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Air pollution poses significant risks to public health, ecosystems, and regional economies, particularly in rapidly developing regions. Despite its importance, the Middle East remains relatively understudied in regional air quality, with limited evaluations of pollutant transport and model performance. This study applies the WRF (Weather Research and Forecasting) model coupled with the CAMx (Comprehensive Air Quality Model with Extensions) model to simulate meteorology and air quality over West Asia, with a focus on the United Arab Emirates (UAE). Six representative months are analyzed, including three winter periods (January 2018, 2020, 2022) and three summer periods (June 2017, 2019, 2021). WRF shows good agreement with observations, reproducing near-surface temperature with an index of agreement (IOA) between 0.90 and 1.00 and generally low wind speed (MB < ±0.5 m s⁻¹) and wind direction biases (MB < ±0.5), although cloud-radiative forcing is underestimated during winter. CAMx reproduces PM_2.5 concentrations with moderate-to-high correlations (r = 0.44–0.65) and low bias, while AOD and O₃ column concentration show larger uncertainties. Satellite-based evaluation indicates good performance for NO₂ and CO column abundances but larger discrepancies for HCHO and SO₂, particularly during summer. Overall, the results demonstrate that the WRF-CAMx modeling system provides a reliable framework for regional air quality simulations over West Asia, while highlighting uncertainties associated with emissions, atmospheric chemistry, and satellite retrieval products. Full article

Siziwang Banner in Inner Mongolia is a typical arid and semi-arid grassland region where ecological environmental quality is highly sensitive to climate variability and land use and land cover change (LULCC). Clarifying the long-term coupling relationship between LULCC and ecological environmental quality is essential for regional ecological protection and sustainable land management. Based on the Google Earth Engine (GEE) platform, this study integrated multi-temporal Landsat imagery and CLCD-based land use datasets, including an updated 2024 land use layer, to construct a Remote Sensing Ecological Index (RSEI) using standardized and direction-corrected principal component analysis. land use transition matrix analysis, spatial autocorrelation analysis, ecological contribution rate calculation, and GeoDetector were further applied to reveal the spatiotemporal evolution patterns, ecological effects, and driving mechanisms of LULCC in Siziwang Banner from 2000 to 2024. The results showed that: (1) grassland was consistently the dominant land use type, accounting for more than 90% of the total area. The overall land use pattern was characterized by stable grassland dominance, decreasing farmland and unused land, and slight increases in grassland and construction land; forestland showed a high relative growth rate but remained very small in absolute area. (2) The regional ecological environmental quality remained at a lower-to-medium level, with mean RSEI values ranging from 0.27 to 0.47. RSEI showed a phased pattern of initial improvement, subsequent decline, and partial recovery; the marked decline around 2015 was associated with the combined effects of drought stress and land use degradation rather than a single driving factor. RSEI exhibited significant positive spatial autocorrelation, with Moran’s I values ranging from 0.898 to 0.993. High-value clusters were mainly distributed in the southern region, whereas low-value clusters were concentrated in the central and northern regions. (3) Different land use transitions produced differentiated ecological effects. The conversion of unused land to grassland contributed positively to ecological restoration, while grassland degradation and construction land expansion exerted negative effects. The positive RSEI response of some grassland-to-farmland transitions should be interpreted cautiously in relation to local irrigation and intensive farmland management. (4) GeoDetector results indicated that land use type and DEM were the dominant factors controlling the spatial differentiation of RSEI, with average q values of 0.7188 and 0.6178, respectively. The interaction between DEM and land use type showed the strongest explanatory power, indicating that ecological quality was jointly shaped by land use structure and natural background conditions. This study provides a scientific basis for grassland protection, unused-land restoration, farmland management, and spatially differentiated ecological restoration in Siziwang Banner and similar ecologically fragile arid and semi-arid grassland regions. Full article

Understanding the structural characteristics of Pinus taiwanensis plantations in climatically transitional regions is essential for developing science-based management strategies under global change. This study investigated 23 plots in Huangbai Mountain Forest Farm, Henan Province, China, classified into low-, medium-, and high-density stands (n = 9, 9, and 5, respectively). Diameter distributions were fitted using six probability functions, and four spatial structure parameters—mixing degree (Mc), size ratio (U), uniform angle index (W), and forest layer index (S)—were quantified. In addition, five comprehensive spatial structure indices—average superiority coefficient index (SPV), spatial structure comprehensive index (Q), stand spatial structure distance index (FSI), Comprehensive Distance Evaluation (CDEV), and Comprehensive Assessment of Proximity Vector (CAPV)—were constructed using a combined analytic hierarchy process and entropy weight method. Given the unbalanced sample sizes, non-parametric Kruskal–Wallis tests were employed for comparisons, and bootstrap resampling (1000 iterations) was performed to assess the reliability of mean estimates. The results showed that both the Gamma and Weibull distributions were equally suitable for describing diameter distribution under different stand densities, as their AIC differences were below 2 for all density classes. Correlation analysis indicated that the relative importance of spatial parameters followed the order S > U > Mc > W. Medium-density stands exhibited the most optimal spatial structure, whereas low-density stands showed the poorest performance. These findings suggest that both overly dense and sparse stands negatively affect spatial organization. Appropriate management practices, such as thinning or enrichment planting, are recommended to optimize stand structure and enhance ecological resilience. Full article

Iron metabolism has long been linked to metabolic syndrome (MetS), but it is still unclear at which step—iron sensing, hepcidin regulation, export, transport, or storage—genetic variation matters the most. There are almost no studies on iron metabolism genes in Kazakhs in particular. Using whole-exome sequencing (WES) data from 96 Kazakh adults (52 with MetS), we examined 18 SNPs across six iron metabolism genes—HFE, SLC40A1, TMPRSS6, FTL, TFR2, and TF. Associations with iron biomarkers and MS components were tested by linear regression adjusted for age, sex, and BMI, with FDR correction, haplotype analysis, and bootstrap mediation analysis. Significant effects clustered at two distinct steps of iron metabolism: hepcidin regulation (TMPRSS6) and iron transport (TF). The T allele of TF rs12769 raised serum transferrin (β = +0.32 g/L; p_FDR = 0.002) while lowering both TSAT (β = −4.25%) and ferritin (β = −0.36 log-units); haplotype analysis confirmed rs12769 as the driver. The TMPRSS6 C–G–C haplotype was associated with lower fasting glucose (β = −1.19 mmol/L; p = 0.023), and TF rs12769 emerged as a robust FDR-significant determinant of serum transferrin (p_FDR = 0.002). Bootstrap mediation analysis (5000 iterations) showed that the TMPRSS6 effect on glucose is not mediated by ferritin, serum iron, transferrin, TSAT, or sTfR (all ACME p > 0.20), while Total and Direct Effects remained robust (p ≤ 0.054). In Kazakhs, iron-metabolism genes appear to influence fasting glucose through direct mechanisms not captured by the standard iron biomarker panel; alternative pathways involving hepatic enzymes, hepcidin, or inflammation warrant investigation in larger cohorts. Full article

Forest disturbances significantly affect the terrestrial carbon cycle, yet high-resolution detection, driver attribution, and carbon loss quantification remain challenging in cloudy and complex terrains. Here, we investigated the Northeast China and Southwest Hengduan Mountains forest regions from 2021 to 2024. We developed a Bayesian Model Averaging (BMA) framework integrating multi-source remote sensing (Sentinel-1/2, Landsat 8/9) and multi-algorithm ensembles (LandTrendr, CCDC, 1D-CNN) to extract 10 m disturbance features. Automated driver attribution and carbon loss quantification were achieved utilizing the Fire Information for Resource Management System (FIRMS), Dynamic World, and GEDI L4B LiDAR data. Validation yielded overall spatial accuracies of 91.15% in the Northeast and 89.62% in the Hengduan Mountains, with corresponding ensemble F1-Scores of 0.92 in both regions. Results indicated the disturbed area in the Northeast (1084.58 ha) significantly exceeded the Hengduan region (133.48 ha). Natural degradation dominated both regions (Northeast: 72.25%; Hengduan: 88.43%), though the Northeast experienced more wildfires and anthropogenic activities. Topographically, Northeast disturbances clustered on low-lying, gentle landscapes, whereas Hengduan events occurred on steep, high-altitude terrains. Due to denser per-pixel carbon storage, the Hengduan area exhibited higher carbon emission costs per unit area. Ultimately, this framework provides a quantitative technical foundation supporting high-resolution forest conservation and spatial evaluations for carbon neutrality commitments. Full article

Natural saline–alkaline soils are widespread in Central Asia, yet microbial responses to salinity gradients and ionic composition remain poorly resolved. We profiled bacterial communities (16S rRNA V3–V4, Illumina MiSeq) in 20 topsoil (0–20 cm) samples from four regions of Kazakhstan spanning non-saline to highly saline conditions. Soil chemistry included pH, total mineralization (dry residue), and major ions (Na⁺, Cl⁻, SO₄²⁻, HCO₃⁻, Ca²⁺, Mg²⁺, K⁺). Alpha (Chao1, Shannon, observed ASVs) and beta diversity (Bray–Curtis; ANOSIM; PCoA) were evaluated across salinity classes. Soils were alkaline (pH 7.91–10.47) and covered a broad salinity range (256–26,312 mg/L), driven mainly by Na⁺ with chloride and/or sulfate. Alpha diversity remained stable across salinity classes, though dispersion increased under high salinity. Community composition differed significantly among classes (ANOSIM R = 0.428, p = 0.005), with partial PCoA separation and overlap, indicating gradual turnover along the salinity gradient. In contrast, fungal communities showed no significant response to salinity, with stable alpha and beta diversity across all samples and consistent dominance of Ascomycota. Communities were dominated by Actinomycetota (formerly Actinobacteriota), Bacteroidota, and Pseudomonadota (formerly Proteobacteria). Bacteroidota increased in highly saline soils (FDR q = 0.036), whereas Acidobacteriota decreased (FDR q = 0.052). Thermodesulfobacteriota (formerly Desulfobacterota) correlated positively with sulfate, and Cyanobacteriota negatively with chloride. Overall, Kazakhstan’s saline–alkaline soils show stable bacterial alpha diversity but moderate, ion-linked compositional shifts with enrichment of halotolerant taxa. Full article

Background/Objectives: Antimicrobial resistance (AMR) in Staphylococcus spp. associated with poultry production is an emerging concern with implications for animal and public health. This study aimed to characterize antimicrobial susceptibility patterns and detect targeted methicillin resistance determinants in Staphylococcus isolates recovered from broiler chickens affected by bacterial chondronecrosis with osteomyelitis (BCO). Methods: A total of 200 bacterial isolates were evaluated, of which 167 were confirmed as Staphylococcus spp. Species identification was performed using presumptive phenotypic characterization followed by 16S rRNA gene sequencing. Antimicrobial susceptibility was assessed using disk diffusion, while presumptive methicillin-resistant phenotypes were evaluated using oxacillin screening and CHROMagar MRSA. Targeted molecular detection of mecA and mecC was performed by PCR. Results: The isolates demonstrated substantial species diversity, with S. aureus as the predominant species. Antimicrobial resistance was mainly observed against β-lactam antibiotics, particularly penicillin (33.5%), whereas high susceptibility was retained for non-β-lactam agents, including ciprofloxacin, tetracycline, trimethoprim–sulfamethoxazole, and azithromycin. A targeted PCR detected mecA in 7.2% of isolates, while mecC was not detected. The detection of mecA in oxacillin-susceptible isolates suggested genotype–phenotype discordance. Conclusions: BCO-associated Staphylococcus spp. from broiler chickens showed diverse species distribution, penicillin-dominant resistance, and targeted mecA detection across multiple species, supporting the use of combined phenotypic and molecular approaches for methicillin resistance surveillance. Full article

The tomato leafminer Tuta absoluta (Meyrick) is a devastating invasive pest that threatens tomato production worldwide. Reliance on chemical insecticides raises sustainability concerns, highlighting the need for effective biological alternatives. Combining predators with entomopathogenic nematodes (EPNs) represents a promising strategy, yet their interactions remain poorly characterized. Here, we conducted laboratory bioassays to assess the individual and joint effects of the predatory bug Arma chinensis (Fallou) and four EPN species, Steinernema carpocapsae, S. feltiae, S. riobrave, and Heterorhabditis bacteriophora, against T. absoluta larvae. Under these controlled conditions, H. bacteriophora showed the highest compatibility with A. chinensis, exhibiting the lowest virulence against the predator. Female A. chinensis exhibited strong predation on freely exposed second-instar larvae, but efficiency declined markedly against leaf-mining larvae. Heterorhabditis bacteriophora caused consistently high mortality in second instars regardless of protection. Their combined application resulted in additive mortality with significantly reduced LT₅₀ values. We also observed A. chinensis preying on nematode-infected larvae and occasional infection of the predator under confined conditions. These laboratory findings demonstrate additive effects against T. absoluta, providing preliminary evidence for stage-specific integrated biological control strategies. Full article

Dandruff (DF) is a prevalent, recurrent inflammatory scalp disorder increasingly recognized as a complex state of functional dysbiosis rather than a simple Malassezia overcolonization. The scalp microbiome is predominantly shaped by Malassezia species (M. restricta and M. globosa), Cutibacterium, and Staphylococcus species. Recent multi-omics evidence indicates that DF pathogenesis is driven by the destabilization of microbial interaction networks and strain-level functional heterogeneity, characterized by the disruption of the C. acnes/S. epidermidis balance and the opportunistic expansion of Staphylococcus aureus. Mechanistically, Malassezia utilizes its lipolytic repertoire to hydrolyze host sebum into irritant free fatty acids and peroxides. Concurrently, oxidative metabolites like squalene peroxide (SQOOH) penetrate the stratum corneum to activate the NF-κB and aryl hydrocarbon receptor (AhR) pathways, triggering a pro-inflammatory cascade that overexpresses keratins (K6/16/17) and downregulates filaggrin. This molecular cascade drives abnormal keratinocyte turnover and lipidomic remodeling, establishing a self-perpetuating “metabolism–inflammation–barrier disruption” pathological cycle. This review systematically elucidates the molecular etiology of DF as an ecological disorder driven by a tripartite imbalance among the microbiome, host physiology, and the environmental niche. We propose that next-generation therapeutic paradigms must transcend traditional antifungal eradication, focusing instead on targeted molecular intervention and microecological restoration to recalibrate overall scalp homeostasis. Full article

Sovereign credit ratings are central to a country’s access to international capital markets, yet the relative informational content of debt service obligations versus aggregate debt stock for rating outcomes remains empirically unsettled. This systematic review synthesises econometric evidence on both measures across 23 primary studies published between 1996 and 2024. The central message of this paper is that debt service indicators—capturing near-term liquidity and refinancing pressure—are at least as informative as, and on average more informative than, debt stock ratios for sovereign credit assessments, particularly in emerging-market contexts and ordered-response specifications. This finding holds across heterogeneous study designs and is confirmed by meta-regression analysis, which shows that debt service effects are significantly more negative than debt stock effects (β = −0.09, p = 0.004) after controlling for sample composition, model family, and rating agency. Emerging-market samples and ordered-response estimators yield stronger associations than advanced-economy samples and linear (OLS) specifications. No consistent differences across the major rating agencies are found once study-design moderators are included. Because primary studies differ in model families, samples, and variable construction, we emphasise transparent reporting, avoid over-interpreting pooled magnitudes, and focus on robust qualitative patterns and moderator-based explanations of heterogeneity. The findings contribute to the literature on sovereign rating determinants and have practical implications for fiscal monitoring, suggesting that debt management aimed at improving near-term servicing capacity matters for credit assessments in ways that are not fully captured by stock-based fiscal anchors. Full article

Autonomous navigation in unstructured off-road environments presents fundamental challenges due to terrain heterogeneity, the absence of structured road markings, and the necessity for real-time traversability reasoning from raw sensory observations. We present TerrainFormer, a hierarchical framework that integrates a world model for terrain dynamics prediction with a temporal decision transformer for action selection. Our methodology employs a two-phase training paradigm: (1) self-supervised world model pretraining on LiDAR point clouds to learn terrain representations encompassing traversability, elevation, and semantic segmentation; (2) behavioral cloning of the decision transformer conditioned on frozen world model features with temporally derived goal directions. The world model processes raw 3D LiDAR point clouds through a PointPillars encoder for real-time bird’s-eye-view (BEV) projection, followed by a Vision Transformer backbone that produces latent terrain representations. A principal contribution is our cross-dataset generalization paradigm: the world model is trained on separate datasets while the decision transformer is trained on separate sequences, ensuring zero data overlap between training phases. We introduce automatic goal direction computation from vehicle pose trajectories, enabling the model to learn directionally conditioned navigation policies. To address the class imbalance inherent in off-road driving data, we employ focal loss with inverse-frequency class weighting and action-chunk supervision. Experimental evaluation on the RELLIS-3D dataset achieves 87.31% test accuracy with 0.7948 macro F1 across all 12 action classes. The world model’s predicted future frames produce only a 0.79% accuracy drop versus ground-truth observations, with 98.82% action agreement, demonstrating effective cross-dataset generalization for real-time off-road navigation. Full article