Search Results (1,680)

Wildlife plays a vital role in maintaining ecological balance and biodiversity, relying on habitats that provide shelter, food, and essential resources. This study investigated wildlife distribution and diversity across the REDD+ program area in Cambodia’s Prey Lang Wildlife Sanctuary, a lowland evergreen forest ecosystem, and assessed the effects of forest habitats and anthropogenic pressure on their distribution. We used square transects for sampling and ArcGIS to calculate forest cover and distance to the nearest village as a proxy for human impact. Overall, we recorded seven mammals and two birds, with the great hornbill (Buceros bicornis) most frequently detected, followed by pileated gibbon (Hylobates pileatus), wild pig (Sus scrofa), long-tailed macaque (Macaca fascicularis), green peafowl (Pavo muticus), northern red muntjac (Muntiacus vaginalis), and Indochinese silvered langur (Trachypithecus germaini), while gaur (Bos gaurus) and sambar deer (Rusa unicolor) were least detected. Wildlife richness and abundance were higher in evergreen-dominated habitats than in mixed deciduous–evergreen forests. Certain K-selected species, including pileated gibbon, Indochinese silvered langur, and great hornbill, were highly specialized and preferred intact forests, whereas generalist species such as northern red muntjac, long-tailed macaque, and wild pig showed ecological flexibility in habitat use. These findings emphasize tailored conservation strategies: protecting intact evergreen forests via REDD+ supports specialized species, while adaptive management in mosaic landscapes benefits generalists, enhancing wildlife conservation and sustainable management of the Prey Lang Wildlife Sanctuary. Full article

Madhuca longifolia plays a significant role in combating malnutrition, aligning with food security for healthier and more sustainable food options. This review highlights the latest advancements in utilizing minor forest produce and developing various mahua products, including bars, laddoos, biscuits, burfis, and pickles. This review highlights the nutritional potential of Madhuca longifolia in combating the triple burden of malnutrition, co-existence of undernutrition, overnutrition, and micronutrient deficiency, while focusing on the utilization of regional foods to safeguard tribal people’s livelihoods and empowerment. The regional food sources provide a viable, long-term alternative for populations with limited access to conventional food systems. Evidence regarding the health-promoting properties of Madhuca longifolia originates from multiple sources, including traditional ethnomedicinal knowledge, in vitro bioactivity assays, and a limited number of human investigations. A comprehensive literature search across major scientific databases identified evidence indicating that Madhuca longifolia is a nutritionally valuable underutilized food resource with promising applications in dietary diversification, functional food development, and sustainable nutrition systems. The reviewed evidence indicates that Madhuca longifolia is a nutrient-rich underutilized food resource with potential applications in dietary diversification, value-added food development, and sustainable nutrition initiatives. Full article

Volatile fingerprints provide useful information for characterizing Astragali Radix (AR), a food–medicine homologous plant material, but differences among wild, wild-simulated, and cultivated samples remain unclear. In this study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC–MS) and headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) were integrated with multivariate analysis and interpretable machine learning to characterize volatile profiles and identify candidate discriminatory compounds in 117 AR samples from different cultivation patterns. HS-SPME-GC–MS tentatively identified 29, 34, and 45 volatile compounds in wild, wild-simulated, and cultivated samples, respectively. Esters were the predominant class in all groups, although the relative abundance of esters and the overall chemical-class composition varied among cultivation patterns. HS-GC–IMS tentatively identified 57, 50, and 55 compounds, respectively, comprising mainly low-molecular-weight aldehydes, alcohols, and ketones and thereby providing complementary volatile fingerprint information. Partial least squares discriminant analysis (PLS-DA) showed that the volatile fingerprints captured cultivation-pattern-associated differences, with the HS-GC–IMS model showing clearer group separation. Random forest, support vector machine, and CatBoost models were further constructed using the HS-SPME-GC–MS profiling results. By integrating variable importance in projection (VIP) and SHapley Additive exPlanations (SHAP) values, γ-hexalactone, methyl eugenol, methyl (9Z,11E)-octadeca-9,11-dienoate, eugenol, and ethyl linoleate were selected as candidate discriminatory compounds. Based on the HS-GC–IMS results, 1-octen-3-one, pentyl acetate, (Z)-2-penten-1-ol, 2-heptanone, and the monomeric signal of 2-ethyl-6-methylpyrazine were also identified as candidate discriminatory compounds. These compounds may be related to fatty acid-derived metabolism, aromatic secondary metabolism, and terpenoid-related processes. The integration of two complementary volatile-analysis platforms with VIP- and SHAP-based interpretation provided broader coverage of volatile features and improved the interpretability of candidate-compound screening. These findings provide an interpretable analytical workflow and candidate discriminatory compounds that may support future rapid screening, cultivation-pattern authentication, and volatile-profile-based differentiation of AR, pending independent external validation. Full article

An informed society can contribute to the conservation of nature. Environmental interpretation encompasses experiences and topics that involve direct interaction between visitors and nature, and such interaction promotes ecological awareness. Exhibits in public places constitute educational settings that complement traditional institutions, promoting learning that involves not only teachers and students but also society at large, including visitors and tourists. The city of Mazatlán, Sinaloa (Mexico), presents a landscape dominated by wetlands, which are a representative ecosystem of this city. However, their conservation requires the active participation of the population. Currently, Mazatlán has 501,441 inhabitants, who affect the wetlands. By 2030, it is expected to have around 756,823 inhabitants, and by 2050, 1,306,423. Additionally, Mazatlán receives 3,872,691 tourists per year. The objective was to evaluate the themes of environmental (interpretation) activities and determine their contribution to the Sustainable Development Goals. Three sites were analyzed to collect information on the topics and develop a correspondence matrix. The results showed that the information provided contributes to 13 of the 17 Sustainable Development Goals: 1. End of poverty, 2. Hunger and food security, 3. Health, 4. Education, 6. Water and sanitation, 8. Economic growth, 9. Resilient infrastructures, 11. Cities and settlements, 12. Sustainable production and consumption, 13. Climate change, 14. Ocean Conservation, 15. Forests, desertification, and biodiversity, and 17. Partnerships. Full article

Litter and understory are key components of forest ecosystems, playing vital roles in soil carbon inputs and microhabitat regulation. Their removal is a common forest management practice: litter removal (L) reduces fire and pest risks while promoting nutrient cycling, whereas understory removal (U) alleviates nutrient competition between understory plants and trees. However, the combined effects and underlying mechanisms of litter removal (L) and understory removal (U) on soil nematode communities remain unclear. This study investigated the individual and interactive effects of L and U on soil nematode communities in a coniferous–broadleaved mixed forest in a subtropical–warm temperate transition zone. The results showed that L significantly reduced soil nematode abundance by 18.1%, increased the relative abundance of bacterivores and omnivores-predators by 21.1% and 103.3%, respectively. U significantly elevated the relative abundance of fungivores by 30.8%, while both L and U reduced the relative abundance of plant-parasitic nematodes. Significant interactive effects between L and U were observed on soil nematode abundance, relative abundances of fungivores, plant-parasites, maturity index, and plant parasite index. Litter plus understory removal (LU) alleviated soil nematode resource limitation through synergistic regulation of resource pulses and microhabitat modification. Faunal analysis based on structure and enrichment indices indicated that LU enhanced food web structural complexity and resource-use efficiency. Redundancy analysis identified microbial biomass carbon, microbial biomass nitrogen, and soil total carbon as key drivers of nematode community differentiation. This study reveals the synergistic regulatory patterns of litter and understory vegetation on soil nematode communities, providing a theoretical reference for understanding the responses of soil nematodes to understory disturbance during the growing season in climate transitional forests, and offering basic data for long-term monitoring and forest soil management. Full article

Background: Assessing overall diet quality has become increasingly important in nutritional epidemiology. The Healthy Eating Index (HEI) is one of the most widely used measures of diet quality; however, comparable data on Hungarian children are scarce. The current study aimed to assess the diet quality of Hungarian children using the Healthy Eating Index and to examine demographic factors associated with diet quality. Methods: This cross-sectional study included 666 children aged 4–10 years. Dietary intake was measured using three-day dietary records, and sociodemographic factors were collected via parental questionnaires. Diet quality was assessed using the Healthy Eating Index-2020 (HEI-2020). For the statistical analysis, linear regression, random forest models and one-way ANOVA with Tukey’s post hoc test were used. Results: The mean HEI score was 48.2 (SD 8.02), indicating low diet quality. Settlement type was significantly associated with the HEI score (p = 0.009). The multiplicity-corrected p-values for pairwise comparisons showed that children living in towns had significantly lower HEI scores (44.9, SD 7.97) than those living in county capitals (3.9, 95% CI:0.8–7.0, p = 0.006) and villages (−3.6, 95% CI: −0.61–−6.6, p = 0.011), but not significantly lower than those living in the capital (3.09, 95% CI: −0.25–6.4, p = 0.08). These differences were primarily related to whole-fruit and whole-grain component scores. Sex, age, and maternal education were not significantly associated with HEI score. The random forest model showed weak predictive performance (RMSE = 7.66). Conclusions: Diet quality among Hungarian children was generally suboptimal. The examined sociodemographic characteristics accounted for only a small proportion of the variability in the HEI score. This highlights the importance of ongoing research to understand dietary patterns and to uncover additional social, environmental, and behavioral aspects of dietary habits across cultures. Furthermore, the results indicate that interventions should also consider local food environments. Full article

Accurate and efficient soybean yield prediction is essential for ensuring food security and optimizing agricultural management. Remote-sensing approaches based on a single phenological stage or sensor may not fully capture the physiological and structural dynamics associated with soybean yield formation, thereby constraining prediction accuracy under field-level spatial heterogeneity. This study therefore developed and evaluated an integrated multi-source and multi-temporal UAV-based framework for plot-scale soybean yield prediction under production-field conditions. A UAV platform equipped with multispectral, RGB, and LiDAR sensors was used to extract multidimensional remote-sensing features at four key growth stages: beginning pod, full seed, beginning maturity, and full maturity. By integrating spectral indices, texture metrics, and three-dimensional canopy structural parameters, seven machine-learning algorithms—Ridge, LASSO, Random Forest, MLP, LightGBM, XGBoost, and CatBoost—were evaluated under single-temporal and multi-temporal scenarios. Nonlinear tree-based ensemble models generally achieved higher predictive accuracy than the linear models. On the independent test set, CatBoost performed best under the full multi-temporal, three-sensor fusion scenario, with an R² of 0.816 and an RMSE of 245 kg ha⁻¹. Among individual growth stages, the full seed stage (R6) produced the highest single-stage prediction accuracy. Three-sensor fusion improved prediction relative to the single-source multispectral scheme, and integrating features across phenological stages further improved performance by representing cumulative crop-growth dynamics. The proposed framework provides methodological support for UAV-based precision management and data-driven decision-making in soybean production. Full article

Satellite -derived environmental features can predict county-level life expectancy (LE) across the contiguous United States with a mean absolute error of 1.08 years over two decades, without using any census or sociodemographic inputs. We assembled 61,680 county-year observations across 3084 counties from 2000–2019, integrating features from 11 satellite and gridded data streams. The data streams include the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, Sentinel-1 synthetic aperture radar, Sentinel-2 and Landsat optical imagery, the United States Department of Agriculture (USDA) Cropland Data Layer, the European Commission Joint Research Centre (JRC) Global Surface Water layer, the Copernicus Digital Elevation Model, the European Space Agency Climate Change Initiative (ESA CCI) soil moisture record, and the Food and Agriculture Organization (FAO) gridded livestock densities. After a supervised pruning step that removed low-importance variables, a Random Forest regressor was trained and evaluated using 5-fold cross-validation grouped by county. The grouping places all 20 years of each county exclusively in either the training set or the test set, which prevents spatial information leakage between folds. Coefficient of determination, mean absolute error, and root mean squared error are reported as $R^2=0.631\pm 0.013$, MAE $=1.08\pm 0.02$ years, and RMSE $=1.48\pm 0.04$ years. Moran’s I, a measure of residual spatial autocorrelation, is $0.0988$ ($p=0.001$), which supports geographic generalisation. Multimodal fusion reduces unexplained variance by approximately one-third relative to the strongest single-modality baseline (MODIS land surface temperature alone, $R^2=0.442$). TreeSHAP attribution analysis reveals a feature hierarchy in which nighttime land surface temperature features carry roughly $6.16\times$ the cumulative attribution weight of all daytime channels combined. The model response shows a protective inflection near a minimum overnight temperature of about 7.5 °C. Because all input streams are globally available, the framework is architecturally extensible to regions where civil registration and vital statistics systems are incomplete; however, the trained model and its thresholds require recalibration against local mortality data before application outside the contiguous United States. With that caveat, the approach supports satellite-based monitoring of United Nations Sustainable Development Goal (UN SDG) Target 3.9. Full article

In 2019, the United Nations’ General Assembly established 2021 to 2030 as the Decade on Ecosystem Restoration, and ecological restoration should be adopted by the member nations. In 2015, Brazil had already committed to restoring (replanting) twelve million hectares of forests, and this commitment was reaffirmed in the National Plan for the Recovery of Native Vegetation in 2017 and relaunched at COP16 on diversity in 2024. Despite Brazil’s leadership in establishing the Tropical Forests Forever Fund (TFFF) in 2023, which was launched at COP30 in Belem in 2025, the expansion of the agricultural frontier remains the main driver of deforestation in the Amazon/Rain Forest and the Cerrado biomes. This article aims to examine the social and ecological consequences of the capitalist occupation and expansion of the agricultural frontier in the Cerrado. It will also study the counterpoint of the land struggles and initiatives of peasant organizations focused on conservation and restoration as possibilities and perspectives for the social and ecological restoration of the Cerrado landscapes. Based on an interdisciplinary approach, the specialized literature, and official agricultural data, the study shows that, in addition to degrading nature (deforestation, water and soil contamination, and desertification) and threatening the historical ways of life of countryside peoples, the frontier’s expansion blocks possibilities for restoration and hinders initiatives to protect the remaining nature of Brazil’s second-largest biome. On the other hand, resistance to expropriation and appropriation, and struggles for land and territory, have emerged as possibilities for socio-environmental restoration, beyond reforestation and the recovery of destroyed nature, by transforming landscapes, ways of life, and production, and by creating conditions for food sovereignty and sustainability in the countryside. Therefore, agroecological actions by agrarian movements and rural organizations in general, and those of the Movement of Landless Rural Workers (MST) in particular, have become emblematic in opposing agrarian extractivism and unsustainable monocrops imposed upon and disseminated throughout the Brazilian Cerrado. Full article

Peanut allergy is a major food allergy associated with severe IgE-mediated hypersensitivity reactions and strong T-cell responses to peanut-derived epitopes. In this study, complementary computational approaches were developed to identify digestion-stable peanut peptides binding to HLA-DRB5*01:01, an HLA class II molecule associated with peanut allergy. A dataset of experimentally validated binders and non-binders was used to construct one sequence logo model and three machine learning (ML) models based on Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) algorithms. Peptide sequences were encoded using Wold’s z-scale descriptors representing hydrophobic, steric, and electronic amino acid properties. All ML models showed excellent predictive performance, with ROC AUC values between 0.978 and 0.980, while the SVM model achieved the best overall accuracy (95.8%). Permutation-importance analysis identified the dominant peptide properties for HLA-DRB5 binding. The developed models were applied to major peanut allergens Ara h 1, Ara h 2, Ara h 3, and Ara h 6 following simulated gastrointestinal digestion. Multiple consensus HLA-DRB5-binding peptides were identified, including several experimentally reported allergenic epitopes. The identified peptides should be regarded as candidate HLA-DRB5*01:01-restricted CD4+ T-cell epitopes that warrant further experimental validation using peptide–HLA binding assays and T-cell functional studies. Full article

Background: Benzo[a]pyrene (BaP), a common food contaminant, is a recognized gastric carcinogen. This study aimed to identify therapeutic targets and repurposed drugs for gastric cancer (GC) using BaP as a network toxicology query. Methods: An integrated strategy combining network toxicology, multi-omics, machine learning (Random Forest, LASSO, SVM-RFE), and experimental validation was applied. Results: By intersecting GC-associated genes with BaP-related targets and machine learning, we identified three hub genes. The logistic regression model further revealed KCNE2 as a protective factor (OR = 0.515, 95% CI: 0.383–0.692), while SULF1 (OR = 2.940, 95% CI: 1.399–6.179) and TIMP1 (OR = 5.351, 95% CI: 2.020–16.743) were identified as potential risk factors. Survival analysis confirmed their prognostic significance. Single-cell transcriptomics descriptively showed TIMP1 and SULF1 enrichment in malignant/stromal cells and fibroblasts, respectively, whereas KCNE2 was restricted to normal epithelial cells and silenced in tumors. GSVA implicated epigenetic regulation, ECM remodeling, and TGF-β signaling. Molecular docking and dynamics simulations suggested that BaP can form stable complexes with DNMT1 and DNMT3A. Accordingly, drug enrichment analysis identified DNMT inhibitor 5-azacytidine as a top candidate. Cellular experiments confirmed that 5-azacytidine selectively inhibited GC cells and was associated with modulation of the DNMT3A–KCNE2 axis. Conclusions: Our findings provide a novel molecular target and a repurposed drug for GC from the perspective of a food contaminant. Full article

Flash drought, an abruptly intensifying meteorological anomaly, poses a growing threat to agricultural production, ecosystem stability, and regional carbon cycling, particularly in croplands of monsoon regions. Existing studies have largely focused on point-scale identification or conventional vegetation indices, whereas the regional spatiotemporal evolution of flash droughts and crop-specific differences in photosynthetic recovery remain poorly understood. Using multi-source remote sensing data for the North China Plain and the Middle–Lower Yangtze Plain during 2001–2024, this study integrated triple-collocation error assessment, root-zone soil-moisture percentile identification, connected-component tracking, and Random Forest–SHAP analysis to characterize flash drought trajectories and their vegetation impacts. The results showed that the southern Middle–Lower Yangtze Plain exhibited a high-frequency but low-intensity pattern, whereas the central North China Plain was characterized by lower frequency yet higher intensity and longer duration. Rice-based systems were more vulnerable to frequent flash drought shocks, whereas rainfed and rotation systems faced stronger cumulative risks. Solar-induced chlorophyll fluorescence (SIF) responded to flash droughts 6–9 days earlier than gross primary productivity (GPP), and all cropping systems displayed a “rapid physiological response–lagged carbon-assimilation recovery” pattern. The month of occurrence, drought duration, and decline rate were identified as the dominant factors governing photosynthetic recovery. These findings extend the flash drought monitoring framework to incorporate regional connectivity and crop recovery mechanisms, providing a remote-sensing basis for agricultural early warning, drought mitigation, and food-security management. Full article

Groundwater-fed irrigation sustains global food production but drives chronic aquifer depletion, creating an urgent need for quantitative tools that link irrigation intensity to groundwater response. This study proposes a gray-box machine learning (ML) framework that learns the parametric coefficients of polynomial irrigation–groundwater response functions—rather than predicting state variables directly—thereby embedding physical interpretability into the ML output. Using a well-validated SWAT-GW model of a representative over-exploited piedmont plain in the North China Plain as the training data generator, gradient irrigation scenarios were constructed for 70 hydrological response units over 20 years, producing 21,000 paired records of winter-wheat irrigation intensity versus three groundwater response variables: vertical recharge, aquifer storage change, and water table depth change. Quadratic polynomials were identified as the optimal functional form through joint evaluation of fitting accuracy (R² > 0.994) and ML learnability. Ensemble boosting algorithms predicted the three quadratic coefficients, with R² ranging from 0.74 to 0.97, and retained acceptable accuracy even when input features were restricted to readily available meteorological and soil data. Four management-critical hydrogeological parameters—the precipitation infiltration coefficient (α), irrigation infiltration coefficient (β), natural recharge (R_nat), and recharge–abstraction equilibrium point (IRR_eq)—were successfully inverted from the predicted coefficients and validated against independent regional groundwater resource assessments. The SHapley Additive exPlanations and Causal Forest analyses confirmed that the learned relationships are governed by physically interpretable drivers. The framework advances groundwater machine learning from state-variable prediction toward functional-structure extraction, offering a transferable approach for deriving irrigation–groundwater response curves and sustainability thresholds in over-exploited aquifer systems. Full article

To advance crop type mapping in support of global food and water security, this study compared three spectral configurations: (A) the full 60-band DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral narrowband (HNB) dataset, (B) a 14-band subset of DESIS-derived HNBs aligned with the planned Landsat 10 (formerly Landsat Next) spectral configuration (400–1000 nm), and (C) DESIS-based simulations of Landsat 10 superspectral broadbands. The analysis was conducted in California’s Central Valley, hereafter referred to as “the Central Valley”, during the peak growing month of August. DESIS imagery from August 2021, 2022, and 2023 was used sequentially for model development, testing, and independent validation. Over these three years, DESIS provided extensive hyperspectral coverage of much of the 4 million hectares in the Central Valley’s. Analyses were performed on Google Earth Engine using two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), to differentiate three major crop classes: row crops, grapes and tree crops, and winter wheat/fallow/other. The highest overall accuracy (86%) was achieved using SVM in combination with either the full DESIS hyperspectral dataset or the 14 DESIS narrowbands corresponding to Landsat 10. This finding aligns with earlier studies showing a small number of strategically positioned narrowbands can be optimal for crop type classification. Use of the narrowband datasets resulted in substantially higher accuracy (overall accuracy of 86%) compared to the simulated Landsat 10 broadbands (overall accuracy of 75%), supporting previous studies highlighting the utility of narrowbands. Despite the high accuracy using August imagery, the study indicates more granular crop type classification will require multi-temporal observations spanning the full phenological cycle (June–October), especially for a large number of crop classes. Acquiring task-based hyperspectral imagery over such large areas throughout the growing season remains operationally challenging. In contrast, Landsat 10 superspectral imagery could provide routine coverage across seasons and years that is practical and scalable for future large area crop type mapping and agricultural monitoring. Full article

Mushrooms, forest fruits and game meat represent three major categories of forest foods in Czechia. This study compares their acquisition mechanisms, market visibility and value-chain positions and provides reference-year, category-specific physical estimates and stage-specific indicative economic values. The analysis integrates pooled national survey data on mushrooms and forest fruits from 2021 to 2025 (N = 5025), a 2022 survey extension on game meat (N = 1000), qualitative interviews with 12 stakeholders in the Czech game-meat value chain conducted by the research team between 2023 and 2024, and official hunting statistics. In the 2024 reference year, mushrooms and forest fruits were estimated through household-collected quantities, whereas game meat was estimated as gross carcass-weight equivalent at the primary procurement stage. The three categories together represented an indicative stage-specific economic value of approximately EUR 324.3 million, but their physical quantities are interpreted as product-specific estimates rather than as directly equivalent units of provisioning value. Mushrooms showed the strongest household-collection profile: 70.4% of respondents reported collection and 20.1% reported purchase. Forest fruits displayed a more mixed acquisition pattern, with particularly high purchase shares for blueberries and raspberries. Collection and purchase were largely independent for mushrooms, whereas complementary relationships prevailed among forest fruits. Game meat had an indicative primary procurement value of EUR 33.57 million and reflected a regulated hunting-based value chain. The findings identify a differentiated forest-food system in which socio-economic significance is shaped by product-specific relationships among household acquisition, market access, value-chain organisation and stage-specific value creation. Full article