Investigating five key predictive text entry with combined distance and keystroke modelling

This paper investigates text entry on mobile devices using only five-keys. Primarily to support text entry on smaller devices than mobile phones, this method can also be used to maximise screen space on mobile phones. Reported combined Fitt's law and keystroke modelling predicts similar performance with bigram prediction using a five-key keypad as is currently achieved on standard mobile phones using unigram prediction. User studies reported here show similar user performance on five-key pads as found elsewhere for novice nine-key pad users

Sedentary time and markers of inflammation in people with newly diagnosed type 2 diabetes

This is the final version. Available on open access from Elsevier via the DOI in this recordBACKGROUND AND AIMS: We investigated whether objectively measured sedentary time was associated with markers of inflammation in adults with newly diagnosed type 2 diabetes. METHODS AND RESULTS: We studied 285 adults (184 men, 101 women, mean age 59.0 ± 9.7) who had been recruited to the Early ACTivity in Diabetes (Early ACTID) randomised controlled trial. C-reactive protein (CRP), adiponectin, soluble intracellular adhesion molecule-1 (sICAM-1), interleukin-6 (IL-6), and accelerometer-determined sedentary time and moderate-vigorous physical activity (MVPA) were measured at baseline and after six-months. Linear regression analysis was used to investigate the independent cross-sectional and longitudinal associations of sedentary time with markers of inflammation. At baseline, associations between sedentary time and IL-6 were observed in men and women, an association that was attenuated following adjustment for waist circumference. After 6 months of follow-up, sedentary time was reduced by 0.4 ± 1.2 h per day in women, with the change in sedentary time predicting CRP at follow-up. Every hour decrease in sedentary time between baseline and six-months was associated with 24% (1, 48) lower CRP. No changes in sedentary time between baseline and 6 months were seen in men. CONCLUSIONS: Higher sedentary time is associated with IL-6 in men and women with type 2 diabetes, and reducing sedentary time is associated with improved levels of CRP in women. Interventions to reduce sedentary time may help to reduce inflammation in women with type 2 diabetes.National Institute for Health Research (NIHR

A discrete random model describing bedrock profile abrasion

We use a simple, collision-based, discrete, random abrasion model to compute the profiles for the stoss faces in a bedrock abrasion process. The model is the discrete equivalent of the generalized version of a classical, collision based model of abrasion. Three control parameters (which describe the average size of the colliding objects, the expected direction of the impacts and the average volume removed from the body due to one collision) are sufficient for realistic predictions. Our computations show the robust emergence of steady state shapes, both the geometry and the time evolution of which shows good quantitative agreement with laboratory experiments.Comment: 9 pages, 6 figure

Adjunctive long-acting risperidone in patients with bipolar disorder who relapse frequently and have active mood symptoms

<p>Abstract</p> <p>Background</p> <p>The objective of this exploratory analysis was to characterize efficacy and onset of action of a 3-month treatment period with risperidone long-acting injection (RLAI), adjunctive to an individual's treatment regimen, in subjects with symptomatic bipolar disorder who relapsed frequently and had significant symptoms of mania and/or depression.</p> <p>Methods</p> <p>Subjects with bipolar disorder with ≥4 mood episodes in the past 12 months entered the open-label stabilization phase preceding a placebo-controlled, double-blind study. Subjects with significant depressive or manic/mixed symptoms at baseline were analyzed. Significant depressive symptoms were defined as Montgomery-Åsberg Depression Rating Scale (MADRS) ≥16 and Young Mania Rating Scale (YMRS) < 16; manic/mixed symptoms were YMRS ≥16 with any MADRS score. Subjects received open-label RLAI (25-50 mg every 2 weeks) for 16 weeks, adjunctive to a subject's individualized treatment for bipolar disorder (mood stabilizers, antidepressants, and/or anxiolytics). Clinical status was evaluated with the Clinical Global Impressions of Bipolar Disorder-Severity (CGI-BP-S) scale and changes on the MADRS and YMRS scales. Within-group changes were evaluated using paired <it>t </it>tests; categorical differences were assessed using Fisher exact test. No adjustment was made for multiplicity.</p> <p>Results</p> <p>162 subjects who relapsed frequently met criteria for significant mood symptoms at open-label baseline; 59/162 (36.4%) had depressive symptoms, 103/162 (63.6%) had manic/mixed symptoms. Most subjects (89.5%) were receiving ≥1 medication for bipolar disorder before enrollment. Significant improvements were observed for the total population on the CGI-BP-S, MADRS, and YMRS scales (p < .001 vs. baseline, all variables). Eighty-two (53.3%) subjects achieved remission at the week 16 LOCF end point. The subpopulation with depressive symptoms at open-label baseline experienced significant improvement on the CGI-BP-S and MADRS scales (p < .001 vs. baseline, all variables). Subjects with manic/mixed symptoms at baseline had significant improvements on the CGI-BP-S and YMRS scales (p < .001 vs. baseline, all variables). No unexpected tolerability findings were observed.</p> <p>Conclusions</p> <p>Exploratory analysis of changes in overall clinical status and depression/mania symptoms in subjects with symptomatic bipolar disorder who relapse frequently showed improvements in each of these areas after treatment with RLAI, adjunctive to a subject's individualized treatment. Prospective controlled studies are needed to confirm these findings.</p

Annotation and query of tissue microarray data using the NCI Thesaurus

<p>Abstract</p> <p>Background</p> <p>The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult.</p> <p>Results</p> <p>We developed methods to map these annotations to the NCI thesaurus. Using the NCI-T we can effectively represent annotations for about 86% of the samples. We demonstrate how this mapping enables ontology driven integration and querying of tissue microarray data. We have deployed the mapping and ontology driven querying tools at the TMAD site for general use.</p> <p>Conclusion</p> <p>We have demonstrated that we can effectively map the diagnosis-related terms describing a sample in TMAD to the NCI-T. The NCI thesaurus terms have a wide coverage and provide terms for about 86% of the samples. In our opinion the NCI thesaurus can facilitate integration of this resource with other biological data.</p

ChromaSig: A Probabilistic Approach to Finding Common Chromatin Signatures in the Human Genome

Computational methods to identify functional genomic elements using genetic information have been very successful in determining gene structure and in identifying a handful of cis-regulatory elements. But the vast majority of regulatory elements have yet to be discovered, and it has become increasingly apparent that their discovery will not come from using genetic information alone. Recently, high-throughput technologies have enabled the creation of information-rich epigenetic maps, most notably for histone modifications. However, tools that search for functional elements using this epigenetic information have been lacking. Here, we describe an unsupervised learning method called ChromaSig to find, in an unbiased fashion, commonly occurring chromatin signatures in both tiling microarray and sequencing data. Applying this algorithm to nine chromatin marks across a 1% sampling of the human genome in HeLa cells, we recover eight clusters of distinct chromatin signatures, five of which correspond to known patterns associated with transcriptional promoters and enhancers. Interestingly, we observe that the distinct chromatin signatures found at enhancers mark distinct functional classes of enhancers in terms of transcription factor and coactivator binding. In addition, we identify three clusters of novel chromatin signatures that contain evolutionarily conserved sequences and potential cis-regulatory elements. Applying ChromaSig to a panel of 21 chromatin marks mapped genomewide by ChIP-Seq reveals 16 classes of genomic elements marked by distinct chromatin signatures. Interestingly, four classes containing enrichment for repressive histone modifications appear to be locally heterochromatic sites and are enriched in quickly evolving regions of the genome. The utility of this approach in uncovering novel, functionally significant genomic elements will aid future efforts of genome annotation via chromatin modifications

HumMeth27QCReport: an R package for quality control and primary analysis of Illumina Infinium methylation data

<p>Abstract</p> <p>Background</p> <p>The study of the human DNA methylome has gained particular interest in the last few years. Researchers can nowadays investigate the potential role of DNA methylation in common disorders by taking advantage of new high-throughput technologies. Among these, Illumina Infinium assays can interrogate the methylation levels of hundreds of thousands of CpG sites, offering an ideal solution for genome-wide methylation profiling. However, like for other high-throughput technologies, the main bottleneck remains at the stage of data analysis rather than data production.</p> <p>Findings</p> <p>We have developed <it>HumMeth27QCReport</it>, an R package devoted to researchers wanting to quickly analyse their Illumina Infinium methylation arrays. This package automates quality control steps by generating a report including sample-independent and sample-dependent quality plots, and performs primary analysis of raw methylation calls by computing data normalization, statistics, and sample similarities. This package is available at CRAN repository, and can be integrated in any Galaxy instance through the implementation of ad-hoc scripts accessible at Galaxy Tool Shed.</p> <p>Conclusions</p> <p>Our package provides users of the Illumina Infinium Methylation assays with a simplified, automated, open-source quality control and primary analysis of their methylation data. Moreover, to enhance its use by experimental researchers, the tool is being distributed along with the scripts necessary for its implementation in the Galaxy workbench. Finally, although it was originally developed for HumanMethylation27, we proved its compatibility with data generated with the HumanMethylation450 Bead Chip.</p

DNA methylation in interleukin-11 predicts clinical response to antidepressants in GENDEP

Transcriptional differences in interleukin-11 (IL11) after antidepressant treatment have been found to correspond to clinical response in major depressive disorder (MDD) patients. Expression differences were partly mediated by a single-nucleotide polymorphism (rs1126757), identified as a predictor of antidepressant response as part of a genome-wide association study. Here we attempt to identify whether DNA methylation, another baseline factor known to affect transcription factor binding, might also predict antidepressant response, using samples collected from the Genome-based Therapeutic Drugs for Depression project (GENDEP). DNA samples from 113 MDD individuals from the GENDEP project, who were treated with either escitalopram (n=80) or nortriptyline (n=33) for 12 weeks, were randomly selected. Percentage change in Montgomery-� sberg Depression Rating Scale scores between baseline and week 12 were utilized as our measure of antidepressant response. The Sequenom EpiTYPER platform was used to assess DNA methylation across the only CpG island located in the IL11 gene. Regression analyses were then used to explore the relationship between CpG unit methylation and antidepressant response. We identified a CpG unit predictor of general antidepressant response, a drug by CpG unit interaction predictor of response, and a CpG unit by rs1126757 interaction predictor of antidepressant response. The current study is the first to investigate the potential utility of pharmaco-epigenetic biomarkers for the prediction of antidepressant response. Our results suggest that DNA methylation in IL11 might be useful in identifying those patients likely to respond to antidepressants, and if so, the best drug suited to each individual

The low-virulent African swine fever virus (ASFV/NH/P68) induces enhanced expression and production of relevant regulatory cytokines (IFNα, TNFα and IL12p40) on porcine macrophages in comparison to the highly virulent ASFV/L60

The impact of infection by the low-virulent ASFV/NH/P68 (NHV) and the highly virulent ASFV/L60 (L60) isolates on porcine macrophages was assessed through the quantification of IFNα, TNFα, IL12p40, TGFβ and ASFV genes by real-time PCR at 2, 4 and 6 h post-infection. Increased IFNα, TNFα and IL12p40 expression was found in infection with NHV, in which expression of TGFβ was lower than in infection with L60. Principal component analysis showed a positive interaction of cytokines involved in cellular immune mechanisms, namely IFNα and IL12p40 in the NHV infection. Quantification by ELISA confirmed higher production of IFNα, TNFα and IL12p40 in the NHV-infected macrophages. Overall, our studies reinforce and clarify the effect of the NHV infection by targeting cellular and cellular-based immune responses relevant for pig survival against ASFV infection