Search Results (1,395)

Marine data assets are increasingly recognized as important drivers of value creation in the blue economy, yet their valuation remains challenging due to difficulties in isolating data-related earnings in capital-intensive maritime enterprises. This study proposes a methodological valuation framework that integrates the multi-period excess earnings method with the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation (FCE) approach, incorporating both financial and non-financial dimensions. The framework follows a “total synergistic return–data contribution separation” logic to isolate data-related excess earnings and applies an AHP–FCE-based adjustment coefficient to account for data quality, application value, and risk. A representative container shipping enterprise is used as an illustrative application to demonstrate the implementation logic of the framework. The results indicate that marine data assets can constitute a non-negligible component of enterprise value under reasonable parameter settings, while sensitivity analysis highlights the influence of key parameters such as the data contribution coefficient and discount rate. The proposed framework provides a transparent methodological reference for marine data asset valuation and supports sustainability-oriented research and practice in the blue economy. Full article

In vitro fertilisation via oocyte donation is a unique reproductive technique in which the embryo is fully separate from the receiver. This compels the immune system to exert more effort at the interface between the uterus and the remainder of the body. This setting has maintained interest in peri-transfer glucocorticoid treatment as a possible approach to modify endometrial immunity and enhance implantation. Nevertheless, the data for this procedure are disjointed and mostly derive from investigations on autologous in vitro fertilisation. This narrative review consolidates contemporary evidence on endometrial immunology in oocyte donation cycles, analysing the mechanistic basis, clinical results, and constraints related to peri-implantation glucocorticoid therapy. Outcomes from randomised studies in autologous cycles consistently demonstrate that there is no advantage in live birth rates, but the claimed improvements in clinical pregnancy rates are from heterogeneous and low-quality data. Limited research exists on people who have received oocyte donations. The majority are diminutive and non-random, often integrating glucocorticoids with other therapies such as antibiotics, granulocyte colony-stimulating factor, or endometrial damage. These designs inhibit the dissociation of the independent impact of glucocorticoids. Recent comprehensive randomised studies on recurrent implantation failure further demonstrate the lack of advantages in live births and highlight possible safety issues. The current data do not support the usual use of peri-transfer glucocorticoids in oocyte donation for in vitro fertilisation; nevertheless, short-term, low-dose treatment may be justified in meticulously chosen immunological profiles. There is an urgent need for rigorously designed randomised studies focused only on oocyte-donation recipients to elucidate the therapeutic effectiveness, safety, and suitable clinical context for glucocorticoid treatment in this expanding patient demographic. Full article

Background/Objectives: Bacterial outer membrane vesicles (OMVs) play a role in bacterial communication, virulence, antimicrobial resistance, and host–pathogen interaction. OMV isolation is a key step for studying these particles’ functions; nevertheless, isolation procedures can greatly influence the yield, purity, and structural integrity of OMVs, thereby affecting downstream biological analyses and functional interpretation. Methods: In this study, we compared the efficacy of two OMV isolation techniques, differential ultracentrifugation (dUC) and size-exclusion chromatography (SEC), in separating and concentrating vesicles produced by two Escherichia coli strains belonging to uropathogenic (UPEC) and Shiga toxin-producing (STEC) pathotypes. The isolated OMVs were characterized using a multi-analytical approach including transmission and scanning electron microscopy (TEM, SEM), nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), ζ-potential measurement, and protein quantification to assess the purity of the preparations. Results: Samples obtained by dUC exhibited higher total protein content, broader particle size distributions, and more pronounced contamination by non-vesicular material. In contrast, SEC yielded morphologically homogeneous and structurally well-preserved vesicles, higher particle-to-protein ratios, and lower total protein content, reflecting reduced co-isolation of protein aggregates. NTA and DLS analyses revealed polydisperse populations in samples obtained with both isolation methods, with DLS measurements highlighting the contribution of larger or transient aggregates. ζ-potential values were close to neutrality for all samples, consistent with limited electrostatic repulsion and with the aggregation tendencies observed in some preparations. Conclusions: This study describes features of OMV produced by two relevant E. coli strains considering two isolation strategies which exert method- and strain-dependent effects on vesicle properties, including size distribution and surface charge, and emphasizes the trade-offs between yield, purity, and vesicle integrity. Full article

Split G-quadruplex DNAzymes offer unique opportunities for building gated biosensors with a wide range of applications. Splitting G4 DNAzymes involves separating guanine tracts in the G-quadruplex DNA sequence into two non-functional sequences that reconstitute into a functional G-quadruplex with peroxidase activity upon hybridisation of the aptamer probe region within the split system with the target molecule. Several studies have demonstrated the reassembly of split G4 DNAzymes and their applications in the detection of various analytes. This approach offers unique opportunities for modular biosensor construction, target-dependent activation, lack of requirement for labelling, amplification-free high sensitivity, and specificity over traditional G4 sensing. In this review, we explore the strategies of splitting G-quadruplex and their applications in biomedical diagnosis, environmental sensing, food safety monitoring, cell detection, and the integration of the technology with nanomaterials for enhanced stability and sensitivity. We considered the classical intermolecular split strategies that utilise binary probes and intramolecular split systems, which integrate the spacer DNA that allow for single probes as the model G4 sequence. Finally, we explore the current challenges required to develop split G-quadruplex DNAzymes into tools for routine practical applications. Full article

Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems. Full article

Deep generative models trained on sensitive data pose significant privacy risks, yet enforcing differential privacy (DP) in high-dimensional generators often leads to severe utility degradation. We propose Differentially Private Vector-Quantized Generation (DP-VQG), a three-stage generative framework that introduces a discrete latent bottleneck as the interface for privacy preservation. DP-VQG separates geometric structure learning, differentially private discrete latent projection, and non-private prior modeling, ensuring that privacy-induced randomness operates on a finite codebook aligned with the decoder’s effective support. This design avoids off-support degradation while providing formal end-to-end DP guarantees through composition and post-processing. We provide a theoretical analysis of privacy and utility, including explicit bounds on privacy-induced distortion. Empirically, under the privacy budget of $\epsilon =10$, DP-VQG attains Fréchet Inception Distance (FID) scores of 18.21 on MNIST and 77.09 on Fashion-MNIST, surpassing state-of-the-art differentially private generative models of comparable scale. Moreover, DP-VQG produces visually coherent samples on high-resolution datasets such as Flowers102, Food101, CelebA-HQ, and Cars, demonstrating scalability beyond prior end-to-end DP generative approaches. Full article

Microplastics (MPs) are emerging pollutants with widespread global distribution, continuously accumulating in soils and posing risks of cross-media pollution. Current soil MP detection methods lack unified standards, suffering from high inter-laboratory variability and cost, which become key bottlenecks limiting data comparability and global microplastics pollution control. Here, we systematically reviewed soil MPs studies (2020–2024) and based on stepwise verification, we established a standardized, reproducible detection method: soil samples were dried at 80 °C for 12 h; density separation was performed in Erlenmeyer flasks with decantation, 10 s glass rod stirring, and 12 h settling, repeated five times; digestion was conducted using a 1:2 volume ratio of H₂O₂ to supernatant at 80 °C for 8 h; and MPs were quantified via stereo-microscopy combined with ImageJ. It should be noted that the use of NaCl limits the recovery of high-density polymers (e.g., PVC, PET), and the minimum detectable particle size is approximately 127 µm. The method was validated in sandy, loam, and clay soils, achieving an average recovery rate of 96.4%, with a processing time of 68 h and a cost of USD 9.77 per sample. In contrast to previous fragmented, non-standardized protocols, this workflow synergistically optimizes high recovery efficiency, cost-effectiveness, and broad applicability, offering a low-cost, efficient, and widely applicable approach for soil MPs monitoring, supporting data comparability across studies and contributing to global pollution assessment and the United Nations 2030 Sustainable Development Goals. Full article

Background: Major Depressive Disorder (MDD) is a pervasive psychiatric condition. Electroencephalography (EEG) is employed to detect MDD-specific neural patterns because it is non-invasive and temporally precise. However, manual interpretation of EEG signals is labor-intensive and subjective. This problem was addressed by proposing machine learning (ML) and deep learning (DL) methods. Although DL methods are promising for MDD detection, they face limitations, including high model complexity, overfitting due to subject-specific noise, excessive channel requirements, and limited interpretability. Methods: To address these challenges, we propose MS-MDDNet, a new lightweight CNN model specifically designed for EEG-based MDD detection, along with an ensemble-like method built on it. The architecture of MS-MDDNet incorporates spatial, temporal, and depth-wise separable convolutions, along with average pooling, to enhance discriminative feature extraction while maintaining computational efficiency with a small number of learnable parameters. Results: The method was evaluated using 10-fold Cross-Subjects Cross-Validation (CS-CV), which mitigates the risks of overfitting associated with subject-specific noise, thereby contributing to generalization robustness. Across three public datasets, the proposed method achieved performance comparable to state-of-the-art approaches while maintaining lower computational complexity. It achieved a 9% improvement on the MODMA dataset, with an accuracy of 99.33%, whereas on MUMTAZ and PRED + CT it achieved accuracies of 98.59% and 96.61%, respectively. Conclusions: The predictions of the proposed method are interpretable, with interpretability achieved through correlation analysis between gamma energy and learned features. This makes it a valuable tool for assisting clinicians and individuals in diagnosing MDD with confidence, thereby enhancing transparency in decision-making and promoting clinical credibility. Full article

This study aimed to develop a robust morphometric-based framework for classifying Apis cerana populations using deep learning and machine learning approaches. Previous studies on Apis cerana population differentiation have primarily relied on manual morphometrics or genetic markers, which are labor-intensive and often lack scalability for large image-based datasets. Forewing landmarks were automatically detected through a deep learning model employing a heatmap regression and Hourglass Network architecture. The extracted coordinates were processed by Principal Component Analysis (PCA) for dimensionality reduction, and shape alignment was further refined through Procrustes ANOVA to minimize non-biological variation. Nine machine learning algorithms were trained and compared under identical preprocessing and validation settings. Among them, the Extra Trees classifier achieved the highest accuracy (99.7%) in distinguishing the three populations—A. cerana cerana from China and A. cerana indica from Thailand, the northern and southern populations. After applying error-based data filtering and retraining, classification accuracy improved further, with almost perfect population separation. The Procrustes ANOVA confirmed that individual variation significantly exceeded residual error (Pillai’s trace = 1.13, p < 0.0001), validating the biological basis of shape differences. Mahalanobis distance and permutation tests (10,000 rounds) revealed significant morphological divergence among populations (p < 0.0001). The integration of geometric alignment and ensemble learning demonstrated a highly reliable strategy for population identification, supporting morphometric and evolutionary studies in Apis cerana. Full article

This paper investigates the global polynomial synchronization (GPS) problem for quaternion-valued neural networks (QVNNs) featuring proportional delay, parameter uncertainty, and external disturbance. A combined approach of sliding mode control (SMC) and a non-separation strategy is adopted to achieve this goal. First, an integral-type sliding surface is designed for the system. Then, by constructing a delay-free Lyapunov functional and leveraging the properties of the quaternion vector norm and inequality techniques, sufficient conditions are derived to achieve GPS for the sliding mode dynamics. Furthermore, both a SMC law and an adaptive SMC law are designed, with a reachability analysis confirming that the system trajectories reach the predefined sliding surface in finite time. Finally, numerical examples with graphical analysis are provided to verify the obtained results, along with their application in color image pattern storage and retrieval. Full article

Lung cancer (LC) remains the leading cause of cancer-related death worldwide, largely due to late-stage diagnosis and the limited performance of current screening strategies. In this preliminary study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was used to comprehensively characterize the urinary volatilome of LC patients and healthy controls (HCs), with the dual aim of defining an LC-associated volatilomic signature and identifying volatile organic metabolites (VOMs) with discriminatory potential. A total of 56 VOMs spanning multiple chemical classes were identified, revealing a distinct metabolic footprint between groups. LC patients exhibited markedly increased levels of terpenoids and aldehydes, consistent with heightened oxidative stress, including lipid peroxidation, and perturbed metabolic pathways, whereas HCs showed a predominance of sulphur-containing compounds and volatile phenols, likely reflecting active sulphur amino acid metabolism and/or microbial-derived processes. Multivariate modelling using partial least squares-discriminant analysis (PLS-DA, R² = 0.961; Q² = 0.941; p < 0.001), supported by hierarchical clustering, demonstrated robust and clearly separated group stratification. Among the detected VOMs, octanal, dehydro-p-cymene, 2,6-dimethyl-7-octen-2-ol and 3,7-dimethyl-3-octanol displayed the highest discriminative power, emerging as promising candidate urinary biomarkers of LC. These findings provide proof-of-concept that HS-SPME/GC-MS-based urinary volatilomic profiling can capture disease-specific molecular signatures and may serve as a non-invasive approach to support the early detection of LC, warranting validation in independent cohorts and integration within future multi-omics diagnostic frameworks. Full article

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that utilizes magnetic fields to induce cortical electric currents, enabling both the measurement and modulation of neuronal activity. Initially developed as a diagnostic tool, TMS now serves dual roles in clinical neurology, offering insight into neurophysiological dysfunctions and the therapeutic modulation of abnormal cortical excitability. This review examines key TMS outcome measures, including motor thresholds (MT), input–output (I/O) curves, cortical silent periods (CSP), and paired-pulse paradigms such as short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), intracortical facilitation (ICF), long interval cortical inhibition (LICI), interhemispheric inhibition (IHI), and short-latency afferent inhibition (SAI). These biomarkers reflect underlying neurotransmitter systems and can aid in differentiating neurological conditions. Diagnostic applications of TMS are explored in Parkinson’s disease (PD), dystonia, essential tremor (ET), Alzheimer’s disease (AD), and mild cognitive impairment (MCI). Each condition displays characteristic neurophysiological profiles, highlighting the potential for TMS-derived biomarkers in early or differential diagnosis. Therapeutically, repetitive TMS (rTMS) has shown promise in modulating cortical circuits and improving motor and cognitive symptoms. High- and low-frequency stimulation protocols have demonstrated efficacy in PD, dystonia, ET, AD, and MCI, targeting the specific cortical regions implicated in each disorder. Moreover, the successful application of TMS in differentiating and treating AD and MCI underscores its clinical utility and translational potential across all neurodegenerative conditions. As research advances, increased attention and investment in TMS could facilitate similar diagnostic and therapeutic breakthroughs for other neurological disorders that currently lack robust tools for early detection and effective intervention. Moreover, this review also aims to underscore the importance of maintaining standardized TMS protocols. By highlighting inconsistencies and variability in outcomes across studies, we emphasize that careful methodological design is critical for ensuring the reproducibility, comparability, and reliable interpretation of TMS findings. In summary, this review emphasizes the value of TMS as a distinctive, non-invasive approach to probing brain function and highlights its considerable promise as both a diagnostic and therapeutic modality in neurology—roles that are often considered separately. Full article

This paper presents the results of a comprehensive technological study of three bronze sculptures from the Jin Empire period (1115–1234 AD) from the collection of the Museum of Archaeology and Ethnography at the Institute of History, Archaeology and Ethnography of the Peoples of the Far East, Far Eastern Branch of the Russian Academy of Sciences (IHAE FEB RAS). Using photon-counting computed tomography (PCCT) and energy-dispersive X-ray spectroscopy (EDS), the production techniques were reconstructed, differences in alloy composition were identified, and specific features of the casting processes were determined. Tomographic analysis revealed two fundamentally different manufacturing approaches: a multi-stage technology involving the use of different alloys and the assembly of separately cast elements, and a single-cast technology with a homogeneous structure. Elemental analysis of the three sculptures using EDS demonstrated significant compositional variability—from 21% to 67% copper and from 9% to 69% tin in different parts of the objects—confirming the complexity of the technological processes. An expanded study of 20 bronze sculptures using portable X-ray fluorescence analysis (pXRF) allowed for the identification of four typological alloy groups: classic balanced lead–tin bronzes (Cu 30–58%, Sn 16–23%, Pb 16–28%), high-lead bronzes (Pb up to 52%), high-tin bronzes (Sn up to 30%), and low-tin alloys (Sn less than 11%). The morphological features of the sculptures suggest one of their possible interpretations as ancestor spirits used in ritual practices. The research findings contribute to the study of Jurchen metallurgical traditions and demonstrate the potential of interdisciplinary, non-destructive analytical methods for reconstructing the technological, social, and cultural aspects of medieval Far Eastern societies. Full article

Although technologies used in non-fossil methane and fossil resources to produce blue hydrogen are relatively mature, a system-integrated approach to reference system (RS)-based purification of H₂, CO₂ capture and storage, and UHS is relatively unexplored and requires research to fill gaps in the literature regarding balanced permutations and geological viability for net-zero requirements. This research proposes a system-integrated process for H₂ production through a PSA-based purification technique coupled with amine-based CO₂ capture and underground hydrogen storage (UHS). The intellectual novelty of the research is its first quantitative treatment of synergistic effects such as heat recovery and pressure-matching across units. Additionally, a site separation technique is applied, where H₂ and CO₂ reservoirs are selected based on the permeability of rock formations and fluids. On a research methodology front, a base case of a steam methane reforming process with the production of 99.99% pure H₂ at a production rate of 5932 kg/h is modeled and simulated using Aspen Plus™ to create a balanced permutation of mass and energy across units. As per the CO₂ capture requirements of this research, a capture of 90% of CO₂ is accomplished from the production of 755 t/d CO₂ within the model. The compressed CO₂ is permanently stored at specifically identified rock strata separated from storage reservoirs of H₂ to avoid empirically identified hazards of rock–fluid interaction at high temperatures and pressures. The lean amine cooling of CO₂ to 60 °C and elimination of tail-gas recompression simultaneously provides 5.4 MW_th of recovered heat. The integrated design achieves a net primary energy penalty of 18% of hydrogen’s LHV, down from ~25% in a standalone configuration. This corresponds to an energy saving of 8–12 MW, or approximately 15–18% of the primary energy demand. The research computes a production cost of H₂ of 0.98 USD per kg of H₂ within a production atmosphere of a commercialized WGS and non-fossil methane-based production of H₂. Additionally, a sensitivity analysis of ±23% of the energy requirements of the reference system shows no marked sensitivity within a production atmosphere of a commercially available WGS process. Full article

CRISPR/Cas systems have transformed molecular medicine, yet the field still lacks principled guidance on when transient editing suffices versus when sustained exposure through in vivo viral delivery is necessary and how to keep prolonged exposure safe. Notably, EDIT-101 was designed for a permanent edit in post-mitotic photoreceptors with lifelong Cas9 persistence. This review addresses this gap by defining the biological and therapeutic conditions that drive benefit from extended Cas activity while minimizing risk. We will (i) examine relationships between expression window and efficacy across Cas9/Cas12/Cas13 modalities, (ii) identify genome-wide off-target liabilities alongside orthogonal assays, and (iii) discuss controllable, self-limiting, and recallable editor platforms. By separating durable edits from persistent nuclease exposure, and by providing validated control levers, this work establishes a generalizable framework for safe, higher-efficacy CRISPR medicines. Furthermore, we highlight key studies in cell lines, murine models, non-human primates, and humans that examine the long-term effects of sustained expression of CRISPR/Cas systems and discuss the safety and efficacy of such approaches. Current evidence demonstrates promising therapeutic outcomes with manageable safety profiles, although there is a need for continued monitoring as CRISPR/Cas therapies are increasingly applied in clinical contexts and therapies are developed for broader clinical applications. Full article