Genetic Incorporation of Human Metallothionein into the Adenovirus Protein IX for Non-Invasive SPECT Imaging

As the limits of existing treatments for cancer are recognized, clearly novel therapies must be considered for successful treatment; cancer therapy using adenovirus vectors is a promising strategy. However tracking the biodistribution of adenovirus vectors in vivo is limited to invasive procedures such as biopsies, which are error prone, non-quantitative, and do not give a full representation of the pharmacokinetics involved. Current non-invasive imaging strategies using reporter gene expression have been applied to analyze adenoviral vectors. The major drawback to approaches that tag viruses with reporter genes is that these systems require initial viral infection and subsequent cellular expression of a reporter gene to allow non-invasive imaging. As an alternative to conventional vector detection techniques, we developed a specific genetic labeling system whereby an adenoviral vector incorporates a fusion between capsid protein IX and human metallothionein. Our study herein clearly demonstrates our ability to rescue viable adenoviral particles that display functional metallothionein (MT) as a component of their capsid surface. We demonstrate the feasibility of 99mTc binding in vitro to the pIX-MT fusion on the capsid of adenovirus virions using a simple transchelation reaction. SPECT imaging of a mouse after administration of a 99mTc-radiolabeled virus showed clear localization of radioactivity to the liver. This result strongly supports imaging using pIX-MT, visualizing the normal biodistribution of Ad primarily to the liver upon injection into mice. The ability we have developed to view real-time biodistribution in their physiological milieu represents a significant tool to study adenovirus biology in vivo

Efficient multi-agent path planning

Abstract. Animating goal-driven agents in an environment with obstacles is a time consuming process, particularly when the number of agents is large. In this paper, we introduce an efficient algorithm that creates path plans for objects that move between user defined goal points and avoids collisions. In addition, the system allows “culling ” of some of the computation for invisible agents: agents are accurately simulated only if they are visible to the user while the invisible objects are approximated probabilistically. The approximations ensure that the agent’s behaviors match those that would occur had they been fully simulated, and result in significant speedups over running the accurate simulation for all agents

Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations

Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so – and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the Pvalues they produce) can lead to small P-values even if the declared test hypothesis is correct, and can lead to large P-values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P-values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting