Author(s): Khaked AA; Oishi N; Roggen D; Lago P;

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated ...

Article GUID: 39860799

Author(s): Guo J; Fan W; Amayri M; Bouguila N;

This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering methods use the Gaussian mixture model (GMM) as a prior on the latent space. We employ a more flexible asy ...

Article GUID: 39662201

Author(s): McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovic ...

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in indi ...

Article GUID: 39590726

Author(s): Abicumaran Uthamacumaran

Pediatric glioblastoma is a complex dynamical disease that is difficult to treat due to its multiple adaptive behaviors driven largely by phenotypic plasticity. Integrated data science and network theory pipelines offer novel approaches to studying glioblastoma cell fate dynamics, particularly phenotypic transitions over time. Here we used various single- ...

Article GUID: 39420135

Author(s): D' Arcey J; Torous J; Asuncion TR; Tackaberry-Giddens L; Zahid A; Ishak M; Foussias G; Kidd S;

Background: Digital mental health is a rapidly growing field with an increasing evidence base due to its potential scalability and impacts on access to mental health care. Further, within underfunded service systems, leveraging personal technologies to deliver or support specialized service deliv ...

Article GUID: 39348196

Author(s): Mosley JD; Dunn WB; Kuligowski J; Lewis MR; Monge ME; Ulmer Holland C; Vuckovic D; Zanetti KA; Schock TB;

Introduction: During the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics. Objectives: The Best Practices Working Group disseminated recent findings from community forums and ...

Article GUID: 38980450

Author(s): Qi Y; Vianna P; Cadrin-Chênevert A; Blanchet K; Montagnon E; Belilovsky E; Wolf G; Mullie LA; Cloutier G; Chassé M; Tang A;

We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset ...

Article GUID: 38858500

Author(s): Amine El Mahdi Safhi

This manuscript delineates the assembly and structure of an extensive dataset encompassing more than 2500 self-consolidating concrete (SCC) mixtures, meticulously compiled from 176 scholarly sources. The dataset has been subjected to a thorough curation process to eliminate feature redundancy, rectify transcriptional inaccuracies, and excise duplicative e ...

Article GUID: 38533116

Author(s): Islam M; Zunair H; Mohammed N;

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relie ...

Article GUID: 38492455

Author(s): Vaz S; Mendes M; Khattar G; Macedo M; Ronquillo C; Zarzo-Arias A; Hortal J; Silveira L;

Background: We compiled a database of firefly species records from the Atlantic Forest hotspot in Brazil and made it available at GBIF. Data were gathered from literature and from several key entomological collections, including: Coleção entomológica Prof. José Alfredo Pinheiro Dutra (DZRJ/UFRJ) and Coleção do Laboratório de Ecologia de Insetos from Unive ...

Article GUID: 38327309

Author(s): Valevicius D; Beck N; Kasper L; Boroday S; Bayer J; Rioux P; Caron B; Adalat R; Evans AC; Khalili-Mahani N;

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scal ...

Article GUID: 37841811