Browse Articles

Validation of the Chinese version of the self-injurious thoughts and behaviors interview for adolescent outpatients in Taiwan: a cross-sectional study

Correlation of visual acuity changes and optical coherence tomography imaging in patients with central retinal artery occlusion post-arterial thrombolysis

Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments

Abstract Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stable environmental statistics, but showed evidence of surprisal and discarded prior information following environmental changes. Second, we observed distinct EEG activity patterns associated with prior uncertainty and surprisal in both the time- and time–frequency domain, which are in line with previous studies using visual tasks. Third, these EEG activity patterns were predictive of our participants’ sound localization error, response uncertainty, and prior bias on a trial-by-trial basis. In summary, our work provides novel behavioral and neural evidence for Bayesian inference during dynamic auditory localization.

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution

Abstract Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the early detection of skin cancer. This task is challenging due to the irregularity of skin lesions in dermatoscopic images, significant color variations, boundary blurring, and other complexities. Artifacts like hairs, blood vessels, and air bubbles further complicate automatic segmentation. Inspired by U-Net and its variants, this paper proposes a Multiscale Input Fusion Residual Attention Pyramid Convolution Network (MRP-UNet) for dermoscopic image segmentation. MRP-UNet includes three modules: the Multiscale Input Fusion Module (MIF), Res2-SE Module, and Pyramid Dilated Convolution Module (PDC). The MIF module processes lesions of different sizes and morphologies by fusing input information from various scales. The Res2-SE module integrates Res2Net and SE mechanisms to enhance multi-scale feature extraction. The PDC module captures image information at different receptive fields through pyramid dilated convolution, improving segmentation accuracy. Experiments on ISIC 2016, ISIC 2017, ISIC 2018, PH2, and HAM10000 datasets show that MRP-UNet outperforms other methods. Ablation studies confirm the effectiveness of its main modules. Both quantitative and qualitative analyses demonstrate MRP-UNet’s superiority over state-of-the-art methods. MRP-UNet enhances skin lesion segmentation by combining multiscale fusion, residual attention, and pyramid dilated convolution. It achieves higher accuracy across multiple datasets, showing promise for early skin disease diagnosis and improved patient outcomes.

Preparation, characterization and application of chitosan/thyme essential oil composite film

Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP

Abstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson’s algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where $$\:{R}^{2}$$ =0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators.

Mechanical mechanism of soil consolidation by plant roots in loess area of northern Shaanxi

Author Correction: Optimal design of a novel modified electric eel foraging optimization (MEEFO) based super twisting sliding mode controller for controlling the speed of a switched reluctance motor

Prevalence and risk factors of high-risk sexual behavior among elderly men with HIV infection in Chongqing, China

Hybrid neural network method for damage localization in structural health monitoring

Abstract The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. In this study, however, we explore an innovative approach based on numerical data, which is characterized by greater cost efficiency and offers intriguing research implications. This study emphasizes the evaluation of hybrid RNN-CNN models in comparison to the pure CNN models previously utilized in related research. Our proposed model incorporates a single RNN layer, complemented by essential supporting layers, which contributes to a reduction in complexity and a decrease in the number of parameters. This design choice results in a more streamlined and efficient architecture. Our experimental results reveal an accuracy of 78.9%, which, while slightly lower than the performance of conventional CNN models, underscores the potential of RNN layers in crack detection tasks. Importantly, this work demonstrates that integrating additional RNN layers can effectively enhance crack detection capabilities, particularly given the significance of preserving spatial information for accurate crack segmentation. These findings open avenues for further exploration and optimization of RNN-based methods in structural damage analysis, suggesting that the strategic use of RNNs can complement CNN models to achieve robust performance in this domain.

Combined indicator assists in early recognition of retinopathy of prematurity

Abstract This research aimed to investigate the value of clinical data in preterm infants on admission for the early prediction of retinopathy of prematurity (ROP). 98 preterm infants (66 males and 32 females) with an average gestational age of 30.42 ± 1.20 weeks were included. Basic vital signs, clinical tests, and maternal information were collected at admission. Preterm infants were divided into a non-ROP group and a ROP group based on whether they eventually developed ROP. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the above indexes and the combined indexes in the ROP of preterm infants. (1) The differences in systolic blood pressure (SBP), red blood cell count (RBC), hemoglobin (HGB), direct bilirubin (DBIL), and total bilirubin (TBIL) were statistically significant between the non-ROP group and ROP group (all P < 0.05). (2) RBC, HGB, DBIL, and TBIL, all of which have diagnostic value for ROP [area under curve (AUC) 0.643, 0644, 0.887, and 0.744, respectively, all P < 0.05]. (3) The combined indicator possessed a good diagnostic value for ROP (AUC of 0.962, P < 0.05), with a sensitivity and specificity of 88.64% and 91.49%, respectively. (4) Combined indicator (body temperature, body weight, heart rate, SBP, diastolic blood pressure, mean arterial pressure, RBC, HGB, red blood cell distribution width, DBIL, TBIL) has better diagnostic value for ROP than each of RBC, HGB, DBIL, and TBIL alone (Z-value 5.386, 5.475, 2.410 and 4.420, respectively, all P < 0.05). Combined indicator has good predictive value for ROP in preterm infants.

Author Correction: Monocytes to Apolipoprotein A1 ratio is associated with metabolic dysfunction-associated fatty liver disease in type 2 diabetes mellitus

A real-world pharmacovigilance study of omalizumab using disproportionality analysis in the FDA adverse drug events reporting system database

Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support

Improvement of carboplatin chemosensitivity in lung cancer cells by siRNA-mediated downregulation of DLGAP1-AS2 expression

Author Correction: Hydropower Station diversion tunnel layered excavation deformation mechanism under high crustal stress

Increased activation of the WNT pathway in brain tissue from patients with cortical dysplasia type IIb

Measurement of axial dose profile for wide detector CT using radiochromic films

A comparative analysis of supraharmonic emission by DC fast chargers used for electric vehicle charging

Spectroscopic and molecular docking studies on binding interactions of camptothecin drugs with bovine serum albumin

Abstract This study investigates the binding interactions between bovine serum albumin (BSA) and camptothecin (CPT) drugs (camptothecin, 10-hydroxycamptothecin, topotecan, and irinotecan) using UV–Vis spectroscopy, fluorescence spectroscopy, three-dimensional fluorescence spectroscopy, and molecular docking techniques. The fluorescence quenching of BSA by CPT drugs follows a static mechanism, with binding constants (Kb) ranging from 4.23 × 103 M− 1 (CPT) to 101.30 × 103 M− 1 (irinotecan), demonstrating significant drug binding selectivity. Thermodynamic analysis reveals distinct interaction mechanisms: topotecan binding is driven by hydrogen bonding (ΔH = − 10.96 kJ·mol− 1) and hydrophobic interactions (ΔS = 0.066 kJ·mol− 1·K− 1), while irinotecan exhibits stronger binding dominated by electrostatic forces (ΔH = − 86.77 kJ·mol− 1) with significant entropy loss (ΔS = − 0.161 kJ·mol− 1·K− 1). Molecular docking confirms preferential binding at Sudlow site I of BSA, with hydrophobic interactions and hydrogen bonding as the primary driving forces. These findings provide a comprehensive understanding of CPT-BSA interactions, offering valuable insights for the design of albumin-based drug delivery systems with optimized pharmacokinetic profiles.