Machine Learning-based Linear regression way to deal with making data science model for checking the sufficiency of night curfew in Maharashtra, India

The birthplace of the novel Covid-19 sickness or COVID-19 began its spread around Wuhan city, China. The spread of this novel infection sickness began toward the start of December 2019. The Covid-19 illness spreads from one individual to another through hacking, sniffling, etc. To stop the spreading of the novel Covid-19 infection the distinctive nation has presented diverse strategies. Some regularly utilized methods are lockdown, night curfew, etc. The fundamental intention of the systems was to stop the social events and leaving homes without serious issues. Utilizing a diverse system Covid-19 first stage can address for saving individuals. Presently the second influx of this novel Covid illness has begun its top from the mid of April-May. The second convergence of this novel Covid disorder flooded all through the world and in India too. To stop the spread of this novel Covid sickness India's richest state Maharashtra government constrained the decision of night curfew. In this paper, we are taking as a relevant examination the night curfew on a schedule of Maharashtra. Here, we study that this system may or may not be able to stop the spread of pandemics. We are using the Machine learning(ML) approach to managing regulate study this case. ML has various systems yet among all of those here we use Linear Regression for the current circumstance. The reproduced insight that readies the plan orchestrated to learn with no other person. Linear Regression is the affirmed strategy for looking over the connection between two sections. Between the two segments, one is astute and another is a seen variable

Bioequivalence study of two formulations containing 400 mg dexibuprofen in healthy Indian subjects

Objective: This study presents the results of two-period, two-treatment crossover investigations on 24 healthy Indian male subjects to assess the bioequivalence of two oral formulations containing 400 mg of dexibuprofen (CAS 51146-56-6). An attempt was also made to study the pharmacokinetics of dexibuprofen in the local population of Indian origin.Method: Both of the formulations were administered orally as a single dose separated by a one-week washout period. The concentration of dexibuprofen in plasma was determined by a validated HPLC method with UV detection using carbamazepine as internal standard. The formulations were compared using the parameters area under the plasma concentration-time curve (AUC0-t), area under the plasma concentration-time curve from zero to infinity (AUC0-∞), peak plasma concentration (Cmax), and time to reach peak plasma concentration (tmax).Results: The results of this investigation indicated that there were no statistically significant differences between the logarithmically transformed AUC0-∞ and Cmax values of the two preparations. The 90 % confidence interval for the ratio of the logarithmically transformed AUC0-t, AUC0-∞ and Cmax were within the bioequivalence limit of 0.8-1.25 and the relative bioavailability of the test formulation was 99.04 % of that of reference formulationjok?.Conclusion: Thus, these findings clearly indicate that the two formulations are bioequivalent in terms of rate and extent of drug absorption. Both preparations were well tolerated with no adverse reactions observed throughout the study

Texture Synthesis Guided Deep Hashing for Texture Image Retrieval

With the large-scale explosion of images and videos over the internet, efficient hashing methods have been developed to facilitate memory and time efficient retrieval of similar images. However, none of the existing works uses hashing to address texture image retrieval mostly because of the lack of sufficiently large texture image databases. Our work addresses this problem by developing a novel deep learning architecture that generates binary hash codes for input texture images. For this, we first pre-train a Texture Synthesis Network (TSN) which takes a texture patch as input and outputs an enlarged view of the texture by injecting newer texture content. Thus it signifies that the TSN encodes the learnt texture specific information in its intermediate layers. In the next stage, a second network gathers the multi-scale feature representations from the TSN's intermediate layers using channel-wise attention, combines them in a progressive manner to a dense continuous representation which is finally converted into a binary hash code with the help of individual and pairwise label information. The new enlarged texture patches also help in data augmentation to alleviate the problem of insufficient texture data and are used to train the second stage of the network. Experiments on three public texture image retrieval datasets indicate the superiority of our texture synthesis guided hashing approach over current state-of-the-art methods.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 Video Presentation: https://www.youtube.com/watch?v=tXaXTGhzaJ

Evidence of a compensated semimetal with electronic correlations at the CNP of twisted double bilayer graphene

Recently, magic-angle twisted bilayer graphene (MATBLG) has shown the emergence of various interaction-driven novel quantum phases at the commensurate fillings of the moir'e superlattice, while the charge neutrality point (CNP) remains mostly a vanilla insulator. Here, we show an emerging phase of nearly compensated semimetallicity at the CNP of twisted double bilayer graphene (TDBLG), a close cousin of MATBLG, with signatures of electronic correlation. Using electrical and thermal transport, we find almost two orders of magnitude enhancement of the thermopower in magnetic fields much smaller than the extreme quantum limit, accompanied by a large magnetoresistance($\sim 2500\%$) at CNP. This provides indisputable experimental evidence that TDBLG near CNP is a compensated semimetal. Moreover, at low temperatures, we observe an unusual sublinear temperature dependence of resistance. A recent theory predicts the formation of an excitonic metal near CNP, where small electron and hole pockets coexist. We understand the sublinear temperature dependence in terms of critical fluctuations in this theory

Genomic characterization of Plasmodium falciparum genes associated with anti-folate drug resistance and treatment outcomes in eastern India: A molecular surveillance study from 2008 to 2017

IntroductionAfter being used vigorously for the previous two decades to treat P. falciparum, chloroquine and sulfadoxine-pyrimethamine were replaced in 2009 with an artemisinin-based combination therapy (artesunate-sulfadoxine-pyrimethamine) in an effort to combat multidrug-resistant parasites.MethodsWe set out to assess the genetic variants of sulfadoxine-pyrimethamine resistance and the effectiveness of its treatment in eastern India prior to, during, and 6 to 8 years following the introduction of the new pharmacological regime. In 2008-2009, 318 P. falciparum–positive patients got the recommended doses of sulfadoxine-pyrimethamine. We used 379 additional isolates from 2015 to 2017 in addition to the 106 isolates from 2010. All 803 isolates from two study sites underwent in vitro sulfadoxine-pyrimethamine sensitivity testing and genomic characterisation of sulfadoxine-pyrimethamine resistance (pfdhfr and pfdhps).ResultsIn Kolkata and Purulia, we observed early treatment failure in 30.7 and 14.4% of patients, respectively, whereas recrudescence was found in 8.1 and 13.4% of patients, respectively, in 2008–2009. In 2017, the proportion of in vitro pyrimethamine and sulfadoxine resistance steadily grew in Kolkata and Purulia despite a single use of sulfadoxine-pyrimethamine. Treatment failures with sulfadoxine-pyrimethamine were linked to quintuple or quadruple pfdhfr- pfdhps mutations (AICII-AGKAT, AICII-AGKAA, AICII-SGKGT, AICII-AGKAA, AICNI-AGKAA) in 2008–2009 (p < 0.001). The subsequent spread of mutant-haplotypes with higher in vitro sulfadoxine-pyrimethamine resistance (p < 0.001), such as the sextuple (dhfr-AIRNI+dhps-AGEAA, dhfr-ANRNL+dhps-AGEAA) and septuple (dhfr-AIRNI+dhps-AGEAT), mutations were observed in 2015-2017.DiscussionThis successive spread of mutations with high in vitro sulfadoxine-pyrimethamine resistance confirmed the progressive increase in antifolate resistance even after an 8-year withdrawal of sulfadoxine-pyrimethamine