Reducing the babel in plant volatile communication: using the forest to see the trees

While plants of a single species emit a diversity of volatile organic compounds (VOCs) to attract or repel interacting organisms, these specific messages may be lost in the midst of the hundreds of VOCs produced by sympatric plants of different species, many of which may have no signal content. Receivers must be able to reduce the babel or noise in these VOCs in order to correctly identify the message. For chemical ecologists faced with vast amounts of data on volatile signatures of plants in different ecological contexts, it is imperative to employ accurate methods of classifying messages, so that suitable bioassays may then be designed to understand message content. We demonstrate the utility of `Random Forests' (RF), a machine-learning algorithm, for the task of classifying volatile signatures and choosing the minimum set of volatiles for accurate discrimination, using datam from sympatric Ficus species as a case study. We demonstrate the advantages of RF over conventional classification methods such as principal component analysis (PCA), as well as data-mining algorithms such as support vector machines (SVM), diagonal linear discriminant analysis (DLDA) and k-nearest neighbour (KNN) analysis. We show why a tree-building method such as RF, which is increasingly being used by the bioinformatics, food technology and medical community, is particularly advantageous for the study of plant communication using volatiles, dealing, as it must, with abundant noise.

Chemical communication in Ropalidia marginata: Dufour's gland contains queen signal that is perceived across colonies and does not contain colony signal

Queens of the primitively eusocial wasp Ropalidia marginata appear to maintain reproductive monopoly through pheromone rather than through physical aggression. Upon queen removal, one of the workers (potential queen, PQ) becomes extremely aggressive but drops her aggression immediately upon returning the queen. If the queen is not returned, the PQ gradually drops her aggression and becomes the next queen of the colony. In a previous study, the Dufour's gland was found to be at least one source of the queen pheromone. Queen-worker classification could be done with 100% accuracy in a discriminant analysis, using the compositions of their respective Dufour's glands. In a bioassay, the PQ dropped her aggression in response to the queen's Dufour's gland macerate, suggesting that the queen's Dufour's gland contents mimicked the queen herself. In the present study, we found that the PQ also dropped her aggression in response to the macerate of a foreign queen's Dufour's gland. This suggests that the queen signal is perceived across colonies. This also suggests that the Dufour's gland in R. marginata does not contain information about nestmateship, because queens are attacked when introduced into foreign colonies, and hence PQ is not expected to reduce her aggression in response to a foreign queen's signal. The latter conclusion is especially significant because the Dufour's gland chemicals are adequate to classify individuals correctly not only on the basis of fertility status (queen versus worker) but also according to their colony membership, using discriminant analysis. This leads to the additional conclusion (and precaution) that the ability to statistically discriminate organisms using their chemical profiles does not necessarily imply that the organisms themselves can make such discrimination. (C) 2010 Elsevier Ltd. All rights reserved.

Modelling electricity demand with representative load curves

Models for electricity planning require inclusion of demand. Depending on the type of planning, the demand is usually represented as an annual demand for electricity (GWh), a peak demand (MW) or in the form of annual load-duration curves. The demand for electricity varies with the seasons, economic activities, etc. Existing schemes do not capture the dynamics of demand variations that are important for planning. For this purpose, we introduce the concept of representative load curves (RLCs). Advantages of RLCs are demonstrated in a case study for the state of Karnataka in India. Multiple discriminant analysis is used to cluster the 365 daily load curves for 1993-94 into nine RLCs. Further analyses of these RLCs help to identify important factors, namely, seasonal, industrial, agricultural, and residential (water heating and air-cooling) demand variations besides rationing by the utility. (C) 1999 Elsevier Science Ltd. All rights reserved.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Classification of Myopathies on Molecular basis in Drosophila using Raman spectroscopy

Myopathies are muscular diseases in which muscle fibers degenerate due to many factors such as nutrient deficiency, infection and mutations in myofibrillar etc. The objective of this study is to identify the bio-markers to distinguish various muscle mutants in Drosophila (fruit fly) using Raman Spectroscopy. Principal Components based Linear Discriminant Analysis (PC-LDA) classification model yielding >95% accuracy was developed to classify such different mutants representing various myopathies according to their physiopathology.

Classification of Myopathies on Molecular basis in Drosophila using Raman spectroscopy

Myopathies are muscular diseases in which muscle fibers degenerate due to many factors such as nutrient deficiency, infection and mutations in myofibrillar etc. The objective of this study is to identify the bio-markers to distinguish various muscle mutants in Drosophila (fruit fly) using Raman Spectroscopy. Principal Components based Linear Discriminant Analysis (PC-LDA) classification model yielding >95% accuracy was developed to classify such different mutants representing various myopathies according to their physiopathology.

Phenotypic characters of rice landraces reveal independent lineages of short-grain aromatic indica rice

Rice landraces are lineages developed by farmers through artificial selection during the long-term domestication process. Despite huge potential for crop improvement, they are largely understudied in India. Here, we analyse a suite of phenotypic characters from large numbers of Indian landraces comprised of both aromatic and non-aromatic varieties. Our primary aim was to investigate the major determinants of diversity, the strength of segregation among aromatic and non-aromatic landraces as well as that within aromatic landraces. Using principal component analysis, we found that grain length, width and weight, panicle weight and leaf length have the most substantial contribution. Discriminant analysis can effectively distinguish the majority of aromatic from non-aromatic landraces. More interestingly, within aromatic landraces long-grain traditional Basmati and short-grain non-Basmati aromatics remain morphologically well differentiated. The present research emphasizes the general patterns of phenotypic diversity and finds out the most important characters. It also confirms the existence of very unique short-grain aromatic landraces, perhaps carrying signatures of independent origin of an additional aroma quantitative trait locus in the indica group, unlike introgression of specific alleles of the BADH2 gene from the japonica group as in Basmati. We presume that this parallel origin and evolution of aroma in short-grain indica landraces are linked to the long history of rice domestication that involved inheritance of several traits from Oryza nivara, in addition to O. rufipogon. We conclude with a note that the insights from the phenotypic analysis essentially comprise the first part, which will likely be validated with subsequent molecular analysis.

Raman Spectroscopic Studies on Screening of Myopathies

Myopathies are among the major causes of mortality in the world. There is no complete cure for this heterogeneous group of diseases, but a sensitive, specific, and fast diagnostic tool may improve therapy effectiveness. In this study, Raman spectroscopy is applied to discriminate between muscle mutants in Drosophila on the basis of associated changes at the molecular level. Raman spectra were collected from indirect flight muscles of mutants, upheld1 (up1), heldup(2) (hdp(2)), myosin heavy chain7 (Mhc7), actin88F(KM88) (Act88F(KM88)), upheld101 (up101), and Canton-S (CS) control group, for both 2 and 12 days old flies. Difference spectra (mutant minus control) of all the mutants showed an increase in nucleic acid and beta-sheet and/or random coil protein content along with a decrease in a-helix protein. Interestingly, the 12th day samples of up1 and Act88F(KM88) showed significantly higher levels of glycogen and carotenoids than CS. A principal components based linear discriminant analysis classification model was developed based on multidimensional Raman spectra, which classified the mutants according to their pathophysiology and yielded an overall accuracy of 97% and 93% for 2 and 12 days old flies, respectively. The up1 and Act88F(KM88) (nemaline-myopathy) mutants form a group that is clearly separated in a linear discriminant plane from up101 and hdp2 (cardiomyopathy) mutants. Notably, Raman spectra from a human sample with nemaline-myopathy formed a cluster with the corresponding Drosophila mutant (up1). In conclusion, this is the first demonstration in which myopathies, despite their heterogeneity, were screened on the basis of biochemical differences using Raman spectroscopy.

Development of the 2003 CU-HTK conversational telephone speech transcription system

This paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for Conversational Telephone Speech (CTS). The system was designed based on a multi-pass, multi-branch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques such as Speaker Adaptive Training, Heteroscedastic Linear Discriminant Analysis, Minimum Phone Error estimation and specially constructed Single Pronunciation dictionaries were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 Rich Transcription (RT-03) evaluation test set is discussed.

Automatic transcription of conversational telephone speech

This paper discusses the Cambridge University HTK (CU-HTK) system for the automatic transcription of conversational telephone speech. A detailed discussion of the most important techniques in front-end processing, acoustic modeling and model training, language and pronunciation modeling are presented. These include the use of conversation side based cepstral normalization, vocal tract length normalization, heteroscedastic linear discriminant analysis for feature projection, minimum phone error training and speaker adaptive training, lattice-based model adaptation, confusion network based decoding and confidence score estimation, pronunciation selection, language model interpolation, and class based language models. The transcription system developed for participation in the 2002 NIST Rich Transcription evaluations of English conversational telephone speech data is presented in detail. In this evaluation the CU-HTK system gave an overall word error rate of 23.9%, which was the best performance by a statistically significant margin. Further details on the derivation of faster systems with moderate performance degradation are discussed in the context of the 2002 CU-HTK 10 × RT conversational speech transcription system. © 2005 IEEE.

Evaluación perceptivo auditiva de voces degradas y su correlación con medidas acústicas

Resumen: El objetivo es determinar utilizando las mediciones acústicas, qué información es más relevante para el oyente al momento de categorizar el grado general de disfonía. Se eligieron 8 (4 voces femeninas y 4 voces masculinas. Cada emisión fue evaluada auditivo perceptualmente a través del item G de la escala GRBAS por 10 oyentes experimentados y acústicamente mediante medidas de aperiodicidad, ruido y caos. El estudio estadístico de análisis discriminante señala la importancia de GNE, Jit y Jitter_cc y Lyapunov como parámetros predictores del grado general de disfonía. La aplicación del método k-means evidencia que existen rasgos en los parámetros acústicos empleados que permiten agrupar objetivamente las voces estudiadas con 100% de precisión para la clase 0, 96% a la clase 2 y 79% a la clase 3. Un mayor número y variabilidad de casos se necesita a fin de verificar los resultados preliminares.

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

O uso do boto-cinza (Sotalia guianensis) como sentinela da poluição continental por compostos organoclorados (DDT, PCB, HCH, HCB e Mirex) em baías costeiras do Estado do Rio de Janeiro

Concentrações de compostos organoclorados (DDTs, PCBs, HCHs, Mirex e HCB) foram determinadas em camadas externas e internas do tecido adiposo subcutâneo de 17 botos-cinza (Sotalia guianensis) da região Sudeste do Brasil. Não houve diferenças estatísticas significativas entre os estratos, relativo aos 37 compostos determinados, assim como ΣDDT, ΣPCB, ΣHCH, e as razões p,p-DDE/ΣDDT e ΣDDT/ΣPCB. Entretanto, foram observadas diferenças significativas nas concentrações de alguns compostos organoclorados de animais encalhados ou capturados acidentalmente quando comparados com animais biopsiados remotamente, sendo assim as comparações entre esses dois conjuntos de dados, devem ser vistas com cuidado. No presente estudo, as concentrações dos compostos organoclorados foram determinadas em biópsias de botos-cinza obtidas de 2007 a 2009, nas baías de Sepetiba (n=13) e Ilha Grande (n=11), Sudeste do Brasil. As concentrações (ng/g de lipídio) variaram de discriminante, os dados dos machos capturados acidentalmente gerados no presente estudo foram analisados juntamente com dados dos machos de um estudo recente que utilizou amostras do tecido adiposo subcutâneo de botos-cinza capturados acidentalmente. Através dessa análise, foi possível verificar que as populações de boto-cinza de 2 baías costeiras vizinhas (Sepetiba e Guanabara), bem como da Baía de Paranaguá, apresentaram padrões distintos de acumulação dos compostos organoclorados. O último achado demonstra a existência de separações ecológicas entre os botoscinza a partir de diferentes áreas, o que constitui informação de grande importância para a conservação e o manejo da espécie

Assobios de delfinídeos na costa do Estado do Rio de Janeiro: caracterização e comparação interespecífica

Os delfinídeos possuem um variado repertório de emissões sonoras, que são produzidos em diferentes contextos comportamentais e são importantes para as relações entre os indivíduos. As emissões sonoras dos delfinídeos são predominantemente utilizadas para a comunicação e são divididas em duas categorias: os sons pulsantes e os assobios. O presente estudo apresenta comparações entre os repertórios de assobios de três espécies de delfinídeos encontrados na costa do Estado do Rio de Janeiro: Stenella frontalis, Steno bredanensis e Sotalia guianensis. Três sistemas de gravação foram utilizados. Estes foram compostos por hidrofones HTI-96-MIN e C54XRS, e gravadores PMD 671 Marantz, FOSTEX (taxa de amostragem de 96 kHz) e SONY TCD-T8 (taxa de amostragem de 48 kHz). As análises dos espectrogramas foram realizadas no software Raven 1.4. Os assobios foram classificados em categorias de formas de contorno e 15 parâmetros acústicos foram mensurados em cada um destes sinais. A estatística descritiva foi realizada para os assobios de cada espécie, e estes foram comparados a partir de testes de comparação de médias e análise discriminante. Um total de 838 assobios foi analisado. Assobios com forma de contorno ascendente de S. frontalis, S. bredanensis, S. guianensis da Baía de Guanabara, da Baía de Ilha Grande e da Baía de Sepetiba corresponderam a 48,1% (N=63), 40,8% (N=47), 49,8% (N=98), 63,9% (N=126) e 58,1% (N=115) do repertório de cada grupo, respectivamente. Diferenças foram encontradas em praticamente todos os parâmetros entre assobios de S. bredanensis e S. guianensis. O maior número de semelhanças ocorreu entre assobios das populações distintas de S. guianensis. A taxa de classificação correta geral foi de 52,4%. Assobios de S. bredanensis apresentaram a maior classificação correta (84,3%). Assobios de S. frontalis apresentaram taxa de classificação correta de 55,7% e os de S. guianensis da Baía de Guanabara, Baía de Ilha Grande e Baía de Sepetiba apresentaram taxas de 57,9%, 48,7% e 29,8%, respectivamente. A análise discriminante realizada entre assobios ascendentes resultou em uma taxa de classificação correta menor (49%). As variáveis consideradas mais importantes para a discriminação entre espécies foram: FF, 3Q, 1Q, MOD e FM. Por meio de parâmetros acústicos foi possível discriminar grande parte dos assobios de espécies simpátricas, apesar de haver ainda sobreposições entre variáveis acústicas dos assobios das espécies comparadas neste estudo.

Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study

The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.