Evaluation of smartphone-based cough data in amyotrophic lateral sclerosis as a potential predictor of functional disability.

Objectives: Cough dysfunction is a feature of patients with amyotrophic lateral sclerosis (ALS). The cough sounds carry information about the respiratory system and bulbar involvement. Our goal was to explore the association between cough sound characteristics and the respiratory and bulbar functions in ALS.

Voice Assessment in Patients with Amyotrophic Lateral Sclerosis: An Exploratory Study on Associations with Bulbar and Respiratory Function.

Background: Speech production is a possible way to monitor bulbar and respiratory functions in patients with amyotrophic lateral sclerosis (ALS). Moreover, the emergence of smartphone-based data collection offers a promising approach to reduce frequent hospital visits and enhance patient outcomes. Here, we studied the relationship between bulbar and respiratory functions with voice characteristics of ALS patients, alongside a speech therapist's evaluation, at the convenience of using a simple smartphone.

Digested galactoglucomannan mitigates oxidative stress in human cells, restores gut bacterial diversity, and provides chemopreventive protection against colon cancer in rats.

Galactoglucomannan (GGM) is the predominant hemicellulose in coniferous trees, such as Norway spruce, and has been used as a multipurpose emulsifier in the food industry. In vitro digestion with a cellular antioxidant activity assay was performed to determine the bioaccessibility and antioxidant activity of phenolic compounds, and the behaviour of GGM on in vivo experimental assay against induced colon cancer. The results showed that digestion decreased the bioaccessibility and antioxidant capacity of phenolic compounds.

Leverage demand-side policies for energy security.

Conventional supply-side approaches overlook potential benefits.

Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System.

The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and export of coffee in Brazil and the technological application of the Remotely Piloted Aircraft System (RPAS) this study aimed to compare and select models based on different data classification techniques by different classification algorithms for the prediction of different coffee cultivars (Coffea arabica L.) recently planted.

Exploring Regularization Methods for Domain Generalization in Accelerometer-Based Human Activity Recognition.

The study of Domain Generalization (DG) has gained considerable momentum in the Machine Learning (ML) field. Human Activity Recognition (HAR) inherently encompasses diverse domains (e.g.

Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition.

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance.

Promoting evidence-based practice: training health professionals for the evidence synthesis.

Objective: To report the experience of the Portugal Centre For Evidence Based Practice (PCEBP): a JBI Centre of Excellence in the training of health professionals, researchers, and professors in the Comprehensive Systematic Review Training Program, a course on Evidence Synthesis, specifically on Systematic Literature Reviews.

Method: This article aims to report the experience of the Portugal Centre For Evidence Based Practice: a JBI Centre of Excellence in the implementation of the Comprehensive Systematic Review Training Program that trains health professionals, researchers, and teachers to develop Systematic Reviews, according to the JBI approach.

Results: By the end of 2020, 11 editions of the course had been developed with 136 participants from different educational and health institutions, from different countries.

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification.

The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN).

Numerical study of the influence of electrode arrangements in electrokinetic remediation technique.

The effectiveness of electrokinetic remediation for soils depends on several factors such as the arrangement and shape of electrodes. This paper presents a numerical study on external electrostatic field generated by seven different electrode configurations in any unbounded two-dimensional domain. The boundary condition at infinity for the voltage is approximated by the iterative algorithm that expands the domain till the limit of the specified tolerance (threshold value).

Composites based on PET and red mud residues as catalyst for organic removal from water.

In this study, we obtained a composite based on carbon/iron oxide from red mud and PET (poly(ethylene terephthalate)) wastes by mechanical mixture (10, 15 and 20wt.% of PET powder/red mud) followed by a controlled thermal treatment at 400°C under air. XRD analyses revealed that the α-Fe2O3 is the main phase formed from red mud.