Search Results (401)

Despite their ecological and conservation significance, morphometric relations remain scarce for elasmobranch species in the Mediterranean. This study examined morphometric parameters of the eight elasmobranch species (one shark and seven batoids) presented in the Amvrakikos Gulf that has been designated as a National Park. A total of 1247 specimens were sampled between 2022 and 2025, caught by small-scale fishing vessels using trammel nets, gillnets or bottom longlines and collected through onboard surveys or landing sites monitoring. Linear regressions were applied to describe relations between total length and other body measures (disc length, disc width, fork length), and length measurements and body weight. Results showed strong relations across morphometric traits, with R² values exceeding 0.655 for most relations. Growth patterns varied: four species (Aetomylaeus bovinus, Dasyatis pastinaca, D. tortonesei, Mustelus mustelus) exhibited positive allometry, one species (D. marmorata) displayed negative allometry and Gymnura altavela showed near-isometric growth. Sexual dimorphism was generally absent, although significant differences were found between sex in disc width slopes for D. marmorata, Myliobatis aquila and Torpedo torpedo, and in length–weight relations for M. mustelus. These findings substantially fill regional data gaps, offering new baseline estimates for rare and threatened elasmobranchs. Full article

Precision livestock farming (PLF), by integrating multimodal sensor data, provides opportunities to enhance welfare monitoring and management in small ruminants. This study evaluated whether environmental, physiological, and behavioral measurements—including the temperature–humidity index (THI), carbon dioxide (CO₂) and ammonia (NH₃) concentrations measured at the barn level, body condition score (BCS), rectal and ocular temperatures, GPS-derived locomotion metrics, accelerometry data, and fixed animal traits—can serve as key predictors of welfare and productivity in dairy sheep. Data were collected from 90 ewes: all animals underwent the same repeated welfare assessments, while 30 of them were additionally equipped with GPS–accelerometer sensor collars; environmental conditions were continuously recorded for the entire flock, generating 773 complete multimodal records. All predictive models were developed using data from all 90 ewes; collar-derived behavioral variables were included only for individuals equipped with GPS–accelerometer collars. Nine regression methods (linear regression (LR), partial least square regression (PLSR), elastic net (EN), mixed-effects models, random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), neural networks (multilayer perceptron, MLP), and an ensemble of RF–XGBoost–EN were evaluated using a combination of nested cross-validation (CV) and leave-one-animal-out CV (LOAOCV) to ensure robustness and generalization at the individual animal level. Nonlinear models—particularly RF, XGBoost, SVR, and the ensemble—consistently delivered superior performance across traits. For behavioral (e.g., daily distance movement) and thermal indicators (e.g., medial canthus temperature), the highest predictive capacity (R² ≈ 0.60–0.70) was achieved, while moderate predictive capacity (R² ≈ 0.40–0.50 and ≈0.35–0.45), respectively, was observed for respiratory rate and milk yield, reflecting their multifactorial nature. Feature importance analyses underscored the relevance of THI, CO₂, NH₃, concentrations, and BCS across results. Overall, these findings demonstrate that multimodal sensor fusion can effectively support the prediction of welfare and productivity indicators in intensively reared dairy sheep and emphasize the need for larger and more diverse datasets to further enhance model generalizability and model transferability. Full article

Temperature strongly influences physiological processes in ectotherms, including digestion, yet its effects on digestive enzyme activity remain poorly understood. We examined the temperature dependence of digestive performance in eight Mediterranean wall lizard species (Podarcis spp.) from mainland and island populations. Under controlled laboratory conditions, we measured the activity of three key enzymes, protease, lipase, and maltase, across a temperature gradient (20–55 °C), alongside gastrointestinal (GI) morphology. Enzyme activity generally increased with temperature up to 50 °C and declined thereafter, reflecting typical thermal kinetics. Lipase activity was consistently higher in island species, while protease and maltase showed no significant geographic or phylogenetic trends. Island lizards also exhibited longer and heavier GI tracts relative to body size (SVL), suggesting enhanced nutrient absorption capacity. Phylogenetic signal analyses (Pagel’s λ and Abouheif’s C_mean) revealed no significant evolutionary constraints on digestive traits, indicating that observed differences reflect ecological adaptation rather than ancestry. Overall, island species appear to have evolved digestive traits that improve energy extraction under resource-limited conditions, but may be more sensitive to extreme heat. These findings highlight contrasting adaptive strategies between island and mainland reptiles and underscore the importance of digestive physiology in predicting the response of species to warming climates. Full article

The adaptation strategies of species to local environments are reflected in phenotypic variations, which could be expressed as trait patterns across the community level. Here, we compiled a dataset of small mammal traits to evaluate the classic ecological rules and to assess predictions related to drought resistance. In June 2017, July 2023, and May–June 2024, a field survey was conducted in Baima Snow Mountain, southwest China, using standardized methods to capture small mammals. Traits potentially corresponding to variations in temperature, productivity, and water availability were measured in the field or calculated in the laboratory. We applied ordinary least squares (OLS) linear regressions to determine the community-level trait variations along the gradients of environmental factors influenced by rain-shadow effects of the mountain system. Results showed that (1) body size decreased with increasing temperature, aligning well with conventional prediction; (2) the proportion of appendage size attributable to allometry decreased with temperature but increased slightly with productivity, thereby violating Allen’s rule while being partly consistent with the resource rule; (3) the renal features did not support the expected negative association concerning water availability but its converse, which may be explained by microhabitat conditions and broad-scale zoogeographic influences within the local community. We conclude that community-level phenotypic variations in small mammals result from complex influences, including climate, productivity, habitat characteristics, and adaptive strategies operating at both micro and macro scales. Full article

In modern industrial environments, camera-based monitoring is essential for workflow optimization, safety, and process control, yet it raises significant privacy concerns when people are recorded. Realistic full-body anonymization offers a potential solution by obscuring visual identity while preserving information needed for automated analysis. Whether such methods also conceal biometric traits from human pose and gait remains uncertain, although these biomarkers enable person identification without appearance cues. This study investigates the impact of full-body anonymization on gait-related identity recognition using DeepPrivacy2 and a custom CCTV-like industrial dataset comprising original and anonymized sequences. This study provides the first systematic evaluation of whether pose-preserving anonymization disrupts identity-relevant gait characteristics. The analysis quantifies keypoint shifts introduced by anonymization, examines their influence on downstream gait-based person identification, and tests cross-domain linkability between original and anonymized recordings. Identification accuracy, domain transfer between data types, and distortions in derived pose keypoints are measured to assess anonymization effects while retaining operational utility. Findings show that anonymization removes appearance but leaves gait identity largely intact, indicating that pose-driven anonymization is insufficient for privacy protection. Effective privacy requires anonymization strategies that explicitly target gait characteristics or incorporate domain-adaptation mechanisms. Full article

Accurate estimation of live weight is essential for efficient management and precision control in poultry production. This study evaluated the potential of supervised machine learning (ML) algorithms and digital image analysis for non-invasive prediction of live weight in Anadolu-T broilers, a locally developed genotype in Türkiye. A total of 4200 records were collected from 100 broilers (50 males and 50 females) over 42 days, including daily measurements of back length, back width, and live weight. Five ML algorithms—Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR)—were trained and validated to estimate live weight based on morphometric traits. Among all algorithms, KNN achieved the highest accuracy (R² = 0.982, RMSE = 111.509 g, MAPE = 8.205%), followed by RF and XGB, which also produced stable and reliable predictions. The image-based models using log-transformed regression between body surface pixel area and live weight yielded similar accuracy (R² = 0.989, RMSE = 101.197 g, MAPE = 7.266%), confirming that projected surface area can effectively represent growth progression. The results demonstrate that integrating ML algorithms with digital imaging offers a practical, cost-effective, and non-invasive approach for real-time broiler weight estimation. This approach supports the advancement of precision poultry farming through automated, data-driven growth monitoring. Full article

Sexual pleasure has attracted increasing scientific attention in recent years. In this cross-sectional study, we investigated self-esteem and body appreciation as potential determinants of sexual pleasure. We examined data from a community sample of young adult women and men. Specifically, we tested the hypotheses that self-esteem and body appreciation positively predict sexual pleasure, and that body appreciation moderates the association between self-esteem and sexual pleasure. We exploratively investigated the moderation of these effects by gender. Sexual pleasure was measured using seven items from the trait scales of the Amsterdam Sexual Pleasure Index 1.0. Self-esteem was measured with the Dutch translation of the Rosenberg Self-Esteem Scale. Body appreciation was measured using the Dutch translation of the Body Appreciation Scale 2. The results showed that self-esteem positively predicted a substantial proportion (19%) of the variance of sexual pleasure, whereas body appreciation did not directly predict sexual pleasure. Body appreciation did not moderate the association between self-esteem and sexual pleasure in the full sample and in women, but it was found to moderate this association in men; however, in the opposite direction of what was predicted in the hypothesis. The findings were discussed, and suggestions were made for future research. Full article

Background/Objectives: Understanding the genetic basis of growth and fat deposition is crucial for improving beef productivity in Kalmyk cattle, a breed well adapted to the extreme climatic conditions of Kazakhstan. The present study aimed to determine the effects of single-nucleotide polymorphisms (SNPs) in the CRTC2 and ELOVL6 genes on intramuscular fat content and to evaluate their associations with growth and meat quality traits in 18-month-old Kalmyk heifers raised under different environmental conditions. Methods: A total of 400 clinically healthy Kalmyk heifers (200 from LLP “Qazaq Asyldary” and 200 from LLP “Agrofirma Turikpen”) were examined. All animals originated from closed breeding herds, and only unrelated individuals without common ancestors to the third generation were included. Zootechnical measurements— live weight, withers height, chest depth, chest girth, and body length—were performed twice by a trained specialist. Backfat thickness and musculus longissimus dorsi depth were measured postmortem. Blood samples were collected for genomic DNA extraction using the GeneJET purification kit, and DNA quality was assessed by Nanodrop, Qubit, and agarose gel electrophoresis. Target fragments of CRTC2 and ELOVL6 were amplified (150–200 bp) and sequenced on an ABI 3500 system. SNP identification, allele frequencies, and genotyping were performed by alignment to the Bos taurus ARS-UCD1.2 reference genome. Statistical analyses were conducted in RStudio using linear and mixed models with “farm” as a random effect. Results: Only one informative polymorphism, g.133528A>G in ELOVL6, was detected. Three genotypes (AA, AG, GG) were observed, with the heterozygous AG genotype showing significantly higher live weight, greater body length, and improved linear measurements compared to AA and GG. No significant associations were detected with backfat thickness or muscle depth. The g.133528A>G polymorphism in ELOVL6 positively influences growth traits without increasing fatness, aligning with the naturally lean phenotype of Kalmyk cattle. Conclusions: The AG genotype may serve as a promising marker for selecting faster-growing animals in marker-assisted breeding programs. Full article

The objective of this study was to evaluate how parity (primiparous vs. multiparous) influences metabolic profile, performance, and offspring growth in grazing Nellore cows, and to explore potential associations among key metabolic traits, milk yield, and performance indicators. Thirty-four pregnant Nellore cows at approximately 230 ± 20 days of gestation were sorted by parity group and randomly allocated into one of six Uruchloa decumbens pastures, totaling 17 multiparous cows (597.8 ± 39 kg; age = 4 to 6 yrs) and 17 primiparous cows (407 ± 33 kg, age = 2 to 3 yrs). Considering calving day as day 0, cow full BW and BCS were collected on days −63, −21, −7, 0, 7, 21, 63, 91, 140 and 203. Cow blood samples were collected on days −21, −14, −7, 0, 7, 14, 21, 42, 63, 91, 140 and 203. Milk yield was measured in the following day after all blood collections. Primiparous cows had higher glucose on days −7 and 0 (p = 0.04), greater NEFA concentration from −14 to 63 days (p < 0.001), and lower IGF-1 concentration from days 7 to 42 but higher on days 140 and 203 (p < 0.001). They also exhibited lower total proteins, albumin, and globulins, but higher serum urea N concentration (p < 0.001). Primiparous cows also had lower milk yield (p = 0.02), calf birth (p = 0.02) and weaning weights (p = 0.01) compared with multiparous cows. Pearson correlation analysis revealed distinct metabolic and physiological interrelationships across gestational and lactational stages in multiparous and primiparous cows. Overall, multiparous cows showed a less catabolic metabolic profile during the periparturient period to lactation. Primiparous cows experienced a more intense mobilization of body reserves during the early lactation period, but a more positive nutritional status towards late lactation, evidenced by recovery of IGF-1 levels and body weight gain. Full article

Background: Childhood obesity is a global issue, influenced by energy-dense foods and powerful cues that affect brain areas regulating food intake. The olfactory system, linked to food preferences and consumption, is inversely related to body mass index. However, no studies have assessed the possible effect of eating behavior traits on the relationship between olfactory capacity and obesity. Objectives: The aim of this study was to examine whether olfactory capacity, eating behavior traits, and body mass index are associated with obesity in adolescents. Methods: An analysis of 204 Chilean adolescents was undertaken in a cross-sectional study. The proportion of participants with normal weight was found to be 39.2%, that of overweight was 25.9%, and that of obesity was 34.8%. Anthropometric measurements (weight, height, BMI Z-score), eating behavior, and olfactory capacity were evaluated. The Child Eating Behavior Questionnaire (CEBQ) and Food Reinforcement Value Questionnaire (FRVQ) were used to assess eating behavior. The Sniffing sticks test was used to assess olfactory capacity. Results: In the global sample, 1.0% had anosmia, 20.5% had hyposmia, 61.0% had normosmia, and 17.5% were supersmellers. Girls showed higher odor identification percentages than boys (p = 0.01). No gender differences were found in olfactory threshold, discrimination, identification, or TDI (threshold–discrimination–identification) scores, nor nutritional status. Stratified analysis revealed that girls with obesity had significantly lower odor discrimination capacity compared to those with normal weight. Conclusions: the study highlights a potential link between olfactory function and obesity, with obese girls showing reduced odor discrimination compared to normal-weight girls. Further research is needed to explore these mechanisms and their implications for targeted obesity interventions. Full article

Morphometric traits are closely linked to body condition, health, welfare, and productivity in livestock. In recent years, contactless 3D reconstruction technologies have been increasingly adopted to improve the accuracy and efficiency of morphometric evaluations. Conventional approaches for 3D reconstruction mainly employ Light Detection and Ranging (LiDAR) or photogrammetry. In contrast, emerging Artificial Intelligence (AI)-based methods, such as Neural Surface Reconstruction, 3D Gaussian Splatting, and Neural Radiance Fields, offer new opportunities for high-fidelity digital modeling. Smartphones’ affordability represents a cost-effective and portable platform for deploying these advanced tools, potentially supporting enhanced agricultural performance, accelerating sector digitalization, and thus reducing the urban–rural digital gap. This preliminary study assessed the viability of using smartphone-based LiDAR, photogrammetry, and AI models to obtain body measurements of Marchigiana cattle. Five morphometric traits manually collected on animals were compared with those extracted from smartphone-based 3D reconstructions. LiDAR measurements offer more consistent estimates, with relative error ranging from −1.55% to 4.28%, while photogrammetry demonstrated accuracy ranging from 0.75 to −14.56. AI-based models (NSR, 3DGS, NeRF) reported more variability between accuracy results, pointing to the need for further refinement. Overall, the results highlight the preliminary potential of portable 3D scanning technologies, particularly LiDAR-equipped smartphones, for non-invasive morphometric data collection in cattle. Full article

High-throughput phenotypic acquisition and analysis allow us to accurately quantify trait expressions, which is essential for developing intelligent breeding strategies. However, there is still much potential to explore in the field of high-throughput phenotyping for edible fungi. In this study, we developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras. We developed an innovative Unet-based semantic segmentation model by integrating the ASPP structure with the VGG16 architecture. This allows for precise segmentation of the cap, gills and stem of the fruiting body. By leveraging depth images from RGB-D cameras, we can effectively collect phenotypic information about Pleurotus eryngii. By combining K-means clustering with Lab color space thresholds, we are able to achieve more precise automatic classification of Pleurotus eryngii cap colors. Moreover, AlexNet is utilized to classify the shapes of the fruiting bodies. The Aspp-VGGUnet network demonstrates remarkable performance with a mean Intersection over Union (mIoU) of 96.47% and a mean pixel accuracy (mPA) of 98.53%. These results reflect respective improvements of 3.03% and 2.23% compared to the standard Unet model, respectively. The average error in size phenotype measurement is just 0.15 ± 0.03 cm. The accuracy for cap color classification reaches 91.04%, while fruiting body shape classification achieves 97.90%. The proposed multi-phenotype acquisition system reduces the measurement time per sample from an average of 76 s (manual method) to about 2 s, substantially increasing data acquisition throughput and providing robust support for scalable phenotyping workflows in breeding research. Full article

Background: Elevated heart rate (HR) reactivity to psychomotor challenge mirrors greater proneness to acute stress, which is a disadvantage in competitive sports. This study investigated whether temperament and adiposity predict HR reactivity during a reaction time (RT) task in adolescent athletes, with a focus on identifying their role in psychophysiological vulnerability. Participants and procedure: The participants were 20 adolescent canoe athletes (15 boys, 5 girls; mean age = 14.3 ± 1.88 years). They were volunteers recruited from a canoe club, with the permission of their coaches and parents. The study was conducted in a controlled laboratory setting, where participants underwent anthropometric tests, completed a questionnaire, had a HR monitor fitted, and rested in an armchair until a relatively stable HR (±5 beats per minute) was recorded. Subsequently, their HR was monitored across three 5 min phases: baseline, RT task, and recovery. Reactivity was calculated as the difference between task and recovery, because pre-task HR was influenced by anticipation. Data analyses were performed using AI-assisted and verified Bootstrapped Spearman correlations, Lasso regression with five-fold cross-validation, and stability analysis with 25 repeated cross-validations. Results: Shyness and body fat percentage were positively related to HR reactivity, whereas other temperament traits and RT performance showed no statistically significant associations. The Lasso regression results revealed shyness and adiposity as significant predictors, with their interaction consistently identified as the strongest effect (selected in 76% of models). The independent measures did not affect HR in the recovery phase. Conclusions: Shy adolescents with higher adiposity demonstrate heightened stress responses, as evidenced by HR reactivity, underscoring the importance of addressing stress vulnerability in young athletes and extending this line of inquiry to a broader spectrum of junior athletes. Full article

As critical reservoirs of biodiversity and providers of ecosystem services, wetland ecosystems play a pivotal role in maintaining global ecological balance. They not only serve as habitats for diverse aquatic and terrestrial organisms but also play substantial roles in water purification, carbon sequestration, and climate regulation. However, intensified anthropogenic activities—including drainage, fertilization, invasion by alien species, grazing, and urbanization—pose unprecedented threats, leading to profound alterations in the functional traits of wetland plants. This review synthesizes findings from peer-reviewed studies published between 2005 and 2024 to elucidate the mechanisms by which human disturbances affect plant functional traits in wetlands. Drainage was found to markedly reduce plant biomass in swamp ecosystems, while mesophyte and tree biomass increased, likely reflecting altered water availability and species-specific adaptive capacities. Mowing and grazing enhanced aboveground biomass and specific leaf area in the short term but ultimately reduced plant height and leaf dry matter content, indicating potential long-term declines in ecological adaptability. Invasive alien species strongly suppressed the growth of native species, reducing biomass and height and thereby threatening ecosystem stability. Eutrophication initially promoted aboveground biomass, but excessive nutrient inputs led to subsequent declines, highlighting ecosystems’ vulnerability to shifts in trophic state. Similarly, fertilization played a dual role: moderate inputs stimulated plant growth, whereas excessive inputs impaired growth performance and exacerbated eutrophication of soils and water bodies. Urbanization further diminished key plant traits, reduced habitat extent, and compromised ecological functions. Overall, this review underscores the profound impacts of anthropogenic disturbances on wetland plant functional traits and their cascading effects on ecosystem structure and function. It provides a scientific foundation for conservation and management strategies aimed at enhancing ecosystem resilience. Future research should focus on disentangling disturbance-specific mechanisms across different wetland types and developing ecological engineering and management practices. Recommended measures include rational land-use planning, effective control of invasive species, and optimized fertilization regimes to safeguard wetland biodiversity, restore ecosystem functions, and promote sustainable development. Full article

Background: Autism spectrum disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder. This study aims to demonstrate the potential of comprehensive polygenic scores (PGSs) as clinical biomarkers for stratifying individuals with ASD and for advancing the understanding of ASD’s heterogeneous etiology. Methods: We calculated 2602 PGSs—representing all publicly available, license-cleared PGSs in the PGS Catalog—for 75 individuals with ASD by utilizing the database of the Tohoku Medical Megabank Birth and Three-generation cohort study. Results: Unsupervised clustering revealed three ASD subgroups. We identified twenty PGSs with the most significant differences among these subgroups as distinctive PGSs for each subgroup. PGS set enrichment analysis associated these distinctive PGSs with different traits in each subgroup: high-density lipoprotein cholesterol measurements, urea measurement, and body mass index. Furthermore, distinctive PGSs indicated consistent genetic predisposition directions: lower high-density lipoprotein cholesterol levels in subgroup 1, higher urea levels in subgroup 2, and lower body mass index in subgroup 3. Conclusions: Comprehensive PGSs extending beyond psychiatry-related traits represent promising clinical biomarkers for identifying ASD subgroups with different genetic predispositions. Such stratification may enhance understanding of heterogenous genetic backgrounds and targeted drug development. Full article