Climate change is accelerating the circulation of the chikungunya virus (CHIKV), posing a significant threat to both endemic areas and immunologically naive temperate regions. This study aims to synthesize empirical climatic drivers, future transmission projections, and global adaptation strategies to evaluate current evidence and identify key research gaps. This scoping review completed protocol registration on the Open Science Framework prior to literature retrieval, with systematic searches conducted across PubMed, Web of Science, Scopus and EBSCOhost to collect all relevant English peer-reviewed papers published between Jan 1, 2000 and Nov 1, 2025. Multi-level screening filtered out unqualified literature, and unified datasets related to epidemiological parameters, predictive models and adaptation measures were extracted from 104 eligible studies. The results indicate that: (1) Current evidence reveals post-2014 research favors Europe and South America, disproportionately focusing on temperature (n = 38) and precipitation (n = 32). CHIKV transmission exhibits a non-linear thermal optimum (23–30 °C) and a 1-to-4-week lag following extreme precipitation. (2) Future projections (n = 24) consistently indicate transboundary vector expansion into higher latitudes and altitudes. (3) Regarding adaptation strategies, we conceptualized a three-tiered intervention framework. However, a stark socioeconomic-climatic gap emerged: while 43

BACKGROUND:Rhipicephalus microplus, a one-host tick species, serves as a principal vector of tick-borne diseases in agricultural ecosystems worldwide by harboring and transmitting various pathogens through blood-feeding. In China, the climatically suitable range of R. microplus has been gradually expanding. However, the climatic suitability of R. microplus under future climate change scenarios remains unclear. METHODS:This study evaluates both current and projected climatic suitability of R. microplus by integrating climatic variables, thereby providing insight into shifts in climatic suitability under present and future climate conditions. The MaxEnt model was applied using 78 occurrence records of R. microplus collected from 1970 to 2023, along with 19 environmental variables obtained from WorldClim. By identifying the most influential environmental factors affecting the climatic suitability of R. microplus, we predicted future changes under three Shared Socioeconomic Pathways (SSP126, SSP245, SSP585) for three future periods (2021-2040, 2041-2060, and 2061-2080). RESULTS:This study indicates that the current climatically suitable areas for R. microplus are mainly located in southern China, covering approximately 1,051,406 km2, which accounts for about 10.91% of China's total land area. The minimum temperature of the coldest month (Bio06, 69.6%) and precipitation of the warmest quarter (Bio18, 20.1%) were identified as the most influential climatic variables. Under future climate scenarios, the suitable habitat for R. microplus is projected to expand and shift northward. By 2061-2080 under the SSP585, the suitable area could reach up to 2,994,700 km2, representing a 2.85-fold increase relative to its current extent. CONCLUSIONS:This study projects a significant northward expansion of climatic suitability for R. microplus in mainland China under future climate scenarios, driven primarily by rising minimum winter temperatures. These findings highlight an urgent need for proactive, climate-integrated surveillance and adaptive control strategies to mitigate the growing threat of this tick vector and its associated diseases in newly vulnerable regions.

2-Ethylhexyl diphenyl phosphate (EHDPHP) is a widely used organophosphorus flame retardant frequently detected in environmental matrices and poses potential health risks. However, its cardiotoxic effects on mammalian cardiomyocytes and the underlying molecular mechanisms remain largely unclear. Niacin (NIA), an essential water-soluble vitamin, exhibits potent antioxidant, anti-inflammatory, and mitochondrial-protective activities. In this study, we investigated EHDPHP-induced toxicity in H9C2 cardiomyocytes and the protective effects of NIA. Cells were exposed to 100 μM EHDPHP alone or in combination with 400, 600, and 800 μM NIA. EHDPHP exposure significantly reduced cell viability, disrupted the balance between pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-18) and the anti-inflammatory cytokine IL-10, and induced oxidative stress, as evidenced by elevated ROS, MitoSOX, and MDA levels alongside decreased activities of antioxidant enzymes (SOD, GSH, GSH-Px, and CAT). Additionally, EHDPHP disturbed mitochondrial dynamics by promoting fission and inhibiting fusion, impaired mitochondrial biogenesis via downregulation of AMPK and PGC-1α, triggered excessive mitophagy through PINK1, PRKN, and LC3 upregulation, and activated pyroptosis via GSDMD, NLRP3, and Caspase-1. Bioinformatics analyses confirmed the interconnected regulatory network among mitochondrial dynamics, mitophagy, pyroptosis, and inflammatory signaling in EHDPHP-induced cardiomyocyte injury. Notably, NIA intervention dose-dependently mitigated these detrimental effects, restoring cell viability, alleviating inflammation and oxidative stress, rebalancing mitochondrial fusion and fission, rescuing biogenesis, normalizing mitophagy, and inhibiting pyroptosis. These findings reveal a pathological cascade through which EHDPHP induces cardiomyocyte injury and demonstrate that NIA confers cardioprotection by targeting multiple pathological pathways. This study provides novel mechanistic insights into EHDPHP-induced cardiotoxicity and highlights NIA as a promising nutritional intervention for reducing cardiovascular risks associated with environmental pollutants.

Cattle diseases severely threaten the global agricultural economy. Traditional disease management, heavily reliant on manual observation, is inherently delayed and subjective. Artificial intelligence (AI) is driving a profound paradigm shift in veterinary diagnostics. By enabling proactive and scalable health monitoring, AI effectively mitigates the economic losses associated with delayed interventions. However, existing reviews primarily focus on individual diseases or specific scenarios, lacking a systematic analysis of cross-disease applications and technological evolution. Consequently, this paper provides a comprehensive review of AI applications in cattle disease diagnosis, with a focus on technological evolution, key challenges, and future directions. A systematic search across four databases (Web of Science, PubMed, Scopus, and EBSCOhost) yielded 316 studies for bibliometric and thematic analysis. Grounded in pathophysiological systems and veterinary clinical practice, the literature is systematically categorized into six core domains: locomotor and superficial anomalies, reproductive and lactation diseases, metabolic and digestive disorders, respiratory conditions, infectious disease prevention, and non-specific health monitoring. The analysis reveals a clear methodological transition from early contact-based sensing and traditional machine learning to non-contact techniques centered on computer vision and deep learning, with current advances toward multimodal data fusion. Vision-based methods are primarily applied to diseases with clear phenotypic traits, whereas multimodal approaches integrating physiological, behavioral, and environmental data demonstrate superior performance in identifying complex and subclinical conditions. Despite these advancements, real-world applications remain constrained by high data heterogeneity, insufficient standardization, multimodal alignment challenges, limited model interpretability, and high deployment costs. Furthermore, practical deployment is also constrained by ethical considerations and environmental complexity. In summary, AI provides valuable technical tools for the early identification, risk assessment, and intervention of cattle diseases. Future research should prioritize multimodal fusion and cloud-edge-device collaborative architectures, while improving deployment efficiency in complex environments through hierarchical computing, real-time decision-making, and advances in explainable AI.

BACKGROUND:Malaria, transmitted by Anopheles mosquitoes, remains a major global health threat. While China achieved malaria-free certification in 2021, the country remains vulnerable to imported cases, a risk exacerbated by climate change and human activities. Accurate predictions of malaria vector distributions are essential for effective prevention and management. RESULTS:We employed the Bayesian additive regression trees (BART) model to assess the current distribution of Anopheles sinensis and Anopheles lesteri in China, incorporating recent occurrence data alongside climatic, soil, human activity, and vegetation variables. The model projected future distribution patterns under three Shared Socioeconomic Pathways (SSP126, SSP245, SSP585) for the periods 2041-2060, 2061-2080, and 2081-2100. An. sinensis is currently widespread in southern, eastern, and central China, primarily influenced by the human footprint and precipitation during the driest month. An. lesteri is more confined to southern and central regions, largely shaped by the normalized difference vegetation index (NDVI) and human footprint. Both species are projected to shift northward under future climate scenarios. By 2081-2100, under the high-emission SSP585 scenario, the potential suitability of An. sinensis is expected to decline by 5.59%, while An. lesteri's suitability is projected to increase by 61.32%. CONCLUSION:Our findings suggest An. lesteri is more resilient to climate change than An. sinensis, with a greater potential for northern expansion. The complex interplay of environmental factors underscores the need for integrated vector management strategies. Adopting a One Health framework - recognizing the interconnected health of humans, animals, and ecosystems - is essential for maintaining China's malaria-free status and preventing the re-establishment of malaria transmission. © 2025 Society of Chemical Industry.

Accurately estimating the contribution of afforestation/deforestation to gross primary productivity (GPP) of an ecosystem is necessary to develop future afforestation policies. However, there is currently a lack of quantitative assessments of the potential consequences of afforestation and deforestation on GPP at a global scale. In this study, we used a 30 m high-resolution forest raster map and a satellite-driven GPP product to assess GPP differences under various afforestation/deforestation scenarios, using spatial rather than temporal comparisons. Our results showed that (1) the simultaneous occurrence of high-intensity afforestation and deforestation was extremely low globally (4.64%). Under this hypothetical scenario, the potential GPP of afforestation could reach 734.13 g C m-2 yr-1, significantly higher than that in the other scenarios. While the percentage of concurrent medium- to low-intensity afforestation and deforestation was up to 41.37%, the potential value of afforestation to promote GPP increase was only 219.56 g C m-2 yr-1. (2) The potential of afforestation to boost GPP varied significantly across space and time. Proximity to equatorial forests, such as evergreen broad-leaved forests, generally facilitate GPP accumulation. However, as latitudinal zonality increased, the fixed GPP potential of high-latitude coniferous forests decreased significantly. Summer (particularly June) showed the highest potential for afforestation to enhance GPP, more than twice as much as in the other seasons, and this pattern was consistent globally. (3) Afforestation costs vary substantially depending on forest type and cover. Afforestation in rainforest areas with a better water-heat balance often requires a higher GPP to achieve the desired effect. Low-density forests dominated by temperate or cold zones yield significantly lower GPP benefits than afforestation in tropical rainforests. This study quantifies the potential impact of afforestation on GPP for the first time and provides guidelines for future afforestation planning across various regions.

This study aims to analyze the driving factors and threshold responses of the NDVI across different regional scales in Hunan Province, revealing the main influences on vegetation cover and the corresponding threshold effects and providing essential data for precise future afforestation planning. We use NDVI data and its associated driving factors, employing correlation analysis methods to investigate the spatial differentiation and threshold effects of vegetation driving factors at different regional scales. First, various analytical techniques, including Sen’s trend analysis, the Mann–Kendall significance test, and the Hurst index, are applied to assess changes in vegetation cover between 2000 and 2020 and to predict future trends. Second, to explore the differences in vegetation’s driving mechanisms at different regional scales, the optimal parameters-based geographic detector model is employed, which integrates continuous variable discretization methods and selects the optimal parameter set by maximizing explanatory power. This approach is particularly suitable for analyzing nonlinear relationships. Lastly, threshold regression analysis is conducted on the key driving factors identified through the optimal parameters-based geographic detector model. The results show that vegetation cover in most areas of Hunan significantly increased from 2000 to 2020; however, our predictions suggest slight degradation in the future. The optimal parameters-based geographic detector model identified topography and geomorphology as the primary factors affecting the spatial and temporal distribution of the NDVI, with notable regional differences in other factors. The influence of natural factors has weakened over time, while anthropogenic activities increasingly affect vegetation. Moreover, dual-factor influences exhibit stronger explanatory power than single-factor influences. The threshold response analysis reveals that slope is a key factor influencing the NDVI, with a positive threshold relationship observed at both the provincial and subregional scales, although the threshold points vary by subregion. The temperature and NDVI are negatively correlated, with varying threshold points across regions. The abovementioned research findings suggest that future afforestation efforts in Hunan should take into account the distinct characteristics of each subregion. Afforestation strategies should be tailored based on the specific threshold relationships observed in each area to enhance their effectiveness.

2-ethylhexyl diphenyl phosphate (EHDPHP), a ubiquitous organic phosphorus flame retardant, is extensively employed in food packaging, flame retardancy applications, and other industrial processes as a crucial chemical. However, recent discoveries have underscored its diverse toxic effects, posing a severe potential threat to biological health. Skeletal muscle, vital for locomotion, relies heavily on proper differentiation during development and maintenance for strength and functionality. This study investigated the impact of EHDPHP on skeletal muscle differentiation by culturing C2C12 cells with varying concentrations of EHDPHP during induction of differentiation, as well as constructing a mouse muscle injury repair model through oral administration of 30 μg/kg EHDPHP for 7 weeks. Exposure to EHDPHP was found to impede skeletal muscle differentiation in mice. Utilizing techniques such as western blot analysis, RNA-seq, and qRT-PCR, our preliminary analysis revealed that low concentrations of EHDPHP inhibited the expression of ACTC1, subsequently suppressing MyoG and MYH2 expression levels, thereby hindering C2C12 myoblast differentiation. Furthermore, EHDPHP exposure disrupted the repair process of muscle injury in mice and compromised the integrity of the repaired tissue. In conclusion, these findings elucidate the detrimental effects of EHDPHP exposure on muscle differentiation, uncovering its muscle toxicity.

This study aims to provide a theoretical foundation for the future management of diabetes at various stages induced by a high-fat diet. Specifically, it seeks to determine the appropriate pharmacological interventions for each phase of diabetes development and the targeted therapeutic directions at different stages of diabetes progression. This investigation employed C57BL6 mice as experimental subjects, successfully establishing an insulin resistance model through a 12-week high-fat diet. Clinical manifestations, weight, body composition, and overall health of each mouse group were observed on the first day of the 6th, 8th, 10th, and 12th week of high-fat feeding to analyze insulin resistance. Subsequently, open-field test of each mouse group, and histopathological changes in the skeletal muscle and myocardium of each mouse group, along with the detection of protein-level expression of relevant genes, were performed to assess alterations in mitochondrial energy metabolism during insulin resistance. This endeavor aims to contribute insights for future in-depth veterinary research. The outcomes demonstrated that a continuous 12-week high-fat diet successfully induced stable insulin resistance in C57BL6 mice. Following insulin resistance, the motor activity of mice decreased, gradual pathological damage and functional decline were observed in the skeletal muscle and myocardium. The insulin signaling pathway was inhibited, resulting in reduced glucose transport and increased gluconeogenesis. Additionally, mitochondrial dysfunction manifested as diminished ATP synthesis capacity, weakened mitochondrial biogenesis, reduced mitochondrial fusion, increased division, and diminished autophagy. Notably, during insulin resistance progression, skeletal muscles and myocardium in C57BL6 mice predominantly relied on glycolytic pathways for energy supply. In the early stages of insulin resistance, the glycogen synthesis pathway in C57BL6 mouse skeletal muscles was inhibited. Our findings underscore a distinct mechanism in skeletal muscle and myocardium that ensures the utilization of anaerobic fermentation to meet energy demands in instances of inadequate aerobic respiration (Fig 1).

Abstract Effective visualization of infectious disease risks is crucial for the development of efficient prevention and control strategies. However, the efficacy of mainstream models is hindered by a scarcity of reliable data in target areas, a situation that is particularly acute when dealing with emerging or re‐emerging infectious diseases. Additionally, these models typically fail to integrate local disease‐related risk factors in line with the ‘One Health’ concept, resulting in inaccurate predictions. Consequently, accurately assessing infectious disease risks without reliable data is challenging. This study introduces SpatMCDA, an innovative R package designed to assess infectious disease risk areas through spatial multi‐criteria decision analysis (MCDA). SpatMCDA is structured around six core modelling steps: standardizing risk factors, determining factor weights, constructing risk maps, performing One‐at‐a‐Time sensitivity analysis, calculating the Mean of Absolute Change Rates and conducting an uncertainty analysis. By examining the case of West Nile virus (WNV) in China, this study illustrates how SpatMCDA can be useful in identifying disease transmission risks in the absence of reliable outbreak data. The assessment identified areas at risk for WNV in northwestern, eastern and southern China. By integrating spatial and epidemiological data, SpatMCDA enhances infectious diseases risk assessment in situations where data are limited. Its efficiency in using available data for accurate risk mapping and adaptability in weighting various risk factors enables tailored analyses. This tool enhances public health strategies, contributing to global health security.

Cartilage, a flexible and smooth connective tissue that envelops the surfaces of synovial joints, relies on chondrocytes for extracellular matrix (ECM) production and the maintenance of its structural and functional integrity. Melatonin (MT), renowned for its anti-inflammatory and antioxidant properties, holds the potential to modulate cartilage regeneration and degradation. Therefore, the present study was devoted to elucidating the mechanism of MT on chondrocytes. The in vivo experiment consisted of three groups: Sham (only the skin tissue was incised), Model (using the anterior cruciate ligament transection (ACLT) method), and MT (30 mg/kg), with sample extraction following 12 weeks of administration. Pathological alterations in articular cartilage, synovium, and subchondral bone were evaluated using Safranin O-fast green staining. Immunohistochemistry (ICH) analysis was employed to assess the expression of matrix degradation-related markers. The levels of serum cytokines were quantified via Enzyme-linked immunosorbent assay (ELISA) assays. In in vitro experiments, primary chondrocytes were divided into Control, Model, MT, negative control, and inhibitor groups. Western blotting (WB) and Quantitative RT-PCR (q-PCR) were used to detect Silent information regulator transcript-1 (SIRT1)/Nuclear factor kappa-B (NF-κB)/Nuclear factor erythroid-2-related factor 2 (Nrf2)/Transforming growth factor-beta (TGF-β)/Bone morphogenetic proteins (BMPs)-related indicators. Immunofluorescence (IF) analysis was employed to examine the status of type II collagen (COL2A1), SIRT1, phosphorylated NF-κB p65 (p-p65), and phosphorylated mothers against decapentaplegic homolog 2 (p-Smad2). In vivo results revealed that the MT group exhibited a relatively smooth cartilage surface, modest chondrocyte loss, mild synovial hyperplasia, and increased subchondral bone thickness. ICH results showed that MT downregulated the expression of components related to matrix degradation. ELISA results showed that MT reduced serum inflammatory cytokine levels. In vitro experiments confirmed that MT upregulated the expression of SIRT1/Nrf2/TGF-β/BMPs while inhibiting the NF-κB pathway and matrix degradation-related components. The introduction of the SIRT1 inhibitor Selisistat (EX527) reversed the effects of MT. Together, these findings suggest that MT has the potential to ameliorate inflammation, inhibit the release of matrix-degrading enzymes, and improve the cartilage condition. This study provides a new theoretical basis for understanding the role of MT in decelerating cartilage degradation and promoting chondrocyte repair in in vivo and in vitro cultured chondrocytes.

Chondrocytes maintain the normal morphological structure and physiological function of articular cartilage. However, the increased expression of inflammatory mediators and matrix-degrading enzymes in chondrocytes can promote ECM degradation, consequently disrupting the physiological function of articular cartilage and ultimately leading to the onset of osteoarthritis (OA). Usnic acid (UA), an extract derived from plants, possesses multiple bioactive properties including anti-inflammatory and antioxidant effects. Hence, this study aims to explore the molecular mechanisms underlying its protective effects in an in vitro simulated model of OA in rat chondrocytes. In vivo experiments encompassed three groups: Control, Model, and Usnic acid (50 mg/kg), with sample extraction following 12 weeks of administration. Pathological alterations in articular cartilage were evaluated using Safranin O-fast green staining. Immunohistochemistry (ICH) analysis was employed to assess the expression of matrix degradation-related markers. The levels of serum cytokines were quantified via ELISA assays. In this study, primary rat chondrocytes were employed as the experimental subject. After a 24-hour treatment period, culture supernatants, total RNA, total protein, and nuclear protein were extracted from the different chondrocyte groups. ELISA and qPCR methods were used to measure levels of IL-6, TNF-α, and PGE2. WB and qPCR were employed to assess levels of p65, p-p65, IκBα, p-IκBα, Nrf2, HO-1, NQO1 ADAMTS-4, MMP-1, MMP-3, MMP-13, INOS, COX-2, and COL2A1. IF analysis was performed to examine Nrf2, p-p65 and COL2A1. Results: (1) In vivo, intraperitoneal injection of usnic acid (50mg/kg) can alleviate pathological changes in articular cartilage and the levels of inflammatory and oxidative-related matrix in the serum. (2) UA reduced the level of inflammatory cytokines in IL-1β-induced chondrocytes supernatant. (3) UA can produce anti-inflammatory and antioxidant effects and inhibit chondrocytes matrix degradation through the Nrf2 and NF-κB pathways. (3) The mechanism by which UA exerts a protective role in chondrocytes is mediated by Nrf2 Conclusion: UA inhibits the expression of chondrocyte matrix degradation-related components through Nrf2 mediated Nrf2 pathway and NF-κB pathway, promotes the production of protective substances, and ultimately exerts a protective effect on chondrocytes.

Background West Nile virus (WNV), the most widely distributed flavivirus causing encephalitis globally, is a vector-borne pathogen of global importance. The changing climate is poised to reshape the landscape of various infectious diseases, particularly vector-borne ones like WNV. Understanding the anticipated geographical and range shifts in disease transmission due to climate change, alongside effective adaptation strategies, is critical for mitigating future public health impacts. This scoping review aims to consolidate evidence on the impact of climate change on WNV and to identify a spectrum of applicable adaptation strategies.Main body We systematically analyzed research articles from PubMed, Web of Science, Scopus, and EBSCOhost. Our criteria included English-language research articles published between 2007 and 2023, focusing on the impacts of climate change on WNV and related adaptation strategies. We extracted data concerning study objectives, populations, geographical focus, and specific findings. Literature was categorized into two primary themes: 1) climate-WNV associations, and 2) climate change impacts on WNV transmission, providing a clear understanding. Out of 2168 articles reviewed, 120 met our criteria. Most evidence originated from North America (59.2%) and Europe (28.3%), with a primary focus on human cases (31.7%). Studies on climate-WNV correlations (n = 83) highlighted temperature (67.5%) as a pivotal climate factor. In the analysis of climate change impacts on WNV (n = 37), most evidence suggested that climate change may affect the transmission and distribution of WNV, with the extent of the impact depending on local and regional conditions. Although few studies directly addressed the implementation of adaptation strategies for climate-induced disease transmission, the proposed strategies (n = 49) fell into six categories: 1) surveillance and monitoring (38.8%), 2) predictive modeling (18.4%), 3) cross-disciplinary collaboration (16.3%), 4) environmental management (12.2%), 5) public education (8.2%), and 6) health system readiness (6.1%). Additionally, we developed an accessible online platform to summarize the evidence on climate change impacts on WNV transmission (https://2xzl2o-neaop.shinyapps.io/WNVScopingReview/).Conclusions This review reveals that climate change may affect the transmission and distribution of WNV, but the literature reflects only a small share of the global WNV dynamics. There is an urgent need for adaptive responses to anticipate and respond to the climate-driven spread of WNV. Nevertheless, studies focusing on these adaptation responses are sparse compared to those examining the impacts of climate change. Further research on the impacts of climate change and adaptation strategies for vector-borne diseases, along with more comprehensive evidence synthesis, is needed to inform effective policy responses tailored to local contexts.

近3年来,受新冠疫情影响,国内农林院校动物医学专业学生的毕业实践实习工作面临着前所未有的教学困难.首先,疫情期间,学生不能出校,理论教学尚可通过线上进行系统性讲授,但校外毕业实践实习地点因疫情影响封闭不能正常营业接诊,使得连续的实践实习教学不能正常开展.其次,受疫情影响,大部分动物医院、科研机构等出现关闭状态,使本专业应参加实践实习的学生未能如期进行线下学习,导致学生在专业技能上与往届学生比较有所欠缺.第三,由于学生接触实践实习及社会的时间减少,导致目前毕业实践实习的学生主观惰性心理增强、对未来的盲目性和专业技能掌握能力下降,也是当前动物医学专业毕业前景不明确,就业困难,专业技能不完善的主要因素.针对以上疫情下动物医学专业学生的实际情况,东北农业大学动物临床教学医院制定了科学合理的应对策略措施.在保障基本实践实习教学的基础上,协助学生渡过这段艰难的时期.通过线上线下教学相结合,设立新课程及新教学模式应用,教学医院与校外企业相结合等多项措施,为动物医学专业学生毕业临床实践教学在疫情下的新教学模式的构建与实行打下基础.

Species in transitional areas often display adaptive responses to climate change and such areas may be crucial for long-term biodiversity conservation. Evaluation of spatial multidimensional biodiversity patterns and the identification of biodiversity hotspots and priority conservation areas may help mitigate the effects of climate change. Here, we examine the spatial distribution patterns, evolutionary and functional levels of Lauraceae from Chinese evergreen broad-leaved forests. The results show species richness (SR), corrected weighted endemism (CWE), phylogenetic diversity (PD), and phylogenetic endemism (PE) for Chinese Lauraceae are congruent, whereas evolutionarily distinct and globally endangered (EDGE) and function diversity (FD) are incongruent. Areas of paleo-endemism are present in the border region of Yunnan and Guangxi, whereas neo-endemic regions are distributed mainly along the Yarlung Zangbo River and the Himalayas in southern Tibet. Priority conservation areas are located in southern Tibet, the northern Hengduan Mountains, the north–south boundary of Qinling and Huaihe River, southern and south-eastern Yunnan, and south China. Biodiversity hotspots for Chinese Lauraceae overlap with transitional zones for several other vegetation types in adjacent areas. Climate factors are estimated to account for 82.72% of the SR and 86.86% of the PD for Lauraceae spatial distribution patterns, reflecting higher diversity under warmer and wetter conditions. This study confirms the conservation value of transitional areas and the significance of using multiple diversity facets as part of integrative approaches to maximize biodiversity protection in Chinese broad-leaved forests, especially under climate change.

Classical scrapie is a transmissible spongiform encephalopathy that attacks the central nervous system of sheep and goats. Since its discovery in the 18th century, the disease has caused enormous economic losses and public health impacts in continental Europe. In the late 20th century, classical scrapie began to spread to places, such as Asia and the Americas, becoming a disease of global concern. In this study, based on prion occurrence records and high-resolution environmental layers, a risk assessment of classical scrapie in China was performed using a maximum entropy model. The model achieved an area under the curve value of 0.906 (95% confidence interval, 0.0883–0.0929). Sheep distribution density, road density, goat distribution density, minimum temperature of the coldest month, port density, and precipitation of the driest quarter were identified as important factors affecting the occurrence of classical scrapie. The risk map showed that potential high-risk areas in China were mainly located in Northeast China, North China, and Northwest China. This study can provide a valuable reference for the prevention of classical scrapie in China. According to the environmental variables and risk areas of classical scrapie, implementing monitoring and early warning measures in these areas is recommended to reduce the possibility of classical scrapie occurrence and transmission.

猪巴氏杆菌病是流行在养猪过程中的一种高度传染性疾病,危害养殖户的经济收入,猪巴氏杆菌病的暴发给养猪业带来严重的经济损失.为了解我国2010-2020年猪巴氏杆菌病的流行情况及空间分布特征,本研究应用年流行情况分析、月流行情况分析、全局空间自相关分析和局部空间自相关分析对2010-2020年我国猪巴氏杆菌病疫情发生数据进行统计与分析.结果表明:自2015年后我国猪巴氏杆菌病疫情的发生呈下降趋势,夏季(6～8月份)为猪巴氏杆菌病发生主要季节;全局空间自相关分析和局部空间自相关分析表明我国猪巴氏杆菌病的发生呈聚集分布模式,主要发生于我国的中南部地区,如四川省、云南省、重庆市和广西壮族自治区.本文总结归纳2010-2020年我国猪巴氏杆菌病的年、月流行情况及空间分布,为了解猪巴氏杆菌病在我国的流行情况和时空分布特征提供了一定的参考.

Ketosis is considered to be the most important metabolic disease affecting dairy herds, surpassing ruminal acidosis and milk fever. On dairy farms, assessment and monitoring of ketosis risk are necessary for the health management of dairy cows. Changes in the content of hematological and serum biochemical parameters are commonly used to monitor ketosis in dairy cows. However, the collection and detection of these indicators are complex and may lead to stress reactions in cows. This study attempted to predict the risk of ketosis in dairy cows using machine learning models based on noninvasive prenatal indicators of parity, body condition score, dystocia score, daily rumination time, daily activity, and season of calving. Results showed the extreme gradient boosting (XGBoost) model had the highest prediction ability and the most accuracy, compared to random forest (RF), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN). In the XGBoost model, daily rumination time (60.15%) and daily activity (16.73%) were identified with the highest percentage contribution to the model, followed by parity (10.41%), body condition score (6.42%), season of calving (4.23%), and dystocia score (2.06%). The probability of ketosis increased with decreasing daily rumination time and daily activity. Moreover, parity 3+ and summer may also increase the probability of ketosis. Finally, an open Shiny web application for predicting the risk of ketosis in dairy cows based on the XGBoost model was developed (https://2xzl2o-neaop.shinyapps.io/PreCowKetosis/). The application PreCowKetosis can provide decision support for researchers and farmers to prevent ketosis in dairy cows.

Wasmannia auropunctata , the little fire ant, is an invasive pest threatening native biodiversity, agricultural and forestry production, and public health. In January 2022, wild populations of W . auropunctata were reported for the first time on the Chinese mainland. However, the stage and degree of W . auropunctata invasion in China are unclear. Therefore, assessing the risk of establishment and the potential impacts associated with this pest is crucial for preventing further spread. This study used the Bayesian additive regression trees (BARTs) model and global occurrence records to assess the potential distribution of W. auropunctata in southern China and the associated risk of invading urban, rural, agricultural, and forest lands. The results from our models indicate that: (1) coastal areas, southwest, and central areas are particularly suitable for W . auropunctata establishment; (2) temperature, clay content, mean normalized differential vegetation index (NDVI), and urban area are important factors in the distribution of W . auropunctata ; (3) the agricultural lands in the coastal areas and the Yunnan–Guangxi border, the urban and rural lands in the coastal areas, and grasslands in the southwest should prepare for possible W. auropunctata invasion; and (4) forest lands have the highest area at risk of W. auropunctata invasion in southern China. These results provide valuable information for planning and implementing the monitoring and control strategies against W. auropunctata invasion in China.

禽霍乱是一种由多杀性巴氏杆菌引起的接触性、败血性传染病,鸡、鸭和鹅等禽类均易感.由于目前我国广泛使用的禽霍乱弱毒疫苗和灭活疫苗副作用大、免疫期短且保护率低,免疫后的禽类仍有患病的风险.因此,对禽霍乱的监控尤为重要.利用MATLAB 2020b软件构建了基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和长短期记忆(long short-term memory,LSTM)模型的EEMD-LSTM组合模型的禽霍乱预测方法.利用2006年-2015年禽霍乱的发病数训练模型,预测2016年-2020年禽霍乱的发病数,并与实际发病数验证,然后通过计算实际发病数与预测发病数的线性回归系数R2值和组内相关系数(intraclass correlation coefficient,ICC)值,分析实际发病数与预测发病数的一致性.结果显示,模型训练期的R2值和ICC值分别为0.9935和0.997,其ICC值大于0.75并接近于1,表明该模型具有良好的预测能力,可用于预测禽霍乱的发病趋势;模型预测期的R2值和ICC值分别为0.7507和0.825,其ICC值大于0.75,同时大于Landis和Koch的建议值0.80,表明该模型具有良好的可信度.该模型的建立可为禽霍乱的防控提供参考,同时也为该模型的其他应用研究提供理论依据.