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The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide. • An integrated framework combining DBN, SD, PLUS and InVEST models was proposed. • Impact of natural and socioeconomic factors on ESs was determined under climate scenarios. • The spatial pattern of ESs was optimized and partitioned using DBNs. • ESs in arid oasis urban agglomerations are mainly affected by natural factors. [ABSTRACT FROM AUTHOR]

Background: Vitamin D is related to human immunity, so we used Bayesian network model to analyze and infer the relationship between vitamin D level and the acid-fast bacilli (AFB) smear-positive after two months treatment among pulmonary tuberculosis (TB) patients. Methods: This is a cross-sectional study. 731 TB patients whose vitamin D level were detected and medical records were collected from December 2019 to December 2020 in XinJiang of China. Logistic regression was used to analyze the influencing factors of second AFB smear-positive. Bayesian network was used to further analyze the causal relationship among vitamin D level and the second AFB smear-positive. Results: Baseline AFB smear-positive (OR = 6.481, 95%CI: 1.604~26.184), combined cavity (OR = 3.204, 95%CI: 1.586~6.472), full supervision (OR = 8.173, 95%CI:1.536~43.492) and full management (OR = 6.231, 95%CI:1.031~37.636) were not only the risk factors and can also be considered as the reasons for second AFB smear-positive in TB patients (Ensemnle > 0.5). There was no causal relationship between vitamin D level and second AFB smear-positive (Ensemnle = 0.0709). Conclusions: The risk factors of second AFB smear-positive were baseline AFB smear-positive, combined cavity, full supervision and full management. The vitamin D level in TB patients was not considered as one of the reasons for the AFB smear-positive. [ABSTRACT FROM AUTHOR]

Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems. [ABSTRACT FROM AUTHOR]