About: Amrita Vishwa Vidyapeetham is a education organization based out in Coimbatore, India. It is known for research contribution in the topics: Population & Support vector machine. The organization has 14354 authors who have published 11082 publications receiving 76150 citations. The organization is also known as: Amrita University & Amrita Vishwa Vidyāpeetham.
Topics: Population, Support vector machine, Deep learning, Feature extraction, Wireless sensor network
Papers published on a yearly basis
TL;DR: The number of adults with raised blood pressure increased from 594 million in 1975 to 1·13 billion in 2015, with the increase largely in low-income and middle-income countries, and the contributions of changes in prevalence versus population growth and ageing to the increase.
Abstract: Summary Background Raised blood pressure is an important risk factor for cardiovascular diseases and chronic kidney disease. We estimated worldwide trends in mean systolic and mean diastolic blood pressure, and the prevalence of, and number of people with, raised blood pressure, defined as systolic blood pressure of 140 mm Hg or higher or diastolic blood pressure of 90 mm Hg or higher. Methods For this analysis, we pooled national, subnational, or community population-based studies that had measured blood pressure in adults aged 18 years and older. We used a Bayesian hierarchical model to estimate trends from 1975 to 2015 in mean systolic and mean diastolic blood pressure, and the prevalence of raised blood pressure for 200 countries. We calculated the contributions of changes in prevalence versus population growth and ageing to the increase in the number of adults with raised blood pressure. Findings We pooled 1479 studies that had measured the blood pressures of 19·1 million adults. Global age-standardised mean systolic blood pressure in 2015 was 127·0 mm Hg (95% credible interval 125·7–128·3) in men and 122·3 mm Hg (121·0–123·6) in women; age-standardised mean diastolic blood pressure was 78·7 mm Hg (77·9–79·5) for men and 76·7 mm Hg (75·9–77·6) for women. Global age-standardised prevalence of raised blood pressure was 24·1% (21·4–27·1) in men and 20·1% (17·8–22·5) in women in 2015. Mean systolic and mean diastolic blood pressure decreased substantially from 1975 to 2015 in high-income western and Asia Pacific countries, moving these countries from having some of the highest worldwide blood pressure in 1975 to the lowest in 2015. Mean blood pressure also decreased in women in central and eastern Europe, Latin America and the Caribbean, and, more recently, central Asia, Middle East, and north Africa, but the estimated trends in these super-regions had larger uncertainty than in high-income super-regions. By contrast, mean blood pressure might have increased in east and southeast Asia, south Asia, Oceania, and sub-Saharan Africa. In 2015, central and eastern Europe, sub-Saharan Africa, and south Asia had the highest blood pressure levels. Prevalence of raised blood pressure decreased in high-income and some middle-income countries; it remained unchanged elsewhere. The number of adults with raised blood pressure increased from 594 million in 1975 to 1·13 billion in 2015, with the increase largely in low-income and middle-income countries. The global increase in the number of adults with raised blood pressure is a net effect of increase due to population growth and ageing, and decrease due to declining age-specific prevalence. Interpretation During the past four decades, the highest worldwide blood pressure levels have shifted from high-income countries to low-income countries in south Asia and sub-Saharan Africa due to opposite trends, while blood pressure has been persistently high in central and eastern Europe. Funding Wellcome Trust.
TL;DR: Toxicity toward the human cancer cell line was considerably higher than previously observed by other researchers on the corresponding primary cells, suggesting selective toxicity of the ZnO to cancer cells.
Abstract: The specific role of size scale, surface capping, and aspect ratio of zinc oxide (ZnO) particles on toxicity toward prokaryotic and eukaryotic cells was investigated. ZnO nano and microparticles of controlled size and morphology were synthesized by wet chemical methods. Cytotoxicity toward mammalian cells was studied using a human osteoblast cancer cell line and antibacterial activity using Gram-negative bacteria (Escherichia coli) as well as using Gram-positive bacteria (Staphylococcus aureus). Scanning electron microscopy (SEM) was conducted to characterize any visual features of the biocidal action of ZnO. We observed that antibacterial activity increased with reduction in particle size. Toxicity toward the human cancer cell line was considerably higher than previously observed by other researchers on the corresponding primary cells, suggesting selective toxicity of the ZnO to cancer cells. Surface capping was also found to profoundly influence the toxicity of ZnO nanoparticles toward the cancer cell line, with the toxicity of starch-capped ZnO being the lowest. Our results are found to be consistent with a membrane-related mechanism for nanoparticle toxicity toward microbes.
TL;DR: This work employed structure-based design with a focused chemical library to discover specific MRE11 endo- or exonuclease inhibitors and define distinct nuclease roles in DSB repair, and support a mechanism whereby M RE11 endonucleasing initiates resection, thereby licensing HR followed by MRE 11 exonuclelease and EXO1/BLM bidirectional resection toward and away from the DNA end, which commits to HR.
Abstract: MRE11 within the MRE11-RAD50-NBS1 (MRN) complex acts in DNA double-strand break repair (DSBR), detection, and signaling; yet, how its endo- and exonuclease activities regulate DSBR by nonhomologous end-joining (NHEJ) versus homologous recombination (HR) remains enigmatic. Here, we employed structure-based design with a focused chemical library to discover specific MRE11 endo- or exonuclease inhibitors. With these inhibitors, we examined repair pathway choice at DSBs generated in G2 following radiation exposure. While nuclease inhibition impairs radiation-induced replication protein A (RPA) chromatin binding, suggesting diminished resection, the inhibitors surprisingly direct different repair outcomes. Endonuclease inhibition promotes NHEJ in lieu of HR, while exonuclease inhibition confers a repair defect. Collectively, the results describe nuclease-specific MRE11 inhibitors, define distinct nuclease roles in DSB repair, and support a mechanism whereby MRE11 endonuclease initiates resection, thereby licensing HR followed by MRE11 exonuclease and EXO1/BLM bidirectional resection toward and away from the DNA end, which commits to HR.
TL;DR: A highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet is proposed which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.
Abstract: Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.
TL;DR: Various mechanisms by which polymer systems are assembled in situ to form implanted devices for sustained release of therapeutic macromolecules are discussed, and various applications in the field of advanced drug delivery are highlighted.
Abstract: Smart polymers have enormous potential in various applications. In particular, smart polymeric drug delivery systems have been explored as "intelligent" delivery systems able to release, at the appropriate time and site of action, entrapped drugs in response to specific physiological triggers. These polymers exhibit a non-linear response to a small stimulus leading to a macroscopic alteration in their structure/properties. The responses vary widely from swelling/contraction to disintegration. Synthesis of new polymers and crosslinkers with greater biocompatibility and better biodegradability would increase and enhance current applications. The most fascinating features of the smart polymers arise from their versatility and tunable sensitivity. The most significant weakness of all these external stimuli-sensitive polymers is slow response time. The versatility of polymer sources and their combinatorial synthesis make it possible to tune polymer sensitivity to a given stimulus within a narrow range. Development of smart polymer systems may lead to more accurate and programmable drug delivery. In this review, we discuss various mechanisms by which polymer systems are assembled in situ to form implanted devices for sustained release of therapeutic macromolecules, and we highlight various applications in the field of advanced drug delivery.
Showing all 14354 results
|Shantikumar V. Nair||73||392||21376|
|Pradeep K. Chintagunta||67||216||14728|
|Krishna Prasad Chennazhi||44||91||6037|
|T. S. Keshava Prasad||41||184||12106|
|Sudip Kumar Batabyal||38||130||5376|
|A. Sreekumaran Nair||37||89||7019|
|Peter H. L. Notten||35||129||4137|
|P. Venkat Rangan||32||110||5873|
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