Search Results (4,096)

Background: Cardiovascular diseases (CVDs) remain the leading global cause of mortality, yet a critical “translational gap” persists: Conventional biomarkers often fail to detect subclinical stages or predict individual disease trajectories. While single-omics studies have proliferated, the field lacks a unified framework synthesizing these molecular layers with advanced computational intelligence. Aim: This review addresses this gap by evaluating the synergistic integration of multi-omics and Artificial Intelligence (AI) to transition from descriptive markers toward predictive precision cardiology. Scope: Evidence from non-coding RNA networks (miRNAs, lncRNAs) and exosomal trafficking is synthesized alongside a critical assessment of Machine Learning (ML) architectures, including supervised, unsupervised, and deep learning (DL) models. Findings: Unlike traditional reviews, this work delineates the specific pipelines required to deconvolute high-dimensional signatures—such as TMAO, acylcarnitines, and cardiac-enriched miRNAs—into actionable risk models for heart failure (HF) and post-infarction outcomes. The primary barrier to clinical translation is identified not as data scarcity but as the lack of standardized bioinformatic workflows and model interpretability. Conclusions: This review distinguishes itself by proposing an integrated molecular–computational framework that prioritizes Explainable AI (XAI) and standardized multi-omic protocols. Such a shift is essential to bridge the gap between high-dimensional biological insights and routine clinical decision-making. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

The formation of tectonic–magmatic–sedimentary processes during the Permian in the Beishan region represents a highly debated research topic along the southern margin of the Central Asian Orogenic Belt and even globally: does it mark the final subduction and amalgamation of the Paleo-Asian Ocean, or does it instead represent rifting superimposed upon an earlier orogen? New field observations combined with geochemical analyses reveal that the Liuyuan area is dominated by Early Permian basalts, associated with a rifting sedimentary sequence. During the Mid–Late Permian, gabbro–rhyolite associations were emplaced, accompanied by minor lacustrine sedimentation. The late stage was characterized by minor granitic intrusions or dikes with adakitic affinities, culminating in the emplacement of lamprophyre dikes. The basalts and gabbros in the Liuyuan area display mantle-derived geochemical signatures, with compositions intermediate between MORB and OIB. The exposed Permian basalt–rhyolite bimodal magmatic suite represents a genetically integrated rift-related rock series. Geochemical data from the Ordovician granites and schists within the belt reveal adakitic characteristics, implying that the Permian granitic rocks largely represent remelting products of these early granitic and schistose protoliths. Collectively, the lithological characteristics and magmatic associations clearly demonstrate that the tectonic setting during the Early Permian corresponded to a post-collisional extensional environment superimposed upon the early Paleozoic orogenic belt (Caledonian Huitongshan ophiolite–arc accretionary orogen), which subsequently underwent tectonic inversion to form the present-day orogenic structure. This paper proposes a theoretical model wherein the bimodal magmatic suite was generated by the upwelling of enriched asthenospheric mantle material, providing the driving mechanism for rifting. It formed within a post-collisional extensional environment developed over a complex pre-existing orogenic belt and was subsequently inverted, forming the current tectonic belt—a typical intracontinental Pyrenees-type orogeny. Full article

Carotenoids are essential metabolites involved in photosynthesis, cellular protection, pigmentation, and antioxidant activities. Phytoene synthase (PSY/CrtB) utilizes C20 substrates in carotenoid biosynthesis, whereas its structural homolog, dehydrosqualene synthase (CrtM), preferentially accepts C15 substrates. Although previous studies have identified CrtM mutations that expand substrate scope, the molecular basis of substrate discrimination in PSY/CrtB remains poorly understood, largely because of the absence of experimentally determined three-dimensional structures. Here, we integrated comparative sequence analysis, homology modeling, and molecular dynamics (MD) simulations to investigate the structural basis of substrate discrimination in PSY/CrtB. Comparative sequence analysis suggested distinct overall conservation landscapes in PSY/CrtB and CrtM, with 20 highly conserved positions shared between them and clustered around the catalytic core. MD simulations suggest that PSY models exhibit minimal differentiation under cross-ligand conditions, consistent with its greater conformational plasticity. Surface property analysis suggested hydrophobic patches and an amphipathic helix (Helix-13) in PSY that were preferentially conserved in PSY homologs relative to CrtM homologs. Taken together, our analyses suggest that greater conformational plasticity may facilitate the accommodation of C20 substrates in PSY and that its conserved hydrophobic surface architecture may shape its surface physicochemistry. These findings suggest that differences in substrate accommodation between PSY/CrtB and CrtM may reflect coordinated variation in conformational dynamics, pocket hydrophobicity, and surface architecture, rather than substantial alterations to the conserved catalytic core. Full article

Breast cancer is a heterogeneous disease characterized by substantial molecular diversity and variable treatment outcomes across patients. Despite advances in targeted and systemic therapies, anticipating individual benefit remains a major clinical challenge. In this context, Artificial Intelligence (AI) can support precision oncology by integrating high-dimensional molecular profiles with clinical and pharmacological information. Here, we present B.R.E.A.S.T. (Breast canceR Enhanced AI-Supported Therapy), an interpretable machine learning framework designed to predict therapy outcome from tumor proteomic profiles integrated with clinical and treatment annotations. Proteomic data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA) were harmonized with outcome and therapy information, and thirteen supervised classifiers were systematically evaluated using stratified 5-fold cross-validation. Therapeutic outcome labels were operationally defined by integrating available treatment response annotations with complementary clinical outcome information. Across both cohorts, ensemble-based models consistently achieved the most stable and highest discriminative performance, supported by learning-curve analyses and consistent behavior across independent datasets. To enhance interpretability, we implemented a two-step feature selection strategy combining model-specific importance measures with a global consensus ranking, enabling the identification of a compact set of robust proteomic biomarkers associated with therapeutic outcome. Top-ranked features mapped to molecular programs relevant to breast cancer progression and treatment sensitivity, including regulators of cell survival, DNA damage response, PI3K/AKT/mTOR signaling, and invasion-related processes. Re-evaluation using only the top 30 globally ranked features preserved high predictive performance across both independent breast cancer cohorts, indicating that a parsimonious proteomic signature captures core molecular determinants of outcome. Overall, B.R.E.A.S.T. provides a robust and generalizable proteomics-driven framework for modeling outcome-associated therapeutic response patterns and supporting biologically informed biomarker discovery in breast cancer. Full article

The beef production chain plays a strategic role in Brazilian and global agri-food systems and faces growing demands for sustainability, transparency, and traceability. Building on official Animal Transit Guide (GTA) records from Mato Grosso do Sul, Brazil, this study examines whether a parsimonious agent-based model (ABM) can generate the main structural signatures of an observed cattle-trade network. The empirical benchmark is a directed and weighted network with 20,827 nodes and 258,120 weighted edges. The ABM represents producers and slaughterhouses as spatial agents connected by trade decisions based on three mechanisms: destination attractiveness, defined as the accumulated pull of a slaughterhouse based on previous simulated throughput; geographic distance, representing spatial friction; and relational memory, representing the tendency to repeat previous commercial ties. Producer choice is formalized through a local utility function that combines attractiveness, distance penalty, and relational memory under capacity, sourcing-radius, and saturation constraints. In the simulated scenarios, the top-five slaughterhouses accounted for 38.49 ± 2.56% of throughput at reduced scale and 14.40 ± 0.65% at intermediate scale, while weighted mean distances were 11.94 ± 0.56 and 9.07 ± 0.39 model units, respectively. The model reproduced, in structural and mechanistic terms, the emergence of dominant hubs, the concentration of flows, and the bounded increase in transaction distance with connectivity around the empirical threshold of kw ≈ 256. Sensitivity analyses indicated that attractiveness increases concentration, distance localizes transactions, and relational memory can stabilize repeated ties when recurrent activation is represented. Rather than reconstructing individual transactions, estimating policy impacts, or identifying a unique parameter vector, the model provides a generative explanation of how local trade rules can produce macro-level network patterns consistent with the observed cattle-trade regime. These findings support future prospective analyses of cattle governance, traceability, and sustainability within the broader context of Livestock 4.0. Full article

Precision in estimating the ripeness of fruits is critical in quality control and minimizing losses in supply chains of agricultural produce following harvesting. Conventional ripeness assessment techniques tend to be destructive, time-consuming and unsuited to monitoring in real-time. In order to avoid these drawbacks, this research suggests a cloud-integrated smart electronic nose (E-nose) system to predict fruit ripeness in a non-destructive and real-time manner. The system uses a low-priced, non-selective gas sensor array with an ESP8266-based Internet of Things (IoT) board to record volatile organic compound (VOC) signatures released at various maturation phases of fruits. The obtained sensor data will be sent to a cloud server to be preprocessed centrally and classified using machine learning, thus reducing the computational needs at the edge. There is a collection of 953 samples of the unripe, ripe, and rotten stages of banana under controlled conditions. Several supervised machine learning algorithms are tested, and methods of ensemble boosting proved to be more effective. The Light Gradient Boosting Machine (LightGBM) is the most accurate in terms of classification of 96.50% and weighted F1-score of 96.49%. The confusion matrix analysis shows that the majority of misclassifications are observed among the neighboring stages of ripeness, indicating the gradual biochemical changes. The system is practically applicable as visualization of the predicted ripeness levels occurs in real time via a mobile application. The suggested model provides a scalable, low-cost, and smart solution to precision agriculture, which can allow efficient, automated, and non-destructive measurement of fruit quality. Full article

The proliferation of AI-generated content and personalized AI systems has sharpened two fundamental and related computational problems: the progressive erosion of authentic identity in AI-mediated representations, and the growing difficulty of distinguishing human-originated from AI-generated behavioral and textual streams. This paper proposes a rigorous computational framework in which digital identity is formalized as a holomorphic function of complex time T = (a + ib) ∈ ℂ, where the real component Re$\left(T\right)$ encodes chronological progression and the imaginary component Im$\left(T\right)$ spans a continuum from episodic memory ($Im\left(T\right)<0$) through the present moment ($Im\left(T\right)=0$) to prospective imagination ($Im\left(T\right)>0$). We argue that holomorphicity—enforced via Cauchy–Riemann regularization during CTNN learning (Proposition 1)—provides a theoretically grounded encoding of identity coherence, and discuss its advantages over alternative mathematical choices, including Lipschitz continuity, ${\mathit{C}}^{\mathbf{\infty }}$ smoothness, piecewise analytic functions, and stochastic models. Under four explicit Assumptions 1–4 covering the Markovian structure and fixed context window of current LLM architectures, we establish via Lemmas 1–2 and Theorem 1 that AI-generated behavioral trajectories exhibit structural limitations in satisfying the Cauchy–Riemann conditions at temporal depths characteristic of human biographical memory—limitations that do not arise for human trajectories learned under CTNN regularization. Building on this result, we introduce the Human–AI Authenticity Discriminant (HAAD), a theoretically grounded classifier with a fully specified calibration algorithm and sensitivity analysis ($\mathit{\kappa }$ ΔAUROC ≤ 0.04 over ±30% perturbation). Five metrics—TCS, ISI, PAS, GAS, and HAAD—are derived analytically from the holomorphic structure. The algorithmic framework is instantiated on four real-world datasets: MovieLens 25M, the Pushshift Reddit corpus, the Stack Overflow Data Dump, and the LIAR dataset. On the LIAR benchmark, TDT-HAAD achieves AUROC = 0.82 (95% CI: [0.79, 0.85]), exceeding a RoBERTa-based LLM detector baseline (AUROC = 0.75, DeLong p < 0.01); an ablation study supports the structural contribution of each component. A credibility harvesting signature is detectable 45.3 ± 12.1 days before standard temporal models reach statistical significance. Full article

Hulunbuir short-tailed sheep (HSTS) and Hu sheep (HS) exhibit distinct tail phenotypes linked to ecological adaptation, with HSTS carrying a loss-of-function mutation (c.G334T) in the T gene while HS retain the wild-type allele. However, the cellular and molecular mechanisms underlying T-mediated tail development remain unclear. Here, we performed single-cell RNA sequencing on HSTS and HS embryos at embryonic days 16 and 19 (E16 and E19), complemented by cross-species validation using a CRISPR/Cas9 mouse model carrying the same mutation. We identified 12 cell types in E16 HSTS and E16 HS embryos, and 15 cell types in E19 HSTS and E19 HS embryos and found that the MDK_ITGA6+ITGB1 ligand–receptor pair consistently mediated core intercellular communication. The MDK_ITGA6+ITGB1 axis mediates intercellular communication critical for tail bud formation; BMP activation and FGF repression disrupt AER survival, leading to tail shortening. Developmental trajectories showed a shift from early progenitor states at E16 to terminal differentiation at E19. Crucially, HSTS embryos showed transcriptomic signatures consistent with premature AER regression. The T mutation showed transcriptomic signatures of increased BMP pathway activity and reduced FGF8 expression, which may disrupt AER survival and contribute to the short-tail phenotype. In the mouse model, mutant T expression was reduced, and expression dynamics of WNT5B and FGF8 were perturbed, corroborating the sheep findings; however, homozygous T mutation causes embryonic lethality in mice but not in sheep, indicating species-specific differences. This study provides single-cell transcriptomic evidence linking the T c.G334T mutation to premature AER regression in sheep, complemented by cross-species validation in a CRISPR/Cas9 mouse model, offering new insights into the cellular mechanisms of tail development and may provide a basis for future investigations into tail-related breeding markers, pending experimental validation. These changes are associated with AER maintenance and tail outgrowth. Full article

Structured Query Language injection (SQLi) remains a persistent threat to web applications due to the obfuscation, diversity, and evolving structure of malicious payloads, which limit the effectiveness of conventional rule and signature-based Web Application Firewalls (WAFs). Although prior studies have reported high detection performance using individual feature extraction methods or offline classification models, limited work has addressed deployment-oriented SQLi prevention through an integrated real-time inspection framework. This paper proposes a Machine Learning (ML)-based SQLi detection and prevention framework that combines hybrid feature representation, supervised dimensionality reduction, Genetic Algorithm (GA)-based hyperparameter optimization, and real-time WAF validation. Multiple public SQLi datasets were merged, cleaned, and deduplicated to improve exposure to diverse query patterns. SQL queries were encoded using Term Frequency–Inverse Document Frequency (TF-IDF), Word2Vec, and FastText features, which were fused and transformed through a Supervised Autoencoder into a compact discriminative representation. GA was then employed to optimize multiple classifiers, including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP). The MLP achieved the best overall performance, with an accuracy of 0.998681. The optimized model was deployed within a lightweight Flask-based WAF for real-time Hypertext Transfer Protocol (HTTP) request inspection and malicious input blocking. SQLMap v1.8.4-based robustness testing and runtime analysis demonstrate that the proposed framework provides effective SQLi prevention with practical deployment efficiency beyond conventional offline benchmark evaluation. Full article

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening cerebrovascular condition characterized by a dynamic clinical course spanning distinct pathophysiological stages, including aneurysm rupture, early brain injury (EBI), delayed cerebral vasospasm, and long-term neurological outcome. Despite extensive research, no clinically applicable molecular biomarkers exist to predict disease trajectory across these stages. MicroRNAs (miRNAs), small non-coding RNA molecules detectable in blood and cerebrospinal fluid (CSF), have emerged as promising candidates due to their stability and close association with vascular, inflammatory, and neuronal processes. However, existing studies have largely evaluated miRNAs in isolation, without integrating findings into a unified temporal framework. This review provides a structured, translational synthesis of miRNA dynamics in aSAH and proposes a stage-specific conceptual model integrating prospective clinical evidence with the broader literature. Dual-biofluid profiling has identified miR-29a, miR-200a-3p, and miR-451a as robust rupture-associated biomarkers, with distinct compartment-specific expression patterns. CSF-based profiling has demonstrated that miR-221-3p, miR-9-3p, and miR-183-5p predict vasospasm within 24 h of hemorrhage, while miR-24 and miR-21-5p correlate with disease severity and poor outcome. Integrating these findings with the broader literature, we categorize miRNA signatures across four stages: rupture discrimination, early brain injury, vasospasm prediction, and outcome stratification. This stage-specific framework highlights the biological continuum linking endothelial injury, vascular dysfunction, and secondary brain damage. The proposed model provides a foundation for multi-marker biomarker development, prospective validation studies, and future precision medicine strategies in aSAH. Full article

Environmental exposures can influence phenotypes across generations, yet the cellular routes by which somatic stress signals reach the germline remain poorly defined. piRNAs are attractive candidates for transgenerational epigenetic inheritance because they silence transposable elements, guide chromatin regulation, carry a stabilizing 3′ 2′-O-methyl modification, and participate in self-reinforcing amplification pathways, including ping-pong amplification in animals and RNA-dependent RNA polymerase (RdRP)-mediated secondary small-RNA amplification in systems such as C. elegans. This review examines evidence linking piRNAs, macrophage biology, and environmentally induced inheritance. We first summarize small-RNA inheritance in animals, plants, and ciliates, emphasizing C. elegans piRNA-triggered epigenetic memory and plant RNA-directed DNA methylation as parallel small-RNA-based inheritance systems. We then discuss emerging evidence that macrophage polarization states contain distinct piRNA signatures and release extracellular vesicles carrying non-coding RNAs. Finally, we revisit the Drosophila ectopic large bristle outgrowth (ELBO) phenotype as a possible example of macrophage-like hemocytes linking stress, tissue remodeling, and heritable morphological variation. We propose the macrophage-mediated morphological evolution (M3) model as a testable framework connecting environmental stress to transgenerational phenotypes. Full article

To elucidate the solute sources, migration and enrichment mechanisms of water bodies in the endorheic lake region of the Qiangtang Plateau on the Tibetan Plateau and clarify the hydrogeochemical cycling patterns in alpine arid environments, this study focuses on two core scientific objectives: quantitative identification of the multi-source contributions of aquatic solutes, and revelation of the key processes governing the enrichment of strategic elements including lithium (Li) and boron (B). To achieve these goals, we conducted systematic hydrogeological field investigations and collected 28 multi-type water samples, covering springs, rivers, thermal springs, freshwater lakes, salt lake brines, atmospheric precipitation, and glacial meltwater. The physicochemical properties, major ions, and trace elements of all samples were comprehensively analyzed. On this basis, the hydrogeochemical characteristics, evolutionary processes, and solute origins of regional waters were systematically explored. Combined with PHREEQC numerical simulation, principal component analysis (PCA), and Pearson correlation analysis, the dominant controlling factors of water geochemistry were quantified, and a conceptual hydrogeochemical evolution model was established. The results reveal a clear hydrogeochemical evolutionary gradient across the study area: water bodies evolve from low-salinity HCO₃-Ca recharge end-members and transitional HCO₃·SO₄-Ca(Mg) type water to highly mineralized Cl-Na (SO₄·Cl-Na) salt lake brines, accompanied by synchronous enrichment of Li, B, arsenic (As), and other characteristic elements. Solute accumulation in regional waters is governed by the ternary coupling effects of evaporative concentration, rock weathering and leaching, and deep geothermal fluid input, while cation exchange and mineral dissolution–precipitation reactions further modulate ionic composition and ratios. Elements including As, Li, B, and chloride (Cl⁻) exhibit conservative migration behaviors in non-hydrothermal waters, whereas thermal springs possess unique geochemical signatures driven by deep fluid recharge. PCA results indicate that evaporative concentration serves as the primary controlling factor with a contribution rate of 55.39%; rock weathering provides the basic solute load (17.09%); and the coupled processes of deep fluid mixing and carbonate precipitation regulate elemental fractionation (14.21%). These findings systematically clarify the hydrogeochemical evolution laws and multi-source coupling mechanisms of inland lake waters in the Qiangtang Plateau. Furthermore, this study establishes a conceptual framework of “multi-source recharge–water–rock interaction–evaporative concentration”, advances the understanding of alpine hydrological cycling under climate change, and provides a solid scientific foundation for hydrological cycle research and green exploration of strategic mineral resources in endorheic salt lake regions. Full article

Tauopathies, including Alzheimer’s disease, involve progressive neurodegeneration and sustained neuroinflammation. We present a multi-compartment transcriptomic atlas of 9.6-month-old PS19 tauopathy mice compared with wild-type (WT) controls (n = 8/group), profiling cortical mRNA, cortical non-coding RNA (ncRNA), and plasma small extracellular vesicle (pEV) ncRNA. In the PS19 cortex, mRNA sequencing identified 917 differentially expressed genes (DEGs), with microglial deconvolution revealing an association toward disease-associated microglia (DAM) gene signature and downregulation of genes involved in oxidative phosphorylation and cholesterol biosynthesis relative to WT. Cortical ncRNA profiling identified 466 differentially expressed ncRNAs, primarily circular RNAs (circRNAs; n = 331). In pEVs, 822 ncRNAs were differentially abundant, of which 657 circRNAs were identified in PS19 compared to WT mice. Cross-compartment integration suggest that pEV miRNA gene targets functionally mirrored genes involved in the brain’s inflammatory and metabolic failure. We identified a preliminary candidate signature of 33 ncRNAs, including miR-5114 (up in brain, down in pEV), circ_0008242 and circ_0002153 (up in brain and pEV), and circ_0007688 (down in brain and pEV), differentially enriched across both brain and periphery in PS19 compared to WT mice. These results suggest that the pEV non-coding landscape may partially reflect central tau-mediated changes in the brain transcriptional response. This study identifies circRNAs as the most numerically perturbed ncRNA class and provides a foundation for potential peripheral indicators of central brain tau pathology. Full article

Ground-penetrating radar is widely used in non-destructive pavement evaluation, but the occurrence of multiple hyperbolic signatures in radargrams of cracked pavements remains insufficiently characterized, particularly for top-down and bottom-up cracking. This study investigates the occurrence of detectable hyperbolas in numerical GPR radargrams by comparing two crack models under a controlled two-dimensional numerical design. Model A represents top-down cracking, and Model B represents bottom-up cracking. For each model, four parametric studies were performed by varying crack width, crack depth, asphalt-layer thickness, and granular-layer thickness, yielding 32 simulations in total. All cases were modeled in gprMax2D at 2300 MHz and processed in MATLAB through radargram pre-processing, central A-scan candidate detection, lateral tracking of hyperbolic events, and final classification based on stable retained trajectories. Model A was predominantly characterized by 3H responses, whereas Model B was predominantly characterized by 2H responses, with no 3H case observed. In Model A, crack-width increase was associated with the strongest occurrence change, whereas in Model B, greater asphalt-layer thickness was associated with a reduction from 2H to 1H. The first apex TWT provided a complementary discriminator between the two models. These findings provide controlled numerical reference trends that may support the interpretation of hyperbola occurrence in GPR-based pavement crack assessment. Full article