Global and Regional Burden of Ischemic Stroke Disease from 1990 to 2021: An Age-Period-Cohort Analysis.

Ischemic stroke is a significant global public health issue that impacts health burdens across various regions. This study analyzed data from the Global Burden of Disease Study 2021 to assess the incidence, mortality, and disability-adjusted life years (DALYs) associated with ischemic stroke worldwide and across different Socio-demographic Index (SDI) regions. Using joinpoint regression and age-period-cohort (APC) models, we examined trends in disease burden and made projections for 2022 to 2035.

TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting.

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time.

Comparative Genomics Reveals Genetic Diversity and Variation in Metabolic Traits in Strains.

is a significant and dominant bacterial species of sourdough microbiota from ecological and functional perspectives. Despite the remarkable prevalence of different strains of this species in sourdoughs worldwide, the drivers behind the genetic diversity of this species needed to be clarified. In this research, 14 strains were isolated from sourdough samples to evaluate the genetic diversity and variation in metabolic traits.

BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on employing DCE-MR images for breast cancer diagnosis due to the lack of publicly available DCE-MRI datasets. To address this issue, our work releases a new DCE-MRI dataset called BreastDM for breast tumor segmentation and classification.

Graph structure learning layer and its graph convolution clustering application.

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph.

Parallelly Adaptive Graph Convolutional Clustering Model.

Benefiting from exploiting the data topological structure, graph convolutional network (GCN) has made considerable improvements in processing clustering tasks. The performance of GCN significantly relies on the quality of the pretrained graph, while the graph structures are often corrupted by noise or outliers. To overcome this problem, we replace the pre-trained and fixed graph in GCN by the adaptive graph learned from the data.

Proteomic Analysis Explores Interactions between and during Sourdough Fermentation.

Sourdough is a fermentation culture which is formed following metabolic activities of a multiple bacterial and fungal species on raw dough. However, little is known about the mechanism of interaction among different species involved in fermentation. In this study, Sx3 and Sq7 were selected.

Application of logistic regression and convolutional neural network in prediction and diagnosis of high-risk populations of lung cancer.

Objectives: The early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment.

Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

Background: To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC.

Patients And Methods: This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software.

Quantitative evaluation of computed and voxelwise computed diffusion-weighted imaging in breast cancer.

Objectives: To assess the value of computed diffusion-weighted imaging (cDWI) and voxelwise computed diffusion-weighted imaging (vcDWI) in breast cancer.

Methods: This retrospective study involved 130 patients (age range, 25-70 years; mean age ± standard deviation, 48.6 ± 10.

Copper-Catalyzed Asymmetric Hydroboration of 1,1-Disubstituted Alkenes.

A mild and efficient approach for highly regio- and enantioselective copper-catalyzed hydroboration of 1,1-diaryl substituted alkenes with bis(pinacolato)diboron (BPin) was developed for the first time, providing facile access to a series of valuable β,β-diaryl substituted boronic esters with high enantiomeric purity. Moreover, this approach could also be suitable for hydroboration of α-alkyl styrenes for the synthesis of enantioenriched β,β-arylalkyl substituted boronic esters. Gram-scale reaction, stereospecific derivatizations, and the application of important antimuscarinic drug (R)-tolterodine for concise enantioselective synthesis further highlighted the attractiveness of this new approach.