The global outbreak of Novel Corona Virus 2019 (SARS-CoV-2) has made worldwide lockdown including India since March 24, 2020. The current research aims at the improvements of nitrogen dioxide (NO) during the COVID-19 lockdown in India. This research has been done using both the open source data sets taken from satellite and ground based for better analysis. For the satellite-based analysis, the Sentinel 5 Precauser's Tropospheric NO from the European Space Agency and for the ground-based numeric data sets from Central Pollution Control Board (CPCB) has been used. During the COVID-19 disease, outbreak the world has set in quarantine and as an overcome air quality improved in Asian countries after national lockdown, the average NO rates plummeted calculated by 40-50%. Similarly, it dramatically decreased in Asia during the COVID-19 pandemic quarantine period. The basic statistical patterns of the NO concentration spectrum of historical data sets (2018-2020) bi-weekly showed during October to March were seen higher in each year. Related with National Ambient Air Quality Standards of mean of NO in India our result shown in the NO levels fall in 21 μg/m during the national lockdown, from the Central Pollution Control Board's air quality standards it almost decreased 50% of the hourly mean in India. This caused by the sudden restriction to the development of manufacturing and the transportations which ultimately minimized the fossil fuel burning which cause the most of the NO releases to the atmosphere. Nowadays, people are aware about comparatively prosperous future with clear blue skies and uses of renewable energy sources from the nature.
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http://dx.doi.org/10.1007/s40808-021-01172-x | DOI Listing |
J Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
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View Article and Find Full Text PDFJ Proteome Res
January 2025
European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, U.K.
The PRIDE database is the largest public data repository of mass spectrometry-based proteomics data and currently stores more than 40,000 data sets covering a wide range of organisms, experimental techniques, and biological conditions. During the past few years, PRIDE has seen a significant increase in the amount of submitted data-independent acquisition (DIA) proteomics data sets. This provides an excellent opportunity for large-scale data reanalysis and reuse.
View Article and Find Full Text PDFThe admixture model is widely applied to estimate and interpret population structure among individuals. Here we consider a "standard admixture" model that assumes the admixed populations are unrelated and also a generalized model, where the admixed populations themselves are related via coancestry (or covariance) of allele frequencies. The generalized model yields a potentially more realistic and substantially more flexible model that we call "super admixture".
View Article and Find Full Text PDFSci Data
January 2025
Brain and Language Lab, Department of Psychology, Faculty of Psychology and Education Science, University of Geneva, Geneva, Switzerland.
This paper introduces the "NEBULA101 - Neuro-behavioural Understanding of Language Aptitude" dataset, which comprises behavioural and brain imaging data from 101 healthy adults to examine individual differences in language and cognition. Human language, a multifaceted behaviour, varies significantly among individuals, at different processing levels. Recent advances in cognitive science have embraced an integrated approach, combining behavioural and brain studies to explore these differences comprehensively.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Research Unit Structural Chemistry and Computational Biophysics, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin 13125, Germany.
Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity prediction from Cell Painting image data, we introduce here a semisupervised contrastive (SemiSupCon) learning approach. This approach combines the strengths of using biological annotations in supervised contrastive learning and leveraging large unannotated image data sets with self-supervised contrastive learning.
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