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Novel polysaccharides from Metarhizium pinghaense modulate bovine satellite cell fate for cultured meat production

CocoaDeep: A preliminary study of the performance sensitivity to datasets of Faster RCNN, YOLO and transformer networks for cocoa pod detection

Farmers must be able to estimate their crop yields at various growth stages for effective management of their farms and to enable them to interact with cooperatives or traders as early as possible. Here we developed an AI-based cocoa pod detection method using low resolution colour images of cocoa trees on farms in Côte d’Ivoire. We compared nano and extra-large architectures of six neural networks, including Faster RCNN, Baidu’s Real-Time Detection Transformer (RTDetr), Detr-ResNet Vision Transformer (ViT), YOLOv5, YOLOv8 and YOLOv11. These networks were trained with 7,850 annotated cocoa pods on 400 low resolution images, and validated in two independent datasets: a 42 low resolution images containing 990 annotated pods, and a 100 low resolution images containing 2,400 annotated pods. The performances of the nano YOLOv8 and YOLOv11 networks were 2% higher than that of the RTDetr networks and 5% higher than that of the YOLOv5, ViT and Faster RCNN networks with an F1-score of 77% on all images and up to 90% on foreground trees. The dominance of nano architectures suggests that the extra-large architectures, which contain 20–30-times more neurons, may not have been fully trained. The study of learning performance curves showed that extra-large networks were unable to outperform nano networks, which contradicts the theory. After review, the annotated dataset was found to contain inconsistencies. The inconsistency of the training and validation data and their limited quantity restricted the objectivity of comparisons between network architectures. Finally, although the average detection performance of RTDetr for cocoa pods was only 2% lower than that of the YOLOv8 network, it was definitively excluded from the candidate models because its per-image processing time was 15–20% higher than that of YOLOv8 and YOLOv11. However, with a performance sensitivity to data of less than 0.5%, YOLOv8 Nano became the best option.

Digital phenotyping accelerates soil biodiversity discovery

Understanding driver behaviour after consuming conventional wines with different sulphite contents: Insights from a driving simulator study

Background Alcohol consumption is a contributing factor to traffic crashes and road fatalities worldwide. While the effects of blood alcohol concentration (BAC) on driving performance are well documented, less is known about the role of sulphites, used for their antimicrobial and antioxidant properties, on driving performance. Method Using a triple-blind, randomised, within-subjects design, thirty-two participants completed three driving simulation trials under sober conditions and after consuming a conventional red wine with ‘low’ (86 mg/l) and ‘high’ (126 mg/l) sulphite content, while maintaining a BAC at about the Italian legal limit (0.5‰). The driving behaviour was assessed in rural, urban and rural-urban transition environments Results In less demanding rural environments, where participants drove in free-flow conditions (i.e., rural and transitional), only BAC affected lateral vehicle control along curves, increasing the standard deviation of lateral position relative to the sober baseline, but no significant differences emerged between the two sulphite levels. However, in the more demanding urban environment, when drivers consumed wine with higher sulphite content, they showed more cautious behaviour during sudden interactions with pedestrians at zebra crossings compared with the sober condition. Conversely, in a car following task, when they drank high-sulphite wine, they behaved more dangerously, maintaining higher speeds and shorter headways with respect to the baseline. Conclusions Within legal BAC limits, higher sulphite content in wine was associated with context-dependent changes in driving behaviour, promoting greater caution in complex urban scenarios but riskier behaviour in car-following tasks. The small number of significant effects supports the effectiveness of current legal limits, and the findings should be interpreted strictly within regulated conditions, while highlighting the need for further research on sulphite-related effects.

An energy-aware and lightweight spiking neural network framework for real-time intrusion detection in IoT networks

Optimal design of dose and drug pharmacokinetic characteristics to achieve the desired pharmacodynamic profile in repeated drug dosing

Oral drug therapy requires achieving a delicate balance between therapeutic efficacy and patient safety, yet current dosing strategies often rely on empirical trial-and-error methods that overlook the complex nonlinear dynamic nature of drug behavior in the human body. Conventional pharmacokinetic/pharmacodynamic (PK–PD) approaches provide valuable insights but lack a systematic method for designing dose sequences and formulations that achieve an optimal therapeutic response. This work introduces a structured optimization framework that combines PK–PD modeling, impulsive dosing concepts, and nonlinear optimization to determine optimal repeated oral dosing regimens. We model each orally administered dose as an impulsive input in a linear compartmental PK system and couple the resulting drug concentration profile with a nonlinear Hill-type PD model. To enable efficient optimization, we derive sensitivity functions describing how the therapeutic effect depends on dose size and adjustable drug-formulation parameters, allowing to construct the Jacobian required by the Gauss–Newton nonlinear least-squares algorithm. The proposed method jointly optimizes dose magnitude and formulation-dependent liberation (release) rate to match a clinically meaningful therapeutic effect trajectory. Using a four-compartment pharmacokinetic model, we demonstrate in silico that the method achieves rapid onset, stable long-term therapeutic effect, and reduced fluctuations of the therapeutic effect across repeated dosing cycles.

HECA-MobileNet: an explainable lightweight attention-based deep learning framework for custard apple disease detection

Participation of Endothelin in the postnatal development of the rat kidney: Molecular mechanisms involved

Some adult diseases such as hypertension and kidney disease may have their origins in early life, due to exposure to different adverse stressors. Previously we demonstrated that the administration of a dual endothelin receptor antagonist (ERA) to Sprague-Dawley (SD) rats from day 1–21 of life decreased glomerular number, predisposing adult male rats to salt sensitivity. This new study explores some early molecular mechanisms underlying the alterations observed in the kidneys of ERA-treated rats during the postnatal period, evaluating sex differences. Newborn male and female SD rats were treated with a dual ERA from day 1–6 and then sacrificed on day 7 of life to obtain the kidneys for the preparation of homogenates and mitochondrial fractions to assess: renal cell proliferation and apoptosis, nitric oxide synthases (Nos), neuronal isoform (Nos1) and endothelial isoform (Nos3) mRNA expression, NADPH- diaphorase (NADPH-d) activity, oxidative stress markers and antioxidant enzymes. ERA-treated male rats showed increased thiobarbituric acid-reacting substances (TBARS) and decreased nitric oxide (NO) to superoxide anion (O 2 - ) ratio, with lower NADPH-d activity in the structures that give rise to glomeruli. Sex differences were observed in Nos1 and Nos3 mRNA expression, H 2 O 2 production, and catalase activity, being females more protected than males. The alterations observed in the kidneys of ERA-treated rats during the early postnatal period could be due to a renal imbalance between NO and ROS, with increased oxidative stress, and a misbalance between proliferation and apoptosis. Our current findings show some molecular mechanisms underlying Endothelin inhibition in the early postnatal period, with potential utility for designing reprogramming strategies.

An efficient Draco lizard optimized stacked Bi-LSTM framework for risk mitigation and resource allocation in project management systems

Spatial and neighborhood data in the collaborative cohort of cohorts for COVID-19 Research (C4R)

Neighborhood factors, encompassing social, built, and natural environments, may explain geographic differences in the impact of COVID-19 pandemic on populations. Data from pre-existing national, population-based cohorts could be leveraged to better understand how pre-existing conditions (both individual and neighborhood) contribute to risk factor development and disease progression. We catalogued spatial and neighborhood data in the Collaborative Cohort of Cohorts for COVID-19 Research (C4R), comprising 14 diverse US cohorts (>50,000 participants). The C4R sample is generally spatially and socially representative of the overall nation, with C4R’s calculated spatial coverage representing 28% of US land area and 52% of the total US population. However, C4R (vs. non C4R) areas were more urban, wealthy, with more foreign-born residents, and less car-dependent with lower proportion employed and green. Twelve cohorts collected neighborhood characteristics – most commonly social environment data on neighborhood socioeconomic status– based on participants’ addresses. The most common built environment measures were related to food access, followed by other destination-based measures such as walkability. Natural environment data were available in the fewest cohorts, with emphasis on air quality or greenspace. This work provides clarity on available neighborhood and spatial data and facilitates future harmonization of data from C4R cohorts. Ultimately, this may enable future longitudinal and comparative analyses of neighborhood influences on COVID-19.

Systemic loss of Rubicon aggravates pathological features in the SOD1G93A mouse model

Fabric defect detection using fine-tuned Yolo-12

Defect identification is critical for ensuring the reliability and price of fabrics. Defective fabrics result in significant waste and losses. Automatic defect identification using the use of deep learning is a faster and more efficient way to analyze fabric quality, replacing human inspection. Furthermore, both plain and printed textiles are produced concurrently in enterprises; hence, one design should be effective in identifying faults in both types of fabric. As a result, a strong deep learning algorithm must be trained to detect defects within fabric datasets produced during manufacturing with excellent performance and cheap computing costs. This study incorporates a local dataset collected from Chenab Textiles and validated with three publicly available datasets, such as Tildav2, DPFD-DET, and ZJU-Leaper. The experiment provides a comprehensive and diversified range of defective images. To identify textile defects successfully, the suggested approach, universal and optimized YOLOv12, named universal defect detect network (UniDefectNet-Omni) for robust identification of a wide spectrum of fabric defects with multi and diverse types of fabric using YOLOv12 by fine-tuning and optimizing training, integrating high determination feature learning, heterogeneous defect representation, and adaptive augmentation. As a consequence, UniDefectNet-Omni is a lightweight, computationally efficient, and robust framework across varied fabrics.The Chenab textile dataset mean Average Precision (mAP) is 85.1%, precision is 84.5%, and recall is 81.7% over seven separate fabric defect categories. The proposed fine-tuned YOLOv12 outperformed on validated datasets, such as the TILDAv2 dataset, having a mean Average Precision (mAP) score 86.7%, precision about 83.7%, in addition recall about 83.6% over four separate fabric defect categories. DPFD-DET with a mean Average Precision (mAP) score 93.6%, precision is 92.2%, and recall is 87.8% over four separate fabric defect categories. ZJU-Leaper with groups 1, 2, 3, and 4 having a mean Average Precision (mAP) score 93%, precision about 78.1%, in addition recall about 90.3% over twelve separate fabric defect categories.

Heart rate variability, eye temperature, and spontaneous blink rates as measures of emotions in working donkeys

Metabolomic profiling of backfat in Ningxiang pigs reveals lipid dynamics and carcass trait associations during the fattening stage

Understanding the metabolic profile of backfat is essential for optimizing breeding strategies and improving pork production. While the early stages of adipose tissue development have been partially characterized, metabolic alterations during the fattening phase (180–360 days)— a critical period for physiological maturation and carcass trait formation—remain insufficiently understood. In this study, we characterized the metabolomic profiles of backfat from Ningxiang pigs across four developmental stages (180d, 240d, 300d, 360d) and evaluated their relationships with carcass traits. A total of 154 metabolites exhibited significant temporal variation (q < 0.05), including functional lipids such as methanandamide phosphate and oleoylethanolamide, which are associated with appetite regulation and lipid metabolism. The progressive accumulation of bioactive exogenous metabolites like α-tocotrienol and cephaeline, indicated potential stage-dependent immunometabolic adaptations. In contrast, several synthetic xenobiotics, potentially derived from feed additives or environmental exposure, accumulated in backfat tissue and may represent potential risks to animal health and pork safety. Co-expression network analysis identified a metabolite module strongly associated (|R| > 0.7, q < 0.05) with key carcass traits, within which psychotrine—an understudied plant-derived alkaloid not previously associated with animal growth— was identified as a hub metabolite. This study establishes a comprehensive metabolic characterization of backfat during the fattening phase in an indigenous pig breed. Collectively, these findings provide novel candidate biomarkers for carcass trait prediction, highlight the potential impact of synthetic compound accumulation, and offer valuable insights for precision breeding, nutritional management, and meat safety assessment in swine production.

Research on resource sharing and allocation incentive mechanism of new energy vehicle charging cloud platform

Psychometric properties and standardization of the shortened latvian personality inventory (LPI-v3s) in athlete sample: Implications for evidence-based assessment

Background and objective Personality traits are relevant to sport participation and athlete development. Many personality measures used in sport lack systematic psychometric evaluation within athletic populations and are rarely accompanied by population-appropriate normative references. The present research comprised two interlinked studies aimed at empirically examining and refining a shortened version of the Latvian Personality Inventory (LPI-v3s) for use in athlete samples, and at exploring associations between personality traits and sport achievement status. Methods A total of 925 athletes (aged 15–45 years) representing 84 sports participated in the study. Following data screening, two subsamples were formed: Study 1 ( n  = 436) for psychometric evaluation and norm development, and Study 2 ( n  = 753) for exploratory criterion-related analyses. In Study 1, exploratory and confirmatory factor analyses were conducted, and measurement invariance across gender was examined using multigroup CFA. Continuous regression-based norming procedures were applied to derive age- and gender-specific T-scores. In Study 2, hierarchical binary logistic regression was used to explore associations between personality traits and competitive status (elite/pre-elite vs. non-elite). Results The resulting 53-item LPI-v3s demonstrated an interpretable five-factor structure comprising 14 trait scales, acceptable to good internal consistency (α = 0.72–0.88), and configural, metric, and scalar invariance across gender groups. Exploratory hierarchical binary logistic regression analyses indicated that selected trait-level variables (most notably Anxious Insecurity, Sociability and Orderliness) showed small-to-moderate associations with competitive status, with modest overall classification performance (AUC = 0.64). Conclusions The findings support the internal consistency and measurement adequacy of the LPI-v3s within this athlete sample and underscore the importance of contextualized, cautious interpretation of personality scores in sport-related research. Given the provisional nature of the factorial structure and the modest predictive performance observed in Study 2, the LPI-v3s may be useful for research and exploratory assessment contexts involving Latvian-speaking athletes, but should not be used as a diagnostic, selection, or talent-identification tool.

Estimating time of death from smartwatch data: initial empirical results

Spatiotemporal evolution and clustering patterns of settlements from the Neolithic to the Bronze Age (9000–3000 BP) in the Songshan Mountain region, China

This study proposes a transparent and methodologically explicit GIS-based framework for delineating prehistoric settlement clusters and tracking their spatiotemporal evolution. Integrating nearest-neighbor analysis, kernel density estimation, least-cost path (LCP) modeling, and K-medoids clustering, we analyze 1,975 settlement sites from four successive cultural phases (Peiligang, Yangshao, Longshan, and Xia-Shang; 9000–3000 BP) in the Songshan Mountain region, a core area of early Chinese civilization. Results show persistent clustered distribution across all periods, with aggregation intensity peaking during the Xia-Shang period. High-density zones shifted progressively westward, and by the Xia-Shang period, a spatial pattern that appears consistent with a potential dual-core structure can be identified centered on Yanshi and Zhengzhou. K-medoids clustering reveals a marked reduction in optimal cluster numbers from nine to two despite a five-fold increase in site numbers. This trend is consistent with spatial integration and is suggestive of socio-political centralization. Environmental factors provided the primary context for settlement distribution from the Peiligang to Longshan period, but spatial patterns suggest socio-political factors may have exerted an important influence on aggregation during the Xia-Shang period even under deteriorating climate conditions. Our framework offers a replicable approach for analyzing settlement clustering and human-environment interactions.

Investigation of the effectiveness of the thermal conductivity of the Bingham fluid subjected to the dispersion of nanoparticles and dissipation effects

Abstract Several fluids exhibit viscoplastic behavior, and several rheological models for viscoplastic fluids have been proposed. The Bingham-Papanastasiou rheological model is one of them. This article examines heat transfer in a Bingham fluid over a heated wedge. Three types of nanoparticles are considered to be dispersed in the Bingham fluid, and their impact on enhancing the thermal effectiveness of the Bingham fluid is examined. For this purpose, the basic equations of fluid dynamics, energy equations, and Bingham-Papanastasiou rheological models are used for modeling of heat transfer under magnetic and porous medium forces. A set of partial differential equations (PDEs) is transformed into ordinary differential equations (ODEs) and numerically solved under no-slip boundary conditions by applying the Galerkin finite element method (GFEM). The roles of magnetic field and porous media forces in thermal enhancement are noted, and it is found that both porous medium and magnetic forces are not favorable for enhancing thermal transfer. An increase in thermal radiation intensity leads to an increase in the Nusselt number. The porous medium causes dissipation due to the resistive force experienced by the fluid flow. For thermal and cooling systems to be thermally efficient and sustainable, the working Bingham fluid should not be heat dissipative, either because of Joule heating or viscous dissipation. Among the different types of nanofluids, the most prominent is the case of tri-nanofluids due to their higher thermal conductivity and stronger fluid–thermal interactions, which makes them more sensitive to parameter variations compared to mono- and di-nanofluids. This study predicts that the skin friction coefficient increases when the Bingham number is increased. Hence, the viscoplastic fluids with higher yield stress exert higher drag on the solid surface to which such fluid interacts. Moreover, the viscoplastic fluid (the Bingham fluid) exerts higher drag on the surface in comparison with Newtonian fluids.

Study on chloride ion erosion resistance of recycled aggregate concrete based on an improved TOPSIS model integrating entropy weight and AHP

Recycled coarse aggregate (RA) is prone to deteriorating the performance of recycled aggregate concrete (RAC) due to inherent defects such as adhered old cement paste and internal micro – cracks, while calcined layered double hydroxides (CLDHs) exhibit significant potential for enhancing concrete performance. However, the synergistic mechanism between CLDHs and RA remains unclear. To address this, this study employs compressive strength, chloride ion (Cl - ) penetrability, X - ray diffraction (XRD), scanning electron microscopy (SEM), and nuclear magnetic resonance (NMR) to investigate the effects of varying CLDHs content (0%, 1%, 3%, 6%) and RA replacement rates (0%, 10%, 20%, 30%) on the mechanical properties, chloride ion permeability resistance, and microstructure of RAC. Results indicate that an appropriate combination of CLDHs and RA significantly improves RAC performance: the mix with 1% CLDHs and 20% RA increased the 28 d compressive strength by 10.3% compared to the reference group, while the combination of 3% CLDHs and 30% RA enhanced chloride ion penetration resistance by 19.1%, with electrical flux as low as approximately 1135 C, achieving a “low” permeability rating. Microstructural analysis confirms that the synergistic interaction of suitable CLDHs and RA promotes the formation of dense flocculent C – S – H gel, fills pores, reduces the proportion of harmful pores, increases the ratio of gel pores, thereby optimizing pore structure and enhancing system compactness. Additionally, it delays crack initiation and propagation, resulting in no penetrating cracks upon specimen failure. Based on these findings, an improved TOPSIS comprehensive evaluation model integrating the entropy weight (EW) method and the Analytic Hierarchy Process (AHP) was developed. This model systematically evaluates the chloride ion erosion resistance of Recycled Aggregate Concrete (RAC) by synthesizing multidimensional indicators such as compressive strength, electrical flux, and pore structure, thereby overcoming the limitation of single-factor weighting inherent in the entropy weight method. Concurrently, through economic analysis, an adjustable decision-making framework for RAC mix proportion selection under various corrosive environments has been proposed. This study elucidates the mechanism by which CLDHs and RA synergistically improve RAC performance, providing a theoretical foundation and methodological support for the engineering application of recycled concrete in aggressive environments.