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Cai, T., Sutter, C., Donovan, S. M., & Fiese, B. H. (2023). The Relationship Between Maternal and Infant Sleep Duration Across the First Two Years. Journal of Developmental and Behavioral Pediatrics, 44(6), E421-E428. https://doi.org/10.1097/DBP.0000000000001195
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Holthaus, T. A., Sethi, S., Cannavale, C. N., Aguiñaga, S., Burd, N. A., Holscher, H. D., & Khan, N. A. (2023). MIND dietary pattern adherence is inversely associated with visceral adiposity and features of metabolic syndrome. Nutrition Research, 116, 69-79. https://doi.org/10.1016/j.nutres.2023.06.001
Stroet, M., Caron, B., Engler, M. S., van der Woning, J., Kauffmann, A., van Dijk, M., El-Kebir, M., Visscher, K. M., Holownia, J., Macfarlane, C., Bennion, B. J., Gelpi-Dominguez, S., Lightstone, F. C., van der Storm, T., Geerke, D. P., Mark, A. E., & Klau, G. W. (2023). OFraMP: a fragment-based tool to facilitate the parametrization of large molecules. Journal of Computer-Aided Molecular Design, 37(8), 357-371. https://doi.org/10.1007/s10822-023-00511-7
Robben, M., Nasr, M. S., Das, A., Veerla, J. P., Huber, M., Jaworski, J., Weidanz, J., & Luber, J. (2023). Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology. Journal of Computational Biology, 30(7), 766-782. https://doi.org/10.1089/cmb.2022.0370
Baldeon, A. D., McDonald, D., Gonzalez, A., Knight, R., & Holscher, H. D. (2023). Diet Quality and the Fecal Microbiota in Adults in the American Gut Project. Journal of Nutrition, 153(7), 2004-2015. https://doi.org/10.1016/j.tjnut.2023.02.018
Unger, A. L., Astrup, A., Feeney, E. L., Holscher, H. D., Gerstein, D. E., Torres-Gonzalez, M., & Brown, K. (2023). Harnessing the Magic of the Dairy Matrix for Next-Level Health Solutions: A Summary of a Symposium Presented at Nutrition 2022. Current Developments in Nutrition, 7(7), Article 100105. https://doi.org/10.1016/j.cdnut.2023.100105
Baur, B., Shin, J., Schreiber, J., Zhang, S., Zhang, Y., Manjunath, M., Song, J. S., Noble, W. S., & Roy, S. (2023). Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation. PLoS computational biology, 19(7), Article e1011286. https://doi.org/10.1371/journal.pcbi.1011286
Ivanovic, S., & El-Kebir, M. (2023). Modeling and predicting cancer clonal evolution with reinforcement learning. Genome Research, 33(7), 1078-1088. https://doi.org/10.1101/gr.277672.123
Morton, J. T., Jin, D. M., Mills, R. H., Shao, Y., Rahman, G., McDonald, D., Zhu, Q., Balaban, M., Jiang, Y., Cantrell, K., Gonzalez, A., Carmel, J., Frankiensztajn, L. M., Martin-Brevet, S., Berding, K., Needham, B. D., Zurita, M. F., David, M., Averina, O. V., ... Taroncher-Oldenburg, G. (2023). Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles. Nature Neuroscience, 26(7), 1208-1217. https://doi.org/10.1038/s41593-023-01361-0
Karakoc, D. B., Konar, M., Puma, M. J., & Varshney, L. R. (2023). Structural chokepoints determine the resilience of agri-food supply chains in the United States. Nature Food, 4(7), 607-615. https://doi.org/10.1038/s43016-023-00793-y
Jops, K., & O’Dwyer, J. P. (2023). Life history complementarity and the maintenance of biodiversity. Nature, 618(7967), 986-991. https://doi.org/10.1038/s41586-023-06154-w
Basu, S., Sattigeri, P., Ramamurthy, K. N., Chenthamarakshan, V., Varshney, K. R., Varshney, L. R., & Das, P. (2023). Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Technical Tracks 6 (pp. 6788-6796). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). American Association for Artificial Intelligence (AAAI) Press.
Choraria, M., Ferwana, I., Mani, A., & Varshney, L. R. (2023). Learning Optimal Features via Partial Invariance. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Technical Tracks 6 (pp. 7175-7183). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). American Association for Artificial Intelligence (AAAI) Press.
Bleier, N., Wezelis, A., Varshney, L., & Kumar, R. (2023). Programmable Olfactory Computing. In ISCA 2023 - Proceedings of the 2023 50th Annual International Symposium on Computer Architecture (pp. 358-371). (Proceedings - International Symposium on Computer Architecture). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3579371.3589061
Cannavale, C. N., Edwards, C. G., Liu, R., Keye, S. A., Iwinski, S. J., Holscher, H. D., Renzi-Hammond, L., & Khan, N. A. (2023). Macular pigment is inversely related to circulating C-reactive protein concentrations in school-aged children. Nutrition Research, 114, 13-19. https://doi.org/10.1016/j.nutres.2023.03.003
Bailey, M. A., Thompson, S. V., Mysonhimer, A. R., Bennett, J. N., Vanhie, J. J., De Lisio, M., Burd, N. A., Khan, N. A., & Holscher, H. D. (2023). Dietary fiber intake and fecal short chain fatty acid concentrations are associated with lower plasma lipopolysaccharide-binding protein and inflammation. American Journal of Physiology - Gastrointestinal and Liver Physiology, 324(5), G369-G377. https://doi.org/10.1152/ajpgi.00176.2021
Mysonhimer, A. R., & Holscher, H. D. (2023). Nondigestible Carbohydrate Consumption: Balancing Therapeutics With Gastrointestinal Effects and Tolerance. Nutrition Today, 58(3), 100-104. https://doi.org/10.1097/NT.0000000000000605
Pan, C., Chien, E., & Milenkovic, O. (2023). Unlearning Graph Classifiers with Limited Data Resources. In ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 (pp. 716-726). (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023). Association for Computing Machinery. https://doi.org/10.1145/3543507.3583547
Hughes, R. L., Pindus, D. M., Khan, N. A., Burd, N. A., & Holscher, H. D. (2023). Associations between Accelerometer-Measured Physical Activity and Fecal Microbiota in Adults with Overweight and Obesity. Medicine and Science in Sports and Exercise, 55(4), 680-689. https://doi.org/10.1249/MSS.0000000000003096
O’Shaughnessy, M. R., Schiff, D. S., Varshney, L. R., Rozell, C. J., & Davenport, M. A. (2023). What governs attitudes toward artificial intelligence adoption and governance? Science and Public Policy, 50(2), 161-176. Article scac056. https://doi.org/10.1093/scipol/scac056
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Mysonhimer, A. R., Cannavale, C. N., Bailey, M. A., Khan, N. A., & Holscher, H. D. (2023). Prebiotic Consumption Alters Microbiota but Not Biological Markers of Stress and Inflammation or Mental Health Symptoms in Healthy Adults: A Randomized, Controlled, Crossover Trial. Journal of Nutrition, 153(4), 1283-1296. https://doi.org/10.1016/j.tjnut.2023.02.015
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Reichhardt, C., Regev, I., Dahmen, K., Okuma, S., & Reichhardt, C. J. O. (2023). Reversible to irreversible transitions in periodic driven many-body systems and future directions for classical and quantum systems. Physical Review Research, 5(2), Article 021001. https://doi.org/10.1103/PhysRevResearch.5.021001
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Chakraborty, R., Xiong, M., Athreya, N., Tabatabaei, S. K., Milenkovic, O., & Leburton, J. P. (2023). Solid-State MoS2 Nanopore Membranes for Discriminating among the Lengths of RNA Tails on a Double-Stranded DNA: A New Simulation-Based Differentiating Algorithm. ACS Applied Nano Materials, 6(6), 4651-4660. https://doi.org/10.1021/acsanm.3c00129
Alexander, F. J., Reyes, K. R., Varshney, L. R., & Yoon, B. J. (2023). AI for optimal experimental design and decision-making. In Artificial Intelligence For Science: A Deep Learning Revolution (pp. 609-625). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789811265679_0032
Burroughs, C. H., Montes, C. M., Moller, C. A., Mitchell, N. G., Michael, A. M., Peng, B., Kimm, H., Pederson, T. L., Lipka, A. E., Bernacchi, C. J., Guan, K., & Ainsworth, E. A. (2023). Reductions in Leaf Area Index, Pod Production, Seed Size and Harvest Index Drive Yield Loss to High Temperatures in Soybean. Journal of experimental botany, 74(5), 1629–1641. Article erac503. https://doi.org/10.1093/jxb/erac503
Wen, B., Ravishankar, S., Zhao, Z., Giryes, R., & Ye, J. C. (2023). Physics-Driven Machine Learning for Computational Imaging: Part 2 [From the Guest Editors]. IEEE Signal Processing Magazine, 40(2), 13-15. https://doi.org/10.1109/MSP.2023.3236492
Rashid, F., Dubinkina, V., Ahmad, S., Maslov, S., & Irudayaraj, J. M. K. (2023). Gut Microbiome-Host Metabolome Homeostasis upon Exposure to PFOS and GenX in Male Mice. Toxics, 11(3), Article 281. https://doi.org/10.3390/toxics11030281
Wang, T., Wang, X. W., Lee-Sarwar, K. A., Litonjua, A. A., Weiss, S. T., Sun, Y., Maslov, S., & Liu, Y. Y. (2023). Predicting metabolomic profiles from microbial composition through neural ordinary differential equations. Nature Machine Intelligence, 5(3), 284-293. https://doi.org/10.1038/s42256-023-00627-3
Fei, F., Mia, M. S., Elbanna, A. E., & Choo, J. (2023). A phase-field model for quasi-dynamic nucleation, growth, and propagation of rate-and-state faults. International Journal for Numerical and Analytical Methods in Geomechanics, 47(2), 187-211. https://doi.org/10.1002/nag.3465
Holscher, H. D. (2023). Let's do the math: embracing mathematical modeling to advance nutrition research. American Journal of Clinical Nutrition, 117(2), 220-221. https://doi.org/10.1016/j.ajcnut.2022.12.011
Splichal, I., Donovan, S. M., Kindlova, Z., Stranak, Z., Neuzil Bunesova, V., Sinkora, M., Polakova, K., Valaskova, B., & Splichalova, A. (2023). Release of HMGB1 and Toll-like Receptors 2, 4, and 9 Signaling Are Modulated by Bifidobacterium animalis subsp. lactis BB-12 and Salmonella Typhimurium in a Gnotobiotic Piglet Model of Preterm Infants. International journal of molecular sciences, 24(3), Article 2329. https://doi.org/10.3390/ijms24032329
Rana, P., & Varshney, L. R. (2023). Exploring limits to tree planting as a natural climate solution. Journal of Cleaner Production, 384, Article 135566. https://doi.org/10.1016/j.jclepro.2022.135566
Shinn, L. M., Mansharamani, A., Baer, D. J., Novotny, J. A., Charron, C. S., Khan, N. A., Zhu, R., & Holscher, H. D. (2023). Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults. The Journal of nutrition, 152(12), 2956-2965. Article nxac195. https://doi.org/10.1093/jn/nxac195
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Kim, Y., Shin, J., Cassuto, Y., & Varshney, L. R. (2023). Distributed Boosting Classification Over Noisy Communication Channels. IEEE Journal on Selected Areas in Communications, 41(1), 141-154. https://doi.org/10.1109/JSAC.2022.3221972
Zhao, Z., Ye, J. C., & Bresler, Y. (2023). Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications. IEEE Signal Processing Magazine, 40(1), 148-163. https://doi.org/10.1109/MSP.2022.3215282
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Gabrys, R., Pattabiraman, S., & Milenkovic, O. (2023). Reconstruction of Sets of Strings From Prefix/Suffix Compositions. IEEE Transactions on Communications, 71(1), 3-12. https://doi.org/10.1109/TCOMM.2022.3222341
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McMath, A. L., Iwinski, S., Shen, S., Bost, K. F., Donovan, S. M., & Khan, N. A. (2023). Adherence to screen time and physical activity guidelines is associated with executive function in US toddlers participating in the STRONG Kids 2 birth cohort study. The Journal of Pediatrics, 252, 22-30.e6. https://doi.org/10.1016/j.jpeds.2022.08.026
Luo, D., Chen, Z., Hu, K., Zhao, Z., Hur, V. M., & Clark, B. K. (2023). Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models. Physical Review Research, 5(1), Article 013216. https://doi.org/10.1103/PhysRevResearch.5.013216
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