Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets

As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques—e.g., NanoString, microarrays, or DNA methylation assays—which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning–based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We validated our results using an independent microarray-based dataset of 92 samples of all four subgroups. In addition, we performed a qPCR validation on a cohort of 18 patients diagnosed with SHH, Group 3 and Group 4 MB. We found that the 22-gene set can be reduced to only six genes (IMPG2, NPR3, KHDRBS2, RBM24, WIF1, and EMX2) without compromising accuracy. The identified gene set is sufficiently small to make a qPCR-based MB subgroup classification easily accessible to clinicians, even in developing, poorly equipped countries

Development of theoretical basics of technology for shaping packs of textile threads and practical realization thereof in textiles industry

Available from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Scalable Classification in Large Scale Spatiotemporal Domains Applied to Voltage-Sensitive Dye Imaging

We present an approach for learning models that obtain accurate classification of large scale data objects, collected in spatiotemporal domains. The model generation is structured in three phases: pixel selection (spatial dimension reduction), spatiotemporal features extraction and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. Model generation based on the combinations of techniques from each phase is explored. The introduced methodology is applied on datasets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the generated classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI currently is the best technique enabling simultaneous high spatial (10,000 points) and temporal (10 ms or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities

Trends in impact factors of ophthalmology journals

Purpose: To test whether there is an association between the growth in the number of ophthalmic journals in the past years and their mean and maximum impact factor (IF) as a common sign of scientific proliferation. Methods: Using data from the 2013 Journal Citation Report database a study of the major clinical medical fields was conducted to assess the correlation between the number of journals and maximum IF in a given field in the year 2013. In the field of ophthalmology, we examined the correlation between year, number of journals, mean IF and maximum IF in the field of ophthalmology throughout the years 2000–2013. Results: In the major medical fields, a positive correlation was found between the number of journals and the maximum IF (quadratic R2 = 0.71, P< 0.001). When studying the field of ophthalmology a positive correlation between the number of journals and mean IF (R2 = 0.84, P< 0.001) and between number of journals and maximum IF (R2 = 0.71, P< 0.001) was detected. Conclusions: Our findings suggest that the variation in the IF can be explained by the number of journals in the field of ophthalmology. In the future, the formation of additional ophthalmology journals is likely to further increase the IFs of existing journals