Graduated embodiment for sophisticated agent evolution and optimization.

We summarize the results of a project to develop evolutionary computing methods for the design of behaviors of embodied agents in the form of autonomous vehicles. We conceived and implemented a strategy called graduated embodiment. This method allows high-level behavior algorithms to be developed using genetic programming methods in a low-fidelity, disembodied modeling environment for migration to high-fidelity, complex embodied applications. This project applies our methods to the problem domain of robot navigation using adaptive waypoints, which allow navigation behaviors to be ported among autonomous mobile robots with different degrees of embodiment, using incremental adaptation and staged optimization. Our approach to biomimetic behavior engineering is a hybrid of human design and artificial evolution, with the application of evolutionary computing in stages to preserve building blocks and limit search space. The methods and tools developed for this project are directly applicable to other agent-based modeling needs, including climate-related conflict analysis, multiplayer training methods, and market-based hypothesis evaluation

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Identification of prescription opioid misuse and abuse behaviors and risk factors in chronic pain patients using the Prescription Opioid Misuse and Abuse Questionnaire (POMAQ)

To identify patient risk factors associated with prescription opioid misuse and abuse as well as groupings of misuse and abuse behaviors as measured by the Prescription Opioid Misuse and Abuse Questionnaire (POMAQ). Adults with chronic pain requiring long-term treatment with opioids completed the POMAQ and other study questionnaires. Latent class analysis (LCA) was used to examine underlying subgroups exhibiting particular risk profiles. Patient demographic and clinical characteristics were examined as covariates and the concordance between the identified latent classes at-risk classifications and the POMAQ clinical scoring algorithm was assessed. Analysis of data from 809 patients revealed 4 classes: “chronic pain, low risk” (n = 473, low to no prevalence of POMAQ behaviors), “chronic pain, comorbid condition” (n = 152, high prevalence of anti-anxiety, sleeping pill, and antihistamine use), “at risk” (n = 154, taking more opioids than prescribed and drinking alcohol with opioids more frequently than other groups), and “high risk” (n = 30, highest prevalence of each behavior). The “high risk” group was associated with being younger, less educated, and unemployed compared to other groups. When examining the LCA classes by groups defined by the original POMAQ clinical scoring algorithm, the “high risk” class had the highest proportion of participants identified with abuse behaviors (46.7%), compared to just 4.7% in the “chronic pain, low risk” group. Findings suggest there are four distinct subgroups of patients defined by chronic opioid misuse and abuse behaviors and support the use of the POMAQ to identify risk factors associated with prescription opioid misuse and abuse.</p