Pressure-Sensitive Paint: Effect of Substrate

There are numerous ways in which pressure-sensitive paint can be applied to a surface. The choice of substrate and application method can greatly affect the results obtained. The current study examines the different methods of applying pressure-sensitive paint to a surface. One polymer-based and two porous substrates (anodized aluminum and thin-layer chromatography plates) are investigated and compared for luminescent output, pressure sensitivity, temperature sensitivity and photodegradation. Two luminophores [tris-Bathophenanthroline Ruthenium(II) Perchlorate and Platinum-tetrakis (pentafluorophenyl) Porphyrin] will also be compared in all three of the substrates. The results show the applicability of the different substrates and luminophores to different testing environments

Analysis of long branch extraction and long branch shortening.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Long branch attraction (LBA) is a problem that afflicts both the parsimony and maximum likelihood phylogenetic analysis techniques. Research has shown that parsimony is particularly vulnerable to inferring the wrong tree in Felsenstein topologies. The long branch extraction method is a procedure to detect a data set suffering from this problem so that Maximum Likelihood could be used instead of Maximum Parsimony. RESULTS: The long branch extraction method has been well cited and used by many authors in their analysis but no strong validation has been performed as to its accuracy. We performed such an analysis by an extensive search of the branch length search space under two topologies of six taxa, a Felsenstein-like topology and Farris-like topology. We also examine a long branch shortening method. CONCLUSIONS: The long branch extraction method seems to mask the majority of the search space rendering it ineffective as a detection method of LBA. A proposed alternative, the long branch shortening method, is also ineffective in predicting long branch attraction for all tree topologies

Phylogenetic search through partial tree mixing.

BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques. RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda CONCLUSIONS: The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution

Inferring gene regulatory networks from asynchronous microarray data with AIRnet

Background Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost. Results Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments. Conclusions By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data

Executive function testing to assist identifi cation of pitch-side concussion in elite rugby players

Abstract Background Current methods for assessing concussion during rugby matches rely on rudimentary behavioural assessment, focusing on balance and gross motor function. Cognitive testing with the Sports Concussion Assessment Tool has recently been included, but there are a paucity of normative and baseline data for this test. This study examined the utility of the Trail Making Test (TMT), which is a neuropsychological test of executive function in two parts (TMT-A and TMT-B), to assist identification of cognitive impairments caused by impacts during rugby games. Methods 27 elite male rugby league players contracted to a professional rugby club were recruited towards the end of the season. Each player was tested on three occasions within a 2 week period with both TMT-A and TMT-B for baseline assessment. Each player was additionally assessed after full contact training on 2 consecutive days and during preseason training. Individual baseline data were calculated from the best of the baseline assessments, and time differences were examined with ANOVA. Findings No instances of concussion occurred during data collection. For TMT-A there was no significant difference (F(3, 24)=2·88, I2=0·27) between baseline (mean 13·79 s [SD 5·32], 95% CI 9·34–18·23), post-training day 1 (11·38 [2·63], 9·18–13·58), post-training day 2 (11·16 [1·94], 9·55–12·79), and preseason (11·79 [2·64], 9·58–13·99). For TMT-B there was no significant difference between baseline (31·50 [5·37], 27·01–35·99), post-training day 1 (28·07 [8·82], 20·70–35·44), post-training day 2 (26·18 [6·16], 21·03–31·33), and preseason (26·98 [4·89], 22·89–31·07). Interpretation These findings indicate that there were no significant differences in performance of these executive tasks from baseline to post-training (end of season and preseason). These data show stability of TMT-A and TMT-B data across a competitive rugby league season. Importantly, use of measures of variation such as CIs for these tasks can provide a metric for calculating minimally important clinical differences within cognition. Funding None

On the use of cartographic projections in visualizing phylo-genetic tree space

Phylogenetic analysis is becoming an increasingly important tool for biological research. Applications include epidemiological studies, drug development, and evolutionary analysis. Phylogenetic search is a known NP-Hard problem. The size of the data sets which can be analyzed is limited by the exponential growth in the number of trees that must be considered as the problem size increases. A better understanding of the problem space could lead to better methods, which in turn could lead to the feasible analysis of more data sets. We present a definition of phylogenetic tree space and a visualization of this space that shows significant exploitable structure. This structure can be used to develop search methods capable of handling much larger data sets

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts