Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Principal Component Analysis of Results Obtained from Finite-Difference Time-Domain Algorithms

Finite-Differences Time-Domain (FDTD) algorithms are well established tools of computational electromagnetism. Because of their practical implementation as computer codes, they are affected by many numerical artefact and noise. In order to obtain better results we propose using Principal Component Analysis (PCA) based on multivariate statistical techniques. The PCA has been successfully used for the analysis of noise and spatial temporal structure in a sequence of images. It allows a straightforward discrimination between the numerical noise and the actual electromagnetic variables, and the quantitative estimation of their respective contributions. Besides, The GDTD results can be filtered to clean the effect of the noise. In this contribution we will show how the method can be applied to several FDTD simulations: the propagation of a pulse in vacuum, the analysis of two-dimensional photonic crystals. In this last case, PCA has revealed hidden electromagnetic structures related to actual modes of the photonic crystal.

Characterization of dynamic speckle sequences using principal component analysis and image descriptors

Speckle is being used as a characterization tool for the analysis of the dynamic of slow varying phenomena occurring in biological and industrial samples. The retrieved data takes the form of a sequence of speckle images. The analysis of these images should reveal the inner dynamic of the biological or physical process taking place in the sample. Very recently, it has been shown that principal component analysis is able to split the original data set in a collection of classes. These classes can be related with the dynamic of the observed phenomena. At the same time, statistical descriptors of biospeckle images have been used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, principal component analysis requires longer computation time but the results contain more information related with spatial-temporal pattern that can be identified with physical process. This contribution merges both descriptions and uses principal component analysis as a pre-processing tool to obtain a collection of filtered images where a simpler statistical descriptor can be calculated. The method has been applied to slow-varying biological and industrial processes

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

LOPES-DOS-SANTOS, V. , CONDE-OCAZIONEZ, S. ; NICOLELIS, M. A. L. , RIBEIRO, S. T. , TORT, A. B. L. . Neuronal assembly detection and cell membership specification by principal component analysis. Plos One, v. 6, p. e20996, 2011.

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

LOPES-DOS-SANTOS, V. , CONDE-OCAZIONEZ, S. ; NICOLELIS, M. A. L. , RIBEIRO, S. T. , TORT, A. B. L. . Neuronal assembly detection and cell membership specification by principal component analysis. Plos One, v. 6, p. e20996, 2011.

Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multimethod approach and principal component analysis (PCA)

This paper characterizes humic substances (HS) extracted from soil samples collected in the Rio Negro basin in the state of Amazonas, Brazil, particularly investigating their reduction capabilities towards Hg(II) in order to elucidate potential mercury cycling/volatilization in this environment. For this reason, a multimethod approach was used, consisting of both instrumental methods (elemental analysis, EPR, solid-state NMR, FIA combined with cold-vapor AAS of Hg(0)) and statistical methods such as principal component analysis (PCA) and a central composite factorial planning method. The HS under study were divided into groups, complexing and reducing ones, owing to different distribution of their functionalities. The main functionalities (cor)related with reduction of Hg(II) were phenolic, carboxylic and amide groups, while the groups related with complexation of Hg(II) were ethers, hydroxyls, aldehydes and ketones. The HS extracted from floodable regions of the Rio Negro basin presented a greater capacity to retain (to complex, to adsorb physically and/or chemically) Hg(II), while nonfloodable regions showed a greater capacity to reduce Hg(II), indicating that HS extracted from different types of regions contribute in different ways to the biogeochemical mercury cycle in the basin of the mid-Rio Negro, AM, Brazil. (c) 2007 Published by Elsevier B.V.

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.

Selection of cowpea progenies with enhanced drought-tolerance traits using principal component analysis.

Vigna unguiculata (L.) Walp (cowpea) is a food crop with high nutritional value that is cultivated throughout tropical and subtropical regions of the world. The main constraint on high productivity of cowpea is water deficit, caused by the long periods of drought that occur in these regions. The aim of the present study was to select elite cowpea genotypes with enhanced drought tolerance, by applying principal component analysis to 219 first-cycle progenies obtained in a recurrent selection program. The experimental design comprised a simple 15 x 15 lattice with 450 plots, each of two rows of 10 plants. Plants were grown under water-deficit conditions by applying a water depth of 205 mm representing one-half of that required by cowpea. Variables assessed were flowering, maturation, pod length, number and mass of beans/pod, mass of 100 beans, and productivity/plot. Ten elite cowpea genotypes were selected, in which principal components 1 and 2 encompassed variables related to yield (pod length, beans/pod, and productivity/plot) and life precocity (flowering and maturation), respectively.

Assessment of heavy metals in soils of a vineyard region with the use of principal component analysis.

Agricultural management with chemicals may contaminate the soil with heavy metals. The objective of this study was to apply Principal Component Analysis and geoprocessing techniques to identify the origin of the metals Cu, Fe, Mn, Zn, Ni, Pb, Cr and Cd as potential contaminants of agricultural soils. The study was developed in an area of vineyard cultivation in the State of São Paulo, Brazil. Soil samples were collected and GPS located under different uses and coverings. The metal concentrations in the soils were determined using the DTPA method. The Cu and Zn content was considered high in most of the samples, and was larger in the areas cultivated with vineyards that had been under the application of fungicides for several decades. The concentrations of Cu and Zn were correlated. The geoprocessing techniques and the Principal Component Analysis confirmed the enrichment of the soil with Cu and Zn because of the use and management of the vineyards with chemicals in the preceding decades.

Assemblage of immature Odonata (Insecta, Anisoptera) in streams of the Mato Grosso do Sul State: spatial implications

ABSTRACT This study investigated the assemblages attributes (composition, abundance, richness, diversity and evenness) and the most representative genera of Odonata, Anisoptera at Água Boa and Perobão Streams, Iguatemi River basin, Brazil. Both are first order streams with similar length that are impacted by riparian forest removal and silting. Quarterly samplings were conducted from March to December 2008 in the upper, intermediate and lower stretch of each stream. The Mantel test was used to check the influence of spatial autocorrelation on the Odonata composition. Spatial variations in the composition were summarized by the Principal Coordinates Analysis (PCoA) using Mantel test residuals. The effects of spatial correlation on richness and abundance were investigated by the spatial correlogram of Moranʼs I coefficients. The most representative genera in each stream were identified by the Indicator Value Method. The spatial variations in the attributes of the assemblages were assessed using analysis of variance of null models. We collected 500 immature individuals of 23 genera and three families. Among the attributes analyzed only the composition and abundance showed significant spatial differences, with the highest mean abundance found in the Perobão Stream. Miathyria and Zenithoptera were the indicator genera of the Água Boa Stream and Erythrodiplax, Libellula, Macrothemis, Progomphus and Tramea were the indicator genera of the Perobão Stream.

Spatial variation of dung beetle assemblages associated with forest structure in remnants of southern Brazilian Atlantic Forest

ABSTRACT The Brazilian Atlantic Forest is one of the world's biodiversity hotspots, and is currently highly fragmented and disturbed due to human activities. Variation in environmental conditions in the Atlantic Forest can influence the distribution of species, which may show associations with some environmental features. Dung beetles (Coleoptera: Scarabaeinae) are insects that act in nutrient cycling via organic matter decomposition and have been used for monitoring environmental changes. The aim of this study is to identify associations between the spatial distribution of dung beetle species and Atlantic Forest structure. The spatial distribution of some dung beetle species was associated with structural forest features. The number of species among the sampling sites ranged widely, and few species were found in all remnant areas. Principal coordinates analysis indicated that species composition, abundance and biomass showed a spatially structured distribution, and these results were corroborated by permutational multivariate analysis of variance. The indicator value index and redundancy analysis showed an association of several dung beetle species with some explanatory environmental variables related to Atlantic Forest structure. This work demonstrated the existence of a spatially structured distribution of dung beetles, with significant associations between several species and forest structure in Atlantic Forest remnants from Southern Brazil.

Extreme homogeneity among Brazilian wheat genotypes determined by RAPD markers

Random amplified polymorphic DNA markers (RAPD) were used to estimate the variability of 14 genotypes of Brazilian wheat (Triticum aestivum L.), using a set of 50 random 10mer primers. A total of 256 reproducibly scorable DNA amplification products were obtained from 48 of the primers, 83% of which were polymorphic. Genetic distances among genotypes were calculated and a dendrogram and a principal coordinates analysis showing the genetic relationships among them were obtained. Despite the low variability found (average genetic distance of 27%), two groups of genotypes could be identified, which probably reflect how they were formed. Studies such as this one may be important in the planning and development of future improvement programs for this plant species.

Populationsgenetische Fragmentanalyse von europäischen Heterobasidion annosum s. s.-Stämmen

Die fünf Arten des Heterobasidion annosum s. l.-Komplexes sind in Nordamerika und Eurasien dafür bekannt große Schäden in Nadelforsten zu verursachen. In Deutschland sind mit H. annsosum s. s., H. parviporum und H. abietinum alle drei eurasischen Weißfäuleerreger sympatrisch vertreten. Besonders in der Norddeutschen Tiefebene fallen H. annsoums s. s.-Stämme durch ansteigende Befallsherde und hohe Pathogenität auf. Seit Jahren untersucht die Nordwestdeutsche forstliche Versuchsanstalt (NW-FVA) Göttingen die Zusammenhänge der sogenannte „Ackersterbe“ in einzelnen Regionen. In diesem Zusammenhang sollte die vorliegende Arbeit den molekular-genetischen Aspekt behandeln und die inner- und zwischenartlichen Strukturen sowie das Beziehungsgefüge von H. annosum s. s. und H. parviporum-Individuen aus 18 europäischen Ländern darstellen. Für die molekularen Untersuchungen wurde die AFLP-Analyse (amplified fragment length polymorphism) ausgewählt. Die Vorteile der AFLP liegen in der großen Anzahl polymorpher und selektionsneutraler Marker, der guten Reproduzierbarkeit und der damit verbundenen Robustheit dieser Methode. Basierend auf acht AFLP-Primerpaarkombinationen mit 209 Individuen wurde eine binäre Matrix erstellt. Diese bildete die Grundlage für die molekulargenetischen Analysen und folgende Fragestellungen: Können die Heterobasidion-Individuen in Netzwerk- und Dendrogramm-Analysen den herkunftsspezifischen Populationen zugeordnet werden und lässt sich ein geografisches Muster erkennen? Wie groß ist das Ausmaß der genetischen Diversität innerhalb und zwischen den untersuchten europäischen Heterobasidion annosum s. l.-Populationen? Ist das gesteigerte Schadbild in der norddeutschen Tiefebene auf eine mögliche invasive Heterobasidion-Art zurückzuführen? Wie sieht die Populationsstruktur von Heterobasidion annosum s. l. in Europa bzw. Deutschland aus und gibt es Hinweise auf Subpopulationen oder Hybridisierungsereignisse? Die von der AFLP-Analyse erzeugten 888 informativen Marker wurden in Neighbor-Joining- und UPGMA-Phänogrammen, Median-joining Netzwerk und einem Principal coordinates analysis (PCoA)-Koordinatensystem dargestellt. Übereinstimmend zeigten sich die Arten H. annosum und H. parviporum eindeutig distinkt. Die Boostrapunterstützung der meisten H. annosum s. s.-Individuen erwies sich jedoch innerartlich als überwiegend schwach. Häufig kam es zu Gruppierungen der Individuen gemäß ihrer regionalen Herkunft. Seltener bildeten die Populationen gemeinsame Cluster entsprechend ihrer Länderzugehörigkeit. Eine zonale Gruppierungsstruktur der Individuen nach Süd-, Mittel-, und Nordeuropa konnte in keiner Analyse beobachtet werden. Mit der Analysis of Molecular Variance (AMOVA) wurde mit der AFLP-Methode eine genetische Varianz zwischen den beiden untersuchten Arten von 72 % nachgewiesen. Intraspezifisch ließ H. annosum s. s eine sehr hohe genetische Varianz innerhalb der Populationen (73%) und eine geringe Differenzierung zwischen diesen erkennen. Lediglich die deutschen Populationen zeigten eine gewisse Abgrenzung zueinander (33%). Als Ursache für die hohe individuelle genetische Vielfalt wird zum Einen das dichte Vorkommen der H. annosum s. s.- Wirte in Europa angesehen. Darüber hinaus sind gebietsfremde Sporeneinträge durch überregionale Forstarbeiten, globale Holzimporte und die für H. annosum s. s. nachgewiesenen weiten Sporenflüge zu erklären. Das Populationsannahmemodell von STRUCTURE generierte für den Datensatz beider Arten eine optimale Populationsanzahl von ΔK=4. Drei der Subpopulationen wurden H. annosum s. s. zugeordnet. Eine Korrelation dieser genetischen Cluster mit ihrem geografischen Ursprung oder der Wirtsbaumart war nicht feststellbar. Ein möglicher Zusammenhang der Subpopulationen mit weiteren ökologischen Parametern wie z. B. Substratgrundlage, Pathogenität, Sporulationsverhalten und sich änderte klimatische Umweltbedingungen konnten in dieser Studie nicht untersucht werden. Des Weiteren zeigte die STRUCTURE-Analyse, dass einige H. parviorum-Individuen Anteile eines H. annosum s. s.-Clusters führten. Um zu klären, ob es sich hierbei um einen erebten und konservierten Polymorphisums, oder um Introgression handelt, wären weiterführende Analysen notwendig.

Friends or relatives? Phylogenetics and species delimitation in the controversial European orchid Genus ophrys

Background and Aims Highly variable, yet possibly convergent, morphology and lack of sequence variation have severely hindered production of a robust phylogenetic framework for the genus Ophrys. The aim of this study is to produce this framework as a basis for more rigorous species delimitation and conservation recommendations. Methods Nuclear and plastid DNA sequencing and amplified fragment length polymorphism (AFLP) were performed on 85 accessions of Ophrys, spanning the full range of species aggregates currently recognized. Data were analysed using a combination of parsimony and Bayesian tree-building techniques and by principal coordinates analysis. Key Results Complementary phylogenetic analyses and ordinations using nuclear, plastid and AFLP datasets identify ten genetically distinct groups (six robust) within the genus that may in turn be grouped into three sections (treated as subgenera by some authors). Additionally, genetic evidence is provided for a close relationship between the O. tenthredinifera, O. bombyliflora and O. speculum groups. The combination of these analytical techniques provides new insights into Ophrys systematics, notably recognition of the novel O. umbilicata group. Conclusions Heterogeneous copies of the nuclear ITS region show that some putative Ophrys species arose through hybridization rather than divergent speciation. The supposedly highly specific pseudocopulatory pollination syndrome of Ophrys is demonstrably 'leaky', suggesting that the genus has been substantially over-divided at the species level.