Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 5 Articles
Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.\nNormal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take\nthe average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not\ncorrelated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely\nproportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy\ncurrently used to detect ERPs, which is based on calculating the average of all ERPââ?¬â?¢s waveform, these waveforms\nbeing time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP\nusing a mathematical method based only on Pearsonââ?¬â?¢s correlation. The result is a graph with the same time\nresolution as the classical ERP and which shows only positive peaks representing the increaseââ?¬â?in consonance with\nthe stimuliââ?¬â?in EEG signal correlation over all channels. This new method is also useful for selectively identifying\nand highlighting some hidden components of the ERP response that are not phase-locked, and that are usually\nhidden in the standard and simple method based on the averaging of all the epochs. These hidden components\nseem to be caused by variations (between each successive stimulus) of the ERPââ?¬â?¢s inherent phase latency period\n(jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason,\nthis new method could be very helpful to investigate these hidden components of the ERP response and to\ndevelop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG\nartifacts than the standard calculations of the average and could be very useful in research and neurology. The\nmethod we are proposing can be directly used in the form of a process written in the well-known Matlab\nprogramming language and can be easily and quickly written in any other software language....
Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association\nstudies (GWAS) across genes and pathways is a strategy to improve statistical power and\ngain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful\ntool for computing gene and pathway scores from SNP-phenotype association summary\nstatistics. For gene score computation, we implemented analytic and efficient numerical\nsolutions to calculate test statistics. We examined in particular the sum and the maximum of\nchi-squared statistics, which measure the strongest and the average association signals\nper gene, respectively. For pathway scoring, we use a modified Fisher method, which offers\nnot only significant power improvement over more traditional enrichment strategies, but\nalso eliminates the problem of arbitrary threshold selection inherent in any binary membership\nbased pathway enrichment approach.We demonstrate the marked increase in power\nby analyzing summary statistics from dozens of large meta-studies for various traits. Our\nextensive testing indicates that our method not only excels in rigorous type I error control,\nbut also results in more biologically meaningful discoveries....
Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is\ncommon practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule\nfrom a new point of view. Here, we try to find the optimal threshold based on the differences between the gene\nexpression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to\nmeasure the overall differential gene expression between patients with smaller tumors and those with larger\ntumors among multiple expression datasets from different studies. We confirmed the performance of the proposed\nalgorithm by a simulation study and then applied it to three different studies conducted at two Norwegian\nhospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2ââ?¬â??2.4 cm, and\nwe confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly\navailable expression datasets. Furthermore, we observed a significant differentiation between the two threshold\ngroups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated\nnetwork and canonical pathway analyses for the genes differentially expressed between tumors below and above\nthe given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a\ncellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an\nenrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds...
The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor\nsuperfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological\nprocesses such as lipid metabolism, inflammation, and wound healing. Here, we describe the first database of PPAR target genes,\nPPARgene.Among the 225 experimentally verified PPARtarget genes, 83 are for PPAR...
Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this\nevolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more\nefficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools\nis machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure\nevolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the\nprediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts\nthe genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes\nbased on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains,\nthen a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is\napplied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from\ntwo sources are used in the validation of these techniques. The results show that the accuracy of this technique in\npredicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis\nof the correlation between different nucleotides in the same RNA sequence....
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