Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools.

Endothelial to mesenchymal transition (EndMT) plays a major role during development, and also contributes to several adult cardiovascular diseases. Importantly, mesenchymal cells including fibroblasts are prominent in atherosclerosis, with key functions including regulation of: inflammation, matrix and collagen production, and plaque structural integrity. However, little is known about the origins of atherosclerosis-associated fibroblasts. Here we show using endothelial-specific lineage-tracking that EndMT-derived fibroblast-like cells are common in atherosclerotic lesions, with EndMT-derived cells expressing a range of fibroblast-specific markers. In vitro modelling confirms that EndMT is driven by TGF-β signalling, oxidative stress and hypoxia; all hallmarks of atherosclerosis. ‘Transitioning' cells are readily detected in human plaques co-expressing endothelial and fibroblast/mesenchymal proteins, indicative of EndMT. The extent of EndMT correlates with an unstable plaque phenotype, which appears driven by altered collagen-MMP production in EndMT-derived cells. We conclude that EndMT contributes to atherosclerotic patho-biology and is associated with complex plaques that may be related to clinical events.

This paper reports on an algorithm for automatic, targetless, extrinsic calibration of a lidar and optical camera system based upon the maximization of mutual information between the sensor-measured surface intensities. The proposed method is completely data-driven and does not require any fiducial calibration targets-making in situ calibration easy. We calculate the Cramér-Rao lower bound (CRLB) of the estimated calibration parameter variance, and we show experimentally that the sample variance of the estimated parameters empirically approaches the CRLB when the amount of data used for calibration is sufficiently large. Furthermore, we compare the calibration results to independent ground-truth (where available) and observe that the mean error empirically approaches zero as the amount of data used for calibration is increased, thereby suggesting that the proposed estimator is a minimum variance unbiased estimate of the calibration parameters. Experimental results are presented for three different lidar-camera systems: (i) a three-dimensional (3D) lidar and omnidirectional camera, (ii) a 3D time-of-flight sensor and monocular camera, and (iii) a 2D lidar and monocular camera. C