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This study uses the economic complexity index to examine how knowledge accumulation and its uses affect energy-related environmental risks in the USA over the period 1995:Q1–2020:Q4. To this end, we extend the traditional bivariate Quantile-on-Quantile Regression to the multivariate case. The empirical results provide time-varying effects of economic complexity, economic growth, FDI, trade openness, and urbanization on energy-related environmental risks. Particularly, the effect of economic complexity is negative and weak in the extremely lower quantiles of energy-related environmental risks, while it is positive and stronger in the middle and higher quantiles. The implication of these results is that economic complexity only condenses energy-related environmental risks when such environmental risks caused by energy-related factors are extremely low. Furthermore, economic growth and tradeopenness stimulate energy-related environmental risks but the effects of FDI and urbanization reduce energy-related environmental risks. Therefore, these findings provide insights into achieving environmental sustainability targets in the USA.

Apolipoprotein E (APOE) is one of the main proteins responsible for cholesterol transport. It has three major isoforms, APOE2, APOE3, and APOE4. The purpose of this study is to investigate the possible effects of single nucleotide variations (SNVs) in the APOE gene, which cause amino acid substitution, on the function, structure and stabilization of the APOE protein using bioinformatics/s tools. SNVs and protein sequence information were obtained from NCBI and UniProt databases. Bioinformatical analysis was performed using a series of tools such as SIFT, PolyPhen-2, SNPs&GO, Mutation Assessor, PROVEAN, SNAP2, I-Mutant-3, MUPro, and Project HOPE. As a result, 321 missense SNVs were analyzed and rs7412 (R176C), rs769455 (R163C), rs11542029 (R50C), rs121918393 (R154S), rs121918394 (K164Q), rs200703101 (R154P), rs387906567 (R160C), rs11542040 (P102T), rs11542041 (R132S) and rs41382345 (E139V) were predicted to be deleterious/disease related after functional analysis and pathological effect analysis via all of the bioinformatics/s tools. According to the protein stabilization results, it was determined that all SNVs decreased protein stabilization with the MUPro software tool, and two SNVs (rs121918394, rs41382345) increased protein stabilization with the I-Mutant-3 software tool. The models of protein and amino acid properties were obtained via Project HOPE for all high-risk SNVs. We hope our analysis will be valuable for further proteomic, genomic, and clinical research.

Alzheimer's disease (AD) is a multifactorial disease resulting from both genetic and environmental factors, which are pathologically defined by the accumulation of intracellular hyperphosphorylated tau protein, neurofibrils tangles, and extracellular amyloid β protein in the brain. The purpose of this study is to estimate the potentially damaging effects of missense single nucleotide polymorphisms (SNPs) in the BID, MAPK10 and AGER genes associated with AD using various in silico tools and to determine the effects of SNPs on miRNAs. In addition, it is aimed to determine the gene-gene and protein-protein interactions through various software tools. Consequently, it was estimated that there may be harmful effects of seven polymorphisms in the BID gene, twenty-seven in the MAPK10 gene and three in the AGER gene. It was obtained that some SNPs decrease the effectiveness of miRNA-mRNA binding, enhance, break, create a new binding zone and/or destroy the miRNA-mRNA binding zone in the BID and MAPK10 genes. miRNA-SNP analyses could not provide information on the AGER gene. In this study, SNPs in the BID, MAPK10, and AGER genes, which are estimated to be high-risk SNPs, will be able to provide data for future genotyping studies. SNPs that are estimated to be high-risk and SNPs that may have a role in miRNA- mRNA activity can be assessed as a priority in experimental studies related to AD. In the future, experimental studies are proposed to investigate the clinical effects of harmful/disease-related missense SNPs and SNPs affecting mRNA-miRNA interaction.