GTT Communications (United States)

Pulmonary arterial hypertension (PAH) is a complex disease characterized by progressive right ventricular (RV) failure leading to significant morbidity and mortality. Investigating metabolic features and pathways associated with RV dilation, mortality, and measures of disease severity can provide insight into molecular mechanisms, identify subphenotypes, and suggest potential therapeutic targets. We collected data from a prospective cohort of PAH participants and performed untargeted metabolomic profiling on 1045 metabolites from circulating blood. Analyses were intended to identify metabolomic differences across a range of common metrics in PAH (eg, dilated versus nondilated RV). Partial least squares discriminant analysis was first applied to assess the distinguishability of relevant outcomes. Significantly altered metabolites were then identified using linear regression, and Cox regression models (as appropriate for the specific outcome) with adjustments for age, sex, body mass index, and PAH cause. Models exploring RV maladaptation were further adjusted for pulmonary vascular resistance. Pathway enrichment analysis was performed to identify significantly dysregulated processes. A total of 117 participants with PAH were included. Partial least squares discriminant analysis showed cluster differentiation between participants with dilated versus nondilated RVs, survivors versus nonsurvivors, and across a range of NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels, REVEAL 2.0 composite scores, and 6-minute-walk distances. Polyamine and histidine pathways were associated with differences in RV dilation, mortality, NT-proBNP, REVEAL score, and 6-minute walk distance. Acylcarnitine pathways were associated with NT-proBNP, REVEAL score, and 6-minute walk distance. Sphingomyelin pathways were associated with RV dilation and NT-proBNP after adjustment for pulmonary vascular resistance. Distinct plasma metabolomic profiles are associated with RV dilation, mortality, and measures of disease severity in PAH. Polyamine, histidine, and sphingomyelin metabolic pathways represent promising candidates for identifying patients at high risk for poor outcomes and investigation into their roles as markers or mediators of disease progression and RV adaptation.

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Scrumming is an important set play in rugby football which frequently determines who wins the game. A pack of eight forwards (players) are required to coordinate their pushes, and timing, to exert pressure in the desired direction and so defeat the opposing pack. However, it has been difficult to objectively assess their cooperation due to technical limitations. We propose a convenient measurement method designed to evaluate the cooperation of players while scrumming by attaching compact inertial measurement units (IMUs) to the waists of the players. The acceleration and quaternions captured by the IMU allow us to calculate the acceleration vector and its peak time, and player posture. We found inter-trial changes in acceleration and posture between players during the scrums. Our proposed measurement system makes it easy to detect player coordination during joint actions in sports such as rugby.

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We forecast internal temperature in two homes, using variants of regression with data from the readings of multiple sensors. We use 48 separate models, where each forecasts mean temperatures that will occur in one future 15-minute interval, to compose a forecast for the next 12 hours. The sensors report internal and external atmospheric and environmental conditions such as temperature, pressure, sunlight, rain and wind, as well as evidence of human activity, including CO2 saturation, motion sensors and electrical load from areas within the house and large appliances. The models use both current and historical sensor values, each of which increases the number of predictors in the linear regression model. We use model simplification techniques including forward stepwise regression, principal component regression, and partial least squares regression. In both houses the forecast accuracy is stable; the mean absolute error over 12 hours is less than 1, while the root mean squared error is less than 1.3. Our accuracy compares favorably to previous work. Our work indicates long sensor histories for forecasts in the next 12 hours do not significantly improve accuracy.

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text M. De Leenheer, A. Koshibe, Y. Higuchi, N. Shiota, H. Wu, T. Furusawa, T. Tofigh, and G. Parulkar, "Open and Disaggregated Multi-Layer Networks," in Optical Fiber Communication Conference, OSA Technical Digest (online) (Optica Publishing Group, 2017), paper Tu3L.8. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article

v0.7.1 (June 2016) Added the ability to put "caps" on the error bars that are drawn by barplot or pointplot (and, by extension, factorplot). Additionally, the line width of the error bars can now be controlled. These changes involve the new parameters capsize and errwidth. See the github pull request for examples of usage. Improved the row and column colors display in clustermap. It is now possible to pass Pandas objects for these elements and, when possible, the semantic information in the Pandas objects will be used to add labels to the plot. When Pandas objects are used, the color data is matched against the main heatmap based on the index, not on position. This is more accurate, but it may lead to different results if current code assumed positional matching. Improved the luminance calculation that determines the annotation color in heatmap. The annot parameter of heatmap now accepts a rectangular dataset in addition to a boolean value. If a dataset is passed, its values will be used for the annotations, while the main dataset will be used for the heatmap cell colors. Fixed a bug in FacetGrid that appeared when using col_wrap with missing col levels. Made it possible to pass a tick locator object to the heatmap colorbar. Made it possible to use different styles (e.g., step) for PairGrid histograms when there are multiple hue levels. Fixed a bug in scipy-based univariate kernel density bandwidth calculation. The reset_orig function (and, by extension, importing seaborn.apionly) resets matplotlib rcParams to their values at the time seaborn itself was imported, which should work better with rcParams changed by the jupyter notebook backend. Removed some objects from the top-level seaborn namespace. Improved unicode compatibility in FacetGrid.