Boston University

About: Boston University is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 48688 authors who have published 119622 publications receiving 6276020 citations. The organization is also known as: BU & Boston U.

Synergistic drug combinations tend to improve therapeutically relevant selectivity.

Abstract: Drug combinations are a promising strategy to overcome the compensatory mechanisms and unwanted off-target effects that limit the utility of many potential drugs. However, enthusiasm for this approach is tempered by concerns that the therapeutic synergy of a combination will be accompanied by synergistic side effects. Using large scale simulations of bacterial metabolism and 94,110 multi-dose experiments relevant to diverse diseases, we provide evidence that synergistic drug combinations are generally more specific to particular cellular contexts than are single agent activities. We highlight six combinations whose selective synergy depends on multitarget drug activity. For one anti-inflammatory example, we show how such selectivity is achieved through differential expression of the drugs' targets in cell types associated with therapeutic, but not toxic, effects and validate its therapeutic relevance in a rat model of asthma. The context specificity of synergistic combinations creates many opportunities for therapeutically relevant selectivity and enables improved control of complex biological systems.

Changedetection.net: A new change detection benchmark dataset

Abstract: Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video dataset exists for benchmarking different methods. Presented here is a unique change detection benchmark dataset consisting of nearly 90,000 frames in 31 video sequences representing 6 categories selected to cover a wide range of challenges in 2 modalities (color and thermal IR). A distinguishing characteristic of this dataset is that each frame is meticulously annotated for ground-truth foreground, background, and shadow area boundaries — an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of change detection algorithms. This paper presents and discusses various aspects of the new dataset, quantitative performance metrics used, and comparative results for over a dozen previous and new change detection algorithms. The dataset, evaluation tools, and algorithm rankings are available to the public on a website1 and will be updated with feedback from academia and industry in the future.

Stochastic process with ultraslow convergence to a Gaussian: The truncated Lévy flight.

Abstract: We introduce a class of stochastic process, the truncated L\'evy flight (TLF), in which the arbitrarily large steps of a L\'evy flight are eliminated. We find that the convergence of the sum of $n$ independent TLFs to a Gaussian process can require a remarkably large value of $n$---typically $n\ensuremath{\approx}{10}^{4}$ in contrast to $n\ensuremath{\approx}10$ for common distributions. We find a well-defined crossover between a L\'evy and a Gaussian regime, and that the crossover carries information about the relevant parameters of the underlying stochastic process.

Numerical simulation and experimental validation of blood flow in arteries with structured-tree outflow conditions.

Abstract: Blood flow in the large systemic arteries is modeled using one-dimensional equations derived from the axisymmetric Navier–Stokes equations for flow in compliant and tapering vessels. The arterial tree is truncated after the first few generations of large arteries with the remaining small arteries and arterioles providing outflow boundary conditions for the large arteries. By modeling the small arteries and arterioles as a structured tree, a semi-analytical approach based on a linearized version of the governing equations can be used to derive an expression for the root impedance of the structured tree in the frequency domain. In the time domain, this provides the proper outflow boundary condition. The structured tree is a binary asymmetric tree in which the radii of the daughter vessels are scaled linearly with the radius of the parent vessel. Blood flow and pressure in the large vessels are computed as functions of time and axial distance within each of the arteries. Comparison between the simulations and magnetic resonance measurements in the ascending aorta and nine peripheral locations in one individual shows excellent agreement between the two. © 2000 Biomedical Engineering Society.