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Maternal stress triggers early-life eczema through fetal mast cell programming
Life course socioeconomic position and the risk of cardiovascular events and all-cause mortality in a Norwegian twin study
Toughened self-assembled monolayers for durable perovskite solar cells
Stochastic carbon-aware planning of renewable DGs and EV charging stations with demand flexibility in smart urban grids
Advanced deep learning framework for soil texture classification
Abstract In soil texture classification, accuracy with interpretability is the key to sustainable agriculture and environmental management. The presented ATFEM (Advanced Triptych Feature Engineering and Modeling framework) framework synergizes handcrafted texture features with learned deep representations through a three-stream architecture: VGG-RTPNet (Residual Texture-Preserving Network based on Visual Geometry Group-16) for texture, ResNet-DANet (Residual Network integrated with Dual Attention Network) for semantics, and Swin-FANet (Shifted Window-based Frequency-Aware Network based on Transformer) for spectral spatial correlation. Subsequently, these branches help in extracting fine-grained structural, dual-attention-enhanced semantic, and spectral-spatial correlation-wise features of soil-image data. To further eliminate redundancy from the feature sets and arrive at the best representation, a Feature Fusion and Selection strategy employing an enhanced hybrid metaheuristic method termed EWJFO (Enhanced Wombat-Jellyfish Feature Optimization) is proposed. It synthesizes the adaptive exploration behavior of Wombat Optimization Algorithm (WOA) with the swift control convergence tempo of the Jellyfish Search Optimizer (JSO) to select the best feature subset. In addition, a new handcrafted descriptor for soil texture image analysis referred as Farthing Ornament of Histogram of Oriented Gradients (F-HOG) has been introduced with adapative. Conventional HOG is burdened with having high-dimensional redundancy and hence suffers from noise sensitivity, F-HOG combines the effect of a Butterworth frequency filter to remove the unwanted high-frequency artifacts and then goes on to perform the statistical selection of the most frequent gradient bins, thus reducing dimensions and retaining quite a bit of the discriminative structural information. The experiments were conducted on a self-built soil texture image dataset consisting of 4,000 labeled images distributed among five texture classes. ATFEM achieved an accuracy of 98.10%, an F1 score of 89.60%, Cohen’s kappa rating of 94.80%, and an AUC of 98.10%, outperforming cutting-edge methods such as CatBoost-DNN, GBDT-CNN, and SVC-RF. This work offers an upscalable, explainable, and expressively accurate solution for soil texture mapping in precision agriculture and environmental monitoring.
Estimating thermal radiation of vertical jet fires of hydrogen pipeline based on linear integral and machine learning
VARNet-6G with FIERO model for anomaly detection and enhancing network stability in future-ready communication systems
Rifaximin-induced changes in the gut microbiome associated to improvement of neurotransmission alterations and learning in rats with chronic liver disease
Abstract Rifaximin, a gut-targeted antibiotic, improves cognitive function and reduces the risk of hepatic encephalopathy (HE), yet its effects on the gut-brain axis remain unknown. This study explores how rifaximin influences gut microbiota functions and its association with cognitive function and molecular alterations in rats with liver injury. Liver injury was induced by chronic administration of carbon tetrachloride (CCl4), and rifaximin was administered daily. Fecal samples were collected after eight weeks of CCl4 administration, and taxonomic and functional changes in the gut microbiome were analyzed. Rifaximin altered microbiota diversity and composition, increasing α diversity in liver-injured rats but reducing diversity in healthy rats. It influenced microbiota interactions with neurotransmission alterations, where Dorea, Lachnospiraceae A2, and possibly Erysipelotricaceae might be important contributors. Functionally, butyric acid levels negatively correlated with gene orthologues associated with GABA, tryptophan, and glutamate degradation pathways. In healthy rats, fecal short-chain fatty acid (SCFA) levels were positively correlated with each other, a pattern absent in other groups. Rifaximin significantly influenced gut microbiota and promoted bacterial groups linked to improved cognition and neurotransmission in liver disease. Our findings underscored the direct relationship between a healthy microbiome and the maintenance of balanced SCFA concentrations.
Delegation to artificial intelligence can increase dishonest behaviour
Abstract Although artificial intelligence enables productivity gains from delegating tasks to machines 1 , it may facilitate the delegation of unethical behaviour 2 . This risk is highly relevant amid the rapid rise of ‘agentic’ artificial intelligence systems 3,4 . Here we demonstrate this risk by having human principals instruct machine agents to perform tasks with incentives to cheat. Requests for cheating increased when principals could induce machine dishonesty without telling the machine precisely what to do, through supervised learning or high-level goal setting. These effects held whether delegation was voluntary or mandatory. We also examined delegation via natural language to large language models 5 . Although the cheating requests by principals were not always higher for machine agents than for human agents, compliance diverged sharply: machines were far more likely than human agents to carry out fully unethical instructions. This compliance could be curbed, but usually not eliminated, with the injection of prohibitive, task-specific guardrails. Our results highlight ethical risks in the context of increasingly accessible and powerful machine delegation, and suggest design and policy strategies to mitigate them.
EBV induces CNS homing of B cells attracting inflammatory T cells
Asian elephants (Elephas maximus) recognise human visual attention from body and face orientation
Abstract Visual attention has mostly been studied in primarily visual species, such as nonhuman primates. Although elephants rely more on acoustic and olfactory cues, they also use visual displays and gestures to communicate. Smet and Byrne (2014) showed that African savanna elephants (Loxodonta africana) recognise human visual attention based on face and body orientation, but this has not been investigated in Asian elephants (Elephas maximus). We tested ten captive female elephants in Thailand and analysed the frequency of experimenter-directed signals in a food-requesting task based on the experimenter’s body and face orientation. Elephants gestured most when both the experimenter’s body and face were oriented towards them, and body orientation appeared to be a stronger visual cue than face orientation, but this effect was only observed when her face was also oriented towards the elephant. This suggests that elephants are not sensitive to face or body orientation alone and rely on a combination of body and face cues to recognise human visual attention. These findings suggest that Asian elephants understand the importance of visual attention for effective communication, contributing to our understanding of cognitive abilities across the elephant taxon and visual attention in animals.
Patients’ satisfaction rate after penile implant surgery: a cross-sectional investigation from the middle east
Laser irradiation of human skin tissue after gold nanoparticles injection for thermal ablation processes – a combined experimental and numerical approach
Abstract In recent years, investigation of new or combined therapeutic modalities for cancer treatment lead to relevant scientific advances. Among these innovative therapeutic modalities photothermal therapy (PTT) attracts attention as an alternative or complementary possibility to current anti-cancer treatments as it allows for the selective ablation of cancer cells. In the present study, PTT is investigated by using gold nanoparticles (AuNPs) as photothermal transducers. AuNPs are injected into human skin and used for PTT-assisted tumor ablation. The effectiveness of the PTT is evaluated as a function of AuNPs morphology, dimensions and the wavelength of the laser source. Five different laser sources with wavelengths ranging from 465 to 980 nm are used. The experimental findings point out that 808 and 980 nm are the optimal laser wavelengths for the PTT of human skin. Especially gold nanorods stabilized by poly(ethylene glycol) layer are identified as effective photothermal transducers. The experimental results are fully corroborated by numerical calculations. The outcomes emphasize the potential and the relevance of synthetically tailoring AuNPs particularly in optimizing their optical properties and assessing the effects of AuNP aggregation for specific applications.