Search Results (30,491)

Prostate cancer (PCa) remains one of the most prevalent urological malignancies worldwide, with early and accurate diagnosis being critical for improving patient outcomes. Traditional screening approaches, such as digital rectal examination and prostate-specific antigen (PSA) testing, have long served as frontline tools; however, their limited specificity and sensitivity contribute to high rates of false positives, unnecessary biopsies, and overtreatment. Recent UK guidelines and international consensus increasingly question the role of PSA-based population screening, advocating for risk-stratified pathways and multiparametric MRI as first-line investigations. In parallel, advances in molecular biology have identified promising cancer-specific biomarkers, such as prostate cancer antigen 3 (PCA3) and transmembrane protease serine 2 (TMPRSS2:ERG), that outperform PSAs in terms of specificity and prognostic value. These developments have catalysed innovation in biosensor technologies, enabling rapid, cost-effective, and non-invasive detection of single and multiplex biomarkers in urine and serum. Electrochemical and optical affinity-based biosensors offer transformative potential for the development of personalised point-of-care platforms and diagnostics, reducing the reliance on invasive procedures and improving clinical decision-making. The latter can be augmented with artificial intelligence (AI) tools. This review critically examines the limitations of PSAs, synthesises evidence on novel biomarkers and imaging-led strategies, and evaluates the design, performance, and translational challenges of biosensor-based assays. Furthermore, it outlines future directions, including standardisation, large-scale clinical validation, and integration of multiplex biosensors with AI for precision diagnostics. By bridging molecular insights with engineering innovations, these approaches promise to redefine PCa screening and enable accurate, patient-centred care. Full article

This study introduces a novel unsupervised framework, ICR-LLS, for detecting pathological gait patterns using instantaneous center of rotation (ICR) trajectories of the shank in the sagittal plane. ICR trajectories were computed from two-dimensional kinematic data captured at the lateral femoral epicondyle and lateral malleolus for both shanks, producing four-dimensional multivariate time series for each gait trial. Pairwise trajectory dissimilarities were quantified using circularly aligned Dynamic Time Warping (DTW), preserving temporal and spatial structure. The resulting dissimilarity matrix was embedded into a three-dimensional space using a force-directed network layout, enabling intuitive visualization of inter-subject gait relationships. Density-based clustering (DBSCAN), enhanced with a consensus-based ensemble approach, was employed to automatically identify clusters representing typical (healthy) gait patterns and outliers corresponding to pathological deviations. The framework is evaluated on a public dataset comprising individuals with Parkinson’s disease (PD) and healthy controls, achieving a normalized mutual information (NMI) of 0.449 and a Separation-to-Compactness Ratio (SCR) of 6.754, indicating a meaningful cluster structure. In addition, classification-oriented metrics yield an accuracy of 90%, sensitivity of 70%, and specificity of 96.7%, supporting the method’s effectiveness in distinguishing pathological gait. By combining minimal 2D kinematic inputs with unsupervised learning, ICR-LLS provides an interpretable framework for the exploratory analysis of gait variability, and although further validation is required, the findings suggest that ICR trajectories may serve as a meaningful biomechanical descriptor for characterizing pathological locomotion. Full article

Surface plasmon resonance (SPR) is a label-free, real-time biosensing technology with high sensitivity for the detection of biomolecular interactions. This review highlights recent advances in SPR biosensors for the detection of SARS-CoV-2. First, we outline design strategies, especially advanced plasmonic nanostructures and precise surface functionalization, that improve the specificity and binding affinity to viral targets. Next, we cover signal amplification methods, such as nanoparticle conjugation and plasmonic photothermal effects, which enhance the sensitivity for low-abundance viral components. Subsequently, we conducted a comparative analysis of SPR biosensors alongside traditional and emerging detection approaches for SARS-CoV-2, elucidating their individual merits and drawbacks. We also discuss how machine learning improves data interpretation and diagnostic accuracy. Finally, we discuss the current challenges and future development directions, particularly for clinical diagnostics, epidemic monitoring, and public health security. These advances support faster, more reliable, and accessible diagnostics for current and future viral outbreaks. Full article

The collaboration of ground vehicles (GVs) and drones offers a powerful approach for enhancing drone capabilities. Current research focuses on drone-only or single-type target reconnaissance, failing to adequately account for practical scenarios. This paper introduces a GV–drone collaboration routing problem with multi-type target reconnaissance (GVD-MTR), which explicitly integrates GV–drone collaboration with simultaneous reconnoitering of point, line, and area targets. To address this problem, we propose a knowledge-driven two-stage hybrid algorithm (KDHA). In the first stage, K-means clustering combined with heuristic operators is applied to generate and refine routes for the GV. In the second stage, an improved Adaptive Large Neighborhood Search (IALNS) method is implemented to produce optimized drone routes. KDHA leverages hybrid search strategies, such as a population-based initialization strategy and a multi-level acceptance strategy, to efficiently generate high-quality solutions. Regarding recognizing the impacts of different target types on the total travel distance, we incorporate the related domain knowledge to design problem-specific search operators. Extensive simulation experiments demonstrate that KDHA consistently outperforms several state-of-the-art heuristics in terms of solution quality and runtime. Sensitivity analyses further confirm the robustness of the proposed approach across a range of parameter settings and problem instances. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

Bacterial stem and root rot (BSRR), caused by Dickeya dadantii, poses a severe threat to global sweetpotato production, yet the genetic architecture underlying resistance remains elusive. To dissect these mechanisms, we conducted a high-resolution genome-wide association study (GWAS) on 135 diverse accessions, integrating two-year field phenotyping with best linear unbiased prediction (BLUP) and 6.8 million single-nucleotide polymorphism (SNP) markers. This approach mapped nine quantitative trait loci (QTLs) exhibiting significant allelic dosage-dependent effects, with the major locus, qBSRR.6.1 was the primary discriminator between resistant and susceptible genotypes. Crucially, transcriptomic profiling within these loci revealed distinct expression patterns: IbTCP5 and IbERF003 (located in qBSRR.5.1 and qBSRR.6.2) were highly expressed in the susceptible cultivar ‘Xinxiang’ but suppressed in the resistant ‘Guangshu87’. Furthermore, BSRR challenge identified IbPUB4, IbKCS5, and IbLig1 as priority candidate genes involved in defense, with expression patterns suggesting roles in ubiquitin-mediated protein turnover, cuticular wax biosynthesis, and DNA repair, respectively. In stark contrast, IbPUB25 was constitutively upregulated in ‘Xinxiang’, potentially acting as a negative regulator of immunity via degradation of target proteins. These findings elucidate the polygenic, dosage-sensitive nature of BSRR resistance and prioritize specific targets for future functional characterization. Pyramiding favorable alleles of positive candidates while silencing potential negative regulators like IbPUB25 offers a promising avenue for developing durable, high-resistance sweetpotato varieties. Full article

Objective: Seaweed and microalgae provide antioxidants, polyunsaturated fatty acids, and bioactive compounds that may enhance exercise performance and accelerate recovery. However, evidence remains inconsistent. This systematic review and meta-analysis aimed to evaluate the effects of algae-derived supplementation on exercise performance and physiological recovery outcomes in healthy and athletic adults. Methods: This review was registered in PROSPERO (CRD420251166723) and conducted in accordance with PRISMA 2020 guidelines. PubMed, Web of Science, Embase, Cochrane Library, EBSCO, and CNKI were systematically searched for randomized controlled trials (RCTs) evaluating algae supplementation in exercise contexts. Inclusion and exclusion criteria were defined based on the PICOS framework. Primary outcomes included VO₂max, Time to exhaustion (TTE), maximal power output (WRmax), Time-Trial (TT) performance, and creatine kinase (CK). Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup, sensitivity, and publication bias analyses were performed. Results: Twenty-two RCTs (n = 822) investigating Spirulina, Chlorella, brown-algal polysaccharides, or astaxanthin met inclusion criteria. Algae supplementation showed a suggestive improvement in VO₂max (SMD = 0.88, 95%CI: 0.00–1.75) and significantly improved in TTE (SMD = 1.06, 95%CI: 0.16–1.96), with smaller effects on WRmax (SMD = 0.29, 95%CI: 0.03–0.55), and no significant benefit for TT performance (SMD = −0.27, 95%CI: −0.74 to 0.21). Regarding recovery, CK concentrations were significantly reduced (SMD = −0.78, 95%CI: −1.28 to −0.28). Subgroup analysis suggested greater effects for Chlorella supplementation, higher dosages, and aerobic training contexts; reductions in muscle-damage markers were more evident following resistance exercise. Sensitivity analyses supported the robustness of the main findings with minimal evidence of publication bias. Conclusions: Algae-derived supplements—particularly Spirulina and Chlorella—may modestly enhance aerobic exercise performance and attenuate exercise-induced muscle damage under certain conditions. Effects appear to depend on algae species, dosing strategies, intervention duration, and training modality. High-quality, multi-center RCTs incorporating mechanistic endpoints are needed to clarify optimal application and to develop athlete-specific recommendations. Full article

The increasing adoption of Large Language Models (LLMs) in software development has enabled automatic code generation from natural language, yet the influence of communicative factors such as prompt tone remains underexplored. This work introduces PromptTone, a controlled dataset designed to investigate how variations in prompt politeness affect LLM-based code generation in web development. The dataset is constructed through a structured experimental design combining three variables: programming paradigm (Vue.js Composition API vs. Options API), LLM provider (GPT, Claude, Gemini), and prompt tone (impolite, neutral, polite), resulting in 396 generated components across 22 implementations. Data were collected in an educational setting under a single-prompt constraint to capture first-shot model behavior, and are provided in both hierarchical and CSV formats, including prompts, generated code, and error annotations. Preliminary analysis reveals that prompt tone influences output characteristics such as verbosity, with model-specific patterns: for instance, some models exhibit increased output length with more polite prompts, while others remain stable. Differences also emerge across programming paradigms, suggesting an interaction between tone and code structure. These findings highlight that LLMs are sensitive not only to semantic content but also to pragmatic aspects of input. Overall, the dataset provides a novel benchmark for studying human–LLM interaction in code generation, supporting future research on prompt engineering, model evaluation, and socially-aware Artificial Intelligence (AI)-assisted development tools. Full article

Recent automated search methods have improved lower bounds for several Ramsey numbers, but the strongest gains often depend on structured seeding and cell-specific heuristic discovery. This leaves open a more fundamental question: Can a useful search structure be transferred across related Ramsey cells rather than rediscovered independently for each target instance? This work proposes a teacher–student framework for transferable structural search in Ramsey graph construction, inspired by the structure-distillation logic of Physics Structure-Informed Neural Networks (Ψ-NNs). The framework builds compressed structural representations from teacher witnesses and search traces, extracts reusable motifs and relations, and reconstructs transfer candidates. These are refined by balanced search and, for weak R(3, s) cells, by exact small-cell supervision. The framework is evaluated as a proof of concept across five Ramsey cells under transfer, matched-compute, search, ablation, and interpretability settings, including a proportional shift-scaling baseline and a greedy triangle-closing baseline that probe the structure-validity frontier from complementary directions. Supplementary experiments cover seed robustness, budget sensitivity, transfer-neighborhood variation, structural-resolution changes, stronger exact supervision, cross-r teacher pooling, single-teacher configurations, and scaling behavior across graph sizes. The results show that the portfolio version of the framework is the strongest balanced transfer method in the current study, while a structure-dominant oracle achieves stronger witness-shape agreement but worse Ramsey-valid construction. These findings reveal a clear structure-validity frontier and suggest that transferable Ramsey search should be evaluated by how well structural priors survive the validity constraints of new cells. Full article

Background: Tumor immune microenvironment (TIME) heterogeneity limits immunotherapy efficacy in hepatocellular carcinoma (HCC), underscoring the need for predictive biomarkers and therapeutic targets. We previously identified dual specificity phosphatase 4 (DUSP4) as a mediator of sorafenib resistance, but its immunomodulatory role remains unknown. Methods: Glypican-3 (GPC3)-specific chimeric antigen receptor (CAR) T-cell cytotoxicity assays were performed to assess the impact of DUSP4 on HCC immune susceptibility. A subcutaneous tumor model using Dusp4-overexpressing cells in female C57BL/6J mice was established to evaluate DUSP4-mediated microenvironment remodeling and anti-PD-L1 therapy efficacy. Bulk RNA sequencing of DUSP4-overexpressing HCC cells identified downstream pathways. Public datasets were interrogated to correlate DUSP4 expression with immune checkpoint blockade (ICB) response and immune infiltration in HCC. Results: DUSP4 overexpression significantly enhanced HCC cell susceptibility to CAR-T cell killing in vitro and potentiated anti-PD-L1 efficacy in vivo, accompanied by TIME remodeling. Mechanistically, RNA sequencing revealed DUSP4-mediated downregulation of the TGF-β signaling pathway, functionally confirmed using a neutralizing antibody that abrogated the enhanced CAR-T killing. Public datasets confirmed associations between DUSP4 expression and enhanced immune cytolytic activity with favorable prognostic outcomes in HCC. Conclusions: DUSP4 serves as a critical molecular nexus linking targeted therapy resistance to enhanced immunotherapy sensitivity. By attenuating the TGF-β signaling pathway, DUSP4 reprograms TIME toward an immunologically active state, thereby augmenting the efficacy of immunotherapy. These findings establish DUSP4 as a promising dynamic biomarker for guiding sequential therapy in HCC and highlight its potential as a novel therapeutic target to improve outcomes in solid tumor immunotherapy. Full article

Background: To explore the correlation between spatial dose distribution and post-implant urinary toxicity, aiming to assist decision making in low-dose-rate (LDR) treatment planning, thereby improving patient outcomes. Methods: Eighty-five prostate LDR patients with >12-month follow-up were included. Patient-reported urinary toxicity was collected prospectively using the International Prostate Symptom Score (IPSS) questionnaire, from before implant (baseline) to post-implant follow-up. Patients were then grouped into those whose symptom scores returned to ≤2 points above baseline by 12 months (no long-term toxicity) vs. those who did not (long-term toxicity). A total of 106 features were extracted for each patient, including principal components of dose-volume histograms (DVHs) from multiple prostate subzones, the whole prostate and urethra, along with baseline IPSS, implantation characteristics, and additional DVH indicators for the prostate and the urethra. A machine learning (ML) model incorporating backward feature selection algorithm was developed to predict long-term toxicity status, using a shuffle-and-split validation strategy for model evaluation during feature selection. A univariate statistical analysis was conducted on the model’s selected features. Results: Out of 85 patients, 41 (48%) had long-term urinary toxicity. Seven features were selected during model training, including baseline IPSS and six dosimetric features from several prostate subzones primarily located in the posterior prostate. The model achieved a high mean area under the receiver operating characteristic curve (AUC) of 0.81, with a balanced sensitivity and specificity of 0.78 by adjusting the probability threshold. In univariate analysis, only baseline IPSS and one selected dose feature were significantly correlated with long-term toxicity with AUC < 0.71. Conclusions: The proposed ML model, integrating baseline IPSS and spatial dosimetric features, effectively predicts long-term urinary toxicity after prostate LDR. This approach offers a practical method for risk stratification, allowing clinicians to identify patients at elevated risk and prioritize them for targeted preventative measures and closer follow-up. Full article

The MarR (multiple antibiotic resistance regulator) family regulators, which are widely conserved across various organisms, play pivotal roles in metabolism, stress response mechanisms, and virulence factor production. However, the regulatory functions of these factors in the degradation of aromatic compounds within Corynebacterium glutamicum remain largely uncharacterized. In this study, we identified a MarR-type regulator, designated AcsR (encoded by ncgl2425), which directly represses the expression of the catechol 2,3-dioxygenase gene ncgl2007 (c23o) and the heavy metal (nickel) transport system permease gene ncgl2351, while activating the expression of ncgl2258 encoding an ABC-type C4-dicarboxylate-binding periplasmic protein. AcsR binds specifically as a dimer to a 6 bp inverted repeat sequence, and this binding is disrupted by catechol in vitro. Correspondingly, catechol induces the expression of c23o in vivo. Phenotypic analysis revealed that the ΔacsR mutant exhibited enhanced resistance to multiple aromatic compounds but increased sensitivity to antibiotics, heavy metals, and oxidants. Collectively, these findings demonstrate that AcsR is an important regulator of stress adaptation in C. glutamicum and provide new insights into the regulatory mechanisms of aromatic compound degradation in this industrially important bacterium. Full article

Pruning residues from medicinal and aromatic plant cultivations represent an under-exploited biomass rich in bioactive metabolites. In this study, pruning by-products from Lavandula angustifolia Mill. ‘Essence Purple’ and Helichrysum italicum (Roth) G.Don were investigated as sources of essential oils (EOs) within a circular economy perspective. Micromorphological analyses confirmed the presence of secretory glandular trichomes in the residual biomass. EOs were obtained by steam distillation (0.33% and 0.15% yield for lavender and helichrysum, respectively) and chemically characterized by GC-FID and GC-MS. A total of 51 and 55 compounds were identified, accounting for 99.68% and 99.57% of the total composition. The main constituents were τ-cadinol (23.09%) and linalyl acetate (14.07%) in lavender EO and γ-curcumene (15.47%) and eudesm-4(14)-en-11-ol (10.71%) in helichrysum EO. Pruning-derived EOs showed a higher sesquiterpene content than those from conventional plant organs, indicating a compositional shift. Phytotoxic assays on Hordeum vulgare, Raphanus sativus, Lolium multiflorum, and Sinapis alba revealed concentration-dependent effects, with a stronger inhibition of radicle elongation than seed germination. These concentrations should be interpreted as indicative of intrinsic phytotoxic potential under controlled conditions. Ecotoxicological tests showed no significant reduction in viability in Artemia salina, whereas concentration- and time-dependent immobilization was observed in Daphnia magna, highlighting species-specific sensitivity, likely related to differences in the uptake and membrane interactions of lipophilic compounds. These findings highlight pruning residues as a promising biomass for the recovery of bioactive phytocomplexes with potential applications in sustainable weed management, although further studies under agronomically relevant conditions and comprehensive environmental assessments are required to validate their practical applicability. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article