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Economic development fosters a favorable ecosystem for entrepreneurs. Economic development and entrepreneurship ecosystem have positive causality with each other. Subsequently, economic development is also a useful determinant of the entrepreneurship ecosystem and vice-versa. The entrepreneurship ecosystem cannot be nurtured without financial development. However, limited studies have observed the causal association between an entrepreneurship ecosystem and financial inclusion. As financial development has a vital contribution to increasing the entrepreneurship ecosystem, this study examines the cause-and-effect relationship between entrepreneurship ecosystem and financial inclusion across countries. The regression coefficients of selected independent variables are estimated through robust empirical models. For said investigation, the global financial inclusion index and global entrepreneurship ecosystem index score were used as proxy variables for financial inclusion and entrepreneurship ecosystem, respectively in regression analysis. The empirical findings revealed that financial inclusion can be improved with an increase in entrepreneurship ecosystem, economic development, ease of doing business, employers, foreign direct investment net inflow, use of internet facilities, and wage and salaried workers. In the same way, entrepreneurship ecosystem can also be nurtured with financial inclusion, economic development, trade openness, ease of doing business, total employers, use of internet facilities and wage and salaried workers increase. The empirical results also inferred that entrepreneurship ecosystem, gross domestic per capita, foreign direct investment net inflow, ease of doing business, employer's start-up procedures to register a business and wages and salaries are important indicators to expand financial inclusion. Further, the results claimed that entrepreneurship ecosystem and financial inclusion are complementary to each other., Competing Interests: The authors declare no competing interests., (© 2023 Published by Elsevier Ltd.)

Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference., (© 2022 Wiley Periodicals LLC.)

Poverty is not only the focal issue that has drawn worldwide attention but is also an essential issue in people's livelihoods. This research examines the primary factors of poverty in the Birbhum district. Multivariate statistical techniques have been used to identify the primary determinants. Ten parameters have been identified as significant drivers of poverty, six of which are physical, viz. slope, elevation, drainage density, pond frequency, soil texture, and rainfall. The remaining four sociocultural and economic parameters are literacy, major market center, population growth, and road density. A linear relationship has been established between the explanatory and response variables where the R -square or coefficient of determination value is 0.741, and this relationship explains more than 74% of the variables. The P -value of multi-linear regression is 0.000, which validates the model and permits the data for factor analysis to extract the major determinants. Factor analysis indicates that five essential factors have been found based on their eigenvalue viz., agro-climatic factor, infrastructural and educational factors, hydrological factor, demographic factor, and pedological factors. All the p-values of the correlation matrix are < 0.05, meaning all the relationships are valid and significant. This research also demonstrates the spatial analysis of data using GIS technology. The western part of the study area has been affected by the high influence of all factors due to the presence of plateau fringe and associated low productivity. The outcomes of the research are scientifically significant and this study helps the planners, higher authorities, and social workers to eradicate poverty from this region through formulating better policies and management., Competing Interests: Conflict of interestThe author declared that there are no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Objective  ABO typing constitutes cell grouping and serum grouping. The discrepancy may arise in ABO typing due to a mismatch in cell grouping and serum grouping. It may be due to technical errors, missing or weak ABO antibodies (type I), weak ABO subgroups (type II), Rouleaux formation (type III), or other miscellaneous reasons (type IV). This study was carried out to determine the prevalence and cause of ABO blood group discrepancy in donor samples at our center. Methods  A retrospective study of ABO blood group typing of blood donors was conducted at our center. The blood group typing was routinely performed using gel cards and a microcentrifuge system (Tulip Diagnostics(P) Ltd, Goa, India). If any discrepancy in ABO typing was noted, the test was repeated using the conventional tube technique. After sorting clerical/technical error, the causes of discrepancy were analyzed and resolved using anti-A 1 , anti-H, anti-AB, and other immunohematological tests like antibody screening and identification, saliva inhibition test, adsorption-elution studies. Results  A total of 12,715 (98.6% males and 1.4% females) donor samples were tested. The number of ABO discrepancies detected were 15 (0.12%). The discrepancies were characterized as type I (6 cases; 40%), type II (1 case; 6.7%), type III (0 cases; 0%), and type IV (8 cases; 53.3%). Three cases, each of anti-M and anti-Le b , were detected in the study population. A single case of A 3 , a subgroup of A blood group, was found during the study. Conclusion  The prevalence of ABO group discrepancy was 0.12% at our center. Discrepancy arising during ABO typing of blood donor must be resolved before reporting ABO blood group to minimize the recipient's chances of transfusion reaction. The serum grouping is equally crucial as cell grouping for reporting the ABO group of an individual., Competing Interests: Conflict of Interest The authors declare no conflict of interest., (The Indian Association of Laboratory Physicians. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).)

The diffusion of COVID-19 or Coronavirus since last few months is the prime matter of concern for the entire world. The government of India had declared the complete lockdown from 24th March. After the second step lockdown, now third step lockdown was declared. So, India in Lockdown 3.0 situation. Although the economy of our country has severely been affected by the impact of lockdown, this situation is good for natural healing. Major metropolitan cities of India are trying to recover from various pollution. This study, therefore, attempts to analyze the trend of air pollution before and during a lockdown situation in Kolkata metropolitan and surrounding areas. To identify air pollution trends before and during the lockdown, the non-parametric Maan-Kendall test and Sen's slope estimator have been applied in this study. This research has been done based on air quality index data of the Kolkata metropolitan region's observatory stations. The time range of the data set is from mid of February to 2nd May. The study results show that air pollution has been reduced up to 80% in almost all the stations due to strict lockdown., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier B.V. All rights reserved.)

Competing Interests: There are no conflicts of interest.