Explainable AI: A review of applications to neuroimaging data.

Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret.

The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.

In December 2019, China reported a new virus identified as SARS-CoV-2, causing COVID-19, which soon spread to other countries and led to a global pandemic. Although many countries imposed strict actions to control the spread of the virus, the COVID-19 pandemic resulted in unprecedented economic and social consequences in 2020 and early 2021. To understand the dynamics of the spread of the virus, we evaluated its chaotic behavior in Japan.

Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.

Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or neural networks models designed to leverage the properties of graph representations, have recently shown promise in task fMRI decoding studies.

The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach.

Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan.

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.

Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals.

Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks.

Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of active virus at the time of vaccination. This research developed sequence-learning models to predict the behavior of the COVID-19 pandemic across the US, based on previously reported information.

A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter in the United States: A Natural Language Processing Approach.

The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered.

Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks.

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic.

Competitive fitness analysis using Convolutional Neural Network.

We developed a procedure for estimating competitive fitness by using as a model organism and a Convolutional Neural Network (CNN) as a tool. Competitive fitness is usually the most informative fitness measure, and competitive fitness assays often rely on green fluorescent protein (GFP) marker strains. CNNs are a class of deep learning neural networks, which are well suited for image analysis and object classification.

Convolutional Neural Networks-Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps.

Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections. Currently applied methods for NETs release quantification are often limited by the use of unspecific dyes and technical difficulties. Therefore, we aimed to develop a fully automatic image processing method for the detection and quantification of NETs based on live imaging with the use of DNA-staining dyes.

Comparison of muscle activity during hand rim and lever wheelchair propulsion over flat terrain.

Purpose: The aim of this study was to compare the activity of upper limb muscles during hand rim wheelchair propulsion and lever wheelchair propulsion at two different velocity levels.

Methods: Twenty male volunteers with physical impairments participated in this study. Their task was to push a lever wheelchair and a hand rim wheelchair on a mechanical wheelchair treadmill for 4 minutes at a speed of 3.

Physiological parameters depending on two different types of manual wheelchair propulsion.

Objective: The purpose of this study was to compare aerobic parameters in the multistage field test (MFT) in hand rim wheelchair propulsion and lever wheelchair propulsion.

Methods: Twenty-one men performed MFT using two different types of propulsion, i.e.

How does lever length and the position of its axis of rotation influence human performance during lever wheelchair propulsion?

The purpose of this study was to investigate empirically how lever length and its axis of rotation position influences human performance during lever wheelchair propulsion. In order to fulfill this goal, a dedicated test stand allowing easy implementation of various lever positions was created. In the experiment, 10 young, healthy, male subjects performed 8 tests consisting of propulsion work with levers of different lengths and lever axis of rotation positions.