Maja Temerinac-Ott

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. DOI: http://dx.doi.org/10.7554/eLife.12620.001

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.

Spatially-variant Lucy-Richardson deconvolution for multiview fusion of microscopical 3D images

A framework for fast multiview fusion of Single Plane Illumination Microscopy (SPIM) images based on a spatially-variant point spread function (PSF) model is presented. For the multiview fusion a new algorithm based on the regularized Lucy-Richardson deconvolution and the Overlap-Save method is developed and tested on SPIM images. In the algorithm the image is decomposed into small blocks which are processed separately thus saving memory space and allowing for parallel processing.

Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF)

With volumetric data from wide field fluorescence microscopy, many emerging questions in biological and biomedical research are being investigated. Data can be recorded with high temporal resolution while the specimen is only exposed to a low amount of photo toxicity. These advantages come at the cost of strong recording blur caused by the infinitely extended point spread function (PSF). For wide field microscopy, its magnitude only decays with the square of the distance to the focal point and consists of an airy bessel pattern which is intricate to describe in the spatial domain. However, the Fourier transform of the incoherent PSF (denoted as Optical Transfer Function (OTF)) is well localized and smooth. In this paper, we present a blind deconvolution method that improves results of state-of-the-art deconvolution methods on wide field data by exploiting the properties of the wide field OTF.

Evaluation of a New Point Clouds Registration Method Based on Group Averaging Features

Registration of point clouds is required in the processing of large biological data sets. The trade off between computation time and accuracy of the registration is the main challenge in this task. We present a novel method for registering point clouds in two and three dimensional space based on Group Averaging on the Euclidean transformation group. It is applied on a set of neighboring points whose size directly controls computing time and accuracy. The method is evaluated regarding dependencies of the computing time and the registration accuracy versus the point density assuming their random distribution. Results are verified in two biological applications on 2D and 3D images.

Multichannel image restoration based on optimization of the structural similarity index

In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the human visual system than the mean square error (MSE). It has not yet been explored for the multi channel restoration task. The construction of an optimization algorithm is difficult due to the non-linearity of the SSIM measure. The existing solution based on a quasi-convex problem formulation is successfully extended for the multichannel image restoration. The correctness of the algorithm is verified on sample images and it is shown that multi-view information can significantly improve the restoration results.

Blind deconvolution with PSF regularization for wide-field microscopy

We propose to use a kernel intensity penalizer (KIP) in the blind maximum likelihood expectation maximization (MLEM) deconvolution scheme. With this very general kernel regularization term, we can stabilize the blind MLEM scheme even for the deconvolution of wide-field microscopic recordings. No complex prior point spread function models are needed. We combine state of the art optimization schemes using Tikhonov-Miller and TV regularization with our new kernel regularization. The proposed method improves the conventional deconvolution methods in terms of SNR on real and simulated datasets.

Discrete Fourier-Invariant Signals: Design and Application

In this paper, two methods for the design of discrete Fourier-invariant signals are proposed. The direct design method provides splitting between independent and dependent signal parts and calculation of the dependent part for any given independent part. The iterative design method generates a family of discrete Fourier-invariant signals by a successive approach. Further we show how the proposed direct design method can be combined with the Gabor uncertainty principle to generate discrete Fourier-invariant signals with the minimum product of their bandwidth (B) and their time-width (T). We show that these signals as well as signal families generated with the iterative design method achieve the theoretical lower BT bound. Also, it is shown that the BT product of discrete Hermite-Gauss signals converges to the theoretical lower bound. Finally, possible applications are illustrated in the case of time-frequency spectral analysis using the obtained discrete Fourier-invariant signals as the window that provides isoresolution.

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

BackgroundActive 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.ResultsWe 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.ConclusionsWe 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.

Deciding When to Stop: Efficient Experimentation to Learn to Predict Drug-Target Interactions (Extended Abstract)

An active learning method for identifying drug-target interactions is presented which considers the interaction between multiple drugs and multiple targets at the same time. The goal of the proposed method is not simply to predict such interactions from experiments that have already been conducted, but to iteratively choose as few new experiments as possible to improve the accuracy of the predictive model. Kernelized Bayesian matrix factorization (KBMF) is used to model the interactions. We demonstrate on four previously characterized drug effect data sets that active learning driven experimentation using KBMF can result in highly accurate models while performing as few as 14% of the possible experiments, and more accurately than random sampling of an equivalent number. We also provide a method for estimating the accuracy of the current model based on the learning curve; and show how it can be used in practice to decide when to stop an active learning process.