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Post-pleistocene colonisation rather than the contemporary environment has most influenced the current population structure of Scottish Atlantic salmon (Salmo salar)
Genetic structuring in populations is the result of both historical and contemporary environmental factors driving genetic drift, natural selection and gene flow, as well as purely genetic factors, such as mutation and recombination. In Atlantic salmon (Salmo salar), re-colonisation of rivers after the last Ice Age was shown to be an important factor in shaping contemporary population structure, though the observed structure was more complex than was predicted through founder effects. Thus, other, perhaps more contemporary factors may also play a role. Here, we investigated the influence of the time since deglaciation, distance to the sea, population connectivity, temperature, water quality, waterbody modifications, and environmental protections on spatial structuring of genetic diversity, based on microsatellite data (33 loci) collected from 48 Scottish S. salar populations. The results confirmed that recently deglaciated areas are less genetically diverse and more differentiated. Modified waterbodies also exhibit less genetic diversity and greater differentiation, although this effect differs between rivers draining on the east and west coasts of Scotland. Distance to the sea also had a non-negligible effect, while the other considered factors did not have a significant effect.
An intelligent fuzzy-neural framework for autism sensory assessment using hierarchical linguistic modeling and risk-based temporal decision-making
Ecological suitability evaluation of traditional village locations in Jiangxi Province based on multi-model integration using artificial intelligence
Traditional villages have evolved over time to adapt to their environmental characteristics, demonstrating high ecological suitability. Ideal village locations not only provide comfortable living spaces but also ensure safety and sustainability, reflecting the ancestors’ profound understanding of the natural environment and ecological wisdom. This study employs an artificial intelligence-based multi-model integration approach to evaluate the ecological suitability of 413 traditional village sites in Jiangxi Province. Key influencing factors are identified, and the ecological wisdom of ancestral site selection is analyzed, resulting in an ecological suitability evaluation map for traditional village locations in Jiangxi Province. The study draws upon environmental characteristic data of Jiangxi Province, including topography, climate, habitat quality, land use, air quality, vegetation cover, and river network density. GIS technology is utilized for spatial analysis and result visualization, with raster data being extracted and standardized. Machine learning methods, such as Random Forest, Support Vector Machine, and Gradient Boosting Decision Trees, along with deep learning methods like Convolutional Neural Networks and Multilayer Perceptrons, are applied. Multi-model integration techniques combine diverse predictive outputs, thereby enhancing the overall accuracy and robustness of ecological suitability evaluations. Experimental results indicate that elevation, slope, habitat quality, actual distance to water bodies, and average temperature are the main influencing factors for village site selection. The multi-model integration method performs excellently in evaluating ecological suitability, effectively identifying key ecological factors. The model’s accuracy and reliability are verified through confusion matrix, feature importance analysis, and ROC curve. By analyzing the impact weights of various ecological factors, this study constructs a Composite Suitability Index (CSI) and generates an ecological suitability evaluation map that clearly displays suitability levels. This provides a scientific basis for the protection and rational development of traditional villages and serves as a reference for ecological site selection studies in other regions.
Enhanced deep learning model for predicting hydraulic performance in recycled porous pipe irrigation systems
Abstract This study evaluates the hydraulic performance of irrigation systems using recycled porous pipes and the predictive modeling capabilities of deep learning algorithms for discharge rates, comparing Type A (recycled rubber-polyethylene blend) and Type B (recycled rubber only). Laboratory experiments measured discharge rates, coefficient of variation (CV), and emission uniformity (EU) across pressures (20–80 kPa) and pipe lengths (3–9 m). Results showed strong discharge-pressure correlations (R2 = 0.95–0.97). Type B achieved superior performance at 80 kPa, with lower CV (8.80%) and higher EU (87.25%) versus Type A (CV = 9.54%, EU = 84.60%), indicating enhanced flow efficiency. Statistical analysis confirmed significant differences (p < 0.05) between pipe types. Four deep learning models—Enhanced Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and Artificial Neural Network (ANN)—were developed to predict discharge rates based on pressure, pipe length, and material type. Synthetic data augmentation (GANs) was used to overcome limited experimental samples.The Enhanced MLP mode achieved the highest predictive accuracy (R2 = 0.9891, RMSE = 0.2762), outperforming all other models.This integration of hydraulic evaluation and AI modeling supports real-time irrigation scheduling, enhances water efficiency in water-scarce regions, and highlights the critical influence of material choice.
A blueprint of synergistic effect in Crataegus pinnatifida and obesity-related gut microbiota against obesity via systems biology concept
Background Current obesity treatments include behavioral interventions, pharmacotherapy and surgery. Recently, the combination of ‘medicinal food’ products such as the plant Crataegus pinnatifida and its interaction with the gut microbiota has shown promise as an alternative therapeutic strategy to treat obesity. Methods We obtained secondary metabolites (SMs) of obesity-related gut microbiota and Crataegus pinnatifida from gutMGene database and NAPSS database. bioinformatics analysis was used to elucidate key target and signaling pathways, whereas molecular docking (MD), molecular dynamics simulation and quantum chemical calculations identified crucial SMs involved in these pathways. The toxicity and physicochemical properties of these SMs were also assessed. Results Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), a key mediator in the phosphoinositide 3-kinase (PI3K)/ Protein Kinase B (Akt) pathway that is crucial for regulating insulin signaling and adipogenesis, emerged as the central hub within the PPI network. Strong binders to PIK3R1 were predicted to be quercetin, kaempferol and naringenin chalcone, suggesting their potential as therapeutic agents to treat obesity. Conclusion The synergistic combination of Crataegus pinnatifida and the obesity-related gut microbiota holds promise as a novel therapeutic strategy for obesity by targeting PIK3R1 and modulating the PI3K/Akt signaling pathway. Further experimental validation is necessary to confirm these findings.
Reduction in Asymptomatic falciparum malaria infection amongst schoolchildren in three ecological zones of Ghana between 2017 and 2023
Prebiotic food intake and biochemical measures in diabetic patients: A cross-sectional study from the Sabzevar Persian Cohort
Background With the high prevalence of diabetes worldwide and the known benefits of functional foods in controlling diabetes, this study aimed to explore the connection between prebiotic food intake and biochemical indices in diabetic patients. Methods This cross-sectional study was conducted on 504 participants with type 2 diabetes who were part of the Sabzevar Persian Cohort study that was initiated in February 2018. A 148-items food frequency questionnaire was utilized to assess the daily amount of prebiotic food consumption. Blood serum samples were collected from participants to measure fasting blood sugar levels, lipid profile, and kidney function indicators. Additionally, anthropometric measurements were taken following standard protocols. Statistical analyses were performed using SPSS version 20, with correlation tests adjusted for confounders, and significance set at p < 0.05. Results This study involved 44.9% male and 55.1% female participants, with an average age of 54.81 ± 7.65 years. A significant correlation was observed between soybean consumption and serum low density lipoprotein (LDL) status (R = −0.110, P = 0.014). Moreover, a higher intake of banana was linked to lower blood urea nitrogen (BUN) levels (R = −0.109, P = 0.015). Furthermore, the consumption of honey exhibited a negative association with both systolic blood pressure (BP) (R = −0.106, P = 0.018) and diastolic BP (R = −0.132, P = 0.003). Green peas intake was also inversely associated with DBP (R = −0.092, P = 0.039). Conclusion This study found a positive correlation between the regular intake of prebiotic foods and improved management of BUN, LDL, and BP in individuals with type 2 diabetes. However, further mechanistic studies are necessary to better understand the potential causal effects of prebiotic foods on metabolic health in this population.
Investigation of secondary charged particles emerged in the interaction of $$^{84}Kr$$ + emulsion at 1 A GeV
Methodological issues in visible LED therapy dermatological research and reporting
Background The advent of mass-market Light Emitting Diodes (LEDs) has seen considerable interest in potential dermatological applications of LED light photobiomodulation (PBM) for a range of conditions, with a thriving market for direct-to-consumer LED treatments, including red light, blue light, and yellow light wavelengths. Evidence of efficacy is however mixed, and studies report a wide range of irradiances and wavelengths as well as outcome measures, rendering interpretation, comparison, and even efficacy evaluation prohibitive and impeding evidence synthesis. Methods This work establishes a model for comparing patient received doses, applying this to existent studies to ascertain potential inhomogeneity in reported doses and wavelengths employed. Patient doses were contrasted to equivalent solar exposure time needed to achieve fluences reported at specified wavelengths in the red light (RL), blue light (BL), and yellow light (YL) portion of the spectrum, yielding a comparison of reported doses to typical solar irradiance at the Earth’s surface. Methodological aspects including dose validation, blinding, and bias were also analysed. Results 27 relevant studies for dermatological conditions including acne vulgaris (n=9, 33.3%), wrinkle-reduction (n=5, 18.5%), wound-healing (n=3, 11.1%), psoriasis severity (n=3, 11.1%), and erythemal index (n=7, 25.9%) were assessed. Outcome measures were highly heterogeneous between studies, with total patients ranging from 14 – 105 (median: 26). Fluences and wavelengths used in treatment differed over three orders of magnitude across studies even for the same conditions (0.1 J cm−2−126 J cm−2, median: 40.5 J cm−2). Derived equivalent solar time ranged from 0.01-19.35 hours (median: 3.3 hours), with central wavelengths between 405nm (BL) - 660nm (RL). No studies reported any dose validation, 10 (37.0%) were sponsored by the device manufacturer with a further 3 (11.1%) conducted by commercial dermatology practices offering the therapy under investigation. Assessors were unblinded to the treatment/ control groups in 33.3% (n=9), while a further 9 (33.3%) did not have any non-light control group, leaving only 33.3% (n=9) with both control group and blinded outcome assessment. Conclusions Results of this analysis suggest that fluences, wavelengths, and effective dose vary inconsistently between studies with often scant biological justification. This analysis suggests that better dose quantification and understanding of the underlying biophysics as well as plausible biological justifications for various wavelengths and fluences are imperative if LED therapy studies for dermatology are to be informative and research replicability improved.
Rhizoctonia solani causes okra (Abelmoschus esculentus) seedling damping-off in South China with biological characterization and fungicide sensitivity profiling
Artificial intelligence literacy, sustainability of digital learning and practice achievement: A study of vocational college students
The rapid expansion of AI and the massive use of digital learning are creating a huge change in higher education. In contrast to general higher education, which is at the center of change, the changes in vocational higher education do not seem to have received sufficient attention from researchers. Focusing on artificial intelligence literacy and sustainability of digital learning competences, this study examined the operational behaviors of 1004 students in a practical course in a higher education institution in mainland China by using the COM-B theory. The results showed that AI literacy was positively correlated with sustainable digital learning ability, and AI literacy was positively correlated with students’ sustainable digital learning behaviors through sustainability of digital learning competences. However, students in vocational institutions are not able to translate this learning behavior into a path to achieve good performance in practice, and even sustainability of digital learning competences can be slightly counterproductive. There is no excessive effect on the model after controlling for variables such as gender, family resources, and ethnic minorities, which contributes to benefit equally from quality education.
Leveling up fun: learning progress, expectations, and success influence enjoyment in video games
Abstract What factors influence how much fun people have when engaging in inherently enjoyable tasks? Several theories predict that people will have the most fun in environments of intermediate difficulty because these environments usually offer the most progress in learning about the world. Past studies have frequently focused on simple experimental paradigms in which learning was still instrumental for later tasks. Here, we put these theories to a test in three large and realistic video game data sets: a puzzle game (with 7,994 levels and 376,341 votes), a racing game (138,662 levels and 614,770 votes), and a platformer game (115,032 levels and 795,313 votes). As predicted, people preferred levels of intermediate difficulty in all games. Yet, additional factors influencing people’s enjoyment also emerged: players preferred levels that matched closely with their prior expectations of difficulty and were also motivated by success. We further confirmed these factors in two precisely controlled experiments. Taken together, these results advance our understanding of the dynamics of fun in realistic environments and emphasize the importance of using both realistic, game-like environments and highly controlled experiments to refine theories of human learning and decision-making.
Targeting vascular dementia: Molecular docking and dynamics of natural ligands against neuroprotective proteins
Vascular dementia (VaD), a neurodegenerative disease driven by vascular pathology, requires multi-targeted therapeutic strategies. This study employs an integrated in silico approach to evaluate the neuroprotective potential of natural ligands against key proteins implicated in VaD pathogenesis. Using molecular docking and normal mode analysis (NMA), four natural compounds (Galangin, Resveratrol, Curcumin, and Licocumarone) were assessed for their binding affinity and structural influence on six target proteins: APLP1, APOE, CLDN5, SOD1, MMP9, and MTHFR. Docking analysis revealed that galangin exhibited the highest binding affinity to APLP1 (−8.5 kcal/mol), resveratrol to MTHFR (−8.1 kcal/mol), and curcumin showed dual efficacy toward APOE (−7.2 kcal/mol) and MMP9 (−8.0 kcal/mol). Licocumarone demonstrated notable stabilization of CLDN5 and SOD1. The NMA results indicated ligand-induced stabilization of protein cores and enhanced flexibility in loop regions, which may impact amyloid aggregation, oxidative stress, and blood-brain barrier integrity. Pathway enrichment using the KEGG and Reactome databases identified significant involvement of the IL-17 and TNF signaling pathways, along with leukocyte transendothelial migration, linking inflammation with vascular dysfunction. APOE emerged as a central node within the protein-protein interaction network, highlighting its regulatory importance. This study highlights the therapeutic relevance of natural ligands as cost-effective modulators of multiple VaD-associated pathways. The combined use of molecular docking, protein dynamics, and enrichment analyses provides a comprehensive computational framework for early-stage drug discovery. These findings warrant further experimental validation to advance the development of targeted, mechanism-driven interventions for vascular dementia.
Research on steel structure weld seam recognition algorithm based on improved YOLOv5
Treatment and monitoring of a high-density population of bare-nosed wombats for sarcoptic mange
Sarcoptes scabiei causes a fatal disease (mange) in bare-nosed wombats (BNWs) (Vombatus ursinus) across their range and can threaten isolated populations with extinction. Repeated dosing of moxidectin (Cydectin®) at a dosage rate of 0.5 mg/kg is effective at treating individual BNWs but is difficult to administer on a population basis where treatment success has varied. This paper documents the temporary (~20 month) eradication of mange from a semi-isolated population of BNWs using repeated dosing of Cydectin® administered by burrow flaps. Treated BNWs were marked with nontoxic paint and selected burrows were monitored with camera traps demonstrating that 64–96% of wombats in the population were treated with each dosage. Treatment success was attributed to the installation of burrow flaps on all burrows in the treated area. This treatment program shows that isolated high-density populations can be successfully treated for S. scabiei infection with repeated dosages of Cydectin® (0.5 mg/kg) and questions the need for higher dosages that have been advocated. Mange returned to the population of BNWs after 20 months possibly as the result of migration of an infected BNW from a nearby population, suggesting mange affected populations may require periodic retreatment. Monitoring of burrow entrances confirmed that burrows provide habitat used by many species of birds, reptiles, and mammals, and suggest burrows could be occasional sites of mange spillover among species. Camera trap monitoring also showed when BNWs in this population leave and return to their burrows; how many BNWs enter a burrow and explore the burrow entrances each night; and how these parameters are impacted by season and mange status; variables that are valuable to know when treating populations of BNW for mange.
The mediating role of self-regulation in fostering Intelligent-TPACK and ethics in physical education teacher education students
Environment and weight class linked to skin microbiome structure of juvenile Eastern hellbenders (Cryptobranchus alleganiensis alleganiensis) in human care
Amphibian skin is integral to promoting normal physiological processes in the body and promotes both innate and adaptive immunity against pathogens. The amphibian skin microbiota is comprised of a complex assemblage of microbes and is shaped by internal host characteristics and external influences. Skin disease is a significant source of morbidity and mortality in amphibians, and increasing research has shown that the amphibian skin microbiota is an important component in host health. The Eastern hellbender (Cryptobranchus alleganiensis alleganiensis) is a giant salamander declining in many parts of its range, and captive-rearing programs are important to hellbender recovery efforts. Survival rates of juvenile hellbenders in captive-rearing programs are highly variable, and mortality rates are overall poorly understood. Deceased juvenile hellbenders often present with low body condition and skin abnormalities. To investigate potential links between the skin microbiota and body condition, we collected skin swab samples from 116 juvenile hellbenders and water samples from two holding tanks in a captive-rearing program. We used 16s rRNA gene sequencing to characterize the skin and water microbiota and observed significant differences in the skin microbiota by weight class and tank. The skin microbiota of hellbenders that were housed in tanks in close proximity were generally more similar than those housed physically distant. A single taxa, Parcubacteria, was differentially abundant by weight class only and observed in higher abundance in low weight hellbenders. These results suggest a specific association between this taxa and Low weight hellbenders. Additional research is needed to investigate how husbandry factors and potential pathogenic organisms, such as Parcubacteria, impact the skin microbiota of hellbenders and ultimately morbidity and mortality in the species.
A model including CD15, ACE2 and age efficiently predicts COVID-19 severity
An investigation of the effects of different text formats on middle school students’ reading comprehension performance
This study is a quasi-experimental research that aims to compare the reading comprehension levels of students who read texts in textbooks through printed, digital, and augmented reality (AR)-supported formats. The sample of the study consisted of 150 students aged 11–12 who were enrolled in schools in Turkey. The participating students were randomly assigned to three equal groups (print, digital, augmented reality). During the data collection process, reading comprehension was assessed in five dimensions (literal comprehension, reorganization, inferential comprehension, evaluation, and appreciation) based on Barrett’s Taxonomy. In order to determine the students’ reading comprehension achievement levels, a “reading comprehension achievement test” was used. Initially, reading activities using the same printed texts were conducted with all three groups. Afterwards, a pre-test was administered. Following a five-week break, different reading activities were conducted with the three groups during the post-test phase. Intra-group comparisons of pre-test and post-test data were analyzed using the paired-samples t-test. ANCOVA analysis was used to test whether the differences in post-test reading comprehension scores between the groups were statistically significant. The results showed that the AR-supported reading activity improved students’ reading comprehension performance (p < .05, η2 = .388). The same trend was observed in all sub-dimensions of reading comprehension (literal comprehension p < .05, η2 = .140; reorganization p < .05, η2 = .217; inferential comprehension p < .05, η2 = .322; evaluation p < .05, η2 = .225; appreciation p < .05, η2 = .327). Therefore, it can be concluded that augmented reality books enhance the reading comprehension performance of children aged 11–12. On the other hand, there was no significant difference in reading comprehension performance between traditional reading and screen-based reading (p = .542). These findings indicate that AR content is more effective in improving students’ reading comprehension compared to printed and digital texts when reading storybooks. Consequently, the present research provides insights into the effects of different text formats on middle school students’ reading comprehension performance.