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Stimulus Predictability and Liking Enhance Auditory-Motor Encoding and Memory for Melodies

Author(s): Albury AW; Bianco R; Johnson AP; Penhune VB;

The ability to predict or anticipate musical events contributes to music-related pleasure and memory; however, their contributions to learning to play a melody have been less well-explored. In this study, we investigated how musical predictability and pleasure influenced how nonmusicians learned to play short melodies, as well as post-learning recall. P ...

Article GUID: 42229373


Computational optical streak microscopy of megahertz acoustic microbubble dynamics

Author(s): Marquez M; Lai Y; Liu M; Memari E; Helfield B; Liang J;

Real-time dynamic imaging of microbubbles is crucial for understanding their microscale biophysical interactions and advancing ultrasound therapy. Despite progress in time-resolved optical imaging, existing techniques still face trade-offs between acquisition speed, spatial resolution, affordability, and system complexity. Here, we introduce compressed ...

Article GUID: 42078858


Emotional competence and help-seeking intentions as predictors of educational success in vocational training students

Author(s): Gilbert W; Stack DM; Barker ET; Dubeau A; Serbin LA; Véronneau MH;

Given the high prevalence of psychological distress among vocational training (VT) students, this study aimed to assess the role of interpersonal emotional competence as a resilience factor promoting the educational success of this population. We postulated that emotional competence would promote educational success, both directly and indirectly by fost ...

Article GUID: 42005820


Deep learning-based femoral reconstruction from intraoperative point clouds for enhanced knee arthroplasty registration

Author(s): Kafian Safari M; Mirzae M; Gheflati B; Sharifzade M; Zuhars J; Rottoo S; Rivaz H;

Purpose: Computer- and robotic-assisted technologies improve total knee arthroplasty (TKA) through intraoperative bone registration. However, limited bone exposure restricts point collection to the distal femur, omitting key geometric features and reducing registration accuracy and the surgical outcomes. Methods: We introduce a deep learning-based meth ...

Article GUID: 41984365


Sagittal abdominal diameter and abdominal aortic calcification are associated with incident major adverse cardiovascular events: The Manitoba Bone Density Registry

Author(s): Abraha HN; Gebre AK; Sim M; Smith C; Gilani SZ; Ilyas Z; Zarzour F; Schousboe JT; Lix LM; Binkley N; Reid S; Monchka BA; Kimelman D; Lewis JR; Leslie WD;

Background: Sagittal abdominal diameter (SAD), a measure of visceral adiposity, has been linked to major adverse cardiovascular events (MACE). However, the relationship between SAD and abdominal aortic calcification (AAC), a marker of subclinical vascular disease, and whether they independently ...

Article GUID: 41903786


Efficient self-supervised Barlow Twins from limited tissue slide cohorts for colonic pathology diagnostics

Author(s): Notton C; Sharma V; Quoc-Huy Trinh V; Chen L; Xu M; Varma S; Hosseini MS;

Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyone over 50 years of age in Canada is eligible for CRC screening. About 20% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up ...

Article GUID: 41793844


Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search

Author(s): Alicia Estefania Antonio Figueroa

This work implements deep learning models to capture non-linear and complex data behavior in aluminum price data. Deep learning models include the long short-term memory (LSTM) and deep feedforward neural networks (FFNN). The support vector regression (SVR) is employed as a base model for comparison. Each predictive model is tuned by using two different ...

Article GUID: 41751647


Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods

Author(s): Ekwe M; Fernando H; James G; Adeluyi O; Verrelst J; Kross A;

Leaf area index (LAI) is a key indicator of crop growth and development and is widely used in both agricultural research and precision farming applications. PlanetScope imagery is generally used for monitoring crop growth due to its high revisit frequency, broad spatial coverage, and cost-effective access to consistent high-resolution multispectral data ...

Article GUID: 41682534


Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity

Author(s): Deng S; Harlaar N; Zhang J; Dekker SO; Kudryashova NN; Zhou H; Bart CI; Jin T; Derevyanko G; van Driel W; Panfilov AV; Poelma RH; de Vries AAF; Zhang G; De Coster T; Pijnappels DA;

Control theory underpins the stabilization of dynamic systems, including cardiac tissue, where disruptions in electrical conduction cause arrhythmias. Current treatments either act rapidly but without precision or deliver targeted interventions that cannot adapt in real time. We present an inte ...

Article GUID: 41684280


ADPv2: A hierarchical histological tissue type-annotated dataset for potential biomarker discovery of colorectal disease

Author(s): Yang Z; Li K; Ramandi SG; Brassard P; Khellaf A; Trinh VQ; Zhang J; Chen L; Rowsell C; Varma S; Plataniotis K; Hosseini MS;

Computational pathology (CPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce ...

Article GUID: 41658283


Towards smart PFAS management: Integrating artificial intelligence in water and wastewater systems

Author(s): Yaghoobian S; An J; Jeong DW; Hwang JH;

Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into Per- and polyfluoroalkyl substances (PFAS) research; however, the field remains fragmented with substantial variation in modeling objectives. This review provides one of the most comprehensive and detailed syntheses to date of AI/ML methods across the PFAS contaminat ...

Article GUID: 41483514


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