We examined the learning process with 3 sets of stimuli that have identical symbolic structure but differ in appearance (meaningless letter strings, arrangements of geometric shapes, and sequences of cities). One hypothesis is that the learning process aims to encode symbolic regularity in the same way, largely regardless of appearance. Another is that different types of stimuli bias the learning process to operate in different ways. Using the experimental paradigm of artificial grammar learning, we provided a preliminary test of these hypotheses. In Experiments 1 and 2 we measured performance in terms of grammaticality and found no difference across the 3 sets of stimuli. In Experiment 3 we analyzed performance in terms of both grammaticality and chunk strength. Again we found no differences in performance. Our tentative conclusion is that the learning process aims to encode symbolic regularity independent of stimulus appearance.
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BMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFBMC Nutr
January 2025
Centre for Lifecourse Nutrition, Department of Nutrition and Public Health, Faculty of Health and Sport Sciences, University of Agder, Postbox 422, Kristiansand, 4604, Norway.
Background: Early Childhood Education and Care (ECEC) centers play an important role in fostering healthy dietary habits. The Nutrition Now project focusing on improving dietary habits during the first 1000 days of life. Central to the project is the implementation of an e-learning resource aimed at promoting feeding practices among staff and healthy dietary behaviours for children aged 0-3 years in ECEC.
View Article and Find Full Text PDFHealth Res Policy Syst
January 2025
Centre for Epidemic Interventions Research, Norwegian Institute of Public Health, Oslo, Norway.
During public health crises such as pandemics, governments must rapidly adopt and implement wide-reaching policies and programs ("public policy interventions"). A key takeaway from the coronavirus disease 2019 (COVID-19) pandemic was that although numerous randomized controlled trials (RCTs) focussed on drugs and vaccines, few policy experiments were conducted to evaluate effects of public policy interventions across various sectors on viral transmission and other consequences. Moreover, many quasi-experimental studies were of spurious quality, thus proving unhelpful for informing public policy.
View Article and Find Full Text PDFJ Transl Med
January 2025
School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, Guizhou, 550000, China.
Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.
Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.
BMC Med Genomics
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China.
Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.
Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability.
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