Purpose: Maturation of neural processes for language may lag in some children who stutter (CWS), and event-related potentials (ERPs) distinguish CWS who have recovered from those who have persisted. The current study explores whether ERPs indexing semantic processing may distinguish children who will eventually persist in stuttering (CWS-ePersisted) from those who will recover from stuttering (CWS-eRecovered).
Method: Fifty-six 5-year-old children with normal receptive language listened to naturally spoken sentences in a story context. ERP components elicited for semantic processing (N400, late positive component [LPC]) were compared for CWS-ePersisted, CWS-eRecovered, and children who do not stutter (CWNS).
Results: The N400 elicited by semantic violations had a more focal scalp distribution (left lateralized and less anterior) in the CWS-eRecovered compared with CWS-ePersisted. Although the LPC elicited in CWS-eRecovered and CWNS did not differ, the LPC elicited in the CWS-ePersisted was smaller in amplitude compared with that in CWNS.
Conclusions: ERPs elicited in 5-year-old CWS-eRecovered compared with CWS-ePersisted suggest that future recovery from stuttering may be associated with earlier maturation of semantic processes in the preschool years. Subtle differences in ERP indices offer a window into neural maturation processes for language and may help distinguish the course of stuttering development.
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http://dx.doi.org/10.1044/2017_JSLHR-S-17-0081 | DOI Listing |
JDS Commun
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Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.
Hock scoring in dairy cattle is a crucial welfare assessment tool used to evaluate the condition of a cow's hocks, particularly for signs of injury, swelling, or lesions. These scores provide insight into the overall well-being of the animals and are essential for ensuring proper management and housing conditions. Accurate hock scoring is vital because it can indicate issues such as poor bedding quality or inadequate space, which directly affect the health and productivity of the herd.
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View Article and Find Full Text PDFPsychon Bull Rev
January 2025
Boston University, Boston, USA.
Individuals with "agrammatic" receptive aphasia have long been known to rely on semantic plausibility rather than syntactic cues when interpreting sentences. In contrast to early interpretations of this pattern as indicative of a deficit in syntactic knowledge, a recent proposal views agrammatic comprehension as a case of "noisy-channel" language processing with an increased expectation of noise in the input relative to healthy adults. Here, we investigate the nature of the noise model in aphasia and whether it is adapted to the statistics of the environment.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
View Article and Find Full Text PDFSci Data
January 2025
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210000, China.
Travelable area boundaries not only constrain the movement of field robots but also indicate alternative guiding routes for dynamic objects. Publicly available road boundary datasets have outlined boundaries by binary segmentation labels. However, hard post-processes have to be done to extract from detected boundaries further semantics including the shapes of the boundaries and guiding routes, which poses challenges to a real-time visual navigation system without detailed prior maps.
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