Generalized LASSO with under-determined regularization matrices.

This paper studies the intrinsic connection between a generalized LASSO and a basic LASSO formulation. The former is the extended version of the latter by introducing a regularization matrix to the coefficients. We show that when the regularization matrix is even- or under-determined with full rank conditions, the generalized LASSO can be transformed into the LASSO form via the Lagrangian framework. In addition, we show that some published results of LASSO can be extended to the generalized LASSO, and some variants of LASSO, e.g., robust LASSO, can be rewritten into the generalized LASSO form and hence can be transformed into basic LASSO. Based on this connection, many existing results concerning LASSO, e.g., efficient LASSO solvers, can be used for generalized LASSO.

Source separation approach for the analysis of spatially resolved multiply excited autofluorescence spectra during optical clearing of ex vivo skin.

Spatially resolved multiply excited autofluorescence spectroscopy is a valuable optical biopsy technique to investigate skin UV-visible optical properties in vivo in clinics. However, it provides bulk fluorescence signals from which the individual endogenous fluorophore contributions need to be disentangled. Skin optical clearing allows for increasing tissue transparency, thus providing access to more accurate in-depth information. The aim of the present contribution was to study the time changes in skin spatially resolved and multiply excited autofluorescence spectra during skin optical clearing. The latter spectra were acquired on an ex vivo human skin strip lying on a fluorescent gel substrate during 37 minutes of the optical clearing process of a topically applied sucrose-based solution. A Non Negative Matrix Factorization-based blind source separation approach was proposed to unmix skin tissue intrinsic fluorophore contributions and to analyze the time evolution of this mixing throughout the optical clearing process. This spectral unmixing exploited the multidimensionality of the acquired data, i.e., spectra resolved in five excitation wavelengths, four source-to-detector separations, and eight measurement times. Best fitting results between experimental and estimated spectra were obtained for optimal numbers of 3 and 4 sources. These estimated spectral sources exhibited common identifiable shapes of fluorescence emission spectra related to the fluorescent gel substrate and to known skin intrinsic fluorophores matching namely dermis collagen/elastin and epidermis flavins. The time analysis of the fluorophore contributions allowed us to highlight how the clearing process towards the deepest skin layers impacts skin autofluorescence through time, namely with a strongest contribution to the bulk autofluorescence signal of dermis collagen (respectively epidermis flavins) fluorescence at shortest (respectively longest) excitation wavelengths and longest (respectively shortest) source-to-detector separations.

DDB2 (damaged-DNA binding 2) protein: a new modulator of nanomechanical properties and cell adhesion of breast cancer cells.

DDB2, known for its role in DNA repair, was recently shown to reduce mammary tumor invasiveness by inducing the transcription of IκBα, an inhibitor of NF-κB activity. Since cellular adhesion is a key event during the epithelial to mesenchymal transition (EMT) leading to the invasive capacities of breast tumor cells, the aim of this study was to investigate the role of DDB2 in this process. Thus, using low and high DDB2-expressing MDA-MB231 and MCF7 cells, respectively, in which DDB2 expression was modulated experimentally, we showed that DDB2 overexpression was associated with a decrease of adhesion abilities on glass and plastic areas of breast cancer cells. Then, we investigated cell nanomechanical properties by atomic force microscopy (AFM). Our results revealed significant changes in the Young's Modulus value and the adhesion force in MDA-MB231 and MCF7 cells, whether DDB2 was expressed or not. The cell stiffness decrease observed in MDA-MB231 and MCF7 expressing DDB2 was correlated with a loss of the cortical actin-cytoskeleton staining. To understand how DDB2 regulates these processes, an adhesion-related gene PCR-Array was performed. Several adhesion-related genes were differentially expressed according to DDB2 expression, indicating that important changes are occurring at the molecular level. Thus, this work demonstrates that AFM technology is an important tool to follow cellular changes during tumorigenesis. Moreover, our data revealed that DDB2 is involved in early events occurring during metastatic progression of breast cancer cells and will contribute to define this protein as a new marker of metastatic progression in this type of cancer.

An optimal method to segment piecewise poisson distributed signals with application to sequencing data.

To analyze the next generation sequencing data, the so-called read depth signal is often segmented with standard segmentation tools. However, these tools usually assume the signal to be a piecewise constant signal and contaminated with zero mean Gaussian noise, and therefore modeling error occurs. This paper models the read depth signal with piecewise Poisson distribution, which is more appropriate to the next generation sequencing mechanism. Based on the proposed model, an opti- mal dynamic programming algorithm with parallel computing is proposed to segment the piecewise signal, and furthermore detect the copy number variation.

Polygonal and polyhedral contour reconstruction in computed tomography.

This paper is about three-dimensional (3-D) reconstruction of a binary image from its X-ray tomographic data. We study the special case of a compact uniform polyhedron totally included in a uniform background and directly perform the polyhedral surface estimation. We formulate this problem as a nonlinear inverse problem using the Bayesian framework. Vertice estimation is done without using a voxel approximation of the 3-D image. It is based on the construction and optimization of a regularized criterion that accounts for surface smoothness. We investigate original deterministic local algorithms, based on the exact computation of the line projections, their update, and their derivatives with respect to the vertice coordinates. Results are first derived in the two-dimensional (2-D) case, which consists of reconstructing a 2-D object of deformable polygonal contour from its tomographic data. Then, we investigate the 3-D extension that requires technical adaptations. Simulation results illustrate the performance of polygonal and polyhedral reconstruction algorithms in terms of quality and computation time.

Does deblurring improve geometrical hyperspectral unmixing?

In this paper, we consider hyperspectral unmixing problems where the observed images are blurred during the acquisition process, e.g., in microscopy and spectroscopy. We derive a joint observation and mixing model and show how it affects end-member identifiability within the geometrical unmixing framework. An analysis of the model reveals that nonnegative blurring results in a contraction of both the minimum-volume enclosing and maximum-volume enclosed simplex. We demonstrate this contraction property in the case of a spectrally invariant point-spread function. The benefit of prior deconvolution on the accuracy of the restored sources and abundances is illustrated using simulated and real Raman spectroscopic data.

Fast positive deconvolution of hyperspectral images.

In this brief, we provide an efficient scheme for performing deconvolution of large hyperspectral images under a positivity constraint, while accounting for spatial and spectral smoothness of the data.

Automated force volume image processing for biological samples.

Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.