In this study, we concurrently investigated 3 possible causes of dyslexia-a phonological deficit, visual stress, and a reduced visual attention span-in a large population of 164 dyslexic and 118 control French children, aged between 8 and 13 years old. We found that most dyslexic children showed a phonological deficit, either in terms of response accuracy (92.1% of the sample), speed (84.8%), or both (79.3%). Deficits in visual attention span, as measured by partial report ability, affected 28.1% of dyslexic participants, all of which also showed a phonological deficit. Visual stress, as measured by subjective reports of visual discomfort, affected 5.5% of dyslexic participants, not more than controls (8.5%). Although phonological variables explained a large amount of variance in literacy skills, visual variables did not explain any additional variance. Finally, children with comorbid phonological and visual deficits did not show more severe reading disability than children with a pure phonological deficit. These results (a) confirm the importance of phonological deficits in dyslexia; (b) suggest that visual attention span may play a role, but a minor one, at least in this population; (c) do not support any involvement of visual stress in dyslexia. Among the factors that may explain some differences with previously published studies, the present sample is characterized by very stringent inclusion criteria, in terms of the severity of reading disability and in terms of exclusion of comorbidities. This may exacerbate the role of phonological deficits to the detriment of other factors playing a role in reading acquisition. (PsycINFO Database Record
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Sci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
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January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFSurv Ophthalmol
January 2025
Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China; Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing 100730, China. Electronic address:
Because of its benign nature and rarity, circumscribed choroidal hemangioma (CCH) often receives limited attention, leading to a high rate of misdiagnosis and a lack of standardized treatment protocols. We provide a thorough clarification of the demographics, clinical features, diagnosis, management, and prognosis of CCH. We conducted a systematic search of the PubMed, EMBASE, and Ovid databases up to December, 2023, to identify relevant studies.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Laboratory Medicine, School of Medicine, Yangtze University, Jingzhou 434023, P.R. China.
Acylaminoacyl-peptide hydrolase (APEH), a serine peptidase that belongs to the prolyl oligopeptidase (POP) family, catalyzes removal of N-terminal acetylated amino acid residues from peptides. As a key regulator of protein N-terminal acetylation, APEH was involved in many important physiological processes while its aberrant expression was correlated with progression of various diseases such as inflammation, diabetics, Alzheimer's disease (AD), and cancers. However, while emerging attention has been attracted in APEH-related disease diagnosis and drug discovery, the mechanisms behind APEH and related disease progression are still unclear; thus, further investigating the physiological role and function of APEH is of great importance.
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
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