Cyanobacteria use micro-optics to sense light direction.

Bacterial phototaxis was first recognized over a century ago, but the method by which such small cells can sense the direction of illumination has remained puzzling. The unicellular cyanobacterium Synechocystis sp. PCC 6803 moves with Type IV pili and measures light intensity and color with a range of photoreceptors. Here, we show that individual Synechocystis cells do not respond to a spatiotemporal gradient in light intensity, but rather they directly and accurately sense the position of a light source. We show that directional light sensing is possible because Synechocystis cells act as spherical microlenses, allowing the cell to see a light source and move towards it. A high-resolution image of the light source is focused on the edge of the cell opposite to the source, triggering movement away from the focused spot. Spherical cyanobacteria are probably the world's smallest and oldest example of a camera eye.

Cyanobacteria are blue-green bacteria that are abundant in the environment. Cyanobacteria in the oceans are among the world's most important oxygen producers and carbon dioxide consumers. Synechocystis is a spherical single-celled cyanobacterium that measures about three thousandths of a millimetre across. Because Synechocystis needs sunlight to produce energy, it is important for it to find places where the light is neither too weak nor too strong. Unlike some bacteria, Synechocystis can't swim, but it can crawl across surfaces. It uses this ability to move to places where the light conditions are better. It was already known that Synechocystis cells move towards a light source that is shone at them from one side, which implies that the cyanobacteria can "see" where the light is. But how can such a tiny cell accurately detect where light is coming from? Schuergers et al. tracked how Synechocystis moved in response to different light conditions, and found that the secret of "vision" in these cyanobacteria is that the cells act as tiny spherical lenses. When a light is shone at the cell, an image of the light source is focused at the opposite edge of the cell. Light-detecting molecules called photoreceptors respond to the focused image of the light source, and this provides the information needed to steer the cell towards the light. Although the details are different, and although a Synechocystis cell is in terms of volume about 500 billion times smaller than a human eyeball, vision in Synechocystis actually works by principles similar to vision in humans. Schuergers et al.'s findings open plenty of further questions, as other types of bacteria may also act as tiny lenses. More also remains to be learnt about how the cyanobacteria process visual information.

Deciding when to stop: efficient experimentation to learn to predict drug-target interactions.

BACKGROUND: Active learning is a powerful tool for guiding an experimentation process. Instead of doing all possible experiments in a given domain, active learning can be used to pick the experiments that will add the most knowledge to the current model. Especially, for drug discovery and development, active learning has been shown to reduce the number of experiments needed to obtain high-confidence predictions. However, in practice, it is crucial to have a method to evaluate the quality of the current predictions and decide when to stop the experimentation process. Only by applying reliable stopping criteria to active learning can time and costs in the experimental process actually be saved. RESULTS: We compute active learning traces on simulated drug-target matrices in order to determine a regression model for the accuracy of the active learner. By analyzing the performance of the regression model on simulated data, we design stopping criteria for previously unseen experimental matrices. We demonstrate on four previously characterized drug effect data sets that applying the stopping criteria can result in upto 40 % savings of the total experiments for highly accurate predictions. CONCLUSIONS: We show that active learning accuracy can be predicted using simulated data and results in substantial savings in the number of experiments required to make accurate drug-target predictions.

Multiview deblurring for 3-D images from light-sheet-based fluorescence microscopy.

We propose an algorithm for 3-D multiview deblurring using spatially variant point spread functions (PSFs). The algorithm is applied to multiview reconstruction of volumetric microscopy images. It includes registration and estimation of the PSFs using irregularly placed point markers (beads). We formulate multiview deblurring as an energy minimization problem subject to L1-regularization. Optimization is based on the regularized Lucy-Richardson algorithm, which we extend to deal with our more general model. The model parameters are chosen in a profound way by optimizing them on a realistic training set. We quantitatively and qualitatively compare with existing methods and show that our method provides better signal-to-noise ratio and increases the resolution of the reconstructed images.