Abstract: The Mediator complex is a central regulator of eukaryotic transcription, functioning as a dynamic molecular bridge between gene-specific transcription factors and RNA polymerase II (Pol II). Although decades of research have established its modular architecture and fundamental role in transcriptional control, recent advances have significantly expanded our understanding of its structural conformations, subunit-specific functions, and links to human disease. This review provides a comprehensive overview of the Mediator complex, highlighting key structural and functional discoveries from the past decade and synthesizing its diverse roles in transcriptional regulation. We further discuss emerging concepts and future directions for therapeutically targeting Mediator, particularly in cancer. Together, these insights position the Mediator complex as a highly conserved yet adaptable, signal-responsive regulatory hub with broad implications for both normal physiology and disease pathogenesis.

Abstract: Resistant mechanisms to venetoclax, a selective BCL-2 inhibitor approved for hematological malignancies, are frequently mediated by the G101V mutation in BCL-2. Sonrotoclax illustrates superior potency against both wild-type and G101V-mutated BCL-2, yet the mechanistic basis remains unclear. This study employed computational methods to investigate the binding dynamics of both inhibitors. Structures were predicted with AlphaFold, refined via molecular dynamics simulations (MDS), and ligands were docked with AutoDock Vina. Four systems were subjected to triplicate 200 ns MDS, with analyses including RMSD, RMSF, buried surface area, protein-ligand interaction fingerprint, and MM/GBSA binding free energies. Results indicate venetoclax exhibits progressive dissociation from G101V BCL-2, with elevated RMSD, reduced buried surface area, and increased unbound states. In contrast, Sonrotoclax maintains a steady correlation, shows persistence with entropy-enthalpy compensation, displays negligible unbound time, higher binding free energies, and constant van der Waals anchors. Having all these results in mind, a "Dynamic Blockade" hypothesis is proposed, where Sonrotoclax's flexibility enables sustained BH3 groove occupancy, blocking pro-apoptotic BH3-only proteins and overcoming allosteric perturbations induced by G101V. This mechanistic perspective proposes the optimal approach for designing resilient inhibitors to accelerate drug repurposing and development in oncology.

Abstract: The blackbody radiation problem gave rise to Planck's hypothesis of energy quantization, which is regarded as the inception of quantum theory and ultimately led to a fundamental conceptual schism between the emerging quantum description and the established classical framework of physics. This paper argues that this historical turning point stems from a profound misunderstanding of the concept of the "quantum". Through a systematic critique of the three fundamental errors in the Rayleigh-Jeans formula, we propose, based on a revised classical electrodynamics framework, that the elimination of the ultraviolet catastrophe does not require the introduction of the assumption of energy discreteness. The key lies in recognizing that continuous energy transfer occurs only when electrons undergo accelerated or decelerated motion, and that the essence of the minimum energy unit ε is a natural measurement benchmark for this continuous process, rather than a physically discrete "energy packet". Building on this, we have derived a blackbody radiation formula that fully matches experimental data. This formula is consistent with the Rayleigh-Jeans formula in the low-frequency region and naturally exhibits exponential decay in the high-frequency region, successfully eliminating the ultraviolet catastrophe. This research fundamentally clarifies the physical origin of the "quantization" feature: it arises from the measurement discreteness of the energy transfer process and the constraints of thermodynamic statistics, rather than a change in the intrinsic nature of energy itself. This achievement not only fulfills Planck’s unfulfilled desire for a classical explanation but also demonstrates that blackbody radiation, and even a series of "quantum phenomena", can be fully explained within a purely self-consistent classical physics framework. This lays a crucial foundation for bridging the "classical-quantum" divide and reconstructing a unified theoretical system in physics.

Abstract: In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using Complementary Metal-Oxide-Semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the extremely high frequency (EHF) range. The SHF mode resonates at 8.5 GHz and exhibits ultrawideband (UWB) behavior, while the EHF mode resonates at 36.6 GHz. Both resonance frequencies can be tuned in a non-volatile fashion by controlling the ferroelectric polarization state of a Hafnium Zirconium Oxide (HZO) varactor monolithically integrated into the feed line. This programmability arises from the ferroelectric switching of the embedded HZO film, which results in a non-volatile variation of its permittivity upon application of a voltage pulse. Ferroelectric switching occurs at approximately ±3 V and induces maximum resonance frequency shifts of 381 MHz for the SHF mode and 3 GHz for the EHF mode, corresponding to fractional frequency changes of 4.5% and 8.3%, respectively. Unlike previously reported ferroelectrically tunable antennas, our reported antenna combines full integration, CMOS compatibility, higher operating frequency, compact footprint, and non-volatile programmability.

Abstract: Wine is widely consumed across cultures and is often perceived as a benign or even beneficial alcoholic beverage, particularly when consumed in moderation and within the context of healthy dietary patterns. At the same time, alcohol is one of the most commonly used substances to self-manage sleep problems. This short narrative review critically examines evidence published over the past decade (2015–2025) on the impact of wine and alcohol more broadly on sleep health in community-dwelling adults. Priority was given to systematic reviews and meta-analyses, followed by high-quality observational and experimental studies. Across study designs, evidence consistently demonstrates that although alcohol may reduce sleep onset latency, it disrupts sleep architecture, suppresses rapid eye movement sleep, increases sleep fragmentation, and impairs breathing during sleep, particularly during the second half of the night. Habitual alcohol consumption is associated with poorer subjective sleep quality, insomnia symptoms, and increased risk of sleep-disordered breathing. Mechanistic pathways include effects on neurotransmission, sleep homeostasis, circadian regulation, thermoregulation, and alcohol metabolism during sleep. A short section also examines the reciprocal relationship, highlighting evidence that circadian disruption, shift work, and evening chronotype are associated with higher alcohol consumption. Although wine contains bioactive compounds such as melatonin and polyphenols, current evidence does not support a clinically meaningful protective effect of wine on sleep. Overall, wine should not be considered a sleep aid, and public health messaging should emphasize dose, timing, and regularity of alcohol consumption in relation to sleep health.

Abstract: Externally applied electric fields are widely employed during thin-film deposition to im-prove film uniformity, texture and densification. Despite extensive experimental evidence, the physical mechanisms by which such fields influence nucleation, surface diffusion, is-land coalescence and interface stability remain theoretically fragmented. Classical thin-film growth models assume a field-free energetic landscape and therefore provide limited predictive guidance for field-assisted manufacturing strategies. In this work, we introduce the Field-Driven Growth Model (FDGM), a unified theoretical framework that incorporates field–matter interactions directly into the free-energy func-tional governing thin-film growth. By explicitly accounting for effective dipolar coupling arising from field-induced polarization of surface species, predominantly quadratic in the field amplitude and consistent with linear-response polarization, the model consistently modifies the fundamental processes of nucleation, surface diffusion and coalescence. At the continuum scale, the FDGM predicts a field-induced stabilization mechanism that suppresses long-wavelength roughening modes and defines a field-controlled morpho-logical crossover wavelength (field-controlled cutoff). The FDGM demonstrates that field-assisted nucleation bias, anisotropic surface diffusion, field-biased coalescence pathways and morphological stabilization are not independent phenomena, but multiscale manifestations of a single energy-minimization principle act-ing on a field-modified energy landscape. By providing analytical stability criteria and explicit links between external field parameters and morphological outcomes, the model establishes a predictive foundation for the manufacturing of thin films with improved uniformity in advanced thin-film-based devices. The framework is broadly applicable to deposition techniques such as sputtering, pulsed-laser deposition, chemical vapor depo-sition and atomic layer deposition.

Abstract: The increasing demand for high-quality dragon fruit in the European market requires efficient quality assessment methods. This study explores a non-destructive image analysis approach for classifying ripe dragon fruits based on fruit ripeness and weight. A low-cost system equipped with visible and ultraviolet lighting was employed to capture images of 60 ripe dragon fruits over a storage period, extracting parameters such as visible and ultraviolet perimeter, maximum and minimum diameter and area, and RGB color coordinates. In a first step, the main characterization magnitudes were confirmed. A ripening index was calculated based on soluble solid content and acidity. Then, a cluster analysis was used to segregate the fruits into three quality characteristics based on the ripening index and weight. In a second step, a step-by-step discriminant analysis was conducted to classify the fruits into the three quality categories (based on the laboratory measured weight, soluble solid content and total acidity) using the non-destructive magnitudes extracted from the image analysis. The proposed classification system achieved an accuracy of nearly 85 \% of well classified dragon fruits, effectively segregating dragon fruits into the three established categories. urthermore, the established model could select the very high-quality dragon fruit (riper and larger fruits) with 93 \% of correctly dentified products.Compared to conventional destructive methods, this non-destructive approach offers a promising, cost-effective, and reliable solution for quality assessment. The findings highlight the potential for integrating smart technologies into fruit classification processes, during automatic harvest and postharvest operations, ultimately improving efficiency, reducing labor costs, and enhancing product consistency in the dragon fruit industry.

Abstract: This article is devoted to constructing of fractional powers of operators and their matrix approximations. A key feature of this study is the use of a spectral approach that remains applicable even when the base operator does not generate a semigroup. Our main results include the convergence rate of matrix approximation, derived from resolvent estimates, and a practical algorithm for constructing matrix approximations. The theory is supported by examples.

Abstract: Malaysia's 2012 amendment to the Uniform Building By-Laws introduced mandatory water efficiency requirements for new construction, yet the extensive inventory of public buildings constructed before this regulatory milestone remains largely uncharacterized in terms of water consumption patterns and efficiency potential. This study develops a comprehensive assessment framework specifically designed for evaluating water supply and demand in four critical public building types, namely government offices, hospitals, police stations, and mosques, constructed before the UBBL 2012 amendment. Through systematic analysis of international water benchmarking literature and synthesis of building-specific consumption patterns, an integrated assessment methodology is proposed combining water auditing protocols, high-resolution metering strategies, cluster-based benchmarking approaches, and building-type-specific performance indicators. Literature synthesis reveals substantial variability in public building water consumption internationally, with hospitals demonstrating consumption ranging from 103 to 458 cubic meters per bed per year, government offices showing documented savings potential of 31 to 82 percent through systematic monitoring programs, and mosques achieving approximately 45.5 percent fresh water savings through greywater reuse from ablution facilities. However, police stations represent a critical research gap with zero documented consumption studies in the available literature. The proposed framework establishes building-type-specific indicators, standardized data collection protocols, and benchmarking clusters to enable systematic assessment and prioritization of retrofitting interventions for Malaysia's pre-2012 public building stock.

Abstract: Soil heavy metal (HM) pollution poses a severe threat to ecological security and human health. Selenium (Se) is an essential trace element for the human body and can regulate crop growth and development as well as HM uptake in HM-contaminated soils. The regulatory mechanisms of Se on HMs are mainly reflected in four aspects: Geochemical immobilization promotes the formation of metal selenide precipitates and the adsorption of HMs by soil colloids by regulating the rhizosphere redox potential (Eh) and pH value. Rhizosphere microbial remodeling drives the enrichment of functional microorganisms such as Se redox bacteria, plant growth-promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF) through the dual selective pressure of Se toxicity and root exudates, so as to synergistically realize Se speciation transformation and HM adsorption/chelation. Root barrier reinforcement constructs physical and chemical dual defense barriers by inducing the formation of iron plaques on the root surface, remodeling root morphology and strengthening cell wall components such as lignin and polysaccharides. Intracellular transport regulation down-regulates the genes encoding HM uptake transporters, up-regulates the genes encoding HM efflux proteins, and promotes the synthesis of phytochelatins (PCs) to form HM complexes and finally realizes vacuolar sequestration. Finally, we summarize current research gaps in the interaction mechanisms of different Se species, precise application strategies, and long-term environmental risk assessment, providing a theoretical basis and technical outlook for the green remediation of HM-contaminated farmlands and Se biofortification of crops.

Abstract: Inflammation is a normal and essential feature of pregnancy, supporting implantation, placental development, and parturition. When dysregulated, however, inflammatory pathways contribute to major obstetric complications such as preeclampsia, fetal growth restriction (FGR), and preterm birth, which account for substantial maternal and perinatal morbidity and mortality. This review synthesizes current understanding of the maternal–fetal immune interface, examines how inflammation contributes to pregnancy disorders, and explores therapeutic strategies that link pathogenic mechanisms to targeted interventions. The placenta functions as an active immunological hub, coordinating innate and adaptive immune responses to maintain tolerance while protecting against infection. In preeclampsia and FGR, excessive activation—driven by inflammasome signaling, Th1/Th17 polarization, and altered natural killer and macrophage function—impairs placental perfusion, promotes antiangiogenic pathways, and induces systemic endothelial dysfunction. Established therapies, including low-dose aspirin, low-molecular-weight heparin, and antenatal corticosteroids, aim to mitigate inflammation and optimize fetal outcomes, while adjunctive strategies target oxidative stress, nutritional deficits, and the maternal microbiome. Emerging approaches such as cytokine-targeted biologics, inflammasome inhibitors, and mesenchymal stem cell therapies show promise but require rigorous evaluation of safety and efficacy. A deeper understanding of placental immunology and inflammatory signaling is essential to develop precision therapies. Future research should prioritize biomarker validation, pathway-specific interventions, and equitable implementation to reduce inflammation-driven pregnancy complications.

Abstract:

We present a rigorous reformulation of Einstein’s General Relativity using the real Clifford algebra Cl_1,3, constructed from Dirac gamma matrices. In this framework, all geometric and dynamical structures—including the metric, spin connection, curvature, and energy-momentum tensor—are expressed using algebraic operations (symmetrized products, commutators, and traces) of Clifford generators. Rather than invoking the full machinery of differential geometry, we reconstruct the Einstein field equations entirely within an operator algebra framework, while maintaining exact equivalence with the classical theory. The underlying metric structure is assumed through the anticommutation relations defining the Clifford algebra, and is algebraically reconstructed using trace identities. This approach provides a unified representation of both geometry and spinor fields and may offer conceptual and pedagogical advantages in connecting gravity with operator-based formulations. Potential extensions involving bivector sectors and torsion are briefly discussed.

Abstract: Black hole singularities still remain a central challenge in gravitational physics. In this work, we present a geometric interpretation of non-singular black hole cores within teleparallel gravity based on geometric drift vectors. Gravitational effects are encoded in a comoving tetrad framework through a dynamical drift field whose gradients generate torsion rather than spacetime curvature. While the teleparallel equivalent of general relativity reproduces the Schwarzschild behavior in the weak-field regime, nonlinear invariant contributions dominate in the strong-field region, replacing the central singular behavior with a smooth de Sitter–like core. Event horizons emerge as drift horizons associated with the limiting behavior of the geometric flow, and null and timelike trajectories admit analytic extensions across the horizon and central region.

Abstract: Sports science is rapidly changing with new discoveries in molecular biology and artificial intelligence. Modern “omics” tools, such as genomics, proteomics, and metabolomics along with AI-based analytics, help us understand how a child’s body builds muscle, responds to training, and recovers after exercise. These technologies also help identify factors that may increase the risk of injury. Simple genetic tests, including variations like ACE I/D and ACTN3 R577X, provide insights into traits linked to endurance, strength, and muscle performance. Protein and metabolite testing, supported by AI models, can reveal how efficiently the body uses energy or repairs tissues after activity. This review article provides the most recent and up-to-date knowledge regarding modern technologies used for performance enhancement. These scientific tools are not meant to label or limit children. Instead, they help parents and coaches understand each child’s individual needs and support healthier training decisions. AI-driven interpretations can guide choices about training intensity, rest, recovery, and nutrition in a safe and personalized manner. Overall, this paper offers practical guidance for using molecular and AI-driven sportomics responsibly. Our goal is to empower parents and coaches with informed, balanced, and child-centric strategies for enhancing performance.

Abstract: Background: Epigenetic regulation must preserve stable functional states under molecular stochasticity and changing environments, yet an operational model linking context-level signals to measurable chromatin remodeling is limited. Method: This study proposes Epigenetic Teleonomy, a stochastic control framework in which epigenomic observables relax toward an empirically estimated within-subject baseline regime (reference distribution) with lagged mediator-driven inputs and feedback. Results: A local approximation yields mean-reverting dynamics. Simulations illustrate that without effective feedback, diffusion-like drift leads to increasing dispersion, whereas sufficient regulation gain yields bounded fluctuations and recovery. In the isotropic local Ornstein–Uhlenbeck (OU) regime, stationary fluctuations scale with effective diffusion and inversely with return rate (gain). Conclusion: The framework is testable in longitudinal designs by (i) estimating a subject-specific baseline from a stable run-in window, (ii) quantifying deviation using reduced-dimensional proxies, and (iii) fitting gain and diffusion from return-to-baseline statistics.

Abstract: In the age of the Internet of Things (IoT), IoT devices scattered across various locations gather and store data in a decentralized manner to improve computational efficiency. Nevertheless, within IoT networks, factors such as fragile devices, challenging deployment conditions, and unreliable data transmission are raising the likelihood of data gaps, potentially having a substantial impact on the subsequent data processing resulting in failure of the system. Conventional imputation approach relies on using historical trend or sensor fusion techniques to combine information from different sensors to fill in the gaps in where information is missing. Historical trend struggles to capture new or emerging patterns, whereas using sensor fusion, even though it shows promising results, relies on information from multiple sensors from same target environment, making it vulnerable to single-point failures. This article presents an alternative strategy: using sensor-based fusion, but in this case, multiple sensors gather data from different targets independently. The architecture intelligently looks and gathers the sensor information from other location/target (multiple locations), sensing the same environmental information, learns the distribution and correlation and employ algorithm to generate synthetic data for imputing missing information. The study conducted experiments by fusing weather station data from various US locations and comparing the effectiveness of this approach to conventional methods. Further, the proposed synthetic data generation approach outperformed other algorithms when applied to the fused weather station dataset. This innovative approach mitigates the risk of single-point failures and offers a more robust solution for dealing with missing data in IoT networks.

Abstract: The localization of the Sustainable Development Goals (SDGs) has become a central dimension of sustainable urban development, as local governments play an increasingly important role in translating global sustainability agendas into place-based action. This study aims to provide a state-of-the-art assessment of how scholarly research has examined the relationship between local governance and SDG implementation over the period 2018–2025. A mixed-method review approach was employed, combining bibliometric mapping using VOSviewer with qualitative content analysis conducted through NVivo. Based on predefined inclusion criteria, 143 peer-reviewed articles indexed in the Web of Science database were systematically analyzed. The results reveal several dominant thematic clusters, including institutional coordination, sustainable urban planning, data-driven governance, accountability mechanisms, and the growing use of policy tools such as Voluntary Local Reviews (VLRs). The findings indicate an increasing emphasis on performance-based monitoring, participatory governance approaches, and multilevel institutional frameworks supporting the integration of the SDGs into local policy and planning processes. At the same time, persistent challenges are identified, particularly with regard to equity considerations, data inconsistencies, and the limited inclusion of marginalized urban communities in SDG-related decision-making. Overall, this review offers a structured and comprehensive overview of current research on SDG localization in urban governance and identifies key gaps and priorities for future research and policy development aimed at more inclusive, measurable, and context-sensitive pathways to sustainable urban development.

Abstract: Background: TNXB-related classical-like Ehlers-Danlos syndrome (clEDS) is caused by biallelic pathogenic variants in TNXB, encoding the extracellular matrix glycoprotein tenascin-X. Although traditionally classified as a connective tissue disorder based on joint hypermobility and skin findings, accumulating clinical, electrophysiological, and imaging data indicate prominent neuromuscular involvement that likely reflects a central disease mechanism. Methods: A qualitative evidence synthesis was conducted following PRISMA 2020 guidelines. A comprehensive search of PubMed, OMIM, and GeneReviews was performed on January 5, 2026. Data from 18 studies representing 56 individuals with biallelic TNXB variants were synthesized narratively, with findings stratified by assessment method and zygosity. Due to heterogeneity in study designs, assessment methods, and outcome definitions, quantitative meta-analysis was not feasible. Results: Among 56 individuals with biallelic TNXB variants, subjective muscle weakness was reported in only 37% of cases. However, systematic neuromuscular assessment demonstrated objective muscle weakness in 85% of patients examined. Electromyography revealed mixed neurogenic-myopathic patterns in 60%, and muscle imaging abnormalities were present in approximately 50%. A clear dose-effect relationship was observed, with heterozygous individuals exhibiting milder phenotypes correlating with reduced serum tenascin-X levels. Conclusion: Neuromuscular involvement in TNXB-related disorders is frequent, progressive, and mechanistically linked to dysfunction at the muscle-extracellular matrix interface. These findings support the reclassification of TNXB-related disease alongside myopathic Ehlers-Danlos syndrome as a muscle-ECM interface disorder.

Abstract: Older adults with cancer face disproportionately high rates of severe treatment-related toxicities, yet current prediction tools rarely incorporate biomarkers that capture physiological resilience. The hypothalamic–pituitary–adrenal (HPA) axis—central to stress adaptation, immune regulation, and tissue repair—undergoes pronounced age-related alterations, including elevated basal cortisol, reduced dehydroepiandrosterone (DHEA) and its sulphate form DHEAS, and an increased cortisol:DHEA(S) ratio. These changes may impair immune function, delay recovery, and exacerbate vulnerability to treatment toxicity. This narrative review synthesizes mechanistic and clinical evidence linking HPA-axis dysregulation to treatment tolerance in geriatric oncology. Common patterns include blunted diurnal cortisol slopes, elevated evening cortisol, and low DHEA(S), which are associated with fatigue, functional decline, and reduced survival across cancer types. However, their predictive value for acute treatment toxicities remains underexplored due to methodological heterogeneity, lack of age-specific reference ranges, and absence from existing geriatric toxicity models. This review proposes a translational roadmap that prioritizes (1) standardization of salivary cortisol/DHEA(S) protocols; (2) prospective, age-stratified validation studies using standardized toxicity endpoints; (3) interventional testing of behavioral or pharmacological strategies to modulate HPA function; and (4) integration into oncology workflows and electronic decision-support tools. Incorporating endocrine biomarkers into risk prediction could refine treatment stratification, enable targeted supportive care, and ultimately improve outcomes for older patients with cancer.

Abstract: This paper develops a generative AI decision-support and optimisation framework for advancing sustainability and resilience in industrial logistics. The framework combines data aggregation, generative scenario creation, simulation-based evaluation, and multi-objective optimisation to support evidence-based management under tightening European Union sustainability regulations. Building upon the decision-aid lineage of the International Journal of Production Research, it integrates policy variables such as the Carbon Border Adjustment Mechanism (CBAM), the EU Emissions Trading System for maritime transport, FuelEU Maritime, the Digital Product Passport (DPP), and the Corporate Sustainability Reporting Directive (CSRD) directly into logistics-planning equations. Recent studies on digital twins and adaptive optimisation (Longo et al., 2023; Flores-García et al., 2025) highlight the need for AI systems that translate these policies into dynamic cost and carbon trade-offs. The proposed model responds to this need by coupling generative scenario synthesis with traceable optimisation and governance controls consistent with the EU AI Act (European Commission, 2025). An illustrative case from the mining-rope industry demonstrates how global sourcing and transport routes in European, South African, and Chinese configurations can be simulated within the generative environment to evaluate comparative cost, emission, and compliance profiles. Both SME-light and enterprise implementations achieved reduced analysis time and improved transparency of carbon-related decisions. The study contributes a replicable methodology that transforms generative AI from a creative text tool into a quantifiable governance instrument, linking strategic foresight with operational resilience in sustainable logistics networks.