Abstract: Background/Objectives: Astragalus root, a traditional Chinese herbal remedy, has shown potential benefits against diabetic nephropathy (DN). However, the mechanisms driving its effects remain poorly understood. This study explored the molecular pathways through which Astragalus root improves DN. Methods: To identify possible targets and mechanisms of Astragalus root in DN treatment, we applied network pharmacology, molecular docking, molecular dynamics simulation, and in vitro assays. Results: Network pharmacology screening uncovered 46 overlapping targets between Astragalus root and DN. Protein-protein interaction (PPI) network analysis identified five core candidate targets: CASP3, VEGFA, CTNNB1, MYC, and PRKCB. KEGG pathway analysis indicated that the AGE-RAGE signaling pathway was the most significantly enriched. Molecular docking revealed that quercetin, β-carotene, daidzein, capsaicin, and kaempferol—major bioactive components of Astragalus root—bound strongly to each of the five core targets. Molecular dynamics simulations further confirmed the conformational stability of kaempferol when complexed with these target proteins. In vitro experiments showed that kaempferol markedly reduced protein levels of α-SMA, Col I, and Col IV; lowered secretion of TNF-α, IL-6, and IL-1β; and decreased ROS and MDA content. Additionally, kaempferol's therapeutic effects were mediated through suppression of the AGE-RAGE-PKC-TGF-β signaling axis. Conclusions: This work identified kaempferol, a bioactive ingredient of Astragalus root, as a potential therapeutic agent against DN, along with its target pathways. These findings provide a scientific foundation for its clinical translation.

Abstract: The existence of supermassive black holes (SMBHs) within the first 800 million years after the Big Bang remains difficult to explain and is still under active debate. At the same time, a dynamical vacuum energy density has been proposed as a possible solution to the cosmological coincidence problem. It is therefore natural to explore its implications for black hole evolution. In this work, we study the rapid growth of SMBHs in a decaying-vacuum cosmology with a time-dependent cosmological constant. In this framework, black holes can grow at rates far exceeding the Eddington limit, which can be phenomenologically described as an effective conversion of vacuum energy into black hole mass. This mechanism may offer a new perspective on the formation and early growth of SMBHs.

Abstract: Interventions implemented in geographic space (geo-interventions), have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To solve this, we produced a framework for transforming spatial data to model potential actions for generating geo-interventions. Finally, we conducted a case study of reducing traffic collisions in Toronto, Canada, to demonstrate the framework, which produced a machine learning model that discovered geo-interventions modifying red light camera, transit shelter, and wayfinding infrastructure predicted to reduce collisions by 5.7%. We highlight the importance of the framework for bridging research and practice through unified understanding, actionable outputs, human guidance, and iterative refinement. With recent advances in big data and artificial intelligence, we envision an acceleration in the discovery of geo-interventions, and emergence of interdisciplinary work towards predicting accurate and precise future real-world outcomes at scale.

Abstract: Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework for lightweight and efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured pruning strategy that applies asymmetric channel pruning to convolutional and feature-fusion modules while compressing the Swin Transformer prediction heads and bottleneck stacks, thereby reducing parameters and computation with limited impact on multi-scale representation capability. Second, we develop a hardware-aware mixed-precision quantization-aware training (QAT) scheme that maps computation-intensive backbone layers to INT8 while preserving the Transformer-related modules in FP16, improving inference efficiency while mitigating the accuracy loss caused by uniform low-bit quantization. Third, we compile the optimized network with TensorRT and integrate the resulting inference engine into a DeepStream-based asynchronous video pipeline on the edge platform, enabling end-to-end acceleration by reducing decoding, preprocessing, and memory-transfer overheads. Experimental results on the VisDrone2019-DET dataset and the NVIDIA Jetson Orin NX demonstrate that DUST-YOLO achieves 43.7% mAP@0.5 acuracy with an end-to-end latency of 36.3 ms and a throughput of 27.5 FPS. Compared with the state-of-the-art detector, DUST-YOLO reduces end-to-end latency by 56.9% and improves end-to-end video throughput by ×2.31, while lowering total energy consumption by 68.5%.

Abstract: Recognizing emotions from written text is a very important part of Natural Language Processing (NLP) and is commonly used for feeling or sentiment analysis or keeping track of someone’s mental health status. This study uses a readable emotion-detecting framework with a RoBERTa-base model that has been modified and trained specifically for the Emotions for NLP dataset and provides an accuracy of 0.924% and f1 score of 0.925%. The main contributions of this study are the use of four different techniques that will help understand how the model works: SHAP (SHapley Additive exPlanations) provides global token credit attribution; LIME (Linear Interpretable Model-Agnostic Explanation) provides instance-level explanations; multi-head Attention Visualization provides structural interpretability; and Integrated Gradients via Captum provides gradient-based attribution using integration. The combination of these four techniques works together to improve transparency, help identify bias in the models, and support the responsible use of this model. Finally, the developers of this model performed many experiments that demonstrated the consistency with which the model could identify important emotional tokens (words or phrases) as predictive indicators of emotion.

Abstract: Lentinula edodes (L. edodes) is a significant edible and medicinal mushroom with essential nutrient elements for its growth, including Fe²⁺, K⁺, and Mn²⁺. However, the molecular mechanisms by which these metal ions regulate the mycelial growth of L. edodes have been poorly elucidated at the transcriptomic level. In this study, plate culture was performed using concentration gradients to screen for optimal concentrations. Transcriptome sequencing (RNA‑seq) and qRT‑PCR validation were performed to elucidate the regulatory effects and molecular mechanisms of the three metal ions on the mycelial growth of L. edodes. The results showed that Fe²⁺ at concentrations above 20 µg/mL significantly inhibited mycelial growth; K⁺ at 1200 µg/mL and Mn²⁺ at 50 µg/mL significantly promoted mycelial growth, with increases of 21.22% and 10.77%, respectively. Transcriptomic analysis revealed that Fe²⁺ primarily induced abnormal protein folding and suppressed material and energy metabolism, thus inhibiting mycelial growth. Mycelial growth is promoted by K⁺ by enhancing detoxification and secondary metabolism and by activating mitochondrial function and the oxidative phosphorylation pathway. The proliferation and growth of mycelial cells are regulated by Mn²⁺ through mechanisms that govern DNA repair and recombination, cell cycle progression, and detoxification. This study elucidates the differential regulatory mechanisms of the three metal ions on the mycelial growth of L. edodes at the transcriptomic level, offering a rationale for enhancing mineral nutrition and high‑yield cultivation of L. edodes.

Abstract: Institutional Review Boards (IRBs) exercise veto power on most empirical research in the social and behavioural sciences. Although widely regarded as essential safeguards in behavioural research, their overall impact on knowledge production has been seldom scrutinized, much less systematically examined. Rather than evaluating IRBs in terms of their stated aims, this article considers them as institutions based on process characteristics: that is, as decision making units facing bureaucratic incentives to impose costs on others. From this political economic perspective, ethics review functions not as a neutral guardrail, but as an active agent influencing the selection pressures within the scientific ecosystems they regulate. This article examines the following key mechanisms through which IRBs affect knowledge production: (1) cost inflation and quality dilution that reduces both the supply of and demand for the knowledge produced by research; (2) selection effects operating on researcher characteristics and on the bureaucratization of decision-making processes in a direction detrimental to the quality and integrity of research production; and (3) non-random distortions of methods, topics, and rates of independent replication are all expected to contribute to a reduction in the practical significance and societal benefit of affected academic institutions. These impacts escalate because of asymmetric accountability and motivated mission expansion in a system where overreach more often self-reinforces than becomes restrained by corrective feedback. This points to empirical predictions and highlights the need to quantify the real costs of unchecked IRB expansion.

Abstract: General Relativity predicts the formation of cosmological and gravitational singularities and, being fundamentally time-reversal invariant, lacks an intrinsic mechanism for the emergence of an arrow of time. In this work, we construct a covariant effective field theory (EFT) extension of gravity based on curvature invariants that implements a dual regularization mechanism. The framework combines (i) a bounded-curvature kernel (sinR-type operator) that dynamically saturates high-curvature growth, and (ii) a geometric memory contribution (“slip”) that correlates the expansion rate with its temporal variation, thereby regulating curvature flow. Within a controlled regime below an explicit curvature cutoff, the resulting field equations remain second order and admit a nonempty, algebraically characterized perturbatively stable parameter domain. A canonical Hamiltonian (ADM) analysis fixes the degree-of-freedom counting and supports the absence of additional pathological propagating modes within the EFT regime. In homogeneous cosmology, the dual mechanism yields nonsingular bouncing solutions with finite curvature invariants and ultraviolet damping driven by geometric memory. The bounce can be interpreted as a transition between contracting and expanding phases governed by curvature regulation rather than singular dynamics. Perturbative analysis indicates stability of both tensor and scalar sectors throughout the EFT-consistent domain. Geometric memory introduces an effective temporal ordering of cosmological solutions: a relational time variable can be defined that evolves monotonically along dynamical trajectories, while the underlying action remains local, covariant, and CPT invariant. This suggests a dynamical origin for the arrow of time without explicit symmetry breaking. The framework predicts characteristic observational signatures, particularly in gravitational-wave physics. These include curvature-dependent damping of tensor modes, potential deviations in the primordial stochastic gravitational-wave spectrum, and imprints associated with nonsingular bounce dynamics, providing concrete avenues for observational tests. Rather than an ultraviolet completion, the theory is a structurally consistent curvature-based EFT with explicit stability control and a well-defined domain of validity, offering a controlled setting to explore singularity resolution, emergent temporal structure, and testable deviations from standard cosmology.

Abstract: Whole-slide imaging has transformed histopathology into a data-intensive domain, with current approaches dominated by end-to-end deep learning that encode morphology implicitly within latent representations. This limits interpretability, reproducibility, and cross-dataset generalization. This review positions histomics as an intermediate phenotype representation layer that maps histological images to structured, multi-scale descriptors of tissue morphology, spatial organization, and architectural context. A unified taxonomy of histomic features across biological scales is presented, along with an analysis of artificial intelligence frameworks spanning classical machine learning, deep learning, weakly supervised learning, and multimodal integration. The review presents core failure modes in histomic pipelines, including segmentation dependence, feature instability, and domain shift, and examines their impact on robustness and generalization. Emerging trends in representation learning and multimodal modeling are analyzed in the context of phenotype-centric inference. Overall, this work reframes histomics as a representation-driven paradigm and outlines directions for developing stable, interpretable, and generalizable computational pathology systems.

Abstract: The purpose of the study is to analyse and to identify structural characteristics reffering to melanocytic nevi, in youth patients. Using both optical and electronic microscope, could be possible a better describtion related specificity in melanocytic nevi characteristics. Epiderm is composed by specific layers. From a curently research pespective we can mention that in utero, specific stem cells from the neuroectoderm play a signifiant role such as migration to the skin as melanoblasts. Future trends, are important key points in management, including preventive and prophylactic methods.

Abstract: While Large Language Models (LLMs) have demonstrated transformative potential in credit risk assessment, existing evaluation frameworks primarily focus on general financial NLP tasks, failing to capture the specialized reasoning required by professionals. To bridge this gap, we introduce the Credit Context Log Understanding and Prediction Evaluation (CCLUPE) benchmark. CCLUPE addresses the unique challenges of the Chinese credit market, where assessment relies heavily on synthesizing nuanced transaction logs and inferring latent financial behaviors. Unlike previous benchmarks, CCLUPE specifically targets Expenditure and Spending Pattern Recognition, evaluating the ability of LLMs to integrate heterogeneous inputs combining textual descriptions with time-series transactional data to perform causal inference and multi-stage reasoning. The dataset encompasses over 4,000 high-quality samples across personal and micro-enterprise client profiles, featuring 7 major log types and 16 subtypes. We ensure data integrity through a rigorous validation mechanism involving over 20 professional annotators. Furthermore, we enter Log-Score, a robust evaluation metric that incorporates log misunderstanding penalties and multi-dimensional capability assessment. Extensive experiments demonstrate that even state-of-the-art (SOTA) models exhibit unsatisfactory performance on these high-stakes tasks. CCLUPE serves as a rigorous testbed for the next generation of financial LLMs, ensuring their robustness for deployment in complex real-world credit scenarios.

Abstract: Anchor-based bipartite graph methods provide linear scalability for multi-view clustering, but most of them construct graphs in the original feature space, where high dimensionality distorts the proximity between samples and anchors and degrades graph quality. In addition, the K-means step commonly used to discretize spectral embeddings produces different cluster assignments across random seeds. To address these limitations, this paper proposes Projection-Enhanced Bipartite Graph Learning (PEBGL), a unified framework that jointly performs subspace projection, bipartite graph construction, consensus graph fusion with adaptive view weighting, and discrete label assignment. Every subproblem admits a closed-form or deterministic solution, so the algorithm runs in linear time and produces reproducible cluster labels for any fixed initialization. Experiments on six benchmark datasets demonstrate that PEBGL achieves consistently competitive accuracy across all evaluation settings and improves over the strongest baseline by up to 4.8 percentage points. These results confirm the effectiveness and generality of the proposed framework.

Abstract: The transition from smart to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart-intelligent city as an urban Cyber-Physical System across environmental, economic, and social sustainability pillars, metrics and their tradeoffs. A methodology based on Deep Reinforcement Learning and reward-shaping mechanisms is proposed to represent and assess sustainability pillar dependencies and their interplay. A case study on a Low-Power Wide-Area Network planning, deployment and management in a Sicilian municipality has been developed to demonstrate the effectiveness of the proposed approach in dealing with the dynamics and the non-linear dependencies of the sustainability pillars. The results thus obtained provide a blueprint for urban planners to develop sustainable, resilient, cost-effective, and environmentally friendly smart-intelligent city frameworks.

Abstract: Nicotinamide adenine dinucleotide (NAD+/NADH) metabolism holds a central position in both tumor pathogenesis and cellular aging processes. Current therapeutic strategies pursue apparently contradictory objectives: oncology aims to deplete NAD+ in cancer cells, while anti-aging medicine administers NAD+ intravenously to restore levels that decline with age. This work proposes a paradigm shift: the pulsed administration of exogenous reducing equivalents — with NADH (the reduced form of the coenzyme) as the primary but not exclusive vehicle — as an integrated anti-cancer and anti-aging strategy. The rationale is based on intrinsic metabolic selectivity: cancer cells, characterized by mitochondrial dysfunction and dependence on fermentative glycolysis (Warburg effect), are unable to dispose of an acute excess of NADH through the electron transport chain, thereby suffering selectively lethal reductive stress. Healthy cells, endowed with functional mitochondria, can manage the reductive overload by oxidizing excess NADH in the respiratory chain, with respiratory control mechanisms regulating the flux. A protocol of brief, intense pulses (redox press-pulse) followed by recovery phases is proposed, in synergistic combination with glucose restriction (ketogenic diet/fasting) and optimization of intracellular magnesium. This triad — reducing substrate, enzymatic structure, and environment — aims to restore respiratory chain efficiency in healthy cells and selectively destabilize cancer cell metabolism. The convergence between anti-cancer and anti-aging mechanisms mediated by cyclic reductive impulses is also discussed. A speculative appendix explores the implications of quantum biology for understanding the efficiency of mitochondrial electron transfer.

Abstract: Dissolving microneedles (DMN) could be considered as a promising platform for transdermal delivery of naltrexone hydrochloride (NTX), providing a minimally inva-sive alternative to conventional administration routes. In the present study, DMN patches with an advanced design were developed via a two-step micromoulding tech-nique. The systems were composed of drug-free polyvinylpyrrolidone (PVP) and poly-vinyl alcohol (PVA) blend microneedle tips, combined with a drug-loaded backing layer based on PVP and the thermoresponsive polymer Poloxamer 407. The influence of polymer concentration into DMN tips and backing layer composition on morpholo-gy, mechanical properties, drug release and permeation was evaluated. Mechanical studies as well as SEM observation revealed that intermediate polymer concentration (formulation MN-20%/2:1), used for DMN tips preparation, provided optimal mi-croneedle geometry, superior structural integrity and penetration efficiency. Incorpo-ration of NTX into backing layer allowed high and uniform drug loading. In vitro per-meation studies demonstrated significantly enhanced NTX delivery from DMN sys-tems compared to simple matrix patches, with the thermoresponsive backing layer contributing to controlled drug release. These findings highlight the importance of polymer composition in DMN design and demonstrate the potential of the developed systems as an effective platform for transdermal delivery of NTX.

Abstract: Metabolic disorders, including obesity, type 2 diabetes mellitus (T2DM), dyslipidemia, and metabolic dysfunction–associated fatty liver disease (MAFLD), represent a major and escalating global health burden. These conditions are now recognized as systemic disorders arising from dysregulated inter-organ communication among metabolically active tissues. Central mechanisms include insulin resistance, chronic low-grade inflammation, oxidative stress, mitochondrial dysfunction, and neuroendocrine dysregulation. Exercise is increasingly recognized as a potent multisystem therapeutic intervention. Beyond energy expenditure, it induces coordinated molecular adaptations across tissues, including improved mitochondrial function, reduced inflammation, and enhanced metabolic flexibility. Exercise-induced signaling molecules (exerkines) and gut microbiota remodeling further mediate systemic metabolic benefits. This review synthesizes current evidence on exercise as an integrative therapy for metabolic disorders, with emphasis on molecular mechanisms, organ-specific adaptations, and clinical applications. Emerging roles of membrane microdomains such as caveolae are discussed as potential regulators of metabolic signaling, although their role in exercise adaptation remains incompletely defined.

Abstract: Rho GTPases—including RhoA, Rac1, and Cdc42—are key molecular switches that regulate cytoskeletal dynamics and transduce biochemical and mechanical signals essential for skeletal and dental tissue development. These small GTPases orchestrate fundamental cellular processes such as proliferation, migration, polarity, and differentiation, thereby guiding the morphogenesis, homeostasis, and regeneration of bone and teeth. In bone, Rho GTPases modulate osteoblast proliferation and matrix mineralization, osteoclast-mediated bone resorption, and mechanotransductive responses to physical stimuli. They are also critical for the behavior and fate specification of skeletal stem cells, integrating environmental cues to balance self-renewal and lineage commitment. In dental tissues, Rho GTPases regulate epithelial–mesenchymal interactions, odontoblast and ameloblast polarization, and the formation of enamel and dentin. Additionally, they play vital roles in craniofacial suture development, where their spatially and temporally controlled activity maintains suture patency and regulates ossification. Dysregulation of Rho GTPase signaling is implicated in a variety of pathological conditions, including osteoporosis, craniosynostosis, and dentinogenesis and amelogenesis imperfecta. Despite their therapeutic potential, targeting Rho GTPases remains challenging due to their pleiotropic functions and broad tissue distribution. This review highlights the mechanistic roles, regulatory networks, and developmental relevance of RhoA, Rac1, and Cdc42 in skeletal and dental biology, and discusses emerging strategies for modulating their activity in regenerative and disease contexts.

Abstract: Elementary particles exhibit intrinsic phase recurrences, \rev{implicit in the undulatory description}, so each can serve as a ``virtually perfect'' reference relativistic clock. From this perspective, Rovelli's ``timeless'' viewpoint is best read not as denying time, but as denying the fundamentality of any preferred external time coordinate: time persists as internal cyclic variables carried by particles, covariantly modulated by energy exchange and relativistic transformations. \rev{Then,} macroscopic flow arises from records and thermodynamic coarse-graining. This Letter shows that cyclic internal times of elementary systems are fully compatible with the ordinary non-compact relativistic time flow observed in nature. In particular, it identifies the fundamental ``internal'' variables underlying physical relativistic time with the particles' intrinsic cyclic times and their relational covariant modulations. Supported by theoretical and phenomenological results established in previous works, intrinsic temporal periodicity constitutes the fundamental principle of Elementary Cycles Theory and acts as exact quantization condition.

Abstract: The ongoing expansion of Highly The ongoing expansion of Highly Pathogenic Avian Influenza Virus (HPAIV) H5N1 is driving unprecedented wildlife mortality and raising global health concerns. To date, Oceania remains the last region free of HPAIV, offering a critical opportunity to anticipate and mitigate future emergence. Here, we assess the risk of HPAIV introduction and spread within seabird communities of New Caledonia, a key biodiversity hotspot of the South Pacific located along major transoceanic migratory routes. We compiled a comprehensive list of seabird species previously exposed to HPAIV and evaluated their likelihood of occurrence in New Caledonia using literature and global biodiversity databases. Species were classified as breeding or non-breeding, and their potential roles in virus dynamics were quantified using trait-based indices. Additionally, seabird migratory connectivity between New Caledonia and surrounding regions was estimated. Among 71 retained seabird species, several long-distance migrant species—particularly within Procellariidae and Charadriiformes—emerged as potential high-risk vectors, although often with low probability of occurrence locally. In contrast, highly colonial breeding species, including Thalasseus bergii and Fregata minor, showed the greatest potential to amplify local transmission. Network analyses revealed that the strongest ecological connections occur with nearby regions not yet affected by HPAIV, whereas links to infected areas involve distances > 2000 km, potentially constraining virus emergence in the South Pacific. Our results identify priority species and critical knowledge gaps, providing a framework to guide targeted surveillance and proactive management strategies in the South Pacific.

Abstract: Dixit et al. proposed an asymptotic drag scaling for zero-pressure-gradient flat-plate turbulent boundary layers based on the approximation M∼U_τ²δ, where M is the kinematic momentum rate through the boundary layer, U_τ is the friction velocity, and δ is the boundary-layer thickness. In the present paper, an explicit Reynolds-number-dependent correction to this approximation is derived from the logarithmic mean-velocity profile. Integration of the log law across the layer yields M∼U_τ²δf(Re_τ), where Re_τ=δU_τ/ν is the friction Reynolds number and f(Re_τ) is given analytically. Application of the correction to the dataset compiled by Dixit et al. shows that the corrected scaling gives an exponent closer to the asymptotic value −1/2 than the uncorrected formulation. The correction should be viewed as a leading-order amendment, since the derivation uses the logarithmic law outside its strict range of validity.