Risk factors for severe acute malnutrition in Central India

Aim: To identify risk factors for severe malnutrition in children <5 years age. Background: Severe acute malnutrition (SAM) is a major cause of childhood morbidity and mortality in our country. However, little information is available on determinants of SAM. Materials and Methods: This was a prospective study done over a period of one year at a tertiary care hospital. Children under five years of age with SAM were enrolled, and the risk factors leading to malnutrition were identified. Results: The risk factors contributing to malnutrition were poor socioeconomic status, parental illiteracy, overcrowding, lack of immunization, lack of awareness among caregivers regarding appropriate child feeding practices. Conclusion: To decrease the prevalence of malnutrition, it is essential to provide education to caregivers about appropriate feeding practices

NRFixer: Sentiment Based Model for Predicting the Fixability of Non-Reproducible Bugs

Software maintenance is an essential step in software development life cycle. Nowadays, software companies spend approximately 45\% of total cost in maintenance activities. Large software projects maintain bug repositories to collect, organize and resolve bug reports. Sometimes it is difficult to reproduce the reported bug with the information present in a bug report and thus this bug is marked with resolution non-reproducible (NR). When NR bugs are reconsidered, a few of them might get fixed (NR-to-fix) leaving the others with the same resolution (NR). To analyse the behaviour of developers towards NR-to-fix and NR bugs, the sentiment analysis of NR bug report textual contents has been conducted. The sentiment analysis of bug reports shows that NR bugs' sentiments incline towards more negativity than reproducible bugs. Also, there is a noticeable opinion drift found in the sentiments of NR-to-fix bug reports. Observations driven from this analysis were an inspiration to develop a model that can judge the fixability of NR bugs. Thus a framework, {NRFixer,} which predicts the probability of NR bug fixation, is proposed. {NRFixer} was evaluated with two dimensions. The first dimension considers meta-fields of bug reports (model-1) and the other dimension additionally incorporates the sentiments (model-2) of developers for prediction. Both models were compared using various machine learning classifiers (Zero-R, naive Bayes, J48, random tree and random forest). The bug reports of Firefox and Eclipse projects were used to test {NRFixer}. In Firefox and Eclipse projects, J48 and Naive Bayes classifiers achieve the best prediction accuracy, respectively. It was observed that the inclusion of sentiments in the prediction model shows a rise in the prediction accuracy ranging from 2 to 5\% for various classifiers

Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?

Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports

Analytical Study on Bug Triaging Practices

Software bugs are inevitable and fixing these bugs is a difficult and time consuming task. Bug report assignment is the activity of designating a developer who makes source code changes in order to fix the bug. Many bug assignment techniques have been proposed in the existing studies. These studies use different datasets, varied input and evaluation parameters to validate their work. This diversification in bug triaging results in perplexity among researchers. Hence, this paper organizes the work performed in bug triaging in a structured manner. This paper aims to present current state of the art to provide a structured consolidation of bug triaging approaches. The paper has identified six research questions under five dimensions to address the various aspects of bug triaging. 60 articles from 36 venues have been reviewed and categorized in order to organize and substructure existing work in the field of bug report assignment. This study will help researchers to wisely decide the weapons for bug triaging. Also, it will act as a ready reference for the bug triaging practitioners.</p

Potential Candidates against COVID-19 Targeting RNA-Dependent RNA Polymerase: A Comprehensive Review

Abstract: Due to the extremely contagious nature of SARS-COV-2, it presents a significant threat to humans worldwide. A plethora of studies are going on all over the world to discover the drug to fight SARS-COV-2. One of the most promising targets is RNA-dependent RNA polymerase (RdRp), responsible for viral RNA replication in host cells. Since RdRp is a viral enzyme with no host cell homologs, it allows the development of selective SARS-COV-2 RdRp inhibitors. A variety of studies used in silico approaches for virtual screening, molecular docking, and repurposing of already existing drugs and phytochemicals against SARS-COV-2 RdRp. This review focuses on collating compounds possessing the potential to inhibit SARS-COV-2 RdRp based on in silico studies to give medicinal chemists food for thought so that the existing drugs can be repurposed for the control and treatment of ongoing COVID-19 pandemic after performing in vitro and in vivo experiments. </jats:sec

Bug Handling in Service Sector Software

The technology enabled service industry is emerging as the most dynamic sectors in world's economy. Various service sector industries such as financial services, banking solutions, telecommunication, investment management, etc. completely rely on using large scale software for their smooth operations. Any malwares or bugs in these software is an issue of big concern and can have serious financial consequences. This chapter addresses the problem of bug handling in service sector software. Predictive analysis is a helpful technique for keeping software systems error free. Existing research in bug handling focus on various predictive analysis techniques such as data mining, machine learning, information retrieval, optimisation, etc. for bug resolving. This chapter provides a detailed analysis of bug handling in large service sector software. The main emphasis of this chapter is to discuss research involved in applying predictive analysis for bug handling. The chapter also presents some possible future research directions in bug resolving using mathematical optimisation techniques.</jats:p

Ischemic preconditioning: Interruption of various

Ischemic heart diseases are the leading cause of morbidity and mortality worldwide. Reperfusion of an ischemic heart is necessary to regain the normal functioning of the heart. However, abrupt reperfusion of an ischemic heart elicits a cascade of adverse events that leads to injury of the myocardium, i.e., ischemia–reperfusion injury. An endogenous powerful strategy to protect the ischemic heart is ischemic preconditioning, in which the myocardium is subjected to short periods of sublethal ischemia and reperfusion before the prolonged ischemic insult. However, it should be noted that the cardioprotective effect of preconditioning is attenuated in some pathological conditions. The aim of this article is to review present knowledge on how menopause and some metabolic disorders such as diabetes and hyperlipidemia affect myocardial ischemic preconditioning and the mechanisms involved