Search

Your search keyword '"Niu, Sheng"' showing total 591 results
Start Over Author "Niu, Sheng"
591 results on '"Niu, Sheng"'

2. High risk of renal outcome of metabolic syndrome independent of diabetes in patients with CKD stage 1–4: The ICKD database

Author

Lin, Hugo Y‐H, Chang, Li‐Yun, Niu, Sheng‐Wen, Kuo, I‐Ching, Yen, Chia‐Hung, Shen, Feng‐Ching, Chen, Phang‐Lang, Chang, Jer‐Ming, and Hung, Chi‐Chih

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Nutrition, Diabetes, Cardiovascular, Kidney Disease, Renal and urogenital, Good Health and Well Being, Humans, Metabolic Syndrome, Kidney Failure, Chronic, Renal Insufficiency, Chronic, Kidney, Diabetes Mellitus, Glomerular Filtration Rate, Risk Factors, chronic kidney disease, diabetes mellitus, metabolic syndrome, renal outcomes, Endocrinology & Metabolism, Clinical sciences
Abstract

AimsTo investigate whether metabolic syndrome (MetS) could predict renal outcome in patients with established chronic kidney disease (CKD).Materials and methodsWe enroled 2500 patients with CKD stage 1-4 from the Integrated CKD care programme, Kaohsiung for delaying Dialysis (ICKD) prospective observational study. 66.9% and 49.2% patients had MetS and diabetes (DM), respectively. We accessed three clinical outcomes, including all-cause mortality, RRT, and 50% decline in estimated glomerular filtration rate events.ResultsThe MetS score was positively associated with proteinuria, inflammation, and nutrition markers. In fully adjusted Cox regression, the hazard ratio (HR) (95% confidence interval) of MetS for composite renal outcome (renal replacement therapy, and 50% decline of renal function) in the DM and non-DM subgroups was 1.56 (1.15-2.12) and 1.31 (1.02-1.70), respectively, while that for all-cause mortality was 1.00 (0.71-1.40) and 1.27 (0.92-1.74). Blood pressure is the most important component of MetS for renal outcomes. In the 2 by 2 matrix, compared with the non-DM/non-MetS group, the DM/MetS group (HR: 1.62 (1.31-2.02)) and the non-DM/MetS group (HR: 1.33 (1.05-1.69)) had higher risks for composite renal outcome, whereas the DM/MetS group had higher risk for all-cause mortality (HR: 1.43 (1.09-1.88)).ConclusionsMetS could predict renal outcome in patients with CKD stage 1-4 independent of DM.

Published
2023

3. Pivotal role for S-nitrosylation of DNA methyltransferase 3B in epigenetic regulation of tumorigenesis

Author

Okuda, Kosaku, Nakahara, Kengo, Ito, Akihiro, Iijima, Yuta, Nomura, Ryosuke, Kumar, Ashutosh, Fujikawa, Kana, Adachi, Kazuya, Shimada, Yuki, Fujio, Satoshi, Yamamoto, Reina, Takasugi, Nobumasa, Onuma, Kunishige, Osaki, Mitsuhiko, Okada, Futoshi, Ukegawa, Taichi, Takeuchi, Yasuo, Yasui, Norihisa, Yamashita, Atsuko, Marusawa, Hiroyuki, Matsushita, Yosuke, Katagiri, Toyomasa, Shibata, Takahiro, Uchida, Koji, Niu, Sheng-Yong, Lang, Nhi B, Nakamura, Tomohiro, Zhang, Kam YJ, Lipton, Stuart A, and Uehara, Takashi

Subjects
Biological Sciences, Medicinal and Biomolecular Chemistry, Chemical Sciences, Genetics, Cancer, Digestive Diseases, Prevention, Aetiology, 2.1 Biological and endogenous factors, Animals, Humans, Mice, Cell Transformation, Neoplastic, DNA (Cytosine-5-)-Methyltransferase 1, DNA (Cytosine-5-)-Methyltransferases, DNA Methylation, DNA Modification Methylases, Epigenesis, Genetic
Abstract

DNA methyltransferases (DNMTs) catalyze methylation at the C5 position of cytosine with S-adenosyl-L-methionine. Methylation regulates gene expression, serving a variety of physiological and pathophysiological roles. The chemical mechanisms regulating DNMT enzymatic activity, however, are not fully elucidated. Here, we show that protein S-nitrosylation of a cysteine residue in DNMT3B attenuates DNMT3B enzymatic activity and consequent aberrant upregulation of gene expression. These genes include Cyclin D2 (Ccnd2), which is required for neoplastic cell proliferation in some tumor types. In cell-based and in vivo cancer models, only DNMT3B enzymatic activity, and not DNMT1 or DNMT3A, affects Ccnd2 expression. Using structure-based virtual screening, we discovered chemical compounds that specifically inhibit S-nitrosylation without directly affecting DNMT3B enzymatic activity. The lead compound, designated DBIC, inhibits S-nitrosylation of DNMT3B at low concentrations (IC50 ≤ 100 nM). Treatment with DBIC prevents nitric oxide (NO)-induced conversion of human colonic adenoma to adenocarcinoma in vitro. Additionally, in vivo treatment with DBIC strongly attenuates tumor development in a mouse model of carcinogenesis triggered by inflammation-induced generation of NO. Our results demonstrate that de novo DNA methylation mediated by DNMT3B is regulated by NO, and DBIC protects against tumor formation by preventing aberrant S-nitrosylation of DNMT3B.

Published
2023

6. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity

Author

Luo, Chongyuan, Liu, Hanqing, Xie, Fangming, Armand, Ethan J, Siletti, Kimberly, Bakken, Trygve E, Fang, Rongxin, Doyle, Wayne I, Stuart, Tim, Hodge, Rebecca D, Hu, Lijuan, Wang, Bang-An, Zhang, Zhuzhu, Preissl, Sebastian, Lee, Dong-Sung, Zhou, Jingtian, Niu, Sheng-Yong, Castanon, Rosa, Bartlett, Anna, Rivkin, Angeline, Wang, Xinxin, Lucero, Jacinta, Nery, Joseph R, Davis, David A, Mash, Deborah C, Satija, Rahul, Dixon, Jesse R, Linnarsson, Sten, Lein, Ed, Behrens, M Margarita, Ren, Bing, Mukamel, Eran A, and Ecker, Joseph R

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Neurosciences, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Good Health and Well Being
Abstract

Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types that are associated with diseases.

Published
2022

7. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Author

Yao, Zizhen, Liu, Hanqing, Xie, Fangming, Fischer, Stephan, Adkins, Ricky S, Aldridge, Andrew I, Ament, Seth A, Bartlett, Anna, Behrens, M Margarita, Van den Berge, Koen, Bertagnolli, Darren, de Bézieux, Hector Roux, Biancalani, Tommaso, Booeshaghi, A Sina, Bravo, Héctor Corrada, Casper, Tamara, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather, Crichton, Kirsten, Crow, Megan, Dee, Nick, Dougherty, Elizabeth L, Doyle, Wayne I, Dudoit, Sandrine, Fang, Rongxin, Felix, Victor, Fong, Olivia, Giglio, Michelle, Goldy, Jeff, Hawrylycz, Mike, Herb, Brian R, Hertzano, Ronna, Hou, Xiaomeng, Hu, Qiwen, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, Li, Yang Eric, Lucero, Jacinta D, Luo, Chongyuan, Mahurkar, Anup, McMillen, Delissa, Nadaf, Naeem M, Nery, Joseph R, Nguyen, Thuc Nghi, Niu, Sheng-Yong, Ntranos, Vasilis, Orvis, Joshua, Osteen, Julia K, Pham, Thanh, Pinto-Duarte, Antonio, Poirion, Olivier, Preissl, Sebastian, Purdom, Elizabeth, Rimorin, Christine, Risso, Davide, Rivkin, Angeline C, Smith, Kimberly, Street, Kelly, Sulc, Josef, Svensson, Valentine, Tieu, Michael, Torkelson, Amy, Tung, Herman, Vaishnav, Eeshit Dhaval, Vanderburg, Charles R, van Velthoven, Cindy, Wang, Xinxin, White, Owen R, Huang, Z Josh, Kharchenko, Peter V, Pachter, Lior, Ngai, John, Regev, Aviv, Tasic, Bosiljka, Welch, Joshua D, Gillis, Jesse, Macosko, Evan Z, Ren, Bing, Ecker, Joseph R, Zeng, Hongkui, and Mukamel, Eran A

Subjects
Human Genome, Neurosciences, Genetics, Bioengineering, Biotechnology, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Atlases as Topic, Datasets as Topic, Epigenesis, Genetic, Epigenomics, Female, Gene Expression Profiling, Male, Mice, Motor Cortex, Neurons, Organ Specificity, Reproducibility of Results, Single-Cell Analysis, Transcriptome, General Science & Technology
Abstract

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Published
2021

8. Epigenomic diversity of cortical projection neurons in the mouse brain

Author

Zhang, Zhuzhu, Zhou, Jingtian, Tan, Pengcheng, Pang, Yan, Rivkin, Angeline C, Kirchgessner, Megan A, Williams, Elora, Lee, Cheng-Ta, Liu, Hanqing, Franklin, Alexis D, Miyazaki, Paula Assakura, Bartlett, Anna, Aldridge, Andrew I, Vu, Minh, Boggeman, Lara, Fitzpatrick, Conor, Nery, Joseph R, Castanon, Rosa G, Rashid, Mohammad, Jacobs, Matthew W, Ito-Cole, Tony, O’Connor, Carolyn, Pinto-Duartec, António, Dominguez, Bertha, Smith, Jared B, Niu, Sheng-Yong, Lee, Kuo-Fen, Jin, Xin, Mukamel, Eran A, Behrens, M Margarita, Ecker, Joseph R, and Callaway, Edward M

Subjects
Neurosciences, Human Genome, Genetics, Brain Disorders, Underpinning research, 1.1 Normal biological development and functioning, Neurological, Animals, Brain Mapping, Cerebral Cortex, Epigenome, Epigenomics, Female, Male, Mice, Neural Pathways, Neurons, General Science & Technology
Abstract

Neuronal cell types are classically defined by their molecular properties, anatomy and functions. Although recent advances in single-cell genomics have led to high-resolution molecular characterization of cell type diversity in the brain1, neuronal cell types are often studied out of the context of their anatomical properties. To improve our understanding of the relationship between molecular and anatomical features that define cortical neurons, here we combined retrograde labelling with single-nucleus DNA methylation sequencing to link neural epigenomic properties to projections. We examined 11,827 single neocortical neurons from 63 cortico-cortical and cortico-subcortical long-distance projections. Our results showed unique epigenetic signatures of projection neurons that correspond to their laminar and regional location and projection patterns. On the basis of their epigenomes, intra-telencephalic cells that project to different cortical targets could be further distinguished, and some layer 5 neurons that project to extra-telencephalic targets (L5 ET) formed separate clusters that aligned with their axonal projections. Such separation varied between cortical areas, which suggests that there are area-specific differences in L5 ET subtypes, which were further validated by anatomical studies. Notably, a population of cortico-cortical projection neurons clustered with L5 ET rather than intra-telencephalic neurons, which suggests that a population of L5 ET cortical neurons projects to both targets. We verified the existence of these neurons by dual retrograde labelling and anterograde tracing of cortico-cortical projection neurons, which revealed axon terminals in extra-telencephalic targets including the thalamus, superior colliculus and pons. These findings highlight the power of single-cell epigenomic approaches to connect the molecular properties of neurons with their anatomical and projection properties.

Published
2021

9. Comparative cellular analysis of motor cortex in human, marmoset and mouse

Author

Bakken, Trygve E, Jorstad, Nikolas L, Hu, Qiwen, Lake, Blue B, Tian, Wei, Kalmbach, Brian E, Crow, Megan, Hodge, Rebecca D, Krienen, Fenna M, Sorensen, Staci A, Eggermont, Jeroen, Yao, Zizhen, Aevermann, Brian D, Aldridge, Andrew I, Bartlett, Anna, Bertagnolli, Darren, Casper, Tamara, Castanon, Rosa G, Crichton, Kirsten, Daigle, Tanya L, Dalley, Rachel, Dee, Nick, Dembrow, Nikolai, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Fang, Rongxin, Fischer, Stephan, Goldman, Melissa, Goldy, Jeff, Graybuck, Lucas T, Herb, Brian R, Hou, Xiaomeng, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, van Lew, Baldur, Li, Yang Eric, Liu, Christine S, Liu, Hanqing, Lucero, Jacinta D, Mahurkar, Anup, McMillen, Delissa, Miller, Jeremy A, Moussa, Marmar, Nery, Joseph R, Nicovich, Philip R, Niu, Sheng-Yong, Orvis, Joshua, Osteen, Julia K, Owen, Scott, Palmer, Carter R, Pham, Thanh, Plongthongkum, Nongluk, Poirion, Olivier, Reed, Nora M, Rimorin, Christine, Rivkin, Angeline, Romanow, William J, Sedeño-Cortés, Adriana E, Siletti, Kimberly, Somasundaram, Saroja, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Wang, Xinxin, Xie, Fangming, Yanny, Anna Marie, Zhang, Renee, Ament, Seth A, Behrens, M Margarita, Bravo, Hector Corrada, Chun, Jerold, Dobin, Alexander, Gillis, Jesse, Hertzano, Ronna, Hof, Patrick R, Höllt, Thomas, Horwitz, Gregory D, Keene, C Dirk, Kharchenko, Peter V, Ko, Andrew L, Lelieveldt, Boudewijn P, Luo, Chongyuan, Mukamel, Eran A, Pinto-Duarte, António, Preissl, Sebastian, Regev, Aviv, Ren, Bing, Scheuermann, Richard H, Smith, Kimberly, Spain, William J, White, Owen R, Koch, Christof, Hawrylycz, Michael, Tasic, Bosiljka, Macosko, Evan Z, McCarroll, Steven A, and Ting, Jonathan T

Subjects
Human Genome, Neurosciences, Genetics, Biotechnology, Underpinning research, 1.1 Normal biological development and functioning, Neurological, Animals, Atlases as Topic, Callithrix, Epigenesis, Genetic, Epigenomics, Female, GABAergic Neurons, Gene Expression Profiling, Glutamates, Humans, In Situ Hybridization, Fluorescence, Male, Mice, Middle Aged, Motor Cortex, Neurons, Organ Specificity, Phylogeny, Single-Cell Analysis, Species Specificity, Transcriptome, General Science & Technology
Abstract

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Published
2021

15. Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images

Author

Niu, Sheng-Yong, Guo, Lun-Zhang, Li, Yue, Wang, Tzung-Dau, Tsao, Yu, and Liu, Tzu-Ming

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

As the rapid growth of high-speed and deep-tissue imaging in biomedical research, it is urgent to find a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress perturbative noises in high contrast images. However, for low photon budget multi-photon images, high detector gain will not only boost signals, but also bring huge background noises. In such stochastic resonance regime of imaging, sub-threshold signals may be detectable with the help of noises. Therefore, a denoising filter that can smartly remove noises without sacrificing the important cellular features such as cell boundaries is highly desired. In this paper, we propose a convolutional neural network based autoencoder method, Fully Convolutional Deep Denoising Autoencoder (DDAE), to improve the quality of Three-Photon Fluorescence (3PF) and Third Harmonic Generation (THG) microscopy images. The average of the acquired 200 images of a given location served as the low-noise answer for DDAE training. Compared with other widely used denoising methods, our DDAE model shows better signal-to-noise ratio (26.6 and 29.9 for 3PF and THG, respectively), structure similarity (0.86 and 0.87 for 3PF and THG, respectively), and preservation of nuclear or cellular boundaries.

Published
2019

24. Cross-species recognition of SARS-CoV-2 to bat ACE2

Author

Liu, Kefang, Tan, Shuguang, Niu, Sheng, Wang, Jia, Wu, Lili, Sun, Huan, Zhang, Yanfang, Pan, Xiaoqian, Qu, Xiao, Du, Pei, Meng, Yumin, Jia, Yunfei, Chen, Qian, Deng, Chuxia, Yan, Jinghua, Wang, Hong-Wei, Wang, Qihui, Qia, Jianxun, and Gao, George Fu

Published
2021

29. The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

Author

Lin, Hugo Y.-H., Shien, Tiffany, Xu, Juan-Wei, Kuo, Yen-Jung, Chen, Phang-Lang, Niu, Sheng-Wen, Kuo, I-Ching, Kuo, Hsuan-Fu, Yang, Kai-Chieh, and Yeh, Yi-Ren

Subjects
CONVOLUTIONAL neural networks, CHRONIC kidney failure, RENAL replacement therapy, BLOOD flow, MACHINE learning
Abstract

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (HD) remaining one of the most prevalent kidney replacement therapies. Ensuring the longevity and functionality of arteriovenous accesses is challenging for HD patients. Blood flow sound, which contains valuable information, has often been neglected in the past. However, machine learning offers a new approach, leveraging data non-invasively and learning autonomously to match the experience of healthcare professionas. This study aimed to devise a model for detecting arteriovenous grafts (AVGs) stenosis. A smartphone stethoscope was used to record the sound of AVG blood flow at the arterial and venous sides, with each recording lasting one minute. The sound recordings were transformed into mel spectrograms, and a 14-layer convolutional neural network (CNN) was employed to detect stenosis. The CNN comprised six convolution blocks with 3x3 kernel mapping, batch normalization, and rectified linear unit activation function. We applied contrastive learning to train the pre-training audio neural networks model with unlabeled data through self-supervised learning, followed by fine-tuning. In total, 27,406 dialysis session blood flow sounds were documented, including 180 stenosis blood flow sounds. Our proposed framework demonstrated a significant improvement (p<0.05) over training from scratch and a popular pre-trained audio neural networks (PANNs) model, achieving an accuracy of 0.9279, precision of 0.8462, and recall of 0.8077, compared to previous values of 0.8649, 0.7391, and 0.6538. This study illustrates how contrastive learning with unlabeled blood flow sound data can enhance convolutional neural networks for detecting AVG stenosis in HD patients. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

30. Structural basis and analysis of hamster ACE2 binding to different SARS-CoV-2 spike RBDs

Author

Niu, Sheng, primary, Zhao, Zhennan, additional, Liu, Zhimin, additional, Rong, Xiaoyu, additional, Chai, Yan, additional, Bai, Bin, additional, Han, Pengcheng, additional, Shang, Guijun, additional, Ren, Jianle, additional, Wang, Ying, additional, Zhao, Xin, additional, Liu, Kefang, additional, Tian, Wen-xia, additional, Wang, Qihui, additional, and Gao, George Fu, additional

Published
2024
Full Text
View/download PDF

43. Author Correction: Comparative cellular analysis of motor cortex in human, marmoset and mouse

Author

Bakken, Trygve E., Jorstad, Nikolas L., Hu, Qiwen, Lake, Blue B., Tian, Wei, Kalmbach, Brian E., Crow, Megan, Hodge, Rebecca D., Krienen, Fenna M., Sorensen, Staci A., Eggermont, Jeroen, Yao, Zizhen, Aevermann, Brian D., Aldridge, Andrew I., Bartlett, Anna, Bertagnolli, Darren, Casper, Tamara, Castanon, Rosa G., Crichton, Kirsten, Daigle, Tanya L., Dalley, Rachel, Dee, Nick, Dembrow, Nikolai, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Fang, Rongxin, Fischer, Stephan, Goldman, Melissa, Goldy, Jeff, Graybuck, Lucas T., Herb, Brian R., Hou, Xiaomeng, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, van Lew, Baldur, Li, Yang Eric, Liu, Christine S., Liu, Hanqing, Lucero, Jacinta D., Mahurkar, Anup, McMillen, Delissa, Miller, Jeremy A., Moussa, Marmar, Nery, Joseph R., Nicovich, Philip R., Niu, Sheng-Yong, Orvis, Joshua, Osteen, Julia K., Owen, Scott, Palmer, Carter R., Pham, Thanh, Plongthongkum, Nongluk, Poirion, Olivier, Reed, Nora M., Rimorin, Christine, Rivkin, Angeline, Romanow, William J., Sedeño-Cortés, Adriana E., Siletti, Kimberly, Somasundaram, Saroja, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Wang, Xinxin, Xie, Fangming, Yanny, Anna Marie, Zhang, Renee, Ament, Seth A., Behrens, M. Margarita, Bravo, Hector Corrada, Chun, Jerold, Dobin, Alexander, Gillis, Jesse, Hertzano, Ronna, Hof, Patrick R., Höllt, Thomas, Horwitz, Gregory D., Keene, C. Dirk, Kharchenko, Peter V., Ko, Andrew L., Lelieveldt, Boudewijn P., Luo, Chongyuan, Mukamel, Eran A., Pinto-Duarte, António, Preiss, Sebastian, Regev, Aviv, Ren, Bing, Scheuermann, Richard H., Smith, Kimberly, Spain, William J., White, Owen R., Koch, Christof, Hawrylycz, Michael, Tasic, Bosiljka, Macosko, Evan Z., McCarroll, Steven A., Ting, Jonathan T., Zeng, Hongkui, Zhang, Kun, Feng, Guoping, Ecker, Joseph R., Linnarsson, Sten, and Lein, Ed S.

Published
2022
Full Text
View/download PDF

48. Phenotypic Hit Discovery Oriented Workshop for Undergraduates: Integrating Cutting-Edge Knowledge and Discovery-Based Laboratory Experiences

Author

Feng, Yi, primary, Wang, Na, additional, Wang, Li-Na, additional, Zhang, Qian-Hui, additional, Cheng, Qi, additional, Wang, Quan-Ling, additional, Niu, Sheng-Li, additional, Gu, Jing, additional, Zeng, Rong, additional, Yang, Jie, additional, Yuan, Yi, additional, Ouyang, Qin, additional, Shi, Wen-Ting, additional, Wu, Chun-Ling, additional, Shang, Wei-Long, additional, and Zheng, Peng-Fei, additional

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results