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

A sensitive electrochemical sensor based on polypyrrole/electrochemically reduced graphene oxide for the determination of imidacloprid

The glassy carbon electrode (GCE) was modified by electrochemically reduced graphene oxide (ERGO) and polypyrrole (PPy) prepared by simple cyclic voltammetry (CV) electropoly­merization. The PPy/ERGO modified electrode (PPy/ERGO/GCE) was used as a platform of electrochemical sensor to detect imidacloprid (IMI) insecticide. CV and differential pulse voltammetry (DPV) were chosen as the methods to investigate of the electrochemical behavior of IMI on PPy/ERGO/GCE surface. Scanning electron microscopy (SEM) and Raman spectra were utilized to describe the morphology and structure of the modified electrode. Experimental parameters were optimized, such as the number of polymerization cycles, scan rate and the pH value of electrolyte. Under the optimized conditions, when the concentration of IMI was in the range of 1-10 μM and 10-60 μM, the increase of reduction peak current was linear with the concentration of IMI, and the low detection limit was found to be 0.18 μM (S/N = 3). Results showed that PPy/ERGO/GCE demonstrated satisfactory reproducibility and stability, and has great potential in actual sample testing

Extrapolating Large Language Models to Non-English by Aligning Languages

Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often stronger than on tasks of other languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages. We start from targeting individual languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i.e. tuning it with translation task data and cross-lingual general task data to obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws to investigate the advantages of using scalable translation data. Then we perform multilingual instruction-tuning (MuIT) with mixed resources to build multilingual m-LLaMA. We also illustrate how we leverage the scaling laws to optimize data allocation in a resource-constrained setting. Experiment results on cross-lingual benchmarks XQUAD and MLQA show that x-LLaMAs surpass the English instruction-tuned counterpart (Alpaca) by an average of 27.83% across six non-English languages. Evaluation results on translation dataset Flores-101 show that x-LLaMAs outperform previous LLaMA-based models by an average of 18.89%. Encouragingly, m-LLaMA achieves comparable performance to x-LLaMAs on individual languages and demonstrates the ability to follow multilingual instructions. Further analysis on response content and representation space reveals the alignment of the multilingual semantic space within the middle layers of m-LLaMA

Green synthesis of biogenetic Te(0) nanoparticles by high tellurite tolerance fungus Mortierella sp. AB1 with antibacterial activity

Tellurite [Te(IV)] is a high-toxicity metalloid. In this study, a fungus with high Te(IV) resistance was isolated. Strain AB1 could efficiently reduce highly toxic Te(IV) to less toxic Te(0). The reduced products formed rod-shaped biogenetic Te(0) nanoparticles (Bio-TeNPs) intracellularly. Further TEM-element mapping, FTIR, and XPS analysis showed that the extracted Bio-TeNPs ranged from 100 to 500 nm and consisted of Te(0), proteins, lipids, aromatic compounds, and carbohydrates. Moreover, Bio-TeNPs exhibited excellent antibacterial ability against Shigella dysenteriae, Escherichia coli, Enterobacter sakazakii, and Salmonella typhimurium according to inhibition zone tests. Further growth and live/dead staining experiments showed that E. coli and S. typhimurium were significantly inhibited by Bio-TeNPs, and cells were broken or shriveled after treatment with Bio-TeNPs based on SEM observation. Additionally, the antioxidant and cytotoxicity tests showed that the Bio-TeNPs exhibited excellent antioxidant capacity with no cytotoxicity. All these results suggested that strain AB1 showed great potential in bioremediation and Bio-TeNPs were excellent antibacterial nanomaterials with no cytotoxicity.Peer reviewe

Evaluation of multiple voxel-based morphometry approaches and applications in the analysis of white matter changes in temporal lobe epilepsy

Abstract. The purpose of this study was to compare multiple voxel-based morphometry (VBM) approaches and analyze the whole-brain white matter (WM) changes in the unilateral temporal lobe epilepsy (TLE) patients relative to controls. In our study, the performance of the VBM approaches, including standard VBM, optimized VBM and VBM-DARTEL, was evaluated via a simulation, and then these VBM approaches were applied to the real data obtained from the TLE patients and controls. The results from simulation show that VBM-DARTEL performs the best among these VBM approaches. For the real data, WM reductions were found in the ipsilateral temporal lobe, the contralateral frontal and occipital lobes, the bilateral parietal lobes, cingulated gyrus, parahippocampal gyrus and brainstem of the left-TLE patients by VBM-DARTEL, which is consistent with previous studies. Our study demonstrated that DARTEL was the most robust and reliable approach for VBM analysis

Skeletal Muscle Regeneration on Protein-Grafted and Microchannel-Patterned Scaffold for Hypopharyngeal Tissue Engineering

In the field of tissue engineering, polymeric materials with high biocompatibility like polylactic acid and polyglycolic acid have been widely used for fabricating living constructs. For hypopharynx tissue engineering, skeletal muscle is one important functional part of the whole organ, which assembles the unidirectionally aligned myotubes. In this study, a polyurethane (PU) scaffold with microchannel patterns was used to provide aligning guidance for the seeded human myoblasts. Due to the low hydrophilicity of PU, the scaffold was grafted with silk fibroin (PU-SF) or gelatin (PU-Gel) to improve its cell adhesion properties. Scaffolds were observed to degrade slowly over time, and their mechanical properties and hydrophilicities were improved through the surface grafting. Also, the myoblasts seeded on PU-SF had the higher proliferative rate and better differentiation compared with those on the control or PU-Gel. Our results demonstrate that polyurethane scaffolds seeded with myoblasts hold promise to guide hypopharynx muscle regeneration

Risk factors for community-acquired pneumonia among inpatients with mental disorders in a tertiary general hospital

IntroductionCommunity-acquired pneumonia (CAP) is an important cause of hospitalization and death in patients with mental disorders. It is critical to understand the risk factors of CAP and determine prevention strategies to reduce CAP. The aim of this study is to explore the characteristics of inpatients with mental disorders who have CAP and analyze the risk factors.MethodsThis retrospective study included 16,934 inpatients with mental disorders who were admitted for the first time to a tertiary general hospital between January 2017 and July 2021 (excluding January 2020–May 2020). Risk factors for CAP were identified by logistic regression analysis after propensity score matching (PSM, 1:4) for age, gender, and BMI.ResultsThe CAP rate of inpatients with mental disorders was 1.78%. Inpatients who had CAP had a significantly prolonged hospital stay, and were more often admitted to a closed ward or the ICU. After PSM, the multivariable analysis revealed that clozapine use (OR = 3.212, 95% CI = 1.744–5.915, P < 0.001), schizophrenia spectrum disorder (OR = 2.785, 95% CI = 1.684–4.607, P < 0.001), alcohol consumption (OR = 2.549, 95% CI = 1.586–4.096, P < 0.001), cardiovascular disease (OR = 2.299, 95% CI = 1.362–3.879, P = 0.002), Charlson comorbidity index (CCI) ≥ 3 (OR = 2.092, 95% CI = 1.342–3.260, P = 0.001), organic mental disorder (OR = 1.941, 95% CI = 1.194–3.156, P = 0.007), antipsychotic drug use (OR = 1.886, 95% CI = 1.312–2.711, P = 0.001), unmarried status (OR = 1.720, 95% CI = 1.164–2.541, P = 0.006) and junior high school education (OR = 1.591, 95%CI = 1.010–2.508, P = 0.045) were independent risk factors for CAP in inpatients with mental disorders.ConclusionCAP was common in inpatients with mental disorders. Patients with mental disorders have unique risk factors for CAP. Further research is required to explore the relationship and mechanism between different mental disorders, antipsychotic drugs and CAP