YOLO-OD: Obstacle Detection for Visually Impaired Navigation Assistance.

Visually impaired individuals frequently encounter difficulties in detecting and avoiding obstacles in the wild. To address this issue, we propose an obstacle detection method for visual navigation assistance, named YOLO-OD. To improve the ability to detect and differentiate between different sized obstacles in outdoor environments, we introduce the Feature Weighting Block (FWB), which improves feature importance discrimination.

A multi-modal and multi-stage region of interest-based fusion network convolutional neural network model to differentiate progressive mild cognitive impairment from stable mild cognitive impairment.

Background: Accurately differentiating stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) is clinically relevant, and identification of pMCI is crucial for timely treatment before it evolves into Alzheimer's disease (AD).

Objective: To construct a convolutional neural network (CNN) model to differentiate pMCI from sMCI integrating features from structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) images.

Methods: We proposed a multi-modal and multi-stage region of interest (ROI)-based fusion network (m2ROI-FN) CNN model to differentiate pMCI from sMCI, adopting a multi-stage fusion strategy to integrate deep semantic features and multiple morphological metrics derived from ROIs of sMRI and PET images.

A longitudinal multimodal MRI study of the visual network in postoperative delirium.

Although structural and functional damage to the brain is considered to be an important neurobiological mechanism of postoperative delirium (POD), alterations in the visual cortical network related to this vulnerability have not yet been determined. In this study, we investigated the impact of alterations in the visual network (VN), as measured by structural and functional magnetic resonance imaging (MRI), on the development of POD. Thirty-six adult patients with frontal glioma who underwent elective craniotomy were recruited.

Metacognition and Mathematical Modeling Skills: The Mediating Roles of Computational Thinking in High School Students.

This study was to investigate the relationship between metacognition and the mathematical modeling skills of high school students, as well as the mediating role of computational thinking. A cluster sampling method was adopted to investigate 661 high school students, using the metacognition scale, computational thinking scale, and mathematical modeling skill test questions. The results showed that metacognitive knowledge and metacognitive monitoring had a direct and positive correlation with high school students' mathematical modeling skills.

Distance-specific functional connectivity strength alterations in human immunodeficiency virus asymptomatic neurocognitive impairment patients: a cross-sectional study.

Background: Asymptomatic neurocognitive impairment (ANI) is the mildest form of human immunodeficiency virus (HIV)-associated neurocognitive disorders (HANDs), and functional connectivity strength (FCS) alternations have been observed in the ANI stage. However, it is not clear whether the FCS alterations are influenced by the anatomical distance. This study sought to investigate distance-specific FCS changes in HIV ANI patients.

A radiomics-based brain network in T1 images: construction, attributes, and applications.

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest.

One model, two brains: Automatic fetal brain extraction from MR images of twins.

Fetal brain extraction from magnetic resonance (MR) images is of great importance for both clinical applications and neuroscience studies. However, it is a challenging task, especially when dealing with twins, which are commonly existing in pregnancy. Currently, there is no brain extraction method dedicated to twins, raising significant demand to develop an effective twin fetal brain extraction method.

Multi-Scale FC-Based Multi-Order GCN: A Novel Model for Predicting Individual Behavior From fMRI.

Predicting individual behavior from brain imaging data using machine learning is a rapidly growing field in neuroscience. Functional connectivity (FC), which captures interactions between different brain regions, contains valuable information about the organization of the brain and is considered a crucial feature for modeling human behavior. Graph convolutional networks (GCN) have proven to be a powerful tool for extracting graph structure features and have shown promising results in various FC-based classification tasks, such as disease classification and prognosis prediction.

Image Recovery Matters: A Recovery-Extraction Framework for Robust Fetal Brain Extraction From MR Images.

The extraction of the fetal brain from magnetic resonance (MR) images is a challenging task. In particular, fetal MR images suffer from different kinds of artifacts introduced during the image acquisition. Among those artifacts, intensity inhomogeneity is a common one affecting brain extraction.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics.

Introduction: Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods: In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing.

Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis: new insights into neurocognitive impairments.

Background: Changes in white matter (WM) underlie the neurocognitive damages induced by a human immunodeficiency virus (HIV) infection. This study aimed to examine using a bundle-associated fixel-based analysis (FBA) pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV patients.

Methods: This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls (HCs) at the Beijing Youan Hospital between January 1, 2016 and December 30, 2016.

Structure bionic topology design method based on biological unit cell.

The mechanical structure topology design based on substructure always adopts the traditional substructure design method, which often comes from the experience and is limited by the inherent or stereotyped design thinking. A substructure design method based on biological unit cell (UC) is proposed, which draws inspiration from the biological efficient load-bearing topology structure. Especially, the thought of the formalized problem-solving of extension matter-element is introduced.

Phloretin decreases microglia-mediated synaptic engulfment to prevent chronic mild stress-induced depression-like behaviors in the mPFC.

Stress is an important risk factor to induce psychiatric disorders such as depression. Phloretin (PHL), a natural dihydrochalcone compound, has been shown to exhibit anti-inflammatory and anti-oxidative effects. However, the impact of PHL on the depression and the underlying mechanism remain unclear.

Radiation hematologic toxicity prediction for locally advanced rectal cancer using dosimetric and radiomics features.

Background: Hematologic toxicity (HT) is a common adverse tissue reaction during radiotherapy for rectal cancer patients, which may lead to various negative effects such as reduced therapeutic effect, prolonged treatment period and increased treatment cost. Therefore, predicting the occurrence of HT before radiotherapy is necessary but still challenging.

Purpose: This study proposes a hybrid machine learning model to predict the symptomatic radiation HT in rectal cancer patients using the combined demographic, clinical, dosimetric, and Radiomics features, and ascertains the most effective regions of interest (ROI) in CT images and predictive feature sets.

Influence of mixed ventilation on particulate-gas diffusion and distribution of diesel engine exhaust in fully mechanized excavation face.

Tail gas emitted by underground trackless rubber wheel cars poses a serious threat to the health and safety of underground workers. To effectively reduce the tail gas concentration of a comprehensive excavation face, this study adopted a numerical simulation method to investigate the influence of air suction volume Q and distance L between trackless rubber wheel cars and headfaces on the diffusion law of diesel particulate matter, CO, and NO under long suction and short pressure ventilation. The results showed that under the condition of L = 20 m, the trackless rubber wheel car is closer to the suction air duct.

Long-term ANT-DBS effects in pilocarpine-induced epileptic rats: A combined 9.4T MRI and histological study.

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) appears to be effective against seizures in animals and humans however, its therapeutic mechanisms remain elusive. This study aimed to combine 9.4T multimodal magnetic resonance imaging (MRI) with histology to investigate the longitudinal effects of long-term ANT-DBS in pilocarpine-induced epileptic rats.

Interactivity in learning instructional videos: Sending danmaku improved parasocial interaction but reduced learning performance.

Introduction: The instructional video is considered to be one of the most distinct and effective virtual learning tools. However, one of its biggest drawbacks is the lack of social interaction that occurs. This study tested the impact of participants sending zero danmaku (sending messages on the screen), three danmaku sending, and unlimited danmaku as an instructional video plays on learning performance.

The impact of perceived teacher support on Chinese junior high school students' academic self-efficacy: The mediating roles of achievement goals and academic emotions.

Introduction: Teacher support is an important external factor that influences students academic self-efficacy, however, the mechanisms of the two factors are not yet fully explored. The purpose of this study was to investigate whether achievement goals and academic emotions could play a chain mediating role between perceived teacher support and academic self-efficacy.

Methods: The study sample was made up of 1,074 Chinese junior high school students, and three structural equation models were constructed using data collected from on questionnaires.

Fluoxetine shows neuroprotective effects against LPS-induced neuroinflammation via the Notch signaling pathway.

Aim: To determine the neuroprotective effects of fluoxetine on depression-like and motor behaviors in rats treated with lipopolysaccharide (LPS) and the mechanisms involved.

Methods: A rat model of depression in Parkinson's disease (dPD) was established by administering LPS (0.5 mg/kg, i.

LPS activates neuroinflammatory pathways to induce depression in Parkinson's disease-like condition.

This study aimed to observe the effects of lipopolysaccharide (LPS) intraperitoneal (i.p.) injection on rats and investigate how neuroinflammation contributes to the pathogenesis of depression in Parkinson's disease (dPD).

A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment.

Background: Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC).

Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard-Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively.