The primary aim of the current study was to identify the predictive relations of both vocabulary and mathematical language to executive functioning (EF) development using a sample of 558 preschool children (M = 57.75 months, SD = 3.71). Monthly family income ranged from $0 to $5539 (M = $1508.18, SD = $892.92). Among the sample, 44% of the children were African American, 32% were Caucasian, 12% were Hispanic, 11% were multiracial, and 1% were Asian. Although the primary study goal was to examine the extent to which language predicted EF development, a secondary aim was to explore whether EF also predicted vocabulary and mathematical language development. Regression analyses accounting for classroom-level variance and key covariates revealed that vocabulary was a significant predictor of EF at the end of preschool after accounting for fall EF. When mathematical language was added into the models, it was a significant predictor of EF, but vocabulary was no longer significant. Furthermore, EF predicted vocabulary and mathematical language. These findings suggest that young children's mathematical language skills are related to the acquisition of higher levels of EF during the preschool year and that there may be bidirectional associations between EF and mathematical language in preschool. Implications for future research are discussed.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jecp.2018.12.005 | DOI Listing |
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Associations between variants in the FTO locus and plasma concentrations of appetite related hormones are inconsistent, and might not work in a dose dependent fashion in people with obesity. Moreover, it is relevant to report meal related plasma concentrations of these hormones in persons with obesity given the growing interest in their pharmacological potential in obesity therapy. We find it clinically relevant to examine associations between the SNP rs9939609 genotypes and homeostatic appetite regulation in individuals with BMI ≥35 kg/m2.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Adv Skin Wound Care
January 2025
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!