Mapping an uncertainty zone between interpolated types of a categorical variable

Categorical data cannot be interpolated directly because they are outcomes of discrete random variables. Thus, types of categorical variables are transformed into indicator functions that can be handled by interpolation methods. Interpolated indicator values are then backtransformed to the original types of categorical variables. However, aspects such as variability and uncertainty of interpolated values of categorical data have never been considered. In this paper we show that the interpolation variance can be used to map an uncertainty zone around boundaries between types of categorical variables. Moreover, it is shown that the interpolation variance is a component of the total variance of the categorical variables, as measured by the coefficient of unalikeability. (C) 2011 Elsevier Ltd. All rights reserved.

VALIDATION OF THE LAND WATER STORAGE FROM GRAVITY RECOVERY AND CLIMATE EXPERIMENT (GRACE) WITH GAUGE DATA IN THE AMAZON BASIN

The Amazon basin is a region of constant scientific interest due to its environmental importance and its biodiversity and climate on a global scale. The seasonal variations in water volume are one of the examples of topics studied nowadays. In general, the variations in river levels depend primarily on the climate and physics characteristics of the corresponding basins. The main factor which influences the water level in the Amazon Basin is the intensive rainfall over this region as a consequence of the humidity of the tropical climate. Unfortunately, the Amazon basin is an area with lack of water level information due to difficulties in access for local operations. The purpose of this study is to compare and evaluate the Equivalent Water Height (Ewh) from GRACE (Gravity Recovery And Climate Experiment) mission, to study the connection between water loading and vertical variations of the crust due to the hydrologic. In order to achieve this goal, the Ewh is compared with in-situ information from limnimeter. For the analysis it was computed the correlation coefficients, phase and amplitude of GRACE Ewh solutions and in-situ data, as well as the timing of periods of drought in different parts of the basin. The results indicated that vertical variations of the lithosphere due to water mass loading could reach 7 to 5 cm per year, in the sedimentary and flooded areas of the region, where water level variations can reach 10 to 8 m.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Synthesis and Solar Cell Application of New Alternating Donor-Acceptor Copolymers Based on Variable Units of Fluorene, Thiophene, and Phenylene

A new series of donor acceptor copolymers were synthesized via the Witting route and applied as an active layer in organic thin-films solar cells. These copolymers are composed of fluorene thiophene and phenylene thiophene units. The ratio between those was systematically varied, and copolymers containing 0%, 50%, and 75% of phenylene thiophene were characterized and evaluated when used in photovoltaic devices. The copolymers' composition, photophysical, electrical, and morphological properties are addressed and correlated with device performance. The 50% copolymer ratio was found to be the best copolymer of the series, yielding a power conversion efficiency (PCE) under air mass (AM) 1.5 conditions of 2.4% in the bilayer heterojunction with the C-60 molecule. Aiming at flexible electronics applications, solutions based on the heterojunction of this copolymer with PCBM (6,6-phenyl-C-61-butyric acid methyl ester) were also successfully deposited using an inkjet printing method and used as an active layer in solar cells.

Studying the influence of a secondary variable in Collocated Cokriging estimates

In this paper the influence of a secondary variable as a function of the correlation with the primary variable for collocated cokriging is examined. For this study five exhaustive data sets were generated in computer, from which samples with 60 and 104 data points were drawn using the stratified random sampling method. These exhaustive data sets were generated departing from a pair of primary and secondary variables showing a good correlation. Then successive sets were generated by adding an amount of white noise in such a way that the correlation gets poorer. Using these samples, it was possible to find out how primary and secondary information is used to estimate an unsampled location according to the correlation level.

Assessing biophysical variable parameters of bean crop with hyperspectral measurements

Recently high spectral resolution sensors have been developed, which allow new and more advanced applications in agriculture. Motivated by the increasing importance of hyperspectral remote sensing data, the need for research is important to define optimal wavebands to estimate biophysical parameters of crop. The use of narrow band vegetation indices (VI) derived from hyperspectral measurements acquired by a field spectrometer was evaluated to estimate bean (Phaseolus vulgaris L.) grain yield, plant height and leaf area index (LAI). Field canopy reflectance measurements were acquired at six bean growth stages over 48 plots with four water levels (179.5; 256.5; 357.5 and 406.2 mm) and tree nitrogen rates (0; 80 and 160 kg ha-1) and four replicates. The following VI was analyzed: OSNBR (optimum simple narrow-band reflectivity); NB_NDVI (narrow-band normalized difference vegetation index) and NDVI (normalized difference index). The vegetation indices investigated (OSNBR, NB_NDVI and NDVI) were efficient to estimate LAI, plant height and grain yield. During all crop development, the best correlations between biophysical variables and spectral variables were observed on V4 (the third trifoliolate leaves were unfolded in 50 % of plants) and R6 (plants developed first flowers in 50 % of plants) stages, according to the variable analyzed.

Automatic (data-based) learning of expressive indicators for machine failure detection : a proposal

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Inferences on earthquake kinematic properties from data inversion: two different approaches

During my PhD, starting from the original formulations proposed by Bertrand et al., 2000 and Emolo & Zollo 2005, I developed inversion methods and applied then at different earthquakes. In particular large efforts have been devoted to the study of the model resolution and to the estimation of the model parameter errors. To study the source kinematic characteristics of the Christchurch earthquake we performed a joint inversion of strong-motion, GPS and InSAR data using a non-linear inversion method. Considering the complexity highlighted by superficial deformation data, we adopted a fault model consisting of two partially overlapping segments, with dimensions 15x11 and 7x7 km2, having different faulting styles. This two-fault model allows to better reconstruct the complex shape of the superficial deformation data. The total seismic moment resulting from the joint inversion is 3.0x1025 dyne.cm (Mw = 6.2) with an average rupture velocity of 2.0 km/s. Errors associated with the kinematic model have been estimated of around 20-30 %. The 2009 Aquila sequence was characterized by an intense aftershocks sequence that lasted several months. In this study we applied an inversion method that assumes as data the apparent Source Time Functions (aSTFs), to a Mw 4.0 aftershock of the Aquila sequence. The estimation of aSTFs was obtained using the deconvolution method proposed by Vallée et al., 2004. The inversion results show a heterogeneous slip distribution, characterized by two main slip patches located NW of the hypocenter, and a variable rupture velocity distribution (mean value of 2.5 km/s), showing a rupture front acceleration in between the two high slip zones. Errors of about 20% characterize the final estimated parameters.

Variable Na(v)1.5 protein expression from the wild-type allele correlates with the penetrance of cardiac conduction disease in the Scn5a(+/-) mouse model

BACKGROUND: Loss-of-function mutations in SCN5A, the gene encoding Na(v)1.5 Na+ channel, are associated with inherited cardiac conduction defects and Brugada syndrome, which both exhibit variable phenotypic penetrance of conduction defects. We investigated the mechanisms of this heterogeneity in a mouse model with heterozygous targeted disruption of Scn5a (Scn5a(+/-) mice) and compared our results to those obtained in patients with loss-of-function mutations in SCN5A. METHODOLOGY/PRINCIPAL FINDINGS: Based on ECG, 10-week-old Scn5a(+/-) mice were divided into 2 subgroups, one displaying severe ventricular conduction defects (QRS interval>18 ms) and one a mild phenotype (QRS< or = 18 ms; QRS in wild-type littermates: 10-18 ms). Phenotypic difference persisted with aging. At 10 weeks, the Na+ channel blocker ajmaline prolonged QRS interval similarly in both groups of Scn5a(+/-) mice. In contrast, in old mice (>53 weeks), ajmaline effect was larger in the severely affected subgroup. These data matched the clinical observations on patients with SCN5A loss-of-function mutations with either severe or mild conduction defects. Ventricular tachycardia developed in 5/10 old severely affected Scn5a(+/-) mice but not in mildly affected ones. Correspondingly, symptomatic SCN5A-mutated Brugada patients had more severe conduction defects than asymptomatic patients. Old severely affected Scn5a(+/-) mice but not mildly affected ones showed extensive cardiac fibrosis. Mildly affected Scn5a(+/-) mice had similar Na(v)1.5 mRNA but higher Na(v)1.5 protein expression, and moderately larger I(Na) current than severely affected Scn5a(+/-) mice. As a consequence, action potential upstroke velocity was more decreased in severely affected Scn5a(+/-) mice than in mildly affected ones. CONCLUSIONS: Scn5a(+/-) mice show similar phenotypic heterogeneity as SCN5A-mutated patients. In Scn5a(+/-) mice, phenotype severity correlates with wild-type Na(v)1.5 protein expression.

Mining tissue microarray data to uncover combinations of biomarker expression patterns that improve intermediate staging and grading of clear cell renal cell cancer

PURPOSE: Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches. EXPERIMENTAL DESIGN: Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme. RESULTS: Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression. CONCLUSIONS: The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer.

Getting in touch-3D printing in Forensic Imaging

With the increasing use of medical imaging in forensics, as well as the technological advances in rapid prototyping, we suggest combining these techniques to generate displays of forensic findings. We used computed tomography (CT), CT angiography, magnetic resonance imaging (MRI) and surface scanning with photogrammetry in conjunction with segmentation techniques to generate 3D polygon meshes. Based on these data sets, a 3D printer created colored models of the anatomical structures. Using this technique, we could create models of bone fractures, vessels, cardiac infarctions, ruptured organs as well as bitemark wounds. The final models are anatomically accurate, fully colored representations of bones, vessels and soft tissue, and they demonstrate radiologically visible pathologies. The models are more easily understood by laypersons than volume rendering or 2D reconstructions. Therefore, they are suitable for presentations in courtrooms and for educational purposes.

China Urban Competitiveness in Industrialization: Based on the Panel Data of 25 Cities in China from 1990 to 2009

There is a consensus in China that industrialization, urbanization, globalization and information technology will enhance China's urban competitiveness. We have developed a methodology for the analysis of urban competitiveness that we have applied to China's 25 principal cities during three periods from 1990 through 2009. Our model uses data for 12 variables, to which we apply appropriate statistical techniques. We are able to examine the competitiveness of inland cities and those on the coast, how this has changed during the two decades of the study, the competitiveness of Mega Cities and of administrative centres, and the importance of each variable in explaining urban competitiveness and its development over time. This analysis will be of benefit to Chinese planners as they seek to enhance the competitiveness of China and its major cities in the future.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Estimating Patterns of CD4+ Lymphocyte Decline Using Data from a Prevalent Cohort of HIV Infected Individuals

In natural history studies of chronic disease, it is of interest to understand the evolution of key variables that measure aspects of disease progression. This is particularly true for immunological variables in persons infected with the Human Immunodeficiency Virus (HIV). The natural timescale for such studies is time since infection. However, most data available for analysis arise from prevalent cohorts, where the date of infection is unknown for most or all individuals. As a result, standard curve fitting algorithms are not immediately applicable. Here we propose two methods to circumvent this difficulty. The first uses repeated measurement data to provide information not only on the level of the variable of interest, but also on its rate of change, while the second uses an estimate of the expected time since infection. Both methods are based on the principal curves algorithm of Hastie and Stuetzle, and are applied to data from a prevalent cohort of HIV-infected homosexual men, giving estimates of the average pattern of CD4+ lymphocyte decline. These methods are applicable to natural history studies using data from prevalent cohorts where the time of disease origin is uncertain, provided certain ancillary information is available from external sources.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.