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202. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U., Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D., Graham, Sarah E., Mukamel, Ronen E., Spracklen, Cassandra N., Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H., Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E., Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S., Akiyama, Masato, Allison, Matthew A., Alvarez, Marcus, Andersen, Mette K., Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F., Bollepalli, Sailalitha, Bonnycastle, Lori L., Bork-Jensen, Jette, Bradfield, Jonathan P., Bradford, Yuki, Braund, Peter S., Brody, Jennifer A., Burgdorf, Kristoffer S., Cade, Brian E., Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E. Warwick, Degenhard, Frauke, Delgado, Graciela E., Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B., Engmann, Jorgen E., Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D., Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E., Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P., Goel, Anuj, Gordon, Scott D., Gorski, Mathias, Grove, Jakob, Guo, Xiuqing, Gustafsson, Stefan, Haessler, Jeffrey, Hansen, Thomas F., Havulinna, Aki S., Haworth, Simon J., He, Jing, Heard-Costa, Nancy, Hebbar, Prashantha, Hindy, George, Ho, Yuk-Lam A., Hofer, Edith, Holliday, Elizabeth, Horn, Katrin, Hornsby, Whitney E., Hottenga, Jouke-Jan, Huang, Hongyan, Huang, Jie, Huerta-Chagoya, Alicia, Huffman, Jennifer E., Hung, Yi-Jen, Huo, Shaofeng, Hwang, Mi Yeong, Iha, Hiroyuki, Ikeda, Daisuke D., Isono, Masato, Jackson, Anne U., Jäger, Susanne, Jansen, Iris E., Johansson, Ingegerd, Jonas, Jost B., Jonsson, Anna, Jørgensen, Torben, Kalafati, Ioanna-Panagiota, Kanai, Masahiro, Kanoni, Stavroula, Kårhus, Line L., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kawaguchi, Takahisa, Kember, Rachel L., Kentistou, Katherine A., Kim, Han-Na, Kim, Young Jin, Kleber, Marcus E., Knol, Maria J., Kurbasic, Azra, Lauzon, Marie, Le, Phuong, Lea, Rodney, Lee, Jong-Young, Leonard, Hampton L., Li, Shengchao A., Li, Xiaohui, Li, Xiaoyin, Liang, Jingjing, Lin, Honghuang, Lin, Shih-Yi, Liu, Jun, Liu, Xueping, Lo, Ken Sin, Long, Jirong, Lores-Motta, Laura, Luan, Jian’an, Lyssenko, Valeriya, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamakou, Vasiliki, Mangino, Massimo, Manichaikul, Ani, Marten, Jonathan, Mattheisen, Manuel, Mavarani, Laven, McDaid, Aaron F., Meidtner, Karina, Melendez, Tori L., Mercader, Josep M., Milaneschi, Yuri, Miller, Jason E., Millwood, Iona Y., Mishra, Pashupati P., Mitchell, Ruth E., Møllehave, Line T., Morgan, Anna, Mucha, Soeren, Munz, Matthias, Nakatochi, Masahiro, Nelson, Christopher P., Nethander, Maria, Nho, Chu Won, Nielsen, Aneta A., Nolte, Ilja M., Nongmaithem, Suraj S., Noordam, Raymond, Ntalla, Ioanna, Nutile, Teresa, Pandit, Anita, Christofidou, Paraskevi, Pärna, Katri, Pauper, Marc, Petersen, Eva R. B., Petersen, Liselotte V., Pitkänen, Niina, Polašek, Ozren, Poveda, Alaitz, Preuss, Michael H., Pyarajan, Saiju, Raffield, Laura M., Rakugi, Hiromi, Ramirez, Julia, Rasheed, Asif, Raven, Dennis, Rayner, Nigel W., Riveros, Carlos, Rohde, Rebecca, Ruggiero, Daniela, Ruotsalainen, Sanni E., Ryan, Kathleen A., Sabater-Lleal, Maria, Saxena, Richa, Scholz, Markus, Sendamarai, Anoop, Shen, Botong, Shi, Jingchunzi, Shin, Jae Hun, Sidore, Carlo, Sitlani, Colleen M., Slieker, Roderick C., Smit, Roelof A. J., Smith, Albert V., Smith, Jennifer A., Smyth, Laura J., Southam, Lorraine, Steinthorsdottir, Valgerdur, Sun, Liang, Takeuchi, Fumihiko, Tallapragada, Divya Sri Priyanka, Taylor, Kent D., Tayo, Bamidele O., Tcheandjieu, Catherine, Terzikhan, Natalie, Tesolin, Paola, Teumer, Alexander, Theusch, Elizabeth, Thompson, Deborah J., Thorleifsson, Gudmar, Timmers, Paul R. H. J., Trompet, Stella, Turman, Constance, Vaccargiu, Simona, van der Laan, Sander W., van der Most, Peter J., van Klinken, Jan B., van Setten, Jessica, Verma, Shefali S., Verweij, Niek, Veturi, Yogasudha, Wang, Carol A., Wang, Chaolong, Wang, Lihua, Wang, Zhe, Warren, Helen R., Bin Wei, Wen, Wickremasinghe, Ananda R., Wielscher, Matthias, Wiggins, Kerri L., Winsvold, Bendik S., Wong, Andrew, Wu, Yang, Wuttke, Matthias, Xia, Rui, Xie, Tian, Yamamoto, Ken, Yang, Jingyun, Yao, Jie, Young, Hannah, Yousri, Noha A., Yu, Lei, Zeng, Lingyao, Zhang, Weihua, Zhang, Xinyuan, Zhao, Jing-Hua, Zhao, Wei, Zhou, Wei, Zimmermann, Martina E., Zoledziewska, Magdalena, Adair, Linda S., Adams, Hieab H. H., Aguilar-Salinas, Carlos A., Al-Mulla, Fahd, Arnett, Donna K., Asselbergs, Folkert W., Åsvold, Bjørn Olav, Attia, John, Banas, Bernhard, Bandinelli, Stefania, Bennett, David A., Bergler, Tobias, Bharadwaj, Dwaipayan, Biino, Ginevra, Bisgaard, Hans, Boerwinkle, Eric, Böger, Carsten A., Bønnelykke, Klaus, Boomsma, Dorret I., Børglum, Anders D., Borja, Judith B., Bouchard, Claude, Bowden, Donald W., Brandslund, Ivan, Brumpton, Ben, Buring, Julie E., Caulfield, Mark J., Chambers, John C., Chandak, Giriraj R., Chanock, Stephen J., Chaturvedi, Nish, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Ching-Yu, Christophersen, Ingrid E., Ciullo, Marina, Cole, John W., Collins, Francis S., Cooper, Richard S., Cruz, Miguel, Cucca, Francesco, Cupples, L. Adrienne, Cutler, Michael J., Damrauer, Scott M., Dantoft, Thomas M., de Borst, Gert J., de Groot, Lisette C. P. G. M., De Jager, Philip L., de Kleijn, Dominique P. V., Janaka de Silva, H., Dedoussis, George V., den Hollander, Anneke I., Du, Shufa, Easton, Douglas F., Elders, Petra J. M., Eliassen, A. Heather, Ellinor, Patrick T., Elmståhl, Sölve, Erdmann, Jeanette, Evans, Michele K., Fatkin, Diane, Feenstra, Bjarke, Feitosa, Mary F., Ferrucci, Luigi, Ford, Ian, Fornage, Myriam, Franke, Andre, Franks, Paul W., Freedman, Barry I., Gasparini, Paolo, Gieger, Christian, Girotto, Giorgia, Goddard, Michael E., Golightly, Yvonne M., Gonzalez-Villalpando, Clicerio, Gordon-Larsen, Penny, Grallert, Harald, Grant, Struan F. A., Grarup, Niels, Griffiths, Lyn, Gudnason, Vilmundur, Haiman, Christopher, Hakonarson, Hakon, Hansen, Torben, Hartman, Catharina A., Hattersley, Andrew T., Hayward, Caroline, Heckbert, Susan R., Heng, Chew-Kiat, Hengstenberg, Christian, Hewitt, Alex W., Hishigaki, Haretsugu, Hoyng, Carel B., Huang, Paul L., Huang, Wei, Hunt, Steven C., Hveem, Kristian, Hyppönen, Elina, Iacono, William G., Ichihara, Sahoko, Ikram, M. Arfan, Isasi, Carmen R., Jackson, Rebecca D., Jarvelin, Marjo-Riitta, Jin, Zi-Bing, Jöckel, Karl-Heinz, Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Kähönen, Mika, Kamatani, Yoichiro, Kang, Kui Dong, Kaprio, Jaakko, Kardia, Sharon L. R., Karpe, Fredrik, Kato, Norihiro, Kee, Frank, Kessler, Thorsten, Khera, Amit V., Khor, Chiea Chuen, Kiemeney, Lambertus A. L. M., Kim, Bong-Jo, Kim, Eung Kweon, Kim, Hyung-Lae, Kirchhof, Paulus, Kivimaki, Mika, Koh, Woon-Puay, Koistinen, Heikki A., Kolovou, Genovefa D., Kooner, Jaspal S., Kooperberg, Charles, Köttgen, Anna, Kovacs, Peter, Kraaijeveld, Adriaan, Kraft, Peter, Krauss, Ronald M., Kumari, Meena, Kutalik, Zoltan, Laakso, Markku, Lange, Leslie A., Langenberg, Claudia, Launer, Lenore J., Le Marchand, Loic, Lee, Hyejin, Lee, Nanette R., Lehtimäki, Terho, Li, Huaixing, Li, Liming, Lieb, Wolfgang, Lin, Xu, Lind, Lars, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Loeffler, Markus, London, Barry, Lubitz, Steven A., Lye, Stephen J., Mackey, David A., Mägi, Reedik, Magnusson, Patrik K. E., Marcus, Gregory M., Vidal, Pedro Marques, Martin, Nicholas G., März, Winfried, Matsuda, Fumihiko, McGarrah, Robert W., McGue, Matt, McKnight, Amy Jayne, Medland, Sarah E., Mellström, Dan, Metspalu, Andres, Mitchell, Braxton D., Mitchell, Paul, Mook-Kanamori, Dennis O., Morris, Andrew D., Mucci, Lorelei A., Munroe, Patricia B., Nalls, Mike A., Nazarian, Saman, Nelson, Amanda E., Neville, Matt J., Newton-Cheh, Christopher, Nielsen, Christopher S., Nöthen, Markus M., Ohlsson, Claes, Oldehinkel, Albertine J., Orozco, Lorena, Pahkala, Katja, Pajukanta, Päivi, Palmer, Colin N. A., Parra, Esteban J., Pattaro, Cristian, Pedersen, Oluf, Pennell, Craig E., Penninx, Brenda W. J. H., Perusse, Louis, Peters, Annette, Peyser, Patricia A., Porteous, David J., Posthuma, Danielle, Power, Chris, Pramstaller, Peter P., Province, Michael A., Qi, Qibin, Qu, Jia, Rader, Daniel J., Raitakari, Olli T., Ralhan, Sarju, Rallidis, Loukianos S., Rao, Dabeeru C., Redline, Susan, Reilly, Dermot F., Reiner, Alexander P., Rhee, Sang Youl, Ridker, Paul M., Rienstra, Michiel, Ripatti, Samuli, Ritchie, Marylyn D., Roden, Dan M., Rosendaal, Frits R., Rotter, Jerome I., Rudan, Igor, Rutters, Femke, Sabanayagam, Charumathi, Saleheen, Danish, Salomaa, Veikko, Samani, Nilesh J., Sanghera, Dharambir K., Sattar, Naveed, Schmidt, Börge, Schmidt, Helena, Schmidt, Reinhold, Schulze, Matthias B., Schunkert, Heribert, Scott, Laura J., Scott, Rodney J., Sever, Peter, Shiroma, Eric J., Shoemaker, M. Benjamin, Shu, Xiao-Ou, Simonsick, Eleanor M., Sims, Mario, Singh, Jai Rup, Singleton, Andrew B., Sinner, Moritz F., Smith, J. Gustav, Snieder, Harold, Spector, Tim D., Stampfer, Meir J., Stark, Klaus J., Strachan, David P., ‘t Hart, Leen M., Tabara, Yasuharu, Tang, Hua, Tardif, Jean-Claude, Thanaraj, Thangavel A., Timpson, Nicholas J., Tönjes, Anke, Tremblay, Angelo, Tuomi, Tiinamaija, Tuomilehto, Jaakko, Tusié-Luna, Maria-Teresa, Uitterlinden, Andre G., van Dam, Rob M., van der Harst, Pim, Van der Velde, Nathalie, van Duijn, Cornelia M., van Schoor, Natasja M., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Völzke, Henry, Wacher-Rodarte, Niels H., Walker, Mark, Wang, Ya Xing, Wareham, Nicholas J., Watanabe, Richard M., Watkins, Hugh, Weir, David R., Werge, Thomas M., Widen, Elisabeth, Wilkens, Lynne R., Willemsen, Gonneke, Willett, Walter C., Wilson, James F., Wong, Tien-Yin, Woo, Jeong-Taek, Wright, Alan F., Wu, Jer-Yuarn, Xu, Huichun, Yajnik, Chittaranjan S., Yokota, Mitsuhiro, Yuan, Jian-Min, Zeggini, Eleftheria, Zemel, Babette S., Zheng, Wei, Zhu, Xiaofeng, Zmuda, Joseph M., Zonderman, Alan B., Zwart, John-Anker, Chasman, Daniel I., Cho, Yoon Shin, Heid, Iris M., McCarthy, Mark I., Ng, Maggie C. Y., O’Donnell, Christopher J., Rivadeneira, Fernando, Thorsteinsdottir, Unnur, Sun, Yan V., Tai, E. Shyong, Boehnke, Michael, Deloukas, Panos, Justice, Anne E., Lindgren, Cecilia M., Loos, Ruth J. F., Mohlke, Karen L., North, Kari E., Stefansson, Kari, Walters, Robin G., Winkler, Thomas W., Young, Kristin L., Loh, Po-Ru, Yang, Jian, Esko, Tõnu, Assimes, Themistocles L., Auton, Adam, Abecasis, Goncalo R., Willer, Cristen J., Locke, Adam E., Berndt, Sonja I., Lettre, Guillaume, Frayling, Timothy M., Okada, Yukinori, Wood, Andrew R., Visscher, Peter M., and Hirschhorn, Joel N.

Published
2022
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206. Associations of autozygosity with a broad range of human phenotypes.

Author

Clark, David W, Okada, Yukinori, Moore, Kristjan HS, Mason, Dan, Pirastu, Nicola, Gandin, Ilaria, Mattsson, Hannele, Barnes, Catriona LK, Lin, Kuang, Zhao, Jing Hua, Deelen, Patrick, Rohde, Rebecca, Schurmann, Claudia, Guo, Xiuqing, Giulianini, Franco, Zhang, Weihua, Medina-Gomez, Carolina, Karlsson, Robert, Bao, Yanchun, Bartz, Traci M, Baumbach, Clemens, Biino, Ginevra, Bixley, Matthew J, Brumat, Marco, Chai, Jin-Fang, Corre, Tanguy, Cousminer, Diana L, Dekker, Annelot M, Eccles, David A, van Eijk, Kristel R, Fuchsberger, Christian, Gao, He, Germain, Marine, Gordon, Scott D, de Haan, Hugoline G, Harris, Sarah E, Hofer, Edith, Huerta-Chagoya, Alicia, Igartua, Catherine, Jansen, Iris E, Jia, Yucheng, Kacprowski, Tim, Karlsson, Torgny, Kleber, Marcus E, Li, Shengchao Alfred, Li-Gao, Ruifang, Mahajan, Anubha, Matsuda, Koichi, Meidtner, Karina, Meng, Weihua, Montasser, May E, van der Most, Peter J, Munz, Matthias, Nutile, Teresa, Palviainen, Teemu, Prasad, Gauri, Prasad, Rashmi B, Priyanka, Tallapragada Divya Sri, Rizzi, Federica, Salvi, Erika, Sapkota, Bishwa R, Shriner, Daniel, Skotte, Line, Smart, Melissa C, Smith, Albert Vernon, van der Spek, Ashley, Spracklen, Cassandra N, Strawbridge, Rona J, Tajuddin, Salman M, Trompet, Stella, Turman, Constance, Verweij, Niek, Viberti, Clara, Wang, Lihua, Warren, Helen R, Wootton, Robyn E, Yanek, Lisa R, Yao, Jie, Yousri, Noha A, Zhao, Wei, Adeyemo, Adebowale A, Afaq, Saima, Aguilar-Salinas, Carlos Alberto, Akiyama, Masato, Albert, Matthew L, Allison, Matthew A, Alver, Maris, Aung, Tin, Azizi, Fereidoun, Bentley, Amy R, Boeing, Heiner, Boerwinkle, Eric, Borja, Judith B, de Borst, Gert J, Bottinger, Erwin P, Broer, Linda, Campbell, Harry, Chanock, Stephen, Chee, Miao-Li, and Chen, Guanjie

Subjects
Humans, Body Size, Risk-Taking, Cognition, Health Status, Consanguinity, Fertility, Haplotypes, Homozygote, Alleles, Inbreeding Depression
Abstract

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p

Published
2019

207. Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

Author

de Vries, Paul S, Brown, Michael R, Bentley, Amy R, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Schwander, Karen, Kraja, Aldi T, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Huffman, Jennifer E, Musani, Solomon K, Li, Changwei, Feitosa, Mary F, Richard, Melissa A, Noordam, Raymond, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Deng, Xuan, Dorajoo, Rajkumar, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Evangelou, Evangelos, Graff, Mariaelisa, Alver, Maris, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gandin, Ilaria, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, Hartwig, Fernando P, He, Meian, Horimoto, Andrea RVR, Hsu, Fang-Chi, Jackson, Anne U, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Lee, Joseph H, Luan, Jian'an, Lyytikäinen, Leo-Pekka, Matoba, Nana, Nolte, Ilja M, Pietzner, Maik, Riaz, Muhammad, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stančáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Wang, Yajuan, Ware, Erin B, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Ballantyne, Christie, Boerwinkle, Eric, Broeckel, Ulrich, Campbell, Archie, Canouil, Mickaël, Charumathi, Sabanayagam, Chen, Yii-Der Ida, Connell, John M, de Faire, Ulf, de las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Ding, Jingzhong, Dominiczak, Anna F, Duan, Qing, Eaton, Charles B, Eppinga, Ruben N, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Franco, Oscar H, Friedlander, Yechiel, Ghanbari, Mohsen, and Giulianini, Franco

Subjects
Epidemiology, Health Sciences, Atherosclerosis, Substance Misuse, Human Genome, Alcoholism, Alcohol Use and Health, Genetics, 2.1 Biological and endogenous factors, Cardiovascular, Adolescent, Adult, Aged, Alcohol Drinking, Cholesterol, HDL, Cholesterol, LDL, Female, Genome-Wide Association Study, Genotype, Humans, Life Style, Lipids, Male, Middle Aged, Phenotype, Racial Groups, Triglycerides, Vascular Endothelial Growth Factor B, Young Adult, alcohol consumption, cholesterol, gene-environment interactions, gene-lifestyle interactions, genome-wide association studies, lipids, triglycerides, InterAct Consortium, Lifelines Cohort, Groningen, The Netherlands, Mathematical Sciences, Medical and Health Sciences
Abstract

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.

Published
2019

242. Open challenges and opportunities in federated foundation models towards biomedical healthcare.

Author

Li, Xingyu, Peng, Lu, Wang, Yu-Ping, and Zhang, Weihua

Abstract

This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data. This approach not only enhances the capabilities of FMs in medical diagnostics and personalized treatment but also addresses critical concerns about data privacy and security in healthcare. This survey reviews the current applications of FMs in federated settings, underscores the challenges, and identifies future research directions including scaling FMs, managing data diversity, and enhancing communication efficiency within FL frameworks. The objective is to encourage further research into the combined potential of FMs and FL, laying the groundwork for healthcare innovations. [ABSTRACT FROM AUTHOR]

Published
2025
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243. The calcium‐sensing receptor alleviates endothelial inflammation in atherosclerosis through regulation of integrin β1‐NLRP3 inflammasome.

Author

Jiang, Yunge, Xing, Wenjing, Li, Zhong, Zhao, Defeng, Xiu, Bingxu, Xi, Yuhui, Bai, Shuzhi, Li, Xiaoxue, Zhang, Zheqi, Zhang, Weihua, and Li, Hongxia

Abstract

Atherosclerosis (AS) is a chronic inflammatory disease of arteries. Endothelial inflammation is key to the initiation and development of AS. The calcium‐sensing receptor (CaSR) is expressed in endothelial cells (ECs) but its role in endothelial inflammation during AS remains unclear. This study focused on the involvement of CaSR in regulating endothelial inflammation and its underlying mechanisms, providing novel insights for AS therapy. Here, we observed that CaSR agonist NPS‐R568 significantly reduced atherosclerotic lesions and aortic inflammation in high‐fat diet (HFD)‐fed ApoE−/− mice, while enhancing the expression of CaSR in aortic tissues. In vitro, human umbilical vein endothelial cells (HUVECs) exposed to oxidized low‐density lipoprotein (oxLDL) at 20 μg·mL−1 triggered inflammation, as indicated by the upregulation of vascular cell adhesion molecule‐1 (VCAM‐1), interleukin (IL)‐6, and IL‐1β expression, along with increased adherence of THP‐1 or U937 cells to the HUVECs. Additionally, treatment with 20 μg·mL−1 oxLDL led to downregulation of CaSR expression in HUVECs. The administration of CaSR agonist NPS‐R568 or overexpression of CaSR in HUVECs resulted in a significant reversal of inflammation induced by oxLDL. Mechanistically, CaSR was found to mitigate NLRP3 inflammasome activation by downregulating the protein level of integrin β1. In conclusion, our study elucidates the beneficial role of CaSR in reducing endothelial inflammation in AS through the regulation of integrin β1 and the subsequent NLRP3 inflammasome. CaSR emerges as a promising target for potential therapeutic interventions in AS. [ABSTRACT FROM AUTHOR]

Published
2025
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244. Impact load identification of high-speed train scale carbody based on multi-objective optimisation and TwIST sparse regularisation.

Author

Zhang, Ying, Miao, Bingrong, Zhang, Weihua, Yuan, Zhefeng, and Jin, Yuehao

Subjects
IMPACT loads, DYNAMIC loads, THRESHOLDING algorithms, SENSOR placement, INVERSE problems
Abstract

Effective identification of dynamic loads on vehicle systems is crucial for evaluating the structural integrity of railway vehicles. The inverse problem method has been proposed for dynamic load identification to determine the impact load on the carbody. Due to the sparse nature of the impact load, the combination of the l1-norm and TwIST (two-step iterative threshold shrinkage method) methods is used to reconstruct the impact load, taking into account the influence of sensor placement on the identification results. The MOHBA (Multi-objective Optimisation Honey Badger Algorithm) was proposed to simultaneously select the regularisation and iteration parameters. This algorithm aims to improve the accuracy of subsequence load reconstruction by considering the influence of noise levels when selecting the measured data. The impact load experiment was conducted on the scale carbody, and the single-point impact load and multi-point impact load matched well with the actual load. Satisfactory load identification results can still be achieved when the noise level is within 15%. When arranging measurement points on the boundary of the vehicle body, satisfactory load identification results can also be achieved. [ABSTRACT FROM AUTHOR]

Published
2025
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245. Research on the change in lubrication state and temperature control strategy of high-speed train axle box bearings using non-destructive testing.

Author

Song, Dongli, Zheng, Zejun, Zhang, Weihua, Zhang, Wei, Zhao, Minming, and Pan, Huanhuan

Subjects
TEMPERATURE control, NONDESTRUCTIVE testing, HIGH speed trains, DEBYE temperatures, TEST methods
Abstract

The heat generation problem of axle box bearing during churning phase leads to abnormal temperature rise and temperature alarm, which affects the normal operation of high-speed train. Aiming at this problem, the temperature characteristics of bearing initial lubrication phase is studied, and the multi-speed grade cycle running schemes are used in tests to run multiple bearings for multiple times. The temperature data and friction data in tests are collected by using non-destructive testing method, and characteristic extraction is carried out. Under the premise of not destroying the internal structure and lubrication state of the bearing, the lubrication state inside the bearing is reflected by the change of the characteristic values. A 118 min scheme is proposed, and the bearing using this scheme to run on the test rig is used in the actual operation. The results show that the temperature of the bearing with running-in test is lower than that of the bearing without running-in test, which indicates the effectiveness of the proposed temperature control strategy. [ABSTRACT FROM AUTHOR]

Published
2025
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246. LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT

Author

Chen, Hu, Zhang, Yi, Chen, Yunjin, Zhang, Junfeng, Zhang, Weihua, Sun, Huaiqiaing, Lv, Yang, Liao, Peixi, Zhou, Jiliu, and Wang, Ge

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly use regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold a state-of-the-art "fields of experts" based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts' Assessment-based Reconstruction Network ("LEARN") for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a competitive performance with the well-known Mayo Clinic Low-Dose Challenge Dataset relative to several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 12, reducing the computational complexity of typical iterative algorithms by orders of magnitude., Comment: 18 pages, 15 figures, accepted by IEEE TMI

Published
2017

247. Dynamic Modeling and Vibration Response Analysis of Rolling Bearings With Composite Faults Considering the Influence of Elastohydrodynamic Lubrication.

Author

Xiong, Qing, Tang, Jianghu, Ding, Tianxia, Wang, Anyu, Yu, Xiang, Zhang, Weihua, and Bai, Xiaotian

Subjects
ROLLER bearings, ROLLING contact, DYNAMIC models, BENCHES, DIAGNOSIS
Abstract

At present, in the dynamic modeling of rolling bearings with composite faults, Hertz contact theory is mostly used to describe the contact between the rolling element and the raceway, while ignoring the influence of lubricating oil film under elastohydrodynamic lubrication (EHL) on key modeling parameters, which brings errors to the accurate calculation of parameters such as stiffness and damping. On the basis of composite fault modeling, this article carefully considers the influence of EHL on the stiffness and damping of rolling bearings, establishes a five degree‐of‐freedom (DOF) composite fault model of rolling bearings, and verifies the correctness of the model through bench test data. The vibration response characteristics of rolling bearings with composite faults under different loads and fault sizes are mainly studied. The results indicate that the frequency‐domain characteristics of composite faults are the result of coupling and superimposing the frequency‐domain characteristics of single‐point fault signals. As the radial load increases, the amplitude of the vibration response also increases gradually. In the inner ring–outer ring composite fault and the outer ring–rolling element composite fault, the outer ring fault is more sensitive to change in radial load. If the radial load is appropriately increased, it is beneficial to identify the outer ring fault in both kinds of composite faults. However, excessive radial loads may inhibit the diagnosis of other fault frequencies other than the outer ring, which may affect the diagnosis of all components in the composite fault. In the inner ring–rolling element composite fault, the inner ring fault is more sensitive to change in radial load. If the radial load is appropriately increased, it is beneficial to identify the inner ring fault in the inner ring–rolling element composite fault. Similarly, in the diagnosis of such composite faults, excessive radial load may affect the detection and diagnosis of rolling element faults. [ABSTRACT FROM AUTHOR]

Published
2024
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248. Sintering Ag 33 Nanoclusters on TiO 2 Nanoparticles as an Efficient Catalyst for Nitroarene Reduction.

Author

Zhang, Weihua, Yang, Wenwen, Yuan, Jianglu, Zhao, Huiping, Han, Qingwen, Fang, Wanggang, Nie, Defu, He, Liqing, and Tian, Fan

Subjects
*OXYGEN vacancy, *CATALYTIC activity, *CATALYTIC reduction, *METALLIC oxides, *TITANIUM dioxide
Abstract

Polydispersed Ag species-modified TiO2 samples with abundant oxygen vacancies were successfully prepared through the calcination of atomically precise Ag33 nanocluster-loaded TiO2 at an optimal temperature under a nitrogen atmosphere. The ligands of the Ag33 nanoclusters are removed by extracting lattice oxygen from TiO2 during the calcination, leading to the formation of CO2, SO2, and H2O vapor. This process simultaneously induces Ag species sintering on the surface of TiO2. The resulting nanocomposites exhibited excellent catalytic activity for the reduction of nitroarenes with NaBH4 as the reductant. This is attributed to the produced Ag species on the oxygen-deficient TiO2, which act as active centers for the catalytic process. [ABSTRACT FROM AUTHOR]

Published
2024
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250. 复杂工况下受电弓升弓碰撞特性.

Author

WEI Haifei, ZHOU Ning, LI Mudi, ZHANG Weihua, ZHANG Min, SHI Changyou, and WU Kaiyang

Abstract

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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