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Influence of early life adversity and breed on aggression and fear in dogs
Abstract Among the animals on this planet, dogs are uniquely adapted for life with humans, a status that exposes them to risks of human-mediated traumatic experiences. At the same time, some lineages of dogs have undergone artificial selection for behavioral phenotypes that might increase risk or resilience to stress exposure, providing an opportunity to examine interactions between heritable and acquired traits. In a large-scale study ( N = 4,497), English-speaking dog guardians reported on their dogs’ life histories, current living environments, and provided observer ratings of dog behavior using the Canine Behavior Assessment and Research Questionnaire (C-BARQ). Our analysis revealed that adverse experiences in the first six months of life, such as abuse and relinquishment, were significantly associated with increased aggression and fearfulness in adulthood, even when accounting for factors such as acquisition source, sex, and neuter status. Additionally, effects of adversity on fearful and aggressive behavior systematically varied at the breed level, suggesting heritable factors for risk and resilience for developing particular phenotypes. Our findings establish that breed ancestry and individual experience interact to show fear and aggressive behavior in pet dogs, confirming that socioemotional behavior is shaped by gene-environment interactions.
Growth and skeletal structure of the parasitic zoantharian Savalia savaglia (Bertoloni, 1819)
An ALE meta-analysis on the effects of neural changes due to exercise on executive function in a healthy population
Spatiotemporal trends in sunshine hours over India during three decades from 1988 to 2018
Aqueous extract of Acer truncatum leaves retards Drosophila melanogaster senescence by regulating amino acid metabolism and gut microbiota
Abstract Acer truncatum is a unique tree species indigenous to northern China. The Chinese government approved the utilization of Acer truncatum leaves as a raw material for food. These leaves have been traditionally used in Inner Mongolia as a form of anti-aging medicine. However, the specific mechanism responsible for the anti-aging properties of Acer truncatum leaves remains unidentified. In this study, an aqueous extract of Acer truncatum leaves (AAL) was prepared and analyzed using UPLC-QTOF-MS/MS. the UPLC-MS/MS profile detected a total of 989 compounds in AAL, with 5 compounds of high concentration selected for quantitative analysis via UPLC-QTOF-MS/MS employing the internal standard method. Subsequently, Drosophila melanogaster served as a model organism to assess the impact of AAL on the lifespan and locomotor abilities. The results demonstrated a significant extension of the lifespan of D. melanogaster in response to AAL supplementation. Moreover, the addition of AAL to the medium enhanced the physical and anti-stress abilities of D. melanogaster, while preserving the integrity of their intestinal barrier. Gut microbiome analysis revealed that AAL administration positively influenced the structure and composition of gut microbes in aged D. melanogaster, notably reducing the prevalence of detrimental bacteria like Enterococcus and increasing beneficial bacteria such as Lactococcus. Metabolomic analysis annotated 30 potentially significant metabolites in AAL that contribute to delaying aging, predominantly associated with Phenylalanine metabolic pathways. Through a comprehensive multi-omics correlation analysis, a strong link was established between gut microbiota and metabolites following AAL treatment, highlighting how AAL prolongs the lifespan of D. melanogaster by modulating metabolic pathways via the gut microbiota. This study offers valuable insights into the anti-aging properties of AAL, emphasizing its ability to delay aging primarily through the regulation of metabolic pathways mediated by the gut microbiota and sets a foundation for the potential future application of AAL as a functional food.
Author Correction: A Ctf4 trimer couples the CMG helicase to DNA polymerase α in the eukaryotic replisome
Test pattern optimization scheme based on Hybrid Ant Colony Optimization
Unveiling the beneficial techniques in lung segmentectomy by using a stapler tractor for vascular dissection based on surgical video replay
Experimental investigation on impact resistance of stacked composite material hybridization by 3D printed CF-PEEK and aluminium foils
An incentive-aware federated bargaining approach for client selection in decentralized federated learning for IoT smart homes
Abstract Federated Learning (FL) has emerged as a promising solution for privacy-preserving model training across distributed IoT devices. Despite its advantages, FL faces challenges such as inefficient client selection, data heterogeneity, security vulnerabilities, and exposure to Man-in-the-Middle (MITM) attacks. To address these issues, the Incentive-Aware Federated Bargaining (IAFB) framework is proposed, integrating Nash Bargaining for optimal client selection, Shapley-value-based incentives for fair reward distribution, and decentralized peer-to-peer (P2P) aggregation to eliminate single points of failure. To enhance security, IAFB employs AES-GCM encryption, ensuring data confidentiality, authenticity, and integrity during transmission, effectively mitigating MITM attacks. Experimental results demonstrate that IAFB improves participation fairness by 28%, boosts model accuracy by 6.5%, and reduces convergence time by 35% compared to FedAvg. Additionally, IAFB reduces communication overhead by 39.5% and enhances resilience against adversarial threats, making it highly suitable for secure and scalable FL deployment in resource-constrained IoT environments.
Tipsy bats and perfect pasta: Ig Nobels celebrate ‘improbable’ research
Population viability analyses provide key insights into how alternative conservation efforts can prevent the extinction of a marsh passerine
Can AI chatbots trigger psychosis? What the science says
Feasibility-guided evolutionary optimization of pump station design and operation in water networks
Abstract Pumping stations are critical elements of water distribution networks (WDNs), as they ensure the required pressure for supply but represent the highest energy consumption within these systems. In response to increasing water scarcity and the demand for more efficient operations, this study proposes a novel methodology to optimize both the design and operation of pumping stations. The approach combines Feasibility-Guided Evolutionary Algorithms (FGEAs) with a Feasibility Predictor Model (FPM), a machine learning-based classifier designed to identify feasible solutions and filter out infeasible ones before performing hydraulic simulations. This significantly reduces the computational burden. The methodology is validated through a real-scale case study using four FGEAs, each incorporating a different classification algorithm: Extreme Gradient Boosting, Random Forest, K-Nearest Neighbors, and Decision Tree. Results show that the number of objective function evaluations was reduced from 50,000 to fewer than 25,000. Additionally, The FGEAs based on Extreme Gradient Boosting and Random Forest outperformed the original algorithm in terms of objective value. These results confirm the effectiveness of integrating machine learning into evolutionary optimization for solving complex engineering problems and highlight the potential of this methodology to reduce operational costs while improving computational efficiency in WDNs.
Molecular detection of Anaplasma Capra and Anaplasma marginale in Rhipicephalus microplus ticks infesting cows
Quantity of fat consumed predisposes cardiac tissues to greater metabolic risk than their level of saturation
Volcanic scoria as a sustainable alternative to sand in structural lightweight concrete
ScaleFusionNet: transformer-guided multi-scale feature fusion for skin lesion segmentation
Abstract Melanoma is a malignant tumor that originates from skin cell lesions. Accurate and efficient segmentation of skin lesions is essential for quantitative analysis but remains a challenge owing to blurred lesion boundaries, gradual color changes, and irregular shapes. To address this, we propose ScaleFusionNet, a hybrid model that integrates a Cross-Attention Transformer Module (CATM) and adaptive fusion block (AFB) to enhance feature extraction and fusion by capturing both local and global features. We introduce CATM, which utilizes Swin transformer blocks and Cross Attention Fusion (CAF) to adaptively refine feature fusion and reduce semantic gaps in the encoder-decoder to improve segmentation accuracy. Additionally, the AFB uses Swin Transformer-based attention and deformable convolution-based adaptive feature extraction to help the model gather local and global contextual information through parallel pathways. This enhancement refines the lesion boundaries and preserves fine-grained details. ScaleFusionNet achieves Dice scores of 92.94%, 91.80%, and 95.37% on the ISIC-2016, ISIC-2018, and HAM10000 datasets, respectively, demonstrating its effectiveness in skin lesion analysis. Simultaneously, independent validation experiments were conducted on the PH 2 dataset using the pretrained model weights. The results show that ScaleFusionNet demonstrates significant performance improvements compared with other state-of-the-art methods. Our code implementation is publicly available at https://github.com/sqbqamar/ScaleFusionNet .
Pincer-cobalt boosts divergent alkene carbonylation under tandem electro-thermo-catalysis
Abstract Catalytic multicomponent carbonylation reactions with high regio- and chemoselectivity represent one of the long-pursued goals in C1 chemistry. We herein disclose a practical cobalt-catalyzed divergent radical alkene carbonylative functionalization under 1 atm of CO at 23 °C. The leverage of the tridentate NNN-type pincer ligand is the key to avoid the formation of catalytically inert Co 0 (CO) n species and overcome the occurrence of oxidative carbonylation of organozincs, selectively tuning the catalytic reactivity of cobalt center for dictating a full cobalt-catalyzed four-component carbonylation. Moreover, direct use CO 2 as the C1 source in the multicomponent alkene carbonylative couplings can be achieved under a tandem electro-thermo-catalysis, thus allowing us to rapidly and reliably construct unsymmetric ketones with ample scope and excellent functional group compatibility. Remarkably, our protocol encompasses a broader of polyhaloalkanes as the electrophiles, which underwent radical-relay couplings in a completely regio- and chemoselective fashion. Finally, facile modifications of drug-like molecules demonstrate the synthetic utility of this method.