Background/objectives: This study evaluated the clinical utility of a virtual reality (VR)-based kitchen error task for children (VKET-C) to assess functional cognition in children.
Methods: In total, 38 children aged 7-12 years were included, comprising 23 typically developing (TD) children and 15 children with developmental disabilities (DDs), including autism spectrum disorder, attention deficit hyperactivity disorder, and intellectual disability. While performing the VKET-C, performance errors were analyzed. The Stockings of Cambridge (SOC) and Spatial Working Memory (SWM) tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were used to assess cognitive function. The Brunner-Munzel test was performed to compare performance errors between the TD and DD groups, and correlations between performance errors and cognitive measures were analyzed.
Results: Omission and commission errors were significantly different between the groups ( < 0.001), with no significant difference in motor errors ( > 0.05). Omission errors were correlated with the initial thinking time mean (ITMN) in all items of the SOC task and the between errors (BE) of the SWM task. Commission errors were correlated with the ITMN in the difficult items of the SOC task and the BE of the SWM task. Additionally, motor errors were significantly correlated with problems solved in minimum moves (PSMM) and ITMN in the difficult items of the SOC task and BE in the SWM task.
Conclusions: The VKET-C shows promise as an effective tool for assessing executive function and working memory in children with DDs, offering an engaging and ecologically valid alternative to traditional methods.
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http://dx.doi.org/10.3390/children11111291 | DOI Listing |
Sci Rep
January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
View Article and Find Full Text PDFSci Rep
January 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.
View Article and Find Full Text PDFSci Rep
January 2025
Electronics and Communication Engineering Dept. Faculty of Engineering, Horus University, New Damietta, Egypt.
Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.
Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.
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