Use of LIDAR-based Elevation Data for Highway Drainage Analysis: A Qualitative Assessment, 2003

US Geological Survey (USGS) based elevation data are the most commonly used data source for highway hydraulic analysis; however, due to the vertical accuracy of USGS-based elevation data, USGS data may be too “coarse” to adequately describe surface profiles of watershed areas or drainage patterns. Additionally hydraulic design requires delineation of much smaller drainage areas (watersheds) than other hydrologic applications, such as environmental, ecological, and water resource management. This research study investigated whether higher resolution LIDAR based surface models would provide better delineation of watersheds and drainage patterns as compared to surface models created from standard USGS-based elevation data. Differences in runoff values were the metric used to compare the data sets. The two data sets were compared for a pilot study area along the Iowa 1 corridor between Iowa City and Mount Vernon. Given the limited breadth of the analysis corridor, areas of particular emphasis were the location of drainage area boundaries and flow patterns parallel to and intersecting the road cross section. Traditional highway hydrology does not appear to be significantly impacted, or benefited, by the increased terrain detail that LIDAR provided for the study area. In fact, hydrologic outputs, such as streams and watersheds, may be too sensitive to the increased horizontal resolution and/or errors in the data set. However, a true comparison of LIDAR and USGS-based data sets of equal size and encompassing entire drainage areas could not be performed in this study. Differences may also result in areas with much steeper slopes or significant changes in terrain. LIDAR may provide possibly valuable detail in areas of modified terrain, such as roads. Better representations of channel and terrain detail in the vicinity of the roadway may be useful in modeling problem drainage areas and evaluating structural surety during and after significant storm events. Furthermore, LIDAR may be used to verify the intended/expected drainage patterns at newly constructed highways. LIDAR will likely provide the greatest benefit for highway projects in flood plains and areas with relatively flat terrain where slight changes in terrain may have a significant impact on drainage patterns.

Local re-inversion coronary MR angiography: arterial spin-labeling without the need for subtraction.

PURPOSE: To implement a double-inversion bright-blood coronary MR angiography sequence using a cylindrical re-inversion prepulse for selective visualization of the coronary arteries. MATERIALS AND METHODS: Local re-inversion bright-blood magnetization preparation was implemented using a nonselective inversion followed by a cylindrical aortic re-inversion prepulse. After an inversion delay that allows for in-flow of the labeled blood-pool into the coronary arteries, three-dimensional radial steady-state free-precession (SSFP) imaging (repetition/echo time, 7.2/3.6 ms; flip angle, 120 degrees, 16 profiles per RR interval; field of view, 360 mm; matrix, 512, twelve 3-mm slices) is performed. Coronary MR angiography was performed in three healthy volunteers and in one patient on a commercial 1.5 Tesla whole-body MR System. RESULTS: In all subjects, coronary arteries were selectively visualized with positive contrast. In addition, a middle-grade stenosis of the proximal right coronary artery was seen in one patient. CONCLUSION: A novel T1 contrast-enhancement strategy is presented for selective visualization of the coronary arteries without extrinsic contrast medium application. In comparison to former arterial spin-labeling schemes, the proposed magnetization preparation obviates the need for a second data set and subtraction.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Inferring the mode of origin of polyploid species from next-generation sequence data.

Many eukaryote organisms are polyploid. However, despite their importance, evolutionary inference of polyploid origins and modes of inheritance has been limited by a need for analyses of allele segregation at multiple loci using crosses. The increasing availability of sequence data for nonmodel species now allows the application of established approaches for the analysis of genomic data in polyploids. Here, we ask whether approximate Bayesian computation (ABC), applied to realistic traditional and next-generation sequence data, allows correct inference of the evolutionary and demographic history of polyploids. Using simulations, we evaluate the robustness of evolutionary inference by ABC for tetraploid species as a function of the number of individuals and loci sampled, and the presence or absence of an outgroup. We find that ABC adequately retrieves the recent evolutionary history of polyploid species on the basis of both old and new sequencing technologies. The application of ABC to sequence data from diploid and polyploid species of the plant genus Capsella confirms its utility. Our analysis strongly supports an allopolyploid origin of C. bursa-pastoris about 80 000 years ago. This conclusion runs contrary to previous findings based on the same data set but using an alternative approach and is in agreement with recent findings based on whole-genome sequencing. Our results indicate that ABC is a promising and powerful method for revealing the evolution of polyploid species, without the need to attribute alleles to a homeologous chromosome pair. The approach can readily be extended to more complex scenarios involving higher ploidy levels.

Univariate versus multivariate modeling of panel data

Panel data can be arranged into a matrix in two ways, called 'long' and 'wide' formats (LFand WF). The two formats suggest two alternative model approaches for analyzing paneldata: (i) univariate regression with varying intercept; and (ii) multivariate regression withlatent variables (a particular case of structural equation model, SEM). The present papercompares the two approaches showing in which circumstances they yield equivalent?insome cases, even numerically equal?results. We show that the univariate approach givesresults equivalent to the multivariate approach when restrictions of time invariance (inthe paper, the TI assumption) are imposed on the parameters of the multivariate model.It is shown that the restrictions implicit in the univariate approach can be assessed bychi-square difference testing of two nested multivariate models. In addition, commontests encountered in the econometric analysis of panel data, such as the Hausman test, areshown to have an equivalent representation as chi-square difference tests. Commonalitiesand differences between the univariate and multivariate approaches are illustrated usingan empirical panel data set of firms' profitability as well as a simulated panel data.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Fiber flows in global paper and pulp industry. A panel data approach.

The purpose of this thesis is to study factors that explain the bilateral fiber trade flows. This is done by analyzing bilateral trade flows during 1990-2006. It will be studied also, whether there are differences between fiber types. This thesis uses a gravity model approach to study the trade flows. Gravity model is mostly used to study the aggregate data between trading countries. In this thesis the gravity model is applied to single fibers. This model is then applied to panel data set. Results from the regression show clearly that there are benefits in studying different fibers in separate. The effects differ considerably from each other. Furthermore, this thesis speaks for the existence of Linder’s effect in certain fiber types.

GENETIC ARCHITECTURE, EVOLUTION AND MAINTENANCE OF A COLOR CLINE AT A CONTINENTAL SCALE

On a geological time scale the conditions on earth are very variable and biological patterns (for example the distributions of species) are very dynamic. Understanding large scale patterns of variation observed today thus requires a deep understanding of the historical factors that drove their evolution. In this thesis, we reevaluated the evolution and maintenance of a continental color cline observed in the European barn owl (Tyto alba) using population genetic tools. The colour cline spans from south-est Europe where most individual have pure white underparts to north and east Europe where most individuals have rufous-brown underparts. Our results globally showed that the old scenario, stipulating that the color cline evolved by secondary contact of two color morphs (white and rufous) that evolved in allopatry during the last ice age has to be revised. We collected samples of about 700 barn owls from the Western Palearctic to establish the first population genetic data set for this species. Individuals were genotyped at 22 microsatellites markers, at one mitochondrial gene, and at a candidate color gene. The color of each individuals was assessed and their sex determined by molecular methods. We first showed that the genetic variation in Western Europe is very limited compared to the heritable color variation. We found no evidences of different glacial lineages, and showed that selection must be involved in the maintenance of the color cline (chapter 1). Using computer simulations, we demonstrated that the post-glacial colonization of Europe occurred from the Iberian Peninsula and that the color cline could not have evolved by neutral demographic processes during this colonization (chapter 2). Finally we reevaluated the whole history of the establishment of the Western Palearctic variation of the barn owl (chapter 3): This study showed that all Western European barn owls descend from white barn owls phenotypes from the Middle East that colonized the Iberian Peninsula via North-Africa. Following the end of the last ice age (20'000 years ago), these white barn owls colonized Western Europe and under selection a novel rufous phenotype evolved (during or after the colonization). An important part of the color variation could be explained by a single mutation in the melanocortin-1-receptor (MC1R) gene that appeared during or after the colonization. The colonization of Europe reached until Greece, where the rufous birds encountered white ones (which reached Greece from the Middle East over the Bosporus) in a secondary contact zone. Our analyses show that white and rufous barn owls in Greece interbreed only to a limited extent. This suggests that barn owls are at the verge of becoming two species in Greece and demonstrates that European barn owls represent an incipient ring species around the Mediterranean. The revisited history of the establishment of the European barn owl color cline makes this model system remarkable for several aspects. It is a very clear example of strong local adaptation that can be achieved despite high gene flow (strong color and MC1R differentiation despite almost no neutral genetic differentiation). It also offers a wonderful model system to study the interactions between colonization processes and selection processes which have, for now, been remarkably understudied despite their potentially ubiquitous importance. Finally it represents a very interesting case in the speciation continuum and appeals for further studying the amount of gene flow that occurs between the color morphs in Greece. -- Sur l'échelle des temps géologiques, les conditions sur terre sont très variables et les patrons biologiques (telle que la distribution des espèces) sont très dynamiques. Si l'on veut comprendre des patrons que l'on peut observer à large échelle aujourd'hui, il est nécessaire de d'abord comprendre les facteurs historiques qui ont gouverné leur établissement. Dans cette thèse, nous allons réévaluer, grâce à des outils modernes de génétique des populations, l'évolution et la maintenance d'un cline de couleur continental observé chez l'effraie des clochers européenne (Tyto alba). Globalement, nos résultats montrent que le scenario accepté jusqu'à maintenant, qui stipule que le cline de couleur a évolué à partir du contact secondaire de deux morphes de couleur (blanches et rousses) ayant évolué en allopatrie durant les dernières glaciations, est à revoir. Afin de constituer le premier jeu de données de génétique des populations pour cette espèce, nous avons récolté des échantillons d'environ 700 effraies de l'ouest Paléarctique. Nous avons génotypé tous les individus à 22 loci microsatellites, sur un gène mitochondrial et sur un autre gène participant au déterminisme de la couleur. Nous avons aussi mesuré la couleur de tous les individus et déterminé leur sexe génétiquement. Nous avons tout d'abord pu montrer que la variation génétique neutre est négligeable en comparaison avec la variation héritable de couleur, qu'il n'existe qu'une seule lignée européenne et que de la sélection doit être impliquée dans le maintien du cline de couleur (chapitre 1). Grâce à des simulations informatiques, nous avons démontré que l'ensemble de l'Europe de l'ouest a été recolonisé depuis la Péninsule Ibérique après les dernières glaciations et que le cline de couleur ne peut pas avoir évolué par des processus neutre durant cette colonisation (chapitre 2). Finalement, nous avons réévalué l'ensemble de l'histoire postglaciaire de l'espèce dans l'ouest Paléarctique (chapitre 3): l'ensemble des effraies du Paléarctique descendent d'effraie claire du Moyen-Orient qui ont colonisé la péninsule ibérique en passant par l'Afrique du nord. Après la fin de la dernière glaciation (il y a 20'000 ans), ces effraies claires ont colonisé l'Europe de l'ouest et ont évolués par sélection le phénotype roux (durant ou après la colonisation). Une part importante de la variation de couleur peut être expliquée par une mutation sur le gène MC1R qui est apparue durant ou juste après la colonisation. Cette vague de colonisation s'est poursuivie jusqu'en Grèce où ces effraies rousses ont rencontré dans une zone de contact secondaire des effraies claires (qui sont remontées en Grèce depuis le Moyen-Orient via le Bosphore). Nos analyses montrent que le flux de gènes entre effraies blanches et rousses est limité en Grèce, ce qui suggère qu'elles sont en passe de former deux espèces et ce qui montre que les effraies constituent un exemple naissant de spéciation en anneaux autour de la Méditerranée. L'histoire revisitée des effraies des clochers de l'ouest Paléarctique en fait un système modèle remarquable pour plusieurs aspects. C'est un exemple très claire de forte adaptation locale maintenue malgré un fort flux de gènes (différenciation forte de couleur et sur le gène MC1R malgré presque aucune structure neutre). Il offre également un très bon système pour étudier l'interaction entre colonisation et sélection, un thème ayant été remarquablement peu étudié malgré son importance. Et il offre finalement un cas très intéressant dans le « continuum de spéciation » et il serait très intéressant d'étudier plus en détail l'importance du flux de gènes entre les morphes de couleur en Grèce.

Analysis of laboratory intercomparison data: a matter of independence

When laboratory intercomparison exercises are conducted, there is no a priori dependence of the concentration of a certain compound determined in one laboratory to that determined by another(s). The same applies when comparing different methodologies. A existing data set of total mercury readings in fish muscle samples involved in a Brazilian intercomparison exercise was used to show that correlation analysis is the most effective statistical tool in this kind of experiments. Problems associated with alternative analytical tools such as mean or paired 't'-test comparison and regression analysis are discussed.

A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets

We present a participant study that compares biological data exploration tasks using volume renderings of laser confocal microscopy data across three environments that vary in level of immersion: a desktop, fishtank, and cave system. For the tasks, data, and visualization approach used in our study, we found that subjects qualitatively preferred and quantitatively performed better in the cave compared with the fishtank and desktop. Subjects performed real-world biological data analysis tasks that emphasized understanding spatial relationships including characterizing the general features in a volume, identifying colocated features, and reporting geometric relationships such as whether clusters of cells were coplanar. After analyzing data in each environment, subjects were asked to choose which environment they wanted to analyze additional data sets in - subjects uniformly selected the cave environment.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Occurrence, biological activities and 13C NMR data of amides from Piper (Piperaceae)

This manuscript describes an update review with up to 285 references concerning the occurrence of amides from a variety of species of the genus Piper (Piperaceae). Besides addressing occurrence, this review also describes the biological activities attributed to extracts and pure compounds, a compiled 13C NMR data set, the main correlations between structural and NMR spectroscopic data of these compounds, and employment of hyphened techniques such as LC-MS, GC-MS and NMR for analysis of amides from biological samples and crude Piper extracts.

Analysis of Patterns in Electricity Spot Market Time Series

Due to its non-storability, electricity must be produced at the same time that it is consumed, as a result prices are determined on an hourly basis and thus analysis becomes more challenging. Moreover, the seasonal fluctuations in demand and supply lead to a seasonal behavior of electricity spot prices. The purpose of this thesis is to seek and remove all causal effects from electricity spot prices and remain with pure prices for modeling purposes. To achieve this we use Qlucore Omics Explorer (QOE) for the visualization and the exploration of the data set and Time Series Decomposition method to estimate and extract the deterministic components from the series. To obtain the target series we use regression based on the background variables (water reservoir and temperature). The result obtained is three price series (for Sweden, Norway and System prices) with no apparent pattern.