Source-oriented health risk assessment of groundwater nitrate by using EMMTE coupled with HHRA model.

Conventional concentration-oriented approaches for nitrate risk diagnosis only provide overall risk levels without identifying risk values of individual sources or sources accountable for potential health risks. Therefore, a hybrid model combining the end-member mixing model tool on Excel™ (EMMTE) with human health risk assessment (HHRA) was developed to assess the source-oriented health risks for groundwater nitrate, particularly in the Poyang Lake Plain (PLP) region. The results indicated that the EMMTE and the Bayesian stable isotope mixing model (MixSIAR) exhibited remarkable consistency in source apportionment of groundwater nitrate. The source contribution of groundwater nitrate in PLP was related to land use types, hydrogeological conditions, and soil properties. Notably, manure and sewage sources, contributing up to 53.4 %, represented the largest nitrate pollution sources, with a significant contribution of soil nitrogen and nitrogen fertilizers. The non-carcinogenic risk for four potential sources was below the acceptable threshold of 1. Given the factors including rainfall dilution and economic development, attention should be directed towards mitigating the health risks posed by manure and sewage. This study can verify the efficacy of EMMTE in source apportionment and offer valuable insights for decision-makers to regulate the largest sources of nitrate contamination and enhance groundwater management efficiency.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Learning verbs in English and Korean: The roles of word order and argument drop.

To learn new words, particularly verbs, child learners have been shown to benefit from the linguistic contexts in which the words appear. However, cross-linguistic differences affect how this process unfolds. One previous study found that children's abilities to learn a new verb differed across Korean and English as a function of the sentence in which the verb occurred (Arunachalam et al., 2013). The authors hypothesized that the properties of word order and argument drop, which vary systematically in these two languages, were driving the differences. In the current study, we pursued this finding to ask if the difference persists later in development, or if children acquiring different languages come to appear more similar as their linguistic knowledge and learning capacities increase. Preschool-aged monolingual English learners (N = 80) and monolingual Korean learners (N = 64) were presented with novel verbs in contexts that varied in word order and argument drop and accompanying visual stimuli. We assessed their learning by measuring accuracy in a forced-choice pointing task, and we measured eye gaze during the learning phase as an indicator of the processes by which they mapped the novel verbs to meaning. Unlike previous studies which identified differences between English and Korean learning 2-year-olds in a similar task, our results revealed similarities between the two language groups with these older preschoolers. We interpret our results as evidence that over the course of early childhood, children become adept at learning from a larger variety of contexts, such that differences between learners of different languages are attenuated.

Burdock miR8175 in diet improves insulin resistance induced by obesity in mice through food absorption.

The incidence of type 2 diabetes mellitus (T2DM) induced by obesity is rapidly increasing. Although there are many synthetic drugs for treating T2DM, they have various side effects. Here, we report that miR8175, a plant miRNA from burdock root, has effective antidiabetic activity. After administration of burdock decoction or synthetic miR8175 by gavage, both burdock decoction and miR8175 can significantly improve the impaired glucose metabolism of diabetic mice induced by a high-fat diet (HFD). Our results demonstrate that burdock decoction and miR8175 enhance the insulin sensitivity of the hepatic insulin signaling pathway by targeting Ptprf and Ptp1b, which may be the reason for the improvement in metabolism. This study provides a theoretical basis for the main active component and molecular mechanism of burdock to improve insulin resistance. And the study also suggests that plant miRNA may be an indispensable nutrient for maintaining human health.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater.

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Evaluating the risk of developing hyperuricemia in patients with type 2 diabetes mellitus using least absolute shrinkage and selection operator regression and machine learning algorithm.

Background: Hyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease. Methods: Patients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated. Results: A total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%. Conclusions: We developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.

A commercial humic acid inhibits benzo(a)pyrene biodegradation by Paracoccus aminovorans HPD-2.

Benzo(a)pyrene (BaP) is posing serious threats to soil ecosystems and its bioremediation usually limited by environmental factors and microbial activity. Humic acid (HA), a ubiquitous heterogeneous organic matter, which could affect the fate of environmental pollutants. However, the impact of HA on bioremediation of organic contamination remains controversial. In the present study, the biodegradation of BaP by Paracoccus aminovorans HPD-2 with and without HA was explored. Approximately 87.4 % of BaP was biodegraded in the HPD-2 treatment after 5 days of incubation, whereas the addition of HA dramatically reduced BaP biodegradation to 56.0 %. The limited BaP biodegradation in the HA + HPD-2 treatment was probably due to the decrease of BaP bioavailability which induced by the adsorption of HA with unspecific interactions. The excitation-emission matrix (EEM) of fluorescence characteristics showed that strain HPD-2 was responsible for the presence of protein-like substances and the microbial original humic substances in the HPD-2 treatment. Addition of HA would result in the increase of soluble microbial humic-like material, which should ascribe to the biodegradation of BaP and probably utilization of HA. Furthermore, both the growth and survival of strain HPD-2 were inhibited in the HA + HPD-2 treatment, because of the limited available carbon source (i.e. BaP) at the presence of HA. The expression of gene1789 and gene2589 dramatically decreased in the HA + HPD-2 treatment, and this should be responsible for the decrease of BaP biodegradation as well. This study reveals the mechanism that HA affect the BaP biodegradation, and the decrease of biodegradation should ascribe to the interaction of HA and bacterial strain. Thus, the bioremediation strategies of PAHs need to consider the effects of organic matter in environment.

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.

Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.

Electrooxidation of chlorophene and dichlorophen by reactive electrochemical membrane: Key determining factors of removal efficiency.

This study systematically investigated the variable main electrooxidation mechanism of chlorophene (CP) and dichlorophen (DCP) with the change of reaction conditions at Ti4O7 anode operated in batch and reactive electrochemical membrane (REM) modes. Significant degradation of CP and DCP was observed, that is, CP exhibited greater removal efficiency in batch mode at 0.5-3.5 mA cm-2 and REM operation (0.5 mA cm-2) with a permeate flow rate of 0.85 cm min-1 under the same reaction conditions, while DCP exhibited a faster degradation rate with the increase of current density in REM operation. Density functional theory (DFT) simulation and electrochemical performance tests indicated that the electrooxidation efficiency of CP and DCP in batch mode was primarily affected by the mass transfer rates. And the removal efficiency when anodic potentials were less than 1.7 V vs SHE in REM operation was determined by the activation energy for direct electron transfer (DET) reaction, however, the adsorption function of CP and DCP on the Ti4O7 anode became a dominant factor in determining the degradation efficiency with the further increase of anodic potential due to the disappeared activation barrier. In addition, the degradation pathways of CP and DCP were proposed according to intermediate products identification and frontier electron densities (FEDs) calculation, the acute toxicity of CP and DCP were also effectively decreased during both batch and REM operations.

Incorporating environmental capacity considerations to prioritize control factors for the management of heavy metals in soil.

Heavy metals (HMs) pollution threatens food security and human health. While previous studies have evaluated source-oriented health risk assessments, a comprehensive integration of environmental capacity risk assessments with pollution source analysis to prioritize control factors for soil contamination is still lacking. Herein, we collected 837 surface soil samples from agricultural land in the Nansha District of China in 2019. We developed an improved integrated assessment model to analyze the pollution sources, health risks, and environmental capacities of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. The model graded pollution source impact on environmental capacity risk to prioritize control measures for soil HMs. All HMs except Pb exceeded background values and were sourced primarily from natural, transportation, and industrial activities (31.26%). Approximately 98.92% (children), 97.87% (adult females), and 97.41% (adult males) of carcinogenic values exceeded the acceptable threshold of 1E-6. HM pollution was classified as medium capacity (3.41 kg/hm2) with mild risk (PI = 0.52). Mixed sources of natural backgrounds, transportation, and industrial sources were identified as priority sources, and As a priority element. These findings will help prioritize control factors for soil HMs and direct resources to the most critical pollutants and sources of contamination, particularly when resources are limited.

Prioritization of control factors for heavy metals in groundwater based on a source-oriented health risk assessment model.

Heavy metals (HMs) in groundwater seriously threaten ecological safety and human health. To facilitate the effective management of groundwater contamination, priority control factors of HMs in groundwater need to be categorized. A total of 86 groundwater samples were collected from the Huangpi district of Wuhan city, China, during the dry and wet seasons. To determine priority control factors, a source-oriented health risk assessment model was applied to compare the pollution sources and health risks of seven HMs (Cu, Pb, Zn, Cr, Ni, As, and Fe). The results showed that the groundwater had higher As and Fe contents. The sources of HM pollution during the wet period were mainly industrial and agricultural activities and natural sources. During the dry period, origins were more complex due to the addition of domestic discharges, such as sewage wastewater. Industrial activities (74.10% during the wet period), agricultural activities (53.84% during the dry period), and As were identified as the priority control factors for groundwater HMs. The results provide valuable insights for policymakers to coordinate targeted management of HM pollution in groundwater and reduce the cost of HM pollution mitigation.

Synthesis of uniformly dispersed Fe2TiO5 nanodisks: a sensitive photoelectrochemical sensor for glucose monitoring in human blood serum.

A novel photoelectrochemical (PEC) sensor was constructed, using Fe2TiO5 nanodisks under visible-light irradiation, for the determination of glucose in human blood serum. The uniformly dispersed Fe2TiO5 nanodisks were synthesized for the first time by an ion exchange method and subsequent heat treatment. As excellent catalysts, the Fe2TiO5 nanodisks can directly catalyze the oxidation of glucose to produce current in the absence of glucose oxidase. Compared with commercial TiO2, the Fe2TiO5 nanodisks exhibit better activity in the electrocatalytic oxidation of glucose and can generate a photocurrent as a signal for glucose detection. The PEC sensor shows a wide linear range (4 µM-10 mM), a low limit of detection (0.588 µM) and a super sensitivity of 2653 µA mM-1 cm-2, which are much better than similar configurations reported previously. This PEC sensor has been successfully used to monitor glucose in human blood serum. Moreover, this PEC glucose sensor based on Fe2TiO5 nanodisks possesses great potential for application in point-of-care clinical diagnosis.

Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting.

Background: Acute kidney injury (AKI) is a common and important complication in patients with gastrointestinal bleeding who are admitted to the intensive care unit. The present study proposes an artificial intelligence solution for acute kidney injury prediction in patients with gastrointestinal bleeding admitted to the intensive care unit. Methods: Data were collected from the eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The prediction model was developed using the extreme gradient boosting (XGBoost) model. The area under the receiver operating characteristic curve, accuracy, precision, area under the precision-recall curve (AUC-PR), and F1 score were used to evaluate the predictive performance of each model. Results: Logistic regression, XGBoost, and XGBoost with severity scores were used to predict acute kidney injury risk using all features. The XGBoost-based acute kidney injury predictive models including XGBoost and XGBoost+severity scores model showed greater accuracy, recall, precision AUC, AUC-PR, and F1 score compared to logistic regression. Conclusion: The XGBoost model obtained better risk prediction for acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit than the traditional logistic regression model, suggesting that machine learning (ML) techniques have the potential to improve the development and validation of predictive models in patients with gastrointestinal bleeding admitted to the intensive care unit.

Assessment of groundwater hydrochemistry, water quality, and health risk in Hainan Island, China.

Groundwater is an important source of water for human sustenance. The determination of groundwater quality at island sites is an urgent priority in China, but there are lacking systematic reports relating to them. Here, 63 groups of groundwater samples were collected and analyzed of Hainan Island. The groundwater in the study area is weakly alkaline, mainly comprising hard and soft freshwater. The predominant anions and cations are HCO3-, and Ca2+ and Na+, respectively, and the main water chemistry types are HCO3-Cl-Na and HCO3-Cl-Na-Ca. The chemical evolution of groundwater is mainly affected by water-rock interactions, cation exchange, and human activity. The groundwater is mostly of high quality and, in most areas, is suitable for drinking and irrigation. Contrastingly, the water quality in the west of the island is relatively poor. The spatial distribution of the risk coefficient (HQ) is consistent with the spatial variation in the NO3- concentrations in the groundwater. Notably, there are unacceptable health risks for different groups of people, with infants having the greatest level of impact, followed by children, teenagers, and adults. This study provides a valuable reference for the development and utilization of groundwater resources, as well as the improvement of aquatic ecological conditions on Hainan Island and other island areas worldwide.

Rolling circle transcription amplification-directed construction of tandem spinach-based fluorescent light-up biosensor for label-free sensing of ß-glucosyltransferase activity.

ß-glucosyltransferase (ß-GT) can specifically catalyze the conversion of 5-hydroxymethylcytosine (5-hmC) to 5-glucosylhydroxy methylcytosine (5-ghmC), and it is associated with the control of phage-specific gene expression by affecting transcription process in vivo and in vitro. The current strategies for ß-GT assay usually involve expensive equipment, laborious treatment, radioactive hazard, and poor sensitivity. Here, we report a Spinach-based fluorescent light-up biosensor for label-free measurement of ß-GT activity by utilizing 5-hmC glucosylation-initiated rolling circle transcription amplification (RCTA). We design a 5-hmC-modified multifunctional circular detection probe (5-hmC-MCDP) that integrates the functions of target-recognition, signal transduction, and transcription amplification in one probe. The introduction of ß-GT catalyzes 5-hmC glucosylation of 5-hmC-MCDP probe, protecting the glucosylated 5-mC-MCDP probe from the cleavage by MspI. The remaining 5-hmC-MCDP probe can initiate RCTA reaction with the aid of T7 RNA polymerase, generating tandem Spinach RNA aptamers. The tandem Spinach RNA aptamers can be lightened up by fluorophore 3,5-difluoro-4-hydroxybenzylidene imidazolinone, facilitating label-free measurement of ß-GT activity. Notably, the high specificity of MspI-catalyzed cleavage of nonglucosylated probe can efficiently inhibit nonspecific amplification, endowing this assay with a low background. Due to the higher efficiency of RCTA than the canonical promoter-initiated RNA synthesis, the signal-to-noise ratio of RCTA is 4.6-fold higher than that of linear template-based transcription amplification. This method is capable of sensitively detecting ß-GT activity with a limit of detection of 2.03 × 10-5 U/mL, and it can be used for the screening of inhibitors and determination of kinetic parameters, with great potential in epigenetic research and drug discovery.

Combined effects of microplastics and cadmium on the soil-plant system: Phytotoxicity, Cd accumulation and microbial activity.

Microplastics (MPs), an emerging pollutant of concern, widely cooccurred with heavy metals in soil, however, little is known about the combined effects of the interactions of MPs and cadmium (Cd) on the soil-plant system. In this study, the combined effects of several types of MPs and soil Cd contamination on Brassica juncea growth, Cd uptake, and soil microbial carbon metabolism were investigated in a 50-day pot experiment. Aged polyethylene (PE), aged polypropylene (PP), biodegradable polybutylene adipate terephthalate (PBAT) and polylactic acid (PLA) displayed moderate phytotoxicity, with reductions in leaf chlorophyll content and shoot biomass. Compared with the control treatment without MPs or B. juncea, B. juncea growth significantly increased the soil pH by 0.3 pH units, and the growth of B. juncea in the presence of biodegradable PBAT or PLA MPs increased the soil pH by an additional 0.4 or 0.6 pH units, respectively. The presence of PBAT or PLA MPs greatly reduced soil diethylenetriamine pentaacetic acid (DTPA)-extractable Cd concentrations and plant Cd accumulation. The Cd bioconcentration factor was higher in roots than shoots in all treatments except the treatment containing PBAT MPs. The average well color development (AWCD), an indicator of metabolic activity, was highest in the treatment with B. juncea alone and was reduced by both biodegradable and conventional MPs. The microbial utilization efficiency of esters and alcohols was enhanced in the treatment with PBAT MPs, whereas carboxylic acids were preferentially utilized in the treatment with PLA MPs. These findings indicate that co-exposure to MPs and Cd may alter soil microenvironmental characteristics such as soil pH, leading to changes in Cd bioavailability, plant growth and Cd accumulation, and the microbial community's capacity to metabolize carbon. These effects of MPs in soil warrant further exploration.

A polyaniline functionalized NiFeP nanosheet array-based electrochemical immunosensor using Au/Cu2O nanocubes as a signal amplifier for the detection of SARS-CoV-2 nucleocapsid protein.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged as a novel pathogen in 2019. The virus is responsible for a severe acute respiratory syndrome outbreak, affecting the respiratory system of infected individuals. COVID-19 is a super amplifier of basic diseases, and the disease with basic diseases is often more serious. Controlling the spread of the COVID-19 pandemic relies heavily on the timely and accurate detection of the virus. To resolve the problem, a polyaniline functionalized NiFeP nanosheet array-based electrochemical immunosensor using Au/Cu2O nanocubes as a signal amplifier is fabricated for the detection of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP). Polyaniline (PANI) functionalized NiFeP nanosheet arrays are synthesized as an ideal sensing platform for the first time. PANI is coated on the surface of NiFeP by electropolymerization to enhance biocompatibility, beneficial for the efficient loading of the capture antibody (Ab1). Significantly, Au/Cu2O nanocubes possess excellent peroxidase-like activity and exhibit outstanding catalytic activity for the reduction of H2O2. Therefore, Au/Cu2O nanocubes combine with a labeled antibody (Ab2) through the Au-N bond to form labeled probes, which can effectively amplify current signals. Under optimal conditions, the immunosensor for the detection of SARS-CoV-2 NP shows a wide linear range of 10 fg mL-1-20 ng mL-1 and a low detection limit of 1.12 fg mL-1 (S/N = 3). It also exhibits desirable selectivity, repeatability, and stability. Meanwhile, the excellent analytical performance in human serum samples confirms the practicality of the PANI functionalized NiFeP nanosheet array-based immunosensor. The electrochemical immunosensor based on the Au/Cu2O nanocubes as a signal amplifier demonstrates great potential for application in the personalized point-of-care (POC) clinical diagnosis.

Priority control factors for heavy metal groundwater contamination in peninsula regions based on source-oriented health risk assessment.

Peninsula regions in China face serious environmental issues, such as heavy metal (HM) groundwater contamination. However, attempts to investigate the pollution sources and health risks of HM contamination in such regions require considerable resources and costs. Moreover, the priority control factors for groundwater HMs remain unclear. In this study, absolute principal component score/multiple linear regression (APCS/MLR) was used to quantify the groundwater pollution sources of typical peninsular areas in China, and a health risk assessment (HRA) was performed to assess the health risks caused by different sources. The results showed that the concentrations of Mn and Fe were higher than those of other HMs, and HM pollution was high in shallow groundwater. The dominant source of HMs in groundwater was agricultural activities (31.12 %), followed by natural sources (26.33 %), industrial activities (22.47 %), and atmospheric deposition (20.09 %). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were high. Agricultural sources, atmospheric deposition sources, and Cr and As were identified as the priority control factors for HM groundwater contamination. This study has implications for improving the control of groundwater HM contamination in peninsula regions and ensuring sustainable groundwater development.

High-throughput Synthesis of Solution-Processable van der Waals Heterostructures through Electrochemistry.

Two-dimensional van der Waals heterostructures (2D vdWHs) have recently gained widespread attention because of their abundant and exotic properties, which open up many new possibilities for next-generation nanoelectronics. However, practical applications remain challenging due to the lack of high-throughput techniques for fabricating high-quality vdWHs. Here, we demonstrate a general electrochemical strategy to prepare solution-processable high-quality vdWHs, in which electrostatic forces drive the stacking of electrochemically exfoliated individual assemblies with intact structures and clean interfaces into vdWHs with strong interlayer interactions. Thanks to the excellent combination of strong light absorption, interfacial charge transfer, and decent charge transport properties in individual layers, thin-film photodetectors based on graphene/In2 Se3 vdWHs exhibit great promise for near-infrared (NIR) photodetection, owing to a high responsivity (267 mA W-1 ), fast rise (72 ms) and decay (426 ms) times under NIR illumination. This approach enables various hybrid systems, including graphene/In2 Se3 , graphene/MoS2 and graphene/MoSe2 vdWHs, providing a broad avenue for exploring emerging electronic, photonic, and exotic quantum phenomena.

Correlation Between Ontological Insecurity and Daily Epidemic Prevention Behavior Among Inpatients During the COVID-19 Pandemic.

This study evaluates inpatients' ontological insecurity and daily epidemic prevention behavior during the pandemic and explores the factors influencing daily epidemic prevention behaviors. The outbreak of coronavirus disease (COVID-19) in December 2019 caused a global public health crisis that has affected the very structure of society and the order of daily life. Ontological security is the ability to predict the impact of changes in social environments on personal security, such as during the pandemic. A cross-sectional study was used to collect data from 1185 inpatients of a hospital in Zhejiang, China, from July 11 to August 9, 2021. Our questionnaire recorded information on demographics, ontological insecurity, and daily epidemic prevention behaviors. The Mann-Whitney U test, Kruskal-Wallis H test, Spearman's correlation analysis, and logistic regression analysis were used to determine the influencing factors of daily epidemic prevention behavior on ontological security. Results showed a negative correlation between inpatients' ontological insecurity and daily epidemic prevention behavior (r = -.253, P < .001). The logistic regression analysis showed that the independent factors affecting daily epidemic prevention behavior include ontological insecurity (OR: 0.952; 95% CI: 0.937-0.968) (P < .001), sex (OR: 1.292; 95% CI: 1.004-1.663), age (OR: 0.880; 95%: 0.790-0.980), education (OR: 1.307; 95% CI: 1.098-1.556), and occupation [famers vs civil servants, staff or professional (OR: 0.596; 95% CI: 0.374-0.949),other versus civil servants, staff, or professional (OR: 0.693; 95% CI: 0.503-0.953)] (P < .05). Inpatients were shown to have good ontological security during the COVID-19 epidemic, younger patients, female patients, patients with stronger ontological security, patients with a higher educational level, and those who work in a fixed unit or organization showed higher levels of daily epidemic prevention behavior. Hospital managers should strengthen the intervention management of epidemic prevention behavior based on patient characteristics.