Neogene palaeoceanographic and palaeoclimatic events inferred from palynological data: Cape Basin off South Africa, ODP Leg 175

Sites 1085, 1086 and 1087 were drilled off South Africa during Ocean Drilling Program (ODP) Leg 175 to investigate the Benguela Current System. While previous studies have focused on reconstructing the Neogene palaeoceanographic and palaeoclimatic history of these sites, palynology has been largely ignored, except for the Late Pliocene and Quaternary. This study presents palynological data from the upper Middle Miocene to lower Upper Pliocene sediments in Holes 1085A, 1086A and 1087C that provide complementary information about the history of the area. Abundant and diverse marine palynomorphs (mainly dinoflagellate cysts), rare spores and pollen, and dispersed organic matter have been recovered. Multivariate statistical analysis of dispersed organic matter identified three palynofacies assemblages (A, B, C) in the most continuous hole (1085A), and they were defined primarily by amorphous organic matter (AOM), and to a lesser extent black debris, structured phytoclasts, degraded phytoclasts, and marine palynomorphs. Ecostratigraphic interpretation based on dinoflagellate cyst, spore-pollen and palynofacies data allowed us to identify several palaeoceanographic and palaeoclimatic signals. First, the late Middle Miocene was subtropical, and sediments contained the highest percentages of land-derived organic matter, even though they are rich in AOM (palynofacies assemblage A). Second, the Late Miocene was cool-temperate and characterized by periods of intensified upwelling, increase in productivity, abundant and diverse oceanic dinoflagellate cysts, and the highest percentages of AOM (palynofacies assemblage C). Third, the Early to early Late Pliocene was warm-temperate with some dry intervals (increase in grass pollen) and intensified upwelling. Fourth, the Neogene "carbonate crash" identified in other southern oceans was recognized in two palynofacies A samples in Hole 1085A that are nearly barren of dinoflagellate cysts: one Middle Miocene sample (590 mbsf, 13.62 Ma) and one Upper Miocene sample (355 mbsf, 6.5 Ma). Finally, the extremely low percentages of pollen suggest sparse vegetation on the adjacent landmass, and Namib desert conditions were already in existence during the late Middle Miocene.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50���100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Extracci��n de conocimientos de la diabetes tipo 1 utilizando la metodolog��a de "Data Mining"

La diabetes mellitus es un trastorno en la metabolizaci��n de los carbohidratos, caracterizado por la nula o insuficiente segregaci��n de insulina (hormona producida por el p��ncreas), como resultado del mal funcionamiento de la parte endocrina del p��ncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos qu��micos m��s peque��os mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polip��ptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentraci��n de glucosa en sangre, ya que las c��lulas no pueden metabolizarla; consumo de ��cidos grasos mediante el h��gado, liberando cuerpos cet��nicos para aportar la energ��a a las c��lulas. Esta situaci��n expone al enfermo cr��nico, a una concentraci��n de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo m��ltiples problemas m��dicos: oftalmol��gicos, renales, cardiovasculares, cerebrovasculares, neurol��gicos��� La diabetes representa un gran problema de salud p��blica y es la enfermedad m��s com��n en los pa��ses desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparici��n de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentaci��n cl��nica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las c��lulas beta del p��ncreas (productoras de insulina) resultando necesaria la administraci��n de insulina ex��gena. Dicho esto, el paciente con diabetes tipo 1 deber�� seguir un tratamiento con insulina administrada por la v��a subcut��nea, adaptado a sus necesidades metab��licas y a sus h��bitos de vida. Para abordar esta situaci��n de regulaci��n del control metab��lico del enfermo, mediante una terapia de insulina, no serviremos del proyecto ���P��ncreas Endocrino Artificial��� (PEA), el cual consta de una bomba de infusi��n de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisi��n, eficacia y seguridad en cuanto a la normalizaci��n del control gluc��mico y reducci��n del riesgo de hipoglucemias. El PEA se instala mediante v��a subcut��nea, por lo que, el retardo introducido por la acci��n de la insulina, el retardo de la medida de glucosa, as�� como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducci��n de variables de entrada. De este modo lo que conseguimos, es una predicci��n de los estados futuros en los que se puede encontrar la glucosa del paciente, sirvi��ndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando par��metros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se est��n empleando actualmente en el PEA no est��n funcionando correctamente por la cantidad de informaci��n y variables que debe de manejar. Data Mining, tambi��n referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracci��n no trivial de informaci��n impl��cita, previamente desconocida y potencialmente ��til. Todo ello, sirvi��ndonos las siguientes fases del proceso de extracci��n del conocimiento: selecci��n de datos, pre-procesado, transformaci��n, miner��a de datos, interpretaci��n de los resultados, evaluaci��n y obtenci��n del conocimiento. Con todo este proceso buscamos generar un ��nico modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con c��lculos en tiempo real, a trav��s de unos par��metros introducidos. Este trabajo busca extraer la informaci��n contenida en una base de datos de pacientes diab��ticos tipo 1 obtenidos a partir de la experimentaci��n cl��nica. Para ello emplearemos t��cnicas de Data Mining. Para la consecuci��n del objetivo impl��cito a este proyecto hemos procedido a implementar una interfaz gr��fica que nos gu��a a trav��s del proceso del KDD (con informaci��n gr��fica y estad��stica) de cada punto del proceso. En lo que respecta a la parte de la miner��a de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a trav��s de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el c��digo. Mediante estos dispositivos y lo expuesto en el proyecto se podr��an implementar o incluso crear nuevas aplicaciones novedosas y muy ��tiles para este campo. Como conclusi��n del proyecto, y tras un exhaustivo an��lisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological ��� The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the ���Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin���glucose model for each patient.

Statistical methods for characterizing similarities and differences between semantic structures

This paper describes a variety of statistical methods for obtaining precise quantitative estimates of the similarities and differences in the structures of semantic domains in different languages. The methods include comparing mean correlations within and between groups, principal components analysis of interspeaker correlations, and analysis of variance of speaker by question data. Methods for graphical displays of the results are also presented. The methods give convergent results that are mutually supportive and equivalent under suitable interpretation. The methods are illustrated on the semantic domain of emotion terms in a comparison of the semantic structures of native English and native Japanese speaking subjects. We suggest that, in comparative studies concerning the extent to which semantic structures are universally shared or culture-specific, both similarities and differences should be measured and compared rather than placing total emphasis on one or the other polar position.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Statistical analysis of shape of objects based on landmark data.

Two objects with homologous landmarks are said to be of the same shape if the configurations of landmarks of one object can be exactly matched with that of the other by translation, rotation/reflection, and scaling. The observations on an object are coordinates of its landmarks with reference to a set of orthogonal coordinate axes in an appropriate dimensional space. The origin, choice of units, and orientation of the coordinate axes with respect to an object may be different from object to object. In such a case, how do we quantify the shape of an object, find the mean and variation of shape in a population of objects, compare the mean shapes in two or more different populations, and discriminate between objects belonging to two or more different shape distributions. We develop some methods that are invariant to translation, rotation, and scaling of the observations on each object and thereby provide generalizations of multivariate methods for shape analysis.

Joint interpretation of magnetotelluric, seismic, and well-log data in Hontom��n (Spain)

Acknowledgements. This work is dedicated to the memory of Andr��s P��rez-Esta��n, brilliant scientist, colleague, and friend. The authors sincerely thank Ian Ferguson and an anonymous reviewer for their useful comments on the manuscript. X��nia Ogaya is currently supported in the Dublin Institute for Advanced Studies by a Science Foundation Ireland grant IRECCSEM (SFI grant 12/IP/1313). Juan Alcalde is funded by NERC grant NE/M007251/1, on interpretational uncertainty. Juanjo Ledo, Pilar Queralt and Alex Marcuello thank Ministerio de Econom��a y Competitividad and EU Feder Funds through grant CGL2014- 54118-C2-1-R. Funding for this Project has been partially provided by the Spanish Ministry of Industry, Tourism and Trade, through the CIUDEN-CSIC-Inst. Jaume Almera agreement (ALM-09-027: Characterization, Development and Validation of Seismic Techniques applied to CO2 Geological Storage Sites), the CIUDEN-Fundaci�� Bosch i Gimpera agreement (ALM-09-009 Development and Adaptation of Electromagnetic techniques: Characterisation of Storage Sites) and the project PIERCO2 (Progress In Electromagnetic Research for CO2 geological reservoirs CGL2009-07604). The CIUDEN project is co-financed by the European Union through the Technological Development Plant of Compostilla OXYCFB300 Project (European Energy Programme for Recovery).

Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data ��� modelling and validation

Subsidence is a natural hazard that affects wide areas in the world causing important economic costs annually. This phenomenon has occurred in the metropolitan area of Murcia City (SE Spain) as a result of groundwater overexploitation. In this work aquifer system subsidence is investigated using an advanced differential SAR interferometry remote sensing technique (A-DInSAR) called Stable Point Network (SPN). The SPN derived displacement results, mainly the velocity displacement maps and the time series of the displacement, reveal that in the period 2004���2008 the rate of subsidence in Murcia metropolitan area doubled with respect to the previous period from 1995 to 2005. The acceleration of the deformation phenomenon is explained by the drought period started in 2006. The comparison of the temporal evolution of the displacements measured with the extensometers and the SPN technique shows an average absolute error of 3.9��3.8 mm. Finally, results from a finite element model developed to simulate the recorded time history subsidence from known water table height changes compares well with the SPN displacement time series estimations. This result demonstrates the potential of A-DInSAR techniques to validate subsidence prediction models as an alternative to using instrumental ground based techniques for validation.

A Sociocybernetics Data Analysis Using Causality in Tourism Networks

The aim of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on a pre-designed algorithm and applying our interpretation of chaos theory developed in the context of General Systems Theory. This article sets out the causal relationships associated with tourist flows in order to enable the formulation of appropriate strategies. Our results can be applied to numerous cases. For example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups and to analyse tourist behaviour in terms of the most relevant variables. Unlike statistical analyses that merely provide information on current data, our method uses orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.