Prediction is often regarded as an integral aspect of incremental language comprehension, but little is known about the cognitive architectures and mechanisms that support it. We review studies showing that listeners and readers use all manner of contextual information to generate multifaceted predictions about upcoming input. The nature of these predictions may vary between individuals owing to differences in language experience, among other factors. We then turn to unresolved questions which may guide the search for the underlying mechanisms. (i) Is prediction essential to language processing or an optional strategy? (ii) Are predictions generated from within the language system or by domain-general processes? (iii) What is the relationship between prediction and memory? (iv) Does prediction in comprehension require simulation via the production system? We discuss promising directions for making progress in answering these questions and for developing a mechanistic understanding of prediction in language.
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http://dx.doi.org/10.1016/j.tics.2023.08.003 | DOI Listing |
Bioinformatics
January 2025
Biocomputing Group, University of Bologna, Italy.
Motivation: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.
Results: In this work we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations.
BMC Bioinformatics
January 2025
College of Artificial Intelligence, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu, China.
Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models.
View Article and Find Full Text PDFJ Hum Hypertens
January 2025
Department of Pediatrics and Child Health, University of Ilorin, Ilorin, Nigeria.
Red cell distribution width (RDW) quantifies the degree of variation in erythrocyte size, is identified as a potential marker of adverse cardiovascular events, and may be a surrogate marker for assessing cardiovascular disease (CVD) risk in low-resource settings. We evaluated RDW as a predictor of CVD risk compared to the World Health Organization (WHO) CVD risk score among adults with hypertension attending primary healthcare centers (PHCs) in Ghana and Nigeria. Adults with hypertension attending selected PHCs in Ghana and Nigeria participated in a cross-sectional study.
View Article and Find Full Text PDFAnn Dyslexia
January 2025
Department of Special Education, National Taiwan Normal University, 162, Section 1Heping E. Rd., Taipei City, 10610, Taiwan.
With a focus on content-area reading, this study aimed to (a) understand the sources and prevalence of concurrent and specific difficulties in word-level skills, vocabulary, and knowledge among adolescent struggling readers (ASRs) and (b) explore the relations among reading skills, profiles, and reading comprehension. A dual-measure screening approach was used to classify a sample of 492 seventh- and eighth-graders. Among the subgroup of 225 ASRs, five distinct profiles were identified by latent profile analysis.
View Article and Find Full Text PDFSLAS Technol
January 2025
Department of General Medicine, The First Afiliated Hospital of Jinan University, Guangzhou, Guangdong, 510000, China. Electronic address:
Chronic kidney disease (CKD) significantly increases the risk of CVD diseases, particularly among elderly patients. Understanding the interaction between several biomarkers and cardiovascular (CVD) risks is crucial for improving patient outcomes and tailoring personalized treatment strategies. There is much more to learn about the intricate relationship between biomarkers and CVD risks in elderly CKD patients.
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