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Since China’s reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a “slow decrease–rapid increase” trend, forming a “V-shaped” pattern, while abandonment shows a “rapid decrease–slow decrease–slow increase” trend, forming a “U-shaped” pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation’s center of gravity is “slightly southeast–slightly northeast–southwest”, whereas the abandonment’s center of gravity shifts “northeast–southwest–northeast”. Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen.

IntroductionAs a systemic autoimmune disorder, the prognosis of rheumatoid arthritis (RA) is intricately linked to inflammation. This study aimed to investigate the association between the advanced lung cancer inflammation index (ALI), a comprehensive indicator of inflammation combined with nutritional status, and all-cause and cardiovascular mortality among patients diagnosed with RA.MethodsThe 2,305 RA patients from NHANES (2001–2018) included in the analysis were categorized into three groups according to ALI tertiles. Weighted Kaplan–Meier and multivariate COX regression analyses evaluated the relationship between ALI and mortality. The time-dependent characteristic curve (ROC) was used to assess the prediction accuracy of ALI.ResultsDuring a median follow-up of 7.92 years, 591 participants died from all causes, including 197 from cardiovascular diseases. Increased ALI was associated with a decreased probability of death. The full COX model revealed lower all-cause mortality hazard risks in the T2 (HR: 0.67, 95%CI: 0.54–0.83) and T3 (HR: 0.47 95%CI: 0.33–0.67, p for tend

ABSTRACTOasis cities are deeply affected by human activities in arid and semi-arid regions. Vegetation is an important repository in the carbon cycle of oasis urban ecosystems. The continuous expansion of urban has a disproportionate impact on the carbon sequestration capacity of vegetation. Till now, studies have been conducted to quantify the impact of urbanization on vegetation carbon sequestration capacity, the mechanism of such impact remains unclear and lacks systematic investigations, especially in oasis urban. Understanding the impact mechanism greatly benefits the sustainable development of oasis urban and regional carbon neutrality. To fill this knowledge gap, we design a theoretical framework to analyze the impact of urbanization on vegetation carbon sequestration capacity by isolating the direct and indirect impacts in Urumqi, China. Some results based on Landsat images indicated that the Impervious Surface Areas (ISAs) expanded by 436.98 km2 during 2000 – 2019. The Net Primary Productivity (NPP) calculated using the Vegetation Photosynthesis Model (VPM) was directly caused by the loss of 51.45 Gg C (1 Gg = 109 g). Another important finding proved that the direct carbon loss caused by urbanization did not change the upward trend of the total carbon sequestration capacity of vegetation, but only slowed down this upward trend by 20.86%. Our results provide new insights into urban vegetation carbon sinks and contribute to a better understanding of the uncertainty of urbanization toward carbon neutrality. This study will provide scientific data support and suggestions for the sustainable development of oasis cities and the regional carbon budget.

ObjectivesThis study delves into the association between dietary niacin intake and Helicobacter pylori seropositivity, a topic gaining prominence in academic discourse. However, the precise role of Niacin in the development and progression of Helicobacter pylori seropositivity remains inadequately understood. Thus, this research aims to investigate the connections between H. pylori seropositivity and dietary niacin intake using a nationally representative sample of adults.MethodsA cross-sectional analysis encompassed 4,000 participants from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States between 1999 and 2000, all aged 20 years or older. The study employed the generalized additive model (GAM) and multivariate logistic regression to explore the potential relationship between niacin intake and Helicobacter pylori seropositivity. Subgroup analyses were performed based on gender, age, diabetes, hypertension, and hyperlipemia.ResultsAnalyzing cross-sectional data from NHANES 1999-2000 involving individuals aged 20 years and above revealed that out of 4,000 participants, 1,842 tested positive for H. pylori via serology. Multivariate analyses unveiled a significant inverse correlation between niacin intake and H. pylori seropositivity. Adjusted odds ratios (ORs) for dietary niacin intake in quartiles Q2 (13.31-19.26 mg/d), Q3 (19.27-27.42 mg/d), and Q4 (>27.42 mg/d) compared to Q1 (ConclusionThis study suggests a potential link between increased dietary niacin intake and reduced prevalence of Helicobacter pylori seropositivity. This correlation is bolstered by plausible mechanisms involving immunomodulatory function, mitochondrial dysfunction, and cellular oxidative stress.

Mounting evidence indicates a complex link between circulating saturated fatty acids (SFAs) and cardiovascular disease (CVD) risk factors, but research on erythrocyte membrane SFA associations with metabolic markers remains limited. Our study sought to investigate the correlations between erythrocyte membrane SFAs and key metabolic markers within glycemic and lipid metabolism in a Chinese population of 798 residents aged 41 to 71 from Guangzhou. Using gas chromatography–mass spectrometry, we assessed the erythrocyte membrane saturated fatty acid profile and performed multiple linear regression to evaluate the relationship between different SFA subtypes and metabolic markers. Our findings revealed that the odd-chain SFA group (C15:0 + C17:0) exhibited negative associations with fasting blood glucose (FBG), homeostatic model assessment for insulin resistance (HOMA-IR), and triglycerides (TG). Conversely, the very-long-chain SFA group (C20:0 + C22:0 + C23:0 + C24:0) exhibited positive associations with fasting insulins (FINS), HOMA-IR, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Furthermore, there was no evidence supporting an association between the even-chain group (C14:0 + C16:0 + C18:0) and metabolic markers. Our findings suggest that different subtypes of SFAs have diverse effects on glycemic and lipid metabolic markers, with odd-chain SFAs associated with a lower metabolic risk. However, the results concerning the correlations between even-chain SFAs and very-long-chain SFAs with markers of glycemic and lipid metabolism pathways are confusing, highlighting the necessity for further exploration and investigation.

Soil organic carbon stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal–spatial and vertical distribution characteristics of the SOCS in the Western Sichuan Plateau (WSP) using the sparrow search algorithm–random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal–spatial variations in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS in the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed a significant increase in the SOCS in the study area since 2010. There was a 27.88 Tg C increase in the SOCS in 2020 compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forests and grasslands are the main sources of SOCS the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total soil organic carbon (SOC) pool in 2020, respectively. The expansion of the wetlands, forest, and grassland areas could increase the SOCS in the study area. A timely and accurate understanding of the dynamics of SOC is crucial for developing effective land management strategies to enhance carbon sequestration and mitigate land degradation.

Net primary productivity (NPP) has been substantially changed under the intense oasification in the urban agglomerations on the northern slopes of mid-Tianshan Mountain (UANSTM) and climate change. However, the temporal variations of NPP under the oasification remain unclear, and the relative contribution of oasification and climate change on annual NPP variation is still under debate. By using remote sensing data, reanalysis data, modified Carnegie–Ames-Stanford Approach (CASA) model, and a machine learning method, we explored the spatial–temporal variation of NPP in the UANSTM region and quantified the contribution of oasification and climate change to NPP variation from 2001 to 2020. Our study indicated that: (1) the NPP presents an overall increasing trend in the most of region and the region presented decreasing trend mainly due to the cropland conversion to the urban area; (2) the oasification-dominated NPP area concentrated in the built-up land and cropland; (3) during 2001–2020, the NPP increased by about 5.4 Tg·C, and the contribution of climatic and oasification to NPP increase were quantified (73.1% and 26.9%, respectively); (4) water-related factors was the main driver of NPP variation in the UANSTM region.

There has been an increasing number of studies on the potential effects of land-use change on the carbon (C) balance. However, few of these studies have focused on arid regions. Cropland in Xinjiang, a typical arid region in China, has expanded dramatically over the last 40 years. This study applied the Carbon Bookkeeping Model to estimate the changes in C stocks resulting from cropland expansion in Xinjiang from 1975 to 2015. The results showed that the area of cropland increased by a factor of ∼1.6. This increase was driven by advancements in agricultural technology and favorable agricultural policies. The increase in cropland area of 2.03 Mha (M = 106) was the result of the clearing of ∼4.09 Mha land for cropland and the conversion of 2.06 Mha cropland to other land cover types. The expansion in cropland resulted in substantial sequestration of C, with that in Xinjiang amounting to 94.24 Tg C (1Tg = 1012g), accounting for 1.4% of the regional C stocks. Land clearing for cropland (LCC) had the greatest contribution to C sequestration in Xinjiang. The rate of increase in C density through LCC was 0.61 Mg C ha−1a−1 and 1.54 Mg C ha−1a−1 from 1975 to 2004 and 2005 to 2015, respectively. C sequestration due to cropland loss (CLO) of 29.40 Tg C was attributed to the expansion of built-up land and afforestation. Sustainable agricultural activities represented by large-scale clearing for cropland were a major C sink in Xinjiang. Therefore, sustainable management of cropland is essential for maintaining a high C density and preventing loss of C to the atmosphere through cropland abandonment in the future.

Urban vegetation (UV) and its carbon storage capacity are critical for terrestrial carbon cycling and global sustainable development goals (SDGs). With complex spatial distribution, composition and ecological functions, UV is essential for global carbon cycling and climate change. Therefore, improving UV carbon storage capacity modeling is a research hotspot that deserves extensive investigation. However, the uniqueness of UV lead to great challenges in carbon storage modeling, including (1) limitations in data and algorithms due to complex and sensitive urban environments; (2) the severe scarcity of in-city field observation data (e.g., EC towers and field surveys); (3) difficulty in parameter inversion (e.g., canopy height, LAI, etc.); (4) poor transferability when migrating estimation models from natural vegetation to urban scenarios. The progress in carbon storage modeling in urban settings is reviewed, with detailed discussions on carbon storage modeling methods and major challenges. We then propose strategies to overcome existing challenges, including (1) implementing novel and improved remote sensing (RS) techniques (e.g., hyper-spectral, LiDAR, carbon satellites, etc.) to obtain enhanced structural and functional information on UV; (2) improving critical nodes of the earth observation sensor network, especially the distribution of EC towers in urban settings; (3) leveraging “Model-Data Fusion” technology by integrating big earth data with carbon estimation models to reduce the uncertainty in UV carbon storage estimations. This review provides new insights for modeling UV carbon storage and is expected to help the research community to achieve a better understanding of UV towards carbon neutrality.

Stipa purpurea (S. purpurea), a dominant herbage resource in alpine steppe, has an important impact on animal husbandry and the maintenance of natural environmental stability in the Tibetan Plateau (TP). As a hot spot of global warming, the TP has been greatly affected by climate change. Based on the MaxEnt model, we simulated the potential habitat changes of S. purpurea on the TP in the 21st century using 197 distribution records combined with current (2000–2018) and future (2021–2100) climate data. We also analyzed the critical factors influencing habitat distribution and driving habitat change. The results showed that the MaxEnt model performed well, with the area under the curve (AUC) value of training data and test data being 0.845 and 0.722, respectively. Annual precipitation, elevation, and annual average temperature were identified as the most critical environmental factors shaping the potential distribution of S. purpurea, and temperature was identified as the most critical factor driving S. purpurea habitat change. Under the current climate, the potential habitat area of S. purpurea was 55.15 × 104 km2, mainly distributed in the Eastern Qinghai-Qilian montane steppe, Southern Tibet montane shrub-steppe, and Ngari montane desert. By the end of the 21st century, the potential habitat area of S. purpurea showed an overall increasing trend, under two shared socio-economic pathways (SSP245 and SSP585) was 87.37 × 104 km2 and 57.80 × 104 km2, respectively. A comparison of warming scenarios indicated that warming had a positive effect on the distribution of S. purpurea within a certain temperature range, but continued warming would limit distribution ranges. In response to climate warming in the 21st century, potential habitats of S. purpurea will shift northwestward and to higher altitudes. These results will provide a reference for implementing long-term conservation and management strategies on the TP and similar regions.

Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc.

Accurate monitoring of aboveground biomass (AGB) and its spatial distribution is essential for sustainable management and development. The most frequently used method for estimating AGB with remotely sensed data is based on the relationship between AGB measured data and vegetation indices. In the past, there were some shortcomings in analyzing the spatial heterogeneity and solving scale difference. Therefore, this paper realized the space-air-ground integrated based on Sentinel-2 Multispectral Instrument (MSI), Unmanned Aerial Vehicle (UAV) and measured data. The results indicated that the RFR models have excellent adaptability to invert AGB of three different coverage regions in study area, with R2 of 0.69 and root mean square error (RMSE) of 116.22 kg·hm−2 for low-coverage region, R2 of 0.65 and RMSE of 164.16 kg·hm−2 for medium-coverage region, R2 of 0.73 and RMSE of 149.99 kg·hm−2 for high-coverage region. We found that the main driving vegetation indices was different in each region during the process of model building and it was related to the characteristics of vegetation indices. Moreover, regions with different coverage have different responses to topography. The findings showed that the spatial distribution and driving mechanism of AGB in the study area could be more clearly analyzed based on the coverage division. The introduction of coverage division reasonably enhanced the accuracy of RFR modeling AGB. This study confirmed that using the Sentinel-2 MSI, UAV images, measured data and the RFR model to realize the space-air-ground integrated is an applicable method for monitoring and assessment of AGB in Qilian Mountains, which can offer scientific basis and theoretical support to rational development of land resources and maintaining the balance of ecosystem.

Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of “climate-urbanization-soil-AMPs” on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of “climate-urbanization-soil” that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security.

The potential impact of natural factors on the runoff of intermittent rivers and ephemeral streams (IRES) has been largely ignored in the Tarim Basin, China. A representative example is the Keriya River. To quantify the long-term dynamic variations in lower reach surface runoff of IRES, river length, defined as the distance between a selected fix point along the perennial river segment to its dynamic, ephemeral end, was used as an indicator. Using a total of 272 remote sensing images, we digitized and measured the distance (river length) between the center of Yutian County and the river's end point on each image, and then calculated monthly inter-annual and intra-annual variations in length of the lower Keriya River from 2000 to 2019. Hydrometeorological data were combined with descriptors of anthropogenic disturbances to assess the relative influence of natural factors and anthropogenic disturbances on lower reach river runoff. The results showed that intra-annual variations in river length fluctuated seasonally, with the minimum value occurring in June; two main peaks occurred in March and August. The minimum June value in river length was closely linked to an increase in agricultural water demand and a decrease in upper reach runoff. The August peak in river length was related to the peak values in upper reach runoff and agricultural water demand; upper reach runoff made a significant contribution because the former was about 20% more than the latter in summer. The March peak corresponded to elevated lower reach groundwater levels and to the melting of ice along river channels. Inter-annual variations in river length were due to inter-annual variations in upper reach runoff and middle reach agricultural water use which increased slightly during the study period. Inter-annual variations in frequency and amplitude of the fluctuations in river length were mainly controlled by changes in upper reach runoff. The minimum in river length in 2009 was consistent with the low in upper reach runoff of the Keriya River and other rivers in the Tarim Basin. The most significant factors controlling variations in river length are natural in origin.

Quantifying the variation of biophysical parameters and their driving mechanisms is essential for monitoring land surface environmental changes and for understanding the land–atmosphere interaction in the arid region. Due to the complexity of human activities, most researches are limited to climate change, whereas the response analysis of human activities to changes in biophysical parameters are still lacking or not comprehensively considered. Therefore, large biases and uncertainties still exist in the estimates of regional responses to global change. Firstly, we specifically quantified the main human activities related to land use/land cover change (LULCC) in the northern Tianshan Mountains (NTM), and identified the spatiotemporal changes of primary biophysical parameters, including Albedo, leaf area index (LAI), land surface temperature (LST), and Normalized Difference Vegetation Index (NDVI). Then, we tested the performance of the five models used, including multiple linear regression (MLR), random forest (RF), support vector regression (SVR), multi-layer perceptron (MLP), and K-nearest neighbor (KNN). RF outperformed others and was used to quantify and disaggregate the contribution of climate change and human activities to land surface parameters in the NTM. We found a strong spatial heterogeneity in the spatial variation of all biophysical parameters. Except for LST, the annual maximum Albedo, LAI, and NDVI showed a significant increasing trend in the NTM from 2000 to 2019 (p

Lake ice phenology is an indicator of past and present climate, it is sensitive to regional and global climate change. In the past few decades, the climate of Central Asia has changed significantly due to global warming and anthropogenic activities. However, there are few studies on the lake ice phenology in Central Asia. In this study, the lake ice phenology of 53 lakes in Central Asia were extracted using MODIS daily LST products from 2002 to 2020. The results show that MODIS-extracted lake ice phenology is generally consistent with Landsat-extracted and AVHRR-extracted lake ice phenology. Generally, lakes in Central Asia start to freeze from October to December. The trends in the lake ice phenology show strong regional differences. Lakes distributed along the Kunlun Mountains show overall delayed trends in all lake ice phenology variables, while lakes located in southwestern Central Asia show clear advancing trends in the freeze-up start dates (7.06 days) and breakup end dates (6.81 days). Correlations between the phenology of lake ice and local and climatic factors suggest that the ice breakup process and the duration of its complete coverage depend more on heat, while precipitation mainly affects the freezing time of the ice. Wind speed mainly affects the time of completely frozen of ice. In general, the breakup process is more susceptible to climatic factors, while local factors have strong influences on the freeze-up process.

The ecological water diversion project (EWDP) in the Tarim River Basin, China, aims to allocate more surface water to downstream reaches to restore the degraded ecosystems. However, seasonal changes in ecological water diversion; the factors (natural and anthropogenic) controlling the ecological water diversion, whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands; and the benefits of the ecological water diversion through overflowing surface water irrigation are unclear. To address the above issues, this study examines the intra-annual changes and its influencing factors in ecological water diversion (inundation) in the Daliyaboyi Oasis in the lower Keriya River valley within the Tarim Basin, discusses whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands, and assesses the ecological benefits of overflowing surface water irrigation. Inundation was quantified by digitizing monthly changes in the inundated area from 2000 to 2018 in the oasis using 184 Landsat images. The results demonstrate that seasonal changes in the inundated area varied significantly, with maximum peaks occurring in February and August; a period of minimal inundation occurred in May. Differences in the July/August peak (i.e., July or August) in inundation dominated the inter-annual variations in the inundated area over the 19-year study period. The two peaks in the inundation area were temporally consistent with the vegetation’s seasonal water demand. Local residents have used ecological water to irrigate vegetation in different parts of the oasis during different seasons, an approach that expanded the inundated area. The February peak in the inundated area is closely linked to elevated downstream groundwater levels and the melting of ice along the river. The August peak is related to a peak in runoff from headwater areas. The minimum May value is correlated to a relatively low value in upstream runoff and an increase in agricultural water demand. Thus, natural factors control the intra-annual and inter-annual variations in the inundated area. Humans changed the spatial distribution of the inundated area and enhanced the water’s ecological benefits, but did not alter the correlation between peak periods of inundation and vegetation water demand. The results from this study improve our understanding of the benefits of the EWDP in the Tarim River Basin.

BACKGROUND:Cattleyak are the hybrid offspring between cattle and yak and combine yak hardiness with cattle productivity. Much attempt has been made to examine the mechanisms of male sterility caused by spermatogenic arrest, but yet there is no research systematically and precisely elucidated testis gene expression profiling between cattleyak and yak. METHODS:To explore the higher resolution comparative transcriptome map between the testes of yak and cattleyak, and further analyze the mRNA expression dynamics of spermatogenic arrest in cattleyak. We characterized the comparative transcriptome profile from the testes of yak and cattleyak using high-throughput sequencing. Then we used quantitative analysis to validate several differentially expressed genes (DEGs) in testicular tissue and spermatogenic cells. RESULTS:Testis transcriptome profiling identified 6477 DEGs (2919 upregulated and 3558 downregulated) between cattleyak and yak. Further analysis revealed that the marker genes and apoptosis regulatory genes for undifferentiated spermatogonia were upregulated, while the genes for differentiation maintenance were downregulated in cattleyak. A majority of DEGs associated with mitotic checkpoint, and cell cycle progression were downregulated in cattleyak during spermatogonial mitosis. Furthermore, almost all DEGs related to synaptonemal complex assembly, and meiotic progression presented no sign of expression in cattleyak. Even worse, dozens of genes involved in acrosome formation, and flagellar development were dominantly downregulated in cattleyak. CONCLUSION:DEGs indicated that spermatogenic arrest of cattleyak may originate from the differentiation stage of spermatogonial stem cells and be aggravated during spermatogonial mitosis and spermatocyte meiosis, which contributes to the scarcely presented sperms in cattleyak.

Background Vegetation dynamics is defined as a significant indictor in regulating terrestrial carbon balance and climate change, and this issue is important for the evaluation of climate change. Though much work has been done concerning the correlations among vegetation dynamics, precipitation and temperature, the related questions about relationships between vegetation dynamics and other climatic factors (e.g., specific humidity, net radiation, soil moisture) have not been thoroughly considered. Understanding these questions is of primary importance in developing policies to address climate change. Methods In this study, the least squares regression analysis method was used to simulate the trend of vegetation dynamics based on the normalized difference vegetation index (NDVI) from 1981 to 2018. A partial correlation analysis method was used to explore the relationship between vegetation dynamics and climate change; and further,the revised greyscale model was applied to predict the future growth trend of natural vegetation. Results The Mann-Kendall test results showed that th e air temperature rose sharply in 1997 and had been in a state of high fluctuations since then. Strong changes in hydrothermal conditions had major impact on vegetation dynamics in the area. Specifically, the NDVI value of natural vegetation showed an increasing trend from 1981 to 2018, and the same changes occurred in the precipitation. From 1981 to 1997, the values of natural vegetation increased at a rate of 0.0016 per year. From 1999 to 2009, the NDVI value decreased by an average rate of 0.0025 per year. From 2010 to 2018, the values began an increasing trend and reached a peak in 2017, with an average annual rate of 0.0033. The high vegetation dynamics areas were mainly concentrated in the north and south slopes of the Tianshan Mountains, the Ili River Valley and the Altay area. The greyscale prediction results showed that the annual average NDVI values of natural vegetation may present a fluctuating increasing trend. The NDVI value in 2030 is 0.0196 higher than that in 2018, with an increase of 6.18%. Conclusions Our results indicate that: (i) the variations of climatic factors have caused a huge change in the hydrothermal conditions in Xinjiang; (ii) the vegetation dynamics in Xinjiang showed obvious volatility, and then in the end stage of the study were higher than the initial stage the vegetation dynamics in Xinjiang showed a staged increasing trend; (iii) the vegetation dynamics were affected by many factors,of which precipitation was the main reason; (iv) in the next decade, the vegetation dynamics in Xinjiang will show an increasing trend.

Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver.

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.

A miniature dosage meter for toxic gas is developed based on TaS2 nanosheets, which is capable of indicating the toxic dosage of trace level NO at room temperature. The TaS2 film-based chemiresistor shows an irreversible current response against the exposure of NO. The unique non-recovery characteristic makes the TaS2 film-based device an ideal indicator of total dosage of chronicle exposure.