Of note, the proximal breakpoint of our proposita overlaps the distal breakpoint of the autistic patients studied by Kumar et al. [Kumar et al. (2008); Hum Mol Genet 17:628-638] and Weiss et al. [Weiss et al. (2008); N Eng J Med 358:667-675], confirming that the 16p region carrying
susceptibility to autism is more centromeric. Our observation further defines two different, contiguous 16p genomic regions, responsible for a distinct MCA/1D syndrome, and for autism, respectively. (C) 2009 Wiley-Liss, Inc.”
“Machine classifiers have been used to automate quantitative analysis and avoid intra-inter-reader variability in previous studies. The selection of an appropriate classification scheme is important for improving performance based on the characteristics of the data set. This paper investigated the performance of several machine classifiers for differentiating obstructive lung find more diseases using texture analysis on various ROI (region of interest) sizes. 265 high-resolution computerized tomography (HRCT) images were taken from 92 subjects. On each image, two experienced radiologists selected ROIs with various sizes representing area of severe centrilobular emphysema (PLE, n=63), mild centrilobular emphysema GSI-IX (CLE, n=65), bronchiolitis obliterans (130, n = 70) or normal lung (NL, n = 67). Four machine classifiers were implemented: naive Bayesian classifier, Bayesian classifier,
ANN (artificial neural net) and SVM (support vector machine). For a testing method, 5-fold cross-validation methods were used
and each validation was repeated 20 tunes. The SVM had the best performance in overall accuracy (in ROI size of 32 x 32 and 64 x 64) (t-test, p < 0.05). There was no significant overall accuracy difference between Bayesian and ANN (t-test, p< 0.05). The naive Bayesian method performed significantly worse than the other classifiers (t-test, p < 0.05). SVM showed the best performance for classification of the obstructive lung diseases buy STA-9090 in this study. (C) 2008 Elsevier Ireland Ltd. All rights reserved.”
“Based on an established 3D pharmacophore, a series of quinoline derivatives were synthesized. The opioidergic properties of these compounds were determined by a competitive binding assay using I-125-Dynorphine, H-3-DAMGO and I-125-DADLE for kappa, mu, and delta receptors, respectively. Results showed varying degree of activities of the compounds to kappa and mu opioid receptors with negligible interactions at the delta receptor. The compound, S4 was the most successful in inhibiting the two most prominent quantitative features of naloxone precipitated withdrawal symptoms – stereotyped jumping and body weight loss. Determination of IC50 of S4 revealed a greater affinity towards mu compared to kappa receptor. In conclusion, quinoline derivatives of S4 like structure offer potential tool for treatment of narcotic addictions. (C) 2009 Elsevier Ltd. All rights reserved.