We examined the influence of task complexity on implicit sequence learning in secondary-school-aged children with developmental dyslexia (DD). This was done to determine whether automatization problems in reading extend to the automatization of all skill and depend on the complexity of the to-be-learned skill. A total of 28 dyslexic children between 12 and 15 years and 28 matched control children carried out two serial reaction time tasks using a first-order conditional (FOC) and second-order conditional (SOC) sequence. In both tasks, children incidentally learned a sequence of hidden target positions, but whereas FOC sequence learning could be based on knowledge about the immediate preceding position, SOC sequence learning required more complex knowledge about the previous two positions. The results demonstrated that sequence learning was highly comparable in dyslexic and control children, regardless of the sequence complexity. This shows that implicit sequence learning, as manifested in the present study, is maintained in DD and is unrelated to task complexity. We suggest that previous reports of sequence-learning deficits in DD can be accounted for by attenuated explicit sequence learning, possibly related to malfunctions in prefrontal processing. The present findings indicate that deficits in skill learning and automatization in DD are not general in nature, but task dependent.
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http://dx.doi.org/10.1080/13803390903313556 | DOI Listing |
Nat Microbiol
January 2025
School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China.
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs.
View Article and Find Full Text PDFOncogene
January 2025
Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK.
Clear cell renal cell carcinoma (ccRCC) is characterised by significant genetic heterogeneity, which has diagnostic and prognostic implications. Very limited evidence is available regarding DNA methylation heterogeneity. We therefore generate sequence level DNA methylation data on 136 multi-region tumour and normal kidney tissue from 18 ccRCC patients, along with matched whole exome sequencing (85 samples) and gene expression (47 samples) data on a subset of samples.
View Article and Find Full Text PDFSci Data
January 2025
Universidad Nacional de Colombia, Bogotá, 1100111, Colombia.
Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures.
View Article and Find Full Text PDFNat Commun
January 2025
National-Local Joint Engineering Laboratory of Druggability and New Drug Evaluation, National Engineering Research Center for New Drug and Druggability (cultivation), Guangdong Province Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China.
Epitranscriptomic modifications, particularly N6-methyladenosine (mA), are crucial regulators of gene expression, influencing processes such as RNA stability, splicing, and translation. Traditional computational methods for detecting mA from Nanopore direct RNA sequencing (DRS) data are constrained by their reliance on experimentally validated labels, often resulting in the underestimation of modification sites. Here, we introduce pum6a, an innovative attention-based framework that integrates positive and unlabeled multi-instance learning (MIL) to address the challenges of incomplete labeling and missing read-level annotations.
View Article and Find Full Text PDFComput Biol Med
January 2025
IMT Atlantique, Lab-STICC, UMR CNRS 6285, team RAMBO, F-29238 Brest, France.
Rehabilitation is the process of helping people regain or improve lost or impaired function due to injury, illness, or disease. To assist in tracking the progress of patients undergoing rehabilitation, this paper proposes a lightweight graph-based deep-learning model for the automatic assessment of physical rehabilitation exercises. The model takes as input the 3D skeleton sequence of a patient performing a movement and outputs a continuous quality score, as a means for patient supervision that could complement or even substitute the need for ordinary clinical exams.
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