Responses of vegetation cover to hydro-climatic variations in Bosten Lake Watershed, NW China.

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

Genetic history of Cambridgeshire before and after the Black Death.

The extent of the devastation of the Black Death pandemic (1346-1353) on European populations is known from documentary sources and its bacterial source illuminated by studies of ancient pathogen DNA. What has remained less understood is the effect of the pandemic on human mobility and genetic diversity at the local scale. Here, we report 275 ancient genomes, including 109 with coverage >0.1×, from later medieval and postmedieval Cambridgeshire of individuals buried before and after the Black Death. Consistent with the function of the institutions, we found a lack of close relatives among the friars and the inmates of the hospital in contrast to their abundance in general urban and rural parish communities. While we detect long-term shifts in local genetic ancestry in Cambridgeshire, we find no evidence of major changes in genetic ancestry nor higher differentiation of immune loci between cohorts living before and after the Black Death.

Oligomeric organization of membrane proteins from native membranes at nanoscale spatial and single-molecule resolution.

The oligomeric organization of membrane proteins in native cell membranes is a critical regulator of their function. High-resolution quantitative measurements of oligomeric assemblies and how they change under different conditions are indispensable to understanding membrane protein biology. We report Native-nanoBleach, a total internal reflection fluorescence microscopy-based single-molecule photobleaching step analysis technique to determine the oligomeric distribution of membrane proteins directly from native membranes at an effective spatial resolution of ~10 nm. We achieved this by capturing target membrane proteins in native nanodiscs with their proximal native membrane environment using amphipathic copolymers. We applied Native-nanoBleach to quantify the oligomerization status of structurally and functionally diverse membrane proteins, including a receptor tyrosine kinase (TrkA) and a small GTPase (KRas) under growth-factor binding and oncogenic mutations, respectively. Our data suggest that Native-nanoBleach provides a sensitive, single-molecule platform to quantify membrane protein oligomeric distributions in native membranes under physiologically and clinically relevant conditions.

Study on the Superlubricity Behavior of Ions under External Electric Fields at Steel Interfaces.

Realizing macroscopic superlubricity in the presence of external electric fields (EEFs) at the steel interfaces is still challenging. In this work, macroscopic superlubricity with a coefficient of friction value of approximately 0.008 was realized under EEFs with the lubrication of LiPF6-based ionic liquids at steel interfaces. The roles of cations and anions in the superlubricity realization under EEFs were studied. Based on the experimental results, the macroscopic superlubricity behavior of Li(PEG)PF6 under EEFs at steel interfaces is attributed to the strong hydration effect of Li+ cations and the complete reactions of anions that contributed to the formation of a boundary film on the appropriate surface. Moreover, the reduction in the number of iron oxides in the boundary film on the disc was beneficial for friction reduction. We also provide a calculation model to describe the relationship between the hydration effect and the optimal voltage position, at which the lowest friction might occur. Ultimately, this work proves that macroscopic superlubricity can be realized under EEFs at steel interfaces and provides a foundation for engineering applications of superlubricity in an electrical environment.

[Advances of the novel immunosuppressant brasilicardin A].

Brasilicardin A (BraA) is a natural diterpene glycoside isolated from the pathogenic actinomycete Nocardia brasiliensis IFM 0406 with highly potent immunosuppressive activity (IC50=0.057 µg/mL). BraA potently inhibits the uptake of amino acids that are substrates for amino acid transport system L of T cells, which is different from the existing clinical immunosuppressants. BraA is more potent in a mouse mixed lymphocyte reaction and less toxic against various human cell lines compared with the known clinical immunosuppressants, such as cyclosporin A, ascomycin and tacrolimus. Therefore, BraA attracted more attention as a new promising immunosuppressant. However, the development of this promising immunosuppressant as drug for medical use is so far hindered because BraA has the unusual and synthetically challenging skeleton and shows the low-yield production in the natural pathogenic producer. This review introduces the molecular structure of BraA, its activity, mechanism of action, chemical synthesis of BraA analogs, heterologous expression of gene cluster, and an application of combining microbial and chemical synthesis for production of BraA, with the aim to facilitate the efficient production of BraA and its analogs.

[Mining and identification of a biosynthetic gene cluster producing xanthocillin analogues from Penicillium chrysogenum MT-40, an endophytic fungus of Huperzia serrata].

Xanthocillin is a unique natural product with an isonitrile group and shows remarkable antibacterial activity. In this study, the genome of an endophytic fungus Penicillium chrysogenum MT-40 isolated from Huperzia serrata was sequenced, and the gene clusters with the potential to synthesize xanthocillin analogues were mined by local BLAST and various bioinformatics analysis tools. As a result, a biosynthetic gene cluster (named for) responsible for the biosynthesis of xanthocillin analogues was identified by further heterologous expression of the key genes in Aspergillus oryzae NSAR1. Specifically, the ForB catalyzes the synthesis of 2-formamido-3-(4-hydroxyphenyl) acrylic acid, and the ForG catalyzes the dimerization of 2-formamido-3-(4-hydroxyphenyl) acrylic acid to produce the xanthocillin analogue N, N'-(1, 4-bis (4-hydroxyphenyl) buta-1, 3-diene-2, 3-diyl) diformamide. The results reported here provide a reference for further discovery of xanthocillin analogues from fungi.

[LC-MS analysis of 2-(2-phenylethyl) chromones in sodium chloride-treated suspension cells of Aquilaria sinensis].

Qualitative and quantitative analysis of 2-(2-phenylethyl) chromones in sodium chloride(NaCl)-treated suspension cells of Aquilaria sinensis was conducted by UPLC-Q-Exactive-MS and UPLC-QQQ-MS/MS. Both analyses were performed on a Waters T3 column(2.1 mm×50 mm, 1.8 µm) with 0.1% formic acid aqueous solution(A)-acetonitrile(B) as mobile phases at gradient elution. MS data were collected by electrospray ionization in positive ion mode. Forty-seven phenylethylchromones was identified from NaCl-treated suspension cell samples of A. sinensis using UPLC-Q-Exactive-MS, including 22 flindersia-type 2-(2-phenylethyl) chromones and their glycosides, 10 5,6,7,8-tetrahydro-2-(2-phenylethyl) chromones and 15 mono-epoxy or diepoxy-5,6,7,8-tetrahydro-2-(2-phenylethyl) chromones. Additionally, 25 phenylethylchromones were quantitated by UPLC-QQQ-MS/MS. Overall, the rapid and efficient qualitative and quantitative analysis of phenylethylchromones in NaCl-treated suspension cells of A. sinensis by two LC-MS techniques, provides an important reference for the yield of phenylethylchromones in Aquilariae Lignum Resinatum using in vitro culture and other biotechnologies.

The BET PROTAC inhibitor dBET6 protects against retinal degeneration and inhibits the cGAS-STING in response to light damage.

BACKGROUND: Chronic inflammation significantly contributes to photoreceptor death in blinding retinal diseases such as age-related macular degeneration (AMD) and retinitis pigmentosa (RP). Bromodomain and extraterminal domain (BET) proteins are epigenetic readers that act as key proinflammatory factors. We recently found the first-generation BET inhibitor JQ1 alleviated sodium iodate-induced retinal degeneration by suppressing cGAS-STING innate immunity. Here, we investigated the effects and mechanism of dBET6, a proteolysis­targeting chimera (PROTAC) small molecule that selectively degrades BET by the ubiquitin‒proteasome system, in light-induced retinal degeneration. METHODS: Mice were exposed to bright light to induce retinal degeneration, and the activation of cGAS-STING was determined by RNA-sequencing and molecular biology. Retinal function, morphology, photoreceptor viability and retinal inflammation were examined in the presence and absence of dBET6 treatment. RESULTS: Intraperitoneal injection of dBET6 led to the rapid degradation of BET protein in the retina without detectable toxicity. dBET6 improved retinal responsiveness and visual acuity after light damage (LD). dBET6 also repressed LD-induced retinal macrophages/microglia activation, Müller cell gliosis, photoreceptor death and retinal degeneration. Analysis of single-cell RNA-sequencing results revealed cGAS-STING components were expressed in retinal microglia. LD led to dramatic activation of the cGAS-STING pathway, whereas dBET6 suppressed LD-induced STING expression in reactive macrophages/microglia and the related inflammatory response. CONCLUSIONS: This study indicates targeted degradation of BET by dBET6 exerts neuroprotective effects by inhibiting cGAS-STING in reactive retinal macrophages/microglia, and is expected to become a new strategy for treatment of retinal degeneration.

Determination of oligomeric organization of membrane proteins from native membranes at nanoscale-spatial and single-molecule resolution.

The oligomeric organization of membrane proteins in native cell membranes is a critical regulator of their function. High-resolution quantitative measurements of oligomeric assemblies and how they change under different conditions are indispensable to the understanding of membrane protein biology. We report a single-molecule imaging technique (Native-nanoBleach) to determine the oligomeric distribution of membrane proteins directly from native membranes at an effective spatial resolution of ∼10 nm. We achieved this by capturing target membrane proteins in "native nanodiscs" with their proximal native membrane environment using amphipathic copolymers. We established this method using structurally and functionally diverse membrane proteins with well-established stoichiometries. We then applied Native-nanoBleach to quantify the oligomerization status of a receptor tyrosine kinase (TrkA) and a small GTPase (KRas) under conditions of growth-factor binding or oncogenic mutations, respectively. Native-nanoBleach provides a sensitive, single-molecule platform to quantify membrane protein oligomeric distributions in native membranes at an unprecedented spatial resolution.

Investigation of the spatial and temporal variation of soil salinity using Google Earth Engine: a case study at Werigan-Kuqa Oasis, West China.

Large-scale soil salinity surveys are time-costly and labor-intensive, and it is also more difficult to investigate historical salinity, while in arid and semi-arid regions, the investigation of the spatial and temporal characteristics of salinity can provide a scientific basis for the scientific prevention of salinity, With this objective, this study uses multi-source data combined with ensemble learning and Google Earth Engine to build a monitoring model to observe the evolution of salinization in the Werigan-Kuqa River Oasis from 1996 to 2021 and to analyze the driving factors. In this experiment, three ensemble learning models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were established using data collected in the field for different years and some environmental variables, After the accuracy validation of the model, XGBoost had the highest accuracy of salinity prediction in this study area, with RMSE of 17.62 dS m-1, R2 of 0.73 and RPIQ of 2.45 in the test set. In this experiment, after Spearman correlation analysis of soil Electrical Conductivity (EC) with environmental variables, we found that the near-infrared band in the original band, the DEM in the topographic factor, the vegetation index based on remote sensing, and the salinity index soil EC had a strong correlation. The spatial distribution of salinization is generally characterized by good in the west and north and severe in the east and south. Non-salinization, light salinization, and moderate salinization gradually expanded southward and eastward from the interior of the western oasis over 25 years. Severe and very severe salinization gradually shifted from the northern edge of the oasis to the eastern and southeastern desert areas during the 25 years. The saline soils with the highest salinity class were distributed in most of the desert areas in the eastern part of the Werigan-Kuqa Oasis study area as well as in smaller areas in the west in 1996, shrinking in size and characterized by a discontinuous distribution by 2021. In terms of area change, the non-salinized area increased from 198.25 in 1996 to 1682.47 km2 in 2021. The area of saline soil with the highest salinization level decreased from 5708.77 in 1996 to 2246.87 km2 in 2021. overall, the overall salinization of the Werigan-Kuqa Oasis improved.

The next-generation Open Targets Platform: reimagined, redesigned, rebuilt.

The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene-disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.

Light damage induces inflammatory factors in mouse retina and vitreous humor.

Purpose: Increased inflammatory factor levels have been reported in the vitreous humor (VH) of diabetic retinopathy and neovascular age-related macular degeneration, ocular diseases generally associated with the formation of new retinal blood vessels and leakage. However, the levels of inflammatory mediators are less known in retinal degeneration without neovascularization. Human retinitis pigmentosa (RP) and animal models of light-induced retinal degeneration (LIRD) share several features, such as photoreceptor death and retinal inflammation. Here, we aimed to determine the levels of inflammatory factors in the VH of the LIRD mouse model. Methods: LIRD was induced by exposing BALB/c mice to white light (15,000 lx, 2 h), and the mice were recovered for 2 days before analysis (n = 50 mice). We assessed retinal morphology using optical coherence tomography and hematoxylin and eosin staining; retinal cell viability was determined using terminal deoxynucleotidyl transferase dUTP nick-end labeling, and retinal responses were measured based on electroretinogram signals. Total retinal RNAs were extracted and subjected to RNA sequencing analysis. VH samples from control (n = 4) and LIRD mice (n = 9) were assayed in triplicate for a panel of four inflammatory mediators using the Simple Plex Cartridge on an Ella System. Results: Retinal degeneration, photoreceptor death, infiltration of microglia/macrophages into the photoreceptor layer, and loss of a- and b-waves were obviously detected after LIRD. RNA sequencing revealed that light damage (LD) led to the significant upregulation of inflammatory factors in mouse retinas. In the VH, LD increased the total protein concentration. Dramatic induction of CCL2 (~3000 fold) and IL6 (~10 fold) was detected in VH in response to LD. Increased but not significant levels of TNFα and IL1ß were also detected in light-exposed VH. Conclusions: Given that the LIRD model mimics RP pathogenesis in some aspects, these results suggest a causative link between retinal degeneration and VH inflammation in RP progression, and the increased CCL2 level in VH may reflect similar elevated CCL2 expression in the degenerative retina.

Probing the Conductive and Tribological Behaviors of Solid Additives in Multiply Alkylated Cyclopentanes for Sliding Electrical Contact.

Sliding electrical contacts need to be lubricated by conductive lubricants to perform low energy dissipation, high reliability, and long service life. This work studied the thermal stability, anti-corrosion capacity, and conductive, and tribological behaviors of several solid additives in multiply alkylated cyclopentanes (MACs), including carbon nanotubes (CNTs), multilayer graphene (MG), and silver microparticles. The results showed that all the additives possessed favorable thermal stability and corrosion resistance; in particular, CNTs and MG exhibited lower and more stable electrical contact resistance (ECR) and better lubricity abilities than Ag microparticles. Moreover, based on the characterization of the worn surfaces and the film thickness calculation, the favorable conductive and tribological properties of CNTs and MG were related to the high conductivity and specific structure of the additives and the good chemical inertness of MACs.

Asymmetric Thermally Activated Delayed Fluorescence Emitter for Highly Efficient Red/Near-Infrared Organic Light-Emitting Diodes.

Developing highly efficient red/near-infrared thermally activated delayed fluorescence (TADF) materials is of great importance for organic light-emitting diodes (OLEDs). Here, we reported an asymmetric TADF emitter (TCPQ), which exhibits a high reverse intersystem crossing rate as well as a low non-radiative rate due to molecular symmetry breaking through multiple donor substitution. The coexistence of multiple donors endows TCPQ with not only near-infrared emission but also excellent device performances. As for the TCPQ-based OLEDs, the 10 and 20 wt % doped devices exhibit outstanding external quantum efficiencies (EQEs) of 21.9 and 19.2% with red emission peaks at 612 and 642 nm, respectively. Meanwhile, the non-doped device achieves an EQE of 5.4% with an emission peak at 718 nm, showing near-infrared emission. These device efficiencies are among the best performances of red/near-infrared TADF-OLEDs, demonstrating that the asymmetry design is a potential strategy for constructing long wavelength TADF materials with high efficiency.

Multidimensional soil salinity data mining and evaluation from different satellites.

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (EC) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well for all satellites (R2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R2 = 0.89, RMSE = 2.57 mS·cm-1, RPD = 2.05), followed by S2 (R2 = 0.86, RMSE = 2.71 mS·cm-1, RPD = 1.90) and L8 (R2 = 0.85, RMSE = 2.84 mS·cm-1, RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases.

Lubrication Performances of Polyalkylene Glycols at Steel Interface under External Electric Fields.

This work studied the lubrication performances of polyalkylene glycols, which are insulating oils, at the steel interface under external electric fields. The results show that external electric fields greatly affect the lubrication performances of polyalkylene glycols, and there is an optimal voltage (-1.0 V) for the improvement in friction reduction performance. The surface analysis and experiment results indicate that the polyalkylene glycol adsorption film and the reduction in the amount of FexOy and FeOOH in the tribochemical film contribute to improved friction performance under the negative voltage condition. This work proves that the lubrication performances of insulating oils can be affected by external electric fields as well. A lubrication model was proposed, hoping to provide a basic understanding of the lubrication mechanisms of ether-bond-containing insulating oils in the electric environment.

Multilayer Coatings for Tribology: A Mini Review.

Friction and wear usually lead to huge energy loss and failure of machine pairs, which usually causes great economic losses. Researchers have made great efforts to reduce energy dissipation and enhance durability through advanced lubrication technologies. Single-layer coatings have been applied in many sectors of engineering, but the performance of single-layer coatings still has many limitations. One solution to overcome these limitations is to use a multilayer coating that combines different components with varied physical and chemical properties. In addition, multilayer coating with alternating layers only containing two components can lead to improved performance compared to a coating with only two different layers. This paper systematically reviews the design concept and properties of different types of multilayer coatings, including transition-metal nitride coatings, diamond-like carbon-based coatings, and other multilayer coatings. The inherent functional mechanisms of the multilayer structures are also detailed and discussed.

Liquid Superlubricity Enabled by the Synergy Effect of Graphene Oxide and Lithium Salts.

In this study, graphene oxide (GO) nanoflakes and lithium salt (LiPF6) were utilized as lubrication additives in ether bond-containing dihydric alcohol aqueous solutions (DA(aq)) to improve lubrication performances. The apparent friction reduction and superlubricity were realized at the Si3N4/sapphire interface. The conditions and laws for superlubricity realization have been concluded. The underlying mechanism was the synergy effect of GO and LiPF6. It was proven that a GO adsorption layer was formed at the interface, which caused the shearing interface to transfer from solid asperities to GO interlayers (weak interlayer interactions), resulting in friction reduction and superlubricity realization. In addition to the GO adsorption layer, a boundary layer containing phosphates and fluorides was formed by tribochemical reactions of LiPF6 and was conducive to low friction. Additionally, a fluid layer contributed to friction reduction as well. This work proved that GO-family materials are promising for friction reduction, and provided new insights into realizing liquid superlubricity at macroscale by combining GO with other materials.

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms.

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions.

PM2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM2.5 concentrations in Xinjiang during 2015-2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM2.5 concentration at a relatively high resolution. (2) The PM2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM2.5 levels year-round. (3) The PM2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m-3) > spring (64.76 µg m-3) > autumn (46.01 µg m-3) > summer (43.40 µg m-3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.