Space-time cluster analysis and maximum entropy modeling of Peste des petits ruminants (PPR) in Türkiye.

Peste des petits ruminants (PPR) is an economically important highly serious transboundary disease that mainly occurs in small ruminants such as sheep and goats. The aim of this study was to identify the probability of risk and and space-time clusters of Peste des Petits Ruminants (PPR) in Türkiye. The occurrence of PPR in Türkiye from 2017 to 2019 was investigated in this study using spatial analysis based on geographic information system (GIS). Between these dates, it was determined that 337 outbreaks and 18,467 cases. The highest number of outbreaks were detected in the Central Anatolia region. It was determined that PPR is seen more intensely in sheep compared to goats in Türkiye. In this study, 34 environmental variables (19 bioclimatic, 12 precipitation, altitude and small livestock density variables) were used to explore the environmental influences on PPR outbreak by maximum entropy modeling (Maxent). The clusters of PPR in Türkiye were identified using the retrospective space-time scan data that were computed using the space-time permutation model. A PPR prediction model was created using data on PPR outbreaks combination with environmental variables. Nineteen significant (p < 0.001) space-time clusters were determined. It was discovered that the variables altitude, sheep density, precipitation in june, and average temperature in the warmest season made important contributions to the model and the PPR outbreak may be strongly related with these variables. In this study, PPR in Türkiye has been characterized significantly spatio-temporal and enviromental factors. In this context, the disease pattern and obtained these findings will contribute to policymakers in the prevention and control of the disease.

Study on the change of global ecological distribution of Nicotiana tabacum L. based on MaxEnt model.

Nicotiana tabacum L. (tobacco) has extremely high economic value, medicinal value, scientific research value and some other uses. Though it has been widely cultivated throughout the world, classification and change of its suitable habitats is not that clear, especially in the context of global warming. In order to achieve rational cultivation and sustainable development of tobacco, current (average from 1970-2000) and future (2070, average from 2061-2080) potential suitable habitats of Nicotiana tabacum L. were forecasted with MaxEnt model and ArcGIS platform based on 854 occurrence data and 22 environmental factors in this study. The results revealed that mean temperature of warmest quarter (bio10), annual precipitation (bio12), solar radiation in September (Srad9), and clay content (CLAY) were the four decisive environment variables for the distribution of Nicotiana tabacum L. Under current climate conditions, suitable habitats of Nicotiana tabacum L. were mainly distributed in south-central Europe, south-central North America, most parts of South America, central Africa, south and southeast Asia, and southeast coast of Australia, and only 13.7% of these areas were highly suitable. By the year 2070, suitable habitats under SSP1-2.6, SSP3-7.0, and SSP5-8.5 climate scenarios would all increase with the largest increase found under SSP3-7.0 scenario, while suitable habitats would reduce under SSP2-4.5 climate scenario. Globally, the center of mass of suitable habitats would migrate to southeast to varying degrees within Libya under four different climate scenarios. The emergence of new habitats and the disappearance of old habitats would all occur simultaneously under each climate scenario, and the specific changes in each area, combined with the prediction results under current climate conditions, will provide an important reference for the adjustment of agronomic practices and rational cultivation of Nicotiana tabacum L. both currently and in the future.

Habitat for Coilia nasus in southern Zhejiang Province, China, based on a maximum entropy model.

As an important fishery resource and endangered species, studying the habitat of Coilia nasus (C. nasus) is highly significant. This study used fishery survey data from southern Zhejiang coastal waters from 2016 to 2020, employing a maximum entropy model (MaxEnt) to map the habitat distribution of C. nasus. Model performance was evaluated using two metrics: the area under the curve (AUC) of the receiver operating characteristic curve for the training and test sets and true skill statistics (TSS). This study aimed to predict the habitat distribution of C. nasus and explore how environmental variables influence habitat suitability. The results indicated that the models for each season had strong predictive performance, with AUC values above 0.8 and TSS values exceeding 0.6, indicating that they could accurately predict the presence of C. nasus. In the study area, C. nasus was primarily found in brackish or marine waters near bays and coastal islands. Among all environmental factors, salinity (S) and bottom temperature (BOT) had the highest correlations with habitat distribution, although these correlations varied across seasons. The findings of this study provide empirical evidence and a reference for the conservation and management of C. nasus and for the designation of its protected areas.

Determination of forest fire risk with respect to Marchalina hellenica potential distribution to protect pine honey production sites in Turkey.

Turkey is the leading producer of pine honey worldwide, accounting for 90% of global production, largely due to the presence of Marchalina hellenica populations. However, in recent years, devastating forest fires have caused substantial damage to Pinus brutia forests and M. hellenica populations, leading to a dramatic decline in pine honey production areas. The urgency for early intervention procedures against forest fires and relocation of M. hellenica populations to other P. brutia forests has become apparent. A comprehensive assessment of 25 criteria was conducted to investigate the thresholds and behaviors of each criterion, which play a vital role in the distribution of M. hellenica, using the maximum entropy model (MaxEnt). To evaluate the impact of forest fires on the distribution of M. hellenica, the potential locations of pine honey production areas were determined for 2022. Furthermore, the susceptibility of forest fires was modeled for all pine honey production months. The findings revealed that forest fires have destroyed 384.9 km2 (12.8% of the total pine honey production area), predominantly in August and September, with the most severe damage in Marmaris (156 km2) and significant impacts in Ula, Köycegiz, and Milas. The analysis facilitates the estimation of new areas suitable for M. hellenica and pine honey production while providing valuable insights into strategies for mitigating forest fires and formulating proactive protection protocols.

Prediction of the potential distribution of Chimonobambusautilis (Poaceae, Bambusoideae) in China, based on the MaxEnt model.

Chimonobambusautilis is a unique edible bamboo species valued for its economic and nutritional benefits. However, its existence in natural habitats is at risk due to environmental shifts and human interventions. This research utilised the maximum entropy model (MaxEnt) to predict potential habitats for Ch.utilis in China, identifying key environmental factors influencing its distribution and analysing changes in suitable habitats under future climate conditions. The results show that the results of the MaxEnt model have high prediction accuracy, with an AUC (Area Under the receiver operating characteristic Curve) value of 0.997. Precipitation in the driest month (Bio14), altitude (Alt) and isothermality (Bio03) emerged as the primary environmental factors influencing the Ch.utilis distribution. Currently, the suitable habitats area for Ch.utilis is 10.55 × 104 km2. Projections for the 2050s and 2090s indicate potential changes in suitable habitats ranging from -3.79% to 10.52%. In general, the most suitable habitat area will decrease and shrink towards higher latitude areas in the future. This study provides a scientific basis for the introduction, cultivation and conservation of Ch.utilis.

Predicting potential and quality distribution of Anisodus tanguticus (Maxim.) Pascher under different climatic conditions in the Qinghai-Tibet plateau.

Anisodus tanguticus (Maxim.) Pascher, a distinctive medicinal plant native to the Qinghai-Tibet Plateau, China, has garnered attention due to increasing market demand. This study explores the impact of environmental factors on the distribution and levels of active compounds namely anisodamine, anisodine, and atropine within A. tanguticus. Our goal was to identify suitable cultivation areas for this plant. This study employs the maximum entropy model to simulate the suitable area of A. tanguticus under current conditions and three climate change scenarios during the 2050s and 2070s. The finding revealed that altitude, precipitation in the warmest season (Bio 18), the average annual temperature (Bio 1) exerted significant influences on the distribution of A. tanguticus. Among the environmental factors considered, temperature difference between day and night (Bio 2) had the most substantial impact on the distribution of anisodamine, temperature seasonal variation variance (Bio 4) predominantly influenced anisodine distribution, and Bio 1 had the greatest effected on the distribution of atropine. The suitable areas primarily exist in the eastern Qinghai-Tibet Plateau in China, encompassing a total area of 30.78 × 104 km2. Under the climate scenarios for the future, the suitable areas exhibit increasing trends of approximately 30.2%, 30.3%, and 39.8% by the 2050s, and 25.1%, 48.8%, and 60.1% by the 2070s. This research would provide theoretical suggestions for the protection, and cultivation management of A. tanguticus resources to face the challenge of global climate change.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Multifaceted analysis of temporal and spatial distribution and risk factors of global poultry HPAI-H5N1, 2005-2023.

The purpose of this study was to analyze the characteristics of occurrence and spread of highly pathogenic avian influenza H5N1 (HPAI-H5N1) globally, understand its spatiotemporal characteristics, investigate the risk factors influencing outbreaks, and identify high-risk areas for disease occurrence. We collected the data on global poultry HPAI-H5N1 outbreaks from January 2005 to April 2023, and conducted a thorough analysis of the spatial and temporal characteristics of the disease through time series decomposition and directional distribution analysis. Additionally, an ecological niche model was established to explore the major factors influencing the occurrence of HPAI-H5N1 and to pinpoint high-risk areas. Our findings revealed that HPAI-H5N1 outbreaks were cyclical, and seasonal, exhibiting a rising trend, with a predominant northwest-southeast transmission direction. The ecological niche model highlighted that species factors and economic trade factors are critical in influencing the outbreak of HPAI-H5N1. Variables such as chicken and duck density, population density, isothermality, and road density, contributed to importantly risk of outbreaks. High-risk areas for HPAI-H5N1 occurrence were primarily identified in Europe, West Africa, Southeast Asia, and Southeast China. This study provided valuable insights into the spatial and temporal distribution characteristics and risk factors of global poultry HPAI-H5N1 outbreaks. The identification of high-risk areas provides essential information that can be used to develop more effective prevention and control policies.

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China. METHODS: Data on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas. RESULTS: From 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province. CONCLUSIONS: The spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

Energy in functional brain states correlates with cognition in adolescent schizophrenia and healthy persons.

Adolescent-onset schizophrenia (AOS) is a relatively rare and under-studied form of schizophrenia with more severe cognitive impairments and poorer outcome compared to adult-onset schizophrenia. Several neuroimaging studies have reported alterations in regional activations that account for activity in individual regions (first-order model) and functional connectivity that reveals pairwise co-activations (second-order model) in AOS compared to controls. The pairwise maximum entropy model, also called the Ising model, can integrate both first-order and second-order terms to elucidate a comprehensive picture of neural dynamics and captures both individual and pairwise activity measures into a single quantity known as energy, which is inversely related to the probability of state occurrence. We applied the MEM framework to task functional MRI data collected on 23 AOS individuals in comparison with 53 healthy control subjects while performing the Penn Conditional Exclusion Test (PCET), which measures executive function that has been repeatedly shown to be more impaired in AOS compared to adult-onset schizophrenia. Accuracy of PCET performance was significantly reduced among AOS compared to controls as expected. Average cumulative energy achieved for a participant over the course of the fMRI negatively correlated with task performance, and the association was stronger than any first-order associations. The AOS subjects spent more time in higher energy states that represent lower probability of occurrence and were associated with impaired executive function suggesting that the neural dynamics may be less efficient compared to controls who spent more time in lower energy states occurring with higher probability and hence are more stable and efficient. The energy landscapes in both conditions featured attractors that corresponded to two distinct subnetworks, namely fronto-temporal and parieto-motor. Attractor basins were larger in the controls than in AOS; moreover, fronto-temporal basin size was significantly correlated with cognitive performance in controls but not among the AOS. The single trial trajectories for the AOS group also showed higher variability in concordance with shallow attractor basins among AOS. These findings suggest that the neural dynamics of AOS features more frequent occurrence of less probable states with narrower attractors, which lack the relation to executive function associated with attractors in control subjects suggesting a diminished capacity of AOS to generate task-effective brain states.

Himalayan Marmot (Marmota himalayana) Redistribution to High Latitudes under Climate Change.

Climate warming and human activities impact the expansion and contraction of species distribution. The Himalayan marmot (Marmota himalayana) is a unique mammal and an ecosystem engineer in the Qinghai-Tibet Plateau (QTP). This pest aggravates grassland degradation and is a carrier and transmitter of plagues. Therefore, exploring the future distribution of Himalayan marmots based on climate change and human activities is crucial for ecosystem management, biodiversity conservation, and public health safety. Here, a maximum entropy model was explored to forecast changes in the distribution and centroid migration of the Himalayan marmot in the 2050s and 2070s. The results implied that the human footprint index (72.80%) and altitude (16.40%) were the crucial environmental factors affecting the potential distribution of Himalayan marmots, with moderately covered grassland being the preferred habitat of the Himalayan marmot. Over the next 30-50 years, the area of suitable habitat for the Himalayan marmot will increase slightly and the distribution center will shift towards higher latitudes in the northeastern part of the plateau. These results demonstrate the influence of climate change on Himalayan marmots and provide a theoretical reference for ecological management and plague monitoring.

[Prediction of potential suitable habitats of Haemphysalis concinna in Heilongjiang Province based on the maximum entropy model].

OBJECTIVE: To predict the potential suitable habitat of Haemaphysalis concinna in Heilongjiang Province under different climatic scenarios. METHODS: The geographic locations of ticks in Heilongjiang Province from 1980 to 2022 were captured from literature review and field ticks monitoring data from Harbin Center for Disease Control and Prevention in Heilongjiang Province, and the tick distribution sites with spatial correlations were removed using the software ArcGIS 10.2. The environment data under historical climatic scenarios from 1970 to 2000 and the climatic shared socioeconomic pathways (SSP) 126 scenario model from 2021 to 2040 and from 2041 to 2060 were downloaded from the WorldClim website, and the elevation (1 km, 2010), population (1 km grid population dataset of China, 2010) and annual vegetation index (1 km, 2010) data were downloaded from the Resource and Environmental Science and Data Center, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences. The contribution of environmental factors to H. concinna distribution was evaluated and environmental variables were screened using the software MaxEnt 3.4.1 and R package 4.1.0, and the areas of suitable habitats of H. concinna and changes in center of gravity were analyzed using the maximum entropy model in Heilongjiang Province under different climatic scenarios. In addition, the accuracy of the maximum entropy model for prediction of H. concinna distribution was assessed using the area under curve (AUC) of the receiver operating characteristic curve. RESULTS: A total of 79 H. concinna distribution sites and 24 environmental variables were collected, and 70 H. concinna distribution sites and 9 environmental factors that contributed to distribution of the potential suitable habitats of H. concinna in Heilongjiang Province were screened. The three most significant contributing factors included precipitation seasonality, annual precipitation, and mean temperature of the driest quarter, with cumulative contributions of 60.7%. The total area of suitable habitats of H. concinna was 29.05 × 104 km2 in Heilongjiang Province under historical climatic scenarios, with the center of gravity of suitable habitats located at (47.31° N, 129.16° E), while the total area of suitable habitats of H. concinna reduced by 0.97 × 104 km2 in Heilongjiang Province under the climatic SSP126 scenario from 2041 to 2060, with the center of gravity shifting to (47.70° N, 129.28° E). CONCLUSIONS: The distribution of suitable habitats of H. concinna strongly correlates with temperature and humidity in Heilongjiang Province. The total area of potential suitable habitats of H. concinna may appear a tendency towards a decline with climatic changes in Heilongjiang Province, and high-, medium- and low-suitable habitats may shift.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e., within-participant reliability) than across different sets of sessions from different participants (i.e., between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Change of geographic distributions of the genus Morchella species responding to climate oscillations in East Asia.

Morchella is a rare macrofungi taxon with high medicinal and edible values. Influenced by recent climate oscillations and human activities, habitat fragmentation of this genus has been critical, leading to a rapid decline of the resource of Morchella. It is thus urgent to preserve Morchella species. Based on maximum entropy model (MaxEnt), and 102 geographic distribution records of Morchella species with 10 environmental factors, we simulated the changes of potential geographic distributions under the climatic conditions of the last glacial maximum (LGM), last interglacial (LIG), in contemporary period and future (2050, 2070). We further analyzed the potential changes of geographic distributions of Morchella species in East Asia under climate change and formulated the effective conservation strategies for Morchella. The results showed that the dominant environmental factors affecting the geographic distributions of Morchella species were mean temperature of coldest quarter, annual precipitation, elevation and temperature annual range, with the mean temperature of coldest quarter having the greatest contribution. Results of the species distribution models showed that the highly suitable regions for Morchella species were mainly distributed in parts of western China under contemporary period. From the LIG to LGM and then the current to the future period, the total suitable regions of Morchella species showed a trend of firstly decrease and then increase, while the highly suitable regions showed similar change with the total suitable regions. At present, there is an urgent need to conduct in situ conservation for the resources of Morchella species in highly suitable regions in western China, and to carry out ex situ conservation in the marginal ranges of highly suitable regions and moderately suitable regions of Shaanxi, Hebei, Shandong, and other regions in China.

Assessing environmental suitability of Ligusticum chuanxiong based on ecological analyses with chemical and molecular verification.

Ligusticum chuanxiong Hort. as an important Chinese medicinal herb clinically used as anti-inflammatory, antioxidant, and hepatoprotective agents, is widely planted in China. However, related studies on L. chuanxiong's distribution and significant environmental factors that affect its growth are insufficient. Based on climatic, topographic and soil factors, this study predicted current and future distributions of L. chuanxiong and analyzed the distribution transformation under different scenarios. Moreover, the most important environmental factors for modeling were explored using maximum entropy models, chemical analysis and molecular analysis. Results suggested that the predicted distribution of L. chuanxiong was wider than previously reported. Among these environmental variables, climate factors, especially the minimum temperature of the coldest month (Bio6, 46.7%) and solar radiation (SRAD, 43.4%) contributed more than others to L. chuanxiong's distribution with optimum values of 0-1.5 °C and 5000-11,000 kJ/m2 per day. Total and highly suitable areas respectively increased by 26,788-943,820 km2 and 34,757-340,417 km2 in the future (2061-2080, 2081-2100). The distribution centers of suitable zones were predicted to migrate north in the future, and the migration distance was 135.74-479.77 km from current center. Results of chemical content determination suggested that L. chuanxiong should be cultivated in high-suitable places to improve medicinal quality by evaluating contents of ferulic acids and Z-ligustilide. Correlation analysis suggested that both chemical contents and gene expression levels decreased with decreasing habitat suitability, suggesting a strong link between environments, chemical constituents, and gene expression. These findings improve the comprehension of the effects of environments on the distribution patterns of L. chuanxiong, as well the relation between environmental suitability and medicinal quality. These findings provide a useful foundation for the planting, cultivation and conservation of L. chuanxiong.

Prediction of potential suitable habitats of Haemphysalis concinna in Heilongjiang Province based on the maximum entropy model / 中国血吸虫病防治杂志

Objective To predict the potential suitable habitat of Haemaphysalis concinna in Heilongjiang Province under different climatic scenarios. Methods The geographic locations of ticks in Heilongjiang Province from 1980 to 2022 were captured from literature review and field ticks monitoring data from Harbin Center for Disease Control and Prevention in Heilongjiang Province, and the tick distribution sites with spatial correlations were removed using the software ArcGIS 10.2. The environment data under historical climatic scenarios from 1970 to 2000 and the climatic shared socioeconomic pathways (SSP) 126 scenario model from 2021 to 2040 and from 2041 to 2060 were downloaded from the WorldClim website, and the elevation (1 km, 2010), population (1 km grid population dataset of China, 2010) and annual vegetation index (1 km, 2010) data were downloaded from the Resource and Environmental Science and Data Center, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences. The contribution of environmental factors to H. concinna distribution was evaluated and environmental variables were screened using the software MaxEnt 3.4.1 and R package 4.1.0, and the areas of suitable habitats of H. concinna and changes in center of gravity were analyzed using the maximum entropy model in Heilongjiang Province under different climatic scenarios. In addition, the accuracy of the maximum entropy model for prediction of H. concinna distribution was assessed using the area under curve (AUC) of the receiver operating characteristic curve. Results A total of 79 H. concinna distribution sites and 24 environmental variables were collected, and 70 H. concinna distribution sites and 9 environmental factors that contributed to distribution of the potential suitable habitats of H. concinna in Heilongjiang Province were screened. The three most significant contributing factors included precipitation seasonality, annual precipitation, and mean temperature of the driest quarter, with cumulative contributions of 60.7%. The total area of suitable habitats of H. concinna was 29.05 × 104 km2 in Heilongjiang Province under historical climatic scenarios, with the center of gravity of suitable habitats located at (47.31° N, 129.16° E), while the total area of suitable habitats of H. concinna reduced by 0.97 × 104 km2 in Heilongjiang Province under the climatic SSP126 scenario from 2041 to 2060, with the center of gravity shifting to (47.70° N, 129.28° E). Conclusions The distribution of suitable habitats of H. concinna strongly correlates with temperature and humidity in Heilongjiang Province. The total area of potential suitable habitats of H. concinna may appear a tendency towards a decline with climatic changes in Heilongjiang Province, and high-, medium- and low-suitable habitats may shift.

ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints.

As a fungus with both medicinal and edible value, Wolfiporia cocos (F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components' content fluctuates in wild and cultivated W. cocos, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of W. cocos, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated W. cocos in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of W. cocos. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated W. cocos in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of W. cocos. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for W. cocos under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of W. cocos, but also provide a basis for quality assessment of medicinal fungi.

Spatial Heterogeneity and Influencing Factors of HFRS Epidemics in Rural and Urban Areas: A Study in Guanzhong Plain of Shaanxi Province, China.

Objective: The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome (HFRS) epidemic area, while HFRS prevalence has decreased in most epidemic areas in China. Little information is available regarding the leading fine-scale influencing factors in this highly HFRS-concentrated area and the roles of natural environmental and socioeconomic factors. To investigate this, two regions in the Guanzhong Plain, that is, the Chang'an District and Hu County, with similar geographical environments, different levels of economic development, and high epidemic prevalence, were chosen as representative areas of the HFRS epidemic. Methods: Maximum entropy models were constructed based on HFRS cases and fine-scale influencing factors, including meteorological, natural environmental, and socioeconomic factors, from 2014 to 2016. Results: More than 95% of the HFRS cases in the study area were located in the northern plains, which has an altitude of less than 800 m, with topography contributed 84.1% of the impact on the spatial differentiation of the HFRS epidemic. In the northern plains, precipitation and population density jointly affected the spatial differentiation of the HFRS epidemic, with contribution rates of 60.7% and 28.0%, respectively. By comparing the influencing factors of the northern plains of Chang'an District and Hu County, we found that precipitation and the normalized difference vegetation index (NDVI) dominated the HFRS epidemic in the relatively developed Chang'an District, while land-use type, temperature, precipitation and population density dominated the HFRS epidemic in the relatively undeveloped Hu County. Conclusion: Topography was the primary key factor for HFRS prevalence in the Chang'an District and Hu County, and the spatial differentiation of HFRS was dominated by precipitation and population density in the northern plains. Compared with the influencing factors of the relatively developed Chang'an District, the developing Hu County was more affected by socioeconomic factors. When formulating targeted HFRS epidemic prevention and control strategies in the targeted areas, it is crucial to consider the local economic development state and combine natural environmental factors, including the meteorological environment and vegetation coverage.

Identifying priority reserves favors the sustainable development of wild ungulates and the construction of Sanjiangyuan National Park.

Sanjiangyuan National Park (SNP), the first national park in China, is one of the most important biodiversity conservation areas in the Sanjiangyuan National Nature Reserve (SNNR) and even the world. The threatened ungulates play an irreplaceable role in maintaining the ecosystem diversity and stability in SNNR. Here, based on 1434 occurrence records of six ungulates, the maximum entropy model, with two different strategies, was utilized to determine the priority reserves. The results indicated that the priority reserves in SNNR was mainly located in and around SNP, which were mainly distributed in the middle east, middle west, and southwest of SNNR. Six ungulates shared preference for altitude ranging 4000-5000 m, the average annual temperature below -3.0°C, and average annual precipitation ranging 200-400 mm on meadow, steppe, and unused land. The proportion of high and medium suitable areas for ungulates in SNP was higher than that in SNNR. As the SNP is not contiguously spaced in space, and some core wildlife habitats are not included, it is suggested to optimize the functional areas and adjust the boundary range on the basis of the pilot scope of SNP, so as to enhance the integrity and connectivity of each functional area.

Analysis of peste des petits ruminants virus spread and the risk of its introduction into the territory of the Russian Federation.

Aim: This study examines methods to effectively control peste des petits ruminants (PPR), an emerging, highly contagious, transboundary disease that has been designated as a highly dangerous disease by the World Organization for Animal Health. Mathematical modeling was used as a predictive and preventive tool to assess the risk of PPR virus spread in the model area and the probability of its introduction into the territory of the Russian Federation. Materials and Methods: PPR risk assessment was performed by modeling the pathogen's ecological niche by performing linear regression analysis in the geographic information system ESRI ArcGIS Desktop and maximum entropy methods using MaxEnt software. The territories of Bangladesh, China, and Algeria were used as model countries because they have the highest number of confirmed PPR outbreaks, as reported by the Food and Agriculture Organization of the United Nations from 2009 to 2020. The prepared global model of the PPR pathogen's ecological niche was extrapolated onto the territory of the Far Eastern regions of the Russian Federation to assess the probability of virus introduction in that region. Results: Global model analysis showed that two factors exerted the highest influence on the spread of the PPR pathogen on a global scale: The minimum temperature of the coldest month of the year and the density of roads per unit area, which reflect the overall economic activity within a region. The highest risk of PPR spread was observed in areas with a minimum annual temperature of 16°C and road density of 5000 m/km2 . Conclusion: According to the model, areas with a dominant subtropical climate, where small livestock breeding is performed and where the average daily air temperature is >0°C throughout the year, are at the highest risk of PPR outbreaks. The risk of PPR spreading outside these areas is significantly reduced. Local extrapolation of the PPR ecological niche model demonstrates that the probability of epizootic development does not exceed 3-4% within the territories of the constituent entities of the Russian Federation adjacent to Mongolia and China.