Cognitive Requirements of the Phonological Tests Affect Their Ability to Discriminate Children With and Without Developmental Dyslexia.

PURPOSE: The main purpose of this study was to investigate whether the performance on each of seven phonological processing (PP) tests from the Russian Test of Phonological Processing (RuToPP), with their varying levels of linguistic complexity and composite phonological indices, are significant predictors of developmental dyslexia (DD) and can reliably differentiate children with and without reading impairment. Additionally, we examined the general contribution of phonological skills to text reading fluency in children with various levels of reading performance. METHOD: A total of 173 Russian-speaking 7- to 11-year-old children participated in this study: 124 who were typically developing (TD) and 49 who had been diagnosed with DD. We assessed reading fluency with a standardized reading test and PP with the RuToPP. We investigated the potential of phonological skills to predict the presence or absence of a dyslexia diagnosis using multinomial logistic regression, receiver operating characteristic (ROC) curve analysis, and calculations of the sensitivity and specificity of each test and index. The contribution of phonological skills to reading fluency was also assessed in a mixed group of children. RESULTS: Six of seven RuToPP tests were significant predictors of dyslexia. However, while the RuToPP correctly identified 93%-99% of TD children, for children with dyslexia, it ranged from 4% to 47% depending on the test. In a mixed group of children with and without dyslexia, performance in the more complex phonological tests was a stronger predictor of reading fluency. CONCLUSIONS: Our findings are consistent with the literature on predictors of literacy skills and dyslexia while uniquely demonstrating the impact of the complexity level of the phonological tests on the classification outcome. PP is a significant and necessary predictor of reading skills, but it is not sufficient for diagnostic purposes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.20779294.

The first deaf-blind patient in Russia with Argus II retinal prosthesis system: what he sees and why.

OBJECTIVE: In this study, we propose a new method for evaluating the functional results based on the sizes of phosphenes that the patient drew which were then digitalized. We also describe the methodology of psychological testing and support for a deaf-blind patient. APPROACH: A 59-year-old man with retinitis pigmentosa and hearing loss (clinical Usher syndrome) underwent surgery to implant the Argus II retinal prosthesis system in his right eye. MAIN RESULTS: Correlation analysis showed a weak dependency between the size of a phosphene and the perceptual threshold. Significant correlations between a phosphene and the height of the interface, impedance or retinal thickness was not found. The patient with the retinal prosthesis felt more independent and confident, and more healthy. This is the first case of retinal implant surgery in Russia. SIGNIFICANCE: The results of this study add to the understanding in the field of retinal implants functioning. The experience of the successful rehabilitation of the deaf-blind patient after implantation of Argus II allowed us to design a methodology that can be used in future similar cases.

Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.

BACKGROUND: In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis. RESULTS: We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership. CONCLUSIONS: The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.