Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition

This paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new theory and algorithms for nonlinear eigenvalue problems via nonlinear spectral decompositions have been developed. Those methods open new directions for advanced image filtering. However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches. In this context, $L^1$ data fidelities are particularly helpful due to their interesting multi-scale properties such as contrast invariance. Hence, the novelty of this work is the combination of $L^1$-based multi-scale methods with nonlinear spectral decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of spectral image representation and decomposition. We show that the contrast invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral response providing more informative decompositions. We provide a numerical method and analyze synthetic and biomedical images at which decomposition leads to improved segmentation.Comment: 13 pages, 7 figures, conference SSVM 201

Dominance-solvable lattice games

This paper derives sufficient and necessary conditions for dominance-solvability of so-called lattice games whose strategy sets have a lattice structure while they simultaneously belong to some metric space. The argument combines and extends Moulin's (1984) approach for nice games and Milgrom and Roberts' (1990) approach for supermodular games. The analysis covers - but is not restricted to - the case of actions being strategic complements as well as the case of actions being strategic substitutes. Applications are given for n-firm Cournot oligopolies, auctions with bidders who are optimistic - respectively pessimistic - with respect to an imperfectly known allocation rule, and Two-player Bayesian models of bank runs

Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin

Development of the Fetal Vermis: New Biometry Reference Data and Comparison of 3 Diagnostic Modalities-3D Ultrasound, 2D Ultrasound, and MR Imaging

Normal biometry of the fetal posterior fossa rules out most major anomalies of the cerebellum and vermis. Our aim was to provide new reference data of the fetal vermis in 4 biometric parameters by using 3 imaging modalities, 2D ultrasound, 3D ultrasound, and MR imaging, and to assess the relation among these modalities. A retrospective study was conducted between June 2011 and June 2013. Three different imaging modalities were used to measure vermis biometry: 2D ultrasound, 3D ultrasound, and MR imaging. The vermian parameters evaluated were the maximum superoinferior diameter, maximum anteroposterior diameter, the perimeter, and the surface area. Statistical analysis was performed to calculate centiles for gestational age and to assess the agreement among the 3 imaging modalities. The number of fetuses in the study group was 193, 172, and 151 for 2D ultrasound, 3D ultrasound, and MR imaging, respectively. The mean and median gestational ages were 29.1 weeks, 29.5 weeks (range, 21-35 weeks); 28.2 weeks, 29.05 weeks (range, 21-35 weeks); and 32.1 weeks, 32.6 weeks (range, 27-35 weeks) for 2D ultrasound, 3D ultrasound, and MR imaging, respectively. In all 3 modalities, the biometric measurements of the vermis have shown a linear growth with gestational age. For all 4 biometric parameters, the lowest results were those measured by MR imaging, while the highest results were measured by 3D ultrasound. The inter- and intraobserver agreement was excellent for all measures and all imaging modalities. Limits of agreement were considered acceptable for clinical purposes for all parameters, with excellent or substantial agreement defined by the intraclass correlation coefficient. Imaging technique-specific reference data should be used for the assessment of the fetal vermis in pregnanc

Preventing Pseudomonas aeruginosa and Chromobacterium violaceum infections by anti-adhesion-active components of edible seeds

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas aeruginosa </it>adhesion to animal/human cells for infection establishment involves adhesive proteins, including its galactose- and fucose-binding lectins PA-IL (LecA) and PA-IIL (LecB). The lectin binding to the target-cell receptors may be blocked by compatible glycans that compete with those of the receptors, functioning as anti-adhesion glycodecoys. The anti-adhesion treatment is of the utmost importance for abrogating devastating antibiotic-resistant <it>P. aeruginosa </it>infections in immunodeficient and cystic fibrosis (CF) patients. This strategy functions in nature in protecting embryos and neonates. We have shown that PA-IL, PA-IIL, and also CV-IIL (a PA-IIL homolog produced in the related pathogen <it>Chromobacterium violaceum</it>) are highly useful for revealing natural glycodecoys that surround embryos in diverse avian eggs and are supplied to neonates in milks and royal jelly. In the present study, these lectins were used as probes to search for seed embryo-protecting glycodecoys.</p> <p>Methods</p> <p>The lectin-blocking glycodecoy activities were shown by the hemagglutination-inhibition test. Lectin-binding glycoproteins were detected by Western blotting with peroxidase-labeled lectins.</p> <p>Results</p> <p>The present work reports the finding - by using PA-IL, PA-IIL, and CV-IIL - of rich glycodecoy activities of low (< 10 KDa) and high MW (> 10 kDa) compounds (including glycoproteins) in extracts of cashew, cocoa, coffee, pumpkin, and tomato seeds, resembling those of avian egg whites, mammal milks, and royal jelly.</p> <p>Conclusions</p> <p>Edible seed extracts possess lectin-blocking glycodecoys that might protect their embryos from infections and also might be useful for hampering human and animal infections.</p

Searching for network modules

When analyzing complex networks a key target is to uncover their modular structure, which means searching for a family of modules, namely node subsets spanning each a subnetwork more densely connected than the average. This work proposes a novel type of objective function for graph clustering, in the form of a multilinear polynomial whose coefficients are determined by network topology. It may be thought of as a potential function, to be maximized, taking its values on fuzzy clusterings or families of fuzzy subsets of nodes over which every node distributes a unit membership. When suitably parametrized, this potential is shown to attain its maximum when every node concentrates its all unit membership on some module. The output thus is a partition, while the original discrete optimization problem is turned into a continuous version allowing to conceive alternative search strategies. The instance of the problem being a pseudo-Boolean function assigning real-valued cluster scores to node subsets, modularity maximization is employed to exemplify a so-called quadratic form, in that the scores of singletons and pairs also fully determine the scores of larger clusters, while the resulting multilinear polynomial potential function has degree 2. After considering further quadratic instances, different from modularity and obtained by interpreting network topology in alternative manners, a greedy local-search strategy for the continuous framework is analytically compared with an existing greedy agglomerative procedure for the discrete case. Overlapping is finally discussed in terms of multiple runs, i.e. several local searches with different initializations.Comment: 10 page

Construction and refinement of preference ordered decision classes

Preference learning methods are commonly used in multicriteria analysis. The working principle of these methods is similar to classical machine learning techniques. A common issue to both machine learning and preference learning methods is the difficulty of the definition of decision classes and the assignment of objects to these classes, especially for large datasets. This paper proposes two procedures permitting to automatize the construction of decision classes. It also proposes two simple refinement procedures, that rely on the 80-20 principle, permitting to map the output of the construction procedures into a manageable set of decision classes. The proposed construction procedures rely on the most elementary preference relation, namely dominance relation, which avoids the need for additional information or distance/(di)similarity functions, as with most of existing clustering methods. Furthermore, the simplicity of the 80-20 principle on which the refinement procedures are based, make them very adequate to large datasets. Proposed procedures are illustrated and validated using real-world datasets

Under stochastic dominance Choquet-expected utility and anticipated utility are identical

The aim of this paper is to convince the reader that Choquet-expected utility, as initiated by Schmeidler (1982, 1989) for decision making under uncertainty, when formulated for decision making under risk naturally leads to anticipated utility, as initiated by Quiggin/Yaari. Thus the two generalizations of expected utility in fact are one

Quantification of mRNA expression using single-molecule nanopore sensing

RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases of rare transcripts or inhibiting contaminants in the sample, an extensive amplification could bias the copy number estimation, leading to quantification errors and false diagnosis. Single-molecule techniques may bypass amplification, but commonly rely on fluorescence detection and probe hybridization, which introduces noise and limits multiplexing. Here, we introduce reverse transcription quantitative nanopore sensing (RT-qNP), an RNA quantification method that involves synthesis and single-molecule detection of gene-specific cDNAs without the need for purification or amplification. RT-qNP allows us to accurately quantify the relative expression of metastasis-associated genes MACC1 and S100A4 in non-metastasizing and metastasizing human cell lines, even at levels for which RT-qPCR quantification produces uncertain results. We further demonstrate the versatility of the method by adapting it to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA against a human reference gene. This internal reference circumvents the need for producing a calibration curve for each measurement, an imminent requirement in RT-qPCR experiments. In summary, we describe a general method to process complicated biological samples with minimal losses, adequate for direct nanopore sensing. Thus, harnessing the sensitivity of label-free single-molecule counting, RT-qNP can potentially detect minute expression levels of RNA biomarkers or viral infection in the early stages of disease and provide accurate amplification-free quantification