A Learning Approach to Optical Tomography

We describe a method for imaging 3D objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of cells

Fast fluorescence microscopy for imaging the dynamics of embryonic development

Live imaging has gained a pivotal role in developmental biology since it increasingly allows real-time observation of cell behavior in intact organisms. Microscopes that can capture the dynamics of ever-faster biological events, fluorescent markers optimal for in vivo imaging, and, finally, adapted reconstruction and analysis programs to complete data flow all contribute to this success. Focusing on temporal resolution, we discuss how fast imaging can be achieved with minimal prejudice to spatial resolution, photon count, or to reliably and automatically analyze images. In particular, we show how integrated approaches to imaging that combine bright fluorescent probes, fast microscopes, and custom post-processing techniques can address the kinetics of biological systems at multiple scales. Finally, we discuss remaining challenges and opportunities for further advances in this field

Microtome-integrated microscope system for high sensitivity tracking of in-resin fluorescence in blocks and ultrathin sections for correlative microscopy

Many areas of biological research demand the combined use of different imaging modalities to cover a wide range of magnifications and measurements or to place fluorescent patterns into an ultrastructural context. A technically difficult problem is the efficient specimen transfer between different imaging modalities without losing the coordinates of the regions-of-interest (ROI). Here, we report a new and highly sensitive integrated system that combines a custom designed microscope with an ultramicrotome for in-resin-fluorescence detection in blocks, ribbons and sections on EM-grids. Although operating with long-distance lenses, this system achieves a very high light sensitivity. Our instrumental set-up and operating workflow are designed to investigate rare events in large tissue volumes. Applications range from studies of individual immune, stem and cancer cells to the investigation of non-uniform subcellular processes. As a use case, we present the ultrastructure of a single membrane repair patch on a muscle fiber in intact muscle in a whole animal context

Control of three-axis manipulator

Práca sa zaoberá riadením a presným polohovaním dopravníka v 3D priestore. Pre riadenie, polohovania bolo využité hardwarové a softvérové vybavenie od spoločnosti Bernecker&Reiner. Medzi ciele práce patrilo aj oboznámenie sa z programovacím prostredím AUTOMATION STUDIO, v ktorom prebiehala celá práca.This thesis deal swith controlling and precision positioning of conveyor in 3D space. Equipment for controling of positioning is made by Bernecker&Reinercompany. Important objective of work is also familiarization with programing environment AUTOMATION STUDIO, which powers whole operation.

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry

Octahedral molybdenum cluster complexes with aromatic sulfonate ligands

This article describes the synthesis, structures and systematic study of the spectroscopic and redox properties of a series of octahedral molybdenum metal cluster complexes with aromatic sulfonate ligands (nBu4N)2[{Mo6X8}(OTs)6] and (nBu4N)2[{Mo6X8}(PhSO3)6] (where X- is Cl-, Br- or I-; OTs- is p-toluenesulfonate and PhSO3 - is benzenesulfonate). All the complexes demonstrated photoluminescence in the red region and an ability to generate singlet oxygen. Notably, the highest quantum yields (>0.6) and narrowest emission bands were found for complexes with a {Mo6I8}4+ cluster core. Moreover, cyclic voltammetric studies revealed that (nBu4N)2[{Mo6X8}(OTs)6] and (nBu4N)2[{Mo6X8}(PhSO3)6] confer enhanced stability towards electrochemical oxidation relative to corresponding starting complexes (nBu4N)2[{Mo6X8}X6]

The FlyCatwalk: A High-Throughput Feature-Based Sorting System for Artificial Selection in Drosophila

Experimental evolution is a powerful tool for investigating complex traits. Artificial selection can be applied for a specific trait and the resulting phenotypically divergent populations pool-sequenced to identify alleles that occur at substantially different frequencies in the extreme populations. To maximize the proportion of loci that are causal to the phenotype among all enriched loci, population size and number of replicates need to be high. These requirements have, in fact, limited evolution studies in higher organisms, where the time investment required for phenotyping is often prohibitive for large-scale studies. Animal size is a highly multigenic trait that remains poorly understood, and an experimental evolution approach may thus aid in gaining new insights into the genetic basis of this trait. To this end, we developed the FlyCatwalk, a fully automated, high-throughput system to sort live fruit flies (Drosophila melanogaster) based on morphometric traits. With the FlyCatwalk, we can detect gender and quantify body and wing morphology parameters at a four-old higher throughput compared with manual processing. The phenotyping results acquired using the FlyCatwalk correlate well with those obtained using the standard manual procedure. We demonstrate that an automated, high-throughput, feature-based sorting system is able to avoid previous limitations in population size and replicate numbers. Our approach can likewise be applied for a variety of traits and experimental settings that require high-throughput phenotyping.ISSN:2160-183