Dengue virus pathogenesis and host molecular machineries.

Dengue viruses (DENV) are positive-stranded RNA viruses belonging to the Flaviviridae family. DENV is the causative agent of dengue, the most rapidly spreading viral disease transmitted by mosquitoes. Each year, millions of people contract the virus through bites from infected female mosquitoes of the Aedes species. In the majority of individuals, the infection is asymptomatic, and the immune system successfully manages to control virus replication within a few days. Symptomatic individuals may present with a mild fever (Dengue fever or DF) that may or may not progress to a more critical disease termed Dengue hemorrhagic fever (DHF) or the fatal Dengue shock syndrome (DSS). In the absence of a universally accepted prophylactic vaccine or therapeutic drug, treatment is mostly restricted to supportive measures. Similar to many other viruses that induce acute illness, DENV has developed several ways to modulate host metabolism to create an environment conducive to genome replication and the dissemination of viral progeny. To search for new therapeutic options, understanding the underlying host-virus regulatory system involved in various biological processes of the viral life cycle is essential. This review aims to summarize the complex interaction between DENV and the host cellular machinery, comprising regulatory mechanisms at various molecular levels such as epigenetic modulation of the host genome, transcription of host genes, translation of viral and host mRNAs, post-transcriptional regulation of the host transcriptome, post-translational regulation of viral proteins, and pathways involved in protein degradation.

Markov decision process design: A framework for integrating strategic and operational decisions.

We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates the design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem setting arises naturally in several application areas, as we illustrate through examples. We derive a bilevel mixed-integer linear programming formulation for the problem and perform a computational study to demonstrate that realistic instances can be solved numerically.

Development and optimization of oleogel made with soy protein isolate and xanthan gum using emulsion template approach and its comparison with solid fats.

This study aims to develop oleogel as a potential substitute for solid fats in the diet. A novel combination of unmodified Soy Protein Isolate (SPI) and Xanthan Gum (XG) have been utilized to gelate sunflower oil, using an emulsion template approach. The experimental trials employing Response Surface Methodology are conducted to optimize various parameters that affect the oil binding capacity, textural and rheological properties of the oleogel. The concentration of soy protein varies in the range of 5-15 %, the ratio of soy protein to xanthan gum ranges from 1:2 to 1:4, and the ionic strength varies from 0.2 to 1 M. The goal is to formulate oleogel that closely resembles solid fats. Responses namely the oil binding capacity and gel strength value of oleogel were observed best fitted to a linear model whereas, the hardness of oleogel found following a quadratic model. The SPI-XG combination was found effective in entraping more than 95 % of the oil. The best formulation of SPI: XG, 1:4; SPI concentration, 15 % and ionic strength of 1.0 M with 95.5 % of oil retention and hardness and gel strength value comparable to commercial solid fats.

Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature.

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) late last year has not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 200,000 scholarly articles, we a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers developed an innovative natural language processing (NLP) platform that combines an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract information relating to clinical risk factors for COVID-19 by presenting the results in a cluster format to support knowledge discovery. Here we describe the principles behind the development, the model and the results we obtained.