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4-ethyl phenyl sulfate (EPS), a protein-bound uremic toxin found in serum of patients suffering from autism spectrum disorders (ASD) and chronic kidney disease (CKD). As per recent advances in the field, gut metabolites after their formation goes to blood stream crosses blood brain barrier and causes neuro related problems. Increased levels of 4-EPS in human body causes anxiety in patients and its role remains elusive. 4-EPS interacts with serum albumin in human body and thus, a model study of interaction of BSA with 4-EPS is presented in support of it. Absorption spectroscopy result demonstrated decrease in bovine serum albumin (BSA) absorption upon interaction with increasing concentration of EPS in a range from 2 μM to 100 μM. Moreover, this interaction was confirmed by the fluorescence quenching in presence of metabolite. The change in secondary structure was demonstrated by circular dichroism, synchronous fluorescence and Fourier transform infra-red spectroscopy. Docking studies reveals binding score of −5.28 Kcal mol−1, demarking that 4-EPS is involved in interaction with BSA via amino acid residues, forming the stable complex. This interaction study may be helpful in devising strategies for the treatment of chronic kidney disease and other neuro related diseases, by producing synthetic compound that competes with albumin binding sites to allow 4-EPS clearance from the body. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

These days users are able to save their time and effort by purchasing products online via various e-commerce websites. Their experience with a product exists in the form of textual reviews/feedbacks provided by them. Recommender systems offer personalized choices to users by capturing their interests and preferences. Through this paper identification of underlying topics using existing topic modeling techniques in user provided reviews of Moto e5 mobile on e-commerce website Amazon has been done and these techniques contrasted. Topic modeling is unsupervised learning technique used to identify hidden topics from a document (all the reviews of a product in this paper's context). Coherence score, a measure of goodness of a topic reflecting the quality of human judgment compares these techniques. The higher the coherence score, the topic is more coherent. Experiments performed reveal that LDA technique performed better on the scrapped dataset. [ABSTRACT FROM AUTHOR]

Summary: As it is known that the whole world is battling against the Corona Virus Disease or COVID19 and trying their level best to stop the spread of this pandemic. To avoid the spread, several countries like China, Italy, Spain, America took strict measures like nationwide lockdown or by cordoning off the areas that were suspected of having risks of community spread. Taking cues from the foreign counterparts, the government of India undertook an important decision of nationwide full lockdown on March 25th which was further extended till May 4th, 2020 (47 days‐full lockdown). Looking at the current situation government of India pushed the lockdown further with eased curbs, divided the nation into green, orange and red zones, rapid testing of citizens in containment area, mandatory wearing of masks and following social distancing among others. The outbreak of the pandemic, has led to the large economic shock to the world which was never been experienced since decades. Moreover it brought a great uncertainty over the world wide electricity sector as to slow down the spread of the virus, many countries have issued restrictions, including the closure of malls, educational institutions, halting trains, suspending of flights, implemented partial or full lockdowns, insisted work from home to the employees. In this paper, the impact analysis of electricity consumption of state Haryana (India) is done using machine learning conventional algorithms and artificial neural network and electricity load forecasting is done for a week so as to aid the electricity board to know the consumption of the area pre hand and likewise can restrict the electricity production as per requirement. Thus, it will help power system to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. For this the dataset from regional electricity boards of Haryana that is, Dakshin Haryana Bijli Vitran Nigam and Uttar Haryana Bijli Vitran Nigam were analysed and electricity loads of state were predicted using python programming and as per result analysis it was observed that artificial neural network out performs conventional machine learning models. [ABSTRACT FROM AUTHOR]

Background: Novel corona virus (2019-nCoV) has spread in the world since its first human infection in December 2019. India has also witnessed a rising number of infections since March 2020. The Indian government imposed lockdowns in the nation to control the movement of its citizens thereby confining the spread of the virus. Tweeters resorted to usage of social media platform to express their mind. Aim: Through this article, an attempt has been made to understand the mind-set of Indian people using Python and R statistical software, during the recent lockdown 2.0 (15 April 2020 to 3 May 2020) and lockdown 3.0 (4 May 2020 to 17 May 2020) through their tweets on the social media platform Twitter. Also, opinion on e-commerce during this pandemic has been analysed. Method: Analysis has been performed using Python and R statistical software. Also, recent articles related to COVID-19 have been considered and reviewed. Result: Although the country had a positive approach in lockdown 2.0 with only few instances of sadness, disgust and others, the majority of the people had a negative approach in lockdown 3.0. Conclusion: This analysis can help the health specialists to understand people's mind-set, the authorities to take further corresponding measures in washing out the virus and the e-commerce stakeholders to adapt to the changing attitudes by adjusting demand and supply plans accordingly. [ABSTRACT FROM AUTHOR]

The ambitious drive of the Government of India to transform the Indian economy from a cash based economy to a cashless economy was contingent in a big way on the people's acceptance of cashless modes of transactions, adaptability to cashless transactions and change in the transaction behavior of consumers and retailers, besides several other factors. As the small retailers form the bulk of the Indian retail sector, their ability to go cashless, their psychological preparedness and their willingness to adopt cashless transactions become an issue of prime importance in the movement towards cashless. The present study was conducted to assess the awareness of the small retailers regarding the cashless transaction and its modes, to understand their apprehensions and perceived convenience in dealing with a cashless system of the transaction and to measure their extent of participation in cashless transactions. The study was conducted in Bareilly, an important city in the state of Uttar Pradesh, with a population of around ten lakhs. Data for the study was obtained from 117 retailers using a structured questionnaire. The study found that though a significant number of small retailers were aware of the possibility of the cashless transaction and its modes, their frequency of engaging in cashless transactions was abysmally less as compared to that of cash based transactions. They were found to believe that dealing with cash was easier in comparison to handling cashless transactions. Fear of losing money due to the faulty transaction was found to be a major deterrent in going cashless amongst the small retailers surveyed during the study. [ABSTRACT FROM AUTHOR]