Search Results (21,078)

Obesity is increasingly recognized as a complex systemic disorder rather than a simple consequence of excess energy intake and fat accumulation. This review presents a systems biology framework that examines how obesity-driven disruption of inter-organ communication networks contributes to chronic disease susceptibility, with particular emphasis on colorectal cancer (CRC). Disrupted signaling among the brain, adipose tissue, liver, skeletal muscle, gut, and immune system generates maladaptive feedback loops that promote chronic metabolic inflammation (metaflammation), loss of physiological resilience, and progressive metabolic dysfunction. Within this framework, obesity is redefined as a network disease characterized by neuroendocrine dysregulation, adipose tissue remodeling, immune dysfunction, impaired organ crosstalk, and alterations in the gut microbiome. A central feature of this dysregulation is persistent low-grade inflammation driven by immune-metabolic reprogramming and sustained activation of inflammatory pathways. Obesity-associated metaflammation is further linked to accelerated biological aging through mechanisms involving cellular senescence, mitochondrial dysfunction, oxidative stress, and impaired metabolic resilience. These interconnected processes create a tumor-promoting environment by enhancing oncogenic signaling, disrupting intestinal barrier integrity, altering microbial and metabolic signaling, impairing immune surveillance, and promoting epithelial dysfunction, thereby increasing susceptibility to CRC. The review also examines how behavioral, circadian, environmental, and socioeconomic factors influence metabolic health and cancer risk. Finally, emerging translational opportunities, including biomarker-guided risk stratification, precision prevention, metabolic network restoration, and integrative lifestyle and pharmacological interventions, are discussed. Collectively, this review reframes obesity as a whole-body regulatory disorder and provides an integrated conceptual framework linking metabolism, inflammation, aging, and colorectal carcinogenesis to inform future prevention and therapeutic strategies. Full article

Chronic hepatitis B virus (HBV) infection remains one of the main causes of hepatocellular carcinoma (HCC), even though vaccination and long-term viral suppression have reduced new infections and circulating viral replication. This residual cancer risk suggests that serum HBV DNA alone does not capture the full biology of HBV-related carcinogenesis. Hepatitis B virus X protein (HBx) is a relevant entry point because it maintains the transcriptional competence of covalently closed circular DNA (cccDNA), engages host chromatin regulators, and may persist in tumors as cccDNA-derived, integration-derived, full-length, truncated, or fusion forms. This review focuses on a specific question: does the available literature support HBx-associated reactivation of the IGF2 locus in chronic HBV infection and HBV-related hepatocarcinogenesis, and, if so, at which regulatory layer is the claim defensible? The most direct evidence remains promoter-proximal. Classic mechanistic work shows acute HBx-dependent activation of IGF2 promoter P4 through Sp1- and PKC/ERK-dependent signaling. Human tissue and cell-based studies also support a broader fetal-promoter compartment, including P3/P4 transcript enrichment, local promoter hypomethylation, MBD2-HBx-CBP/p300 recruitment, and increased histone H3/H4 acetylation. These observations do not, however, establish HBV exclusivity, uniform loss of imprinting, or direct HBx-mediated rewiring of the human IGF2/H19 topological domain. Recent integration-aware and long-read studies further argue against treating tumor-stage HBx as a single biological variable. In the present evidence framework, HBx-associated IGF2 locus reactivation is therefore more appropriately viewed as a stage-aware, promoter-resolved, biomarker-oriented hypothesis than as a universal mechanism or a treatment algorithm for HBV-related HCC. Full article

Ionic liquids (ILs) are salts that are liquid at or below 100 °C. They are composed entirely of ions and have unique properties like negligible vapor pressure, high thermal stability, and tunable structures. These characteristics make them a promising alternative to traditional, often volatile and toxic organic solvents in the petrochemical industry. They have broad applications in chemical and petrochemical industry processes. Ionic liquids may be applied in the following processes: desulfurization, benzene toluene xylene (BTX) separation, alkylation, and carbon capture units. Two different ionic liquid-based process configurations have been evaluated for BTX separation. It has been found that the process configuration working with 1-ethyl-3methylimidazolium tricyanomethanide ([emim][TCM]) reduces the energy costs and capital expenditures associated with the Morphylane process by 67 and 63%, respectively. It also reduces solvent costs, confirming it as a cleaner alternative. The hydrodesulfurization (HDS) process is operated under harsh conditions, such as high temperature and high pressure and the requirement of a noble catalyst and hydrogen. High-Temperature Hydrogen Attack (HTHA) failure occurs at high temperatures between the gaseous molecular hydrogen contained inside the steel pressure vessel and the carbon atoms located in the steel matrix or in carbides. Methane molecules are produced during this reaction. This phenomenon can consequently lead to a loss of mechanical properties due to surface decarburization and to the formation of defects caused by methane bubbles mainly located at grain boundaries. The application of ionic liquids (ILs) in oil refineries offers significant advantages, such as safety, environmental sustainability, and process efficiency, primarily by serving as versatile alternatives to hazardous traditional solvents and catalysts. Across BTX extraction, carbon capture, and desulfurization/HDS-adjacent service, the recurring barriers are high viscosity, difficult regeneration, solvent cost/inventory and uncertain long-term stability. Full article

The rapid expansion of high-protein dairy-based powders (HPDPs), including milk protein concentrates and isolates (MPC/MPI), whey protein concentrates and isolates (WPC/WPI), and micellar casein concentrates and isolates (MCC/MCI), has intensified the need to understand instability phenomena that emerge during processing and storage. These products are governed by protein-rich amorphous matrices, in which molecular mobility, interfacial composition, and mineral interactions dictate both physical stability and functional performance. Importantly, these physical instabilities are directly coupled with functional deterioration, particularly in terms of impaired wetting, dispersion, and dissolution during rehydration. This review presents an integrated mechanistic framework linking these instability phenomena across processing, storage, and reconstitution, thereby consolidating concepts that remain fragmented across the current literature on high-protein dairy matrices. Key controlling factors include glass transition temperature (Tg), water activity-induced plasticization, protein–protein and protein–mineral interactions, and surface compositional heterogeneity established during spray drying. These factors govern the progression from surface stickiness through uncontrolled agglomeration to caking, forming a consolidation continuum. In contrast to lactose-driven matrices, caking and agglomeration in HPDPs arise primarily from protein-mediated restructuring and inter-particle bonding, with lactose crystallization acting only as a secondary mechanism in mixed-composition grades. The review further distinguishes engineered agglomeration from storage-induced consolidation and evaluates advances in molecular mobility characterization and Tg-based stability mapping. Significant gaps remain in linking localized surface evolution, mineral redistribution, and inter-particle bridge chemistry under realistic environmental conditions. The review concludes by proposing a mobility-centered “stability-by-design” framework that integrates composition, processing, particle architecture, and storage conditions to guide the development of future HPDPs with improved physical stability and functional recovery. Full article

Background/Aims: While pathogenic germline variants play a critical role in pediatric cancer susceptibility, traditional clinical genetics primarily focuses on single-gene interpretations. Transitioning to a systems-level analysis of inherited variation can uncover shared biological vulnerabilities, informing genetic counseling, surveillance, and targeted therapeutics. This study aims to implement an unsupervised machine learning framework to identify and characterize Germline Mutation Signatures (GMS) across diverse pediatric malignancies, elucidating latent genomic patterns that reveal shared oncogenic mechanisms. Methods: We analyzed germline whole-exome and whole-genome sequencing (WES/WGS) data from a retrospective cohort of 420 pediatric cancer patients and matched non-cancer controls. Variants were deeply annotated to capture multi-dimensional features, including predicted pathogenicity, splice-site disruption, regulatory impact, population frequency, and sequence context. To enable robust modeling, we integrated an augmented feature set encompassing evolutionary constraint, loss-of-function intolerance, and compositionally normalized substitution spectra. These high-dimensional annotations were processed using a deep autoencoder for non-linear representation learning, followed by Gaussian Mixture Modeling (GMM) of the latent space. Results: The framework delineated 13 signatures (GMS1–GMS13), yielding an optimal Davies–Bouldin index of 1.051. These signatures map to fundamental biological processes, including DNA repair deficiencies, transcription-coupled damage, replication stress, and aberrant RNA regulation. Crucially, these GMSs transcend traditional tissue-of-origin classifications, manifesting across multiple distinct cancer types. This observation indicates convergent germline etiologies and suggests potential shared susceptibilities to pathway-directed therapies. Conclusions: The discovery of these cross-cancer signatures provides a scalable, biologically interpretable framework for decoding inherited pediatric cancer risk. While the therapeutic mapping networks identified are currently exploratory and serve as a hypothesis-generating foundation, this deep learning-driven paradigm establishes a robust basis for stratified precision medicine. Pending prospective clinical validation, this approach holds significant translational potential to move beyond single-gene paradigms toward unified, systems-level precision oncology strategies. Full article

The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-risk forecasting and early warning remain challenging because carbon-market losses are affected by nonlinear dependence, episodic liquidity stress, and time-varying volatility. This study proposes an Interpretable Quantile Temporal Network (IQTN) as a systems-oriented risk-monitoring framework for China’s national CEA market. By integrating a feature-gating mechanism, a causal temporal convolutional encoder, and a non-crossing quantile output layer, IQTN directly models the conditional tail distribution of future carbon-market losses. The framework produces multi-horizon Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) forecasts for 1-day, 5-day, and 10-day horizons and converts predicted tail risk into operational early-warning signals. Compared with historical simulation, EWMA, GARCH-type models, machine-learning quantile models, and deep temporal benchmarks, IQTN achieved the lowest 95% VaR pinball loss across all horizons, with values of 0.1765, 0.3958, and 0.5732. VaR backtesting showed empirical exceedance rates of 5.23%, 6.04%, and 6.94%, closest to the nominal 5% level. Interpretability analysis identified rolling volatility, maximum loss, intraday range, trading value, and illiquidity as key risk drivers. The temporal importance results also show that recent observations dominated the risk forecasts, suggesting that the risk state of the CEA market is highly sensitive to short-term market information. This supports the use of a short-horizon temporal network as a systems-oriented tool for carbon-market tail-risk monitoring and early warning. Full article

Conventional defect inspection for warp-knitted lace relies on manual work and negative-sample-based training, resulting in low efficiency, frequent false detections and poor adaptability. This study presents a novel AI visual inspection system centered on positive-sample learning, which is built upon a five-layer 5G + Industrial Internet distributed architecture. Supported by modified looms, high-precision imaging devices and an optimized YOLOv5s model, the system accomplishes intelligent defect detection. A positive-sample self-learning paradigm and dual-model collaboration mechanism are proposed to reduce the demand for negative samples and cut labeling expenses. The integration of CBAM, FPN + PAN structure, self-supervised learning and hybrid loss further strengthens the recognition performance for subtle defects under complex patterns. Industrial tests show that the system reaches a grid-level classification accuracy of 95% and a frame-level detection rate over 98%, with a detection speed of 30 m/min. It reduces labor costs and product reject rates by 40% and 30% correspondingly while running stably in real production. This method breaks the constraints of traditional training modes, provides a scalable intelligent solution for the digital upgrading of the warp-knitted lace industry, and promotes the high-quality development of textile manufacturing. Full article

The design of intravascular ultrasound (IVUS) catheters involves inherently coupled acoustic, hemodynamic, and structural requirements. Existing design strategies, which often rely on empirical geometric refinement or single-physics optimization, are limited in their ability to simultaneously ensure acoustic transmission efficiency, flow compatibility, and mechanical reliability. A multiphysics topology optimization method for the integrated design of IVUS catheters under acoustic–fluid–structure interactions is proposed in this paper. A density-based design variable is introduced to characterize the material distribution within the design domain, and consistent interpolation schemes are employed to relate this variable to the effective acoustic properties in the Helmholtz equation, the Brinkman penalization coefficient in the incompressible Navier–Stokes equations, and the elastic stiffness tensor in the structural equilibrium equation. The optimization problem is formulated as a normalized multi-objective minimization of acoustic transmission loss, flow resistance, and structural compliance, subject to constraints on material volume, received acoustic energy, wall shear stress, and structural displacement. Density filtering and smooth Heaviside projection are incorporated to regularize the design field and promote well-defined material boundaries. An adjoint sensitivity formulation is further developed to enable efficient gradient evaluation for the coupled system. Compared with the initial design, the average acoustic transmission efficiency has increased by $59.01\%$, the shear stress has decreased by $53.87\%$, and the stiffness matching rate has reached $98.27\%$. The objective function converged after 35 iterations, demonstrating the numerical stability of the proposed acoustic–fluid–structure topology optimization framework. Full article

Contemporary deep learning-based image steganography techniques for embedding images within images are hindered by inadequate utilization of frequency-domain features and limited steganographic security, restricting their effectiveness in practical privacy protection contexts. To mitigate these limitations, we introduce a frequency–spatial dual-path fusion-based deep steganography approach, termed FS-Stego. This method incorporates a frequency–spatial dual-path architecture within the generator network. Specifically, the frequency-domain processing module facilitates feature embedding in the complex domain, while the spatial-domain processing module maintains the image’s structural integrity, thereby enabling the co-optimization of multi-dimensional features. Second, an adaptive fusion module is developed to dynamically adjust the weights of the two paths, while residual connections and attention mechanisms are utilized to mitigate feature loss. Third, a multi-objective loss function is implemented to simultaneously optimize the quality of the stego images and the reconstruction accuracy of the secret images. The proposed method utilizes three open-source datasets as cover images and the LFW dataset as the secret images. Experimental results demonstrate that, compared to existing deep steganographic techniques, the stego and recovered images achieve superior peak signal-to-noise ratios (PSNR) and structural similarity (SSIM). Regarding model efficiency, the number of parameters is reduced to below 0.98 million, significantly enhancing practical performance. The proposed method ensures high-quality image recovery while maintaining steganographic security, thereby offering an effective solution for privacy protection. Full article

This study examined acute pre- to post-match changes in perceived mental fatigue, subjective workload, and psychomotor performance in professional male soccer players, and whether cognitive changes were associated with GPS-derived external-load metrics, match outcome, and playing position. The dataset comprised 101 player–match measurements from 40 elite players, with paired pre–post psychomotor assessments yielding n = 202 total measurements. Pre–post comparisons were analysed using repeated-measures ANOVA, supplemented by linear mixed-effects models with a random intercept for player. Soccer matches produced large increases in perceived exertion, mental fatigue, mental demand, physical demand, and effort (all p < 0.001), and significant deteriorations in reaction time, accuracy, processing speed, and response variability (all p ≤ 0.005), confirmed in the mixed-effects analyses (all p ≤ 0.014). In the initial player–match-level analyses, high-intensity accelerations (>3 m·s⁻²) were weakly associated with greater Δreaction-time slowing (r = 0.203), increased response variability (r = 0.276), and reduced Rate Correct Score (r = −0.242), while high metabolic load distance was weakly associated with post-match perceived mental fatigue but not with psychomotor-performance changes. One-way ANOVAs indicated greater post-match psychomotor decrements following losses than draws. Once within-player dependence was modelled, the effects of match outcome, playing position, and most external-load metrics were attenuated, except for a residual match-outcome effect on accuracy and a high-intensity deceleration effect on accuracy. These findings indicate that competitive soccer match play is followed by acute psychomotor-performance decrements and increased perceived mental fatigue, whereas the contributions of mechanical load, match outcome, and playing position appear modest and partly reflect stable between-player differences. Full article

Uncoordinated and instantaneous charging decisions made by electric vehicle (EV) drivers create bottlenecks in existing infrastructure, leading to inefficiencies and prolonged waiting times, and resource losses that challenge sustainable transportation systems. This study proposes a “scenario-based” optimization approach targeting the stochastic behaviors of independent EV drivers, incorporating individual risk-taking profiles and balking mechanisms to promote infrastructure sustainability. The proposed algorithm integrates a discrete-event simulation with a Genetic Algorithm (GA) as a decision support mechanism. The optimization focuses on a vehicle cohort entering the route once the system reaches a steady-state saturation point during peak evening hours. GA parameters are optimized using the Taguchi method to maximize robustness. The results demonstrate that, compared to the baseline scenario where drivers act individually, the proposed decision-making mechanism can achieve up to a 20% reduction in the total travel time of the optimized vehicle group. Overall, the proposed model offers a scalable framework for optimizing individual charging behaviors, thereby contributing to more predictable, resource-efficient, and sustainable management of electric vehicle charging infrastructures. Full article

The interindividual phenotypic heterogeneity in Alpha-1 Antitrypsin Deficiency (AATD), despite a shared genetic etiology (the Z-allele of SERPINA1), is explained by the interaction of dual pathogenic mechanisms (gain-of-function vs. loss-of-function), additional genetic modifiers, and environmental or metabolic factors. Building on recent evidence suggesting divergent disease trajectories, we investigated whether pulmonary and hepatic impairments represent coupled manifestations or independent clinical dimensions within a large European cohort. Methods: This international multicenter study utilized the European Alpha-1 Research Collaboration (EARCO) registry (n = 1217). Pulmonary and hepatic severities were quantified using concurrent 0.0–10.0 composite indices. Independence was evaluated via partial Spearman correlations, multivariable multinomial regression, and geometric mapping across a continuous phenotypic space. Results: Cross-domain correlations between respiratory metrics and liver stiffness were near zero (r = −0.03), demonstrating statistical independence. Phenotypic dominance classification isolated distinct profiles; the lung-dominant group exhibited a higher age (57.0 vs. 54.0 years; p < 0.001) and tobacco exposure, while the liver-dominant group registered a higher body mass index (25.8 vs. 24.4 kg/m²; p < 0.001). Multivariable models identified age (OR 1.03; 95% CI 1.02–1.05) and smoking as independent predictors of lung dominance, whereas body mass index was independently associated with liver dominance (OR 1.04; 95% CI 1.01–1.07). Geometric mapping revealed advanced disease clusters at orthogonal margins rather than forming a systemic continuum. Conclusions: Hepatic and pulmonary impairments in AATD operate as independent clinical dimensions modulated by distinct metabolic and environmental factors. Risk stratification must transition toward organ-specific prognostic models. Full article

(1) Introduction: Transit-oriented development is often framed as a strategy to expand opportunity and advance equitable transportation. However, evidence suggests it can also contribute to rising housing costs and displacement in historically marginalized communities. This study examines how a light rail expansion reshaped displacement risk in a Latina/o community in the U.S. Southwest, identifying early mechanisms through residents’ interpretations of the expansion during construction. (2) Materials and Methods: Using a qualitative, community-engaged design, the study draws on ten in-depth pláticas with Latina/o residents conducted during construction of a major rail expansion. Data were analyzed abductively and guided by Critical Race Ecological Systems Theory (CrEST) to identify multilevel mechanisms linking infrastructure policy to lived social conditions. (3) Results: Findings identify three mechanisms through which transit investment generated displacement risk prior to relocation. First, historical and intergenerational memory shaping anticipatory displacement. Second, place-based belonging intensifying psychosocial stress and loss. Third, policy-mediated mobility constraining residents’ ability to remain or benefit from reinvestment. (4) Discussion: Transit infrastructure operates as a structural policy intervention that reorganizes risk, belonging, and stability when histories of racialized disinvestment are not incorporated into policy design. These findings position infrastructure planning as a critical site for social work policy analysis and prevention-oriented intervention. Full article

Noise exposure has become an increasingly prevalent public health concern, with effects extending beyond the auditory system. Accumulating evidence indicates that chronic noise exposure induces both structural and functional alterations in the central nervous system, ultimately affecting cognitive and emotional processes. This review summarizes the impact of noise on key brain regions, including the hippocampus, prefrontal cortex, and auditory cortex. Structurally, noise exposure is associated with reduced neurogenesis, dendritic remodeling, synaptic loss, alterations in white matter and changes in glial activity. Functionally, it disrupts synaptic plasticity mechanisms—such as long-term potentiation and long-term depression—as well as neuronal connectivity, leading to impairments in higher-order cognitive and behavioral functions. These effects are mediated by interconnected mechanisms, including activation of the hypothalamic–pituitary–adrenal axis, neuroinflammation, oxidative stress, and alterations in neurotrophic signaling. Full article

Adversarial camouflage has attracted growing research attention owing to its ability to execute multi-view, persistent attacks in real physical environments, outperforming conventional single-view adversarial patches. However, most existing methods are confined to non-targeted attacks, which induce arbitrary incorrect detection results without specifying target categories. This ambiguity weakens attack destructiveness and stealthiness, posing limitations for security evaluation of real-world vision systems. To address this gap, we present TACT, an approach built upon the full-coverage physical camouflage pipeline. By replacing the original category supervision with a predefined target class, TACT redirects the optimization gradient to guide 3D texture toward the target category features. Such a scheme only employs the inherent feature alignment mechanism of off-the-shelf object detectors, without redesigning network modules, defining novel loss functions, or modifying the rendering pipeline. Extensive experiments across digital and physical domains validate its effectiveness: on seven mainstream general-purpose object detectors, TACT-person achieves an average targeted attack success rate of 51.91%, and delivers cross-architecture and cross-version transferability. In physical tests, TACT-bird reduces mAP50-95 by 59.87% on YOLOv8, yet a TCER–TASR gap suggests that the physical pipeline acts as a low-pass filter: coarse-grained target classes transfer robustly while fine-grained ones suffer feature collapse. These results confirm the viability of native supervision redirection and reveal an empirical pattern: coarse-grained target classes transfer more robustly through the physical pipeline than fine-grained ones, suggesting that target class feature granularity consistently influences physical-domain attack effectiveness. Full article