The Boston Naming Test (BNT) is the most widely used naming test worldwide in research and clinical settings. This study aimed to develop a method for adapting the BNT to suit different linguistic and cultural characteristics using the example of Maltese in a bilingual context. In addition, it investigated the effects in Malta of age and level of education on naming performance. The words of the BNT were first translated into Maltese. The test was then piloted to establish target and alternative responses. Naming performance data were later collected from individuals of different ages and levels of education. Only 38 BNT items had at least 70% name agreement. Main effects of age and education were found. A Maltese adaptation was proposed using 38 items and lenient scoring. Similar procedures may be used in other bilingual populations. The study suggests that normative data should be stratified according to age and education.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/02699206.2016.1181106 | DOI Listing |
Data Brief
February 2025
Tashkent institute of textile and light industry, 5, Shoxdjaxon str., Tashkent city 100100, Uzbekistan.
In this study, the authors presented a dataset for named entity recognition in the Uzbek language. The dataset consists of 2000 sentences and 25,865 words, and the sources were legal documents and hand-crafted sentences annotated using the BIOES scheme. The study is complemented by the fact that the authors demonstrated the applications of the created dataset by training a language model using the CNN + LSTM architecture, which achieves high accuracy in NER tasks, with an F1 score of 90.
View Article and Find Full Text PDFBiol Methods Protoc
January 2025
Department of Physics, George Washington University, Washington, DC 20052, United States.
A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference.
View Article and Find Full Text PDFCurr Dev Nutr
January 2025
United States Army Research Institute of Environmental Medicine (USARIEM), Military Performance Division, Natick, MA, United States.
Background: Dietary intake is a modifiable factor linked to short-term and long-term health. The Healthy Eating Index (HEI) is an objective measure to assess diet quality and population-level comparisons, like military to civilian.
Objectives: This study aimed to characterize diet quality of early-career and mid-career female soldiers compared with that of age-matches and sex-matched civilians and to link indicators of cardiometabolic disease risk to dietary outcomes and health status.
Anticancer Agents Med Chem
January 2025
Department of Immunology, School of Basic Medicine, Beihua University. No. 3999, East Binjiang Road, Jilin, China.
Background: Programmed cell death-ligand 1 (PD-L1) is overexpressed in tumor cells, which promotes tumor cell survival and cell proliferation and causes tumor cells to escape T-cell killing. Schisanhenol, a biphenyl cyclooctene lignin-like compound, was extracted and isolated from the plant named Schisandra rubriflora (Franch.).
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China (X.W., C.C., W.C., Y.G., X.L., X.J.); Department of Pathology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou 325000, China (X.W., J.W., C.C., W.C., Y.G., K.G., Y.C., Y.S., J.C., X.L., X.J.). Electronic address:
Rationale And Objectives: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop and validate a model that integrates deep learning and sub-regional radiomics from MRI imaging to predict pathological complete response (pCR) in patients with LARC.
Materials And Methods: We retrospectively enrolled 768 eligible participants from three independent hospitals who had received neoadjuvant chemoradiotherapy followed by radical surgery.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!