scispace - formally typeset
Author

Di Wu

Other affiliations: University of California, Los Angeles, Kansas State University, Nanjing University of Aeronautics and Astronautics, Center for Advanced Materials, Stanford University, University of Illinois at Urbana–Champaign, Centers for Disease Control and Prevention, Boehringer Ingelheim, Imperial College London, University of North Carolina at Chapel Hill, Idiap Research Institute, Brigham and Women's Hospital, Monash University, Harvard University, Norwegian University of Science and Technology, New York University, Peking University, Qiqihar University, Hunan University, Nanjing University, Zhejiang University, Beijing Institute of Technology, Centre national de la recherche scientifique, Broad Institute, Oregon State University, Monash Institute of Medical Research, Soochow University (Suzhou), The Chinese University of Hong Kong, Hainan University, University of Melbourne, Beijing Normal University, Shenzhen University, Chinese Ministry of Education, University of Sheffield, Hong Kong Polytechnic University, Sun Yat-sen University, Capital Medical University, Chinese Academy of Sciences, National University of Singapore, Walter and Eliza Hall Institute of Medical Research, Case Western Reserve University, University of Texas at Austin, Tsinghua University, Massachusetts Institute of Technology, Zhejiang Chinese Medical University, Wuhan University, University of Paris-Sud, University of California, Irvine, University of South Australia, Fudan University, Guangzhou Medical University, Nanyang Technological University, Agency for Science, Technology and Research, Cedarville University, Dalian University of Technology, Howard Hughes Medical Institute, Capital Normal University, Hudson Institute
Bio: Di Wu is an academic researcher from Northeast Agricultural University. The author has contributed to research in topic(s): Cloud computing & Thin film. The author has an hindex of 87, co-authored 965 publication(s) receiving 48697 citation(s). Previous affiliations of Di Wu include University of California, Los Angeles & Kansas State University.
Papers
More filters

Journal ArticleDOI
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

13,819 citations


Journal ArticleDOI
TL;DR: The SARS-CoV-2 infection may affect primarily T lymphocytes particularly CD4+T and CD8+ T cells, resulting in decrease in numbers as well as IFN-γ production, which may be of importance due to their correlation with disease severity in COVID-19.
Abstract: BACKGROUNDSince December 2019, an outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, and is now becoming a global threat. We aimed to delineate and compare the immunological features of severe and moderate COVID-19.METHODSIn this retrospective study, the clinical and immunological characteristics of 21 patients (17 male and 4 female) with COVID-19 were analyzed. These patients were classified as severe (11 cases) and moderate (10 cases) according to the guidelines released by the National Health Commission of China.RESULTSThe median age of severe and moderate cases was 61.0 and 52.0 years, respectively. Common clinical manifestations included fever, cough, and fatigue. Compared with moderate cases, severe cases more frequently had dyspnea, lymphopenia, and hypoalbuminemia, with higher levels of alanine aminotransferase, lactate dehydrogenase, C-reactive protein, ferritin, and D-dimer as well as markedly higher levels of IL-2R, IL-6, IL-10, and TNF-α. Absolute numbers of T lymphocytes, CD4+ T cells, and CD8+ T cells decreased in nearly all the patients, and were markedly lower in severe cases (294.0, 177.5, and 89.0 × 106/L, respectively) than moderate cases (640.5, 381.5, and 254.0 × 106/L, respectively). The expression of IFN-γ by CD4+ T cells tended to be lower in severe cases (14.1%) than in moderate cases (22.8%).CONCLUSIONThe SARS-CoV-2 infection may affect primarily T lymphocytes, particularly CD4+ and CD8+ T cells, resulting in a decrease in numbers as well as IFN-γ production by CD4+ T cells. These potential immunological markers may be of importance because of their correlation with disease severity in COVID-19.TRIAL REGISTRATIONThis is a retrospective observational study without a trial registration number.FUNDINGThis work is funded by grants from Tongji Hospital for the Pilot Scheme Project, and partly supported by the Chinese National Thirteenth Five Years Project in Science and Technology for Infectious Disease (2017ZX10202201).

2,445 citations


Journal ArticleDOI
Yukinori Okada1, Yukinori Okada2, Di Wu3, Di Wu1, Di Wu2, Gosia Trynka1, Gosia Trynka2, Towfique Raj2, Towfique Raj1, Chikashi Terao4, Katsunori Ikari, Yuta Kochi, Koichiro Ohmura4, Akari Suzuki, Shinji Yoshida, Robert R. Graham5, A. Manoharan5, Ward Ortmann5, Tushar Bhangale5, Joshua C. Denny6, Robert J. Carroll6, Anne E. Eyler6, Jeff Greenberg7, Joel M. Kremer, Dimitrios A. Pappas8, Lei Jiang9, Jian Yin9, Lingying Ye9, Ding Feng Su9, Jian Yang10, Gang Xie11, E.C. Keystone11, Harm-Jan Westra12, Tõnu Esko2, Tõnu Esko13, Tõnu Esko1, Andres Metspalu13, Xuezhong Zhou14, Namrata Gupta2, Daniel B. Mirel2, Eli A. Stahl15, Dorothee Diogo1, Dorothee Diogo2, Jing Cui1, Jing Cui2, Katherine P. Liao2, Katherine P. Liao1, Michael H. Guo2, Michael H. Guo1, Keiko Myouzen, Takahisa Kawaguchi4, Marieke J H Coenen16, Piet L. C. M. van Riel16, Mart A F J van de Laar17, Henk-Jan Guchelaar18, Tom W J Huizinga18, Philippe Dieudé19, Xavier Mariette20, S. Louis Bridges21, Alexandra Zhernakova18, Alexandra Zhernakova12, René E. M. Toes18, Paul P. Tak22, Paul P. Tak23, Paul P. Tak24, Corinne Miceli-Richard20, So Young Bang25, Hye Soon Lee25, Javier Martin26, Miguel A. Gonzalez-Gay, Luis Rodriguez-Rodriguez27, Solbritt Rantapää-Dahlqvist28, Lisbeth Ärlestig28, Hyon K. Choi29, Hyon K. Choi1, Yoichiro Kamatani30, Pilar Galan19, Mark Lathrop31, Steve Eyre32, Steve Eyre33, John Bowes33, John Bowes32, Anne Barton32, Niek de Vries23, Larry W. Moreland34, Lindsey A. Criswell35, Elizabeth W. Karlson1, Atsuo Taniguchi, Ryo Yamada4, Michiaki Kubo, Jun Liu1, Sang Cheol Bae25, Jane Worthington32, Jane Worthington33, Leonid Padyukov36, Lars Klareskog36, Peter K. Gregersen37, Soumya Raychaudhuri1, Soumya Raychaudhuri2, Barbara E. Stranger38, Philip L. De Jager2, Philip L. De Jager1, Lude Franke12, Peter M. Visscher10, Matthew A. Brown10, Hisashi Yamanaka, Tsuneyo Mimori4, Atsushi Takahashi, Huji Xu9, Timothy W. Behrens5, Katherine A. Siminovitch11, Shigeki Momohara, Fumihiko Matsuda4, Kazuhiko Yamamoto39, Robert M. Plenge2, Robert M. Plenge1 
20 Feb 2014-Nature
TL;DR: A genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries provides empirical evidence that the genetics of RA can provide important information for drug discovery, and sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis.
Abstract: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7, 8, 9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

1,556 citations


Journal ArticleDOI
01 Aug 2009-Nature Medicine
TL;DR: It is found that breast tissue from BRCA1 mutation carriers harbors an expanded luminal progenitor population that shows factor-independent growth in vitro, and the findings suggest that an aberrant luminalprogenitor population is a target for transformation in BRCa1-associated basal tumors.
Abstract: Basal-like breast cancers arising in women carrying mutations in the BRCA1 gene, encoding the tumor suppressor protein BRCA1, are thought to develop from the mammary stem cell. To explore early cellular changes that occur in BRCA1 mutation carriers, we have prospectively isolated distinct epithelial subpopulations from normal mammary tissue and preneoplastic specimens from individuals heterozygous for a BRCA1 mutation. We describe three epithelial subsets including basal stem/progenitor, luminal progenitor and mature luminal cells. Unexpectedly, we found that breast tissue from BRCA1 mutation carriers harbors an expanded luminal progenitor population that shows factor-independent growth in vitro. Moreover, gene expression profiling revealed that breast tissue heterozygous for a BRCA1 mutation and basal breast tumors were more similar to normal luminal progenitor cells than any other subset, including the stem cell-enriched population. The c-KIT tyrosine kinase receptor (encoded by KIT) emerged as a key marker of luminal progenitor cells and was more highly expressed in BRCA1-associated preneoplastic tissue and tumors. Our findings suggest that an aberrant luminal progenitor population is a target for transformation in BRCA1-associated basal tumors .

1,212 citations


Journal ArticleDOI
01 Apr 2018-Nature
TL;DR: Molten-salt-assisted chemical vapour deposition is used to synthesize a wide variety of two-dimensional transition-metal chalcogenides and elaborate how the salt decreases the melting point of the reactants and facilitates the formation of intermediate products, increasing the overall reaction rate.
Abstract: Investigations of two-dimensional transition-metal chalcogenides (TMCs) have recently revealed interesting physical phenomena, including the quantum spin Hall effect1,2, valley polarization3,4 and two-dimensional superconductivity 5 , suggesting potential applications for functional devices6–10. However, of the numerous compounds available, only a handful, such as Mo- and W-based TMCs, have been synthesized, typically via sulfurization11–15, selenization16,17 and tellurization 18 of metals and metal compounds. Many TMCs are difficult to produce because of the high melting points of their metal and metal oxide precursors. Molten-salt-assisted methods have been used to produce ceramic powders at relatively low temperature 19 and this approach 20 was recently employed to facilitate the growth of monolayer WS2 and WSe2. Here we demonstrate that molten-salt-assisted chemical vapour deposition can be broadly applied for the synthesis of a wide variety of two-dimensional (atomically thin) TMCs. We synthesized 47 compounds, including 32 binary compounds (based on the transition metals Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, Pt, Pd and Fe), 13 alloys (including 11 ternary, one quaternary and one quinary), and two heterostructured compounds. We elaborate how the salt decreases the melting point of the reactants and facilitates the formation of intermediate products, increasing the overall reaction rate. Most of the synthesized materials in our library are useful, as supported by evidence of superconductivity in our monolayer NbSe2 and MoTe2 samples21,22 and of high mobilities in MoS2 and ReS2. Although the quality of some of the materials still requires development, our work opens up opportunities for studying the properties and potential application of a wide variety of two-dimensional TMCs.

811 citations


Cited by
More filters

Proceedings ArticleDOI
07 Jun 2015-
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

29,453 citations



Journal ArticleDOI
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

13,819 citations


Journal ArticleDOI
01 Jan 2015-Neural Networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Abstract: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

11,176 citations


Christopher M. Bishop1
01 Jan 2006-
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations


Network Information
Related Authors (5)
Sun Chongde

7 papers, 7 citations

86% related
He Yong

5 papers, 11 citations

85% related
Qiaozhen Li

8 papers, 96 citations

82% related
Zhenrui Zhang

2 papers, 6 citations

82% related
Xiaoqiang Du

3 papers, 30 citations

81% related
Performance
Metrics

Author's H-index: 87

No. of papers from the Author in previous years
YearPapers
20224
2021130
2020123
2019100
201881
201763

Top Attributes

Show by:

Author's top 5 most impactful journals

Applied Physics Letters

48 papers, 1.6K citations

Journal of Applied Physics

14 papers, 446 citations

IEEE Access

12 papers, 72 citations

bioRxiv

11 papers, 20 citations