Cerebellar morphology in developmental dyslexia

Recent evidence has suggested cerebellar anomalies in developmental dyslexia. Therefore, we investigated cerebellar morphology in subjects with documented reading disabilities. We obtained T1-weighted magnetic resonance images in the coronal and sagittal planes from 11 males with prior histories of developmental dyslexia, and nine similarly-aged male controls. Proton magnetic resonance spectra (TE=136 ms, TR=2.4 s) were obtained bilaterally in the cerebellum. Phonological decoding skill was measured using non-word reading. Handedness was assessed using both the Annett questionnaire of hand preference and Annett’s peg moving task. Cerebellar symmetry was observed in the dyslexics but there was significant asymmetry (right grey matter>left grey matter) in controls. The interpretation of these results depended whether a motor- or questionnaire-based method was used to determine handedness. The degree of cerebellar symmetry was correlated with the severity of dyslexics’ phonological decoding deficit. Those with more symmetric cerebella made more errors on a nonsense word reading measure of phonological decoding ability. Left cerebellar metabolite ratios were shown to correlate significantly with the degree of cerebellar asymmetry (P<0.05) in controls. This relationship was absent in developmental dyslexics. Cerebellar morphology reflects the higher degree of symmetry found previously in the temporal and parietal cortex of dyslexics. The relationship of cerebellar asymmetry to phonological decoding ability and handedness, together with our previous finding of altered metabolite ratios in the cerebellum of dyslexics, lead us to suggest that there are alterations in the neurological organisation of the cerebellum which relate to phonological decoding skills, in addition to motor skills and handedness.

A comparison of high-content screening versus manual analysis to assay the effects of mesenchymal stem cell-conditioned medium on neurite outgrowth in vitro

Bone marrow mesenchymal stem cells (MSCs) promote nerve growth and functional recovery in animal models of spinal cord injury (SCI) to varying levels. The authors have tested high-content screening to examine the effects of MSC-conditioned medium (MSC-CM) on neurite outgrowth from the human neuroblastoma cell line SH-SY5Y and from explants of chick dorsal root ganglia (DRG). These analyses were compared to previously published methods that involved hand-tracing individual neurites. Both methods demonstrated that MSC-CM promoted neurite outgrowth. Each showed the proportion of SH-SY5Y cells with neurites increased by ~200% in MSC-CM within 48 h, and the number of neurites/SH-SY5Y cells was significantly increased in MSC-CM compared with control medium. For high-content screening, the analysis was performed within minutes, testing multiple samples of MSC-CM and in each case measuring >15,000 SH-SY5Y cells. In contrast, the manual measurement of neurite outgrowth from >200 SH-SY5Y cells in a single sample of MSC-CM took at least 1 h. High-content analysis provided additional measures of increased neurite branching in MSC-CM compared with control medium. MSC-CM was also found to stimulate neurite outgrowth in DRG explants using either method. The application of the high-content analysis was less well optimized for measuring neurite outgrowth from DRG explants than from SH-SY5Y cells.

Aggregating preference ranking with fuzzy Data Envelopment Analysis

Selecting the best alternative in a group decision making is a subject of many recent studies. The most popular method proposed for ranking the alternatives is based on the distance of each alternative to the ideal alternative. The ideal alternative may never exist; hence the ranking results are biased to the ideal point. The main aim in this study is to calculate a fuzzy ideal point that is more realistic to the crisp ideal point. On the other hand, recently Data Envelopment Analysis (DEA) is used to find the optimum weights for ranking the alternatives. This paper proposes a four stage approach based on DEA in the Fuzzy environment to aggregate preference rankings. An application of preferential voting system shows how the new model can be applied to rank a set of alternatives. Other two examples indicate the priority of the proposed method compared to the some other suggested methods.