Influence of green technology, tourism, and inclusive financial development on ecological sustainability: exploring the path toward green revolution

This study demonstrates the linkages between green technological innovations, sustainable tourism, financial development, economic growth, and ecological sustainability using China’s regional data from 2000 to 2019. The study applies the novel estimation technique, Quantile Autoregressive Distributive Lag (QARDL) approach to examine long-run and short-run relationships between the stated variables. The initial findings confirm non-linearity in the data verified through J-B test statistics. It approves the implication of QARDL estimation for exploring ecological sustainability trends over the study period. The study outcomes confirm that tourism and green technology innovation assists in reducing ecological footprints in China in the long run. Moreover, financial development and economic growth reflect a direct role towards more ecological footprints; therefore, the sustainability dimension has been missing both in financial development and growth. Furthermore, the results in the short run cover the same phenomenon and confirm that ecological innovations and tourism would help in sustaining the natural environment. The study outcomes demonstrate that government officials in China should specifically implement long-term policies to support the natural environment from adverse shocks of more financial development and economic growth

CircTRHDE knockdown protects WI-38 cells against LPS-induced inflammatory injury

Background Circular RNAs (circRNAs) have been reported to be involved in the progression of infantile pneumonia. Here, we investigated the function of circTRHDE in lipopolysaccharide (LPS)-induced cell inflammatory injury to evaluate its role in infantile pneumonia progression. Methods The circTRHDE, microRNA (miR)-381-3p and TNF-receptor associated factor 3 (TRAF3) expression were detected by quantitative real-time PCR. LPS-induced WI-38 cells were used to construct an inflammatory injury model. Cell viability, inflammation and apoptosis were measured by cell counting kit assay, ELISA assay and flow cytometry. Caspase3 activity, MDA level and SOD activity were analysed to assess cell apoptosis and oxidative stress. Protein levels were determined using western blot analysis. The interaction between miR-381-3p and circTRHDE or TRAF3 was confirmed by dual-luciferase activity assay and RNA pull-down assay. Results CircTRHDE had increased expression in infantile pneumonia patients and LPS-induced WI-38 cells. LPS treatment inhibited WI-38 cell viability while promoting inflammation, apoptosis and oxidative stress. However, knockdown of circTRHDE remitted LPS-triggered WI-38 cell injury. CircTRHDE could sponge miR-381-3p to positively regulate TRAF3 expression. MiR-381-3p suppressed LPS-induced WI-38 cell inflammatory injury, and this effect was revoked by TRAF3 overexpression. Also, LPS-induced WI-38 cell inflammatory injury restrained by circTRHDE knockdown also were reversed by miR-318-3p inhibitor or TRAF3 overexpression. Conclusion Our findings demonstrated that circTRHDE might be a target for infantile pneumonia treatment, which relieved LPS-induced cell inflammatory injury by the regulation of the miR-318-3p/TRAF3 axis

Mechanisms of Action of the Antimicrobial Peptide Cecropin in the Killing of <i>Candida albicans</i>

The development of drug resistance has caused fungal infections to become a global health concern. Antimicrobial peptides (AMPs) offer a viable solution to these pathogens due to their resistance to drug resistance and their diverse mechanisms of actions, which include direct killing and immunomodulatory properties. The peptide Cecropin, which is expressed by genetically engineered bacteria, has antifungal effects on Candida albicans. The minimal inhibitory concentration (MIC) and the minimal fungicidal concentration (MFC) of Candida albicans were 0.9 μg/mL and 1.8 μg/mL, respectively, detected by the micro-broth dilution method. According to the killing kinetics, the MFC of Cecropin could kill Candida albicans in 40 min. The electron microscope indicated that Cecropin could cause the cell wall to become rough and nicked, eventually killing Candida albicans. The effects of Cecropin on the cell membrane of treated C. albicans, using the 1,6-diphenyl-1,3,5-hexatriene and propidium iodide protocol, showed that they could change the permeability and fluidity, destroy it, and lead to cell necrosis. In addition, Cecropin can also induce cells to produce excessive reactive oxygen species, causing changes in the mitochondrial membrane potential. Therefore, this study provides a certain theoretical basis for the antifungal infection of new antifungal agents

A Machine-Learning Approach Based on Attention Mechanism for Significant Wave Height Forecasting

Significant wave height (SWH) is a key parameter for monitoring the state of waves. Accurate and long-term SWH forecasting is significant to maritime shipping and coastal engineering. This study proposes a transformer model based on an attention mechanism to achieve the forecasting of SWHs. The transformer model can capture the contextual information and dependencies between sequences and achieves continuous time series forecasting. Wave scale classification is carried out according to the forecasting results, and the results are compared with gated recurrent unit (GRU) and long short-term memory (LSTM) machine-learning models and the key laboratory of MArine Science and NUmerical Modeling (MASNUM) numerical wave model. The results show that the machine-learning models outperform the MASNUM within 72 h, with the transformer being the best model. For continuous 12 h, 24 h, 36 h, 48 h, 72 h, and 96 h forecasting, the average mean absolute errors (MAEs) of the test sets were, respectively, 0.139 m, 0.186 m, 0.223 m, 0.254 m, 0.302 m, and 0.329 m, and the wave scale classification accuracies were, respectively, 91.1%, 99.4%, 86%, 83.3%, 78.9%, and 77.5%. The experimental results validate that the transformer model can achieve continuous and accurate SWH forecasting, as well as accurate wave scale classification and early warning of waves, providing technical support for wave monitoring

Modelling the socio-economic impacts of coal-fired electricity phaseout under carbon neutrality target:A dynamic CGE-based study of China

Given its significant carbon emissions, the phaseout of coal-fired electricity emerges as a crucial strategy in achieving the goal of carbon neutrality, which will fundamentally reshape China's social-economic landscape. In this study, a dynamic computable general equilibrium (CGE) model was constructed to evaluate the economic and social ramifications of phasing out coal-fired electricity, encompassing diverse pathway scenarios. The results suggest that the phaseout of coal-fired electricity will significantly influence the socio-economic system compared to the Business as Usual (BAU) scenario. Different phaseout scenarios are projected to yield a cumulative decrease in carbon dioxide emissions ranging from 36.38 to 60.15 Gt between 2020 and 2060. Consequently, there will be a cumulative GDP ranging from 22.22 to 36.29 trillion yuan, accompanied by a cumulative decline in employment affecting 23.68 to 29.92 million individuals. The anticipated trends in annual GDP and job losses are expected to exhibit a U-shaped curve, initially increasing, and then decreasing from 2020 to 2060 compared to the BAU scenario. A distinct trade-off is anticipated to arise between the potential for carbon reduction and the resultant losses in jobs and economic output. The more optimistic scenario, characterized by a lower peak value, an earlier peak time, and a later phaseout time, is projected to result in a relatively lower socioeconomic impact per unit of CO2 emissions reduction. These findings provide policymakers with vital insights, informing the development of strategies for a gradual transition away from coal-fired electricity while minimizing socio-economic repercussions

MicroRNA-146a Contributes to Abnormal Activation of the Type I Interferon Pathway in Human Lupus by Targeting the Key Signaling Proteins

Objective. MicroRNA have recently been identified as regulators that modulate target gene expression and are involved in shaping the immune response. This study was undertaken to investigate the contribution of microRNA-146a (miR-146a), which was identified in the pilot expression profiling step, to the pathogenesis of systemic lupus erythematosus (SLE). Methods. TaqMan microRNA assays of peripheral blood leukocytes were used for comparison of expression levels of microRNA between SLE patients and controls. Transfection and stimulation of cultured cells were conducted to determine the biologic function of miR-146a. Bioinformatics prediction and validation by reporter gene assay and Western blotting were performed to identify miR-146a targets. Results. Profiling of 156 miRNA in SLE patients revealed the differential expression of multiple microRNA, including miR-146a, a negative regulator of innate immunity. Further analysis showed that underexpression of miR-146a negatively correlated with clinical disease activity and with interferon (IFN) scores in patients with SLE. Of note, overexpression of miR-146a reduced, while inhibition of endogenous miR-146a increased, the induction of type I IFNs in peripheral blood mononuclear cells (PBMCs). Furthermore, miR-146a directly repressed the transactivation downstream of type I IFN. At the molecular level, miR-146a could target IFN regulatory factor 5 and STAT-1. More importantly, introduction of miR-146a into the patients' PBMCs alleviated the coordinate activation of the type I IFN pathway. Conclusion. The microRNA miR-146a is a negative regulator of the IFN pathway. Underexpression of miR-146a contributes to alterations in the type I IFN pathway in lupus patients by targeting the key signaling proteins. The findings provide potential novel strategies for therapeutic interventio