Functional Bipartite Ranking: a Wavelet-Based Filtering Approach

It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA). Given a training set of independent realizations of a (possibly sampled) second-order random function with a (locally) smooth autocorrelation structure and to which a binary label is randomly assigned, the objective is to learn a scoring function s with optimal ROC curve. Based on linear/nonlinear wavelet-based approximations, it is shown how to select compact finite dimensional representations of the input curves adaptively, in order to build accurate ranking rules, using recent advances in the ranking problem for multivariate data with binary feedback. Beyond theoretical considerations, the performance of the learning methods for functional bipartite ranking proposed in this paper are illustrated by numerical experiments

Nonequilibrium fluctuations of an interface under shear

The steady state properties of an interface in a stationary Couette flow are addressed within the framework of fluctuating hydrodynamics. Our study reveals that thermal fluctuations are driven out of equilibrium by an effective shear rate that differs from the applied one. In agreement with experiments, we find that the mean square displacement of the interface is strongly reduced by the flow. We also show that nonequilibrium fluctuations present a certain degree of universality in the sense that all features of the fluids can be factorized into a single control parameter. Finally, the results are discussed in the light of recent experimental and numerical studies.Comment: 14 pages, 4 figure

Defining a robust biological prior from Pathway Analysis to drive Network Inference

Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for inferring gene regulation networks using a robust biological prior on their structure in order to limit the set of candidate networks. Pathways, that represent biological knowledge on the regulatory networks, will be used as an informative prior knowledge to drive Network Inference. This approach is based on the selection of a relevant set of genes, called the "molecular signature", associated with a condition of interest (for instance, the genes involved in disease development). In this context, differential expression analysis is a well established strategy. However outcome signatures are often not consistent and show little overlap between studies. Thus, we will dedicate the first part of our work to the improvement of the standard process of biomarker identification to guarantee the robustness and reproducibility of the molecular signature. Our approach enables to compare the networks inferred between two conditions of interest (for instance case and control networks) and help along the biological interpretation of results. Thus it allows to identify differential regulations that occur in these conditions. We illustrate the proposed approach by applying our method to a study of breast cancer's response to treatment

2017 Texas Bays and Estuaries Meeting

Program for the 2017 Texas Bays and Estuaries Meeting held in Port Aransas, Texas, April 12-13, 2017.Coastal Bend Bays & Estuaries Program, Coastal Bend Bays Foundation, The University of Texas Marine Science Institute, Sea Grant Texas at Texas A&M University, Harte Research Institute for Gulf of Mexico Studies, and Mission Aransas National Estuarine Research Reserve.Marine Scienc

Gulf Estuarine Research Society 2014 Meeting

Table of Contents: Thank You to Our Sponsors! (p. 3) -- About the Gulf Estuarine Research Society (p. 4) -- Student Travel Award winners (p. 5) -- Abbreviated Schedule (p. 7) -- 2014 Plenary Speaker – Dr. Michael Osland (p. 8) -- 2014 Plenary Speaker – Dr. Maggie Walser (p. 9) -- Full Schedule (p. 10) -- Poster Session Directory (p. 17) -- Oral Presentation Abstracts (p. 21) -- Poster Presentation Abstracts (p. 38) -- Things to Do in Port Aransas (p. 52) -- Greening the Meeting (p. 53) -- Map of University of Texas Marine Science Institute (p. 54)Coastal and Estuarine Research Foundation, Port Aransas, Gulf of Mexico Foundation, Coastal Bend Bays & Estuaries Program, Lotek Wireless Fish & Wildlife Monitoring, Sea Grant Mississippi-Alabama, Sea Grant Louisiana, Sea Grant Texas, The University of Austin Marine Science Institute, Mission-Aransas National Estuarine Research ReserveMarine Scienc