ADHD/ADD

Constructing a Personalized Cross-Day EEG-Based Emotion-Classification Model Using Transfer Learning.

State-of-the-art electroencephalogram (EEG)-based emotion-classification works indicate that a personalized model may not be well exploited until sufficient labeled data are available, given a substantial EEG non-stationarity over days. However, it is impractical to impose a labor-intensive, time-consuming multiple-day data collection. This study proposes a robust principal component analysis (RPC...

A Laboratory-Built Fully Automated Ultrasonication Robot for Filamentous Fungi Homogenization.

This article presents the design and development of a new hands-free ultrasonication robot for filamentous fungi homogenization. The platform was constructed with a modified inexpensive 3D printer, equipped with an upward-facing camera, a custom-designed wash station, and an add-on sonicator. While machine vision accomplished sample well screening based on image subtraction and color thresholding,...

Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback.

BACKGROUND: Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such rob...

Using Recurrent Neural Networks to Compare Movement Patterns in ADHD and Normally Developing Children Based on Acceleration Signals from the Wrist and Ankle.

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of child...

Efficacy of Cardiac Rehabilitation with Assistance from Hybrid Assistive Limb in Patients with Chronic Heart Failure: Protocol for a Randomized Controlled Study.

The hybrid assistive limb (HAL) provides motion assistance based on bioelectrical signals detected on the skin surface when muscle forces are generate...

Deep Learning-Assisted Three-Dimensional Fluorescence Difference Spectroscopy for Identification and Semiquantification of Illicit Drugs in Biofluids.

The fast identification and quantification of illicit drugs in biofluids are of great significance in clinical detection. However, existing drug detec...

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logi...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). ...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

A semi-blind online dictionary learning approach for fMRI data.

BACKGROUND: Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in rece...

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the...

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation.

Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents under...

EEG characteristics of children with attention-deficit/hyperactivity disorder.

The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to c...

ChemSuite: A package for chemoinformatics calculations and machine learning.

Prediction of biological and toxicological properties of small molecules using in silico approaches has become a wide practice in pharmaceutical resea...

Attention Inspired Network: Steep learning curve in an invariant pattern recognition model.

Hubel and Wiesel's study about low areas of the visual cortex (VC) inspired deep models for invariant pattern recognition. In such models, simple and ...

Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.

Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing ...

DCDE: An Efficient Deep Convolutional Divergence Encoding Method for Human Promoter Recognition.

Efficient human promoter feature extraction is still a major challenge in genome analysis as it can better understand human gene regulation and will b...

Deep Learning Based Attenuation Correction of PET/MRI in Pediatric Brain Tumor Patients: Evaluation in a Clinical Setting.

Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes. O-(2...

Learning a discriminant graph-based embedding with feature selection for image categorization.

Graph-based embedding methods are very useful for reducing the dimension of high-dimensional data and for extracting their relevant features. In this ...

Accuracy of B-natriuretic peptide for the diagnosis of decompensated heart failure in muscular dystrophies patients with chronic respiratory failure.

Heart failure and restrictive respiratory insufficiency are complications in muscular dystrophies. We aimed to assess the accuracy of the B-natriureti...