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1. A multiomic atlas identifies a treatment-resistant, bone marrow progenitor-like cell population in T cell acute lymphoblastic leukemia

Author

Xu, Jason, Chen, Changya, Sussman, Jonathan H., Yoshimura, Satoshi, Vincent, Tiffaney, Pölönen, Petri, Hu, Jianzhong, Bandyopadhyay, Shovik, Elghawy, Omar, Yu, Wenbao, Tumulty, Joseph, Chen, Chia-hui, Li, Elizabeth Y., Diorio, Caroline, Shraim, Rawan, Newman, Haley, Uppuluri, Lahari, Li, Alexander, Chen, Gregory M., Wu, David W., Ding, Yang-yang, Xu, Jessica A., Karanfilovski, Damjan, Lim, Tristan, Hsu, Miles, Thadi, Anusha, Ahn, Kyung Jin, Wu, Chi-Yun, Peng, Jacqueline, Sun, Yusha, Wang, Alice, Mehta, Rushabh, Frank, David, Meyer, Lauren, Loh, Mignon L., Raetz, Elizabeth A., Chen, Zhiguo, Wood, Brent L., Devidas, Meenakshi, Dunsmore, Kimberly P., Winter, Stuart S., Chang, Ti-Cheng, Wu, Gang, Pounds, Stanley B., Zhang, Nancy R., Carroll, William, Hunger, Stephen P., Bernt, Kathrin, Yang, Jun J., Mullighan, Charles G., Tan, Kai, and Teachey, David T.

Published
2024
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3. The genomic basis of childhood T-lineage acute lymphoblastic leukaemia

Author

Pölönen, Petri, Di Giacomo, Danika, Seffernick, Anna Eames, Elsayed, Abdelrahman, Kimura, Shunsuke, Benini, Francesca, Montefiori, Lindsey E., Wood, Brent L., Xu, Jason, Chen, Changya, Cheng, Zhongshan, Newman, Haley, Myers, Jason, Iacobucci, Ilaria, Li, Elizabeth, Sussman, Jonathan, Hedges, Dale, Hui, Yawei, Diorio, Caroline, Uppuluri, Lahari, Frank, David, Fan, Yiping, Chang, Yunchao, Meshinchi, Soheil, Ries, Rhonda, Shraim, Rawan, Li, Alexander, Bernt, Kathrin M., Devidas, Meenakshi, Winter, Stuart S., Dunsmore, Kimberly P., Inaba, Hiroto, Carroll, William L., Ramirez, Nilsa C., Phillips, Aaron H., Kriwacki, Richard W., Yang, Jun J., Vincent, Tiffaney L., Zhao, Yaqi, Ghate, Pankaj S., Wang, Jian, Reilly, Colleen, Zhou, Xin, Sanders, Mathijs A., Takita, Junko, Kato, Motohiro, Takasugi, Nao, Chang, Bill H., Press, Richard D., Loh, Mignon, Rampersaud, Evadnie, Raetz, Elizabeth, Hunger, Stephen P., Tan, Kai, Chang, Ti-Cheng, Wu, Gang, Pounds, Stanley B., Mullighan, Charles G., and Teachey, David T.

Published
2024
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5. Incidence and Risk Factors for Retinal Detachment and Retinal Tear after Cataract Surgery: IRIS® Registry (Intelligent Research in Sight) Analysis

Author

Lee, Aaron Y., Lee, Cecilia S., Van Gelder, Russ, Lorch, Alice, Miller, Joan W., Pershing, Suzann, Goldberg, Jeffrey, Morano, Michael J., Khan, M. Ali, Zhang, Qiang, Halfpenny, Colleen P., Wisner, Douglas M., Sharpe, James, Li, Alexander, Tomaiuolo, Maurizio, Haller, Julia A., Hyman, Leslie, and Ho, Allen C.

Published
2023
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6. Open-Globe Injury Repairs in the American Academy of Ophthalmology IRIS® Registry 2014 – 2018: Incidence, Risk Factors, and Visual Outcomes

Author

Lee, Aaron Y., Lee, Cecilia S., Van Gelder, Russ, Lorch, Alice, Miller, Joan W., Pershing, Suzann, Goldberg, Jeffrey, Tomaiuolo, Maurizio, Woreta, Fasika A., Li, Alexander, Yonekawa, Yoshihiro, Zhang, Qiang (Ed), Sharpe, James E., Zafar, Sidra, Syed, Zeba A., Ramesh, Sathyadeepak, Lorch, Alice C., Hall, Nathan E., Shah, Ankoor S., Justin, Grant A., and Hyman, Leslie

Published
2023
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10. Genetic inactivation of zinc transporter SLC39A5 improves liver function and hyperglycemia in obesogenic settings.

Author

Shek Man Chim, Howell, Kristen, Dronzek, John, Weizhen Wu, Van Hout, Cristopher, Ferreira, Manuel A. R., Bin Ye, Li, Alexander, Brydges, Susannah, Arunachalam, Vinayagam, Marcketta, Anthony, Locke, Adam E., Bovijn, Jonas, Verweij, Niek, De, Tanima, Lotta, Luca, Mitnaul, Lyndon, LeBlanc, Michelle, Center, Regeneron Genetics, and Carey, David J.

Published
2024
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11. Ferroptosis as a novel form of regulated cell death: Implications in the pathogenesis, oncometabolism and treatment of human cancer

Author

Pu, Feifei, Chen, Fengxia, Zhang, Zhicai, Shi, Deyao, Zhong, Binlong, Lv, Xiao, Tucker, Andrew Blake, Fan, Jiaming, Li, Alexander J., Qin, Kevin, Hu, Daniel, Chen, Connie, Wang, Hao, He, Fang, Ni, Na, Huang, Linjuan, Liu, Qing, Wagstaff, William, Luu, Hue H., Haydon, Rex C., Shen, Le, He, Tong-Chuan, Liu, Jianxiang, and Shao, Zengwu

Published
2022
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13. Genome-wide analysis provides genetic evidence that ACE2 influences COVID-19 risk and yields risk scores associated with severe disease

Author

Horowitz, Julie E., Kosmicki, Jack A., Damask, Amy, Sharma, Deepika, Roberts, Genevieve H. L., Justice, Anne E., Banerjee, Nilanjana, Coignet, Marie V., Yadav, Ashish, Leader, Joseph B., Marcketta, Anthony, Park, Danny S., Lanche, Rouel, Maxwell, Evan, Knight, Spencer C., Bai, Xiaodong, Guturu, Harendra, Sun, Dylan, Baltzell, Asher, Kury, Fabricio S. P., Backman, Joshua D., Girshick, Ahna R., O’Dushlaine, Colm, McCurdy, Shannon R., Partha, Raghavendran, Mansfield, Adam J., Turissini, David A., Li, Alexander H., Zhang, Miao, Mbatchou, Joelle, Watanabe, Kyoko, Gurski, Lauren, McCarthy, Shane E., Kang, Hyun M., Dobbyn, Lee, Stahl, Eli, Verma, Anurag, Sirugo, Giorgio, Ritchie, Marylyn D., Jones, Marcus, Balasubramanian, Suganthi, Siminovitch, Katherine, Salerno, William J., Shuldiner, Alan R., Rader, Daniel J., Mirshahi, Tooraj, Locke, Adam E., Marchini, Jonathan, Overton, John D., Carey, David J., Habegger, Lukas, Cantor, Michael N., Rand, Kristin A., Hong, Eurie L., Reid, Jeffrey G., Ball, Catherine A., Baras, Aris, Abecasis, Gonçalo R., and Ferreira, Manuel A. R.

Published
2022
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15. ANGPTL7, a therapeutic target for increased intraocular pressure and glaucoma

Author

Praveen, Kavita, Patel, Gaurang C., Gurski, Lauren, Ayer, Ariane H., Persaud, Trikaladarshi, Still, Matthew D., Miloscio, Lawrence, Van Zyl, Tavé, Di Gioia, Silvio Alessandro, Brumpton, Ben, Krebs, Kristi, Åsvold, Bjørn Olav, Chen, Esteban, Chavali, Venkata R. M., Fury, Wen, Gudiseva, Harini V., Hyde, Sarah, Jorgenson, Eric, Lefebvre, Stephanie, Li, Dadong, Li, Alexander, Mclninch, James, Patel, Brijeshkumar, Rabinowitz, Jeremy S., Salowe, Rebecca, Schurmann, Claudia, Seidelin, Anne-Sofie, Stahl, Eli, Sun, Dylan, Teslovich, Tanya M., Tybjærg-Hansen, Anne, Willer, Cristen, Waldron, Scott, Walley, Sabrina, Yang, Hua, Zaveri, Sarthak, Hu, Ying, Hveem, Kristian, Melander, Olle, Milani, Lili, Stender, Stefan, O’Brien, Joan M., Jones, Marcus B., Abecasis, Gonçalo R., Cantor, Michael N., Weyne, Jonathan, Karalis, Katia, Economides, Aris, Della Gatta, Giusy, Ferreira, Manuel A., Yancopoulos, George D., Baras, Aris, Romano, Carmelo, and Coppola, Giovanni

Published
2022
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16. Exome sequencing and analysis of 454,787 UK Biobank participants

Author

Backman, Joshua D., Li, Alexander H., Marcketta, Anthony, Sun, Dylan, Mbatchou, Joelle, Kessler, Michael D., Benner, Christian, Liu, Daren, Locke, Adam E., Balasubramanian, Suganthi, Yadav, Ashish, Banerjee, Nilanjana, Gillies, Christopher E., Damask, Amy, Liu, Simon, Bai, Xiaodong, and Hawes, Alicia

Subjects
Exome sequencing -- Methods, Genetic variation -- Identification and classification, Gene banks -- Usage, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing.sup.1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study.sup.2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P [less than or equal to] 2.18 × 10.sup.-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale. Whole-exome sequencing analysis of 454,787 individuals in the UK Biobank is used to examine the association of protein-coding variants with nearly 4,000 health-related traits, identifying 564 distinct genes with significant trait associations., Author(s): Joshua D. Backman [sup.1] , Alexander H. Li [sup.1] , Anthony Marcketta [sup.1] , Dylan Sun [sup.1] , Joelle Mbatchou [sup.1] , Michael D. Kessler [sup.1] , Christian Benner [...]

Published
2021
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17. Giant vesicles as cell-mimetic vessels: Induced cellular variation and confinement on a cyanobacterial circadian clock

Author

Li, Alexander Zhan Tu

Subjects
Bioengineering, Biology, Cellular biology, Cellular variation, Circadian clock protein, GUV, Liposome, Membrane interaction, Vesicle
Abstract

Giant unilamellar vesicles (GUVs) are spherical structures composed of an aqueous compartment enclosed by a bilayer membrane. They are often seen as a simplified analog of a cell membrane and can be utilized as minimal cell models for studying cellular systems due to their cell-like sizes and capacity to mediate membrane interactions. A paper-based diffusive loading technique termed, OSM-PAPYRUS, is shown to assemble GUVs in physiologically relevant salt solutions with gentle loading of proteins. Characterization of this loading process reveals cell-like variation of encapsulated protein concentrations and a gamma distribution often cited for protein distributions in the cell. This ability to mimic cellular variability in vitro reveals potential in bridging the gap between in vitro and in vivo experimentation. The highly controlled environment of in vitro experiments can be combined with cell-like volumes, phospholipid bilayer, and cellular variation in GUVs. A practical application is explored, encapsulating the post-translation oscillator (PTO) of the cyanobacteria circadian clock system which shows membrane interactions in vivo. The results showed that cellular variation and membrane binding significantly hampers the fidelity of the clock, in contrast to bulk experiments where concentration did not matter once a critical concentration is met. An increase in concentration to cellular levels helps counteract the effect of variation. Modeling the clock reaction using expected distributions and variation of encapsulated proteins, corroborated with the hypothesis that intercellular variation and membrane binding were responsible for trends in the experimental data. The experimental data and model showed that the PTO by itself was not capable of achieving the near 100% fidelity observed in the native cyanobacteria, instead, other cellular components, like SasA and CikA or transcriptional-translational feedback loop (TTFL) would be necessary to achieve in vivo clock fidelity. The GUV model demonstrated advantages over in vivo studies, particularly in the isolation of the PTO, which allowed for the determination that large period variations seen in vivo cannot be produced by the PTO even under cell-like variability and volumes. This demonstrates the ability of GUV in vitro models to obtain context on behaviors not appreciated by either previous bulk in vitro or in vivo studies.

Published
2023

18. Biallelic BRCA Loss and Homologous Recombination Deficiency in Nonbreast/Ovarian Tumors in Germline BRCA1/2 Carriers

Author

Wineland, Dylane, Le, Anh N., Hausler, Ryan, Kelly, Gregory, Barrett, Emanuel, Desai, Heena, Wubbenhorst, Bradley, Pluta, John, Bastian, Paul, Symecko, Heather, DʼAndrea, Kurt, Doucette, Abigail, Gabriel, Peter, Reiss, Kim A., Nayak, Anupma, Feldman, Michael, Domchek, Susan M., Nathanson, Katherine L., Maxwell, Kara N., Mansfield, Adam J., Locke, Adam, Poindexter, Afiya, Shuldiner, Alan, Li, Alexander, Lopez, Alexander, Hawes, Alicia, Averitt, Amelia, Damask, Amy, Deubler, Andrew, Ziyatdinov, Andrey, Marcketta, Anthony, Verma, Anurag, Moscati, Arden, Ayer, Ariane, Baras, Aris, Economides, Aris, Ghosh, Arkopravo, Brock, Ashlei, Kloter, Ashley, Rasool, Ayesha, Guvenek, Aysegul, Ye, Bin, Zhang, Blair, Boutkov, Boris, Forsythe, Caitlin, Sidore, Carlo, Paulding, Charles, Beechert, Christina, Gillies, Christopher, Morse, Colleen, Li, Dadong, Rader, Daniel J., Liu, Daren, Sharma, Deepika, Stahl, Eli, Jorgenson, Eric, Fuller, Erin D., Chen, Esteban, Maxwell, Evan K., Vadivieso, Fred, Tzoneva, Gannie, Hindy, George, Mitra, George, Coppola, Giovanni, Eom, Gisu, Abecasis, Goncalo, Kang, Hyun Min, Kosmicki, Jack, Hernandez, Jaimee, Freudenberg, Jan, Mighty, Jason, Staples, Jeffrey C., Reid, Jeffrey G., Mbatchou, Joelle, Weaver, JoEllen, Overton, John D., Bovijn, Jonas, Marchini, Jonathan, Dunn, Joseph, Backman, Joshua, Rodriguez-Flores, Juan, Siminovitch, Katherine, Sun, Kathie, Praveen, Kavita, Hu-Sain, Khadijah, Manoochehri, Kia, Watanabe, Kyoko, Gurski, Lauren, Dobbyn, Lee, Morrel, Linda, Widom, Louis, Lotta, Luca A., Habegger, Lukas, Mitnaul, Lyndon J., Pradhan, Manasi, Kapoor, Manav, Ferreira, Manuel Allen Revez, Jones, Marcus B., Padilla, Maria Sotiropoulos, Risman, Marjorie, Haas, Mary, Ritchie, Marylyn D., Orelus, Max, Livingstone, Meghan, Cantor, Michael, Feldman, Michael D., Kessler, Michael, Lattari, Michael, LeBlanc, Michelle G., Riaz, Moeen, Nafde, Mona, Rana, Nadia, Lin, Nan, Haubein, Ned, Verweij, Niek, Banerjee, Nilanjana, Nishtala, Nirupama, Krasheninina, Olga, Sosina, Olukayode, Akbari, Parsa, Nakka, Priyanka, Panea, Razvan, Judy, Renae, Ulloa, Ricardo H., Lanche, Rouel, Gelfman, Sahar, Malhotra, Sameer, Wolf, Sarah E., Dudek, Scott, OʼKeeffe, Sean, Khalid, Shareef, Verma, Shefali S., Chen, Siying, DerOhannessian, Stephanie, Balasubramanian, Suganthi, Gokhale, Sujit, Bao, Suying, De, Tanima, Tran, Teo, Drivas, Theodore, Schleicher, Thomas D., Polanco, Tommy, Rajagopal, Veera, Salerno, William, Bai, Xiaodong, Ko, Yi-An, Bradford, Yuki, and Gu, Zhenhua

Published
2023
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21. Low-Grade Glioma Clinical Trials in the United States: A Systematic Review.

Author

Xu, Emily, Patterson, Jonathan, Angione, Angelo, Li, Alexander, Wu, David W., Akca, Ebrar, Elghawy, Omar, Barsouk, Alexander, and Sussman, Jonathan H.

Subjects
RACE, INVESTIGATIONAL therapies, CENTRAL nervous system, CLINICAL trials, AFRICAN Americans
Abstract

Low-grade glioma (LGG) is a malignancy of the central nervous system that is often treatable with surgical resection and chemoradiation. However, despite an initial positive response to standard therapy, most LGG eventually progress to high-grade gliomas which are nearly uniformly fatal. There is a pressing need for more clinical trials and greater clinical trial accessibility to improve the standard of care of LGG to delay or prevent its progression. In this study, we systematically examined the scope and inclusion of clinical trials for LGG based in the United States. This cross-sectional study analyzes trends in trial design and reported demographic data from completed LGG trials registered on ClinicalTrials.gov between 2010 to 2023. Inclusion criteria, investigational therapies, primary outcomes, and preliminary results were compared and summarized. A total of 14 trials with 1067 participants were included in the study. Most of the trials were not exclusive to LGGs and 14% had mutation-specific inclusion criteria. To date, two of the trials have led to new FDA-approved treatments. All trials reported age and sex, while only 57% reported race and 43% reported ethnicity. Individuals identifying as Black or African American and Asian or Pacific Islander were statistically underrepresented. Lastly, we investigated the geographic distributions of trial sites across the United States, which demonstrated several coverage gaps in the Rocky Mountain and Southeast regions. These findings suggest specific areas for improvement in LGG clinical trial reporting and accessibility. [ABSTRACT FROM AUTHOR]

Published
2024
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23. FAMSi: A Synthetic Biology Approach to the Fast Assembly of Multiplex siRNAs for Silencing Gene Expression in Mammalian Cells

Author

He, Fang, Ni, Na, Zeng, Zongyue, Wu, Di, Feng, Yixiao, Li, Alexander J., Luu, Benjamin, Li, Alissa F., Qin, Kevin, Wang, Eric, Wang, Xi, Wu, Xiaoxing, Luo, Huaxiu, Zhang, Jing, Zhang, Meng, Mao, Yukun, Pakvasa, Mikhail, Wagstaff, William, Zhang, Yongtao, Niu, Changchun, Wang, Hao, Huang, Linjuan, Shi, Deyao, Liu, Qing, Zhao, Xia, Fu, Kai, Reid, Russell R., Wolf, Jennifer Moriatis, Lee, Michael J., Hynes, Kelly, Strelzow, Jason, El Dafrawy, Mostafa, Gan, Hua, He, Tong-Chuan, and Fan, Jiaming

Published
2020
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24. A reverse transcriptase-mediated ribosomal RNA depletion (RTR2D) strategy for the cost-effective construction of RNA sequencing libraries

Author

Zeng, Zongyue, Huang, Bo, Wang, Xi, Fan, Jiaming, Zhang, Bo, Yang, Lijuan, Feng, Yixiao, Wu, Xiaoxing, Luo, Huaxiu, Zhang, Jing, Zhang, Meng, He, Fang, Mao, Yukun, Pakvasa, Mikhail, Wagstaff, William, Li, Alexander J., Liu, Bin, Ding, Huimin, Zhang, Yongtao, Niu, Changchun, Wu, Meng, Zhao, Xia, Wolf, Jennifer Moriatis, Lee, Michael J., Huang, Ailong, Luu, Hue H., Haydon, Rex C., and He, Tong-Chuan

Published
2020
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25. Multi-ethnic heterozygote frequencies of cancer susceptibility genes to inform counseling of reproductive risk

Author

Verma, Anurag, Verma, Shefali S., Bradford, Yuki, Brock, Ashlei, DerOhannessian, Stephanie, Dudek, Scott, Dunn, Joseph, Drivas, Theodore, Haubein, Ned, Hu-Sain, Khadijah, Judy, Renae, Kloter, Ashley, Ko, Yi-An, Livingstone, Meghan, Morrel, Linda, Morse, Colleen, Poindexter, Afiya, Risman, Marjorie, Tran, Teo, Vadivieso, Fred, Weaver, JoEllen, Rader, Daniel J., Ritchie, Marylyn D., Feldman, Michael D., Beechert, Christina, Forsythe, Caitlin, Fuller, Erin D., Gu, Zhenhua, Lattari, Michael, Lopez, Alexander, Overton, John D., Padilla, Maria Sotiropoulos, Pradhan, Manasi, Manoochehri, Kia, Schleicher, Thomas D., Widom, Louis, Wolf, Sarah E., Ulloa, Ricardo H., Averitt, Amelia, Banerjee, Nilanjana, Cantor, Michael, Li, Dadong, Malhotra, Sameer, Sharma, Deepika, MHI, Staples, Jeffrey, Bai, Xiaodong, Balasubramanian, Suganthi, Bao, Suying, Boutkov, Boris, Chen, Siying, Eom, Gisu, Habegger, Lukas, Hawes, Alicia, Khalid, Shareef, Krasheninina, Olga, Lanche, Rouel, Mansfield, Adam J., Maxwell, Evan K., Mitra, George, Nafde, Mona, O’Keeffe, Sean, Orelus, Max, B, Panea, Razvan, Polanco, Tommy, Rasool, Ayesha, Reid, Jeffrey G., Salerno, William, Staples, Jeffrey C., Sun, Kathie, Abecasis, Goncalo, Backman, Joshua, Damask, Amy, Dobbyn, Lee, Revez Ferreira, Manuel Allen, Ghosh, Arkopravo, Gillies, Christopher, Gurski, Lauren, Jorgenson, Eric, Kang, Hyun Min, Kessler, Michael, Kosmicki, Jack, Li, Alexander, Lin, Nan, Liu, Daren, Locke, Adam, Marchini, Jonathan, Marcketta, Anthony, Mbatchou, Joelle, Moscati, Arden, Paulding, Charles, Sidore, Carlo, Stahl, Eli, Watanabe, Kyoko, Ye, Bin, Zhang, Blair, Ziyatdinov, Andrey, Ayer, Ariane, Guvenek, Aysegul, Hindy, George, Coppola, Giovanni, Freudenberg, Jan, Bovijn, Jonas, Siminovitch, Katherine, Praveen, Kavita, Lotta, Luca A., Kapoor, Manav, Haas, Mary, Riaz, Moeen, Verweij, Niek, Sosina, Olukayode, Akbari, Parsa, Nakka, Priyanka, Gelfman, Sahar, Gokhale, Sujit, De, Tanima, Rajagopal, Veera, Tzoneva, Gannie, Rodriguez-Flores, Juan, Chen, Esteban, Jones, Marcus B., LeBlanc, Michelle G., Mighty, Jason, Mitnaul, Lyndon J., Nishtala, Nirupama, Rana, Nadia, Hernandez, Jaimee, Baras, Aris, Deubler, Andrew, Economides, Aris, Shuldiner, Alan, Powers, Jacquelyn, Wachtel, Heather, Trujillo, Erica, Desai, Heena, Hausler, Ryan, Conway, Laura, Wubbenhorst, Bradley, Domchek, Susan M., Nathanson, Katherine L., and Maxwell, Kara N.

Published
2025
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26. Exome-wide evaluation of rare coding variants using electronic health records identifies new gene–phenotype associations

Author

Park, Joseph, Lucas, Anastasia M., Zhang, Xinyuan, Chaudhary, Kumardeep, Cho, Judy H., Nadkarni, Girish, Dobbyn, Amanda, Chittoor, Geetha, Josyula, Navya S., Katz, Nathan, Breeyear, Joseph H., Ahmadmehrabi, Shadi, Drivas, Theodore G., Chavali, Venkata R. M., Fasolino, Maria, Sawada, Hisashi, Daugherty, Alan, Li, Yanming, Zhang, Chen, Bradford, Yuki, Weaver, JoEllen, Verma, Anurag, Judy, Renae L., Kember, Rachel L., Overton, John D., Reid, Jeffrey G., Ferreira, Manuel A. R., Li, Alexander H., Baras, Aris, LeMaire, Scott A., Shen, Ying H., Naji, Ali, Kaestner, Klaus H., Vahedi, Golnaz, Edwards, Todd L., Chen, Jinbo, Damrauer, Scott M., Justice, Anne E., Do, Ron, Ritchie, Marylyn D., and Rader, Daniel J.

Published
2021
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27. Stem cell therapy for chronic skin wounds in the era of personalized medicine: From bench to bedside

Author

Coalson, Elam, Bishop, Elliot, Liu, Wei, Feng, Yixiao, Spezia, Mia, Liu, Bo, Shen, Yi, Wu, Di, Du, Scott, Li, Alexander J., Ye, Zhenyu, Zhao, Ling, Cao, Daigui, Li, Alissa, Hagag, Ofir, Deng, Alison, Liu, Winny, Li, Mingyang, Haydon, Rex C., Shi, Lewis, Athiviraham, Aravind, Lee, Michael J., Wolf, Jennifer Moriatis, Ameer, Guillermo A., He, Tong-Chuan, and Reid, Russell R.

Published
2019
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29. BMP9-induced osteoblastic differentiation requires functional Notch signaling in mesenchymal stem cells

Author

Cui, Jing, Zhang, Wenwen, Huang, Enyi, Wang, Jia, Liao, Junyi, Li, Ruidong, Yu, Xinyi, Zhao, Chen, Zeng, Zongyue, Shu, Yi, Zhang, Ruyi, Yan, Shujuan, Lei, Jiayan, Yang, Chao, Wu, Ke, Wu, Ying, Huang, Shifeng, Ji, Xiaojuan, Li, Alexander, Gong, Cheng, Yuan, Chengfu, Zhang, Linghuan, Liu, Wei, Huang, Bo, Feng, Yixiao, An, Liping, Zhang, Bo, Dai, Zhengyu, Shen, Yi, Luo, Wenping, Wang, Xi, Huang, Ailong, Luu, Hue H., Reid, Russell R., Wolf, Jennifer Moriatis, Thinakaran, Gopal, Lee, Michael J., and He, Tong-Chuan

Published
2019
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34. Genetic architecture of laterality defects revealed by whole exome sequencing

Author

Li, Alexander H., Hanchard, Neil A., Azamian, Mahshid, D’Alessandro, Lisa C. A., Coban-Akdemir, Zeynep, Lopez, Keila N., Hall, Nancy J., Dickerson, Heather, Nicosia, Annarita, Fernbach, Susan, Boone, Philip M., Gambin, Tomaz, Karaca, Ender, Gu, Shen, Yuan, Bo, Jhangiani, Shalini N., Doddapaneni, HarshaVardhan, Hu, Jianhong, Dinh, Huyen, Jayaseelan, Joy, Muzny, Donna, Lalani, Seema, Towbin, Jeffrey, Penny, Daniel, Fraser, Charles, Martin, James, Lupski, James R., Gibbs, Richard A., Boerwinkle, Eric, Ware, Stephanie M., and Belmont, John W.

Published
2019
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36. Demographics of Common Compressive Neuropathies in the Upper Extremity.

Author

Rocks, Madeline C., Donnelly, Megan R., Li, Alexander, Glickel, Steven Z., Catalano III, Louis W., Posner, Martin, and Hacquebord, Jacques H.

Abstract

Background: The purpose of this study was to compare the demographic differences of the most common peripheral nerve compressions in the upper extremity—carpal tunnel syndrome (CTS), ulnar nerve compression (UNC) at the elbow, combined CTS and UNC, radial tunnel syndrome (RTS), and posterior interosseous nerve syndrome (PINS)—as a means to better understand the etiologies of each. Methods: A retrospective chart review was performed of all patients over the age of 18 years seen at our institution in the 2018 calendar year. International Classification of Diseases, Tenth Revision codes were used to identify patients with diagnoses of upper extremity peripheral nerve compressions. Demographic details and relevant comorbidities were recorded for each patient and compared with controls, who were seen the same calendar year with no neuropathies. χ2 analyses, independent-samples t tests, and multivariate logistic regressions were performed (P <.05). Results: A total of 7448 patients were identified. Those with CTS were mainly women, former smokers, and diabetic (all P <.001) and with a greater average body mass index (BMI) (P =.006) than controls. Patients with UNC were more often men and younger when compared with controls (both P <.001). A history of smoking, diabetes, and average BMI were similar between patients with UNC and controls (all P >.05). Those patients with combined CTS/UNC were mainly men, former smokers, and diabetic (all P <.001) when compared with controls. Patients with RTS/PINS were also mostly men (P =.007), diabetic (P =.042), and were more often current smokers (P <.001). Conclusions: The demographics of patients with various compressive neuropathies were not homogeneous, suggesting different etiologies. [ABSTRACT FROM AUTHOR]

Published
2024
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40. PMS2CL interference leading to erroneous identification of a pathogenic PMS2 variant in Black patients

Author

Verma, Anurag, Verma, Shefali S., Bradford, Yuki, Brock, Ashlei, DerOhannessian, Stephanie, Dudek, Scott, Dunn, Joseph, Drivas, Theodore, Haubein, Ned, Hu-Sain, Khadijah, Judy, Renae, Kloter, Ashley, Ko, Yi-An, Livingstone, Meghan, Morrel, Linda, Morse, Colleen, Poindexter, Afiya, Risman, Marjorie, Tran, Teo, Vadivieso, Fred, Weaver, JoEllen, Rader, Daniel J., Ritchie, Marylyn D., Feldman, Michael D., Beechert, Christina, Forsythe, Caitlin, Fuller, Erin D., Gu, Zhenhua, Lattari, Michael, Lopez, Alexander, Overton, John D., Padilla, Maria Sotiropoulos, Pradhan, Manasi, Manoochehri, Kia, Schleicher, Thomas D., Widom, Louis, Wolf, Sarah E., Ulloa, Ricardo H., Averitt, Amelia, Banerjee, Nilanjana, Cantor, Michael, Li, Dadong, Malhotra, Sameer, Sharma, Deepika, Staples, Jeffrey, Bai, Xiaodong, Balasubramanian, Suganthi, Bao, Suying, Boutkov, Boris, Chen, Siying, Eom, Gisu, Habegger, Lukas, Hawes, Alicia, Khalid, Shareef, Krasheninina, Olga, Lanche, Rouel, Mansfield, Adam J., Maxwell, Evan K., Mitra, George, Nafde, Mona, O’Keeffe, Sean, Orelus, Max, Panea, Razvan, Polanco, Tommy, Rasool, Ayesha, Reid, Jeffrey G., Salerno, William, Staples, Jeffrey C., Sun, Kathie, Abecasis, Goncalo, Backman, Joshua, Damask, Amy, Dobbyn, Lee, Ferreira, Manuel Allen Revez, Ghosh, Arkopravo, Gillies, Christopher, Gurski, Lauren, Jorgenson, Eric, Kang, Hyun Min, Kessler, Michael, Kosmicki, Jack, Li, Alexander, Lin, Nan, Liu, Daren, Locke, Adam, Marchini, Jonathan, Marcketta, Anthony, Mbatchou, Joelle, Moscati, Arden, Paulding, Charles, Sidore, Carlo, Stahl, Eli, Watanabe, Kyoko, Ye, Bin, Zhang, Blair, Ziyatdinov, Andrey, Ayer, Ariane, Guvenek, Aysegul, Hindy, George, Coppola, Giovanni, Freudenberg, Jan, Bovijn, Jonas, Siminovitch, Katherine, Praveen, Kavita, Lotta, Luca A., Kapoor, Manav, Haas, Mary, Riaz, Moeen, Verweij, Niek, Sosina, Olukayode, Akbari, Parsa, Nakka, Priyanka, Gelfman, Sahar, Gokhale, Sujit, De, Tanima, Rajagopal, Veera, Shuldiner, Alan, Tzoneva, Gannie, Rodriguez-Flores, Juan, Chen, Esteban, Jones, Marcus B., LeBlanc, Michelle G., Mighty, Jason, Mitnaul, Lyndon J., Nishtala, Nirupama, Rana, Nadia, Hernandez, Jaimee, Baras, Aris, Deubler, Andrew, Economides, Aris, Cappadocia, Jacqueline, Aiello, Lisa B., Kelley, Michael J., Katona, Bryson W., and Maxwell, Kara N.

Published
2024
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41. Benchmarking Diverse-Modal Entity Linking with Generative Models

Author

Wang, Sijia, Li, Alexander Hanbo, Zhu, Henry, Zhang, Sheng, Hang, Chung-Wei, Perera, Pramuditha, Ma, Jie, Wang, William, Wang, Zhiguo, Castelli, Vittorio, Xiang, Bing, and Ng, Patrick

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality configuration, such as text-only EL, visual grounding, or schema linking, it is more challenging to design a unified model for diverse modality configurations. To bring various modality configurations together, we constructed a benchmark for diverse-modal EL (DMEL) from existing EL datasets, covering all three modalities including text, image, and table. To approach the DMEL task, we proposed a generative diverse-modal model (GDMM) following a multimodal-encoder-decoder paradigm. Pre-training \Model with rich corpora builds a solid foundation for DMEL without storing the entire KB for inference. Fine-tuning GDMM builds a stronger DMEL baseline, outperforming state-of-the-art task-specific EL models by 8.51 F1 score on average. Additionally, extensive error analyses are conducted to highlight the challenges of DMEL, facilitating future research on this task., 15 pages. ACL 2023

Published
2023

47. Infantile Myofibromatosis of the Femoral Neck: A Case Report.

Author

Gunderson, Ashlynn, Abraham, Vivek M., Li, Alexander D., Lorimer, Shannon D., and Rocchi, Vanna

Subjects
FEMUR neck, BREECH delivery, CONGENITAL hip dislocation, FEMUR, DYSPLASIA
Abstract

Case: A 15-month-old boy who was being followed for developmental dysplasia of the hip because of breech presentation was discovered to have a solitary infantile myofibroma in the left femoral neck. The patient was avoiding weight-bearing on the affected extremity; thus, stabilization of the femoral neck was performed using a proximal femur locking plate. Postoperatively, he achieved all gross motor developmental milestones. Conclusion: This report is the first to describe a solitary infantile myofibroma in the femoral neck and demonstrates the utility of operative stabilization of these lesions. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
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48. Internet Explorer: Targeted Representation Learning on the Open Web

Author

Li, Alexander C., Brown, Ellis, Efros, Alexei A., and Pathak, Deepak

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Robotics, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Robotics (cs.RO), Machine Learning (cs.LG)
Abstract

Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet -- where billions of images are uploaded each day. We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30--40 hours. Results, visualizations, and videos at https://internet-explorer-ssl.github.io/, Website at https://internet-explorer-ssl.github.io/

Published
2023

50. Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness

Author

Chang, Shuaichen, Wang, Jun, Dong, Mingwen, Pan, Lin, Zhu, Henghui, Li, Alexander Hanbo, Lan, Wuwei, Zhang, Sheng, Jiang, Jiarong, Lilien, Joseph, Ash, Steve, Wang, William Yang, Wang, Zhiguo, Castelli, Vittorio, Ng, Patrick, and Xiang, Bing

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustness test sets usually focus on individual phenomena. In this paper, we propose a comprehensive robustness benchmark based on Spider, a cross-domain text-to-SQL benchmark, to diagnose the model robustness. We design 17 perturbations on databases, natural language questions, and SQL queries to measure the robustness from different angles. In order to collect more diversified natural question perturbations, we utilize large pretrained language models (PLMs) to simulate human behaviors in creating natural questions. We conduct a diagnostic study of the state-of-the-art models on the robustness set. Experimental results reveal that even the most robust model suffers from a 14.0% performance drop overall and a 50.7% performance drop on the most challenging perturbation. We also present a breakdown analysis regarding text-to-SQL model designs and provide insights for improving model robustness., ICLR 2023

Published
2023

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