Search Results (7,590)

Obstructive sleep apnea is a prevalent sleep disorder with serious health implications. While previous studies focused on detecting apnea events, little is known about the factors that determine whether an apnea episode continues or terminates. Understanding these mechanisms is crucial for optimizing treatment strategies. In this study, we analyzed 30-s brain activity segments during continuous and ending apnea events to identify neurophysiological markers of event termination, with particular emphasis on the most influential EEG features. Frequency-domain and complexity features were extracted, and several ensemble machine learning models were trained and evaluated. Our results show that the Extra Trees model achieved the highest performance, with an accuracy of 0.88, F1-score for ending apnea of 0.87, and an area under the receiver operating characteristic curve of 0.95. Feature importance analyses and SHAP visualizations highlighted frequency-band energy, Teager–Kaiser energy, and signal complexity as key contributors. Temporal analyses revealed how these features evolve during apnea termination. These findings suggest that cortical activation and transient arousal processes play a decisive role in ending apnea events and may facilitate the development of more advanced adaptive or closed-loop sleep apnea therapies. Full article

Endocrine-disrupting chemicals (EDCs) are widespread contaminants that interfere with hormonal signaling and compromise reproductive success in aquatic organisms. Vitellogenin (VTG) is one of the most widely established biomarkers of estrogenic exposure, especially in males and juveniles. However, evidence from multi-omics studies indicates that VTG induction occurs within broader transcriptional and regulatory networks, involving genes such as cyp19a1 (aromatase), cyp1a (cytochrome P4501A), and other stress-responsive genes, underscoring the complexity of endocrine disruption. This review focuses on nuclear receptor isoforms, including estrogen receptor alpha (ERα), estrogen receptor beta (ERβ), and androgen receptor (AR) variants. We examine the diversification of vtg gene repertoires across teleost genomes and epigenetic mechanisms, such as DNA methylation and microRNAs, that modulate sex-dependent sensitivity. In addition, we discuss integrative approaches that combine VTG with transcriptomic, epigenetic, and histological endpoints. Within the Adverse Outcome Pathway (AOP) and weight-of-evidence (WoE) frameworks, these strategies provide mechanistic links between receptor activation and reproductive impairment. Finally, we outline future directions, focusing on the development of sex-specific biomarker panels, the integration of omics-based data with machine learning, and advances in ecogenomics. Embedding molecular responses into ecological and regulatory contexts will help bridge mechanistic insights with environmental relevance and support sustainability goals such as SDG 14 (Life Below Water). Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

The identification of producer typologies is a crucial tool for understanding the heterogeneity of agroecosystems and designing targeted policies. Andean agroecosystems, particularly those in rapidly suburbanizing areas, have been understudied in this regard, creating a critical knowledge gap. This study addressed this void by determining the typologies of smallholder agroecosystems in the suburban periphery of Cuenca, Ecuador, by applying an unsupervised machine learning technique, Partitioning Around Medoids (PAM) Clustering, to survey data from 293 farmers. Our analysis revealed three distinct typologies, highlighting a socio-economic and productive gradient defined by income sources, market access, and agrochemical use. The typologies range from economically vulnerable households to more commercially oriented and environmentally sustainable ones, underscoring the complex interplay between livelihoods strategies and environmental management. This research provides one of the first empirical typologies of suburban Andean agroecosystems, demonstrating the value of unsupervised learning for capturing farm heterogeneity in data-scarce contexts. The findings offer a robust evidence base for moving beyond one-size-fits-all approaches, enabling the design of differentiated agricultural and territorial policies that enhance sustainability, equity, and resilience at the rural–urban interface. Full article

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, often diagnosed at late stages due to the limited sensitivity of current screening tools. This study explores whether blood-based lipidomic profiling, combined with explainable artificial intelligence (XAI), can improve early and interpretable detection of HCC. Methods: We analyzed lipidomic data from 219 HCC patients and 219 matched healthy controls using liquid chromatography-mass spectrometry. An Explainable Boosting Machine (EBM) was employed to identify discriminatory lipid biomarkers and was compared against several standard machine learning algorithms. Results: The EBM model achieved superior performance with 87.0% accuracy, 87.7% sensitivity, 86.3% specificity, and an AUC of 91.8%, outperforming other models. Key lipid biomarkers identified included specific phosphatidylcholines (PC 38:2, PC 40:4), sphingomyelins (SM d40:2 B), and lysophosphatidylcholines (LPC 18:2), which exhibited significant alterations in HCC patients and highlighted disruptions in sphingolipid metabolism. Conclusions: Integration of lipidomics with explainable machine learning offers a powerful, transparent approach for HCC biomarker discovery, achieving high diagnostic accuracy while providing biological insights. This strategy holds promise for developing non-invasive, clinically interpretable screening tools to improve early detection of liver cancer. Full article

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu₂O, g-C₃N₄/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies. Full article

Accurate estimation of aboveground biomass (AGB) is essential for monitoring forage availability and guiding sustainable management in high-altitude pastures, where grazing sustains livelihoods but also drives ecological degradation. Although remote sensing has advanced biomass modeling in rangelands, applications in Andean–Amazonian ecosystems remain limited, particularly using UAV-based structural and spectral data. This study evaluated the potential of UAV LiDAR and multispectral imagery to estimate fresh and dry AGB in ryegrass (Lolium multiflorum Lam.) pastures of Amazonas, Peru. Field data were collected from subplots within 13 plots across two sites (Atuen and Molinopampa) and modeled using Random Forest (RF), Support Vector Machines, and Elastic Net. AGB maps were generated at 0.2 m and 1 m resolutions. Results revealed clear site- and month-specific contrasts, with Atuen yielding higher AGB than Molinopampa, linked to differences in climate, topography, and grazing intensity. RF achieved the best accuracy, with chlorophyll-sensitive indices dominating fresh biomass estimation, while LiDAR-derived height metrics contributed more to dry biomass prediction. Predicted maps captured grazing-induced heterogeneity at fine scales, while aggregated products retained broader gradients. Overall, this study shows the feasibility of UAV-based multi-sensor integration for biomass monitoring and supports adaptive grazing strategies for sustainable management in Andean–Amazonian ecosystems. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

Cancer metabolomics has become a powerful way of understanding tumor biology, identifying biomarkers and metabolites, and helping precision oncology. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), among many other analytical platforms, has gained popularity over the past two and a half decades due to its unique ability of directly analyzing metabolites in tissue with spatial resolution. This review will study 2000–2025 MALDI-based strategies for cancer metabolite detection, spanning from early proof-of-concept protein profiling to the development of high-resolution MALDI-MS imaging (MALDI-MSI), which is capable of mapping thousands of metabolites at near single-cell resolution. Its applications include the differentiation of tumor versus normal tissue, discovery of stage and subtype specific biomarkers, mapping of metabolic heterogeneity, and the visualization of drug metabolism in situ. Breakthrough technological milestones, such as the advanced matrices, on-tissue derivatization, MALDI-2 post-ionization, and the integration with Orbitrap or Fourier-transform ion cyclotron resonance (FT-ICR) platforms, have significantly improved the overall sensitivity, metabolite coverage, and spatial fidelity. Clinically, MALDI-MS has shown its purpose in breast, prostate, colorectal, lung, and liver cancers by providing metabolic fingerprints that are linked to tumor microenvironments, hypoxia, and therapeutic response. However, challenges such as the inclusion of matrix interface with low-mass metabolites, limited quantitation, ion suppression, and the lack of standardized procedures do not yet allow for the transition from translation to routine diagnostics. Even with these hurdles, the future of MALDI-MS in oncology remains in a good position with major advancements in multimodal imaging, machine learning-based data integration, portable sampling devices, and clinical validation studies that are pushing the field towards precision treatment. Full article

Radiative transfer models (RTMs) are foundational to optical remote sensing for simulating vegetation and atmospheric properties. However, their significant computational cost, especially for 3D RTMs and large-scale applications, severely limits their utility. Emulation, or surrogate modeling, has emerged as a highly effective strategy, accurately and efficiently replicating RTM outputs. This review comprehensively surveys recent developments in emulating vegetation and atmospheric RTMs. We discuss the methodological underpinnings, including suitable machine learning regression algorithms (MLRAs), effective training sampling strategies (e.g., Latin Hypercube Sampling, active learning), and spectral dimensionality reduction (DR) methods (e.g., PCA, autoencoders). Emulators commonly achieve ${10}^2-{10}^6\times$ per-evaluation acceleration, but accuracy–efficiency trade-offs remain inherently context-dependent, governed by the MLRA design and the coverage/quality of training data. DR consistently shifts this trade-off toward lower cost at comparable accuracy, positioning latent-space training as a pragmatic choice for hyperspectral applications. We synthesize key emulation applications such as global sensitivity analysis, synthetic scene generation, scene-to-scene translation (e.g., multispectral-to-hyperspectral), and retrieval of geophysical variables using remote sensing data. The paper concludes by outlining persistent challenges in generalizability, interpretability, and scalability, while also proposing future research avenues: investigating advanced deep learning algorithms (e.g., physics-informed and explainable architectures), developing multimodal/multitemporal frameworks, and establishing community benchmarks, tools and libraries. Emulation ultimately empowers remote sensing workflows with unparalleled scalability, transforming previously unmanageable tasks into viable solutions for operational Earth observation applications. Full article

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient global–local regression (EGLR) framework by integrating XGBoost-SHAP with global–local regression (GLR), enabling accelerated estimation of LST. In a case study of Wuhan, the EGLR reduces the computation time of GLR by 44.21%. The main contribution of computational efficiency improvement lies in the procedure of Moran eigenvector selecting executed by XGBoost-SHAP. Results of validation experiments also show significant time decrease of the EGLR for a larger sample size; in addition, transplanting the framework of the EGLR to two machine learning models not only reduces the executing time, but also increases model fitting. Furthermore, the inherent merits of XGBoost-SHAP and GLR also enables the EGLR to simultaneously capture nonlinear causal relationships and decompose spatial effects. Results identify population density as the most sensitive LST-increasing factor. Impervious surface percentage, building height, elevation, and distance to the nearest water body are positively correlated with LST, while water area, normalized difference vegetation index, and the number of bus stops have significant negative relationships with LST. In contrast, the impact of the number of points of interest, gross domestic product, and road length on LST is not significant overall. Full article

The Bactrian camel exemplifies mammalian adaptation to deserts, but the spatial organization of its innate immune system remains uncharacterized. This study integrated transcriptomes from 110 samples across 11 major tissues and organs to resolve tissue-specific gene expression and innate immune modularity. Through differential expression analysis, Tau specificity index (τ > 0.8), and machine learning validation (Random Forest F1-score = 0.86 ± 0.11), we identified 4242 high-confidence tissue-specific genes (e.g., LIPE/PLIN1 in adipose). Weighted gene co-expression network analysis (WGCNA) of 1522 innate immune genes revealed 11 co-expression modules, with six exhibiting significant tissue associations (FDR < 0.01): liver-specific (r = 0.96), spleen-adipose-enriched (r = 0.88), muscle-associated (r = 0.82), and blood-specific (r = 0.80) modules. These networks demonstrated multifunctional coordination of immune pathways—including Pattern Recognition, Cytokine Signaling, and Phagocytosis—rather than isolated functions. Our results establish that camel innate immunity is organized into spatially modular networks tailored to tissue microenvironments, providing the first systems-level framework for understanding immune resilience in desert-adapted mammals and may inform strategies for enhancing livestock resilience in arid regions. Full article

Chronic hemophilic synovitis (CHS), driven by hemosiderin-laden macrophages from recurrent hemarthrosis, is a major cause of joint damage in hemophilia. Platelet-rich plasma (PRP) is a promising regenerative therapy for joint diseases. This study investigated PRP’s ability to modulate macrophage polarization from a pro-inflammatory (M1) to a pro-resolving, tissue-repairing (M2) phenotype in CHS. We analyzed synovial fluid (SF) from CHS patients (N = 22), both pre- and post-PRP treatment. Ex vivo analysis revealed a predominant M1 profile with an increased proportion of CD11⁺CD14⁺CD64^hi compared with CD206⁺ or CD163⁺ M2 macrophages in CHS SF. In vitro experiments showed that CHS SF skewed monocyte-derived macrophages toward an M1 inflammatory program, evaluated by flow cytometry, qPCR, and ELISA. However, adding PRP significantly modulated the pro-inflammatory macrophage program, promoting an M2 tissue repair profile. Furthermore, a random forest machine learning algorithm, applied to public scRNAseq data, confirmed PRP’s macrophage reprogramming effect. Functional assays also showed increased TGF-β secretion and macrophage fusion when challenged with neutrophil extracellular traps (NETs). A small patient follow-up cohort treated with intra-articular PRP showed similar results, including normalization of cellular content and reduced CD64/CD206 expression. These findings indicate that PRP treatment effectively shifts SF-associated M1 macrophages to an M2-like phenotype, highlighting its potential as a therapeutic strategy for CHS. Full article

Impedimetric biosensors have emerged as a versatile class of electrochemical devices, enabling highly sensitive and real-time detection of diverse analytes. Their applications extend across healthcare diagnostics, environmental monitoring, food safety, and agriculture. By virtue of their compact size, high sensitivity, selectivity, portability, and ease of operation, these sensors have advanced rapidly in both research and practical applications. This review consolidates the wide spectrum of current applications and technological advances reported in the literature. Additionally, it examines the prospects of integrating impedimetric biosensors with emerging technology fields, including artificial intelligence, machine learning, and flexible and wearable devices. By providing an overview of the different categories of impedimetric biosensors, their detection strategies, sensing modalities, and applications, this review presents a comprehensive perspective on the current progress and future opportunities in impedimetric biosensing. Full article

Air pollution, outstanding particulate matter (PM_2.5), poses severe risks to human health and the environment in densely populated urban areas. Accurate short-term forecasting of PM_2.5 concentrations is therefore crucial for timely public health advisories and effective mitigation strategies. This work proposes a hybrid approach that combines machine learning models with STL decomposition to provide precise short-term PM_2.5 predictions. Daily PM_2.5 series from four major Pakistani cities—Islamabad, Lahore, Karachi, and Peshawar—are first pre-processed to handle missing values, outliers, and variance instability. The data are then decomposed via seasonal-trend decomposition using Loess (STL), which explicitly exploits the symmetric and recurrent structure of seasonal patterns. Each decomposed component (trend, seasonality, and remainder) is modeled independently using an ensemble of statistical and machine learning approaches. Forecasts are combined through a weighted aggregation scheme that balances bias–variance trade-offs and preserves the distributional consistency. The final recombined forecasts provide one-day-ahead PM_2.5 predictions with associated uncertainty measures. The model evaluation employs multiple statistical accuracy metrics, distributional diagnostics, and out-of-sample validation to assess its performance. The results demonstrate that the proposed framework consistently outperforms conventional benchmark models, yielding robust, interpretable, and probabilistically coherent forecasts. This study demonstrates how periodic and recurrent seasonal structure decomposition and probabilistic ensemble methods enhance the statistical modeling of environmental time series, offering actionable insights for urban air quality management. Full article