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Author

Payel Roy

Other affiliations: JIS College of Engineering
Bio: Payel Roy is an academic researcher from La Jolla Institute for Allergy and Immunology. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 11, co-authored 25 publications receiving 413 citations. Previous affiliations of Payel Roy include JIS College of Engineering.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors review key molecular mechanisms of immune cell activation implicated in modulating atherogenesis and provide an update on the contributions of innate and adaptive immune cell subsets in atherosclerosis.
Abstract: Atherosclerosis is the root cause of many cardiovascular diseases. Extensive research in preclinical models and emerging evidence in humans have established the crucial roles of the innate and adaptive immune systems in driving atherosclerosis-associated chronic inflammation in arterial blood vessels. New techniques have highlighted the enormous heterogeneity of leukocyte subsets in the arterial wall that have pro-inflammatory or regulatory roles in atherogenesis. Understanding the homing and activation pathways of these immune cells, their disease-associated dynamics and their regulation by microbial and metabolic factors will be crucial for the development of clinical interventions for atherosclerosis, including potentially vaccination-based therapeutic strategies. Here, we review key molecular mechanisms of immune cell activation implicated in modulating atherogenesis and provide an update on the contributions of innate and adaptive immune cell subsets in atherosclerosis.

103 citations

Proceedings ArticleDOI
10 Jul 2014
TL;DR: A comparative study on adaptive thresholding techniques to choose the accurate method for binarizing an image based on the contrast, texture, resolution etc. of an image.
Abstract: — With the growth of image processing applications, image segmentation has become an important part of image processing. The simplest method to segment an image is thresholding. Using the thresholding method, segmentation of an image is done by fixing all pixels whose intensity values are more than the threshold to a foreground value. The remaining pixels are set to a background value. Such technique can be used to obtain binary images from grayscale images. The conventional thresholding techniques use As previously noted, recently a number of worksa global threshold for all pixels, whereas done adaptive thresholding changes the threshold value dynamically over the image. This paper offers a comparative study on adaptive thresholding techniques to choose the accurate method for binarizing an image based on the contrast, texture, resolution etc. of an image. Keywords —Threshold, Otsu’s Method, Kapur’s threshold, Rosin’s threshold, Entropy based thresholding,

94 citations

Journal ArticleDOI
01 Jul 2014
TL;DR: In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly to provide a stable and better framework forimage segmentation.
Abstract: In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

77 citations

Book ChapterDOI
01 Jan 2016
TL;DR: A new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.
Abstract: MEDLINE is the largest biomedical literature database. It is updated daily with 200–4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.

72 citations

Journal ArticleDOI
TL;DR: This review article catalogs NF-κB activating genetic mutations and microenvironmental cues associated with multiple myeloma, and proposes that mechanistic understanding of NF-kkB deregulations may provide for improved therapeutic and prognostic tools in multipleMyeloma.
Abstract: Multiple myeloma(MM), an incurable plasma cell cancer, represents the second most prevalent hematological malignancy. Deregulated activity of the nuclear factor kappaB (NF-κB) family of transcription factors has been implicated in the pathogenesis of multiple myeloma. Tumor microenvironment-derived cytokines and cancer-associated genetic mutations signal through the canonical as well as the non-canonical arms to activate the NF-κB system in myeloma cells. In fact, frequent engagement of both the NF-κB pathways constitutes a distinguishing characteristic of myeloma. In turn, NF-κB signaling promotes proliferation, survival and drug-resistance of myeloma cells. In this review article, we catalog NF-κB activating genetic mutations and microenvironmental cues associated with multiple myeloma. We then describe how the individual canonical and non-canonical pathways transduce signals and contribute towards NF-κB -driven gene-expressions in healthy and malignant cells. Furthermore, we discuss signaling crosstalk between concomitantly triggered NF-κB pathways, and its plausible implication for anomalous NF-κB activation and NF-κB driven pro-survival gene-expressions in multiple myeloma. Finally, we propose that mechanistic understanding of NF-κB deregulations may provide for improved therapeutic and prognostic tools in multiple myeloma.

53 citations


Cited by
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Journal ArticleDOI
TL;DR: The signalling mechanisms and the biological function of the non-canonical NF-κB pathway are reviewed and recent progress in elucidating the molecular mechanisms regulating non- canonical NF-σB pathway activation is discussed, which may provide new opportunities for therapeutic strategies.
Abstract: The nuclear factor-κB (NF-κB) family of transcription factors is activated by canonical and non-canonical signalling pathways, which differ in both signalling components and biological functions Recent studies have revealed important roles for the non-canonical NF-κB pathway in regulating different aspects of immune functions Defects in non-canonical NF-κB signalling are associated with severe immune deficiencies, whereas dysregulated activation of this pathway contributes to the pathogenesis of various autoimmune and inflammatory diseases Here we review the signalling mechanisms and the biological function of the non-canonical NF-κB pathway We also discuss recent progress in elucidating the molecular mechanisms regulating non-canonical NF-κB pathway activation, which may provide new opportunities for therapeutic strategies

995 citations

Journal ArticleDOI
TL;DR: The nuclear factor kappa B (NFκB) family of transcription factors is a key regulator of immune development, immune responses, inflammation, and cancer as mentioned in this paper, which can reveal deeper insights about the regulatory design principles.
Abstract: The nuclear factor kappa B (NFκB) family of transcription factors is a key regulator of immune development, immune responses, inflammation, and cancer. The NFκB signaling system (defined by the interactions between NFκB dimers, IκB regulators, and IKK complexes) is responsive to a number of stimuli, and upon ligand-receptor engagement, distinct cellular outcomes, appropriate to the specific signal received, are set into motion. After almost three decades of study, many signaling mechanisms are well understood, rendering them amenable to mathematical modeling, which can reveal deeper insights about the regulatory design principles. While other reviews have focused on upstream, receptor proximal signaling (Hayden MS, Ghosh S. Signaling to NF-κB. Genes Dev 2004, 18:2195-2224; Verstrepen L, Bekaert T, Chau TL, Tavernier J, Chariot A, Beyaert R. TLR-4, IL-1R and TNF-R signaling to NF-κB: variations on a common theme. Cell Mol Life Sci 2008, 65:2964-2978), and advances through computational modeling (Basak S, Behar M, Hoffmann A. Lessons from mathematically modeling the NF-κB pathway. Immunol Rev 2012, 246:221-238; Williams R, Timmis J, Qwarnstrom E. Computational models of the NF-KB signalling pathway. Computation 2014, 2:131), in this review we aim to summarize the current understanding of the NFκB signaling system itself, the molecular mechanisms, and systems properties that are key to its diverse biological functions, and we discuss remaining questions in the field. WIREs Syst Biol Med 2016, 8:227-241. doi: 10.1002/wsbm.1331 For further resources related to this article, please visit the WIREs website.

597 citations

Journal ArticleDOI
TL;DR: The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.
Abstract: In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.

301 citations

Journal ArticleDOI
TL;DR: The latest advances in the understanding of the role of Tcell subsets in atherosclerosis are described, the process of T cell homing to atherosclerotic plaques is discussed, and potential T cell-related therapies for atheros sclerosis are highlighted.
Abstract: Atherosclerosis is a chronic inflammatory disease of the arterial wall and the primary underlying cause of cardiovascular disease. Data from in vivo imaging, cell-lineage tracing and knockout studies in mice, as well as clinical interventional studies and advanced mRNA sequencing techniques, have drawn attention to the role of T cells as critical drivers and modifiers of the pathogenesis of atherosclerosis. CD4+ T cells are commonly found in atherosclerotic plaques. A large body of evidence indicates that T helper 1 (TH1) cells have pro-atherogenic roles and regulatory T (Treg) cells have anti-atherogenic roles. However, Treg cells can become pro-atherogenic. The roles in atherosclerosis of other TH cell subsets such as TH2, TH9, TH17, TH22, follicular helper T cells and CD28null T cells, as well as other T cell subsets including CD8+ T cells and γδ T cells, are less well understood. Moreover, some T cells seem to have both pro-atherogenic and anti-atherogenic functions. In this Review, we summarize the knowledge on T cell subsets, their functions in atherosclerosis and the process of T cell homing to atherosclerotic plaques. Much of our understanding of the roles of T cells in atherosclerosis is based on findings from experimental models. Translating these findings into human disease is challenging but much needed. T cells and their specific cytokines are attractive targets for developing new preventive and therapeutic approaches including potential T cell-related therapies for atherosclerosis. Accumulating evidence supports the critical role of T cells as drivers and modifiers of atherosclerosis. In this Review, Ley and colleagues describe the latest advances in our understanding of the role of T cell subsets in atherosclerosis, discuss the process of T cell homing to atherosclerotic plaques and highlight potential T cell-related therapies for atherosclerosis.

297 citations

Journal ArticleDOI
TL;DR: The basic concepts, operations and characteristics on the rough set theory are introduced, and then the extensions of rough set model, the situation of their applications, some application software and the key problems in applied research for the roughSet theory are presented.

185 citations