Fast Selection of Spectral Variables with B-Spline Compression

The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of the selected variables. Since the optimal approach of testing all possible subsets of variables with the prediction model is intractable, an incremental selection approach using a nonparametric statistics is a good option, as it avoids the computationally intensive use of the model itself. It has two drawbacks however: the number of groups of variables to test is still huge, and colinearities can make the results unstable. To overcome these limitations, this paper presents a method to select groups of spectral variables. It consists in a forward-backward procedure applied to the coefficients of a B-Spline representation of the spectra. The criterion used in the forward-backward procedure is the mutual information, allowing to find nonlinear dependencies between variables, on the contrary of the generally used correlation. The spline representation is used to get interpretability of the results, as groups of consecutive spectral variables will be selected. The experiments conducted on NIR spectra from fescue grass and diesel fuels show that the method provides clearly identified groups of selected variables, making interpretation easy, while keeping a low computational load. The prediction performances obtained using the selected coefficients are higher than those obtained by the same method applied directly to the original variables and similar to those obtained using traditional models, although using significantly less spectral variables

Resampling methods for parameter-free and robust feature selection with mutual information

Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time. However, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward procedure. These two choices are difficult to make because, as the dimensionality of the subset increases, the estimation of the mutual information becomes less and less reliable. This paper proposes to use resampling methods, a K-fold cross-validation and the permutation test, to address both issues. The resampling methods bring information about the variance of the estimator, information which can then be used to automatically set the parameter and to calculate a threshold to stop the forward procedure. The procedure is illustrated on a synthetic dataset as well as on real-world examples

A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis

Prediction problems from spectra are largely encountered in chemometry. In addition to accurate predictions, it is often needed to extract information about which wavelengths in the spectra contribute in an effective way to the quality of the prediction. This implies to select wavelengths (or wavelength intervals), a problem associated to variable selection. In this paper, it is shown how this problem may be tackled in the specific case of smooth (for example infrared) spectra. The functional character of the spectra (their smoothness) is taken into account through a functional variable projection procedure. Contrarily to standard approaches, the projection is performed on a basis that is driven by the spectra themselves, in order to best fit their characteristics. The methodology is illustrated by two examples of functional projection, using Independent Component Analysis and functional variable clustering, respectively. The performances on two standard infrared spectra benchmarks are illustrated.Comment: A paraitr

Advances in Feature Selection with Mutual Information

The selection of features that are relevant for a prediction or classification problem is an important problem in many domains involving high-dimensional data. Selecting features helps fighting the curse of dimensionality, improving the performances of prediction or classification methods, and interpreting the application. In a nonlinear context, the mutual information is widely used as relevance criterion for features and sets of features. Nevertheless, it suffers from at least three major limitations: mutual information estimators depend on smoothing parameters, there is no theoretically justified stopping criterion in the feature selection greedy procedure, and the estimation itself suffers from the curse of dimensionality. This chapter shows how to deal with these problems. The two first ones are addressed by using resampling techniques that provide a statistical basis to select the estimator parameters and to stop the search procedure. The third one is addressed by modifying the mutual information criterion into a measure of how features are complementary (and not only informative) for the problem at hand

Inter- and intra-annual patterns of seed rain in the black spruce stands of Quebec, Canada

Divergent reproductive strategies of tree species generate differences in the dynamics of seed production and dispersion. The spatial and temporal variability in seed rain abundance and viability was monitored during the period 2000-2007 in four boreal stands in Quebec, Canada. The aim was to compare the inter-and intra-annual patterns of seed dispersal between species with diverging adaptive characteristics and reproductive strategies by testing the hypothesis that sympatric species can exhibit different patterns of seed dispersal according to specific ecological adaptations. The coefficient of variation (CV), representing the inter-annual variability in seed rain, was close to or higher than 1 in balsam fir (Abies balsamea [L.] P. Mill.) and white birch (Betula papyrifera Marsh.) and confirmed the mast seeding habit of the two species. In contrast, CV in black spruce (Picea mariana [Mill.] BSP) ranged between 0.24 and 0.54, indicating a more homogeneous inter-annual amount of seed dispersal because of its semiserotinous cones that preserve seeds for an indefinite period of time. The species showed divergent intra-annual patterns of seed dispersal. Most seed dispersal of the companion species was observed in September-November, while black spruce concentrated seed rain in spring, when the proportion of germinated seeds was higher. Boreal stands experience annual seed rains constituted by a gradual dispersal of seeds of different ages and originating from cones belonging to multiple cohorts. However, asynchronous seed rains in terms of quantity and quality can occur if companion species are associated to the dominant black spruce

Modélisation de la distribution géographique de deux scolytes, Tomicus destruens et Tomicus piniperda, en Europe et dans la région méditerranéenne

Article publié suite à l'événement : MEDINSECT 3 ; Hammamet-Tunis (Tunisie) - (2012-05-08 - 2012-05-11).Preliminary note – Data and results presented in that paper come from the original article: Horn A., Kerdelhué C., Lieutier F., Rossi J.P., 2012. Predicting the distribution of the two bark beetles Tomicus destruens and Tomicus piniperda in Europe and the Mediterranean region. Agricultural and Forest Entomology, 14, 358-366.Various factors such as climate and resource availability influence the geographical distributions of organisms. Species that are susceptible to small temperature variations are known to experience rapid distribution shifts as a result of current global warming, sometimes leading to new threats to agriculture and forests. Tomicus destruens and Tomicus piniperda (Coleoptera, Curculionidae, Scolytinae) cause economic damage to pines in Europe and around the Mediterranean Basin. However, their respective potential distributions have not yet been studied at a large scale, mostly because these two species have long been misidentified. The present study aimed to investigate the influence of climatic factors on the geographical distributions of both Tomicus species in Europe and around the Mediterranean Sea, and to establish maps of suitable areas. We used occurrence data from 114 published localities where the presence or absence of both species was unambiguously recorded and confirmed by molecular data, and we gathered WorldClim meteorological records to link the occurrence of insects and climatic data and to build potential distribution maps. The two studied Tomicus species presented parapatric distributions and opposite climate requirements. T. destruens occurs in locations with warmer temperatures, whereas T. piniperda occurs under colder, continental climates. Amongst the investigated climatic variables, temperature appeared to be most correlated with both species distributions. We further extended our approach to explore potential geographical distributions under climate change scenarios. This showed that the distribution of both species is expected to exhibit strong alteration in the near future (2080) corresponding to a marked expansion of T. destruens towards northern Europe and a retractation and fragmentation of the distribution of T. piniperda.Résumé - Différents facteurs, tels que le climat ou la disponibilité de certaines ressources, affectent la distribution géographique des organismes. Aujourd’hui, les espèces sensibles à de faibles variations de température connaissent des modifications de leur aire de distribution sous l’effet du changement climatique, avec parfois de nouveaux risques pour l’agriculture et les forêts. Tomicus destruens et Tomicus piniperda (Coleoptera, Curculionidae, Scolytinae) causent des dégâts importants aux pins en Europe et sur le pourtour méditerranéen mais leur aire de distribution potentielle à une large échelle est encore mal connue, en particulier parce que ces deux espèces ont longtemps été confondues. L’étude présentée ici a pour objectif de mieux comprendre l’impact des variables climatiques sur la distribution géographique des deux espèces et d’établir des cartes de distribution potentielle, actuelles et futures. Nous avons utilisé 114 données d’occurrence publiées pour lesquelles la présence et l’absence des deux espèces sont avérées par des diagnostiques moléculaires. Nous avons utilisé les données climatiques disponibles dans la base « WorldClim » afin d’ajuster des modèles linéaires généralisés pour modéliser les relations climat – occurrences de T. destruens et T. piniperda. Les résultats obtenus montrent que les deux espèces ont des distributions parapatriques et des préférences climatiques différentes. T. destruens est présent dans les zones les plus chaudes tandis que T. piniperda occupe les régions les plus froides ayant probablement des saisons marquées. Nous avons utilisé les modèles précédents pour réaliser une projection permettant d’estimer la distribution potentielle de chaque espèce dans le cadre du scénario de changement climatique IPCC 4 du CIAT (modèle CCCM avec un scénario d’émission a2a). Les résultats indiquent que les distributions des deux espèces devraient connaître d’importants changements à court terme (2080). Ainsi, T. destruens remonterait vers le nord de l’Europe tandis que l’aire de distribution de T. piniperda serait à la fois réduite et très fragmentée

Osteoclast Cytomorphometry and Scanning Electron Microscopy of Bone Eroded Surfaces During Leukemic Disorders

Tartrate resistant acid phosphatase (TRAP) is a reliable histochemical marker of osteoclasts when used on tissue sections of undecalcified bone. This paper presents an original morphometric analysis which can be done after histochemical identification of osteoclasts. These bone resorbing cells were demonstrated on undecalcified bone biopsies from control subjects and patients presenting a malignant disease of the lymphocyte B lineage. Computerized analysis of the osteoclastic population revealed that: (1) all TRAP positive cells along bone trabeculae belong to a osteoclastic population; (2) that B cell malignancies had an increased bone resorption. At the scanning electron microscopic level small resorption bays (about 10 μm in diameter) were observed either associated or separated from eroded surfaces presenting a normal appearance; TRAP staining of histological sections of undecalcified bone, coupled with morphometric studies, may help in the understanding of bone disease pathobiology

Cutting force sensor based on digital image correlation for segmented chip formation analysis

Conventional piezoelectric sensors cannot record the force fluctuations at high frequencies to monitor serrated chip formation. Recently, force measurements by using digital image correlation (DIC) have been reported thanks to imaging devices that become more and more efficient, thereby opening possibilities of high rate acquisition. This study proposes to apply DIC based on closed-form solutions in order to measure cutting forces at camera acquisition frequency. The considered displacement fields are obtained from the Flamant–Boussinesq solution. This method is first applied to picture pairs shot during the cut and then to a full sequence of pictures recorded upon orthogonal cutting of hardened AISI 52100 steel with a c-BN tool. To validate part of the corresponding mechanism, the change of cutting forces is finally investigated when chip segments are formed