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Background: Early intervention is effective for reducing ADHD symptoms and related impairments, yet methods of identifying young children in need of services are lacking. Most early predictors of ADHD previously identified are of limited clinical utility. This study examines several theoretically relevant predictors of ADHD in infancy and toddlerhood and whether assessment at multiple time points improves prediction.

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Sound-symbol learning and the relationship to spelling in first-grade children.

J Exp Child Psychol

January 2025

Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, LMU University Hospital, LMU Munich, 80336 München, Germany.

Early spelling depends on the ability to understand the alphabetic principle and to translate speech sounds into visual symbols (letters). Thus, the ability to associate sound-symbol pairs might be an important predictor of spelling development. Here, we examined the relation between sound-symbol learning (SSL) and early spelling skills.

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Objective: The agitation that can occur in patients undergoing vitreoretinal surgery on awakening from general anesthesia is a serious post-operative problem. In our study, we aimed to compare the effects of different anesthesia methods on emergence agitation in patients undergoing vitreoretinal surgery.

Method: Patients undergoing vitreoretinal surgery were divided into two groups: Total intravenous anesthesia (Group T) and inhalation anesthesia (Group D) according to the maintenance of anesthesia applied by consulting the records.

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Background: The mental health crisis among college students intensified amid the COVID-19 pandemic, suggesting an urgent need for innovative solutions to support them. Previous efforts to address mental health concerns have been constrained, often due to the underuse or shortage of services. Mobile health (mHealth) technology holds significant potential for providing resilience-building support and enhancing access to mental health care.

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Improving Recall Accuracy in Sparse Associative Memories That Use Neurogenesis.

Neural Comput

January 2025

Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, U.K.

The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their noisy variants. Solutions to this problem may be required to memorize vast numbers of patterns based on limited training data and subsequently recall the patterns in the presence of noise.

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