Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

Constraints on the permeability structure of alluvial aquifers from the poro-elastic inversion of multi-frequency P-wave velocity logs

We have explored the possibility of obtaining first-order permeability estimates for saturated alluvial sediments based on the poro-elastic interpretation of the P-wave velocity dispersion inferred from sonic logs. Modern sonic logging tools designed for environmental and engineering applications allow one for P-wave velocity measurements at multiple emitter frequencies over a bandwidth covering 5 to 10 octaves. Methodological considerations indicate that, for saturated unconsolidated sediments in the silt to sand range and typical emitter frequencies ranging from approximately 1 to 30 kHz, the observable velocity dispersion should be sufficiently pronounced to allow one for reliable first-order estimations of the permeability structure. The corresponding predictions have been tested on and verified for a borehole penetrating a typical surficial alluvial aquifer. In addition to multifrequency sonic logs, a comprehensive suite of nuclear and electrical logs, an S-wave log, a litholog, and a limited number laboratory measurements of the permeability from retrieved core material were also available. This complementary information was found to be essential for parameterizing the poro-elastic inversion procedure and for assessing the uncertainty and internal consistency of corresponding permeability estimates. Our results indicate that the thus obtained permeability estimates are largely consistent with those expected based on the corresponding granulometric characteristics, as well as with the available evidence form laboratory measurements. These findings are also consistent with evidence from ocean acoustics, which indicate that, over a frequency range of several orders-of-magnitude, the classical theory of poro-elasticity is generally capable of explaining the observed P-wave velocity dispersion in medium- to fine-grained seabed sediments

Effects of exercise on the isoelectric patterns of erythropoietin.

OBJECTIVES: Recombinant erythropoietin has a strong impact on aerobic power and is therefore one of the most potent doping agents in endurance sports. The anti-doping control of this synthetic hormone relies on the detection, in the urine, of its isoelectric pattern, which differs from that of the corresponding natural hormone, the latter being typically more acidic than the former. However, a small number of natural urinary patterns, referred to as "atypical patterns," are less acidic than the dominant form. Based on anecdotal evidence, the occurrence of such patterns seems to be related to particular strenuous exercises. This study aimed to demonstrate this relation using a strenuous exercise protocol. DESIGN: Seven athletes took part in a training protocol including a series of supramaximal short-duration exercises. Urine and blood samples were collected throughout the protocols. SETTINGS: World Cycling Center, Aigle, Switzerland, and research laboratories. PARTICIPANTS: Seven top-level athletes (cyclists) were involved in this study. MAIN OUTCOME MEASURES: Erythropoietin (EPO) isoelectric patterns were obtained by submitting blood and urine samples to isoelectric focusing. Additional protein dosages were performed. RESULTS: Supramaximal short-duration exercises induced the transformation of typical urinary natural EPO patterns into atypical ones. None of the obtained atypical patterns fulfilled the 3 criteria mandatory for reporting an adverse analytical finding. Serum EPO patterns were not affected by the exercises that caused the transformation of urinary patterns. CONCLUSION: An exercise-induced transient renal dysfunction is proposed as a hypothetic explanation for these observations that rely on parallel investigations of proteinuria in the same samples.

Cuando Frankenstein encontró a Dorian Gray: dandysmo, post-identidad y sujetos virtuales

The appearance and popularization of the internet has created new forms of writing, which compel us to think anew about identity and subjectivity. Webjournals or blogs are specially interesting because they are a massive phenomenon that use autobiographical writing in a peculiar way. These forms of writing stress a particular paradox of the genre: the coexistence between a purpose of private, confessional and spontaneous writing and a public image, carefully built, as a result of its writing. The technology is new, but, in fact, the paradox is old. This paper tries to explore this old paradox, our eternal condition of cyborgs, our use of technologies in order to construct a public, unique and recognizably identity. In oder to do so, I will try to show the virtual condition of any written individual ¿this issue has already been dealt with by autobiographical studies¿, focusing on blogs, and especially on concrete example (Lord Whimsy¿s Journal). I will pay attention to gender as a technology that constructs identity and, at the same time, is deconstructed by the autobiographical narratives analyzed. In short, I attempt to show that virtual and autobiographical discourse do not bring forth a new kind of subject but the permanence of an old phenomenon "clearly developed by dandyism, for instance¿: the use of tehnologies to re-invent, re-formulate and re-construct us as multiple, hybrid and mixed subjects."

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Cuando Frankenstein encontró a Dorian Gray: dandysmo, post-identidad y sujetos virtuales

The appearance and popularization of the internet has created new forms of writing, which compel us to think anew about identity and subjectivity. Webjournals or blogs are specially interesting because they are a massive phenomenon that use autobiographical writing in a peculiar way. These forms of writing stress a particular paradox of the genre: the coexistence between a purpose of private, confessional and spontaneous writing and a public image, carefully built, as a result of its writing. The technology is new, but, in fact, the paradox is old. This paper tries to explore this old paradox, our eternal condition of cyborgs, our use of technologies in order to construct a public, unique and recognizably identity. In oder to do so, I will try to show the virtual condition of any written individual ¿this issue has already been dealt with by autobiographical studies¿, focusing on blogs, and especially on concrete example (Lord Whimsy¿s Journal). I will pay attention to gender as a technology that constructs identity and, at the same time, is deconstructed by the autobiographical narratives analyzed. In short, I attempt to show that virtual and autobiographical discourse do not bring forth a new kind of subject but the permanence of an old phenomenon "clearly developed by dandyism, for instance¿: the use of tehnologies to re-invent, re-formulate and re-construct us as multiple, hybrid and mixed subjects."

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Cancer in Iowa, 2003

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of Epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. In 2003 data will be collected on an estimated 14,700 new cancers among Iowa residents. A follow-up program tracks more than 97 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for follow-up and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. Since 1973 the Iowa Registry has been funded by the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI). Iowa represents rural and midwestern populations and provides data included in many NCI publications. Beginning in 1990 about 5-10 percent of the Registry’s annual operating budget has been provided by the state of Iowa. The Registry also receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.

2004 Cancer in Iowa Report, 2004

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of Epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. In 2004 data will be collected on an estimated 15,200 new cancers among Iowa residents. A follow-up program tracks more than 97 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for follow-up and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. Since 1973 the Iowa Registry has been funded by the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI). Iowa represents rural and midwestern populations and provides data included in many NCI publications. Beginning in 1990 about 5-10 percent of the Registry’s annual operating budget has been provided by the state of Iowa. Beginning in 2003, the University of Iowa is also providing cost-sharing funds. The Registry also receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.

2005 Cancer in Iowa Report: Prostate Cancer in Iowa, 2005

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of Epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. A follow-up program tracks more than 97 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for follow-up and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. In 2005 data will be collected on an estimated 15,800 new cancers among Iowa residents. Beginning with 2005 Cancer in Iowa, in situ cases of bladder cancer are included in the estimates for bladder cancer, to be in agreement with the definition of reportable cases of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. Since 1973 the Iowa Registry has been funded by the SEER Program of the National Cancer Institute. Iowa represents rural and midwestern populations and provides data included in many NCI publications. Beginning in 1990 about 5-10 percent of the Registry’s annual operating budget has been provided by the state of Iowa. Beginning in 2003, the University of Iowa has also been providing cost-sharing funds. The Registry also receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.

2007 Cancer in Iowa Report, 2007

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of Epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. A follow-up program tracks more than 99 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for follow-up and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. In 2007 data will be collected on an estimated 15,700 new cancers among Iowa residents. In situ cases of bladder cancer are included in the estimates for bladder cancer, to be in agreement with the definition of reportable cases of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. Since 1973 the Iowa Registry has been funded by the SEER Program of the National Cancer Institute. Iowa represents rural and Midwestern populations and provides data included in many NCI publications. Beginning in 1990 about 5-10 percent of the Registry’s annual operating budget has been provided by the state of Iowa. Beginning in 2003, the University of Iowa has also been providing cost-sharing funds. In addition, the Registry receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.

2008 Cancer in Iowa Report, 2008

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of Epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. A follow-up program tracks more than 99 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for followup and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. In 2008 data will be collected on an estimated 16,000 new cancers among Iowa residents. Noninvasive cases of bladder cancer are included in the estimates for bladder cancer, to be in agreement with the definition of reportable cases of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. Since 1973 the Iowa Registry has been funded primarily by the SEER Program of the National Cancer Institute. Iowa represents rural and Midwestern populations and provides data included in many National Cancer Institute publications. Beginning in 1990 a small percent of the Registry’s annual operating budget has been provided by the state of Iowa. Beginning in 2003, the University of Iowa has also been providing cost-sharing funds. The Registry also receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.

2009 Cancer in Iowa Report: Data Sources, 2009

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of Epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. A follow-up program tracks more than 99 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for follow-up and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. In 2009 data will be collected on an estimated 16,000 new cancers among Iowa residents. In situ cases of bladder cancer are included in the estimates for bladder cancer, to be in agreement with the definition of reportable cases of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. Since 1973 the Iowa Registry has been funded primarily by the SEER Program of the National Cancer Institute. Iowa represents rural and Midwestern populations and provides data included in many National Cancer Institute publications. Beginning in 1990 between 5 and 10 percent of the Registry’s annual operating budget has been provided by the state of Iowa. Beginning in 2003, the University of Iowa has been providing cost-sharing funds. The Registry also receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.