Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert. © 2010 The Author(s

Application of different classification methods for litho-fluid facies prediction: A case study from the offshore Nile Delta

In this work we test four classification methods for litho-fluid facies identification in a clastic reservoir located in offshore Nile Delta. The ultimate goal of this study is to find an optimal classification method for the area under examination. The geologic context of the investigated area allows us to consider three different facies in the classification: shales, brine sands and gas sands. The depth at which the reservoir zone is located (2300-2700 m) produces a significant overlap of the P- and S-wave impedances of brine sands and gas sands that makes the discrimination between these two litho-fluid classes particularly problematic. The classification is performed on the feature space defined by the elastic properties that are derived from recorded reflection seismic data by means of Amplitude Versus Angle (AVA) Bayesian inversion. As classification methods we test both deterministic and probabilistic approaches: the quadratic discriminant analysis and the neural network methods belong to the first group, whereas the standard Bayesian approach and the Bayesian approach that includes a 1D Markov chain prior model to constrain the vertical continuity of litho-fluid facies, belong to the second group. The capability of each method to discriminate the different facies is evaluated both on synthetic seismic data (computed on the basis of available borehole information) and on field seismic data. The outcomes of each classification method are compared with the known facies profile derived from well log data and the goodness of the results is quantitatively evaluated using the so called confusion matrix. It results that all methods return vertical facies profiles in which the main reservoir zone is correctly identified. However, the consideration of as much prior information as possible in the classification process is the winning choice to derive a reliable and a physically plausible predicted facies profile

Classification of chirp signals using hierarchical bayesian learning and MCMC methods

This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm

Object Classification in Astronomical Multi-Color Surveys

We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of >65000 color templates. The method aims for extracting the information content of object colors in a statistically correct way and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach. For the redshift estimation, we use an advanced version of the MEV estimator which determines the redshift error from the redshift dependent probability density function. The method was originally developed for the CADIS survey, where we checked its performance by spectroscopy. The method provides high reliability (6 errors among 151 objects with R<24), especially for quasar selection, and redshifts accurate within sigma ~ 0.03 for galaxies and sigma ~ 0.1 for quasars. We compare a few model surveys using the same telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. In practice, medium-band surveys show superior performance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, and is most critical for surveys with few, broad and deeply exposed filters, but less severe for many, narrow and less deep filters.Comment: 21 pages including 10 figures. Accepted for publication in Astronomy & Astrophysic

The forelimbs of Octodontidae (Rodentia: Mammalia): substrate use, morphology, and phylogenetic signal

Rodents of the family Octodontidae, endemic to South America, represent a group with low taxonomic richness group (six genera and 14 species) but have great ecomorphological diversity with epigean, semi-fossorial, fossorial, and subterranean forms. We analyzed morphometric variation in humerus and ulna, the possible relationship with substrate preference use, and the presence of a phylogenetic signal in the forelimbs traits (five biomechanical indices). Our results show that, in octodontids, the forelimb variation was not primarily associated with their phylogeny and some attributes are highly explanatory in terms of function, with a clear differentiation between the substrate use gradient extremes (i.e. epigean and subterranean forms). The two forelimb traits, the development of humeral epicondyles and the olecranon process of the ulna, indicative of adaptive trends found in Octodontidae are consistent with most of those described for other mammals and corroborate the relevance of forelimb characters to differentiate modes of locomotion or substrate preferences.Fil: Pérez, María Julieta. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Programa de Investigación de Biodiversidad Argentina; ArgentinaFil: Cassini, Guillermo Hernán. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Díaz, María Mónica. División Mastozoología, Museo Argentino de Cs Nat; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Programa de Investigación de Biodiversidad Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Monte Carlo Approach to Ridge Discriminant Analysis

A combined analytical and empirical method to define human performance measures is required for effective automated training. In theory, performance measures can be weighted and combined in a single first order equation upon which an automated training system can track a student\u27s comparative skill level. A form of multivariate discriminant analysis was the statistical technique incorporated into the measurement selection and weighting scheme. (Vreuls and Wooldridge 1977). As with regression analysis, this discriminant technique was subject to debilitating problems such as overfit and undesirable inaccuracies in the coefficients. The author developed and implemented an adjustment to the discriminant function analogous to the ridge regression analysis suggested by Hoerl and Kemmard (1970a). To further demonstrate the effectiveness of ridge discriminant analysis and to determine useful performance criteria the author developed a Monte Carlo Simulation which provided a relative comparison of various performance measurement models. Two sets of human performance data were used to demonstrate this new statistical tool

Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert. © 2010 The Author(s