the characterization of materials and thin films AU - Jones, Jacob L. AU - Broughton, Rachel AU - Iamsasri, Thanakorn AU - Fancher, Chris M. AU - Wilson, Alyson G. AU - Reich, Brian AU - Smith, Ralph C. T2 - ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES DA - 2019/// PY - 2019/// DO - 10.1107/S0108767319097940 VL - 75 SP - A211-A211 SN - 2053-2733 ER - TY - JOUR TI - Large crabgrass (Digitaria sanguinalis) and Palmer amaranth (Amaranthus palmeri) intraspecific and interspecific interference in soybean AU - Basinger, Nicholas T. AU - Jennings, Katherine M. AU - Monks, David W. AU - Jordan, David L. AU - Everman, Wesley J. AU - Hestir, Erin L. AU - Bertucci, Matthew B. AU - Brownie, Cavell T2 - WEED SCIENCE AB - Abstract Field studies were conducted in 2016 and 2017 at Clinton, NC, to quantify the effects of season-long interference of large crabgrass [ Digitaria sanguinalis (L.) Scop.] and Palmer amaranth ( Amaranthus palmeri S. Watson) on ‘AG6536’ soybean [ Glycine max (L.) Merr.]. Weed density treatments consisted of 0, 1, 2, 4, and 8 plants m −2 for A. palmeri and 0, 1, 2, 4, and 16 plants m −2 for D. sanguinalis with (interspecific interference) and without (intraspecific interference) soybean to determine the impacts on weed biomass, soybean biomass, and seed yield. Biomass per square meter increased with increasing weed density for both weed species with and without soybean present. Biomass per square meter of D. sanguinalis was 617% and 37% greater when grown without soybean than with soybean, for 1 and 16 plants m −2 respectively. Biomass per square meter of A. palmeri was 272% and 115% greater when grown without soybean than with soybean for 1 and 8 plants m −2 , respectively. Biomass per plant for D. sanguinalis and A. palmeri grown without soybean was greatest at the 1 plant m −2 density. Biomass per plant of D. sanguinalis plants across measured densities was 33% to 83% greater when grown without soybean compared with biomass per plant when soybean was present for 1 and 16 plants m −2 , respectively. Similarly, biomass per plant for A. palmeri was 56% to 74% greater when grown without soybean for 1 and 8 plants m −2 , respectively. Biomass per plant of either weed species was not affected by weed density when grown with soybean due to interspecific competition with soybean. Yield loss for soybean grown with A. palmeri ranged from 14% to 37% for densities of 1 to 8 plants m −2 , respectively, with a maximum yield loss estimate of 49%. Similarly, predicted loss for soybean grown with D. sanguinalis was 0 % to 37% for densities of 1 to 16 m −2 with a maximum yield loss estimate of 50%. Soybean biomass was not affected by weed species or density. Results from these studies indicate that A. palmeri is more competitive than D. sanguinalis at lower densities, but that similar yield loss can occur when densities greater than 4 plants m −2 of either weed are present. DA - 2019/11// PY - 2019/11// DO - 10.1017/wsc.2019.43 VL - 67 IS - 6 SP - 649-656 SN - 1550-2759 KW - Biomass KW - competition KW - rectangular hyperbola model KW - weed density KW - yield loss ER - TY - JOUR TI - Shape Constrained Tensor Decompositions AU - Lusch, Bethany AU - Chi, Eric C. AU - Kutz, J. Nathan T2 - 2019 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2019) AB - We propose a new low-rank tensor factorization where one mode is coded as a sparse linear combination of elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces r-rank approximations of data tensors via outer products of vectors in each dimension of the data. The SCTD model can leverage prior knowledge about the shape of factors along a given mode, for example in tensor data where one mode represents time. By constraining the vector in the temporal dimension to known analytic forms which are selected from a large set of candidate functions, more readily interpretable decompositions are achieved and analytic time dependencies discovered. The SCTD method circumvents traditional flattening techniques where an N-way array is reshaped into a matrix in order to perform a singular value decomposition. A clear advantage of the SCTD algorithm is its ability to extract transient and intermittent phenomena which is often difficult for SVD-based methods. We motivate the SCTD method using several intuitively appealing results before applying it on a real-world data set to illustrate the efficiency of the algorithm in extracting interpretable spatio-temporal modes. With the rise of data-driven discovery methods, the decomposition proposed provides a viable technique for analyzing multitudes of data in a more comprehensible fashion. DA - 2019/// PY - 2019/// DO - 10.1109/DSAA.2019.00044 SP - 287-297 SN - 2472-1573 KW - tensor decomposition KW - multiway arrays KW - multilinear algebra KW - higher-order singular value decomposition (HOSVD) KW - over-complete libraries KW - sparse regression ER - TY - JOUR TI - Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization AU - Onel, Melis AU - Beykal, Burcu AU - Ferguson, Kyle AU - Chiu, Weihsueh A. AU - McDonald, Thomas J. AU - Zhou, Lan AU - House, John S. AU - Wright, Fred A. AU - Sheen, David A. AU - Rusyn, Ivan AU - Pistikopoulos, Efstratios N. T2 - PLOS ONE AB - A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes-Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity. DA - 2019/10/10/ PY - 2019/10/10/ DO - 10.1371/journal.pone.0223517 VL - 14 IS - 10 SP - SN - 1932-6203 ER - TY - JOUR TI - Identical and Nonidentical Twins: Risk and Factors Involved in Development of Islet Autoimmunity and Type 1 Diabetes AU - Triolo, Taylor M. AU - Fouts, Alexandra AU - Pyle, Laura AU - Yu, Liping AU - Gottlieb, Peter A. AU - Steck, Andrea K. AU - Greenbaum, C. J. AU - Atkinson, M. AU - Baidal, D. AU - Battaglia, M. AU - Becker, D. AU - Bingley, P. 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AU - Campbell, S. AU - Stapleton, A. AU - Stone, N. AU - Donoho, A. AU - Everett, H. AU - Hensley, H. AU - Johnson, M. AU - Marshall, C. AU - Skirvin, N. AU - Taylor, P. AU - Ray, L. AU - Wolverton, C. AU - Nickels, D. A. AU - Dothard, C. AU - Speiser, P. W. AU - Pellizzari, M. AU - Bokor, L. AU - Izuora, K. AU - Abdelnour, S. AU - Cummings, P. AU - Paynor, S. AU - Leahy, M. AU - Riedl, M. AU - Shockley, S. AU - Saad, R. AU - Briones, T. AU - Casella, S. AU - Herz, C. AU - Walsh, K. AU - Greening, J. AU - Hay, F. AU - Hunt, S. AU - Sikotra, N. AU - Simons, L. AU - Karounos, D. G. AU - Oremus, R. AU - Dye, L. AU - Myers, L. AU - Ballard, D. AU - Miers, W. AU - Sparks, R. AU - Thraikill, K. M. AU - Edwards, K. AU - Fowlkes, J. AU - Kemp, S. AU - Morales, A. AU - Holland, L. AU - Johnson, L. AU - Paul, P. AU - Ghatak, A. AU - Phelen, K. AU - Leyland, H. AU - Henderson, T. AU - Brenner, D. AU - Oppenheimer, E. AU - Mamkin, I AU - Moniz, C. AU - Clarson, C. AU - Lovell, M. AU - Peters, A. AU - Ruelas, V AU - Borut, D. AU - Burt, D. AU - Jordan, M. AU - Castilla, S. AU - Flores, P. AU - Ruiz, M. AU - Hanson, L. AU - Green-Blair, J. AU - Sheridan, R. J. AU - Wintergerst, K. A. AU - Pierce, G. AU - Omoruyi, A. AU - Foster, M. AU - Kingery, S. AU - Lunsford, A. AU - Cervantes, I AU - Parker, T. AU - Price, P. AU - Urben, J. AU - Doughty, I AU - Haydock, H. AU - Parker, V AU - Bergman, P. AU - Duncum, S. AU - Rodda, C. AU - Thomas, A. D. AU - Ferry, R. AU - McCommon, D. AU - Cockroft, J. AU - Perelman, A. AU - Calendo, R. AU - Barrera, C. AU - Arce-Nunez, E. AU - Martinez, Y. AU - Portilla, M. AU - Cardenas, I AU - Garrido, L. AU - Villar, M. AU - Lorini, R. AU - Calandra, E. AU - G. D'Annuzio, AU - Perri, K. AU - Minuto, N. AU - Rebora, C. AU - Callegari, R. AU - Ali, O. AU - Kramer, J. AU - Auble, B. AU - Cabrera, S. AU - Donohoue, P. AU - Fiallo-Scharer, R. AU - Hessner, M. AU - Wolfgram, P. AU - Kansra, A. AU - Bettin, N. AU - McCuller, R. AU - Miller, A. AU - Accacha, S. AU - Corrigan, J. AU - Fiore, E. AU - Levine, R. L. AU - Mahoney, T. A. AU - Polychronakos, C. AU - Gagne, V AU - Starkman, H. AU - Fox, M. AU - Chin, D. AU - Melchionne, F. AU - Silverman, L. A. AU - Marshall, I AU - Cerracchio, L. AU - Cruz, J. AU - Viswanathan, A. AU - Wilson, J. AU - Chalew, S. AU - Valley, S. AU - Layburn, S. AU - Lala, A. AU - Clesi, P. AU - Genet, M. AU - Uwaifo, G. AU - Charron, A. AU - Allerton, T. AU - Cefalu, W. AU - Melendez-Ramirez, L. AU - Richards, R. AU - Alleyn, C. AU - Gustafson, E. AU - Lizanna, M. AU - Wahlen, J. AU - Aleiwe, S. AU - Hansen, M. AU - Wahlen, H. AU - Levy, C. J. AU - Bonaccorso, A. AU - Rapaport, R. AU - Tomer, Y. AU - Chia, D. AU - Goldis, M. AU - Iazzetti, L. AU - Klein, M. AU - Levister, C. AU - Waldman, L. AU - Wallach, E. AU - Regelmann, M. O. AU - Antal, Z. AU - Aranda, M. AU - Reynholds, C. AU - Leech, N. AU - Wake, D. AU - Owens, C. AU - Burns, M. AU - Wotherspoon, J. AU - Wynn, L. AU - Wiltshire, E. AU - Krebs, J. AU - Cresswell, P. AU - Faherty, H. AU - Ross, C. AU - Vinik, A. AU - Barlow, P. AU - Bourcier, M. AU - Nevoret, M. L. AU - Couper, J. AU - Beresford, S. AU - Thalagne, N. AU - Roper, H. AU - Gibbons, J. AU - Hill, J. AU - Balleaut, S. AU - Brennan, C. AU - Ellis-Gage, J. AU - Fear, L. AU - Gray, T. AU - Jones, L. AU - McNerney, C. AU - Pointer, L. AU - Price, N. AU - Few, K. AU - Tomlinson, D. AU - Denvir, L. AU - Drew, J. AU - Randell, T. AU - Mansell, P. AU - Bell, S. A. AU - Butler, S. AU - Hooton, Y. AU - Navarra, H. AU - Roper, A. AU - Babington, G. AU - Crate, L. AU - Cripps, H. AU - Ledlie, A. AU - Moulds, C. AU - Norton, R. AU - Petrova, B. AU - Silkstone, O. AU - Smith, C. AU - Ghai, K. AU - Murray, M. AU - Viswanathan, V. AU - Henegan, M. AU - Kawadry, O. AU - Olson, J. A. AU - Patterson, L. AU - Ahmad, T. AU - Flores, B. AU - Domek, D. AU - Domek, S. AU - Copeland, K. AU - George, M. AU - Less, J. AU - Davis, T. AU - Short, M. AU - Dwarakanathan, A. AU - P. O'Donnell, AU - Boerner, B. AU - Larson, L. AU - Phillips, M. AU - Rendell, M. AU - Larson, K. AU - Zebrowski, K. AU - Kuechenmeister, L. AU - Thevarayapillai, M. AU - Daniels, M. AU - Speer, H. AU - Forghani, N. AU - Quintana, R. AU - Reh, C. AU - Bhangoo, A. AU - Desrosiers, P. AU - Ireland, L. AU - Misla, T. AU - Torres, C. AU - Wells, S. AU - Villar, J. AU - Yu, M. AU - Berry, D. AU - Cook, D. AU - Soder, J. AU - Powell, A. AU - Ng, M. AU - Morrison, M. AU - Haslam, Z. AU - Lawson, M. AU - Bradley, B. AU - Courtney, J. AU - Richardson, C. AU - Watson, C. AU - Keely, E. AU - DeCurtis, D. AU - Vaccarcello-Cruz, M. AU - Torres, Z. AU - Sandberg, K. AU - Hsiang, H. AU - Joy, B. AU - McCormick, D. AU - Jones, H. AU - Bell, J. AU - Hargadon, S. AU - Hudson, S. AU - Kummer, M. AU - Sauder, S. AU - Sutton, E. AU - Gensel, K. AU - Aguirre-Castaneda, R. AU - Lopez, V. Benavides AU - Hemp, D. AU - Allen, S. AU - Stear, J. AU - Davis, E. AU - Jones, T. AU - Roberts, A. AU - Dart, J. A. AU - Paramalingam, N. AU - Katz, L. E. Levitt AU - Chaudhary, N. AU - Murphy, K. M. AU - Willi, S. M. AU - Schwartzman, B. AU - Kapadia, C. AU - Larson, D. AU - McClellan, D. AU - Shaibai, G. AU - Kelley, L. A. AU - Villa, G. AU - Kelley, C. AU - Diamond, R. AU - Kabbani, M. AU - Dajani, T. AU - Hoekstra, F. AU - Magorno, M. AU - Holst, J. AU - Chauhan, V AU - Wilson, N. AU - Bononi, P. AU - Sperl, M. AU - Millward, A. AU - Eaton, M. AU - Dean, L. AU - Olshan, J. AU - Renna, H. AU - Milliard, C. AU - Snyder, D. AU - Beaman, S. AU - Burch, K. AU - Chester, J. AU - Ahmann, A. AU - Wollam, B. AU - DeFrang, D. AU - Fitch, R. AU - Jahnke, K. AU - Hanavan, K. AU - Klopfenstein, B. AU - Nicol, L. AU - Bergstrom, R. W. AU - Noland, T. AU - Brodksy, J. AU - Bacon, L. AU - Quintos, J. B. AU - Topor, L. S. AU - Bialo, S. AU - Bancroft, B. AU - Soto, A. G. AU - Lagarde, W. AU - Lockemer, H. AU - Vanderploeg, T. AU - Ibrahim, M. A. AU - Huie, M. AU - Sanchez, V AU - Edelen, R. AU - Marchiando, R. AU - Repas, T. AU - Wasson, M. AU - Auker, P. AU - Culbertson, J. AU - Kieffer, T. AU - Voorhees, D. AU - Borgwardt, T. AU - DeRaad, L. AU - Eckert, K. AU - Isaacson, E. AU - Kuhn, H. AU - Carroll, A. AU - Schubert, M. AU - Francis, G. AU - Hagan, S. AU - Le, T. AU - Penn, M. AU - Wickham, E. AU - Leyva, C. AU - Rivera, K. AU - Padilla, J. AU - Rodriguez, I AU - Jospe, N. AU - Czyzyk, J. AU - Johnson, B. AU - Nadgir, U. AU - Marlen, N. AU - Prakasam, G. AU - Rieger, C. AU - Glaser, N. AU - Heiser, E. C. AU - Harris, B. AU - Foster, C. AU - Slater, H. AU - Wheeler, K. AU - Donaldson, D. L. AU - Hale, D. E. AU - Tragus, R. AU - Word, D. R. AU - Lynch, J. AU - Pankratz, L. AU - Rogers, W. AU - Newfield, R. AU - Holland, S. AU - Hashiguchi, M. AU - Gottschalk, M. AU - Philis-Tsimikas, A. AU - Rosal, R. AU - Franklin, S. AU - Guardado, S. M. AU - Bohannon, N. AU - Garcia, M. AU - Aguinaldo, T. AU - Phan, J. AU - Barraza, V AU - Cohen, D. AU - Pinsker, J. AU - Khan, U. AU - Wiley, J. AU - Jovanovic, L. AU - Misra, P. AU - Wright, M. AU - Huang, K. AU - Skiles, M. AU - Maxcy, S. AU - Pihoker, C. AU - Cochrane, K. AU - Fosse, J. AU - Kearns, S. AU - Klingsheim, M. AU - Wright, N. AU - Viles, L. AU - Smith, H. AU - Heller, S. AU - Cunningham, M. AU - Daniels, A. AU - Zeiden, L. AU - Field, J. AU - Walker, R. AU - Griffin, K. J. AU - Bartholow, L. AU - Erickson, C. AU - Krabbenhoft, B. AU - Sandman, C. AU - Vanveldhuizen, A. AU - Wurlger, J. AU - Zimmerman, A. AU - Hanisch, K. AU - Davis-Keppen, L. AU - Cotterill, A. AU - Kirby, J. AU - Harris, M. AU - Schmidt, A. AU - Kishiyama, C. AU - Flores, C. AU - Milton, J. AU - Martin, W. AU - Whysham, C. AU - Yerka, A. AU - Freels, T. AU - Hassing, J. M. AU - Webster, J. AU - Green, R. AU - Carter, P. AU - Galloway, J. AU - Hoelzer, D. AU - Roberts, S. AU - Said, S. AU - Sullivan, P. AU - Allen, H. F. AU - Reiter, E. AU - Feinberg, E. AU - Johnson, C. AU - Newhook, L. A. AU - Hagerty, D. AU - White, N. H. AU - Levandoski, L. AU - Kyllo, J. AU - Benoit, C. AU - Iyer, P. AU - Diamond, F. AU - Hosono, H. AU - Jackman, S. AU - Barette, L. AU - Jones, P. AU - Sills, I AU - Bzdick, S. AU - Bulger, J. AU - Weinstock, R. AU - Douek, I AU - Andrews, R. AU - Modgill, G. AU - Gyorffy, G. AU - Robin, L. AU - Vaidya, N. AU - Crouch, S. AU - K. O'Brien, AU - Thompson, C. AU - Thorne, N. AU - Blumer, J. AU - Kalic, J. AU - Klepek, L. AU - Paulett, J. AU - Rosolowski, B. AU - Horner, J. AU - Watkins, M. AU - Casey, J. L. AU - Carpenter, K. AU - Burns, C. AU - Horton, J. AU - Pritchard, C. AU - Soetaert, D. AU - Wynne, A. G. AU - Kaiserman, K. AU - Halvorson, M. AU - Chin, C. AU - Molina, O. Y. AU - Patel, C. AU - Senguttuvan, R. AU - Wheeler, M. AU - Furet, O. AU - Steuhm, C. AU - Jelley, D. H. AU - Goudeau, S. AU - Chalmers, L. AU - Greer, D. AU - Panagiotopoulos, C. AU - Metzger, D. L. AU - Nguyen, D. AU - Horowitz, M. AU - Christiansen, M. P. AU - Glades, E. AU - Morimoto, C. AU - Macarewich, M. AU - Norman, R. AU - Patin, K. AU - Vargas, C. AU - Barbanica, A. AU - Yu, A. AU - Vaidyanathan, P. AU - Osborne, W. AU - Mehra, R. AU - Kaster, S. AU - Neace, S. AU - Reeves, G. AU - Cordrey, C. AU - Marrs, L. AU - Miller, T. AU - Dowshen, S. AU - Doyle, D. AU - Walker, S. AU - Catte, D. AU - Dean, H. AU - Drury-Brown, M. AU - Hackman, B. AU - Lee, M. M. C. AU - Malkani, S. AU - Cullen, K. AU - Johnson, K. AU - Hampton, P. AU - McCarrell, M. AU - Curtis, C. AU - Paul, E. AU - Zambrano, Y. T2 - DIABETES CARE AB - There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings.Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A], and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years.At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P < 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody-positive, 13% for single autoantibody-positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody-positive, 12% for single autoantibody-positive, and 0.5% for initially autoantibody-negative subjects.Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody-positive identical twins and multiple autoantibody-positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins. DA - 2019/2/1/ PY - 2019/2/1/ DO - 10.2337/dc18-0288 VL - 42 IS - 2 SP - 192-199 SN - 1935-5548 ER - TY - JOUR TI - Multiclass Probability Estimation With Support Vector Machines AU - Wang, Xin AU - Zhang, Hao Helen AU - Wu, Yichao T2 - JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS AB - Multiclass classification and probability estimation have important applications in data analytics. Support vector machines (SVMs) have shown great success in various real-world problems due to their high classification accuracy. However, one main limitation of standard SVMs is that they do not provide class probability estimates, and thus fail to offer uncertainty measure about class prediction. In this article, we propose a simple yet effective framework to endow kernel SVMs with the feature of multiclass probability estimation. The new probability estimator does not rely on any parametric assumption on the data distribution, therefore, it is flexible and robust. Theoretically, we show that the proposed estimator is asymptotically consistent. Computationally, the new procedure can be conveniently implemented using standard SVM softwares. Our extensive numerical studies demonstrate competitive performance of the new estimator when compared with existing methods such as multiple logistic regression, linear discrimination analysis, tree-based methods, and random forest, under various classification settings. Supplementary materials for this article are available online. DA - 2019/7/3/ PY - 2019/7/3/ DO - 10.1080/10618600.2019.1585260 VL - 28 IS - 3 SP - 586-595 SN - 1537-2715 KW - LDA KW - Logistic regression KW - Multiclass classification KW - Probability estimation KW - Support vector machines ER - TY - JOUR TI - Evaluation of the geometric accuracy of computed tomography and microcomputed tomography of the articular surface of the distal portion of the radius of cats AU - Webster, Caroline E. AU - Marcellin-Little, Denis J. AU - Koballa, Erin M. AU - Stallrich, Jonathan W. AU - Harrysson, Ola L. A. T2 - AMERICAN JOURNAL OF VETERINARY RESEARCH AB - To evaluate accuracy of articular surfaces determined by use of 2 perpendicular CT orientations, micro-CT, and laser scanning.23 cat cadavers.Images of antebrachia were obtained by use of CT (voxel size, 0.6 mm) in longitudinal orientation (CTLO images) and transverse orientation (CTTO images) and by use of micro-CT (voxel size, 0.024 mm) in a longitudinal orientation. Images were reconstructed. Craniocaudal and mediolateral length, radius of curvature, and deviation of the articular surface of the distal portion of the radius of 3-D renderings for CTLO, CTTO, and micro-CT images were compared with results of 3-D renderings acquired with a laser scanner (resolution, 0.025 mm).Measurement of CTLO and CTTO images overestimated craniocaudal and mediolateral length of the articular surface by 4% to 10%. Measurement of micro-CT images underestimated craniocaudal and mediolateral length by 1%. Measurement of CTLO and CTTO images underestimated mediolateral radius of curvature by 15% and overestimated craniocaudal radius of curvature by > 100%; use of micro-CT images underestimated them by 3% and 5%, respectively. Mean ± SD surface deviation was 0.26 ± 0.09 mm for CTLO images, 0.30 ± 0.28 mm for CTTO images, and 0.04 ± 0.02 mm for micro-CT images.Articular surface models derived from CT images had dimensional errors that approximately matched the voxel size. Thus, CT cannot be used to plan conforming arthroplasties in small joints and could lack precision when used to plan the correction of a limb deformity or repair of a fracture. DA - 2019/10// PY - 2019/10// DO - 10.2460/ajvr.80.10.976 VL - 80 IS - 10 SP - 976-984 SN - 1943-5681 ER - TY - JOUR TI - Analysis of compartments-in-series models of liver metabolism as partial differential equations: the effect of dispersion and number of compartments AU - Noorman, Marcella AU - Allen, Richard AU - Musante, Cynthia J. AU - Banks, H. Thomas T2 - MATHEMATICAL BIOSCIENCES AND ENGINEERING AB - Non-alcoholic fatty liver disease is the most common cause of chronic liver disease. Precipitated by the build up of extra fat in the liver not caused by alcohol, it is still not understood why steatosis occurs where it does in the liver microstructure in non-alcoholic fatty liver disease. It is likely, however, that the location of steatosis is due, at least in part, to metabolic zonation (heterogeneity among liver cells in function and enzyme expression). Recently, there has been an influx of computational and mathematical models in order to investigate the relationship between metabolic zonation and steatosis in non-alcoholic fatty liver disease. Of interest among these models are "compartments-in-series" models. Compartments-in-series models include the spatial distribution of metabolite concentrations via series of compartments that are connected through some representation of blood flow. In this paper, we analyze one such model, focusing specifically at how the number of compartments and inclusion of dispersion in the flow affect simulation results. We find the number of compartments to have a much larger effect than the inclusion of dispersion, however this is likely due to numerical artifacts. Overall, we conclude that considering partial differential equations that are equivalent to compartments-in-series models would be beneficial both in computation and in theoretical analyses. DA - 2019/// PY - 2019/// DO - 10.3934/mbe.2019052 VL - 16 IS - 3 SP - 1082-1114 SN - 1551-0018 KW - liver disease KW - zonation KW - computational inverse models KW - dispersion in flows KW - compartments-in-series models ER - TY - JOUR TI - Mixture designs to investigate adverse effects upon co-exposure to environmental cyanotoxins AU - Martin, Rubia M. AU - Stallrich, Jonathan AU - Bereman, Michael S. T2 - TOXICOLOGY AB - The goal of this study was to implement powerful mixture design techniques, commonly used in process optimization, to investigate enhanced adverse effects upon co-exposure to environmental cyanotoxins. Exposure to cyanobacteria, which are found ubiquitously in environmental water reservoirs, have been linked to several neurodegenerative diseases. Despite the known co-occurrence of various cyanotoxins, the majority of studies investigating this link have focused on the investigation of a single cyanotoxin, a noncanonical amino acid called β-methylamino-L-alanine (BMAA), which poorly recapitulates an actual environmental exposure. Interactions amongst cyanotoxic compounds is an area of great concern and remains poorly understood. To this end, we describe the use of a simplex axial mixture design to screen for interactive adverse effects of cyanotoxic mixtures. Using a combination of basic toxicity assays coupled with contemporary proteomic techniques, our results show the existence of a significant (p ≤ 0.01) interaction between BMAA and its isomers aminoethyl glycine (AEG) and 2,4-diaminobutyric acid (2,4DAB). Cyanotoxic mixtures significantly decreased cell viability by an average of 19% and increased caspases 3/7 activities by an average of 110% when compared to individual cyanotoxins (p ≤ 0.05). Cyanotoxic mixtures perturbed various biological pathways associated with neurodegeneration, including inhibition of protective autophagy and activation of mitochondrial dysfunction (z-score >|2|). Additionally, exposure to mixtures perturbed important upstream regulators involved in cellular dysfunction, morbidity, and development. Taken together, our results highlight: (1) the need to study combinations of cyanotoxins when investigating the link between cyanobacteria and neurodegenerative pathologies and (2) the application of design of experiment (DoE) as an efficient methodology to study mixtures of relevant environmental toxins. DA - 2019/6/1/ PY - 2019/6/1/ DO - 10.1016/j.tox.2019.04.013 VL - 421 SP - 74-83 SN - 0300-483X KW - Cyanotoxins KW - Mixture design KW - Interaction KW - Environmental exposure KW - Proteomics ER - TY - JOUR TI - Empirical Priors and Posterior Concentration Rates for a Monotone Density AU - Martin, Ryan T2 - Sankhya A AB - In a Bayesian context, prior specification for inference on monotone densities is conceptually straightforward, but proving posterior convergence theorems is complicated by the fact that desirable prior concentration properties often are not satisfied. In this paper, I first develop a new prior designed specifically to satisfy an empirical version of the prior concentration property, and then I give sufficient conditions on the prior inputs such that the corresponding empirical Bayes posterior concentrates around the true monotone density at nearly the optimal minimax rate. Numerical illustrations also reveal the practical benefits of the proposed empirical Bayes approach compared to Dirichlet process mixtures. DA - 2019/12// PY - 2019/12// DO - 10.1007/s13171-018-0147-5 VL - 81 IS - 2 SP - 493-509 SN - 0976-836X 0976-8378 UR - http://dx.doi.org/10.1007/S13171-018-0147-5 KW - Density estimation KW - Empirical Bayes KW - Grenander estimator KW - Mixture model KW - Shape constraint ER - TY - JOUR TI - Drivers of Elevational Richness Peaks, Evaluated for Trees in the East Himalaya AU - Rana, Suresh K. AU - Gross, Kevin AU - Price, Trevor D. T2 - The Bulletin of the Ecological Society of America AB - A mid-elevation peak in species richness is common in many clades. Here, we studied trees of the east Himalaya and found a richness peak at 500–1,000 m. We argue that this results from a correlation of climate with both the numbers and kinds of species, coupled with a geometric constraint in which range expansions from the plains introduce few new species at the base, whereas just above the base, novel species have ranges extending from both above and below. We develop a mathematical model to derive conditions for this to happen. A prediction is that species’ elevational ranges should be smaller at lower elevations, as we find. These photographs illustrate the article “Drivers of elevational richness peaks, evaluated for trees in the east Himalaya” by Suresh K. Rana, Kevin Gross, and Trevor D. Price published in Ecology. https://doi.org/10.1002/ecy.2548 DA - 2019/1// PY - 2019/1// DO - 10.1002/BES2.1499 VL - 100 IS - 1 SP - e01499 J2 - Bull Ecol Soc Am LA - en OP - SN - 0012-9623 UR - http://dx.doi.org/10.1002/BES2.1499 DB - Crossref ER - TY - JOUR TI - Quantitative trait loci and differential gene expression analyses reveal the genetic basis for negatively associated β-carotene and starch content in hexaploid sweetpotato [Ipomoea batatas (L.) Lam.] AU - Gemenet, Dorcus C. AU - da Silva Pereira, Guilherme AU - De Boeck, Bert AU - Wood, Joshua C. AU - Mollinari, Marcelo AU - Olukolu, Bode A. AU - Diaz, Federico AU - Mosquera, Veronica AU - Ssali, Reuben T. AU - David, Maria AU - Kitavi, Mercy N. AU - Burgos, Gabriela AU - Felde, Thomas Zum AU - Ghislain, Marc AU - Carey, Edward AU - Swanckaert, Jolien AU - Coin, Lachlan J. M. AU - Fei, Zhangjun AU - Hamilton, John P. AU - Yada, Benard AU - Yencho, G. Craig AU - Zeng, Zhao-Bang AU - Mwanga, Robert O. M. AU - Khan, Awais AU - Gruneberg, Wolfgang J. AU - Buell, C. Robin T2 - Theoretical and Applied Genetics AB - β-Carotene content in sweetpotato is associated with the Orange and phytoene synthase genes; due to physical linkage of phytoene synthase with sucrose synthase, β-carotene and starch content are negatively correlated. In populations depending on sweetpotato for food security, starch is an important source of calories, while β-carotene is an important source of provitamin A. The negative association between the two traits contributes to the low nutritional quality of sweetpotato consumed, especially in sub-Saharan Africa. Using a biparental mapping population of 315 F1 progeny generated from a cross between an orange-fleshed and a non-orange-fleshed sweetpotato variety, we identified two major quantitative trait loci (QTL) on linkage group (LG) three (LG3) and twelve (LG12) affecting starch, β-carotene, and their correlated traits, dry matter and flesh color. Analysis of parental haplotypes indicated that these two regions acted pleiotropically to reduce starch content and increase β-carotene in genotypes carrying the orange-fleshed parental haplotype at the LG3 locus. Phytoene synthase and sucrose synthase, the rate-limiting and linked genes located within the QTL on LG3 involved in the carotenoid and starch biosynthesis, respectively, were differentially expressed in Beauregard versus Tanzania storage roots. The Orange gene, the molecular switch for chromoplast biogenesis, located within the QTL on LG12 while not differentially expressed was expressed in developing roots of the parental genotypes. We conclude that these two QTL regions act together in a cis and trans manner to inhibit starch biosynthesis in amyloplasts and enhance chromoplast biogenesis, carotenoid biosynthesis, and accumulation in orange-fleshed sweetpotato. Understanding the genetic basis of this negative association between starch and β-carotene will inform future sweetpotato breeding strategies targeting sweetpotato for food and nutritional security. DA - 2019/10/8/ PY - 2019/10/8/ DO - 10.1007/s00122-019-03437-7 VL - 133 IS - 1 SP - 23-36 J2 - Theor Appl Genet LA - en OP - SN - 0040-5752 1432-2242 UR - http://dx.doi.org/10.1007/s00122-019-03437-7 DB - Crossref ER - TY - ER - TY - JOUR TI - Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping AU - Mollinari, Marcelo AU - Olukolu, Bode A. AU - Pereira, Guilherme da S. AU - Khan, Awais AU - Gemenet, Dorcus AU - Yencho, G. Craig AU - Zeng, Zhao-Bang T2 - G3: Genes|Genomes|Genetics AB - The hexaploid sweetpotato (Ipomoea batatas (L.) Lam., 2n = 6x = 90) is an important staple food crop worldwide and plays a vital role in alleviating famine in developing countries. Due to its high ploidy level, genetic studies in sweetpotato lag behind major diploid crops significantly. We built an ultra-dense multilocus integrated genetic map and characterized the inheritance system in a sweetpotato full-sib family using our newly developed software, MAPpoly. The resulting genetic map revealed 96.5% collinearity between I. batatas and its diploid relative I. trifida We computed the genotypic probabilities across the whole genome for all individuals in the mapping population and inferred their complete hexaploid haplotypes. We provide evidence that most of the meiotic configurations (73.3%) were resolved in bivalents, although a small portion of multivalent signatures (15.7%), among other inconclusive configurations (11.0%), were also observed. Except for low levels of preferential pairing in linkage group 2, we observed a hexasomic inheritance mechanism in all linkage groups. We propose that the hexasomic-bivalent inheritance promotes stability to the allelic transmission in sweetpotato. DA - 2019/11/15/ PY - 2019/11/15/ DO - 10.1534/g3.119.400620 VL - 10 IS - 1 SP - 281-292 J2 - G3 LA - en OP - SN - 2160-1836 UR - http://dx.doi.org/10.1534/g3.119.400620 DB - Crossref KW - Polyploidy KW - Genetic Linkage KW - Hexasomic Inheritance KW - Haplotyping KW - Preferential Pairing KW - Multivalent ER - TY - JOUR TI - Robust regression for optimal individualized treatment rules AU - Xiao, W. AU - Zhang, H. H. AU - Lu, W. T2 - Statistics in Medicine AB - Because different patients may respond quite differently to the same drug or treatment, there is an increasing interest in discovering individualized treatment rules. In particular, there is an emerging need to find optimal individualized treatment rules, which would lead to the "best" clinical outcome. In this paper, we propose a new class of loss functions and estimators based on robust regression to estimate the optimal individualized treatment rules. Compared to existing estimation methods in the literature, the new estimators are novel and advantageous in the following aspects. First, they are robust against skewed, heterogeneous, heavy-tailed errors or outliers in data. Second, they are robust against a misspecification of the baseline function. Third, under some general situations, the new estimator coupled with the pinball loss approximately maximizes the outcome's conditional quantile instead of the conditional mean, which leads to a more robust optimal individualized treatment rule than the traditional mean-based estimators. Consistency and asymptotic normality of the proposed estimators are established. Their empirical performance is demonstrated via extensive simulation studies and an analysis of an AIDS data set. DA - 2019/2/11/ PY - 2019/2/11/ DO - 10.1002/SIM.8102 VL - 38 IS - 11 SP - 2059-2073 J2 - Statistics in Medicine LA - en OP - SN - 0277-6715 1097-0258 UR - http://dx.doi.org/10.1002/SIM.8102 DB - Crossref KW - optimal individualized treatment rules KW - personalized medicine KW - quantile regression KW - robust regression ER - TY - JOUR TI - Altered bile acid profile associates with cognitive impairment in Alzheimer's disease—An emerging role for gut microbiome AU - MahmoudianDehkordi, Siamak AU - Arnold, Matthias AU - Nho, Kwangsik AU - Ahmad, Shahzad AU - Jia, Wei AU - Xie, Guoxiang AU - Louie, Gregory AU - Kueider-Paisley, Alexandra AU - Moseley, M. Arthur AU - Thompson, J. Will AU - St John Williams, Lisa AU - Tenenbaum, Jessica D. AU - Blach, Colette AU - Baillie, Rebecca AU - Han, Xianlin AU - Bhattacharyya, Sudeepa AU - Toledo, Jon B. AU - Schafferer, Simon AU - Klein, Sebastian AU - Koal, Therese AU - Risacher, Shannon L. AU - Kling, Mitchel Allan AU - Motsinger-Reif, Alison AU - Rotroff, Daniel M. AU - Jack, John AU - Hankemeier, Thomas AU - Bennett, David A. AU - De Jager, Philip L. AU - Trojanowski, John Q. AU - Shaw, Leslie M. AU - Weiner, Michael W. AU - Doraiswamy, P. Murali AU - van Duijn, Cornelia M. AU - Saykin, Andrew J. AU - Kastenmüller, Gabi AU - Kaddurah-Daouk, Rima T2 - Alzheimer's & Dementia AB - Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD).Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for confounders and multiple testing.In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid:CA, which reflects 7α-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response-related genes implicated in AD showed associations with BA profiles.We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut-liver-brain axis in the pathogenesis of AD. DA - 2019/1// PY - 2019/1// DO - 10.1016/J.JALZ.2018.07.217 VL - 15 IS - 1 SP - 76-92 J2 - Alzheimer's & Dementia LA - en OP - SN - 1552-5260 UR - http://dx.doi.org/10.1016/J.JALZ.2018.07.217 DB - Crossref KW - Metabolomics KW - Metabolome KW - Lipidomics KW - Alzheimer's disease KW - Gut microbiome KW - Gut-liver-brain axis KW - Atlas for Alzheimer KW - Genetic variants KW - Immunity KW - Inflammation ER - TY - JOUR TI - Multi-element effects on arsenate accumulation in a geochemical matrix determined using mu-XRF, mu-XANES and spatial statistics AU - Sharma, Aakriti AU - Muyskens, Amanda AU - Guinness, Joseph AU - Polizzotto, Matthew L. AU - Fuentes, Montserrat AU - Tappero, Ryan V. AU - Chen-Wiegart, Yu-chen K. AU - Thieme, Juergen AU - Williams, Garth J. AU - Acerbo, Alvin S. AU - Hesterberg, Dean T2 - JOURNAL OF SYNCHROTRON RADIATION AB - Soils regulate the environmental impacts of trace elements, but direct measurements of reaction mechanisms in these complex, multi-component systems can be challenging. The objective of this work was to develop approaches for assessing effects of co-localized geochemical matrix elements on the accumulation and chemical speciation of arsenate applied to a soil matrix. Synchrotron X-ray fluorescence microprobe (µ-XRF) images collected across 100 µm × 100 µm and 10 µm × 10 µm regions of a naturally weathered soil sand-grain coating before and after treatment with As(V) solution showed strong positive partial correlations (r' = 0.77 and 0.64, respectively) between accumulated As and soil Fe, with weaker partial correlations (r' > 0.1) between As and Ca, and As and Zn in the larger image. Spatial and non-spatial regression models revealed a dominant contribution of Fe and minor contributions of Ca and Ti in predicting accumulated As, depending on the size of the sample area analyzed. Time-of-flight secondary ion mass spectrometry analysis of an area of the sand grain showed a significant correlation (r = 0.51) between Fe and Al, so effects of Fe versus Al (hydr)oxides on accumulated As could not be separated. Fitting results from 25 As K-edge microscale X-ray absorption near-edge structure (µ-XANES) spectra collected across a separate 10 µm × 10 µm region showed ∼60% variation in proportions of Fe(III) and Al(III)-bound As(V) standards, and fits to µ-XANES spectra collected across the 100 µm × 100 µm region were more variable. Consistent with insights from studies on model systems, the results obtained here indicate a dominance of Fe and possibly Al (hydr)oxides in controlling As(V) accumulation within microsites of the soil matrix analyzed, but the analyses inferred minor augmentation from co-localized Ti, Ca and possibly Zn. DA - 2019/11/1/ PY - 2019/11/1/ DO - 10.1107/S1600577519012785 VL - 26 SP - 1967-1979 SN - 1600-5775 KW - reactive microsites KW - multi-component complexity KW - arsenic KW - partial correlation KW - spatial regression ER - TY - JOUR TI - Distributional consistency of the lasso by perturbation bootstrap AU - Das, Debraj AU - Lahiri, S. N. T2 - BIOMETRIKA AB - Summary The lasso is a popular estimation procedure in multiple linear regression. We develop and establish the validity of a perturbation bootstrap method for approximating the distribution of the lasso estimator in a heteroscedastic linear regression model. We allow the underlying covariates to be either random or nonrandom, and show that the proposed bootstrap method works irrespective of the nature of the covariates. We also investigate finite-sample properties of the proposed bootstrap method in a moderately large simulation study. DA - 2019/12// PY - 2019/12// DO - 10.1093/biomet/asz029 VL - 106 IS - 4 SP - 957-964 SN - 1464-3510 KW - Distributional consistency KW - Lasso KW - Paired bootstrap KW - Perturbation bootstrap KW - Residual bootstrap ER - TY - JOUR TI - Data-driven priors and their posterior concentration rates AU - Martin, Ryan AU - Walker, Stephen G. T2 - ELECTRONIC JOURNAL OF STATISTICS AB - In high-dimensional problems, choosing a prior distribution such that the corresponding posterior has desirable practical and theoretical properties can be challenging. This begs the question: can the data be used to help choose a prior? In this paper, we develop a general strategy for constructing a data-driven or empirical prior and sufficient conditions for the corresponding posterior distribution to achieve a certain concentration rate. The idea is that the prior should put sufficient mass on parameter values for which the likelihood is large. An interesting byproduct of this data-driven centering is that the asymptotic properties of the posterior are less sensitive to the prior shape which, in turn, allows users to work with priors of computationally convenient forms while maintaining the desired rates. General results on both adaptive and non-adaptive rates based on empirical priors are presented, along with illustrations in density estimation, nonparametric regression, and high-dimensional normal models. DA - 2019/// PY - 2019/// DO - 10.1214/19-EJS1600 VL - 13 IS - 2 SP - 3049-3081 SN - 1935-7524 KW - Adaptation KW - data-dependent prior KW - density estimation KW - empirical Bayes KW - nonparametric regression ER - TY - JOUR TI - Variable selection via adaptive false negative control in linear regression AU - Jeng, X. Jessie AU - Chen, Xiongzhi T2 - ELECTRONIC JOURNAL OF STATISTICS AB - Variable selection methods have been developed in linear regression to provide sparse solutions. Recent studies have focused on further interpretations on the sparse solutions in terms of false positive control. In this paper, we consider false negative control for variable selection with the goal to efficiently select a high proportion of relevant predictors. Different from existing studies in power analysis and sure screening, we propose to directly estimate the false negative proportion (FNP) of a decision rule and select the smallest subset of predictors that has the estimated FNP less than a user-specified control level. The proposed method is adaptive to the user-specified control level on FNP by selecting less candidates if a higher level is implemented. On the other hand, when data has stronger effect size or larger sample size, the proposed method controls FNP more efficiently with less false positives. New analytic techniques are developed to cope with the major challenge of FNP control when relevant predictors cannot be consistently separated from irrelevant ones. Our numerical results are in line with the theoretical findings. DA - 2019/// PY - 2019/// DO - 10.1214/19-EJS1649 VL - 13 IS - 2 SP - 5306-5333 SN - 1935-7524 KW - debiased Lasso KW - FNC-Reg KW - post-selection inference KW - variable screening ER - TY - CHAP TI - Dissecting the Learning Curve of Taxi Drivers: A Data-Driven Approach AU - Pan, Menghai AU - Li, Yanhua AU - Zhou, Xun AU - Liu, Zhenming AU - Song, Rui AU - Lu, Hui AU - Luo, Jun T2 - Proceedings of the 2019 SIAM International Conference on Data Mining AB - Many real world human behaviors can be modeled and characterized as sequential decision making processes, such as taxi driver's choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and improves their working efficiency. Understanding the dynamics of such preferences helps accelerate the learning process of taxi drivers. Prior works on taxi operation management mostly focus on finding optimal driving strategies or routes, lacking in-depth analysis on what the drivers learned during the process and how they affect the performance of the driver. In this work, we make the first attempt to inversely learn the taxi drivers' preferences from data and characterize the dynamics of such preferences over time. We extract two types of features, i.e., profile features and habit features, to model the decision space of drivers. Then through inverse reinforcement learning we learn the preferences of drivers with respect to these features. The results illustrate that self-improving drivers tend to keep adjusting their preferences to habit features to increase their earning efficiency, while keeping the preferences to profile features invariant. On the other hand, experienced drivers have stable preferences over time.MSC codesurban computinginverse reinforcement learningpreference dynamics PY - 2019/5/6/ DO - 10.1137/1.9781611975673.88 SP - 783-791 OP - PB - Society for Industrial and Applied Mathematics SN - 9781611975673 UR - http://dx.doi.org/10.1137/1.9781611975673.88 DB - Crossref ER - TY - JOUR TI - Optimal design for classification of functional data AU - Li, Cai AU - Xiao, Luo T2 - Canadian Journal of Statistics AB - Abstract We study the design problem for the optimal classification of functional data. The goal is to select sampling time points so that functional data observed at these time points can be classified accurately. We propose optimal designs that are applicable to either dense or sparse functional data. Using linear discriminant analysis, we formulate our design objectives as explicit functions of the sampling points. We study the theoretical properties of the proposed design objectives and provide a practical implementation. The performance of the proposed design is evaluated through simulations and real data applications. The Canadian Journal of Statistics 48: 285–307; 2020 © 2019 Statistical Society of Canada DA - 2019/// PY - 2019/// DO - 10.1002/cjs.11531 VL - 12 KW - Covariance function KW - linear discriminant analysis KW - longitudinal data ER - TY - JOUR TI - A functional mixed model for scalar on function regression with application to a functional MRI study AU - Ma, Wanying AU - Xiao, Luo AU - Liu, Bowen AU - Lindquist, Martin A T2 - Biostatistics AB - Motivated by a functional magnetic resonance imaging (fMRI) study, we propose a new functional mixed model for scalar on function regression. The model extends the standard scalar on function regression for repeated outcomes by incorporating subject-specific random functional effects. Using functional principal component analysis, the new model can be reformulated as a mixed effects model and thus easily fit. A test is also proposed to assess the existence of the subject-specific random functional effects. We evaluate the performance of the model and test via a simulation study, as well as on data from the motivating fMRI study of thermal pain. The data application indicates significant subject-specific effects of the human brain hemodynamics related to pain and provides insights on how the effects might differ across subjects. DA - 2019/10// PY - 2019/10// DO - 10.1093/biostatistics/kxz046 VL - 10 KW - fMRI KW - Functional data analysis KW - Functional mixed model KW - Functional principal component KW - Repeated measurements KW - Variance component testing ER - TY - JOUR TI - A nonparametric spatial test to identify factors that shape a microbiome AU - Singh, Susheela P. AU - Staicu, Ana-Maria AU - Dunn, Robert R. AU - Fierer, Noah AU - Reich, Brian J. T2 - The Annals of Applied Statistics AB - The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found within homes across the contiguous United States. DA - 2019/12// PY - 2019/12// DO - 10.1214/19-aoas1262 VL - 13 IS - 4 SP - 2341-2362 J2 - Ann. Appl. Stat. LA - en OP - SN - 1932-6157 UR - http://dx.doi.org/10.1214/19-aoas1262 DB - Crossref KW - Bayesian nonparametrics KW - Dirichlet process KW - high dimensional data KW - spatial modeling KW - spike-and-slab prior KW - variable selection ER - TY - CHAP TI - Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling AU - Yang, Shu AU - Kim, Jae Kwang T2 - Advances in Econometrics AB - Nearest neighbor imputation has a long tradition for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the nearest neighbor imputation estimator for general population parameters, including population means, proportions and quantiles. For variance estimation, we propose novel replication variance estimation, which is asymptotically valid and straightforward to implement. The main idea is to construct replicates of the estimator directly based on its asymptotically linear terms, instead of individual records of variables. The simulation results show that nearest neighbor imputation and the proposed variance estimation provide valid inferences for general population parameters. PY - 2019/3/26/ DO - 10.1108/s0731-905320190000039012 SP - 209-234 OP - PB - Emerald Publishing Limited SN - 9781787567269 9781787567252 UR - http://dx.doi.org/10.1108/s0731-905320190000039012 DB - Crossref ER - TY - JOUR TI - Semiparametric estimation of structural failure time models in continuous-time processes AU - Yang, S AU - Pieper, K AU - Cools, F T2 - Biometrika AB - Summary Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging due to the nonsmoothness of artificial censoring. We propose a class of continuous-time structural failure time models that respects the continuous-time nature of the underlying data processes. Under a martingale condition of no unmeasured confounding, we show that the model parameters are identifiable from a potentially infinite number of estimating equations. Using the semiparametric efficiency theory, we derive the first semiparametric doubly robust estimators, which are consistent if the model for the treatment process or the failure time model, but not necessarily both, is correctly specified. Moreover, we propose using inverse probability of censoring weighting to deal with dependent censoring. In contrast to artificial censoring, our weighting strategy does not introduce nonsmoothness in estimation and ensures that resampling methods can be used for inference. DA - 2019/10/29/ PY - 2019/10/29/ DO - 10.1093/biomet/asz057 VL - 10 LA - en OP - SN - 0006-3444 1464-3510 UR - http://dx.doi.org/10.1093/biomet/asz057 DB - Crossref KW - Causality KW - Cox proportional hazards model KW - Discretization KW - Observational study KW - Semiparametric analysis KW - Survival data ER - TY - JOUR TI - Causal inference with confounders missing not at random AU - Yang, S AU - Wang, L AU - Ding, P T2 - Biometrika AB - Summary It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects. DA - 2019/9/24/ PY - 2019/9/24/ DO - 10.1093/biomet/asz048 VL - 106 IS - 4 SP - 875-888 LA - en OP - SN - 0006-3444 1464-3510 UR - http://dx.doi.org/10.1093/biomet/asz048 DB - Crossref KW - Completeness KW - Identifiability KW - Ill-posed inverse problem KW - Integral equation KW - Outcome-independent missingness KW - Two-stage least squares estimator ER - TY - JOUR TI - Flexible Imputation of Missing Data, 2nd ed. AU - Yang, Shu T2 - Journal of the American Statistical Association AB - "Flexible Imputation of Missing Data, 2nd ed.." Journal of the American Statistical Association, 114(527), p. 1421 DA - 2019/7/3/ PY - 2019/7/3/ DO - 10.1080/01621459.2019.1662249 VL - 114 IS - 527 SP - 1421-1421 J2 - Journal of the American Statistical Association LA - en OP - SN - 0162-1459 1537-274X UR - http://dx.doi.org/10.1080/01621459.2019.1662249 DB - Crossref ER - TY - JOUR TI - Reference equations for spirometry in healthy Asian children aged 5 to 18 years in Taiwan AU - Chang, Sheng-Mao AU - Tsai, Hui-Ju AU - Tzeng, Jung-Ying AU - Yeh, Kuo-Wei AU - Chen, Li-Chen AU - Lai, Shen-Hao AU - Liao, Sui-Ling AU - Hua, Man-Chin AU - Tsai, Ming-Han AU - Huang, Jing-Long AU - Yao, Tsung-Chieh T2 - WORLD ALLERGY ORGANIZATION JOURNAL AB - This study aimed to establish reference equations for spirometry in healthy Taiwanese children and assess the applicability of the Global Lung Function Initiative (GLI)-2012 equations to Taiwanese children.Spirometric data collected from 757 healthy Taiwanese children aged 5 to 18 years in a population-based cohort study. Prediction equations derived using linear regression and the generalized additive models for location, scale and shape (GAMLSS) method, respectively.The GLI-2012 South East Asian equations did not provide a close fit with mean ± standard error z-scores of -0.679 ± 0.030 (FVC), -0.186 ± 0.044 (FEV1), -0.875 ± 0.049 (FEV1/FVC ratio) and -2.189 ± 0.063 (FEF25-75) for girls; and 0.238 ± 0.059, -0.061 ± 0.053, -0.513 ± 0.059 and -1.896 ± 0.077 for boys. The proposed GAMLSS models took age, height, and weight into account. GAMLSS models for boys and girls captured the characteristics of spirometric data in the study population closely in contrast to the linear regression models and the GLI-2012 equations.This study provides up-to-date reference values for spirometry using GAMLSS modeling in healthy Taiwanese children aged 5 to 18 years. Our study provides evidence that the GLI-2012 reference equations are not properly matched to spirometric data in a contemporary Taiwanese child population, indicating the urgent need for an update of GLI reference values by inclusion of more data of non-Caucasian decent. DA - 2019/11// PY - 2019/11// DO - 10.1016/j.waojou.2019.100074 VL - 12 IS - 11 SP - SN - 1939-4551 KW - Asian KW - Children KW - Prediction equations KW - Pulmonary function KW - Reference values KW - Spirometry ER - TY - JOUR TI - Modelling and estimation for optimal treatment decision with interference AU - Su, Lin AU - Lu, Wenbin AU - Song, Rui T2 - Stat AB - In many network-based intervention studies, treatment applied on an individual or his or her own characteristics may also affect the outcome of other connected people. We call this interference along network. Approaches for deriving the optimal individualized treatment regimen remain unknown after introducing the effect of interference. In this paper, we propose a novel network-based regression model that is able to account for interaction between outcomes and treatments in a network. Both Q-learning and A-learning methods are derived. We show that the optimal treatment regimen under our model is independent from interference, which makes its application in practice more feasible and appealing. The asymptotic properties of the proposed estimators are established. The performance of the proposed model and methods is illustrated by extensive simulation studies and an application to a mobile game network data. DA - 2019/1// PY - 2019/1// DO - 10.1002/STA4.219 VL - 8 IS - 1 J2 - STAT LA - en OP - SN - 2049-1573 2049-1573 UR - http://dx.doi.org/10.1002/STA4.219 DB - Crossref KW - A-learning KW - interference KW - network KW - optimal treatment regimen KW - Q-learning ER - TY - JOUR TI - Combining Multiple Observational Data Sources to Estimate Causal Effects AU - Yang, Shu AU - Ding, Peng T2 - Journal of the American Statistical Association AB - The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators. Supplementary materials for this article are available online. DA - 2019/6/11/ PY - 2019/6/11/ DO - 10.1080/01621459.2019.1609973 VL - 6 SP - 1-33 J2 - Journal of the American Statistical Association LA - en OP - SN - 0162-1459 1537-274X UR - http://dx.doi.org/10.1080/01621459.2019.1609973 DB - Crossref KW - Calibration KW - Causal inference KW - Inverse probability weighting KW - Missing confounder KW - Two-phase sampling ER - TY - JOUR TI - Testing and Estimation of Social Network Dependence With Time to Event Data AU - Su, Lin AU - Lu, Wenbin AU - Song, Rui AU - Huang, Danyang T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - Lin Sua, Wenbin Lua*, Rui Songa & Danyang Huangba Department of Statistics, North Carolina State University, Raleigh, NC; b School of Statistics, Remin University, Beijing, China DA - 2019/// PY - 2019/// DO - 10.1080/01621459.2019.1617153 KW - Cox model KW - EM algorithm KW - Social network dependence KW - Time-to-event data ER - TY - JOUR TI - A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects AU - Shi, Chengchun AU - Lu, Wenbin AU - Song, Rui T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - In contrast to the classical “one-size-fits-all” approach, precision medicine proposes the customization of individualized treatment regimes to account for patients’ heterogeneity in response to treatments. Most of existing works in the literature focused on estimating optimal individualized treatment regimes. However, there has been less attention devoted to hypothesis testing regarding the existence of overall qualitative treatment effects, especially when there are a large number of prognostic covariates. When covariates do not have qualitative treatment effects, the optimal treatment regime will assign the same treatment to all patients regardless of their covariate values. In this article, we consider testing the overall qualitative treatment effects of patients’ prognostic covariates in a high-dimensional setting. We propose a sample splitting method to construct the test statistic, based on a nonparametric estimator of the contrast function. When the dimension of covariates is large, we construct the test based on sparse random projections of covariates into a low-dimensional space. We prove the consistency of our test statistic. In the regular cases, we show the asymptotic power function of our test statistic is asymptotically the same as the “oracle” test statistic which is constructed based on the “optimal” projection matrix. Simulation studies and real data applications validate our theoretical findings. Supplementary materials for this article are available online. DA - 2019/// PY - 2019/// DO - 10.1080/01621459.2019.1604368 KW - High-dimensional testing KW - Optimal treatment regime KW - Precision medicine KW - Qualitative treatment effects KW - Sparse random projection ER - TY - JOUR TI - Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework AU - Yang, Shu AU - Kim, Jae Kwang T2 - Scandinavian Journal of Statistics AB - Abstract Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator for finite‐population inference using a superpopulation model framework. We also clarify conditions for its robustness. For variance estimation, the conventional bootstrap inference is invalid for matching estimators with a fixed number of matches due to the nonsmoothness nature of the matching estimator. We propose a new replication variance estimator, which is asymptotically valid. The key strategy is to construct replicates directly based on the linear terms of the martingale representation for the matching estimator, instead of individual records of variables. Simulation studies confirm that the proposed method provides valid inference. DA - 2019/12/17/ PY - 2019/12/17/ DO - 10.1111/sjos.12429 J2 - Scand J Statist LA - en OP - SN - 0303-6898 1467-9469 UR - http://dx.doi.org/10.1111/sjos.12429 DB - Crossref KW - hot deck imputation KW - Jackknife variance estimation KW - martingale central limit theorem KW - missing at random ER - TY - JOUR TI - A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes AU - Hazra, Arnab AU - Reich, Brian AU - Staicu, Ana-Maria T2 - ENVIRONMETRICS AB - Abstract To study trends in extreme precipitation across the United States over the years 1951–2017, we analyze 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation and annual maximum of consecutive five‐day average precipitation. We consider the gridded data produced by the CLIMDEX project ( http://www.climdex.org/gewocs.html ), constructed using daily precipitation data. These indexes exhibit spatial and mutual dependence. In this paper, we propose a multivariate spatial skew‐ t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. In a numerical study, we find that the proposed model outperforms several simpler alternatives in terms of various model selection criteria. We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes. DA - 2019/// PY - 2019/// DO - 10.1002/env.2602 KW - climate change KW - extremal dependence KW - extremal trend analysis KW - extreme precipitation indexes KW - multivariate spatial skew-t process KW - separable covariance ER - TY - JOUR TI - Tensor canonical correlation analysis AU - Min, Eun Jeong AU - Chi, Eric C. AU - Zhou, Hua T2 - Stat AB - Canonical correlation analysis (CCA) is a multivariate analysis technique for estimating a linear relationship between two sets of measurements. Modern acquisition technologies, for example, those arising in neuroimaging and remote sensing, produce data in the form of multidimensional arrays or tensors. Classic CCA is not appropriate for dealing with tensor data due to the multidimensional structure and ultrahigh dimensionality of such modern data. In this paper, we present tensor CCA (TCCA) to discover relationships between two tensors while simultaneously preserving multidimensional structure of the tensors and utilizing substantially fewer parameters. Furthermore, we show how to employ a parsimonious covariance structure to gain additional stability and efficiency. We delineate population and sample problems for each model and propose efficient estimation algorithms with global convergence guarantees. Also we describe a probabilistic model for TCCA that enables the generation of synthetic data with desired canonical variates and correlations. Simulation studies illustrate the performance of our methods. DA - 2019/1// PY - 2019/1// DO - 10.1002/sta4.253 VL - 8 IS - 1 UR - https://doi.org/10.1002/sta4.253 KW - block coordinate ascent KW - CP decomposition KW - multidimensional array data ER - TY - JOUR TI - Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks AU - Clark, Natalie M. AU - Buckner, Eli AU - Fisher, Adam P. AU - Nelson, Emily C. AU - Nguyen, Thomas T. AU - Simmons, Abigail R. AU - Balaguer, Maria A. de Luis AU - Butler-Smith, Tiara AU - Sheldon, Parnell J. AU - Bergmann, Dominique C. AU - Williams, Cranos M. AU - Sozzani, Rossangela T2 - NATURE COMMUNICATIONS AB - Stem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred to here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allows us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together. DA - 2019/12/6/ PY - 2019/12/6/ DO - 10.1038/s41467-019-13132-2 VL - 10 SP - SN - 2041-1723 ER - TY - JOUR TI - Revisiting the sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol AU - Tittlemier, S. A. AU - Chan, J. AU - Gaba, D. AU - Pleskach, K. AU - Osborne, J. AU - Slate, A. B. AU - Whitaker, T. B. T2 - WORLD MYCOTOXIN JOURNAL AB - Fifteen lots of wheat were sampled to characterise the total variance and distribution among sample test results associated with measuring deoxynivalenol (DON) in bulk wheat lots. An unbalanced nested experimental design based on past research was used to determine contributions to the total variance from sampling, sample preparation, and analysis. The wheat lots used in the study contained average DON concentrations that ranged from 0.17 to 24.5 mg/kg. Sampling was determined to be the largest contributor to the total variance of measuring DON at low mg/kg concentrations, which are relevant to existing maximum levels. With the experimental design parameters of 1 kg laboratory samples, sub-division of whole and ground grain using rotary sample division, sample comminution using a commercial-grade coffee grinder, extraction of 100 g test portions, and making one measurement of DON in the test portion by gas chromatography-mass spectrometry, the total variance of DON measurement at 2 mg/kg was 0.046 mg 2 /kg 2 (coefficient of variation=10.7%). At this concentration, sampling contributed 67% to the total variance, followed by sample preparation (18%) and analysis (15%). The DON distribution among sample test results was accurately described by the normal distribution. The mathematical model of variance was used with the normal distribution of DON measurement results to construct operating characteristics curves to model the likelihood of mischaracterising a wheat lot as (non) compliant with a certain decision limit. With realistic laboratory sample and test portion sizes, as well as a practicable decision limit of 1.5 mg/kg, the estimated probability of mischaracterising a wheat lot containing 2 mg/kg DON as less than this concentration was reduced to 1%. DA - 2019/// PY - 2019/// DO - 10.3920/WMJ2019.2450 VL - 12 IS - 4 SP - 319-332 SN - 1875-0796 KW - heterogeneity KW - variance KW - sampling KW - grains KW - operating characteristic curve ER - TY - RPRT TI - A curriculum for foundational Research Data Science skills for Early Career Researchers AU - Shanahan, H. DA - 2019/12/1/ PY - 2019/12/1/ DO - 10.15497/RDA00038 UR - https://www.rd-alliance.org/group/rdacodata-summer-schools-data-science-and-cloud-computing-developing-world-wg/outcomes-0 ER - TY - JOUR TI - A note on cyclic shift permutation testing for large eigenvalues AU - Zhou, Yi‐Hui T2 - Stat AB - Recent publications have described the problem of testing for the “significance” of large sample (empirical) matrix eigenvalues in the presence of modest variation of underlying true eigenvalues. This modest variation often can be ascribed to endemic dependence in one matrix dimension (e.g., rows), whereas the null hypothesis concerns the other dimension (columns). The need for such testing frequently arises in genomics, time‐series analysis, and a variety of other fields. However, the tools available for testing are underdeveloped, with statistical properties that may be sensitive to the true eigenvalues. The purpose of this note is to point the reader to this emerging literature and to suggest that the tool of cyclic shift permutation may be well‐suited to the problem. DA - 2019/1// PY - 2019/1// DO - 10.1002/sta4.257 VL - 8 IS - 1 UR - https://doi.org/10.1002/sta4.257 KW - eigendecomposition KW - Marcenko-Pastur KW - Tracy-Widom ER - TY - JOUR TI - Emerging procurement technology: data analytics and cognitive analytics AU - Handfield, Robert AU - Jeong, Seongkyoon AU - Choi, Thomas T2 - INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT AB - Purpose The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics? Design/methodology/approach This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field. Findings The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture. Originality/value While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports. DA - 2019/// PY - 2019/// DO - 10.1108/IJPDLM-11-2017-0348 VL - 49 IS - 10 SP - 972-1002 ER - TY - JOUR TI - Strain-Specific Differences in Survival of Campylobacter spp. in Naturally Contaminated Turkey Feces and Water AU - Good, Lesley AU - Miller, William G. AU - Niedermeyer, Jeffrey AU - Osborne, Jason AU - Siletzky, Robin M. AU - Carver, Donna AU - Kathariou, Sophia T2 - APPLIED AND ENVIRONMENTAL MICROBIOLOGY AB - Campylobacter jejuni and Campylobacter coli are leading foodborne pathogens, with poultry as a major reservoir. Due to their growth requirements, these Campylobacter spp. may be unable to replicate once excreted by their avian hosts, but their survival in feces and the environment is critical for transmission in the farm ecosystem. Reducing the prevalence of Campylobacter -positive flocks can have major impacts in controlling both contamination of poultry products and environmental dissemination of the pathogens. However, understanding the capacity of these pathogens to survive in transmission-relevant vehicles such as feces and farmhouse water remains poorly understood, and little information is available on species- and strain-associated differences in survival. Here, we employed model conditions to investigate the survival of C. jejuni and C. coli from naturally colonized turkey flocks, and with diverse genotypes and antimicrobial resistance profiles, in turkey feces and in farmhouse water. DA - 2019/11// PY - 2019/11// DO - 10.1128/AEM.01579-19 VL - 85 IS - 22 SP - SN - 1098-5336 KW - Campylobacter KW - Campylobacter coli KW - Campylobacter jejuni KW - turkey KW - antimicrobial resistance KW - feces KW - survival KW - water ER - TY - JOUR TI - Sensitivity of the US Blumeria graminis f. sp. tritici Population to Demethylation Inhibitor Fungicides AU - Meyers, Emily AU - Arellano, Consuelo AU - Cowger, Christina T2 - PLANT DISEASE AB - Wheat powdery mildew, caused by Blumeria graminis f. sp. tritici, is managed in the United States with cultivar resistance and foliar fungicides. Despite high levels of fungicide sensitivity in other cereal mildew populations, fungicide sensitivity of U.S. B. graminis f. sp. tritici has never been evaluated. Almost 400 B. graminis f. sp. tritici isolates were collected from 15 U.S. states over 2 years and phenotyped for sensitivity to two widely used demethylation inhibitor (DMI) fungicides, tebuconazole and prothioconazole. A large range of sensitivity to both DMIs was observed, with more insensitive isolates originating from the eastern United States (Great Lakes, Mid-Atlantic, and Southeast regions) and more sensitive isolates from central states (Plains region, Arkansas, and Missouri). Cross-resistance was indicated by a positive although weak association between tebuconazole and prothioconazole sensitivities at all levels of analysis (EC 50 values, P < 0.0001). A possible fitness cost was also associated with prothioconazole insensitivity (P = 0.0307) when analyzed at the state population level. This is the first assessment of fungicide sensitivity in the U.S. B. graminis f. sp. tritici population, and it produced evidence of regional selection for reduced DMI efficacy. The observation of reduced sensitivity to DMI fungicides in the eastern United States underlines the importance of rotating between chemistry classes to maintain the effectiveness of DMIs in U.S. wheat production. Although cross-resistance was demonstrated, variability in the relationship of EC 50 values for tebuconazole and prothioconazole also suggests that multiple mechanisms influence B. graminis f. sp. tritici isolate responses to these two DMI fungicides. DA - 2019/12// PY - 2019/12// DO - 10.1094/PDIS-04-19-0715-RE VL - 103 IS - 12 SP - 3108-3116 ER - TY - JOUR TI - Diagnostic utility of clinical and laboratory test parameters for differentiating between sudden acquired retinal degeneration syndrome and pituitary-dependent hyperadrenocorticism in dogs AU - Oh, Annie AU - Foster, Melanie L. AU - Williams, Jonathan G. AU - Zheng, Chaowen AU - Ru, Hongyu AU - Lunn, Katharine F. AU - Mowat, Freya M. T2 - VETERINARY OPHTHALMOLOGY AB - Abstract Objective To identify discriminating factors, using clinical ophthalmic examination findings and routine laboratory testing, that differentiate dogs with early sudden acquired retinal degeneration (SARDS; vision loss <6 weeks’ duration), age‐ and breed‐matched control dogs, and dogs with pituitary‐dependent hyperadrenocorticism (PDH). Animals Client‐owned dogs: 15 with SARDS with <6 weeks duration of vision loss, 14 age‐ and breed‐matched control dogs, and 13 dogs with confirmed PDH. Procedures Dogs underwent ophthalmic examination, electroretinography (ERG) fundus photography, and spectral‐domain optical coherence tomography (SD‐OCT) in addition to physical examination, urinalysis, serum biochemistry, complete blood count, and adrenocorticotrophic hormone (ACTH) stimulation testing. Statistical analysis was performed using receiver operating curve area under the curve analysis, principal component analysis with sparse partial least squares analysis, and one‐way ANOVA. Results Dogs with SARDS all had absent vision and ERG a‐ and b‐waves. SD‐OCT demonstrated that dogs with SARDS had significantly thicker inner retina, thinner outer nuclear layer, and thicker photoreceptor inner/outer segment measurements than either controls or dogs with PDH. Discriminating laboratory parameters between dogs with SARDS and PDH with high specificity included post‐ACTH serum cortisol (<19.3 μg/dL), AST:ALT ratio (>0.343), and urine specific gravity (>1.030). Conclusions and Clinical Relevance We have identified significant discriminators between SARDS and PDH. This work provides the basis for future studies that could identify and examine dogs with SARDS prior to vision loss, which may extend the potential therapeutic window for SARDS. DA - 2019/11// PY - 2019/11// DO - 10.1111/vop.12661 VL - 22 IS - 6 SP - 842-858 SN - 1463-5224 KW - biomarker KW - dog KW - hyperadrenocorticism KW - retina KW - sudden acquired retinal degeneration syndrome ER - TY - JOUR TI - Use of Unconventional Mixed Acetone-Butanol-Ethanol Solvents for Anthocyanin Extraction from Purple-Fleshed Sweetpotatoes T2 - Food Chemistry AB - Anthocyanins from purple-fleshed sweetpotatoes constitute highly valued natural colorants and functional ingredients. In the past, anthocyanin extraction conditions and efficiencies using a single acidified solvent have been assessed. However, the potential of solvent mixes that can be generated by fermentation of biomass-derived sugars have not been explored. In this study, the effects of single and mixed solvent, time, temperature, sweetpotato genotype and preparation, on anthocyanin and phenolic extraction were evaluated. Results indicated that unconventional diluted solvent mixes containing acetone, butanol, and ethanol were superior or equally efficient for extracting anthocyanins when compared to commonly used concentrated extractants. In addition, analysis of anthocyanidins concentrations including cyanidin (cy), peonidin (pe), and pelargonidin (pl), indicated that different ratios of pn/cy were obtained depending on the solvent used. These results could be useful when selecting processing conditions that better suit particular end-use applications and more environmentally friendly process development for purple sweetpotatoes. DA - 2019/12// PY - 2019/12// DO - 10.1016/j.foodchem.2019.125959 UR - http://dx.doi.org/10.1016/j.foodchem.2019.125959 KW - Ipomoea batatas KW - Anthocyanidins KW - Phenolics KW - Cyanidin KW - Peonidin KW - Temperature KW - Flour ER - TY - JOUR TI - Comparison of smoking-related DNA methylation between newborns from prenatal exposure and adults from personal smoking AU - Sikdar, Sinjini AU - Joehanes, Roby AU - Joubert, Bonnie R. AU - Xu, Cheng-Jian AU - Vives-Usano, Marta AU - Rezwan, Faisal I. AU - Felix, Janine F. AU - Ward, James M. AU - Guan, Weihua AU - Richmond, Rebecca C. AU - Brody, Jennifer A. AU - Kupers, Leanne K. AU - Baiz, Nour AU - Haberg, Siri E. AU - Smith, Jennifer A. AU - Reese, Sarah E. AU - Aslibekyan, Stella AU - Hoyo, Cathrine AU - Dhingra, Radhika AU - Markunas, Christina A. AU - Xu, Tao AU - Reynolds, Lindsay M. AU - Just, Allan C. AU - Mandaviya, Pooja R. AU - Ghantous, Akram AU - Bennett, Brian D. AU - Wang, Tianyuan AU - Bakulski, Kelly M. AU - Melen, Erik AU - Zhao, Shanshan AU - Jin, Jianping AU - Herceg, Zdenko AU - Meurs, Joyce AU - Taylor, Jack A. AU - Baccarelli, Andrea A. AU - Murphy, Susan K. AU - Liu, Yongmei AU - Munthe-Kaas, Monica Cheng AU - Deary, Ian J. AU - Nystad, Wenche AU - Waldenberger, Melanie AU - Annesi-Maesano, Isabella AU - Conneely, Karen AU - Jaddoe, Vincent W. V. AU - Arnett, Donna AU - Snieder, Harold AU - Kardia, Sharon L. R. AU - Relton, Caroline L. AU - Ong, Ken K. AU - Ewart, Susan AU - Moreno-Macias, Hortensia AU - Romieu, Isabelle AU - Sotoodehnia, Nona AU - Fornage, Myriam AU - Motsinger-Reif, Alison AU - Koppelman, Gerard H. AU - Bustamante, Mariona AU - Levy, Daniel AU - London, Stephanie J. T2 - EPIGENOMICS AB - Aim: Cigarette smoking influences DNA methylation genome wide, in newborns from pregnancy exposure and in adults from personal smoking. Whether a unique methylation signature exists for in utero exposure in newborns is unknown. Materials & methods: We separately meta-analyzed newborn blood DNA methylation (assessed using Illumina450k Beadchip), in relation to sustained maternal smoking during pregnancy (9 cohorts, 5648 newborns, 897 exposed) and adult blood methylation and personal smoking (16 cohorts, 15907 participants, 2433 current smokers). Results & conclusion: Comparing meta-analyses, we identified numerous signatures specific to newborns along with many shared between newborns and adults. Unique smoking-associated genes in newborns were enriched in xenobiotic metabolism pathways. Our findings may provide insights into specific health impacts of prenatal exposure on offspring. DA - 2019/10// PY - 2019/10// DO - 10.2217/epi-2019-0066 VL - 11 IS - 13 SP - 1487-1500 SN - 1750-192X KW - cigarette smoking KW - epigenetics KW - infant KW - maternal exposure KW - methylation ER - TY - JOUR TI - Discussion on “Effective interdisciplinary collaboration between statisticians and other subject matter experts” AU - Typhina, Eli AU - Wilson, Alyson T2 - Quality Engineering AB - Anderson-Cook, Lu, and Parker’s article offers numerous suggestions for ways statisticians can facilitate effective interdisciplinary collaboration, with particular focus on project teams. Their article comes at a time when the importance of collaboration to support innovation is becoming more broadly recognized, bringing with it the inherent challenges of engaging in collaboration. In our discussion, we expand on Anderson-Cook et al.’s insights by describing our experiences working with collaborators from different disciplines and sectors. We contextualize our recommendations with examples of collaborations from our organization, the Laboratory for Analytic Sciences. DA - 2019/1/2/ PY - 2019/1/2/ DO - 10.1080/08982112.2018.1539233 UR - https://doi.org/10.1080/08982112.2018.1539233 ER - TY - JOUR TI - Recovering Trees with Convex Clustering AU - Chi, E. AU - Steinerberger, S. T2 - SIAM Journal on Mathematics of Data Science AB - Hierarchical clustering is a fundamental unsupervised learning task, whose aim is to organize a collection of points into a tree of nested clusters. Convex clustering has been proposed recently as a new way to construct tree organizations of data that are more robust to perturbations in the input data than standard hierarchical clustering algorithms. In this paper, we present conditions that guarantee when the convex clustering solution path recovers a tree and also make explicit how affinity parameters in the convex clustering formulation modulate the structure of the recovered tree. The proof of our main result relies on establishing a novel property of point clouds in a Hilbert space, which is potentially of independent interest. DA - 2019/1// PY - 2019/1// DO - 10.1137/18m121099x VL - 1 IS - 3 SP - 383-407 UR - http://dx.doi.org/10.1137/18m121099x KW - convex optimization KW - fused lasso KW - hierarchical clustering KW - penalized regression KW - sparsity ER - TY - CONF TI - Co-manifold learning with missing data AU - Mishne, Gal AU - Chi, Eric AU - Coifman, Ronald A2 - Chaudhuri, Kamalika A2 - Salakhutdinov, Ruslan C2 - 2019/// C3 - International Conference on Machine Learning CY - Long Beach, California, USA DA - 2019/// VL - 97 SP - 4605-4614 PB - PMLR UR - http://proceedings.mlr.press/v97/mishne19a.html ER - TY - JOUR TI - Synergistic Chemotherapy Drug Response Is a Genetic Trait in Lymphoblastoid Cell Lines AU - Roell, Kyle R. AU - Havener, Tammy M. AU - Reif, David M. AU - Jack, John AU - McLeod, Howard L. AU - Wiltshire, Tim AU - Motsinger-Reif, Alison A. T2 - FRONTIERS IN GENETICS AB - Lymphoblastoid cell lines (LCLs) are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. In the present work, we demonstrate that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Lymphoblastoid cell lines have been commonly employed in association mapping in cancer pharmacogenomics, but it is so far untested as to whether synergistic effects have a genetic etiology. Here we use cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. Additionally, we perform linkage mapping in these pedigrees to identify putative regions linked to this important phenotype. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies. DA - 2019/10/15/ PY - 2019/10/15/ DO - 10.3389/fgene.2019.00829 VL - 10 SP - SN - 1664-8021 KW - synergy KW - heritability KW - chemotherapy KW - lymphoblastoid cell lines KW - linkage mapping ER - TY - JOUR TI - Spatial Signal Detection Using Continuous Shrinkage Priors AU - Jhuang, An-Ting AU - Fuentes, Montserrat AU - Jones, Jacob L. AU - Esteves, Giovanni AU - Fancher, Chris M. AU - Furman, Marschall AU - Reich, Brian J. T2 - TECHNOMETRICS AB - Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. We apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field. DA - 2019/10/2/ PY - 2019/10/2/ DO - 10.1080/00401706.2018.1546622 VL - 61 IS - 4 SP - 494-506 SN - 1537-2723 KW - Bayesian variable selection KW - High-dimensional data KW - Image analysis KW - X-ray diffraction ER - TY - JOUR TI - Retrofitting a grass swale with rock check dams: hydrologic impacts AU - Winston, Ryan J. AU - Powell, Jacob T. AU - Hunt, William F. T2 - URBAN WATER JOURNAL AB - The hydrologic performance of a grass swale, a common stormwater control measure often utilized to drain roads, may potentially be improved using simple retrofits. Two rock check dams were retrofitted into an existing grass swale located in Knightdale, North Carolina, USA. The swale was monitored before and after check dam installation, and the addition of check dams improved runoff volume reduction (17%), peak flow mitigation, and hydraulic retention time in the swale, particularly for small (< 19 mm) and moderate (19–38 mm) rainfall events. The check dams were effective filters of gross solids, which eventually led to clogging and caused extended inundation and subsequent loss of swale vegetation. Because check dams are relatively inexpensive and simple vis-à-vis other stormwater control measure enhancements, their use for stormwater treatment is encouraged, provided they are adequately maintained. DA - 2019/7/3/ PY - 2019/7/3/ DO - 10.1080/1573062X.2018.1455881 VL - 16 IS - 6 SP - 404-411 SN - 1744-9006 KW - Clogging KW - highway runoff KW - peak flow rate KW - low impact development KW - runoff reduction KW - vegetated swale ER - TY - JOUR TI - LOCAL SENSITIVITY VIA THE COMPLEX-STEP DERIVATIVE APPROXIMATION FOR 1D PORO-ELASTIC AND PORO-VISCO-ELASTIC MODELS AU - Banks, H. Thomas AU - Bekele-Maxwell, Kidist AU - Bociu, Lorena AU - Noorman, Marcelle AU - Guidiboni, Giovanna T2 - MATHEMATICAL CONTROL AND RELATED FIELDS AB - Poro-elastic systems have been used extensively in modeling fluid flow in porous media in petroleum and earthquake engineering. Nowadays, they are frequently used to model fluid flow through biological tissues, cartilages, and bones. In these biological applications, the fluid-solid mixture problems, which may also incorporate structural viscosity, are considered on bounded domains with appropriate non-homogeneous boundary conditions. The recent work in [12] provided a theoretical and numerical analysis of nonlinear poro-elastic and poro-viscoelastic models on bounded domains with mixed boundary conditions, focusing on the role of visco-elasticity in the material. Their results show that higher time regularity of the sources is needed to guarantee bounded solution when visco-elasticity is not present. Inspired by their results, we have recently performed local sensitivity analysis on the solutions of these fluid-solid mixture problems with respect to the boundary source of traction associated with the elastic structure [3]. Our results show that the solution is more sensitive to boundary datum in the purely elastic case than when visco-elasticity is present in the solid matrix. In this article, we further extend this work in order to include local sensitivities of the solution of the coupled system to the boundary conditions imposed on the Darcy velocity. Sensitivity analysis is the first step in identifying important parameters to control or use as control terms in these poro-elastic and poro-visco-elastic models, which is our ultimate goal. DA - 2019/12// PY - 2019/12// DO - 10.3934/mcrf.2019044 VL - 9 IS - 4 SP - 623-642 SN - 2156-8499 KW - Sensitivity KW - poro-elastic KW - poro-visco-elastic KW - biological tissues KW - complex-step method ER - TY - JOUR TI - Development of the Texas A&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis AU - Miikherjee, Rajib AU - Onel, Melis AU - Beykal, Burcu AU - Szafran, Adam T. AU - Stossi, Fabio AU - Mancini, Michael A. AU - Zhou, Lan AU - Wright, Fred A. AU - Pistikopoulos, Efstratios N. T2 - 29TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT A AB - The National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program (SRP) aims to support university-based multidisciplinary research on human health and environmental issues related to hazardous substances and pollutants. The Texas A&M Superfund Research Program comprehensively evaluates the complexities of hazardous chemical mixtures and their potential adverse health impacts due to exposure through a number of multi-disciplinary projects and cores. One of the essential components of the Texas A&M Superfund Research Center is the Data Science Core, which serves as the basis for translating the data produced by the multi-disciplinary research projects into useful knowledge for the community via data collection, quality control, analysis, and model generation. In this work, we demonstrate the Texas A&M Superfund Research Program computational platform, which houses and integrates large-scale, diverse datasets generated across the Center, provides basic visualization service to facilitate interpretation, monitors data quality, and finally implements a variety of state-of-the-art statistical analysis for model/tool development. The platform is aimed to facilitate effective integration and collaboration across the Center and acts as an enabler for the dissemination of comprehensive ad-hoc tools and models developed to address the environmental and health effects of chemical mixture exposure during environmental emergency-related contamination events. DA - 2019/// PY - 2019/// DO - 10.1016/B978-0-12-818634-3.50162-4 VL - 46 SP - 967-972 SN - 1570-7946 KW - Data analytics KW - data integration KW - statistical analysis KW - collaborative networks ER - TY - JOUR TI - Results of the Brief Jail Mental Health Screen Across Repeated Jail Bookings AU - Zottola, Samantha A. AU - Desmarais, Sarah L. AU - Neupert, Shevaun D. AU - Dong, Lin AU - Laber, Eric AU - Lowder, Evan M. AU - Van Dorn, Richard A. T2 - PSYCHIATRIC SERVICES AB - Objective: The Brief Jail Mental Health Screen (BJMHS) is widely used at intake in county jails to identify detainees who may have serious mental illness and who should be referred for further mental health evaluation. The BJMHS may be administered multiple times across repeated jail bookings; however, the extent to which results may change over time is unclear. To that end, the authors examined the odds of screening positive on the BJMHS across repeated jail bookings. Methods: Data were drawn from the administrative and medical records of a large, urban county jail that used the BJMHS at jail booking. The study sample comprised BJMHS results for the 12,531 jail detainees who were booked at least twice during the 3.5-year period (N=41,965 bookings). Multilevel logistic modeling was used to examine changes over time overall and within the four decision rules (current psychiatric medication, prior hospitalization, two or more current symptoms, and referral for any other reason). Results: Results show that the odds of a positive screen overall increased with each jail booking, as did the odds of referral for any other reason. In contrast, the odds of screening positive for two or more current symptoms and prior hospitalization decreased. There was no change in the odds of screening positive for current psychiatric medication across bookings. Conclusions: Findings show that BJMHS results changed across bookings. Further research is needed to determine whether changes reflect true changes in mental health status, issues with fidelity, the repeated nature of the screening process, or other factors. DA - 2019/11// PY - 2019/11// DO - 10.1176/appi.ps.201800377 VL - 70 IS - 11 SP - 1006-1012 SN - 1557-9700 ER - TY - JOUR TI - Measurement of peripheral muscle oxygen saturation in conscious healthy horses using a near-infrared spectroscopy device AU - Gingold, Benjamin M. C. AU - Killos, Maria B. AU - Griffith, Emily AU - Posner, Lysa T2 - VETERINARY ANAESTHESIA AND ANALGESIA AB - Objective Maintaining adequate muscle tissue oxygenation is of paramount importance during equine general anesthesia. The objectives of this study were to assess the feasibility, reliability and repeatability of near-infrared spectroscopy (NIRS) muscle oximetry using the Inspectra m650 in conscious healthy adult horses. Study design Prospective, observational study. Animals A group of 30 healthy client-owned adult horses admitted to the equine hospital between July 2017 and July 2018. Methods The probe of an Inspectra m650 NIRS tissue oximeter was placed on the hairless surface of five muscle sites (omotransversarius, triceps long head, extensor carpi ulnaris, vastus lateralis and lateral digital extensor) on the left side of the body of each standing, unsedated horse. Each site had muscle oxygenation (StO2) recordings measured in triplicate and statistical modeling used to assess the reading reliability and repeatability within and between muscle sites. Results The readings acquired at the vastus lateralis and extensor carpi ulnaris muscle sites had highly repeatable values [mean (90% confidence interval): StO2, 95% (93.8%, 96.5%) and 93% (91.6%, 93.9%), respectively; intraclass correlation coefficients, 0.92 and 0.80, respectively]. These two sites also had high reliability (represented by the percentage of successful readings; 70% and 86%, respectively). Conclusions and clinical relevance The use of NIRS muscle oxygenation technology is a clinically feasible means to assess tissue oxygenation in horses. The vastus lateralis and extensor carpi ulnaris muscle sites provided the most reliable and repeatable readings when using the Inspectra m650 machine in horses. DA - 2019/11// PY - 2019/11// DO - 10.1016/j.vaa.2019.07.001 VL - 46 IS - 6 SP - 789-795 SN - 1467-2995 KW - equine KW - horse KW - microcirculation KW - muscle oxygenation KW - NIRS KW - tissue oximetry ER - TY - JOUR TI - ON OPTIMAL DESIGNS FOR NONREGULAR MODELS AU - Lin, Yi AU - Martin, Ryan AU - Yang, Min T2 - ANNALS OF STATISTICS AB - Classically, Fisher information is the relevant object in defining optimal experimental designs. However, for models that lack certain regularity, the Fisher information does not exist, and hence, there is no notion of design optimality available in the literature. This article seeks to fill the gap by proposing a so-called Hellinger information, which generalizes Fisher information in the sense that the two measures agree in regular problems, but the former also exists for certain types of nonregular problems. We derive a Hellinger information inequality, showing that Hellinger information defines a lower bound on the local minimax risk of estimators. This provides a connection between features of the underlying model—in particular, the design—and the performance of estimators, motivating the use of this new Hellinger information for nonregular optimal design problems. Hellinger optimal designs are derived for several nonregular regression problems, with numerical results empirically demonstrating the efficiency of these designs compared to alternatives. DA - 2019/12// PY - 2019/12// DO - 10.1214/18-AOS1780 VL - 47 IS - 6 SP - 3335-3359 SN - 0090-5364 KW - E-optimality KW - experimental design KW - Fisher information KW - Hellinger distance KW - information inequality ER - TY - JOUR TI - Discussion of 'Nonparametric generalized fiducial inference for survival functions under censoring' AU - Martin, Ryan T2 - BIOMETRIKA AB - It is a pleasure to participate in the discussion of the article by Cui & Hannig (2019). The authors are to be congratulated for their efforts and ingenuity in developing what is, to my knowledge, the first fiducial solution to a problem involving an infinite-dimensional parameter of interest. Specifically, the authors build on some existing machinery, summarized recently in Hannig et al. (2016) and the references therein, to construct a generalized fiducial distribution for the full event time survival function, |$S$|⁠, in the presence of right censoring. They proceed to show that the generalized fiducial distribution satisfies a Bernstein–von Mises theorem, that is, it asymptotically resembles a Gaussian process centred at the Kaplan–Meier estimator, |$\hat S$|⁠, with covariance function matching that of the limiting sampling distribution of |$\hat S$|⁠. This implies that summaries of the generalized fiducial distribution, such as hypothesis testing rules and confidence sets for |$S$|⁠, will control the frequentist error rates at the nominal level, asymptotically. With theoretical justification in hand, they go on to demonstrate that the methods derived from their generalized fiducial distribution perform as well as or better than state-of-the-art methods in survival analysis. Here I will focus mainly on general features of the generalized fiducial approach, leaving the specific results in survival analysis for the experts to discuss. DA - 2019/9// PY - 2019/9// DO - 10.1093/biomet/asz022 VL - 106 IS - 3 SP - 519-522 SN - 1464-3510 ER - TY - JOUR TI - Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts AU - St John-Williams, Lisa AU - Mahmoudiandehkordi, Siamak AU - Arnold, Matthias AU - Massaro, Tyler AU - Blach, Colette AU - Kastenmueller, Gabi AU - Louie, Gregory AU - Kueider-Paisley, Alexandra AU - Han, Xianlin AU - Baillie, Rebecca AU - Motsinger-Reif, Alison A. AU - Rotroff, Daniel AU - Nho, Kwangsik AU - Saykin, Andrew J. AU - Risacher, Shannon L. AU - Koal, Therese AU - Moseley, M. Arthur AU - Enenbaum, Jessica D. T. AU - Thompson, J. Will AU - Kaddurah-Daouk, Rima T2 - SCIENTIFIC DATA AB - Abstract Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI. DA - 2019/10/17/ PY - 2019/10/17/ DO - 10.1038/s41597-019-0181-8 VL - 6 SP - SN - 2052-4463 ER - TY - JOUR TI - Miscellanea Calibrating general posterior credible regions AU - Syring, Nicholas AU - Martin, Ryan T2 - BIOMETRIKA AB - An advantage of methods that base inference on a posterior distribution is that credible regions are readily obtained. Except in well-specified situations, however, there is no guarantee that such regions will achieve the nominal frequentist coverage probability, even approximately. To overcome this difficulty, we propose a general strategy that introduces an additional scalar tuning parameter to control the posterior spread, and we develop an algorithm that chooses this parameter so that the corresponding credible region achieves the nominal coverage probability. DA - 2019/6// PY - 2019/6// DO - 10.1093/biomet/asy054 VL - 106 IS - 2 SP - 479-486 SN - 1464-3510 KW - Bootstrap KW - Coverage probability KW - Gibbs posterior distribution KW - Model misspecification KW - Stochastic approximation ER - TY - JOUR TI - Marker-Trait Complete Analysis AU - Zhou, Yi-Hui AU - Gallins, Paul AU - Wright, Fred AB - 1 Abstract A recurring problem in genomics involves testing association of one or more traits of interest to multiple genomic features. Feature-trait squared correlations r 2 are commonly-used statistics, sensitive to trend associations. It is often of interest to perform testing across collections { r 2 } over markers and/or traits using both maxima and sums. However, both trait-trait correlations and marker-marker correlations may be strong and must be considered. The primary tools for multiple testing suffer from various shortcomings, including p -value inaccuracies due to asymptotic methods that may not be applicable. Moreover, there is a lack of general tools for fast screening and follow-up of regions of interest.To address these difficulties, we propose the MTCA approach, for M arker- T rait C omplete A nalysis. MTCA encompasses a large number of existing approaches, and provides accurate p -values over markers and traits for maxima and sums of r 2 statistics. MTCA uses the conditional inference implicit in permutation as a motivational frame-work, but provides an option for fast screening with two novel tools: (i) a multivariate-normal approximation for the max statistic, and (ii) the concept of eigenvalue-conditional moments for the sum statistic. We provide examples for gene-based association testing of a continuous phenotype and cis-eQTL analysis, but MTCA can be applied in a much wider variety of settings and platforms. DA - 2019/11/9/ PY - 2019/11/9/ DO - 10.1101/836494 VL - 11 UR - https://doi.org/10.1101/836494 ER - TY - JOUR TI - On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs AU - Cape, Joshua AU - Tang, Minh AU - Priebe, Carey E. T2 - NETWORK SCIENCE AB - Abstract Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations such as the graph Laplacian or adjacency matrix. In this paper, we analyze the asymptotic information-theoretic relative performance of Laplacian spectral embedding and adjacency spectral embedding for block assignment recovery in stochastic blockmodel graphs by way of Chernoff information. We investigate the relationship between spectral embedding performance and underlying network structure (e.g., homogeneity, affinity, core-periphery, and (un)balancedness) via a comprehensive treatment of the two-block stochastic blockmodel and the class of K -blockmodels exhibiting homogeneous balanced affinity structure. Our findings support the claim that, for a particular notion of sparsity, loosely speaking, “Laplacian spectral embedding favors relatively sparse graphs, whereas adjacency spectral embedding favors not-too-sparse graphs.” We also provide evidence in support of the claim that “adjacency spectral embedding favors core-periphery network structure.” DA - 2019/9// PY - 2019/9// DO - 10.1017/nws.2019.23 VL - 7 IS - 3 SP - 269-291 SN - 2050-1250 KW - stochastic blockmodel KW - Laplacian matrix KW - adjacency matrix KW - spectral embedding KW - network structure KW - core-periphery KW - Chernoff information ER - TY - JOUR TI - BAYESIAN MODELING OF THE STRUCTURAL CONNECTOME FOR STUDYING ALZHEIMER'S DISEASE AU - Roy, Arkaprava AU - Ghosal, Subhashis AU - Prescott, Jeffrey AU - Choudhury, Kingshuk Roy T2 - ANNALS OF APPLIED STATISTICS AB - We study possible relations between Alzheimer’s disease progression and the structure of the connectome which is white matter connecting different regions of the brain. Regression models in covariates including age, gender and disease status for the extent of white matter connecting each pair of regions of the brain are proposed. Subject inhomogeneity is also incorporated in the model through random effects with an unknown distribution. As there is a large number of pairs of regions, we also adopt a dimension reduction technique through graphon (J. Combin. Theory Ser. B 96 (2006) 933–957) functions which reduces the functions of pairs of regions to functions of regions. The connecting graphon functions are considered unknown but the assumed smoothness allows putting priors of low complexity on these functions. We pursue a nonparametric Bayesian approach by assigning a Dirichlet process scale mixture of zero to mean normal prior on the distributions of the random effects and finite random series of tensor products of B-splines priors on the underlying graphon functions. We develop efficient Markov chain Monte Carlo techniques for drawing samples for the posterior distributions using Hamiltonian Monte Carlo (HMC). The proposed Bayesian method overwhelmingly outperforms a competing method based on ANCOVA models in the simulation setup. The proposed Bayesian approach is applied on a dataset of 100 subjects and 83 brain regions and key regions implicated in the changing connectome are identified. DA - 2019/9// PY - 2019/9// DO - 10.1214/19-AOAS1257 VL - 13 IS - 3 SP - 1791-1816 SN - 1932-6157 KW - ADNI KW - B-spline prior KW - brain image KW - connectome KW - graphical model KW - Graphon KW - HMC ER - TY - JOUR TI - Exploration and Inference in Spatial Extremes Using Empirical Basis Functions AU - Morris, Samuel A. AU - Reich, Brian J. AU - Thibaud, Emeric T2 - JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS AB - Statistical methods for inference on spatial extremes of large datasets are yet to be developed. Motivated by standard dimension reduction techniques used in spatial statistics, we propose an approach based on empirical basis functions to explore and model spatial extremal dependence. Based on a low-rank max-stable model, we propose a data-driven approach to estimate meaningful basis functions using empirical pairwise extremal coefficients. These spatial empirical basis functions can be used to visualize the main trends in extremal dependence. In addition to exploratory analysis, we describe how these functions can be used in a Bayesian hierarchical model to model spatial extremes of large datasets. We illustrate our methods on extreme precipitations in eastern USA. Supplementary materials accompanying this paper appear online DA - 2019/12// PY - 2019/12// DO - 10.1007/s13253-019-00359-1 VL - 24 IS - 4 SP - 555-572 SN - 1537-2693 KW - Dimension reduction KW - Max-stable process KW - Non-stationary data analysis ER - TY - JOUR TI - Spectral measure of color variation of black-orange-black (BOB) pattern in small parasitoid wasps (Hymenoptera: Scelionidae), a statistical approach T2 - PLOS ONE AB - Small parasitoid wasps are abundant and extremely diverse, yet their colors have not been analyzed. One of the more common color patterns observed in these wasps is a black-orange-black pattern, which is especially common among neotropical species of Scelionidae ranging in size from 2 to 10 mm. Due to the methodological challenges involved in extracting and analyzing pigments from small-sized insects, other methods for examining colors need to be explored. In this work, we propose the use of microspectrophotometry in combination with statistical analysis methods in order to 8 study the spectral properties in such cases. We examined 8 scelionid genera and 1 genus from a distantly related family (Evaniidae), all showing the black-orange-black pattern. Functional Data Analysis and statistical analysis of Euclidean distances for color components were applied to study color differences both between and within genera. The Functional Data Analysis proved to be a better method for treating the reflectance data because it gave a better representation of the physical information. Also, the reflectance spectra were separated into spectral color component contributions and each component was labeled according to its own dominant wavelength at the maximum of the spectrum: Red, Green and Blue. When comparing spectral components curves, the spectral blue components of the orange and black colors, independent of the genera being compared, result almost identical, suggesting that there is a common compound for the pigments. The results also suggest that cuticle from different genera, but with the same color might have a similar chemical composition. This is the first time that the black and orange colors in small parasitoid wasps has been analyzed and our results provide a basis for future research on the color patterns of an abundant but neglected group of insects. DA - 2019/10/24/ PY - 2019/10/24/ DO - 10.1371/journal.pone.0218061 VL - 14 IS - 10 UR - http://dx.doi.org/10.1371/journal.pone.0218061 ER - TY - JOUR TI - Long-term incidence and risk of noncardiovascular and all-cause mortality in apparently healthy cats and cats with preclinical hypertrophic cardiomyopathy AU - Fox, Philip R. AU - Keene, Bruce W. AU - Lamb, Kenneth AU - Schober, Karsten E. AU - Chetboul, Valerie AU - Fuentes, Virginia Luis AU - Payne, Jessie Rose AU - Wess, Gerhard AU - Hogan, Daniel F. AU - Abbott, Jonathan A. AU - Haeggstroem, Jens AU - Culshaw, Geoffrey AU - Fine-Ferreira, Deborah AU - Cote, Etienne AU - Trehiou-Sechi, Emilie AU - Motsinger-Reif, Alison A. AU - Nakamura, Reid K. AU - Singh, Manreet AU - Ware, Wendy A. AU - Riesen, Sabine C. AU - Borgarelli, Michele AU - Rush, John E. AU - Vollmar, Andrea AU - Lesser, Michael B. AU - Van Israel, Nicole AU - Lee, Pamela Ming-Show AU - Bulmer, Barret AU - Santilli, Roberto AU - Bossbaly, Maribeth J. AU - Quick, Nadine AU - Bussadori, Claudio AU - Bright, Janice AU - Estrada, Amara H. AU - Ohad, Dan G. AU - Palacio, Maria Josefa Fernandez AU - Brayley, Jennifer Lunney AU - Schwartz, Denise S. AU - Gordon, Sonya G. AU - Jung, SeungWoo AU - Bove, Christina M. AU - Brambilla, Paola G. AU - Moise, N. Sydney AU - Stauthammer, Christopher AU - Quintavalla, Cecilia AU - Manczur, Ferenc AU - Stepien, Rebecca L. AU - Mooney, Carmel AU - Hung, Yong-Wei AU - Lobetti, Remo AU - Tamborini, Alice AU - Oyama, Mark A. AU - Komolov, Andrey AU - Fujii, Yoko AU - Pariaut, Romain AU - Uechi, Masami AU - Ohara, Victoria Yukie Tachika T2 - JOURNAL OF VETERINARY INTERNAL MEDICINE AB - Abstract Background Epidemiologic knowledge regarding noncardiovascular and all‐cause mortality in apparently healthy cats (AH) and cats with preclinical hypertrophic cardiomyopathy (pHCM) is limited, hindering development of evidence‐based healthcare guidelines. Objectives To characterize/compare incidence rates, risk, and survival associated with noncardiovascular and all‐cause mortality in AH and pHCM cats. Animals A total of 1730 client‐owned cats (722 AH, 1008 pHCM) from 21 countries. Methods Retrospective, multicenter, longitudinal, cohort study. Long‐term health data were extracted by medical record review and owner/referring veterinarian interviews. Results Noncardiovascular death occurred in 534 (30.9%) of 1730 cats observed up to 15.2 years. Proportion of noncardiovascular death did not differ significantly between cats that at study enrollment were AH or had pHCM ( P = .48). Cancer, chronic kidney disease, and conditions characterized by chronic weight‐loss‐vomiting‐diarrhea‐anorexia were the most frequently recorded noncardiovascular causes of death. Incidence rates/risk of noncardiac death increased with age in AH and pHCM. All‐cause death proportions were greater in pHCM than AH (65% versus 40%, respectively; P < .001) because of higher cardiovascular mortality in pHCM cats. Comparing AH with pHCM, median survival (study entry to noncardiovascular death) did not differ (AH, 9.8 years; pHCM, 8.6 years; P = .10), but all‐cause survival was significantly shorter in pHCM ( P = .0001). Conclusions and Clinical Importance All‐cause mortality was significantly greater in pHCM cats due to disease burden contributed by increased cardiovascular death superimposed upon noncardiovascular death. DA - 2019/11// PY - 2019/11// DO - 10.1111/jvim.15609 VL - 33 IS - 6 SP - 2572-2586 SN - 1939-1676 KW - cancer KW - chronic kidney disease KW - epidemiology KW - mortality KW - survival ER - TY - JOUR TI - Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes AU - Zhu, Wensheng AU - Zeng, Donglin AU - Song, Rui T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - Wensheng Zhua, Donglin Zengb & Rui Songc*a Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Chinab Departments of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NCc Department of Statistics, North Carolina State University, Raleigh, NC DA - 2019/7/3/ PY - 2019/7/3/ DO - 10.1080/01621459.2018.1506341 VL - 114 IS - 527 SP - 1404-1417 SN - 1537-274X KW - Hard threshold KW - Q-learning KW - Value function inference KW - Variable selection ER - TY - JOUR TI - Evaluation of Emamectin Benzoate and Propiconazole for Management of a New Invasive Shot Hole Borer (Euwallacea nr. fornicatus, Coleoptera: Curculionidae) and Symbiotic Fungi in California Sycamores AU - Grosman, Donald M. AU - Eskalen, Akif AU - Brownie, Cavell T2 - JOURNAL OF ECONOMIC ENTOMOLOGY AB - The polyphagous shot hole borer (Euwallacea nr. fornicatus, Coleoptera: Curculionidae: Scolytinae), an exotic and invasive ambrosia beetle, was recently found attacking a number of tree species in Los Angeles, Orange, Riverside, and San Diego Counties in southern California. Their colonization and subsequent inoculation of a suite of symbiotic fungi that cause Fusarium Dieback, has resulted in extensive mortality of some tree species, including, California sycamore (Platanus racemose Nutt.). There are no sustainable control options for polyphagous shot hole borer other than maintaining tree vigor and removal of severely infested host material. The effectiveness of therapeutic treatments of an injected systemic insecticide containing emamectin benzoate (EB) alone and in combination with a systemic fungicide, propiconazole (P), was evaluated over a 4-yr period for maintaining the health of individual sycamore trees infested by polyphagous shot hole borer. All treatments containing EB reduced levels of polyphagous shot hole borer colonization and associated sap flow at attack sites compared to untreated controls. A second trial evaluated preventative treatments of EB and P alone or combined to protect individual sycamore from colonization by polyphagous shot hole borer. After 45 mo posttreatment, all treatments significantly reduced polyphagous shot hole borer attack levels and successful attacks compared to untreated controls (EB + P > EB alone > P alone). We concluded that EB alone or combined with P are acceptable therapeutic and preventative treatments for management of polyphagous shot hole borer in California sycamore in southern California. DA - 2019/6// PY - 2019/6// DO - 10.1093/jee/toy423 VL - 112 IS - 3 SP - 1267-1273 SN - 1938-291X KW - ambrosia beetle KW - chemical control KW - polyphagous shot hole borer KW - tree injection ER - TY - JOUR TI - Linkage Analysis and Haplotype Phasing in Experimental Autopolyploid Populations with High Ploidy Level Using Hidden Markov Models AU - Mollinari, Marcelo AU - Garcia, Antonio Augusto Franco T2 - G3: Genes|Genomes|Genetics AB - Abstract Modern SNP genotyping technologies allow measurement of the relative abundance of different alleles for a given locus and consequently estimation of their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels for a set of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters by maximizing the likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results show the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations. DA - 2019/8/12/ PY - 2019/8/12/ DO - 10.1534/g3.119.400378 VL - 9 IS - 10 SP - 3297-3314 J2 - G3 LA - en OP - SN - 2160-1836 UR - http://dx.doi.org/10.1534/g3.119.400378 DB - Crossref KW - Polyploidy KW - Recombination KW - Fraction KW - Bivalent Pairing KW - Multilocus KW - Analysis ER - TY - JOUR TI - Nucleo-cytoplasmic Partitioning of ARF Proteins Controls Auxin Responses in Arabidopsis thaliana AU - Powers, Samantha K. AU - Holehouse, Alex S. AU - Korasick, David A. AU - Schreiber, Katherine H. AU - Clark, Natalie M. AU - Jing, Hongwei AU - Emenecker, Ryan AU - Han, Soeun AU - Tycksen, Eric AU - Hwang, Ildoo AU - Sozzani, Rosangela AU - Jez, Joseph M. AU - Pappu, Rohit V. AU - Strader, Lucia C. T2 - MOLECULAR CELL AB - The phytohormone auxin plays crucial roles in nearly every aspect of plant growth and development. The auxin response factor (ARF) transcription factor family regulates auxin-responsive gene expression and exhibits nuclear localization in regions of high auxin responsiveness. Here we show that the ARF7 and ARF19 proteins accumulate in micron-sized assemblies within the cytoplasm of tissues with attenuated auxin responsiveness. We found that the intrinsically disordered middle region and the folded PB1 interaction domain of ARFs drive protein assembly formation. Mutation of a single lysine within the PB1 domain abrogates cytoplasmic assemblies, promotes ARF nuclear localization, and results in an altered transcriptome and morphological defects. Our data suggest a model in which ARF nucleo-cytoplasmic partitioning regulates auxin responsiveness, providing a mechanism for cellular competence for auxin signaling. DA - 2019/10/3/ PY - 2019/10/3/ DO - 10.1016/j.molcel.2019.06.044 VL - 76 IS - 1 SP - 177-+ SN - 1097-4164 ER - TY - JOUR TI - A varying-coefficient generalized odds rate model with time-varying exposure: An application to fitness and cardiovascular disease mortality AU - Zhou, Jie AU - Zhang, Jiajia AU - Mclain, Alexander C. AU - Lu, Wenbin AU - Sui, Xuemei AU - Hardin, James W. T2 - BIOMETRICS AB - Abstract Varying-coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time-varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying-coefficient generalized odds rate model that allows flexible estimation of age-modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed-effects model, and its associated age-varying coefficient function is represented by cubic B-splines. An expectation-maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian-Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E-step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study. DA - 2019/9// PY - 2019/9// DO - 10.1111/biom.13057 VL - 75 IS - 3 SP - 853-863 SN - 1541-0420 KW - B-splines KW - expectation-maximization algorithm KW - generalized odds rate model KW - joint modeling KW - varying coefficient ER - TY - JOUR TI - ADAPTION OF AKAIKE INFORMATION CRITERION UNDER LEAST SQUARES FRAMEWORKS FOR COMPARISON OF STOCHASTIC MODELS AU - Banks, H. T. AU - Joyner, Michele L. T2 - QUARTERLY OF APPLIED MATHEMATICS AB - In this paper, we examine the feasibility of extending the Akaike information criterion (AIC) for deterministic systems as a potential model selection criteria for stochastic models. We discuss the implementation method for three different classes of stochastic models: continuous time Markov chains (CTMC), stochastic differential equations (SDE), and random differential equations (RDE). The effectiveness and limitations of implementing the AIC for comparison of stochastic models is demonstrated using simulated data from the three types of models and then applied to experimental longitudinal growth data for algae. DA - 2019/12// PY - 2019/12// DO - 10.1090/qam/1542 VL - 77 IS - 4 SP - 831-859 SN - 1552-4485 KW - Continuous time Markov chain models KW - CTMC KW - stochastic differential equations KW - SDE KW - random differential equations KW - RDE KW - inverse problems KW - model comparison techniques KW - Akaike information criterion KW - AIC ER - TY - JOUR TI - Estimation of probability distributions of parameters using aggregate population data: analysis of a CAR T-cell cancer model AU - Schacht, Celia AU - Meade, Annabel AU - Banks, H. T. AU - Enderling, Heiko AU - Abate-Daga, Daniel T2 - MATHEMATICAL BIOSCIENCES AND ENGINEERING AB - In this effort we explain fundamental formulations for aggregate data inverse problems requiring estimation of probability distribution parameters. We use as a motivating example a class of CAR T-call cancer models in mice. After ascertaining results on model stability and sensitivity with respect to parameters, we carry out first elementary computations on the question how much data is needed for successful estimation of probability distributions. DA - 2019/// PY - 2019/// DO - 10.3934/mbe.2019365 VL - 16 IS - 6 SP - 7299-7326 SN - 1551-0018 KW - aggregate data KW - CAR T-cell therapy KW - cancer model KW - inverse problems KW - design of experiments ER - TY - JOUR TI - Bayesian variable selection for logistic regression AU - Tian, Yiqing AU - Bondell, Howard D. AU - Wilson, Alyson T2 - STATISTICAL ANALYSIS AND DATA MINING AB - Abstract A key issue when using Bayesian variable selection for logistic regression is choosing an appropriate prior distribution. This can be particularly difficult for high‐dimensional data where complete separation will naturally occur in the high‐dimensional space. We propose the use of the Normal‐Gamma prior with recommendations on calibration of the hyper‐parameters. We couple this choice with the use of joint credible sets to avoid performing a search over the high‐dimensional model space. The approach is shown to outperform other methods in high‐dimensional settings, especially with highly correlated data. The Bayesian approach allows for a natural specification of the hyper‐parameters. DA - 2019/10// PY - 2019/10// DO - 10.1002/sam.11428 VL - 12 IS - 5 SP - 378-393 SN - 1932-1872 KW - joint credible region KW - Laplace prior KW - LASSO KW - Normal-gamma prior ER - TY - JOUR TI - Set‐based differential covariance testing for genomics AU - Zhou, Yi‐Hui T2 - Stat AB - The problem of detecting the changes in covariance for a single pair of genomic features has been studied in some detail but may be limited in importance or general applicability. For testing equality of covariance matrices of a set of features, many methods have been limited to the two-sample problem and involve varying assumptions on the number of features p versus the sample size n. More general covariance regression approaches are appealing but have been insufficiently structured to provide interpretable testing. To address these deficiencies, we propose a simple uniform framework to test association of covariance matrices with an experimental variable, whether discrete or continuous. We describe four different summary statistics, to ensure power and flexibility under various alternatives, including a new "connectivity" statistic that is sensitive to the changes in overall covariance magnitude. For continuous experimental variables, a natural individual "risk score" is associated with several of the statistics. We establish asymptotic results applicable to both continuous and discrete responses, with relatively mild conditions and allowing for situations where p>n. We also show that the proposed statistics are permutationally equivalent to some existing methods in the two-sample special case. We demonstrate the power and utility of our approaches via simulation and analysis of real data. The R package CorDiff is published on R CRAN. DA - 2019/1// PY - 2019/1// DO - 10.1002/sta4.235 VL - 8 IS - 1 UR - https://doi.org/10.1002/sta4.235 KW - asymptotics KW - covariance testing KW - permutation ER - TY - JOUR TI - Carbon monoxide releasing molecule enhances coagulation and decreases fibrinolysis in canine plasma exposed to Crotalus viridis venom in vitro and in vivo AU - Johnson, Tyler E. AU - Wells, Raegan J. AU - Bell, Amy AU - Nielsen, Vance G. AU - Olver, Christine S. T2 - BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY AB - Carbon monoxide releasing molecule-2 (CORM-2), an emerging therapeutic in human medicine, enhances plasmatic coagulation and attenuates fibrinolysis in vitro in human, rabbit and horse plasma and ameliorates hypocoagulation and hyperfibrinolysis secondary to venom exposure in human plasma in vitro. Fibrinogenases in rattlesnake venom cause decreased clot strength, and in the presence of tissue plasminogen activator (tPA) in vitro, a markedly increased rate of clot lysis. CO interacts with a haem group on fibrinogen, changing its configuration so that the fibrin clot is strengthened and more resistant to fibrinolysis. We hypothesized that CORM-2 enhances coagulation and attenuates fibrinolysis in canine plasma exposed to C viridis venom. We measured the effects of C viridis venom on clot strength, rates of coagulation and fibrinolysis in both pooled canine plasma and plasma from individual naturally envenomed dogs, with and without CORM-2, using thromboelastography (TEG). We tested venom effects on coagulation using tissue factor (TF) activated TEG and on both coagulation and fibrinolysis using TF-activated TEG with added tPA. We found that 17.9 µg/mL of venom causes a mean 26.4% decrease in clot strength, a 61.8% decrease in maximum rate of thrombus generation, 75% faster clot lysis, a 226% increase in maximum rate of lysis and a 92% decrease in total clot life span (CLS). CORM-2 ameliorated these effects, increasing CLS in the presence of venom by 603%. Additionally, we showed that CORM-2 has similar effects in vitro on plasma from naturally envenomed dogs, showing promise as an adjunct therapy for snake envenomation. DA - 2019/10// PY - 2019/10// DO - 10.1111/bcpt.13242 VL - 125 IS - 4 SP - 328-336 SN - 1742-7843 KW - Carbon monoxide releasing molecules KW - coagulation KW - coagulopathy KW - dog KW - venom ER - TY - JOUR TI - Individual and environmental risk factors associated with fecal glucocorticoid metabolite concentrations in zoo-housed Asian and African elephants AU - Brown, Janine L. AU - Carlstead, Kathy AU - Bray, Jessica D. AU - Dickey, David AU - Farin, Charlotte AU - Ange-van Heugten, Kimberly T2 - PLOS ONE AB - A recent large-scale welfare study in North America involving 106 Asian (Elephas maximus) and 131 African (Loxodonta africana) elephants at 64 accredited facilities identified links (i.e., risk factors) between zoo environmental factors and a number of welfare outcomes (stereotypic behavior, ovarian acyclicity, hyperprolactinemia, walking and recumbence, body condition, health status, serum cortisol). For this population of elephants, we used the same epidemiological methods to examine associations between those risk factors and two additional welfare outcomes, mean concentration and individual variability (CV) of fecal glucocorticoid metabolite concentrations (FGM) as indicators of stress. Results indicate that African elephants are more responsive to social stressors than Asians, and that poor joint health is a stress-related welfare problem for Asian, but not African elephants in the North American population. For both species, higher FGM concentrations were associated with zoos located at more northern latitudes, whereas lower FGM concentrations were associated with having free access to indoor/outdoor spaces, and spending more time in managed interactions with staff. Also important for captive management, elephants having diverse enrichment options and belonging to compatible social groups exhibited reduced intra-individual variability in FGM concentrations. Our findings show that aspects of the zoo environment can be potential sources of stress for captive elephants, and that there are management activities that may facilitate coping with zoo conditions. Given species differences in factors that affected FGM, targeted, species-specific management approaches likely are needed to ensure good welfare for all elephants. DA - 2019/9/4/ PY - 2019/9/4/ DO - 10.1371/journal.pone.0217326 VL - 14 IS - 9 SP - SN - 1932-6203 ER - TY - JOUR TI - False confidence, non-additive beliefs, and valid statistical inference AU - Martin, Ryan T2 - INTERNATIONAL JOURNAL OF APPROXIMATE REASONING AB - Statistics has made tremendous advances since the times of Fisher, Neyman, Jeffreys, and others, but the fundamental and practically relevant questions about probability and inference that puzzled our founding fathers remain unanswered. To bridge this gap, I propose to look beyond the two dominating schools of thought and ask the following three questions: what do scientists need out of statistics, do the existing frameworks meet these needs, and, if not, how to fill the void? To the first question, I contend that scientists seek to convert their data, posited statistical model, etc., into calibrated degrees of belief about quantities of interest. To the second question, I argue that any framework that returns additive beliefs, i.e., probabilities, necessarily suffers from {\em false confidence}---certain false hypotheses tend to be assigned high probability---and, therefore, risks systematic bias. This reveals the fundamental importance of {\em non-additive beliefs} in the context of statistical inference. But non-additivity alone is not enough so, to the third question, I offer a sufficient condition, called {\em validity}, for avoiding false confidence, and present a framework, based on random sets and belief functions, that provably meets this condition. Finally, I discuss characterizations of p-values and confidence intervals in terms of valid non-additive beliefs, which imply that users of these classical procedures are already following the proposed framework without knowing it. DA - 2019/10// PY - 2019/10// DO - 10.1016/j.ijar.2019.06.005 VL - 113 SP - 39-73 SN - 1873-4731 KW - Bayes KW - Fiducial KW - Inferential model KW - p-Value KW - Plausibility function KW - Random set ER - TY - JOUR TI - ENTROPY LEARNING FOR DYNAMIC TREATMENT REGIMES AU - Jiang, Binyan AU - Song, Rui AU - Li, Jialiang AU - Zeng, Donglin AU - Lu, Wenbin AU - He, Xin AU - Xu, Shirong AU - Wang, Junhui AU - Qian, Min AU - Cheng, Bin AU - Qiu, Hongxiang AU - Luedtke, Alex AU - Laan, Mark AU - Wager, Stefan AU - Zhang, Yichi AU - Laber, Eric B. AU - Kallus, Nathan T2 - STATISTICA SINICA AB - Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not been well established in machine learning based approaches. In this paper, we propose a entropy learning approach to estimate the optimal individualized treatment rules (ITRs). We obtain the asymptotic distributions for the estimated rules so further provide valid inference. The proposed approach is demonstrated to perform well in finite sample through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings that are otherwise not revealed with existing approaches. DA - 2019/10// PY - 2019/10// DO - 10.5705/ss.202018.0076 VL - 29 IS - 4 SP - 1633-1710 SN - 1996-8507 KW - Dynamic treatment regime KW - entropy learning KW - personalized medicine ER - TY - JOUR TI - TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS AU - Zhang, Xiang AU - Li, Lexin AU - Zhou, Hua AU - Zhou, Yeqing AU - Shen, Dinggang T2 - STATISTICA SINICA AB - Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs). The proposed GEE approach accounts for intra-subject correlation, and an imposed low-rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative. DA - 2019/10// PY - 2019/10// DO - 10.5705/ss.202017.0153 VL - 29 IS - 4 SP - 1977-2005 SN - 1996-8507 KW - Generalized estimating equations KW - longitudinal imaging KW - low rank tensor decomposition KW - magnetic resonance imaging KW - multidimensional array KW - tensor regression ER - TY - JOUR TI - Conference report: 2018 materials and data science hackathon (MATDAT18) AU - Ferguson, Andrew L. AU - Mueller, Tim AU - Rajasekaran, Sanguthevar AU - Reich, Brian J. T2 - MOLECULAR SYSTEMS DESIGN & ENGINEERING AB - MATDAT18 organizers and participants. DA - 2019/6/1/ PY - 2019/6/1/ DO - 10.1039/c9me90018g VL - 4 IS - 3 SP - 462-468 SN - 2058-9689 ER - TY - JOUR TI - Genome-wide DNA copy number analysis and targeted transcriptional analysis of canine histiocytic malignancies identifies diagnostic signatures and highlights disruption of spindle assembly complex AU - Kennedy, Katherine AU - Thomas, Rachael AU - Durrant, Jessica AU - Jiang, Tao AU - Motsinger-Reif, Alison AU - Breen, Matthew T2 - CHROMOSOME RESEARCH DA - 2019/9// PY - 2019/9// DO - 10.1007/s10577-019-09606-0 VL - 27 IS - 3 SP - 179-202 SN - 1573-6849 KW - Histiocytic sarcoma KW - Chromothripsis KW - MMP9 KW - Aurora kinase KW - Dendritic cell sarcoma ER - TY - JOUR TI - Longitudinal dynamic functional regression AU - Staicu, Ana‐Maria AU - Islam, Md Nazmul AU - Dumitru, Raluca AU - Heugten, Eric van T2 - Journal of the Royal Statistical Society: Series C (Applied Statistics) AB - Summary The paper develops a parsimonious modelling framework to study the time-varying association between scalar outcomes and functional predictors observed at many instances, in longitudinal studies. The methods enable us to reconstruct the full trajectory of the response and are applicable to Gaussian and non-Gaussian responses. The idea is to model the time-varying functional predictors by using orthogonal basis functions and to expand the time-varying regression coefficient by using the same basis. Numerical investigation through simulation studies and data analysis show excellent performance in terms of accurate prediction and efficient computations, when compared with existing alternatives. The methods are inspired and applied to an animal science application, where of interest is to study the association between the feed intake of lactating sows and the minute-by-minute temperature throughout the 21 days of their lactation period. R code and an R illustration are provided. DA - 2019/9/12/ PY - 2019/9/12/ DO - 10.1111/rssc.12376 VL - 69 IS - 1 SP - 25-46 J2 - J. R. Stat. Soc. C LA - en OP - SN - 0035-9254 1467-9876 UR - http://dx.doi.org/10.1111/rssc.12376 DB - Crossref ER - TY - JOUR TI - Effects of extended powered knee prosthesis stance time via visual feedback on gait symmetry of individuals with unilateral amputation: a preliminary study AU - Brandt, Andrea AU - Riddick, William AU - Stallrich, Jonathan AU - Lewek, Michael AU - Huang, He Helen T2 - JOURNAL OF NEUROENGINEERING AND REHABILITATION AB - Establishing gait symmetry is a major aim of amputee rehabilitation and may be more attainable with powered prostheses. Though, based on previous work, we postulate that users transfer a previously-learned motor pattern across devices, limiting the functionality of more advanced prostheses. The objective of this study was to preliminarily investigate the effect of increased stance time via visual feedback on amputees' gait symmetry using powered and passive knee prostheses.Five individuals with transfemoral amputation or knee disarticulation walked at their self-selected speed on a treadmill. Visual feedback was used to promote an increase in the amputated-limb stance time. Individuals were fit with a commercially-available powered prosthesis by a certified prosthetist and practiced walking during a prior visit. The same protocol was completed with a passive knee and powered knee prosthesis on separate days. We used repeated-measures, two-way ANOVA (alpha = 0.05) to test for significant effects of the feedback and device factors. Our main outcome measures were stance time asymmetry, peak anterior-posterior ground reaction forces, and peak anterior propulsion asymmetry.Increasing the amputated-limb stance time via visual feedback significantly improved the stance time symmetry (p = 0.012) and peak propulsion symmetry (p = 0.036) of individuals walking with both prostheses. With the powered knee prosthesis, the highest feedback target elicited 36% improvement in stance time symmetry, 22% increase in prosthesis-side peak propulsion, and 47% improvement in peak propulsion symmetry compared to a no feedback condition. The changes with feedback were not different with the passive prosthesis, and the main effects of device/ prosthesis type were not statistically different. However, subject by device interactions were significant, indicating individuals did not respond consistently with each device (e.g. prosthesis-side propulsion remained comparable to or was greater with the powered versus passive prosthesis for different subjects). Overall, prosthesis-side peak propulsion averaged across conditions was 31% greater with the powered prosthesis and peak propulsion asymmetry improved by 48% with the powered prosthesis.Increasing prosthesis-side stance time via visual feedback favorably improved individuals' temporal and propulsive symmetry. The powered prosthesis commonly enabled greater propulsion, but individuals adapted to each device with varying behavior, requiring further investigation. DA - 2019/9/11/ PY - 2019/9/11/ DO - 10.1186/s12984-019-0583-z VL - 16 IS - 1 SP - SN - 1743-0003 KW - Gait KW - Amputation KW - Visual Feedback KW - Rehabilitation KW - Knee Prosthesis ER - TY - JOUR TI - Bioinspired Bistable Soft Actuators AU - Wei, S. AU - Shao, H. AU - Ghosh, T. K. T2 - ELECTROACTIVE POLYMER ACTUATORS AND DEVICES (EAPAD) XXI AB - DEAs have been studied for decades as a potential polymer artificial muscle for its excellent mechanical properties and large electric field-induced strains. The structural design of DEAs enhances the actuator performances and converts the electrically–controlled strain to diverse motions including linear motion, bending, twisting and moving with multiple degree of freedom. Inspired by the Venus Flytrap (VFT), whose bistable leaves and local strain redistribution are crucial to the fast closure speed, we developed cylindrically-curved bistable laminated DEAs, and activated the bistable shape transformation by electrically tuning the strain field. To obtain the bistable structure, two elastomeric films are prestrained biaxially and bonded orthogonally to a stiffer elastic film in the middle. Due to the elastic energy minimization, the originally flat laminate immediately self-equilibrated to two bistable cylindrical shapes, with the curvatures orthogonal to each other. Basic theoretical analyses on the interaction of prestrains and bending curvatures provide guidance to the design of bistable morphing shapes. The prestrains on the DE films not only generate various curved shapes, but also decreases the film thickness and therefore reduces the actuation voltage. Similar to the fast closure of VFT, which is activated by the strain redistribution resulted from the motor cell enlargement, our bistable DEA achieves reversible bistable shape transformation by voltage-induced strain change at the area covered by compliant electrodes. DA - 2019/// PY - 2019/// DO - 10.1117/12.2522123 VL - 10966 SP - SN - 1996-756X KW - Bioinspired KW - bistable KW - dielectric elastomer ER - TY - JOUR TI - A smoothing-based goodness-of-fit test of covariance for functional data AU - Chen, Stephanie T. AU - Xiao, Luo AU - Staicu, Ana-Maria T2 - BIOMETRICS AB - Abstract Functional data methods are often applied to longitudinal data as they provide a more flexible way to capture dependence across repeated observations. However, there is no formal testing procedure to determine if functional methods are actually necessary. We propose a goodness-of-fit test for comparing parametric covariance functions against general nonparametric alternatives for both irregularly observed longitudinal data and densely observed functional data. We consider a smoothing-based test statistic and approximate its null distribution using a bootstrap procedure. We focus on testing a quadratic polynomial covariance induced by a linear mixed effects model and the method can be used to test any smooth parametric covariance function. Performance and versatility of the proposed test is illustrated through a simulation study and three data applications. DA - 2019/6// PY - 2019/6// DO - 10.1111/biom.13005 VL - 75 IS - 2 SP - 562-571 SN - 1541-0420 KW - functional data analysis KW - Functional principal components analysis KW - hypothesis testing KW - linear mixed effects models KW - longitudinal data analysis ER - TY - JOUR TI - Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation AU - Guan, Yawen AU - Sampson, Christian AU - Tucker, J. Derek AU - Chang, Won AU - Mondal, Anirban AU - Haran, Murali AU - Sulsky, Deborah T2 - JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS AB - Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online. DA - 2019/9// PY - 2019/9// DO - 10.1007/s13253-019-00353-7 VL - 24 IS - 3 SP - 444-463 SN - 1537-2693 KW - Arctic sea ice KW - Calibration KW - Emulation KW - Gaussian process KW - Image registration ER - TY - JOUR TI - Performance of the Population Bioequivalence (PBE) Statistical Test with Impactor Sized Mass Data AU - Chen, Stephanie AU - Morgan, Beth AU - Beresford, Hayden AU - Getz, Elise Burmeister AU - Christopher, David AU - Langstrom, Goran AU - Strickland, Helen AU - Wiggenhorn, Christopher AU - Lyapustina, Svetlana T2 - AAPS PHARMSCITECH DA - 2019/10// PY - 2019/10// DO - 10.1208/s12249-019-1507-8 VL - 20 IS - 7 SP - SN - 1530-9932 KW - average bioequivalence KW - inhalation products KW - in vitro performance KW - regulatory KW - US Food and Drug Administration (FDA) ER - TY - JOUR TI - Hunting interacts with socio-demographic predictors of human perceptions of urban coyotes AU - Drake, Michael D. AU - Peterson, M. Nils AU - Griffith, Emily H. AU - Olfenbuttel, Colleen AU - Moorman, Christopher E. AU - Deperno, Christopher S. T2 - WILDLIFE SOCIETY BULLETIN AB - ABSTRACT Recent research suggests hunting participation interacts with other variables (e.g., bird‐watching participation) to shape attitudes about wildlife. We build on this research by evaluating how hunting participation interacted with key variables to predict affectual attitudes toward coyotes ( Canis latrans ), support for coyotes on the landscape, and support for coyote management approaches in urban North Carolina, USA. We conducted surveys of urban hunters and nonhunting urban residents during 2015, and modeled relationships between respondent attributes and perceptions of coyotes. Among nonhunters, men liked coyotes more than women did, but the relationship was reversed among hunters. Similarly, men supported killing coyotes more than women did, but the difference was less pronounced among hunters. Pet owners liked coyotes and opposed killing coyotes more than non–pet owners did, but those differences disappeared among hunters. Having a rural background predicted lower tolerance for coyotes among hunters but not nonhunters. Finally, age was negatively related to support for lethal coyote management among hunters but positively related to support among nonhunters. Participation in hunting may moderate how socio‐demographic variables predict perceptions of coyotes and change or reverse previously described relationships between these variables and perceptions of wildlife. © 2019 The Wildlife Society. DA - 2019/9// PY - 2019/9// DO - 10.1002/wsb.993 VL - 43 IS - 3 SP - 447-454 SN - 1938-5463 KW - Canis latrans KW - carnivore KW - coyotes KW - hunting KW - moderating effects KW - urban ER - TY - JOUR TI - Guest Editors' Introduction to the Special Issue on "Climate and the Earth System" AU - Hammerling, Dorit AU - Reich, Brian J. T2 - JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS AB - The Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on the Climate and Earth System highlights recent statistical develops that aim to refine our understanding of this complex system. New methods are required to process the massive environmental data that often fuels climate analysis and to properly account for uncertainty in the results. This special issue proudly features eight papers that span a wide range of computational and methodological problems related to the climate and earth system. In this brief introduction, we identify common themes among the papers and point to areas of future research. DA - 2019/9// PY - 2019/9// DO - 10.1007/s13253-019-00373-3 VL - 24 IS - 3 SP - 395-397 SN - 1537-2693 KW - Climate models KW - Computational statistics KW - Extreme value analysis KW - Spatiotemporal data ER - TY - JOUR TI - Creation of a Geospatially Explicit, Agent-based Model of a Regional Healthcare Network with Application to Clostridioides difficile Infection AU - Rhea, Sarah AU - Hilscher, Rainer AU - Rineer, James I AU - Munoz, Breda AU - Jones, Kasey AU - Endres-Dighe, Stacy M. AU - DiBiase, Lauren M. AU - Sickbert-Bennett, Emily E. AU - Weber, David J. AU - MacFarquhar, Jennifer K. AU - Dubendris, Heather AU - Bobashev, Georgiy T2 - HEALTH SECURITY AB - Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers. Agent-based models describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. They are increasingly used to study healthcare-associated infections, including Clostridioides difficile infection. The authors describe their methods of creating a geospatially explicit model of a major regional healthcare network using a synthetic population as microdata input. DA - 2019/8/1/ PY - 2019/8/1/ DO - 10.1089/hs.2019.0021 VL - 17 IS - 4 SP - 276-290 SN - 2326-5108 KW - Geospatial KW - Agent-based model KW - Synthetic population KW - Healthcare-associated infection KW - Clostridioides difficile infection KW - Healthcare network ER - TY - JOUR TI - Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene–environment interaction effect AU - Zhao, N. AU - Zhang, H. AU - Clark, J.J. AU - Maity, A. AU - Wu, M.C. T2 - Biometrics AB - Abstract Most common human diseases are a result from the combined effect of genes, the environmental factors, and their interactions such that including gene–environment (GE) interactions can improve power in gene mapping studies. The standard strategy is to test the SNPs, one‐by‐one, using a regression model that includes both the SNP effect and the GE interaction. However, the SNP‐by‐SNP approach has serious limitations, such as the inability to model epistatic SNP effects, biased estimation, and reduced power. Thus, in this article, we develop a kernel machine regression framework to model the overall genetic effect of a SNP‐set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function andwe model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. Extensive simulations and real data analysis show that our proposed method has correct type I error and can have higher power than score‐based approaches under many situations. DA - 2019/6// PY - 2019/6// DO - 10.1111/biom.13003 VL - 75 IS - 2 SP - 625-637 UR - http://dx.doi.org/10.1111/biom.13003 KW - gene-environment interactions KW - kernel machine testing KW - likelihood ratio test KW - multiple variance components KW - spectral decomposition KW - unidentifiable conditions ER - TY - JOUR TI - Genomic Selection with Allele Dosage in Panicum maximum Jacq. AU - Lara, Leticia A. de C. AU - Santos, Mateus F. AU - Jank, Liana AU - Chiari, Lucimara AU - Vilela, Mariane de M. AU - Amadeu, Rodrigo R. AU - Santos, Jhonathan P. R. AU - Pereira, Guilherme da S. AU - Zeng, Zhao-Bang AU - Garcia, Antonio Augusto F. T2 - G3-GENES GENOMES GENETICS AB - Genomic selection is an efficient approach to get shorter breeding cycles in recurrent selection programs and greater genetic gains with selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species have been limited to a rough approximation of studies in diploid species. The major challenge is to distinguish the different types of heterozygotes present in polyploid populations. In this work, we evaluated different genomic prediction models applied to a recurrent selection population of 530 genotypes of Panicum maximum, an autotetraploid forage grass. We also investigated the effect of the allele dosage in the prediction, i.e., considering tetraploid (GS-TD) or diploid (GS-DD) allele dosage. A longitudinal linear mixed model was fitted for each one of the six phenotypic traits, considering different covariance matrices for genetic and residual effects. A total of 41,424 genotyping-by-sequencing markers were obtained using 96-plex and Pst1 restriction enzyme, and quantitative genotype calling was performed. Six predictive models were generalized to tetraploid species and predictive ability was estimated by a replicated fivefold cross-validation process. GS-TD and GS-DD models were performed considering 1,223 informative markers. Overall, GS-TD data yielded higher predictive abilities than with GS-DD data. However, different predictive models had similar predictive ability performance. In this work, we provide bioinformatic and modeling guidelines to consider tetraploid dosage and observed that genomic selection may lead to additional gains in recurrent selection program of P. maximum. DA - 2019/8// PY - 2019/8// DO - 10.1534/g3.118.200986 VL - 9 IS - 8 SP - 2463-2475 SN - 2160-1836 KW - Plant Breeding KW - Guinea Grass KW - Quantitative Genotyping KW - Polyploidy KW - Genotyping-by-sequencing (GBS) KW - Recurrent Genomic Selection KW - Genomic Prediction KW - GenPred KW - Shared Data Resources ER - TY - JOUR TI - Satellite conjunction analysis and the false confidence theorem AU - Balch, Michael Scott AU - Martin, Ryan AU - Ferson, Scott T2 - PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES AB - Satellite conjunction analysis is the assessment of collision risk during a close encounter between a satellite and another object in orbit. A counterintuitive phenomenon has emerged in the conjunction analysis literature, namely, probability dilution, in which lower quality data paradoxically appear to reduce the risk of collision. We show that probability dilution is a symptom of a fundamental deficiency in probabilistic representations of statistical inference, in which there are propositions that will consistently be assigned a high degree of belief, regardless of whether or not they are true. We call this deficiency false confidence. In satellite conjunction analysis, it results in a severe and persistent underestimate of collision risk exposure. We introduce the Martin--Liu validity criterion as a benchmark by which to identify statistical methods that are free from false confidence. Such inferences will necessarily be non-probabilistic. In satellite conjunction analysis, we show that uncertainty ellipsoids satisfy the validity criterion. Performing collision avoidance maneuvers based on ellipsoid overlap will ensure that collision risk is capped at the user-specified level. Further, this investigation into satellite conjunction analysis provides a template for recognizing and resolving false confidence issues as they occur in other problems of statistical inference. DA - 2019/7// PY - 2019/7// DO - 10.1098/rspa.2018.0565 VL - 475 IS - 2227 SP - SN - 1471-2946 KW - belief functions KW - filter estimates KW - space situational awareness KW - statistical theory and methods ER - TY - JOUR TI - Effects of acellular equine amniotic allografts on the healing of experimentally induced full-thickness distal limb wounds in horses AU - Fowler, Alexander W. AU - Gilbertie, Jessica M. AU - Watson, Victoria E. AU - Prange, Timo AU - Osborne, Jason A. AU - Schnabel, Lauren V T2 - VETERINARY SURGERY AB - To characterize the growth factors contained in equine amniotic membrane allograft (eAM; StemWrap scaffold and StemWrap+ injection) and to evaluate the effect of eAM on equine distal limb wound healing.Prospective experimental controlled study.Eight adult horses.Transforming growth factor (TGF)-β1, vascular endothelial growth factor (VEGF), epidermal growth factor, platelet-derived growth factor-BB, and prostaglandin E2 (PGE2 ) concentrations in StemWrap+ were assessed with enzyme-linked immunosorbent assay. Two full-thickness 6.25-cm2 skin wounds were created on each metacarpus. On one forelimb, one wound was treated with eAM, and the other was left untreated (eAM control). On the contralateral limb, one wound was treated with a silicone dressing, and the other served as negative control. Three-dimensional images were obtained to determine wound circumference and surface area analyses at each bandage change until healed. Excessive granulation tissue was debrided once weekly for 4 weeks. Biopsy samples were taken to evaluate quality of wound healing via histologic and immunohistochemistry assays.StemWrap+ contained moderate concentrations of TGF-β1 (494.10 pg/mL), VEGF (212.52 pg/mL), and PGE2 (1811.61 pg/mL). Treatment of wounds with eAM did not affect time to healing or histologic quality of the healing compared with other groups but was associated with increased granulation tissue production early in the study, particularly on day 7.Application of eAM resulted in increased granulation tissue production while maintaining appropriate healing of experimental wounds.Use of eAM is likely most beneficial for substantial wounds in which expedient production of large amounts of granulation tissue is desirable. DA - 2019/11// PY - 2019/11// DO - 10.1111/vsu.13304 VL - 48 IS - 8 SP - 1416-1428 SN - 1532-950X ER - TY - JOUR TI - Matrix completion from a computational statistics perspective AU - Chi, Eric C. AU - Li, Tianxi T2 - WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS AB - Abstract In the matrix completion problem, we seek to estimate the missing entries of a matrix from a small sample of the total number of entries in a matrix. While this task is hopeless in general, structured matrices that are appropriately sampled can be completed with surprising accuracy. In this review, we examine the success behind low‐rank matrix completion, one of the most studied and employed versions of matrix completion. Formulating the matrix completion problem as a low‐rank matrix estimation problem admits several strengths: good empirical performance on real data, statistical guarantees, and practical algorithms with convergence guarantees. We also examine how matrix completion relates to the classical study of missing data analysis (MDA) in statistics. By drawing on the MDA perspective, we see opportunities to weaken the commonly enforced assumption of missing completely at random in matrix completion. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis DA - 2019/9// PY - 2019/9// DO - 10.1002/wics.1469 VL - 11 IS - 5 SP - SN - 1939-0068 UR - https://doi.org/10.1002/wics.1469 KW - Collaborative filtering KW - low-rank approximation KW - missing data KW - optimization KW - recommender systems ER - TY - JOUR TI - Computation of exact probabilities associated with overlapping pattern occurrences AU - Martin, Donald E. K. T2 - WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS AB - Abstract Searching for patterns in data is important because it can lead to the discovery of sequence segments that play a functional role. The complexity of pattern statistics that are used in data analysis and the need of the sampling distribution of those statistics for inference renders efficient computation methods as paramount. This article gives an overview of the main methods used to compute distributions of statistics of overlapping pattern occurrences, specifically, generating functions, correlation functions, the Goulden‐Jackson cluster method, recursive equations, and Markov chain embedding. The underlying data sequence will be assumed to be higher‐order Markovian, which includes sparse Markov models and variable length Markov chains as special cases. Also considered will be recent developments for extending the computational capabilities of the Markov chain‐based method through an algorithm for minimizing the size of the chain's state space, as well as improved data modeling capabilities through sparse Markov models. An application to compute a distribution used as a test statistic in sequence alignment will serve to illustrate the usefulness of the methodology. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition Data: Types and Structure > Categorical Data Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms DA - 2019/9// PY - 2019/9// DO - 10.1002/wics.1477 VL - 11 IS - 5 SP - SN - 1939-0068 KW - Auxiliary Markov chain KW - distribution of a pattern statistic KW - Markovian sequences KW - sparse Markov models KW - VLMC ER - TY - JOUR TI - Population estimates of Antillean manatees in Puerto Rico: an analytical framework for aerial surveys using multi-pass removal sampling AU - Collazo, Jaime A. AU - Krachey, Matthew J. AU - Pollock, Kenneth H. AU - Perez-Aguilo, Francisco J. AU - Zegarra, Jan P. AU - Mignucci-Giannoni, Antonio A. T2 - JOURNAL OF MAMMALOGY AB - Abstract Effective management of the threatened Antillean manatee (Trichechus manatus manatus) in Puerto Rico requires reliable estimates of population size. Estimates are needed to assess population responses to management actions, and whether recovery objectives have been met. Aerial surveys have been conducted since 1976, but none adjusted for imperfect detection. We summarize surveys since 1976, report on current distribution, and provide population estimates after accounting for apparent detection probability for surveys between June 2010 and March 2014. Estimates in areas of high concentration (hotspots) averaged 317 ± 101, three times higher than unadjusted counts (104 ± 0.56). Adjusted estimates in three areas outside hotspots also differed markedly from counts (75 ± 9.89 versus 19.5 ± 3.5). Average minimum island-wide estimate was 386 ± 89, similar to the maximum estimate of 360 suggested in 2005, but fewer than the 700 recently suggested by the Puerto Rico Manatee Conservation Center. Manatees were more widespread than previously understood. Improving estimates, locally or island-wide, will require stratifying the island differently and greater knowledge about factors affecting detection probability. Sharing our protocol with partners in nearby islands (e.g., Cuba, Jamaica, Hispaniola), whose populations share genetic make-up, would contribute to enhanced regional conservation through better population estimates and tracking range expansion. El manejo efectivo del manatí antillano amenazado en Puerto Rico requiere estimados de tamaños de poblaciónes confiables. Dichas estimaciones poblacionales son necesarias para evaluar las respuestas a las acciones de manejo, y para determinar si los objetivos de recuperación han sido alcanzados. Se han realizado censos aéreos desde 1976, pero ninguno de ellos han sido ajustados para detecciones imperfectas. Aquí resumimos los censos desde 1976, actualizamos la distribución, y reportamos los primeros estimados poblacionales ajustados para la probabilidad de detección aparente en los censos de Junio 2010 a Marzo 2014. Las estimaciones poblacionales en áreas de mayor concentración del manatí promedió 317 ± 103, tres veces más abundante que los conteos sin ajuste (104 ± 0.56). Las estimaciones poblacionales en tres áreas fuera de las áreas de mayor concentración del manatí también fueron marcadamente diferentes (75 ± 9.89 vs 19.5 ± 3.5). El estimado mínimo poblacional en la isla entera fue de 386 ± 89, similar al estimado máximo de 360 sugerido en el año 2005, pero menor a los 700 sugeridos recientemente por el Centro de Conservación de Manatíes de Puerto Rico. Documentamos que el manatí tiene una distribución más amplia de lo que se sabía con anterioridad. El mejoramiento de los estimados poblacionales locales o a nivel de isla requerirá que se estratifique a la isla en forma diferente y que se investiguen los factores que influencian a la probabilidad de detección. Compartir protocolos como este con colaboradores de islas vecinas (por. ej., Cuba, Jamaica, Española), cuyas poblaciones de manatíes comparten material genético, contribuiría a la conservación regional mediante mejores estimaciones poblacionales y monitoreo de la expansión de su ámbito doméstico. DA - 2019/7/27/ PY - 2019/7/27/ DO - 10.1093/jmammal/gyz076 VL - 100 IS - 4 SP - 1340-1349 SN - 1545-1542 KW - aerial survey KW - Antillean manatee KW - detection probability KW - population size KW - Puerto Rico KW - removal method KW - repeated counts ER - TY - JOUR TI - LINEAR HYPOTHESIS TESTING FOR HIGH DIMENSIONAL GENERALIZED LINEAR MODELS AU - Shi, Chengchun AU - Song, Rui AU - Chen, Zhao AU - Li, Runze T2 - ANNALS OF STATISTICS AB - This paper is concerned with testing linear hypotheses in high dimensional generalized linear models. To deal with linear hypotheses, we first propose the constrained partial regularization method and study its statistical properties. We further introduce an algorithm for solving regularization problems with folded-concave penalty functions and linear constraints. To test linear hypotheses, we propose a partial penalized likelihood ratio test, a partial penalized score test and a partial penalized Wald test. We show that the limiting null distributions of these three test statistics are $\chi^{2}$ distribution with the same degrees of freedom, and under local alternatives, they asymptotically follow noncentral $\chi^{2}$ distributions with the same degrees of freedom and noncentral parameter, provided the number of parameters involved in the test hypothesis grows to $\infty$ at a certain rate. Simulation studies are conducted to examine the finite sample performance of the proposed tests. Empirical analysis of a real data example is used to illustrate the proposed testing procedures. DA - 2019/10// PY - 2019/10// DO - 10.1214/18-AOS1761 VL - 47 IS - 5 SP - 2671-2703 SN - 0090-5364 KW - High dimensional testing KW - linear hypothesis KW - likelihood ratio statistics KW - score test KW - Wald test ER - TY - JOUR TI - Histological evaluation of five suture materials in the telson ligament of the American horseshoe crab (Limulus polyphemus) AU - Krasner, Ami E. AU - Hancock-Ronemus, Amy AU - Christian, Larry S. AU - Griffith, Emily H. AU - Lewbart, Gregory A. AU - Law, Jerry M. T2 - PEERJ AB - An ideal suture material supports healing, minimizes inflammation, and decreases the likelihood of secondary infection. While there are published recommendations for suture materials in some invertebrates, there are no published recommendations for Limulus polyphemus or any chelicerate. This study evaluates the histological reaction of horseshoe crabs to five commonly used suture materials: monofilament nylon, silk, poliglecaprone, polydioxanone, and polyglycolic acid. None of the materials were superior with regards to holding nor was there any dehiscence. Nylon evoked the least amount of tissue reaction. This work also provides a histopathological description of the soft membrane at the hinge area between the opisthosoma and telson (telson ligament) and comments on euthanasia with intracardiac eugenol. DA - 2019/8/1/ PY - 2019/8/1/ DO - 10.7717/peerj.7061 VL - 7 SP - SN - 2167-8359 KW - Limulus KW - Suture reaction KW - Telson ligament KW - Eugenol KW - Histopathology ER - TY - JOUR TI - Maximum Entropy-based Probabilistic Mass-Radius Relation of Exoplanets AU - Ma, Qi AU - Ghosh, Sujit K. T2 - ASTRONOMICAL JOURNAL AB - Transiting planet surveys of recent years like the Kepler and K2 missions have provided a great deal of data for studying the compositional constituents of exoplanets through the relationship between their masses and radii (M-R relation). However, it is often the case that only one of the mass or radius measurement is available for newly discovered planets, which makes it necessary to estimate the M-R relation conditioned on a sample of planets with both masses and radii measurements available but subject to measurement errors. The majority of the statistical models available in the literature on probabilistic M-R relation are based on the assumption that the planetary masses are normally distributed around the means determined by the power law without any justification. Given the power-law relation, using the well known Maximum Entropy Principle, it is shown that the conditional distribution of masses (given radii) follow an exponential distribution where the conditional mean is modeled using a flexible multiple knot-based power-law structure. Parameter estimation is carried out using Bayesian methods that not only account for measurement errors in building the likelihood function, but also perform proper imputation using posterior predictive distributions. Two data sets (from exoplanets.org site) are used to illustrate the flexibility and broad applicability of the proposed model. DA - 2019/8// PY - 2019/8// DO - 10.3847/1538-3881/ab2990 VL - 158 IS - 2 SP - SN - 1538-3881 KW - methods: statistical KW - planets and satellites: composition ER - TY - JOUR TI - A Genetic Locus on Chromosome 2q24 Predicting Peripheral Neuropathy Risk in Type 2 Diabetes: Results From the ACCORD and BARI 2D Studies AU - Tang, Yaling AU - Lenzini, Petra A. AU - Pop-Busui, Rodica AU - Ray, Pradipta R. AU - Campbell, Hannah AU - Perkins, Bruce A. AU - Callaghan, Brian AU - Wagner, Michael J. AU - Motsinger-Reif, Alison A. AU - Buse, John B. AU - Price, Theodore J. AU - Mychaleckyj, Josyf C. AU - Cresci, Sharon AU - Shah, Hetal AU - Doria, Alessandro T2 - DIABETES AB - Genetic factors have been postulated to be involved in the etiology of diabetic peripheral neuropathy (DPN), but their identity remains mostly unknown. The aim of this study was to conduct a systematic search for genetic variants influencing DPN risk using two well-characterized cohorts. A genome-wide association study (GWAS) testing 6.8 million single nucleotide polymorphisms was conducted among participants of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial. Included were 4,384 white case patients with type 2 diabetes (T2D) and prevalent or incident DPN (defined as a Michigan Neuropathy Screening Instrument clinical examination score &gt;2.0) and 784 white control subjects with T2D and no evidence of DPN at baseline or during follow-up. Replication of significant loci was sought among white subjects with T2D (791 DPN-positive case subjects and 158 DPN-negative control subjects) from the Bypass Angioplasty Revascularization Investigation in Type 2 Diabetes (BARI 2D) trial. Association between significant variants and gene expression in peripheral nerves was evaluated in the Genotype-Tissue Expression (GTEx) database. A cluster of 28 SNPs on chromosome 2q24 reached GWAS significance (P &lt; 5 × 10−8) in ACCORD. The minor allele of the lead SNP (rs13417783, minor allele frequency = 0.14) decreased DPN odds by 36% (odds ratio [OR] 0.64, 95% CI 0.55–0.74, P = 1.9 × 10−9). This effect was not influenced by ACCORD treatment assignments (P for interaction = 0.6) or mediated by an association with known DPN risk factors. This locus was successfully validated in BARI 2D (OR 0.57, 95% CI 0.42–0.80, P = 9 × 10−4; summary P = 7.9 × 10−12). In GTEx, the minor, protective allele at this locus was associated with higher tibial nerve expression of an adjacent gene (SCN2A) coding for human voltage-gated sodium channel NaV1.2 (P = 9 × 10−4). To conclude, we have identified and successfully validated a previously unknown locus with a powerful protective effect on the development of DPN in T2D. These results may provide novel insights into DPN pathogenesis and point to a potential target for novel interventions. DA - 2019/8// PY - 2019/8// DO - 10.2337/db19-0109 VL - 68 IS - 8 SP - 1649-1662 SN - 1939-327X ER - TY - JOUR TI - A Feed Forward Neural Network Based on Model Output Statistics for Short-Term Hurricane Intensity Prediction AU - Cloud, Kirkwood A. AU - Reich, Brian J. AU - Rozoff, Christopher M. AU - Alessandrini, Stefano AU - Lewis, William E. AU - Delle Monache, Luca T2 - WEATHER AND FORECASTING AB - Abstract A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt ≈ 0.51 m s−1) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index. DA - 2019/8// PY - 2019/8// DO - 10.1175/WAF-D-18-0173.1 VL - 34 IS - 4 SP - 985-997 SN - 1520-0434 KW - Neural networks KW - Regression analysis KW - Forecasting techniques KW - Operational forecasting KW - Short-range prediction ER - TY - JOUR TI - Modeling Zika Virus Transmission Dynamics: Parameter Estimates, Disease Characteristics, and Prevention AU - Rahman, Munsur AU - Bekele-Maxwell, Kidist AU - Cates, LeAnna L. AU - Banks, H. T. AU - Vaidya, Naveen K. T2 - SCIENTIFIC REPORTS AB - Abstract Because of limited data, much remains uncertain about parameters related to transmission dynamics of Zika virus (ZIKV). Estimating a large number of parameters from the limited information in data may not provide useful knowledge about the ZIKV. Here, we developed a method that utilizes a mathematical model of ZIKV dynamics and the complex-step derivative approximation technique to identify parameters that can be estimated from the available data. Applying our method to epidemic data from the ZIKV outbreaks in French Polynesia and Yap Island, we identified the parameters that can be estimated from these island data. Our results suggest that the parameters that can be estimated from a given data set, as well as the estimated values of those parameters, vary from Island to Island. Our method allowed us to estimate some ZIKV-related parameters with reasonable confidence intervals. We also computed the basic reproduction number to be from 2.03 to 3.20 across islands. Furthermore, using our model, we evaluated potential prevention strategies and found that peak prevalence can be reduced to nearly 10% by reducing mosquito-to-human contact by at least 60% or increasing mosquito death by at least a factor of three of the base case. With these preventions, the final outbreak-size is predicted to be negligible, thereby successfully controlling ZIKV epidemics. DA - 2019/7/22/ PY - 2019/7/22/ DO - 10.1038/s41598-019-46218-4 VL - 9 SP - SN - 2045-2322 ER - TY - JOUR TI - VARIANCE COMPONENT TEST FOR CROSS-DISORDER PATHWAY ANALYSIS AU - Szatkiewicz, Jin AU - Marceau, Rachel AU - Yilmaz, Zeynep AU - Bulik, Cynthia AU - Crowley, James AU - Mattheisen, Manuel AU - Sullivan, Patrick AU - Lu, Wenbin AU - Maity, Arnab AU - Tzeng, Jung-Ying AU - al., T2 - EUROPEAN NEUROPSYCHOPHARMACOLOGY DA - 2019/// PY - 2019/// DO - 10.1016/j.euroneuro.2018.08.252 VL - 29 SP - 1204-1205 SN - 1873-7862 ER - TY - JOUR TI - Managing Fusarium Head Blight in Winter Barley With Cultivar Resistance and Fungicide AU - Cowger, Christina AU - Arellano, Consuelo AU - Marshall, David AU - Fitzgerald, Joshua T2 - PLANT DISEASE AB - Although there has been research on managing Fusarium head blight (FHB) in spring barley, little has been published on cultivar resistance and optimal fungicide timing for FHB management in winter barley. A 3-year (2015 to 2017) field experiment was conducted to measure FHB resistance of winter barley varieties, gauge the potential benefit from a fungicide, and help determine the optimal timing for fungicide application. The split-plot experiment took place in a misted, inoculated nursery in Raleigh, North Carolina using main plots of four winter barley cultivars (Atlantic, Endeavor, Nomini, and Thoroughbred). Three fungicide treatments were applied to subplots: prothioconazole + tebuconazole at full spike emergence, the same fungicide 6 days later, or no fungicide. The late applications significantly reduced FHB index in each of 3 years and significantly reduced deoxynivalenol (DON) in harvested grain in 2 of the 3 years. Applications at full spike emergence also yielded significant benefit in 1 of the 3 years for each parameter. Neither disease symptoms nor DON gave reason to prefer one of the fungicide timings over the other. Across the 3 years, DON ranked the cultivars Endeavor < Nomini = Thoroughbred < Atlantic. Combining the moderate resistance of Endeavor with a fungicide application and averaging the two timings resulted in a 75% DON reduction compared with unsprayed Atlantic. Taken together, our results indicate that barley growers concerned about minimizing DON should both plant moderately resistant varieties and apply fungicide if there is scab risk. During the same period, 16 commercial winter barley cultivars were tested in from three to seven Virginia and North Carolina environments each, and the DON results were compared after standardization across environments. The winter two-row malting barley cultivars Endeavor and Calypso displayed superior and robust DON resistance across environments. DA - 2019/8// PY - 2019/8// DO - 10.1094/PDIS-09-18-1582-RE VL - 103 IS - 8 SP - 1858-1864 SN - 1943-7692 KW - cereals and grains KW - chemical KW - disease management KW - field crops KW - fungi ER - TY - JOUR TI - Gibbs posterior inference on value-at-risk AU - Syring, Nicholas AU - Hong, Liang AU - Martin, Ryan T2 - SCANDINAVIAN ACTUARIAL JOURNAL AB - Accurate estimation of value-at-risk (VaR) and assessment of associated uncertainty is crucial for both insurers and regulators, particularly in Europe. Existing approaches link data and VaR indirectly by first linking data to the parameter of a probability model, and then expressing VaR as a function of that parameter. This indirect approach exposes the insurer to model misspecification bias or estimation inefficiency, depending on whether the parameter is finite- or infinite-dimensional. In this paper, we link data and VaR directly via what we call a discrepancy function, and this leads naturally to a Gibbs posterior distribution for VaR that does not suffer from the aforementioned biases and inefficiencies. Asymptotic consistency and root-n concentration rate of the Gibbs posterior are established, and simulations highlight its superior finite-sample performance compared to other approaches. DA - 2019/8/9/ PY - 2019/8/9/ DO - 10.1080/03461238.2019.1573754 IS - 7 SP - 548-557 SN - 1651-2030 KW - Direct posterior KW - discrepancy function KW - M-estimation KW - model misspecification KW - risk capital KW - robust estimation ER - TY - JOUR TI - Conditional Analysis for Mixed Covariates, with Application to Feed Intake of Lactating Sows AU - Park, S. Y. AU - Li, C. AU - Benavides, S. M. Mendoza AU - Heugten, E. AU - Staicu, A. M. T2 - JOURNAL OF PROBABILITY AND STATISTICS AB - We propose a novel modeling framework to study the effect of covariates of various types on the conditional distribution of the response. The methodology accommodates flexible model structure, allows for joint estimation of the quantiles at all levels, and provides a computationally efficient estimation algorithm. Extensive numerical investigation confirms good performance of the proposed method. The methodology is motivated by and applied to a lactating sow study, where the primary interest is to understand how the dynamic change of minute-by-minute temperature in the farrowing rooms within a day (functional covariate) is associated with low quantiles of feed intake of lactating sows, while accounting for other sow-specific information (vector covariate). DA - 2019/7/16/ PY - 2019/7/16/ DO - 10.1155/2019/3743762 VL - 2019 SP - SN - 1687-9538 ER - TY - JOUR TI - Impact of heat stress and antioxidant supplements in feed or drinking water on growth, intestinal morphology, and oxidative and immune status in growing pigs. AU - Silva-Guillen, Ysenia Victoria AU - Padilla, Gabriela E. Martinez AU - Wiegert, Jeffrey AU - Arellano, Consuelo AU - Boyd, R. Dean AU - Heugten, Eric T2 - JOURNAL OF ANIMAL SCIENCE AB - Abstract The objective of this study was to evaluate the impact of vitamin E (vitE) or polyphenols supplemented in feed or drinking water as a heat abatement strategy in growing pigs. Individually housed pigs (n = 128, 47.3 ± 5.0 kg BW) were assigned within weight blocks and sex to a 2x4 factorial arrangement consisting of 2 environments (thermo-neutral [21.2°C] or heat-stressed [30.9°C]) and 4 supplementation treatments (control diet [25 IU/kg dl-α-tocopherol acetate]; control+100 IU/L vitE [d-α-tocopherol] in water; control+200 IU/kg vitE [dl-α-tocopherol acetate] in feed; or control+400 mg/kg polyphenols in feed). Supplementation was started 7 d prior to temperature treatments applied for 28 d. Heat stress reduced (P ≤ 0.001) final BW, ADG, and ADFI (-7.4 kg, -26.7%, and -25.4%, respectively) and increased (P &lt; 0.001) respiration rate and rectal temperature, but no effects of supplementation were detected. Serum vitamin E concentration increased (P &lt; 0.001) with vitE supplementation (1.64, 3.59, 3.24, and 1.67 mg/kg for control, vitE in water, vitE in feed, and polyphenols, respectively) and was greater when supplemented in water vs. feed (P = 0.002), especially when measured on d 28 (chronic) vs. d 2 (acute) of heat stress. Liver vitamin E increased (P &lt; 0.001) with vitE supplementation, especially in water, but not polyphenols (3.9, 31.8, 18.0, 4.9 ppm for control, vitE in water, vitE in feed, and polyphenols, respectively). Serum malondialdehyde (MDA) was greater (P &lt; 0.05) for supplemented pigs compared to control, and heat stress reduced (P = 0.014) serum MDA on d 2, but not d 28. No differences were detected for intestinal morphology or MDA in mucosa of jejunum or ileum. Heat stress decreased (P &lt; 0.03) TNF-α in mucosa of ileum and jejunum, and supplementation reduced (P &lt; 0.05) TNF-α in mucosa of the ileum, but not jejunum. Heat stress markedly reduced performance of growing pigs, and supplementing antioxidants in feed or water was not effective in alleviating the impact of heat stress. DA - 2019/7// PY - 2019/7// DO - 10.1093/jas/skz122.131 VL - 97 SP - 71-71 SN - 1525-3163 KW - antioxidants KW - growing pigs KW - heat stress ER - TY - JOUR TI - Statistical Analysis of Zero-Inflated Nonnegative Continuous Data: A Review AU - Liu, Lei AU - Shih, Ya-Chen Tina AU - Strawderman, Robert L. AU - Zhang, Daowen AU - Johnson, Bankole A. AU - Chai, Haitao T2 - STATISTICAL SCIENCE AB - Zero-inflated nonnegative continuous (or semicontinuous) data arise frequently in biomedical, economical, and ecological studies. Examples include substance abuse, medical costs, medical care utilization, biomarkers (e.g., CD4 cell counts, coronary artery calcium scores), single cell gene expression rates, and (relative) abundance of microbiome. Such data are often characterized by the presence of a large portion of zero values and positive continuous values that are skewed to the right and heteroscedastic. Both of these features suggest that no simple parametric distribution may be suitable for modeling such type of outcomes. In this paper, we review statistical methods for analyzing zero-inflated nonnegative outcome data. We will start with the cross-sectional setting, discussing ways to separate zero and positive values and introducing flexible models to characterize right skewness and heteroscedasticity in the positive values. We will then present models of correlated zero-inflated nonnegative continuous data, using random effects to tackle the correlation on repeated measures from the same subject and that across different parts of the model. We will also discuss expansion to related topics, for example, zero-inflated count and survival data, nonlinear covariate effects, and joint models of longitudinal zero-inflated nonnegative continuous data and survival. Finally, we will present applications to three real datasets (i.e., microbiome, medical costs, and alcohol drinking) to illustrate these methods. Example code will be provided to facilitate applications of these methods. DA - 2019/5// PY - 2019/5// DO - 10.1214/18-STS681 VL - 34 IS - 2 SP - 253-279 SN - 2168-8745 KW - Two-part model KW - Tobit model KW - health econometrics KW - semiparametric regression KW - joint model KW - cure rate KW - frailty model KW - splines ER - TY - JOUR TI - Evaluating the Hydrologic Benefits of a Bioswale in Brunswick County, North Carolina (NC), USA AU - Purvis, Rebecca A. AU - Winston, Ryan J. AU - Hunt, William F. AU - Lipscomb, Brian AU - Narayanaswamy, Karthik AU - McDaniel, Andrew AU - Lauffer, Matthew S. AU - Libes, Susan T2 - WATER AB - Bioswales are a promising stormwater control measure (SCM) for roadway runoff management, but few studies have assessed performance on a field scale. A bioswale is a vegetated channel with underlying engineered media and a perforated underdrain to promote improved hydrologic and water quality treatment. A bioswale with a rip-rap lined forebay was constructed along state highway NC 211 in Bolivia, North Carolina, USA, and monitored for 12 months. Thirty-seven of the 39 monitored rain events exfiltrated into underlying soils, resulting in no appreciable overflow or underdrain volume. The bioswale completely exfiltrated a storm event of 86.1 mm. The one event to have underdrain-only flow was 4.8 mm. The largest and third-largest rainfall depth events (82.6 and 146 mm, respectively) had a large percentage (85%) of volume exfiltrated, but also had appreciable overflow and underdrain volumes exiting the bioswale, resulting in no peak flow mitigation. Overall, this bioswale design was able to capture and manage storms larger than the design storm (38 mm), showing the positive hydrologic performance that can be achieved by this bioswale. The high treatment capabilities were likely due to the high infiltration rate of the media and the underlying soil, longer forebay underlain with media, gravel detention layer with an underdrain, and shallow slope. DA - 2019/6// PY - 2019/6// DO - 10.3390/w11061291 VL - 11 IS - 6 ER - TY - JOUR TI - Breeding maize under biodynamic-organic conditions for nutritional value and N efficiency/N-2 fixation AU - Goldstein, W. AU - Jaradat, A. A. AU - Hurburgh, C. AU - Pollak, L. M. AU - Goodman, M. T2 - OPEN AGRICULTURE AB - Abstract An overview is given for an ongoing maize breeding program that improves populations, inbreds, and hybrids in the Midwestern USA. Breeding and selection occurred under biodynamic conditions in Wisconsin, on an organic winter nursery in Puerto Rico, a biodynamic winter nursery in Hawaii, and a conventional winter nursery in Chile. Emphasis is on improving protein quality, carotenoid content, competitiveness with weeds, nitrogen (N) efficiency/N 2 fixation, and cross incompatibility to pollen from genetically engineered (GE) maize. Philosophy is that the plant species is a responding partner in the breeding process. Adaptation and selection emphasizes vigor and yield under N limited conditions. The Ga1 and Tcb1 alleles were utilized to induce cross incompatibility. The program resulted in inbreds and hybrids with increased N efficiency and protein quality coupled with softer grain texture, more chlorophyll in foliage, and densely branched root growth in the topsoil relative to conventionally bred cultivars under N limited conditions. Grain protein quality was improved by utilizing opaque kernels that emerged in populations during the course of the program in surprisingly high frequencies. N efficiency was accentuated by breeding with landraces that may fix N 2 with microbes coupled with selection for response traits under N-limited conditions. When grown next to conventional hybrids, the best hybrids from this program have exhibited 30% more methionine and 16% more protein in grain and more protein/ha. DA - 2019/1// PY - 2019/1// DO - 10.1515/opag-2019-0030 VL - 4 IS - 1 SP - 322-345 SN - 2391-9531 KW - methionine KW - gametophytic incompatibility KW - epigenetics KW - isolation by environment ER - TY - JOUR TI - Interspecific and intraspecific interference of Palmer amaranth (Amaranthus palmeri) and large crabgrass (Digitaria sanguinalis) in sweetpotato AU - Basinger, Nicholas T. AU - Jennings, Katherine M. AU - Monks, David W. AU - Jordan, David L. AU - Everman, Wesley J. AU - Hestir, Erin L. AU - Waldschmidt, Matthew D. AU - Smith, Stephen C. AU - Brownie, Cavell T2 - WEED SCIENCE AB - Abstract Field studies were conducted in 2016 and 2017 in Clinton, NC, to determine the interspecific and intraspecific interference of Palmer amaranth ( Amaranthus palmeri S. Watson) or large crabgrass [ Digitaria sanguinalis (L.) Scop.] in ‘Covington’ sweetpotato [ Ipomoea batatas (L.) Lam.]. Amaranthus palmeri and D. sanguinalis were established 1 d after sweetpotato transplanting and maintained season-long at 0, 1, 2, 4, 8 and 0, 1, 2, 4, 16 plants m −1 of row in the presence and absence of sweetpotato, respectively. Predicted yield loss for sweetpotato was 35% to 76% for D. sanguinalis at 1 to 16 plants m −1 of row and 50% to 79% for A. palmeri at 1 to 8 plants m −1 of row. Weed dry biomass per meter of row increased linearly with increasing weed density. Individual dry biomass of A. palmeri and D. sanguinalis was not affected by weed density when grown in the presence of sweetpotato. When grown without sweetpotato, individual weed dry biomass decreased 71% and 62% from 1 to 4 plants m −1 row for A. palmeri and D. sanguinalis , respectively. Individual weed dry biomass was not affected above 4 plants m −1 row to the highest densities of 8 and 16 plants m −1 row for A. palmeri and D. sanguinalis , respectively. DA - 2019/7// PY - 2019/7// DO - 10.1017/wsc.2019.16 VL - 67 IS - 4 SP - 426-432 SN - 1550-2759 KW - Carlene Chase KW - University of Florida KW - Biomass KW - competition KW - linear-plateau model KW - rectangular hyperbola model KW - weed density KW - yield loss ER - TY - JOUR TI - Using data from multiple studies to develop a child growth correlation matrix AU - Anderson, Craig AU - Xiao, Luo AU - Checkley, William T2 - STATISTICS IN MEDICINE AB - In many countries, the monitoring of child growth does not occur in a regular manner, and instead, we may have to rely on sporadic observations that are subject to substantial measurement error. In these countries, it can be difficult to identify patterns of poor growth, and faltering children may miss out on essential health interventions. The contribution of this paper is to provide a framework for pooling together multiple datasets, thus allowing us to overcome the issue of sparse data and provide improved estimates of growth. We use data from multiple longitudinal growth studies to construct a common correlation matrix that can be used in estimation and prediction of child growth. We propose a novel 2‐stage approach: In stage 1, we construct a raw matrix via a set of univariate meta‐analyses, and in stage 2, we smooth this raw matrix to obtain a more realistic correlation matrix. The methodology is illustrated using data from 16 child growth studies from the Bill and Melinda Gates Foundation's Healthy Birth Growth and Development knowledge integration project and identifies strong correlation for both height and weight between the ages of 4 and 12 years. We use a case study to provide an example of how this matrix can be used to help compute growth measures. DA - 2019/8/30/ PY - 2019/8/30/ DO - 10.1002/sim.7696 VL - 38 IS - 19 SP - 3540-3554 SN - 1097-0258 KW - child health KW - correlation KW - growth KW - SDS ER - TY - JOUR TI - Ten Hot Topics around Scholarly Publishing AU - Tennant, Jonathan P. AU - Crane, Harry AU - Crick, Tom AU - Davila, Jacinto AU - Enkhbayar, Asura AU - Havemann, Johanna AU - Kramer, Bianca AU - Martin, Ryan AU - Masuzzo, Paola AU - Nobes, Andy AU - Rice, Curt AU - Rivera-Lopez, Barbara AU - Ross-Hellauer, Tony AU - Sattler, Susanne AU - Thacker, Paul D. AU - Vanholsbeeck, Marc T2 - PUBLICATIONS AB - The changing world of scholarly communication and the emerging new wave of ‘Open Science’ or ‘Open Research’ has brought to light a number of controversial and hotly debated topics. Evidence-based rational debate is regularly drowned out by misinformed or exaggerated rhetoric, which does not benefit the evolving system of scholarly communication. This article aims to provide a baseline evidence framework for ten of the most contested topics, in order to help frame and move forward discussions, practices, and policies. We address issues around preprints and scooping, the practice of copyright transfer, the function of peer review, predatory publishers, and the legitimacy of ‘global’ databases. These arguments and data will be a powerful tool against misinformation across wider academic research, policy and practice, and will inform changes within the rapidly evolving scholarly publishing system. DA - 2019/6// PY - 2019/6// DO - 10.3390/publications7020034 VL - 7 IS - 2 SP - SN - 2304-6775 KW - peer review KW - copyright KW - open access KW - open science KW - scholarly communication KW - web of science KW - Scopus KW - impact factor KW - research evaluation ER - TY - JOUR TI - MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL AU - Hu, Liuyi AU - Lu, Wenbin AU - Zhou, Jin AU - Zhou, Hua T2 - STATISTICA SINICA AB - Logistic linear mixed models are widely used in experimental designs and genetic analyses of binary traits. Motivated by modern applications, we consider the case of many groups of random effects, where each group corresponds to a variance component. When the number of variance components is large, fitting a logistic linear mixed model is challenging. Thus, we develop two efficient and stable minorization-maximization (MM) algorithms for estimating variance components based on a Laplace approximation of the logistic model. One of these leads to a simple iterative soft-thresholding algorithm for variance component selection using the maximum penalized approximated likelihood. We demonstrate the variance component estimation and selection performance of our algorithms by means of simulation studies and an analysis of real data. DA - 2019/7// PY - 2019/7// DO - 10.5705/ss.202017.0220 VL - 29 IS - 3 SP - 1585-1605 SN - 1996-8507 KW - Generalized linear mixed model (GLMM) KW - Laplace approximation KW - MM algorithm KW - variance components selection ER - TY - JOUR TI - Using Collaborative Cross Mouse Population to Fill Data Gaps in Risk Assessment: A Case Study of Population-Based Analysis of Toxicokinetics and Kidney Toxicodynamics of Tetrachloroethylene AU - Luo, Yu-Syuan AU - Cichocki, Joseph A. AU - Hsieh, Nan-Hung AU - Lewis, Lauren AU - Wright, Fred A. AU - Threadgill, David W. AU - Chiu, Weihsueh A. AU - Rusyn, Ivan T2 - ENVIRONMENTAL HEALTH PERSPECTIVES AB - Background: Interindividual variability in susceptibility remains poorly characterized for environmental chemicals such as tetrachloroethylene (PERC). Development of population-based experimental models provide a potential approach to fill this critical need in human health risk assessment. Objectives: In this study, we aimed to better characterize the contribution of glutathione (GSH) conjugation to kidney toxicity of PERC and the degree of associated interindividual toxicokinetic (TK) and toxicodynamic (TD) variability by using the Collaborative Cross (CC) mouse population. Methods: Male mice from 45 strains were intragastrically dosed with PERC (1,000mg/kg) or vehicle (5% Alkamuls EL-620 in saline), and time-course samples were collected for up to 24 h. Population variability in TK of S-(1,2,2-trichlorovinyl)GSH (TCVG), S-(1,2,2-trichlorovinyl)-L-cysteine (TCVC), and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine (NAcTCVC) was quantified in serum, liver, and kidney, and analyzed using a toxicokinetic model. Effects of PERC on kidney weight, fatty acid metabolism–associated genes [Acot1 (Acyl-CoA thioesterase 1), Fabp1 (fatty acid-binding protein 1), and Ehhadh (enoyl-coenzyme A, hydratase/3-hydroxyacyl coenzyme A dehydrogenase)], and a marker of proximal tubular injury [KIM-1 (kidney injury molecule-1)/Hepatitis A virus cellular receptor 1 (Havcr1)] were evaluated. Finally, quantitative data on interstrain variability in both formation of GSH conjugation metabolites of PERC and its kidney effects was used to calculate adjustment factors for the interindividual variability in both TK and TD. Results: Mice treated with PERC had significantly lower kidney weight, higher kidney-to-body weight (BW) ratio, and higher expression of fatty acid metabolism–associated genes (Acot1, Fabp1, and Ehhadh) and a marker of proximal tubular injury (KIM-1/Havcr1). Liver levels of TCVG were significantly correlated with KIM-1/Havcr1 in kidney, consistent with kidney injury being associated with GSH conjugation. We found that the default uncertainty factor for human variability may be marginally adequate to protect 95%, but not more, of the population for kidney toxicity mediated by PERC. Discussion: Overall, this study demonstrates the utility of the CC mouse population in characterizing metabolism–toxicity interactions and quantifying interindividual variability. Further refinement of the characterization of interindividual variability can be accomplished by incorporating these data into in silico population models both for TK (such as a physiologically based pharmacokinetic model), as well as for toxicodynamic responses. https://doi.org/10.1289/EHP5105 DA - 2019/6// PY - 2019/6// DO - 10.1289/EHP5105 VL - 127 IS - 6 SP - SN - 1552-9924 ER - TY - JOUR TI - A TEST FOR ISOTROPY ON A SPHERE USING SPHERICAL HARMONIC FUNCTIONS AU - Sahoo, Indranil AU - Guinness, Joseph AU - Reich, Brian J. T2 - STATISTICA SINICA AB - Analysis of geostatistical data is often based on the assumption that the spatial random field is isotropic. This assumption, if erroneous, can adversely affect model predictions and statistical inference. Nowadays many applications consider data over the entire globe and hence it is necessary to check the assumption of isotropy on a sphere. In this paper, a test for spatial isotropy on a sphere is proposed. The data are first projected onto the set of spherical harmonic functions. Under isotropy, the spherical harmonic coefficients are uncorrelated whereas they are correlated if the underlying fields are not isotropic. This motivates a test based on the sample correlation matrix of the spherical harmonic coefficients. In particular, we use the largest eigenvalue of the sample correlation matrix as the test statistic. Extensive simulations are conducted to assess the Type I errors of the test under different scenarios. We show how temporal correlation affects the test and provide a method for handling temporal correlation. We also gauge the power of the test as we move away from isotropy. The method is applied to the near-surface air temperature data which is part of the HadCM3 model output. Although we do not expect global temperature fields to be isotropic, we propose several anisotropic models with increasing complexity, each of which has an isotropic process as model component and we apply the test to the isotropic component in a sequence of such models as a method of determining how well the models capture the anisotropy in the fields. DA - 2019/7// PY - 2019/7// DO - 10.5705/ss.202017.0475 VL - 29 IS - 3 SP - 1253-1276 SN - 1996-8507 KW - Anisotropy KW - spatial statistics KW - spherical harmonic representation ER - TY - JOUR TI - Bayesian nonparametric estimation of ROC surface under verification bias AU - Zhu, Rui AU - Ghosal, Subhashis T2 - STATISTICS IN MEDICINE AB - The receiver operating characteristic (ROC) surface, as a generalization of the ROC curve, has been widely used to assess the accuracy of a diagnostic test for three categories. A common problem is verification bias, referring to the situation where not all subjects have their true classes verified. In this paper, we consider the problem of estimating the ROC surface under verification bias. We adopt a Bayesian nonparametric approach by directly modeling the underlying distributions of the three categories by Dirichlet process mixture priors. We propose a robust computing algorithm by only imposing a missing at random assumption for the verification process but no assumption on the distributions. The method can also accommodate covariates information in estimating the ROC surface, which can lead to a more comprehensive understanding of the diagnostic accuracy. It can be adapted and hugely simplified in the case where there is no verification bias, and very fast computation is possible through the Bayesian bootstrap process. The proposed method is compared with other commonly used methods by extensive simulations. We find that the proposed method generally outperforms other approaches. Applying the method to two real datasets, the key findings are as follows: (1) human epididymis protein 4 has a slightly better diagnosis ability compared to CA125 in discriminating healthy, early stage, and late stage patients of epithelial ovarian cancer. (2) Serum albumin has a prognostic ability in distinguishing different stages of hepatocellular carcinoma. DA - 2019/8/15/ PY - 2019/8/15/ DO - 10.1002/sim.8181 VL - 38 IS - 18 SP - 3361-3377 SN - 1097-0258 KW - Bayesian bootstrap KW - Dirichlet process KW - MAR assumption KW - ROC surface KW - verification bias correction ER - TY - JOUR TI - Saying Sayonara to the Farm: Hierarchical Bayesian Modeling of Farm Exits in Japan AU - Ramsey, A. Ford AU - Ghosh, Sujit K. AU - Sonoda, Tadashi T2 - JOURNAL OF AGRICULTURAL ECONOMICS AB - Abstract Off‐farm employment opportunities are thought to have an effect on farm exit rates, though evidence on the sign of this effect has been mixed. Examining this issue in the context of Japanese agriculture, we find that farm exits are related to off‐farm income as a share of household income, and more specifically to the nature of off‐farm work. Two econometric models are developed: a hierarchical Bayesian linear model and a hierarchical Bayesian Poisson model. Both models perform well in predicting exit rates across the towns and prefectures of Japan. DA - 2019/6// PY - 2019/6// DO - 10.1111/1477-9552.12290 VL - 70 IS - 2 SP - 372-391 SN - 1477-9552 KW - Bayesian estimation KW - Japan KW - off-farm employment KW - off-farm income KW - prediction of farm exits ER - TY - JOUR TI - Resolving misaligned spatial data with integrated species distribution models AU - Pacifici, Krishna AU - Reich, Brian J. AU - Miller, David A. W. AU - Pease, Brent S. T2 - ECOLOGY AB - Abstract Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change‐of‐support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser‐quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion. DA - 2019/6// PY - 2019/6// DO - 10.1002/ecy.2709 VL - 100 IS - 6 SP - SN - 1939-9170 KW - black-throated blue warbler KW - change of support KW - Data integration for population models Special Feature KW - integrated species distribution modeling KW - occupancy modeling KW - spatial modeling ER - TY - JOUR TI - Correcting observation model error in data assimilation AU - Hamilton, Franz AU - Berry, Tyrus AU - Sauer, Timothy T2 - CHAOS AB - Standard methods of data assimilation assume prior knowledge of a model that describes the system dynamics and an observation function that maps the model state to a predicted output. An accurate mapping from model state to observation space is crucial in filtering schemes when adjusting the estimate of the system state during the filter's analysis step. However, in many applications, the true observation function may be unknown and the available observation model may have significant errors, resulting in a suboptimal state estimate. We propose a method for observation model error correction within the filtering framework. The procedure involves an alternating minimization algorithm used to iteratively update a given observation function to increase consistency with the model and prior observations using ideas from attractor reconstruction. The method is demonstrated on the Lorenz 1963 and Lorenz 1996 models and on a single-column radiative transfer model with multicloud parameterization. DA - 2019/5// PY - 2019/5// DO - 10.1063/1.5087151 VL - 29 IS - 5 SP - SN - 1089-7682 ER - TY - JOUR TI - Assessing Tuning Parameter Selection Variability in Penalized Regression AU - Hu, Wenhao AU - Laber, Eric B. AU - Barker, Clay AU - Stefanski, Leonard A. T2 - TECHNOMETRICS AB - Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractable when there are more than a handful of predictors. However, automated methods usually fail to incorporate domain-knowledge, exploratory analyses, or other factors that might guide a more interactive model-building approach. A hybrid approach is to use penalized regression to identify a set of candidate models and then to use interactive model-building to examine this candidate set more closely. To identify a set of candidate models, we derive point and interval estimators of the probability that each model along a solution path will minimize a given model selection criterion, for example, Akaike information criterion, Bayesian information criterion (AIC, BIC), etc., conditional on the observed solution path. Then models with a high probability of selection are considered for further examination. Thus, the proposed methodology attempts to strike a balance between algorithmic modeling approaches that are computationally efficient but fail to incorporate expert knowledge, and interactive modeling approaches that are labor intensive but informed by experience, intuition, and domain knowledge. Supplementary materials for this article are available online. DA - 2019/4/3/ PY - 2019/4/3/ DO - 10.1080/00401706.2018.1513380 VL - 61 IS - 2 SP - 154-164 SN - 1537-2723 KW - Conditional distribution KW - Lasso KW - Prediction sets ER - TY - JOUR TI - Electrically Conductive Coatings for Fiber-Based E-Textiles AU - Chatterjee, Kony AU - Tabor, Jordan AU - Ghosh, Tushar K. T2 - FIBERS AB - With the advent of wearable electronic devices in our daily lives, there is a need for soft, flexible, and conformable devices that can provide electronic capabilities without sacrificing comfort. Electronic textiles (e-textiles) combine electronic capabilities of devices such as sensors, actuators, energy harvesting and storage devices, and communication devices with the comfort and conformability of conventional textiles. An important method to fabricate such devices is by coating conventionally used fibers and yarns with electrically conductive materials to create flexible capacitors, resistors, transistors, batteries, and circuits. Textiles constitute an obvious choice for deployment of such flexible electronic components due to their inherent conformability, strength, and stability. Coating a layer of electrically conducting material onto the textile can impart electronic capabilities to the base material in a facile manner. Such a coating can be done at any of the hierarchical levels of the textile structure, i.e., at the fiber, yarn, or fabric level. This review focuses on various electrically conducting materials and methods used for coating e-textile devices, as well as the different configurations that can be obtained from such coatings, creating a smart textile-based system. DA - 2019/6// PY - 2019/6// DO - 10.3390/fib7060051 VL - 7 IS - 6 SP - SN - 2079-6439 KW - flexible electronics KW - smart textiles KW - conductive coatings KW - e-textiles ER - TY - JOUR TI - MM Algorithms for Variance Components Models AU - Zhou, Hua AU - Hu, Liuyi AU - Zho, Jin AU - Lange, Kenneth T2 - JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS AB - Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation and restricted maximum likelihood estimation of variance component models remain numerically challenging. Building on the minorization-maximization (MM) principle, this paper presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush-Kuhn-Tucker (KKT) point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite. DA - 2019/4/3/ PY - 2019/4/3/ DO - 10.1080/10618600.2018.1529601 VL - 28 IS - 2 SP - 350-361 SN - 1537-2715 KW - Global convergence KW - Linear mixed model (LMM) KW - Matrix convexity KW - Maximum a posteriori (MAP) estimation KW - Minorization-maximization (MM) KW - Multivariate response KW - Penalized estimation KW - Variance components model ER - TY - JOUR TI - Robust and Accurate Inference via a Mixture of Gaussian and Student's t Errors AU - Tak, Hyungsuk AU - Ellis, Justin A. AU - Ghosh, Sujit K. T2 - JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS AB - A Gaussian measurement error assumption, that is, an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student’s t distribution helps reduce the bias. However, it may be less efficient in estimating parameters if the heavy tailed assumption is uniformly applied to all of the data when most of them are normally observed. We propose a mixture error assumption that selectively converts Gaussian errors into Student’s t errors according to latent outlier indicators, leveraging the best of the Gaussian and Student’s t errors; a parameter estimation can be not only robust but also accurate. Using simulated hospital profiling data and astronomical time series of brightness data, we demonstrate the potential for the proposed mixture error assumption to estimate parameters accurately in the presence of outliers. Supplemental materials for this article are available online. DA - 2019/4/3/ PY - 2019/4/3/ DO - 10.1080/10618600.2018.1537925 VL - 28 IS - 2 SP - 415-426 SN - 1537-2715 KW - Gaussian process KW - Gibbs sampling KW - Hierarchical model KW - Huber's M-estimator KW - Linear mixed model KW - Outlier KW - Time series ER - TY - JOUR TI - A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction AU - Zhou, Yi-Hui AU - Gallins, Paul T2 - FRONTIERS IN GENETICS AB - With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided. DA - 2019/6/25/ PY - 2019/6/25/ DO - 10.3389/fgene.2019.00579 VL - 10 SP - SN - 1664-8021 KW - disease KW - phenotype KW - modeling KW - machine learning KW - prediction ER - TY - JOUR TI - Cheminformatics approach to exploring and modeling trait-associated metabolite profiles AU - Ash, Jeremy R. AU - Kuenemann, Melaine A. AU - Rotroff, Daniel AU - Motsinger-Reif, Alison AU - Fourches, Denis T2 - JOURNAL OF CHEMINFORMATICS AB - Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers. DA - 2019/6/24/ PY - 2019/6/24/ DO - 10.1186/s13321-019-0366-3 VL - 11 SP - SN - 1758-2946 KW - Metabolomics KW - Data mining KW - Cheminformatics KW - Molecular fragmentation KW - Statistics KW - Visualization KW - Chemical structure ER - TY - JOUR TI - Asymptotic analysis of non-periodical cointegration with high seasonals AU - Dickey, David A. AU - Gonzalez-Farias, Graciela AU - Muriel, Nelson T2 - BOLETIN DE LA SOCIEDAD MATEMATICA MEXICANA DA - 2019/7// PY - 2019/7// DO - 10.1007/s40590-018-0201-2 VL - 25 IS - 2 SP - 443-459 SN - 2296-4495 KW - Cointegration KW - Asymptotic approximation KW - Periodicity KW - Nonstationarity ER - TY - JOUR TI - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen AU - Menden, Michael P. AU - Wang, Dennis AU - Mason, Mike J. AU - Szalai, Bence AU - Bulusu, Krishna C. AU - Guan, Yuanfang AU - Yu, Thomas AU - Kang, Jaewoo AU - Jeon, Minji AU - Wolfinger, Russ AU - Nguyen, Tin AU - Zaslavskiy, Mikhail AU - Jang, In Sock AU - Ghazoui, Zara AU - Ahsen, Mehmet Eren AU - Vogel, Robert AU - Neto, Elias Chaibub AU - Norman, Thea AU - Tang, Eric K. Y. AU - Garnett, Mathew J. AU - Di Veroli, Giovanni Y. AU - Fawell, Stephen AU - Stolovitzky, Gustavo AU - Guinney, Justin AU - Dry, Jonathan R. AU - Saez-Rodriguez, Julio AU - Abante, Jordi AU - Abecassis, Barbara Schmitz AU - Aben, Nanne AU - Aghamirzaie, Delasa AU - Aittokallio, Tero AU - Akhtari, Farida S. AU - Al-lazikani, Bissan AU - Alam, Tanvir AU - Allam, Amin AU - Allen, Chad AU - Almeida, Mariana Pelicano AU - Altarawy, Doaa AU - Alves, Vinicius AU - Amadoz, Alicia AU - Anchang, Benedict AU - Antolin, Albert A. AU - Ash, Jeremy R. AU - Romeo Aznar, Victoria AU - Ba-alawi, Wail AU - Bagheri, Moeen AU - Bajic, Vladimir AU - Ball, Gordon AU - Ballester, Pedro J. AU - Baptista, Delora AU - Bare, Christopher AU - Bateson, Mathilde AU - Bender, Andreas AU - Bertrand, Denis AU - Wijayawardena, Bhagya AU - Boroevich, Keith A. AU - Bosdriesz, Evert AU - Bougouffa, Salim AU - Bounova, Gergana AU - Brouwer, Thomas AU - Bryant, Barbara AU - Calaza, Manuel AU - Calderone, Alberto AU - Calza, Stefano AU - Capuzzi, Stephen AU - Carbonell-Caballero, Jose AU - Carlin, Daniel AU - Carter, Hannah AU - Castagnoli, Luisa AU - Celebi, Remzi AU - Cesareni, Gianni AU - Chang, Hyeokyoon AU - Chen, Guocai AU - Chen, Haoran AU - Chen, Huiyuan AU - Cheng, Lijun AU - Chernomoretz, Ariel AU - Chicco, Davide AU - Cho, Kwang-Hyun AU - Cho, Sunghwan AU - Choi, Daeseon AU - Choi, Jaejoon AU - Choi, Kwanghun AU - Choi, Minsoo AU - De Cock, Martine AU - Coker, Elizabeth AU - Cortes-Ciriano, Isidro AU - Cserzo, Miklos AU - Cubuk, Cankut AU - Curtis, Christina AU - Van Daele, Dries AU - Dang, Cuong C. AU - Dijkstra, Tjeerd AU - Dopazo, Joaquin AU - Draghici, Sorin AU - Drosou, Anastasios AU - Dumontier, Michel AU - Ehrhart, Friederike AU - Eid, Fatma-Elzahraa AU - ElHefnawi, Mahmoud AU - Elmarakeby, Haitham AU - Engelen, Bo AU - Engin, Hatice Billur AU - Esch, Iwan AU - Evelo, Chris AU - Falcao, Andre O. AU - Farag, Sherif AU - Fernandez-Lozano, Carlos AU - Fisch, Kathleen AU - Flobak, Asmund AU - Fornari, Chiara AU - Foroushani, Amir B. K. AU - Fotso, Donatien Chedom AU - Fourches, Denis AU - Friend, Stephen AU - Frigessi, Arnoldo AU - Gao, Feng AU - Gao, Xiaoting AU - Gerold, Jeffrey M. AU - Gestraud, Pierre AU - Ghosh, Samik AU - Gillberg, Jussi AU - Godoy-Lorite, Antonia AU - Godynyuk, Lizzy AU - Godzik, Adam AU - Goldenberg, Anna AU - Gomez-Cabrero, David AU - Gonen, Mehmet AU - Graaf, Chris AU - Gray, Harry AU - Grechkin, Maxim AU - Guimera, Roger AU - Guney, Emre AU - Haibe-Kains, Benjamin AU - Han, Younghyun AU - Hase, Takeshi AU - He, Di AU - He, Liye AU - Heath, Lenwood S. AU - Hellton, Kristoffer H. AU - Helmer-Citterich, Manuela AU - Hidalgo, Marta R. AU - Hidru, Daniel AU - Hill, Steven M. AU - Hochreiter, Sepp AU - Hong, Seungpyo AU - Hovig, Eivind AU - Hsueh, Ya-Chih AU - Hu, Zhiyuan AU - Huang, Justin K. AU - Huang, R. Stephanie AU - Hunyady, Laszlo AU - Hwang, Jinseub AU - Hwang, Tae Hyun AU - Hwang, Woochang AU - Hwang, Yongdeuk AU - Isayev, Olexandr AU - Walk, Oliver Bear Don't AU - Jack, John AU - Jahandideh, Samad AU - Ji, Jiadong AU - Jo, Yousang AU - Kamola, Piotr J. AU - Kanev, Georgi K. AU - Karacosta, Loukia AU - Karimi, Mostafa AU - Kaski, Samuel AU - Kazanov, Marat AU - Khamis, Abdullah M. AU - Khan, Suleiman Ali AU - Kiani, Narsis A. AU - Kim, Allen AU - Kim, Jinhan AU - Kim, Juntae AU - Kim, Kiseong AU - Kim, Kyung AU - Kim, Sunkyu AU - Kim, Yongsoo AU - Kim, Yunseong AU - Kirk, Paul D. W. AU - Kitano, Hiroaki AU - Klambauer, Gunter AU - Knowles, David AU - Ko, Melissa AU - Kohn-Luque, Alvaro AU - Kooistra, Albert J. AU - Kuenemann, Melaine A. AU - Kuiper, Martin AU - Kurz, Christoph AU - Kwon, Mijin AU - Laarhoven, Twan AU - Laegreid, Astrid AU - Lederer, Simone AU - Lee, Heewon AU - Lee, Jeon AU - Lee, Yun Woo AU - Leppaho, Eemeli AU - Lewis, Richard AU - Li, Jing AU - Li, Lang AU - Liley, James AU - Lim, Weng Khong AU - Lin, Chieh AU - Liu, Yiyi AU - Lopez, Yosvany AU - Low, Joshua AU - Lysenko, Artem AU - Machado, Daniel AU - Madhukar, Neel AU - De Maeyer, Dries AU - Malpartida, Ana Belen AU - Mamitsuka, Hiroshi AU - Marabita, Francesco AU - Marchal, Kathleen AU - Marttinen, Pekka AU - Mason, Daniel AU - Mazaheri, Alireza AU - Mehmood, Arfa AU - Mehreen, Ali AU - Michaut, Magali AU - Miller, Ryan A. AU - Mitsopoulos, Costas AU - Modos, Dezso AU - Van Moerbeke, Marijke AU - Moo, Keagan AU - Motsinger-Reif, Alison AU - Movva, Rajiv AU - Muraru, Sebastian AU - Muratov, Eugene AU - Mushthofa, Mushthofa AU - Nagarajan, Niranjan AU - Nakken, Sigve AU - Nath, Aritro AU - Neuvial, Pierre AU - Newton, Richard AU - Ning, Zheng AU - De Niz, Carlos AU - Oliva, Baldo AU - Olsen, Catharina AU - Palmeri, Antonio AU - Panesar, Bhawan AU - Papadopoulos, Stavros AU - Park, Jaesub AU - Park, Seonyeong AU - Park, Sungjoon AU - Pawitan, Yudi AU - Peluso, Daniele AU - Pendyala, Sriram AU - Peng, Jian AU - Perfetto, Livia AU - Pirro, Stefano AU - Plevritis, Sylvia AU - Politi, Regina AU - Poon, Hoifung AU - Porta, Eduard AU - Prellner, Isak AU - Preuer, Kristina AU - Angel Pujana, Miguel AU - Ramnarine, Ricardo AU - Reid, John E. AU - Reyal, Fabien AU - Richardson, Sylvia AU - Ricketts, Camir AU - Rieswijk, Linda AU - Rocha, Miguel AU - Rodriguez-Gonzalvez, Carmen AU - Roell, Kyle AU - Rotroff, Daniel AU - Ruiter, Julian R. AU - Rukawa, Ploy AU - Sadacca, Benjamin AU - Safikhani, Zhaleh AU - Safitri, Fita AU - Sales-Pardo, Marta AU - Sauer, Sebastian AU - Schlichting, Moritz AU - Seoane, Jose A. AU - Serra, Jordi AU - Shang, Ming-Mei AU - Sharma, Alok AU - Sharma, Hari AU - Shen, Yang AU - Shiga, Motoki AU - Shin, Moonshik AU - Shkedy, Ziv AU - Shopsowitz, Kevin AU - Sinai, Sam AU - Skola, Dylan AU - Smirnov, Petr AU - Soerensen, Izel Fourie AU - Soerensen, Peter AU - Song, Je-Hoon AU - Song, Sang Ok AU - Soufan, Othman AU - Spitzmueller, Andreas AU - Steipe, Boris AU - Suphavilai, Chayaporn AU - Tamayo, Sergio Pulido AU - Tamborero, David AU - Tang, Jing AU - Tanoli, Zia-ur-Rehman AU - Tarres-Deulofeu, Marc AU - Tegner, Jesper AU - Thommesen, Liv AU - Tonekaboni, Seyed Ali Madani AU - Tran, Hong AU - De Troyer, Ewoud AU - Truong, Amy AU - Tsunoda, Tatsuhiko AU - Turu, Gabor AU - Tzeng, Guang-Yo AU - Verbeke, Lieven AU - Videla, Santiago AU - Vis, Daniel AU - Voronkov, Andrey AU - Votis, Konstantinos AU - Wang, Ashley AU - Wang, Hong-Qiang Horace AU - Wang, Po-Wei AU - Wang, Sheng AU - Wang, Wei AU - Wang, Xiaochen AU - Wang, Xin AU - Wennerberg, Krister AU - Wernisch, Lorenz AU - Wessels, Lodewyk AU - Westen, Gerard J. P. AU - Westerman, Bart A. AU - White, Simon Richard AU - Willighagen, Egon AU - Wurdinger, Tom AU - Xie, Lei AU - Xie, Shuilian AU - Xu, Hua AU - Yadav, Bhagwan AU - Yau, Christopher AU - Yeerna, Huwate AU - Yin, Jia Wei AU - Yu, Michael AU - Yu, MinHwan AU - Yun, So Jeong AU - Zakharov, Alexey AU - Zamichos, Alexandros AU - Zanin, Massimiliano AU - Zeng, Li AU - Zenil, Hector AU - Zhang, Frederick AU - Zhang, Pengyue AU - Zhang, Wei AU - Zhao, Hongyu AU - Zhao, Lan AU - Zheng, Wenjin AU - Zoufir, Azedine AU - Zucknick, Manuela T2 - NATURE COMMUNICATIONS AB - The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. DA - 2019/6/17/ PY - 2019/6/17/ DO - 10.1038/s41467-019-09799-2 VL - 10 SP - SN - 2041-1723 ER - TY - JOUR TI - FSEM: Functional Structural Equation Models for Twin Functional Data AU - Luo, S. AU - Song, R. AU - Styner, M. AU - Gilmore, J. H. AU - Zhu, H. T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - The aim of this article is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white matter tracts obtained from the UNC early brain development study. Supplementary materials for this article are available online. DA - 2019/1/2/ PY - 2019/1/2/ DO - 10.1080/01621459.2017.1407773 VL - 114 IS - 525 SP - 344-357 SN - 1537-274X KW - Covariance function KW - Genetic and environmental effects KW - Weighted likelihood ratio test ER - TY - JOUR TI - COMPLETE SPATIAL MODEL CALIBRATION AU - Huang, Yen-Ning AU - Reich, Brian J. AU - Fuentes, Montserrat AU - Sankarasubramanian, A. T2 - ANNALS OF APPLIED STATISTICS AB - Computer simulation models are central to environmental science. These mathematical models are used to understand complex weather and climate patterns and to predict the climate’s response to different forcings. Climate models are of course not perfect reflections of reality, and so comparison with observed data is needed to quantify and to correct for biases and other deficiencies. We propose a new method to calibrate model output using observed data. Our approach not only matches the marginal distributions of the model output and gridded observed data, but it simultaneously postprocesses the model output to have the same spatial correlation as the observed data. This comprehensive calibration method permits realistic spatial simulations for regional impact studies. We apply the proposed method to global climate model output in North America and show that it successfully calibrates the model output for temperature and precipitation. DA - 2019/6// PY - 2019/6// DO - 10.1214/18-AOAS1219 VL - 13 IS - 2 SP - 746-766 SN - 1941-7330 KW - Bayesian methods KW - calibration KW - spatial statistics ER - TY - JOUR TI - Discussion of "Penalized Spline of Propensity Methods for Treatment Comparison" by Zhou, Elliott, and Little AU - Yang, Shu AU - Zeng, Donglin T2 - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION AB - "Discussion of “Penalized Spline of Propensity Methods for Treatment Comparison” by Zhou, Elliott, and Little." Journal of the American Statistical Association, 114(525), pp. 30–32 DA - 2019/1/2/ PY - 2019/1/2/ DO - 10.1080/01621459.2018.1537916 VL - 114 IS - 525 SP - 31-32 SN - 1537-274X ER - TY - JOUR TI - Bayesian mode and maximum estimation and accelerated rates of contraction AU - Yoo, William Weimin AU - Ghosal, Subhashis T2 - BERNOULLI AB - We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise. We adopt the Bayesian approach by using tensor-product B-splines and endowing the coefficients with Gaussian priors. In the usual fixed-in-advanced sampling plan, we establish posterior contraction rates for mode and maximum and show that they coincide with the minimax rates for this problem. To quantify estimation uncertainty, we construct credible sets for these two quantities that have high coverage probabilities with optimal sizes. If one is allowed to collect data sequentially, we further propose a Bayesian two-stage estimation procedure, where a second stage posterior is built based on samples collected within a credible set constructed from a first stage posterior. Under appropriate conditions on the radius of this credible set, we can accelerate optimal contraction rates from the fixed-in-advanced setting to the minimax sequential rates. A simulation experiment shows that our Bayesian two-stage procedure outperforms single-stage procedure and also slightly improves upon a non-Bayesian two-stage procedure. DA - 2019/8// PY - 2019/8// DO - 10.3150/18-BEJ1056 VL - 25 IS - 3 SP - 2330-2358 SN - 1573-9759 KW - anisotropic Holder space KW - credible set KW - maximum value KW - mode KW - nonparametric regression KW - posterior contraction KW - sequential KW - tensor-product B-splines KW - two-stage ER - TY - JOUR TI - Empirical Likelihood for a Long Range Dependent Process Subordinated to a Gaussian Process AU - Lahiri, Soumendra N. AU - Das, Ujjwal AU - Nordman, Daniel J. T2 - JOURNAL OF TIME SERIES ANALYSIS AB - This article develops empirical likelihood methodology for a class of long range dependent processes driven by a stationary Gaussian process. We consider population parameters that are defined by estimating equations in the time domain. It is shown that the standard block empirical likelihood (BEL) method, with a suitable scaling, has a non‐standard limit distribution based on a multiple Wiener–Itô integral. Unlike the short memory time series case, the scaling constant involves unknown population quantities that may be difficult to estimate. Alternative versions of the empirical likelihood method, involving the expansive BEL (EBEL) methods are considered. It is shown that the EBEL renditions do not require an explicit scaling and, therefore, remove this undesirable feature of the standard BEL. However, the limit law involves the long memory parameter, which may be estimated from the data. Results from a moderately large simulation study on finite sample properties of tests and confidence intervals based on different empirical likelihood methods are also reported. DA - 2019/7// PY - 2019/7// DO - 10.1111/jtsa.12465 VL - 40 IS - 4 SP - 447-466 SN - 1467-9892 KW - Geweke-Porter-Hudak estimator KW - long memory KW - M-estimators KW - non-central limit theorems KW - Wiener-Ito integrals ER - TY - JOUR TI - The role of cellular contact and TGF-beta signaling in the activation of the epithelial mesenchymal transition (EMT) AU - Gasior, Kelsey AU - Wagner, Nikki J. AU - Cores, Jhon AU - Caspar, Rose AU - Wilson, Alyson AU - Bhattacharya, Sudin AU - Hauck, Marlene L. T2 - CELL ADHESION & MIGRATION AB - The epithelial mesenchymal transition (EMT) is one step in the process through which carcinoma cells metastasize by gaining the cellular mobility associated with mesenchymal cells. This work examines the dual influence of the TGF-β pathway and intercellular contact on the activation of EMT in colon (SW480) and breast (MCF7) carcinoma cells. While the SW480 population revealed an intermediate state between the epithelial and mesenchymal states, the MC7 cells exhibited highly adhesive behavior. However, for both cell lines, an exogenous TGF-β signal and a reduction in cellular confluence can push a subgroup of the population towards the mesenchymal phenotype. Together, these results highlight that, while EMT is induced by the synergy of multiple signals, this activation varies across cell types. DA - 2019/// PY - 2019/// DO - 10.1080/19336918.2018.1526597 VL - 13 IS - 1 SP - 63-75 SN - 1933-6926 UR - https://doi.org/10.1080/19336918.2018.1526597 KW - EMT KW - TGF- KW - cellular adhesion KW - epithelial KW - mesenchymal KW - breast carcinoma KW - colon carcinoma ER - TY - JOUR TI - Interdisciplinarity of Ph.D. students across the Atlantic. A Case of Interdisciplinary Research Team Building at the Student Level AU - Park, Jinoh AU - Kim, Byungsoo AU - Lee, Boyeun AU - Hands, David AU - Rider, Traci Rose T2 - DESIGN JOURNAL AB - This research explores the process of building an interdisciplinary design research team at the doctorate student level across institutions and disciplines. This study aims to establish a case addressing how to: 1) define aligned research goals, 2) outline overlapped approaches, such as methodologies, to achieve the research goals, and 3) organize a research team to conduct an interdisciplinary research project addressing overarching characteristics and research interests of members. This study was conducted in four phases: 1) understanding context, 2) framing inputs, 3) discussing processes (repeatable), and 4) analyzing outputs (products). Framed by Action Research, five data collection methods were used within the interdisciplinary team (participants) over two weeks. The interdisciplinary team building process, the benefits and shortcomings of the methods used, and the resulting research study with aligned research goals are presented in this paper. DA - 2019/// PY - 2019/// DO - 10.1080/14606925.2019.1594970 VL - 22 SP - 1453-1466 ER - TY - JOUR TI - Minimal auxiliary Markov chains through sequential elimination of states AU - Martin, Donald E. K. T2 - COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION AB - When using an auxiliary Markov chain to compute the distribution of a pattern statistic, the computational complexity is directly related to the number of Markov chain states. Theory related to minimal deterministic finite automata have been applied to large state spaces to reduce the number of Markov chain states so that only a minimal set remains. In this paper, a characterization of equivalent states is given so that extraneous states are deleted during the process of forming the state space, improving computational efficiency. The theory extends the applicability of Markov chain based methods for computing the distribution of pattern statistics. DA - 2019/4/21/ PY - 2019/4/21/ DO - 10.1080/03610918.2017.1406505 VL - 48 IS - 4 SP - 1040-1054 SN - 1532-4141 KW - completion strings KW - failure states KW - Markov chain embedding KW - minimal deterministic finite automaton KW - spaced seed coverage KW - structured motifs ER - TY - JOUR TI - Population-Based Analysis of DNA Damage and Epigenetic Effects of 1,3-Butadiene in the Mouse AU - Lewis, Lauren AU - Borowa-Mazgaj, Barbara AU - Conti, Aline AU - Chappell, Grace A. AU - Luo, Yu-Syuan AU - Bodnar, Wanda AU - Konganti, Kranti AU - Wright, Fred A. AU - Threadgill, David W. AU - Chiu, Weihsueh A. AU - Pogribny, Igor P. AU - Rusyn, Ivan T2 - CHEMICAL RESEARCH IN TOXICOLOGY AB - Metabolism of 1,3-butadiene, a known human and rodent carcinogen, results in formation of reactive epoxides, a key event in its carcinogenicity. Although mice exposed to 1,3-butadiene present DNA adducts in all tested tissues, carcinogenicity is limited to liver, lung, and lymphoid tissues. Previous studies demonstrated that strain- and tissue-specific epigenetic effects in response to 1,3-butadiene exposure may influence susceptibly to DNA damage and serve as a potential mechanism of tissue-specific carcinogenicity. This study aimed to investigate interindividual variability in the effects of 1,3-butadiene using a population-based mouse model. Male mice from 20 Collaborative Cross strains were exposed to 0 or 635 ppm 1,3-butadiene by inhalation (6 h/day, 5 days/week) for 2 weeks. We evaluated DNA damage and epigenetic effects in target (lung and liver) and nontarget (kidney) tissues of 1,3-butadiene-induced carcinogenesis. DNA damage was assessed by measuring N-7-(2,3,4-trihydroxybut-1-yl)-guanine (THB-Gua) adducts. To investigate global histone modification alterations, we evaluated the trimethylation and acetylation of histones H3 and H4 across tissues. Changes in global cytosine DNA methylation were evaluated from the levels of methylation of LINE-1 and SINE B1 retrotransposons. We quantified the degree of variation across strains, deriving a chemical-specific human variability factor to address population variability in carcinogenic risk, which is largely ignored in current cancer risk assessment practice. Quantitative trait locus mapping identified four candidate genes related to chromatin remodeling whose variation was associated with interstrain susceptibility. Overall, this study uses 1,3-butadiene to demonstrate how the Collaborative Cross mouse population can be used to identify the mechanisms for and quantify the degree of interindividual variability in tissue-specific effects that are relevant to chemically induced carcinogenesis. DA - 2019/5// PY - 2019/5// DO - 10.1021/acs.chemrestox.9b00035 VL - 32 IS - 5 SP - 887-898 SN - 1520-5010 ER - TY - JOUR TI - PERTURBATION BOOTSTRAP IN ADAPTIVE LASSO AU - Das, Debraj AU - Gregory, Karl AU - Lahiri, S. N. T2 - ANNALS OF STATISTICS AB - The Adaptive Lasso (Alasso) was proposed by Zou [J. Amer. Statist. Assoc. 101 (2006) 1418–1429] as a modification of the Lasso for the purpose of simultaneous variable selection and estimation of the parameters in a linear regression model. Zou [J. Amer. Statist. Assoc. 101 (2006) 1418–1429] established that the Alasso estimator is variable-selection consistent as well as asymptotically Normal in the indices corresponding to the nonzero regression coefficients in certain fixed-dimensional settings. In an influential paper, Minnier, Tian and Cai [J. Amer. Statist. Assoc. 106 (2011) 1371–1382] proposed a perturbation bootstrap method and established its distributional consistency for the Alasso estimator in the fixed-dimensional setting. In this paper, however, we show that this (naive) perturbation bootstrap fails to achieve second-order correctness in approximating the distribution of the Alasso estimator. We propose a modification to the perturbation bootstrap objective function and show that a suitably Studentized version of our modified perturbation bootstrap Alasso estimator achieves second-order correctness even when the dimension of the model is allowed to grow to infinity with the sample size. As a consequence, inferences based on the modified perturbation bootstrap will be more accurate than the inferences based on the oracle Normal approximation. We give simulation studies demonstrating good finite-sample properties of our modified perturbation bootstrap method as well as an illustration of our method on a real data set. DA - 2019/8// PY - 2019/8// DO - 10.1214/18-AOS1741 VL - 47 IS - 4 SP - 2080-2116 SN - 0090-5364 KW - Alasso KW - naive perturbation bootstrap KW - modified perturbation bootstrap KW - second-order correctness KW - oracle ER - TY - JOUR TI - On an algorithm for solving Fredholm integrals of the first kind AU - Chae, Minwoo AU - Martin, Ryan AU - Walker, Stephen G. T2 - STATISTICS AND COMPUTING AB - In this paper we use an iterative algorithm for solving Fredholm equations of the first kind. The basic algorithm is known and is based on an EM algorithm when involved functions are non-negative and integrable. With this algorithm we demonstrate two examples involving the estimation of a mixing density and a first passage time density function involving Brownian motion. We also develop the basic algorithm to include functions which are not necessarily non-negative and again present illustrations under this scenario. A self contained proof of convergence of all the algorithms employed is presented. DA - 2019/7// PY - 2019/7// DO - 10.1007/s11222-018-9829-z VL - 29 IS - 4 SP - 645-654 SN - 1573-1375 KW - Brownian motion first passage time KW - Convergence KW - Expectation-maximization KW - Iterative algorithm KW - Mixture model ER - TY - JOUR TI - Determining Normal Precipitation Ranges for Hydric Soil Assessments AU - Vepraskas, Michael J. AU - Berkowitz, Jacob F. AU - Arellano, Consuelo T2 - SOIL SCIENCE SOCIETY OF AMERICA JOURNAL AB - Core Ideas Normal rainfall ranges are best defined by the 30th and 70th percentiles of historic data. Mean ± SD produces a normal rainfall range twice as large as that of percentiles. Mean ± SD normal rainfall will cause some upland soils to be classified as hydric soils. Water table data collected for hydric soil and wetland identification studies require supporting analysis of rainfall normality. Water table measurements made after periods when precipitation is within a normal range are believed to represent long‐term trends, whereas data collected following periods of abnormally high precipitation represent rare events, potentially resulting in erroneous hydric soil determinations. The USDA‐NRCS currently uses two different methods to assess normal precipitation ranges; both have been used to assess hydric soils. This study compared methodologies that identify normal precipitation periods by using: (i) the range defined by the 30th and 70th percentiles observed within a 30‐yr period [i.e., the Climate Analysis for Wetlands Tables (WETS) method] and (ii) long‐term monthly mean precipitation ± one SD (i.e., the U.S. Soil Taxonomy method). Comparisons were made for 30 geographically diverse locations and soil moisture regimes. The results demonstrated that the U.S. Soil Taxonomy method yielded normal precipitation ranges approximately twice as large as those from the WETS method. As a result, the U.S. Soil Taxonomy method precluded the occurrence of drier than normal conditions in many instances and displayed increased sensitivity to infrequent high rainfall events. Three case studies evaluated the implications of method selection on hydric soil identification, demonstrating that the U.S. Soil Taxonomy method identified normal conditions more frequently than the WETS method. As a result, the adoption of the WETS method, which accounts for the non‐normal distribution of precipitation data, as the sole method to determine normal precipitation periods for hydric soil assessment is recommended. DA - 2019/// PY - 2019/// DO - 10.2136/sssaj2018.09.0333 VL - 83 IS - 2 SP - 503-510 SN - 1435-0661 ER - TY - JOUR TI - Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia AU - Ruppert, A. S. AU - Yin, J. AU - Davidian, M. AU - Tsiatis, A. A. AU - Byrd, J. C. AU - Woyach, J. A. AU - Mandrekar, S. J. T2 - ANNALS OF ONCOLOGY AB - Ibrutinib therapy is safe and effective in patients with chronic lymphocytic leukemia (CLL). Currently, ibrutinib is administered continuously until disease progression. Combination regimens with ibrutinib are being developed to deepen response which could allow for ibrutinib maintenance (IM) discontinuation. Among untreated older patients with CLL, clinical investigators had the following questions: (i) does ibrutinib + venetoclax + obinutuzumab (IVO) with IM have superior progression-free survival (PFS) compared with ibrutinib + obinutuzumab (IO) with IM, and (ii) does the treatment strategy of IVO + IM for patients without minimal residual disease complete response (MRD- CR) or IVO + IM discontinuation for patients with MRD- CR have superior PFS compared with IO + IM.Conventional designs randomize patients to IO with IM or IVO with IM to address the first objective, or randomize patients to each treatment strategy to address the second objective. A sequential multiple assignment randomized trial (SMART) design and analysis is proposed to address both objectives.A SMART design strategy is appropriate when comparing adaptive interventions, which are defined by an individual's sequence of treatment decisions and guided by intermediate outcomes, such as response to therapy. A review of common applications of SMART design strategies is provided. Specific to the SMART design previously considered for Alliance study A041702, the general structure of the SMART is presented, an approach to sample size and power calculations when comparing adaptive interventions embedded in the SMART with a time-to-event end point is fully described, and analyses plans are outlined.SMART design strategies can be used in cancer clinical trials with adaptive interventions to identify optimal treatment strategies. Further, standard software exists to provide sample size, power calculations, and data analysis for a SMART design. DA - 2019/4// PY - 2019/4// DO - 10.1093/annonc/mdz053 VL - 30 IS - 4 SP - 542-550 SN - 1569-8041 KW - SMART KW - CLL KW - design strategies KW - adaptive interventions KW - randomized clinical trials ER - TY - JOUR TI - Bisphenol F has different effects on preadipocytes differentiation and weight gain in adult mice as compared with Bisphenol A and S AU - Drobna, Zuzana AU - Talarovicova, Alzbeta AU - Schrader, Hannah E. AU - Fennell, Timothy R. AU - Snyder, Rodney W. AU - Rissman, Emilie F. T2 - TOXICOLOGY AB - Bisphenol S (2,2-bisulfone, BPS) and Bisphenol F (2,2-bis [4-hydroxyphenol]methane, BPF) are analogs of Bisphenol A (2,2-bis[4-hydroxyphenyl]propane, BPA), a widely used endocrine disrupting compound present in polycarbonate plastics, thermal receipts and epoxy resins that line food cans. Here we examined effects of BPA, BPS, and BPF in low concentrations on differentiation in murine 3T3-L1 preadipocytes. We also fed adult male mice chow with one of three doses of BPF (0, 0.5, 5, 50 mg/kg chow, or approximately 0.044, 0.44 and 4.4 mg/kg body weight per day) for 12 weeks, collected body weights, food intake, and tested for glucose tolerance. The doses of BPF used produced mean concentrations of 0, 6.2, 43.6, and 561 ng/mL in plasma. In 3T3-L1 cells BPS had the greatest effects, along with BPA, both increased expression of several genes required for preadipocyte differentiation over 12 days in culture. In contrast, BPF decreased expression of several genes late in differentiation. This dichotomy was also reflected in lipid accumulation as BPA and BPS treated cells had elevated lipid concentrations compared to controls or cells treated with BPF. Male mice fed either the highest or lowest concentrations of BPF gained less weight than controls with no effects on glucose levels or glucose tolerance. Plasma levels of BPF reflected doses in food with no overlap between doses. In summary, our results suggest that BPS has a strong potential to be obesogenic while effects of BPF are subtler and potentially in the opposite direction. DA - 2019/5/15/ PY - 2019/5/15/ DO - 10.1016/j.tox.2019.03.016 VL - 420 SP - 66-72 SN - 0300-483X KW - Bisphenol KW - Metabolism KW - Fat KW - BPF KW - Alternatives to BPA KW - Diabetes ER - TY - JOUR TI - ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS AU - Shi, Chengchun AU - Song, Rui AU - Lu, Wenbin T2 - ANNALS OF STATISTICS AB - Precision medicine is an emerging medical paradigm that focuses on finding the most effective treatment strategy tailored for individual patients. In the literature, most of the existing works focused on estimating the optimal treatment regime. However, there has been less attention devoted to hypothesis testing regarding the optimal treatment regime. In this paper, we first introduce the notion of conditional qualitative treatment effects (CQTE) of a set of variables given another set of variables and provide a class of equivalent representations for the null hypothesis of no CQTE. The proposed definition of CQTE does not assume any parametric form for the optimal treatment rule and plays an important role for assessing the incremental value of a set of new variables in optimal treatment decision making conditional on an existing set of prescriptive variables. We then propose novel testing procedures for no CQTE based on kernel estimation of the conditional contrast functions. We show that our test statistics have asymptotically correct size and nonnegligible power against some nonstandard local alternatives. The empirical performance of the proposed tests are evaluated by simulations and an application to an AIDS data set. DA - 2019/8// PY - 2019/8// DO - 10.1214/18-AOS1750 VL - 47 IS - 4 SP - 2348-2377 ER - TY - JOUR TI - Implementation of an occupancy-based monitoring protocol for a widespread and cryptic species, the New England cottontail (Sylvilagus transitionalis) AU - Shea, Colin P. AU - Eaton, Mitchell J. AU - MacKenzie, Darryl I. T2 - WILDLIFE RESEARCH AB - Context Designing effective long-term monitoring strategies is essential for managing wildlife populations. Implementing a cost-effective, practical monitoring program is especially challenging for widespread but locally rare species. Early successional habitat preferred by the New England cottontail (NEC) has become increasingly rare and fragmented, resulting in substantial declines from their peak distribution in the mid-1900s. The introduction of a possible competitor species, the eastern cottontail (EC), may also have played a role. Uncertainty surrounding how these factors have contributed to NEC declines has complicated management and necessitated development of an appropriate monitoring framework to understand possible drivers of distribution and dynamics. Aims Because estimating species abundance is costly, we designed presence–absence surveys to estimate species distributions, test assumptions about competitive interactions, and improve understanding of demographic processes for eastern cottontails (EC) and New England cottontails (NEC). The survey protocol aimed to balance long-term management objectives with practical considerations associated with monitoring a widespread but uncommon species. Modelling data arising from these observations allow for estimation of covariate relationships between species status and environmental conditions including habitat and competition. The framework also allows inference about species status at unsurveyed locations. Methods We designed a monitoring protocol to collect data across six north-eastern USA states and, using data collected from the first year of monitoring, fit a suite of single-season occupancy models to assess how abiotic and biotic factors influence NEC occurrence, correcting for imperfect detectability. Key results Models did not provide substantial support for competitive interactions between EC and NEC. NEC occurrence patterns appear to be influenced by several remotely sensed habitat covariates (land-cover classes), a habitat-suitability index, and, to a lesser degree, plot-level habitat covariates (understorey density and canopy cover). Conclusions We recommend continuing presence–absence monitoring and the development of dynamic occupancy models to provide further evidence regarding hypotheses of competitive interactions and habitat influences on the underlying dynamics of NEC occupancy. Implications State and federal agencies responsible for conserving this and other threatened species can engage with researchers in thoughtful discussions, based on management objectives, regarding appropriate monitoring design to ensure that the allocation of monitoring efforts provides useful inference on population drivers to inform management intervention. DA - 2019/3// PY - 2019/3// DO - 10.1071/WR18058 VL - 46 IS - 3 SP - 222-235 SN - 1448-5494 KW - co-occurrence models KW - lagomorphs KW - species distribution models KW - Sylvilagus transitionalis KW - wildlife management ER - TY - JOUR TI - Population model for the decline of Homalodisca vitripennis (Hemiptera: Cicadellidae) over a ten-year period AU - Banks, H. T. AU - Banks, John E. AU - Cody, Natalie G. AU - Hoddle, Mark S. AU - Meade, Annabel E. T2 - JOURNAL OF BIOLOGICAL DYNAMICS AB - The glassy-winged sharpshooter, Homalodisca vitripennis (Germar), is an invasive pest which presents a major economic threat to grape industries in California, because it spreads a disease-causing bacterium, Xylella fastidiosa. In this note we develop a time and temperature dependent mathematical model to analyze aggregate population data for H. vitripennis from a 10-year study consisting of biweekly monitoring of H. vitripennis populations on unsprayed citrus, during which H. vitripennis decreased significantly. This model was fitted to the aggregate H. vitripennis time series data using iterative reweighted weighted least squares (IRWLS) with assumed probability distributions for certain parameter values. Results indicate that the H. vitripennis model fits the phenological and temperature data reasonably well, but the observed population decrease may possibly be attributed to factors other than the abiotic effect of temperature. A key factor responsible for this decline but not analyzed here could be biotic, for example, potentially parasitism of H. vitripennis eggs by Cosmocomoidea ashmeadi. A biological control program targeting H. vitripennis utilizing the mymarid egg parasitoid Cosmocomoidea (formerly Gonatocerus) ashmeadi (Girault) is described. DA - 2019/1/1/ PY - 2019/1/1/ DO - 10.1080/17513758.2019.1616839 VL - 13 IS - 1 SP - 422-446 SN - 1751-3766 KW - Population model KW - data fitting KW - inverse problems KW - weighted least squares KW - generalized least squares KW - biological control KW - Homalodisca vitripennis KW - Gonatocerus ashmeadi KW - Cosmocomoidea ashmeadi KW - density dependence ER - TY - JOUR TI - Quality Control of Quantitative High Throughput Screening Data AU - Shockley, Keith R. AU - Gupta, Shuva AU - Harris, Shawn F. AU - Lahiri, Soumendra N. AU - Peddada, Shyamal D. T2 - FRONTIERS IN GENETICS AB - Quantitative high throughput screening (qHTS) experiments can generate thousands of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and precision of concentration at half-maximal response (AC50) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with “inconsistent” response patterns and produces trustworthy AC50 values. DA - 2019/5/9/ PY - 2019/5/9/ DO - 10.3389/fgene.2019.00387 VL - 10 SP - SN - 1664-8021 KW - ANOVA KW - clustering KW - concentration-response KW - potency KW - quantitative high throughput screening KW - toxicological response ER - TY - JOUR TI - Registration of USDA-Max x Soja Core Set-1: Recovering 99% of Wild Soybean Genome from PI 366122 in 17 Agronomic Interspecific Germplasm Lines AU - Eickholt, David AU - Carter, Thomas E., Jr. AU - Taliercio, Earl AU - Dickey, David AU - Dean, Lisa O. AU - Delheimer, Jake AU - Li, Zenglu T2 - JOURNAL OF PLANT REGISTRATIONS AB - USDA‐ Max × Soja Core Set‐1 (USDA‐MxS‐CS1‐1 to USDA‐MxS‐CS1‐17 [Reg. No. GP‐417 to GP‐433, PI 689053 to PI 689069]) is a group of 17 interspecific breeding lines developed from the hybridization of lodging‐resistant soybean cultivar N7103 [ Glycine max (L.) Merr.] with wild soybean plant introduction PI 366122 [ G. soja Siebold & Zucc.]. These materials were released by the USDA‐ARS and the North Carolina Agricultural Research Service (March 2017) to expand the North American soybean breeding pool. The full‐sib breeding lines are 50% wild soybean by pedigree and developed through bulk breeding and pedigree selection. Marker analysis of 2455 well‐distributed polymorphic single‐nucleotide polymorphism loci revealed that individual breeding lines ranged from 21 to 40% alleles derived from wild soybean. Collectively, most of the wild soybean genome was transferred to the core set in that 5, 10, and 17 breeding lines captured 83, 98, and 99% of G. soja –derived polymorphic alleles. Physical linkage maps suggested that extensive recombination occurred between the G. max and G. soja genomes. The 17 breeding lines are well adapted to the southeastern United States, exhibited seed yield ranging from 75 to 97% of the domesticated parent, and are group VI or VII maturity. Some breeding lines displayed increased seed protein, oil, or methionine content, and all exhibited increased seed size as compared to the domesticated parent. The novel genetic diversity, positive agronomic performance, and improved seed composition of these lines suggest that they are valuable genetic resources for US soybean breeding. DA - 2019/5// PY - 2019/5// DO - 10.3198/jpr2017.09.0059crg VL - 13 IS - 2 SP - 217-236 SN - 1940-3496 ER - TY - JOUR TI - A comparison of testing methods in scalar-on-function regression AU - Tekbudak, M.Y. AU - Alfaro-Córdoba, M. AU - Maity, A. AU - Staicu, A.-M. T2 - AStA Advances in Statistical Analysis AB - A scalar-response functional model describes the association between a scalar response and a set of functional covariates. An important problem in the functional data literature is to test nullity or linearity of the effect of the functional covariate in the context of scalar-on-function regression. This article provides an overview of the existing methods for testing both the null hypotheses that there is no relationship and that there is a linear relationship between the functional covariate and scalar response, and a comprehensive numerical comparison of their performance. The methods are compared for a variety of realistic scenarios: when the functional covariate is observed at dense or sparse grids and measurements include noise or not. Finally, the methods are illustrated on the Tecator data set. DA - 2019/9// PY - 2019/9// DO - 10.1007/s10182-018-00337-x VL - 103 IS - 3 SP - 411-436 UR - http://dx.doi.org/10.1007/s10182-018-00337-x KW - Functional regression KW - Functional linear model KW - Nonparametric regression KW - Mixed-effects model KW - Hypothesis testing ER - TY - JOUR TI - A spatio-temporal model for longitudinal image-on-image regression AU - Hazra, A. AU - Reich, B.J. AU - Reich, D.S. AU - Shinohara, R.T. AU - Staicu, A.M. T2 - Statistics in Biosciences AB - Neurologists and radiologists often use magnetic resonance imaging (MRI) in the management of subjects with multiple sclerosis (MS) because it is sensitive to inflammatory and demyelinative changes in the white matter of the brain and spinal cord. Two conventional modalities used for identifying lesions are T1-weighted (T1) and T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging, which are used clinically and in research studies. Magnetization transfer ratio (MTR), which is available only in research settings, is an advanced MRI modality that has been used extensively for measuring disease-related demyelination both in white matter lesions as well across normal-appearing white matter. Acquiring MTR is not standard in clinical practice, due to the increased scan time and cost. Hence, prediction of MTR based on the modalities T1 and FLAIR could have great impact on the availability of these promising measures for improved patient management. We propose a spatio-temporal regression model for image response and image predictors that are acquired longitudinally, with images being co-registered within the subject but not across subjects. The model is additive, with the response at a voxel being dependent on the available covariates not only through the current voxel but also on the imaging information from the voxels within a neighboring spatial region as well as their temporal gradients. We propose a dynamic Bayesian estimation procedure that updates the parameters of the subject-specific regression model as data accummulates. To bypass the computational challenges associated with a Bayesian approach for high-dimensional imaging data, we propose an approximate Bayesian inference technique. We assess the model fitting and the prediction performance using longitudinally acquired MRI images from 46 MS patients. DA - 2019/// PY - 2019/// DO - 10.1007/s12561-017-9206-z VL - 11 IS - 1 SP - 22-46 SN - 1867-1772 KW - Spatio-temporal regression model KW - Longitudinal imaging study KW - Dynamic Bayesian updating KW - Multiple sclerosis KW - Magnetization transfer ratio KW - T1-weighted KW - T2-weighted fluid-attenuated inversion recovery KW - Composite likelihood ER - TY - JOUR TI - Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion AU - Chi, Eric C. AU - Hu, Liuyi AU - Saibaba, Arvind K. AU - Rao, Arvind U. K. T2 - JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS AB - We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an information criterion to select an appropriate amount of row and column smoothing, namely, to perform model selection. We also discuss how to exploit the special structure of the problem to scale up the estimation and model selection procedure via the Hutchinson estimator, combined with a stochastic Quasi-Newton approach. Supplementary material for this article is available online. DA - 2019/1/2/ PY - 2019/1/2/ DO - 10.1080/10618600.2018.1482763 VL - 28 IS - 1 SP - 36-47 SN - 1537-2715 UR - https://doi.org/10.1080/10618600.2018.1482763 KW - Convex optimization KW - Degrees of freedom KW - Hutchinson estimator KW - Information criterion KW - Penalization KW - Sparse linear systems ER - TY - JOUR TI - A Spatial Markov Model for Climate Extremes AU - Reich, Brian J. AU - Shaby, Benjamin A. T2 - JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS AB - Spatial climate data are often presented as summaries of areal regions such as grid cells, either because they are the output of numerical climate models or to facilitate comparison with numerical climate model output. Extreme value analysis can benefit greatly from spatial methods that borrow information across regions. For Gaussian outcomes, a host of methods that respect the areal nature of the data are available, including conditional and simultaneous autoregressive models. However, to our knowledge, there is no such method in the spatial extreme value analysis literature. In this article, we propose a new method for areal extremes that accounts for spatial dependence using latent clustering of neighboring regions. We show that the proposed model has desirable asymptotic dependence properties and leads to relatively simple computation. Applying the proposed method to North American climate data reveals several local and continental-scale changes in the distribution of precipitation and temperature extremes over time. Supplementary material for this article is available online. DA - 2019/1/2/ PY - 2019/1/2/ DO - 10.1080/10618600.2018.1482764 VL - 28 IS - 1 SP - 117-126 SN - 1537-2715 KW - Areal data KW - Bayesian data analysis KW - Climate change KW - Conditionally autoregressive prior KW - Generalized extreme value distribution ER - TY - JOUR TI - Binormal Precision-Recall Curves for Optimal Classification of Imbalanced Data AU - Liu, Zhongkai AU - Bondell, Howard D. T2 - STATISTICS IN BIOSCIENCES DA - 2019/4// PY - 2019/4// DO - 10.1007/s12561-019-09231-9 VL - 11 IS - 1 SP - 141-161 SN - 1867-1772 KW - Binary classification KW - Binormal assumption KW - Imbalanced data KW - Precision-Recall curve KW - ROC curve ER - TY - JOUR TI - Asymptotic theory of penalized splines AU - Xiao, Luo T2 - ELECTRONIC JOURNAL OF STATISTICS AB - The paper gives a unified study of the large sample asymptotic theory of penalized splines including the O-splines using B-splines and an integrated squared derivative penalty [22], the P-splines which use B-splines and a discrete difference penalty [13], and the T-splines which use truncated polynomials and a ridge penalty [24]. Extending existing results for O-splines [7], it is shown that, depending on the number of knots and appropriate smoothing parameters, the $L_{2}$ risk bounds of penalized spline estimators are rate-wise similar to either those of regression splines or to those of smoothing splines and could each attain the optimal minimax rate of convergence [32]. In addition, convergence rate of the $L_{\infty }$ risk bound, and local asymptotic bias and variance are derived for all three types of penalized splines. DA - 2019/// PY - 2019/// DO - 10.1214/19-EJS1541 VL - 13 IS - 1 SP - 747-794 SN - 1935-7524 KW - Nonparametric regression KW - penalized splines KW - L-infinity convergence KW - L-2 convergence KW - local asymptotics KW - rate optimality ER - TY - JOUR TI - Cultures of Gossypium barbadense cotton ovules offer insights into the microtubule-mediated control of fiber cell expansion AU - Pierce, Ethan T. AU - Graham, Benjamin P. AU - Stiff, Michael R. AU - Osborne, Jason A. AU - Haigler, Candace H. T2 - PLANTA DA - 2019/5// PY - 2019/5// DO - 10.1007/s00425-019-03106-5 VL - 249 IS - 5 SP - 1551-1563 SN - 1432-2048 KW - Colchicine KW - Cotton fiber KW - Cytoskeleton KW - Fluridone KW - Ovule culture KW - Plant cell growth ER - TY - JOUR TI - An integrated model decomposing the components of detection probability and abundance in unmarked populations AU - Hostettere, Nathan J. AU - Gardner, Beth AU - Sillett, T. Scott AU - Pollock, Kenneth H. AU - Simons, Theodore R. T2 - ECOSPHERE AB - Abstract Accurate estimates of population abundance are essential to both theoretical and applied ecology. Rarely are all individuals detected during a survey and abundance models often incorporate some form of imperfect detection. Detection probability, however, consists of three components: probability of presence during a survey, probability of availability given presence, and probability of detection given availability and presence. We develop an integrated model to separate these three detection components and provide abundance estimates for the available, present, and superpopulation of individuals. Our framework integrates several common survey methods for unmarked populations: spatially and temporally replicated counts, distance sampling data, and time‐of‐detection data. Simulations indicated relatively unbiased estimates for detection and availability probabilities. Negative bias in estimated superpopulation abundance was present with three temporally replicated surveys, but greatly reduced with six surveys. In a case study of Island Scrub‐Jays ( Aphelocoma insularis ), posterior modes for presence, availability, and detection probabilities were 0.78, 0.96, and 0.26, respectively, from 10‐min point counts repeated at 97 sites on three occasions, with noticeable differences among available, present, and superpopulation abundance estimates. This generalizable framework integrates common sampling protocols and provides joint inferences on the components of detection probability, spatial and non‐spatial temporary emigration, and abundance in unmarked populations. DA - 2019/3// PY - 2019/3// DO - 10.1002/ecs2.2586 VL - 10 IS - 3 SP - SN - 2150-8925 KW - detection probability KW - distance sampling KW - integrated model KW - N-mixture model KW - temporary emigration KW - time-of-detection KW - unmarked populations ER - TY - JOUR TI - Effect of controlled drainage on nitrogen fate and transport for a subsurface drained grass field receiving liquid swine lagoon effluent AU - Liu, Yu AU - Youssef, Mohamed A. AU - Chescheir, George M. AU - Appelboom, Timothy W. AU - Poole, Chad A. AU - Arellano, Consuelo AU - Skaggs, R. Wayne T2 - AGRICULTURAL WATER MANAGEMENT AB - Application of livestock manure has become a principal nutrient source in groundwater and surface water. The goal of this research was to investigate the effect of controlled drainage (CD) on nitrogen (N) fate and transport for a subsurface drained grass field receiving liquid swine lagoon effluent (SLE). A four-year field experiment was conducted on a naturally poorly drained pasture in eastern North Carolina. The 1.25 ha experimental field was artificially drained by subsurface drains installed at 1.0 m depth and 12.5 m spacing. Two treatments, replicated twice were implemented: conventional drainage (FD) and CD. The CD management protocol was more intensive compared to previous studies. The drain outlets of CD plot were set at 36 cm below soil surface all year round except several days before irrigation application when water table depth was shallower than 65 cm below surface. Controlled drainage significantly reduced drainage flow and TN loading via subsurface drain lines by an average of 397 mm yr−1 (93%) and 34.5 kg N ha−1 yr−1 (94%), respectively. DRAINMOD hydrologic simulations indicated that 96% of the reduction in predicted drain flow was attributed to increased lateral seepage. The nitrogen that did not drain from the field in response to CD was lost via enhanced denitrification (67%) and lateral seepage to adjacent fields (33%). This study clearly demonstrated how CD management affects the N fate and transport through seepage and denitrification process. DA - 2019/5/20/ PY - 2019/5/20/ DO - 10.1016/j.agwat.2019.02.018 VL - 217 SP - 440-451 SN - 1873-2283 KW - Controlled drainage KW - Swine lagoon effluent KW - Drainage water quality KW - Nitrogen KW - Denitrification KW - Lateral seepage ER - TY - JOUR TI - IMMUNOSUPPRESSANT TREATMENT DYNAMICS IN RENAL TRANSPLANT RECIPIENTS: AN ITERATIVE MODELING APPROACH AU - Murad, Neha AU - Tran, H. T. AU - Banks, H. T. AU - Everett, R. A. AU - Rosenberg, Eric S. T2 - DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B AB - Finding the optimal balance between over-suppression and under-suppression of the immune response is difficult to achieve in renal transplant patients, all of whom require lifelong immunosuppression. Our ultimate goal is to apply control theory to adaptively predict the optimal amount of immunosuppression; however, we first need to formulate a biologically realistic model. The process of quantitively modeling biological processes is iterative and often leads to new insights with every iteration. We illustrate this iterative process of modeling for renal transplant recipients infected by BK virus. We analyze and improve on the current mathematical model by modifying it to be more biologically realistic and amenable for designing an adaptive treatment strategy. DA - 2019/6// PY - 2019/6// DO - 10.3934/dcdsb.2018274 VL - 24 IS - 6 SP - 2781-2797 SN - 1553-524X KW - Inverse problem KW - infection dynamics KW - differential equations KW - renal transplantation KW - immunosuppression therapy KW - iterative modeling process KW - BKV ER - TY - JOUR TI - Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers AU - Grimm, Fabian A. AU - House, John S. AU - Wilson, Melinda R. AU - Sirenko, Oksana AU - Iwata, Yasuhiro AU - Wright, Fred A. AU - Ball, Nicholas AU - Rusyn, Ivan T2 - REGULATORY TOXICOLOGY AND PHARMACOLOGY AB - High-content screening data derived from physiologically-relevant in vitro models promise to improve confidence in data-integrative groupings for read-across in human health safety assessments. The biological data-based read-across concept is especially applicable to bioactive chemicals with defined mechanisms of toxicity; however, the challenge of data-derived groupings for chemicals that are associated with little or no bioactivity has not been explored. In this study, we apply a suite of organotypic and population-based in vitro models for comprehensive bioactivity profiling of twenty E-Series and P-Series glycol ethers, solvents with a broad variation in toxicity ranging from relatively non-toxic to reproductive and hematopoetic system toxicants. Both E-Series and P-Series glycol ethers elicited cytotoxicity only at high concentrations (mM range) in induced pluripotent stem cell-derived hepatocytes and cardiomyocytes. Population-variability assessment comprised a study of cytotoxicity in 94 human lymphoblast cell lines from 9 populations and revealed differences in inter-individual variability across glycol ethers, but did not indicate population-specific effects. Data derived from various phenotypic and transcriptomic assays revealed consistent bioactivity trends between both cardiomyocytes and hepatocytes, indicating a more universal, rather than cell-type specific mode-of-action for the tested glycol ethers in vitro. In vitro bioactivity-based similarity assessment using Toxicological Priority Index (ToxPi) showed that glycol ethers group according to their alcohol chain length, longer chains were associated with increased bioactivity. While overall in vitro bioactivity profiles did not correlate with in vivo toxicity data on glycol ethers, in vitro bioactivity of E-series glycol ethers were indicative of and correlated with in vivo irritation scores. DA - 2019/2// PY - 2019/2// DO - 10.1016/j.yrtph.2018.11.011 VL - 101 SP - 91-102 SN - 1096-0295 KW - New assessment methodologies KW - Glycol ethers KW - In vitro KW - ToxPi KW - Read-across KW - Safety assessment ER - TY - JOUR TI - EFFECT OF A NUTRIENT ENEMA ON SERUM NUTRIENT CONCENTRATIONS IN WHITE-SPOTTED BAMBOO SHARKS (CHILOSCYLLIUM PLAGIOSUM) AU - Parkinson, Lily AU - Gaines, Brian AU - Nollens, Hendrik T2 - JOURNAL OF ZOO AND WILDLIFE MEDICINE AB - Ill and anorectic captive sharks present a unique challenge for husbandry and veterinary staff. Providing adequate fluid and nutritional support to sharks while minimizing handling remains difficult. This study aimed to evaluate the ability of a nutrient enema to alter blood analyte concentrations. Thirty-six healthy, fasted white-spotted bamboo sharks (Chiloscyllium plagiosum) were enrolled in the study with 18 sharks receiving a nutrient enema and 18 sharks receiving a non-nutrient saline enema. The metabolic state of sharks was evaluated via measurement of blood glucose, blood urea nitrogen, and β-hydroxybutyrate as well as other serum biochemistry parameters. Changes in sodium, chloride, calcium, β-hydroxybutyrate, glucose, total protein, and triglyceride concentrations were seen across time in both groups. Blood glucose absolute concentrations and changes over time differed between the nutrient and nonnutrient groups. This pilot study indicates that it is possible to influence the glucose metabolism of healthy sharks via nutrient enema. Further study is needed to better understand potential therapeutics for ill and anorectic sharks. DA - 2019/3// PY - 2019/3// DO - 10.1638/2017-0106 VL - 50 IS - 1 SP - 55-61 SN - 1937-2825 KW - Chiloscyllium plagiosum KW - enema KW - fasting KW - glucose KW - nutrition KW - white-spotted bamboo shark ER - TY - JOUR TI - Predictor ranking and false discovery proportion control in high-dimensional regression AU - Jeng, X. Jessie AU - Chen, Xiongzhi T2 - JOURNAL OF MULTIVARIATE ANALYSIS AB - We propose a ranking and selection procedure to prioritize relevant predictors and control false discovery proportion (FDP) in variable selection. Our procedure utilizes a new ranking method built upon the de-sparsified Lasso estimator. We show that the new ranking method achieves the optimal order of minimum non-zero effects in ranking relevant predictors ahead of irrelevant ones. Adopting the new ranking method, we develop a variable selection procedure to asymptotically control FDP at a user-specified level. We show that our procedure can consistently estimate the FDP of variable selection as long as the de-sparsified Lasso estimator is asymptotically normal. In simulations, our procedure compares favorably to existing methods in ranking efficiency and FDP control when the regression model is relatively sparse. DA - 2019/5// PY - 2019/5// DO - 10.1016/j.jmva.2018.12.006 VL - 171 SP - 163-175 ER - TY - JOUR TI - Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse AU - Bates, Savannah AU - Leonenko, Vasiliy AU - Rineer, James AU - Bobashev, Georgiy T2 - COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY DA - 2019/3// PY - 2019/3// DO - 10.1007/s10588-018-09281-2 VL - 25 IS - 1 SP - 36-47 SN - 1572-9346 KW - Opioids KW - Synthetic populations KW - Data linkage KW - Overdose ER - TY - JOUR TI - Relationships between urban green land cover and human health at different spatial resolutions AU - Tsai, Wei-Lun AU - Leung, Yu-Fai AU - McHale, Melissa R. AU - Floyd, Myron F. AU - Reich, Brian J. T2 - URBAN ECOSYSTEMS DA - 2019/4// PY - 2019/4// DO - 10.1007/s11252-018-0813-3 VL - 22 IS - 2 SP - 315-324 SN - 1573-1642 KW - MAUP KW - Landscape metrics KW - Life expectancy ER - TY - JOUR TI - Bayesian modeling and test planning for multiphase reliability assessment AU - Gilman, James F. AU - Fronczyk, Kassandra M. AU - Wilson, Alyson G. T2 - QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL AB - Abstract We propose a Bayesian hierarchical model to assess the reliability of a family of vehicles, based on the development of the joint light tactical vehicle (JLTV). The proposed model effectively combines information across three phases of testing and across common vehicle components. The analysis yields estimates of failure rates for specific failure modes and vehicles as well as an overall estimate of the failure rate for the family of vehicles. We are also able to obtain estimates of how well vehicle modifications between test phases improve failure rates. In addition to using all data to improve on current assessments of reliability and reliability growth, we illustrate how to leverage the information learned from the three phases to determine appropriate specifications for subsequent testing that will demonstrate if the reliability meets a given reliability threshold. DA - 2019/4// PY - 2019/4// DO - 10.1002/qre.2406 VL - 35 IS - 3 SP - 750-760 ER - TY - JOUR TI - Bayesian Method for Causal Inference in Spatially-Correlated Multivariate Time Series AU - Ning, Bo AU - Ghosal, Subhashis AU - Thomas, Jewell T2 - BAYESIAN ANALYSIS AB - Measuring the causal impact of an advertising campaign on sales is an essential task for advertising companies. Challenges arise when companies run advertising campaigns in multiple stores which are spatially correlated, and when the sales data have a low signal-to-noise ratio which makes the advertising effects hard to detect. This paper proposes a solution to address both of these challenges. A novel Bayesian method is proposed to detect weaker impacts and a multivariate structural time series model is used to capture the spatial correlation between stores through placing a G-Wishart prior on the precision matrix. The new method is to compare two posterior distributions of a latent variable—one obtained by using the observed data from the test stores and the other one obtained by using the data from their counterfactual potential outcomes. The counterfactual potential outcomes are estimated from the data of synthetic controls, each of which is a linear combination of sales figures at many control stores over the causal period. Control stores are selected using a revised Expectation-Maximization variable selection (EMVS) method. A two-stage algorithm is proposed to estimate the parameters of the model. To prevent the prediction intervals from being explosive, a stationarity constraint is imposed on the local linear trend of the model through a recently proposed prior. The benefit of using this prior is discussed in this paper. A detailed simulation study shows the effectiveness of using our proposed method to detect weaker causal impact. The new method is applied to measure the causal effect of an advertising campaign for a consumer product sold at stores of a large national retail chain. DA - 2019/3// PY - 2019/3// DO - 10.1214/18-BA1102 VL - 14 IS - 1 SP - 1-28 SN - 1936-0975 KW - advertising campaign KW - Bayesian variable selection KW - causal inference KW - graphical model KW - stationarity KW - time series ER - TY - JOUR TI - Precision Medicine AU - Kosorok, Michael R. AU - Laber, Eric B. AU - Louis, TA T2 - ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 6 AB - Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges. DA - 2019/// PY - 2019/// DO - 10.1146/annurev-statistics-030718-105251 VL - 6 SP - 263-286 SN - 2326-831X KW - data-driven decision science KW - dynamic treatment regimes KW - machine learning KW - patient heterogeneity KW - statistical inference ER - TY - JOUR TI - Identifying individual risk rare variants using protein structure guided local tests (POINT) AU - West, Rachel Marceau AU - Lu, Wenbin AU - Rotroff, Daniel M. AU - Kuenemann, Melaine A. AU - Chang, Sheng-Mao AU - Wu, Michael C. AU - Wagner, Michael J. AU - Buse, John B. AU - Motsinger-Reif, Alison A. AU - Fourches, Denis AU - Tzeng, Jung-Ying T2 - PLOS COMPUTATIONAL BIOLOGY AB - Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data. DA - 2019/2// PY - 2019/2// DO - 10.1371/journal.pcbi.1006722 VL - 15 IS - 2 SP - SN - 1553-7358 ER - TY - JOUR TI - High strain rate compressive response of the C-f/SiC composite AU - Luan, Kun AU - Liu, Jianjun AU - Sun, Baozhong AU - Zhang, Wei AU - Hu, Jianbao AU - Fang, Xiaomeng AU - Ming, Chen AU - Song, Erhong T2 - CERAMICS INTERNATIONAL AB - Carbon fiber reinforced ceramic owns the properties of lightweight, high fracture toughness, excellent shock resistance, and thus overcomes ceramic's brittleness. The researches on the advanced structure of astronautics, marine have exclusively evaluated the quasi-static mechanical response of carbon fiber reinforced ceramics, while few investigations are available in the open literature regarding elastodynamics. This paper reports the dynamic compressive responses of a carbon fiber reinforced silicon carbide (Cf/SiC) composite (CFCMC) tested by the material test system 801 machine (MTS) and the split Hopkinson pressure bar (SHPB). These tests were to determine the rate dependent compression response and high strain rate failure mechanism of the Cf/SiC composite in in-plane and out-plane directions. The in-plane compressive strain rates are from 0.001 to 2200 s−1, and that of the out-plane direction are from 0.001 to 2400 s−1. The compressive stress-strain curves show the Cf/SiC composite has a property of strain rate sensitivity in both directions while under high strain rate loadings. Its compressive stiffness, compressive stress, and corresponding strain are also strain rate sensitive. The compressive damage morphologies after high strain rate impacting show different failure modes for each loading direction. This study provides knowledge about elastodynamics of fiber-reinforced ceramics and extends their design criterion with a reliable evaluation while applying in the scenario of loading high strain rate. DA - 2019/4/15/ PY - 2019/4/15/ DO - 10.1016/j.ceramint.2018.12.174 VL - 45 IS - 6 SP - 6812-6818 SN - 1873-3956 KW - Ceramic matrix composite KW - Compression KW - Strain rate sensitivity KW - Energy absorption KW - Damage morphology ER - TY - JOUR TI - Bayesian Semiparametric ROC surface estimation under verification bias AU - Zhu, Rui AU - Ghosal, Subhashis T2 - Computational Statistics & Data Analysis AB - The Receiver Operating Characteristic (ROC) surface is a generalization of the ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories. Verification bias occurs when not all subjects have their labels observed. This is a common problem in disease diagnosis since the gold standard test to get labels, i.e., the true disease status, can be invasive and expensive. The same situation happens in the evaluation of semi-supervised learning, where the unlabeled data are incorporated. A Bayesian approach for estimating the ROC surface is proposed based on continuous data under a semi-parametric trinormality assumption. The proposed method is then extended to situations in the presence of verification bias. The posterior distribution is computed under the trinormality assumption using a rank-based likelihood. The consistency of the posterior under a mild condition is also established. The proposed method is compared with existing methods for estimating an ROC surface. Simulation results show that it performs well in terms of accuracy. The method is applied to evaluate the performance of CA125 and HE4 in the diagnosis of epithelial ovarian cancer (EOC) as a demonstration. DA - 2019/5// PY - 2019/5// DO - 10.1016/j.csda.2018.09.003 VL - 133 SP - 40-52 J2 - Computational Statistics & Data Analysis LA - en OP - SN - 0167-9473 UR - http://dx.doi.org/10.1016/J.CSDA.2018.09.003 DB - Crossref KW - ROC surface KW - Verification bias correction KW - Trinormal model KW - MAR assumption ER - TY - JOUR TI - Optimal control of immunosuppressants in renal transplant recipients susceptible to BKV infection AU - Murad, Neha AU - Tran, H. T. AU - Banks, H. T. T2 - OPTIMAL CONTROL APPLICATIONS & METHODS AB - Summary Kidney transplant recipients are put on a lifelong regime of immunosuppressants to prevent the body from rejecting the allograft. Suppressing the immune system renders the body susceptible to infections. The key to a successful transplant is to ensure the immune system is sufficiently suppressed to prevent organ rejection but adequately strong to fight infections. Finding the optimal balance between over and undersuppression of the immune response is crucial in preventing allograft failure. In this paper, we design a feedback control formulation to predict the optimal amount of immunosuppression required by renal transplant recipients in the context of infections caused by BK virus. We use a receding horizon control methodology to construct the feedback control. Data, as they are currently collected, provide information for only some model states, so we use nonlinear Kalman filtering to estimate the remaining model states for feedback control. We conclude that, using the presented methodology, an individualized adaptive treatment schedule can be built for renal transplant recipients. DA - 2019/3// PY - 2019/3// DO - 10.1002/oca.2478 VL - 40 IS - 2 SP - 292-309 SN - 1099-1514 KW - BK virus KW - immunosuppression KW - Kalman filtering KW - optimal feedback control KW - receding horizon control KW - renal transplant ER - TY - JOUR TI - A Bayesian multivariate functional model with spatially varying coefficient approach for modeling hurricane track data AU - Rekabdarkolaee, Hossein Moradi AU - Krut, Christopher AU - Fuentes, Montserrat AU - Reich, Brian J. T2 - SPATIAL STATISTICS AB - Abstract Hurricanes are massive storm systems with enormous destructive capabilities. Understanding the trends across space and time of a hurricane track and intensity leads to improved forecasts and minimizes their damage. Viewing the hurricane’s latitude, longitude, and wind speed as functions of time, we propose a novel spatiotemporal multivariate functional model to simultaneously allow for multivariate, longitudinal, and spatially observed data with noisy functional covariates. The proposed procedure is fully Bayesian and inference is performed using MCMC. This new approach is illustrated through simulation studies and analyzing the hurricane track data from 2004 to 2013 in the Atlantic basin. Simulation results indicate that our proposed model offers a significant reduction in the mean square error and averaged interval and increases the coverage probability. In addition, our method offers a 10% reduction in location and wind speed prediction error. DA - 2019/3// PY - 2019/3// DO - 10.1016/j.spasta.2018.12.006 VL - 29 SP - 351-365 SN - 2211-6753 KW - Atlantic basin KW - Bayesian inference KW - Markov chain Monte Carlo KW - Spatially varying coefficient KW - Spline KW - Tropical cyclones ER - TY - PCOMM TI - Genetic loci determining total immunoglobulin E levels from birth through adulthood AU - Yao, Tsung-Chieh AU - Chung, Ren-Hua AU - Lin, Chung-Yen AU - Tsai, Pei-Chien AU - Chang, Wei-Chiao AU - Yeh, Kuo-Wei AU - Tsai, Ming-Han AU - Liao, Sui-Ling AU - Hua, Man-Chin AU - Lai, Shen-Hao AU - Chen, Li-Chen AU - Chang, Su-Wei AU - Yu, Ya-Wen AU - Hsu, Jing-Ya AU - Chang, Su-Ching AU - Cheng, Wen-Chih AU - Hu, Donglei AU - Hong, Xiumei AU - Burchard, Esteban G. AU - Wang, Xiaobin AU - Tzeng, Jung-Ying AU - Tsai, Hui-Ju AU - Huang, Jing-Long AB - Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. DA - 2019/3// PY - 2019/3// DO - 10.1111/all.13654 SP - 621-625 ER - TY - JOUR TI - Ecosystem function in predator-prey food webs-confronting dynamic models with empirical data AU - Curtsdotter, Alva AU - Banks, H. Thomas AU - Banks, John E. AU - Jonsson, Mattias AU - Jonsson, Tomas AU - Laubmeier, Amanda N. AU - Traugott, Michael AU - Bommarco, Riccardo T2 - JOURNAL OF ANIMAL ECOLOGY AB - Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured >70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size. DA - 2019/2// PY - 2019/2// DO - 10.1111/1365-2656.12892 VL - 88 IS - 2 SP - 196-210 SN - 1365-2656 KW - agricultural pests KW - allometry KW - body mass KW - conservation biological control KW - herbivore suppression KW - multitrophic functioning KW - predator-prey interactions KW - species traits ER - TY - JOUR TI - Impacts on soil nitrogen availability of converting managed pine plantation into switchgrass monoculture for bioenergy AU - Cacho, Julian F. AU - Youssef, Mohamed A. AU - Shi, Wei AU - Chescheir, George M. AU - Skaggs, R. Wayne AU - Tian, Shiying AU - Leggett, Zakiya H. AU - Sucre, Eric B. AU - Nettles, Jami E. AU - Arellano, Consuelo T2 - SCIENCE OF THE TOTAL ENVIRONMENT AB - Biofuels derived from lignocellulosic materials is one of the options in addressing issues on climate change and energy independence. One of the most promising bioenergy crops is switchgrass (Panicum virgatum L.), particularly in North America. Future advancement in large-scale conversion of lignocellulosic feedstocks and relatively more competitive price for biomass and other economic advantages could lead to landowners opting to venture on switchgrass monoculture (SWITCH) in lieu of loblolly pine monoculture (PINE). Therefore, we investigated the conversion of previously managed loblolly pine stand into SWITCH in eastern North Carolina, U.S.A. on soil N availability. Treatments included PINE, SWTICH, and mature loblolly pine stand (REF). Each treatment was replicated three times on 0.8 ha plots drained by open ditches dug 1.0–1.2 m deep and spaced at 100 m. Rates of net N mineralization (Nm) and nitrification (Nn) at the top 20 cm were measured using sequential in-situ techniques in 2011 and 2012 (the 3rd and 4th years of establishment, respectively) along with a one-time laboratory incubation. On average, PINE, SWITCH, and REF can have field net Nm rates up to 0.40, 0.34 and 0.44 mg N·kg soil−1·d−1, respectively, and net Nn rates up to 0.14, 0.08 and 0.10 mg N·kg soil−1·d−1, respectively. Annually, net Nm rates ranged from 136.98 to 167.21, 62.00 to 142.61, and 63.57 to 127.95 kg N·ha−1, and net Nn rates were 56.31–62.98, 16.45–30.45, 31.99–32.94 kg N·ha−1 in PINE, SWITCH, and REF, respectively. Treatment effect was not significant on field Nm rate (p = 0.091). However, SWITCH significantly reduced nitrate-N production (p < 0.01). Overall, results indicated that establishment of SWITCH on poorly drained lands previously under PINE is less likely to significantly impact total soil N availability and potentially has minimum N leaching losses since soil mineral N under this system will be dominated by ammonium-N. DA - 2019/3/1/ PY - 2019/3/1/ DO - 10.1016/j.scitotenv.2018.11.133 VL - 654 SP - 1326-1336 SN - 1879-1026 KW - Managed forests KW - Switchgrass KW - Biomass KW - Biofuel KW - Soil N cycling KW - Soil N dynamics ER - TY - JOUR TI - Untargeted Metabolomic Profiling Identifies Disease-Specific Pathways in Food Allergy and Asthma AU - Crestani, Elena AU - Leirer, Jonathan AU - Motsinger-Reif, Alison AU - Phipatanakul, Wanda AU - Kaddurah-Daouk, Rima AU - Chatila, Talal A. T2 - JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY AB - Food allergy (FA) affects an increasing proportion of children in the US and other developed countries for reasons that remain largely unknown. A deeper understanding of pathogenic mechanisms active in FA may lead to much needed diagnostic and prognostic biomarkers of disease and improved treatment options. Children with asthma alone, children with FA alone, children with both FA and asthma as well as healthy pediatric controls were recruited in the Allergy clinic at Boston Children’s Hospital. Mass spectrometry-based untargeted metabolomic profiling was performed on serum samples (n=35 for FA or asthma alone and FA/asthma; n=20 for controls) looking at global metabolism. Untargeted metabolomic analysis revealed differential profiles of altered metabolites in patients’ groups. In comparison to both controls and children with asthma, FA was uniquely associated with a marked decrease in sphingolipids - in particular sphingomyelins and ceramides - as well as lysophospholipids. Among atopic children, differences in aromatic amino acid metabolism and metabolism of secondary bile acids were observed between food allergic and asthmatic children. Among children with FA, the metabolomic profile of those with asthma was indistinguishable from that of children without asthma. Both unique and shared metabolomic alterations were detected in children with FA, asthma, or both, likely reflecting overlapping but distinct mechanisms and environmental influences operative in these disorders. Lower levels of sphingolipids and ceramides observed in food allergic children may affect the interplay between microbiota and immune cell subsets in the gut, including iNKT cells. DA - 2019/2// PY - 2019/2// DO - 10.1016/j.jaci.2018.12.778 VL - 143 IS - 2 SP - AB255-AB255 SN - 1097-6825 ER - TY - JOUR TI - The recent past and promising future for data integration methods to estimate species' distributions AU - Miller, David A. W. AU - Pacifici, Krishna AU - Sanderlin, Jamie S. AU - Reich, Brian J. T2 - METHODS IN ECOLOGY AND EVOLUTION AB - Abstract With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. Key to recent advances has been using point‐process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts. DA - 2019/1// PY - 2019/1// DO - 10.1111/2041-210X.13110 VL - 10 IS - 1 SP - 22-37 SN - 2041-2096 KW - data fusion KW - integrated distribution model KW - joint likelihood KW - spatial point process KW - species distribution modelling ER - TY - JOUR TI - Contest models highlight inherent inefficiencies of scientific funding competitions AU - Gross, Kevin AU - Bergstrom, Carl T. T2 - PLOS BIOLOGY AB - Scientific research funding is allocated largely through a system of soliciting and ranking competitive grant proposals. In these competitions, the proposals themselves are not the deliverables that the funder seeks, but instead are used by the funder to screen for the most promising research ideas. Consequently, some of the funding program's impact on science is squandered because applying researchers must spend time writing proposals instead of doing science. To what extent does the community's aggregate investment in proposal preparation negate the scientific impact of the funding program? Are there alternative mechanisms for awarding funds that advance science more efficiently? We use the economic theory of contests to analyze how efficiently grant proposal competitions advance science, and compare them with recently proposed, partially randomized alternatives such as lotteries. We find that the effort researchers waste in writing proposals may be comparable to the total scientific value of the research that the funding supports, especially when only a few proposals can be funded. Moreover, when professional pressures motivate investigators to seek funding for reasons that extend beyond the value of the proposed science (e.g., promotion, prestige), the entire program can actually hamper scientific progress when the number of awards is small. We suggest that lost efficiency may be restored either by partial lotteries for funding or by funding researchers based on past scientific success instead of proposals for future work. DA - 2019/1// PY - 2019/1// DO - 10.1371/journal.pbio.3000065 VL - 17 IS - 1 SP - SN - 1545-7885 ER - TY - JOUR TI - Best linear estimation via minimization of relative mean squared error AU - Su, Lin AU - Bondell, Howard D. T2 - STATISTICS AND COMPUTING DA - 2019/1// PY - 2019/1// DO - 10.1007/s11222-017-9792-0 VL - 29 IS - 1 SP - 33-42 SN - 1573-1375 KW - Biased linear estimator KW - Smallest relative mean squared error KW - Ridge regression KW - Ordinary least squares ER - TY - JOUR TI - Effect of wheat infection timing on Fusarium head blight causal agents and secondary metabolites in grain AU - Beccari, Giovanni AU - Arellano, Consuelo AU - Covarelli, Lorenzo AU - Tini, Francesco AU - Sulyok, Michael AU - Cowger, Christina T2 - INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY AB - Fusarium head blight (FHB) results in yield loss and damaging contamination of cereal grains and can be caused by several Fusarium species. The objective of the present study was to determine, in a greenhouse experiment on winter wheat, how FHB was affected by timing of infection (0, 3, 6 or 9 days after anthesis, daa) by the aggressive species Fusarium graminearum compared to the relatively weak species Fusarium avenaceum, Fusarium poae and Fusarium acuminatum. Measures of FHB development were: symptoms in spikes (visually assessed), fungal biomass (quantified by real time quantitative PCR) and accumulation of fungal secondary metabolites (quantified by liquid chromatography-tandem mass spectrometry) in kernels. With regard to symptoms, F. graminearum was unaffected by inoculation timing, while the weaker pathogens caused greater disease severity at later timings. In contrast, the accumulation of F. graminearum biomass was strongly affected by inoculation timing (3 daa ≥ 6 daa ≥ 0 daa = 9 daa), while colonization by the weaker pathogens was less influenced. Similarly, F. graminearum secondary metabolite accumulation was affected by inoculation timing (3 daa ≥ 6 daa ≥ 0 daa = 9 daa), while that of the weaker species was less affected. However, secondary metabolites produced by these weaker species tended to be higher from intermediate-late inoculations (6 daa). Overall, infection timing appeared to play a role particularly in F. graminearum colonization and secondary metabolite accumulation. However, secondary metabolites of weaker Fusarium species may be relatively more abundant when environmental conditions promote spore dispersal later in anthesis, while secondary metabolites produced by F. graminearum are relatively favored by earlier conducive conditions. DA - 2019/2/2/ PY - 2019/2/2/ DO - 10.1016/j.ijfoodmicro.2018.10.014 VL - 290 SP - 214-225 SN - 1879-3460 KW - Anthesis KW - Fusarium graminearum KW - Fusarium avenaceum KW - Fusarium poae KW - Fusarium acuminatum KW - Mycotoxins ER - TY - JOUR TI - Real-time Bayesian non-parametric prediction of solvency risk AU - Hong, Liang AU - Martin, Ryan T2 - ANNALS OF ACTUARIAL SCIENCE AB - Abstract Insurance regulation often dictates that insurers monitor their solvency risk in real time and take appropriate actions whenever the risk exceeds their tolerance level. Bayesian methods are appealing for prediction problems thanks to their ability to naturally incorporate both sample variability and parameter uncertainty into a predictive distribution. However, handling data arriving in real time requires a flexible non-parametric model, and the Monte Carlo methods necessary to evaluate the predictive distribution in such cases are not recursive and can be too expensive to rerun each time new data arrives. In this paper, we apply a recently developed alternative perspective on Bayesian prediction based on copulas. This approach facilitates recursive Bayesian prediction without computing a posterior, allowing insurers to perform real-time updating of risk measures to assess solvency risk, and providing them with a tool for carrying out dynamic risk management strategies in today’s “big data” era. DA - 2019/3// PY - 2019/3// DO - 10.1017/S1748499518000039 VL - 13 IS - 1 SP - 67-79 SN - 1748-5002 KW - Density estimation KW - Mixture model KW - Non-parametric Bayes KW - Risk management KW - Value-at-risk ER - TY - JOUR TI - A spatial kernel density method to estimate the diet composition of fish AU - Binion-Rock, Samantha M. AU - Reich, Brian J. AU - Buckel, Jeffrey A. T2 - CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES AB - We present a novel spatially explicit kernel density approach to estimate the proportional contribution of a prey to a predator’s diet by mass. First, we compared the spatial estimator to a traditional cluster-based approach using a Monte Carlo simulation study. Next, we compared the diet composition of three predators from Pamlico Sound, North Carolina, to evaluate how ignoring spatial correlation affects diet estimates. The spatial estimator had lower mean squared error values compared with the traditional cluster-based estimator for all Monte Carlo simulations. Incorporating spatial correlation when estimating the predator’s diet resulted in a consistent increase in precision across multiple levels of spatial correlation. Bias was often similar between the two estimators; however, when it differed it mostly favored the spatial estimator. The two estimators produced different estimates of proportional contribution of prey to the diets of the three field-collected predator species, especially when spatial correlation was strong and prey were consumed in patchy areas. Our simulation and empirical data provide strong evidence that data on food habits should be modeled using spatial approaches and not treated as spatially independent. DA - 2019/2// PY - 2019/2// DO - 10.1139/cjfas-2017-0306 VL - 76 IS - 2 SP - 249-267 SN - 1205-7533 ER - TY - JOUR TI - Chemometric Approaches for Developing Infrared Nanosensors To Image Anthracyclines AU - Del Bonis-O'Donnell, Jackson Travis AU - Pinals, Rebecca L. AU - Jeong, Sanghwa AU - Thakrar, Arni AU - Wolfinger, Russ D. AU - Landry, Markita P. T2 - BIOCHEMISTRY AB - Generation, identification, and validation of optical probes to image molecular targets in a biological milieu remain a challenge. Synthetic molecular recognition approaches leveraging the intrinsic near-infrared fluorescence of single-walled carbon nanotubes are promising for long-term biochemical imaging in tissues. However, generation of nanosensors for selective imaging of molecular targets requires a heuristic approach. Here, we present a chemometric platform for rapidly screening libraries of candidate single-walled carbon nanotube nanosensors against biochemical analytes to quantify the fluorescence response to small molecules, including vitamins, neurotransmitters, and chemotherapeutics. We further show this method can be applied to identify biochemical analytes that selectively modulate the intrinsic near-infrared fluorescence of candidate nanosensors. Chemometric analysis thus enables identification of nanosensor-analyte "hits" and also nanosensor fluorescence signaling modalities such as wavelength shifts that are optimal for translation to biological imaging. Through this approach, we identify and characterize a nanosensor for the chemotherapeutic anthracycline doxorubicin (DOX), which provides a ≤17 nm fluorescence red-shift and exhibits an 8 μM limit of detection, compatible with peak circulatory concentrations of doxorubicin common in therapeutic administration. We demonstrate the selectivity of this nanosensor over dacarbazine, a chemotherapeutic commonly co-injected with doxorubicin. Lastly, we establish nanosensor tissue compatibility for imaging of doxorubicin in muscle tissue by incorporating nanosensors into the mouse hindlimb and measuring the nanosensor response to exogenous DOX administration. Our results motivate chemometric approaches to nanosensor discovery for chronic imaging of drug partitioning into tissues and toward real-time monitoring of drug accumulation. DA - 2019/1/8/ PY - 2019/1/8/ DO - 10.1021/acs.biochem.8b00926 VL - 58 IS - 1 SP - 54-64 SN - 0006-2960 ER - TY - JOUR TI - Drivers of elevational richness peaks, evaluated for trees in the east Himalaya AU - Rana, Suresh K. AU - Gross, Kevin AU - Price, Trevor D. T2 - ECOLOGY AB - Abstract Along elevational gradients, species richness often peaks at intermediate elevations and not the base. Here we refine and test eight hypotheses to evaluate causes of a richness peak in trees of the eastern Himalaya. In the field, we enumerated trees in 50 plots of size 0.1 ha each at eight zones along an elevational gradient and compared richness patterns with interpolation of elevational ranges of species from a thorough review of literature, including floras from the plains of India. The maximum number of species peaks at similar elevations in the two data sets (at 500 m in the field sampling and between 500 m and 1,000 m in range interpolation); concordance between the methods implies that statistical artefacts are unlikely to explain the peak in the data. We reject most hypotheses (e.g., area, speciation rate, mixing of distinct floras). We find support for a model in which climate (actual evapotranspiration [ AET ] or its correlates) sets both the number of species and each species optimum, coupled with a geometric constraint. We consider that AET declines with elevation, but an abrupt change in the association of AET with geographical distance into the plains means that the location of highest AET , at the base of the mountain, receives range overlaps from fewer species than the location just above the base. We formalize this explanation with a mathematical model to show how this can generate the observed low‐elevation richness peak. DA - 2019/1// PY - 2019/1// DO - 10.1002/ecy.2548 VL - 100 IS - 1 SP - SN - 1939-9170 KW - beta diversity KW - diversification rate KW - elevational gradient KW - geometric constraints KW - Himalaya KW - mid-elevational peak KW - species richness KW - trees ER - TY - JOUR TI - Educational attainment predicts negative perceptions women have of their own climate change knowledge AU - Selm, Kathryn R. AU - Peterson, M. Nils AU - Hess, George R. AU - Beck, Scott M. AU - McHale, Melissa R. T2 - PLOS ONE AB - Education may encourage personal and collective responses to climate change, but climate education has proven surprisingly difficult and complex. Self-perception of knowledge and intelligence represent one factor that may impact willingness to learn about climate change. We explored this possibility with a case study in Raleigh, North Carolina in 2015 (n = 200). Our goal was to test how gender and ethnicity influenced perceptions people had of their own climate change knowledge. Survey respondents were asked how strongly they agreed with the statement “I feel knowledgeable about climate change” (1 = strongly disagree, and 5 = strongly agree). Our survey instrument also included demographic questions about race, age, income, gender, and education, as well as respondent’s experience with natural disasters and drought. We observed an interaction between education and gender where women’s self-perceived knowledge was higher than men among people with low levels of educational attainment, but was higher for men than women among people with high levels of educational attainment. In addition, minority respondents self-reported lower perceived climate change knowledge than white respondents, regardless of educational attainment. This study enhances our understanding of the gender gap in self-perceptions of climate knowledge by suggesting it is contingent on educational attainment. This could be the result of stereotype-threat experienced by women and minorities, and exacerbated by educational systems. Because people who question their knowledge are often more able to learn, particularly in ideologically charged contexts, highly educated women and minorities may be more successful learning about climate change than white men. DA - 2019/1/4/ PY - 2019/1/4/ DO - 10.1371/journal.pone.0210149 VL - 14 IS - 1 SP - SN - 1932-6203 ER - TY - JOUR TI - Predictors of Response to 4-Aminopyridine in Chronic Canine Spinal Cord Injury AU - Lewis, Melissa J. AU - Laber, Eric AU - Olby, Natasha J. T2 - JOURNAL OF NEUROTRAUMA AB - 4-Aminopyridine (4AP), a potassium channel antagonist, can improve hindlimb motor function in dogs with chronic thoracolumbar spinal cord injury (SCI); however, individual response is variable. We hypothesized that injury characteristics would differ between dogs that do and do not respond to 4AP. Our objective was to compare clinical, electrodiagnostic, gait, and imaging variables between dogs that do and do not respond to 4AP, to identify predictors of response. Thirty-four dogs with permanent deficits after acute thoracolumbar SCI were enrolled. Spasticity, motor and sensory evoked potentials (MEPs, SEPs), H-reflex, F-waves, gait scores, and magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) were evaluated at baseline and after 4AP administration. Baseline variables were assessed as predictors of response; response was defined as ≥1 point change in open field gait score. Variables were compared pre- and post-4AP to evaluate 4AP effects. Fifteen of 33 (45%) dogs were responders, 18/33 (55%) were non-responders and 1 was eliminated because of an adverse event. Pre-H-reflex threshold <1.2 mA predicted non-response; pre-H-reflex threshold >1.2 mA and Canine Spasticity Scale overall score <7 were predictive of response. All responders had translesional connections on DTI. MEPs were more common post-4AP than pre-4AP (10 vs. 6 dogs) and 4AP decreased H-reflex threshold and increased spasticity in responders. 4-AP impacts central conduction and motor neuron pool excitability in dogs with chronic SCI. Severity of spasticity and H-reflex threshold might allow prediction of response. Further exploration of electrodiagnostic and imaging characteristics might elucidate additional factors contributing to response or non-response. DA - 2019/5/1/ PY - 2019/5/1/ DO - 10.1089/neu.2018.5975 VL - 36 IS - 9 SP - 1428-1434 SN - 1557-9042 KW - canine KW - chronic SCI KW - motor neuron pool excitability KW - potassium channel antagonist KW - spasticity ER - TY - JOUR TI - Field Monitoring of Downspout Disconnections to Reduce Runoff Volume and Improve Water Quality along the North Carolina Coast AU - Taguchi, Vinicius J. AU - Carey, Erin S. AU - Hunt, William F., III T2 - JOURNAL OF SUSTAINABLE WATER IN THE BUILT ENVIRONMENT AB - Virtually all land development increases stormwater runoff and disrupts the natural hydrologic cycle. This is a particularly important issue for areas developed prior to the widespread application of stormwater control measures (SCMs). Among the simplest and most underused SCMs are downspout disconnections (DSDs), whereby stormwater gutters and downspouts are disconnected from storm sewers, and stormwater is instead routed across a lawn or other pervious surfaces on the property before entering the storm drain. Seven such DSD sites within the Hewletts Creek watershed in Wilmington, North Carolina were monitored for stormwater volume, total suspended solids (TSS), and nutrient reductions over a full hydrologic year. The DSD sites had loading ratios of contributing roof area to infiltrating lawn area ranging from 4:1 to 14:1. Sandy underlying soils within this watershed were expected to improve the effectiveness of these inexpensive SCMs. Significant cumulative volume reductions (α=0.05 level) were found at each site and ranged from 42% to 87%. Significant nitrogen mass reductions (across 4 DSD sites monitored for water quality) ranged from 44% to 90% for the pollutants evaluated; however, none of the sites demonstrated significant reductions in total phosphorus (TP) or orthophosphorus (ortho-P). All TSS mass reductions were significant and ranged from 44% to 88%, with a median value of 75%. It was found that infiltration rates most significantly impacted volume reductions, whereas nutrient mass reductions correlated primarily with infiltration area length. DA - 2019/2// PY - 2019/2// DO - 10.1061/JSWBAY.0000872 VL - 5 IS - 1 SP - SN - 2379-6111 KW - Downspout disconnection KW - Low-impact development KW - Impervious surface KW - Vegetated filter strip KW - Best management practice KW - Stormwater ER - TY - JOUR TI - Second order correctness of perturbation bootstrap M-estimator of multiple linear regression parameter AU - Das, Debraj AU - Lahiri, S. N. T2 - BERNOULLI AB - Consider the multiple linear regression model $y_{i}=\mathbf{x}'_{i}\boldsymbol{\beta}+\varepsilon_{i}$, where $\varepsilon_{i}$’s are independent and identically distributed random variables, $\mathbf{x}_{i}$’s are known design vectors and $\boldsymbol{\beta}$ is the $p\times1$ vector of parameters. An effective way of approximating the distribution of the M-estimator $\bar{\boldsymbol{\beta}}_{n}$, after proper centering and scaling, is the Perturbation Bootstrap Method. In this current work, second order results of this non-naive bootstrap method have been investigated. Second order correctness is important for reducing the approximation error uniformly to $o(n^{-1/2})$ to get better inferences. We show that the classical studentized version of the bootstrapped estimator fails to be second order correct. We introduce an innovative modification in the studentized version of the bootstrapped statistic and show that the modified bootstrapped pivot is second order correct (S.O.C.) for approximating the distribution of the studentized M-estimator. Additionally, we show that the Perturbation Bootstrap continues to be S.O.C. when the errors $\varepsilon_{i}$’s are independent, but may not be identically distributed. These findings establish perturbation Bootstrap approximation as a significant improvement over asymptotic normality in the regression M-estimation. DA - 2019/2// PY - 2019/2// DO - 10.3150/17-BEJ1001 VL - 25 IS - 1 SP - 654-682 SN - 1573-9759 KW - Edgeworth expansion KW - generalized bootstrap KW - M-estimation KW - perturbation bootstrap KW - residual bootstrap KW - SOC KW - Studentization KW - wild bootstrap ER - TY - JOUR TI - Toward Fully Manufacturable, Fiber Assembly–Based Concurrent Multimodal and Multifunctional Sensors for e‐Textiles AU - Kapoor, Ashish AU - McKnight, Michael AU - Chatterjee, Kony AU - Agcayazi, Talha AU - Kausche, Hannah AU - Bozkurt, Alper AU - Ghosh, Tushar K. T2 - Advanced Materials Technologies AB - Abstract Soft polymer‐based sensors as an integral part of textile structures have attracted considerable scientific and commercial interest recently because of their potential use in healthcare, security systems, and other areas. While electronic sensing functionalities can be incorporated into textiles at one or more of the hierarchical levels of molecules, fibers, yarns, or fabrics, arguably a more practical and inconspicuous means to introduce the desired electrical characteristics is at the fiber level, using processes that are compatible to textiles. Here, a prototype multimodal and multifunctional sensor array formed within a woven fabric structure using bicomponent fibers with ordered insulating and conducting segments is reported. The multifunctional characteristics of the sensors are successfully demonstrated by measuring tactile, tensile, and shear deformations, as well as wetness and biopotential. While the unobtrusive integration of sensing capabilities offers possibilities to preserve all desirable textile qualities, this scaled‐up fiber‐based approach demonstrates the potential for scalable and facile manufacturability of practical e‐textile products using low‐cost roll‐to‐roll processing of large‐area flexible sensor systems and can be remarkably effective in advancing the field of e‐textiles. DA - 2019/1// PY - 2019/1// DO - 10.1002/admt.201800281 UR - https://doi.org/10.1002/admt.201800281 KW - bicomponent fibers KW - e-textiles KW - fiber-based sensors KW - flexible electronics KW - wearable sensors ER - TY - JOUR TI - Multivariate Gaussian network structure learning AU - Du, Xingqi AU - Ghosal, Subhashis T2 - Journal of Statistical Planning and Inference AB - We consider a graphical model where a multivariate normal vector is associated with each node of the underlying graph and estimate the graphical structure. We minimize a loss function obtained by regressing the vector at each node on those at the remaining ones under a group penalty. We show that the proposed estimator can be computed by a fast convex optimization algorithm. We show that as the sample size increases, the estimated regression coefficients and the correct graphical structure are correctly estimated with probability tending to one. By extensive simulations, we show the superiority of the proposed method over comparable procedures. We apply the technique on two real datasets. The first one is to identify gene and protein networks showing up in cancer cell lines, and the second one is to reveal the connections among different industries in the US. DA - 2019/3// PY - 2019/3// DO - 10.1016/j.jspi.2018.07.009 VL - 199 SP - 327-342 J2 - Journal of Statistical Planning and Inference LA - en OP - SN - 0378-3758 UR - http://dx.doi.org/10.1016/J.JSPI.2018.07.009 DB - Crossref KW - Graphical model KW - Group penalty KW - Multivariate normal KW - Rate of convergence ER -