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Health risk assessment of potentially toxic elements in bread from Iranian markets using Monte Carlo simulation
A Fragmentation behavior-guided UHPLC-Q-Orbitrap HRMS method for the quantitative analysis of 26 perfluoroalkyl substances and their alternatives in water
Perfluoroalkyl substances (PFASs), including newly introduced alternatives, are of global concern due to their environmental persistence, bioaccumulation, and potential health risks. We developed and validated a sensitive, selective UHPLC–Q-Orbitrap HRMS method for the comprehensive analysis of 26 PFASs—including perfluorocarboxylic acids (PFCAs), perfluorosulfonic acids (PFSAs), and emerging substitutes—in diverse water matrices. Sample preparation used WAX solid-phase extraction with isotope-labeled internal standards and separation on a BEH C18 column; quantitative performance was assessed under Full MS, targeted SIM, and PRM. The method achieved excellent linearity, sub-ng/L detection capability, and robust recoveries and precision across matrices, with PRM offering the best balance of sensitivity and selectivity. Characteristic fragmentation patterns, like decarboxylation and desulfonation, supported structural confirmation and resulted in diagnostic fragments that facilitated isomer differentiation and increased the reliability of identifying low-abundance compounds. This fragmentation-guided UHPLC-HRMS approach enables high-confidence detection and quantification of both legacy and novel PFASs and provides a practical tool for environmental monitoring and risk assessment.
Repairing the inconsistent pairwise comparison matrix using a cosine distance and grey wolf optimiser-based framework in multi-criteria decision-making
Naturalistic sleep tracking in a longitudinal cohort: Uncertainty and bias in short duration sampling
Background Despite broad interest in the health implications of sleep duration, traditional measurements via polysomnography or actigraphy are often limited to one or a few nights per person. Inferential uncertainty remains an important issue for interpreting descriptive statistics in this common research setting. Methods This retrospective analysis of observational data used a combined approach of simulated data and real-world data (30–365 nights) analysis from over 35,000 participants who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep data. Results Simulations demonstrate that the degree of uncertainty and bias, compared to truth defined by 1000 simulated nights, depended on several factors: sub-sample size, the simulated distribution (normal versus skewed), and the computed metrics of central tendency (mean, median) and dispersion (standard deviation (SD), interquartile range (IQR)). For example, the SD computed from n = 7 observations from a simulated normal distribution (7 ± 1 hours) showed a median 6.7% under-estimation bias, and an uncertainty range with IQR from 24% under- to 14.7% over-estimation. Defining ground truth with a small sample (7–14 nights) yielded overly optimistic estimates of bias and uncertainty when sub-sampled. Real-world sleep duration data, when randomly sub-sampled and compared to longer observations within-participant, showed similar SD bias and rates of convergence as the normal distribution simulations. Sub-sampled sleep stage durations also varied substantially from “true” values computed from longer observations. Finally, simulated cohorts with sleep durations of 7 ± 1 hours mixed with a subset of 6 ± 1 hours sleepers showed that a random single-night observation of “short sleep” (6 hours) is more likely from random variation of a 7-hour sleeper, than from an actual 6-hour sleeper. Extending the mean duration calculation to n = 7 nights mitigates this mis-classification risk. Conclusion The simulation and empiric data approaches both suggest that bias and uncertainty due to sub-sampling depend on: a) the sample size of observations within each participant, b) the descriptive statistic used to capture centrality or dispersion, and c) the distribution shape of the data (normal or skewed). Longer duration tracking provides important and tangible benefits to reduce bias and uncertainty in sleep health research that historically relies on small observation windows.
New robust estimator for handling outliers and multicollinearity in gamma regression model with application to breast cancer data
A neural network approach to sarcopenia prediction based on bioelectrical impedance in community-dwelling older adults
This study aimed to apply a neural network to raw bioelectrical impedance analysis data and to test whether sarcopenia could be predicted with high accuracy. The study population comprised 727 community-dwelling older adults aged 65–85 years who participated in the Kasama Study from 2015 to 2018. Sarcopenia was determined using the standard values set by the Asian Working Group for Sarcopenia 2019. Skeletal muscle mass index, grip strength, and five-times sit-to-stand test (Dataset 1) or skeletal muscle mass index, grip strength, and gait speed (Dataset 2) were used. The characteristic variables were sex, age, height, and body mass index, as well as parameters from bioelectrical impedance analysis, such as reactance, resistance, and impedance for six frequencies (1, 5, 50, 250, 500, and 1000 kHz) in six body parts measured using a multi-frequency body composition analyzer (MC-980A, Tanita). For analysis, a neural network was used to construct a model. For verification of the model’s accuracy, a receiver operating characteristic analysis was performed to calculate the sensitivity, specificity, area under the curve, and positive and negative predictive values. Among the participants analyzed, 21 (3.3%) in Dataset 1 and 24 (3.7%) in Dataset 2 had sarcopenia. In Dataset 1, the model that used 5, 50, and 250 kHz showed the highest prediction accuracy (sensitivity: 1.00, specificity: 0.91, area under the curve: 0.96, accuracy: 0.91, positive predictive value: 0.28, negative predictive value: 1.00). In Dataset 2, the model that used 50 kHz exhibited the highest prediction accuracy (sensitivity: 0.91, specificity: 0.84, area under the curve: 0.88, accuracy: 0.84, positive predictive value: 0.17, negative predictive value: 1.00). In conclusion, highly accurate predictions are possible by applying a neural network to the raw data obtained from bioelectrical impedance analysis. As a highly accurate sarcopenia screening method, it is expected to be used in various settings, ranging from clinical practice to local communities.
Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features
Correction: Diabetes rescue, engagement, and management (D-REM) for hypoglycemia: Clinical trial protocol of a community paramedic program to improve diabetes management among adults with severe hypoglycemia
Messages from co-partisan elected officials can increase climate mitigation intentions without changing climate beliefs
Investigation of optical properties of three dimensional reflectance rulers fabricated by size-controlled monodisperse polystyrene colloidal particles
Stability analysis of heterogeneous oligopoly games of increasing players: A computational approach
In this paper, we study an oligopolistic market endowed with an isoelastic demand function and a quadratic cost function, where heterogeneous firms coexist and produce the same product. We create new games by adding additional heterogeneous firms one after the other, and we examine the relative size of the stability region as the number of heterogeneous firms increases. For each model studied, we use the cylindrical algebraic decomposition method to analytically investigate the conditions for the local stability of the Cournot–Nash equilibrium. We find that the stability regions become larger as the number of heterogeneous firms involved increases. We also perform numerical simulations to investigate complex dynamics, such as periodic orbits and chaos, when the equilibrium loses its stability. Furthermore, we investigate the case of distinct cost parameters through numerical simulations and find that the dynamics seem more complicated than the case of identical cost parameters.
Enhanced spatial clustering of single-molecule localizations with graph neural networks
Abstract Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multifunctional Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.
Identification of a m6A-immune-related risk model for predicting prognosis, immune microenvironment, and drug responses in acute myeloid leukemia
Abstract This study utilized TCGA database to explore the role of m6A modification and immune infiltration in AML. Through unsupervised clustering and WGCNA analysis, 8 hub genes were identified, and a risk model with EHBP1L1 and ZNF385A was established using LASSO regression. A nomogram incorporating hub gene risk score and age showed satisfactory prognostic prediction. External validation of GEO confirmed the model’s effectiveness. TME analysis revealed correlations with monocytes and Treg cells, while immune checkpoints and HLA genes were associated with risk scores. Drug sensitivity analysis suggested potential responses to specific chemotherapy drugs. TIDE analysis indicated reduced ICI treatment benefit in high-risk patients. RT-qPCR validations revealed the significance of prognosis and risk stratification of ZNF385A . The noticeable trend of EHBP1L1 was observed. In addition, the accurate predictive capability of the risk model has been validated by clinical samples. Therefore, the risk model enables a quantitative evaluation of disease severity and progression risk in AML patients, based on their clinical and biological characteristics. This precise prediction not only informs treatment decisions but also guides the selection of chemotherapy regimens, overall improving patient outcomes.
Diagnosis of colorectal cancer using residual transformer with mixed attention and explainable AI
Colorectal cancer (CRC) is the leading cause of cancer disease and poses a significant threat to global health. Although deep learning models have been utilized to accurately diagnose CRC, they still face challenges in capturing the global correlations of spatial features, especially in complex textures and morphologically similar features. To overcome these challenges, we propose a hybrid model using a residual network and transformer encoder with mixed attention. The Residual Next Transformer Network (RNTNet) extracts spatial features from CRC images using ResNeXt. ResNeXt utilizes group convolution and skip connections to capture fine-grained features. Furthermore, a vision transformer (ViT) encoder containing a mixed attention block is designed using multiscale feature aggregation to provide global attention to the spatial features. In addition, a Grad-CAM module is added to visualize the model’s decision process to support oncologists with a second opinion. Two publicly available datasets, Kather and KvasirV1, were utilized for model training and testing. The model achieved classification accuracies of 97.96% and 98.20% on the KvasirV1 and Kather datasets, respectively. Model efficacy is also further confirmed by ROC curve analysis, where AUC values of 0.9895 and 0.9937 on the KvasirV1 and Kather datasets are obtained, respectively. Comparative study findings support that RNTNet delivers improvements in accuracy and efficiency compared to state-of-the-art methods.