Abstract: Modern user authentication systems increasingly need user and device behavior aware adaptive mechanism to detect evolving threats beyond the traditional authentication framework of static credential verification. This paper proposes a hybrid multi-model framework for personalized user-level anomaly detection using a data-driven Hybrid Anomaly Score (HAS). Unlike static thresholding approaches, The proposed framework derives algorithm that integrates multiple anomaly detection methodologies to compute HAS through adaptive per-user thresholds (using cohort maturity and percentile-based optimization). The framework is evaluated on 72 million real-world data set. The framework demonstrate 96% precision, 92% recall, and an F1-score of 0.94, while maintaining inference latency within 2-3 ms per authentication event. The ablation analysis of the framework confirms the contribution of dynamic weighting and personalized threshold optimization to improved detection stability and convergence. The proposed framework outperforms existing approaches in both scalability and latency satisfying real-time operational constraint. The results indicate that data-driven adaptive thresholding combined with hybrid anomaly modeling provides an effective and deployable solution for large-scale authentication environments.

Abstract:

Summary: The potential of deep learning (DL) in genomic selection (GS) is constrained by the significant technical expertise required to design and implement neural networks. While DL has revolutionized fields like language processing and structural biology, its application in GS has not yet consistently outperformed traditional models like mixed linear models. The key to unlocking DL's power in GS lies in the exploration of network architectures tailored to genomic data, a process that demands intensive programming and poses a barrier for many researchers. To overcome this challenge, we developed Artificial Intelligence for Efficient and Versatile Evaluation and Representation (AI4EVER), a freely available graphical software platform that enables users to explore and apply machine learning (ML) models without any coding. AI4EVER integrates a graphical user interface (GUI) with a Python-based ML backend. The platform currently supports five models: Ridge Regression, Random Forest, Gradient Boosted Decision Trees, Multi-Layer Perceptron, and a customizable Keras-based neural network that can simultaneously predict multiple traits in a single model. A key feature of AI4EVER is optional incorporation of genome-wide association study (GWAS) results (p-values) as feature weights during model training, enabling biologically informed DL workflows. The platform further provides real-time visualization of model performance metrics and automated feature-importance outputs to enhance interpretability. AI4EVER also separates model training and prediction workflows, allowing trained models to be reused for independent prediction datasets. Using a representative maize dataset, we demonstrate that AI4EVER enables access to advanced AI, empowers genomic researchers to accelerate data-driven decision-making in breeding programs, ultimately lowering the barrier to artificial intelligence-enabled genetic improvement in crops and animals and human health management.

Abstract: The hierarchy problem---the seventeen-order-of-magnitude separation between the electroweak scale and the Planck scale---remains one of the most compelling open questions in theoretical physics. Within the Standard Model, quadratically divergent quantum corrections to the Higgs mass require extraordinary fine-tuning at every loop order, with no underlying physical explanation. We propose a geometric suppression mechanism in which the electroweak scale arises naturally from the local curvature geometry of singular cycles within a six-dimensional Calabi--Yau compactification of Type~IIB superstring theory. When toroidal cycles degenerate, string-theoretic $D$-brane defects form at the resulting singular loci, and monopole-brane recoil governed by the Nambu--Goto action produces massless spin-2 gravitons that propagate into the higher-dimensional bulk. The local singularity energy density, controlled entirely by the curvature scale of the collapsed cycle, determines the electroweak mass scale without free parameters. A complementary brane-instanton mechanism generates the hierarchy exponentially from a geometric action of order thirty-seven, naturally reproducing the observed ratio of electroweak to Planck scales. We derive an explicit four-dimensional effective action from Kaluza--Klein reduction, demonstrate three independent consistency limits, compare the mechanism with Randall--Sundrum warped geometry and supersymmetric approaches, and outline a programme for embedding the proposal in explicit compactification models.

Abstract: This paper proposes a unified gravitational theory framework based on discrete spatial element dynamics, grounded in two fundamental principles: matter conservation in discrete space and global configurational covariance. It posits that spacetime consists of indivisible discrete spatial elements, where quantum virtual processes generate new elements by consuming conserved spatial raw materials. The resulting local density gradient constitutes the microscopic essence of spacetime curvature. The framework eliminates the concept of action at a distance, achieving self-consistency with general relativity under covariance constraints. It fundamentally resolves four major physics puzzles: dark matter, dark energy, black hole singularity, and vacuum catastrophe.This paper first elucidates the core concept of "holistic covariant" and provides the ultimate explanation for symmetry breaking—symmetry breaking is the local cost paid to achieve global covariance. Subsequently, it systematically expounds the twelve core tenets of the framework, using the second-order discrete wave equation of complex fields as the sole foundational equation . Through rigorous step-by-step derivation, it rigorously establishes all fundamental laws of classical and quantum physics, including the Newtonian gravity limit, mass-energy equivalence, principle of constancy of light speed, Maxwell's equations, Newton's three laws of motion, Schrödinger equation, Dirac equation, and others. It explicitly clarifies the geometric origin of spin-1/2 and presents the geometric formula for the fine-structure constant. All physical laws are derived theoretically rather than based on external inputs.To address the theoretical shortcomings—including ambiguous definitions of spatiotemporal structures, lack of quantitative mapping for densification, unclear mechanisms of Laplacian approximation, and undefined density-curvature relationships—this study introduces refined solutions: an asymmetric nanograin model, Landau free energy theory for densification, third-order accurate discrete Laplacian, and differential geometry-derived field mapping. All quantification metrics (error <1%, fit>0.95) are derived through first-principles calculations constrained by observational data, with no artificial adjustments. This paper further conducts cross-verification of the theory from eight modern geometric perspectives, including fiber bundles, complex geometry, and conformal geometry, unifying the standard model constants into geometric invariants of discrete spacetime. Based on discrete compact manifolds and genus geometry, it achieves parameter-free numerical calculations of the lepton mass ratio, derives Friedmann equations with discrete geometric corrections, and provides a natural geometric origin for the cosmic lithium problem. Ultimately, it offers eight quantitative predictions verifiable by future high-energy physics and cosmological experiments, providing a self-consistent, complete, and falsifiable new path toward the unification of quantum gravity and the standard model.

Abstract: Mycorrhizae utilization is an integral part of adaptation strategy to climate change in semiarid regions. A controlled experiment was conducted at the University of Limpopo, South Africa, to determine the effects of arbuscular mycorrhiza fungi (AMF) and soil type on crop growth and root colonization of Jew’s mallow. Treatments comprised two levels of mycorrhiza (with and without), two soil sources (Ofcolaco and Syferkuil), and three Jew’s mallow cultivars ("Amugbadu", "Oniyaya", and a Landrace). The results revealed that the Amugbadu cultivar produced the highest Mycorrhizal growth response (MGR) in Syferkuil soil, whereas Oniyaya cultivar was the highest in Ofcolaco soil. MGR in Ofcolaco soil was six times higher than in Syferkuil soil. AMF-inoculated Amugbadu consistently resulted in the highest crop growth rate (CGR) in Ofcolaco soil. Inoculated Landrace was superior in CGR, compared to uninoculated Landrace in Syferkuil soil. Approximately 71.23-75.86% of the Jew’s mallow roots were colonized by AMF in both soil sources, with inoculated ʺAmugbaduʺ producing the highest root colonization. The landrace root colonization was inferior in both soil sources. Our results indicate that incorporating AMF in Jew’s mallow could improve root coloni-zation, growth rate, and productivity in the semi-arid regions of South Africa, depending on soil type.

Abstract: Why do different swarm algorithms achieve different performance on the same fitness landscape? This paper proposes that navigability—the structural capacity to find improving paths—is observer-dependent: different algorithms perceive different navigability on the same landscape, and this difference is irreducible to landscape properties alone. We formalise this through the decomposition F = P/D, where Perception (P) measures an algorithm’s differentiation capacity and Distortion (D) measures structural resistance. Three claims are advanced and tested at scale on the Deucalion supercomputer. First, D is confirmed as multiplicative (geometric), not additive (R² = 0.993 vs 0.643, 10,000 simulations). Second, P is observer-dependent: six different strategies on the same graph yield six different P values, with hidden variable reconstruction achieving only R² = 0.069 (57,518 trials across 10,000 graphs). Third, step-wise alignment—the fraction of moves that reduce distance to the optimum—is the dominant predictor of navigation efficiency (R² = 0.82), outperforming all tested graph-theoretic and landscape metrics (maximum R² = 0.03). Complementarity is demonstrated: system-level and agent-level Perception are anti-correlated (ρ = −0.393), meaning that increasing the number of available paths decreases the fraction of improving paths per step. Eight counterfactual tests, mediation analysis, and cross-domain validation (graph navigation, 2D continuous landscapes, PSO, ACO) support the framework. All results are simulation-based. Falsification criteria are specified. The framework is a hypothesis under test, not a proven law.

Abstract: The development of postgraduate course cases for clinical pharmacy plays a pivotal role in facilitating professional knowledge acquisition for postgraduate students majoring in this discipline. The Kern Six-Step Approach serves as an innovative paradigm for curriculum construction, comprising six core components: needs assessment, objective formulation, content design, implementation strategies, evaluation and feedback, and continuous improvement. This paper reviews the existing challenges in the construction of course cases for postgraduate clinical pharmacy programs, the implementation procedures, cur-rent application status, as well as the effectiveness evaluation of the Kern Six-Step Teaching Approach.

Abstract: Background/Objectives: Abdominal sepsis remains a major contributor to morbidity and mortality among surgical and critically ill patients worldwide. Timely diagnosis is frequently hindered by the overlapping clinical and biochemical features of postoperative inflammatory responses and evolving intra-abdominal infections, which may resemble systemic sepsis. Conventional biomarkers, including C-reactive protein (CRP) and procalcitonin (PCT), are widely implemented in clinical practice but demonstrate suboptimal specificity in differentiating infectious from sterile inflammatory conditions in the early postoperative phase. Presepsin (soluble CD14 subtype, sCD14-ST), a circulating fragment released during monocyte–macrophage activation in response to bacterial endotoxins, has emerged as a biomarker reflecting innate immune engagement. This review aims to critically evaluate the current evidence regarding the diagnostic accuracy, prognostic relevance, and potential clinical role of presepsin in abdominal sepsis. Methods: A comprehensive narrative review of the biomedical literature was performed using MEDLINE (via PubMed) and supplementary academic sources. Studies assessing the diagnostic performance, prognostic associations, and clinical applicability of presepsin in abdominal infections, postoperative infectious complications, and sepsis were systematically examined. Where available, comparative analyses with established biomarkers such as CRP and PCT were evaluated to contextualize its incremental value within existing diagnostic frameworks. Results: The accumulated evidence indicates that presepsin concentrations increase early during bacterial infections and correlate with validated severity indices, organ dysfunction scores, and mortality outcomes. Across multiple surgical and intensive care settings, presepsin demonstrated moderate-to-high diagnostic performance, frequently comparable to and occasionally exceeding that of traditional inflammatory biomarkers, particularly in distinguishing septic from non-septic inflammatory states. Moreover, dynamic changes in circulating levels appear to provide additional prognostic information and may support longitudinal clinical assessment. Nonetheless, substantial heterogeneity in study design, patient populations, sampling strategies, and reported cut-off values limits direct cross-study comparability and constrains definitive clinical recommendations. Conclusions: Presepsin represents a biologically plausible and clinically promising biomarker for the early identification and risk stratification of abdominal sepsis. Although current findings are encouraging, further large-scale, methodologically standardized prospective investigations are required to define optimal diagnostic thresholds and to clarify its role within multimodal biomarker strategies in contemporary sepsis management.

Abstract: Equitable access to rehabilitation services is essential for individuals living with disa-bility, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpa-tient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate travel time to the nearest clinic and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2,255 outpatient rehabilitation clinics across 6,896 census tracts. Travel times varied substantially, with rural areas ex-periencing the longest travel burdens. The 2SFCA analysis revealed pronounced dis-parities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, sug-gesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access.

Abstract: Prominin-1 (Prom1/CD133) has long been recognized as a structural determinant of photoreceptor outer segment (OS) morphogenesis, yet rapidly accumulating evidence extends its role to retinal pigment epithelium (RPE) homeostasis, encompassing autophagy–lysosomal flux, outer segment phagocytosis, mitochondrial function, and regulation of inflammatory stress. This review synthesizes mechanistic and transcriptomic insights that position Prom1 as a central regulator of photoreceptor and RPE integrity, reframing Prom1 disease as a multi-compartment retinal disorder relevant to both inherited retinal dystrophies (IRDs) and atrophic age-related macular degeneration (AMD). We develop a dual-axis conceptual model in which Prom1 dysfunction can initiate pathology in either the photoreceptors (OS morphogenesis failure) or the RPE, including impaired autophagic flux, lysosomal activity, defective phagocytosis, and EMT-like de-differentiation, with secondary cross-compartmental degeneration. Clinically, autosomal dominant missense variants associate with macular or cone rod dystrophy, whereas biallelic truncating/splice site mutations drive early onset rod–cone disease and panretinal/RPE atrophy, illustrating genotype–phenotype diversity. By integrating recent high-resolution transcriptomic data from Prom1-deficient RPE cells with long-standing insights into photoreceptor biology, we highlight converging pathways of degeneration that challenge a photoreceptor-centric view and unify disparate phenotypes within a single molecular framework. These insights broaden the therapeutic landscape, advancing gene augmentation and pathway-targeted strategies to preserve RPE integrity, sustain photoreceptor function, and modify disease course in Prom1-associated IRDs and atrophic AMD.

Abstract: Organic facies distribution exerts a primary control on hydrocarbon generation potential in clastic-dominated passive margin basins. This study evaluates the spatial and stratigraphic distribution of organic facies and their hydrocarbon potential in the Niger Delta Basin using an extensive organic geochemical dataset. A total of 715 source rock samples from onshore, shallow offshore, and deepwater wells were analyzed using total organic carbon (TOC) and Rock-Eval pyrolysis parameters (S1, S2, S3, HI, OI, Tmax). Organic facies were classified following the Pepper organofacies scheme to assess variations in organic matter type, richness, and generative potential across depositional settings and depobelts. The results show that source rocks of the Akata Formation are dominated by organofacies B and D/E, reflecting mixed marine and terrigenous organic matter with moderate to high hydrogen indices and predominantly oil-prone to mixed oil–gas generative potential. In contrast, source rocks of the Agbada Formation are characterized mainly by organofacies F, dominated by terrestrial organic matter with low hydrogen indices, indicating a gas-prone character. Cretaceous shales beneath the Niger Delta contain mixed organofacies D/E and F and locally exhibit fair to good hydrocarbon potential. TOC values range from 0.1 to 16.9 wt%, with the highest organic richness concentrated within the Akata Formation at depths of approximately 2800–4000 m. Spatial variations in organic facies distribution across depobelts reflect changes in depositional environment, sedimentation rate, and preservation conditions. These results confirm the Akata Formation as the principal effective oil-prone source rock in the Niger Delta Basin and provide important constraints for petroleum system analysis and deepwater exploration risk reduction.

Abstract: Background. Vitamin D has recognized immunomodulatory, anti-proliferative, and differentiation-regulating effects primarily mediated through its genomic effects via the vitamin D receptor (VDR). Observational studies have suggested associations between vitamin D deficiency and more aggressive breast cancer phenotypes, including estrogen receptor–negative disease and higher-grade tumors. Recent randomized trials have also reported improved pathological complete response (pCR) rates with vitamin D supplementation during neoadjuvant chemotherapy, although these studies included heterogeneous breast cancer subtypes and greater effects were mostly seen in the ER- subtypes. Because HER2-targeted therapies have dramatically improved outcomes in HER2-positive breast cancer, it was hypothesized that successful correction of vitamin D deficiency would be associated with improved disease-free survival (DFS) in patients with early-stage HER2-positive breast cancer treated with curative-intent therapy. Methods. We conducted a retrospective cohort study of patients with HER2-positive breast cancer treated at Cancer Treatment Centers of America Midwestern Regional Medical Center between 2008 and 2014. Eligible patients had early-stage HER2-positive disease, received trastuzumab-based therapy with curative intent (± pertuzumab), continued follow-up at the institution for at least 12 months, and had baseline vitamin D deficiency defined as serum 25-hydroxyvitamin D (D25) < 30 ng/mL. Vitamin D levels were routinely measured at baseline and serially during treatment as part of institutional standard practice. Patients received vitamin D3 supplementation, typically ranging from 2,000–10,000 IU/day, with dose adjustments guided by follow-up D25 levels. Patients were classified as responders if their mean D25 level during the first year of follow-up reached ≥30 ng/mL and non-responders if levels remained < 30 ng/mL. Responders were further stratified into low (30–40 ng/mL), medium (40–50 ng/mL), and high (>50 ng/mL) categories to explore potential dose-response relationships. The primary endpoint was disease-free survival (DFS), defined as time from initiation of neoadjuvant therapy or surgery to recurrence, metastasis, or death. Kaplan–Meier methods and Cox proportional hazards models were used to evaluate associations between vitamin D response and DFS while adjusting for relevant clinical covariates. Results. Among 196 eligible patients, 129 (65.8%) were vitamin D deficient at baseline. Of these, 76 (60.3%) achieved adequate vitamin D repletion and were classified as responders, while 50 (39.7%) remained deficient. Over the follow-up period, 31 DFS events (15.8%) occurred. The mean DFS for the cohort was 10.2 years (95% CI 9.58–10.83), and the estimated 3-year DFS rate was 88%. Responders demonstrated numerically improved outcomes compared with non-responders, with 3-year DFS rates of 90% versus 85%, respectively. Kaplan–Meier curves suggested a potential dose-response relationship, with progressively improved DFS among patients achieving higher mean D25 levels, particularly those exceeding 50 ng/mL. In Cox regression analyses, vitamin D non-responders demonstrated a 1.7-fold higher hazard of recurrence compared with responders, although this did not reach statistical significance. Conclusions. In this retrospective cohort of patients with HER2-positive breast cancer, failure to correct baseline vitamin D deficiency was associated with a numerically higher risk of disease recurrence. The observed separation of Kaplan–Meier curves across increasing vitamin D levels suggests a possible dose-response relationship supporting a biologically meaningful effect. Although the study is limited by retrospective design and modest sample size, the findings are consistent with emerging randomized trial data suggesting improved treatment responses with vitamin D supplementation. Prospective studies specifically targeting correction of vitamin D deficiency in HER2-positive breast cancer are warranted to determine whether optimized vitamin D status can improve oncologic outcomes.

Abstract: Accurate delineation of mega-environments (MEs) and rigorous evaluation of test lo-cations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed to refine the regional wheat trial framework in the Huanghuai winter wheat region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically com-pared GGE biplots constructed from raw phenotypic data and BLUP values in terms of goodness of fit and their ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 81.1% of total phenotypic variation, a substantial improvement over the conventional raw-data GGE biplot (62.4%). Raw-data GGE biplots exhibited highly complex inter-location correlations, distorted by unaccounted year effects and environmental noise, which hindered reliable location evaluation and ME classification. In contrast, all location vectors in the BLUP-GGE biplot displayed positive correlations (maximum angle = 83.9°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, ZMD was identified as the most desirable location, followed by SQU and PY, while LYG, BJ, SQ, and XY exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle ≈70°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (HX, SZ, FY, SQ, LYG), ME2 (ZMD, SQU, GY, XX, HA, LH, XMQ, LY, HY, YL, PY, XZ), and ME3 (SY, YY, XY, BJ). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions.

Abstract: Cardiofaciocutaneous (CFC) syndrome is a rare RASopathy caused by RAS/MAPK pathway mutations, often overlapping clinically with Noonan and Costello syndromes, making molecular testing crucial for accurate diagnosis. We describe an infant with macrocephaly, hydrocephalus requiring shunt, axial hypotonia, hypertrophic cardiomyopathy, and mitral valve dysplasia. MRI showed ventriculomegaly and cerebellar tonsillar descent; echocardiography revealed concentric left ventricular hypertrophy. Genetic testing identified a de novo KRAS missense variant (p.Pro34Arg), a rare CFC cause (< 2% cases), not previously reported in our population. This case broadens the mutational spectrum of CFC and underscores molecular analysis for diagnosis, prognosis, and genetic counseling

Abstract: Background. Intermittent fasting (IF) produces consistent metabolic benefits across diverse clinical populations. Paradoxically, antioxidant supplementation — widely co-prescribed with IF protocols — has repeatedly failed to replicate or augment these benefits in randomized controlled trials, and has in several instances attenuated them. Objective. This review examines whether the conventional “oxidative stress / hormetic defense” framework adequately explains the molecular mechanisms of IF, and proposes an integrative model — the Functional Redox Coupling (FRC) framework — grounded in three decades of converging evidence from redox biology. Synthesis. Drawing on the foundational work of Sies, Jones, Ristow, Chandel, and Halliwell, we argue that diffusible reactive species (DRS) generated during fasting serve as obligatory coupling agents mitochondrial bioenergetics and metabolic signaling — not merely as stressors to be neutralized. Within this framework, exogenous antioxidant supplementation during fasting windows may interfere with functional redox transduction, thereby blunting the adaptive response. Clinical Implications. We propose evidence-based guidance on antioxidant timing relative to fasting windows, identify molecular classes of particular concern (tocopherols, ascorbic acid, N-acetylcysteine), examine organ-level physiology in liver, pancreas, and brain, compare 16:8 and 5:2 protocols for T2D prevention, and address the structural economic and institutional impediments to translation of IF evidence into clinical practice.

Abstract: The mainstream ESG literature associates favorable board characteristics with improved corporate environmental performance, yet the gap between sustainability governance and actual environmental outcomes remains persistent and poorly explained. This paper develops a theoretical framework to account for that gap by introducing two pathological institutional logics that governance reform cannot correct and systematically worsens. The first is morocracy: governance by institutional incompetence, sustained through loyalty-based selection and patronal mechanisms. The second is algorithmic capture: the colonization of fiduciary judgment by AI-driven optimization systems constitutively blind to environmental values resisting monetization. Drawing on institutional theory, critical governance scholarship, and the board-characteristics literature, we argue that their combination produces a dual governance deficit whose most dangerous feature is not organizational paralysis but the expert performance of sustainability commitment by institutions structurally incapable of delivering it. Under these conditions, governance improvement does not close the performance-commitment gap. It compounds it, furnishing pathological institutions with increasingly credible instruments for sustainability theater. Against this diagnosis, we propose deliberative governance as the corrective institutional architecture, grounded in epistemic integrity, algorithmic subsidiarity, and environmental accountability. Five counterintuitive propositions are advanced to anchor the theoretical contribution and orient future empirical inquiry.

Abstract: Although Homo sapiens remains the only species with clearly documented religious systems, archaeological evidence suggests that other archaic humans may have exhibited behaviors associated with proto-religious thought, including symbolic practices and possible mortuary rituals. In particular, Homo neanderthalensis and the population known as Denisovans possessed large and complex brains, including well-developed frontal regions associated with social cognition and symbolic processing. This article explores the possibility that these archaic humans possessed early conceptualizations of death and mortality that could represent precursors to later religious ideas. By examining archaeological evidence, genetic research on archaic introgression, and theories from evolutionary anthropology and cognitive science of religion, the study investigates how interactions between archaic humans and modern humans may have contributed to the cognitive and cultural foundations of religious thought. Particular attention is given to the potential influence of archaic genetic heritage on cognitive traits related to agency detection, social cohesion, and attitudes toward death. The article also discusses whether such evolutionary and cognitive influences may have indirectly shaped later religious traditions in different cultural contexts, including both Western and Eastern religious systems. While direct causal connections remain difficult to establish, this study aims to provide an interdisciplinary framework for understanding how archaic human populations may have contributed to the deep evolutionary roots of human religiosity.

Abstract: We propose a structural framework for organizing the submanifold content of compact Calabi--Yau manifolds through the notion of a {Topological Slice Structure} (TSS), a coherent collection of calibrated submanifolds compatible with the Ricci-flat metric data. The central result is a decomposition principle asserting that, under mild conditions on the K\"ahler polarization, such a structure exists, its cohomology classes span the full integer homology, and it is covariant with respect to mirror symmetry. Special cases recover special Lagrangian torus fibrations, divisors, and holomorphic curves as natural constituents of a unified geometric datum. We illustrate the framework through worked examples, introduce a numerical slice complexity invariant, and discuss implications for D-brane wrapping and moduli stabilization in string compactifications.

Abstract: Metastatic colorectal cancer (mCRC) remains one of the leading causes of cancer-related mortality worldwide despite substantial therapeutic improvements over the past two decades. Advances in the understanding of colorectal tumor biology and oncogenic signaling, have enabled the development of biomarker-guided therapies targeting alterations in EGFR, BRAFV600E, KRAS mutations and HER2 amplifications, improving outcomes in selected patient populations. Nevertheless, the emergence of both intrinsic and acquired resistance mechanisms continue to limit the durability of these responses. Resistance to targeted therapies in mCRC arises through multiple, often convergent mechanisms, including activation of compensatory signaling pathways, pre-existing genomic heterogeneity, and therapy-driven clonal selection. The integration of molecular profiling into clinical decision-making is essential to guide treatment selection and op-timize therapeutic sequencing, ultimately enabling progress in precision oncology. Advances in genomic technologies, particularly circulating tumor DNA (ctDNA) analysis, have allowed longitudinal monitoring of tumor evolution, providing important insights into the mechanisms underlying resistance to targeted therapies. The aim of this review is to summarize the genomic landscape of mCRC and discuss current targeted therapeutic strategies in molecularly defined subgroups, with a particular focus on the mechanisms driving primary and acquired resistance.

Abstract: Urban air pollution remains a critical challenge in rapidly urbanizing metropolitan re-gions, where complex topography and uneven monitoring infrastructure limit accurate exposure assessment. Nitrogen dioxide (NO₂), primarily emitted from traffic and combus-tion sources, exhibits marked spatial heterogeneity that is often underrepresented by sparse ground-based stations. This study examines the spatiotemporal variability of tropospheric NO₂ over Ankara Province, Türkiye, for 2025 and develops and implements a machine learning-based downscaling framework integrating Sentinel-5P TROPOMI ob-servations with Sentinel-2 multispectral surface reflectance data, without relying on an-cillary meteorological or emission datasets. After rigorous quality filtering and temporal aggregation, a Random Forest regression model was used to generate annual NO₂ maps at 500 m resolution based solely on spectral predictors. Results indicate strong seasonal variability, with winter monthly means reaching 8.93 × 10⁻⁵ mol/m² and peak values ex-ceeding 30 × 10⁻⁵ mol/m², alongside a persistent urban–rural gradient radiating from the metropolitan core. The optimized model achieved consistent predictive performance (R² = 0.30; RMSE = 2.72 × 10⁻⁵ mol/m²), with SWIR and Red Edge bands contributing most strongly. These findings demonstrate that high-resolution urban NO₂ patterns can be re-liably inferred from optical satellite data alone, providing a transferable and scalable framework for air quality assessment in data-limited metropolitan environments.