Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface

The steady state visual evoked protocol has recently become a popular paradigm in brain–computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control

Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics

Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a pilot study that reveal the combined use of brain and heart modalities provide improved classification performance and further-more, an improvement in the stability of the features over time through the use of binaural brain entrainment. The classification rate was increased, for the case of the neural network classifier from 92.4% to 95.1% and for the case of LDA, from 98.6% to 99.8%. The average standard deviation with binaural brain entrainment using all the inter-session features (from all the subjects) was 1.09, as compared to 1.26 without entrainment. This result suggests the improved stability of both the EEG and ECG features over time and hence resulting in higher classification performance. Overall, the results indicate that combining ECG and EEG gives improved classification performance and that through the use of binaural brain entrainment, both the ECG and EEG features are more stable over time

A family case of fertile human 45,X,psu dic(15;Y) males

We report on a familial case including four male probands from three generations with a 45,X,psu dic(15;Y)(p11.2;q12) karyotype. 45,X is usually associated with a female phenotype and only rarely with maleness, due to translocation of small Y chromosomal fragments to autosomes. These male patients are commonly infertile because of missing azoospermia factor regions from the Y long arm. In our familial case we found a pseudodicentric translocation chromosome, that contains almost the entire chromosomes 15 and Y. The translocation took place in an unknown male ancestor of our probands and has no apparent effect on fertility and phenotype of the carrier. FISH analysis demonstrated the deletion of the pseudoautosomal region 2 (PAR2) from the Y chromosome and the loss of the nucleolus organizing region (NOR) from chromosome 15. The formation of the psu dic(15;Y) chromosome is a reciprocal event to the formation of the satellited Y chromosome (Yqs). Statistically, the formation of 45,X,psu dic(15;Y) (p11.2;q12) is as likely as the formation of Yqs. Nevertheless, it has not been described yet. This can be explained by the dicentricity of this translocation chromosome that usually leads to mitotic instability and meiotic imbalances. A second event, a stable inactivation of one of the two centromeres is obligatory to enable the transmission of the translocation chromosome and thus a stably reduced chromosome number from father to every son in this family

Acoustic Based Footstep Detection in Pervasive Healthcare

Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older adults in their home environment. Acoustic footstep detection is a promising approach for nonintrusive detection of footsteps. So far there has been limited work in developing robust acoustic footstep detection systems that can operate in noisy home environments. In this paper, we propose a novel application of the Attention based Recurrent Deep Neural Network to detect human footsteps in noisy overlapping audio streams. The model is trained on synthetic data which simulates the acoustic scene in a home environment. To evaluate performance, we reproduced two footstep detection models from literature and compared them using the newly developed Polyphonic Sound Detection Scores (PSDS). Our model achieved the highest PSDS and is close to the highest score achieved by generic indoor AED models in DCASE. The proposed system is designed to both detect and track footsteps within a home setting, and to enhance state-of-the-art digital health-care solutions for empowering older adults to live autonomously in their own homes

Big Domains Are Novel Ca2+-Binding Modules: Evidences from Big Domains of Leptospira Immunoglobulin-Like (Lig) Proteins

binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. with dissociation constants of 2–4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm), probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. binding

Ebolavirus species-specific interferon antagonism mediated by VP24

Members of the Ebolavirus genus demonstrate a marked differences in pathogenicity in humans with Ebola (EBOV) being the most pathogenic, Bundibugyo (BDBV) less pathogenic, and Reston (RESTV) is not known to cause a disease in humans. The VP24 protein encoded by members of the Ebolavirus genus blocks type I interferon (IFN-I) signaling through interaction with host karyopherin alpha nuclear transporters, potentially contributing to virulence. Previously, we demonstrated that BDBV VP24 (bVP24) binds with lower affinities to karyopherin alpha proteins relative to EBOV VP24 (eVP24), and this correlated with a reduced inhibition in IFN-I signaling. We hypothesized that modification of eVP24-karyopherin alpha interface to make it similar to bVP24 would attenuate the ability to antagonize IFN-I response. We generated a panel of recombinant EBOVs containing single or combinations of point mutations in the eVP24-karyopherin alpha interface. Most of the viruses appeared to be attenuated in both IFN-I-competent 769-P and IFN-I-deficient Vero-E6 cells in the presence of IFNs. However, the R140A mutant grew at reduced levels even in the absence of IFNs in both cell lines, as well as in U3A STAT1 knockout cells. Both the R140A mutation and its combination with the N135A mutation greatly reduced the amounts of viral genomic RNA and mRNA suggesting that these mutations attenuate the virus in an IFN-I-independent attenuation. Additionally, we found that unlike eVP24, bVP24 does not inhibit interferon lambda 1 (IFN-λ1), interferon beta (IFN-β), and ISG15, which potentially explains the lower pathogenicity of BDBV relative to EBOV. Thus, the VP24 residues binding karyopherin alpha attenuates the virus by IFN-I-dependent and independent mechanisms

The integrated microbial genomes (IMG) system in 2007: data content and analysis tool extensions

The integrated microbial genomes (IMG) system is a data management, analysis and annotation platform for all publicly available genomes. IMG contains both draft and complete JGI microbial genomes integrated with all other publicly available genomes from all three domains of life, together with a large number of plasmids and viruses. IMG provides tools and viewers for analyzing and annotating genomes, genes and functions, individually or in a comparative context. Since its first release in 2005, IMG's data content and analytical capabilities have been constantly expanded through quarterly releases. IMG is provided by the DOE-Joint Genome Institute (JGI) and is available from http://img.jgi.doe.gov

IMG/M: a data management and analysis system for metagenomes

IMG/M is a data management and analysis system for microbial community genomes (metagenomes) hosted at the Department of Energy's (DOE) Joint Genome Institute (JGI). IMG/M consists of metagenome data integrated with isolate microbial genomes from the Integrated Microbial Genomes (IMG) system. IMG/M provides IMG's comparative data analysis tools extended to handle metagenome data, together with metagenome-specific analysis tools. IMG/M is available at http://img.jgi.doe.gov/

IMG/M: the integrated metagenome data management and comparative analysis system

The integrated microbial genomes and metagenomes (IMG/M) system provides support for comparative analysis of microbial community aggregate genomes (metagenomes) in a comprehensive integrated context. IMG/M integrates metagenome data sets with isolate microbial genomes from the IMG system. IMG/M’s data content and analytical capabilities have been extended through regular updates since its first release in 2007. IMG/M is available at http://img.jgi.doe.gov/m. A companion IMG/M systems provide support for annotation and expert review of unpublished metagenomic data sets (IMG/M ER: http://img.jgi.doe.gov/mer)

Predicting Java Computer Programming Task Difficulty Levels Using EEG for Educational Environments

Understanding how difficult a learning task is for a person allows teaching material to be appropriately designed to suit the person, especially for programming material. A first step for this would be to predict on the task difficulty level. While this is possible through subjective questionnaire, it could lead to misleading outcome and it would be better to do this by tapping the actual thought process in the brain while the subject is performing the task, which can be done using electroencephalogram. We set out on this objective and show that it is possible to predict easy and difficult levels of mental tasks when subjects are attempting to solve Java programming problems. Using a proposed confidence threshold, we obtained a classification performance of 87.05% thereby showing that it is possible to use brain data to determine the teaching material difficulty level which will be useful in educational environments