Reliable discharge estimation is essential for water resource management, yet many regions lack sufficient hydrological stations. To address this limitation, we propose the Spatial Hydraulic Geometry Interpolation (SHGI) method, which estimates discharge (Q), hydraulic depth (D), and mean velocity (V) from river width (W) obtained via surveys or satellite imagery. SHGI integrates hydraulic geometry theory with multiquadric radial basis interpolation, applied to the Meta and Atrato river basins in Colombia. Parameters of at‑station hydraulic geometry (coefficients , c, k and exponents b, f, m) were derived using least squares and transformed into log‑ratio space to preserve their compositional constraints. Interpolation along upstream distance ensures spatial continuity, and closure operations guarantee internal consistency. Validation against observed data in basins with contrasting geomorphology and data density confirmed the method's robustness. The principal contributions of SHGI are:•Longitudinal continuity: explicit incorporation of upstream distance to interpolate parameters consistently along channels and tributaries.•Compositional integrity: preservation of the multiplicative and additive constraints of hydraulic geometry parameters during interpolation.•Estimation under data scarcity: enabling calculation of Q, D, and V at ungauged sites using only river width.

Reply to: PHOENICS trial: robust methods, outdated question and can PHOENICS resurrect hydroxyethyl starch? A cautious view.

PHOENICS trial: robust methods, outdated question.

Low-field magnetic resonance imaging (LF-MRI) has emerged as a transformative technology, offering portable and cost-effective solutions for medical imaging in resource-limited settings. However, LF-MRI systems face inherent challenges, including low signal-to-noise ratio (SNR) and reduced spatial resolution, which can compromise diagnostic accuracy. To systematically address these fundamental limitations, which involve complex, nonlinear image transformations, deep learning (DL) presents a powerful solution due to its proven capability in learning such mappings from data. This review explores the pivotal role of DL techniques in overcoming these limitations, focusing on two critical applications: denoising and super-resolution. Recent advancements in DL architectures-such as U-Net, generative adversarial networks (GANs), and diffusion models (DMs)-have demonstrated remarkable success in enhancing LF-MRI image quality. For denoising, supervised models like U-Net and denoising auto-encoders (DAEs) effectively suppress noise while preserving anatomical details, while unsupervised approaches (e.g., Cycle-GANs) leverage unpaired datasets to bridge the gap between low-field (LF) and high-field (HF) MRI. In super-resolution, DL models like 3D U-Net and residual channel attention networks (RCANs) reconstruct high-resolution images from LF inputs, enabling finer detail visualization for clinical diagnostics. Key findings highlight the superiority of DL over conventional iterative methods in adaptability, robustness, and real-time performance. However, challenges persist, including data dependency, computational costs, and limited interpretability. Innovations such as diffusion-driven neural representations and dual-acquisition 3D super-resolution further push the boundaries of LF-MRI quality. This review underscores DL's potential to democratize MRI access, particularly in low-resource regions, while outlining future directions: improving generalization, reducing training data requirements, and integrating postprocessing pipelines. By directly tackling the key barriers of image quality, DL-enhanced LF-MRI is poised to make a significant impact in clinical scenarios where accessibility and speed are paramount, such as point-of-care diagnostics and emergency clinic, thereby helping bridge global healthcare disparities.

SEQUAL: Self-refining and effective querying active learning with pseudo label divergence score for carotid intima-media segmentation in ultrasound.

Seaweed are primary producers and potential vectors of microplastics (MPs) contamination, yet robust extraction methods that digest complex algal matrices while preserving polymer integrity remain limited. A sequential enzymatic-oxidative digestion was optimized for three seaweeds (Fucus vesiculosus, Chondrus crispus and Ulva lactuca). The optimized process involved the initial addition of cellulase (1% w/v, 24 h, 50 °C) followed by H₂O₂ (30% v/v, 48-72 h, 65 °C). Across nine 0.5 g dry-weight sub-replicates (3 per seaweed), 30 MPs were found (6.7 MPs/g⁻¹). The integrity of polymers was assessed for 12 MPs polymers, with acceptable performance being defined as ≥ 90% recovery and spectroscopic (through FTIR analysis) identifiability. Eight polymers met this threshold (90-101%). Four polymers were adversely affected with the long 72 H₂O₂ incubation, namely: cellulose-acetate (53% recovery), polyamide (61%), acrylic (3%) and rayon (2%). Although polymers remained identifiable, sequential digestion produced mass loss and visible changes (e.g. polyamide opacity, cellulose-acetate brittleness), which may increase fragmentation and miss-identification. Therefore, the protocol is suitable for most common MPs, but not for rayon and acrylic, and should be applied cautiously where cellulose-acetate or polyamide are expected.

A new estimator with Monte Carlo simulation and theoretical framework: Its implementations in the sports and radiation sciences

Beyond lipid homochirality: Analytical strategies and biological implications.

Informal caregivers increasingly support people with chronic kidney disease, yet caregiving is often associated with substantial physical, emotional, and financial burden. To synthesise the characteristics and effectiveness of interventions designed to reduce burden among informal caregivers of people with chronic kidney disease. Systematic review and meta-analysis. Informal caregivers of people with chronic kidney disease. A search was conducted of CINAHL, PubMed, Embase, PsycINFO, and Web of Science databases from inception to February 2025, as well as citation chaining and hand searching. Randomised control trials, quasi-and non-randomised controlled trials, and single-group pre-post studies, conducted at any location and published in English were included. Risk of bias was assessed using Joanna Briggs Institute Checklists. GRADE was used to assess certainty of evidence. Random-effects meta-analyses were conducted for caregiver burden outcomes. Thirteen studies were included in the review, investigating education interventions, mindfulness, relaxation, logotherapy, peer mentoring, and behaviour modification. Twelve studies identified a significant reduction in caregiver burden. A meta-analysis of five education and skills training interventions found overall reduction in caregiver burden. Heterogeneity was high, which limited comparability. Certainty of evidence for caregiver burden was low. Brief education-focused programs and selected behaviour-change interventions may reduce the burden for informal caregivers of people with chronic kidney disease. However, additional high-quality studies conducted in a variety of contexts incorporating robust methods are required to identify the benefits and optimal characteristics of interventions to inform kidney care teams. PROSPERO (CRD420250651266).

While controlled interrupted time series (CITS) are commonly used to evaluate public health policies, how to incorporate control(s) into their statistical modelling has received limited attention. We aimed to compare the statistical performance of different model formulations for including control groups in various segmented regression model specifications (with a particular focus on CITS and Difference-in-Difference [DiD] designs) under conditions where their assumptions are met, as well as when they are violated. Based on a real-world dataset, we simulated and compared the statistical performance of four model formulations grounded on segmented regressions for including control groups in a pre- and post-evaluation. The compared model formulations were: (1) CITS segmented regression, (2) DiD segmented regression, (3) single ITS of the difference between control and intervention series, and (4) incorporating the control as a covariate in a single ITS. Models were tested across scenarios challenging assumptions around the control group (e.g., non-parallel trends -challenging DiD assumptions-, or inconsistent trend difference over time between groups -challenging CITS assumption-) or regression errors (e.g., heteroscedasticity or autocorrelation). We also included models, including restricted cubic splines of time, which may mitigate distortions from assumption violations. Additionally, we tested for detecting non-parallel trends. Standard DiD, CITS, and the ITS of the difference between series yielded the lowest bias whenever their design assumptions were satisfied. Overall, including time splines as covariates into ITS of the difference between series achieved the lowest bias and highest coverage also when design assumptions were violated. This makes it a valuable tool for causal inference in settings with parallel, non-parallel or inconsistent trend patterns between groups. Since violations of the trends assumption are often undetectable, methods robust to such violations are extremely valuable. Modelling CITS as an ITS of the difference between series is among the most robust methods to embed control series into model specifications. Incorporating time splines as model covariates within an ITS of the difference has the potential of reducing bias from assumption violations (including parallel trends) without negative impacts when assumptions hold.

Sufficiency-based absolute sustainability position assessment for passenger vehicles

Handling missing patient-reported outcomes in longitudinal clinical trials: a simulation study.

Cell labeling approaches for tracking stromal vascular fraction fate.

The long-term physiologic effects of thyroid problems make them one of the most important endocrine disorders. Even if a lot of machine learning and deep learning techniques have been presented out for the early detection of thyroid disease, it is still difficult to achieve reliable and clinically accurate multi-class diagnostic performance. In this work, we suggest an Enhanced Extreme Learning Machine (EELM) that uses Drop-Connect regularization to enhance generalization and reduce over-fitting that is frequently seen in traditional ELM models. The pipeline for the suggested framework consists of seven steps: data preprocessing, model building, training, and evaluation. To simulate a clinically relevant diagnostic scenario, the model was assessed on a unified four-class thyroid classification task (hypothyroidism, hyperthyroidism, sick-euthyroid, and normal). The suggested EELM demonstrated steady and reliable multi-class performance with an average accuracy of approximately 82% under 10-fold cross-validation. The model achieved up to 99.89% accuracy in comparative binary classification studies (e.g., hypothyroid vs. normal), indicating the better division of some thyroid diseases. Accuracy, precision, recall, specificity, sensitivity, F1-score, ROC, and AUC measures were used to evaluate performance. The suggested method's robustness and significance were validated statistically using ANOVA and paired t-tests. Significant improvements over baseline models were confirmed by statistical validation with paired t-tests and ANOVA (p < 0.05). Overall, the findings show that the suggested EELM offers a clinically applicable, statistically supported, and computationally effective method for classifying thyroid diseases.

Microplastics (MiPs, ×5 mm in size) harbor complex biofilms that facilitate pathogen dissemination, yet standardized extraction protocols are lacking. Here, we developed and optimized a method for biofilm extraction from environmentally weathered MiPs. To reflect real-world conditions, the protocol was applied directly to bulk, heterogeneous, field-collected MiP mixtures (size range: 80 µm-5 mm) without prior sorting by polymer type or morphology. By optimizing extraction buffers, mechanical disruption, and MiP quantities (100-150 particles), we established an optimal protocol combining phosphate-buffered saline with 0.1% Tween 80, ultrasonication (40 kHz, 10 min), vortexing with glass beads, and a two-cycle extraction-disaggregation workflow. This approach involves an initial extraction followed by a repeated, exhaustive extraction step designed to maximize the recovery of recalcitrant biofilm residues. This protocol markedly enhanced recovery of viable, culturable cells, delivering a 2,950-fold enhancement in the recovery of viable, culturable cells (evaluated via CFU counts; 28,020 ± 11,034 CFU MiP⁻¹) vs. conventional PBS extraction (9.5 ± 3 CFU MiP⁻¹) and 102-fold vs. passive extraction (274 ± 59 CFU MiP⁻¹). The 10-min sonication empirically maximized viable cell recovery within the tested duration range. The two-step protocol with Tween 80-mediated disaggregation proved critical, increasing recovery 208-fold by disaggregating biofilm fragments. While DNA yields (26.5 ± 3.93 ng µL⁻¹) were sufficient for targeted PCR-based pathogen detection (Aeromonas spp., Salmonella enterica), the co-extraction of complex environmental matrices (A260/A280 ratio: 0.17-0.19) strictly requires an additional purification step prior to next-generation sequencing. Validation across contrasting aquatic environments confirmed the method's robustness. Comparative analysis demonstrates that conventional single-step approaches fail to recover the majority of viable cells trapped within weathered MiP biofilms. This optimized and validated protocol provides a critical methodological foundation for investigating plastisphere microbial ecology and pathogen transport dynamics, supporting evidence-based risk assessment of MiP contamination, especially public health risks associated with microplastic pollution.IMPORTANCEMicroplastic-associated biofilms (the "plastisphere") serve as vectors for waterborne pathogens and antibiotic resistance genes; however, the persistent use of inadequate extraction methods has systematically underestimated microbial abundance, presenting a critical barrier to global environmental risk assessment. By overcoming the limitations of conventional extractions-which fail to penetrate recalcitrant extracellular polymeric matrices on environmentally weathered microplastics-our standardized methodology liberates previously undetectable bacterial populations. The ability to accurately quantify these hidden communities, including key pathogens like Aeromonas spp. and Salmonella enterica, fundamentally transforms our understanding of microplastics as hidden biological reservoirs. Ultimately, this methodological advancement bridges a critical gap in microbial ecology, delivering the reliable, quantitative data strictly required by policymakers, environmental agencies, and public health officials to establish evidence-based guidelines mitigating the impacts of microplastic pollution on global water systems.

3D Flower-Like Bi2O2CO3/2D rGO composite modified with GCE for electrochemical nanomolar quantification of chloramphenicol in biological and environmental matrices.

The rapid advancements in PET technology, coupled with the need for accurate and efficient imaging, necessitate the development of robust and generalizable methods for CT-free attenuation and scatter correction (ASC). Deep learning offers a promising solution, but exhibits limited performance when tested in diverse clinical settings and varying imaging conditions. We propose a few-shot fine-tuning paradigm that enables efficient adaptation of models from a source domain to a new target domain. Our backbone network incorporates statistical modulation to extract domain-specific distribution information and employs pixel-wise factor scaling modeling to disentangle ASC factor maps from input images. On a large and diverse dataset of 1539 subjects across multiple tracers, scanners, and centers, we evaluate model performance under single-tracer training, multi-tracer joint training, and few-shot adaptation strategies. Although joint training demonstrates strong performance on known tracers, the proposed few-shot adaptation approach, CrossPET-Adapt, excels at adapting to unseen domains with minimal data, outperforming joint training. This method significantly reduces radiation exposure and data requirements, offering a rapid and robust solution for CT-free PET ASC in varied clinical environments.

This work presents a novel method for fitting superquadrics to point clouds under the contamination of noise and outliers, which has many applications for shape modeling across diverse fields. Unlike prior approaches that either exclusively focus on fitting rigid or deformable superquadrics, or suffer from robustness and numerical instability issues, our method redefines the problem from a new unsupervised clustering perspective, enabling the holistic fitting of both rigid and deformable superquadrics within a unified framework. Central to our approach is a stable optimization function inspired by unsupervised clustering analysis, where we formulate the point cloud data and samples from the potential parametric surface as clustering members and centroids, respectively. Then, the clustering process with dynamic updates to centroid locations serves as a direct proxy for optimizing superquadric parameters, establishing a principled link between geometric fitting and clustering dynamics. We further derive the relationship between pairwise computations of clustering centroids and clustering members to orthogonal distances, effectively eliminating the need for the time-consuming surface sampling process. Moreover, our formulation provides closed-form analytical solutions for both the fuzzy membership degree vector and the covariance matrix, ensuring efficient iteration optimization and enabling more effective handling of geometric deformations. In addition, we provide a theoretical certificate of convergence analysis and demonstrate that the clustering-inspired fitting method can escape local minima by inherently increasing the convexity of the objective function. We experimentally demonstrate the improvements of our superquadric fitting method in accuracy, robustness, and stability over state-of-the-art approaches. We also show that our method effectively avoids convergence to local minima, yielding smaller point-to-surface distances, particularly for the highly tapered shapes. Additionally, we illustrate the versatility of our method in diverse applications, including via multiple superquadric representation, type-specific primitive fitting, geometry editing, and medical modeling.

E-cigarette use is a common form of tobacco consumption; however, just like secondhand smoke, its long-term health effects remain uncertain. At present, risk assessment indicators for e-cigarettes and second-hand smoke are not sufficiently comprehensive, and robust technical methods for holistic exposure evaluation are lacking. In this study, a questionnaire survey was conducted and urine samples from 100 non-smokers and 301 e-cigarette users were collected. Urinary metabolites of nicotine, volatile organic compounds, tobacco-specific nitrosamines, polycyclic aromatic hydrocarbons, 1,2-propylene glycol, and glycerol were systematically analyzed, making this study the most comprehensive investigation to date of risk assessment indicators for e-cigarettes. The results showed significant differences in multiple urinary biomarkers between non-smokers and e-cigarette users, including total nicotine equivalents, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, 2-amino-1,3,4-thiadiazole-5-carboxylic acid, S-phenylmercapturic acid, N-acetyl-S-(2-cyanoethyl)-L-cysteine, 2-hydroxypropyl mercapturic acid, 3-hydroxypropyl mercapturic acid, and total hydroxylated naphthalenes. Using integrated biomarker response (IBR) calculations, a risk assessment model was established for e-cigarette exposure. The scientific validity of this model was evaluated using the cigarette consumption test for dependence and the Fagerström test for nicotine dependence scales, and it indicates that the higher the degree of addiction to tobacco products, the higher the risk of exposure. The biomarkers and IBR-based assessment model developed in this study may provide valuable tools for clinical evaluation of the health risk status of tobacco product users and second-hand smoke.

PurposeTo characterize the genetic landscape of congenital ectopia lentis (EL) and assess genotype–phenotype correlations with implications for surgical decision-making.MethodsThis retrospective study enrolled patients with congenital EL who presented to Fudan University Eye and ENT Hospital between 2017 and 2025. We performed targeted next-generation sequencing for probands, with candidate variants confirmed by Sanger sequencing. Patients were categorized into FBN1 and non-FBN1 groups. The ocular features and surgical options were compared across genotypes.ResultsA total of 497 probands were enrolled. The molecular diagnostic yield was 93.36%, with FBN1 variants accounting for 82.93% and non-FBN1 variants for 10.44%. Compared with FBN1 cases, non-FBN1 patients exhibited higher EL severity (P < 0.001), lower corneal curvature radius (CCR) (P < 0.001), and higher incidence of ocular comorbidities (P < 0.01). Surgically, non-FBN1 patients more often required robust intraocular lens fixation methods than did FBN1 patients (P < 0.001). Within the FBN1 cohort, the DN(Cys+CaB)+HI subgroup exhibited longer axial length (AL) (P < 0.001), thinner central corneal thickness (CCT) (P = 0.015), and a higher proportion of clinically diagnosed Marfan syndrome (P < 0.001) compared with the DN(Others) subgroup. In contrast, the FBN1 DN(Others) subgroup showed comparable AL, CCT, and CCR to the non-FBN1 group (all P > 0.05). No significant difference of ocular biometrics or surgical options were observed within the non-FBN1 group, except for the highest CCR in patients harboring CPAMD8 variants (P = 0.004).ConclusionsGenetic characterization of congenital EL extends beyond diagnosis to inform ocular phenotype variability and surgical decision-making.