A novel prognostic model based on telomere-related lncRNAs in gastric cancer.

Background: Telomeres are specialized structures at the ends of chromosomes that are important for their protection. Over time, long non-coding RNAs (lncRNAs) have gradually come into the spotlight as essential biomarkers of proliferation, migration, and invasion of human malignant tumors. Nevertheless, the impact of telomere-related lncRNAs (TRLs) in gastric cancer is currently unknown.

MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.

Background: Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images.

Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.

Both the primate visual system and artificial deep neural network (DNN) models show an extraordinary ability to simultaneously classify facial expression and identity. However, the neural computations underlying the two systems are unclear. Here, we developed a multi-task DNN model that optimally classified both monkey facial expressions and identities.

The default mode network is affected in the early stage of simian immunodeficiency virus infection: a longitudinal study.

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode network. Most recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral therapy. Specifically, when brain imaging data at only one time point are analyzed, determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency syndrome.

Dissociation and hierarchy of human visual pathways for simultaneously coding facial identity and expression.

Humans have an extraordinary ability to recognize facial expression and identity from a single face simultaneously and effortlessly, however, the underlying neural computation is not well understood. Here, we optimized a multi-task deep neural network to classify facial expression and identity simultaneously. Under various optimization training strategies, the best-performing model consistently showed 'share-separate' organization.

Sex differences in resting state network (RSN) functional connections with mild cognitive impairment (MCI) progression.

Objective: Sex plays an important role in many diseases. The purpose of current study is to explore whether there are different lesion patterns in the RSN functional connections between males and females with MCI progression, and identify the differences in brain network changes due to sex.

Methods: Resting state fMRI data included 37 normal controls (NC), 39 early MCI (EMCI) patients and 37 late MCI (LMCI) patients were collected, and network model based on graph theory was performed to compare the differences of brain network at different stages caused by sex from three aspects: functional connectivity between ROIs, intra-functional connectivity within RSN and inter-functional connectivity between RSN and white matter (WM).

Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

The main symptom of patients with Alzheimer's disease is cognitive dysfunction. Alzheimer's disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure.

Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.

Introduction: Alzheimer's disease (AD) is a chronic neurodegenerative disease that generally starts slowly and leads to deterioration over time. Finding biomarkers more effective to predict AD transition is important for clinical medicine. And current research indicated that the lesion regions occur in both gray matter (GM) and white matter (WM).