Search Results (4,800)

This paper presents a high-sensitivity two-dimensional fiber-optic hydrophone designed for the detection and localization of underwater acoustic sources. The device comprises two sensing heads, each incorporating a fiber Bragg grating (FBG) embedded within a customized 3D-printed encapsulation. To enhance acoustic sensitivity, the design utilizes a silicone thin-film coupled with a pyramidal channel that spatially concentrates acoustic energy from the base to the apex, where the FBG is positioned. Incident acoustic pressure induces vibrations in the film, which are amplified by the channel structure, imparting strain on the FBG and resulting in a shift in the Bragg wavelength. The acoustic frequency response is demodulated by converting the overlapping optical power between the sensing and reference gratings into an electrical signal via a photodetector. By arranging the two sensing heads orthogonally, the system effectively determines the direction and angle of the acoustic source. Experimental results show a peak sensitivity of −210.59 dB re 1 V/μPa, with a FWHM of 57.92–66.27 Hz and a figure of merit (FOM) up to 3.64 dB/Hz. In addition, the acoustic-field SNR is approximately 26 dB in the dominant band, and the LOD is 64.19 dB re 1 μPa (10–400 Hz). Experimental validation confirms the hydrophone’s high sensitivity and localization accuracy, demonstrating its significant potential for underwater acoustic sensing applications. Full article

Gene editing, particularly CRISPR/Cas technology, represents a promising approach for the treatment of rare genetic diseases, including inherited retinal dystrophies, for which effective therapies are largely unavailable. Despite extensive research investigating gene editing across a wide range of cell types, transient delivery of CRISPR/Cas components and efficient homology-directed repair (HDR) in differentiated cells remain challenging. In this study, we employed hiPSCs derived from patients with Stargardt disease or Best disease, carrying pathogenic variants in ABCA4 or BEST1, respectively, to explore gene editing in human models. CRISPR/Cas9 and Cas12 nucleases were delivered into hiPS-derived retinal pigment epithelium (RPE) and retinal organoids using lipoplexes and compared with electroporation. We evaluated transfection efficiency, sgRNA-mediated DNA cleavage, and HDR-based correction. Precise repair of the pathogenic BEST1 variant was successfully achieved in hiPS-derived RPE cells using both nucleases, with Cas12 yielding the highest efficiency, exceeding 10% of HDR correction. Edited RPE cells preserved normal morphology and expressed specific maturity markers. In contrast, retinal organoids exhibited moderate transfection efficiency but showed no detectable CRISPR/Cas-induced DNA cleavage, highlighting the need for further optimization of gene editing in more complex cellular tissues. This study demonstrates, for the first time, precise correction of a single-nucleotide mutation in patient-derived RPE using CRISPR/Cas9 and Cas12 delivered using lipoplexes. These findings underscore the therapeutic potential of CRISPR/Cas-based strategies for inherited retinal dystrophies and provide a proof of concept for future clinical approximations. Full article

The integration of renewable energy sources (RESs) into electric power systems introduces new challenges for system operation, reliability, and emergency management. Causal analysis, as a powerful data analysis tool, can reveal the interactions and influences between components in the power system, thus supporting the design, operation and optimization of the system. This review examines causal analysis methods applied to electric power systems with high-RES penetration, highlighting their effectiveness in identifying interactions among system components, detecting potential risks, and supporting operational decision-making. Key system properties, including safety, efficiency, flexibility, survivability, and reliability, are discussed in the context of high renewable integration. The review also analyzes lessons from systemic accidents and explores strategies to mitigate risks associated with excessive RES penetration. Finally, directions for future research are outlined, emphasizing real-time monitoring, advanced causal modeling, and methods to enhance the resilience of modern power systems. Full article

Plastic pollutants, including phthalates, bisphenol A (BPA), per- and polyfluoroalkyl substances (PFAS), and microplastics (MPs), are increasingly recognized as emerging environmental cofactors that intersect with infectious disease dynamics. These compounds, once considered inert, can alter immune function, reshape host–pathogen interactions, and directly influence viral survival and transmission. In this review, we compile current evidence on the chemistry, environmental occurrence, and biological activity of major plastic-associated pollutants with emphasis on their role in viral infections. Phthalates such as di(2-ethylhexyl) phthalate (DEHP) and its metabolite MEHP modulate innate immune signaling and have been shown to exacerbate infections, including Dengue and Coxsackievirus B3. Other DEHP-like phthalates, such as dibutyl phthalate (DBP), exhibit consistent infection-enhancing effects, while high molecular weight or cyclical phthalates such as polyvinyl acetate phthalate (PVAP) display conflicting results in their modulation of viral infections. BPA, widely detected in human tissues, acts through endocrine and immune disruption, worsening viral myocarditis, and altering influenza outcomes. PFAS, persistent “forever chemicals,” reshape adaptive immune responses and are associated with increased susceptibility, viral persistence, or severity of infection of herpesvirus (HCMV, EBV, HSV-1), hepatitis virus, and influenza infection. Microplastics represent a distinct risk by acting as physical carriers for viruses and bacteria, stabilizing viral RNA, enhancing host cell uptake, and skewing immune responses. Together, these pollutants extend beyond toxicology into virology, providing novel insights into how environmental exposures converge with viral pathogenesis. We highlight mechanistic advances and critical knowledge gaps and propose future directions for integrating environmental health and infectious disease research. Full article

The use of artificial intelligence (AI) to recognize postures is a promising approach for the prevention of work-related musculoskeletal disorders (WMSDs). The aim was to conduct a systematic review with meta-analysis to assess the performance of work posture recognition systems during occupational activity. The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were screened without date restrictions. Two authors independently selected articles and extracted data. Studies were included if they presented a performance analysis of an AI deep learning (DL) or machine learning (ML) method that assessed the WMSD risk associated with working postures. Only peer-reviewed studies written in English including accuracy, precision, specificity, sensitivity, or F1-score values were included. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Of the 157 unique records, 58 studies were selected. The five performance parameters were investigated and averaged for seven occupational activities, eight posture categories, and the AI methods (ML vs. DL). Statistical analyses showed that DL methods produced better results. The reported systems detected sitting and standing postures with high accuracy. The solutions proposed in Manufacturing and Construction were the most numerous and the most effective on average. The major limitation lies in the wide variety of methods used. This analysis is a valuable source of information for designing new detection systems that are effective, ergonomic, easy to use, and acceptable so that humans remain at the center of the production process as defined by Industry 5.0. Full article

Background: We previously established that striatal, but not cortical, dopaminergic activation stimulates movement, indicating that the crucial and original site of dopaminergic stimulation of motor function is the striatum, not the motor cortex. In the present study, we have further investigated the potential effects of the cortical and striatal dopaminergic activity on cortical pyramidal neuron physiology. Methods and Results: First, under a constant fluorescence imaging condition, we established that DA innervation and D1R and D2R expression were very low in the cerebral cortex but very high in the striatum. Second, we performed cellular neurophysiological experiments on layer 2/3 pyramidal neurons in the primary motor cortex (M1) in tyrosine hydroxylase gene knockout (TH-KO) DA-depleted mice that have hyperfunctional DA receptors. Using brain slice–whole-cell patch-clamping techniques, we found that M1 layer 2/3 pyramidal neurons had lower input resistance, stronger inward rectification, more negative RMP, and fired fewer spikes in DA-depleted TH-KO mice than in DA-intact WT mice; M1 layer 2/3 pyramidal neurons also had a diminished synaptic release function with reduced frequencies for spontaneous and miniature excitatory synaptic currents in TH-KO mice compared to WT mice. Third, we also found that when TH-KO mice were treated with L-dopa before brain slice preparation, these neurophysiological deficits of M1 layer 2/3 pyramidal neurons were reversed, but 30 min incubation of cortical brain slices with 10–20 μM DA produced no detectable effect in M1 layer 2/3 pyramidal neurons in TH-KO mice and WT mice. Fourth, Golgi staining showed that cortical pyramidal neuron morphology was indistinguishable between WT mice and TH-KO mice. Conclusions: Our results indicate that DA loss in the striatum, not in the cortex, indirectly reduces cortical pyramidal neuron membrane excitability and weakens synaptic function. Our data also indicate that (1) the normal direct effects of the cortical DA system on cortical pyramidal neurons are weak, (2) the striatal DA system is the dominant DA system in the brain, and (3) striatal DA activity can indirectly increase cortical neuron activity (spike firing and synaptic activity) and thus critically contribute to brain function. Additionally, our data suggest that in DA depletion rodent PD models, DA loss-induced effects on cortical pyramidal neurons and other neurons are functional rather than structural, such that DA replenishment restores motor function almost instantaneously. These findings provide important insights into how the brain’s dopaminergic system controls our motor and cognitive functions and indicate that the striatum is the main therapeutic target of dopaminergic drugs. Full article

Background: The relationship between maternal thyroid function and psychiatric morbidity remains inconclusive, particularly regarding the association with antepartum depression (APD). This meta-analysis aimed to precisely quantify the association between the three primary maternal thyroid hormone concentrations—thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3)—measured in late pregnancy and in the presence of APD. Methods: We conducted a systematic review and meta-analysis of observational studies identified through comprehensive database searches (PubMed, Web of Science, Scopus). Four exploratory studies were ultimately included, enrolling a total of 689 participants. We used random-effects models to pool the mean difference (MD) in hormone concentrations between depressed and non-depressed cohorts. Subgroup analyses were performed based on the study population (general versus hypothyroid), and publication bias was assessed using Begg’s and Egger’s tests. Results: None of the pooled hormone concentrations demonstrated a statistically significant association with APD. The overall MDs were non-significant for TSH (MD = −0.07, 95% CI: [−0.32, 0.18], p = 0.59), FT4 (MD = −0.11, 95% CI: [−1.14, 0.92], p = 0.83), and FT3 (MD = 0.53, 95% CI: [−0.20, 1.25], p = 0.15). Substantial and significant heterogeneity was detected across all models (I² ranging from 70% to 94%). This heterogeneity was largely driven by conflicting directional findings (some studies linking APD to hypothyroid trends, others to hyperthyroid trends), masking a potential non-linear or categorical effect. Statistical tests found no significant evidence of publication bias for TSH (p = 0.33), FT4 (p = 0.12), or FT3 (p = 0.33). Conclusions: The absolute mean concentrations of TSH, FT4, and FT3 in late pregnancy are not robust concurrent biomarkers for antepartum depressive symptoms. The high heterogeneity suggests that APD may be associated with categorical dysfunction (i.e., TSH levels at the extreme high or low ends of the reference range) rather than linear changes in hormone concentration. Future research should prioritize investigating categorical risks, the influence of thyroid autoimmunity, and employing gold-standard diagnostic interviews to better delineate the complex endocrinological risk factors for APD. Due to the limited number of studies, these results should be considered hypothesis-generating rather than confirmatory. PROSPERO registration: CRD420251233154. Full article

The acceleration of economic, technological, geopolitical and environmental processes has significantly increased the exposure of national economies to interconnected financial and non-financial risks. While global financial risks and corporate risks have been extensively analyzed, significant national risks—such as fiscal sustainability, debt vulnerability, systemic inefficiency and investment uncertainty—are often treated fragmentarily or descriptively within conventional sovereign risk frameworks. This article offers a comparative analytical approach to identifying national financial and non-financial risks by examining the degree of convergence and divergence between risks identified in four different sources: national expert scientific studies, World Economic Forum global risk assessments, strategic development documents of Bulgaria and national media coverage. Using expert data, structured content analysis, a modified media visibility index, and nonparametric statistical tests for linked binary data, the study identifies risks that are consistently recognized across sources and therefore pose an increased threat to financial stability, as well as risks that remain systematically underestimated despite their potential fiscal and macroeconomic consequences. The results show that cross-source comparison significantly improves the detection of national risks and reveals blind spots in fiscal planning, investment, and social policy. This article contributes to the literature on the management of risks with a direct negative financial effect or with an indirect financial impact on the national economy by positioning national risk identification within a governance-oriented, multi-source analytical framework. Full article

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48–49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments. Full article

This paper proposes a novel method for synthesizing a discrete optimization algorithm based on the moth–flame paradigm for application to the architecture of deceptive systems incorporating decoys and traps. Unlike existing approaches that primarily rely on continuous search spaces or static deception strategies, the proposed method enables the formation of a discrete search space with a coordinate-based representation of deception objects and system states. A spiral search trajectory is synthesized by modeling the dynamic interaction between moths and flames, which allows the algorithm to balance exploration and exploitation effectively and to mitigate premature convergence to local optima. The problem of selecting subsequent operational steps of a deceptive system, which includes the control and reconfiguration of decoys and traps in response to detected events, is formulated as a discrete optimization problem. The objective of this optimization is to increase the effectiveness of cyberattack and malware detection in corporate network environments. The decision variables include the sequence of deception actions, process models, and architectural characteristics of the system, while the constraints are defined by the operational conditions, resource limitations, and structural features of corporate networks. The proposed method supports the identification of an optimal sequence of deception actions under dynamically changing conditions and provides mechanisms for operational adaptation to attacker behavior in real time. This adaptability enables the creation of deceptive systems capable of long-term autonomous operation without continuous administrative intervention, while simultaneously increasing their resistance to adversarial reconnaissance and reverse engineering of their operational principles. The experimental results confirm the feasibility and effectiveness of the proposed approach and demonstrate the potential of integrating population-based optimization algorithms into deceptive system architectures. Comparative analysis shows that the proposed method outperforms its closest competitor, the genetic algorithm, achieving an improvement of 4.82% in terms of the objective function value. Future research directions include deeper integration of population-based optimization methods into decoy-and-trap architectures and the development of a comprehensive framework for organizing their operation in accordance with the proposed conceptual model. Overall, the results contribute to enhancing the cyber-resilience of corporate networks through intelligent, adaptive, and autonomous systems for countering modern cyberattacks and malware. Full article

Background: The PROSPET-BX trial compared [⁶⁸Ga]PSMA-11 PET/CT (PSMA-PET) with multiparametric MRI (mpMRI) in parallel in men with suspicion of prostate cancer (PCa) after at least one previously negative biopsy (ClinicalTrials.gov:NCT05297162; GR-2018-12366240). In this study, we performed the cost analysis of the two imaging modalities with respect to the detection of clinically significant PCa (csPCa). Methods: We analyzed the data from patients enrolled in the trial who met the inclusion criteria. For the cost analysis, we identified six competing triage strategies, each defined as a binary decision rule for referral to prostate biopsy: (1) biopsy-all; (2) elevated PSA-density (PSAD; biopsy if PSAD > 0.15 ng/mL/cc; (3) mpMRI positive (PIRADS 3–5); (4) PSMA-PET positive (PRIMARY 3–5); (5) mpMRI or PSMA-PET positive; (6) PSAD and mpMRI. For each strategy, we yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for csPCa. Direct hospital costs were modeled from a provider perspective, incorporating testing and procedural costs. Unit costs (in EUR) were sourced from our institutional accounting records. Pairwise cost-effectiveness comparisons were performed using incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB). Results: Among the six triage strategies evaluated, the “biopsy-all” approach achieved perfect sensitivity, whereas the PSAD + mpMRI pathway was the most parsimonious strategy but missed 14 csPCa cases (53.8%). The combined “mpMRI or PSMA-PET” strategy maximized detection (22 cPCa, missing only 4) at an intermediate cost (EUR 81.991 total; EUR 3.727 per csPCa). The pairwise comparison of each strategy with mpMRI alone showed for the mpMRI or PSMA-PET pathway a low ICER (~EUR 2.900/extra csPCa), with consistently positive and increasing INB across higher WTP (willingness-to-pay). Therefore, this combination provided the most favorable cost-effectiveness profile, balancing detection, efficiency, and cost. Conclusions: To the best of our knowledge, this is the first cost analysis study to compare different strategies incorporating PSMA-PET in the re-biopsy setting, demonstrating that the combined “mpMRI or PSMA-PET” pathway is the most cost-effective diagnostic pathway for csPCa detection. Full article

Background/Objectives: Colistin, a polymyxin antibiotic introduced in the mid-20th century, has regained clinical importance as a last-resort agent for the treatment of infections caused by multidrug-resistant (MDR) Gram-negative bacteria. The global dissemination of carbapenem-resistant pathogens has intensified colistin use, leading to a concerning rise in resistance. This review aims to provide a comprehensive and up-to-date synthesis of colistin’s pharmacological characteristics, resistance mechanisms, epidemiology, and current and emerging therapeutic strategies. Methods: A narrative review of the literature was conducted, encompassing studies on the chemistry, mechanism of action, pharmacodynamics, clinical use, dosing, and resistance to colistin. Data on chromosomal and plasmid-mediated resistance mechanisms, detection methodologies, epidemiological trends, and clinical outcomes were examined. In addition, evidence on colistin-based treatment strategies and novel non-antibiotic approaches was analyzed. Results: Colistin remains active against many MDR Gram-negative pathogens, including Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii; however, resistance is increasingly reported worldwide. Both chromosomally mediated modifications of lipid A and plasmid-mediated mcr genes contribute to resistance, with heteroresistance posing diagnostic and therapeutic challenges. Carbapenem resistance has emerged as a major driver of colistin use and subsequent resistance selection. Combination therapies, inhaled formulations, and guideline-directed use may improve outcomes, while emerging alternatives such as antimicrobial peptides, bacteriophages, nanoparticles, photodynamic therapy, and CRISPR-based technologies show promise. Conclusions: The escalating prevalence of colistin resistance threatens the effectiveness of this critical last-line antibiotic. Optimized use, robust resistance surveillance, accurate detection methods, and the development of innovative therapeutic strategies are essential to preserve colistin’s clinical utility and address the growing burden of MDR Gram-negative infections. Full article

Maintaining stable research productivity is critical for sustainable knowledge production, yet institutional mobility—an increasingly common form of organizational transition in higher education—may disrupt scientists’ output trajectories. This study examines how mobility direction shapes sustainable research productivity and how co-authorship network structure conditions these effects. Using curriculum vitae records and 74,336 Web of Science publications for 531 preliminary candidates for Chinese Academy of Sciences academicians (2005–2019), we estimate random-effects negative binomial models to assess the quantity and quality dimensions of sustainable research productivity in the third-to-fifth years after mobility events. Downward mobility to lower-ranked institutions is associated with significant declines in both dimensions, whereas upward mobility shows no detectable effect within the same window. Network structure matters: higher co-authorship network density buffers the adverse effect of downward mobility, while higher betweenness centrality amplifies it. These findings suggest that cohesive collaboration structures help sustain knowledge production under adverse transitions, whereas brokerage-oriented positions may increase vulnerability when collaborations are reconfigured. By conceptualizing post-mobility outcomes as sustainable research productivity, this study extends the talent mobility literature and offers implications for universities and science policy on supporting high-level scientists during institutional transitions. Full article

Electrochemical ozone production (EOP) for generating ozone (O₃) directly in pure water presents a sustainable alternative to conventional methods. This review focuses on the advanced Membrane Electrode Assembly (MEA) electrolyzer, analyzing its fundamental thermodynamics, mechanisms, and key performance indicators. The core discussion centers on the MEA architecture, with an in-depth critique of anode catalysts (including cost-effective alternatives) and strategies to enhance O₃ selectivity over the competing oxygen evolution reaction. The role of the solid polymer electrolyte membrane is also examined. Furthermore, the review assesses how operational parameters and detection methods determine overall system efficiency and stability. It concludes by identifying challenges and future directions, underscoring the potential of MEA-based EOP for practical, on-site O₃ generation in pure water. Full article

Accelerated industrialization has caused complex mixed toxicant pollution, where synergistic or antagonistic interactions render conventional detection methods inadequate. Herein, we develop an integrated framework by pioneering the integration of microbial electrochemical systems (MECs) with machine learning (ML) for quantifying formaldehyde, tetracycline, Ag⁺, and Cu²⁺ in multi-component, multi-ratio, and multi-concentration mixtures. MECs generated dynamic current–time (I–t) signals responsive to toxicant stress, though signal overlap from mixed toxicants hindered direct quantification. Guided by toxicokinetics and electrochemical mechanisms, we developed a novel mechanism-driven feature engineering strategy with exclusively original indicators, which extracted 22 multidimensional features capturing instantaneous characteristics, kinetic patterns, and microbial stress-adaptive responses to resolve signal ambiguity, and provided biologically meaningful, high-information feature inputs that effectively bridge electrochemical response signals and ML modeling. Comparative analysis of four ML models (SVM, KNN, PLS, and RF) showed RF outperformed others, achieving R² > 0.9 for all toxicants (formaldehyde: 0.959; tetracycline: 0.934; Ag⁺: 0.936; Cu²⁺: 0.957) with minimized MAE and RMSE. Microbial community analysis identified Geobacter anodireducens (71.5%, electroactive for heavy metals) and Comamonas testosteroni (12.9%, organic degrader) as key functional taxa, supported by KEGG enzyme abundance data. This work overcomes traditional MEC limitations via innovative feature engineering and pioneering ML integration, providing a rapid, low-cost, and high-accuracy tool for environmental mixed toxicant monitoring. Full article