Search Results (564)

Background: Neonatal mortality remains a significant public health challenge in Ethiopia. Despite efforts to implement key evidence-based interventions, their coverage and utilization remain low. The Saving Little Lives (SLL) program aims to scale-up a Minimum Care Package (MCP) of synergistic, life-saving interventions for all liveborn neonates, with a focus on preterm and low birth weight (LBW) infants, across 290 hospitals in Ethiopia (206 primary, 69 general, and 15 referral hospitals), representing 82% of all hospitals in the country at the time of the study, and evaluate the impact on neonatal mortality. Methods: A non-randomized stepped-wedge trial will be conducted to evaluate the impact of implementing the SLL MCP interventions. Quantitative evaluation data will be collected from 36 primary hospitals, selected from 206 primary hospitals across four regions, receiving the interventions. An independent evaluation research assistant will be deployed in each of the hospitals to collect data using Open Data Kit (ODK) through interviewing mothers before discharge, on the 29th day of life if discharged, and reviewing medical records. A mixed-method, cross-sectional formative assessment will be conducted prior to implementation, employing quantitative facility assessment and qualitative interviews with mothers, healthcare providers, and facility managers. This will be followed by continuous program learning assessment once implementation begins. Descriptive data will be presented using numbers, percentages, tables, and graphs. Regression modeling and generalized estimating equations (GEEs) will be used to estimate the impact of the SLL MCP interventions. Qualitative data will be gathered through in-depth interviews, digitally recorded, transcribed, and thematically analyzed using ATLAS.ti Version 7.5 software to assess facility readiness, barriers, and enablers of implementing the SLL MCP interventions. Expected Outcome: We hypothesize that achieving 80% coverage of the SLL MCP interventions among eligible neonates will result in a 35% reduction in neonatal mortality at implementation facilities. Full article

Background and Objectives: Primary open-angle glaucoma (POAG) is one of the leading ocular diseases leading to irreversible blindness and is often asymptomatic until advanced cases. While intraocular pressure reduction remains the cornerstone of treatment, neuroprotective strategies targeting retinal ganglion cell metabolism are actively investigated. Niacinamide (nicotinamide, vitamin B3), a precursor of NAD+, has shown neuroprotective potential in preclinical models. This exploratory study evaluated the short-term functional, structural, and electrophysiological effects of oral niacinamide supplementation in POAG. Materials and Methods: In this interventional study, patients with POAG received oral niacinamide 500 mg daily for six months. Visual field (VF) global and localized sensitivity (Mean Deviation [MD], Pattern Standard Deviation [PSD]), Optic Coherence Tomography (OCT)-derived peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC), and Visual evoked potentials (VEP) latency parameters (P2 1.4 Hz, P100 1°, and P100 15′) were assessed at baseline and at six months. Because both eyes from some participants were included, primary longitudinal inference was based on clustered analyses using generalized estimating equations and linear mixed-effects models to account for inter-eye correlation. Eye-level paired analyses were used for exploratory comparison. Change–change relationships across modalities were explored using Spearman correlation. Results: After accounting for inter-eye correlation, no statistically significant change in MD was detected (mean ΔMD +0.43 dB; GEE p = 0.099; LME p = 0.101), and PSD remained stable. RNFL thickness showed a small decrease (−1.26 µm; GEE p = 0.046), while GCC did not change significantly. VEP P100 latencies remained stable, whereas P2 latency showed a small increase (+3.9 ms; GEE p = 0.039). Correlation analysis revealed a moderate association between changes in GCC and MD (ρ = 0.44), suggesting concordance between macular structural stability and global visual field performance. Conclusions: When inter-eye correlation is appropriately accounted for, six months of niacinamide supplementation in POAG is associated with overall functional, structural, and electrophysiological stability, without evidence of clinically meaningful improvement or progression. These findings support short-term safety and highlight the importance of clustered analytical approaches and macular-centered biomarkers in future glaucoma neuroprotection trials. Full article

Kazakhstan, located in Central Asia, is experiencing faster warming than the global trend, making it an important region regarding the study of how climate change is affecting climatic zones. This research aims to identify projected shifts in Köppen–Geiger climate zones under high-emission Shared Socioeconomic Pathway (SSP) 5-8.5 climate scenarios. The Köppen–Geiger climate classification system is a practical tool that effectively captures climate types based on just two variables: temperature and precipitation. Monthly temperature and precipitation data from Climatic Research Unit (CRU,) ERA5-Land, and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles from 1951 to 2100 were used to generate climatic zone maps. CMIP6 models were evaluated against meteorological station data and ERA5-Land, with bias metrics used to identify the best-performing models for temperature and precipitation in Kazakhstan. Based on these results, two inter-model datasets were developed and used to generate Köppen–Geiger climate maps for high-emission scenarios for the 2061–2100 time period. This research resulted in two key outcomes. First, to facilitate this analysis, a Google Earth Engine (GEE) application was developed as an open accessible tool for dynamic visualization of Köppen–Geiger climate maps. Second, projected maps based on CMIP6 SSP5-8.5 scenario projections indicate that southern Kazakhstan may shift to BSh (Hot Semi-Arid) and Csa (Mediterranean) climates, and the southwest region of the country is projected to shift to a BWh (Hot Desert) climate. These projected Köppen–Geiger climate maps contributed to climate adaptation efforts by identifying regions at risk of desertification and aridification. This study provides a comprehensive analysis of climate zone transformations in Kazakhstan and offers a practical scalable geovisualization tool for monitoring climate change impacts. This allows users easy access to climate-related information and insights into data processing procedures. Full article

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification. Full article

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Crop production in the desert Southwest of the United States, as well as in other arid and semi-arid regions, requires tools that provide accurate crop evapotranspiration (ET) estimates to support efficient irrigation management. Such tools include the web-based OpenET platform, which provides real-time ET data generated from six satellite-based models, their Ensemble, and a field-based system (LI-710, LI-COR Inc., Lincoln, NE, USA). This study evaluated simulated ET (ET_SIM) of cotton (Gossypium hirsutum L.) derived from OpenET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop), their Ensemble approach, and LI-710. Field data were utilized to estimate cotton ET using the soil water balance (SWB) method (ET_SWB) from June to October 2025 in Gila Bend, AZ, USA. Four evaluation metrics, the normalized root-mean-squared error (NRMSE), mean bias error (MBE), simulation error (S_e), and coefficient of determination (R²), were employed to evaluate the performance of OpenET models, their Ensemble, and the LI-710 in estimating cotton ET. Statistical analysis indicated that the ALEXI/DisALEXI, geeSEBAL, and PT-JPL models substantially underestimated ET_SWB, with simulation errors ranging from −26.92% to −20.57%. The eeMETRIC, SIMS, SSEBop, and Ensemble provided acceptable ET estimates (22.57% ≤ NRMSE ≤ 29.85%, −0.36 mm. day⁻¹ ≤ MBE ≤ 0.16 mm. day⁻¹, −7.58% ≤ S_e ≤ 3.42%, 0.57 ≤ R² ≤ 0.74). Meanwhile, LI-710 simulated cotton ET acceptably with a slight tendency to overestimate daily ET by 0.21 mm. A strong positive correlation was observed between daily ET_SIM from LI-710 and ET_SWB, with S_e and NRMSE of 4.40% and 23.68%, respectively. Based on our findings, using a singular OpenET model, such as eeMETRIC, SIMS, or SSEBop, the OpenET Ensemble, and the LI-710 can offer growers and decision-makers reliable guidance for efficient irrigation management of late-planted cotton in arid and semi-arid climates. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

Wildfires are increasing globally and pose major challenges for assessing post-fire vegetation recovery and ecosystem resilience. We analyzed long-term Landsat time series in two contrasting fire-prone ecosystems in the United States and Australia. Vegetation area was extracted using the Enhanced Vegetation Index (EVI) with Otsu thresholding. Recovery to pre-fire baseline levels was modeled using linear, logistic, locally estimated scatterplot smoothing (LOESS), and generalized additive models (GAM), and their performance was compared using multiple metrics. The results indicated rapid recovery of Australian forests to baseline levels, whereas this was not the case for forests in the United States. Among climatic factors, temperature was the dominant parameter in Australia (Spearman ρ = 0.513, p < 10⁻⁸), while no climatic variable significantly influenced recovery in California. Methodologically, GAM consistently performed best in both regions due to its success in capturing multiphase and heterogeneous recovery patterns, yielding the lowest values of AIC (United States: 142.89; Australia: 46.70) and RMSE_cv (United States: 112.86; Australia: 2.26). Linear and logistic models failed to capture complex recovery dynamics, whereas LOESS was highly sensitive to noise and unstable for long-term prediction. These findings indicate that post-fire recovery is inherently nonlinear and ecosystem-specific and that simple models are insufficient for accurate estimation, with GAM emerging as an appropriate method for assessing vegetation recovery using remote sensing data. This study provides a transferable approach using remote sensing and GAM to monitor forest resilience under accelerating global fire regimes. Full article

Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models for oilseed flax, this study proposes a CNN–Informer hybrid framework that integrates convolutional neural networks (CNNs) with the Informer architecture to model multi-source spatio-temporal data. Unlike conventional Transformer-based approaches, the proposed framework combines CNN-based local temporal feature extraction with the ProbSparse attention mechanism of Informer, enabling the efficient modeling of long-range temporal dependencies across multiple years while reducing the computational burden of attention-based time-series modeling. The model incorporates multi-source inputs, including remote sensing indices (NDVI, EVI, SAVI, KNDVI), TerraClimate meteorological variables, soil properties, and historical yield records. Comprehensive experiments conducted at the county level in Gansu Province, China, demonstrate that the CNN–Informer model consistently outperforms representative machine learning and deep learning baselines (Transformer, Informer, LSTM, and XGBoost), achieving an average performance of R² = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%. Results from feature ablation and historical yield window analyses reveal that a three-year historical yield window yields optimal performance, with remote sensing features contributing most strongly to predictive accuracy, while meteorological and soil variables enhance spatial adaptability under heterogeneous environmental conditions. Model robustness was further verified through fivefold county-based spatial cross-validation, indicating stable performance and strong generalization capability in unseen regions. Overall, the proposed CNN–Informer framework provides a reliable and interpretable solution for county-level oilseed flax yield prediction and offers practical insights for precision management of specialty crops in arid and semi-arid regions. Full article

Background/Objectives: Evidence links children’s health-related quality of life (HRQoL) to overall diet, but data on specific, actionable habits are limited. We tested whether vegetable intake ≥2 portions/day and fish intake ≥2–3 times/week were associated with risk of low HRQoL (KIDSCREEN-10 Index score <40) and assessed their joint effect and robustness to overall diet quality. Methods: In three waves (2020–2023) in Madrid (Spain), 1127 observations from 771 children (8–12 years) were analysed. Logistic Generalised Estimating Equations (GEE) adjusted for age, sex, socioeconomic status (four levels), moderate-to-vigorous physical activity (MVPA), screen time, body mass index (BMI) z-score, wave and school ownership. Marginal predicted probabilities were computed for four exposure combinations (neither, vegetables only, fish only, both). Sensitivity models added school area and the Mediterranean Diet Quality Index (KIDMED; KIDMED_wo_FV and total); hybrid within–between GEE and a linear mixed model for continuous KIDSCREEN-10 were also fitted. Results: Vegetables ≥2/day and fish ≥2–3/week were inversely associated with low HRQoL (odds ratio (OR) 0.49 (95% confidence interval (CI) 0.30–0.82) and 0.61 (0.43–0.87)). The interaction was positive (OR 2.50 (1.39–4.53)). Adjusted probabilities were 40.1% (neither), 25.8% (vegetables only; −14.3 percentage points (p.p.)), 29.7% (fish only; −10.5 p.p.), and 34.0% (both; −6.1 p.p.). Findings persisted with KIDMED_wo_FV and attenuated with total KIDMED. MVPA related inversely and screen time directly to risk. Conclusions: Vegetables ≥2/day and fish ≥2–3/week were associated with lower odds of low HRQoL, with non-additive combined effects. These simple targets may complement physical-activity promotion and reduced screen time; longitudinal/experimental studies should test causality and dose–response. Full article

Coconut palms (Cocos nucifera L.) are a critical economic and ecological resource in Wenchang City, Hainan. Accurate mapping of their spatial distribution is essential for precision agricultural planning and effective pest and disease management. However, in tropical monsoon regions, persistent cloud cover, spectral similarity with other evergreen species, and redundancy among high-dimensional features hinder the performance of optical classification. To address these challenges, we developed a scalable multi-source remote sensing framework on the Google Earth Engine (GEE) with an emphasis on species-oriented feature design rather than generic feature stacking. The framework integrates Sentinel-1 SAR, Sentinel-2 MSI, and SRTM topographic data to construct a 42-dimensional feature set encompassing spectral, polarimetric, textural, and topographic attributes. Using Random Forest (RF) importance ranking and out-of-bag (OOB) error analysis, an optimal 15-feature subset was identified. Four feature combination schemes were designed to assess the contribution of each data source. The fused dataset achieved an overall accuracy (OA) of 92.51% (Kappa = 0.8928), while the RF-OOB optimized subset maintained a comparable OA of 92.83% (Kappa = 0.8975) with a 64% reduction in dimensionality. Canopy Water Index (CWI), Green Chlorophyll Index (GCI), and VV-polarized backscattering coefficient (${\mathsf{\sigma }}_{\mathrm{V}\mathrm{V}}$) were identified as the most discriminative features. Independent UAV validation (0.07 m resolution) in a 50 km² area of Chongxing Town confirmed the model’s robustness (OA = 90.17%, Kappa = 0.8617). This study provides an efficient and robust framework for large-scale monitoring of tropical economic forests such as coconut palms. Full article

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration ($ET$) being a key ecohydrological process. However, monitoring $ET$ over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of $ET$ remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based $ET$ models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on $ET$ patterns. Our results showed a good agreement between modeled and observed $ET$ using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day⁻¹ for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on $ET$, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor $ET$ in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions. Full article

In this research, we sought to methodically examine the protective effects of Gastrodia elata extract (GEE) on liver damage induced by D-galactose (D-gal) in mice and clarify the underlying mechanisms. The chemical composition of GEE was characterized using Ultra-Performance Liquid Chromatography–Tandem Mass Spectrometry (UPLC-MS/MS), while network pharmacology analysis was employed to predict potential molecular targets and signaling pathways. A mouse model of liver injury was established through daily intraperitoneal injection of D-gal over a 42-day period, during which the hepatoprotective efficacy of GEE was evaluated. Biochemical, histopathological, and molecular analyses were subsequently performed. UPLC-MS/MS identified ingredients such as amino acids, aromatic compounds, fatty acids, and terpenoids in GEE. A network pharmacology analysis enabled the identification of 272 common targets linked to GEE and liver damage, demonstrating notable enrichment within the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway. In vivo experiments demonstrated that GEE effectively alleviated D-gal-induced body weight loss and elevated liver index values, alleviated hepatic histological damage, and reduced serum levels of Alanine Aminotransferase (ALT), Aspartate Aminotransferase (AST), and Alkaline Phosphatase (ALP). Furthermore, GEE enhanced the activities of the antioxidant enzymes superoxide dismutase (SOD) and catalase (CAT), decreased malondialdehyde (MDA) levels, and downregulated the mRNA expression of the pro-inflammatory cytokines Interleukin-6 (IL-6), Interleukin-1 beta (IL-1β), and Tumor Necrosis Factor-alpha (TNF-α). Western blot analysis confirmed that GEE activated the PI3K/Akt pathway, as evidenced by increased ratios of phosphorylated Phosphatidylinositol 3-kinase/Phosphatidylinositol 3-kinase (p-PI3K/PI3K) and phosphorylated AKT/Protein Kinase B (p-AKT/AKT); restored the B-cell lymphoma 2-associated X protein/B-cell lymphoma-2 (Bax/Bcl-2) balance; and reduced cyclin-dependent kinase inhibitor 1 (p21) expression. The results suggest that GEE protects against D-gal-induced liver damage by reducing oxidative stress, inhibiting inflammatory responses, and modulating apoptosis through the activation of the PI3K/Akt signaling pathway, providing support for its potential use in hepatoprotection. Full article

The Kubuqi Desert represents a key ecologically fragile region in northern China, primarily functioning as a windbreak and sand-fixation barrier while also contributing to regional ecological balance. However, the area’s ecological vulnerability is pronounced, and investigating the spatiotemporal dynamics of vegetation carbon storage and associated driving mechanisms is essential for the scientific formulation of ecological restoration strategies. This research incorporates multi-source remote-sensing datasets (including Landsat 8 OLI/TIRS Level 2, Sentinel-1 Synthetic Aperture Radar (SAR), ERA5 daily meteorological data, GEDI Level 4B, SRTM GL1 v003, and ESA WorldCover v100) based on the Google Earth Engine (GEE) platform, and employs multiple machine-learning algorithms (validation metrics of the machine learning model: $R^2$ = 0.917, RMSE = 0.251) to develop a dynamic monitoring model of vegetation carbon storage in the Kubuqi Desert during the period 2019–2023. The analysis systematically evaluates the influence of climatic variables and anthropogenic activities on the spatiotemporal differentiation of carbon storage. The results indicate a slight upward trend in overall carbon storage across the study area (average annual increase of 0.4%), with high values predominantly concentrated in vegetated regions (up to 5.22 Mg/Ha) and low values distributed in bare lands and desert zones (0.5–0.7 Mg/Ha). Altitude, temperature, and slope serve as the primary driving factors governing carbon-storage variability. The findings suggest that scientifically guided vegetation restoration and optimized water-resource management can enhance the carbon-sink capacity of the Kubuqi Desert, offering a robust scientific basis for ecological governance and carbon budget assessment in arid and semi-arid desert ecosystems. Full article