Mental state space visualization for interactive modeling of personalized BCI control strategies.

OBJECTIVE: Numerous studies in the area of BCI are focused on the search for a better experimental paradigm-a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are motor imagery, mental computations, etc. We propose a technique that instead allows the user to try different mental actions in the search for the ones that will work best. APPROACH: The system is based on a modification of the self-organizing map (SOM) algorithm and enables interactive communication between the user and the learning system through a visualization of user's mental state space. During the interaction with the system the user converges on the paradigm that is most efficient and intuitive for that particular user. MAIN RESULTS: Results of the two experiments, one allowing muscular activity, another permitting mental activity only, demonstrate soundness of the proposed method and offer preliminary validation of the performance improvement over the traditional closed-loop feedback approach. SIGNIFICANCE: The proposed method allows a user to visually explore their mental state space in real time, opening new opportunities for scientific inquiry. The application of this method to the area of brain-computer interfaces enables more efficient search for the mental states that will allow a user to reliably control a BCI system.

A critique of national solidarity in transnational organ sharing in Europe.

In this article, I critically examine the principle of national solidarity in organ sharing across national borders. More specifically, I analyse the policy foundations of solidarity in the transnational allocation of organs and its implementation in the system of national balance points adopted in Europe. I argue that the system of national balance points is based on statist collectivism and therefore is oriented more toward collective, rather than individual welfare. The same collective welfare rationale is also evident from leading policy statements about self-sufficiency in organ donation that seem to assume that cross-border organ sharing can be wrong if collective welfare is violated. This collectivist system of organ sharing can produce unjust results to individual candidates for organ transplantation. I propose several measures to reform the existing solidarity-based framework for the procurement and allocation of organs in order to balance the collective and the individual welfare of the donors and recipients of organs. I also discuss the implications of adopting that proposal.

Age-related profiling of DNA methylation in CD8+ T cells reveals changes in immune response and transcriptional regulator genes.

Human ageing affects the immune system resulting in an overall decline in immunocompetence. Although all immune cells are affected during aging, the functional capacity of T cells is most influenced and is linked to decreased responsiveness to infections and impaired differentiation. We studied age-related changes in DNA methylation and gene expression in CD4+ and CD8+ T cells from younger and older individuals. We observed marked difference between T cell subsets, with increased number of methylation changes and higher methylome variation in CD8+ T cells with age. The majority of age-related hypermethylated sites were located at CpG islands of silent genes and enriched for repressive histone marks. Specifically, in CD8+ T cell subset we identified strong inverse correlation between methylation and expression levels in genes associated with T cell mediated immune response (LGALS1, IFNG, CCL5, GZMH, CCR7, CD27 and CD248) and differentiation (SATB1, TCF7, BCL11B and RUNX3). Our results thus suggest the link between age-related epigenetic changes and impaired T cell function.

Fast probabilistic file fingerprinting for big data.

BACKGROUND: Biological data acquisition is raising new challenges, both in data analysis and handling. Not only is it proving hard to analyze the data at the rate it is generated today, but simply reading and transferring data files can be prohibitively slow due to their size. This primarily concerns logistics within and between data centers, but is also important for workstation users in the analysis phase. Common usage patterns, such as comparing and transferring files, are proving computationally expensive and are tying down shared resources. RESULTS: We present an efficient method for calculating file uniqueness for large scientific data files, that takes less computational effort than existing techniques. This method, called Probabilistic Fast File Fingerprinting (PFFF), exploits the variation present in biological data and computes file fingerprints by sampling randomly from the file instead of reading it in full. Consequently, it has a flat performance characteristic, correlated with data variation rather than file size. We demonstrate that probabilistic fingerprinting can be as reliable as existing hashing techniques, with provably negligible risk of collisions. We measure the performance of the algorithm on a number of data storage and access technologies, identifying its strengths as well as limitations. CONCLUSIONS: Probabilistic fingerprinting may significantly reduce the use of computational resources when comparing very large files. Utilisation of probabilistic fingerprinting techniques can increase the speed of common file-related workflows, both in the data center and for workbench analysis. The implementation of the algorithm is available as an open-source tool named pfff, as a command-line tool as well as a C library. The tool can be downloaded from http://biit.cs.ut.ee/pfff.

G =  MAT: linking transcription factor expression and DNA binding data.

Transcription factors are proteins that bind to motifs on the DNA and thus affect gene expression regulation. The qualitative description of the corresponding processes is therefore important for a better understanding of essential biological mechanisms. However, wet lab experiments targeted at the discovery of the regulatory interplay between transcription factors and binding sites are expensive. We propose a new, purely computational method for finding putative associations between transcription factors and motifs. This method is based on a linear model that combines sequence information with expression data. We present various methods for model parameter estimation and show, via experiments on simulated data, that these methods are reliable. Finally, we examine the performance of this model on biological data and conclude that it can indeed be used to discover meaningful associations. The developed software is available as a web tool and Scilab source code at http://biit.cs.ut.ee/gmat/.

Comprehensive transcriptome analysis of mouse embryonic stem cell adipogenesis unravels new processes of adipocyte development.

BACKGROUND: The current epidemic of obesity has caused a surge of interest in the study of adipose tissue formation. While major progress has been made in defining the molecular networks that control adipocyte terminal differentiation, the early steps of adipocyte development and the embryonic origin of this lineage remain largely unknown. RESULTS: Here we performed genome-wide analysis of gene expression during adipogenesis of mouse embryonic stem cells (ESCs). We then pursued comprehensive bioinformatic analyses, including de novo functional annotation and curation of the generated data within the context of biological pathways, to uncover novel biological functions associated with the early steps of adipocyte development. By combining in-depth gene regulation studies and in silico analysis of transcription factor binding site enrichment, we also provide insights into the transcriptional networks that might govern these early steps. CONCLUSIONS: This study supports several biological findings: firstly, adipocyte development in mouse ESCs is coupled to blood vessel morphogenesis and neural development, just as it is during mouse development. Secondly, the early steps of adipocyte formation involve major changes in signaling and transcriptional networks. A large proportion of the transcription factors that we uncovered in mouse ESCs are also expressed in the mouse embryonic mesenchyme and in adipose tissues, demonstrating the power of our approach to probe for genes associated with early developmental processes on a genome-wide scale. Finally, we reveal a plethora of novel candidate genes for adipocyte development and present a unique resource that can be further explored in functional assays.