Search Results (1,488)

Prostate adenocarcinoma is characterized by substantial inter-patient heterogeneity, limiting the clinical reliability of conventional diagnostic tools, including prostate-specific antigen testing. This limitation underscores the need for robust molecular biomarkers that may complement conventional diagnostic tools, highlighting the urgent need for biomarkers capable of enhancing diagnostic accuracy and enabling more precise risk stratification. In the present study, transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed using an integrative bioinformatics and machine learning pipeline., The proposed workflow was designed as a stepwise and reproducible biomarker prioritization framework in which differential expression analysis, functional enrichment, protein–protein interaction (PPI) based network interpretation, graph-convolutional feature selection, and hybrid ensemble machine learning were sequentially integrated. Differential gene expression analysis was combined with pathway enrichment (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome), protein–protein interaction network construction, and graph-convolutional feature selection. Multiple machine learning algorithms, including Random Forest, Gradient Boosting Machine, Support Vector Classifier, Artificial Neural Network, and AdaBoost, were systematically evaluated. A hybrid ensemble model integrating Gradient Boosting Machine and Random Forest (GBM+RF) was subsequently developed. Model performance was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic (ROC) and externally validated using the independent GSE14206 dataset. The analysis revealed a coordinated molecular pattern characterized by dysregulated cell cycle activity and enhanced interferon-mediated immune signaling. Protein–protein interaction analysis identified STAT1 and PLK1 as highly connected network hub genes within immune-related and cell-cycle-associated modules. Among the evaluated models, the hybrid GBM+RF framework achieved the highest predictive performance on the TCGA dataset, with AUC: 0.9526; Accuracy: 97.49%. External validation using the GSE14206 dataset confirmed the robustness of this model (AUC: 0.9156; Accuracy: 91.53%). These findings support a broader multi-gene candidate signature in prostate adenocarcinoma, in which machine learning prioritized genes such as XAF1, APP, RPA3, IFIH1, UBE2D2, RSAD2, KIF2C, and PLK1, while STAT1 and PLK1 provided complementary network-level biological relevance. The proposed framework provides a robust and transferable strategy for biomarker discovery and precision oncology. Full article

Learning transferable region embeddings is fundamental to urban computing, supporting applications from economic forecasting to public safety. Existing multi-view fusion methods predominantly rely on coarse-grained fusion, applying uniform weights across an entire city or task, thereby neglecting spatial heterogeneity—the fact that the dominant view driving a region’s function varies significantly across regions. To address this, we propose RAMEN, a two-stage framework for Region-adaptive Mixture of Ego-Networks learning. In the pre-training stage, a unified spatially aware Transformer distills universal urban semantics. In the fine-grained adaptation stage, a Mixture of Ego-Networks (MoEN) module employs a region-adaptive gating mechanism to dynamically allocate exclusive view weights for each region. A Region Ego-aware Spatial Transformer (REST) then aggregates these fused local subgraphs by explicitly injecting degree and physical distance priors, overcoming the over-smoothing limitations of traditional GNNs. Extensive experiments on real-world datasets for check-in, crime, service call and population prediction show that RAMEN consistently outperforms state-of-the-art baselines, achieving up to 35.3% MAE improvement. Visualizations of gating weights further suggest that RAMEN’s fusion aligns well with real-world urban physical characteristics. Full article

Water conservation is among the most important ecosystem service functions, and its dynamic changes can reflect the health of regional ecosystems. As one of the ten major tributaries of the Yellow River, the Yi–Luo River in Henan Province plays a significant role in maintaining regional ecological health through the ecosystem water conservation functions of its basin. To explore the characteristics of these functions, this study employed ecosystem transfer matrix and water balance methods to analyze the changes in ecosystem types in the basin and the dynamic changes in the corresponding water conservation functions from 2010 to 2020. The results showed that from 2010 to 2020, the dominant ecosystem types were farmland, forestland, and shrubland, and the main transformations between different ecosystem types were from farmland to urban areas and wetland, from grassland to shrubland, and from shrubland to forestland. The water conservation functions of the different ecosystem types varied significantly, with the average water conservation capacities ranked as follows: forestland > wetland > shrubland > grassland > farmland > others > urban areas. Due to the decreased water conservation depths in the forestland and shrubland ecosystems, the overall water conservation depth decreased over the study period. In addition, the water conservation capacities of the farmland, forestland, and shrubland ecosystems were the highest, reaching 12.94 × 10⁸, 12.15 × 10⁸ and 9.73 × 10⁸ tons, respectively, accounting for 34.47%, 32.35%, and 25.91% of the total water conservation capacity, respectively. Overall, water conservation in the study area was high in the south and low in the north, and there was an overall decreasing trend over time. Moreover, the regions with critical and important water conservation functions accounted for the largest proportion (47.2%). The critical and important regions were mainly located in the higher altitudes of Luanchuan County and Lushi county, while the moderate and average regions were primarily located in the plains, and weak regions were distributed mainly in the urban area of Luoyang city. Through analysis of the spatiotemporal changes and degree of importance of water conservation functions in the Yi–Luo River Basin, the key regions for ecological protection and construction were identified. The study results can be used to help enhance ecological security in the Yellow River Basin of Henan Province and play a significant role in promoting the regional economy as well as the health and sustainable development of the ecological environment. Full article

In the Neotropical Savanna domain, few equations are available for estimating biomass stocks in Cerrado sensu stricto (CSS), despite the importance of such tools for estimating carbon stocks, understanding ecosystem functioning, and supporting conservation actions. We conducted forest inventories in 40 temporary 1000 m² plots in southeastern Brazil to estimate total and compartmental biomass stocks and to model biomass using structural and edaphic predictors. Total biomass (TB) included aboveground woody biomass (AGWB), necromass, litter, and belowground biomass (BGB). AGWB was estimated for trees with basal diameter ≥ 5 cm using a previously fitted regional equation. Root biomass was sampled using a 1 m³ trench excavated at a single point adjacent to each plot. Necromass was quantified using the line-intersect method along a 50 m transect, considering debris with diameter ≥ 3 cm. Litter was sampled using a 0.25 m² frame placed at the center of each plot. Biomass was modeled on an area basis using a hierarchical approach for TB, total tree biomass (TTB = AGWB + BGB), and AGWB. Mean stocks (Mg ha⁻¹ ± s.d.) were 45.24 ± 17.32 (TB), 20.47 ± 11.26 (AGWB), 18.47 ± 10.88 (BGB), 5.49 ± 4.18 (litter), and 0.81 ± 1.62 (necromass). Models selected using the Akaike Information Criterion (AIC) and validated by repeated k-fold cross-validation achieved rŷy = 0.76, 0.72, and 0.94 and RMSE = 24.40%, 27.06%, and 18.10% for TB, TTB, and AGWB. As the equations were calibrated for CSS under the environmental conditions of the Brazilian semiarid region, their transferability to other Cerrado regions should be considered with caution. The inclusion of soil variables (e.g., Al, Mg, and sand) improved predictions, reducing relative costs and taxonomic dependence, while incorporating nutritional adaptations to the acidic soils characteristic of the biome. Full article

Spectral analysis technology has emerged as a vital tool for quantifying complex samples because of its simplicity and high efficiency. However, spectral data has a high-dimensional characteristic and traditional variable selection methods struggle to balance computational efficiency and prediction accuracy. Hence, a discretized wild horse optimizer (WHO) algorithm was introduced in this study. Firstly, transfer functions were introduced to solve the discrete optimization in spectral variable selection. The optimal number of latent variables (LVs) in partial least squares (PLS), DWHO iterations and the population size were determined to establish the DWHO-PLS quantitative analysis model. Then, three spectral datasets of pork, marzipan and DOSY samples were used to assess the effectiveness of the method. Finally, the DWHO-PLS model was compared with full-spectrum PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo-UVE-PLS (MC-UVE-PLS), randomization test-PLS (RT-PLS), gray wolf optimizer-PLS (GWO-PLS) and whale optimization algorithm-PLS (WOA-PLS). Results show that the number of variables selected by DWHO-PLS was the smallest and the root mean squared error of prediction (RMSEP) had the lowest value compared with other methods for the three datasets. The research indicates that DWHO can effectively simplify the PLS model while enhancing its accuracy and stability. Full article

Distributed Acoustic Sensing (DAS) provides dense spatial measurements of the dynamic strain along fiber optic cables, offering high-resolution wave sensing for ground motion monitoring and subsurface imaging applications. However, DAS records the axial strain or strain rate, whereas traditional seismic and engineering ground motion equipment and derived metrics are based on particle displacement, velocity, or acceleration, necessitating reliable strain-to-velocity conversion methods. This study evaluates three widely used conversion approaches: the fk-rescaling, curvelet-based conversion, and slant-stack methods. These approaches are applied to a unique high-energy, near-field, active-source dataset collected at the Birds Landing Site in Sherman Island, California. The dataset includes wavefields generated by a large transmission tower collapse and sledgehammer impacts used for subsurface imaging. The wavefields were recorded simultaneously by a 1.4 km DAS array and 71 collocated nodal stations (NSs). Using 63 DAS–NS pairs, we quantify the strain-to-velocity conversion method performance using amplitude and phase transfer functions (TFs) between DAS-derived and NS particle velocity records, with the root-mean-square error (RMSE) evaluated across three frequency bands: 0.5–100 Hz, 1–10 Hz, and 10–100 Hz. The results show that fk-rescaling provides the most stable amplitude response across both source types, while both the fk-rescaling and slant-stack methods generally yield the best phase agreement. Curvelet-based conversion shows a greater variability and larger RMSE values. All methods yield a poorer amplitude reconstruction at higher frequencies, while the phase content is generally preserved more reliably than amplitudes. Differences between the tower collapse and sledgehammer sources demonstrate the influence of the source characteristics and spatial processing window length on the conversion performance. The findings provide practical guidance for selecting suitable strain-to-velocity conversion methods for active-source DAS applications, particularly where collocated reference sensors are unavailable. Full article

The co-contamination of aquatic environments by antibiotic residues and organic dyes poses a serious threat to ecological security and human health, underscoring the urgent need for high-efficiency, recyclable, and environmentally benign adsorbents. Chitosan, a naturally occurring alkaline polysaccharide rich in reactive functional groups, has attracted considerable attention in water treatment applications. Nevertheless, its practical use is often constrained by intrinsic limitations, including poor stability in acidic media, inadequate mechanical strength, and difficulties in solid–liquid separation. Chitosan-based hydrogels, featuring unique three-dimensional cross-linked networks, high porosity, and strong hydrophilicity, provide efficient mass-transfer pathways for macromolecular contaminants and thus offer a promising strategy to overcome the shortcomings of pristine chitosan. This review comprehensively summarizes recent advances in chitosan-based hydrogel adsorbents, with a focus on elucidating the critical structure–performance relationships that link molecular/structural design to adsorption efficacy. First, fabrication strategies are systematically reviewed, ranging from molecular-level modifications (e.g., grafting, chemical cross-linking, and interpenetrating polymer networks) to macroscopic structural engineering approaches (e.g., mechanically reinforced, magnetic, and stimuli-responsive hydrogels). Subsequently, adsorption behaviors toward representative classes of antibiotics, including tetracyclines, fluoroquinolones, and sulfonamides, are critically examined, with emphasis on the underlying mechanisms such as electrostatic interactions, hydrogen bonding, π–π stacking, and pore-filling effects. In addition, the removal performance of chitosan-based hydrogels for organic dyes with varying charge characteristics is summarized, together with an analysis of how environmental factors (e.g., pH and ionic strength) influence adsorption kinetics and thermodynamics. Finally, key challenges related to mechanical robustness, selective adsorption, and recyclability are discussed, and future perspectives are proposed for the development of multifunctional, synergistic, and intelligent, environmentally responsive chitosan-based hydrogel materials. This review aims to provide systematic insights and guidance for the rational design of advanced hydrogel adsorbents for the treatment of complex wastewater. Full article

The super-resolution (SR) of Magnetic Resonance Imaging (MRI) is essential for utilizing clinical scans with limited resolution, noise, and anisotropic sampling, such as multi-modal brain tumor imaging. In this work, we propose a Tri-Representation hybrid framework for MRI SR, TRT-GLA, that redefines the MRI SR task as a joint spatial–spectral–structural high-resolution image generation problem. TRT-GLA utilizes (i) spatial global–local attentions for modeling the spatial anatomy, (ii) a Fourier spectral transfer mechanism for upholding spectral consistency, and (iii) multi-scale hierarchical spectral decomposition for improved edge details. To adapt the learning framework to medical imaging characteristics, we introduce a tri-representation consistent loss function that explicitly combines pixel-wise, spectral, and edge structure priors from the high-resolution ground-truth, as well as a progressive resolution learning strategy. Our large-scale brain tumor experiments, on the IXI, BraTS 2019, 2020, and 2023 datasets, show that TRT-GLA achieves state-of-the-art results at upsampling factors of ×2, ×4, and ×8, respectively, achieving substantial improvements across CNN, GAN, and transformer-based methods in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Multi-scale Structural Similarity Index (MS-SSIM). We further demonstrate how SR benefits brain tumor segmentation through the downstream task evaluation of a dual-branch segmentation framework. TRT-GLA produces highly accurate tumor segmentation results from low-resolution inputs, improving over native high-resolution inputs at ×8 in critical tumor boundary regions and in small tumor regions. There remains a small gap between native, high-resolution imaging and SR-enhanced performance, which TRT-GLA nearly closes under realistic scenarios. Our results highlight the importance of synthesizing unified priors over spatial, spectral, and structural domains within a transformer for anatomically faithful reconstructions. Importantly, we also establish the utility of TRT-GLA in supporting quantitative analysis through a downstream tumor segmentation experiment that is clinically relevant. Full article

Self-assembled nanosphere structures are widely used as bottom-up platforms for ordered micro- and nanostructures, with applications in photonic crystals, sensing platforms, functional coatings, drug delivery, and nanosphere lithography. Their performance and reproducibility depend on structural order, packing density, interparticle spacing, and defect density and distribution. Thus, reliable imaging and quantitative defect detection are needed for quality evaluation and process optimization. This review provides an overview of defect characteristics, imaging systems, and defect detection algorithms for self-assembled nanosphere structures. It first introduces representative zero-, one-, two-, and three-dimensional assemblies, followed by a summary of common defects, including vacancies, interstitial particles, dislocations, grain boundaries, stacking faults, voids, and cracks. Optical microscopy, electron microscopy, atomic force microscopy, scanning near-field optical microscopy, and correlative techniques are compared in terms of resolution, field of view, temporal resolution, contrast mechanism, in situ capability, and compatibility with feedback control. Algorithmic approaches are also reviewed, encompassing classical image processing, machine learning, and deep learning, along with their applications in segmentation, localization, classification, and high-throughput analysis. Overall, reliable defect inspection requires integrated workflows. These workflows should combine appropriate imaging systems, image quality control, transferable algorithms, standardized datasets, and closed-loop feedback. Full article

Hydrodynamic cavitation has attracted increasing attention in water and wastewater treatment because it can generate localized shear, pressure fluctuations, interfacial renewal, and reactive species in relatively simple continuous-flow devices. This review critically examines its main application domains, including microbial disinfection, cyanobacterial bloom control, organic micropollutant degradation, real wastewater treatment, sludge pretreatment for energy recovery, and hybrid process configurations. Rather than treating hydrodynamic cavitation as a single treatment mode, the discussion compares applications in relation to reactor design, matrix characteristics, treatment target, operating conditions, and assigned process function. The analysis shows that performance depends strongly on the interaction among device geometry, treated matrix, process configuration, and evaluation metrics. The same nominal process may therefore act as direct treatment, pretreatment, mass-transfer intensifier, oxidant-activation module, or support to downstream biological and polishing steps. The most consolidated evidence concerns microbial disinfection, sludge pretreatment, and several classes of organic contaminants, whereas PFAS treatment, field-scale validation, and system-level assessment remain less mature. Overall, hydrodynamic cavitation is best interpreted as a process-intensification platform rather than as a universally applicable stand-alone solution. Further progress will require more transparent assessment criteria, more comparable metrics, stronger validation in real matrices, more controllable reactors, and more rigorous energy, techno-economic, and scale-up evaluation. Full article

Respiratory syncytial virus (RSV) remains a major cause of severe lower respiratory tract infection in early infancy, a period characterized by immunological immaturity and limited capacity for effective antiviral responses. Maternal RSV vaccination has emerged as a successful strategy to protect newborns by inducing high concentrations of IgG1-dominant, prefusion F-specific antibodies, which are selectively and actively transported across the placenta. This review synthesizes current mechanistic insights into how maternally derived antibodies confer neonatal protection, focusing on (i) FcRn-mediated transplacental transport, (ii) IgG subclass-specific differences in transfer and half-life, and (iii) the role of Fc glycosylation in modulating Fcγ receptor engagement and effector functions. Beyond neutralization, vaccine-induced antibodies mediate Fc-dependent mechanisms such as antibody-dependent cellular cytotoxicity and phagocytosis, which may be preferentially enriched in the neonatal circulation. Although emerging systems serology data suggest qualitative selectivity in placental transfer, evidence remains heterogeneous and highlights the need for further clarification of glycosylation-dependent and glycosylation-independent pathways. The timing of vaccination, maternal antibody characteristics, and placental integrity critically influence neonatal antibody levels and the duration of passive immunity. Understanding these molecular determinants is essential for optimizing maternal RSV immunization strategies and improving early-life protection. Full article

This paper revisits a delayed tumor–immune model by replacing the discrete delay with weak and strong Gamma distributed memories, reflecting the realistic spread of immune-response times. Because the Gamma kernels are normalized, the biologically relevant equilibria of the reference model are preserved. The local stability problem, however, changes substantially: a careful linearization shows that the characteristic equation contains both the first and the second power of the memory transfer function, since delayed immune and tumor variables enter coupled feedback terms. Consequently, the weak Gamma chain leads to a quintic characteristic polynomial, whereas the strong Gamma chain leads to a seventh-degree polynomial. Routh–Hurwitz conditions and explicit Hopf bifurcation tests are derived for both memory structures, including simplicity and transversality requirements. Numerical simulations performed with the parameter sets of the reference study show that distributed memory reproduces the main biological regimes while shifting stability thresholds and modifying transient oscillations. The results indicate that not only the mean immune-response time but also the shape of its distribution can influence tumor–immune dynamics. Full article

Exhaled breath analysis has emerged as a promising non-invasive approach for disease diagnosis, leveraging gas sensors for their high sensitivity, portability, and real-time monitoring capabilities. Two-dimensional nanomaterials, such as graphene, transition metal dichalcogenides (TMDs), MXenes, black phosphorus, and metal–organic frameworks (MOFs), exhibit exceptional gas-sensing properties due to their atomic-scale thickness, ultra-large specific surface area, and tunable electronic structures. These characteristics enable enhanced gas adsorption and room-temperature operation, making them ideal for detecting ppb-level biomarkers like acetone, ammonia, and nitric oxide in breath. However, sensors based on pristine 2D materials face challenges including slow response/recovery kinetics, poor stability, weak humidity resistance, and limited selectivity in complex breath environments. To address these limitations, functionalization strategies have been developed to engineer material properties. Key approaches include heteroatom doping to modulate electronic band structures, heterojunction construction to facilitate charge transfer and improve selectivity, and noble metal decoration for catalytic enhancement of gas adsorption. Additionally, light irradiation has been employed to regulate the carrier concentration on the surface of sensitive materials. These strategies significantly boost sensor performance, achieving ppb-level detection limits, robust humidity resistance, and rapid response. Future directions involve integrating functionalized 2D materials into wearable, multiplexed sensor arrays for simultaneous biomarker detection, coupled with machine learning for real-time diagnostic platforms. Full article

Microencapsulated phase change material (MPCM)-modified cement composites have attracted increasing attention for energy-efficient buildings and thermal energy storage applications. Accurate prediction of thermal conductivity is essential for optimizing thermal management performance. However, quantitative understanding of the multiscale heat transfer mechanisms in MPCM-modified cement composites remains relatively limited. In this study, the thermal transport behavior of MPCM-modified cement composites was investigated through experimental characterization and multiscale theoretical modeling. A micro–macro combinatorial accumulation approach was developed based on representative volume element (RVE) construction and cumulative thermal interactions to characterize hierarchical heat transfer mechanisms within the composite system. The proposed model enables quantitative prediction of thermal conductivity for inclusions with different geometries and accumulation states. The results revealed that the proposed MMCA model successfully captured the multiscale evolution of thermal conductivity by considering cumulative RVE effects and inclusion geometrical characteristics. The effective thermal conductivity decreased from 0.802 to 0.519 W/(m·K) as the MPCM volume fraction increased from 0 to 0.217, corresponding to a reduction of approximately 35.3%. Furthermore, the accumulation of RVEs exhibited a rapid reduction followed by stabilization of thermal conductivity, revealing the scale-dependent heat transfer behavior induced by hierarchical inclusion interactions. The main contribution of this work is the establishment of a physics-based multiscale framework that quantitatively links MPCM inclusion characteristics, cumulative thermal interactions, and macroscopic thermal conductivity, providing new insights into the micro-to-macro heat transfer mechanisms of PCM-modified cement composites. This study offers theoretical support for the multiscale design and thermal performance optimization of PCM-modified energy-functional cementitious materials. Full article

The Crab pulsar produces extremely intense, short-duration radio bursts known as giant pulses (GPs). We introduce a cumulative-energy diagnostic to quantify the apparent spectral extent of individual Crab giant pulses across the ultra-wideband low-frequency (UWL) receiver band, aiming to build a reproducible method for describing the frequency-domain concentration of emission and to characterize the observed spectral diversity of Crab GPs. Using UWL receiver on the Murriyang (Parkes) radio telescope, we present a systematic study of GPs from the Crab pulsar (PSR J0534+2200). We introduce an empirical classification scheme based on the cumulative distribution function of the pulse energy as a function of observing frequency. We use this diagnostic to separate events with apparent spectral concentration from events with broader spectral coverage. Under this empirical classification scheme, most detected events show apparent spectral concentration within a limited frequency range. Events classified as apparently spectrally concentrated contain most of their measured relative energy within limited frequency ranges, whereas broadband events show more extended spectral coverage. We emphasize that this classification describes the observed spectral extent and should not by itself be interpreted as proof of intrinsically narrow-band emission. Spectral fitting shows that most apparently spectrally concentrated (ASC) GPs have negative spectral indices, while a few events exhibit positive slopes, indicating substantial spectral diversity within the sample. The 3$\sigma$ widths of ASC main pulse GPs appear to cluster around two characteristic ranges, although this feature should be interpreted with caution given the finite time resolution of the data. The energy distribution of ASC main pulse GPs is broadly consistent with a log-normal functional form at low-to-intermediate energies and resembles a power-law-like tail at the high-energy end. The waiting-time distribution can be described by a Weibull function, while a sliding-window comparison with Monte Carlo realizations of a Poisson distribution shows no statistically significant deviation from temporal independence over the present 18.9-min observing span. The CDF-based classification method developed here is transferable to other wideband receiver data, provided that careful consideration is given to the instrumental bandpass, frequency-dependent sensitivity, RFI masking, and signal-to-noise thresholds. These results provide observational constraints on the phenomenology of Crab GPs and may be useful for future studies of pulsar coherent emission and related radio transients. Full article