Detecting errors and adapting behavior accordingly constitutes an integral aspect of cognition. Previous studies have linked neural correlates of error processing (e.g., error-related negativity (ERN) and error-related positivity (Pe)) to task performance and broader behavioral constructs, but few studies examined how these associations manifest in adolescence. In this study, we examined neural error processing markers and their behavioral associations in an adolescent/emerging adult sample (N = 143, M = 18.0 years, range 11-25 years), employing a stop-signal task. Linear regressions were conducted using bootstrap resampling to explore associations between ERN/Pe peak amplitudes and latencies, stop accuracy, stop-signal reaction time (SSRT), and post-error slowing, as well as self-reported substance-related risks and problems and externalizing problems. After adjusting for age and sex, smaller frontocentral Pe amplitude and later Pe latency were associated with longer SSRT, and later Pe latency was associated with lower stop accuracy. This might indicate that the Pe, which is thought to reflect conscious error processing, reflects task performance on a response inhibition task better than the ERN, which reflects subconscious error processing. After correcting for multiple testing, there were no associations between ERN/Pe parameters and substance-related or externalizing problems, and no age interactions for these associations were detected.
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http://dx.doi.org/10.1016/j.dcn.2024.101500 | DOI Listing |
J Biomed Inform
December 2024
Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia; STACC, 51009 Tartu, Estonia.
Objective: This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes.
Methods: We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format.
Biodegradation
December 2024
Department of Civil engineering, Islamic Azad university, Mashhad Branch, Iran.
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).
View Article and Find Full Text PDFJ Athl Train
December 2024
Musculoskeletal Adaptations to Aging and eXercise (MAAX) Laboratory, Oklahoma State University, Stillwater, OK, USA.
A female NCAA Division I track athlete experienced non-localized shin pain midway through her first season, which was diagnosed as medial tibial stress syndrome. Treatments included strengthening and range of motion exercises, reduced training volume, and pain control modalities, but symptoms worsened. It was revealed she had been suffering from severe sleep deprivation (<3 hours/night) contributing to bilateral tibial and fibular stress reactions.
View Article and Find Full Text PDFSkelet Muscle
December 2024
Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada.
Background: INTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.
Methods: We detail an open-source fully-automated iterative threshold-seeking algorithm (ITSA) for segmenting IMF from T1-weighted MRI of the calf and thigh within three cohorts (CaMos Hamilton (N = 54), AMBERS (N = 280), OAI (N = 105)) selecting adults 45-85 years of age.
Sci Rep
December 2024
Department of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Ossification of the ligamentum flavum (OLF) is the main causative factor of spinal stenosis, but how to accurately and efficiently identify the ossification region is a clinical pain point and an urgent problem to be solved. Currently, we can only rely on the doctor's subjective experience for identification, with low efficiency and large error. In this study, a deep learning method is introduced for the first time into the diagnosis of ligamentum flavum ossificans, we proposed a lightweight, automatic and efficient method for identifying ossified regions, called CDUNeXt.
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