Objectives. Our objective was to study the ecological relationship of many risk factors and personal characteristics with mean age at death (AD) after a 50-year follow-up of nearly extinct cohorts. Material and Methods. There were 16 cohorts totaling 12,763 middle-aged men enrolled in the Seven Countries Study (SCS), and 58 variables were measured, including traditional risk factors, dietary nutrition and anthropometric variables. A follow-up of 50 years allowed the use of AD as the end-point. Analysis included simple linear regression correlation and multivariate modelling using Principal Component Analysis and regression and Ridge regression. Results. Out of 58 variables, only 11 (10 nutrition-dietary items plus age) showed a significant linear correlation coefficient (R) ≥ 0.50 and a p value ≤ 0.05. Linear regression was computed by using as a predictor the dietary factor score derived from a Principal Component Analysis of the 11 significant variables, which were used as independent variables, whose coefficients were significantly related with AD, and the final R2 was 0.52. The Principal Component regression and Ridge regression documented the direct relationship of food groups of vegetable origin (including olive oil) with the AD and the inverse relationship for food groups of animal origin. Conclusions. A few variables, all related to diet and nutrition, were able to statistically explain about 50% of the different AD in 16 cohorts of men followed up with nearly until death. Other variables, including traditional cardiovascular disease risk factors, did not contribute in a significant way for this purpose.

Highlights What are the main findings? Visual mental imagery engages ventral and dorsal stream systems in a content- and stage-dependent manner, with evidence for stream interaction that varies across paradigms and populations. Structural and clinico-radiological evidence is broadly consistent with disconnection frameworks, suggesting that disruption of long-range pathways (e.g., inferior longitudinal fasciculus (ILF)/inferior fronto-occipital fasciculus (IFOF)/ superior longitudinal fasciculus (SLF)) may contribute to imagery deficits beyond focal cortical damage. What are the implications of the main findings? Stream-sensitive phenotyping (object vs. spatial imagery) and stage-aware paradigms are essential to develop interpretable neuroradiological biomarkers. Multimodal protocols combining structural MRI, diffusion imaging/tractography, functional connectivity, and lesion mapping can improve clinical interpretation of imagery complaints. Abstract Visual mental imagery, the ability to generate and manipulate internal visual experiences without direct sensory input, links perception with memory, planning, and higher cognition. In this targeted narrative review, we synthesize neuroimaging and lesion evidence on the brain basis of visual imagery, with a focus on neuroradiological correlates of the ventral and dorsal visual pathways. Unlike prior cognitive neuroscience reviews that primarily emphasize functional mechanisms, this review is neuroradiology-oriented and integrates lesion patterns and white-matter disconnection to support clinico-radiological interpretation of imagery complaints. Using a dual-stream framework, we contrast ventral occipito-temporal systems that preferentially support object imagery (appearance-based features such as form, faces/objects, and color, with texture remaining under-studied) with dorsal occipito-parietal systems that preferentially support spatial imagery (relations, transformations, and navigation). Across studies, imagery recruitment is strongly task- and stage-dependent: ventral regions are most often engaged during object-focused imagery, whereas parietal regions are prominent during spatial transformation tasks, with evidence for interaction between pathways when demands require both content and spatial operations. Structural and clinico-radiological findings indicate that imagery impairment can arise from focal posterior lesions and posterior neurodegenerative syndromes but also from network disruption affecting long-range connections that support top-down access to posterior representations. Finally, emerging work on aphantasia and hyperphantasia supports a network-level view in which imagery vividness relates to how effectively higher-order systems engage visual representations. We conclude that standardized, stream-sensitive tasks and multimodal approaches combining functional and structural imaging with lesion-based evidence are key to discovering clinically actionable biomarkers of imagery dysfunction.

Background/Objectives: Hidradenitis suppurativa (HS) is a chronic, immune-mediated inflammatory skin disease characterized by painful nodules, abscesses, sinus tracts, and progressive fibrosis. Vascular activation is becoming increasingly acknowledged as an important factor in HS pathogenesis; however, the effects of tumor necrosis factor alpha (TNF-α) blockade on vascular remodeling in HS remain poorly characterized. This study investigated the impact of TNF-α inhibition by adalimumab (ADA) on endothelial and fibroblast-associated markers in HS lesions. Methods: Formalin-fixed paraffin-embedded skin samples from 71 HS patients were analyzed, including treatment-naive (n = 38) and adalimumab-treated (n = 33) cases. Histopathology and immunofluorescence were performed using antibodies against CD31, von Willebrand factor (vWF), α-smooth muscle actin (αSMA), vimentin, Ki-67 (proliferation), and cleaved Caspase-3 (apoptosis). ImageJ software was used to determine the immunoexpression of selected markers and vascular density. Vascular density, assessed as vessel count per mm2, was designated as the primary endpoint. Sex-related differences were analyzed as exploratory endpoints. Results: Adalimumab-treated tissue exhibited significantly reduced vascular density (p < 0.01) compared to the treatment-naive group. Conversely, vimentin immunoexpression was significantly higher (p < 0.01) in the adalimumab-treated group. No significant differences were found in endothelial Ki-67 or cleaved Caspase-3 expression between treatment groups, indicating that the observed reduction in vascular density is not associated with direct effects on endothelial cell proliferation or apoptosis, but rather may occur indirectly through attenuation of the pro-angiogenic inflammatory milieu. Exploratory sex-stratified analysis revealed that treatment-naive males had significantly higher endothelial proliferation (Ki-67; p = 0.031) and vimentin expression (p = 0.017) compared to treatment-naive females. In the ADA-treated group, males exhibited significantly lower vascular density (p = 0.036) and higher endothelial apoptosis (p = 0.039) compared to females, whereas females showed a significant increase in vimentin expression following treatment (p = 0.008), suggesting possible sex-dependent differences in vascular remodeling. Conclusions: TNF-α blockade is associated with reduced vascular density, consistent with indirect anti-angiogenic effects, suggesting that adalimumab exerts disease-modifying effects on the microenvironment beyond inflammatory cytokine suppression. Sex-dependent differences in vascular regression underscore the importance of considering sex as a biological variable in HS pathogenesis and treatment response. These results highlight the significance of vascular interactions in HS and support adalimumab as a disease-modifying treatment. These exploratory findings require confirmation in longitudinal studies with paired biopsies.

Abstract Sustainable development demands research into safe, renewable energy sources. Wood briquettes offer numerous advantages, but they can contain heavy metal(oid)s, posing environmental challenges, particularly in the ash produced during combustion. This study examines the concentrations of heavy metal(oid)s (Cd, Cr, Cu, Fe, Mn, Ni, Pb, Co, Zn, and As) in wood briquettes and their residual ash. Samples were prepared via wet digestion using 65% nitric acid (HNO3) in polytetrafluoroethylene vessels, followed by analysis using flame and graphite furnace atomic absorption spectrometry. The results showed that arsenic (As) had the lowest concentration in wood briquettes, while iron (Fe) was the highest. In the ash, chromium (Cr) was detected at the lowest concentration (0.80 mg/kg), while iron (Fe) reached 5830 mg/kg. Heavy metal concentrations in wood briquettes often exceeded permissible limits, and the concentrations in ash were significantly higher, making some ash samples unsuitable for agricultural use. The ash content ranged from 0.70% to 2.34%. This study provides valuable quantitative data on heavy metal(oid)s before and after combustion, highlighting their potential environmental impact and emphasizing the need for careful management of wood briquette ash.

Psychological interventions represent a core component of contemporary interdisciplinary chronic pain treatment, yet treatment initiation following referral to pain psychology services remains consistently low. Empirical studies across behavioral health and pain medicine demonstrate that referral alone is insufficient to ensure patient engagement with psychological care. This gap between referral and treatment initiation represents a major implementation barrier limiting the impact of evidence-based psychological pain interventions. The present article synthesizes contemporary literature on behavioral health treatment initiation and chronic pain psychology to propose a structured engagement framework designed to improve initiation rates following referral. Using a targeted narrative review methodology, empirical literature published between 2021 and 2025 was examined to identify key determinants of treatment initiation across pain medicine and integrated behavioral health settings. Findings indicate that treatment initiation is best conceptualized as a multistep process involving referral communication, structural and attitudinal barriers, patient readiness, psychoeducation, and system-level facilitation. Evidence from collaborative care models suggests that active engagement strategies embedded within medical workflows can substantially improve treatment initiation rates compared with passive referral approaches. The proposed Active Engagement Model of Pain Psychology Referral integrates individual-level and system-level interventions designed to address common barriers to treatment initiation. Improving initiation requires a shift from passive referral models toward proactive engagement strategies embedded within interdisciplinary pain care. Implementing structured engagement approaches may substantially improve access to evidence-based psychological interventions for chronic pain.

Data scarcity and weak supervision continue to limit the performance of machine learning models in many real-world applications, such as mammography, where Multiple Instance Learning (MIL) often offers the best formulation. While recent foundation models provide strong semantic representations out of the box, effective augmentation of such representations of MIL data remains limited, as existing methods operate at the instance level and fail to capture intra-bag dependencies. In this work, we introduce SetFlow, a generative architecture that models entire MIL bags (i.e., sets) directly in the representation space. Our approach leverages the flow matching paradigm combined with a Set Transformer-inspired design, enabling it to handle permutation-invariant inputs while capturing interactions between instances within each bag. The model is conditioned on both class labels and input scale, allowing it to generate coherent and semantically consistent sets of representations. We evaluate SetFlow on a large-scale mammography benchmark using a state-of-the-art MIL-PF classification pipeline. The generated samples are shown to closely match the original data distribution and even improve downstream performance when used for augmentation. Furthermore, training on synthetic data alone shows competitive results, demonstrating the effectiveness of representation-space generative modeling for data-scarce and privacy-sensitive tasks.

The growing global demand for effective and safe therapeutics has accelerated advances in biomaterials for drug delivery applications. Biomaterials, including polymers, metals, ceramics, and composites, play a central role in modern medical devices and therapeutic systems by enabling controlled interactions with biological environments. Initially defined as inert materials interfacing with biological systems, biomaterials are now rationally engineered to treat, replace, or evaluate tissue and organ functions. Recent progress in regenerative medicine, nanotechnology, and precision healthcare has expanded their use in drug delivery, where tunable physicochemical properties—such as degradation kinetics, surface chemistry, and mechanical stability—allow controlled release, protection of labile therapeutics, and enhanced accumulation at target sites. Polymer-based biomaterials enable sustained drug release through diffusion-controlled, degradation-mediated, or stimulus-responsive mechanisms, thereby extending therapeutic exposure and reducing systemic dosing frequency compared with conventional formulations. Nanostructured carriers, including liposomes, micelles, and dendrimers, further enhance drug delivery by improving solubility, cellular uptake, and site-specific targeting via size control, surface functionalization, and ligand-mediated interactions. Despite these advances, clinical translation remains limited by challenges related to immune–biomaterial interactions, batch-to-batch variability, long-term biodegradation behavior, and the scalability of manufacturing under regulatory constraints. Future biomaterial development must therefore emphasize precision fabrication, good manufacturing practice–compatible production, and biologically informed design strategies that account for patient-specific variability. This review provides a focused overview of biomaterial-based drug delivery systems, summarizes recent technological advances, and critically discusses mechanistic and translational challenges, including immune compatibility, degradation control, and regulatory compliance, with particular emphasis on their implications for personalized drug delivery.

Malware detection using deep learning faces challenges in model selection for practical deployment. We systematically compare five transfer learning architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0) on the MaleBin RGB malware dataset ($\text{1 2, 0 0 0 +}$ images through March 2025). Experiments on NVIDIA A100 GPU evaluated accuracy, efficiency, and deployment suitability. DenseNet121 achieved highest accuracy ($91.20 \%, 8 \mathrm{M}$ parameters), MobileNetV2 provided optimal edge deployment (90.39 %, 3.5 M parameters), while ResNet50 and EfficientNetB0 unexpectedly underperformed $(77.34 \%, 71.16 \%)$. Directions for practitioners are to deploy DenseNet121 for cloud environments, prioritizing accuracy, and MobileNetV2 for resource-constrained edge devices.

Perfluorination of the terminal methyl group in ethanol gives rise to different thermodynamics of mixing with water. To understand its origin, we probe structure, thermodynamics and collective vibration modes in aqueous solutions of ethanol (EtOH) or 2,2,2-trifluoroethanol (TFE) by using Terahertz (THz) spectroscopy and molecular dynamics simulations. The THz spectra show mainly two features: a mostly entropy-related peak (below 200 cm-1) related to weaker water-water hydrogen bonds, and an enthalpy-related large band above 200 cm-1 related to water-solute hydrogen bonds. The entropic feature is red-shifted for TFE relative to EtOH, consistent with TFE's subpopulation of weaker solvation shell water-water hydrogen bonds found in the simulations. By contrast, the thermodynamics of mixing is dominated by three effects: the higher probability of forming water-water hydrogen bonds in the solvation shell of either solute than in the bulk; the fact that TFE induces a smaller perturbation per water molecule than EtOH, despite perturbing a slightly larger number of water molecules than EtOH, and TFE's weaker solute-water hydrogen bond. The three effects determine the more negative (favourable) enthalpy of mixing and more negative (unfavourable) entropy of mixing of EtOH relative to TFE at low concentrations. The results confirm that hydrophobic solvation of perfluorinated groups is fundamentally different from that of their alkylated equivalents and have implications for the development of models to predict solubility of perfluorinated molecules.

Atrial fibrillation (AF) is the most common persistent cardiac arrhythmia in clinical practice and a significant, often underdiagnosed risk factor for stroke. The electrocardiogram (ECG) is the primary method for its detection, typically manifesting as irregular $\mathbf{R R}$ intervals and the absence of P-waves. Numerical ECG parameters enable quantitative analysis of these changes and provide a foundation for the development of automated detection systems. This study examines the association between atrial fibrillation and numerical ECG parameters using the ECG-ViEW II database. From 12-lead ECG recordings, key temporal and morphological parameters were extracted, and descriptive statistics were calculated to form the final dataset. Descriptive statistical analysis, inferential tests, and graphical visualizations were applied to compare AF and non-AF groups. The results indicate that parameters describing RR-interval variability show a strong association with atrial fibrillation, confirming their potential for application in automated systems for early AF detection.

This paper presents a novel UART-based debugging interface for resource-constrained RISC-V soft-core implementations on FPGAs. Unlike traditional JTAG-based approaches that require dedicated hardware and tools, our design leverages the ubiquitous UART peripheral to provide comprehensive debug capabilities through a dual-mode architecture. The interface operates in standard ASCII mode for command-line interaction and switches to a binary protocol for advanced operations including bulk instruction memory programming (IMPR), singleinstruction hot-patching (IMWR), and direct memory/CSR bus read/write operations (BUSR/BUSW). A key innovation is the bus mastering mechanism that enables real-time memory inspection and modification without permanent CPU halting, facilitating live debugging and in-field firmware updates. The FSM-based protocol incorporates checksum verification and timeout recovery for robust operation. Implemented on an 80 MHz RISC-V based SoC for WireGuard VPN acceleration, the interface consumes less than 2% additional FPGA resources while providing functionality comparable to more complex debug modules. Experimental results demonstrate successful hot-patching of running programs, sub-second firmware updates, and effective production diagnostics without requiring specialized JTAG hardware or tools.

This paper explores the application of FPGA (Field Programmable Gate Array) technology based on the Basys 3 board in video game development. Given that the hardware of most classic games is no longer functional, the programmable nature of FPGA allows for precise replication, allowing modern devices to run these games and provide an authentic gaming experience. The paper presents the design and emulation of a video game on the Basys 3 FPGA development system. It also examines the use of open-source tools for the development of FPGA applications, comparing them with commercial alternatives. The Basys 3 system was prepared for game emulation by implementing a RISC-V processor, a VGA controller, and memory in Verilog. The game was initially developed in $\mathbf{C}$ for Windows and later ported to the FPGA environment, with particular attention paid to memory management to ensure proper image display via the VGA controller. The results demonstrate that FPGA systems are viable platforms for video game emulation and complex application development, with open-source tools proving efficient and effective.

As web applications have grown to become more dynamic, frontend rendering strategies have also become more central in the architectural discussions. The three main strategies are client-side, server-side, and hybrid rendering, yet empirical comparisons of them under controlled conditions are limited. Each of them has their own set of trade-offs, and this study systematically evaluates them using three functionally and visually identical frontends which are connected to a single backend. The metrics which were collected described the rendering strategies' performance, user experience, search engine optimisation and crawlability, and resource utilisation. Results show that server-side and hybrid rendering significantly improve initial load performance and search engine optimisation compared to client-side rendering, reducing First Contentful Paint by approximately 65% on average, while client-side rendering reduces server resource usage but suffers from delayed rendering and poor crawlability. Hybrid rendering achieves the best balance between performance and search engine optimisation, but with higher resource consumption. These findings highlight that rendering strategy selection should be driven by application requirements, considering crawlability, infrastructure, and performance objectives.

Implementing a cognitive Sense-Think-Act-Learn (STAL) architecture for automated cultural heritage visualization, this paper illustrates a multi-agent system. The system consists of three specialized agents: an Exhibition Curator Agent that applies Large Language Models (LLMs) to classify and generate exhibitions, a Conversational Guide Agent that provides interactive visitor engagement, and an Image Acquisition Agent that performs automated visual content enrichment. Evaluated on a dataset including 6,398 historical events across multiple civilizations and epochs, the system effectively automates the curation of the data by adopting a hybrid approach via deterministic classification algorithms as well as LLM-based analysis. The architecture allows adaptive learning based on administrative feedback, improving classification accuracy over time. This work provides a concrete framework to leverage generative $A I$ in cultural heritage digitization while also addressing issues related to scale, multilingual content, and domain-specific curation needs.

This study investigates the application of machine learning clustering techniques, specifically Dynamic Time Warping (DTW), to define typical load profiles (TLPs) for industrial facilities. Utilizing $\mathbf{1 5}$-minute smart meter data from a plastics manufacturing plant, the research analyzes total factory consumption alongside individual chiller and compressor loads. Cluster quality is assessed using the Silhouette score, Dunn index, and mean intra-cluster distance. Results indicate that while DTW effectively captures temporal shapes, industrial profiles are highly enterprise-specific and noise-intensive, resulting in fair-to-weak cluster quality. The findings suggest that primary electricity datasets and basic temporal metadata are insufficient for high-quality profiling compared to existing household models. The study concludes that integrating production-related metadata, such as work orders, is essential for improving industrial consumption forecasting and capacity planning.