H
Himanshu Kumar
Researcher at Indian Institute of Science
Publications - 127
Citations - 11986
Himanshu Kumar is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Innate immune system & Immune system. The author has an hindex of 32, co-authored 97 publications receiving 10353 citations. Previous affiliations of Himanshu Kumar include Osaka University & Indian Institute of Science Education and Research, Bhopal.
Papers
More filters
Journal ArticleDOI
Viral Infection Augments Nod1/2 Signaling to Potentiate Lethality Associated with Secondary Bacterial Infections
Yun Gi Kim,Jong Hwan Park,Thornik Reimer,Darren P. Baker,Taro Kawai,Himanshu Kumar,Himanshu Kumar,Shizuo Akira,Christiane E. Wobus,Gabriel Núñez +9 more
TL;DR: It is shown that crosstalk between type I IFNs and Nod1/Nod2 signaling promotes bacterial recognition, but induces harmful effects in the virally infected host.
Journal ArticleDOI
NLRC5 Deficiency Does Not Influence Cytokine Induction by Virus and Bacteria Infections
Himanshu Kumar,Surya Pandey,Jian Zou,Yutaro Kumagai,Ken Takahashi,Ken Takahashi,Shizuo Akira,Taro Kawai +7 more
TL;DR: It is indicated that NLRC5 is dispensable for cytokine induction in virus and bacterial infections under physiologic conditions and controls IL-1β production through an unidentified pathway.
Journal ArticleDOI
Cutting Edge: TLR-Dependent Viral Recognition Along with Type I IFN Positive Feedback Signaling Masks the Requirement of Viral Replication for IFN-α Production in Plasmacytoid Dendritic Cells
TL;DR: Results showed that detection of viruses via TLRs together with a type I IFN feedback system circumvents the requirement for viral replication-dependent recognition in pDCs.
Posted Content
Robust Loss Functions under Label Noise for Deep Neural Networks
TL;DR: In this article, the authors provide sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems, and show that standard back propagation is enough to learn the true classifier even under label noise.
Journal ArticleDOI
dropClust: efficient clustering of ultra-large scRNA-seq data.
Debajyoti Sinha,Debajyoti Sinha,Akhilesh Kumar,Himanshu Kumar,Sanghamitra Bandyopadhyay,Debarka Sengupta +5 more
TL;DR: Locality Sensitive Hashing is exploited, an approximate nearest neighbour search technique, to develop a de novo clustering algorithm for large-scale single cell data that outperformed the existing best practice methods in terms of execution time, clustering accuracy and detectability of minor cell sub-types.