Research on the etiology of dyslexia typically uses an approach based on a single core deficit, failing to understand how variations in combinations of factors contribute to reading development and how this combination relates to intervention outcome. To fill this gap, this study explored links between 28 cognitive, environmental, and demographic variables related to dyslexia by employing a network analysis using a large clinical database of 1,257 elementary school children. We found two highly connected subparts in the network: one comprising reading fluency and accuracy measures, and one comprising intelligence-related measures. Interestingly, phoneme awareness was functionally related to the controlled and accurate processing of letter-speech sound mappings, whereas rapid automatized naming was more functionally related to the automated convergence of visual and speech information. We found evidence for the contribution of a variety of factors to (a)typical reading development, though associated with different aspects of the reading process. As such, our results contradict prevailing claims that dyslexia is caused by a single core deficit. This study shows how the network approach to psychopathology can be used to study complex interactions within the reading network and discusses future directions for more personalized interventions.
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http://dx.doi.org/10.1017/S0954579421000365 | DOI Listing |
Neurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
BMC Med Educ
January 2025
Division of General Internal Medicine, Department of Medicine, The Ottawa Hospital, Ottawa, Canada.
Introduction: Hospital strain has been shown to negatively impact physician wellness, educational experience, and patient care. To address rising service demands, a non-academic hospitalist service was implemented to reduce daily clinical teaching unit (CTU) census by approximately 30%. Secondary aims were to evaluate physician and trainee wellness on CTU as well as assess unintended adverse patient outcomes.
View Article and Find Full Text PDFSci Rep
January 2025
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK.
In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP.
View Article and Find Full Text PDFSci Rep
January 2025
Office for the Advancement of Educational Information, Chengdu Normal University, Chengdu, 610000, China.
In the training of teacher students, simulated teaching is a key method for enhancing teaching skills. However, traditional evaluations of simulated teaching typically rely on direct teacher involvement and guidance, increasing teachers' workload and limiting the opportunities for teacher students to practice independently. This paper introduces a Retrieval-Augmented Generation (RAG) framework constructed using various open-source tools (such as FastChat for model inference and Whisper for speech-to-text) combined with a local large language model (LLM) for audio analysis of simulated teaching.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.
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