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In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 10 6 km 2 and 1.55 × 10 6 km 2 , respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity (NPP) inorder to understand the driving mechanism of NPP and prevent desertification. This study investigated the spatial and temporal differentiation features of actual net primary productivity (ANPP) in the Ili River Basin, a transboundary river between China and Kazakhstan, as well as the proportional contributions of climate and human causes to ANPP variation. Additionally, we analyzed the pixel-scale relationship between ANPP and significant climatic parameters. ANPP in the Ili River Basin increased from 2001 to 2020 and was lower in the northeast and higher in the southwest; furthermore, it was distributed in a ring around the Tianshan Mountains. In the vegetation improvement zone, human activities were the dominant driving force, whereas in the degraded zone, climate change was the primary major driving force. The correlation coefficients of ANPP with precipitation and temperature were 0.322 and 0.098, respectively. In most areas, there was a positive relationship between vegetation change, temperature and precipitation. During 2001 to 2020, the basin’s climatic change trend was warm and humid, which promoted vegetation growth. One of the driving factors in the vegetation improvement area was moderate grazing by livestock.

The urbanization process is complex and lengthy, typically resulting in dual changes in the socioeconomic structure and ecological environment. However, in the context of arid environments and initial urbanization, emerging towns undergo evolutionary processes different from those of traditional cities. This study focuses on a typical town, analyzing its growth under the combined effects of arid conditions and incipient urbanization. The results reveal a unique urbanization trajectory in the hinterland of southern Xinjiang: transitioning from refined agricultural planting to shrubland and bare land dominated by natural factors and then to impervious surfaces. While the pattern was complex, the direction of transition was clear. Using the town’s establishment in 2014 as a critical node for urbanization initiation, shrubland emerged as the most sensitive land type, with a proportional increase by a factor of 2.6 from 2010 to 2015. This was driven by the abandonment of cultivated land, which decreased by 11.3% during the study period, with 78% of the newly added shrubland area converted from cropland. By applying the Markov model together with the InVEST model, the study predicted urban land-use transition patterns over the next 5 years and revealed that urbanization primarily exacerbates the instability of water yield in the surrounding region. This study uniquely addresses the gap in understanding the impacts of the urbanization process of emerging towns in arid regions and its associated ecological processes. A detailed investigation of such urbanization is crucial to mitigating issues like disorderly land use and promoting the sustainable development of small and medium-sized towns.

Eolagurus luteus (yellow steppe lemming Eolagurus luteus Eversmann, 1840) is a keystone species in the desert steppe of northern Xinjiang, one of the regions most affected by global climate change. Their behavior of eating grassland vegetation and digging holes has resulted in the reduction of grassland vegetation and soil erosion in northern Xinjiang, which has seriously affected the ecological balance of the grassland in northern Xinjiang, and pathogens carried by E. luteus pose a great threat to human health. Climate change exacerbates the uncertainty of the outbreak of E. luteus. Predicting the suitable habitat area of this species under climate change scenarios will help farmers and herders deal with the potential threat of an E. luteus outbreak. In this study, 117 actual occurrence points of E. luteus were used, and 24 climate models, 6 soil factors and 3 topographic factors from the Coupled Model Intercomparison Project (CMIP6) were taken into account. Combining the MaxEnt model and the overlay analysis function of ArcGIS software, the potential geographic distribution of E. luteus in 2030 and 2050 for the green development path (SSP126), the intermediate development path (SSP245), the regional competition path (SSP370), and the high development path (SSP585) was predicted. The change trend of the suitable area and distribution pattern of E. luteus in Xinjiang under future climate conditions was analyzed, and the main environmental factors affecting the distribution of E. luteus are discussed. The results show that the average area under curve (AUC) and true skill statistics (TSS) of the MaxEnt model are 0.993 and 0.8816, respectively, indicating that the model has a good prediction effect. The analysis of environmental factors showed that the main environmental factors affecting the potential geographical distribution of E. luteus are average annual temperature, isotherm, average temperature in the wettest quarter, average temperature in the driest quarter, and precipitation variation coefficient. With the increase of radiation intensity and time, the suitable areas of E. luteus will continue to decrease. Especially in the 2050s under the SSP585 scenario, the middle and high suitable areas will decrease by 2.58 × 104 km2 and 1.52 × 104 km2, respectively. Although the potential habitat area of E. luteus is shrinking, the future threat of E. luteus to grassland ecological security and human health should not be underestimated due to ecological adaptation of the community and the frequent occurrence of extreme weather. Therefore, studying changes in the potential geographic distribution of E. luteus under climate change scenarios and developing appropriate monitoring programs are of great importance for grassland ecological security and human health. This study fills in the gaps in the study of the potential geographical distribution of E. luteus and provides methodological and literature support for the study of the potential geographical distribution of other rodents.

Eolagurus luteus (yellow steppe lemming Eolagurus luteus Eversmann, 1840) is a keystone species in the desert steppe of northern Xinjiang, one of the regions most affected by global climate change. Their behavior of eating grassland vegetation and digging holes has resulted in the reduction of grassland vegetation and soil erosion in northern Xinjiang, which has seriously affected the ecological balance of the grassland in northern Xinjiang, and pathogens carried by E. luteus pose a great threat to human health. Climate change exacerbates the uncertainty of the outbreak of E. luteus. Predicting the suitable habitat area of this species under climate change scenarios will help farmers and herders deal with the potential threat of an E. luteus outbreak. In this study, 117 actual occurrence points of E. luteus were used, and 24 climate models, 6 soil factors and 3 topographic factors from the Coupled Model Intercomparison Project (CMIP6) were taken into account. Combining the MaxEnt model and the overlay analysis function of ArcGIS software, the potential geographic distribution of E. luteus in 2030 and 2050 for the green development path (SSP126), the intermediate development path (SSP245), the regional competition path (SSP370), and the high development path (SSP585) was predicted. The change trend of the suitable area and distribution pattern of E. luteus in Xinjiang under future climate conditions was analyzed, and the main environmental factors affecting the distribution of E. luteus are discussed. The results show that the average area under curve (AUC) and true skill statistics (TSS) of the MaxEnt model are 0.993 and 0.8816, respectively, indicating that the model has a good prediction effect. The analysis of environmental factors showed that the main environmental factors affecting the potential geographical distribution of E. luteus are average annual temperature, isotherm, average temperature in the wettest quarter, average temperature in the driest quarter, and precipitation variation coefficient. With the increase of radiation intensity and time, the suitable areas of E. luteus will continue to decrease. Especially in the 2050s under the SSP585 scenario, the middle and high suitable areas will decrease by 2.58 × 10[sup.4] km[sup.2] and 1.52 × 10[sup.4] km[sup.2], respectively. Although the potential habitat area of E. luteus is shrinking, the future threat of E. luteus to grassland ecological security and human health should not be underestimated due to ecological adaptation of the community and the frequent occurrence of extreme weather. Therefore, studying changes in the potential geographic distribution of E. luteus under climate change scenarios and developing appropriate monitoring programs are of great importance for grassland ecological security and human health. This study fills in the gaps in the study of the potential geographical distribution of E. luteus and provides methodological and literature support for the study of the potential geographical distribution of other rodents.

Despite many attempts to establish pre-treatment prognostic markers to understand the clinical biology of esophageal adenocarcinoma (EAC), validated clinical biomarkers or parameters remain elusive. We generated and analyzed tumor transcriptome to develop a practical biomarker prognostic signature in EAC. Untreated esophageal endoscopic biopsy specimens were obtained from 64 patients undergoing surgery and chemoradiation. Using DNA microarray technology, genome-wide gene expression profiling was performed on 75 untreated cancer specimens from 64 EAC patients. By applying various statistical and informatical methods to gene expression data, we discovered distinct subgroups of EAC with differences in overall gene expression patterns and identified potential biomarkers significantly associated with prognosis. The candidate marker genes were further explored in formalin-fixed, paraffin-embedded tissues from an independent cohort (52 patients) using quantitative RT-PCR to measure gene expression. We identified two genes whose expression was associated with overall survival in 52 EAC patients and the combined 2-gene expression signature was independently associated with poor outcome (P<0.024) in the multivariate Cox hazard regression analysis. Our findings suggest that the molecular gene expression signatures are associated with prognosis of EAC patients and can be assessed prior to any therapy. This signature could provide important improvement for the management of EAC patients.

Acute gouty arthritis (AGA) is characterized by the accumulation of pro-inflammatory cytokines, which are immunological responses to monosodium urate (MSU) crystals. It has been demonstrated that long non-coding RNA (lncRNA)-MM2P is a novel regulator of M2 polarization of macrophages. The aim of the present study was to investigate whether lncRNA-MM2P regulates the MSU-induced inflammatory process. In cell models of RAW 264.7 and THP-1-derived macrophages, decreased expression of lncRNA-MM2P was observed in lipopolysaccharide- and MSU-treated macrophages, which was accompanied with obvious inflammatory responses. Using small interfering RNA to knockdown lncRNA-MM2P led to the upregulation of MSU-mediated inflammatory responses, both in RAW 264.7 and THP-1-derived macrophages. In conclusion, lncRNA-MM2P could be an important regulator of MSU-induced inflammation, and therefore could be involved in the development of AGA.

Background IGFBP-4 has been considered as a factor involving in development of the central nervous system (CNS), but its role needs to be further clarified. In present study, the localization of IGFBP-4 expression in the embryonic forebrain, midbrain and hindbrain was determined using immunohistochemistry, and the levels of IGFBP-4 protein and mRNA were semi-quantified using RT-PCR and Western blot in the embryonic (forebrain, midbrain and hindbrain) and postnatal brain (cerebral cortex, cerebellum and midbrain). Results A clear immunoreactivity of IGFBP-4 covered almost the entire embryonic brain (forebrain, midbrain, hindbrain) from E10.5 to E18.5, except for the area near the ventricle from E14.5. The change of IGFBP-4 mRNA level was regularly from E10.5 to E18.5: its expression peaked at E13.5 and E14.5, followed by gradual decreasing from E15.5. The expression of IGFBP-4 protein was similar to that of mRNA in embryonic stage. After birth, the pattern of IGFBP-4 expression was shown to be rather divergent in different brain areas. In the cerebral cortex, the IGFBP-4 mRNA increased gradually after birth (P0), while the protein showed little changes from P0 to P28, but decreased significantly at P70. In the cerebellum, the IGFBP-4 mRNA decreased gradually from P0, reached the lowest level at P21, and then increased again. However, its protein level gradually increased from P0 to P70. In the midbrain, the IGFBP-4 mRNA first decreased and reached its lowest level at P28 before it increased, while the protein remained constant from P0 to P70. At P7, P14, P21, P28 and P70, the levels of IGFBP-4 mRNA in the cerebral cortex were significantly higher than that in the cerebellum or in the midbrain. Differently, the protein levels in the cerebellum were significantly higher than that either in the cerebral cortex or in the midbrain at P14, P21, P28 and P70. Conclusions The temporal expression pattern of IGFBP-4 in the embryonic brain from E10.5 to E18.5 was consistent with the course of neurogenesis in the ventricular zone, suggesting an important role of IGFBP-4 in regulating differentiation of neural stem cells. A strikingly higher abundance of the IGFBP-4 protein observed in the cerebellum from P14 to P70 suggests that IGFBP-4 may participate in the maintenance of cerebellar plasticity.