Comparative Computational Analysis of Mycobacterium Species by using Different Techniques in Study

Mycobacterium tuberculosis (MTB) is a pathogenic bacteria species in the genus Mycobacterium and the causative agent of most cases of tuberculosis. It is spread through the air when people who have an active MTB infection cough, sneeze, or otherwise transmit their saliva through the air. Most infections in humans result in an asymptomatic, latent infection, and about one in ten latent infections eventually progresses to active disease, which, if left untreated, kills more than 50% of its victims.  Mycobacterium tuberculosis is a member of the genus ‘tuberculosis’ in which contains various other mycobacterium species also. These species within a gene must have some similarity in them. In spite of this similarity only mycobacterium tuberculosis cause the tuberculosis disease, the remaining does not. This signifies that mycobacterium tuberculosis must be having some specific genes or proteins which are uniquely present only in it and not in the other species. This fact is used in this research and blast program is executed recursively for the comparison between these mycobacterium species. Keywords: BLAST, Mycobacterium Tuberculosis, Nontuberculous mycobacterium group, EEA1, KEG

Pregnancy in unicornuate uterus without rudimentary horn: a case report

A unicornuate uterus is associated with numerous obstetric and gynaecological complications such as infertility, endometriosis, miscarriage, malpresentations, and intrauterine growth restriction. Around 2.3-13% of Mullerian duct anomalies present as unicornuate uterus. Management of unicornuate uterus is still uncertain and it leads to poorer pregnancy outcome. We present here a case of 26-year-old primigravida who presented to us with 40-weeks pregnancy associated with breech presentation. She was taken for elective caesarean section and intra-operatively she was found to have unicornuate uterus without rudimentary horn. Unicornuate uterus is associated with poor pregnancy outcome but a successful pregnancy is possible. Usual presentation of patients with unicornuate uterus is near their menarche and they have higher than usual gynaecological complications. Pregnancies in unicornuate uterus are prone to intra uterine growth restriction hence serial ultrasound should be done for regular fetal growth monitoring

Mini-batch stochastic subgradient for functional constrained optimization

In this paper we consider finite sum composite convex optimization problems with many functional constraints. The objective function is expressed as a finite sum of two terms, one of which admits easy computation of (sub)gradients while the other is amenable to proximal evaluations. We assume a generalized bounded gradient condition on the objective which allows us to simultaneously tackle both smooth and nonsmooth problems. We also consider the cases of both with and without a strong convexity property. Further, we assume that each constraint set is given as the level set of a convex but not necessarily differentiable function. We reformulate the constrained finite sum problem into a stochastic optimization problem for which the stochastic subgradient projection method from [17] specializes to a collection of mini-batch variants, with different mini-batch sizes for the objective function and functional constraints, respectively. More specifically, at each iteration, our algorithm takes a mini-batch stochastic proximal subgradient step aimed at minimizing the objective function and then a subsequent mini-batch subgradient projection step minimizing the feasibility violation. By specializing different mini-batching strategies, we derive exact expressions for the stepsizes as a function of the mini-batch size and in some cases we also derive insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime. We also prove sublinear convergence rates for the mini-batch subgradient projection algorithm which depend explicitly on the mini-batch sizes and on the properties of the objective function. Numerical results also show a better performance of our mini-batch scheme over its single-batch counterpart.Comment: 24 page

A stochastic moving ball approximation method for smooth convex constrained minimization

In this paper, we consider constrained optimization problems with convex, smooth objective and constraints. We propose a new stochastic gradient algorithm, called the Stochastic Moving Ball Approximation (SMBA) method, to solve this class of problems, where at each iteration we first take a gradient step for the objective function and then perform a projection step onto one ball approximation of a randomly chosen constraint. The computational simplicity of SMBA, which uses first-order information and considers only one constraint at a time, makes it suitable for large-scale problems with many functional constraints. We provide a convergence analysis for the SMBA algorithm using basic assumptions on the problem, that yields new convergence rates in both optimality and feasibility criteria evaluated at some average point. Our convergence proofs are novel since we need to deal properly with infeasible iterates and with quadratic upper approximations of constraints that may yield empty balls. We derive convergence rates of order $\mathcal{O} (k^{-1/2})$ when the objective function is convex, and $\mathcal{O} (k^{-1})$ when the objective function is strongly convex. Preliminary numerical experiments on quadratically constrained quadratic problems demonstrate the viability and performance of our method when compared to some existing state-of-the-art optimization methods and software.Comment: 28 page

A study of expressed emotion, perceived stress and socio-demographic profile in patients of dissociative disorder

Background: Dissociation is understood as one of coping mechanism to deal with intense stressors. Individuals vary widely in their subjective response to a similar stressful event depending on number of factors including their family and social support system. So, authors tried to study the expressed emotion in patients of dissociative disorder along with other socio-demographic factors and its relation with perceived stress.Methods: This cross-sectional descriptive study was done on 100 patients with primary diagnosis of dissociative disorder. Hamilton depression rating scale (HAM-D) was used to assess comorbid Depressive symptoms and Hamilton anxiety rating scale (HAM-A) was used to asses comorbid anxiety symptoms. Perceived stress scale (PSS) was used to assess the perception of stress. Family emotional involvement and criticism scale (FEICS) was used to measure perceived criticism (PC) and intensity of emotional involvement (EI).Results: Mean perceived stress in this study was 25.8. Mean score for perceived criticism (PC) was 16.5 and emotional involvement (EI) was 15.7. Both measures of expressed emotions were significantly higher in females and subjects belonging to joint families and rural area. In this study perceived stress by subjects was significantly (p=0.001) correlated to perceived criticism (Pearson r = 0.78) and emotional involvement (Pearson r = 0.77).Conclusions: High perceived criticism and emotion over involvement of family member was associated with perceived stress in dissociation patients

Effect of obesity and metabolic syndrome on severity, quality of life, sleep quality and inflammatory markers in patients of asthma in India

Introduction: The study aimed to compare the effect of obesity with and without metabolic syndrome on asthma severity, quality of life, sleep quality, sleep disordered breathing and inflammatory markers as compared to non-obese asthma patients. Material and methods: 60 asthma patients recruited for the study were divided equally into non-obese (NOA), obese without metabolic syndrome (OANMS) and obese with metabolic syndrome (OAMS) groups. Study cohorts were assessed for severity of asthma, quality of life and quality of sleep using questionnaires and inflammatory markers (FENO, hs-CRP, IL-5, IL-6 and leptin). Institutional ethical committee approved the study. Results: The results suggests OAMS patients may be a subtype of asthmatics having significantly severe asthma (p < 0.05), poor quality of life (p < 0.05), high risk of OSA (p < 0.05), decreased lung volumes (FRC) (p < 0.05), higher levels of inflammatory markers (leptin and IL-6) (p < 0.05), and high incidence of sleep disordered breathing (p < 0.05) in comparison to NOA and OANMS patients. Conclusions: The present study has shown that obese asthmatics especially with metabolic syndrome represent a subtype of asthmatic population. Hence, the treatment of metabolic syndrome may be necessary in addition to asthma to achieve optimal control.  INTRODUCTION: The study aimed to compare the effect of obesity with and without metabolic syndrome on asthma severity, quality of life, sleep quality, sleep disordered breathing and inflammatory markers as compared to non-obese asthma patients. MATERIAL AND METHODS: 60 asthma patients recruited for the study were divided equally into non-obese (NOA), obese without metabolic syndrome (OANMS) and obese with metabolic syndrome (OAMS) groups. Study cohorts were assessed for severity of asthma, quality of life and quality of sleep using questionnaires and inflammatory markers (FENO, hs-CRP, IL-5, IL-6 and leptin). Institutional ethical committee approved the study. RESULTS: The results suggests OAMS patients may be a subtype of asthmatics having significantly severe asthma (p < 0.05), poor quality of life (p  < 0.05), high risk of OSA (p < 0.05), decreased lung volumes (FRC) (p < 0.05), higher levels of inflammatory markers (leptin and IL-6) (p  < 0.05), and high incidence of sleep disordered breathing (p < 0.05) in comparison to NOA and OANMS patients. CONCLUSIONS: The present study has shown that obese asthmatics especially with metabolic syndrome represent a subtype of asthmatic population. Hence, the treatment of metabolic syndrome may be necessary in addition to asthma to achieve optimal control.

Applying machine learning to predict patient-specific current CD4 cell count in order to determine the progression of human immunodeficiency virus (HIV) infection

This work shows the application of machine learning to predict current CD4 cell count of an HIV-positive patient using genome sequences, viral load and time. A regression model predicting actual CD4 cell counts and a classification model predicting if a patient’s CD4 cell count is less than 200 was built using a support vector machine and neural network. The most accurate regression and classification model took as input the viral load, time, and genome and produced a correlation of co-efficient of 0.9 and an accuracy of 95%, respectively, proving that a CD4 cell count measure may be accurately predicted using machine learning on genotype, viral load and time.Keywords: Human immunodeficiency virus (HIV), antigens, CD4, computational biology, artificial intelligence, data mining, pattern recognition.African Journal of Biotechnology Vol. 12(23), pp. 3724-373