A statistical approach for array CGH data analysis

BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome

Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Comment: 13 pages, 7 figure

Parameter-independent battery control based on series and parallel impedance emulation

Appropriate voltage control is essential in order to extend the useful life of a battery. However, when universal chargers are used, the design of this control becomes more complicated, given the fact that the battery impedance value may vary considerably, depending not only on the operating point but also on the type, size and aging level of the battery. This paper firstly shows how the voltage regulation can become extremely variable or even unstable when the controller is designed according to the proposals in the literature. We then go on to propose the emulation of a series and parallel impedance with the battery, which is easy to implement and achieves a control that is completely independent of the battery connected. The simulation results obtained for batteries with resistances ranging from 10 mO to 1 O, show the problems with existing controls and confirm that the proposed control response is similar for all the possible range of battery resistances.Peer ReviewedPostprint (published version

Dynamic habitat models reflect interannual movement of cetaceans within the California current ecosystem

This modeling project was funded by the Navy, Commander, U.S. Pacific Fleet (U.S. Navy), the Bureau of Ocean Energy Management (BOEM), and by the National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS), Southwest Fisheries Science Center (SWFSC). The 2018 survey was conducted as part of the Pacific Marine Assessment Program for Protected Species (PacMAPPS), a collaborative effort between NOAA Fisheries, the U.S. Navy, and BOEM to collect data necessary to produce updated abundance estimates for cetaceans in the CCE study area. BOEM funding was provided via Interagency Agreement (IAA) M17PG00025, and Navy funding via IAA N0007018MP4C560, under the Mexican permit SEMARNAT/SGPA/DGVS/013212/18. The methods used to derive uncertainty estimates were developed as part of “DenMod: Working Group for the Advancement of Marine Species Density Surface Modeling” funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, and managed by the U.S. Navy’s Living Marine Resources (LMR) program under Contract No. N39430-17-C-1982. Other permits included INEGI: Oficio núm. 400./331/2018, INEGI.GMA 1.03 SAGARPA de Oficio B00.02.04.1530/2018 NMFS Permit No. 19091.The distribution of wide-ranging cetacean species often cross national or jurisdictional boundaries, which creates challenges for monitoring populations and managing anthropogenic impacts, especially if data are only available for a portion of the species’ range. Many species found off the U.S. West Coast are known to have continuous distributions into Mexican waters, with highly variable abundance within the U.S. portion of their range. This has contributed to annual variability in design-based abundance estimates from systematic shipboard surveys off the U.S. West Coast, particularly for the abundance of warm temperate species such as striped dolphin, Stenella coeruleoalba, which increases off California during warm-water conditions and decreases during cool-water conditions. Species distribution models (SDMs) can accurately describe shifts in cetacean distribution caused by changing environmental conditions, and are increasingly used for marine species management. However, until recently, data from waters off the Baja California peninsula, México, have not been available for modeling species ranges that span from Baja California to the U.S. West Coast. In this study, we combined data from 1992–2018 shipboard surveys to develop SDMs off the Pacific Coast of Baja California for ten taxonomically diverse cetaceans. We used a Generalized Additive Modeling framework to develop SDMs based on line-transect surveys and dynamic habitat variables from the Hybrid Coordinate Ocean Model (HYCOM). Models were developed for ten species: long- and short-beaked common dolphins (Delphinus delphis delphis and D. d. bairdii), Risso’s dolphin (Grampus griseus), Pacific white-sided dolphin (Lagenorhynchus obliquidens), striped dolphin, common bottlenose dolphin (Tursiops truncatus), sperm whale (Physeter macrocephalus), blue whale (Balaenoptera musculus), fin whale (B. physalus), and humpback whale (Megaptera novaeangliae). The SDMs provide the first fine-scale (approximately 9 x 9 km grid) estimates of average species density and abundance, including spatially-explicit measures of uncertainty, for waters off the Baja California peninsula. Results provide novel insights into cetacean ecology in this region as well as quantitative spatial data for the assessment and mitigation of anthropogenic impacts.Publisher PDFPeer reviewe

Are the Baltic Countries Ready to Adopt the Euro? A Generalised Purchasing Power Parity Approach

This paper focuses on macroeconomic interdependencies between the Euro area and three transition economies (Estonia, Lithuania and Latvia), with the aim of establishing whether the latter are ready to adopt the Euro. The theoretical framework is based on the Generalised Purchasing Power Parity (GPPP) hypothesis, which is empirically tested within a Vector Error Correction (VEC) model. Using both monthly and quarterly data over the period 1993-2005, it is found that GPPP holds for the real exchange rate vis-à-vis the Euro of each Baltic country, reflecting a degree of real convergence consistent with Optimum Currency Area criteria. Further, the adopted joint modelling approach for the real exchange rates of the Baltic region outperforms a number of alternative models in terms of out-of-sample forecasts.transition economies, Euro area, (Generalised) Purchasing Power Parity, Vector Error Corrector models

Construction of Bayesian Deformable Models via Stochastic Approximation Algorithm: A Convergence Study

The problem of the definition and the estimation of generative models based on deformable templates from raw data is of particular importance for modelling non aligned data affected by various types of geometrical variability. This is especially true in shape modelling in the computer vision community or in probabilistic atlas building for Computational Anatomy (CA). A first coherent statistical framework modelling the geometrical variability as hidden variables has been given by Allassonni\`ere, Amit and Trouv\'e (JRSS 2006). Setting the problem in a Bayesian context they proved the consistency of the MAP estimator and provided a simple iterative deterministic algorithm with an EM flavour leading to some reasonable approximations of the MAP estimator under low noise conditions. In this paper we present a stochastic algorithm for approximating the MAP estimator in the spirit of the SAEM algorithm. We prove its convergence to a critical point of the observed likelihood with an illustration on images of handwritten digits

Least costly energy management for series hybrid electric vehicles

Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and battery degradation - is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign