Implementation of Feature Engineering in Prediction of AQI in India using Machine Learning

Prediction of Air Quality Index (AQI) is the necessity of today’s era but for the prediction, analysis of different preprocessing techniques that can be applied, needs to be considered. In this study, first of all we explored various feature engineering techniques such as Data Imputation, Scaling, Extraction, Selection, and Data Split that can be used before applying machine learning algorithm for better results. Second, we used MLR and SVR (Linear, Gaussian) to build the prediction models. Finally, we used root mean square error (RMSE), R2, Mean Squared Error (MSE) and Mean Absolute Error (MAE) to evaluate the performance of the regression models in collaboration with the feature engineering techniques. The results shows that the performance of Linear SVR is better when coupled with imputation and robust scaler (R2=0.7557834846394744) as compared to the others, the performance of Gaussian SVR is better when coupled with the imputation only as compared to the others. In case of MLR, results (R2=0.7769187383819041) are almost same in all the 4 cases and performance degraded when PCA was applied

Comparison of two efficient numerical techniques based on Chelyshkov polynomial for solving stochastic It\^o-Volterra integral equation

In this study, two reliable approaches to solving the nonlinear stochastic It\^o-Volterra integral equation are provided. These equations have been evaluated using the orthonormal Chelyshkov spectral collocation technique and the orthonormal Chelyshkov spectral Galerkin method. The techniques presented here transform this problem into a collection of nonlinear algebraic equations that have been numerically solved using the Newton method. Also, the convergence analysis has been studied for both approaches. Two illustrative examples have been provided to show the efficacy, plausibility, proficiency, and applicability of the current approaches

A new numerical technique based on Chelyshkov polynomials for solving two-dimensional stochastic It\^o-Volterra Fredholm integral equation

In this paper, a two-dimensional operational matrix method based on Chelyshkov polynomials is implemented to numerically solve the two-dimensional stochastic It\^o-Volterra Fredholm integral equations. These equations arise in several problems such as an exponential population growth model with several independent white noise sources. In this paper a new stochastic operational matrix has been derived first time ever by using Chelyshkov polynomials. After that, the operational matrices are used to transform the It\^o-Volterra Fredholm integral equation into a system of linear algebraic equations by using Newton cotes nodes as collocation point that can be easily solved. Furthermore, the convergence and error bound of the suggested method are well established. In order to illustrate the effectiveness, plausibility, reliability, and applicability of the existing technique, two typical examples have been presented

Seroprevalence of human immunodeficiency virus and various risk factors responsible for spread of human immunodeficiency virus in pregnant women in Jammu, India

Background: Human immunodeficiency virus (HIV) is increasing at an alarming rate among pregnant women in various parts of India. Purpose of present study is to investigate the seroprevalence of HIV infection in Jammu region of India and to trace various risk factors responsible for its spread. Another objective is to look for strategies which can be adapted to curtail transmission of this dreadful infection.Methods: Pregnant women attending the antenatal clinic of Government Medical College and Hospital, Jammu (India) from October 2013 to September 2014, were counseled and those who agreed to undergo testing, were subjected to HIV testing by ELISA method. Pre-designed and post-testing questionnaire was used for collecting the data. In addition, all the unbooked HIV positive patients, who were directly admitted in labor ward and delivered in this hospital during this period, were also included in the present study.Results: Out of 17918 women attending the antenatal clinic, 5695 agreed for HIV testing at ICTC (integrated counseling and testing center), SMGS hospital and only 5 cases were confirmed positive. Prevalence rate of HIV positivity was found to be 0.088%. Majority of women were between 21-25 years of age, primigravidas, from rural background, lower middle class and spouses of laborers/drivers.Conclusions: Seroprevalence of HIV in Jammu region is relatively low when compared to the national figures. More attention is to be focused on the risk factors to control the transmission of HIV infection

IDENTIFICATION AND MOLECULAR CHARACTERIZATION OF BACTERIA HAVING ANTIMICROBIAL AND ANTIBIOFILM ACTIVITY

Objective: The aim of the current study was to isolate and identify the bacteriocinogenic strain exhibiting broad range antimicrobial activity and antibiofilm activity from soil of animal farms.Methods: In the current study, bacterial strains were isolated from soil of twelve different regions of animal farm all over India and screened for antimicrobial activity against Staphylococcus epidermidis, Micrococcus luteus, Pseudomonas fluorescence and Escherichia coli. Antibiofilm ability of these selected strains was checked on preformed biofilm of S. epidermidis and in addition biofilm disruption potential was also determined. The potent bacterial strain was identified at molecular level by 16S ribosomal DNA (rDNA) sequencing.Results: 30 out of 231 strains isolated from soil were selected on the basis of antibacterial activity against S. epidermidis. One potential candidate (GAS 101) exhibited ≥99% inhibition against S. epidermidis, M. luteus, P. fluorescence and E. coli and also showed antibiofilm activity. GAS 101 16S rDNA sequencing data identified it as Bacillus subtilis. The sequence of B. subtilis was submitted to genbank under accession no. KJ564301.Conclusion: B. subtilis GAS 101 isolated from soil of animal farm showed the antibacterial activity against all indicator organisms and also displayed antibiofilm activity against preformed biofilm and inhibited biofilm formation of S. epidermidis

Role of biochemical and inflammatory markers in assessing COVID-19 severity among the Indian population: An observational study

Introduction: Different laboratory parameters get altered in coronavirus disease 2019 (COVID-19); therefore, the changes of these parameters could help recognize the patients with severe disease. This study was conducted to achieve a comprehensive biochemical and inflammatory profile of COVID-19 among the Indian population. Methods: The study consisted of 730 patients admitted to Jaya Arogya Hospital, Gwalior, with COVID-19 from August 2020 to December 2020. The patients were divided into mild disease group (MDG) (n=533) and severe disease group (SDG) (n=197) depending on certain criteria, and their biochemical and inflammatory markers were collected. Data were analyzed using SPSS version 25. Results: Statistically significant rise in blood urea (P=0.011), serum creatinine (P=0.008), serum bilirubin (P=0.012), interleukin 6 (IL-6) (P<0.001), and troponin I (P<0.001) was observed in SDG as compared to MDG. Serum electrolytes (sodium and potassium) and serum protein (total protein and albumin) showed a significant fall in SDG as compared to MDG (P<0.001 for electrolytes and P=0.023 for proteins). The area under the receiver operating characteristic curve (AUROC) showed a high diagnostic value of IL-6. Conclusion: Patients with severe COVID-19 showed a high prevalence of hyperbilirubinemia, hypoproteinemia, electrolyte imbalance, and raised inflammatory markers (IL-6, troponin I, and procalcitonin). Results showed their effectiveness in assessing disease severity and predicting outcomes in patients with COVID-19

Leishmania donovani triose phosphate isomerase: a potential vaccine target against visceral leishmaniasis

Visceral leishmaniasis (VL) is one of the most important parasitic diseases with approximately 350 million people at risk. Due to the non availability of an ideal drug, development of a safe, effective, and affordable vaccine could be a solution for control and prevention of this disease. In this study, a potential Th1 stimulatory protein- Triose phosphate isomerase (TPI), a glycolytic enzyme, identified through proteomics from a fraction of Leishmania donovani soluble antigen ranging from 89.9–97.1 kDa, was assessed for its potential as a suitable vaccine candidate. The protein- L. donovani TPI (LdTPI) was cloned, expressed and purified which exhibited the homology of 99% with L. infantum TPI. The rLdTPI was further evaluated for its immunogenicity by lymphoproliferative response (LTT), nitric oxide (NO) production and estimation of cytokines in cured Leishmania patients/hamster. It elicited strong LTT response in cured patients as well as NO production in cured hamsters and stimulated remarkable Th1-type cellular responses including IFN-ã and IL-12 with extremely lower level of IL-10 in Leishmania-infected cured/exposed patients PBMCs in vitro. Vaccination with LdTPI-DNA construct protected naive golden hamsters from virulent L. donovani challenge unambiguously (∼90%). The vaccinated hamsters demonstrated a surge in IFN-ã, TNF-á and IL-12 levels but extreme down-regulation of IL-10 and IL-4 along with profound delayed type hypersensitivity and increased levels of Leishmania-specific IgG2 antibody. Thus, the results are suggestive of the protein having the potential of a strong candidate vaccine