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Abstract A bimetallic heterostructure has been shown effective to enhance the multi-carbon (C2+) product selectivity in CO2 electroreduction. Clarifying the interfacial structure under electrolysis and its decisive role in the pathway selection are crucial, yet challenging. Here, we conceive a well-defined Ag-Cu biphasic heterostructure to understand the interfacial structure-steered product selectivity: The Cu-rich interface prefers ethylene, while the dominant product switch to alcohols with an increasing Ag fraction, and finally to CO as Ag occupying the main surface. We unravel a *CO intermediate-regulated interfacial restructuring, and observe abundant of Cu atoms migrating onto the neighboring Ag surface under a locally high *CO concentration. The evolving structure alters the oxyphilic characteristic at the interface, which profoundly determines the hydrogenation energetics of CO2 and ultimately, the dominant C2+ product. This work explicitly links the evolving interfacial structure with distinct C2+ pathway, formulating design guidelines for bimetallic electrocatalysts with selectively enhanced C2+ yields.

Exposure to environmental chemicals is prevalent. While previous studies reported associations between multiple chemical exposures and metabolic syndrome (MetS), they did not comprehensively account for correlations among exposures. We used machine learning methods including Boruta algorithm and least absolute shrinkage and selection operator (LASSO) regression, combined with weighted quartiles sum (WQS) regression to investigate the associations of phenols, polycyclic aromatic hydrocarbons (PAHs), metals, and phthalates with MetS and its components. Data were drawn from the 2005–2012 National Health and Nutrition Examination Survey (NHANES). The mean (standard deviation (SD)) age of 2596 participants was 48.4 (17.9) years. After adjusting for age, sex, body mass index, race/ethnicity, marital status, education, poverty income ratio, physical activity, smoking, and alcohol, higher 2-Phenanthrene (2-PHE) and mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) concentrations were associated with a higher odds of the MetS (odds ratio (OR) = 4.26, 95 % confidence interval (CI) 2.40–7.58 per ng/mL, and 3.24, 1.75–6.02 per ng/L, respectively). WQS index for environmental chemicals was positively associated with the MetS (OR = 1.31, 95 % CI 1.09–1.57). Moreover, we observed consistent and stronger positive associations with MetS (OR = 1.54, 95 % CI 1.04–2.30) in current smokers. Exposure to phenols, PAHs, metals, and phthalates was positively associated with an increase in metabolic syndrome and its components, which was more pronounced in current smokers.

Abstract Rational design and construction of oxygen reduction reaction (ORR) electrocatalysts with high activity, good stability, and low price are essential for the practical applications of renewable energy conversion devices, such as metal‐air batteries. Electronic modification through constructing metal/semiconductor Schottky heterointerface represents a powerful strategy to enhance the electrochemical performance. Herein, we demonstrate a concept of Schottky electrocatalyst composed of uniform Co nanoparticles in situ anchored on the carbon nanotubes aligned on the carbon nanosheets (denoted as Co@N‐CNTs/NSs hereafter) toward ORR. Both experimental findings and theoretical simulation testify that the rectifying contact could impel the voluntary electron flow from Co to N‐CNTs/NSs and create an internal electric field, thereby boosting the electron transfer rate and improving the intrinsic activity. As a consequence, the Co@N‐CNTs/NSs deliver outstanding ORR activity, impressive long‐term durability, excellent methanol tolerance, and good performance as the air‐cathode in the Zn‐air batteries. The design concept of Schottky contact may provide the innovational inspirations for the synthesis of advanced catalysts in sustainable energy conversion fields.

As alternatives of perfluorooctanoic acid (PFOA), hexafluoropropylene oxide dimeric acid (HFPO-DA) and trimeric acid (HFPO-TA) have been detected increasingly in environmental media and even humans. They have been shown to exhibit reproductive toxicity to model species, but their effects on human remain unclear due to the knowledge gap in their mode of action. Herein, (anti-)androgenic effects of the two HFPOs and PFOA were investigated and underlying toxicological mechanism was explored by combining zebrafish test, cell assay and molecular docking simulation. Exposure of juvenile zebrafish to the chemicals during sex differentiation promoted feminization, with HFPO-TA acting at an environmental concentration of 1 μg/L. The chemicals inhibited proliferation of human prostate cells and transcriptional activity of human and zebrafish androgen receptors (AR), with HFPO-TA displaying the strongest potency. Molecular docking revealed that the chemicals bind to AR in a conformation similar to a known AR antagonist. Combined in vivo, in vitro and in silico results demonstrated that the chemicals disrupted sex differentiation likely by antagonizing AR-mediated pathways, and provided more evidence that HFPO-TA is not a safe alternative to PFOA.

Abstract Ground‐dwelling invertebrates are vital for soil biodiversity and function maintenance. Contemporary biodiversity assessment necessitates novel and automatic monitoring methods because of the threat of sharp reductions in soil biodiversity in farmlands worldwide. Using infrared camera traps (ICTs) is an effective method for assessing richness and abundance of ground‐dwelling invertebrates. However, the influence that the shooting area of ICTs has on the diversity of ground‐dwelling invertebrates has not been strongly considered during survey design. In this study, data from six ICTs with two shooting areas (A1, 38.48 cm2; A2, 400 cm2) were used to investigate ground‐dwelling invertebrates in a farm in a city on the Eastern Coast of China from 20: 00 on July 31 to 00:00 on September 29, 2022. Over the course of 59 days and 1420 h, invertebrates within 9 taxa, 2447 individuals, and 112,909 ind./m2 were observed from 222,912 images. Our results show that ICTs with relatively large shooting areas recorded relatively high taxonomic richness and abundance of total ground‐dwelling invertebrates, relatively high abundance of the dominant taxon, and relatively high daily and hourly abundance of most taxa. The shooting areas of ICTs significantly affected the recorded taxonomic richness and abundance of ground‐dwelling invertebrates throughout the experimental period and at fine temporal resolutions. Overall, these results suggest that the shooting areas of ICTs should be considered when designing experiments, and ICTs with relatively large shooting areas are more favorable for monitoring the diversity of ground‐dwelling invertebrates. This study further provides an automatic tool and high‐quality data for biodiversity monitoring and protection in farmlands.

In the future, the frequency, duration, and intensity of extreme weather may increase, thus posing a threat to soil biodiversity in farmlands, particularly in agricultural production bases. However, little is known about the effect of cold wave events on the soil fauna community compared with other extreme weathers. Laboratory experiments fail to capture the complicated field environment of cold wave events. We investigated soil fauna communities in facility farmland (strawberry) and open farmland (green cabbage) during a cold wave event in Ningbo City, southeastern China. The results showed that the taxonomic richness of the total soil fauna community in facility farmland was significantly lower than that in open farmland, but the difference in abundance was not significant. The taxonomic richness and body size of soil mites and collembolan communities in facility farmland were significantly lower than those in the open farmland. Obvious differences in abundance, mean body length, mean body width, and ratio of body width to body length of Scheloribatidae, Galumnidae, Onychiuridae, Entomobryidae, and Enchytraeidae were detected between facility farmland and open farmland. The results of this study showed that the taxonomic richness, abundance, and body size of the soil fauna community in open farmland were significantly higher than those in facility farmland during a cold wave event. We suggested that the open farmland could be a “refuge” and “hot spot” of soil fauna community during the cold wave events.

The great success of deep learning in the field of computer vision provides a development opportunity for intelligent information extraction of remote sensing images. In the field of agriculture, a large number of deep convolutional neural networks have been applied to crop spatial distribution recognition. In this paper, crop mapping is defined as a semantic segmentation problem, and a multi-scale feature fusion semantic segmentation model MSSNet is proposed for crop recognition, aiming at the key problem that multi-scale neural networks can learn multiple features under different sensitivity fields to improve classification accuracy and fine-grained image classification. Firstly, the network uses multi-branch asymmetric convolution and dilated convolution. Each branch concatenates conventional convolution with convolution nuclei of different sizes with dilated convolution with different expansion coefficients. Then, the features extracted from each branch are spliced to achieve multi-scale feature fusion. Finally, a skip connection is used to combine low-level features from the shallow network with abstract features from the deep network to further enrich the semantic information. In the experiment of crop classification using Sentinel-2 remote sensing image, it was found that the method made full use of spectral and spatial characteristics of crop, achieved good recognition effect. The output crop classification mapping was better in plot segmentation and edge characterization of ground objects. This study can provide a good reference for high-precision crop mapping and field plot extraction, and at the same time, avoid excessive data acquisition and processing.

We evaluated the hourly dynamics of ground-dwelling invertebrate communities in farmland using infrared camera traps between August and September 2022. No significant variations within 24 h nor between any two time points of each day were observed in the taxonomic richness and abundance of the entire community. However, the periods from 4:00 to 7:00 and 13:00 to 20:00 showed relatively high taxonomic richness, while those from 2:00 to 6:00 and 16:00 to 21:00 showed relatively high abundance. Millipede abundance varied significantly in a 24 h period, with higher abundance from 3:00 to 4:00 and 1:00 to 2:00. Additionally, slug, beetle, and grasshopper abundances were significantly higher from 22:00 to 23:00, 17:00 to 18:00, and 23:00 to 24:00, respectively. The abundance of other taxa did not show significant variations between any two time points of a day. Predominant generalist predators showed positive correlation in their activity times. These results suggest that significant variations within each 24 h period are uncommon at either community or taxa (except for millipedes) levels in farmland ground-dwelling invertebrates. Further, while most taxa had significantly preferred active hours, the total community did not. Therefore, hourly dynamics should be considered to understand biodiversity maintenance.

Abstract Rational design of low‐cost and high‐efficiency non‐precious metal‐based catalysts toward the hydrogen evolution reaction (HER) is of paramount significance for the sustainable development of the hydrogen economy. Interfacial manipulation‐induced electronic modulation represents a sophisticated strategy to enhance the intrinsic activity of a non‐precious electrocatalyst. Herein, we demonstrate the straightforward construction of Co/MoS2 hetero‐nanoparticles anchored on inverse opal‐structured N,S‐doped carbon hollow nanospheres with an ordered macroporous framework (denoted as Co/MoS2@N,S–CHNSs hereafter) via a template‐assisted method. Systematic experimental evidence and theoretical calculations reveal that the formation of Co/MoS2 heterojunctions can effectively modulate the electronic configuration of active sites and optimize the reaction pathways, remarkably boosting the intrinsic activity. Moreover, the inverse opal‐structured carbon substrate with an ordered porous framework is favorable to enlarge the accessible surface area and provide multidimensional mass transport channels, dramatically expediting the reaction kinetics. Thanks to the compositional synergy and structural superiority, the fabricated Co/MoS2@N,S–CHNSs exhibit excellent HER activity with a low overpotential of 105 mV to afford a current density of 10 mA/cm2. The rationale of interface manipulation and architectural design herein is anticipated to be inspirable for the future development of efficient and earth‐abundant electrocatalysts for a variety of energy conversion systems.

Using remote sensing images to identify crop plots and estimate crop planting area is an important part of agricultural remote sensing monitoring. High-resolution remote sensing images can provide rich information regarding texture, tone, shape, and spectrum of ground objects. With the advancement of sensor and information technologies, it is now possible to categorize crops with pinpoint accuracy. This study defines crop mapping as a semantic segmentation problem; therefore, a deep learning method is proposed to identify the distribution of corn and soybean using the differences in the spatial and spectral features of crops. The study area is located in the southwest of the Great Lakes in the United States, where corn and soybean cultivation is concentrated. The proposed attention mechanism deep learning model, A2SegNet, was trained and evaluated using three years of Sentinel-2 data, collected between 2019 and 2021. The experimental results show that this method is able to fully extract the spatial and spectral characteristics of crops, and its classification effect is significantly better than that of the baseline method, and it has better classification performance than other deep learning models. We cross verified the trained model on the test sets of different years through transfer learning in both spatiotemporal and spatial dimensions. Proving the effectiveness of the attention mechanism in the process of knowledge transfer, A2SegNet showed better adaptability.

Mapping the crop type can provide a basis for extracting information on crop planting structure, and area and yield estimation. Obtaining large-scale crop-type mapping by field investigation is inefficient and expensive. Traditional classification methods have low classification accuracy due to the fragmentation and heterogeneity of crop planting. However, the deep learning algorithm has a strong feature extraction ability and can effectively identify and classify crop types. This study uses GF-1 high-resolution remote sensing images as the data source for the Shuangcheng district, Harbin city, Heilongjiang Province, China. Two spectral feature data sets are constructed through field sampling and employed for training and verification, combined with basic survey data of grain production functional areas at the plot scale. Traditional machine learning algorithms, such as random forest (RF) and support vector machine (SVM), and a popular deep learning algorithm, convolution neural network have been utilized. The results show that the fusion of multi-spectral information and vegetation index features helps improve classification accuracy. The deep learning algorithm is superior to the machine learning algorithm in both classification accuracy and classification effect. The highest classification accuracy of Crop Segmentation Network (CSNet) based on fine-tuning Resnet-50 is 91.2%, kappa coefficient is 0.882, and mean intersection over union is 0.834. The classification accuracy is 13.3% and 9.5% points higher than RF and SVM, respectively, and the best classification performance is obtained. The classification accuracy and execution efficiency of the model are suitable for a wide range of crop classification tasks and exhibit good transferability.

Abstract Heterogeneous nanocomposites comprising chemically distinct constituents are particularly promising in electrocatalysis. We herein report a synthetic strategy that combines the reduction of Pt and Co ionic precursors at an appropriate ratio with the subsequent phosphating at an elevated temperature for forming heterogeneous nanocomposites consisting of quasi‐spherical Pt3Co alloy domains and rod‐like CoP2 domains for high‐efficiency methanol electro‐oxidation. The strong electronic coupling between Pt3Co and CoP2 domains in the nanocomposites render the electron density around Pt atoms to decrease, which is favorable for reducing the adsorption of poisoning CO‐like intermediates on the catalyst surfaces. Accordingly, the as‐prepared heterogeneous Pt3Co–CoP2 nanocomposites show good performance for methanol electro‐oxidation both in acidic and alkaline media. In specific, at a Pt loading of only 6.4% on a common carbon substrate, the mass‐based activity of Pt3Co–CoP2 nanocomposites in an acidic medium is about 2 and 1.5 times as high as that of commercial Pt/C catalyst (20% mass loading) and home‐made Pt3Co alloy nanoparticles (8.0% mass loading), while in the alkaline medium, these values are 3 and 2, respectively.

High-precision land cover maps of remote sensing images based on an intelligent extraction method are an important research field for many scholars. In recent years, deep learning represented by convolutional neural networks has been introduced into the field of land cover remote sensing mapping. In view of the problem that a convolution operation is good at extracting local features but has limitations in modeling long-distance dependence relationships, a semantic segmentation network, DE-UNet, with a dual encoder is proposed in this paper. The Swin Transformer and convolutional neural network are used to design the hybrid architecture. The Swin Transformer pays attention to multi-scale global features and learns local features through the convolutional neural network. Integrated features take into account both global and local context information. In the experiment, remote sensing images from UAVs were used to test three deep learning models including DE-UNet. DE-UNet achieved the highest classification accuracy, and the average overall accuracy was 0.28% and 4.81% higher than UNet and UNet++, respectively. It shows that the introduction of a Transformer enhances the model fitting ability.

Several models have been employed in the past to estimate interfacial thermal conductance (ITC) for different material interfaces, of which the diffuse mismatch model (DMM) has been generally accepted as reliable for rough material interfaces at high temperature. Even though the DMM has been shown to predict the correct order of magnitude in isotropic material interfaces, it is unable to reproduce the same accuracy for low-dimensional anisotropic layered materials, which have many potential applications. Furthermore, the use of approximated dispersion curves tends to overestimate the ITC. In this work, we propose a new method that utilizes a mode-to-mode comparison within the DMM framework to predict ITC. We employed this model to calculate ITC between layered materials such as MoS2 and graphite and metals such as Al, Au, and Cr. We then compared our values with previous literature data that employ linear dispersion relations and experimental data from time-domain thermoreflectance measurements. This new framework was then used to visualize the phonon focusing effect in anisotropic materials. Further analysis revealed that counting only the three acoustic modes and neglecting the low-frequency optical modes lead to significant underestimation of the ITC using DMM. Our findings indicate that it is imperative to use the exact full phonon dispersion relations in evaluating the ITC for low-dimensional layered materials.

We theoretically investigate the enhancement of thermoelectric cooling performance in thermoelectric refrigerators made of materials with inhomogeneous thermal conductivity, beyond the usual practice of enhancing thermoelectric figure of merit (ZT) of materials. The dissipation of the Joule heat in such thermoelectric refrigerators is asymmetric which can give rise to better thermoelectric cooling performance. Although the thermoelectric figure of merit and the coefficient-of-performance are slightly enhanced, both the maximum cooling power and the maximum cooling temperature difference can be enhanced significantly. This finding can be used to increase the heat absorption at the cold end. We further find that the asymmetric dissipation of Joule heat leads to thermal rectification.

Abstract Objectives The purpose of our study is to further understanding of the depression symptoms of HIV/AIDS patients in Guilin, Guangxi via exploring whether there is a mediating effect of sleep quality on medical-social support and depression symptoms and therefore provide a theoretical basis for application of medical-social support to alleviate depression symptoms of HIV/AIDS patients. Methods A convenience sampling method was used to select 200 HIV/AIDS patients for the study. Depression symptoms, sleep quality, and medical-social support of the study participants were investigated using The Center for Epidemiological Studies Depression Scale (CES-D), The Pittsburg Sleep Quality Index (PSQI), and The Medical Outcomes Study Social Support Survey (MOS-SSS), respectively. Predictors of depression symptoms were explored by multiple linear regression, and Pearson correlation was used to analyze the relationship between sleep quality, medical-social support, and depression symptoms. Mediating effect analysis was performed by nonparametric Bootstrap test. Results In this study, the incidence of depression symptoms was 54.4%. Multiple linear regression analysis showed that leanness (β = 0.161, P = 0.008), obesity (β = 0.186, P = 0.002), sleep quality score > 7 (β = 0.331, P 56 (β = -0.247, P

Perfluorobutanesulfonic acid (PFBS), a short-chain alternative to perfluorooctanesulfonic acid (PFOS), is widely used in various products and is increasingly present in environmental media and human bodies. Recent epidemiological findings have raised concerns about its potential adverse health effects, although the specific toxic mechanism remains unclear. This study aimed to investigate the metabolic toxicity of gestational PFBS exposure in maternal rats. Pregnant Sprague Dawley (SD) rats were randomly assigned to three groups and administered either 3% starch gel (control), 5, or 50 mg/kg bw·d PFBS. Oral glucose tolerance tests (OGTT) and lipid profiles were measured, and integrated omics analysis (transcriptomics and non-targeted metabolomics) was employed to identify changes in genes and metabolites and their relationships with metabolic phenotypes. The results revealed that rats exposed to 50 mg/kg bw·d PFBS exhibited a significant decrease in 1-h glucose levels and the area under the curve (AUC) of OGTT compared with the starch group. Transcriptomics analysis indicated significant alterations in gene expression related to cytochrome P450 exogenous metabolism, glutathione metabolism, bile acid secretion, tumor pathways, and retinol metabolism. Differentially expressed metabolites (DEMs) were enriched in pathways such as pyruvate metabolism, the glucagon signaling pathway, central carbon metabolism in cancer, and the citric acid cycle. Co-enrichment analysis and pairwise correlation analysis among genes, metabolites, and outcomes identified several differentially expressed genes (DEGs), including Gstm1, Kit, Adcy1, Gck, Ppp1r3c, Ppp1r3d, and DEMs such as fumaric acid, L-lactic acid, 4-hydroxynonenal, and acetylvalerenolic acid. These DEGs and DEMs may play a role in the modulation of glucolipid metabolic pathways. In conclusion, our results suggest that gestational exposure to PFBS may induce molecular perturbations in glucose homeostasis. These findings provide insights into the potential mechanisms contributing to the heightened risk of abnormal glucose tolerance associated with PFBS exposure.

Porcine viral diarrhea is caused by many pathogens and can result in watery diarrhea, dehydration and death. Various detection methods, such as polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR), have been widely used for molecular diagnosis. We developed a triplex real-time quantitative reverse transcription PCR (qRT-PCR) for the simultaneous detection of three RNA viruses potentially associated with porcine viral diarrhea: porcine epidemic diarrhea virus (PEDV), porcine transmissible gastroenteritis virus (TGEV), and porcine rotavirus A (PoRVA). The triplex qRT-PCR had R2 values of 0.999 for the standard curves of PEDV, TGEV and PoRVA. Importantly, the limits of detection for PEDV, TGEV and PoRVA were 10 copies/μL. The specificity test showed that the triplex qRT-PCR detected these three pathogens specifically, without cross-reaction with other pathogens. In addition, the approach had good repeatability and reproducibility, with intra-and inter-assay coefficients of variation

Swine leukocyte antigen (SLA) class I molecule-restricted T-cell epitopes, which induce cytotoxic T lymphocyte (CTL) responses, play a critical role in the clearance of porcine reproductive and respiratory syndrome virus (PRRSV) and the development of efficient protective vaccines. The SLA-1*04:01:01, SLA-2*04:01, and SLA-3*04:01 alleles, assigned the Hp-4.0 haplotype, are highly prevalent and usually present in all pig breeds. However, the SLA Hp-4.0 haplotype-restricted CTL epitopes in the structural membrane (M) protein of PRRSV are still unknown. In this study, we predicted 27 possible 9-mer epitope peptides in M protein with high binding scores for SLA-1*04:01:01 using CTL epitope prediction tools. In total, 45 SLA class I complexes, comprising the predicted peptide, extracellular region of the SLA-I molecules, and β2-microglobulin, were constructed in vitro to detect the specific binding of these peptides to SLA-1*04:01:01 (27 complexes), SLA-2*04:01 (9 complexes), and SLA-3*04:01 (9 complexes), respectively. Our results showed that the M27 (T91WKFITSRC), M39 (N130HAFVVRRP), and M49 (G158RKAVKQGV) peptides bind specifically to SLA-1*04:01:01, SLA-2*04:01, and SLA-3*04:01, respectively. Subsequently, using peripheral blood mononuclear cells (PBMCs) isolated from the homozygous Hp-4.0 and Hp-26.0 haplotype piglets vaccinated with commercial PRRSV HuN4-F112 strain, we determined the capacities of these 27 potential peptides to stimulate their proliferation with a Cell Counting Kit-8 and their secretion and expression of interferon gamma (IFN-γ) with an ELISpot assay and real-time qPCR, respectively. The immunological activities of M27, M39, and M49 were therefore confirmed when they efficiently induced PBMC proliferation and IFN-γ secretion in PBMCs from piglets with the prevalent SLA Hp-4.0 haplotype. The amino acid sequence alignment revealed that M27, M39, and M49 are highly conserved among 248 genotype II PRRSV strains collected between 1998 and 2019. These findings contribute to the understanding of the mechanisms of cell-mediated immune responses to PRRSV. Our study also provides a novel strategy for identifying and confirming potential SLA haplotype-restricted CTL epitopes that could be used to develop novel peptide-based vaccines against swine diseases.

Porcine respiratory disease is a significant economic problem for the global swine industry. Haemophilus parasuis (H. parasuis), Streptococcus suis (S. suis), and Pasteurella multocida (P. multocida) are three important pathogenic bacteria of the swine respiratory tract. Notably, the three pathogens not only frequently manifest as mixed infections, but their striking clinical similarities also present difficulties for pig populations in terms of disease prevention and treatment. Thus, we developed a triplex real-time quantitative polymerase chain reaction (qPCR) assay based on a TaqMan probe for the detection of H. parasuis, S. suis serotype 2, and P. multocida. Primers and probes were designed to target the conserved regions of the H. parasuis OmpP2 gene, the S. suis serotype 2 gdh gene, and the P. multocida Kmt1 gene. By optimizing the reaction system and conditions, a triplex qPCR method for simultaneous detection of H. parasuis, S. suis serotype 2, and P. multocida was successfully established. The amplification efficiencies of the standard curves for all three pathogens were found to be highly similar, with values of 102.105% for H. parasuis, 105.297% for S. suis serotype 2, and 104.829% for P. multocida, and all R2 values achieving 0.999. The specificity analysis results showed that the triplex qPCR method had a strong specificity. The sensitivity test results indicated that the limit of detection can reach 50 copies/μL for all three pathogens. Both intra- and inter-assay coefficients of variation for repeatability were below 1%. This triplex qPCR method was shown to have good specificity, sensitivity, and reproducibility. Finally, the triplex qPCR method established in this study was compared with the nested PCR as recommended by the Chinese national standard (GB/T34750-2017) for H. parasuis, the PCR as recommended by the Chinese national standard (GB/T 19915.9-2005) for S. suis serotype 2, and the PCR as recommended by the Chinese agricultural industry standard (NY/T 564-2016) for P. multocida by detecting the same clinical samples. Both methods are reasonably consistent, while the triplex qPCR assay was more sensitive. In summary, triplex qPCR serves not only as a rapid and accurate detection and early prevention method for these pathogens but also constitutes a robust tool for microbial quality control in specific pathogen-free pigs.

Abstract Background This study aims to investigate the relationship between different dietary patterns and non-alcoholic fatty liver disease (NAFLD). Methods Residents over 30 years old in the ecological longevity cohort in Gongcheng Yao Autonomous County, Guangxi Province were the research objects selected from 2018 to 2019. Physical examination, baseline population survey, and food frequency questionnaire (FFQ) survey were conducted. Dietary patterns were analyzed by factor analysis. Influencing factors of NAFLD were analyzed by multiple logistic regression. Results NAFLD was diagnosed in 241 of 2664 participants based on ultrasonography, and the detection rate was 9.0%. Factor analysis yielded a total of three dietary patterns, namely, traditional Chinese, Western, and cereal-potato dietary patterns. Results of multivariate logistic regression analysis showed that after adjusting for confounding factors, participants in the highest quartile of the Western dietary pattern exhibited a higher prevalence of NAFLD (OR = 2.799; 95% CI: 1.620–4.837; p

Abstract Objective To analyze the relationship between plasma metal elements, ApoE gene polymorphisms and the interaction between the two and impaired cognitive function in elderly population. Method A stratified sample was drawn according to the age of the study population, and 911 subjects were included. Baseline information and health indicators were obtained, and cognitive function status was assessed by health examination, a general questionnaire and Mini-Mental Status Examination. Plasma metal elements were measured, and SNP typing was performed. Binary logistic regression was used to analyze the factors influencing cognitive function status and the association between the SNP genetic pattern of the ApoE gene and cognitive function. Results The differences in gene frequencies and genotype frequencies of the ApoE rs7412 and rs7259620 genotype frequencies were statistically different between the cognitive impairment group and the control group (P

The quality governance level of an industry is a multi-index evaluation problem that must consider multiple dimensions and factors. This study is the first to construct a comprehensive quality governance evaluation model for the civil aircraft industry of China (CAIC). The index system for the quality governance evaluation of CAIC was established using a literature review, enterprise investigation, expert interviews, and questionnaire surveys. An Analytic Hierarchy Process (AHP) was employed to determine index weights. Based on the evaluation model, data from 53 aviation manufacturing enterprises were collected, and the quality governance level of the CAIC was empirically evaluated; thus, quantitative and qualitative evaluation results were obtained. This empirical study shows that the quality governance of the CAIC is currently at a “medium to low” level. Furthermore, critical factors and bottleneck indices restricting the quality governance level of the CAIC were identified.