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Abstract Background Neurodevelopmental disorders are disorders that are generally seen in the early developmental period of an individual's life and involve more than one disease that causes disruptions in the central nervous system. These disorders can be given as examples of diseases such as autism, mental retardation, some epileptic disorders, communication disorders, and mental retardation. The aim of this study is to determine the possible harmful effects of missense single nucleotide polymorphisms (SNPs) in the GABRA1, GABRB1, and GABRB3 genes, which are associated with neurodevelopmental disorders, on the structure and stabilization of the protein, using in silico methods. Software tools SIFT, PolyPhen-2 HumVar, PolyPhen-2 HumDiv, PROVEAN, SNAP2, PHD-SNP, SNP&GO, PANTHER, and Meta-SNP were used to predict harmful SNPs. I-Mutant and MUpro software tools were used to predict the effects of predicted harmful SNPs on protein stabilization. The STRING software tool was used for protein–protein interactions, the GeneMANIA software tool for gene–gene interactions, and the Project HOPE software tool for three-dimensional modeling examples. Results As a result of the bioinformatics analysis, rs121434579, rs139163545, and rs267600530 in the GABRA1 gene; rs74608570, rs75612351, rs78815529 in the GABRB1 gene, and rs7819600779, rs1719850690, rs7819600779, rs171985060690, rs7819600779, rs1719850600779, rs149963014 in the GABRB3 gene were predicted as harmful SNPs. Conclusions In this study, protein structure, function, and stabilization of SNPs known to cause amino acid substitutions in GABRA1, GABRB1, and GABRB3 genes associated with some diseases in neurodevelopmental disorders were investigated using bioinformatics tools. As a result of the results obtained in our study, it is thought that it will benefit experimental studies and bioinformatics studies.

Renewable energy, resource efficiency, and globalization are recognized as pivotal factors in the pursuit of environmental sustainability. This study's novelty lies in examining the impact of resource efficiency, the influence of globalization, and the adoption of renewable energy sources on energy‐related CO2 emissions in the United States spanning the years from 1990 to 2020 is the focus of this study. Additionally, this investigation leveraged a diverse range of time‐frequency‐quantile techniques, encompassing quantile‐on‐quantile regressions, wavelet coherence, cross quantile coherence, wavelet‐based quantile correlation, and quantile regression, which is an additional novel aspect of this study. The findings of this research indicate that resource efficiency, the utilization of renewable energy sources, and the forces of globalization collectively contribute to enhancing ecological sustainability by reducing energy‐related CO2 in the United States across a range of quantiles, time periods, and temporal contexts. The study posits that these findings offer a comprehensive framework that can guide policymakers in the United States toward taking tangible measures to advance ecological sustainability while curbing energy‐related CO2 emissions. [ABSTRACT FROM AUTHOR]

This study examines the connectedness among eco-friendly assets and how volatility in fossil energy markets affects this connectedness. Using a broad coverage for eco-friendly assets such as a clean energy index, a clean cryptocurrency (Cardano), a green bond index, and a sustainability-leaders index, our results show that the connectedness is higher in the extremities (tail-risk spillovers) and more significant during essential world events such as a pandemic or war. Further, we employ nonparametric causality in quantiles, quantile regression, and kernel-based regularised least squares methods to investigate the influence of fossil energy markets (crude oil, natural gas, and gas oil) volatility on eco-friendly assets' connectedness. Our results imply that fossil energy market volatility has a positive and causal effect on connectedness dynamics among eco-friendly assets. Our study offers important implications for investors and policymakers in crafting portfolio strategies and policy decisions. [ABSTRACT FROM AUTHOR]

This study presents evidence on how tourism development affects U.S. energy security risks from 1997 to 2020 using a Kernel-based regularized least squares (KRLS) machine learning approach. Our empirical results demonstrate that tourism development amplifies the U.S. energy security-related risks. Also, while technological innovation and urbanization dampen the pressure on energy security-related risks, economic policy-based uncertainty and industrial production increase energy security risks. These results survive in the disaggregated models except for the environmental-related risks sub-index which decreases as a result of tourism development. Our findings, therefore, provide useful insights for policymakers to minimize energy security-related risks. [ABSTRACT FROM AUTHOR]