@article{guo_liu_lu_you_wu_2023, title={Development of Two Early Forecasting Models for Predicting Incidence of Rice Panicle Blast in China}, volume={113}, ISSN={["1943-7684"]}, DOI={10.1094/PHYTO-08-22-0311-R}, abstractNote={ Early forecasting of rice panicle blast is critical to the management of rice blast. To develop early forecasting models for rice panicle blast, the relationship between the seasonal maximum incidence of rice panicle blast ( PBx) and the PBx in the preceding crop, weather conditions, location, and acreage of susceptible varieties was analyzed. Results revealed that PBx in the preceding crop, acreage of the susceptible varieties in class ( SVC), altitude, weather conditions 120 to 180 days before the PBx date (dbPBx) and 30 to 90 dbPBx were significantly correlated with the PBx. Subsequently, a logistic model and a two-step hurdle model were developed to predict rice panicle blast. The logistic model was developed to predict whether the PBx was 0 or not based on the preceding PBx, altitude, acreage of susceptible varieties, the longest stretch of days with soil temperatures between 16 and 24°C for the period 120 to 150 dbPBx, and the longest stretch of rainy days in the period 120 to 180 dbPBx. The hurdle model predicted if the PBx was greater than 0 at the first step, and if the prediction was greater than 0, then a regression model was developed for predicting PBx based on the preceding PBx, SVC, altitude, and weather data 180 to 30 dbPBx. Validation with the test datasets showed that the logistic model could correctly predict whether PBx was 0 at a mean test accuracy of 78.39% and that the absolute prediction error of PBx by the two-step hurdle model was smaller than 6.16% for 90% of the records. The model developed in this study will be helpful in management decisions for rice growers and policy makers and offer a useful basis for further studies on the epidemiology and forecasting of rice panicle blast. }, number={3}, journal={PHYTOPATHOLOGY}, author={Guo, Fangfang and Liu, Wan-Cai and Lu, Ming-Hong and You, Fengzhi and Wu, B. M.}, year={2023}, month={Mar}, pages={448–459} }
 @article{rasmussen_guo_2023, title={Espalier: Efficient Tree Reconciliation and Ancestral Recombination Graphs Reconstruction Using Maximum Agreement Forests}, volume={72}, ISSN={["1076-836X"]}, DOI={10.1093/sysbio/syad040}, abstractNote={Abstract}, number={5}, journal={SYSTEMATIC BIOLOGY}, author={Rasmussen, David A. and Guo, Fangfang}, year={2023}, month={Nov}, pages={1154–1170} }
 @article{guo_carbone_rasmussen_2022, title={Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination}, volume={18}, ISSN={["1553-7358"]}, DOI={10.1371/journal.pcbi.1010422}, abstractNote={Movement of individuals between populations or demes is often restricted, especially between geographically isolated populations. The structured coalescent provides an elegant theoretical framework for describing how movement between populations shapes the genealogical history of sampled individuals and thereby structures genetic variation within and between populations. However, in the presence of recombination an individual may inherit different regions of their genome from different parents, resulting in a mosaic of genealogical histories across the genome, which can be represented by an Ancestral Recombination Graph (ARG). In this case, different genomic regions may have different ancestral histories and so different histories of movement between populations. Recombination therefore poses an additional challenge to phylogeographic methods that aim to reconstruct the movement of individuals from genealogies, although also a potential benefit in that different loci may contain additional information about movement. Here, we introduce the Structured Coalescent with Ancestral Recombination (SCAR) model, which builds on recent approximations to the structured coalescent by incorporating recombination into the ancestry of sampled individuals. The SCAR model allows us to infer how the migration history of sampled individuals varies across the genome from ARGs, and improves estimation of key population genetic parameters such as population sizes, recombination rates and migration rates. Using the SCAR model, we explore the potential and limitations of phylogeographic inference using full ARGs. We then apply the SCAR to lineages of the recombining fungusAspergillus flavussampled across the United States to explore patterns of recombination and migration across the genome.}, number={8}, journal={PLOS COMPUTATIONAL BIOLOGY}, author={Guo, Fangfang and Carbone, Ignazio and Rasmussen, David A.}, year={2022}, month={Aug} }