Enhancing lucerne (Medicago sativa) yield and nutritional quality: a meta-analysis of fertilization types and environmental factors in China.

Introduction: Lucerne (Medicago sativa), is a cornerstone of China's livestock industry, however, due to the backward agronomic strategies and technology, lucerne in China faces cultivation challenges that result in lower productivity and quality than global standards. Therefore, we undertook a meta-analysis to evaluate the impact of five distinct fertilization types on lucerne yield and nutritional quality in various locations in China. The fertilizer practices included manure application, combined mineral fertilizer and manure application (FM), biological fertilizer application, unbalanced application of two or more mineral fertilizer types, and balanced mineral fertilizer application. Furthermore, we investigate influential factors of yield and quality of lucerne under fertilization, including climatic variables (mean annual precipitation, mean annual temperature), initial soil properties (soil organic carbon; total nitrogen, pH), and agronomic factors (seeding rate, harvest frequency, and lucerne stand age). Methods: Our study analyzed 53 published papers to discern the most beneficial fertilizer for enhancing lucerne yield and nutritional quality. Results and discussion: The results showed that the fertilizer practices, on average, significantly improved yield by 31.72% and crude protein content by 11.29%, with FM emerging as the most effective, this is because mineral fertilizers provide available nutrients for lucerne, manure provides essential organic matter for microorganisms and improve soil properties. In addition, the fertilizer practices significantly reduced neutral and acid detergent fiber contents by 6.28% and 8.50%, respectively, while increasing ash content and relative feeding value. Furthermore, climatic variables, soil properties, and planting system factors such as sowing date and harvest frequency significantly affected yield and nutritional quality. The practical implications of our results emphasize the need for balanced and strategic fertilizer application to optimize lucerne production and highlight the potential to adjust cultivation practices according to environmental conditions. Balanced and strategic fertilizer application can simultaneously improve soil properties, enhance soil carbon sequestration, and reduce the emission of greenhouse gases from the soil, which is a vital measure for realizing sustainable agricultural development.

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits.

Background: Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era. Methods: This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI. Results: In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL. Conclusion: Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.

Distributed Frank-Wolfe Solver for Stochastic Optimization With Coupled Inequality Constraints.

Distributed stochastic optimization (DSO) with local set constraints and coupled inequality constraints over a multiagent network is considered in this article. Usually, such problems are tackled by projected primal-dual methods, which require expensive projection operations when set constraints are complicated. In this context, this article focuses on the Frank-Wolfe (FW) framework, which provides computational simplicity by avoiding expensive projection operations, for solving DSO with local set and coupled inequality constraints. By combining recursive momentum and weighted averaging, this article proposes a distributed stochastic FW primal-dual algorithm (DSFWPD), which is the first stochastic FW solver for DSO problems with coupled constraints. The proposed algorithm achieves zero constraint violation on average with a sublinear decay of the optimality gap over a directed and time-varying network. The efficacy of DSFWPD is demonstrated by several numerical experiments.

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk.

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

The Impact of Depression on Detrimental Changes in Bone Microstructure in Female Mice.

Background: Several clinical studies have examined the connection between depression and bone loss, but the cause-and-effect relationship between the two conditions, especially in animal models, is not well-studied. Methods: A total of 32 female mice were, randomly divided into control group (CON, n=19) and depression group (DEP, n=13). The mice in the DEP group were subjected to 21 consecutive days of restraint stress, following depressive-like behaviors were assessment. The femurs were collected using Micro-Computed Tomography (µCT) and histochemical staining. In parallel, levels of serotonin-related proteins in the brain were measured using Western blot analysis, and sex hormone profiles were determined through liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Results: The mice in the DEP group exhibited clear signs of depressive-like behaviors and an increase in serotonin transporter levels (t=-2.435, P< 0.05). In comparison to the CON mice, the DEP mice showed a decrease in bone mineral density (t =3.741, P< 0.05), bone surface area density (t =8.009, P<0.01), percent bone volume (t =4.293, P< 0.05), trabecular number (t =5.844, P<0.01), and connected density (t =11.000, P< 0.05). Additionally, there was an increase in trabecular separation (t =-7.436, P<0.01) in DEP mice. Furthermore, the DEP mice displayed a significant reduction in serum estrogen levels (t =4.340, P< 0.05) and changes in its metabolite (t =-3.325, P< 0.05), while the levels of androgens remained unchanged. Conclusion: The restraint stress not only led to the development of depressive-like behaviors but also disrupted the estrogen metabolism pathway, resulting in damage to bone mass and microstructure in female mice. These findings suggest that stress-induced depression may pose a risk for bone loss in female mice by altering estrogen metabolism pathways.

Protective evaluation of the commercialized porcine reproductive and respiratory syndrome virus vaccines in piglets challenged by NADC34-like strain.

In China, the porcine reproductive and respiratory syndrome virus (PRRSV) has undergone several variations over the decades and contributed to the diversity of the clinical epidemic PRRSV strains. This has complicated the prevention and control of PRRS. In particular, the efficacy of the currently available commercial vaccines against the highly pathogenic NADC34-like strains is unclear. Therefore, the objective of this study was to evaluate the protection efficacy of three commercial PRRS modified-live virus (MLV) vaccines derived from classical PRRS VR2332 MLV and R98 MLV against challenge with a heterologous NADC34-like PRRSV strain, JS2021NADC34, which has high pathogenicity in pigs. PRRSV- and antibody-free piglets were immunized with the PRRS VR2332 MLV vaccine or either of two R98 MLV vaccines (from different manufacturers) and were challenged with the JS2021NADC34 strain 28 days after immunization. Rectal temperature, clinical symptoms, viremia and viral shedding from the nose, gross lesions in the thymus and lungs, microscopic lesions and viral distribution in the lungs, as well as the humoral immune response and mortality rates were recorded over a 14-day post-challenge period. The results showed that PRRS VR2332 MLV had better efficacy against the JS2021NADC34 challenge than PRRS R98 MLV, with vaccinated piglets in the former group showing transient and mild symptoms, mild pathological lesions in the lungs, mild thymic atrophy, and low viral levels in sera and nasal swabs, as well as better growth performance and a 100% survival rate. In contrast, two PRRS R98 MLVs exhibited limited efficacy against the JS2021NADC34 challenge, with the piglets in two R98 groups showing obvious clinical symptoms and pathological changes in the lungs and thymus; moreover, there were two deaths caused by PRRS in two R98 groups, respectively. Despite this, the mortality rate was lower than that of the unvaccinated piglets that were challenged with JS2021NADC34. The cumulative results demonstrate that PRRS VR2332 MLV was partly effective against the highly pathogenic PRRSV NADC34-like strain based on the observations over the 14-day post-challenge period. Thus, it might be a viable option among the commercially available vaccines for control of NADC34-like virus infections in swine herds.

Visualization analysis of research hotspots and trends on gastrointestinal tumor organoids.

BACKGROUND: Gastrointestinal tumor organoids serve as an effective model for simulating cancer in vitro and have been applied in basic biology and preclinical research. Despite over a decade of development and increasing research achievements in this field, a systematic and comprehensive analysis of the research hotspots and future trends is lacking. AIM: To address this problem by employing bibliometric tools to explore the publication years, countries/regions, institutions, journals, authors, keywords, and references in this field. METHODS: The literature was collected from Web of Science databases. CiteSpace-6.2R4, a widely used bibliometric analysis software package, was used for institutional analysis and reference burst analysis. VOSviewer 1.6.19 was used for journal co-citation analysis, author co-authorship and co-citation analysis. The 'online platform for bibliometric analysis (https://bibliometric.com/app)' was used to assess the total number of publications and the cooperation relationships between countries. Finally, we employed the bibliometric R software package (version R.4.3.1) in R-studio, for a comprehensive scientific analysis of the literature. RESULTS: Our analysis included a total of 1466 publications, revealing a significant yearly increase in articles on the study of gastrointestinal tumor organoids. The United States (n = 393) and Helmholtz Association (n = 93) have emerged as the leading countries and institutions, respectively, in this field, with Hans Clevers and Toshiro Sato being the most contributing authors. The most influential journal in this field is Gastroenterology. The most impactful reference is "Long term expansion of epithelial organs from human colon, adenoma, adenocarcinoma, and Barrett's epithelium". Keywords analysis and citation burst analysis indicate that precision medicine, disease modeling, drug development and screening, and regenerative medicine are the most cutting-edge directions. These focal points were further detailed based on the literature. CONCLUSION: This bibliometric study offers an objective and quantitative analysis of the research in this field, which can be considered as an important guide for next scientific research.

Superspreading Surface with Hierarchical Porous Structure for Highly Efficient Vapor-Liquid Phase Change Heat Dissipation.

Superspreading surfaces with excellent water transport efficiency are highly desirable for addressing thermal failures through the liquid-vapor phase change of water in electronics thermal management applications. However, the trade-off between capillary pressure and viscous resistance in traditional superspreading surfaces with micro/ nanostructures poses a longstanding challenge in the development of superspreading surfaces with high cooling efficiency in confined spaces. Herein, a heat-treated hierarchical porous enhanced superspreading surface (HTHP) for highly efficient electronic cooling is proposed. Compared with the single porous structures in nanograss, nanosheets, and copper foam, HTHP with hierarchical honeycomb pores effectively resolves the trade-off effect by introducing large vertical through-pores to reduce viscous resistance, and connected small pores to provide sufficient capillary pressure synergistically. HTHP exhibits excellent capillary performance in both horizontal spreading and vertical rising. Despite a thickness of only 0.33 mm, the as-prepared ultrathin vapor chamber (UTVC) fabricated to exploit the superior capillary performance of HTHP achieved effective heat dissipation with outstanding thermal conductivity (12 121 Wm-1K-1), and low thermal resistance (0.1 KW-1) at a power of 5 W. This regulation strategy based on hierarchical honeycomb porous structures is expected to promote the development of high-performance superspreading surfaces with a wide range of applications in thermal management.

In silico off-target profiling for enhanced drug safety assessment.

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

Transcriptome profiling reveals the impact of various levels of biochar application on the growth of flue-cured tobacco plants.

BACKGROUND: Biochar, a carbon-rich source and natural growth stimulant, is usually produced by the pyrolysis of agricultural biomass. It is widely used to enhance plant growth, enzyme activity, and crop productivity. However, there are no conclusive studies on how different levels of biochar application influence these systems. METHODS AND RESULTS: The present study elucidated the dose-dependent effects of biochar application on the physiological performance, enzyme activity, and dry matter accumulation of tobacco plants via field experiments. In addition, transcriptome analysis was performed on 60-day-old (early growth stage) and 100-day-old (late growth stage) tobacco leaves to determine the changes in transcript levels at the molecular level under various biochar application levels (0, 600, and 1800 kg/ha). The results demonstrated that optimum biochar application enhances plant growth, regulates enzymatic activity, and promotes biomass accumulation in tobacco plants, while higher biochar doses had adverse effects. Furthermore, transcriptome analysis revealed a total of 6561 differentially expressed genes (DEGs) that were up- or down-regulated in the groupwise comparison under different treatments. KEGG pathways analysis demonstrated that carbon fixation in photosynthetic organisms (ko00710), photosynthesis (ko00195), and starch and sucrose metabolism (ko00500) pathways were significantly up-regulated under the optimal biochar dosage (600 kg/ha) and down-regulated under the higher biochar dosage (1800 kg/ha). CONCLUSION: Collectively, these results indicate that biochar application at an optimal rate (600 kg/ha) could positively affect photosynthesis and carbon fixation, which in turn increased the synthesis and accumulation of sucrose and starch, thus promoting the growth and dry matter accumulation of tobacco plants. However, a higher biochar dosage (1800 kg/ha) disturbs the crucial source-sink balance of organic compounds and inhibits the growth of tobacco plants.

Identifying influencing factors of metabolic syndrome in patients with major depressive disorder: A real-world study with Bayesian network modeling.

BACKGROUND: The bidirectional relationships between metabolic syndrome (MetS) and major depressive disorder (MDD) were discovered, but the influencing factors of the comorbidity were barely investigated. We aimed to fully explore the factors and their associations with MetS in MDD patients. METHODS: The data were retrieved from the electronic medical records of a tertiary psychiatric hospital in Beijing from 2016 to 2021. The influencing factors were firstly explored by univariate analysis and multivariate logistic regressions. The propensity score matching was used to reduce the selection bias of participants. Then, the Bayesian networks (BNs) with hill-climbing algorithm and maximum likelihood estimation were preformed to explore the relationships between influencing factors with MetS in MDD patients. RESULTS: Totally, 4126 eligible subjects were included in the data analysis. The proportion rate of MetS was 32.6 % (95 % CI: 31.2 %-34.1 %). The multivariate logistic regression suggested that recurrent depression, uric acid, duration of depression, marriage, education, number of hospitalizations were significantly associated with MetS. In the BNs, number of hospitalizations and uric acid were directly connected with MetS. Recurrent depression and family history psychiatric diseases were indirectly connected with MetS. The conditional probability of MetS in MDD patients with family history of psychiatric diseases, recurrent depression and two or more times of hospitalizations was 37.6 %. CONCLUSION: Using the BNs, we found that number of hospitalizations, recurrent depression and family history of psychiatric diseases contributed to the probability of MetS, which could help to make health strategies for specific MDD patients.

An Identification Method for Road Hypnosis Based on Human EEG Data.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Pan-cancer Analysis Reveals m6A Variation and Cell-specific Regulatory Network in Different Cancer Types.

As the most abundant messenger RNA (mRNA) modification in mRNA, N  6-methyladenosine (m6A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the function and role of the m6A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m6A methylomes across nine human tissues from 97 m6A sequencing (m6A-seq) and RNA sequencing samples. Our findings demonstrate that m6A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m6A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called "m6A-express", we predicted m6A-regulated genes and revealed that cancer type-specific m6A-regulated genes contributed to the prognosis, tumor origin, and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m6A regulators that regulate cancer-specific m6A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m6A regulator CAPRIN1 controls the m6A level of TP53. Overall, our work reveals the clinical relevance of m6A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m6A for cancer precision medicine for patients with different cancer types.

Crystal-Phase-Selective Etching of Heterophase Au Nanostructures.

Selective oxidative etching is one of the most effective ways to prepare hollow nanostructures and nanocrystals with specific exposed facets. The mechanism of selective etching in noble metal nanostructures mainly relies on the different reactivity of metal components and the distinct surface energy of multimetallic nanostructures. Recently, phase engineering of nanomaterials (PEN) offers new opportunities for the preparation of unique heterostructures, including heterophase nanostructures. However, the synthesis of hollow multimetallic nanostructures based on crystal-phase-selective etching has been rarely studied. Here, a crystal-phase-selective etching method is reported to selectively etch the unconventional 4H and 2H phases in the heterophase Au nanostructures. Due to the coating of Pt-based alloy and the crystal-phase-selective etching of 4H-Au in 4H/face-centered cubic (fcc) Au nanowires, the well-defined ladder-like Au@PtAg nanoframes are prepared. In addition, the 2H-Au in the fcc-2H-fcc Au nanorods and 2H/fcc Au nanosheets can also be selectively etched using the same method. As a proof-of-concept application, the ladder-like Au@PtAg nanoframes are used for the electrocatalytic hydrogen evolution reaction (HER) in acidic media, showing excellent performance that is comparable to the commercial Pt/C catalyst.

Decoding Wilson disease: a machine learning approach to predict neurological symptoms.

Objectives: Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods. Methods: The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms. Results: In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur. Conclusions: To sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.

Learning generalizable AI models for multi-center histopathology image classification.

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA's potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.

Structural basis for the H2AK119ub1-specific DNMT3A-nucleosome interaction.

Isoform 1 of DNA methyltransferase DNMT3A (DNMT3A1) specifically recognizes nucleosome monoubiquitylated at histone H2A lysine-119 (H2AK119ub1) for establishment of DNA methylation. Mis-regulation of this process may cause aberrant DNA methylation and pathogenesis. However, the molecular basis underlying DNMT3A1-nucleosome interaction remains elusive. Here we report the cryo-EM structure of DNMT3A1's ubiquitin-dependent recruitment (UDR) fragment complexed with H2AK119ub1-modified nucleosome. DNMT3A1 UDR occupies an extensive nucleosome surface, involving the H2A-H2B acidic patch, a surface groove formed by H2A and H3, nucleosomal DNA, and H2AK119ub1. The DNMT3A1 UDR's interaction with H2AK119ub1 affects the functionality of DNMT3A1 in cells in a context-dependent manner. Our structural and biochemical analysis also reveals competition between DNMT3A1 and JARID2, a cofactor of polycomb repression complex 2 (PRC2), for nucleosome binding, suggesting the interplay between different epigenetic pathways. Together, this study reports a molecular basis for H2AK119ub1-dependent DNMT3A1-nucleosome association, with important implications in DNMT3A1-mediated DNA methylation in development.

Tracing groundwater nitrate sources in an intensive agricultural region integrated of a self-organizing map and end-member mixing model tool.

The traceability of groundwater nitrate pollution is crucial for controlling and managing polluted groundwater. This study integrates hydrochemistry, nitrate isotope (δ15N-NO3- and δ18O-NO3-), and self-organizing map (SOM) and end-member mixing (EMMTE) models to identify the sources and quantify the contributions of nitrate pollution to groundwater in an intensive agricultural region in the Sha River Basin in southwestern Henan Province. The results indicate that the NO3--N concentration in 74% (n = 39) of the groundwater samples exceeded the WHO standard of 10 mg/L. According to the results of EMMTE modeling, soil nitrogen (68.4%) was the main source of nitrate in Cluster-1, followed by manure and sewage (16.5%), chemical fertilizer (11.9%) and atmospheric deposition (3.3%). In Cluster-2, soil nitrogen (60.1%) was the main source of nitrate, with a significant increase in the contribution of manure and sewage (35.5%). The considerable contributions of soil nitrogen may be attributed to the high nitrogen fertilizer usage that accumulated in the soil in this traditional agricultural area. Moreover, it is apparent that most Cluster-2 sampling sites with high contributions of manure and sewage are located around residential land. Therefore, the arbitrary discharge and leaching of domestic sewage may be responsible for these results. Therefore, this study provides useful assistance for the continuous management and pollution control of groundwater in the Sha River Basin.

Nitrogen-Doped 3D-Graphene Advances Near-Infrared Photodetector for Logic Circuits and Image Sensors Overcoming 2D Limitations.

The limitations of two-dimensional (2D) graphene in broadband photodetector are overcome by integrating nitrogen (N) doping into three-dimensional (3D) structures within silicon (Si) via plasma-assisted chemical vapor deposition (PACVD) technology. This contributes to the construction of vertical Schottky heterojunction broad-spectrum photodetectors and applications in logic devices and image sensors. The natural nanoscale resonant cavity structure of 3D-graphene enhances photon capture efficiency, thereby increasing photocarrier generation. N-doping can fine-tune the electronic structure, advancing the Schottky barrier height and reducing dark current. The as-fabricated photodetector exhibits exceptional self-driven photoresponse, especially at 1550 nm, with an excellent photoresponsivity (79.6 A/W), specific detectivity (1013 Jones), and rapid response of 130 µs. Moreover, it enables logic circuits, high-resolution pattern image recognition, and broadband spectra recording across the visible to near-infrared range (400-1550 nm). This research will provide new views and technical support for the development and widespread application of high-performance semiconductor-based graphene broadband detectors.

Self-breakdown statistics of a high-pressure spark gap with a microarray graphite cathode.

Comparative measurements with high-pressure spark gaps (gas pressure: 0.2-0.9 MPa nitrogen, gap spacing 5 mm) are presented, one with a regular Bruce-profile polished graphite cathode (diameter 25 mm, thickness 8 mm) and the other with a microarray graphite cathode of equal dimensions. By microstructuring, a V-type graphite microarray is created by purpose-developed laser treatment of a plane graphite electrode. The microarray graphite cathode brings more initial plasma and then produces more initial electrons. It is beneficial for electron emission, which improves the stability of the switch breakdown. The experimental results are achieved at a gas pressure of 0.9 MPa and a 200-kV voltage pulse applied to the switch. With these parameters, the mean breakdown voltage is 91.7 kV, the minimum is 91.4 kV, and the mean relative standard deviation in breakdown voltage of the first 100 shots is 0.4%. Compared to a plane graphite cathode, the mean breakdown voltage is about 10% lower, and the mean relative standard deviation is reduced by more than 90%. The main result can be stated that microarray graphite cathodes are a suitable choice as electrodes for low-jitter high-pressure spark gaps.