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Precision livestock farming technologies require individual animal identification to integrate specific animal data with farm processes, including health, milk recording, and feeding management. Identification methods, such as physical marks and ear tags, require manual handling and are prone to loss or wear. Radio Frequency Identification (RFID) tags enable contactless identification, but still face limitations under farm conditions, including tag loss, maintenance costs, and proximity to the sensor. Recent advances in computer vision and deep learning have led to alternative approaches for animal identification. Earlier studies in small ruminants reported face or tag-based identification, while cattle studies explored multifeature fusion from different body parts. Our study developed an automated system for individual identification of sheep during drinking visits without human intervention, based on computer vision and deep learning techniques. The aims were to evaluate whether sheep could be identified using visual features from separate body regions (face, back, legs), and whether combining predictions from multiple regions with visual tag recognition improved visit-level identification. Data were collected at the Ivry Dairy Farm in Azarya, central Israel, using a water trough with an overhead Intel RealSense D435 RGB-D (red, green, blue and depth) camera and a Jetson Orin device. A total of 287 visits from 85 sheep were recorded. YOLOv8, an object detection model, was used to detect body parts (face, back, leg) and ear tags. ResNet50, a convolutional neural network (CNN) used for visual feature extraction, was applied to generate image embeddings, and the GLASS text spotting and recognition algorithm was used for visual tag text recognition. Leg-based features alone reported a peak accuracy of 0.64, which was lower than other body regions, and their inclusion in combinations yielded only minor and inconsistent effects. Back-based features alone reported a peak accuracy of 0.79, while combining back and face features reported 0.91, and integration of back, face, and visual tag features reported 0.93. The results suggest that sheep could be identified using multiple body regions, and that combining predictions from these regions provided complementary information and higher accuracy. Adding visual tag recognition further improved prediction results under typical farm conditions without human involvement.

Vaccine-based approaches provide a sustainable method for controlling pathogens in aquaculture. Our team previously created the IPath® vaccine, a recombinant formulation with metal-chelating properties. This study aimed to evaluate the performance of IPath® in salmon vaccinated with the mandatory vaccines used commercially in Chile, and to assess their effects on the transcriptome profiles of Atlantic salmon challenged with Caligus rogercresseyi and co-infected with Piscirickettsia salmonis. Four experimental groups of Atlantic salmon were immunized: IPath®, BlueGuard® (B) + Alpha Ject LiVac® SRS® (L), B + L + IPath®, and PBS as a control. After accumulating 400 thermal units (ATUs), vaccinated salmon were exposed to a coinfection model involving 35 copepodids per fish for 25 days, followed by an intraperitoneal infection with P. salmonis (1 × 10^8) for 16 days, with mortality recorded daily. Head kidney tissue samples were collected for mRNA Illumina sequencing at 25 days post-infection (dpi) for sea lice and at 16 dpi for P. salmonis infections. IPath® and B + L + IPath® showed 73.7 and 69.8% reduction in sea lice burden, respectively. Furthermore, the IPath® vaccinated group showed a delay in salmon mortality following P. salmonis infection compared with the other experimental groups. During sea lice infection, the B + L + IPath® group upregulated key immune-related genes, such as cathelicidin, major histocompatibility complex class I, and interferon regulatory factor, indicating an innate and adaptive immune response. During P. salmonis co-infection, the B + L group downregulated immune-related transcripts, including metalloendopeptidase, interferon regulatory factor 7, and T-lymphocyte surface antigen Ly-9-like. Biological processes and pathways related to stress response were highly enriched in the B + L group. Notably, the IPath® and B + L + IPath® groups triggered gene regulation associated with iron balance, such as ferric chelate reductase 1, in response to both pathogens. A synergy between IPath® and commercial vaccines, especially in iron regulation and immune response, offers a new approach for integrated control of C. rogercresseyi and P. salmonis in salmon farming.

Surface-enhanced Raman spectroscopy (SERS) holds great promise for food safety analysis; however, quantitative analysis at ultratrace levels remains constrained by large signal fluctuations and poor reproducibility. In this study, a statistical quantitative strategy based on digital SERS (dSERS) was integrated with the construction of a high-performance substrate to overcome these limitations. A g-C3N4-CTAB-Ag nanocolloid substrate was successfully fabricated via an in situ mixing strategy, in which cetyltrimethylammonium bromide (CTAB) served simultaneously as a flocculating agent and an electrostatic bridging molecule. While CTAB induced the aggregation of silver nanoparticles (Ag NPs) to generate a high-density hotspot network, it also tightly coupled g-C3N4 nanosheets with Ag aggregates, thereby achieving a synergistic enhancement of both electromagnetic and chemical effects. The optimized substrate exhibited an enhancement factor as high as 1.22 × 1010 for rhodamine 6G, with an ultralow limit of detection of 3.99 × 10-12 g/L, together with excellent homogeneity (relative standard deviation, RSD = 6.79%) and long-term stability (>50 days) achieved through a stepwise storage strategy. The limits of detection for enrofloxacin, malachite green, and nitrofurazone were7.72 × 10-6, 1.32 × 10-8, and 3.48 × 10-9 g/L, respectively. Based on this substrate, a dSERS quantitative method was further developed by using the ratio of positive voxels instead of conventional signal intensity as the quantification metric. The method showed excellent linearity over a wide enrofloxacin concentration range of 10-9-10-6 g/L (R2 = 0.9941), and successfully reduced the RSD for detecting 10 μg/kg enrofloxacin in fish to 4.04%, representing more than a sixfold improvement in quantitative precision compared with the conventional intensity-based approach. This platform provides a new technical tool for rapid and highly robust screening of trace prohibited analytes in food.

Xanthoangelol (XAG), a chalcone isolated from Angelica keiskei, exhibits various biological activities. Evidence supports its anti-cancer effects, including its ability to promote apoptosis in multiple cancer cell types. However, its impact on cervical cancer cells and its underlying mechanisms remain unclear. In this study, we investigated the cytotoxic, and pro-apoptotic effects of XAG in human cervical cancer cell lines (HeLa, and CaSki). XAG significantly reduced cell viability in a concentration-dependent manner and induced apoptosis, as evidenced by DNA fragmentation, caspase-3 activation, and PARP cleavage. Time-course analyses revealed that XAG rapidly disrupted intracellular redox balance, characterized by an early increase in the GSSG/GSH ratio, prior to the execution phase of apoptosis. Western blot analyses demonstrated activation of both extrinsic and intrinsic apoptotic pathways, including cleavage of caspase-8 and caspase-9, as well as an increased Bax/Bcl-2 ratio. Pharmacological inhibition of caspase-8, but not caspase-9, effectively suppressed XAG-induced PARP cleavage, indicating a predominant contribution of the extrinsic pathway in apoptosis induction. Collectively, XAG induces apoptosis in cervical cancer cells via early redox imbalance followed by caspase-dependent signaling, with caspase-8 playing a critical role.

A fully automated 2-dimensional imaging system that uses machine learning to produce real-time mobility scores has been developed and previously externally validated using human mobility scores and foot lesion records as ground truth. This randomized controlled trial evaluated the effect of integrating this system into an early detection and prompt treatment lameness management protocol on a large dairy farm in the UK A total of 419 multiparous cows ≤30 d-in-milk (DIM) were randomly allocated to either a control (CON) group (n = 208), managed under the farm's standard protocol or an intervention (AUTO) group (n = 211). The CON protocol consisted of routine trims at early (approximately 80 DIM) and mid-lactation (approximately 180 DIM), and examination of cows identified as lame by farm staff. In addition to the CON protocol, weekly automated scores were obtained for AUTO cows. Any AUTO cow exceeding the pre-defined threshold (≥50, on a scale of 0 to 100) or those with a ≥20 points increase in absolute scores during the last 2 weeks were scheduled for examination and treatment. Lameness scores from monthly human mobility scoring sessions were compared between groups using Fisher's exact tests or Chi-squared tests, with relative risks (RR) and odds ratios (OR) calculated. Trimming events, foot lesion prevalence and severity, and number of hoof block applications required were compared between groups using Poisson regressions and Chi-squared tests. The effect on weekly average milk yield was assessed with linear mixed effects models. Culling hazard was assessed using Cox proportional hazards regression (COXPHR). Time to 1st artificial insemination (AI) and time to conception by 150 DIM were assessed with COXPHR, whereas odds for pregnancy to the 1st AI were assessed with binary logistic regression. Cows in the AUTO group had a lower proportion of cows that developed severe lameness (2.0% vs. 7.9%, RR = 0.25; 95% CI: 0.09-0.66; OR = 0.24; 95% CI: 0.08-0.69) and chronic lameness (3.9% vs. 9.8%, RR = 0.40; 95% CI: 0.18-0.91; OR = 0.38; 95% CI: 0.16-0.88) compared with CON cows. Cows in the AUTO group underwent 2.67 trimming events per cow compared with 1.83 in the CON group during the study period (as estimated marginal means). At the 180 DIM routine trim, the AUTO group had a higher proportion of lesion-free cows (22.4% vs. 12.0%) and a lower proportion of cows with moderate lesions (16.0% vs. 25.3%). The small subset of second-parity cows in the AUTO group had higher odds of conception to 1st AI (OR = 7.6; 95% CI: 1.6-36.7) and a greater hazard of conception by 150 DIM (HR = 3.1; 95% CI: 1.3-7.3) compared with their CON counterparts. No differences were detected for weekly average milk yield or culling risk. Our findings indicate that automated mobility monitoring can improve lameness control programs by reducing severe and chronic lameness and improving mid-lactation foot health in cows.

Poecilobdella manillensis Lesson is a well-recognized medicinal leech in traditional Chinese medicine and Guangxi Zhuang ethnic medicine. It has long been used to activate blood circulation and remove blood stasis for the treatment of ischemic stroke. Modern pharmacological research has verified its potent anticoagulant and anti-inflammatory activities. Current studies mainly focus on its polypeptide components that exert antithrombotic effects to improve cerebral ischemia, while the neuroprotective potential and related mechanisms of its small-molecule constituents remain largely unclear. This study aimed to investigate the therapeutic effects of the ethyl acetate extract (EA) of P. manillensis on cerebral ischemia-reperfusion injury and to clarify its underlying molecular mechanism. The chemical constituents of EA were identified by UPLC-Q-TOF-MS/MS. Network pharmacology and molecular docking were used to predict and verify core targets and pathways. Neuroprotective and anti-inflammatory effects of EA were evaluated in a rat MCAO/R model, OGD/R-injured SH-SY5Y cells, and LPS-stimulated BV2 cells, using histological staining, Western blot, immunohistochemistry, and RT-qPCR. Seven small-molecule components were identified in EA, and 314 overlapping targets related to ischemic stroke were screened. Network analysis showed that TLR4 was the core target, and the main enriched pathways included NF-κB, Toll-like receptor, apoptosis and TNF signaling pathways. Consistent with the predicted results, EA significantly reduced cerebral infarct volume and improved neurological deficits in MCAO/R rats, and inhibited neuronal apoptosis and microglial inflammation in vivo. In vitro, EA notably improved the survival of OGD/R-injured neurons and suppressed LPS-induced inflammatory responses in BV2 cells. Meanwhile, EA markedly downregulated the expression of TLR4/NF-κB and NLRP3 inflammasome-related molecules. The present study demonstrated that EA protects against cerebral ischemia-reperfusion injury by inhibiting neuronal apoptosis and TLR4/NF-κB-mediated neuroinflammation. These findings provide a scientific basis for the traditional clinical application of P. manillensis and suggest that EA could serve as a potential therapeutic candidate for ischemic stroke.

Chicken egg yolk immunoglobulin Y (IgY) has shown potential in preventing pathogen infections. This study examined the effectiveness of dietary IgY supplementation at 6&#xa0;g/kg in preventing weaned piglets from developing diarrhoea after a challenge with Escherichia coli K88 (E. coli K88). The experiment employed a 2&#x2009;&#xd7;&#x2009;2 factorial design. Twenty-four healthy 17-day-old piglets with similar body weights (4.63&#x2009;&#xb1;&#x2009;0.39&#xa0;kg) were selected and divided into 4 groups, each with 6 replicates (n&#x2009;=&#x2009;6). The experimental period lasted 11 days. The control group (CON) and the E. coli K88 group (ECON) were fed a basal diet supplemented with 6&#xa0;g/kg of common yolk powder. Meanwhile, the anti-E. coli K88 IgY group (IgY) and the E. coli K88 IgY group (EIgY) received a diet supplemented with 6&#xa0;g/kg of anti-E. coli K88 IgY. From days 8 to 11 of the formal trial, piglets in the CON and IgY groups received an equal volume of normal saline (NS), while the ECON and EIgY groups were orally administered 10 mL of NS containing approximately 1&#x2009;&#xd7;&#x2009;1010 CFU/mL of E. coli K88. Results showed that dietary 6&#xa0;g/kg anti-E. coli K88 IgY supplementation increased the IL-10 protein expression level in the jejunum tissue, villus height (VH), the villus height to crypt depth ratio (V: C), sucrase activities, and reduced colonic E. coli counts, diarrhea incidence, and D-LA levels in E. coli K88-infected piglets (P&#x2009;<&#x2009;0.05). Meanwhile, the IgY diet increased Claudin-1 protein levels and decreased IL-8 levels in the jejunum tissue of E. coli K88-challenged piglets (P&#x2009;<&#x2009;0.05). The dietary 6&#xa0;g/kg anti-E. coli K88 IgY supplementation can reduce intestinal structural damage and inflammation, improving growth by enhancing passive immunity and intestinal barrier function in E coli K88-challenged piglets.

Grasslands are among the largest terrestrial biomes and play essential roles in livestock production, carbon sequestration and global food security. The productivity and resilience of these ecosystems are driven by complex molecular interactions between plants and their associated microbiomes. Although recent advances in nucleic acid research and multi-omics approaches have provided new insights into these interactions, the molecular mechanisms underpinning plant-microbiome interactions in these ecosystems remain insufficiently explored. This review synthesizes the latest progress in nucleic-acid and multi-omics approaches to better understand plant-microbiome interactions. It integrates nucleic acid-based technologies with multi-omics frameworks to explain plant-microbiome interactions across molecular, ecological, and management scales. By linking microbial community structure, functional genes, gene expression, metabolite profiles, ecosystem multifunctionality and sustainable grassland management, this review provides a broader framework for translating molecular insights into practical strategies for grassland resilience, productivity, and food security. Advances in amplicon sequencing, shotgun and long-read metagenomics, environmental DNA (eDNA) monitoring, plant and microbiome genome-wide association studies (GWAS) and transcriptomics have provided valuable insights into plant-microbiome interaction. This review highlights how these techniques enable functional and mechanistic understanding by linking microbial diversity with gene expression, nutrient cycling and plant performance. Additionally, long-read sequencing technologies provide genome-resolved analysis, improving the detection of structural and epigenetic variations, which are essential for understanding these interactions. These approaches reveal the role of beneficial microbes in enhancing grassland fertility, ultimately improving grassland productivity. Integrating these findings with metabolomics and phenomics offers a novel approach for predictive modeling in sustainable grassland management. The review concludes by emphasizing the need for standardized protocols, longitudinal field studies and experimental validation through synthetic communities and genome editing to harness plant-microbiome interactions for enhanced productivity and food security.

Native Anatolian goat breeds are genetic resources shaped by traditional management, selection, and adaptation to diverse environments. This study aimed to identify selection signatures in five goat breeds raised in T&#xfc;rkiye: Ankara, Damascus, Honamli, Kil, and Kilis. A total of 192 goats were genotyped using the Axiom Goat Genotyping Array containing 59,795 SNP markers. Genome-wide selection signatures were investigated using three complementary haplotype-based statistics: the integrated haplotype score (iHS), the integrated haplotype homozygosity pooled score (iHH12), and the number of segregating sites by length (nSL). Across all analyses, 2,960 non-redundant gene-method-breed records were identified, corresponding to 2,385 unique candidate genes. The three methods revealed overlapping but complementary signals. iHS highlighted regions associated with production, pigmentation, growth, reproduction, and immunity; iHH12 identified a smaller set related to pigmentation, epithelial biology, immunity, metabolism, and reproduction; whereas nSL detected the broadest set, including regions linked to growth, lactation, lipid metabolism, pigmentation, immunity, reproduction, and vascular development. Repeatability analysis prioritized 276 core genes, 83 strong core genes, and 23 robust core genes recurrent across methods and breeds. Functional enrichment analysis highlighted melanogenesis, endocytosis, phosphatidylinositol signaling, Hippo signaling, calcium signaling, synaptic processes, and cytoskeletal regulation. These findings provide genomic resources for validation, conservation, and sustainable management.

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Abscisic acid (ABA) HYPERSENSITIVE GERMINATION 1 (AHG1) controls seed dormancy, germination, and early seedling establishment in response to ABA in Arabidopsis thaliana. However, whether and how AHG1 is degraded remain unclear. Here, we identified an unreported REALLY INTERESTING NEW GENE E3 ligase ARABIDOPSIS T&#xd3;XICOS EN LEVADURA 17 (ATL17) by RNA-seq analysis and investigated its functions in ABA responses using multiple biochemical, molecular and genetic methods. ATL17 positively regulates ABA-mediated suppression of seed germination and postgerminative seedling growth. It interacts with AHG1 and promotes AHG1 degradation by the 26S proteasome in the cytoplasm in response to ABA. AUXIN RESPONSE FACTOR 2 (ARF2), a crucial regulator of auxin signaling, binds to the ATL17 promoter to repress its transcription and functions in ABA responses. ATL17 prominently affects the phosphorylation of class III SUCROSE NONFERMENTING 1-RELATED KINASE 2.3 and the accumulation of ABA-INSENSITIVE 5 during ABA-arrested seed germination and early seedling growth. Our findings reveal ATL17 as a new and essential regulator of AHG1 turnover and uncover a new function of ARF2 in ABA signaling, highlighting the key roles of ATL17-regulated AHG1 degradation in governing ABA responses and likely mediating the crosstalk among ABA and auxin signaling in A. thaliana.

Bio-stimulants are promising environment friendly alternatives to support sustainable agricultural development, capable of boosting crop growth and yield while cutting down excessive dependence on chemical synthetic fertilizers. Nevertheless, the explicit regulatory mechanisms by which bio-stimulants exert the role of growth-promoting functions still remain largely unclear and require further systematic clarification. In this study, we explored the influences of bio-stimulants (rich in humic acid) on tomato growth performance and rhizosphere microbial community assembly via greenhouse trials, and comparatively analyzed the functional differences between foliar spraying and root irrigation application modes. The results demonstrated that bio-stimulants treatment markedly improved tomato aboveground biomass, plant nitrogen and phosphorus accumulation by 17.1%, 27.4% and 22.7%, respectively. Meanwhile, bio-stimulants application effectively raised soil available nitrogen and soil organic matter levels, and further facilitated phosphorus assimilation in tomato plants. Metagenomic sequencing confirmed that bio-stimulants substantially reshaped the overall structure and composition of tomato rhizosphere microbiome. Specifically, they dramatically enriched the relative abundance of core microbial taxa responsible for soil nitrogen fixation and phosphorus solubilization. Collectively, these results clearly elaborate the underlying action mechanism: bio-stimulants optimize rhizosphere micro-ecological environment, enrich functional nutrient-solubilizing microorganisms, improve soil nutrient availability, and ultimately promote nutrient absorption and vegetative growth of tomato plants. This study confirms that bio-stimulants can serve as efficient and reliable regulators to advance green and sustainable crop production.

Aflatoxins M1 (AFM1) and M2 (AFM2) pose public health risks, necessitating monitoring and mitigation strategies. Climate change increases the risk of aflatoxin contamination in animal feed, particularly maize silage, which may lead to the carry-over of these toxins into milk. This study evaluated the occurrence of AFM1 and AFM2 in raw milk from Polish conventional dairy farms during different feeding seasons and milk powder samples (n = 70) purchased from retail markets in Poland (35), the Czech Republic (6), Slovakia (9), Hungary (10), and Iran (10). Additionally, the efficacy of &#x3b2;-cyclodextrin bead polymers (BCPs) was investigated. Results showed an excellent safety profile of milk powder in the V4 countries (Czech Republic, Slovakia, and Hungary), remaining below the LOQ. Of the 284 raw milk samples tested, AFM1 was detected above the LOQ in 4 samples, whereas AFM2 was detected above the LOQ in 3 samples. Polish raw milk was generally pure, although a few isolated positive findings were recorded, mainly in winter and spring. Among the Polish milk powder samples, AFM1 was detected in 2 samples at concentrations of 0.077 and 0.101 &#xb5;g/kg, and AFM2 was detected in 1 sample at a level of 0.049 &#xb5;g/kg. In the Iranian samples, AFM1 was detected in 2 cases at 0.042 and 0.049 &#xb5;g/kg, whereas AFM2 was detected in 5 samples at concentrations ranging from 0.011 to 0.026 &#xb5;g/kg. Decontamination assays demonstrated that BCPs can effectively remove AFM1 from dairy matrices. The physicochemical properties of milk significantly influenced binding capacity; superior removal efficiency was observed in raw and skim milk compared with homogenized milk. These results underscore the need for region-specific monitoring and suggest that BCP-based technological interventions may represent a complementary safety approach. Nevertheless, further studies are needed to evaluate its applicability in dairy processing.

Groundwater recharge in irrigated agricultural landscapes and surrounding watersheds is critical for sustainable water management and environmental flows. In irrigated Mediterranean regions, quantifying this process is complicated by substantial interannual and spatial variability in precipitation, irrigation practices, and evapotranspiration (ET), which introduces significant uncertainty. Here, we assess field-scale spatiotemporal variability in potential and actual contributions to aquifer replenishment across Mediterranean intermontane irrigated basins. Potential estimates were derived from a remote sensing ET water-balance residual (RSET-WB) and soil water balance modeling (SWBM), whereas the actual component was inferred from groundwater-level fluctuations using the water-table fluctuation method (WTFM). Results reveal strong spatial and crop-specific contrasts among basins and fields. In SWBM, irrigation-season variability was primarily associated with soil available water storage (AWS) and crop type, whereas non-irrigation season patterns were explained largely by interbasin differences in wet-season precipitation. Crop-specific patterns differed between methods, with alfalfa dominating RSET-WB residual estimates and grain and pasture lands showing greater SWBM-derived dry-season deep percolation below the root zone. Within SWBM, low-AWS fields also showed enhanced growing-season drainage. WTFM estimates indicated relatively balanced water table recharge between wet and dry seasons across most basins, contrasting with the wet-season dominance shown by RSET-WB and SWBM. Long-term averages (2008-2023) from RSET-WB and SWBM suggest that the dry season accounted for about 29-34% of annual potential recharge, while the wet-season fraction ranged from 54% to 78% of precipitation. Collectively, these findings underscore that irrigation return flow and late-season precipitation are critical to sustaining groundwater potential recharge in Mediterranean agricultural lands, supporting managed aquifer recharge strategies such as early- or off-season irrigation in low-AWS pasture grasslands.

Banana (Musa spp.) is a critical global fruit crop whose production is severely threatened by sheath rot disease. Klebsiella variicola has been identified as one of the main causative agents of this disease, while the pathogenicity of Herbaspirillum spp. strains isolated from sheath rot-infected banana tissues remains to be clearly defined. We isolated five Herbaspirillum strains from infected banana sheaths, namely Herbaspirillum huttiense Musa1 (hereafter abbreviated as Her1), Herbaspirillum huttiense Musa2 (hereafter Her2), Herbaspirillum sp. Musa3 (hereafter Her3), Herbaspirillum huttiense Musa4 (hereafter Her4), and Herbaspirillum huttiense Musa5 (hereafter Her5), and performed whole-genome sequencing on each isolate. Comparative genomics revealed genomes of similar size and stable GC content across strains. Functional annotation uncovered a core set of carbohydrate-active enzymes (CAZymes) and antibiotic resistance genes (ARGs), indicating conserved metabolic capabilities. Notably, two pectate lyase genes, potentially involved in plant cell wall degradation, were uniquely identified in strain Her3. We observed marked variation in the repertoire of putative virulence determinants across the isolates: strains Her1 and Her4 each encoded 17 predicted type III secretion system (T3SS) effector proteins, a notably higher number than that of the other strains. All strains also harbored a substantial number of predicted type VI secretion system (T6SS) effectors. This study provides the first genomic resource for Herbaspirillum species associated with banana sheath rot. Our comparative analysis highlights key genomic differences, particularly in secreted effector profiles, that likely underlie strain-specific pathogenic mechanisms. These results enhance our fundamental understanding of the phytopathogenicity of Herbaspirillum in plants.

Glyphosate may pose risks to non-target beneficial insects. We investigated the sublethal effects of a laboratory-simulated field-recommended concentration (3.5 g/L) of glyphosate on the predatory stink bug Arma chinensis. Although glyphosate exposure did not affect repellency or longevity, it was associated with significantly reduced weight gain and reproductive output. Integrated transcriptomic and physiological analyses revealed coordinated responses. Glyphosate exposure was associated with disruptions in central energy-sensing pathways, downregulation of lipid biosynthesis genes, and upregulation of pathways involved in lipid mobilization. Concurrently, key reproductive signaling pathways and core vitellogenin genes were suppressed, in association with delayed ovarian development and reduced reproductive fecundity. Although glyphosate altered the relative abundance of certain gut bacterial taxa, these changes were not statistically significant, and overall microbiota diversity remained unchanged. Collectively, our findings suggest that molecular-level perturbations are associated with adverse phenotypic outcomes. However, as this study was correlational, causality could not be established. Field-relevant concentrations of glyphosate may impair the fitness of this natural enemy, but this possibility requires functional validation under realistic conditions.

Natural woods are abundant and renewable biomass resources, the inherent microchannels and micropores make woods the ideal microfluidic device. In this study, a wood-biomimetic microfluidic enzymatic reactor (CALB@BW-C8) was proposed by hydrophobically functionalizing balsa wood column (BW) using octyltrimethoxysilane (C8) and immobilizing lipases for the continuous-flow catalytic synthesis of flavor esters. Results indicate that C8 modification improved the water contact angles of BW from 52.0&#xb0; to 122.7&#xb0;, thus providing the hydrophobic microenvironment to enhance enzyme activity and stability. When two CALB@BW-C8 reactors were connected in series, the continuous-flow conversion of hexyl hexanoate reached 91% under solvent-free conditions. Besides, the superior thermal stability, long-term reusability, broad applicability, mechanical strength and porous stability of CALB@BW-C8 also highlighted its industrial potential. Finally, computational fluid dynamics (CFD) simulations revealed that liquid substrates could migrated through intervessel pits to achieve cross-channel mass transfer, while the vessel perforation plates further enhanced local turbulence to promote the sufficient substrate-enzyme contacted. Hence, this paper innovatively combined the natural microfluidic wood with enzymatic catalysis, to achieve the continuous and sustainable production of flavor esters.

Weed pressure causes global crop yield losses of 10-34%, while the deployment of deep learning-based weed detection systems at scale remains constrained by the high cost of bounding-box annotation across diverse field environments. This study addresses this annotation bottleneck in precision agriculture by proposing WEEDINO-YOLOv12, a label-efficient weed detection framework that transfers global-average-pooled feature distributions from a frozen DINOv3 ViT-B/16 teacher into a lightweight YOLOv12n backbone through feature-distribution distillation on unlabeled agricultural imagery, followed by supervised fine-tuning on a limited labeled subset. To rigorously evaluate the proposed framework, we present a controlled empirical benchmark comparing four training regimes: fully supervised YOLOv12n, semi-supervised Soft Teacher, self-supervised BYOL, and the proposed DINOv3 distillation approach. All methods are assessed using a common YOLOv12n backbone, consistent evaluation metrics, matched controls, and multi-seed reporting. External validation on the multi-class CottonWeedDet12 dataset further examines whether the observed label-efficient behaviour generalises beyond the single-class Roboflow Weeds benchmark. Across matched 20%-label settings, WEEDINO-YOLOv12 improved mAP@0.5:0.95 from 0.6402&#x2009;&#xb1;&#x2009;0.0271 to 0.6517&#x2009;&#xb1;&#x2009;0.0087 on the Roboflow fixed split and from 0.7987&#x2009;&#xb1;&#x2009;0.0154 to 0.8083&#x2009;&#xb1;&#x2009;0.0078 on CottonWeedDet12. Full-label supervision remained the strongest overall setting, indicating that the proposed method provides modest but consistent annotation-efficiency gains rather than replacing fully supervised training. High-resolution fine-tuning at 896&#x2009;&#xd7;&#x2009;896 pixels is analysed separately because it can improve localisation independently of the distillation stage. A Streamlit-based deployment prototype further demonstrates the practical accessibility of the framework for agronomists and precision-agriculture users without requiring direct interaction with deep learning code.

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