Search Results (4,815)

High-resolution LiDAR and individual-tree modeling are generating increasing volumes of tree-level spatial data, including coordinates, tree height, and diameter at breast height (DBH). However, the lack of scalable and spatially explicit identifiers still limits the organization and integration of tree records in digital twin forest systems. This paper presents a GeoSOT-based framework for assigning position-linked identifiers to standardized tree observation records. The proposed code is used as a spatial anchor for record organization, candidate retrieval, and lifecycle-oriented management, rather than as a direct label of biological tree identity. The framework is implemented through a Yukon-based workflow for spatial storage and GeoSOT-code attachment, with a Bigtable-style schema described for time-stamped record organization. In a Mengjiagang forest farm case study, 604 treetop observations were extracted from airborne-LiDAR-derived canopy height models. Perturbation tests, boundary stress testing, controlled candidate matching, and a prototype retrieval benchmark show that fine-level GeoSOT codes are sensitive to positional uncertainty, whereas coarser levels combined with target-cell and adjacent-cell retrieval provide more stable candidate filtering with compact candidate sets under controlled experimental conditions. These results suggest that GeoSOT-based coding can support tree-observation record organization and candidate matching in digital twin forest systems. Independent cross-source identity validation and deployed database-level benchmarking should be addressed using real multi-source datasets and operational database environments. Full article

Background/Objectives: Both adiposity and nutritional–inflammatory status influence glucose metabolism; however, their longitudinal associations with glycemic changes in non-diabetic populations remain unclear. We examined the independent, interactive, and joint associations of body mass index (BMI) and prognostic nutritional index (PNI) with the 3-year change in HbA1c (ΔHbA1c). PNI, a composite marker of serum albumin and peripheral lymphocyte count, reflects both protein nutritional status and systemic immune competence. We hypothesized that BMI and PNI would each independently predict ΔHbA1c and that their joint profiling would identify higher-risk subgroups. Methods: A total of 9414 non-diabetic adults from the Shanghai Suburban Adult Cohort were included. Participants with diabetes at baseline (defined as fasting plasma glucose ≥ 7.0 mmol/L, 2-h post-load glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, or self-reported physician diagnosis of diabetes or use of glucose-lowering medications) were excluded. BMI was measured, and PNI was calculated as serum albumin + 5 × lymphocyte count. ΔHbA1c was assessed over a 3-year period. Multivariable linear regression, interaction testing, and joint stratification were performed. Covariate selection was guided by prior biological plausibility, and model adequacy was evaluated using the Akaike Information Criterion (AIC). Results: Both BMI (β = 0.013% per kg/m², 95% CI: 0.011–0.016, p < 0.001) and PNI (β = 0.002% per unit, 95% CI: 0.000–0.004, p = 0.019) were independently and positively associated with ΔHbA1c. No significant interaction was observed (p = 0.431). High BMI (≥24 kg/m²) was associated with glycemic worsening irrespective of PNI level (β ≈ 0.075%, p < 0.001). Among normal-weight individuals, higher PNI was associated with a modest increase in ΔHbA1c (β = 0.031%, p = 0.007). Conclusions: Although the absolute effect sizes were modest at the individual level, BMI was consistently and independently associated with glycemic deterioration therefore, even small per-unit increases may translate into meaningful risk at the population level given the high prevalence of overweight and obesity. PNI showed a small positive association, suggesting that in relatively healthy populations a higher PNI may partly capture subtle pro-glycemic factors—such as low-grade inflammation or higher protein intake—rather than representing unambiguous nutritional benefit. The absence of interaction suggests that BMI and PNI act through largely independent pathways. These findings extend prior evidence by demonstrating that PNI provides modest additional glycemic information beyond BMI in non-diabetic community-dwelling adults, particularly among those of normal weight. Full article

Epigenetic mechanisms profoundly regulate gene expression, developmental trajectories, and phenotypic variation, extending biological influence beyond DNA sequence alone. A growing body of evidence suggests that environmental exposures, including pollutants, drugs, stress, and diet, can induce germline and early embryonic epimutations that alter developmental programs with lasting consequences for neurodevelopmental and cognitive outcomes. However, the fields most relevant to these processes have largely developed independently. These include germline epigenetics, early embryonic patterning, neurodevelopment and cognitive regulation, and intergenerational or transgenerational inheritance. Each field has its own conceptual frameworks and mechanistic models. This fragmentation obscures the biological reality that these systems are tightly interconnected: environmentally induced epigenetic perturbations in gametes can reshape the epigenetic landscape of the early embryo, influence lineage allocation during gastrulation, and ultimately modify the molecular architecture of the developing central nervous system. A systems–biology perspective capable of linking germline epimutations and early embryonic epigenetic instability to later neurodevelopmental and cognitive phenotypes and their potential inheritance is therefore required. This review synthesizes current evidence across these traditionally isolated domains and proposes a coherent mechanistic framework linking germ cell epimutations and early embryonic epigenetic instability to the emergence of neurodevelopmental and cognitive phenotypes. By bridging these conceptual gaps, we aim to establish a cohesive foundation for understanding how early epigenetic disruptions generate long-lasting and in some cases heritable effects on brain development and cognitive function. Full article

This paper extends the metacybernetic framework by grounding its conceptual descriptions in first principles of information physics. We demonstrate that for living systems to organise efficiently under uncertainty, they must adhere to a strict recursive pattern, a “fractal seed” originating in the third-order interaction between potential and action. By utilising Fisher Information Field Theory (FIFT) within an Informational Realism paradigm, we formalise this process through variational analysis on an implicate–explicate manifold. Under a rigorous informational parsimony constraint (a functional analogue of the holographic principle), we treat the J-field as the dispositional reservoir of latent potential and the I-field as the operative field of structured configurations, and show how their autopoietic coupling generates the system’s Potential–Actuation trait poles as a scale-invariant viability structure This coupling reveals that the boundary substructure, which encodes the holographic content, directly conditions the emergent superstructure through a deterministic parity rule inherited from the dyadic logic of the minimal generic living system represented by ${\widehat{\theta }}_2$. Drawing on the application of Fisher Information, we show that maintaining informational parsimony requires the system’s architecture to oscillate: odd-numbered orders express two traits (dyads), whereas even-numbered orders express three (triads). This produces a canonical 2–3–2–3–2 sequence, preventing a combinatorial explosion of traits as systemic depth increases. We present the Cogitor5 model as a complete fifth-order exemplar of this rule, demonstrating how this rhythmic structural pattern enables self-evolution, systemic coherence, and collective intelligence in both biological and artificial agencies. Full article

Functional antibody-dependent enhancement (ADE) represents a fundamental and context-dependent characteristic of antiviral antibody responses, reflecting the dual capacity of antibodies to mediate both the neutralization and Fc receptor-dependent enhancement of infection. In flavivirus research, this duality complicates the interpretation of conventional serological metrics and limits the reliability of single-parameter correlates of immunity, particularly in populations with complex exposure histories. Over the past decade, functional ADE assays have evolved from specialized mechanistic tools into integrated immune assessment platforms supporting translational immunology, vaccine evaluation, and population-level immune surveillance. These platforms incorporate Fcγ receptor-relevant target cell systems, standardized viral inputs, dilution series-based profiling, quantitative enhancement metrics, and structured quality control frameworks to enable reproducible, comparable, and context-aware functional measurements across cohorts and laboratories. A central concept emerging from these developments is that ADE reflects a dynamic functional immune state rather than an intrinsic property of antibodies or a direct indicator of pathological risk. Accordingly, functional ADE platforms support the contextual interpretation of antibody activity across physiologically relevant conditions, facilitating discrimination between transient functional enhancement and clinically meaningful immunological risk. By integrating functional ADE metrics with serological, cellular, and epidemiological data, these platforms provide a structured framework for interpreting immune profiles in vaccine evaluation, booster strategy design, and population-level risk stratification. This review synthesizes the development, standardization, and global dissemination of functional ADE platforms and discusses key principles governing biological relevance, analytical robustness, and inter-site transferability. Emerging directions integrating functional ADE profiling with systems immunology, immunogenomics, and computational modeling are highlighted as pathways toward predictive, decision-support-oriented frameworks. By positioning ADE platforms as immune assessment infrastructures rather than isolated assays, this review underscores their value for mechanistic inquiry, translational interpretation, and preparedness-oriented responses to emerging viral threats in the absence of definitive correlates of protection. Full article

Electrospun poly(ε-caprolactone) (PCL) membranes incorporating Triticum vulgare extract (TVE) were developed as biomimetic scaffolds for periodontal regeneration. Using a ternary solvent system, two experimental formulations (µF-P10 and µF-P10T1) were fabricated and compared against a commercial dermal matrix. SEM analysis revealed bimodal fiber distributions (0.77–1.74 µm) and a surface porosity of 29.86% for TVE-loaded membranes, significantly higher than that of the commercial control (25.26%). FT-IR confirmed that the PCL chemical integrity was preserved, while mechanical testing showed that extract incorporation reinforced the matrix, increasing the Young’s modulus from 2.90 × 10³ Pa to 3.54 × 10³ Pa. UHPLC–MS identified ferulic acid as the primary bioactive component (90%), with release kinetics following a first-order model (R² = 0.998) over 48 h. Biological assays with human gingival fibroblasts (HGF) confirmed non-cytotoxicity (>70% viability). While both membranes supported healing, the µF-P10 formulation showed superior performance, with 80.2% proliferation and 60.6% wound closure, approaching control levels. These findings demonstrate that PCL-TVE electrospun scaffolds effectively combine favorable morphology and controlled release, offering a promising alternative for subepithelial connective tissue regeneration. Full article

Dental implants are widely used to replace missing teeth, but peri-implantitis remains a major biological complication associated with bacterial biofilm formation on implant surfaces. The increasing incidence of peri-implant infections underscores the need for alternative antimicrobial strategies that effectively decontaminate complex titanium implant surfaces. This study evaluated the inhibitory effect of low-temperature microwave argon plasma on bacteria in an experimental model simulating peri-implant conditions and compared the responses of microorganisms with different biological characteristics. A 3D-printed mandibular bone segment model with an inserted Straumann BLX Roxolid^® dental implant was used to reproduce the peri-implant environment. Bacterial suspensions of Streptococcus mutans NBIMCC 1786 and the extremophilic bacterium Chromohalobacter canadensis NBIMCC 9077 have been exposed to a microwave non-equilibrium argon plasma jet (2.45 GHz, atmospheric pressure) for 1–7 min. Optical density measurements and colony growth analysis were used to assess antimicrobial effects. Plasma treatment induced a pronounced reduction in bacterial growth during the early post-treatment period. In C. canadensis, growth inhibition reached a plateau (~47–55% at 24 h) regardless of exposure time. In contrast, S. mutans showed a nonlinear response, with stable inhibition after short exposures (1–3 min) and partial recovery after longer treatments (5–7 min). These findings indicate that microwave argon plasma exhibits significant antimicrobial activity under controlled in vitro conditions, although its effectiveness depends on microorganism-specific biological characteristics. Because the present model was based on simplified single-species systems, direct clinical extrapolation remains limited and should be addressed in future studies using polymicrobial peri-implant biofilm models. Full article

Intensive monoculture exacerbates soil compaction and sodification in the West Liao River Plain. This study evaluated legacy effects of diversified 3-year rotations on sodic soil health (ESP > 15%, ECe < 4 dS m⁻¹) during two subsequent maize seasons. Rotations incorporating salt-tolerant forages and deep-rooted crops (sugar beet–Echinochloa–sorghum and Echinochloa–tall fescue–silage corn) significantly reduced bulk density (8.6–13.1%) and exchangeable sodium percentage (up to 14.1 percentage points) relative to continuous monoculture. Treatments with maximum desalination (22.6% reduction) enhanced fungal α-diversity by 98.0%, while forage-dominated systems enriched Acidobacteriota by 35.2%, shifting bacterial communities toward oligotrophic dominance. Structural equation modeling confirmed that rotation effects on enzyme activity were mediated through reduced bulk density and ESP. These systems provide effective biological models for sustainable maize cultivation in sodic soils via synergistic physical-chemical-biological amelioration. Full article

Background/Objectives: Artificial intelligence (AI) models are increasingly applied across the assisted reproductive technology (ART) workflow, including male-factor assessment, ovarian stimulation, endometrial receptivity evaluation, embryo selection and prediction of pregnancy outcomes. However, many systems remain difficult to interpret, raising concerns regarding transparency, clinical integration and patient communication. Explainable artificial intelligence (XAI) aims to address these limitations by making model behavior more accessible to clinicians and embryologists. This review aimed to provide a narrative, concept-driven synthesis of how XAI has been implemented in ART, to critically examine methodological quality and clinical relevance and to outline priorities for responsible translation into practice. Methods: A structured narrative review was conducted using PubMed/MEDLINE as the primary database, supplemented by targeted reference-list screening of key primary studies and recent cross-disciplinary reviews relevant to AI in ART. Studies were curated and classified according to stage of the ART workflow, data modality, model family, explanation technique and validation strategy. Methodological features, performance reporting and implementation considerations were qualitatively appraised. Results: Most XAI applications in ART fall into two dominant categories: (i) feature-attribution methods such as SHAP and LIME applied to tabular clinical and laboratory data and (ii) saliency-based approaches, including Grad-CAM and related techniques, applied to embryo and ultrasound imaging. These methods can improve transparency and support counselling by clarifying which variables or image regions influence predictions. However, the majority of studies are retrospective and single centre, with limited external validation and heterogeneous outcome definitions, often prioritising clinical pregnancy over live birth. Calibration, decision-analytic evaluation and prospective assessment remain uncommon. XAI outputs are frequently interpreted as biologically causal despite being derived from observational data, highlighting the need for cautious clinical framing. Conclusions: XAI in ART has progressed from proof-of-concept demonstrations to early clinically oriented tools, but robust validation, standardised reporting and thoughtful workflow integration are still needed. Explanations can enhance auditability and communication, yet they do not compensate for methodological weakness. Future progress will depend on higher-quality multi-centre data, evaluation beyond discrimination metrics and governance frameworks that ensure transparency, fairness and sustained performance in real-world practice. Full article

Early detection remains the most decisive factor in improving outcomes for oral cancer and oral potentially malignant disorders. However, reliance on conventional biopsy-based pathways presents some practical and biological limitations. This scoping review aimed to map recent advances in non- and minimally invasive diagnostic approaches and to clarify how these innovations are being positioned within clinical workflows. Following PRISMA-ScR guidance, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language original studies published between 2020 and 2025. Two independent reviewers screened and charted data on technologies, biomarkers, sampling sources, and clinical applications. Forty-nine studies were included. The literature clustered around four main domains: enhanced cytology (including liquid-based platforms and DNA ploidy analysis), multilayer liquid biopsy strategies (miRNA, cfDNA/ctDNA, methylation panels, and autoantibodies), optical and nanotechnology-based systems (Raman/SERS and sensor platforms), and artificial intelligence-driven decision support tools. Across modalities, a shared emphasis on rapid triage, risk stratification, and follow-up monitoring was evident. Nonetheless, variability in sampling, processing, analytical thresholds, and reporting standards limited cross-study comparability. Recent innovations point toward integrated, panel-based diagnostic models. Broader clinical adoption will require methodological standardization and robust multicenter validation. Full article

Artificial intelligence (AI) is increasingly used in cancer research, enabling integrative analysis of complex biomedical data to identify actionable therapeutic vulnerabilities. This review specifically examines how AI advances mechanistic cancer target discovery and translational drug development, focusing on: (1) the processing of large-scale genomics, transcriptomics, proteomics, metabolomics, single-cell profiling, spatial, and clinical datasets using machine learning (ML) and deep learning (DL) algorithms; (2) the identification of candidate biomarkers, driver genes, dysregulated pathways, tumor dependencies, and molecular targets that traditional methods often miss; (3) the integration of multi-omics data, network biology, causal inference, and systems-level modeling to refine mechanistic understanding of cancer progression and separate functional driver events from passengers; and (4) applications in drug development, including virtual screening, molecular modeling, structure-informed target validation, drug repurposing, synthetic lethality prediction, and de novo drug design, which collectively may enhance early-stage drug discovery efficiency. The review underscores that AI serves as both a predictive tool and a platform for linking molecular mechanisms to hypothesis generation, target prioritization, and rational treatment design. Challenges such as data heterogeneity, algorithmic bias, interpretability, reproducibility, regulatory requirements, and patient privacy must be addressed for robust translation and clinical use. Future directions may focus on hybrid approaches that integrate causal modeling, explainable AI, multimodal data, and experimental validation to yield mechanistically grounded, clinically actionable insights. AI-driven approaches ultimately aim to accelerate mechanism-based cancer target discovery and enable more precise, biologically informed anticancer therapies. Full article

Difficult-to-treat systemic lupus erythematosus (D2T-SLE) remains a major unmet challenge in contemporary lupus care, yet it continues to be defined predominantly by clinical non-response rather than underlying biology. Current biomarkers largely quantify inflammatory burden, immune complex activity, or organ damage and do not reliably capture persistent activation of pathogenic pathways under therapy. Emerging multi-omics, single-cell, and longitudinal studies suggest that, in a subset of patients, apparent treatment failure may reflect incomplete attenuation of dominant immune circuits rather than uniformly elevated inflammation. We propose molecular refractoriness in systemic lupus erythematosus (SLE) as sustained, pathway-level immune activity despite apparently adequate, mechanism-directed therapy. We outline the major immune programs implicated in this process—including interferon-enriched, B-cell/plasmablast-associated, neutrophil extracellular trap (NET)-related, cytotoxic T-cell, and cytokine-associated states—and discuss their relevance for biomarker development and precision trial design. Importantly, we emphasize that interferon gene signatures (IGS) should be interpreted as context-dependent and non-specific markers of interferon responsiveness, reflecting combined activity of type I, II, and III interferons, and functioning primarily as predictive rather than mechanistic biomarkers. We further highlight critical limitations of a purely endotype-based model, including the need to distinguish true molecular refractoriness from damage-dominant and pseudo-refractory states, as well as the emerging role of immune-reset strategies such as cluster of differentiation 19 (CD19)-directed chimeric antigen receptor T-cell (CAR-T) therapy, which may overcome refractoriness independently of specific pathway dominance. These observations suggest that difficult-to-treat SLE encompasses biologically heterogeneous states that may not be fully captured by pathway-resolved stratification alone. Reframing D2T-SLE as a biologically heterogeneous state of incomplete immune attenuation may help bridge the gap between clinical treatment failure and mechanism-informed precision medicine in systemic lupus erythematosus. Full article

Micro- and nanoplastics (MNPs) have now been detected in human blood, placenta, and arterial tissue, yet the oral cavity has received strikingly little mechanistic attention despite serving as a primary portal of environmental exposure and a local site of polymer generation from dental and oral-care materials. This narrative review addresses that gap from an environmental microbiology perspective, synthesizing recent literature on periodontal disease, chronic low-grade inflammation, oral biofilms, dental materials, microbial–plastic interactions, and systemic chronic disease risk. Unlike prior reviews, we apply an explicit three-tier evidentiary framework (established, plausible, unproven) that distinguishes what is directly demonstrated from what is biologically plausible but unproven, and we situate the periodontal environment specifically as a particle-retention and inflammatory-amplification niche. The strongest direct oral evidence shows that human dental calculus harbors at least 26 microplastic types, dominated by polyamide (41.4%), polyethylene (32.7%), and polyurethane (7.0%). Polyethylene isolated from calculus induces cytotoxicity, apoptosis, impaired migration, NF-κB activation, and upregulation of IL-1β and IL-6 in human gingival fibroblasts. From a microbiological standpoint, oral organisms actively degrade methacrylate dental polymers, and the degradation products of these polymers reciprocally modulate oral bacterial virulence gene expression. Across experimental systems, MNPs activate oxidative stress, inflammasome signaling, macrophage polarization, and barrier dysfunction, pathways that overlap extensively with periodontal pathobiology. Adjacent environmental microbiology demonstrates that plastic-associated biofilms enhance extracellular polymeric substance production, quorum sensing, pathogen persistence, and antibiotic resistance gene transfer, supporting a plausible but not yet validated oral plastisphere within plaque and calculus. We argue that periodontitis should be reconceptualized as a chronically inflamed particle-processing interface that may increase local MNP retention, cellular reactivity, and systemic inflammatory spillover, with implications for cardiovascular, metabolic, and other chronic disease risk pathways. Current evidence does not yet prove that environmental MNP exposure causes human periodontitis, and that evidentiary boundary is maintained throughout. A priority research agenda is proposed, centered on contamination-controlled subgingival biomonitoring stratified by periodontal status, spatially resolved multi-species biofilm models, polymer source attribution, and longitudinal clinical studies linking oral plastic burden to inflammatory and systemic outcomes. Full article

Background/Objectives: Understanding the causes of bull subfertility and developing reliable diagnostic tools are critical to reducing economic losses caused by reproductive failure in beef cattle. Metabolomic analysis of sperm from bulls with diverging field fertility may provide insights on sperm metabolism that are associated with fertility. The objective was to determine metabolomic differences in sperm from bulls with differing field fertility. Methods: Angus bulls (n = 15) were classified based on a composite field fertility index (CFI). Frozen–thawed semen straws (n = 10 per bull) underwent a Percoll gradient sperm purification process. Metabolomic analysis was performed through ultra-high performance liquid chromatography coupled high resolution mass spectrometry at the University of Tennessee Biological and Small Molecule Mass Spectrometry Core. The general linear model (GLM) procedure of Statistical Analysis System (SAS) was used to evaluate linear and quadratic relationships between metabolites and CFI. Furthermore, the MIXED procedure was used to determine differences in metabolite abundance between the four highest and lowest fertility bulls. Significance was determined when p ≤ 0.05 and tendency was declared when p ≤ 0.10. Results: A total of 75 metabolites were detected. Quadratic relationships with fertility were observed for kynurenine, xanthine, and ophthalmate. Tricarballylic acid and creatinine showed a negative linear relationship with fertility. When differences in metabolite abundance were assessed between the four highest and lowest fertility bulls, N-acetylglutamate and N-acetylglutamine had greater abundance in low fertility bulls. Conclusions: Metabolites kynurenine, xanthine, ophthalmate, tricarballylic acid, and creatinine are potential fertility markers to identify subfertile bulls from a breeding population. These metabolites have promising future implications in the diagnosis and treatment of beef bull subfertility. Full article

Corner detection is a fundamental task in computer vision that plays a critical role in applications such as image registration, 3D reconstruction, and object tracking. In biological visual systems, simple cells in the primary visual cortex exhibit high selectivity to edge stimuli of specific orientations, while end-stopped cells can detect geometric singular structures such as line segment endpoints and corners. Existing corner detection methods based on visual neural computation typically employ a strategy of densely distributed end-stopped cells for corner localization, which suffers from significant localization deviation under small angle conditions due to mutual interference between responses of adjacent neurons. To address this problem, this paper proposes a bionic corner detection method based on cooperative processing of simple cells and end-stopped cells. The method constructs a two-stage cooperative processing framework: the edge filtering stage employs a Gabor filter bank to simulate the orientation selectivity of simple cells, extracting edge positions and orientation information; the dynamic construction stage builds unilateral end-stopped cells only at filtered edge positions based on local orientation information, fundamentally avoiding computational redundancy and response interference caused by global dense distribution; the corner localization stage determines precise corner coordinates through hierarchical clustering and dual-cluster centroid fusion strategies. Experimental results demonstrate that, in the 15° acute-angle regime where dense end-stopped schemes are most severely affected by response interference, the proposed method reduces the mean localization error from 8.76 to 2.34 pixels, corresponding to a 73.3% improvement; averaged across the eight tested angle levels from 15° to 165°, the improvement is approximately 40.9%, and all per-angle differences are statistically significant (paired t-test, p < 0.01 or below, N = 10 independent runs). On standard test images, the method attains the lowest mean localization error among the eight compared detectors (1.58 pixels, versus 1.68–3.42 pixels for Harris, FAST, COSFIRE, KAZE, SuperPoint, Deep Corner, and Wei et al.), while maintaining competitive detection rate, false-alarm rate, and runtime. Physiological plausibility validation experiments show that the correlation coefficient between the detection deviation of this method and human perceptual deviation reaches 0.923, indicating that the output of the framework aligns with previously reported human perceptual bias patterns and supporting its biological plausibility as a biologically inspired—rather than mechanistic—model of corner perception. The source code, dataset, and experimental results are publicly available (see Data Availability Statement). Full article