Search Results (3,281)

Background: Although blinded independent central review (BICR) can reduce assessment variability, it introduces additional financial and logistical burdens to trial operations. This study analyzed the discrepancy indexes (DIs) to evaluate differences between progression-free survival (PFS) assessments by local investigators (LIs) and BICR in randomized clinical trials (RCTs) of patients with metastatic melanoma. Methods: A comprehensive literature search was conducted on PubMed, Embase, and Cochrane databases up to 30 June 2024. The primary outcome was the DI, which was calculated for each trial as a ratio of the hazard ratios (HR)_BICR by HR_LI. The agreement between PFS HRs was also evaluated using the intraclass correlation coefficient (ICC) and Pearson’s correlation coefficient (r). Results: Twelve studies comprising 4915 patients were included in this study. Of these, 10 (83%) were Phase III, 11 (92%) were cutaneous melanoma, one was uveal, and all identified PFS as the primary endpoint. Most (86%) of the PFS comparisons yielded the same statistical inference by both BICR and LIs. The overall combined DI was calculated at 1.08 (95% CI: 1.01–1.15), indicating a statistically significant, numerically small difference in PFS evaluations driven primarily by the uveal Phase III double-blinded study, while there was a strong overall correlation [(ICC: 0.87, p < 0.001); (r = 0.89, 95% CI 0.67–0.96, p < 0.0001)]. Cutaneous melanoma trials demonstrated strong agreement between BICR and local investigator assessments. Conclusions: In randomized trials of metastatic cutaneous melanoma, LI-assessed PFS closely aligns with BICR and provides equivalent trial-level conclusions in most cases. These findings support the use of LI-assessed PFS as a valid and practical primary endpoint, without routine requirement for BICR. Central review should be reserved for selected scenarios. Full article

Global climate change and spatiotemporal heterogeneity in water resources exacerbate supply-demand imbalances. Accurate outflow simulation for joint reservoir group operations thus becomes critical for scientific water resources management. Existing data-driven models like the Long Short-Term Memory (LSTM) lack the robust integration of physical constraints. Traditional mechanistic methods, by contrast, lack generality and stability under complex hydrological conditions. To address this limitation, we propose MDUPLEX-KG-LSTM—a physically constrained data-driven model for reservoir outflow simulation. The model incorporates multi-round DUPLEX (MDUPLEX) data partitioning, which ensures statistical homogeneity across training, validation, and test datasets. It also features a Knowledge-Guided (KG) loss function that embeds core physical constraints: water balance, dead water level, flood season restricted water level, and inter-reservoir re-regulation mechanisms. Additionally, it adopts an LSTM network optimized via Particle Swarm Optimization (PSO) for enhanced predictive performance. We validate the model using daily hydrological data from 2010 to 2025 for three reservoirs in the Wujiaqu Irrigation District of Xinjiang, China. The model exhibits exceptional stability and predictive accuracy across key evaluation metrics: Nash–Sutcliffe Efficiency (NSE) ≥ 0.82, Pearson correlation coefficient (r) > 0.94, Root Mean Square Error (RMSE) ≤ 1.50 m³/s, and Water Balance Index (WBI) ≤ 0.016. It outperforms conventional data-driven and mechanistic models in extreme flow simulation scenarios. It also eliminates unphysical negative outflow values in all predictive results. The model achieves 100% compliance with flood control standards and an irrigation guarantee rate of no less than 86%. This study advances the development of physically constrained data-driven modeling for water resources engineering. It provides reliable methodological support for the intelligent operation of reservoir groups in smart water conservancy systems. The model also balances training cost and inference efficiency effectively. It demonstrates verified scalability for reservoir groups of varying scales, fully meeting the operational deployment requirements of smart water systems. Full article

Synthetic aperture radar (SAR) imaging is highly vulnerable to radio-frequency interference (RFI) in complex electromagnetic environments, which can introduce structured artifacts and obscure targets in single-look complex (SLC) products. Most existing suppression methods rely on separability along a single dimension or require interference-specific parameter tuning, limiting robustness under multidimensional coupling and strong scatterers. We propose a range-domain sliding-window local tensorization that rearranges SLC data into localized range–azimuth–block-index tensors to better expose multi-mode correlations. On this representation, an energy-guided tensor CUR low-rank projector is embedded into an alternating-projection scheme that alternates complex-valued soft-thresholding for the sparse scene-plus-noise term and CUR-based projection for the structured RFI term. The cleaned SLC image is obtained by de-tensorizing the estimated RFI component and subtracting it from the input SLC. Experiments on semi-synthetic data, where controlled RFI is superimposed on real SLC scenes, and on real Sentinel-1 SLC data containing RFI demonstrate improved Pearson correlation coefficient (PCC) and perceptual image quality while preserving target signatures and scene textures, particularly under strong interference and strong coupling. The proposed approach provides a practical SLC-domain RFI mitigation tool for post-focusing SAR products without requiring explicit interference parameterization. Full article

Past studies have reported that carbon dioxide emissions during combustion vary depending on the tree species used as fuel. It has also been reported that the moisture content of wood affects combustion efficiency. From this perspective, identifying the tree species and moisture content is crucial for utilizing waste wood as a resource. Therefore, this study verified the effectiveness of non-destructive diagnosis using terahertz waves. Samples with adjusted moisture content were prepared for eight types of wood. Each wood sample was irradiated with multiple broadband terahertz electromagnetic waves, and their transmission characteristics were compared. Experimental results revealed a strong negative correlation (Pearson Correlation coefficient: −0.98~−0.71 square meter/gram) between the sample’s specific gravity and transmittance when irradiated with 65 GHz and 90 GHz sub-terahertz waves. This trend was particularly pronounced during 90 GHz sub-terahertz irradiation. Furthermore, it was found that the trend in transmittance variation differed depending on the wood’s moisture content. These results indicate that terahertz waves are effective as a wood identification method capable of distinguishing between coniferous and broadleaf trees. Furthermore, they are considered effective for predicting wood moisture content. This research is expected to contribute to promoting wood recycling and the sustainable use of wood resources. Full article

The purpose of the present study was to validate a new linear encoder by comparing the mean velocity (MV) and peak velocity (PV) of two linear position transducers during free-weight back squat (SQ) and bench press (BP) exercises. Barbell velocity was simultaneously recorded using GymAware (version 5.1.0; reference standard) and Velocity Matters. Fifteen male participants completed two testing sessions, each involving six repetitions (two sets of three) across five velocity ranges: >1.00 to 0.51 m·s⁻¹ (velocity range 1: >1.00 m·s⁻¹; velocity range 2: 0.87–0.99 m·s⁻¹; velocity range 3: 0.75–0.86 m·s⁻¹; velocity range 4: 0.63–0.74 m·s⁻¹; velocity range 5: 0.51–0.62 m·s⁻¹) in SQ and >1.02 to 0.40 m·s⁻¹ (velocity range 1: >1.02 m·s⁻¹; velocity range 2: 0.86–1.01 m·s⁻¹; velocity range 3: 0.70–0.85 m·s⁻¹; velocity range 4: 0.56–0.69 m·s⁻¹; velocity range 5: 0.40–0.55 m·s⁻¹) in BP. In total, 180 repetitions per velocity range were analyzed for each exercise. Validity was assessed using Pearson’s correlation (r), mean absolute error (MAE), Bland–Altman plots, the intraclass correlation coefficient (ICC), and the concordance correlation coefficient (CCC). Pearson’s r indicated good (0.5–0.7) to excellent (>0.9) correlations across all ranges and exercises. However, acceptable MAE values were found only for MV in SQ (except at >1.00 m·s⁻¹) and for both MV and PV in BP at velocities <0.70 m·s⁻¹. Despite an acceptable MAE in some cases, Bland–Altman analyses revealed systematic underestimation by Velocity Matters, with wide limits of agreement of up to −0.08 m·s⁻¹ in SQ and −0.09 m·s⁻¹ in BP, even where MAE was acceptable. ICC values were generally >0.70 but showed wide confidence intervals, indicating high uncertainty. CCC values were consistently poor (<0.90) across all velocity ranges and both exercises, except for PV in the lowest velocity range during BP. In conclusion, Velocity Matters may be cautiously used to monitor MV during SQ at velocities <0.87 m·s⁻¹, but it does not provide sufficient accuracy for use in BP across any load. Full article

The grading of beef appearance quality is crucial for standardizing market circulation and promoting the upgrading of the beef cattle industry. China’s current beef quality grading system, which relies primarily on human sensory-based visual assessment with marbling and meat color as core parameters, suffers from strong subjectivity, low efficiency, and large errors. This study proposes a rapid grading method for beef rib eye muscle using smartphone imaging combined with ImageJ software. Standardized images were acquired, and ImageJ was employed for grayscale conversion, threshold segmentation, and morphological processing to extract length, width, area, and marbling proportion. The R, G, B color channels were separated to calculate the R/(R + G + B) color ratio. Pearson correlation analysis showed that the ImageJ results were highly consistent with manual measurements (correlation coefficients > 0.97), indicating good reliability. A five-level grading standard (A1–A5) was established, characterized by low cost, simple operation, and objective results. It provides an economical technical solution for beef quality grading and facilitates the intelligent development of the industry. It should be noted that this experimental grading model has only been validated under the specific experimental conditions of this study, and further verification is required for broader application. Full article

This paper proposed a stacking-based ensemble model to replace the traditional single machine learning model prediction approach, significantly improving the evaluation efficiency of SoC and SoH of lithium batteries. Firstly, a dataset was constructed including three input variables (temperature, current, and voltage) and two output variables (SoC and SoH). Pearson correlation coefficients and histograms were used for preliminary analysis of the correlations and distributions of the dataset. The multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and extreme gradient boosting tree (XGB) were used as base prediction models. Bayesian optimization (BO) was used to fine-tune the parameters of these models, then three statistical indicators were compared to assess the prediction accuracy of the four ML models. Furthermore, MLP, SVM, and RF were selected as base models, while XGB was used as the meta-model, enhancing the integrated performance of the prediction models. SHAP was used to quantify the influence of the output variables on SoC. Finally, linked measures for the prediction model were proposed to achieve autonomous monitoring of drones. The results showed that XGB exhibited superior prediction accuracy, with R² of 0.93 and RMSE of 0.14. The ensemble model obtained using stacking reduced the number of outliers by 89.4%. Current was identified as the key variable influencing both SoC and SoH. Furthermore, the intelligent prediction model proposed in this paper can be integrated with controllers, visualization web pages, and other systems to enable the health status assessment of drones. Full article

Fluorescent protein (FP) tagging is widely used in imaging experiments to investigate the subcellular distribution of proteins. However, because the fluorescence of most FP chromophores is quenched upon their protonation, their fluorescence intensities are dependent on their pKas and on the environmental pH. Thus, the concentration of a protein tagged with EGFP (pKa = 6.0) is dramatically underestimated in the lysosomal lumen (pH ~4.7) compared to that of the same protein tagged with mCherry (pKa = 4.5). In this study, we examined the effect of differential FP tagging on the apparent subcellular distribution of several proteins that reside on the cytoplasmic surfaces of secretory/endocytic organelles. Due to the presumed uniformity of cytoplasmic conditions (pH ~7.2–7.4), we expected to find essentially complete overlap of fluorescent signals, regardless of the nature of the fused FP. However, we were surprised to observe significant discrepancies in the apparent distributions of a subset of proteins tagged with EGFP vs. mCherry (Pearson’s correlation coefficients of about 0.80). These discrepancies were not evident when comparing proteins tagged with mCherry vs. other FPs with low pKas (e.g., mTurquoise (pKa = 4.5), mCerulean (pKa = 3.2)) (Pearson’s correlation coefficients of about 0.90–0.95). Our results suggest that FP tags may be sensitive to the microenvironments on the cytoplasmic surfaces of different organelles. Full article

To address the issue of reduced fault diagnosis accuracy caused by insufficient samples in laboratory datasets, this study proposes an improved Transfer Component Analysis (TCA) algorithm with dynamic kernel parameter adjustment, combined with Local Mean Decomposition (LMD). Firstly, the original signals are decomposed using LMD, and representative signal components are reconstructed based on the Pearson’s correlation coefficient to enhance feature representativeness. Then, multidimensional features, including Root Mean Square (RMS), kurtosis, and main frequency (MF), are extracted from the reconstructed signals to comprehensively reflect signal characteristics in terms of energy distribution, impact properties, and frequency structure. Subsequently, a dynamic kernel parameter adjustment strategy is incorporated into TCA to adaptively optimize the kernel parameters, effectively reducing the distribution discrepancy between the source and target domains and enhancing the generalization capability of cross-domain feature transfer. Finally, a Least Squares Support Vector Machine (LSSVM) classifier is employed to perform fault diagnosis on the reconstructed features. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic accuracy than traditional approaches under various operating conditions, especially when signals are complex and distribution differences are large, showing strong robustness and adaptability. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders where early detection is vital. However, the need for long-term monitoring is incompatible with data-scarce settings, and methods trained on one subject often fail on another due to cross-subject variability. To address these limitations, this study proposes a cross-subject, single-channel electroencephalography (EEG)-based method that uses Multi-Entropy Feature Concatenation (MEFC) to classify AD and FTD. First, single-channel EEG is processed through the Discrete Wavelet Transform (DWT) to extract five rhythms: delta, theta, alpha, beta, and gamma. Subsequently, Permutation Entropy (PE), Singular Spectrum Entropy (SSE), and Sample Entropy (SE) are calculated for each rhythm and concatenated to form a combined MEFC to characterize the non-linear dynamic properties of EEG. Lastly, Dynamic Time Warping (DTW), Pearson Correlation Coefficient (PCC), Wavelet Coherence (WC), and Hilbert Transform Correlation (HTC) are employed to measure the similarity between unknown rhythmic MEFC and those from AD, FTD, and Healthy Control (HC) groups, performing a data-driven classification via similarity measurement. Experimental results on 88 subjects in the AHEPA dataset demonstrate that the beta-rhythm with PCC yields a three-class accuracy of 76.14% using single-channel FP2. In another dataset, the Florida-Based dataset, involving 48 subjects, theta-rhythm with WC achieves a two-class accuracy of 83.33% using FP2. Furthermore, a MATLAB R2023b-based toolbox is developed using the proposed method. Such outcomes are impressive, given the limited data per individual (data-efficient), reliable performance across new subjects (cross-subject), and compatibility with wearable devices (single-channel), providing a novel entropy-based approach for EEG-based applications in biomedical engineering. Full article

Background: HML-2 subgroup mobile genetic elements of the HERV-K family were described to participate in carcinogenesis processes, but their expression and epigenetic regulation in molecular subtypes of colorectal cancer (CRC) remain partly characterized. The present study aimed to evaluate the expression of HML-2 elements using RNA-sequencing data in paired tumor and normal intestinal tissue samples from 63 patients with CRC to identify patterns of the retrotransposons’ activity in different molecular subtypes (CMSs). Methods: RNA-sequencing and DNA methylation data were analyzed for paired CRC and normal tissue samples. HERV-K expression was assessed using three bioinformatics tools: Telescope (version 1.0.3), TEtranscripts (version 2.2.3), GeneTEFlow (version 2020). Molecular tumor subtypes were defined using the CMScaller (version 0.99.2) program. The results of the HML-2 loci expression analysis were supplemented with the HML-2 proteins expression data obtained by quantitative RT-PCR. Results: HML-2 expression assessment by GeneTEFlow (version 2020), TECount (version 2.2.3) and Telescope (version 1.0.3) showed high convergence: the Pearson correlation coefficient for each tool exceeded 0.88. Several HML-2 loci were identified as differentially expressed in CRC samples of different CMS. The PCR results confirmed an increase in HML-2 expression in tumor tissues. For all CMSs, an inverse association was detected between differential methylation of CpG sites and differential expression of HML-2 loci. Associations of HML-2 expressions with differentially expressed genes in which they are located were found, and for a number of such genes an inverse relationship between the expression level and the methylation level of their promoters were demonstrated, and data on the involvement in the pathogenesis of CRC were described: CR1, CD48, TTLL3, ABCC2 and ZNF420. Expression signatures associated with the activity of the RIG-I-like receptor signaling cascade were identified in CMS1–3 CRC samples, which may indicate the possible implementation of viral mimicry against the background of HML-2 activation. Conclusions: Analysis of the expression of HML-2 and its association with CpG methylation contributes to a comprehensive interpretation of the CRC pathogenesis mechanisms. Full article

Advanced time series interpolation techniques used for estimating marine environmental factors encounter challenges regarding their usability, practical implementation, and reproducibility outside of marine science laboratories. This study aimed to interpolate NIFS Serial Oceanographic Observations and develop a system for analyzing complex factors in offshore and coastal fishing ground formation in South Korea. Additionally, the study explored the potential for integration of spatiotemporally discontinuous in situ data with continuously available satellite data through interpolation methods. Specifically, daily sea temperature and salinity data were generated through conventional time series interpolation techniques such as linear, cubic spline, and STL + PCHIP, and spatial interpolation techniques such as IDW, kriging, and natural neighbor were used to construct monthly raster data. The generated data were compared with the output of the GOFS3.1 model, and statistical indices such as MAE, RMSE, R², and Pearson or Spearman correlation coefficients were used to evaluate the accuracy and reproducibility. Cubic spline temporal and kriging spatial interpolation methods demonstrated strong performance for the sea temperature data; however, the interpolation performance for the salinity data exhibited limited effectiveness owing to unique local variability. This study introduces techniques for transforming discontinuous in situ observational data into high-resolution data and demonstrates that the integrated use of in situ data can enhance our understanding of the fishing ground formation mechanisms and ecosystem-based fishery management. Full article

Umami taste in lager beer not only determined body fullness and the backbone of aftertaste, but also affected the controllability and interpretability of flavor expression across the entire brewing process. Based on stage-wise sampling, peptidomic profiles were established on wort fermentation day 0, day 1, day 3, and day 9. A total of 25,592 peptides were identified by reversed-phase liquid chromatography–quadrupole time-of-flight mass spectrometry (RPLC-QTOF-MS). Molecular informatics screening was performed using UMPred-FRL (a feature representation learning-based meta-predictor for umami peptides) and TastePeptides-Meta (a one-stop platform for taste peptides and prediction models), yielding 7255 potential umami peptides. From these, 145 peptides were further selected for molecular docking. In addition, 6 representative umami peptides were selected for receptor-level validation and structural analysis. Mechanistically, the umami receptor taste receptor type 1 member 1/taste receptor type 1 member 3 (T1R1/T1R3) belonged to class C G protein-coupled receptor (GPCR) and relied on the extracellular Venus flytrap (VFT) domain for ligand capture. Ligand-induced VFT conformational convergence transmitted changes to the transmembrane region and triggered signal transduction. Docking and energy decomposition indicated that the ionic group primarily contributed to orientation and anchoring. Salt-bridge or hydrogen-bond networks were formed around Lys228, Arg240, Glu206, Asp210, Asn141, and Gln138, thereby reducing conformational freedom. Meanwhile, hydrophobic side chains obtained major binding gains within a hydrophobic microenvironment formed by Val135, Ile137, Leu165, Tyr166, Trp78, and His79. These results reflected a synergistic mode in which charge pairing enabled positioning and hydro-phobic complementarity promoted VFT closure. To experimentally confirm sensory relevance, 6 representative peptides were individually spiked into 4 brewing-stage beer samples, which produced a clear stratification pattern across stages. Notably, peptides with favorable docking-derived binding propensity did not necessarily enhance umami perception, and several longer peptides showed persistent negative sensory shifts, supporting that binding affinity alone could not be treated as a proxy for perceived umami in the beer matrix. At the node level, the cumulative abundance of umami peptides showed a significant positive correlation with umami scores, with a Pearson correlation coefficient of r = 0.963 and p = 0.037. This result indicated good linear consistency between umami peptide content and the upward shift in umami taste in lager beer. Umami peptide clusters were further proposed as a more appropriate functional unit, and an umami peptide cluster database spanning the full process was constructed. This database provided a reusable resource for process control and flavor prediction. Full article

To enhance the resilience and sustainability of urban metro systems under operational uncertainties and external disturbances, critical station identification and vulnerability assessment should be further investigated from the perspective of network science. In this paper, the presented comprehensive clustering algorithm and the Pearson correlation coefficient are adopted to explore the origin-destination (OD) passenger flow characteristics on different date classifications, and the different dates should be reasonably classified into three categories, including working day, weekends, and holiday. Meanwhile, this paper proposes the dynamic DomiRank algorithm and flow DomiGCN model to identify critical stations from network structure and function on different data classifications respectively, and further studies the vulnerability property of metro networks under simulated attacks. The Shanghai metro network is selected as case to prove the feasibility and correctness of the model. The results show that the dynamic DomiRank algorithm is relatively effective to identify critical stations from network structure, and the flow DomiGCN model is also relatively effective to identify critical stations from network function. Moreover, simulated attacks to these critical stations detected by the proposed methods can cause more damages than the other methods. These findings provide some supports for protection of metro infrastructure and contribute to the sustainable operation and development of urban rail transit systems. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) affects approximately 12.7% of pregnant women in South Korea. While breastfeeding provides critical health benefits for mothers with GDM and their infants, including improved insulin resistance and reduced Type 2 diabetes risk, no validated GDM-specific breastfeeding knowledge instrument exists. This study aimed to develop and validate a breastfeeding knowledge instrument for women with GDM. Methods: This methodological study employed systematic procedures for the development and validation of knowledge test. Initial item generation yielded 30 items across three domains: postpartum physical characteristics, breastfeeding barriers, and breastfeeding benefits. Content validity was evaluated by six clinical experts and ten experiential experts (women with GDM). An online survey was conducted in October 2022 with 220 women diagnosed with GDM who were either pregnant or within six months postpartum. Item analysis, exploratory factor analysis, and reliability testing were performed. Convergent validity was assessed by calculating the Pearson correlation coefficient with an established breastfeeding knowledge scale. Results: Following expert review and psychometric analysis, the final instrument comprised 14 items across three factors: postpartum physical characteristics (3 items), breastfeeding barriers (2 items), and breastfeeding benefits (9 items). The Kaiser–Meyer–Olkin measure was 0.884, Bartlett’s test was significant (χ² = 838.835, p < 0.001), and factor loadings were satisfactory. The KR-20 reliability coefficient was 0.826, and criterion validity was confirmed. Conclusions: This first validated GDM-specific breastfeeding knowledge instrument enables the identification of knowledge gaps and the development of targeted educational interventions to improve maternal-child health outcomes. Full article