Mercury abatement in the environment: Insights from industrial emissions and fates in the environment.

Mercury's neurotoxic effects have prompted the development of advanced control and remediation methods to meet stringent measures for industries with high-mercury feedstocks. Industries with significant Hg emissions, including artisanal and small-scale gold mining (ASGM)-789.2 Mg year-1, coal combustion-564.1 Mg year-1, waste combustion-316.1 Mg year-1, cement production-224.5 Mg year-1, and non-ferrous metals smelting-204.1 Mg year-1, use oxidants and adsorbents capture Hg from waste streams. Oxidizing agents such as O3, Cl2, HCl, CaBr2, CaCl2, and NH4Cl oxidize Hg0 to Hg2+ for easier adsorption. To functionalize adsorbents, carbonaceous ones use S, SO2, and Na2S, metal-based adsorbents use dimercaprol, and polymer-based adsorbents are grafted with acrylonitrile and hydroxylamine hydrochloride. Adsorption capacities span 0.2-85.6 mg g-1 for carbonaceous, 0.5-14.8 mg g-1 for metal-based, and 168.1-1216 mg g-1 for polymer-based adsorbents. Assessing Hg contamination in soils and sediments uses bioindicators and stable isotopes. Remediation approaches include heat treatment, chemical stabilization and immobilization, and phytoremediation techniques when contamination exceeds thresholds. Achieving a substantially Hg-free ecosystem remains a formidable challenge, chiefly due to the ASGM industry, policy gaps, and Hg persistence. Nevertheless, improvements in adsorbent technologies hold potential.

A GITRL-mTORC1-GM-CSF positive loop promotes pathogenic Th17 response in exacerbating Primary Sjögren's Syndrome.

OBJECTIVES: Glucocorticoid-induced Tumor-Necrosis-Factor-Receptor Family-related Protein (GITR), with its ligand (GITRL), plays an important role in CD4+T-cell-mediated autoimmunity. This study aimed to investigate the underlying mechanisms of GITRL in primary Sjögren's Syndrome (pSS). METHODS: pSS patients and healthy controls were recruited. Serum GITRL and Th17-related cytokines were determinated. RNA-Sequencing was performed to decipher key signal pathways. Non Obese Diabetes (NOD) mice was adopted as experimental Sjögren models and recombinant adeno-associated virus (rAAV) transduction was conducted to verify the therapeutic potentials of targeting GITRL in vivo. RESULTS: Serum GITRL was significantly higher in pSS patients and showed a positive correlation with leukopenia, thrombocytopenia, autoantibodies, lung involvement and disease activity. Serum GITRL was correlated with Th17-related cytokines. GITRL promoted expansion of Th17 and Th17.1 cells. Expansion of granulocyte-macrophage-colony-stimulating-factor (GM-CSF+)CD4+T cells induced by GITRL could be inhibited by blockade of GITRL. Moreover, GM-CSF could stimulate GITRL expression on monocytes. RNA-Sequencing revealed mammalian target of rapamycin complexes 1 (mTORC1) might be the key modulator. The increased phosphorylation of S6 and STAT3 and expansion of Th17 and Th17.1 cells induced by GITRL were effectively inhibited by rapamycin, suggesting a GITRL/mTORC1/GM-CSF positive loop in pathogenic Th17 response in pSS. Administration of rAAV vector expressing shRNA targeting GITRL alleviated disease progression in NOD mice. CONCLUSIONS: Our results identified the pathogenic role of GITRL in exacerbating disease activity and promoting pathogenic Th17 response in pSS through a GITRL/mTORC1/GM-CSF positive loop. These findings suggest GITRL might be a promising therapeutic target in the treatment of pSS.

Transport mechanism of presynaptic high-affinity choline uptake by CHT1.

Choline is a vital nutrient and a precursor for the biosynthesis of essential metabolites, including acetylcholine (ACh), that play a central role in fetal development, especially in the brain. In cholinergic neurons, the high-affinity choline transporter (CHT1) provides an extraordinarily efficient reuptake mechanism to reutilize choline derived from intrasynaptical ACh hydrolysis and maintain ACh synthesis in the presynapse. Here, we determined structures of human CHT1 in three discrete states: the outward-facing state bound with the competitive inhibitor hemicholinium-3 (HC-3); the inward-facing occluded state bound with the substrate choline; and the inward-facing apo open state. Our structures and functional characterizations elucidate how the inhibitor and substrate are recognized. Moreover, our findings shed light on conformational changes when transitioning from an outward-facing to an inward-facing state and establish a framework for understanding the transport cycle, which relies on the stabilization of the outward-facing state by a short intracellular helix, IH1.

Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.

BACKGROUND: Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC. METHODS: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC). RESULTS: In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients. CONCLUSIONS: This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression.

Background: Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses. Methods: Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes. Results: The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified. Conclusion: This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

The role of MrUbp4, a deubiquitinase, in conidial yield, thermotolerance, and virulence in Metarhizium robertsii.

Ubiquitin-specific proteases (UBPs), the largest subfamily of deubiquitinating enzymes, regulate ubiquitin homeostasis and play diverse roles in eukaryotes. Ubp4 is essential for the growth, development, and pathogenicity of various fungal pathogens. However, its functions in the growth, stress responses, and virulence of entomopathogenic fungi remain unclear. In this study, we elucidated the role of the homolog of Ubp4, MrUbp4, in the entomopathogenic fungus Metarhizium robertsii. Deletion of MrUbp4 led to a notable increase in ubiquitination levels, demonstrating the involvement of MrUbp4 in protein deubiquitination. Furthermore, the ΔMrUbp4 mutant displayed a significant reduction in conidial yield, underscoring the pivotal role of MrUbp4 in conidiation. Additionally, the mutant exhibited heightened resistance to conidial heat treatment, emphasizing the role of MrUbp4 in thermotolerance. Notably, insect bioassays unveiled a substantial impairment in the virulence of the ΔMrUbp4 mutant. This was accompanied by a notable decrease in cuticle penetration ability and appressorium formation upon further analysis. In summary, our findings highlight the essential role of MrUbp4 in regulating the conidial yield, thermotolerance, and contributions to the virulence of M. robertsii.

Deoxynivalenol and T-2 toxin cause liver damage and egg quality degradation through endoplasmic reticulum stress in summer laying hens.

The present study aimed to find whether low doses of mixed mycotoxins would affect egg quality in laying hens, and to explore the oxidative stress induced liver damage through endoplasmic reticulum during summer stress. A total of 96 Jinghong laying hens, 36 wks of age, were divided into four treatments, with eight repetitions per treatment and three hens per repetition. All the hens were raised in summer (average temperature: 31.3 ± 0.5℃; average humidity: 85.5 ± 0.2%) for 28d. One treatment was fed a basal diet as control (CON), and the other three treatments were fed the same diets containing 3.0 mg/kg deoxynivalenol (DON), 0.5 mg/kg T-2 toxin (T-2), and 1.5 mg/kg DON + 0.25 mg/kg T-2 toxin (Mix). Albumen height and Haugh unit were decreased (P < 0.05) in the Mix group on day 14 and 28. The activity of total antioxidant capacity, glutathione peroxidase, catalase, and superoxide dismutase were decreased (P < 0.05) in the DON, T-2, and Mix groups. The alkaline phosphatase level in DON, T-2, and Mix groups was significantly increased (P < 0.05). The level of interleukin-1ß, interferon-γ, and tumor necrosis factor-α in the Mix group were higher (P < 0.05) than CON, DON, and T-2 groups. Mix group upregulated the mRNA expressions of protein kinase RNA-like ER kinase, activating transcription factor4, IL-1ß, nuclear factor-κ-gene binding, and nuclear respiratory factor 2 in the liver (P < 0.05). The results showed that low doses of DON and T-2 toxin could cause oxidative stress in the liver, but DON and T-2 toxin have a cumulative effect on virulence, which can reduce egg quality and cause endoplasmic reticulum stress in the liver.

Proteomics-derived biomarker panel facilitates distinguishing primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) from lung metastatic colorectal cancer (lmCRC).

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Pollution control performance of solidified nickel-cobalt tailings on site: Bioavailability of heavy metals and microbial response.

There has been increasing attention given to nickel-cobalt tailings (NCT), which pose a risk of heavy metal pollution in the field. In this study, on site tests and sampling analysis were conducted to assess the physical and chemical characteristics, heavy metal toxicity, and microbial diversity of the original NCT, solidified NCT, and the surrounding soil. The research results show that the potential heavy metal pollution species in NCT are mainly Ni, Co, Mn, and Cu. Simultaneous solidification and passivation of heavy metals in NCT were achieved, resulting in a reduction in biological toxicity and a fivefold increase in seed germination rate. The compressive strength of the original tailings was increased by 20 times after solidification. The microbial diversity test showed that the abundance of microbial community in the original NCT was low and the population was monotonous. This study demonstrates, for the first time, that the use of NCT for solidification in ponds can effectively solidification of heavy metals, reduce biological toxicity, and promote microorganism diversity in mining areas (tended to the microbial ecosystem in the surrounding soil). Indeed, this study provides a new perspective for the environmental remediation of metal tailings.

Fractional Excretion of Urate is Positively Associated with Type 2 Diabetes in HUA Patients: A Cross-Sectional Study.

Purpose: People with hyperuricemia (HUA) are often related to metabolic disorders such as diabetes, metabolic syndrome (MetS), and obesity. However, the correlation between excretion of uric acid and these diseases is unclear. Our study aimed to explore the relationship between uric acid excretion and type 2 diabetes (T2D). Methods: A total of 228 HUA patients from Tianjin Medical University General Hospital from 2022 to 2023 were included in this study. We collected demographic, biochemical, and anthropometric data on each subject. Urine uric acid excretion (UUAE) was calculated enzymatically from a single urine collection that lasted 24 hours. And fractional excretion of uric acid (FEUA) was calculated from serum uric acid and creatinine and uric acid and creatinine. Binary logistic regression modeling assessed the association between uric acid excretion and T2D. Results: Of the 228 subjects, 13.4% had T2D and 48.7% had obesity. The obesity group had a lower FEUA (p<0.05) and a higher UUAE compared to the control group (p<0.05). And FEUA had a stronger correlation with the risk of T2D (p<0.001). Also, there was a negative association between BMI and FEUA and a positive link between BMI and UUAE in the outpatients. Conclusion: Increased FEUA levels were significantly associated with T2D in HUA patients. Therefore, routine calculating of FEUA is essential for proper diagnosis and appropriate treatment T2D of in HUA patients.

Development and validation of a nomogram to predict the risk of residual low back pain after tubular microdiskectomy of lumbar disk herniation.

OBJECTIVE: Tubular microdiskectomy (tMD) is one of the most commonly used for treating lumbar disk herniation. However, there still patients still complain of persistent postoperative residual low back pain (rLBP) postoperatively. This study attempts to develop a nomogram to predict the risk of rLBP after tMD. METHODS: The patients were divided into non-rLBP (LBP VAS score < 2) and rLBP (LBP VAS score ≥ 2) group. The correlation between rLBP and these factors were analyzed by multivariate logistic analysis. Then, a nomogram prediction model of rLBP was developed based on the risk factors screened by multivariate analysis. The samples in the model are randomly divided into training and validation sets in a 7:3 ratio. The Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diskrimination, calibration and clinical value of the model, respectively. RESULTS: A total of 14.3% (47/329) of patients have persistent rLBP. The multivariate analysis suggests that higher preoperative LBP visual analog scale (VAS) score, lower facet orientation (FO), grade 2-3 facet joint degeneration (FJD) and moderate-severe multifidus fat atrophy (MFA) are risk factors for postoperative rLBP. In the training and validation sets, the ROC curves, calibration curves, and DCAs suggested the good diskrimination, predictive accuracy between the predicted probability and actual probability, and clinical value of the model, respectively. CONCLUSION: This nomogram including preoperative LBP VAS score, FO, FJD and MFA can serve a promising prediction model, which will provide a reference for clinicians to predict the rLBP after tMD.

Massive seasonal high-altitude migrations of nocturnal insects above the agricultural plains of East China.

Long-distance migrations of insects contribute to ecosystem functioning but also have important economic impacts when the migrants are pests or provide ecosystem services. We combined radar monitoring, aerial sampling, and searchlight trapping, to quantify the annual pattern of nocturnal insect migration above the densely populated agricultural lands of East China. A total of ~9.3 trillion nocturnal insect migrants (15,000 t of biomass), predominantly Lepidoptera, Hemiptera, and Diptera, including many crop pests and disease vectors, fly at heights up to 1 km above this 600 km-wide region every year. Larger migrants (>10 mg) exhibited seasonal reversal of movement directions, comprising northward expansion during spring and summer, followed by southward movements during fall. This north-south transfer was not balanced, however, with southward movement in fall 0.66× that of northward movement in spring and summer. Spring and summer migrations were strongest when the wind had a northward component, while in fall, stronger movements occurred on winds that allowed movement with a southward component; heading directions of larger insects were generally close to the track direction. These findings indicate adaptations leading to movement in seasonally favorable directions. We compare our results from China with similar studies in Europe and North America and conclude that ecological patterns and behavioral adaptations are similar across the Northern Hemisphere. The predominance of pests among these nocturnal migrants has severe implications for food security and grower prosperity throughout this heavily populated region, and knowledge of their migrations is potentially valuable for forecasting pest impacts and planning timely management actions.

Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis.

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.

Dual roles of α1,4-galactosyltransferase 1 in spermatogenesis of Drosophila melanogaster.

Spermatogenesis is critical for insect reproduction and the process is regulated by multiple genes. Glycosyltransferases have been shown to participate in the development of Drosophila melanogaster; however, their role in spermatogenesis is still unclear. In this study, we found that α1,4-galactosyltransferase 1 (α4GT1) was expressed at a significantly higher level in the testis than in the ovary of Drosophila. Importantly, the hatching rate was significantly decreased when α4GT1 RNA interference (RNAi) males were crossed with w1118 females, with only a few mature sperm being present in the seminal vesicle of α4GT1 RNAi flies. Immunofluorescence staining further revealed that the individualization complex (IC) in the testes from α4GT1 RNAi flies was scattered and did not move synchronically, compared with the clustered IC observed in the control flies. Terminal deoxyribonucleotide transferase (TdT)-mediated dUTP nick end labeling (TUNEL) assay showed that apoptosis signals in the sperm bundles of α4GT1 RNAi flies were significantly increased. Moreover, the expression of several individualization-related genes, such as Shrub, Obp44a and Hanabi, was significantly decreased, whereas the expression of several apoptosis-related genes, including Dronc and Drice, was significantly increased in the testes of α4GT1 RNAi flies. Together, these results suggest that α4GT1 may play dual roles in Drosophila spermatogenesis by regulating the sperm individualization process and maintaining the survival of sperm bundles.

Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis.

OBJECTIVES: This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status. METHODS: A total of 290 patients (mean age, 58.21 ± 9.28) diagnosed with brain (BM, n = 150) or spinal bone metastasis (SM, n = 140) from primary NSCLC were enrolled as a primary cohort. An external validation cohort, consisting of 69 patients (mean age, 59.87 ± 7.23; BM, n = 36; SM, n = 33), was enrolled from another center. Thoracic computed tomography-based features were extracted from the primary tumor and peritumoral area and selected using the least absolute shrinkage and selection operator regression to build a radiomic signature (RS-primary). Contrast-enhanced magnetic resonance imaging-based features were calculated and selected from the BM and SM to build RS-BM and RS-SM, respectively. The RS-BM-Com and RS-SM-Com were developed by integrating the most important features from the primary tumor, BM, and SM. RESULTS: Six computed tomography-based features showed high association with EGFR mutation status: 3 from intratumoral and 3 from peritumoral areas. By combination of features from primary tumor and metastases, the developed RS-BM-Com and RS-SM-Com performed well with areas under curve in the training (RS-BM-Com vs RS-BM, 0.936 vs 0.885, P = 0.177; RS-SM-Com vs RS-SM, 0.929 vs 0.843, P = 0.003), internal validation (RS-BM-Com vs RS-BM, 0.920 vs 0.858, P = 0.492; RS-SM-Com vs RS-SM, 0.896 vs 0.859, P = 0.379), and external validation (RS-BM-Com vs RS-BM, 0.882 vs 0.805, P = 0.263; RS-SM-Com vs RS-SM, 0.865 vs 0.816, P = 0.312) cohorts. CONCLUSIONS: This study indicates that the accuracy of detecting EGFR mutations significantly enhanced in the presence of metastases in primary NSCLC. The established radiomic signatures from this approach may be useful as new predictors for patients with distant metastases.

Contribution of Nutritional, Lifestyle, and Metabolic Risk Factors to Parkinson's Disease.

BACKGROUND: Modifiable risk factors for Parkinson's disease (PD) are poorly known. OBJECTIVES: The aim is to evaluate independent associations of different nutritional components, physical activity, and sedentary behavior and metabolic factors with the risk of PD. METHODS: In this population-based prospective cohort study using the data of the United Kingdom Biobank (from 2006-2010), 502,017 men and women who were free from PD (International Classification of Diseases 10th edition; "G20") at baseline were included. We implemented a Cox proportion hazard's model to evaluate the associations of different levels of physical activity, sitting time, sleep habits, diet quality, alcohol and coffee consumption, smoking, and body mass index with PD risk, adjusting for several confounding variables. RESULTS: During a median follow-up of 12.8 years, lifestyle factors including vigorous physical activity (hazard ration [HR] = 0.84; 95% confidence interval [CI], 0.75-0.94), low-to-moderate sitting time (HR = 0.89; 95% CI, 0.81-0.97), and high sleep quality (HR = 0.89; 95% CI, 0.80-0.99) were associated with a reduced risk of PD. Small amounts of coffee (HR = 0.88; 95% CI, 0.82-0.95), red meat (HR = 0.86; 95% CI, 0.76-0.97), and current smoking (HR = 0.65; 95% CI, 0.56-0.75) were also associated with a lower risk of PD, whereas alcohol intake (HR = 1.29; 95% CI, 1.06-1.56) with higher PD risk. Secondary analysis, including metabolic risk factors, confirmed these findings and highlighted the potential protective effect of plasma vitamin D and uric acid, but of low-density lipoprotein-cholesterol, triglycerides, and C-reactive protein as well. CONCLUSIONS: Vigorous physical activity, reduced sitting time, good sleep quality together with small coffee intake and vitamin D supplementation are potentially neuroprotective lifestyle interventions for the prevention of PD. © 2024 International Parkinson and Movement Disorder Society.

Chronic exposure to ambient air pollution and the risk of non-alcoholic fatty liver disease: A cross-sectional study in Taiwan and Hong Kong.

BACKGROUND: Information on the relation of air pollution with non-alcoholic fatty liver disease (NAFLD) is scarce. We thus conducted a large cross-sectional study in Asia to investigate the role of air pollution in NAFLD. METHODS: We recruited 329,048 adults (mean age: 41.0 years) without other liver disease (hepatitis and cirrhosis) or excessive alcohol consumption in Taiwan and Hong Kong from 2001 to 2018. The concentrations of nitrogen dioxide (NO2) and ozone (O3) were estimated using a space-time regression model, and the concentrations of fine particulate matter (PM2.5) was evaluated using a satellite-based spatio-temporal model. NAFLD was determined using either the fatty liver index (FLI) or the hepatic steatosis index (HSI). The NAFLD-related advanced fibrosis was defined according to BARD score or the fibrosis-4 (FIB-4). A logistic regression model was adopted to explore the relationships of ambient air pollution with the odds of NAFLD and NAFLD-related advanced fibrosis. RESULTS: We found positive relationships between PM2.5 and the odds of NAFLD and advanced fibrosis, with every standard deviation (SD, 7.5 µg/m3) increases in PM2.5 exposure being associated with a 10% (95% confidence interval [CI]: 9%-11%) increment in the prevalence of NAFLD and an 8% (95% CI: 7%-9%) increment in the prevalence of advanced fibrosis. Similarly, the prevalence of NAFLD and advanced fibrosis increased by 8% (95% CI: 7%-9%) and 7% (95% CI: 6%-8%) with per SD (18.9 µg/m3) increasement in NO2 concentration, respectively. Additionally, for every SD (9.9 µg/m3) increasement in O3 concentration, the prevalence of NAFLD and advanced fibrosis decreased by 12% (95% CI: 11%-13%) and 11% (95% CI: 9%-12%), respectively. CONCLUSION: Higher ambient PM2.5 and NO2 are linked with higher odds of NAFLD and advanced fibrosis. Our findings indicate that reducing PM2.5 and NO2 concentrations may be an effective way for preventing NAFLD. Further studies on O3 are warranted.

The Phase Control of Transition Metallic Elements via Facile Chemical and Physical Syntheses.

The crystal phases of metals are important factors to tune the properties of metals, and therefore received extensive attention. Traditionally, phase control is performed within limited numbers of elements by harsh conditions, such as face-centered cubic Fe by high temperature. This review summarizes most reports in metal phase control area, including elements of Fe, Co, Ni, Cu, Ru, Pd, Rh, Os and Au. For every metallic element, the facile phase control methods are systematically introduced, such as epitaxial growth, ball milling, chemical reduction, etc. Their corresponding applications and the mechanisms for phase control are thoroughly discussed.

New energy vehicles' technology innovation coordination strategy based on alliance negotiation under dual credit policy.

The development of new energy vehicles (NEVs) is one of the effective ways to alleviate carbon emissions, environmental pollution, and energy scarcity in the transportation sector. The Chinese government has innovatively proposed the "dual credit policy," but it is still a hot topic whether it can promote the NEVs' technological innovation. In this study, we construct game models and obtain the technological innovation strategies for NEVs under the dual credit policy, considering that the NEV supply chain contains one manufacturer and N suppliers. Further, we construct bargaining game models and study how to encourage manufacturers and suppliers to enhance technological innovation, realize supply chain coordination, and give the alliance strategy to maximize suppliers' profit. We found that the dual credit policy can effectively stimulate technological innovation, and the higher the credit price or technological innovation credit factor, the higher the technical level of NEVs. The findings could guide the government to adjust and revise the policy. Second, we found that the bargaining games could coordinate the NEV supply chain so that decentralized enterprises can achieve optimal technological innovation under centralized decision-making. Third, we found that suppliers can improve their profits by choosing a suitable alliance strategy under the manufacturer's different negotiating power.