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Abstract The optimization of the enzyme-like catalytic selectivity of nanozymes for specific reactive oxygen species (ROS)-related applications is significant, and meanwhile the real-time monitoring of ROS is really crucial for tracking the therapeutic process. Herein, we present a mild oxidation valence-engineering strategy to modulate the valence states of Mo in Pluronic F127-coated MoO3-x nanozymes (denoted as MF-x, x: oxidation time) in a controlled manner aiming to improve their specificity of H2O2-associated catalytic reactions for specific therapy and monitoring of ROS-related diseases. Experimentally, MF-0 (Mo average valence 4.64) and MF-10 (Mo average valence 5.68) exhibit exclusively optimal catalase (CAT)- or peroxidase (POD)-like activity, respectively. Density functional theory (DFT) calculations verify the most favorable reaction path for both MF-0- and MF-10-catalyzed reaction processes based on free energy diagram and electronic structure analysis, disclosing the mechanism of the H2O2 activation pathway on the Mo-based nanozymes. Furthermore, MF-0 poses a strong potential in acute kidney injury (AKI) treatment, achieving excellent therapeutic outcomes in vitro and in vivo. Notably, the ROS-responsive photoacoustic imaging (PAI) signal of MF-0 during treatment guarantees real-time monitoring of the therapeutic effect and post-cure assessment in vivo, providing a highly desirable non-invasive diagnostic approach for ROS-related diseases.

Abstract Background Nursing Practice Environment is an important index to improve nursing quality and patient outcome. To explore the nursing practice environment in the COVID-19 ward during the period of COVID-19 and its impact on nursing quality to provide reference for setting up supporting nursing team in epidemic area in the future. Methods A cross-sectional survey was conducted among 251 nurses working in COVID-19 ward in Shanghai, Hainan and Hunan in December 2022 through stratified proportional sampling. Structured questionnaires, including general information questionnaire, professional practice environment scale and nursing quality questionnaire, were used to investigate the patients. Pearson correlation was used to analyze the correlation between nursing practice environment and nursing quality, and multiple linear regression analysis was used to analyze the influencing factors of nursing quality in the COVID-19 ward. Results The professional practice environment scale score was (3.34 ± 0.40), the nursing quality questionnaire score was (9.47 ± 0.81), both at a high level. Single factor analysis showed that nursing quality was related to educational background, physical condition, professional title, grade of the original hospital and composition of nursing staff in supported departments. Nursing quality were positively correlated with each nursing practice environment dimensions (in addition to teamwork). The results of regression analysis showed that the nursing practice environment in the COVID-19 ward had a positive impact on nursing quality. Conclusions The nursing practice environment and nursing quality of nurses in the COVID-19ward is generally very high. The education, working hospital level and nursing practice environment of nurses are the important factors influencing nursing quality. The relationship between nursing practice environment (include leadership and autonomy in clinical practice, staff relationships with physicians, control over practice, communication about patients, handling disagreement and conflict, internal work motivation and cultural sensitivity) and nursing quality is positive. It is suggested that the hospital should pay special attention to and improve nursing practice environment in order to improve nursing quality when setting up temporary ward in the future epidemic period of infectious diseases, ensure patient safety. Impact on clinical practice Research shows that an active nursing practice environment can improve the quality of care, and nursing managers create and maintain an active practice environment to improve the quality of care and ensure patient safety.

Land use influences soil biota community composition and diversity, and then belowground ecosystem processes and functions. To characterize the effect of land use on soil biota, soil nematode communities in crop land, forest land and fallow land were investigated in six regions of northern China. Generic richness, diversity, abundance and biomass of soil nematodes was the lowest in crop land. The richness and diversity of soil nematodes were 28.8 and 15.1% higher in fallow land than in crop land, respectively. No significant differences in soil nematode indices were found between forest land and fallow land, but their network keystone genera composition was different. Among the keystone genera, 50% of forest land genera were omnivores-predators and 36% of fallow land genera were bacterivores. The proportion of fungivores in forest land was 20.8% lower than in fallow land. The network complexity and the stability were lower in crop land than forest land and fallow land. Soil pH, NH4+-N and NO3−-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land. Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land. Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.

Omics methodologies, such as genomics, transcriptomics, proteomics, metabolomics, lipidomics and microbiomics, have revolutionized biological research by allowing comprehensive molecular analysis in livestock animals. However, despite being widely used in various animal species, research on donkeys has been notably scarce. China, renowned for its rich history in donkey husbandry, plays a pivotal role in their conservation and utilization. China boasts 24 distinct donkey breeds, necessitating conservation efforts, especially for smaller breeds facing extinction threats. So far, omics approaches have been employed in studies of donkey milk and meat, shedding light on their composition and quality. Similarly, omics methods have been utilized to explore the molecular basis associated with donkey growth, meat production, and quality traits. Omics analysis has also unraveled the critical role of donkey microbiota in health and nutrition, with gut microbiome studies revealing associations with factors such as pregnancy, age, transportation stress, and altitude. Furthermore, omics applications have addressed donkey health issues, including infectious diseases and reproductive problems. In addition, these applications have also provided insights into the improvement of donkey reproductive efficiency research. In conclusion, omics methodologies are essential for advancing knowledge about donkeys, their genetic diversity, and their applications across various domains. However, omics research in donkeys is still in its infancy, and there is a need for continued research to enhance donkey breeding, production, and welfare in China and beyond.

Abstract Sol–gel processed zinc oxide (ZnO) is one of the most widely used electron transport layers (ETLs) in inverted organic solar cells (OSCs). The high annealing temperature (≈200 °C) required for sintering to ensure a high electron mobility however results in severe damage to flexible substrates. Thus, flexible organic solar cells based on sol–gel processed ZnO exhibit significantly lower efficiency than rigid devices. In this paper, an indium‐doping approach is developed to improve the optoelectronic properties of ZnO layers and reduce the required annealing temperature. Inverted OSCs based on In‐doped ZnO (IZO) exhibit a higher efficiency than those based on ZnO for a range of different active layer systems. For the PM6:L8‐BO system, the efficiency increases from 17.0% for the pristine ZnO‐based device to 17.8% for the IZO‐based device. The IZO‐based device with an active layer of PM6:L8‐BO:BTP‐eC9 exhibits an even higher efficiency of up to 18.1%. In addition, a 1.2‐micrometer‐thick inverted ultrathin flexible organic solar cell is fabricated based on the IZO ETL that achieves an efficiency of 17.0% with a power‐per‐weight ratio of 40.4 W g−1, which is one of the highest efficiency for ultrathin (less than 10 micrometers) flexible organic solar cells.

The reproductive efficiency of livestock is crucial for agricultural productivity and economic sustainability. One critical factor in successful fertilization and the viability of offspring is the quality of semen. Poor semen quality, especially in frozen-thawed semen used in artificial insemination (AI) have been shown to influence conception outcomes, resulting a negative impact on livestock production. Recent advancements in genetic research have identified specific markers linked to semen quality traits in various livestock species, such as cattle, sheep, goats, pigs, buffalo, and equines. These genetic markers are essential in screening males for breeding suitability, which in turn enhances selective breeding programs. Understanding these markers is crucial for improving reproductive performance and increasing productivity in livestock populations. This review offers a comprehensive overview of the genetic markers associated with semen quality in key livestock. It explores the underlying genetic mechanisms and their practical implications in animal breeding and management. The review underscores the importance of integrating genetic insights into breeding strategies to optimize reproductive efficiency and ensure the sustainable development of livestock industries.

Exposure to arsenic (As) induces adverse effects on human health. Vitamins B1, B6, and C, as indispensable micronutrients for humans, have been proven to influence the metabolism and toxicity of ingested As. To determine the effect of vitamins on health risks associated with soil exposure, As bioaccessibility in 14 soil samples using four in vitro methods of IVG, PBET, SBRC, and UBM was measured with the addition of vitamins B1, B6, and C. With vitamins B1 and B6 addition, the gastric As bioaccessibility in 14 soil samples was reduced by 1.14–3.52 and 1.14–5.02 fold, respectively, and instead an increase in the intestinal bioaccessibility was presented in some cases. Vitamin C supplementation yielded higher As bioaccessibility in the gastric (1.13–13.02 fold) and small intestinal (1.21–33.35 fold) phases, respectively. As evidenced by the X-ray absorption near-edge spectroscopy (XANES) and Fourier transform infrared spectroscopy (FTIR) analysis, arsenic dissolution was promoted by Fe-As and hindered by the formation of Al-As fractions. Soil As dissolution in the simulated gastrointestinal tract was strongly influenced by soil minerals and ingested vitamins, due to the chelation of arsenic with vitamins and soil minerals such as Fe (hydr)oxides, and Fe(III) reductive dissolution to enhance As release by vitamin C as an iron reducer. These findings will expand the knowledge of health risks of exposure to As-contaminated soils and nutritional interventions aiming at the mitigation of As toxicity.

In 2022, China released the Special Action Plan for the Control of Spartina alterniflora (2022–2025) with the goal of completely clearing S. alterniflora from China's coastal wetlands. It is crucial to conduct a thorough analysis of wetlands under artificial intervention and obtain reliable future wetland simulation results. The Yellow River Delta wetland is one of China's important wetland reserves, has faced severe impacts from S. alterniflora invasion over the past decade. This study focuses on the Yellow River Delta wetland, utilizing Landsat imagery and a combined object-oriented and pixel-based approach to achieve wetland mapping from 2013 to 2023. Through wetland analysis, we designed scenarios for natural development, ecological priority, farmland protection, and combined development. The patch generating land use simulation model was used to simulate wetland scenarios for 2030 and analyze factors influencing wetland use cover change and future conversion patterns. The results indicate that between 2013 and 2022, native vegetation was impacted by the invasion of S. alterniflora, with Suaeda glauca being the most affected. In 2023, the removal of S. alterniflora was effective, reducing its area by 42.37 km2 compared to pre-removal conditions. In all three scenarios, the area of Phragmites australis increased and is expected to dominate the wetlands, while the live space of other native salt marsh vegetation has also been restored. However, the predictions indicate that S. alterniflora still poses a potential threat of expansion in the future, necessitating continuous monitoring of its dynamics. We recommend implementing zoned management of the wetland, balancing ecological restoration in coastal areas with agricultural development in the western regions.

Global biodiversity is still being lost at an alarming rate. How to slow the destruction of habitats to protect biodiversity is an urgent issue. As one of the most rapidly developing and active economic regions in China, the habitat quality assessment and impact factor analysis in the Yangtze River Delta region are important for promoting socio-economic development and improving the local ecological environment. The InVEST mode and Geodetector method was used to explore the spatial and temporal variations of the habitat quality and its driving factors in the Yangtze River Delta region. The results show that the averaged habitat quality index showed a decreasing trend during 1980–2020, which accounted about 71.86 % of the Yangtze River Delta region. The habitat quality showed an overall spatial pattern of high in the south and low in the north, and the regional distribution of low habitat quality was dominated. The Moran's I values were larger than 0.8 during 1980–2020, showing a significant positive spatial correlation and a high degree of spatial clustering. The high-high clusters of habitat quality were mainly located in the southwest of the study area, while the low-low clusters were mainly located in the north. During 2025–2060, the habitat quality under the three scenarios exhibited SSP1-RCP19 > SSP2-RCP45 > SSP5-RCP85 and all showed a trend of decreasing in the north of the study area and increasing in the south. Under the two scenarios SSP1-RCP19 and SSP2-RCP45, the habitat quality generally showed an increasing trend, while under the SSP5-RCP85 scenario, it showed a decreasing trend. The slope, DEM, precipitation, construction land index and their interactions played a dominant role in the spatio-temporal variations of the habitat quality. Therefore, the slope, DEM, precipitation and construction land index should be considered when formulating habitat quality protection countermeasures.

Excessive nitrogen (N) fertilization and irrigation prevail in irrigated agriculture worldwide. However, little information is available that systematically evaluates grain yield, plant N uptake, residual soil NO3−-N (RSN), and N leaching under different irrigation–N regimes in an irrigated maize production system. To address such agronomic and environmental issues, a 6-year in situ lysimeter field trial was conducted in the Hetao Irrigation District of China with five treatments: 1) conventional irrigation (3,900 m3/ha) and local farmer fertilizer N rate (490 kg ha−1) (CON); 2) conventional irrigation and the recommended fertilizer N rate (420 kg ha−1) (RN); 3) deficit irrigation (2,730 m3/ha) and the recommended fertilizer N rate (SRN); 4) deficit irrigation and the recommended fertilizer N rate (with ∼ 10 % organic fertilizer N substitution) (SMN); and 5) conventional irrigation and controlled-release N fertilizer (270 kg ha−1) (CRF). Maize yield was not significantly different across treatments in 2015–2020. Averaged across years, SMN, SRN, and CRF significantly increased plant N uptake compared with that in CON by 28.84 %, 19.15 %, and 17.53 %, respectively. Residual soil NO3−-N at 0–90 cm was ranked as SRN > CON ≈ SMN > RN > CRF, whereas cumulative N in leachate from 2015–2020 (Nloss) in CON was 86.2 % to 187.1 % higher than that in other treatments. The Nloss in SRN was equivalent to that in RN and SMN, but Nloss in SMN decreased significantly compared with that in RN, indicating additive benefits of deficit irrigation and organic input on reducing N leaching. Structural equation modelling revealed that irrigation had greater effects on plant N uptake than those of fertilization, whereas N rate had a greater total effect on N leaching than that of irrigation. Overall, rampant excessive irrigation and N fertilization by farmers can be substantially reduced while maintaining grain yield and mitigating N leaching loss, with further benefits gained by applying controlled-release N fertilizer and manure in irrigated maize production.

In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

Liquid metal, as a novel material, has become ideal for the fabrication of flexible conductive fibers and has shown great potential in the field of biomedical sensing. This paper presents a comprehensive review of the unique properties of liquid metals such as gallium-based alloys, including their excellent electrical conductivity, mobility, and biocompatibility. These properties make liquid metals ideal for the fabrication of flexible and malleable biosensors. The article explores common preparation methods for liquid metal conductive fibers, such as internal liquid metal filling, surface printing with liquid metal, and liquid metal coating techniques, and their applications in health monitoring, neural interfaces, and wearable devices. By summarizing and analyzing the current research, this paper aims to reveal the current status and challenges of liquid metal conductive fibers in the field of biosensors and to look forward to their development in the future, which will provide valuable references and insights for researchers in the field of biomedical engineering.

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively.

Endometritis is a common inflammatory condition of the uterine endometrial lining that primarily affects perinatal dairy animals and causes significant economic losses in agriculture. It is usually triggered by pathogenic bacteria and is associated with chronic postpartum reproductive tract infections. Bacterial lipopolysaccharides (LPSs) are known to increase levels of reactive oxygen species (ROS), leading to oxidative stress and inflammation through the activation of the NF-κB signaling pathway and the inhibition of Nrf2 nuclear translocation, which regulates antioxidant response elements (AREs). The effectiveness of the conventional management strategy involving antibiotics is decreasing due to resistance and residual concerns. This review explores the potential therapeutic benefits of targeting the Nrf2/Kelch-like ECH-associated protein 1 (Keap1)/NF-κB signaling pathway to alleviate LPS-induced endometritis. We discuss recent advancements in veterinary medicine that utilize exogenous antioxidants to modulate these pathways, thereby reducing oxidative stress and inflammatory responses in endometrial cells. This review highlights the efficacy of several bioactive compounds that enhance Nrf2 signaling and suppress NF-κB activation, offering protective effects against oxidative damage and inflammation. By examining various in vitro studies, this review emphasizes the emerging role of these signaling pathways in developing new therapeutic strategies that could potentially replace or supplement traditional treatments and mitigate the economic impacts of endometritis in livestock.

Due to the rise in temperature and sea level caused by climate change, the detection rate of aflatoxin B1 (AFB1) in food crops has increased dramatically, and the frequency and severity of aflatoxicosis in humans and animals are also increasing. AFB1 has strong hepatotoxicity, causing severe liver damage and even cancer. However, the mechanism of AFB1 hepatotoxicity remains unclear. By integrating network toxicology, molecular docking and in vivo experiments, this research was designed to explore the potential hepatotoxicity mechanisms of AFB1. Thirty-three intersection targets for AFB1-induced liver damage were identified using online databases. PI3K/AKT1, MAPK, FOXO1 signaling pathways, and apoptosis were significantly enriched. In addition, the proteins of ALB, AKT1, PIK3CG, MAPK8, HSP90AA1, PPARA, MAPK1, EGFR, FOXO1, and IGF1 exhibited good affinity with AFB1. In vivo experiments, significant pathological changes occurred in the liver of mice. AFB1 induction increased the expression levels of EGFR, ERK, and FOXO1, and decreased the expression levsls of PI3K and AKT1. Moreover, AFB1 treatment caused an increase in Caspase3 expression, and a decrease in Bcl2/Bax ratio. By combining network toxicology with in vivo experiments, this study confirms for the first time that AFB1 promotes the FOXO1 signaling pathway by inactivating PI3K/AKT1 and activating EGFR/ERK signaling pathways, hence aggravating hepatocyte apoptosis. This research provides new strategies for studying the toxicity of environmental pollutants and new possible targets for the development of hepatoprotective drugs.

Abstract Hydrogen sulfide for wound healing has drawn a lot of attention recently. In this research, the S‐propargyl‐cysteine (SPRC), an endogenous H2S donor, was loaded on carbomer hydrogel, and a copper sheet rat burn model was developed. Pathological changes in rat skin tissue were examined using hematoxylin–eosin (HE) and Masson staining. The immunohistochemistry (IHC) staining was performed to detect the expression of Collagen I (Col I) and Collagen III (Col III). The mRNA levels of interleukin (IL)‐6, Col Iα2, Col IIIα1, tissue inhibitors of metalloproteinase (TIMP)‐1, matrix metalloproteinase (MMP)‐9, vascular endothelial growth factor (VEGF), and transforming growth factor (TGF)‐β1 were examined by quantitative real‐time chain polymerase reaction. The findings demonstrated that the collagen layer was thicker in the SPRC group during the proliferative phase, SPRC hydrogel promoted VEGF expression. In the late stage of wound healing, the expression of IL‐6, TIMP‐1, MMP‐9, and TGF‐β1 was inhibited, and the Col I content was closer to that of normal tissue. These results surface that SPRC hydrogel can promote wound healing and play a positive role in reducing scar formation. Our results imply that SPRC can facilitate wound healing and play a positive role in reducing scar formation.

The green development of the mariculture industry involves the interaction of economic, resource, environmental and scientific systems, and is a complex and dynamic process. To investigate the effect of different policies on this process, this paper used the system dynamics method to construct a simulation model of the green development of China's mariculture industry, simulating the green development trend of the mariculture industry from 2021 to 2035, and on this basis, four policy simulation models were set up for comparative analysis, with economic input, ecological protection and scientific and technological innovation as the guide. The results showed that under the status quo development model, the overall level of green development of mariculture had improved, but the pressure of resource consumption and environmental pollution had increased, and economic growth was weak. Under a single regulatory policy scenario, economic development policies could hardly achieve green development, science and technology innovation policies could significantly promote the green development of mariculture, and ecological protection policies had the best effect on reducing energy consumption and improving environmental quality. Coordinated development policies promoted the green development of mariculture significantly better than single regulation policies, with economic development and resource utilisation both at the highest level, science and technology significantly improved, energy consumption significantly reduced, and ecological and environmental quality significantly improved.

Abstract Background This study aims to identify the current status and factors influencing self-management of knee discomfort in middle-aged and elderly people in China. Methods A stratified multistage cluster sampling method was used to select participants from communities in China from January 15 to May 31, 2020. A cross-sectional survey was conducted using the general information questionnaire and the Knee Joint Discomfort Self-management Scale. Univariate analysis and a generalized linear model were used to analyze the factors influencing self-management. Results The prevalence of knee discomfort was 77%. Moderate to severe discomfort accounted for 30.5%. The average item score of self-management in 9640 participants was 1.98 ± 0.76. The highest and lowest levels were: ‘daily life management’ and ‘information management’. Gender, ethnicity, education level, economic source, chronic disease, knee pain in the past month, and the degree of self-reported knee discomfort were significant predictors of self-management. Conclusion The self-management of knee discomfort in middle-aged and elderly people is poor, and the degree of discomfort is a significant predictor. Healthcare providers should consider socioeconomic demographic and clinical characteristics to help these individuals improve their self-management skills. Attention should also be given to improving their ability to access health information and making them aware of disease risks.

Mastitis is a significant inflammatory condition of the mammary gland in dairy cows. It is caused by bacterial infections and leads to substantial economic losses worldwide. The disease can be either clinical or sub-clinical and presents challenges such as reduced milk yield, increased treatment costs, and the need to cull affected cows. The pathogenic mechanisms of mastitis involve the activation of Toll-like receptors (TLRs), specifically TLR2 and TLR4. These receptors play crucial roles in recognizing pathogen-associated molecular patterns (PAMPs) and initiating immune responses through the NF-κB signaling pathway. Recent in vitro studies have emphasized the importance of the TLR2/TLR4/NF-κB signaling pathway in the development of mastitis, suggesting its potential as a therapeutic target. This review summarizes recent research on the role of the TLR2/TLR4/NF-κB signaling pathway in mastitis. It focuses on how the activation of TLRs leads to the production of proinflammatory cytokines, which, in turn, exacerbate the inflammatory response by activating the NF-κB signaling pathway in mammary gland tissues. Additionally, the review discusses various bioactive compounds and probiotics that have been identified as potential therapeutic agents for preventing and treating mastitis by targeting TLR2/TLR4/NF-κB signaling pathway. Overall, this review highlights the significance of targeting the TLR2/TLR4/NF-κB signaling pathway to develop effective therapeutic strategies against mastitis, which can enhance dairy cow health and reduce economic losses in the dairy industry.

Microorganisms in the rumen play a crucial role in determining the most efficient utilization rate of nutrients. Among these microorganisms, Prevotella stands out as one of the most representative bacteria within the rumen biological system. Prevotella is a common strict anaerobic bacterium that is found in the gastrointestinal tract of livestock. Prevotella plays a crucial role in breaking down and metabolizing complex nutrients like cellulose and protein during food digestion. Moreover, it is capable of working together with other bacteria in the body’s digestive system. Several studies have shown a strong correlation between the abundance of Prevotella and livestock growth performance. This paper provides a comprehensive review of the current research on the function, mechanisms, and applications of Prevotella in the gastrointestinal tract. The insights provided in this review could serve as a theoretical basis for accurately classifying Prevotella, further investigating its effects and potential mechanisms on livestock growth performance, and exploring its practical applications.

Variation in coat color among equids has attracted significant interest in genetics and breeding research. The range of colors is primarily determined by the type, concentration, and distribution of melanin pigments, with the balance between eumelanin and pheomelanin influenced by numerous genetic factors. Advances in genomic and sequencing technologies have enabled the identification of several candidate genes that influence coat color, thereby clarifying the genetic basis of these diverse phenotypes. In this review, we concisely categorize coat coloration in horses and donkeys, focusing on the biosynthesis and types of melanin involved in pigmentation. Moreover, we highlight the regulatory roles of some key candidate genes, such as MC1R, TYR, MITF, ASIP, and KIT, in coat color variation. Moreover, the review explores how coat color relates to selective breeding and specific equine diseases, offering valuable insights for developing breeding strategies that enhance both the esthetic and health aspects of equine species.

Abstract Background The ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian decline characterized by abnormal ovarian reserve tests is regarded as a critical measure to prevent infertility. However, the relevant data are typically stored in an unstructured format in a hospital’s electronic medical record (EMR) system, and their retrieval requires tedious manual abstraction by domain experts. Computational tools are therefore needed to reduce the workload. Methods We presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based disease scoring model, to automatize the screening procedure of premature ovarian decline using Chinese reproductive EMRs. We used regular expressions (REs) as a text mining method and explored whether REs automatically synthesized by the genetic programming-based online platform RegexGenerator + + could be as effective as manually formulated REs. We also investigated how the representativeness of the learning corpus affected the performance of machine-generated REs. Additionally, we translated the clinical diagnostic criteria into a programmable disease diagnostic model for disease scoring and risk stratification. Four hundred outpatient medical records were collected from a Chinese fertility center. Manual review served as the gold standard, and fivefold cross-validation was used for evaluation. Results The overall F-score of manually built REs was 0.9444 (95% CI 0.9373 to 0.9515), with no significant difference (paired t test p > 0.05) compared with machine-generated REs that could be affected by training set sizes and annotation portions. The extractor performed effectively in automatically tracing the dynamic changes in hormone levels (F-score 0.9518–0.9884) and ultrasonographic measures (F-score 0.9472–0.9822). Applying the extracted information to the proposed diagnostic model, the program obtained an accuracy of 0.98 and a sensitivity of 0.93 in risk screening. For each specific disease, the automatic diagnosis in 76% of patients was consistent with that of the clinical diagnosis, and the kappa coefficient was 0.63. Conclusion A Chinese NLP system named RegEMR was developed to automatically identify high risk of early ovarian aging and diagnose related diseases from Chinese reproductive EMRs. We hope that this system can aid EMR-based data collection and clinical decision support in fertility centers.

Copy number variations (CNVs) have garnered increasing attention within the realm of genetics due to their prevalence in human, animal, and plant genomes. These structural genetic variations have demonstrated associations with a broad spectrum of phenotypic diversity, economic traits, environmental adaptations, epidemics, and other essential aspects of both plants and animals. Furthermore, CNVs exhibit extensive sequence variability and encompass a wide array of genomes. The advancement and maturity of microarray and sequencing technologies have catalyzed a surge in research endeavors pertaining to CNVs. This is particularly prominent in the context of livestock breeding, where molecular markers have gained prominence as a valuable tool in comparison to traditional breeding methods. In light of these developments, a contemporary and comprehensive review of existing studies on CNVs becomes imperative. This review serves the purpose of providing a brief elucidation of the fundamental concepts underlying CNVs, their mutational mechanisms, and the diverse array of detection methods employed to identify these structural variations within genomes. Furthermore, it seeks to systematically analyze the recent advancements and findings within the field of CNV research, specifically within the genomes of herbivorous livestock species, including cattle, sheep, horses, and donkeys. The review also highlighted the role of CNVs in shaping various phenotypic traits including growth traits, reproductive traits, pigmentation and disease resistance etc., in herbivorous livestock. The main goal of this review is to furnish readers with an up-to-date compilation of knowledge regarding CNVs in herbivorous livestock genomes. By integrating the latest research findings and insights, it is anticipated that this review will not only offer pertinent information but also stimulate future investigations into the realm of CNVs in livestock. In doing so, it endeavors to contribute to the enhancement of breeding strategies, genomic selection, and the overall improvement of herbivorous livestock production and resistance to diseases.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Abstract Understanding the molecular mechanisms that regulate grain yield is important for improving agricultural productivity. Protein ubiquitination controls various aspects of plant growth but lacks understanding on how E2-E3 enzyme pairs impact grain yield in major crops. Here, we identified a RING-type E3 ligase SGD1 and its E2 partner SiUBC32 responsible for grain yield control in Setaria italica. The conserved role of SGD1 was observed in wheat, maize, and rice. Furthermore, SGD1 ubiquitinates the brassinosteroid receptor BRI1, stabilizing it and promoting plant growth. Overexpression of an elite SGD1 haplotype improved grain yield by about 12.8% per plant, and promote complex biological processes such as protein processing in endoplasmic reticulum, stress responses, photosystem stabilization, and nitrogen metabolism. Our research not only identifies the SiUBC32-SGD1-BRI1 genetic module that contributes to grain yield improvement but also provides a strategy for exploring key genes controlling important traits in Poaceae crops using the Setaria model system.

A novel, highly efficient terahertz fully polarized transmission line is designed by two-dimensional tellurium photonic crystals consisting of square lattice rod arrays with a complete photonic bandgap. The TE and TM photonic bandgaps of the tellurium photonic crystals, which are computed by plane wave expansion, happen to coincide, and the complete photonic bandgap covers from 2.894 to 3.025 THz. The function of the designed waveguide is simulated by the finite element method, and the transmission characteristics are optimized by accurately adjusting its structural parameters. The transmission efficiency of the waveguide for TE mode achieves a peak value of −0.34 dB at a central frequency of 2.950 THz and keeps above −3 dB from 2.82 THz to 3.02 THz, obtaining a broad relative bandwidth of about 6.84 percent. The operating bandwidth of the tellurium photonic crystals’ waveguide for TM mode is narrower than that of TE mode, whose relative bandwidth is about 4.39 percent or around 2.936 THz above −5 dB. The designed terahertz photonic crystals’ waveguide can transmit both TE and TM waves, and not only can it be used as a high-efficiency transmission line, but it also provides a promising approach for implementing fully polarized THz devices for future 6G communication systems.

This study aims to analyze the load-bearing characteristics of non-pneumatic tires with composite spokes using experimental and finite element simulation methods and to establish a mechanical analysis model based on the Timoshenko beam theory. Subsequently, experiments were conducted on carbon fiber-reinforced plastics and rubbers to establish the corresponding constitutive model. A finite element model of the non-pneumatic tires with composite spokes was also developed. The main structural and material parameters were selected, and their correlation with the vertical stiffness of the non-pneumatic tires with composite spokes was studied using response surface methodology. The stiffness characteristics of the composite spokes were simplified, and a load-bearing characteristic analysis model was established. The results indicated that among the parameters of the reinforcement plate structure and rubber, the constitutive parameter C10 of the rubber in the spokes had the greatest impact, with a comprehensive influence value of 319.83 N/mm. Under a load of 5000 N, the load-bearing characteristic analysis model results were consistent with those of the finite element simulation, with a maximum relative error of 7.49%. The proposed load-bearing characteristic analysis model can assist in the rapid design and performance prediction of non-pneumatic tires with composite spokes.

Meox1 is a critical transcription factor that plays a pivotal role in embryogenesis and muscle development. It has been established as a marker gene for growth-specific muscle stem cells in zebrafish. In this study, we identified the SsMeox1 gene in a large teleost fish, Sebastes schlegelii. Through in situ hybridization and histological analysis, we discovered that SsMeox1 can be employed as a specific marker of growth-specific muscle stem cells, which originate from the somite stage and are primarily situated in the external cell layer (ECL) and myosepta, with a minor population distributed among muscle fibers. The knockdown of SsMeox1 resulted in a significant increase in Ccnb1 expression, subsequently promoting cell cycle progression and potentially accelerating the depletion of the stem cell pool, which ultimately led to significant growth retardation. These findings suggest that SsMeox1 arrests the cell cycle of growth-specific muscle stem cells in the G2 phase by suppressing Ccnb1 expression, which is essential for maintaining the stability of the growth-specific muscle stem cell pool. Our study provides significant insights into the molecular mechanisms underlying the indeterminate growth of large teleosts.

DNA methylation represents a predominant epigenetic modification with broad implications in various biological functions. Its role is particularly significant in the process of collagen deposition, a fundamental aspect of dermal development in donkeys. Despite its critical involvement, the mechanistic insights into how DNA methylation influences collagen deposition in donkey skin remain limited. In this study, we employed whole genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) to investigate the epigenetic landscape and gene expression profiles in the dorsal skin tissues of Dezhou donkeys across three developmental stages: embryonic (YD), juvenile (2-year-old, MD), and mature (8-year-old, OD). Our analysis identified numerous differentially methylated genes that play pivotal roles in skin collagen deposition and overall skin maturation, including but not limited to COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, GLUL, SFRP2, FOSL1, SERPINE1, MMP1, MMP2, MMP9, and MMP13. Notably, we observed an inverse relationship between gene expression and DNA methylation proximal to transcription start sites (TSSs), whereas a direct correlation was detected in regions close to transcription termination sites (TTSs). Detailed bisulfite sequencing analyses of the COL1A1 promoter region revealed a low methylation status during the embryonic stage, correlating with elevated transcriptional activity and gene expression levels. Collectively, our findings elucidate key genetic markers associated with collagen deposition in the skin of Dezhou donkeys, underscoring the significant regulatory role of DNA methylation. This research work contributes to the foundational knowledge necessary for the genetic improvement and selective breeding of Dezhou donkeys, aiming to enhance skin quality attributes.

Idiopathic pulmonary haemosiderosis is a rare disease primarily affecting children. The condition is characterized by widespread bleeding from alveolar capillaries, resulting in symptoms such as haemoptysis, shortness of breath and iron deficiency anaemia. However, it is not a specific disease and sometimes can manifest solely as anaemia, which may be easily overlooked and misdiagnosed. The purpose of this case report was to describe a 1-year-old boy who exhibited haemolytic anaemia as the only symptom of idiopathic pulmonary haemosiderosis, with the intention of offering clinical insights into the precise diagnosis and subsequent management of this rare and easily misdiagnosed disease. Clinicians should keep idiopathic pulmonary haemosiderosis in mind when evaluating children with haemolytic anaemia and promptly initiate testing and treatment to prevent misdiagnosis and improve outcomes.

Abstract Astrocytes play a crucial role in regulating sleep–wake behavior, and adenosine signaling is generally thought to be involved. Here we show multiple lines of evidence supporting that modulation of the sleep–wake behavior by astrocyte Ca2+ activity could occur without adenosine signaling. In the basal forebrain and the brainstem, two brain regions that are known to be essential for sleep–wake regulation, chemogenetically-induced astrocyte Ca2+ elevation significantly modulated the sleep–wake cycle. Although astrocyte Ca2+ level positively correlated with the amount of extracellular adenosine, as revealed by a genetically encoded adenosine sensor, we found no detectable change in adenosine level after suppressing astrocyte Ca2+ elevation, and transgenic mice lacking one of the major extracellular ATP-adenosine conversion enzymes showed similar extracellular adenosine level and astrocyte Ca2+-induced sleep modulation. Furthermore, astrocyte Ca2+ is dependent primarily on local neuronal activity, causing brain region-specific regulation of the sleep–wake cycle. Thus, neural activity-dependent astrocyte activity could regulate the sleep–wake behavior independent of adenosine signaling.

In this article, a broadband frequency measurement system based on optical spectrum manipulation and stimulated Brillouin scattering (SBS) is proposed and experimentally demonstrated, which can implement wide-range and high-resolution frequency measurement, and generate corresponding broadband interference signals in some frequency bands, that is, it has versatility. Since the frequency range of the pump light to excite SBS is expanded based on the optical spectrum manipulation and high resolution is ensured by the narrow scattering spectrum of SBS, high-resolution spectrum measurement covering a wide frequency band is finally implemented. In addition, the double sideband pump light can be used as the source of electronic interference signal. In the experiments, the frequency measurement range of the proposed system is tested to be 0.03–44 GHz, and the resolution is up to 3 MHz. Furthermore, the frequency measurement and electronic interference sharing the same work band can be carried out in the ranges of 6.3367–7.8367 GHz and 20.36–22.16 GHz.

Abstract Machine-learning models have recently encountered enormous success for predicting the properties of materials. These are often trained based on data that present various levels of accuracy, with typically much less high- than low-fidelity data. In order to extract as much information as possible from all available data, we here introduce an approach which aims to improve the quality of the data through denoising. We investigate the possibilities that it offers in the case of the prediction of the band gap using both limited experimental data and density-functional theory relying on different exchange-correlation functionals. After analyzing the raw data thoroughly, we explore different ways to combine the data into training sequences and analyze the effect of the chosen denoiser. We also study the effect of applying the denoising procedure several times until convergence. Finally, we compare our approach with various existing methods to exploit multi-fidelity data and show that it provides an interesting improvement.

The cortex is very active during sleep. Wang et al. used macroscopic Ca2+ imaging to record the global cortical activity from the entire dorsal cortex of mice during sleep and uncover an unexpected role of the cortex in controlling REM sleep.

Liupao tea as a type of dark tea can relieve irritable bowel syndrome by regulating gut microbiota, but the mechanism has not been fully explained. An ultra-high performance liquid chromatography along with quadrupole time of flight tandem mass spectrometry was used to analyze the phytochemicals in Liupao tea. Then, we explored the effects of Liupao tea against IBS. From the results of chemical analysis, we identified catechins, polyphenols, amino acids, caffeine, polysaccharides and other components in Liupao tea. The open-field test, gastrointestinal function-related indexes, histochemical assays, measurements of cytokine and aquaporin 3 (AQP3), and determination of serum metabolites were utilized to monitor the physiological consequences of Liupao tea administration in rats with irritable bowel syndrome. The results showed that Liupao tea had a significant protective effect on irritable bowel syndrome. Liupao tea increased locomotive velocity while reducing interleukin-6, interleukin-1β, and tumor necrosis factor-α levels, as well as gastrointestinal injury. Moreover, Liupao tea increased the AQP3 levels of renal tissues but reduced the AQP3 levels of gastrointestinal tissues. Liupao tea reduced the Firmicutes/Bacteroides ratio and significantly reconstructed the microbial pattern. Liupao tea relieved irritable bowel syndrome by repairing gastrointestinal dysfunction, regulating the secretion of pro-inflammatory cytokines, modulating water metabolism, and restoring microbial homeostasis.

In livestock breeding, the number of vertebrae has gained significant attention due to its impact on carcass quality and quantity. Variations in vertebral traits have been observed across different animal species and breeds, with a strong correlation to growth and meat production. Furthermore, vertebral traits are classified as quantitative characteristics. Molecular marker techniques, such as marker-assisted selection (MAS), have emerged as efficient tools to identify genetic markers associated with vertebral traits. In the current review, we highlight some key potential genes and their polymorphisms that play pivotal roles in controlling vertebral traits (development, length, and number) in various livestock species, including pigs, donkeys, and sheep. Specific genetic variants within these genes have been linked to vertebral development, number, and length, offering valuable insights into the genetic mechanisms governing vertebral traits. This knowledge has significant implications for selective breeding strategies to enhance structural characteristics and meat quantity and quality in livestock, ultimately improving the efficiency and quality of the animal husbandry industry.

Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data.

Climate change and the increasing frequency of high temperature (HT) events are significant threats to global crop yields. To address this, a comprehensive understanding of how plants respond to heat shock (HS) is essential. Signaling pathways involving calcium (Ca2+), a versatile second messenger in plants, encode information through temporal and spatial variations in ion concentration. Ca2+ is detected by Ca2+-sensing effectors, including channels and binding proteins, which trigger specific cellular responses. At elevated temperatures, the cytosolic concentration of Ca2+ in plant cells increases rapidly, making Ca2+ signals the earliest response to HS. In this review, we discuss the crucial role of Ca2+ signaling in raising plant thermotolerance, and we explore its multifaceted contributions to various aspects of the plant HS response (HSR).

Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations.

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase–amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

IntroductionCOVID-related work changes have seriously disrupted employees’ familiar routines and hampered their lives and work. Although this topic has drawn rising attention, to our knowledge, limited studies have investigated the impact of COVID-related work changes on employees’ mentality and behavior. In this paper, we developed a moderated mediation model based on ego depletion theory to test how and when COVID-related work changes impact employees’ mental health, interpersonal conflict, and aggression behavior.MethodsWe collected 536 valid participants by conducting a questionnaire survey in a large Chinese manufacturing company, and tested our proposed theoretical model and hypotheses using SPSS 26.0 and Mplus 8.1.ResultsThe empirical results showed that COVID-related work changes would harm employees’ mental health and boost their interpersonal conflict and aggression via increasing their ego depletion. Moreover, trait resilience has an intervention in the relationship between COVID-related work changes and employees’ ego depletion, which weakens the indirect impact of COVID-related work changes on mental health, interpersonal conflict, and aggression.DiscussionThese findings suggest that although COVID-related work changes were inevitable, managers should take measures to improve the employees’ mental status and avoid conflicts promptly while taking steps to keep organizations on track.

Gut microbiota provides protection against arsenic (As) induced toxicity, and As metabolism is considered an important part of risk assessment associated with soil As exposures. However, little is known about microbial iron(III) reduction and its role in metabolism of soil-bound As in the human gut. Here, we determined the dissolution and transformation of As and Fe from incidental ingestion of contaminated soils as a function of particle size (

Abstract Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker‐trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker‐assisted selection.

Abstract Background Diabetic retinopathy (DR), one of the most common and severe microvascular complication of diabetes mellitus (DM), is mainly caused by diabetic metabolic disorder. So far, there is no effective treatment for DR. Eriocauli Flos, a traditional Chinese herb, has been used in treating the ophthalmic diseases including DR. However, the active ingredients and molecular mechanisms of Eriocauli Flos to treat diabetic retinopathy remain elusive. Methods Here, the systems pharmacology model was developed via constructing network approach. 8 active components which were screened by oral bioavailability (OB ≥ 30%) and drug-likeness (DL ≥ 0.18) and 154 targets were selected from Eriocauli Flos through TCMSP database. Another 3593 targets related to DR were obtained from Genecards, OMIM, TTD, and Drugbank databases. The 103 intersecting targets of DR and Eriocauli Flos were obtained by Draw Venn Diagram. In addition, protein-protein interaction network was established from STRING database and the compound-target network was constructed by Cytoscape which screened top 12 core targets with cytoNCA module. Then the overlapping targets were analyzed by GO and KEGG enrichment. Moreover, two core targets were selected to perform molecular docking simulation. Subsequently, CCK8 assay, RT-PCR and Western blotting were applied to further reveal the mechanism of new candidate active component from Eriocauli Flos in high glucose-induced HRECs. Results The results showed that the overlapping targets by GO analysis were enriched in cellular response to chemical stress, response to oxidative stress, response to reactive oxygen species, reactive oxygen species metabolic process and so on. Besides, the overlapping targets principally regulated pathways such as AGE-RAGE signaling pathway in diabetic complications, lipid atherosclerosis, fluid shear stress and atherosclerosis, and PI3K-Akt signaling pathway. Molecular docking exhibited that VEGFA and TNF-α, had good bindings to the great majority of compounds, especially the compound hispidulin. In vitro, hispidulin ameliorated high-glucose induced proliferation by down-regulating the expression of p-ERK, p-Akt, and VEGFA; meanwhile inhibited the mRNA levels of TNF-α. Conclusions In this study, through network pharmacology analysis and experimental validation, we found that hispidulin maybe has a potential targeted therapy effect for DR by decreasing the expression of p-Akt, p-ERK, and VEGFA, which resulted in ameliorating the proliferation in HRECs.

BackgroundAlthough Alzheimer's disease (AD) mainly affects cognitive function, it is often accompanied by sleep disorders and psychobehavioral symptoms. These symptoms, including depression, agitation, and psychotic symptoms, are prominent hospitalization causes among patients with AD. Currently, relatively more research exists on light therapy for sleep disorders, while those on psychobehavioral symptoms are gradually increasing. However, no consensus exists on these results because of the vulnerability of light therapy to multiple factors, including light intensity and duration. Thus, further research investigating this aspect is warranted.ObjectiveTo evaluate the efficacy of light therapy in improving sleep disorders and psychobehavioural symptoms in patients with AD.MethodsIn this meta-analysis, relevant literature was searched in Embase, the Clinical Trials Registry, Web of Science, PubMed, and the Cochrane Library up to December 2022. Furthermore, a fixed-effects model was used for data analysis.ResultsFifteen randomized controlled trials involving 598 patients with AD were included. In the case of sleep disorders, our meta-analysis revealed that light therapy significantly improved sleep efficiency (MD = -2.42, 95% CI = -3.37 to -1.48, p < 0.00001), increased interdaily stability (MD = -0.04, 95% CI = -0.05 to -0.03, p < 0.00001), and reduced intradaily variability (MD = -0.07, 95% CI = -0.10 to -0.05, p < 0.00001). With respect to psychotic behavior, light therapy was found to alleviate depression (MD = -2.55, 95% CI = -2.98 to -2.12, p < 0.00001) as well as reduce agitation (MD = -3.97, 95% CI = -5.09 to -2.84, p < 0.00001) and caregiver burden (MD = -3.57, 95% CI = -5.28 to -1.87, p < 0.00001).ConclusionLight therapy leads to significant improvement in sleep and psychobehavioral symptoms and is associated with relatively fewer side effects in patients with AD, indicating its potential as a promising treatment option for AD.

Heterologous expression is an indispensable approach to exploiting natural products from phylogenetically diverse microbial communities. In this study, we constructed a heterologous expression system based on strain Burkholderia thailandensis E264 by deleting efflux pump genes and screening constitutive strong promoters. The biosynthetic gene cluster (BGC) of disorazol from Sorangium cellulosum So ce12 was expressed successfully with this host, and the yield of its product, disorazol F2, rather than A1, was improved to 38.3 mg/L by promoter substitution and insertion. In addition to the disorazol gene cluster, the BGC of rhizoxin from Burkholderia rhizoxinica was also expressed efficiently, whereas no specific peak was detected when shuangdaolide BGC from Streptomyces sp. B59 was transformed into the host. This system provides another option to explore natural products from different phylogenetic taxa.

The high Asia region is the water tower for many river valleys in East and South Asia, which has vital significance for regional environmental conservation and ecological security. In the upper Indus Valley, the role of forests is often overlooked, and their loss will lead to a reduction in ecological services, such as water conservation and disaster prevention, and will indirectly affect the water and food security of 230 million people in South Asia. Quantifying the drivers of forest cover change is a prerequisite for ensuring sustainable development in the upper Indus Valley. This study used forest disturbance and recovery data, history fire data, topographic data, and land use data to construct a decision tree model for mining the of drivers of forest cover change. Classification of the drivers of forest disturbance and recovery was achieved in the upper Indus Valley. The results showed that (1) disturbance mainly occurred in the central region of the upper Indus Valley, where forest degradation was the main driver accounting for 69.0%, and agricultural transfer was the secondary driver accounting for 21.6%; commercial cultivation and deforestation drivers both disturbed about 4% of the area, and fire, human activities, and natural hazards disturbed only a small area of forest accounting for less than 1%; (2) the spatial distribution of recovery was basically consistent with the disturbance, with natural recovery being the main driver of forest recovery, accounting for 60.1%, and artificial recovery being the secondary driver, accounting for 39.9%; (3) relevant governance measures or forest conversation policies in the Indus Valley were recommended, such as increasing the supply of non-biomass energy, controlling the scale of livestock, and establishing friendly land use policies, to maintain the growth and balance of forest area and quality.