African-American patients with cancer Talking About Clinical Trials (TACT) with oncologists during consultations: evaluating the efficacy of tailored health messages in a randomised controlled trial—the TACT study protocol

Introduction Low rates of accrual of African-American (AA) patients with cancer to therapeutic clinical trials (CTs) represent a serious and modifiable racial disparity in healthcare that impedes the development of promising cancer therapies. Suboptimal physician–patient consultation communication is a barrier to the accrual of patients with cancer of any race, but communication difficulties are compounded with AA patients. Providing tailored health messages (THM) to AA patients and their physician about CTs has the potential to improve communication, lower barriers to accrual and ameliorate health disparities. Objective (1) Demonstrate the efficacy of THM to increase patient activation as measured by direct observation. (2) Demonstrate the efficacy of THM to improve patient outcomes associated with barriers to AA participation. (3) Explore associations among preconsultation levels of: (A) trust in medical researchers, (B) knowledge and attitudes towards CTs, (C) patient-family member congruence in decision-making, and (D) involvement/information preferences, and group assignment. Methods and analysis First, using established methods, we will develop THM materials. Second, the efficacy of the intervention is determined in a 2 by 2 factorial randomised controlled trial to test the effectiveness of (1) providing 357 AA patients with cancer with THM with 2 different ‘depths’ of tailoring and (2) either providing feedback to oncologists about the patients\u27 trial THM or not. The primary analysis compares patient engaged communication in 4 groups preconsultation and postconsultation. Ethics and dissemination This study was approved by the Virginia Commonwealth University Institutional Review Board. To facilitate use of the THM intervention in diverse settings, we will convene ‘user groups’ at 3 major US cancer centres. To facilitate dissemination, we will post all materials and the implementation guide in publicly available locations

Hi-C Observations of Penumbral Bright Dots

No abstract availabl

On the Maximum Crossing Number

Research about crossings is typically about minimization. In this paper, we consider \emph{maximizing} the number of crossings over all possible ways to draw a given graph in the plane. Alpert et al. [Electron. J. Combin., 2009] conjectured that any graph has a \emph{convex} straight-line drawing, e.g., a drawing with vertices in convex position, that maximizes the number of edge crossings. We disprove this conjecture by constructing a planar graph on twelve vertices that allows a non-convex drawing with more crossings than any convex one. Bald et al. [Proc. COCOON, 2016] showed that it is NP-hard to compute the maximum number of crossings of a geometric graph and that the weighted geometric case is NP-hard to approximate. We strengthen these results by showing hardness of approximation even for the unweighted geometric case and prove that the unweighted topological case is NP-hard.Comment: 16 pages, 5 figure

Extremal Optimization for Graph Partitioning

Extremal optimization is a new general-purpose method for approximating solutions to hard optimization problems. We study the method in detail by way of the NP-hard graph partitioning problem. We discuss the scaling behavior of extremal optimization, focusing on the convergence of the average run as a function of runtime and system size. The method has a single free parameter, which we determine numerically and justify using a simple argument. Our numerical results demonstrate that on random graphs, extremal optimization maintains consistent accuracy for increasing system sizes, with an approximation error decreasing over runtime roughly as a power law t^(-0.4). On geometrically structured graphs, the scaling of results from the average run suggests that these are far from optimal, with large fluctuations between individual trials. But when only the best runs are considered, results consistent with theoretical arguments are recovered.Comment: 34 pages, RevTex4, 1 table and 20 ps-figures included, related papers available at http://www.physics.emory.edu/faculty/boettcher

Submaximal Oxygen Uptake Kinetics, Functional Mobility, and Physical Activity in Older Adults with Heart Failure and Reduced Ejection Fraction

Background: Submaximal oxygen uptake measures are more feasible and may better predict clinical cardiac outcomes than maximal tests in older adults with heart failure (HF). We examined relationships between maximal oxygen uptake, submaximal oxygen kinetics, functional mobility, and physical activity in older adults with HF and reduced ejection fraction. Methods: Older adults with HF and reduced ejection fraction (n = 25, age 75 ± 7 years) were compared to 25 healthy age- and gender-matched controls. Assessments included a maximal treadmill test for peak oxygen uptake (VO2peak), oxygen uptake kinetics at onset of and on recovery from a submaximal treadmill test, functional mobility testing [Get Up and Go (GUG), Comfortable Gait Speed (CGS), Unipedal Stance (US)], and self-reported physical activity (PA). Results: Compared to controls, HF had worse performance on GUG, CGS, and US, greater delays in submaximal oxygen uptake kinetics, and lower PA. In controls, VO2peak was more strongly associated with functional mobility and PA than submaximal oxygen uptake kinetics. In HF patients, submaximal oxygen uptake kinetics were similarly associated with GUG and CGS as VO2peak, but weakly associated with PA. Conclusions: Based on their mobility performance, older HF patients with reduced ejection fraction are at risk for adverse functional outcomes. In this population, submaximal oxygen uptake measures may be equivalent to VO2 peak in predicting functional mobility, and in addition to being more feasible, may provide better insight into how aerobic function relates to mobility in older adults with HF

Finding community structure in networks using the eigenvectors of matrices

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.Comment: 22 pages, 8 figures, minor corrections in this versio