Probabilistic Results on the Architecture of Mathematical Reasoning Aligned by Cognitive Alternation

We envision a machine capable of solving mathematical problems. Dividing the quantitative reasoning system into two parts: thought processes and cognitive processes, we provide probabilistic descriptions of the architecture

Investigation gene and microRNA expression in glioblastoma

Background: Glioblastoma is the most common primary brain tumor in adults. Though a lot of research has been focused on this disease, the causes and pathogenesis of glioblastoma have not been indentified clearly. Results: We indentified 1,236 significantly differentially expressed genes, and 30 pathways enriched in the set of differentially expressed genes among 243 tumor and 11 normal samples. We also indentified 97 differentially expressed microRNAs among 240 tumor and 10 normal samples. 22 of which have been reported to affect glioblastoma and 50 of which were implicated in other cancers and brain diseases. We regressed gene expression on microRNA expression in 237 tumor tissues and 10 normal tissues comprehensively. We found two experimentally validated microRNA targets and 1,094 miRNA-target gene pairs in our datasets which were predicted by miRanda algorithm, 8 of the target genes were tumor suppressor genes and 3 were oncogenes. Further function analysis of target genes suggested that microRNAs most frequently targeted genes associated with Cell Signalling and Nervous System. Conclusion: We investigated gene and microRNA Expression in Glioblastoma and gave a comprehensive function study of differential expressed gene and microRNA in glioblastoma patients. These findings gave important clues to study of the carcinogenic process in glioblastomas.Biotechnology & Applied MicrobiologyGenetics & HereditySCI(E)9ARTICLEnull1

The enhancement of electrochemical capacitance of biomass-carbon by pyrolysis of extracted nanofibers

Biomass-derived carbons have been extensively researched as electrode material for energy storage and conversion recently. However, most of the previous works convert crude biomass directly into carbon and the electrochemical capacitances for the resultant carbons are quite often underestimated as well as large variations in capacitances exist in literatures due to the complex nature of biomass, which practically hinder their applications. In this work, polysaccharide nanofibers were extracted from an inexpensive natural fungus using a hydrothermal method and were converted to porous carbon nanofibers (CNFs) by potassium hydroxide activation. The porous carbons were assembled into symmetric supercapacitors using both potassium hydroxide and an ionic liquid (IL) as electrolytes. Solid state nuclear magnetic resonance characterization showed that the micropores of the as-prepared carbons are accessible to the IL electrolyte when uncharged and thus high capacitance is expected. It is found in both electrolytes the electrochemical capacitances of CNFs are significantly higher than those of the porous carbon derived directly from the crude fungus. Furthermore, the CNFs delivered an extraordinary energy density of 92.3 Wh kg−1 in the IL electrolyte, making it a promising candidate for electrode materials for supercapacitors.<br/

Psoriasis prediction from genome-wide SNP profiles

<p>Abstract</p> <p>Background</p> <p>With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data.</p> <p>Methods</p> <p>Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance.</p> <p>Results</p> <p>The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study.</p> <p>Conclusions</p> <p>The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.</p

ContentAdaptive Spatial Scalability for Scalable Video Coding

ABSTRACT This paper presents an enhancement of the SVC extension of the H.264/AVC standard by content-adaptive spatial scalability (CASS). CASS introduces a novel functionality which is important for high quality content distribution. The video streams (spatial layers), which are used as input to the encoder, are created by content-adaptive and art-directable retargeting of existing high resolution video. Video is retargeted to resolutions and aspect ratios which are mainly dictated by target display devices. Thereby no content is cut off, but visually important content is preserved at the expense of a non-linear distortion of visually unimportant areas. The non-linear dependencies between such video streams are efficiently exploited by CASS for scalable coding. This is achieved by integrating warping-based non-linear texture prediction and warp coding into the SVC framework. The results indicate high prediction accuracy of non-linear predictors and high compression efficiency with limited increase in bit rate and complexity compared to the standard SVC