Identifying the favored mutation in a positive selective sweep.

Most approaches that capture signatures of selective sweeps in population genomics data do not identify the specific mutation favored by selection. We present iSAFE (for "integrated selection of allele favored by evolution"), a method that enables researchers to accurately pinpoint the favored mutation in a large region (∼5 Mbp) by using a statistic derived solely from population genetics signals. iSAFE does not require knowledge of demography, the phenotype under selection, or functional annotations of mutations

Aromatase expression is increased in BRCA1 mutation carriers

<p>Abstract</p> <p>Background</p> <p>Until recently, the molecular mechanisms explaining increased incidence of ovarian and breast cancers in carriers of <it>BRCA1 </it>gene mutations had not been clearly understood. Of significance is the finding that BRCA1 negatively regulates aromatase expression <it>in vitro</it>. Our objective was to characterise aromatase gene <it>(CYP19A1) </it>and its promoter expression in breast adipose and ovarian tissue in <it>BRCA1 </it>mutation carriers and unaffected controls.</p> <p>Methods</p> <p>We measured aromatase transcripts, total and promoter-specific (PII, PI.3, PI.4) in prophylactic oophorectomy or mastectomy, therapeutic mastectomy, ovarian and breast tissue from unaffected women.</p> <p>Results</p> <p>We demonstrate that the lack of functional BRCA1 protein correlates to higher aromatase levels in 85% of <it>BRCA1 </it>mutation carriers. This increase is mediated by aberrant transcriptional regulation of aromatase; in breast adipose by increases in promoter II/I.3 and I.4-specific transcripts; and in the ovary with elevation in promoter I.3 and II-specific transcripts.</p> <p>Conclusion</p> <p>Understanding the link between BRCA1 and aromatase is significant in terms of understanding why carcinogenesis is restricted to estrogen-producing tissues in <it>BRCA1 </it>mutation carriers.</p

Why I tense up when you watch me: inferior parietal cortex mediates an audience’s influence on motor performance

The presence of an evaluative audience can alter skilled motor performance through changes in force output. To investigate how this is mediated within the brain, we emulated real-time social monitoring of participants’ performance of a fine grip task during functional magnetic resonance neuroimaging. We observed an increase in force output during social evaluation that was accompanied by focal reductions in activity within bilateral inferior parietal cortex. Moreover, deactivation of the left inferior parietal cortex predicted both inter- and intra-individual differences in socially-induced change in grip force. Social evaluation also enhanced activation within the posterior superior temporal sulcus, which conveys visual information about others’ actions to the inferior parietal cortex. Interestingly, functional connectivity between these two regions was attenuated by social evaluation. Our data suggest that social evaluation can vary force output through the altered engagement of inferior parietal cortex; a region implicated in sensorimotor integration necessary for object manipulation, and a component of the action-observation network which integrates and facilitates performance of observed actions. Social-evaluative situations may induce high-level representational incoherence between one’s own intentioned action and the perceived intention of others which, by uncoupling the dynamics of sensorimotor facilitation, could ultimately perturbe motor output

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

VEZF1 elements mediate protection from DNA methylation

There is growing consensus that genome organization and long-range gene regulation involves partitioning of the genome into domains of distinct epigenetic chromatin states. Chromatin insulator or barrier elements are key components of these processes as they can establish boundaries between chromatin states. The ability of elements such as the paradigm &#946;-globin HS4 insulator to block the range of enhancers or the spread of repressive histone modifications is well established. Here we have addressed the hypothesis that a barrier element in vertebrates should be capable of defending a gene from silencing by DNA methylation. Using an established stable reporter gene system, we find that HS4 acts specifically to protect a gene promoter from de novo DNA methylation. Notably, protection from methylation can occur in the absence of histone acetylation or transcription. There is a division of labor at HS4; the sequences that mediate protection from methylation are separable from those that mediate CTCF-dependent enhancer blocking and USF-dependent histone modification recruitment. The zinc finger protein VEZF1 was purified as the factor that specifically interacts with the methylation protection elements. VEZF1 is a candidate CpG island protection factor as the G-rich sequences bound by VEZF1 are frequently found at CpG island promoters. Indeed, we show that VEZF1 elements are sufficient to mediate demethylation and protection of the APRT CpG island promoter from DNA methylation. We propose that many barrier elements in vertebrates will prevent DNA methylation in addition to blocking the propagation of repressive histone modifications, as either process is sufficient to direct the establishment of an epigenetically stable silent chromatin stat

On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload

Transition of plasmodium sporozoites into liver stage-like forms is regulated by the RNA binding protein pumilio

Many eukaryotic developmental and cell fate decisions that are effected post-transcriptionally involve RNA binding proteins as regulators of translation of key mRNAs. In malaria parasites (Plasmodium spp.), the development of round, non-motile and replicating exo-erythrocytic liver stage forms from slender, motile and cell-cycle arrested sporozoites is believed to depend on environmental changes experienced during the transmission of the parasite from the mosquito vector to the vertebrate host. Here we identify a Plasmodium member of the RNA binding protein family PUF as a key regulator of this transformation. In the absence of Pumilio-2 (Puf2) sporozoites initiate EEF development inside mosquito salivary glands independently of the normal transmission-associated environmental cues. Puf2- sporozoites exhibit genome-wide transcriptional changes that result in loss of gliding motility, cell traversal ability and reduction in infectivity, and, moreover, trigger metamorphosis typical of early Plasmodium intra-hepatic development. These data demonstrate that Puf2 is a key player in regulating sporozoite developmental control, and imply that transformation of salivary gland-resident sporozoites into liver stage-like parasites is regulated by a post-transcriptional mechanism