scispace - formally typeset
Search or ask a question
Author

Amit Singh

Bio: Amit Singh is an academic researcher from National Institute of Technology, Patna. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 57, co-authored 640 publications receiving 13795 citations. Previous affiliations of Amit Singh include Ithaca College & Center for Infectious Disease Research and Policy.


Papers
More filters
Journal ArticleDOI
TL;DR: It is shown that bioactive lipids synthesized by clinical drug-resistant isolates of Mycobacterium tuberculosis reactivate HIV-1 through modulation of intracellular EGSH, and the expression analysis of U1 and patient peripheral blood mononuclear cells demonstrated a major recalibration of cellular redox homeostatic pathways during persistence and active replication of HIV.

52 citations

Journal ArticleDOI
TL;DR: In this article, a combination of deep learning technology, modulation information recognition, and beam formation is introduced to solve the security problem of the 5G heterogeneous network, which can effectively reduce the computational complexity under different numbers of transmitting antennas, which verifies the superiority of the unsupervised beamforming algorithm based on deep learning proposed in this research.
Abstract: With increasingly complex network structure, requirements for heterogeneous 5G are also growing. The aim of this study is to meet the network security performance under the existing high-capacity and highly reliable transmission. In this context, deep learning technology is adopted to solve the security problem of the 5G heterogeneous network. First, the security problems existing in 5G heterogeneous networks are presented, mainly from two aspects of the physical layer security problems and application prospects of deep learning in communication technology. Then the combination of deep learning and 5G heterogeneous networks is analyzed. The combination of deep learning technology, modulation information recognition, and beam formation is introduced. The application of deep learning in communications technology is analyzed, and the modulation information recognition and beamforming based on deep learning are introduced. Finally, the challenges of solving security problems in 5G heterogeneous networks by deep learning are explored. The results show that the deep learning model can solve the modulation recognition problem well, and the modulation mode of the convolutional neural network can well identify the modulation signals involved in the experiment. Therefore, deep learning has a good advantage in solving modulation recognition. In addition, compared to the traditional algorithm, the unsupervised beamforming algorithm based on deep learning proposed in this research can effectively reduce the computational complexity under different numbers of transmitting antennas, which verifies the superiority of the unsupervised beamforming algorithm based on deep learning proposed in this research. Therefore, the present work provides a good idea for solving the security problem of 5G heterogeneous networks.

50 citations

Journal ArticleDOI
TL;DR: In vivo anticancer activity was evaluated in an orthotopic pancreatic adenocarcinoma tumor bearing SCID beige mice, which confirmed that EGFR-targeted gelatin nanoparticles could efficiently deliver gemcitabine to the tumor leading to higher therapeutic benefit as compared to the drug in solution.

49 citations

Journal ArticleDOI
TL;DR: In this paper, the authors focused on the biodistribution profile and pharmacokinetic parameters of EGFR-targeted chitosan nanoparticles (TG CS nanoparticles) for siRNA/cisplatin combination therapy of lung cancer.

47 citations

Journal Article
TL;DR: Parenteral administration of DAMC to rats caused significant inhibition of AFB1 binding to hepatic DNA in vivo as well as AFB1-induced micronuclei formation in bone marrow cells, highlighting the antimutagenic potential of DamC.

47 citations


Cited by
More filters
28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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

Journal ArticleDOI
TL;DR: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors.
Abstract: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

6,968 citations

Journal ArticleDOI
TL;DR: A comprehensive review of current research activities that center on the shape-controlled synthesis of metal nanocrystals, including a brief introduction to nucleation and growth within the context of metal Nanocrystal synthesis, followed by a discussion of the possible shapes that aMetal nanocrystal might take under different conditions.
Abstract: Nanocrystals are fundamental to modern science and technology. Mastery over the shape of a nanocrystal enables control of its properties and enhancement of its usefulness for a given application. Our aim is to present a comprehensive review of current research activities that center on the shape-controlled synthesis of metal nanocrystals. We begin with a brief introduction to nucleation and growth within the context of metal nanocrystal synthesis, followed by a discussion of the possible shapes that a metal nanocrystal might take under different conditions. We then focus on a variety of experimental parameters that have been explored to manipulate the nucleation and growth of metal nanocrystals in solution-phase syntheses in an effort to generate specific shapes. We then elaborate on these approaches by selecting examples in which there is already reasonable understanding for the observed shape control or at least the protocols have proven to be reproducible and controllable. Finally, we highlight a number of applications that have been enabled and/or enhanced by the shape-controlled synthesis of metal nanocrystals. We conclude this article with personal perspectives on the directions toward which future research in this field might take.

4,927 citations