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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Journal of the Indian Institute of Science
Journal Prestige (SJR): 0.212
Citation Impact (citeScore): 1
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0970-4140 - ISSN (Online) 0019-4964
Published by Springer-Verlag Homepage  [2469 journals]
  • Cross fertilisation of Public Health and Translational Research

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      Abstract: Abstract Public health is defined as the science of protecting the safety and improving the health of communities through education, policy-making and research for the prevention of disease (Gatseva and Argirova in J Public Health 19(3):205–6, 2011, 10.1007/s10389-011-0412-8; Winslow in Mod Med 2(1306):183–91, 1920. 10.1126/science.51.1306.23; What is public health. Centers for Disease Control Foundation. Centers for Disease Control, Atlanta, https://www.cdcfoundation.org/what-public-health; What is the WHO definition of health' from the Preamble to the Constitution of WHO as adopted by the International Health Conference, New York, On 7 April 1948. The definition has not been amended since. 22 July 1946; signed by the representatives of 61 States (Official Records of WHO, no. 2, p. 100) and entered into force, 19 June;1948. https://web.archive.org/web/20190307113324/https:/www.who.int/about/who-we-are/frequently-asked-questions). Translational research in healthcare is not only useful and satisfying for the researchers to bring their work to market but it would also support public health by bringing affordable, attainable and scalable solutions to the community at large. This is of high significance because instead of increasing the GDP spent in public health, we should focus on the increasing the translational research spending, as this would lead to improved solutions. Hence, the public health offering would reach a larger community at an improved cost. The COVID-19 pandemic and the huge number of lives it claimed exposes challenges in the public health. The pandemic has caused economic and social disruption to millions of people around the world, with many falling into extreme poverty. In early 2021, it was estimated nearly 690 million people are undernourished and by end of 2021 to increase further by 132 million (Joint statement by ILO, FAO, IFAD and WHO. Impact of COVID-19 on people's livelihoods, their health and our food systems https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-people's-livelihoods-their-health-and-our-food-systems). The spending for public health has increased many folds during the pandemic and this is where translational research in healthcare can play a transformative role to reduce the burden on government healthcare budget (Covid-19 and its impact on Indian society. https://timesofindia.indiatimes.com/readersblog/covid-19-and-its-impact-on-india/covid-19-and-its-impact-on-indian-society-27565/). Over the past decade, public health research has started playing a major role in Indian academic settings. COVID-19 pandemic has further highlighted the role of public health. However, the potential of using technological advancement has not been fully utilised. This is where translational research and public health can play a role to tap the full potential of technology. This review paper explores the public health practices to understand the different practices to examine how both public health and translational research can cross-fertilise. It concludes with a short discussion on implications on policymakers.
      PubDate: 2022-08-10
       
  • COVID-19: A Veterinary and One Health Perspective

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      Abstract: Abstract Interface with animals has been responsible for the occurrence of a major proportion of human diseases for the past several decades. Recent outbreaks of respiratory, haemorrhagic, encephalitic, arthropod-borne and other viral diseases have underlined the role of animals in the transmission of pathogens to humans. The on-going coronavirus disease-2019 (COVID-19) pandemic is one among them and is thought to have originated from bats and jumped to humans through an intermediate animal host. Indeed, the aetiology, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can infect and cause disease in cats, ferrets and minks, as well as be transmitted from one animal to another. The seriousness of the pandemic along with the zoonotic origin of the virus has been a red alert on the critical need for collaboration and cooperation among human and animal health professionals, as well as stakeholders from various other disciplines that study planetary health parameters and the well-being of the biosphere. It is therefore imminent that One Health principles are applied across the board for human infectious diseases so that we can be better prepared for future zoonotic disease outbreaks and pandemics.
      PubDate: 2022-08-10
       
  • Modularity of Galois Representations and Langlands Functoriality

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      Abstract: Abstract This survey reports on some of the recent developments in the area of Galois representations and automorphic forms, with a particular focus on the author and Thorne’s work on symmetric power functoriality for modular forms.
      PubDate: 2022-07-25
       
  • Environmental Contamination and Chronic Exposure to Endocrine-Disrupting
           Phthalates: An Overlooked and Emerging Determinant for Hormone-Sensitive
           Cancers

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      Abstract: Abstract Despite several modifiable and non-modifiable risk factors of hormone-associated cancers have been established, less heed has been paid to chemicals, those having the potential to thwart the body’s normal detox system and affect the endocrine-hormonal pathways. Phthalates are endocrine-disrupting chemicals, most widely manufactured and used indiscriminately in several industries, including processed, ultra-processed and packaged food, single-use plastics, household and personal care/cosmetic products including diapers and electronics. The general population is regularly being exposed to phthalates on contact with these products, especially women and children are most vulnerable. It is therefore highly crucial to monitor and evaluate the biological burden of plasticizing phthalates in humans and understand the potential mechanisms of etiological link between pervasive exposure to phthalates and development of chronic diseases such as cancer through epigenetic and/or genetic alterations. It is also important to identify sustainable and scalable interventions for increasing public awareness, and restricting chronic phthalate exposure to individual and the population at large through relevant policy legislations, particularly in low-income and middle-income countries, such as India.
      PubDate: 2022-07-20
       
  • Quantum Affine Algebras, Graded Limits and Flags

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      Abstract: Abstract In this survey, we review some of the recent connections between the representation theory of (untwisted) quantum affine algebras and the representation theory of current algebras. We mainly focus on the finite-dimensional representations of these algebras. This connection arises via the notion of the graded and classical limit of finite-dimensional representations of quantum affine algebras. We explain how this study has led to interesting connections with Macdonald polynomials and discuss a BGG-type reciprocity result. We also discuss the role of Demazure modules in this theory and several recent results on the presentation, structure and combinatorics of Demazure modules.
      PubDate: 2022-07-19
       
  • Towards a Sustainable Future with Materials

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      PubDate: 2022-05-26
      DOI: 10.1007/s41745-022-00309-w
       
  • The Development of Bayesian Statistics

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      Abstract: Abstract The incorporation of Bayesian inference into practical statistics has seen many changes over the past century, including hierarchical and nonparametric models, general computing tools that have allowed the routine use of nonconjugate distributions, and the incorporation of model checking and validation in an iterative process of data analysis. We discuss these and other technical advances along with parallel developments in philosophy, moving beyond traditional subjectivist and objectivist frameworks to ideas based on prediction and falsification. Bayesian statistics is a flexible and powerful approach to applied statistics and an imperfect but valuable way of understanding statistics more generally.
      PubDate: 2022-05-21
      DOI: 10.1007/s41745-022-00307-y
       
  • IntroSurvey of Representation Theory

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      Abstract: Abstract There could be thousands of Introductions/Surveys of representation theory, given that it is an enormous field. This is just one of them, quite personal and informal. It has an increasing level of difficulty; the first part is intended for final year undergrads. We explain some basics of representation theory, notably Schur–Weyl duality and representations of the symmetric group. We then do the quantum version, introduce Kazhdan–Lusztig theory, quantum groups and their categorical versions. We then proceed to a survey of some recent advances in modular representation theory. We finish with 20 open problems and a song of despair.
      PubDate: 2022-05-17
      DOI: 10.1007/s41745-022-00301-4
       
  • Guest Editorial: Materials for a Sustainable Future

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      PubDate: 2022-05-10
      DOI: 10.1007/s41745-022-00306-z
       
  • Clinician Scientists in the Indian Context

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      Abstract: Abstract Advances in healthcare are fueled by discovery and innovation in science and technology. However, such discovery and innovation cannot occur without vital inputs from a clinician. Here, we make a case for the training of clinicians as scientists and engineers to help integrate over disciplines and accelerate translational research in India. We identify and suggest possible solutions to the challenges of creating an Indian clinician-scientist. We further take stock of programs across the globe, initiatives in India, including at our institute, and highlight possible paths that could be taken to train such individuals. With an adequate investment of effort and time, we believe that the clinician-scientist will have the ability to transform healthcare in India.
      PubDate: 2022-05-04
      DOI: 10.1007/s41745-022-00303-2
       
  • Editor’s Desk

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      PubDate: 2022-04-28
      DOI: 10.1007/s41745-022-00304-1
       
  • Likelihood Ratio Tests for Elaborate Covariance Structures and for MANOVA
           Models with Elaborate Covariance Structures—A Review

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      Abstract: Abstract In this paper a review is made from the primordia of the history of likelihood ratio tests for covariance structures and equality of mean vectors through the development of likelihood ratio tests that refer to elaborate covariance structures. Relations are established among several covariance structures, taking more elaborate ones as umbrella structures and examining then their particular cases of interest. References are made to bibliography where the corresponding likelihood ratio tests are developed and the distributions of the corresponding statistics addressed. Most of the likelihood ratio test statistics for one-way manova models where the covariance matrices have elaborate structures were developed quite recently. Also for these likelihood ratio tests a similar approach is taken. Although we start with the common test that uses unstructured covariance matrices, then we go on to consider tests with more elaborate covariance structures, and subsequently we specify them to their particular cases of interest. Some special attention is also given to the so-called Wilks \(\Lambda\) statistics.
      PubDate: 2022-04-17
      DOI: 10.1007/s41745-022-00300-5
       
  • Bayesian Modeling of Discrete-Time Point-Referenced Spatio-Temporal Data

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      Abstract: Abstract Discrete-time point-referenced spatio-temporal data are obtained by collecting observations at arbitrary but fixed spatial locations \(\varvec{s}_{1},\varvec{s}_{2},\ldots ,\varvec{s}_{n}\) at regular intervals of time \(t := 1,2,\ldots ,T\) . They are encountered routinely in meteorological and environmental studies. Gaussian linear dynamic spatio-temporal models (LDSTMs) are the most widely used models for fitting and prediction with them. While Gaussian LDSTMs demonstrate good predictive performance at a wide range of scenarios, discrete-time point-referenced spatio-temporal data, often being the end product of complex interactions among environmental processes, are better modeled by nonlinear dynamic spatio-temporal models (NLDSTMs). Several such nonlinear models have been proposed in the context of precipitation, deposition, and sea-surface temperature modeling. Some of the above-mentioned models, although are fitted classically, dynamic spatio-temporal models with their complex dependence structure, are more naturally accommodated within the fully Bayesian framework. In this article, we review many such linear and nonlinear Bayesian models for discrete-time point-referenced spatio-temporal data. As we go along, we also review some nonparametric spatio-temporal models as well as some recently proposed Bayesian models for massive spatio-temporal data.
      PubDate: 2022-03-25
      DOI: 10.1007/s41745-022-00298-w
       
  • Mutations and the Distribution of Lifetime Reproductive Success

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      Abstract: Abstract Evolution proceeds in large part by the establishment of mutations in the genome of organisms, but even an advantageous mutation may be lost by chance. The probability of such loss is the extinction probability of an individual with a random lifetime reproductive success (LRS). We show here that the traditional approximation of extinction probability in terms of the mean and variance of LRS does not always apply, because the LRS has a skewed, often multimodal, distribution. To exemplify distinct life history patters, we use the Hadza and Pacific Chinook salmon. The traditional approximation overestimates the exact extinction probability from complete LRS distribution. An accurate analysis of the distribution of LRS strengthens our ability to successfully analyze evolution.
      PubDate: 2022-03-25
      DOI: 10.1007/s41745-022-00297-x
       
  • Empirical Bayes and Selective Inference

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      Abstract: Abstract We review the empirical Bayes approach to large-scale inference. In the context of the problem of inference for a high-dimensional normal mean, empirical Bayes methods are advocated as they exhibit risk-reducing shrinkage, while establishing appropriate control of frequentist properties of the inference. We elucidate these frequentist properties and evaluate the protection that empirical Bayes provides against selection bias.
      PubDate: 2022-03-07
      DOI: 10.1007/s41745-022-00286-0
       
  • Fractional Processes and Their Statistical Inference: An Overview

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      Abstract: Abstract We give an overview of properties of fractional processes such as fractional Brownian motion, mixed fractional Brownian motion, sub-fractional Brownian motion, fractional Lévy process , fractional Poisson process and present a short review of problems of statistical inference for processes driven by fractional processes.
      PubDate: 2022-02-24
       
  • Statistical Thermal Efficiency and Quantum Interactions

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      Abstract: Abstract We statistically deal with two distinct quantum interactions embedded within an exactly solvable model that mimics some well known nuclear effects. By recourse to finite temperature statistical quantifiers we describe how these two fermion–fermion interactions compete and strongly influence each other. Statistically scrutinizing such competition leads to interesting insights on many body dynamics’ features. We discuss, in particular, the thermal efficiency of the fermion–fermion interactions
      PubDate: 2022-02-23
       
  • Neutralizing Antibodies and Antibody-Dependent Enhancement in COVID-19: A
           Perspective

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      Abstract: Abstract Antibody-dependent enhancement (ADE) is an alternative route of viral entry in the susceptible host cell. In this process, antiviral antibodies enhance the entry access of virus in the cells via interaction with the complement or Fc receptors leading to the worsening of infection. SARS-CoV-2 variants pose a general concern for the efficacy of neutralizing antibodies that may fail to neutralize infection, raising the possibility of a more severe form of COVID-19. Data from various studies on respiratory viruses raise the speculation that antibodies elicited against SARS-CoV-2 and during COVID-19 recovery could potentially exacerbate the infection through ADE at sub-neutralizing concentrations; this may contribute to disease pathogenesis. It is, therefore, of utmost importance to study the effectiveness of the anti-SARS-CoV-2 antibodies in COVID-19-infected subjects. Theoretically, ADE remains a general concern for the efficacy of antibodies elicited during infection, most notably in convalescent plasma therapy and in response to vaccines where it could be counterproductive.
      PubDate: 2022-02-04
       
  • Fifty Years with the Cox Proportional Hazards Regression Model

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      Abstract: Abstract The 1972 paper introducing the Cox proportional hazards regression model is one of the most widely cited statistical articles. In the present article, we give an account of the model, with a detailed description of its properties, and discuss the marked influence that the model has had on both statistical and medical research. We will also review points of criticism that have been raised against the model.
      PubDate: 2022-01-23
       
  • The Kullback–Leibler Divergence Between Lattice Gaussian
           Distributions

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      Abstract: Abstract A lattice Gaussian distribution of given mean and covariance matrix is a discrete distribution supported on a lattice maximizing Shannon’s entropy under these mean and covariance constraints. Lattice Gaussian distributions find applications in cryptography and in machine learning. The set of Gaussian distributions on a given lattice can be handled as a discrete exponential family whose partition function is related to the Riemann theta function. In this paper, we first report a formula for the Kullback–Leibler divergence between two lattice Gaussian distributions and then show how to efficiently approximate it numerically either via Rényi’s \(\alpha \) -divergences or via the projective \(\gamma \) -divergences. We illustrate how to use the Kullback-Leibler divergence to calculate the Chernoff information on the dually flat structure of the manifold of lattice Gaussian distributions.
      PubDate: 2022-01-23
       
 
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