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Remote sensing technology provides an effective means for continuously assessing the chlorophyll content in plants on a broad scale. Given the challenges associated with satellite image quality and spatiotemporal resolution, spatiotemporal fusion algorithms for estimating vegetation chlorophyll content have garnered significant attention in recent years. In this study, we evaluated the performance of four fusion algorithms fusing Gaofen-1 WFV and MODIS data while also exploring their fusion accuracy. A random forest regression model was developed using the fused images and measured SPAD (Soil and Plant Analyzer Development) values, enabling large-scale, accurate, and dynamic monitoring of vegetation chlorophyll content. The results indicate that (1) all four fusion algorithms can effectively address the issue of missing images; (2) the constructed random forest regression model accurately estimates SPAD values; and (3) among the three vegetation indices that exhibit a strong correlation with SPAD values, the fusion strategy “Index-then-Blend” outperforms “Blend-then-Index.” This study provides comprehensive insights into dynamic and large-scale monitoring of vegetation chlorophyll content, particularly in scenarios in which satellite imagery is unavailable.

The pine wilt disease (PWD) can cause destructive death to pine trees in a short period. Utilizing unmanned aerial vehicle (UAV) remote sensing technology to promptly identify PWD-infected trees has become an effective and feasible method for precise PWD monitoring. In this study, UAV multispectral imagery was used to analyze the sensitive spectral bands and different vegetation indices for PWD discriminability. A dataset of optimal spectral combinations from visible light and multispectral images was constructed, along with an improved YOLOv8 deep learning model for rapid and accurate identification of PWD-infected trees. The improved YOLOv8 model used omni-dimensional dynamic convolution (ODConv) to enhance the performance of convolutional networks, designed a dynamic head (DyHead) module to capture PWD features more accurately, and applied MPDioU to improve the regression accuracy and model runtime efficiency. Experimental results showed that the mAP@0.5 of the improved YOLOv8 model increased to 89.1 %, with a user accuracy of 90 % and a recall rate of 93.1 %. This achieved rapid and accurate detection of PWD-infected trees, providing effective technical support for automatic identification of PWD epidemic areas and control of PWD outbreaks based on UAV multispectral imagery.

Soil salinization represents a significant challenge to the ecological environment in arid areas, and digital mapping of soil salinization as well as exploration of its spatial heterogeneity with crop growth have important implications for national food security and salinization management. However, the machine learning models currently used are deficient in mining local information on salinity and do not explore the spatial heterogeneity of salinity impacts on crops. This study developed soil salinization inversion models using CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory Network), and RF (Random Forest) models based on 97 field samples and feature variables extracted from Landsat-8 imagery. By evaluating the accuracy, the best-performing model was selected to map soil salinity at a 30m resolution for the years 2013 and 2022, and to explore the relationship between soil electrical conductivity (EC) values and the expansion of cotton fields as well as their spatial correlation. The results indicate that:(1) The CNN performs best in prediction, with an R2 of 0.84 for the training set and 0.73 for the test set, capable of capturing more local salinity information. (2) The expansion of cotton fields has reduced the level of soil salinization, with the area of severely salinized and saline soils in newly added cotton fields decreasing from 177.91 km2 and 381.46 km2 to 19.49 km2 and 1.12 km2, respectively. (3) Regions with long-term cotton cultivation and newly reclaimed cotton fields exhibit high sensitivity and vulnerability to soil salinity. This study explores the excellent performance of deep learning in salinity mapping and visualizes the spatial distribution of cotton fields that are highly sensitive to soil salinity, providing a scientific theoretical basis for accurate salinity management.

A comprehensive understanding of soil salinity characteristics and the vertical and spatial distribution of particle sizes in lakes and wetlands within arid zones, as well as elucidating their interrelationship, is crucial for effective wetland soil salinization management. In this study, the typical salinized wetland, the Ebinur Lake wetland, was selected as the research object. A total of 50 sampling points were established along the edge of Ebinur Lake, resulting in the collection of 200 soil samples from depths of 0–60 cm. The particle size distribution (PSD) of the soil samples was obtained by laser particle sizer, and the fractal dimension of the soil structure was deduced by applying fractal theory. The soluble salt content (TSS) and salt ions content were measured by laboratory physicochemical experiments. Finally, Pearson correlation and other methods were used to explore the relationship between soil salinity and soil particle size. The results showed the following: (1) Soil salinization in the study area was severe, and the accumulation of surface salts was obvious, with a mean value of 46,410 mg/kg. The spatial distribution of TSS was predominantly influenced by Cl−, SO42−, Na+ + K+, Mg2+, and Ca2+. (2) Across various soil depths, silt and sand were the primary constituents, with soil fractal dimensions (Dsoil) ranging from 1.91 to 2.76, averaging 2.54, and a poor soil textural structure. The spatial distribution of Dsoil closely mirrored that of TSS. (3) According to the correlation analysis results, as TSS increased, Dsoil continued to rise, with an increasing content of clay, while the sand content decreased. Simultaneously, as the soil particles became finer, TSS and Dsoil also increased, suggesting that sandy loam to silty soils in the study area were more prone to salt accumulation.

The water delivery capacity of Karez is gradually declining, and it is possible that such a traditional water supply systems might be completely replaced by modern water conservancy projects. To determine whether these ancient water conservancy projects have a future, we conducted a study in a typical research area of Xinjiang Province, China. Using remote sensing technology, in situ surveying, and the analytic hierarchy process, we evaluated Karez in terms of water delivery capacity, water intake convenience, and capability to maintaining the ecological stability of the surrounding area. The derived results are as follows. (1) During 2005–2020, the length of 14 Karez systems in the study area decreased; however, the calculated water access convenience degree (C) indicated that Karez are more convenient for water intake compared with modern transmission channels, i.e., Ckarez = 0.68 and Cchannel = 0.51. (2) The mean annual runoff of the Karez system was 69.38 × 106 m3, similar to that of the modern water transmission channels in the area (73.49 × 106 m3). (3) Change in ecological sensitivity occurred mostly in regions where Karez systems have disappeared, with increase of 70.69 % and 14.51 % in high and medium sensitivity areas, respectively, and decline of 21.53 % and 9.74 % in low-sensitivity and non-sensitivity areas. Our research shows that Karez have considerable water delivery capacity, and that their existence is beneficial to maintaining the stability of the surrounding ecology. The Karez system can be considered a template for harmonious coexistence between humans and nature.

Carbon use efficiency (CUE) and water use efficiency (WUE) are key metrics for quantifying the coupling between terrestrial ecosystem carbon and water cycles. The impacts of intensifying climate change and human activities on carbon and water fluxes in Central Asian vegetation remain unclear. In this study, the CUE and WUE in Central Asia from 2001 to 2022 were accurately estimated with the help of the Google Earth Engine (GEE) data platform; the Theil–Sen median slope estimation combined with the Manna–Kendall significance test and partial derivative analysis were used to investigate the CUE and WUE trends and their responses to climate change and human activities. CUE and WUE show overall declining trends with significant spatial variability. Among meteorological factors, vapor pressure deficit and temperature show the strongest correlation with CUE, while precipitation and temperature are most correlated with WUE. Compared to human activities, climate change has a greater impact on CUE and WUE, mainly exerting a negative influence. Human activities are the main drivers in regions with developed agriculture, such as oases, farmlands, and areas near rivers and lakes. This study provides scientific references for the optimization of water and soil resources and the integrated regional environmental management in Central Asia.

Xinjiang is a representative dry area in China characterized by oasis agriculture. In recent decades, the amount of farmland has increased considerably. For the regional objectives of “carbon peaking and carbon neutrality”, it is essential to investigate the carbon effects induced by large-scale changes in farmland. This research integrates the PLUS and InVEST models to calculate the carbon effects resulting from the spatiotemporal changes in farmland distribution in Xinjiang. It quantitatively assesses the changes in land-use patterns and carbon storage under four scenarios for 2035—natural development (ND), economic development (ED), ecological protection (EP), and farmland protection (FP)—and explores the spatial agglomeration degree of the carbon effect of cultivated land area change. The analysis reveals the following: (1) From 1990 to 2020, the farmland area in Xinjiang showed a trend of first decreasing and then increasing, resulting in a total increase of 33,328.53 km2 over the 30-year period. The newly added farmland primarily came from grassland and unused land. (2) The terrestrial ecosystem carbon storage in Xinjiang showed a trend of decreasing first and then increasing, with an increase of 57.49 Tg in 30 years. The centroid of carbon storage was located in the northwestern part of the Bayingolin Mongol Autonomous Prefecture, showing an overall southwestward shift. Changes in farmland area contributed to a regional carbon storage increase of 45.03 Tg. The contribution of farmland to carbon storage increased by 3.42%. (3) In 2035, the carbon storage value of different scenarios will increase compared with 2020, and the carbon sink of cultivated land will be the maximum under the cultivated land protection scenario. (4) There is a strong spatial positive correlation between the changes in carbon storage caused by the change in cultivated land area in Xinjiang, and there are more hot spots than cold spots. The carbon storage changes under farmland transformation have the characteristics of “high-high” clustering and “low-low” clustering. Future territorial spatial planning in Xinjiang should comprehensively coordinate ecological protection and farmland conservation measures, improve regional carbon sink capacity, and achieve green and sustainable development.

Research on the carrying capacity of land resources and their outputs to population size will be able to better serve the ecological governance of food security. This paper analyzes the population carrying capacity level of southern Xinjiang by using the population carrying capacity model of land resources, and ANOVA analyzes the significance difference between the newly added arable land and the original arable land. At the same time, the demand of cultivated land development is discussed. The results showed that the sown area of grain crops in all the prefectures showed an overall increase from 2009 to 2019. The population carrying scale is more optimistic under subsistence and well-off conditions. From 2009 to 2019, cropland in Aksu region increased the most, followed by Kashgar region, with both regions exceeding 300,000 ha. ANOVA results show that the new arable land is comprehensively lower than the original arable land in terms of quality grade and agricultural output, and salinization and desertification are more serious than on the original arable land. At the same time, the subsistence type requires the least amount of arable land development while the affluent type requires the most. Therefore, we should adapt to local conditions and develop modern agriculture scientifically and reasonably.

Background: A COVID-19 outbreak in the rural areas of Shijiazhuang City was attributed to the complex interactions among vaccination, host, and non-pharmaceutical interventions (NPIs). Herein, we investigated the epidemiological characteristics of all reported symptomatic cases by picking Shijiazhuang City, Hebei Province in Northern China as research objective. In addition, we established an age-group mathematical model to perform the optimal fitting and to investigate the dynamical profiles under three scenarios. Methods: All reported symptomatic cases of Shijiazhuang epidemic (January 2-February 3, 2021) were investigated in our study. The cases were classified by gender, age group and location, the distributions were analyzed by epidemiological characteristics. Furthermore, the reported data from Health Commission of Hebei Province was also analyzed by using an age-group mathematical model by two phases and three scenarios. Results: Shijiazhuang epidemic caused by SARS-CoV-2 wild strain was recorded with the peak 84 cases out of 868 reported symptomatic cases on January 11, 2021, which was implemented with strong NPIs by local government and referred as baseline situation in this study. The research results showed that R0 under baseline situation ranged from 4.47 to 7.72, and Rt of Gaocheng Distinct took 3.72 with 95% confidence interval from 3.23 to 4.35 on January 9, the declining tendencies of Rt under baseline situation were kept till February 3, the value of Rt reached below 1 on January 19 and remained low value up to February 3 for Gaocheng District and Shijiazhuang City during Shijiazhuang epidemic. This indicated Shijiazhuang epidemic was under control on January 19. However, if the strong NPIs were kept, but remote isolation operated on January 11 was not implemented as of February 9, then the scale of Shijiazhuang epidemic reached 9,482 cases from age group who were 60 years old and over out of 31,017 symptomatic cases. The investigation also revealed that Shijiazhuang epidemic reached 132,648 symptomatic cases for age group who were 60 years old and over (short for G2) under risk-based strategies (Scenario A), 58,048 symptomatic cases for G2 under late quarantine strategies (Scenario B) and 207,124 symptomatic cases for G2 under late quarantine double risk strategies (Scenario C), and that the corresponding transmission tendencies of Rt for three scenarios were consistently controlled on Jan 29, 2021. Compared with baseline situation, the dates for controlling Rt below 1 under three scenarios were delayed 10 days. Conclusions: Shijiazhuang epidemic was the first COVID-19 outbreak in the rural areas in Hebei Province of Northern China. The targeted interventions adopted in early 2021 were effective to halt the transmission due to the implementation of a strict and village-wide closure. However we found that age group profile and NPIs played critical rules to successfully contain Shijiazhuang epidemic, which should be considered by public health policies in rural areas of mainland China during the dynamic zero-COVID policy.

Spherical and whisker-like cobalt particles were doped into the diamond powders to synthesize the PDC compacts under the HPHT conditions, to investigate the effect of their morphology on the properties of PDC. It was found that the two morphologies of cobalt powder have significant differences in physical characteristics, such as microstructure, porosity, magnetic properties, and crystallinity. When they were used to synthesize PDC samples, it was found that there were noticeable differences in interface bonding morphology, abnormal growth of interface WC, impact strength, and wear resistance. PDC synthesized with spherical cobalt powder had better comprehensive performance than PDC synthesized with whisker-like cobalt powder.

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of “salt moves together with water, and when water evaporates, salt is retained in the soil weight”. The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring.

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%–0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023.

As a typical geographic landscape unit in the arid zone of northwest China, the distribution of the thermal environment in the substratum of the desert-oasis is of great significance to the monitoring of the ecological environment and the quality of human habitat in the region. The purpose of this research is to reveal the spatial–temporal evolution pattern of the thermal environment of the desert–oasis in the Turpan–Hami region from 2005 to 2020 and its trend changes, and to investigate the relationship between natural and human factors and the thermal environment and to conduct a long time series analysis. Firstly, the accuracy of MODIS land surface temperature (LST) data combined with site data was verified. Secondly, the mean-standard deviation method is used to identify desert heat island and oasis cold island zones. Again, the spatial–temporal distribution and change trends of thermal environment are explored by using standard deviation ellipse and spatial autocorrelation combined with land use/land cover (LULC) types. Finally, based on the multi-source remote sensing data, the natural factors and human factors are selected to explore their correlation with the thermal environment using Pearson correlation analysis. The results show that (1) the desert heat island zones are distributed in the desert areas on the periphery of urban built-up areas in Gaochang District, Shanshan County and Yizhou District of Hami Region. The oasis cold island zones are concentrated in the urban built-up areas of Gaochang District and Yizhou District, mainly because the vegetation coverage of urban built-up areas is higher than that of the peripheral desert areas. (2) The spatial development characteristics of the extremely high temperature (EHT) zone and the high temperature (HT)zone from 2005 to 2020 are “southeast-northeast-northwest” and “southeast-northwest” respectively, and the area of construction land 16a increased by 0.31%. This indicates that the spatial evolution of the thermal environment is closely related to the LULC and the degree of urbanization development. (3) From a four–period image with P 2.58, the thermal environment displays a high positive spatial correlation with Moran's I values of 0.45, 0.54, 0.47, and 0.45. (4) Temperature (Tem), downward longwave radiation (LWdown), and nighttime light intensity (NPP) all exhibited positive correlations with the LST and are significant in the desert region (p

Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid modeling, we develop a hybrid model based on surface conductance optimization. A hybrid modeling algorithm, two physical process-based ET algorithms (Penman–Monteith-based and Priestley–Taylor-based ET algorithms), and three pure machine learning algorithms (Random Forest, Extreme Gradient Boosting, and K Nearest Neighbors) are comparatively analyzed for estimating the ET. The results showed that, in general, the machine learning model optimized by parameters was able to better predict the surface conductance of the hybrid model. Driver analyses showed that radiation, normalized difference vegetation index (NDVI), and air temperature had high correlations with ET. The hybrid model had a better prediction performance for ET than the other five models, and it improved the R2 of the two physical process-based algorithms to 0.9, reduced the root mean square error (RMSE) to 0.5 mm/day, reduced the BIAS to 0.2 mm/day, and improved the Kling–Gupta efficiency (KGE) to 0.9. The hybrid model outperformed the others across different time scales, displaying lower BIAS, RMSE, and higher KGE. Spatially, its ET patterns aligned with regional vegetation changes, with superior accuracy in annual ET estimation compared to the other models. Comparison with other ET products shows that the estimation results based on the hybrid model have better performance. This approach not only improves the accuracy of ET estimation but also improves the understanding of the physical mechanism of ET estimation by pure machine learning models. This study can provide important support for understanding ET and hydrological processes under different climatic and biotic vegetation in other arid and semi-arid regions.

The World Health Organization (WHO) declared monkeypox as a public health emergency of international concern (PHEIC) on July 23, 2022, their highest level of alert. This raised concerns about the management of the global monkeypox outbreak, as well as the scientific analysis and accurate prediction of the future course of the epidemic. This study used EpiSIX (an analysis and prediction system for epidemics based on a general SEIR model) to analyze the monkeypox epidemic and to forecast the major tendencies based on data from the USA CDC (https://www.cdc.gov) and the WHO (https://www.who.int/health-topics/monkeypox). The global outbreak of monkeypox started in the UK on May 2, 2022, which marked the beginning of an epidemic wave. As of October 28, 2022, the cumulative number of reported cases worldwide was 77,115, with 36 deaths. EpiSIX simulations predict that the global monkeypox epidemic will enter a low epidemic status on March 1, 2023 with the cumulative number of confirmed cases ranging from 85,000 to 124,000, and the total number of deaths ranging from 60 to 87. Our analysis revealed that the basic reproduction number (R0) of monkeypox virus (MPXV) is near to 3.1 and the percentage of asymptomatic individuals is 13.1 %–14.5 %, both of which are similar to the data for SARS. The vaccination efficiency against susceptibility (VEs) of individuals who have had monkeypox is ∼ 79 %, and the vaccination efficiency against infectiousness (VEi) of individuals who have had monkeypox is ∼ 76 %–82 %. The mean incubation period for monkeypox is 8 days. In total, 94.7 % of infected individuals develop symptoms within 20 days and recover within 2 weeks after the confirmation of symptoms. Simulation results using EpiSIX showed that ring vaccination was remarkably effective against monkeypox. Our findings confirmed that a 20-day isolation for close contacts is necessary.

Osteoarthritis impairs the functions of various joints, such as knees, hips, hands and spine, which causes pain, swelling, stiffness and reduced mobility in joints. Multiple factors, including age, joint injuries, obesity, and mechanical stress, could contribute to osteoarthritis development and progression. Evidence has demonstrated that genetics and epigenetics play a critical role in osteoarthritis initiation and progression. Noncoding RNAs (ncRNAs) have been revealed to participate in osteoarthritis development. In this review, we describe the pivotal functions and molecular mechanisms of numerous lncRNAs in osteoarthritis progression. We mention that long noncoding RNAs (lncRNAs) could be biomarkers for osteoarthritis diagnosis, prognosis and therapeutic targets. Moreover, we highlight the several compounds that alleviate osteoarthritis progression in part via targeting lncRNAs. Furthermore, we provide the future perspectives regarding the potential application of lncRNAs in diagnosis, treatment and prognosis of osteoarthritis.

Aimed at the preparation of bimetallic composites by using a liquid–liquid compound casting method with a sound interface, this study focused on the interface evolution with an increase in the pouring time interval. The results revealed that the melt mixing occurred when the pouring interval was 3 s. The transition zone appeared at the interface when the pouring interval was 10 s, and a good metallurgical bond was obtained. When the pouring interval was 20 s, a discontinuous oxide layer appeared at the interface. The oxide layer gap formed a channel for the transport of the SiC particles.

Soil moisture content is an important measure of soil health, and high-precision soil moisture trend analysis is essential for understanding regional ecological quality in the context of climate change, flood monitoring, and water cycle processes. However, in the arid regions of Central Asia, where data are severely lacking, obtaining high-spatial-resolution, continuous soil moisture data is difficult due to the scarcity of stations. Moreover, because soil moisture is easily affected by evaporation time, surface morphology, and anthropogenic factors, mature theoretical models or empirical or semiempirical models to measure soil moisture are also lacking. To investigate the distribution and trend of soil moisture in the Ebinur Lake water, in this study, microwave remote sensing and visible remote sensing data were selected as inputs, and the Global Land Data Assimilation System (GLDAS-2.2) data products were downscaled using the GTWR model, which increased the spatial scale from 27,830 m × 27,830 m to 30 m × 30 m. The phenomena involved in the soil moisture change cycle, spatial distribution, temporal variation, and internal randomness distance were analyzed in the study area through wavelet analysis, Theil–Sen trend analysis, the Mann–Kendall (MK) test, and a variogram. This study obtained high-resolution continuous soil moisture data in the arid and data-scarce region in Central Asia, thus broadening the field of multisource remote sensing analysis and providing a theoretical basis for the construction of precision agriculture in northwest China.

Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan–Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. Firstly, the applicability of TRMM 3B43v7, GPM_3IMERGM 06, and CMORPH CDR satellite precipitation data for the Turpan–Hami Region was evaluated, and the products with better applicability were selected. Next, the Extreme Gradient Boosting Algorithm (XGBoost) and the Shapley Additive Explanations for Machine Learning (SHAP) model were combined to carry out a feature importance analysis on the climate factors affecting precipitation (mean temperature, actual evapotranspiration, wind speed, cloud cover), from which climate factors with a greater influence on precipitation were selected. Combined with climate factors, normalized difference vegetation index (NDVI), slope, aspect, and elevation as explanatory variables, a Multi-Scale Geographically Weighted Regression (MGWR) model was constructed to obtain the monthly precipitation data of 1 km spatial resolution in the Turpan–Hami area from 2001 to 2020. Finally, the spatiotemporal distribution characteristics and changing trend of precipitation in the Turpan–Hami region from 2001 to 2020 were analyzed. The results show that (1) GPM_3IMERGM 06 satellite precipitation data exhibits good applicability in the Turpan–Hami region. (2) The precision verification of the downscaling results from a monthly scale and an annual scale shows that the accuracy and spatial resolution of the data are improved after downscaling. (3) From 2001 to 2020, the precipitation in the Turpan–Hami region showed an insignificantly increasing trend.

Abstract Background Lung adenocarcinoma (LUAD) is one of the tumor‐related diseases with high morbidity worldwide. Epigenetic modifications such as DNA methylation changes may involve in tumorigenesis. This study aimed to explore new biomarkers that have prognostic significance of LUAD. Methods First, we downloaded the gene expression and methylation data set from Gene Expression Omnibus. R software was then used to identify abnormally methylated differentially expressed genes (MDEGs). Next, R package Cluster Profiler was used to analyze the enrichment and pathway of the MDEGs. Analysis using STRING revealed the protein–protein interaction network. The result was then visualized by Cytoscape and obtained 10 hub genes. Afterward, they were further verified by The Cancer Genome Atlas to select candidate genes. Moreover, quantitative real‐time polymerase chain reaction (qRT‐PCR) and immunohistochemistry were used to verify the expression and prognostic value of candidate genes in LUAD patients. Results The results showed that the expressions of ADCY5 and PRKCB are indeed related to LUAD. The clinical relevance to PRKCB was confirmed by its clinical correlation analysis. Gene set enrichment analysis (GSEA) and tumor immune estimation resource (TIMER) tumor immune correlations showed that PRKCB is involved in the cancer‐related Kyoto Encyclopedia of Genes and Genomes pathway and is involved in immune infiltration. It was also verified by qRT‐PCR and immunohistochemistry that PRKCB was lowly expressed in LUAD patients and correlated with prognosis. Conclusions PRKCB is relevant to prognosis of LUAD through methylation and immune infiltration.

Climate change has caused inland wetlands shrinkage and exacerbated problems, such as sustainable development and ecological security, for years. These issues are mainly pronounced in the inland arid area. The ecological environment’s deterioration is especially severe in the drylands of the interior. However, dryland wetland changes and their response to climate are poorly understood. This study uses the K-means algorithm in Google Earth Engine (GEE) to classify two typical dryland wetlands (Ebinur and Bosten Lakes) for rapidly and accurately detecting dryland wetland changes. Moreover, it explores the long-term spatial–temporal variation in wetland distribution. In addition, it investigates the response of various lakes to climate change in northern and southern Xinjiang using wavelet analysis. The study’s results showed that K-means clustering in the GEE platform has a high classification accuracy (Kappa > 0.8) in wetland classification, making it a feasible approach. The terminal lake wetland types, represented by the Ebinur Lake, changed significantly between 2001 and 2021. In contrast, the inflow-outflow lake wetland types, represented by the Bosten Lake, perform more consistently. Significant spatial–temporal variation is observed at Ebinur Lake, with the lake gradually shrinking and transforming into a marsh, where the largest marsh proportion degrades into non-wetland during the year. Bosten Lake experienced frequent conversions between marsh and non-wetland throughout the year. Furthermore, the responses of various dryland lakes to climate change are consistent, and a low precedes precipitation and follows evapotranspiration. However, their sensitivity to climate response varies, with the terminal lake being most affected by climate change. Mastering the dynamic changes and climate response of dryland wetlands achieves the sustainable development goals of drylands, including carbon neutrality and peak carbon dioxide emissions.

As a pioneer part of the major project of space-integrated-ground information network (SIGN), the typical application services provided by the ground information port (GIP) are the basic starting point and footing point for the implementation of major project to serve our national strategy and economic construction.Based on the basic framework of the ground information port and the idea of cloud service construction, the typical case of the ground information port was explored, providing reference for the development of the related technical services in the ground information port.

Abstract Background Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky. Methods The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model. Result Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094). Conclusion Our study provides a robust signature for predicting patients’ prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

Habitat quality has great significance in terms of regional ecological conservation and human welfare. In this study, we evaluated the spatial and temporal characteristics of land use and habitat quality in the temperate desert sub-region of the Ordos Plateau using patch-generating land use simulation (PLUS) and integrated valuation of ecosystem services and trade-offs (InVEST) models. From 2000 to 2020, the areas of grassland, cropland, and unused land in the study area increased significantly; the areas of water bodies and woodland increased slightly; and the area of wasteland decreased significantly. Moreover, the habitat quality in the temperate desert subzone of the Ordos Plateau showed a trend of initial increase and then decrease between 2000 and 2020. The areas of lower and low habitat quality first decreased and then increased, and the overall area decreased over time. Conversely, the areas of high and higher habitat quality initially increased and then decreased, and the overall area increased over time. The area of medium habitat quality first decreased and then increased, although the overall change was minimal. Based on the PLUS model, the predicted habitat quality of the study area in 2025 under the natural development scenario was compared to that predicted under the ecological conservation scenario. The comparison of results showed higher habitat quality and lower habitat degradation under the ecological conservation development scenario. These results can be used to provide a scientific basis and decision reference for the sustainable use of land resources and encouragement of high-quality socio-economic development in the temperate desert sub-region of the Ordos Plateau.

Soil salinization has hampered the achievement of sustainable development goals (SDGs) in many countries worldwide. Several countries have recently launched hyperspectral remote sensing satellites, opening new avenues for accurate soil-salinity monitoring. Among them, Gaofen-5 (GF-5) from China has a high comprehensive performance, including a spectral resolution of 5 nm, 330 bands, and signal-to-noise ratio of 700. However, the potential of GF-5 for estimating soil salinity is not well understood. In this study, we proposed a strategy that includes bootstrap methods, fractional order derivative (FOD) techniques and decision-level fusion models to exploit the soil salinity diagnostic information and reduce estimation uncertainty in the Ebinur Lake oasis in northwestern China. The results showed that the GF-5 data were suitable for assessing soil salinity. The FOD technique enhanced the correlation between soil salinity and spectra, identified more diagnostic bands, improved the accuracy of soil salinity estimation, and reduced model uncertainty. The low-order FOD outperformed the high-order FOD. The spectra processed by the 0.9 order derivative were the most correlated with soil salinity (r = −0.76). The model driven by the 0.8 order derivative produced the optimal estimated model (R2 = 0.95, root mean square error (RMSE) = 3.20 dS m−1 and a ratio of performance to interquartile distance (RPIQ) = 5.96). The model driven by the 0.8 order derivative had less uncertainty than the models based on the original and integer-order derivative (first- and second- derivatives) spectra. This study provides a reference for estimating soil salinity from GF-5 data using the proposed framework with low uncertainty and high accuracy. GF-5 data have great potential for assessing environmental problems and facilitating further SDGs.

BackgroundNon-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.MethodsIn The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.ResultsWith the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.ConclusionThis study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy.

Abstract Lung adenocarcinoma (LUAD) is characterized by high infiltration and rapid growth. The function of the stem cell population is to control and maintain cell regeneration. Therefore, it is necessary to study the prognostic value of stem cell-related genes in LUAD. Signature genes were screened out from 166 stem cell-related genes according to the least absolute shrinkage operator (LASSO) and subsequently multivariate Cox regression analysis, and then established risk model. Immune infiltration and nomogram model were used to evaluate the clinical efficacy of signature. A signature consisting of 10 genes was used to dichotomize the LUAD patients into two groups (cutoff, 1.314), and then validated in GSE20319 and GSE42127. There was a significant correlation between signature and clinical characteristics. Patients with high-risk had a shorter overall survival. Furthermore, significant differences were found in multiple immune cells between the high-risk group and low-risk group. A high correlation was also reflected between signature and immune infiltration. What’s more, the signature could effectively predict the efficacy of chemotherapy in patients with LUAD, and a nomogram based on signature might accurately predict the prognosis of patients with LUAD. The signature-based of stem cell-related genes might be contributed to predicting prognosis of patients with LUAD.

Exploring the future trends of land use/land cover (LULC) changes is significant for the sustainable development of a region. The simulation and prediction of LULC in a large-scale basin in an arid zone can help the future land management planning and rational allocation of resources in this ecologically fragile region. Using the whole Ili-Balkhash Basin as the study area, the patch-generating land use simulation (PLUS) model and a combination of PLUS and Markov predictions (PLUS–Markov) were used to simulate and predict land use in 2020 based on the assessment of the accuracy of LULC classification in the global dataset. The accuracy of simulations and predictions using the model were measured for LULC data covering different time periods. Model settings with better simulation results were selected for simulating and predicting possible future land use conditions in the basin. The future predictions for 2025 and 2030, which are based on historical land change characteristics, indicate that the overall future spatial pattern of LULC in the basin remains relatively stable in general without the influence of other external factors. Over the time scale of the future five years, the expansion of croplands and barren areas in the basin primarily stems from the loss of grasslands. Approximately 48% of the converted grassland areas are transformed into croplands, while around 40% are converted into barren areas. In the longer time scale of the future decade, the conversion of grasslands to croplands in the basin is also evident. However, the expansion phenomenon of urban and built-up lands at the expense of croplands is more significant, with approximately 774.2 km2 of croplands developing into urban and built-up lands. This work provides an effective new approach for simulating and predicting LULC in data-deficient basins at a large scale in arid regions, thereby establishing a foundation for future research on the impact of human activities on basin hydrology and related studies.

At present, forest and fruit resource surveys are mainly based on ground surveys, and the information technology of the characteristic forest and fruit industries is evidently lagging. The automatic extraction of fruit tree information from massive remote sensing data is critical for the healthy development of the forest and fruit industries. However, the complex spatial information and weak spectral information contained in high-resolution images make it difficult to classify fruit trees. In recent years, fully convolutional neural networks (FCNs) have been shown to perform well in the semantic segmentation of remote sensing images because of their end-to-end network structures. In this paper, an end-to-end network model, Multi-Unet, was constructed. As an improved version of the U-Net network structure, this structure adopted multiscale convolution kernels to learn spatial semantic information under different receptive fields. In addition, the “spatial-channel” attention guidance module was introduced to fuse low-level and high-level features to reduce unnecessary semantic features and refine the classification results. The proposed model was tested in a characteristic high-resolution pear tree dataset constructed through field annotation work. The results show that Multi-Unet was the best performer among all models, with classification accuracy, recall, F1, and kappa coefficient of 88.95%, 89.57%, 89.26%, and 88.74%, respectively. This study provides important practical significance for the sustainable development of the characteristic forest fruit industry.

Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone.

Detecting changes in urban areas presents many challenges, including complex features, fast-changing rates, and human-induced interference. At present, most of the research on change detection has focused on traditional binary change detection (BCD), which becomes increasingly unsuitable for the diverse urban change detection tasks as cities grow. Previous change detection networks often rely on convolutional operations, which struggle to capture global contextual information and underutilize category semantic information. In this paper, we propose SIGNet, a Siamese graph convolutional network, to solve the above problems and improve the accuracy of urban multi-class change detection (MCD) tasks. After maximizing the fusion of change differences at different scales using joint pyramidal upsampling (JPU), SIGNet uses a graph convolution-based graph reasoning (GR) method to construct static connections of urban features in space and a graph cross-attention method to couple the dynamic connections of different types of features during the change process. Experimental results show that SIGNet achieves state-of-the-art accuracy on different MCD datasets when capturing contextual relationships between different regions and semantic correlations between different categories. There are currently few pixel-level datasets in the MCD domain. We introduce a new well-labeled dataset, CNAM-CD, which is a large MCD dataset containing 2508 pairs of high-resolution images.

The accurate inversion of actual evapotranspiration (ETa) at a regional scale is crucial for understanding water circulation, climate change, and drought monitoring. In this study, we produced a 1 km monthly ETa dataset for Turpan and Hami, two typical arid cities in northwest China, using multi-source remote sensing data, reanalysis information, and the ETMonitor model from 1980 to 2021. We analyzed the spatiotemporal variation of ETa using various statistical approaches and discussed the impact of climate and land use and cover changes (LUCC) on ETa. The results show the following: (1) the estimation results correlate well with ETa products on monthly scales (coefficient of determination (R2) > 0.85, root mean square error (RMSE) < 15 mm/month) with high reliability. (2) The ETa values were spatially distributed similarly to precipitation and LUCC, with the multi-year (1980–2021) average of 66.31 mm and a slightly fluctuating downward trend (−0.19 mm/a). (3) During the 42-year period, 63.16% of the study area exhibited an insignificant decrease in ETa, while 86.85% experienced pronounced fluctuations (coefficient of variation (CV) > 0.20), and 78.83% will show an upward trend in the future. (4) ETa was significantly positively correlated with precipitation (94.17%) and insignificantly positively correlated with temperature (55.81%). The impact of human activities showed an insignificant decreasing trend (85.41%). Additionally, the intensity of ETa varied considerably among land types, with the largest for cropland (424.12 mm/a). The results of the study have implications for promoting the rational allocation of regional water resources and improving water use efficiency in arid zones.

Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction accuracy, inference speed and model size remains a fundamental and challenging issue for real-time road extraction applications, especially for rural roads. For this purpose, we developed a lightweight dynamic addition network (LDANet) to exploit rural road extraction. Specifically, considering the narrow, complex and diverse nature of rural roads, we introduce an improved Asymmetric Convolution Block (ACB)-based Inception structure to extend the low-level features in the feature extraction layer. In the deep feature association module, the depth-wise separable convolution (DSC) is introduced to reduce the computational complexity of the model, and an adaptation-weighted overlay is designed to capture the salient features. Moreover, we utilize a dynamic weighted combined loss, which can better solve the sample imbalance and boosts segmentation accuracy. In addition, we constructed a typical remote sensing dataset of rural roads based on the Deep Globe Land Cover Classification Challenge dataset. Our experiments demonstrate that LDANet performs well in road extraction with fewer model parameters (

Although understanding the carbon and water cycles of dryland ecosystems in terms of water use efficiency (WUE) is important, WUE and its driving mechanisms are less understood in Central Asia. This study calculated Central Asian WUE for 2001–2021 based on the Google Earth Engine (GEE) platform and analyzed its spatial and temporal variability using temporal information entropy. The importance of atmospheric factors, hydrological factors, and biological factors in driving WUE in Central Asia was also explored using a geographic detector. The results show the following: (1) the average WUE in Central Asia from 2001–2021 is 2.584–3.607 gCkg−1H2O, with weak inter-annual variability and significant intra-annual variability and spatial distribution changes; (2) atmospheric and hydrological factors are strong drivers, with land surface temperature (LST) being the strongest driver of WUE, explaining 54.8% of variation; (3) the interaction of the driving factors can enhance the driving effect by more than 60% for the interaction between most atmospheric factors and vegetation factors, of which the effect of the interaction of temperature (TEM) with vegetation cover (FVC) is the greatest, explaining 68.1% of the change in WUE. Furthermore, the interaction of driving factors with very low explanatory power (e.g., water pressure (VAP), aerosol optical depth over land (AOD), and groundwater (GWS)) has a significant enhancement effect. Vegetation is an important link in driving WUE, and it is important to understand the mechanisms of WUE change to guide ecological restoration projects.

Elevated triglycerides are associated with an increased risk of cardiovascular disease (CVD). Therefore, it is very important to understand the metabolism of triglyceride-rich lipoproteins (TRLs) and their atherogenic role in animal models. Using low-density lipoprotein receptor knockout (LDLR−/−) Syrian golden hamsters, this study showed that unlike LDLR−/− mice, when LDLR−/− hamsters were fed a high cholesterol high-fat diet (HFD), they had very high plasma levels of triglycerides and cholesterol. We found that LDLR−/− hamsters exhibited increased serum TRLs and the ApoB100 and 48 in these particles after being fed with HFD. Treatment with ezetimibe for 2 weeks decreased these large particles but not the LDL. In addition, ezetimibe simultaneously reduced ApoB48 and ApoE in plasma and TRLs. The expression of LRP1 did not change in the liver. These findings suggested that the significantly reduced large particles were mainly chylomicron remnants, and further, the remnants were mainly cleared by the LDL receptor in hamsters. After 40 days on an HFD, LDLR−/− hamsters had accelerated aortic atherosclerosis, accompanied by severe fatty liver, and ezetimibe treatment reduced the consequences of hyperlipidemia. Compared with the serum from LDLR−/− hamsters, that from ezetimibe-treated LDLR−/− hamsters decreased the expression of vascular adhesion factors in vascular endothelial cells and lipid uptake by macrophages. Our results suggested that in the LDLR−/− hamster model, intestinally-derived lipoprotein remnants are highly atherogenic and the inflammatory response of the endothelium and foam cells from macrophages triggered atherosclerosis. The LDL receptor might be very important for chylomicrons remnant clearance in the Syrian golden hamster, and this may not be compensated by another pathway. We suggest that the LDLR−/− hamster is a good model for the study of TRLs-related diseases as it mimics more complex hyperlipidemia.

Extreme climate events have a significant impact both on the ecological environment and human society, and it is crucial to analyze the spatial–temporal evolutionary trends of extreme climate. Based on the RClimDex model, this study used trend analysis, probability density function, and wavelet coherence analysis to analyze the spatiotemporal variation characteristics of extreme climate indices and their response mechanisms to teleconnection patterns. The results of the study show that: (1) All the extreme precipitation indices, except max 1-day precipitation amount, max 5-day precipitation amount, and extremely wet days increased, with no significant abrupt changes. The extreme warm indices increased and extreme cold indices decreased. The years with abrupt changes were mainly distributed between 1988 and 1997. (2) Spatially, the extreme precipitation indices of most meteorological stations decreased, except for the simple daily intensity index and the number of very heavy precipitation days. The extreme warm indices of most meteorological stations increased, and the extreme cold indices decreased. (3) Except for consecutive dry days, the frequency of extreme precipitation indices increased significantly, the severity and frequency of high-temperature events increased, while the frequency of low-temperature events increased, but the severity decreased. The results of rescaled range (R/S) analysis indicated that the climate in the Beijing–Tianjin–Hebei region will further tend to be warm and humid in the future. (4) The Polar/Eurasia Pattern, the East Atlantic Pattern, the Arctic Oscillation, and the East Atlantic/West Russian Pattern were most closely associated with extreme climate events in the Beijing–Tianjin–Hebei region. The multi-factor combination greatly enhanced the explanatory power of the teleconnection pattern for extreme climates.

Interleukin 34 (IL-34), an additional ligand of the colony-stimulating factor-1 receptor (CSF-1R), promotes the secretion of pro-inflammatory cytokines and stimulates NF-κB and JNK-related signaling pathways. However, the potential mechanism and prognostic value of IL-34 in lung adenocarcinoma (LUAD) remain obscure. In this study, IL-34 was found to be downregulated in LUAD tissues compared with para-carcinoma tissues, and loss of IL-34 expression was correlated with shorter overall survival (OS), which was validated by bioinformatics\ analysis in TCGA (The Cancer Genome Atlas) cohort and immunohistochemical analysis in the NTU (Nantong University) cohort, respectively. Subsequently, loss of IL-34 promotes negative regulation of the immune system and inhibits the infiltration of immune cells. Moreover, IL-34 deficiency was shown to be an independent adverse prognostic factor for patients with LUAD, and subgroup analysis indicated that IL-34 might contribute to the stratified management of patients with LUAD. IL-34-based nomogram model significantly improved the accuracy of prognostic predictions for OS of patients with LUAD, both in the TCGA cohort and the NTU cohort. Taken together, our data suggested that loss of IL-34 expression is associated with poor prognosis and negative regulation of the immune system of patients with LUAD, contributing to the stratified management of patients with LUAD.

Background. LIM and SH3 domain protein 1 (LASP1), highly expressed in a variety of tumors, is considered as a novel tumor metastasis biomarker. However, it is unknown which signaling pathway works and how the signal transduces into cell nucleus to drive tumor progression by LASP1. The aim of this study is to explore the essential role of LASP1 in TGF-β1-induced epithelial-mesenchymal transition (EMT) in lung cancer cells. Methods. The gene and protein levels of LASP-1 were successfully silenced or overexpressed by LASP-1 shRNA lentivirus or pcDNA in TGF-β1-treated lung cancer cell lines, respectively. Then, the cells were developed EMT by TGF-β1. The cell abilities of invasion, migration, and proliferation were measured using Transwell invasion assay, wound healing assay, and MTT assay, respectively. Western blotting was used to observe the protein levels of EMT-associated molecules, including N-cadherin, vimentin, and E-cadherin, and the key molecules in the TGF-β1/Smad/Snail signaling pathway, including pSmad2 and Smad2, pSmad3 and Smad3, and Smad7 in cell lysates, as well as Snail1, pSmad2, and pSmad3 in the nucleus. Results. TGF-β1 induced higher LASP1 expression. LASP1 silence and overexpression blunted or promoted cell invasion, migration, and proliferation upon TGF-β1 stimulation. LASP1 also regulated the expression of vimentin, N-cadherin, and E-cadherin in TGF-β1-treated cells. Activity of key Smad proteins (pSmad2 and pSmad3) and protein level of Smad7 were markedly regulated through LASP1. Furthermore, LASP1 affected the nuclear localizations of pSmad2, pSmad3, and Snail1. Conclusion. This study reveals that LASP1 regulates the TGF-β1/Smad/Snail signaling pathway and EMT markers and features, involving in key signal molecules and their nuclear levels. Therefore, LASP1 might be a drug target in lung cancer.

In the water system, antibiotic pollution significantly impacts the human body and the environment. Therefore, it is essential to quickly identify the types of antibiotics in the system and detect their concentration. It has been reported that many metal ions interact with antibiotics, and some of them can also change the enzyme-like catalytic properties of gold clusters (AuNCs). In the experiments, we found significant differences in the experimental results when different antibiotics and metal ions were placed in a TMB-H2O2 system with AuNCs as catalysts. Based on this result, we devised a simple and sensitive colorimetric method for the simultaneous detection of multiple antibiotics using AuNCs-metal ions as the sensor, a multifunctional microplate detector as the detection instrument, and LDA as the analytical method. This method was successfully applied for the identification of antibiotics and the detection of their concentrations in river water.

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas.

Familial hypercholesterolemia (FH) is an autosomal dominant genetic disease caused mainly by LDL receptor (Ldlr) gene mutations. Unlike FH patients, heterozygous Ldlr knockout (KO) mice do not show a dominant FH trait. Hamsters, like humans, have the cholesteryl ester transfer protein, intestine-only ApoB editing and low hepatic cholesterol synthesis. Here, we generated Ldlr-ablated hamsters using CRISPR/Cas9 technology. Homozygous Ldlr KO hamsters on a chow diet developed hypercholesterolemia with LDL as the dominant lipoprotein and spontaneous atherosclerosis. On a high-cholesterol/high-fat (HCHF) diet, these animals exhibited severe hyperlipidemia and atherosclerotic lesions in the aorta and coronary arteries. Moreover, the heterozygous Ldlr KO hamsters on a short-term HCHF diet also had overt hypercholesterolemia, which could be effectively ameliorated with several lipid-lowering drugs. Importantly, heterozygotes on 3-month HCHF diets developed accelerated lesions in the aortas and coronary arteries. Our findings demonstrate that the Ldlr KO hamster is an animal model of choice for human FH and has great potential in translational research of hyperlipidemia and coronary heart disease.

Copper (II) ions have been shown to greatly improve the chemical stability and peroxidase-like activity of gold nanoclusters (AuNCs). Since the affinity between Cu2+ and pyrophosphate (PPi) is higher than that between Cu2+ and AuNCs, the catalytic activity of AuNCs-Cu2+ decreases with the introduction of PPi. Based on this principle, a new colorimetric detection method of PPi with high sensitivity and selectivity was developed by using AuNCs-Cu2+ as a probe. Under optimized conditions, the detection limit of PPi was 0.49 nM with a linear range of 0.51 to 30,000 nM. The sensitivity of the method was three orders of magnitude higher than that of a fluorescence method using AuNCs-Cu2+ as the probe. Finally, the AuNCs-Cu2+ system was successfully applied to directly determine the concentration of PPi in human urine samples.

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

Climate change, especially precipitation change, will significantly change soil moisture, which then influences root growth, further affecting yield and grain quality. Previous studies focused on the drought or flood effects on summer maize growth. However, few studied the effects of drought-flood abrupt alternation (DFAA) on the growth of summer maize. We explored the DFAA impacts on the roots, leaf area index (LAI), yield, and grain quality in field. The main results show that DFAA had different impacts on the summer maize growth in the seeding-jointing stage (SJS) and tasseling-grain filling stage (TGS). In general, the DFAA reduced the yield. Roots at the depth of 40 cm had obviously positive impacts on the yield. The DFAA reduced the LAI and promoted the maximum LAI achieving in advance. The grain crude protein augmented under DFAA. The drought had evidently negative impacts on the grain crude fat in the TGS, while it had no obvious influence in the SJS. DFAA had no apparent impact on the grain crude starch. These results could provide some references for the effects and adaptation-strategies study of extreme climate events and their impacts on growth of summer maize.

The Chang 7 reservoir in Changqing oilfield is rich in tight oil. However, due to the low formation permeability, it is very difficult to obtain economical oil production without stimulation treatments. Volume fracturing seems to be a more efficient tight oil recovery enhancement (EOR) method in Chang 7 pilot tests compared with conventional hydraulic fracturing. In this study, Chang 7 tight oil reservoir was first characterized by its geological property, hydrocarbon source rock distribution, and formation physiochemical property. Tight core flooding tests were then conducted to experimentally investigate the EOR ability of the volume fracturing technique. The field-scale practice was also demonstrated and analyzed. The results show that Chang 7 reservoir is favorable for the generation of a large amount of tight oil. Fractures created in tight cores can significantly improve the fluid flow conductivity and enhance the imbibition of displacing water, resulting in a greater tight oil recovery increment. Volume fracturing is an effective way to generate a larger number of fractures. Field application indicates that volume fracturing treatment can form a much greater reservoir stimulation volume. Daily oil production in the volume-fracturing-treated wells can be more than twice as high as that in the conventional-fracturing-treated wells.

Germinal center lymphoma is a heterogeneous human lymphoma entity. Here we report that constitutive activity of SHP2 (PTPN11) and its downstream kinase ERK is essential for the viability of germinal center lymphoma cells and disease progression. Mechanistically, SHP2/ERK inhibition impedes c-Myc transcriptional activity, which results in the repression of proliferative phenotype signatures of germinal center lymphoma. Furthermore, SHP2/ERK signaling is required to maintain the CD19/c-Myc loop, which preferentially promotes survival of a distinct subtype of germinal center lymphoma cells carrying the MYC/IGH translocation. These findings demonstrate a critical function for SHP2/ERK signaling upstream of c-Myc in germinal center lymphoma cells and provide a rationale for targeting SHP2 in the therapy of germinal center lymphoma.