Qula-derived Limosilactobacillus fermentum TD-3 and Lactococcus lactis MQ1-1 alleviate aging-related intestinal barrier dysfunction via microbiota-short-chain fatty acid-AMPK/MLCK-tight junction axis.

The aging process is associated with detrimental effects on the integrity of the intestinal barrier, consequently elevating susceptibility to various diseases. Qula exhibits outstanding nutritional composition and has a characteristic taste profile, positioning it as a prime candidate for probiotic exploration given its rich reservoir of probiotic potential. In the present study, a probiotics screening strategy was developed based on the production of short-chain fatty acids (SCFA) to reverse aging-related intestinal barrier dysfunction.

Empowering Balochistan's skilled women: Unleashing self-employment potential.

Technical and Vocational Education and Training (TVET) programs are increasingly recognized as equipping women with essential skills to navigate the dynamic labor market. However, there is limited research on the specific impact of TVET on women's self-employability. This study explores the relationship between TVET participation and self-employability among women in Balochistan, addressing a critical gap in the literature.

Programmable ultrasonic modulation of viscoelasticity in polymer-based elastomers: Experiments and constitutive modeling.

One of the central challenges in soft matter mechanics is to achieve reversible and programmable modulation of viscoelasticity in polymer-based elastomers at small strains, which is crucial for precision engineering and advanced functional devices. Conventional approaches are constrained by irreversibility and lack of dynamic control. In this study, it is demonstrated that ultrasonic vibration (19-22 kHz) enables dynamic, reversible, and tunable modulation of the mechanical response in such materials.

Visualizing nexus of porous architecture and reactive transport in heterogeneous catalysis by deep learning computer vision and transfer learning.

Reactive transport in porous media is the key to heterogeneous catalysis, which is the central process in both natural and engineered systems. Elucidating nexus between porous architecture and reactive transport is of importance, but remains a challenge. Conventional text-based approach relies on quantitative structural features (QSFs; porosity, tortuosity, and connectivity), which fails to identify key reaction regions and predict local reaction rate for anisotropic architecture due to isotropic assumption.

Integration of single cell multiomics data by deep transfer hypergraph neural network.

Multi-omics characterization of individual cells offers remarkable potential for analyzing the dynamics and relationships of gene regulatory states across millions of cells. How to integrate multimodal data is an open problem, existing integration methods struggle with accuracy and modality-specific biological variation retention. In this paper, we present scHyper (scalable, interpretable machine learning for single cell integration), a low-code and data-efficient deep transfer model designed for integrating paired and unpaired single-cell multimodal data.

Tuning Intercrystal Interactions between Metal-Organic Frameworks through Water Adsorption.

Naturally powdered metal-organic frameworks (MOFs) usually require packing for practical applications, where intercrystal interactions play pivotal roles. Herein, using an atomic force microscope with a MOF HKUST-1 crystallite-tipped probe, we directly find that the intercrystal adhesion energy of as-synthesized HKUST-1 with adsorbed water molecules is quadruple that of its activated counterpart. A similar large enhancement in the intercrystal adhesion energy of HKUST-1 due to water adsorption is also observed in molecular dynamics simulations, which is ascribed to the formation of strongly interacting hydrogen-bond networks, driven by copper paddlewheels massively exposed on HKUST-1 surfaces and intercalated water molecules adsorbed in surface cavities.

AI may exacerbate the energy trilemma.

The rapid expansion of artificial intelligence (AI) is intensifying the global energy trilemma, threatening security, equity, and sustainability. Its soaring electricity demand-projected to rival Japan's entire annual consumption by 2030-is the primary driver. This surge poses a direct threat to energy security, as demand growth dramatically outpaces grid infrastructure development and provokes regional power crises.

Multi-system toxicity of lead (Pb) in Procambarus clarkii: Integrated analysis of tissue damage, immune dysfunction, oxidative stress, and microbial dysbiosis via multi-omics approaches.

The element lead (Pb) exhibits significant toxicity throughout aquatic habitats. Crustaceans sit at the top of the aquatic food chain and therefore are especially susceptible to Pb-associated toxicity. Nevertheless, there is limited information on the crustaceans exposed to Pb.

Feature fusion based on global-local weighted attention model for automatic epileptic seizure detection.

Objective: Epilepsy is a neurological disorder characterized by recurrent seizures, which present significant challenges in both diagnosis and treatment. Despite advances in seizure detection, existing methods often struggle with accurately capturing the complex and dynamic interactions between temporal, spatial, and spectral features of EEG signals. This leads to limitations in the detection accuracy and generalization across different datasets.

Continuous Monitoring with AI-Enhanced BioMEMS Sensors: A Focus on Sustainable Energy Harvesting and Predictive Analytics.

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources.

Integrating Large Language Models into Fluid Antenna Systems: A Survey.

Fluid antenna system (FAS) has emerged as a promising technology for next-generation wireless networks, offering dynamic reconfiguration capabilities to adapt to varying channel conditions. However, FAS faces critical issues from channel estimation to performance optimization. This paper provides a survey of a how large language model (LLM) can be leveraged to address these issues.

A Style Transfer-Based Fast Image Quality Assessment Method for Image Sensors.

Accurate image quality evaluation is essential for optimizing sensor performance and enhancing the fidelity of visual data. The concept of "image style" encompasses the overall visual characteristics of an image, including elements such as colors, textures, shapes, lines, strokes, and other visual components. In this paper, we propose a novel full-reference image quality assessment (FR-IQA) method that leverages the principles of style transfer, which we call style- and content-based IQA (SCIQA).

A Miniaturized FSS Using the Parallel LC Resonant with Angular Stability.

This paper proposes a highly symmetrical miniaturized, frequency-selective surface (FSS) based on LC parallel resonance to optimize high-frequency passband characteristics, enhancing transmission efficiency under large-angle conditions. Through meandered design optimization, the device size is further reduced. Utilizing cell bending techniques and LC resonators, a single-layer FSS unit with parallel LC resonance is designed, achieving reflection and transmission peaks at approximately 1.

Molecular Dynamics Simulation of Nano-Defects on Fused Silica Surface Induced by Low-Temperature Plasma Cleaning.

Low-temperature plasma cleaning technology has been widely used to clean various optical components precisely. After the complete removal of organic contaminants from fused silica surfaces through plasma cleaning, continuous plasma irradiation can lead to nano-defects on the fused silica surface, resulting in the degradation of optical performance. Thus, the microscale processes underlying plasma-induced surface damage on fused silica were investigated through molecular dynamics simulations, aiming to analyze the mechanisms of surface damage on optical components during plasma cleaning.

Investigations of the Sulfonated Poly(ether ether ketone) Membranes with Various Degrees of Sulfonation by Considering Durability for the Proton Exchange Membrane Fuel Cell (PEMFC) Applications.

The optimum degree of sulfonation (DS) for sulfonated poly(ether ether ketone) (SPEEK) membranes is determined by comprehensive characterization results, including proton conductivity, swelling ratio, water uptake, chemical stability, thermal stability, mechanical indicators, and proton exchange membrane fuel cell (PEMFC) performance. The PEMFC with a membrane electrode assembly containing a SPEEK-62 (DS = 62%) membrane realizes the power density of 482.08 mW/cm, surpassing that of commercial Nafion-212 under identical conditions.

A Review on Comfort of Pedestrian Bridges Under Human-Induced Vibrations and Tuned Mass Damper Control Technologies.

With the development of urban infrastructure construction, while pedestrian bridges meet traffic functions the issue of their comfort has become a core consideration in structural design. This is because the long-span lightweight structures, with their large flexibility and low fundamental frequencies, are also vulnerable to human-induced vibrations. Pedestrian load modellings include the deterministic time-domain model, which is widely adopted in codes due to its simplicity, the random model that takes into account individual variability, and the frequency-domain model.

High Thermal Conductivity Diamond-Copper Composites Prepared via Hot Pressing with Tungsten-Coated Interfacial Layer Optimization.

Diamond-copper composites, due to their exceptional thermal conductivity, hold significant potential in the field of electronic device thermal management. Hot-press sintering is a promising fabrication technique with industrial application prospects; however, the thermal conductivity of composites prepared by this method has yet to reach optimal levels. In this study, tungsten was deposited on the surface of diamond particles by magnetron sputtering as an interfacial transition layer, and hot-press sintering was employed to fabricate the composites.

Mix Design Optimization of Coal Gangue-Based Geopolymer Foamed Concrete Using Response Surface Methodology.

This study develops a novel geopolymer foamed concrete using coal gangue and slag as precursors, along with a composite alkali activator comprising sodium silicate and sodium hydroxide, based on the physical foaming method. The Box-Behnken Design within Response Surface Methodology was applied to optimize the mix proportions of coal gangue-slag-based geopolymer foamed concrete. The effects of alkali activator dosage, sodium silicate modulus, water-to-binder ratio, and foam content on 28-day compressive strength and thermal conductivity were systematically investigated to determine the optimal mix for achieving a balance between mechanical and thermal performance.

Experimental Study on the Impact Compression Properties of Aluminum Honeycomb with Gradient-Thickness Cell Walls Using a Three-Factor Orthogonal Matrix Design.

A novel honeycomb with gradient-thickness cell walls (HGTCWs) is fabricated through chemical etching to achieve progressive thickness reduction in the cell walls. This engineered honeycomb demonstrates superior energy absorption by effectively eliminating the peak load during the linear elastic stage of the load-displacement curve under impact loading, thereby preventing premature structural failure caused by excessive instantaneous loads. To systematically investigate the impact compression mechanics, energy absorption characteristics, and key influencing factors of aluminum HGTCWs, a three-factor orthogonal array of low-velocity impact experiments was designed.

Development and validation of a deep learning model for early detection and screening of diabetic retinopathy.

Early diagnosis and screening of diabetic retinopathy (DR) are crucial for reducing medical burdens and conserving healthcare resources. This study introduces an advanced AI-assisted recognition system designed to enhance the detection of DR lesions through innovative automatic learning methods. Central to our approach are agnostic text instruction templates, which facilitate zero-shot DR detection by integrating text embeddings with visual information.

How long is long enough for traffic conflict observation: An investigation using extreme value theory approaches.

Determining the appropriate duration for traffic conflict observation remains a critical yet unresolved challenge in road safety analysis. Existing approaches lack a quantitative approach to determine adequate sample sizes for general conflict-based applications. This study addresses this gap by proposing an extreme value theory based framework to determine appropriate observation durations, based on the inherent stability of road entity safety for a specified period.

Graph-based feature learning methods for subject-dependent and subject-independent motor imagery EEG decoding.

Unlabelled: The significant intra-individual variability and inter-individual differences in scalp electroencephalogram (EEG) make it difficult to learn task-distinguishable features, posing a challenge for motor imagery brain-computer interfaces. Current feature learning methods often produce an incomplete feature space, struggling to accommodate these variations and differences. Additionally, the weak discriminative nature of this feature space results in diminished EEG classification performance.

reDA: differential abundance testing on scATAC-seq data using random walk with restart.

Summary: Identifying cell states associated with disease progression or experimental perturbations from single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) data is critical for unraveling disease pathogenesis. However, the high dimensionality, extreme sparsity, and nearly binary nature of scATAC-seq data pose significant challenges. Here, we present reDA, a cluster-free computational framework that performs differential abundance testing based on the random walk with restart.

Hierarchical Assembly of Proteinosomes and Coacervates Communities into 3D Protocellular Networks.

Drawing inspiration from the living cells, various life-inspired assemblies have been developed to mimic their remarkable properties and functionalities, and offer valuable insights into the origin of life and the understanding of life behaviors. Advancing the design and construction of multi-compartmentalized hierarchical systems with more advanced structures and functions has always increasingly attracted attention in this field. Here, a type of 3D protocellular networks are designed and constructed, which is formed by the hybrid of two types of protocells communities based on phenylboronic acid-grafted proteinosomes and polysaccharide-based coacervate microdroplets.