Environment-growth nexus and corruption in the MENA region: Novel evidence based on method of moments quantile estimations.

This study contributes to the environment-growth nexus literature by examining corruption's effect on environmental quality in 17 Middle East and North Africa (MENA) countries between 1984 and 2018. We use four different indicators of environmental degradation (ecological footprint, carbon dioxide emissions, greenhouse gas emissions, and carbon emission intensity) to gain a comprehensive view of the relationship. This study's methodological value added is the application of the method of moments quantile regression, which enables us to account for different relationships between the independent variables and environmental quality at different levels of environmental degradation. The results indicate that corruption worsens environmental quality based on three of the four indicators of environmental degradation, while the impact appears to become less severe at higher levels of environmental deterioration. The results also indicate that traditional mean-based panel data estimation methods provide an incomplete picture of the factors behind environmental problems: The explanatory variables' impacts tend to vary at different levels of environmental quality, and the impact's sign can even change when moving from the lowest to the highest environmental degradation quantiles. Overall, the results highlight the importance of curbing corruption to enable enforcement of more stringent environmental regulations.

The influence of shadow economy, environmental policies and geopolitical risk on renewable energy: A comparison of high- and middle-income countries.

Given the alarming rate of climate change and environmental degradation, major countries are seeking ways to curtail environmental damage and attain sustainability in the future. In the quest for a green economy, countries are motivated to adopt renewable energy that can assist in resource conservation and efficiency. Accordingly, this study examines the diverse effects of the underground economy, environmental policy strictness, geopolitical risk, gross domestic product, carbon emissions, population, and oil prices on renewable energy for 30 high- and middle-income countries from 1990 to 2018. The empirical outcomes based on quantile regression document significant variations across two country groups. For instance, for high-income countries, the shadow economy has a detrimental effect across all quantiles but it is statistically significant at the top quantiles. Nonetheless, the effect of the shadow economy on renewable energy is detrimental and significant statistically across all quantiles for middle-income countries. In the context of environmental policy stringency, the effect is positive across both country groups, though there is heterogeneity in outcomes. Geopolitical risk has a positive influence on the deployment of renewable energy for high-income countries but negatively impacts renewables for middle-income countries. As far as policy suggestions are concerned, the policymakers of both high- and middle-income countries need to take steps to constrain the growth of the shadow economy by adopting effective policy strategies. Policies need to be implemented for middle income-countries to reduce the unfavorable effect of geopolitical uncertainty. The findings of this study contribute to a better and more precise understanding of factors shaping the role of renewables whereby the energy crisis would be mitigated.

Interplay of Biologically Active Carbon Filtration and Chlorine-Based Disinfection in Mitigating the Dissemination of Antibiotic Resistance Genes in Water Reuse Distribution Systems.

Appropriate management approaches are needed to minimize the proliferation of antibiotic resistance genes (ARGs) in reclaimed water distribution systems (RWDSs). Six laboratory-scale RWDSs were operated over 3 years receiving influent with or without biologically active carbon (BAC) filtration + chlorination, chloramination, or no disinfectant residual. Shotgun metagenomic sequencing was applied toward comprehensive characterization of resistomes, focusing on total ARGs, ARG mobility, and specific ARGs of clinical concern. ARGs such as aadA, bacA, blaOXA, mphE, msrE, sul1, and sul2 were found to be particularly sensitive to varying RWDS conditions. BAC filtration with chlorination most effectively achieved and maintained the lowest levels of nearly all metagenomically derived antibiotic resistance indicators. However, BAC filtration or addition of residual disinfectants alone tended to increase these indicators. Biofilm and sediment compartments harbored ARGs in disinfected systems, presenting a concern for their release to bulk water. Relative and absolute abundances of most ARGs tended to decrease with water age (up to 5 days), with notable exceptions in BAC-filtered chloraminated and no residual systems. Superchlorination of unfiltered water especially raised concerns in terms of elevation of clinically relevant and mobile ARGs. This study revealed that BAC filtration and disinfection must be carefully coordinated in order to effectively mitigate ARG dissemination via RWDSs.

Mapping the Terrain for Pathogen Persistence and Proliferation in Non-potable Reuse Distribution Systems: Interactive Effects of Biofiltration, Disinfection, and Water Age.

Diverse pathogens can potentially persist and proliferate in reclaimed water distribution systems (RWDSs). The goal of this study was to evaluate interactive effects of reclaimed water treatments and water age on persistence and proliferation of multiple fecal (e.g., Klebsiella, Enterobacter) and non-fecal (e.g., Legionella, mycobacteria) gene markers in RWDSs. Six laboratory-scale RWDSs were operated in parallel receiving the influent with or without biologically active carbon (BAC) filtration + chlorination, chloramination, or no disinfectant residual. After 3 years of operation, the RWDSs were subject to sacrificial sampling and shotgun metagenomic sequencing. We developed an in-house metagenome-derived pathogen quantification pipeline, validated by quantitative polymerase chain reaction and mock community analysis, to estimate changes in abundance of ∼30 genera containing waterborne pathogens. Microbial community composition in the RWDS bulk water, biofilm, and sediments was clearly shaped by BAC filtration, disinfectant conditions, and water age. Key commonalities were noted in the ecological niches occupied by fecal pathogen markers in the RWDSs, while non-fecal pathogen markers were more varied in their distribution. BAC-filtration + chlorine was found to most effectively control the widest range of target genera. However, filtration alone or chlorine secondary disinfection alone resulted in proliferation of some of these genera containing waterborne pathogens.

Full Fatty Acid Amide Hydrolase Inhibition Combined with Partial Monoacylglycerol Lipase Inhibition: Augmented and Sustained Antinociceptive Effects with Reduced Cannabimimetic Side Effects in Mice.

Inhibition of fatty acid amide hydrolase (FAAH) or monoacylglycerol lipase (MAGL), the primary hydrolytic enzymes for the respective endocannabinoids N-arachidonoylethanolamine (AEA) and 2-arachidonylglycerol (2-AG), produces antinociception but with minimal cannabimimetic side effects. Although selective inhibitors of either enzyme often show partial efficacy in various nociceptive models, their combined blockade elicits augmented antinociceptive effects, but side effects emerge. Moreover, complete and prolonged MAGL blockade leads to cannabinoid receptor type 1 (CB1) receptor functional tolerance, which represents another challenge in this potential therapeutic strategy. Therefore, the present study tested whether full FAAH inhibition combined with partial MAGL inhibition would produce sustained antinociceptive effects with minimal cannabimimetic side effects. Accordingly, we tested a high dose of the FAAH inhibitor PF-3845 (N-​3-​pyridinyl-​4-​[[3-​[[5-​(trifluoromethyl)-​2-​pyridinyl]oxy]phenyl]methyl]-​1-​piperidinecarboxamide; 10 mg/kg) given in combination with a low dose of the MAGL inhibitor JZL184 [4-nitrophenyl 4-(dibenzo[d][1,3]dioxol-5-yl(hydroxy)methyl)piperidine-1-carboxylate] (4 mg/kg) in mouse models of inflammatory and neuropathic pain. This combination of inhibitors elicited profound increases in brain AEA levels (>10-fold) but only 2- to 3-fold increases in brain 2-AG levels. This combination produced significantly greater antinociceptive effects than single enzyme inhibition and did not elicit common cannabimimetic effects (e.g., catalepsy, hypomotility, hypothermia, and substitution for Δ(9)-tetrahydrocannabinol in the drug-discrimination assay), although these side effects emerged with high-dose JZL184 (i.e., 100 mg/kg). Finally, repeated administration of this combination did not lead to tolerance to its antiallodynic actions in the carrageenan assay or CB1 receptor functional tolerance. Thus, full FAAH inhibition combined with partial MAGL inhibition reduces neuropathic and inflammatory pain states with minimal cannabimimetic effects.

DBETH: a Database of Bacterial Exotoxins for Human.

Pathogenic bacteria produce protein toxins to survive in the hostile environments defined by the host's defense systems and immune response. Recent progresses in high-throughput genome sequencing and structure determination techniques have contributed to a better understanding of mechanisms of action of the bacterial toxins at the cellular and molecular levels leading to pathogenicity. It is fair to assume that with time more and more unknown toxins will emerge not only by the discovery of newer species but also due to the genetic rearrangement of existing bacterial genomes. Hence, it is crucial to organize a systematic compilation and subsequent analyses of the inherent features of known bacterial toxins. We developed a Database for Bacterial ExoToxins (DBETH, http://www.hpppi.iicb.res.in/btox/), which contains sequence, structure, interaction network and analytical results for 229 toxins categorized within 24 mechanistic and activity types from 26 bacterial genuses. The main objective of this database is to provide a comprehensive knowledgebase for human pathogenic bacterial toxins where various important sequence, structure and physico-chemical property based analyses are provided. Further, we have developed a prediction server attached to this database which aims to identify bacterial toxin like sequences either by establishing homology with known toxin sequences/domains or by classifying bacterial toxin specific features using a support vector based machine learning techniques.

How does external debt and governance quality impact renewable energy consumption: novel policy insights from BRICS countries.

Amidst global environmental reforms, the role of energy systems is under scrutiny to promote ecological welfare through low-carbon alternatives. Amongst the solutions, the role of renewable energy as a clean source has become popular to mitigate climate change. However, the impact of debt on renewable energy consumption remains limited in the economic literature. The debt initiatives provide funding for environmental initiatives primarily, while it is also credited as a barrier to limiting the growth of clean energy programs. Within such discussion, the current study extended the dialogue by examining how external debt impacts energy transition in Brazil, Russia, India, China, and South Africa (BRICS) economies in the presence of institutional quality, education expenditures, and banking development. Using the novel CS-ARDL, AMG, and CCEMG tests, the study results showed that external debt decreases renewable energy consumption, while institutional quality, educational expenditures, banking developments, and economic growth are essential elements of green energy developments. Based on these conclusions, this study provides novel policy guidelines to align BRICS energy and economic agendas.

Aß-oligomers: A potential therapeutic target for Alzheimer's disease.

The cascade of amyloid formation relates to multiple complex events at the molecular level. Previous research has established amyloid plaque deposition as the leading cause of Alzheimer's disease (AD) pathogenesis, detected mainly in aged population. The primary components of the plaques are two alloforms of amyloid-beta (Aß), Aß1-42 and Aß1-40 peptides. Recent studies have provided considerable evidence contrary to the previous claim indicating that amyloid-beta oligomers (AßOs) as the main culprit responsible for AD-associated neurotoxicity and pathogenesis. In this review, we have discussed the primary features of AßOs, such as assembly formation, the kinetics of oligomer formation, interactions with various membranes/membrane receptors, the origin of toxicity, and oligomer-specific detection methods. Recently, the discovery of rationally designed antibodies has opened a gateway for using synthesized peptides as a grafting component in the complementarity determining region (CDR) of antibodies. Thus, the Aß sequence motif or the complementary peptide sequence in the opposite strand of the ß-sheet (extracted from the Protein Data Bank: PDB) helps design oligomer-specific inhibitors. The microscopic event responsible for oligomer formation can be targeted, and thus prevention of the overall macroscopic behaviour of the aggregation or the associated toxicity can be achieved. We have carefully reviewed the oligomer formation kinetics and associated parameters. Besides, we have depicted a thorough understanding of how the synthesized peptide inhibitors can impede the early aggregates (oligomers), mature fibrils, monomers, or a mixture of the species. The oligomer-specific inhibitors (peptides or peptide fragments) lack in-depth chemical kinetics and optimization control-based screening. In the present review, we have proposed a hypothesis for effectively screening oligomer-specific inhibitors using the chemical kinetics (determining the kinetic parameters) and optimization control strategy (cost-dependent analysis). Further, it may be possible to implement the structure-kinetic-activity-relationship (SKAR) strategy instead of structure-activity-relationship (SAR) to improve the inhibitor's activity. The controlled optimization of the kinetic parameters and dose usage will be beneficial for narrowing the search window for the inhibitors.

Renewable energy, forest cover, export diversification, and ecological footprint: a machine learning application in moderating eco-innovations on agriculture in the BRICS-T economies.

The United Nations Climate Change Conference (COP26) recommended that the member nations enhance their technological progression and structural transformation to mitigate the problems of climate change. The BRICS-T countries consisting of Brazil, Russia, India, China, South Africa, and Turkey agreed to implement COP26's policy suggestions. These countries accounted for 40% of global greenhouse gas emissions in 2017, thus posing severe threats to the global environment. The current study explores the role of renewable energy, forest depletion, eco-innovations, and export diversification in impacting the ecological footprint for those BRICS-T countries. We further examine the moderating effect of eco-innovations on agriculture on the BRICS-T nations. The study contributes to the existing literature by providing newer empirical insights on how eco-innovations and export diversification, along with renewable energy, forest cover, and agriculture, affecting the ecological footprint in the BRICS-T nations. It utilizes novel empirical methods like parametric and non-parametric techniques to derive the short-run and long-run empirical results. The empirical findings based on the augmented mean group and the kernel regularized least square methods document that economic growth, agriculture value added, and forest depletion increase the ecological footprint. In contrast, renewable energy and eco-innovations decrease the level of ecological footprint. In the long run, a 1% rise in GDP leads to a rise in the ecological footprint by 0.64% using the augmented mean group (AMG) estimation. The mean marginal effects are - 0.27%, 0.29%, and 0.17% for renewable energy; agriculture and forest cover, respectively, using the kernel-based regularized least square methods. The study suggests that policies designed for controlling the ecological footprints focus on the use of energy efficient technologies, particularly in the agricultural sector.

Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis.

We provide the first empirical study on the role of panic and stress related to the COVID-19 pandemic, including six uncertainties and the four most traded cryptocurrencies, on three green bond market volatilities. Based on daily data covering the period from January 1, 2020 to January 31, 2022, we combine Diebold and Yilmaz's (2012, 2014) time domain spillover approach and Ando et al.'s (2022) quantile regression framework to investigate the time-frequency spillover connectedness among markets and measure the direction and intensity of the net transmission effect under extreme negative and positive event conditions, and normal states. We further provide novel insights into the green finance literature by examining sensitivity to quantile analysis of the net transfer mechanism between green bonds, cryptocurrencies, and pandemic uncertainty. Regarding the network connectedness analysis, the results reveal strong net information spillover transmission among markets under the bearish market. In extremely negative event circumstances, the MSCI Euro green bond acts as the leading net shock receiver in the system, whereas COVID-19 fake news appears as the largest net shock contributor, followed by BTC. According to sensitivity to quantile analysis, the net dynamic shock transfer mechanism is time-varying and quantile-dependent. Overall, our work uncovers crucial implications for investors and policymakers.