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A man in his late 50s was referred by a speech and language therapist for consideration of a palatal lift prosthesis (PLP) to improve his speech intelligibility. He presented with hypokinetic dysarthria characterised by reduced loudness, breathy voice and hypernasality. The patient had a diagnosis of progressive muscular dystrophy and mobilised in a motorised wheelchair.

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Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.

Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.

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Usher syndrome type 1C (USH1C) is a genetic disorder caused by mutations in the USH1C gene, which encodes harmonin, a key component of the mechanoelectrical transduction complex in auditory and vestibular hair cells. USH1C leads to deafness and vestibular dysfunction in humans. An Ush1c knockout (KO) mouse model displaying these characteristic deficits is generated in our laboratory.

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Background: Residual speech sound disorder (RSSD) is a high-prevalence condition that negatively impacts social and academic participation. Telepractice service delivery has the potential to expand access to technology-enhanced intervention methods that can help remediate RSSD, but it is not known whether remote service delivery is associated with a reduction in the efficacy of these methods. This project will systematically measure the outcomes of visual-acoustic biofeedback intervention when delivered in-person or online.

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