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The Covid pandemic has become a serious public health challenge for people across India and other nations. Nowadays, people rely on the online reviews being shared on different review sites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, since these reviews are large in number and are unstructured, patients struggle to get accurate and reliable information about the hospitals, due to which they end up taking admission into a hospital which might not be appropriate for the specific treatment they require. This paper employs the use of sentiment analysis to understand various online reviews of hospitals and provide valuable information to the patients. Approximately 30,000 + reviews were collected from more than 500 hospitals. The broad objective of the study is to give the patients a comprehensive and descriptive rating of the hospitals based on the online reviews given by different patients. In addition to providing a comprehensive summary, the study has conducted aspect-based analysis where it compares the hospitals based on four different aspects of the hospital viz. “Doctors’ services”, “Staff’s services”, “Hospital facilities”, and “Affordability”. The database containing aspect-based ratings of the hospitals will be of great value to the patients by allowing them to compare and select the best hospital based on the optimum fit of the aspects of their preference. [ FROM AUTHOR] Copyright of New Generation Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Identifying and examining the change-prone parts of the software is gaining wide importance in the field of software engineering. This would help software practitioners to cautiously assign the resources for testing and maintenance. Software metrics can be used for constructing various classification models which allow timely identification of change prone classes. There have been various machine learning classification models proposed in the literature. However, due to varying results across studies, more research needs to be done to increase the confidence in the results and provide a valuable conclusion. In this paper, we have used a number of data analysis techniques (14 machine learning techniques and a statistical technique) to construct change prediction models and performed statistical testing to compare the performance of these models. The application of a large number of techniques will allow for fair evaluation and will thus, increase the conclusion validity of the study. The results are validated on five releases of an open source, widely used operating system in mobile phone and tablet computers, ‘Android’. To make the results more generalizable, we have also conducted inter-release and cross-project predictions. The results conclude that the machine learning techniques are effective in predicting change prone classes and thus, should be widely used by researchers and practitioners to reduce maintenance effort and thus efficient and better development of software.

Mycobacterial beta-lactamases are involved in exerting beta-lactam resistance, though many of these proteins remain uncharacterized. Here, we have characterized MSMEG_4455 of Mycobacterium smegmatis as a beta-lactamase using molecular, biochemical and mutational techniques. To elucidate its nature in vivo and in vitro, and to predict its structure-function relationship in silico analysis is done. The MSMEG_4455 is cloned and expressed ectopically in a beta-lactamase deficient Escherichia coli mutant to establish the in vivo beta-lactamase like nature via minimum inhibitory concentration (MIC) determination. Likewise the in vivo results, purified soluble form of MSMEG_4455 showed beta-lactam hydrolysis pattern similar to group 2a penicillinase. In silico analyses of MSMEG_4455 reveal glutamic acid (E)193 and tyrosine (Y)194 of omega-like loop might have importance in strengthening hydrogen bond network around the active-site, though involvement of tyrosine is rare for beta-lactamase activity. Accordingly, these residues are mutated to alanine (A) and phenylalanine (F), respectively. The mutated proteins have partially lost their ability to exert beta-lactamase activity both in vivo and in vitro. The Y194F mutation had more prominent effect on the enzymatic activity. Therefore, we infer that Y194 is the key for beta-lactamase activity of MSMEG_4455.

Background: Insulin/IGF-1 signaling plays a central role in longevity across phylogeny. In C. elegans, the forkhead box O (FOXO) transcription factor, DAF-16, is the primary target of insulin/IGF-1 signaling, and multiple isoforms of DAF-16 (a, b, and d/f) modulate lifespan, metabolism, dauer formation, and stress resistance. Thus far, across phylogeny modulation of mammalian FOXOs and DAF-16 have focused on post-translational regulation with little focus on transcriptional regulation. In C. elegans ,w e have previously shown that DAF-16d/f cooperates with DAF-16a to promote longevity. In this study, we generated transgenic strains expressing near-endogenous levels of either daf-16a or daf-16d/f, and examined temporal expression of the isoforms to further define how these isoforms contribute to lifespan regulation. Results: Here, we show that DAF-16a is sensitive both to changes in gene dosage and to alterations in the level of insulin/IGF-1 signaling. Interestingly, we find that as worms age, the intestinal expression of daf-16d/f but not daf-16a is dramatically upregulated at the level of transcription. Preventing this transcriptional upregulation shortens lifespan, indicating that transcriptional regulation of daf-16d/f promotes longevity. In an RNAi screen of transcriptional regulators, we identify elt-2 (GATA transcription factor) and swsn-1 (core subunit of SWI/SNF complex) as key modulators of daf-16d/f gene expression. ELT-2 and another GATA factor, ELT-4, promote longevity via both DAF-16a and DAF-16d/f while the components of SWI/SNF complex promote longevity specifically via DAF-16d/f. Conclusions: Our findings indicate that transcriptional control of C. elegans FOXO/daf-16 is an essential regulatory event. Considering the conservation of FOXO across species, our findings identify a new layer of FOXO regulation as a potential determinant of mammalian longevity and age-related diseases such as cancer and diabetes.