BACKGROUND: Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window of several months, and cannot assess the cumulative impact of an intervention. RESULTS: We developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly from viral deep-sequencing data, with no need for additional variables. HIV-phyloTSI provides a continuous measure of TSI up to 9 years, with a mean absolute error of less than 12 months overall and less than 5 months for infections with a TSI of up to a year. It performs equally well for all major HIV subtypes based on data from African and European cohorts. CONCLUSIONS: We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.

We extend an earlier automatic motion-synthesis algorithm for physically realistic articulated figures in several ways. First, we summarize several incremental improvements to the original algorithm that improve its efficiency significantly and provide the user with some ability to influence what motions are generated. These techniques can be used by an animator to achieve a desired movement style, or they can be used to guarantee variety in the motions synthesized over several runs of the algorithm. Second, we report on new mechanisms that support the concatenation of existing, automatically generated motion controllers to produce complex, composite movement. Finally, we describe initial work on generalizing the techniques from 2D to 3D articulated figures. Taken together, these results illustrate the promise and challenges afforded by the controller-based approach to automatic motion synthesis for computer animation.