Radiomics for predicting grades, isocitrate dehydrogenase mutation, and oxygen 6-methylguanine-DNA methyltransferase promoter methylation of adult diffuse gliomas: combination of structural MRI, apparent diffusion coefficient, and susceptibility-weighted imaging.

Background: There has been no research investigating susceptibility-weighted imaging (SWI) radiomics features in evaluating molecular makers in gliomas. The aim of this study was to assess the predictive value of radiomics features extracted from structural magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC), and SWI in determining World Health Organization (WHO) Grade, isocitrate dehydrogenase () mutation, and oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with diffuse gliomas.

Methods: Retrospective MRI data of 539 patients from University of California San Francisco and Nanjing Drum Tower Hospital between January 2010 and December 2022 were analyzed in this study.

A simple projection method to correlate the principal mechanical direction with the principal microstructural direction of human osteoporotic femoral heads.

The mechanics of the trabecular bone is related to its structure; this work aimed to propose a simple projection method to clarify the correlation between the principal mechanical direction (PMD) and the principal microstructural direction (PMSD) of trabecular bones from osteoporotic femoral heads. A total of 529 trabecular cubes were cropped from five osteoporotic femoral heads. The micro computed tomography (μCT) sequential images of each cube were first projected onto the three Cartesian coordinate planes to have three overlapped images, and the trabecular orientation distribution in the three images was analyzed.

Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks.

Objective: To propose a transfer learning based method of tumor segmentation in intraoperative fluorescence images, which will assist surgeons to efficiently and accurately identify the boundary of tumors of interest.

Methods: We employed transfer learning and deep convolutional neural networks (DCNNs) for tumor segmentation. Specifically, we first pre-trained four networks on the ImageNet dataset to extract low-level features.

Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks.

Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people's daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results.

On the dynamical model for COVID-19 with vaccination and time-delay effects: A model analysis supported by Yangzhou epidemic in 2021.

The 2019 novel coronavirus (COVID-19) emerged at the end of 2019 has a great influence on the health and lives of people all over the world. The spread principle is still unclear. This paper considers a novel evolution model of COVID-19 in terms of an integral-differential equation, involving vaccination effect and the incubation of COVID-19.

3D brain tumor segmentation using a two-stage optimal mass transport algorithm.

Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object (i.e., the brain) without causing significant distortion, is used to preprocess brain tumor datasets for the first time in this paper.