Procedural learning is a fundamental cognitive function that facilitates efficient processing of and automatic responses to complex environmental stimuli. Here, we examined training-dependent and off-line changes of two sub-processes of procedural learning: namely, sequence learning and statistical learning. Whereas sequence learning requires the acquisition of order-based relationships between the elements of a sequence, statistical learning is based on the acquisition of probabilistic associations between elements. Seventy-eight healthy young adults (58 females and 20 males) completed the modified version of the Alternating Serial Reaction Time task that was designed to measure Sequence and Statistical Learning simultaneously. After training, participants were randomly assigned to one of three conditions: active wakefulness, quiet rest, or daytime sleep. We examined off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased during training, while Statistical Learning plateaued relatively rapidly. After the off-line period, both the acquired sequence and statistical knowledge was preserved, irrespective of the vigilance state (awake, quiet rest or sleep). Sequence Learning further improved during extended practice, while Statistical Learning did not. Moreover, within the sleep group, cortical oscillations and sleep spindle parameters showed differential associations with Sequence and Statistical Learning. Our findings can contribute to a deeper understanding of the dynamic changes of multiple parallel learning and consolidation processes that occur during procedural memory formation.
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http://dx.doi.org/10.3389/fpsyg.2018.02708 | DOI Listing |
BMC Public Health
January 2025
Department of Women's and Children's Health, Karolinska Institutet, Tomtebodavägen 18A, Stockholm, Solna, 171 77, Sweden.
Background: Globally, the quality of maternal and newborn care remains inadequate, as seen through indicators like perineal injuries and low Apgar scores. While midwifery practices have the potential to improve care quality and health outcomes, there is a lack of evidence on how midwife-led initiatives, particularly those aimed at improving the use of dynamic birth positions, intrapartum support, and perineal protection, affect these outcomes.
Objective: To explore how the use of dynamic birth positions, intrapartum support, and perineal protection impact the incidence of perineal injuries and the 5-min Apgar score within the context of a midwife-led quality improvement intervention.
BMC Public Health
January 2025
Al-Barkaat Institute of Management Studies, Aligarh 202122, Dr. A. P. J. Abdul Kalam Technical University, Lucknow 226010, India.
Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan.
View Article and Find Full Text PDFBackground: Population aging and the increase in memory-related diseases have motivated the search for accessible cognitive screening instruments. To develop a digital memory and learning test (DMLT) based on Rey's Auditory Verbal Learning Test (RAVLT) principles to assess cognition in the elderly and identify early cognitive decline.
Methods: The research was divided into two phases: developing the digital test and the experimental phase of comparison with a reference test.
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Ophthalmology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach.
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