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

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

Hydrogen bond directed molecular recognition in water in a strapped-porphyrin-cyclodextrin assembly

A water soluble, phenanthroline-strapped zinc porphyrin bearing four arylsulfonate groups formed a stable host–guest complex with two per-O-methylated β-cyclodextrin cavities. In the host–guest assembly, the zinc porphyrin was capable of binding imidazole within the cavity between the zinc(II) ion and the phenanthroline strap in an aqueous medium. The formation of a hydrogen bond between the imidazole NH and the nitrogen atoms of the phenanthroline was an essential element of the binding event, as shown by comparative binding studies with a non-strapped tetrasulfonated zinc porphyrin and with N-methylimidazole. This hydrogen bonding in an aqueous medium was possible due to the protected hydrophobic environment created by the cyclodextrins around the phenanthroline strap. This type of binding event may provide a biomimetic approach to study water soluble heme protein models

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

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

A spontaneous mutation in MutL-Homolog 3 (HvMLH3) affects synapsis and crossover resolution in the barley desynaptic mutant des10

Although meiosis is evolutionarily conserved, many of the underlying mechanisms show species-specific differences. These are poorly understood in large genome plant species such as barley (Hordeum vulgare) where meiotic recombination is very heavily skewed to the ends of chromosomes. The characterization of mutant lines can help elucidate how recombination is controlled. We used a combination of genetic segregation analysis, cytogenetics, immunocytology and 3D imaging to genetically map and characterize the barley meiotic mutant DESYNAPTIC 10 (des10). We identified a spontaneous exonic deletion in the orthologue of MutL-Homolog 3 (HvMlh3) as the causal lesion. Compared with wild-type, des10 mutants exhibit reduced recombination and fewer chiasmata, resulting in the loss of obligate crossovers and leading to chromosome mis-segregation. Using 3D structured illumination microscopy (3D-SIM), we observed that normal synapsis progression was also disrupted in des10, a phenotype that was not evident with standard confocal microscopy and that has not been reported with Mlh3 knockout mutants in Arabidopsis. Our data provide new insights on the interplay between synapsis and recombination in barley and highlight the need for detailed studies of meiosis in nonmodel species. This study also confirms the importance of early stages of prophase I for the control of recombination in large genome cereals.Isabelle Colas, Malcolm Macaulay, James D. Higgins, Dylan Phillips, Abdellah Barakate ... Robbie Waugh ... et al

Benchmarking Image-Processing Algorithms for Biomicroscopy: Reference Datasets and Perspectives

As the field of bioimage informatics matures, the issue of the validation of image reconstruction algorithms and the definition of proper performance criteria becomes more pressing. In this work, we discuss benchmarking aspects of fluorescence microscopy quantitative tools. We point out the importance of generating realistic datasets and describe our approach to this task. We rely on our experience and present arguments in favor of the use of 3D continuous-domain models of biological structures for simulating bioimaging datasets. We also present physically-realistic models of image formation that that are reasonably efficiently to implement