Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

A large number of the human microRNAs target lentiviruses, retroviruses, and endogenous retroviruses.

Retroelements (including transposons, retrotransposons, retroviruses, and lentiviruses) make up a significant portion of eukaryotic genomes. Given their ability to mutate genes these mobile elements always present a threat to the integrity of the host genomes. Recent studies have revealed complex molecular mechanisms that silence the mutagenic ability of these RE as well strategically express the pieces of the incorporated RE that are utilized to silence human endogenous retroviruses (HERVs) or invading exogenous retroviruses (IERV). We have hypothesized that small endogenous RNA originally evolved to quell "foreign" IERV-genes and subsequently emerged into elaborate silencing systems that include RNA interference, miRNA-based gene regulation and other gene silencing mechanisms. Here, we present evidence that the replication of complex RE are most likely silenced or regulated by homologous miRNA that are found as a part of the cellular repertoire. We analyzed Homo sapiens miRNAs for possible target genetic sequences in selected HERVs and IERV found in humans and other large primates. We identified several miRNAs that have >80% sequence homology with human HERVs; -L, -W, and -K, and IERV like SIVcpz, HTLV-1, and HTLV-2. We found an inverse correlation between the numbers and relative degree of homology of miRNAs to the relative replication capacity of a specific RE. Therefore, larger numbers of miRNAs with greater degree of homology are found against the least active RE and the least numbers of miRNAs with smaller degree of homology are found against the most active RE (i.e. HERV-K). Implications of these observations in RE disease and therapy are discussed.