Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^

(Table 1) Species richness and diversity of tundra plants in herbivore exclosure and control plots near Barrow, Alaska

To determine the role lemmings play in structuring plant communities and their contribution to the 'greening of the Arctic', we measured plant cover and biomass in 50 + year old lemming exclosures and control plots in the coastal tundra near Barrow, Alaska. The response of plant functional types to herbivore exclusion varied among land cover types. In general, the abundance of lichens and bryophytes increased with the exclusion of lemmings, whereas graminoids decreased, although the magnitude of these responses varied among land cover types. These results suggest that sustained lemming activity promotes a higher biomass of vascular plant functional types than would be expected without their presence and highlights the importance of considering herbivory when interpreting patterns of greening in the Arctic. In light of the rapid environmental change ongoing in the Arctic and the potential regional to global implications of this change, further exploration regarding the long-term influence of arvicoline rodents on ecosystem function (e.g. carbon and energy balance) should be considered a research priority.

Measurements of gastropods shell sizes from 23 fossil and extant long-lived lakes

Aim: To investigate shell size variation among gastropod faunas of fossil and recent long-lived European lakes and discuss potential underlying processes. Location: 23 long-lived lakes of the Miocene to Recent of Europe. Methods: Based on a dataset of 1412 species of both fossil and extant lacustrine gastropods, we assessed differences in shell size in terms of characteristics of the faunas (species richness, degree of endemism, differences in family composition) and the lakes (surface area, latitude and longitude of lake centroid, distance to closest neighbouring lake) using multiple and linear regression models. Because of a strong species-area relationship, we used resampling to determine whether any observed correlation is driven by that relationship. Results: The regression models indicated size range expansion rather than unidirectional increase or decrease as the dominant pattern of size evolution. The multiple regression models for size range and maximum and minimum size were statistically significant, while the model with mean size was not. Individual contributions and linear regressions indicated species richness and lake surface area as best predictors for size changes. Resampling analysis revealed no significant effects of species richness on the observed patterns. The correlations are comparable across families of different size classes, suggesting a general pattern. Main conclusions: Among the chosen variables, species richness and lake surface area are the most robust predictors of shell size in long-lived lake gastropods. Although the most outstanding and attractive examples for size evolution in lacustrine gastropods derive from lakes with extensive durations, shell size appears to be independent of the duration of the lake as well as longevity of a species. The analogue of long-lived lakes as 'evolutionary islands' does not hold for developments of shell size because different sets of parameters predict size changes.

Identification of a biomarker panel for colorectal cancer diagnosis

Background Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955)

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

Identification of a biomarker panel for colorectal cancer diagnosis

Background:Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

Estrategias de monitorización de CO2 y otros gases en los estudios de análogos naturales

La Universidad Politécnica de Madrid (UPM) y la Università degli Studi di Firenze (UniFi), bajo la coordinación técnica de AMPHOS21, participan desde 2009 en el proyecto de investigación “Estrategias de Monitorización de CO2 y otros gases en el estudio de Análogos Naturales”, financiado por la Fundación Ciudad de la Energía (CIUDEN) en el marco del Proyecto Compostilla OXYCFB300 (http://www.compostillaproject.eu), del Programa “European Energy Program for Recovery - EEPR”. El objetivo principal del proyecto fue el desarrollo y puesta a punto de metodologías de monitorización superficiales para su aplicación en el seguimiento y control de los emplazamientos donde se realice el almacenamiento geológico de CO2, analizando técnicas que permitan detectar y cuantificar las posibles fugas de CO2 a la atmósfera. Los trabajos se realizaron tanto en análogos naturales (españoles e italianos) como en la Planta de Desarrollo Tecnológico de Almacenamiento de CO2 de Hontomín. Las técnicas analizadas se centran en la medición de gases y aguas superficiales (de escorrentía y manantiales). En cuanto a la medición de gases se analizó el flujo de CO2 que emana desde el suelo a la atmósfera y la aplicabilidad de trazadores naturales (como el radón) para la detección e identificación de las fugas de CO2. En cuanto al análisis químico de las aguas se analizaron los datos geoquímicos e isotópicos y los gases disueltos en las aguas de los alrededores de la PDT de Hontomín, con objeto de determinar qué parámetros son los más apropiados para la detección de una posible migración del CO2 inyectado, o de la salmuera, a los ambientes superficiales. Las medidas de flujo de CO2 se realizaron con la técnica de la cámara de acúmulo. A pesar de ser una técnica desarrollada y aplicada en diferentes ámbitos científicos se estimó necesario adaptar un protocolo de medida y de análisis de datos a las características específicas de los proyectos de captura y almacenamiento de CO2 (CAC). Donde los flujos de CO2 esperados son bajos y en caso de producirse una fuga habrá que detectar pequeñas variaciones en los valores flujo con un “ruido” en la señal alto, debido a actividad biológica en el suelo. La medida de flujo de CO2 mediante la técnica de la cámara de acúmulo se puede realizar sin limpiar la superficie donde se coloca la cámara o limpiando y esperando al reequilibrio del flujo después de la distorsión al sistema. Sin embargo, los resultados obtenidos después de limpiar y esperar muestran menor dispersión, lo que nos indica que este procedimiento es el mejor para la monitorización de los complejos de almacenamiento geológico de CO2. El protocolo de medida resultante, utilizado para la obtención de la línea base de flujo de CO2 en Hontomín, sigue los siguiente pasos: a) con una espátula se prepara el punto de medición limpiando y retirando el recubrimiento vegetal o la primera capa compacta de suelo, b) se espera un tiempo para la realización de la medida de flujo, facilitando el reequilibrio del flujo del gas tras la alteración provocada en el suelo y c) se realiza la medida de flujo de CO2. Una vez realizada la medición de flujo de CO2, y detectada si existen zonas de anomalías, se debe estimar la cantidad de CO2 que se está escapando a la atmósfera (emanación total), con el objetivo de cuantificar la posible fuga. Existen un amplio rango de metodologías para realizar dicha estimación, siendo necesario entender cuáles son las más apropiadas para obtener el valor más representativo del sistema. En esta tesis se comparan seis técnicas estadísticas: media aritmética, estimador insegado de la media (aplicando la función de Sichel), remuestreo con reemplazamiento (bootstrap), separación en diferentes poblaciones mediante métodos gráficos y métodos basados en criterios de máxima verosimilitud, y la simulación Gaussiana secuencial. Para este análisis se realizaron ocho campañas de muestreo, tanto en la Planta de Desarrollo Tecnológico de Hontomón como en análogos naturales (italianos y españoles). Los resultados muestran que la simulación Gaussiana secuencial suele ser el método más preciso para realizar el cálculo, sin embargo, existen ocasiones donde otros métodos son más apropiados. Como consecuencia, se desarrolla un procedimiento de actuación para seleccionar el método que proporcione el mejor estimador. Este procedimiento consiste, en primer lugar, en realizar un análisis variográfico. Si existe una autocorrelación entre los datos, modelizada mediante el variograma, la mejor técnica para calcular la emanación total y su intervalo de confianza es la simulación Gaussiana secuencial (sGs). Si los datos son independientes se debe comprobar la distribución muestral, aplicando la media aritmética o el estimador insesgado de la media (Sichel) para datos normales o lognormales respectivamente. Cuando los datos no son normales o corresponden a una mezcla de poblaciones la mejor técnica de estimación es la de remuestreo con reemplazamiento (bootstrap). Siguiendo este procedimiento el máximo valor del intervalo de confianza estuvo en el orden del ±20/25%, con la mayoría de valores comprendidos entre ±3,5% y ±8%. La identificación de las diferentes poblaciones muestrales en los datos de flujo de CO2 puede ayudar a interpretar los resultados obtenidos, toda vez que esta distribución se ve afectada por la presencia de varios procesos geoquímicos como, por ejemplo, una fuente geológica o biológica del CO2. Así pues, este análisis puede ser una herramienta útil en el programa de monitorización, donde el principal objetivo es demostrar que no hay fugas desde el reservorio a la atmósfera y, si ocurren, detectarlas y cuantificarlas. Los resultados obtenidos muestran que el mejor proceso para realizar la separación de poblaciones está basado en criterios de máxima verosimilitud. Los procedimientos gráficos, aunque existen pautas para realizarlos, tienen un cierto grado de subjetividad en la interpretación de manera que los resultados son menos reproducibles. Durante el desarrollo de la tesis se analizó, en análogos naturales, la relación existente entre el CO2 y los isótopos del radón (222Rn y 220Rn), detectándose en todas las zonas de emisión de CO2 una relación positiva entre los valores de concentración de 222Rn en aire del suelo y el flujo de CO2. Comparando la concentración de 220Rn con el flujo de CO2 la relación no es tan clara, mientras que en algunos casos aumenta en otros se detecta una disminución, hecho que parece estar relacionado con la profundidad de origen del radón. Estos resultados confirmarían la posible aplicación de los isótopos del radón como trazadores del origen de los gases y su aplicación en la detección de fugas. Con respecto a la determinación de la línea base de flujo CO2 en la PDT de Hontomín, se realizaron mediciones con la cámara de acúmulo en las proximidades de los sondeos petrolíferos, perforados en los ochenta y denominados H-1, H-2, H-3 y H-4, en la zona donde se instalarán el sondeo de inyección (H-I) y el de monitorización (H-A) y en las proximidades de la falla sur. Desde noviembre de 2009 a abril de 2011 se realizaron siete campañas de muestreo, adquiriéndose más de 4.000 registros de flujo de CO2 con los que se determinó la línea base y su variación estacional. Los valores obtenidos fueron bajos (valores medios entre 5 y 13 g•m-2•d-1), detectándose pocos valores anómalos, principalmente en las proximidades del sondeo H-2. Sin embargo, estos valores no se pudieron asociar a una fuente profunda del CO2 y seguramente estuvieran más relacionados con procesos biológicos, como la respiración del suelo. No se detectaron valores anómalos cerca del sistema de fracturación (falla Ubierna), toda vez que en esta zona los valores de flujo son tan bajos como en el resto de puntos de muestreo. En este sentido, los valores de flujo de CO2 aparentemente están controlados por la actividad biológica, corroborado al obtenerse los menores valores durante los meses de otoño-invierno e ir aumentando en los periodos cálidos. Se calcularon dos grupos de valores de referencia, el primer grupo (UCL50) es 5 g•m-2•d-1 en las zonas no aradas en los meses de otoño-invierno y 3,5 y 12 g•m-2•d-1 en primavera-verano para zonas aradas y no aradas, respectivamente. El segundo grupo (UCL99) corresponde a 26 g•m-2•d- 1 durante los meses de otoño-invierno en las zonas no aradas y 34 y 42 g•m-2•d-1 para los meses de primavera-verano en zonas aradas y no aradas, respectivamente. Flujos mayores a estos valores de referencia podrían ser indicativos de una posible fuga durante la inyección y posterior a la misma. Los primeros datos geoquímicos e isotópicos de las aguas superficiales (de escorrentía y de manantiales) en el área de Hontomín–Huermeces fueron analizados. Los datos sugieren que las aguas estudiadas están relacionadas con aguas meteóricas con un circuito hidrogeológico superficial, caracterizadas por valores de TDS relativamente bajos (menor a 800 mg/L) y una fácie hidrogeoquímica de Ca2+(Mg2+)-HCO3 −. Algunas aguas de manantiales se caracterizan por concentraciones elevadas de NO3 − (concentraciones de hasta 123 mg/l), lo que sugiere una contaminación antropogénica. Se obtuvieron concentraciones anómalas de of Cl−, SO4 2−, As, B y Ba en dos manantiales cercanos a los sondeos petrolíferos y en el rio Ubierna, estos componentes son probablemente indicadores de una posible mezcla entre los acuíferos profundos y superficiales. El estudio de los gases disueltos en las aguas también evidencia el circuito superficial de las aguas. Estando, por lo general, dominado por la componente atmosférica (N2, O2 y Ar). Sin embargo, en algunos casos el gas predominante fue el CO2 (con concentraciones que llegan al 63% v/v), aunque los valores isotópicos del carbono (<-17,7 ‰) muestran que lo más probable es que esté relacionado con un origen biológico. Los datos geoquímicos e isotópicos de las aguas superficiales obtenidos en la zona de Hontomín se pueden considerar como el valor de fondo con el que comparar durante la fase operacional, la clausura y posterior a la clausura. En este sentido, la composición de los elementos mayoritarios y traza, la composición isotópica del carbono del CO2 disuelto y del TDIC (Carbono inorgánico disuelto) y algunos elementos traza se pueden considerar como parámetros adecuados para detectar la migración del CO2 a los ambientes superficiales. ABSTRACT Since 2009, a group made up of Universidad Politécnica de Madrid (UPM; Spain) and Università degli Studi Firenze (UniFi; Italy) has been taking part in a joint project called “Strategies for Monitoring CO2 and other Gases in Natural analogues”. The group was coordinated by AMPHOS XXI, a private company established in Barcelona. The Project was financially supported by Fundación Ciudad de la Energía (CIUDEN; Spain) as a part of the EC-funded OXYCFB300 project (European Energy Program for Recovery -EEPR-; www.compostillaproject.eu). The main objectives of the project were aimed to develop and optimize analytical methodologies to be applied at the surface to Monitor and Verify the feasibility of geologically stored carbon dioxide. These techniques were oriented to detect and quantify possible CO2 leakages to the atmosphere. Several investigations were made in natural analogues from Spain and Italy and in the Tecnchnological Development Plant for CO2 injection al Hontomín (Burgos, Spain). The studying techniques were mainly focused on the measurements of diffuse soil gases and surface and shallow waters. The soil-gas measurements included the determination of CO2 flux and the application to natural trace gases (e.g. radon) that may help to detect any CO2 leakage. As far as the water chemistry is concerned, geochemical and isotopic data related to surface and spring waters and dissolved gases in the area of the PDT of Hontomín were analyzed to determine the most suitable parameters to trace the migration of the injected CO2 into the near-surface environments. The accumulation chamber method was used to measure the diffuse emission of CO2 at the soil-atmosphere interface. Although this technique has widely been applied in different scientific areas, it was considered of the utmost importance to adapt the optimum methodology for measuring the CO2 soil flux and estimating the total CO2 output to the specific features of the site where CO2 is to be stored shortly. During the pre-injection phase CO2 fluxes are expected to be relatively low where in the intra- and post-injection phases, if leakages are to be occurring, small variation in CO2 flux might be detected when the CO2 “noise” is overcoming the biological activity of the soil (soil respiration). CO2 flux measurements by the accumulation chamber method could be performed without vegetation clearance or after vegetation clearance. However, the results obtained after clearance show less dispersion and this suggests that this procedure appears to be more suitable for monitoring CO2 Storage sites. The measurement protocol, applied for the determination of the CO2 flux baseline at Hontomín, has included the following steps: a) cleaning and removal of both the vegetal cover and top 2 cm of soil, b) waiting to reduce flux perturbation due to the soil removal and c) measuring the CO2 flux. Once completing the CO2 flux measurements and detected whether there were anomalies zones, the total CO2 output was estimated to quantify the amount of CO2 released to the atmosphere in each of the studied areas. There is a wide range of methodologies for the estimation of the CO2 output, which were applied to understand which one was the most representative. In this study six statistical methods are presented: arithmetic mean, minimum variances unbiased estimator, bootstrap resample, partitioning of data into different populations with a graphical and a maximum likelihood procedures, and sequential Gaussian simulation. Eight campaigns were carried out in the Hontomín CO2 Storage Technology Development Plant and in natural CO2 analogues. The results show that sequential Gaussian simulation is the most accurate method to estimate the total CO2 output and the confidential interval. Nevertheless, a variety of statistic methods were also used. As a consequence, an application procedure for selecting the most realistic method was developed. The first step to estimate the total emanation rate was the variogram analysis. If the relation among the data can be explained with the variogram, the best technique to calculate the total CO2 output and its confidence interval is the sequential Gaussian simulation method (sGs). If the data are independent, their distribution is to be analyzed. For normal and log-normal distribution the proper methods are the arithmetic mean and minimum variances unbiased estimator, respectively. If the data are not normal (log-normal) or are a mixture of different populations the best approach is the bootstrap resampling. According to these steps, the maximum confidence interval was about ±20/25%, with most of values between ±3.5% and ±8%. Partitioning of CO2 flux data into different populations may help to interpret the data as their distribution can be affected by different geochemical processes, e.g. geological or biological sources of CO2. Consequently, it may be an important tool in a monitoring CCS program, where the main goal is to demonstrate that there are not leakages from the reservoir to the atmosphere and, if occurring, to be able to detect and quantify it. Results show that the partitioning of populations is better performed by maximum likelihood criteria, since graphical procedures have a degree of subjectivity in the interpretation and results may not be reproducible. The relationship between CO2 flux and radon isotopes (222Rn and 220Rn) was studied in natural analogues. In all emissions zones, a positive relation between 222Rn and CO2 was observed. However, the relationship between activity of 220Rn and CO2 flux is not clear. In some cases the 220Rn activity indeed increased with the CO2 flux in other measurements a decrease was recognized. We can speculate that this effect was possibly related to the route (deep or shallow) of the radon source. These results may confirm the possible use of the radon isotopes as tracers for the gas origin and their application in the detection of leakages. With respect to the CO2 flux baseline at the TDP of Hontomín, soil flux measurements in the vicinity of oil boreholes, drilled in the eighties and named H-1 to H-4, and injection and monitoring wells were performed using an accumulation chamber. Seven surveys were carried out from November 2009 to summer 2011. More than 4,000 measurements were used to determine the baseline flux of CO2 and its seasonal variations. The measured values were relatively low (from 5 to 13 g•m-2•day-1) and few outliers were identified, mainly located close to the H-2 oil well. Nevertheless, these values cannot be associated to a deep source of CO2, being more likely related to biological processes, i.e. soil respiration. No anomalies were recognized close to the deep fault system (Ubierna Fault) detected by geophysical investigations. There, the CO2 flux is indeed as low as other measurement stations. CO2 fluxes appear to be controlled by the biological activity since the lowest values were recorded during autumn-winter seasons and they tend to increase in warm periods. Two reference CO2 flux values (UCL50 of 5 g•m-2•d-1 for non-ploughed areas in autumn-winter seasons and 3.5 and 12 g•m-2•d-1 for in ploughed and non-ploughed areas, respectively, in spring-summer time, and UCL99 of 26 g•m-2•d-1 for autumn-winter in not-ploughed areas and 34 and 42 g•m-2•d-1 for spring-summer in ploughed and not-ploughed areas, respectively, were calculated. Fluxes higher than these reference values could be indicative of possible leakage during the operational and post-closure stages of the storage project. The first geochemical and isotopic data related to surface and spring waters and dissolved gases in the area of Hontomín–Huermeces (Burgos, Spain) are presented and discussed. The chemical and features of the spring waters suggest that they are related to a shallow hydrogeological system as the concentration of the Total Dissolved Solids approaches 800 mg/L with a Ca2+(Mg2+)-HCO3 − composition, similar to that of the surface waters. Some spring waters are characterized by relatively high concentrations of NO3 − (up to 123 mg/L), unequivocally suggesting an anthropogenic source. Anomalous concentrations of Cl−, SO4 2−, As, B and Ba were measured in two springs, discharging a few hundred meters from the oil wells, and in the Rio Ubierna. These contents are possibly indicative of mixing processes between deep and shallow aquifers. The chemistry of the dissolved gases also evidences the shallow circuits of the Hontomín– Huermeces, mainly characterized by an atmospheric source as highlighted by the contents of N2, O2, Ar and their relative ratios. Nevertheless, significant concentrations (up to 63% by vol.) of isotopically negative CO2 (<−17.7‰ V-PDB) were found in some water samples, likely related to a biogenic source. The geochemical and isotopic data of the surface and spring waters in the surroundings of Hontomín can be considered as background values when intra- and post-injection monitoring programs will be carried out. In this respect, main and minor solutes, the isotopic carbon of dissolved CO2 and TDIC (Total Dissolved Inorganic Carbon) and selected trace elements can be considered as useful parameters to trace the migration of the injected CO2 into near-surface environments.

Host–parasite relatedness shown by protein fingerprinting in a brood parasitic bird

Brood parasitism as an alternative female breeding tactic is particularly common in ducks, where hosts often receive eggs laid by parasitic females of the same species and raise their offspring. Herein, we test several aspects of a kin selection explanation for this phenomenon in goldeneye ducks (Bucephala clangula) by using techniques of egg albumen sampling and statistical bandsharing analysis based on resampling. We find that host and primary parasite are indeed often related, with mean r = 0.13, about as high as between first cousins. Relatedness to the host is higher in nests where a parasite lays several eggs than in those where she lays only one. Returning young females parasitize their birth nestmates (social mothers or sisters, which are usually also their genetic mothers and sisters) more often than expected by chance. Such adult relatives are also observed together in the field more often than expected and for longer periods than other females. Relatedness and kin discrimination, which can be achieved by recognition of birth nestmates, therefore play a role in these tactics and probably influence their success.

Chemical composition of the soluble fraction of oxidate portions of Lakes Shebadnowan ferromanganese concretions

Shebandowan Lakes, Ontario, are the site of at least 49 shallow (2-12 m) ferromanganese concretion deposits, widely distributed throughout the 48 km of the watercourse. X-ray diffraction and Mossbauer methods have revealed the presence of goethite in some of the concretions. Chemical analyses of the acid soluble portions of 72 samples gave an average composition of 43.1% Fe and 5.65% Mn with a low content of trace elements. The Shebandowan concretions are among the richest in iron of lake concretions reported, possibly because only the acid soluble portion was analysed. Their low content of trace elements suggests rapid growth rates and a relatively young age. A positive correlation was found between Mn and K, Ca, Mg, Cu, Ni, and Co and the relationship between the last three and Mn was deemed significant. Zn was independent of association with either Mn and Fe, probably due to the presence locally of zinc sulphide deposits. Analyses of lake bottom and influent waters suggested that frequent resampling of the sites would be required throughout the year to permit meaningful interpretation of the effect of water composition of concretions. Analyses of sediment cores from 20 concretion sites indicated an upward increase in Fe and Mn and in the Mn/Fe ratio, consistent with the model of upward migration of the elements, where Mn is more mobile than Fe. This study concludes that a considerable proportion of the elements have been supplied to the Shebandowan concretions via the diagenetic process; generally a minor fraction of the elements has been abstracted directly from the superjacent water.

Partitioned likelihood support and the evaluation of data set conflict

In simultaneous analyses of multiple data partitions, the trees relevant when measuring support for a clade are the optimal tree, and the best tree lacking the clade (i.e., the most reasonable alternative). The parsimony-based method of partitioned branch support (PBS) forces each data set to arbitrate between the two relevant trees. This value is the amount each data set contributes to clade support in the combined analysis, and can be very different to support apparent in separate analyses. The approach used in PBS can also be employed in likelihood: a simultaneous analysis of all data retrieves the maximum likelihood tree, and the best tree without the clade of interest is also found. Each data set is fitted to the two trees and the log-likelihood difference calculated, giving partitioned likelihood support (PLS) for each data set. These calculations can be performed regardless of the complexity of the ML model adopted. The significance of PLS can be evaluated using a variety of resampling methods, such as the Kishino-Hasegawa test, the Shimodiara-Hasegawa test, or likelihood weights, although the appropriateness and assumptions of these tests remains debated.

Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F-0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F-0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (D-LR) appeared to be an effective way to predict whether F-0 immigrants could be identified for a particular pair of populations using a given set of markers.

Advantage of single-trial models for response to selection in wheat breeding multi-environment trials

An investigation was conducted to evaluate the impact of experimental designs and spatial analyses (single-trial models) of the response to selection for grain yield in the northern grains region of Australia (Queensland and northern New South Wales). Two sets of multi-environment experiments were considered. One set, based on 33 trials conducted from 1994 to 1996, was used to represent the testing system of the wheat breeding program and is referred to as the multi-environment trial (MET). The second set, based on 47 trials conducted from 1986 to 1993, sampled a more diverse set of years and management regimes and was used to represent the target population of environments (TPE). There were 18 genotypes in common between the MET and TPE sets of trials. From indirect selection theory, the phenotypic correlation coefficient between the MET and TPE single-trial adjusted genotype means [r(p(MT))] was used to determine the effect of the single-trial model on the expected indirect response to selection for grain yield in the TPE based on selection in the MET. Five single-trial models were considered: randomised complete block (RCB), incomplete block (IB), spatial analysis (SS), spatial analysis with a measurement error (SSM) and a combination of spatial analysis and experimental design information to identify the preferred (PF) model. Bootstrap-resampling methodology was used to construct multiple MET data sets, ranging in size from 2 to 20 environments per MET sample. The size and environmental composition of the MET and the single-trial model influenced the r(p(MT)). On average, the PF model resulted in a higher r(p(MT)) than the IB, SS and SSM models, which were in turn superior to the RCB model for MET sizes based on fewer than ten environments. For METs based on ten or more environments, the r(p(MT)) was similar for all single-trial models.

AVALIAÇÃO DA ACEITAÇÃO DO PORTAL INOVAÇÃO: UM ESTUDO DE CASO

Esta pesquisa apresenta estudo de caso cujo objetivo foi analisar a aceitação do Portal Inovação, identificando os fatores preditivos da intenção comportamental de uso e do comportamento de uso direcionadores da adoção da tecnologia por seus usuários via extensão do Modelo Unificado de Aceitação de Tecnologia, denominado pela sigla UTAUT (Unified Theory of Acceptance and Use of Technololgy) de Venkatesh et al. (2003). O objeto da pesquisa o Portal Inovação foi desenvolvido pelo Ministério da Ciência, Tecnologia e Inovação (MCTI) em parceria com o Centro de Gestão e Estudos Estratégicos (CGEE), Associação Brasileira de Desenvolvimento Industrial (ABDI) e Instituto Stela, visando atender às demandas do Sistema Nacional de Ciência, Tecnologia e Inovação (SNCTI) do País. Para atingir os objetivos propostos, recorreu-se às abordagens qualitativa, que foi subsidiada pelo método estudo de caso (YIN, 2005) e quantitativa, apoiada pela metodologia UTAUT, aplicada a usuários do portal e que contemplou o resultado de 264 respondentes validados. Quanto ao material de análise, utilizou-se da pesquisa bibliográfica sobre governo eletrônico (e-Gov), Internet, Sistema Nacional de Inovação, modelos de aceitação de tecnologia, dados oficiais públicos e legislações atinentes ao setor de inovação tecnológica. A técnica de análise empregada quantitativamente consistiu no uso de modelagem por equações estruturais, com base no algoritmo PLS (Partial Least Square) com bootstrap de 1.000 reamostragens. Os principais resultados obtidos demonstraram alta magnitude e significância preditiva sobre a Intenção Comportamental de Uso do Portal pelos fatores: Expectativa de Desempenho e Influência Social. Além de evidenciarem que as condições facilitadoras impactam significativamente sobre o Comportamento de Uso dos usuários. A conclusão principal do presente estudo é a de que ao considerarmos a aceitação de um portal governamental em que a adoção é voluntária, o fator social é altamente influente na intenção de uso da tecnologia, bem como os aspectos relacionados à produtividade consequente do usuário e o senso de utilidade; além da facilidade de interação e domínio da ferramenta. Tais constatações ensejam em novas perspectivas de pesquisa e estudos no âmbito das ações de e-Gov, bem como no direcionamento adequado do planejamento, monitoramento e avaliação de projetos governamentais.

Functional source separation applied to induced visual gamma activity

Objective of this work was to explore the performance of a recently introduced source extraction method, FSS (Functional Source Separation), in recovering induced oscillatory change responses from extra-cephalic magnetoencephalographic (MEG) signals. Unlike algorithms used to solve the inverse problem, FSS does not make any assumption about the underlying biophysical source model; instead, it makes use of task-related features (functional constraints) to estimate source/s of interest. FSS was compared with blind source separation (BSS) approaches such as Principal and Independent Component Analysis, PCA and ICA, which are not subject to any explicit forward solution or functional constraint, but require source uncorrelatedness (PCA), or independence (ICA). A visual MEG experiment with signals recorded from six subjects viewing a set of static horizontal black/white square-wave grating patterns at different spatial frequencies was analyzed. The beamforming technique Synthetic Aperture Magnetometry (SAM) was applied to localize task-related sources; obtained spatial filters were used to automatically select BSS and FSS components in the spatial area of interest. Source spectral properties were investigated by using Morlet-wavelet time-frequency representations and significant task-induced changes were evaluated by means of a resampling technique; the resulting spectral behaviours in the gamma frequency band of interest (20-70 Hz), as well as the spatial frequency-dependent gamma reactivity, were quantified and compared among methods. Among the tested approaches, only FSS was able to estimate the expected sustained gamma activity enhancement in primary visual cortex, throughout the whole duration of the stimulus presentation for all subjects, and to obtain sources comparable to invasively recorded data.

Visualizing uncertainty in multi-spectral remotely sensed imagery

Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicity identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data. © 2002 Elsevier Science Ltd. All rights reserved.