A deficit in encoding the order of visual sequences in developmental dyslexia: a non -phonological deficit

The present thesis tested the hypothesis of Stanovich, Siegel, & Gottardo (1997) that surface dyslexia is the result of a milder phonological deficit than that seen in phonological dyslexia coupled with reduced reading experience. We found that a group of adults with surface dyslexia showed a phonological deficit that was commensurate with that shown by a group of adults with phonological dyslexia (matched for chronological age and verbal and non-verbal IQ) and normal reading experience. We also showed that surface dyslexia cannot be accounted for by a semantic impairment or a deficit in the verbal learning and recall of lexical-semantic information (such as meaningful words), as both dyslexic subgroups performed the same. This study has replicated the results of our published study that surface dyslexia is not the consequence of a mild retardation or reduced learning opportunities but a separate impairment linked to a deficit in written lexical learning, an ability needed to create novel lexical representations from a series of unrelated visual units, which is independent from the phonological deficit (Romani, Di Betta, Tsouknida & Olson, 2008). This thesis also provided evidence that a selective nonword reading deficit in developmental dyslexia persists beyond poor phonology. This was shown by finding a nonword reading deficit even in the presence of normal regularity effects in the dyslexics (when compared to both reading and spelling-age matched controls). A nonword reading deficit was also found in the surface dyslexics. Crucially, this deficit was as strong as in the phonological dyslexics despite better functioning of the sublexical route for the former. These results suggest that a nonword reading deficit cannot be solely explained by a phonological impairment. We, thus, suggested that nonword reading should also involve another ability relating to the processing of novel visual orthographic strings, which we called 'orthographic coding'. We then investigated the ability to process series of independent units within multi-element visual arrays and its relationship with reading and spelling problems. We identified a deficit in encoding the order of visual sequences (involving both linguistic and nonlinguistic information) which was significantly associated with word and nonword processing. More importantly, we revealed significant contributions to orthographic skills in both dyslexic and control individuals, even after age, performance IQ and phonological skills were controlled. These results suggest that spelling and reading do not only tap phonological skills but also order encoding skills.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.