Regularization Parameter Estimation for Non-Negative Hyperspectral Image Deconvolution.

This paper aims at studying a method to automatically estimate the regularization parameters of non-negative hyperspectral image deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) and the maximum curvature criterion (MCC) are proposed to estimate regularization parameters especially for the non-negativity constrained deconvolution problem. MDC has good theoretical properties (convexity and uniqueness) but requires to choose a reference point. On the contrary, MCC does not need to choose any reference point but does not have interesting theoretical properties. A grid-search-based approach to minimize the computational cost of MDC and MCC is proposed. It results in fast approaches to estimate the regularization parameters. Based on simulated 2D images, the proposed approaches are compared with the state-of-the-art methods, confirming the effectiveness of the MDC and MCC for the non-negativity constrained image deconvolution problem. In the case of non-negative hyperpsectral image deconvolution, the fast MDC yields better performances than the fast MCC. An application to real-world hyperspectral fluorescence microscopy images is also provided; it confirms the superiority of MDC.

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.

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.

Multicolor whole-cell bacterial sensing using a synchronous fluorescence spectroscopy-based approach.

The wide collection of currently available fluorescent proteins (FPs) offers new possibilities for multicolor reporter gene-based studies of bacterial functions. However, the simultaneous use of multiple FPs is often limited by the bleed-through of their emission spectra. Here we introduce an original approach for detection and separation of multiple overlapping fluorescent signals from mixtures of bioreporters strains. The proposed method relies on the coupling of synchronous fluorescent spectroscopy (SFS) with blind spectral decomposition achieved by the Canonical Polyadic (CP) decomposition (also known as Candecomp/Parafac) of three-dimensional data arrays. Due to the substantial narrowing of FP emission spectra and sensitive detection of multiple FPs in a one-step scan, SFS reduced spectral overlap and improved the selectivity of the CP unmixing procedure. When tested on mixtures of labeled E. coli strains, the SFS/CP approach could easily extract the contribution of at least four overlapping FPs. Furthermore, it allowed to simultaneously monitor the expression of three iron responsive genes and pyoverdine production in P. aeruginosa. Implemented in a convenient microplate format, this multiplex fluorescent reporter method provides a useful tool to study complex processes with different variables in bacterial systems.

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.

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.