Ensuring respect for persons in COMPASS: A cluster randomised pragmatic clinical trial

Cluster randomised clinical trials present unique challenges in meeting ethical obligations to those who are treated at a randomised site. Obtaining informed consent for research within the context of clinical care is one such challenge. In order to solve this problem it is important that an informed consent process be effective and efficient, and that it does not impede the research or the healthcare. The innovative approach to informed consent employed in the COMPASS study demonstrates the feasibility of upholding ethical standards without imposing undue burden on clinical workflows, staff members or patients who may participate in the research by virtue of their presence in a cluster randomised facility. The COMPASS study included 40 randomised sites and compared the effectiveness of a postacute stroke intervention with standard care. Each site provided either the comprehensive postacute stroke intervention or standard care according to the randomisation assignment. Working together, the study team, institutional review board and members of the community designed an ethically appropriate and operationally reasonable consent process which was carried out successfully at all randomised sites. This achievement is noteworthy because it demonstrates how to effectively conduct appropriate informed consent in cluster randomised trials, and because it provides a model that can easily be adapted for other pragmatic studies. With this innovative approach to informed consent, patients have access to the information they need about research occurring where they are seeking care, and medical researchers can conduct their studies without ethical concerns or unreasonable logistical impediments. Trial registration number NCT02588664, recruiting. This article covers the development of consent process that is currentlty being employed in the study

Measurement of Bottom versus Charm as a Function of Transverse Momentum with Electron-Hadron Correlations in p+p Collisions at sqrt(s)=200 GeV

The momentum distribution of electrons from semi-leptonic decays of charm and bottom for mid-rapidity |y|<0.35 in p+p collisions at sqrt(s)=200 GeV is measured by the PHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) over the transverse momentum range 2 < p_T < 7 GeV/c. The ratio of the yield of electrons from bottom to that from charm is presented. The ratio is determined using partial D/D^bar --> e^{+/-} K^{-/+} X (K unidentified) reconstruction. It is found that the yield of electrons from bottom becomes significant above 4 GeV/c in p_T. A fixed-order-plus-next-to-leading-log (FONLL) perturbative quantum chromodynamics (pQCD) calculation agrees with the data within the theoretical and experimental uncertainties. The extracted total bottom production cross section at this energy is \sigma_{b\b^bar}= 3.2 ^{+1.2}_{-1.1}(stat) ^{+1.4}_{-1.3}(syst) micro b.Comment: 432 authors, 6 pages text, 3 figures. Submitted to Phys. Rev. Lett. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

Assessing the feasibility of interrupting the transmission of soil-transmitted helminths through mass drug administration: The DeWorm3 cluster randomized trial protocol

DeWorm3 collectionThe file attached is the Published/publisher’s pdf version of the article.© 2018 Ásbjörnsdóttir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Three-dimensional mapping of light transmittance and foliage distribution using lidar

The horizontal and vertical distributions of light transmittance were evaluated as a function of foliage distribution using lidar (light detection and ranging) observations for a sugar maple (Acer saccharum) stand in the Turkey Lakes Watershed. Along the vertical profile of vegetation, horizontal slices of probability of light transmittance were derived from an Optech ALTM 1225 instrument's return pulses (two discrete, 15-cm diameter returns) using indicator kriging. These predictions were compared with (i) below canopy (1-cm spatial resolution) transect measurements of the fraction of photosynthetically active radiation (FPAR) and (ii) measurements of tree height. A first-order trend was initially removed from the lidar returns. The vertical distribution of vegetation height was then sliced into nine percentiles and indicator variograms were fitted to them. Variogram parameters were found to vary as a function of foliage height above ground. In this paper, we show that the relationship between ground measurements of FPAR and kriged estimates of vegetation cover becomes stronger and tighter at coarser spatial resolutions. Three-dimensional maps of foliage distribution were computed as stacks of the percentile probability surfaces. These probability surfaces showed correspondence with individual tree-based observations and provided a much more detailed characterization of quasi-continuous foliage distribution. These results suggest that discrete-return lidar provides a promising technology to capture variations of foliage characteristics in forests to support the development of functional linkages between biophysical and ecological studies

Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy)

In some cases a canopy height model (CHM) is the only available source of forest height information. For these cases it is important to understand the predictive power of CHM data for forest attributes. In this study we examined the use of lidar-derived CHM metrics to predict forest structure classes according to the amount of basal area present in understory, midstory, and overstory trees. We evaluated two approaches to predict sizebased forest classifications: in the first, we attempted supervised classification with both linear discriminant analysis (LDA) and random forest (RF); in the second, we predicted basal areas of lower, mid, and upper canopy trees from CHM-derived variables by k-nearest neighbour imputation (k-NN) and parametric regression, and then classified observations based on their predicted basal areas. We used leave-one-out cross-validation to evaluate our ability to predict forest structure classes from CHM data and in the case of prediction-based classification approach we look at the performances in predicting basal area. The strategies proved moderately successful with a best overall classification accuracy of 41% in the case of LDA. In general, we were most successful in predicting the basal areas of small and large trees (R 2 respectively of 71% and 69% in the case of k-NN imputation)