@article{jiang_mcclure_mao_chen_liu_zhang_2023, title={Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving}, volume={12}, ISSN={["1938-1328"]}, url={https://doi.org/10.1021/acs.jchemed.3c00589}, DOI={10.1021/acs.jchemed.3c00589}, abstractNote={Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and the challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a "codeless" and free ML neural network building software, Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on the color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to students' daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts.}, journal={JOURNAL OF CHEMICAL EDUCATION}, author={Jiang, Shiyan and Mcclure, Jeanne and Mao, Hongjing and Chen, Jiahui and Liu, Yunshu and Zhang, Yang}, year={2023}, month={Dec} }
 @article{neely_zhang_blumensaadt_mao_brenner_sun_zhang_bao_2023, title={Nucleoporin downregulation modulates progenitor differentiation independent of nuclear pore numbers}, volume={6}, ISSN={["2399-3642"]}, url={https://doi.org/10.1038/s42003-023-05398-6}, DOI={10.1038/s42003-023-05398-6}, abstractNote={AbstractNucleoporins (NUPs) comprise nuclear pore complexes, gateways for nucleocytoplasmic transport. As primary human keratinocytes switch from the progenitor state towards differentiation, most NUPs are strongly downregulated, with NUP93 being the most downregulated NUP in this process. To determine if this NUP downregulation is accompanied by a reduction in nuclear pore numbers, we leveraged Stochastic Optical Reconstruction Microscopy. No significant changes in nuclear pore numbers were detected using three independent NUP antibodies; however, NUP reduction in other subcellular compartments such as the cytoplasm was identified. To investigate how NUP reduction influences keratinocyte differentiation, we knocked down NUP93 in keratinocytes in the progenitor-state culture condition. NUP93 knockdown diminished keratinocytes’ clonogenicity and epidermal regenerative capacity, without drastically affecting nuclear pore numbers or permeability. Using transcriptome profiling, we identified that NUP93 knockdown induces differentiation genes related to both mechanical and immune barrier functions, including the activation of known NF-κB target genes. Consistently, keratinocytes with NUP93 knockdown exhibited increased nuclear localization of the NF-κB p65/p50 transcription factors, and increased NF-κB reporter activity. Taken together, these findings highlight the gene regulatory roles contributed by differential NUP expression levels in keratinocyte differentiation, independent of nuclear pore numbers.}, number={1}, journal={COMMUNICATIONS BIOLOGY}, author={Neely, Amy E. and Zhang, Yang and Blumensaadt, Laura A. and Mao, Hongjing and Brenner, Benjamin and Sun, Cheng and Zhang, Hao F. and Bao, Xiaomin}, year={2023}, month={Oct} }