Fast Interscale Wavelet Denoising of Poisson-Corrupted Images

We present a fast algorithm for image restoration in the presence of Poisson noise. Our approach is based on (1) the minimization of an unbiased estimate of the MSE for Poisson noise, (2) a linear parametrization of the denoising process and (3) the preservation of Poisson statistics across scales within the Haar DWT. The minimization of the MSE estimate is performed independently in each wavelet subband, but this is equivalent to a global image-domain MSE minimization, thanks to the orthogonality of Haar wavelets. This is an important difference with standard Poisson noise-removal methods, in particular those that rely on a non-linear preprocessing of the data to stabilize the variance. Our non-redundant interscale wavelet thresholding outperforms standard variance-stabilizing schemes, even when the latter are applied in a translation-invariant setting (cycle-spinning). It also achieves a quality similar to a state-of-the-art multiscale method that was specially developed for Poisson data. Considering that the computational complexity of our method is orders of magnitude lower, it is a very competitive alternative. The proposed approach is particularly promising in the context of low signal intensities and/or large data sets. This is illustrated experimentally with the denoising of low-count fluorescence micrographs of a biological sample

A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples

<p>Abstract</p> <p>Background</p> <p>Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.</p> <p>Methods</p> <p>To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.</p> <p>Results</p> <p>The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.</p> <p>Conclusions</p> <p>We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.</p

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

Roles of retinoic acid receptors and of Hox genes in the patterning of the teeth and of the jaw skeleton.

Retinoic acid receptors and transcriptional factors encoded by Hox genes play key roles in vertebrate development and belong to an integrated functional network. To investigate the actual functions of these molecules during ontogenesis and in particular in the patterning of the cranial neural crest cells giving rise to the teeth and to the jaw bones, we have generated null mutant mice lacking functional retinoic acid receptors or Hox genes by gene targeting in embryonic stem cells.journal articleresearch support, non-u.s. gov'treview1995 Febimporte

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

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

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.

Unlike for Human Monocytes after LPS Activation, Release of TNF-α by THP-1 Cells Is Produced by a TACE Catalytically Different from Constitutive TACE

Tumor necrosis factor-alpha (TNF-α) is a pro-inflammatory cytokine today identified as a key mediator of several chronic inflammatory diseases. TNF-α, initially synthesized as a membrane-anchored precursor (pro-TNF-α), is processed by proteolytic cleavage to generate the secreted mature form. TNF-α converting enzyme (TACE) is currently the first and single protease described as responsible for the inducible release of soluble TNF-α.Here, we demonstrated the presence on THP-1 cells as on human monocytes of a constitutive proteolytical activity able to cleave pro-TNF-α. Revelation of the cell surface TACE protein expression confirmed that the observed catalytic activity is due to TACE. However, further studies using effective and innovative TNF-α inhibitors, as well as a highly selective TACE inhibitor, support the presence of a catalytically different sheddase activity on LPS activated THP-1 cells. It appears that this catalytically different TACE protease activity might have a significant contribution to TNF-α release in LPS activated THP-1 cells, by contrast to human monocytes where the TACE activity remains catalytically unchanged even after LPS activation.On the surface of LPS activated THP-1 cells we identified a releasing TNF-α activity, catalytically different from the sheddase activity observed on human monocytes from healthy donors. This catalytically-modified TACE activity is different from the constitutive shedding activity and appears only upon stimulation by LPS