Search Results (463)

Rice is a globally important staple crop and a major dietary source of inorganic arsenic (As). Compared with upland crops, flooded rice cultivation profoundly alters soil redox conditions, making paddy soils one of the most active agricultural interfaces for As mobilization, transformation, and food-chain transfer. While previous research has primarily focused on total As and inorganic As [As(III)/As(V)], methylated and thiolated As species also carry critical agronomic and health implications. Dimethylarsinic acid (DMA) can accumulate in grain and induce straighthead disease, whereas dimethylmonothioarsenate (DMMTA) shows substantially higher toxicity and uptake potential; DMMTA root uptake can be approximately 10 times higher than DMA, and its straighthead-inducing potency can exceed DMA by more than fivefold. This review synthesizes the sources, biogeochemical transformations, plant uptake, grain accumulation, safety assessment, and management implications of As along the paddy soil–rice–grain continuum. Particular emphasis is placed on how water regimes, redox potential, Fe/Mn/Al oxides, sulfur cycling, dissolved organic matter (DOM), microbial functional genes, and crop genotypes regulate diverse As species. Quantitative evidence indicates that alternate wetting and drying (AWD) can reduce grain total As and inorganic As by medians of 32% and 22%, respectively, but may increase grain cadmium (Cd) by a median of 58%; meanwhile, DMA and DMMTA can account for approximately 10–90% and 1–21% of total grain As, respectively, emphasizing that grain-As risk cannot be evaluated using inorganic As alone. Future research should establish speciation-based monitoring systems for inorganic, methylated, and thiolated As; develop process models linking water regime, Fe/S cycling, microbial transformations, and plant transport; and translate these mechanisms into field decision tools that balance As–Cd risk reduction, crop yield, and rice safety under changing environmental conditions. Full article

Dactylorhiza fuchsii (Druce) Soó is an ecologically sensitive terrestrial orchid and an important indicator of habitat condition, yet its population structure and morphological variability remain poorly studied in the Central Kazakh Uplands. This study evaluated the population size, density, ontogenetic structure, and morphometric differentiation of 21 coenopopulations in Kokshetau, Burabay, and Karkaraly National Parks. Ontogenetic stages were classified as juvenile, immature, virginal, and generative. Twenty-two quantitative traits were measured in 420 generative individuals and analysed using Pearson correlation, principal component analysis (PCA), the Mann–Whitney U test with Bonferroni correction, and PERMANOVA. Generative individuals predominated in most coenopopulations. In Burabay National Park, they comprised 71–100% of all individuals, virginal plants were absent from five of six coenopopulations, and population density was lower than in the other study regions. Balanced ontogenetic spectra were recorded in Kokshetau (CP4, CP6) and Karkaraly (CP1–CP3). Morphometric traits exhibited strong integration, with plant height strongly correlated with lip width (r = 0.723) and median lobe length (r = 0.684), whereas several floral traits varied more independently. The first three principal components explained 86.5% of the total variance, and PERMANOVA revealed significant regional differentiation among the three study regions (p < 0.001). This study provides the first comprehensive assessment of the population structure and morphometric variability of D. fuchsii in the Central Kazakh Uplands and establishes a baseline for long-term population monitoring and conservation planning. Full article

This paper considers the problem of early detection of late blight (Phytophthora infestans) in tomatoes based on computer vision and machine learning methods. The main purpose of the study was to develop a representative dataset of images of tomato leaves and an approach to extracting informative features for classifying the stages of disease development. A new dataset was generated using tomato plants grown under greenhouse conditions, with leaf images subsequently captured under controlled laboratory conditions, including five stages of late blight progression with variability in imaging devices, lighting conditions, and temporal disease dynamics. To improve the quality of image analysis, a preprocessing stage was applied, including conversion to grayscale, median filtering, and binarization using the Otsu method. In addition to the traditional textural features, fractal analysis was used to quantify the structural complexity of the affected leaf areas. To verify the information content of the selected features, classification experiments were conducted using Random Forest, XGBoost, and Support Vector Machine models, and the quality was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of textural and fractal features contributes to a more accurate distinction between the stages of disease. The developed dataset and the proposed approach can be used in further research on plant disease diagnosis, agricultural monitoring, and precision farming systems although it should be acknowledged that the dataset is limited to greenhouse settings, and field-scale generalizability requires further validation. Full article

Biomass-based bioenergy plants are commonly compared using aggregate indices that locate a system on a utilization scale but conceal the dimensional structure of its remaining deficit. This study extends the Biopolygeneration Diagnostic Index (BDI) by defining the biopolygeneration gap as a weighted multidimensional distance between each plant profile and a synthetic componentwise best-demonstrated reference constructed exclusively from real operating plants. Each reference coordinate has been demonstrated independently; simultaneous feasibility of the complete vector is not assumed. The squared Euclidean metric is decomposed into criterion-level contributions to identify the dominant diagnostic leverage, without interpreting that leverage as a cost-optimal retrofit. The framework is evaluated using 34 literature-derived cases (21 real plants and 13 models) covering 11 conversion technologies and 16 countries. Relative gaps range from 0.198 to 0.827, and energy efficiency and exergetic output quality provide the dominant leverage in 31 of 34 baseline cases. Incremental information beyond the aggregate BDI is demonstrated by three real plants with nearly identical BDI values (0.606–0.629) but distinct dominant deficits: energy efficiency, exergetic quality, and coproduct valorization. Rank ordering remains stable under 90th-percentile and top-three-median references ($\rho =0.984$–$0.992$), alternative distance norms, local weight perturbations, and correction for the shared C1/C2 source. A bounded input-uncertainty scenario yields a mean rank correlation of 0.893 and shows that leverage stability is case-specific. The framework therefore supports transparent dimension-level diagnostic prioritization while preserving a clear boundary with techno-economic, environmental, and implementation decisions. Full article

Which runtime governance mechanisms reduce physical risk when a learned controller drives a building’s climate, and under what conditions? We develop a closed-loop software-in-the-loop testbed in which a setpoint model trained on real occupancy data drives a physics-based thermal zone through a declarative governance plane, with outcomes scored by an independent safety oracle, and we run the same governance logic on a real MQTT stack with an in-process policy decision point and a hash-chained audit log. Under distribution shift, admission control reduces unsafe physical exposure by $19.4\%$, from 1185.3 to 954.8 °C·min, whereas adding checkpoint rollback reduces it by only a further $0.2\%$ in the reference run ($0.1$–$0.4\%$ across sensor noise seeds): the governance decision takes 0.44 ms while physical recovery takes a median of 61 min. Prevention therefore outperforms recovery in the studied thermal system, and the remaining avoidable exposure is driven by the policy’s estimate of occupancy context. A deterministic single-rule thermostat incurs $50\%$ more exposure under shift while the learned controller uses a $38\%$ higher heating demand proxy: a safety–demand trade-off, not evidence that learned control is necessary. A plant sweep yields an operating envelope criterion for inertial plants: rollback contributes materially to safety only when the plant is restored before the next command arrives and sampled before it can leave the safe set; on slower plants it removes at most $14.7\%$, with a transition band in between. No physical hardware was operated. Full article

Leaf disease diagnosis needs models that are accurate enough for agronomic use yet small enough for constrained computing settings. This study examines a late-attention MobileNetV2 design in which one Convolutional Block Attention Module (CBAM) is inserted between the last MobileNetV2 convolutional map and global average pooling. The experiments use 54,306 controlled-background PlantVillage images spanning 38 classes. Under a uniform saved-model re-evaluation, MobileNetV2 + CBAM obtained 97.17% accuracy and 97.15% weighted F1-score, whereas MobileNetV2 obtained 96.78% and 96.73%. On the converted models, paired testing gave a 0.64-percentage-point accuracy advantage for the CBAM variant (95% CI: 0.31–0.96; exact McNemar $p<0.001$). The proposed network has 4.02 million parameters, costs 0.604 GFLOPs (about 0.302 GMACs), and yields a 4.07 MiB dynamic-range-quantized TensorFlow Lite file with 96.70% accuracy. Batch-one inference on an Intel i7-11800H CPU with TensorFlow Lite/XNNPACK and eight threads reached a median of 45.42 ms (P95: 102.54 ms), excluding preprocessing. Grad-CAM inspection illustrates both lesion-centered activation and unresolved shared errors. The evidence therefore supports a compact accuracy–cost compromise for the tested conditions, while field robustness, energy use, repeated training runs, and target-device behavior remain open validation requirements. Full article

The large-scale release of natural enemies remains a major challenge in biological control. This study evaluated the viability of using biodegradable capsules produced from pineapple (Ananas comosus) leaf fibers for the release of the predator Euborellia annulipes. The objectives of the current research were: (I) to evaluate the viability of these capsules for predator release; (II) to validate the effectiveness of aerial release by drone (Agras T40); and (III) to evaluate the behavioral repertoire and performance of the predator after exiting the capsule in maize plants infested with susceptible and resistant Spodoptera frugiperda larvae. Predator-induced opening (in the absence of moisture) required a median time of 33 h (77.5% success, mortality ≤ 7.5%), whereas autonomous opening reached 100% in soil at field capacity. Aerial release achieved a 95.04% success rate. Under semi-field conditions, moisture facilitated rapid capsule opening (220 to 269 min); the predator exhibited active search behavior and achieved a higher predation rate on the resistant pest population (40%) than on the susceptible one (25%). The technology is therefore functional, compatible with the predator’s biology, and promising for sustainable integrated pest management strategies. Full article

Urban viaduct corridors represent persistent hotspots of traffic-derived particulate matter (PM), yet the performance of modular living wall systems (MLWS) in such environments remains poorly understood. This study investigated species-dependent deposition of fine-sized (≤2.5 µm) and coarse-sized (>2.5–10 µm) particles on an MLWS installed on viaduct piers in Wuhan, China. Four plant species were evaluated at three installation heights (0 m, 1 m, and 2 m) using 36 leaf samples analysed via environmental scanning electron microscopy and ImageJ-based quantification. Hosta plantaginea and Chlorophytum comosum exhibited substantially higher image-derived particle deposition than Bowles mint and Begonia ‘Escargot’. The observed species-level pattern was consistent with qualitative differences in leaf-surface micromorphology, although the contribution of individual traits could not be determined because these traits were not quantitatively measured. Installation height showed a descriptive but non-significant trend in PM deposition (H = 1.545, p = 0.462), with the highest median value observed at 2 m. Adaxial surfaces showed approximately 2.1 times higher deposited particle counts than abaxial surfaces, based on paired Wilcoxon signed-rank testing (Z = 4.399, p < 0.001). Fine-sized particles accounted for over 80% of total deposited particles across all conditions. These findings offer preliminary indications for species selection in MLWS on transport infrastructure, pending multi-seasonal and multi-site validation. Full article

Accurate assessment of the presence and severity of plant diseases is essential for effective crop monitoring and management. This study presents a deep learning-based pipeline for quantifying blackleg crown canker disease severity in canola stems by combining image segmentation and severity prediction tasks. Three architectures (ResUNet, UNet and SegFormer) were compared for the first step of stem segmentation to isolate relevant regions. The disease severity scores of four experts, and their aggregated median, were used to train models which were evaluated for label consistency, ambiguity, and model robustness. Among the three segmentation architectures, SegFormer achieved the best performance (mean IoU = 0.939, F1 score = 0.962), outperforming ResUNet and UNet. There was a high correlation in disease severity scores across expert labels, with the median-trained model achieving correlation coefficients of 0.924–0.963 against individual expert assessors on the evaluation dataset. Confusion matrix analysis further demonstrated reliable classification across severity levels. This work highlights the influence of segmentation quality, label aggregation strategies and data imbalances on downstream prediction tasks. This study uses controlled imaging conditions, but the proposed framework provides a strong foundation for future application in field environments. The framework enhances model interpretability by generating severity scores that align closely with expert assessments to support users such as agronomists and plant breeders for better decision-making and help track disease resistance by providing consistent, objective disease measurements over time. Future work will focus on exploring multi-task learning for greater efficiency, alongside validating the approach in field conditions to enable broader adoption. Full article

Centrifugal spraying systems are widely used in plant protection unmanned aerial vehicles (UAVs) due to their flexible droplet size adjustment and low-volume application capabilities. These systems can typically generate a wide range of volume median diameters from 50 to over 300 micrometers depending on rotational speed and disc structure. However, field performance is governed by a complex multi-physics coupling process rather than atomizer performance alone. Droplets released from the atomizer undergo near-field expansion and are subsequently entrained by rotor downwash. While strong downwash significantly improves deep canopy penetration compared to traditional application methods, it also dynamically reshapes droplet trajectories and size spectra through high-shear wake vortices. A critical comparative gap identified in this review is the severe discrepancy between static indoor atomization models and dynamic field transport. Current research lacks continuous spatial-temporal tracking of droplet size spectra under these complex field conditions. To address this, this review synthesizes the continuous mapping among droplet generation, spray plume transport, and canopy deposition. Precision application requires coordinated matching of flight parameters, aerodynamic downwash, and canopy architecture rather than single-parameter optimization. Future research must focus on the dynamic reconstruction of droplet size spectra and multisource perception-based feedback to shift centrifugal spraying systems from empirical parameter adjustment to mechanism-driven, closed-loop coordinated control. Full article

Pollinators, particularly bees, are essential for the reproduction of flowering plants, including ornamentals, and for maintaining ecosystem balance, benefitting gardens by supporting plant health and promoting robust flowering. However, pollinator populations are declining, with pesticide exposure recognized as one of several contributing stressors. Thiamethoxam, a commonly used systemic insecticide in ornamental horticulture (and its toxic metabolite clothianidin), has been found in nectar resources at levels harmful to bees. This study evaluated the impact of different thiamethoxam application rates on pollen contamination in blanket flower (Gaillardia pulchella L.), a popular source of pollen for bee pollinators. Container plants were drench-treated with low (0.30 g/L) and high (0.64 g/L) rates of thiamethoxam during the mature floral bud stage. The results show that thiamethoxam and clothianidin were present in pollen at both application rates. However, only clothianidin levels significantly increased with the application rate (p = 0.0009). When the pesticide concentrations measured in pollen were used to calculate the estimated exposure doses based on pollen consumption, the values exceeded published median lethal doses (LD50) for the common eastern bumble bee (Bombus impatiens Cresson) and buff-tailed bumble bee (Bombus terrestris L.), indicating substantial ecological risk. These findings underscore the potential threat posed to some pollinator species by thiamethoxam-treated ornamentals. Full article

Iron oxide-based nanomaterials have attracted considerable interest owing to their unique magnetic properties and potential biomedical and environmental applications. In this study, starch-coated superparamagnetic Fe₃O₄ nanoparticles (Sta-MNP) were synthesized and comprehensively characterized using electron microscopy (TEM, SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), vibrating sample magnetometry (VSM), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), and nanoparticle tracking analysis (NTA). The results confirmed the formation of a magnetite-based iron oxide nanoparticles sample with a median physical diameter of 12.24 nm, superparamagnetic behavior with a saturation magnetization of 59.81 emu/g, and effective starch coating on the nanoparticle surface. The biological effects of Sta-MNP were assessed in Triticum aestivum L. using the mitotic index (MI) and aberration index (AI) as cytogenetic endpoints, respectively. Exposure-induced concentration-dependent increases in both parameters across the tested volume fractions (0–200 µL/L), suggesting a significant interaction between Sta-MNP and dividing cells. Overall, this study provides a comprehensive physicochemical profile of starch-coated magnetite nanoparticles and demonstrates their potential cytogenetic impact in a plant model system, supporting further investigation of their environmental interactions and potential agricultural applications. Full article

Background: Colorectal cancer (CRC) remains a leading cause of mortality worldwide, with a significant proportion of patients presenting with metastatic disease (mCRC). While molecularly targeted therapies, including anti-EGFR and anti-VEGF agents, have improved survival outcomes, their efficacy is often limited by drug resistance, toxicity, and high costs. There is a growing need for sustainable strategies to enhance therapeutic efficacy. Methods: This review explores the emerging role of plant-derived compounds as synergistic adjuvants. Specifically, PubMed, Scopus, and Web of Science were searched for English-language articles published between January 2004 and June 2026, using combination of terms related to colorectal cancer, metastatic disease, anti-EGFR/anti-VEGF targeted therapy, phytochemicals/natural products, and gut microbiota; both primary studies and reviews were eligible. Results: Targeted therapies such as cetuximab and bevacizumab are the standard of care but face challenges related to RAS/BRAF mutations and primary tumour location. Clinical data demonstrate that while cetuximab improves overall survival in patients with RAS wild-type, left-sided tumours (median OS 31 vs. 26 months; HR 0.76, p = 0.012), progression-free survival remains comparable to that of bevacizumab. Concurrently, natural products like Vitis vinifera, Dendrobium candidum, and quercetin demonstrate significant preclinical potential in inhibiting angiogenesis, inducing apoptosis, and modulating the tumour microenvironment. The gut microbiome, particularly Fusobacterium nucleatum (whose reported prevalence varies widely across cohorts and reaches up to ~98% of CRC tissues only in selected series), has emerged as a key factor in chemoresistance. It should be emphasised that the great majority of the phytochemical-targeted therapy combinations discussed here are currently supported primarily by preclinical (in vitro and animal) studies rather than by clinical trials. Conclusions: Integrating evidence-based phytochemicals with conventional targeted therapies is a mechanistically compelling and potentially sustainable strategy that may enhance therapeutic efficacy, help overcome resistance, and mitigate adverse effects in mCRC management. However, because current support is largely preclinical, these combinations should be regarded as hypothesis-generating and require validation in prospective, biomarker-stratified clinical trials before clinical adoption. Full article

Soil fungi are key drivers of terrestrial ecosystem functioning, contributing to organic matter decomposition, nutrient cycling, and plant–microorganism interactions. Despite their importance, the global distribution and structural biases of public sequence records for soil fungi remain incompletely characterized. In this study, we analyzed soil-associated fungal DNA sequences retrieved from the NCBI GenBank database using a custom R-based bioinformatics pipeline. Following filtering and metadata standardization, 544,554 filtered sequence records were obtained. The taxonomic composition of the dataset consisted primarily of Ascomycota (69.62%), followed by Basidiomycota, Glomeromycota, and Mucoromycota, with Trichoderma, Penicillium, and Aspergillus representing the most frequent genera. The geographic distribution revealed strong sampling bias, with China and the United States accounting for over one-third of all records. Ecological metadata indicated that rhizospheric and forest soils were the most common sources of the deposited sequences. At the same time, gene marker analyses confirmed the widespread use of the ITS region as the primary fungal barcode. Sequence diversity analyses revealed continental variation, with Europe and Asia showing higher medians, while the ordination highlighted clustering of sequence profiles, particularly among records from extreme environments. This study demonstrates the potential of public sequence databases for large-scale biodiversity assessments while highlighting the influence of sampling bias and the limitations of metadata. Full article

Glioblastoma (GB) is the most aggressive primary central nervous system tumor in adults and the most common malignant primary brain tumor, representing approximately 50.9% of all malignant CNS tumors, with a median overall survival of approximately 14.6 months despite standard multimodal treatment, consisting of surgical resection, concurrent radiotherapy, and temozolomide (TMZ), followed by adjuvant TMZ (Stupp protocol). Tumor recurrence is inevitable and attributed to diffuse infiltration of neoplastic cells into the brain parenchyma, marked intratumoral heterogeneity, the presence of glioma stem cells, and the protection conferred by the BBB. Flavonoids are plant-derived polyphenolic compounds with more than 8000 identified. They have attracted growing interest as potential therapeutic agents because of their capacity to modulate multiple oncogenic signaling pathways and their favorable toxicity profile. Here we synthesize the preclinical evidence on the main flavonoids with documented activity in GB models, with emphasis on quercetin, apigenin, luteolin, and EGCG, while distinguishing glioblastoma-specific evidence from indirect findings derived from other experimental systems. We analyze their underlying molecular mechanisms, including induction of apoptosis through the intrinsic and extrinsic pathways, inhibition of cell proliferation and angiogenesis, suppression of migration and invasion, epigenetic modulation, and, particularly, the capacity to target the glioma stem cell population. We also examine the limited oral bioavailability and restricted penetration across the BBB, as these factors remain major barriers to translational development. We conclude with an analysis of emerging nanotechnological strategies, targeted delivery systems, and synergistic combinations with conventional chemotherapeutic agents, together with a cautious assessment of the current clinical evidence, which remains insufficient to support the use of flavonoids outside controlled clinical trials. Full article