Diagnosis of dengue virus infection using spectroscopic images and deep learning.

Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers.

Recognition of activities, such as preparing meal or watching TV, performed by a smart home resident, can promote the independent living of elderly in a safe and comfortable environment of their own homes, for an extended period of time. Different activities performed at the same location have commonalities resulting in less inter-class variations; while the same activity performed multiple times, or by multiple residents, varies in its execution resulting in high intra-class variations. We propose a Local Feature Weighting approach (LFW) that assigns weights based on both inter-class and intra-class importance of a feature in an activity. Multiple sensors are deployed at different locations in a smart home to gather information. We exploit the obtained information, such as frequency and duration of activation of sensors, and the total sensors in an activity for feature weighting. The weights for the same features vary among activities, since a feature may have more importance for one activity but less for the other. For the classification, we exploit the two variants of K-Nearest Neighbors (KNN): Evidence Theoretic KNN (ETKNN) and Fuzzy KNN (FKNN). The evaluation of the proposed approach on three datasets, from CASAS smart home project, demonstrates its ability in the correct recognition of activities compared to the existing approaches.

Antimicrobial resistance: more than 70 years of war between humans and bacteria.

Development of antibiotic resistance in bacteria is one of the major issues in the present world and one of the greatest threats faced by mankind. Resistance is spread through both vertical gene transfer (parent to offspring) as well as by horizontal gene transfer like transformation, transduction and conjugation. The main mechanisms of resistance are limiting uptake of a drug, modification of a drug target, inactivation of a drug, and active efflux of a drug. The highest quantities of antibiotic concentrations are usually found in areas with strong anthropogenic pressures, for example medical source (e.g., hospitals) effluents, pharmaceutical industries, wastewater influents, soils treated with manure, animal husbandry and aquaculture (where antibiotics are generally used as in-feed preparations). Hence, the strong selective pressure applied by antimicrobial use has forced microorganisms to evolve for survival. The guts of animals and humans, wastewater treatment plants, hospital and community effluents, animal husbandry and aquaculture runoffs have been designated as "hotspots for AMR genes" because the high density of bacteria, phages, and plasmids in these settings allows significant genetic exchange and recombination. Evidence from the literature suggests that the knowledge of antibiotic resistance in the population is still scarce. Tackling antimicrobial resistance requires a wide range of strategies, for example, more research in antibiotic production, the need of educating patients and the general public, as well as developing alternatives to antibiotics (briefly discussed in the conclusions of this article).

Purification and Characterization of a Thermostable Cellobiohydrolase from Thermotoga petrophila.

BACKGROUND: Cellulose, being the most abundant biopolymer found in nature, can be utilized for bioethanol production to cater the future energy needs. Due to increased usage of fossil fuel it has been predicted that fossil fuel reserves may be depleted by year 2050. These concerns need serious attention and focus should be diverted to renewable fuels that are based on natural resources. Cellulases including exoglucanase (cellobiohydrolases) are the key enzymes that are produced by cellulolytic micro-organisms for the biodegradation of natural resource (cellulose) into fermentable reducing sugars. Many members of genus Clostridium possess supramolecular structures known as cellulosomes which contain various cellulases. Cellulase are composed of catalytic subunits that include endoglucanase, ß-glucosidase and cellobiohydrolases which concurrently can catalyse and subsequently convert cellulose into glucose and other sugars. After the action of cellulases, the sugars can be conveniently converted into bioethanol. OBJECTIVE: In the present study, characterization of a thermostable cellobiohydrolase enzyme from Thermotoga petrophila was carried out. The main purpose of this study is the utilization of thermostable cellobiohydrolase along with other cellulases in the process of saccharification of the cellulosic biomass to produce fermentable sugars that could in turn be converted into bioethanol which is the fuel of the future. METHOD: In this article, we propose a framework for achieving our a forementioned object. We started with the cloning of thermophilic cellobiohydrolase gene in mesophilic hosts to ease enzyme production. After cloning of cellobiohydrolase gene, submerged fermentation was performed for intracellular enzyme production. Microbial pellet obtained after centrifugation was sonicated and subjected to ammonium sulphate precipitation. The fraction obtained was purified to isoelectric homogeneity through ion exchange chromatography. Finally SDS analysis of purified cellobiohydrolase was carried out alongwith its characterization, kinetics and thermodynamics studies. RESULTS: Purification fold of 4.05 was obtained along with enzyme activity and specific activity of 11.5 U ml-1 min-1 and 66.5 U mg-1, respectively. The molecular mass of purified recombinant enzyme was 37 kDa as calculated by means of SDS-PAGE analysis. The enzyme showed 50% residual activity at 90°C and also at a wide pH range of 4-10. The enzyme retained its activity in the presence of most of the metal ions except Fe+2, Hg+2 and Pb+2. EDTA has an inhibitory effect on the function of the enzyme. The catalytic activity of the enzyme was maintained in the presence of the organic solvents. The enzyme had a Km and Vmax of 4.6 mM and 25.64±1.87 µM min-1 for PNP-ß- D-cellobioside under optimal conditions. CONCLUSION: The present study demonstrated that cellobiohydrolase produced from Thermotoga petrophila can be employed in many industries like paper and pulp and food processing. Most recent application of the cellobiohydrolases is their utilization in the production of bioethanol.