Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Quantitative component analysis of the coarse fraction of surface sediments from the Persian Gulf

In the Persian Gulf and the Gulf of Oman marl forms the primary sediment cover, particularly on the Iranian side. A detailed quantitative description of the sediment components > 63 µ has been attempted in order to establish the regional distribution of the most important constituents as well as the criteria governing marl sedimentation in general. During the course of the analysis, the sand fraction from about 160 bottom-surface samples was split into 5 phi° fractions and 500 to 800 grains were counted in each individual fraction. The grains were cataloged in up to 40 grain type catagories. The gravel fraction was counted separately and the values calculated as weight percent. Basic for understanding the mode of formation of the marl sediment is the "rule" of independent availability of component groups. It states that the sedimentation of different component groups takes place independently, and that variation in the quantity of one component is independent of the presence or absence of other components. This means, for example, that different grain size spectrums are not necessarily developed through transport sorting. In the Persian Gulf they are more likely the result of differences in the amount of clay-rich fine sediment brought in to the restricted mouth areas of the Iranian rivers. These local increases in clayey sediment dilute the autochthonous, for the most part carbonate, coarse fraction. This also explains the frequent facies changes from carbonate to clayey marl. The main constituent groups of the coarse fraction are faecal pellets and lumps, the non carbonate mineral components, the Pleistocene relict sediment, the benthonic biogene components and the plankton. Faecal pellets and lumps are formed through grain size transformation of fine sediment. Higher percentages of these components can be correlated to large amounts of fine sediment and organic C. No discernable change takes place in carbonate minerals as a result of digestion and faecal pellet formation. The non-carbonate sand components originate from several unrelated sources and can be distinguished by their different grain size spectrum; as well as by other characteristics. The Iranian rivers supply the greatest amounts (well sorted fine sand). Their quantitative variations can be used to trace fine sediment transport directions. Similar mineral maxima in the sediment of the Gulf of Oman mark the path of the Persian Gulf outflow water. Far out from the coast, the basin bottoms in places contain abundant relict minerals (poorly sorted medium sand) and localized areas of reworked salt dome material (medium sand to gravel). Wind transport produces only a minimal "background value" of mineral components (very fine sand). Biogenic and non-biogenic relict sediments can be placed in separate component groups with the help of several petrographic criteria. Part of the relict sediment (well sorted fine sand) is allochthonous and was derived from the terrigenous sediment of river mouths. The main part (coarse, poorly sorted sediment), however, was derived from the late Pleistocene and forms a quasi-autochthonous cover over wide areas which receive little recent sedimentation. Bioturbation results in a mixing of the relict sediment with the overlying younger sediment. Resulting vertical sediment displacement of more than 2.5 m has been observed. This vertical mixing of relict sediment is also partially responsible for the present day grain size anomalies (coarse sediment in deep water) found in the Persian Gulf. The mainly aragonitic components forming the relict sediment show a finely subdivided facies pattern reflecting the paleogeography of carbonate tidal flats dating from the post Pleistocene transgression. Standstill periods are reflected at 110 -125m (shelf break), 64-61 m and 53-41 m (e.g. coare grained quartz and oolite concentrations), and at 25-30m. Comparing these depths to similar occurrences on other shelf regions (e. g. Timor Sea) leads to the conclusion that at this time minimal tectonic activity was taking place in the Persian Gulf. The Pleistocene climate, as evidenced by the absence of Iranian river sediment, was probably drier than the present day Persian Gulf climate. Foremost among the benthonic biogene components are the foraminifera and mollusks. When a ratio is set up between the two, it can be seen that each group is very sensitive to bottom type, i.e., the production of benthonic mollusca increases when a stable (hard) bottom is present whereas the foraminifera favour a soft bottom. In this way, regardless of the grain size, areas with high and low rates of recent sedimentation can be sharply defined. The almost complete absence of mollusks in water deeper than 200 to 300 m gives a rough sedimentologic water depth indicator. The sum of the benthonic foraminifera and mollusca was used as a relative constant reference value for the investigation of many other sediment components. The ratio between arenaceous foraminifera and those with carbonate shells shows a direct relationship to the amount of coarse grained material in the sediment as the frequence of arenaceous foraminifera depends heavily on the availability of sand grains. The nearness of "open" coasts (Iranian river mouths) is directly reflected in the high percentage of plant remains, and indirectly by the increased numbers of ostracods and vertebrates. Plant fragments do not reach their ultimate point of deposition in a free swimming state, but are transported along with the remainder of the terrigenous fine sediment. The echinoderms (mainly echinoids in the West Basin and ophiuroids in the Central Basin) attain their maximum development at the greatest depth reached by the action of the largest waves. This depth varies, depending on the exposure of the slope to the waves, between 12 to 14 and 30 to 35 m. Corals and bryozoans have proved to be good indicators of stable unchanging bottom conditions. Although bryozoans and alcyonarian spiculae are independent of water depth, scleractinians thrive only above 25 to 30 m. The beginning of recent reef growth (restricted by low winter temperatures) was seen only in one single area - on a shoal under 16 m of water. The coarse plankton fraction was studied primarily through the use of a plankton-benthos ratio. The increase in planktonic foraminifera with increasing water depth is here heavily masked by the "Adjacent sea effect" of the Persian Gulf: for the most part the foraminifera have drifted in from the Gulf of Oman. In contrast, the planktonic mollusks are able to colonize the entire Persian Gulf water body. Their amount in the plankton-benthos ratio always increases with water depth and thereby gives a reliable picture of local water depth variations. This holds true to a depth of around 400 m (corresponding to 80-90 % plankton). This water depth effect can be removed by graphical analysis, allowing the percentage of planktonic mollusks per total sample to be used as a reference base for relative sedimentation rate (sedimentation index). These values vary between 1 and > 1000 and thereby agree well with all the other lines of evidence. The "pteropod ooze" facies is then markedly dependent on the sedimentation rate and can theoretically develop at any depth greater than 65 m (proven at 80 m). It should certainly no longer be thought of as "deep sea" sediment. Based on the component distribution diagrams, grain size and carbonate content, the sediments of the Persian Gulf and the Gulf of Oman can be grouped into 5 provisional facies divisions (Chapt.19). Particularly noteworthy among these are first, the fine grained clayey marl facies occupying the 9 narrow outflow areas of rivers, and second, the coarse grained, high-carbonate marl facies rich in relict sediment which covers wide sediment-poor areas of the basin bottoms. Sediment transport is for the most part restricted to grain sizes < 150 µ and in shallow water is largely coast-parallel due to wave action at times supplemented by tidal currents. Below the wave base gravity transport prevails. The only current capable of moving sediment is the Persian Gulf outflow water in the Gulf of Oman.

NGC 7097: THE ACTIVE GALACTIC NUCLEUS AND ITS MIRROR, REVEALED BY PRINCIPAL COMPONENT ANALYSIS TOMOGRAPHY

Three-dimensional spectroscopy techniques are becoming more and more popular, producing an increasing number of large data cubes. The challenge of extracting information from these cubes requires the development of new techniques for data processing and analysis. We apply the recently developed technique of principal component analysis (PCA) tomography to a data cube from the center of the elliptical galaxy NGC 7097 and show that this technique is effective in decomposing the data into physically interpretable information. We find that the first five principal components of our data are associated with distinct physical characteristics. In particular, we detect a low-ionization nuclear-emitting region (LINER) with a weak broad component in the Balmer lines. Two images of the LINER are present in our data, one seen through a disk of gas and dust, and the other after scattering by free electrons and/or dust particles in the ionization cone. Furthermore, we extract the spectrum of the LINER, decontaminated from stellar and extended nebular emission, using only the technique of PCA tomography. We anticipate that the scattered image has polarized light due to its scattered nature.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

Parallel implementation of vertex component analysis for hyperspectral endmember extraction

International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July 2012, Munich, Germany

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Cochineal, a precious source of red: cochineal dyes characterization by high performance liquid chromatography with diode array detection and principal component analysis

Presented at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles

Principal component analysis of the yield curve

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Pair trading: Clustering based on principal component analysis

This study focuses on the implementation of several pair trading strategies across three emerging markets, with the objective of comparing the results obtained from the different strategies and assessing if pair trading benefits from a more volatile environment. The results show that, indeed, there are higher potential profits arising from emerging markets. However, the higher excess return will be partially offset by higher transaction costs, which will be a determinant factor to the profitability of pair trading strategies. Also, a new clustering approach based on the Principal Component Analysis was tested as an alternative to the more standard clustering by Industry Groups. The new clustering approach delivers promising results, consistently reducing volatility to a greater extent than the Industry Group approach, with no significant harm to the excess returns.