Maxim E. Zaslavsky, Erin Craig, Jackson K. Michuda, Nidhi Sehgal, Nikhil Ram-Mohan, Ji-Yeun Lee, Khoa D. Nguyen, Ramona A. Hoh, Tho D. Pham, Katharina Röltgen, Brandon Lam, Ella S. Parsons, Susan R. Macwana, Wade DeJager, Elizabeth M. Drapeau, Krishna M. Roskin, Charlotte Cunningham-Rundles, M. Anthony Moody, Barton F. Haynes, Jason D. Goldman, James R. Heath, R. Sharon Chinthrajah, Kari C. Nadeau, Benjamin A. Pinsky, Catherine A. Blish, Scott E. Hensley, Kent Jensen, Everett Meyer, Imelda Balboni, Paul J. Utz, Joan T. Merrill, Joel M. Guthridge, Judith A. James, Samuel Yang, Robert Tibshirani, Anshul Kundaje, Scott D. Boyd <<<

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses. <<<

Xin Wei, Mengjiao Chen, Qi Zhang, Junyi Gong, Jie Liu, Kaicheng Yong, Qin Wang, Jiongjiong Fan, Suhui Chen, Hua Hua, Zhaowei Luo, Xiaoyan Zhao, Xuan Wang, Wei Li, Jia Cong, Xiting Yu, Zhihan Wang, Ruipeng Huang, Jiaxin Chen, Xiaoyi Zhou, Jie Qiu, Ping Xu, Jeremy Murray, Hai Wang, Yang Xu, Chenwu Xu, Gen Xu, Jinliang Yang, Bin Han, Xuehui Huang <<<

Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice. <<<

Kimberly Siletti, Rebecca Hodge, Alejandro Mossi Albiach, Ka Wai Lee, Song-Lin Ding, Lijuan Hu, Peter Lönnerberg, Trygve Bakken, Tamara Casper, Michael Clark, Nick Dee, Jessica Gloe, Daniel Hirschstein, Nadiya V. Shapovalova, C. Dirk Keene, Julie Nyhus, Herman Tung, Anna Marie Yanny, Ernest Arenas, Ed S. Lein, Sten Linnarsson <<<

The human brain directs complex behaviors, ranging from fine motor skills to abstract intelligence, but the diversity of cell types that support these skills has not been fully described. In this work, we used single-nucleus RNA sequencing to systematically survey cells across the entire adult human brain. We sampled more than three million nuclei from approximately 100 dissections across the forebrain, midbrain, and hindbrain in three postmortem donors. Our analysis identified 461 clusters and 3313 subclusters organized largely according to developmental origins and revealing high diversity in midbrain and hindbrain neurons. Astrocytes and oligodendrocyte-lineage cells also exhibited regional diversity at multiple scales. The transcriptomic census of the entire human brain presented in this work provides a resource for understanding the molecular diversity of the human brain in health and disease. <<<

Jonas Richiardi, Andre Altmann, Anna-Clare Milazzo, Catie Chang, M. Mallar Chakravarty, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Uli Bromberg, Christian Büchel, Patricia Conrod, Mira Fauth-Bühler, Herta Flor, Vincent Frouin, Jürgen Gallinat, Hugh Garavan, Penny Gowland, Andreas Heinz, Hervé Lemaître, Karl F. Mann, Jean-Luc Martinot, Frauke Nees, Tomáš Paus, Zdenka Pausova, Marcella Rietschel, Trevor W. Robbins, Michael N. Smolka, Rainer Spanagel, Andreas Ströhle, Gunter Schumann, Mike Hawrylycz, Jean-Baptiste Poline, Michael D. Greicius, , Lisa Albrecht, Chris Andrew, Mercedes Arroyo, Eric Artiges, Semiha Aydin, Christine Bach, Tobias Banaschewski, Alexis Barbot, Gareth Barker, Nathalie Boddaert, Arun Bokde, Zuleima Bricaud, Uli Bromberg, Ruediger Bruehl, Christian Büchel, Arnaud Cachia, Anna Cattrell, Patricia Conrod, Patrick Constant, Jeffrey Dalley, Benjamin Decideur, Sylvane Desrivieres, Tahmine Fadai, Herta Flor, Vincent Frouin, Jürgen Gallinat, Hugh Garavan, Fanny Gollier Briand, Penny Gowland, Bert Heinrichs, Andreas Heinz, Nadja Heym, Thomas Hübner, James Ireland, Bernd Ittermann, Tianye Jia, Mark Lathrop, Dirk Lanzerath, Claire Lawrence, Hervé Lemaitre, Katharina Lüdemann, Christine Macare, Catherine Mallik, Jean-François Mangin, Karl Mann, Jean- Luc Martinot, Eva Mennigen, Fabiana Mesquita de Carvahlo, Xavier Mignon, Ruben Miranda, Kathrin Müller, Frauke Nees, Charlotte Nymberg, Marie-Laure Paillere, Tomas Paus, Zdenka Pausova, Jean-Baptiste Poline, Luise Poustka, Michael Rapp, Gabriel Robert, Jan Reuter, Marcella Rietschel, Stephan Ripke, Trevor Robbins, Sarah Rodehacke, John Rogers, Alexander Romanowski, Barbara Ruggeri, Christine Schmäl, Dirk Schmidt, Sophia Schneider, MarkGunter Schumann, Florian Schubert, Yannick Schwartz, Michael Smolka, Wolfgang Sommer, Rainer Spanagel, Claudia Speiser, Tade Spranger, Alicia Stedman, Sabina Steiner, Dai Stephens, Nicole Strache, Andreas Ströhle, Maren Struve, Naresh Subramaniam, Lauren Topper, Robert Whelan, Steve Williams, Juliana Yacubian, Monica Zilbovicius, C Peng Wong, Steven Lubbe, Lourdes Martinez-Medina, Alinda Fernandes, Amir Tahmasebi <<<

Cooperating brain regions express similar genesWhen the brain is at rest, a number of distinct areas are functionally connected. They tend to be organized in networks. Richiardiet al.compared brain imaging and gene expression data to build computational models of these networks. These functional networks are underpinned by the correlated expression of a core set of 161 genes. In this set, genes coding for ion channels and other synaptic functions such as neurotransmitter release dominate.Science, this issue p.1241 <<<

Wenkang Chen, Lu Chen, Xuan Zhang, Ning Yang, Jianghua Guo, Min Wang, Shenghui Ji, Xiangyu Zhao, Pengfei Yin, Lichun Cai, Jing Xu, Lili Zhang, Yingjia Han, Yingni Xiao, Gen Xu, Yuebin Wang, Shuhui Wang, Sheng Wu, Fang Yang, David Jackson, Jinkui Cheng, Saihua Chen, Chuanqing Sun, Feng Qin, Feng Tian, Alisdair R. Fernie, Jiansheng Li, Jianbing Yan, Xiaohong Yang <<<

A better understanding of the extent of convergent selection among crops could greatly improve breeding programs. We found that the quantitative trait locus KRN2 in maize and its rice ortholog, OsKRN2 , experienced convergent selection. These orthologs encode WD40 proteins and interact with a gene of unknown function, DUF1644, to negatively regulate grain number in both crops. Knockout of KRN2 in maize or OsKRN2 in rice increased grain yield by ~10% and ~8%, respectively, with no apparent trade-offs in other agronomic traits. Furthermore, genome-wide scans identified 490 pairs of orthologous genes that underwent convergent selection during maize and rice evolution, and these were enriched for two shared molecular pathways. KRN2 , together with other convergently selected genes, provides an excellent target for future crop improvement. <<<

A high-throughput lithographic method with 25-nanometer resolution and smooth vertical sidewalls is proposed and demonstrated. The technique uses compression molding to create a thickness contrast pattern in a thin resist film carried on a substrate, followed by anisotropic etching to transfer the pattern through the entire resist thickness. Metal patterns with a feature size of 25 nanometers and a period of 70 nanometers were fabricated with the use of resist templates created by imprint lithography in combination with a lift-off process. With further development, imprint lithography should allow fabrication of sub-10-nanometer structures and may become a commercially viable technique for manufacturing integrated circuits and other nanodevices. <<<