Search Results (1,769)

Over the past two decades, advances in the understanding of epigenetic mechanisms—driven by the rapid expansion of omics technologies—have catalyzed a major paradigm shift in biology: from the genetic determinism and linear causality of the Central Dogma toward the dynamic, networked complexity of systems biology and multilevel regulation. This reconceptualization extends to inheritance itself, highlighting the crucial role of the epigenome as a molecular interface between the genome and the exposome—the cumulative set of internal and external environmental influences experienced across the lifespan. Within this evolving framework, neurodevelopmental disorders exemplify the deep entanglement between genetic predisposition, environmental exposure, and epigenetic modulation. Their increasing global prevalence and frequent comorbidities underscore the need for an integrated etiological understanding that transcends reductionist models. This review tries to synthesize current evidence on the shared molecular and systemic mechanisms underlying neurodevelopmental spectrum disorders and examines how environmental and epigenetic factors jointly shape neurodevelopmental trajectories across generations. Finally, it discusses the broader implications of this paradigm shift for early diagnosis, prevention, and public health policies aimed at fostering healthy brain development in future generations. Full article

With the rapid urbanization in China, building energy consumption has become a critical challenge for sustainable urban development. Conventional simulation methods are computationally intensive and inefficient for large-scale urban layout optimization, highlighting the need for fast and reliable predictive approaches. Existing machine learning models often overlook spatial relationships among buildings and rely heavily on manual feature engineering, which limits their applicability at the urban block scale. To address these limitations, the study proposes a building energy consumption prediction model for urban blocks based on Generative Adversarial Networks (GANs), which preserves spatial information while significantly advancing computational speed. The optimal GAN model is further integrated with a Multi-Island Genetic Algorithm (MIGA) to form a GAN-MIGA optimization framework, which is applied to the layout optimization of a target urban block in Lanzhou. Key findings include: (1) the GAN model achieves an average prediction error of 6.8% compared with conventional energy simulations; (2) the GAN-MIGA framework reduces energy consumption by 48.78% relative to the worst-performing solution and by 22.53% compared with the original block layout; (3) the spatial distribution patterns of energy consumption predicted by the GAN are consistent with those obtained from traditional simulation methods; (4) the regression model derived from GAN-MIGA optimization results achieves an R² value exceeding 0.84; and (5) building layout design strategies are formulated based on key morphological indicators in the regression model. Overall, this study demonstrates the effectiveness of the GAN-based method for urban scale building energy prediction and layout optimization. The proposed GAN-MIGA framework provides practical tools and theoretical support for energy-efficient design, policy formulation, and smart city development, contributing to more sustainable urban energy planning. Full article

Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. Full article

Swine enteric coronaviruses (SECoVs), including transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV), are major enteric pathogens causing severe diarrhea, dehydration, high neonatal mortality, and substantial global economic losses. Rapid viral evolution and recombination continually generate antigenically diverse variants that limit cross-protection and undermine vaccine efficacy, particularly for PEDV genogroup II strains that now dominate worldwide circulation. This review synthesizes current knowledge on epidemiology, diagnostic innovations, and emerging vaccine platforms, with emphasis on advances since 2022. Recent progress includes molecular surveillance tools, rapid point-of-care diagnostics, and next-generation vaccine technologies such as mRNA-based and virus-like particle platforms. However, significant knowledge gaps persist regarding viral evolution dynamics, co-infection synergies, and zoonotic spillover potential, particularly following documented human infections with PDCoV. Effective long-term control requires integrated genomic surveillance, strengthened farm-level biosecurity, rationally designed multivalent vaccines targeting conserved epitopes, and harmonized international surveillance systems to reduce outbreak risk and enhance pandemic preparedness at the human–animal interface. Full article

Background: Boron neutron capture therapy (BNCT) represents a highly selective therapeutic modality for recalcitrant cancers, leveraging the nuclear reaction initiated by thermal neutron capture in boron-10 (¹⁰B) to deliver high-linear energy transfer radiation (α-particles and ⁷Li ions) directly within tumor cell boundaries. However, the widespread clinical adoption of BNCT is critically hampered by the pharmacological challenge of achieving sufficiently high, tumor-selective intracellular ¹⁰B concentrations (20–50 μg of ¹⁰B/g tissue). Conventional small-molecule boron carriers often exhibit dose-limiting non-specificity, rapid systemic clearance, and poor cellular uptake kinetics. Methods: To overcome these delivery barriers, we synthesized and characterized a novel dual-modality nanoplatform based on highly biocompatible, functionalized graphene oxide (GO). This platform was structurally optimized via covalent conjugation with high-boron content carborane clusters (dodecacarborane derivatives) for enhanced BNCT efficacy. Crucially, the nanocarrier was further decorated with plasmonic gold nanostructures (AuNPs), endowing the system with intrinsic surface-enhanced Raman scattering (SERS) properties, enabling real-time, high-resolution intracellular tracking and quantification. Results: We evaluated the synthesized GO@Carborane@Au nanoplatforms for their stability, cytotoxicity, and internalization characteristics. Cytotoxicity assays demonstrated excellent biocompatibility against the non-malignant human keratinocyte line (HaCaT) while showing selective toxicity (upon irradiation, if tested) and high cellular uptake efficiency in the aggressive human glioblastoma tumor cell line (T98G). The integrated plasmonic component allowed for the successful, non-destructive monitoring of nanoplatform delivery and accumulation within both HaCaT and T98G cells using SERS microscopy, confirming the potential for pharmacokinetic and biodistribution studies in vivo. Conclusions: This work details the successful synthesis and preliminary in vitro validation of a unique graphene oxide-based dual-modality nanoplatform designed to address the critical delivery and monitoring challenges of BNCT. By combining highly efficient carborane delivery with an integrated photonic trace marker, this system establishes a robust paradigm for next-generation theranostic agents, significantly advancing the potential for precision, image-guided BNCT for difficult-to-treat cancers like glioblastoma. Full article

With the rapid advancement of deepfake generation technologies, forged videos have become increasingly realistic in visual quality and temporal consistency, posing serious threats to multimedia security. Existing detection methods often struggle to effectively model temporal dynamics and capture subtle inter-frame anomalies. To address these challenges, we propose a Stochastic Differential Equation and Quantum Uncertainty Network (SDEQ-Net), a novel deepfake video anomaly detection framework that integrates continuous time stochastic modeling with quantum uncertainty mechanisms. First, a Continuous Time Neural Stochastic Differential Filtering Module (CNSDFM) is introduced to characterize the continuous evolution of latent inter-frame states using neural stochastic differential equations, enabling robust temporal filtering and uncertainty estimation. Second, a Quantum Uncertainty Aware Fusion Module (QUAFM) incorporates Hermitian-symmetric density matrix representations and von Neumann entropy to enhance feature fusion under uncertainty, leveraging the mathematical symmetry properties of quantum state representations for principled uncertainty quantification. Third, a Fractional Order Temporal Anomaly Detection Module (FOTADM) is proposed to generate fine grained temporal anomaly scores based on fractional order residuals, which are used as dynamic weights to guide attention toward anomalous frames. Extensive experiments on three benchmark datasets, including FaceForensics++, Celeb-DF, and DFDC, demonstrate the effectiveness of the proposed method. SDEQ-Net achieves AUC scores of 99.81% on FF++ (c23) and 97.91% on FF++ (c40). In cross dataset evaluations, it obtains 89.55% AUC on Celeb-DF and 86.21% AUC on DFDC, consistently outperforming existing state-of-the-art methods in both detection accuracy and generalization capability. Full article

The rapid development of large language models (LLMs), including ChatGPT, Gemini, and Copilot, has led to their increasing use in health communication and patient education. However, their growing popularity raises important concerns about whether the language they generate aligns with recommended readability standards and patient health literacy levels. This review synthesizes evidence on the readability of medical information generated by chatbots using established linguistic readability indices. A comprehensive search of PubMed, Scopus, Web of Science, and Cochrane Library identified 4209 records, from which 140 studies met the eligibility criteria. Across the included publications, 21 chatbots and 14 readability scales were examined, with the Flesch–Kincaid Grade Level and Flesch Reading Ease being the most frequently applied metrics. The results demonstrated substantial variability in readability across chatbot models; however, most texts corresponded to a secondary or early tertiary reading level, exceeding the commonly recommended 8th-grade level for patient-facing materials. ChatGPT-4, Gemini, and Copilot exhibited more consistent readability patterns, whereas ChatGPT-3.5 and Perplexity produced more linguistically complex content. Notably, DeepSeek-V3 and DeepSeek-R1 generated the most accessible responses. The findings suggest that, despite technological advances, AI-generated medical content remains insufficiently readable for general audiences, posing a potential barrier to equitable health communication. These results underscore the need for readability-aware AI design, standardized evaluation frameworks, and future research integrating quantitative readability metrics with patient-level comprehension outcomes. Full article

Accurate segmentation of 3D dental structures is essential for oral diagnosis, orthodontic planning, and digital dentistry. With the rapid advancement of 3D scanning and modeling technologies, high-resolution dental data have become increasingly common. However, existing approaches still struggle to process such high-resolution data efficiently. Current models often suffer from excessive parameter counts, slow inference, high computational overhead, and substantial GPU memory usage. These limitations compel many studies to downsample the input data to reduce training and inference costs—an operation that inevitably diminishes critical geometric details, blurs tooth boundaries, and compromises both fine-grained structural accuracy and model robustness. To address these challenges, this study proposes DiffusionNet++, an end-to-end segmentation framework capable of operating directly on raw high-resolution dental data. Building upon the standard DiffusionNet architecture, our method introduces a normal-enhanced multi-feature input strategy together with a lightweight SE channel-attention mechanism, enabling the model to effectively exploit local directional cues, curvature variations, and other higher-order geometric attributes while adaptively emphasizing discriminative feature channels. Experimental results demonstrate that the coordinates + normal feature configuration consistently delivers the best performance. DiffusionNet++ achieves substantial improvements in overall accuracy (OA), mean Intersection over Union (mIoU), and individual class IoU across all data types, while maintaining strong robustness and generalization on challenging cases, such as missing teeth and partially scanned data. Qualitative visualizations further corroborate these findings, showing superior boundary consistency, finer structural preservation, and enhanced recovery of incomplete regions. Overall, DiffusionNet++ offers an efficient, stable, and highly accurate solution for high-resolution 3D tooth segmentation, providing a powerful foundation for automated digital dentistry research and real-world clinical applications. Full article

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

In many Latin American cities, rapid densification, shrinking public land reserves, and growing spatial, social and biophysical fragmentation have heightened the urban significance of large, private, closed open spaces (COSs). COS, marked by restricted access and social homogeneity, operate as capsular urban models that limit socio-environmental integration, urban continuity and resilience. Far from being mere enclaves, the reconfiguration of COS emerges as a critical response to contemporary urban challenges with the capacity to reshape urban structures by generating new social and spatial connectivities. This article examines the transformation of COSs in urban contexts, such as golf clubs, into accessible public landscapes as hybrid open spaces (HOSs), a topic that remains underexplored internationally. For that, this research proposes a design-oriented, multiscalar framework (city and zonal/local) that integrates open and closed spatial programs within the wider urban open space system. Considering urban, biophysical, and sociocultural dynamics, and drawing on the concepts of accessibility, connectivity, diversity, and flexibility, the study develops guidelines and design strategies for hybridising private and public recreational and environmental uses to strengthen urban integration. Using El Rodeo Gold Club in Medellín as a case study, the work contributes to landscape architecture by advancing the transformation of underutilised COS into inclusive, multifunctional HOS, positioning COS as a strategic asset for sustainable urban environments. The framework can be replicable in other similar contexts. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Background: Rhinovirus C (RV-C) is one of three species of rhinoviruses (RVs), which cause the common cold, preschool wheezing illnesses and exacerbations of asthma. RV-C types are more virulent, especially in children, but progress in developing treatments is limited by difficulties in generating high-titer virus preparations. The goals of this study were to optimize methods for large-scale production and purification of RV-C to facilitate structure and immune response studies. Methods: We optimized protocols for the propagation and purification of RV-C15a, a clinical isolate adapted to HeLa-E8 cells stably expressing virus receptor CDHR3. We compared virus yields in adherent and suspension cultures, evaluated the effects of calcium supplementation and infection timing, and tested multiple purification strategies, including ultracentrifugation, dialysis, and lipase treatment. Results: RV-C15a yields were significantly lower in suspension vs. adherent cultures despite comparable virus binding and entry, suggesting post-entry replication limitations in suspended cells. In adherent cultures, infecting soon after cell seeding and calcium supplementation reduced the time of virus production and modestly improved virus progeny yields. Surface CDHR3 expression declined over time, potentially restricting viral spread. Among purification methods, lipase treatment of infected cell lysates followed by ultracentrifugation produced highly pure and concentrated virus preparations suitable for structural and immunological applications, with high yields. Conclusions: We present a robust system for large-scale RV-C15a production in adherent HeLa-E8 cells and recommend a lipase-based purification method as a rapid and effective approach for producing high-quality viral preparations. These advances will support structural studies and accelerate the development of RV-C-targeted therapeutics and vaccines. Full article

Brimonidine, a highly selective α₂-adrenergic receptor agonist originally developed for glaucoma treatment, has emerged as an important dermatological agent due to its potent vasoconstrictive and anti-inflammatory properties. This review summarizes its pharmacological characteristics, and clinical applications. By activating α₂-adrenergic receptors in cutaneous vessels, brimonidine induces rapid, reversible vasoconstriction and reduces neurogenic inflammation, leading to significant improvement of facial erythema in rosacea. Beyond its approved indication, topical brimonidine demonstrates efficacy in alcohol flushing syndrome, telangiectasia, post-procedural erythema, and as a local hemostatic agent in dermatologic surgery. Its favorable safety profile and minimal systemic absorption make it suitable for long-term use, though transient rebound erythema may occur. Advances in nanotechnology—such as supramolecular hydrogels and lipid-based carriers—enhance skin retention, prolong therapeutic action, and improve tolerability. These developments, together with ongoing synthesis of new quinoxaline–imidazoline analogues, open prospects for next-generation α₂-agonists with optimized selectivity and dermatologic applicability. Brimonidine’s emerging role extends to dermatologic formulations for transient redness and sensitive skin management. Integrating pharmacological, formulation, and molecular insights may transform brimonidine from a niche rosacea therapy into a versatile platform for vascular, inflammatory, and aesthetic skin treatments. Full article

Ocular disorders such as keratitis, glaucoma, age-related macular degeneration (AMD), diabetic retinopathy (DR), and dry eye disease (DED) are highly prevalent worldwide and remain major causes of visual impairment and blindness. Conventional therapeutic approaches for ocular diseases, such as eye drops, surgery, and laser therapy, are frequently hampered by limited drug bioavailability, rapid clearance, and treatment-related complications, primarily due to the eye’s unique anatomical and physiological barriers. Hydrogels, characterized by their three-dimensional network structure, high water content, excellent biocompatibility, and tunable physicochemical properties, have emerged as promising platforms for ophthalmic drug delivery. This review summarizes the classification, fabrication strategies, and essential properties of hydrogels, and highlights recent advances in their application to ocular diseases, including keratitis management, corneal wound repair, intraocular pressure regulation and neuroprotection in glaucoma, sustained drug delivery for AMD and DR, vitreous substitutes for retinal detachment, and therapies for DED. In particular, we highlight recent advances in stimuli-responsive hydrogels that enable spatiotemporally controlled drug release in response to ocular cues such as temperature, pH, redox state, and enzyme activity, thereby enhancing therapeutic precision and efficacy. Furthermore, this review critically evaluates translational aspects, including long-term ocular safety, clinical feasibility, manufacturing scalability, and regulatory challenges, which are often underrepresented in existing reviews. By integrating material science, ocular pathology, and translational considerations, this review aims to provide a comprehensive framework for the rational design of next-generation hydrogel systems and to facilitate their clinical translation in ophthalmic therapy. Full article