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Background: Strychnos madagascariensis Poir (Loganiaceae) is a drought-tolerant indigenous fruit tree of East and southern Africa, valued for its food, medicinal, and socio-economic contributions to rural communities. Despite its importance as a candidate food crop, intraspecific morphological and genetic diversity had not previously been characterized, and no simple sequence repeat (SSR) markers had been developed for this species, leaving breeders and conservation planners without the basic diversity baseline needed to prioritize material for domestication. Methods: This study assessed vegetative and reproductive trait variation, variance components, and broad-sense heritability, and SSR-based genetic diversity among 27 morphologically defined S. madagascariensis morphotypes at Bonamanzi Game Reserve, KwaZulu-Natal, South Africa. Three trees were measured per morphotype (81 trees total), over two growing seasons. Genetic diversity was characterized in one representative tree per morphotype using seventeen newly developed SSR loci, the first such markers reported for this species, and analyzed with population structure (STRUCTURE version 2.3.4), PCA, and Nei’s genetic distance. Results: Twenty-seven morphotypes were identified based on leaf colour, shape, hairiness and size, dominated by grey (41%), elongated (59%), less hairy (48%), and medium-sized (>50–90 mm) leaves. Fruit diameter and mass showed the highest inter-morphotype variation (r = 0.949) and also the highest broad-sense heritability (H² = 55.3% and 47.8%, respectively), indicating strong genetic control of these traits and their suitability as targets for selective breeding. Environmental variance exceeded genotypic variance for most traits. A total of 144 alleles were identified across 17 SSR loci (mean 4.24 alleles/locus; mean PIC = 0.31). Population structure gave a preliminary, tentative signal of two genetic clusters (K = 2) with substantial admixture, which we interpret cautiously, given the limited sampling depth. Conclusions: This is the first study to characterize intraspecific morphological variation in S. madagascariensis and the first to develop SSR markers for the species. The results provide a preliminary, single-site framework for conservation genetics and crop improvement that should be validated with larger, multi-site samples. Grey morphotypes GyEvH1, GyEvH2, GyEvH3, GyRlH1 and GyEH2 combined consistent fruiting performance with favourable fruit-trait values and are proposed as priority candidates for further evaluation in domestication and breeding programmes. Full article

While Zanthoxylum bungeanum Maxim. (Z. bungeanum) pericarps are a globally prized spice, their leaves are frequently discarded as agricultural waste. This study systematically characterizes the aromatic potential of leaf by-products compared with traditional pericarps under diverse extraction strategies, utilizing an integrated flavoromics and sensomics approach. Qualitative GC-MS-O analysis revealed that leaf-derived fractions possess superior aromatic diversity: leaf essential oil and volatile solvent extract yielded 71 and 68 odorants, respectively, significantly surpassing pericarp counterparts (65 and 43 compounds). Concurrently, HS-GC-IMS profiling confirmed that targeted extraction allows leaf-derived flavors to replicate and exceed traditional spice complexity. Specifically, the leaf solvent extract achieved aromatic parity with pericarps by effectively mirroring the core spicy–citrus profile through cuminaldehyde and limonene retention. Conversely, distilled leaf essential oil unlocked a distinctive herbal–woody sensory innovation, driven by eucalyptol and a broader variety of aldehydes and ketones. Sensomics validation, incorporating aroma recombination, omission experiments, and partial least-squares regression modeling, conclusively identified β-myrcene, limonene, caryophyllene, and humulene as core molecular markers dictating these perceptual shifts. Ultimately, this research provides a robust theoretical foundation for upcycling Z. bungeanum leaves into valuable flavoring resources, facilitating circular bio-economy practices by delivering functional equivalence and entirely novel sensory experiences for the global food industry. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

Urban Park vegetation plays a crucial role in mitigating air pollution by serving as a natural sink for gaseous and particulate pollutants, thereby enhancing the ecological sustainability of cities. Identifying tree species with high tolerance to air pollution is therefore essential for effective urban park planning and management in highly polluted urban environments. This study evaluated the air pollution tolerance of selected tree species commonly found in urban parks of Kandy City, Sri Lanka, using the Air Pollution Tolerance Index (APTI). Five tree species—Terminalia catappa (Indian almond), Cassia fistula (golden shower tree), Pongamia pinnata (Indian beech), Madhuca longifolia (butter tree), and Tabebuia rosea (pink poui)—were assessed at two urban park locations representing contrasting pollution levels, identified based on ambient SO₂, NO₂, and PM_2.5 concentrations. APTI was calculated using four leaf biochemical parameters: pH, ascorbic acid content, relative water content, and total chlorophyll content. Leaf samples were collected from ten replicates of each species at both sites. Madhuca longifolia exhibited the highest APTI values (17.06 at the HP site and 25.17 at the LP site), followed by Cassia fistula, Terminalia catappa, Tabebuia rosea, and Pongamia pinnata. These findings suggest that the identified species, particularly Madhuca longifolia and Cassia fistula, are well-suited for urban greening and can contribute to mitigating air pollution impacts. However, these findings are constrained by a single cross-sectional sampling term, limited species screening, sequential data collection variances, and fixed mathematical equations. Consequently, future research should implement continuous multi-station monitoring arrays, expand species diversity, establish localized biochemical weightings, and initiate long-term multi-seasonal tracking to resolve temporal dynamics in tropical urban ecosystems. Full article

Triticale (×Triticosecale) is an important cereal crop in Poland, valued for its high yield potential and tolerance to biotic and abiotic stresses; however, fungal diseases remain a major constraint to production. This study aimed to assess the resistance and yield performance of selected winter triticale cultivars under varying levels of chemical crop protection across diverse environmental conditions. Field experiments were conducted during the 2023/2024 and 2024/2025 growing seasons at 16 locations in Poland within the framework of Post-Registration Variety Testing (PRVT). Three cultivars (Medalion, Fanfaro, and SU Atletus) were evaluated under two agrotechnical levels differing in fertilization and protection intensity. Disease severity for powdery mildew, brown rust, and septoria leaf blotch was assessed using a 9-point scale, and yield data were analyzed using four-way ANOVA and multivariate methods. The results demonstrated significant effects of management intensity, cultivar, growing season, environment as well as interactions: management intensity × environment, cultivar × environment, growing season × environment, management intensity × growing season × environment and cultivar× growing season × environment were significant for all four traits of the study. Management intensity × cultivar as well as management intensity × cultivar × environment interactions were significant for powdery mildew, brown rust and septoria leaf blotch. Management intensity × growing season interaction was significant for powdery mildew and septoria leaf blotch. Management intensity × cultivar × growing season × environment interaction was significant for powdery mildew and brown rust. The cultivar × growing season interaction was significant only for brown rust and management intensity × cultivar × growing season interaction for septoria leaf blotch. Increased protection intensity generally reduced disease severity and improved yield. Medalion exhibited the highest yield stability, whereas SU Atletus achieved the highest yields under favorable conditions but with greater variability. Fanfaro showed intermediate performance. The findings highlight the importance of cultivar selection and management intensity in optimizing triticale production and support the role of PRVT in guiding agricultural practice under variable climatic conditions. Full article

Alpine shrub meadows on the Qinghai–Tibet Plateau are key warm-season pastures that support pastoral production and ecosystem stability in fragile high-elevation regions. Due to low temperatures, short growing seasons, and slow vegetation recovery, these pastures are highly sensitive to inappropriate grazing management. However, the effects of different grazing–rest time configurations on plant community composition and leaf functional traits in alpine shrub meadows remain insufficiently understood. In this study, we evaluated five grazing treatments in an alpine shrub meadow in Sunan County, central–eastern Qilian Mountains: 10 days grazing–20 days rest (T1), 15 days grazing–15 days rest (T2), 20 days grazing–10 days rest (T3), continuous grazing (CG), and grazing exclusion (CK). In the third year of treatment implementation, we measured the community diversity, species importance values, and leaf functional traits of four dominant species: Elymus nutans, Carex tibetikobresia, Oxytropis kansuensis, and Bistorta vivipara. T1 and T2 significantly increased species richness, Shannon–Wiener diversity, and Simpson diversity compared with CG and CK. NMDS and PERMANOVA further showed significant differences in overall community composition among grazing treatments. Grazing generally reduced the leaf length, leaf width, and leaf area, whereas T2 showed relatively stronger leaf recovery among grazing treatments. Specific leaf area, specific leaf weight, and leaf length–width ratio showed higher variability and calculated plasticity than leaf thickness and leaf dry matter content, suggesting that resource-acquisition and morphological traits were more responsive to grazing than conservative structural traits. The coefficient of variation of leaf traits was positively associated with the plasticity index, although this association should be interpreted cautiously because both indices were calculated from the same underlying trait dataset. Overall, under the conditions of this three-year, single-site experiment and a target moderate grazing intensity, the 15-day grazing–15-day rest regime performed best among the tested treatments. This regime may provide a practical reference for rotational grazing management in similar warm-season alpine shrub meadows, but its broader applicability requires further validation across different grassland types, grazing intensities, climatic conditions, and longer monitoring periods. Full article

Saudi Arabia is expanding its domestic coffee sector under Vision 2030, yet coffee farming remains vulnerable to leaf diseases and pest damage. Image-based artificial intelligence studies conducted under Saudi field conditions remain limited, particularly in relation to assessing image-based visible disease severity. This study designs a hierarchical deep learning framework for screening coffee leaf diseases using field-collected images of Saudi coffee leaves. Three tasks were addressed: binary health status classification, four-class disease or pest damage identification, and binary visible severity classification. A dataset of 550 RGB images was collected from Al-Dayer Governorate, Jazan, under natural field conditions. ResNet50, DenseNet121, and EfficientNet-B0 were evaluated via transfer learning in two phases: a Saudi-only phase and an integrated phase that combined Saudi data with selected JMuBEN and JMuBEN2 samples. In the Saudi-only phase, ResNet50 achieved 96.47% accuracy for binary classification, while DenseNet121 achieved 68.66% and 78.12% for disease and visible severity classification, respectively. In the integrated phase, performance improved to 99.74%, 97.76%, and 97.37%. These integrated-phase results are interpreted as evidence that dataset expansion and increased visual diversity can improve model performance, rather than as definitive estimates of field deployment performance. The results show that binary classification is feasible under limited local data, whereas fine-grained disease classification is more constrained by dataset size and class imbalance. Grad-CAM visualizations were used to support qualitative interpretability and should not be interpreted as biological validation of disease localization. The framework is positioned as a decision-support screening approach that requires further expert-validated, multi-farm, and multi-season evaluation before deployment. Full article

Plant functional traits are central to regulating ecosystem multifunctionality (EMF), yet how coordinated above- and below-ground resource acquisition strategies mediate the effects of forest structural diversity on EMF remain insufficiently understood, particularly in typical south subtropical forests. Here, we applied a trait-based framework to disentangle the pathways linking forest structural diversity to EMF through plant resource acquisition strategies. Typical south subtropical forests were sampled for community-level leaf and root traits, including leaf total nitrogen and total phosphorus content, specific leaf area, leaf dry matter content, root diameter, specific root length, root tissue density, root total nitrogen and root total phosphorus content. EMF was quantified using 13 indicators associated with carbon storage, litter decomposition, primary productivity, and nutrient cycling, evaluated using both averaging and multi-threshold approaches. Principal component analysis was used to summarize trait variation along major functional axes representing the leaf and root economics spectra, and structural equation modeling was employed to quantify direct and trait-mediated pathways linking forest structural diversity to EMF. We found pronounced variation in EMF among forest types, with multifunctionality increasing along the classical fast-slow plant economics spectrum. Communities dominated by fast-growing species exhibited consistently higher EMF than those dominated by slow-growing species, with below-ground traits showing stronger associations with EMF than above-ground traits. In contrast, EMF was unrelated to the root collaboration gradient, suggesting that alternative below-ground foraging strategies contributed little to multifunctionality. Moreover, the positive effects of structural diversity on EMF were indirectly mediated through both leaf and root conservation gradients. Notably, the relative importance of these trait-mediated pathways was threshold-dependent. Root conservation gradients dominated EMF at low multifunctionality thresholds, whereas leaf conservation gradients became increasingly important at higher thresholds. Our findings show that forest structural diversity enhances ecosystem multifunctionality through coordinated leaf and root strategies. By revealing trait-mediated links between biodiversity and EMF, this study clarifies how community composition and species turnover shape multifunctionality in typical south subtropical forests. Full article

Strigolactones (SLs) have emerged as important regulators of plant adaptation to abiotic stress, functioning not as isolated hormones but as integrative signaling molecules. Beyond stress responses, SLs regulate key biological processes, including shoot branching, root architecture, leaf senescence, nutrient acquisition, rhizosphere communication, flowering-related development, and growth–developmental plasticity. This review synthesizes current knowledge on how SLs modulate plant responses to drought, salinity, heavy metal toxicity, high temperature, and low temperature through crosstalk with abscisic acid, auxin, cytokinin, ethylene, and gibberellin. We examine SL structural diversity, biosynthesis, transport, and signaling together with their roles in growth–stress coordination, hormonal networking, and stress-specific mitigation, while distinguishing endogenous SL functions from responses inferred from exogenous analogs such as GR24. Across stresses, SL-mediated resilience converges on adaptive modules, including water regulation, root–shoot architectural remodeling, redox protection, ion and osmotic homeostasis, photosynthetic maintenance, and rhizosphere-assisted resource acquisition. The mechanistic basis involves transcriptional reprogramming, ROS/RNS-linked redox regulation, metabolic protection, and root–microbe interactions. Translational prospects include SL analogs, genetic manipulation, and breeding for adaptive plasticity, nutrient efficiency, and stress tolerance. However, species specificity, dosage dependence, limited field validation, unclear structure–function relationships, and parasitic-weed stimulation remain major constraints. Full article

Vegetation restoration is an effective measure to improve soil carbon storage in rocky desertification areas. However, the underlying mechanisms driving differences in soil carbon storage among different vegetation types remain unclear. In this study, we selected four typical vegetation types (monospecific broadleaf plantation, mixed conifer–broadleaf plantation, monospecific coniferous plantation, and natural shrubland) from the comprehensive control zone for moderately rocky desertification of Xuyong County in the Chishui River Basin. We investigated the effects of vegetation patterns on soil carbon storage through tree layer functional traits, tree diversity, shrub layer functional traits, shrub diversity, and soil fungal/bacterial diversity. The results showed that soil carbon storage was highest in monospecific broadleaf plantations, followed by mixed conifer–broadleaf plantations, then natural shrubland, and it was lowest in monospecific coniferous plantations. Significant differences were mainly observed in the 0–10 cm and 10–20 cm soil layers, while no significant difference was found in the 20–30 cm layer. Tree layer community-weighted mean of leaf nitrogen content, soil fungal Shannon–Wiener index, and shrub layer community-weighted mean of specific leaf area were the core positive drivers of soil carbon differentiation. Among these, tree layer community-weighted mean of leaf nitrogen content was the most important factor, whereas the regulatory effect of shrub layer community-weighted mean of specific leaf area intensified with increasing soil depth. Furthermore, tree layer community-weighted mean of leaf nitrogen content and shrub layer community-weighted mean of specific leaf area not only directly promoted soil carbon accumulation but also indirectly promoted it by enhancing soil fungal diversity. In contrast, the effects of tree and shrub layer diversity and soil bacterial diversity were negligible. This study demonstrates that increasing soil carbon storage in rocky desertification ecosystems depends on the direct effect of soil fungal diversity, as well as the direct effects of tree and shrub layer functional traits and their indirect effects via regulating soil fungal diversity. We recommend that in moderately rocky desertified areas, priority should be given to tree species with high leaf nitrogen content; on sites unsuitable for afforestation, promoting natural shrublands with high specific leaf area can effectively enhance carbon sequestration capacity. Full article

The objective of this study was to determine the diversity and genetic structure of M. oleifera populations cultivated in Mexico through SilicoDArT markers. Seeds were collected from 14 populations in various states of the country. The seeds were germinated in greenhouse conditions. DNA was extracted from young leaves using a CTAB-based protocol. The extracted DNA was used to genotype each population with DArtseqTM technology. A total of 11,156 SilicoDArT markers were obtained, all of which were polymorphic. On average, the expected heterozygosity of the populations was 0.46, the number of effective alleles was 1.84 and the rareness was 280.53. Principal coordinate analysis and cluster analysis identified three clusters, with no clear association according to cultivation site. Genetic structure analysis determined three original populations (K = 3). Seven populations were assigned to the same ancestral group, six populations exhibited shared ancestries and one population displayed a distinct genetic background, suggesting anthropogenic management and exchange plant material among leaf producers. The identified genetic diversity and population structure constitute a basis for the conservation and management of Mexican moringa germplasm. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking effective targeted therapies, highlighting the need for new anticancer agents. Natural products remain a valuable source of bioactive compounds with diverse mechanisms of action. In this study, a pyrone glucoside, 7-hydroxymaltol-3-O-β-D-glucoside, was isolated from the methanolic leaf extract of Maerua angolensis and evaluated for its anticancer activity against TNBC cells. Structural elucidation was achieved using NMR and LC–MS analyses. Both the crude extract and the isolated compound exhibited dose-dependent cytotoxicity against MDA-MB-468 cells, with IC₅₀ values of 2.94 and 0.78 µg/mL, respectively, while showing reduced toxicity toward MCF10A normal cells. Mechanistic studies revealed induction of apoptosis, evidenced by activation of caspase-9 and caspase-7 and PARP cleavage. Confocal imaging further demonstrated lysosomal disruption and nuclear morphological alterations consistent with stress-associated cell death. Gene expression analysis indicated minimal involvement of the PI3K/AKT/mTOR pathway. Molecular docking showed favorable binding of the compound to AKT1, PARP-1, and caspase-7, suggesting a multi-target mode of action. ADMET analysis indicated low oral bioavailability but a favorable safety profile. These findings highlight the potential of this compound as a lead for TNBC therapy. Full article

Background: The rapid expansion of functional ready-to-drink (RTD) beverages—formulated with prebiotic fibers, botanical extracts, and reduced sugar—has outpaced systematic characterization of their small-molecule composition. Methods: We applied dual-mode untargeted high-resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS), integrating hydrophilic interaction (HILIC) and reversed-phase C18 separations, to profile five commercial RTD beverages spanning distinct formulation categories: Coca-Cola^®, Poppi^® Orange, OLIPOP^® Cream Soda, Pure Leaf^® Unsweetened Black Tea, and BeePop™ Peach + Orange Blossom Honey. Results: Across all products, 478 compounds were structurally annotated at Metabolomics Standards Initiative (MSI) Levels 1 and 2, of which 42 matched compounds with reported bioactivity in a curated literature-based reference database. Seventeen compounds—including the NAD+ precursor trigonelline and multiple B vitamins—were detected across all five products. The number and diversity of compounds with reported bioactivity varied substantially by product and correlated with botanical ingredient complexity. Conclusions: This work presents a qualitative molecular survey of the RTD beverage category using standardized, dual-mode untargeted metabolomics, providing a reference dataset for future targeted quantitation studies. Full article

Chromium is an environmental pollutant with high toxicity and carcinogenicity. It can induce severe oxidative stress and DNA damage after entering the human body through the food chain. As a plant growth-promoting rhizobacterium (PGPR) with both heavy metal tolerance and plant growth-promoting properties, Klebsiella variicola has considerable potential for the remediation of chromium contamination. In this study, chicory served as the experimental plant to explore the mitigating impacts of K. variicola on stress induced by hexavalent chromium (Cr(VI)) at a concentration of 400 mg/kg. The results showed that chromium severely inhibited the growth of chicory. In contrast, K. variicola significantly reduced the soil chromium content. As the chromium content decreased, the activities of soil urease, sucrase, catalase, and alkaline phosphatase were restored, increasing by 32.60–53.69%. Accordingly, the contents of total phosphorus, available phosphorus, total nitrogen, available nitrogen, soil organic carbon, and available potassium also increased by 34.71–51.81%. In addition, K. variicola reversed the decline in microbial diversity induced by chromium stress, promoted the growth of beneficial bacteria such as Acidobacteriota and Chloroflexota, and enhanced the stability of soil ecosystem functions. Ultimately, the growth inhibition of chicory caused by chromium stress was alleviated, with fresh weight, root length, maximum leaf width, maximum leaf length, plant height, and stem diameter significantly increasing by 21.89–61.60%. This study enhances our comprehension of the various functions of PGPR when exposed to heavy metal stress, and provides support for the development of microbe–plant combined strategies in the remediation of chromium-contaminated soils. Full article

Individual tree classification has a long history of diverse development, with recent trends focusing on the adoption of machine learning and deep learning approaches. It is a simple and powerful approach that allows the model to auto-pilot while reducing the need for physical characteristic understanding. Over more than a decade of research, we have focused on establishing a direct representation of individual trees that bridges 2D top-down imagery and true 3D models. In this study, we investigated the fundamental question of the influence of the input data on these ML/DL models. In 2024, we introduced a novel data transformation method, the Pseudo Tree Crown (PTC), which provides a pseudo-3D pixel-value perspective that enhances the informational richness of images and significantly improves classification performance. Our original implementation was successfully tested on urban and deciduous trees in 2024 and was later extended to Canadian natural conifer species under snow conditions in 2025. However, the original PTC relied on the green band, limiting its applicability to green-leaf species. In this study, we analyzed and compared the performance of different data variations and transformations, such as the Green–Red Vegetation Index (GRVI) and principal component analysis (PCA), as direct input and used their PTC forms. Classifications were conducted using Random Forest (RF), ResNet50, YOLOv10 and Segment Anything (SA). The results confirmed the effectiveness of the PTC, which consistently improves the classification accuracy by at least 5% without introducing additional computational time or complexity. Furthermore, PTC exhibits robust, consistent behavior across all data forms, demonstrating its strong resilience and reliability. Full article