Transcatheter coil embolization of multiple bilateral congenital coronary artery fistulae.

Coronary artery fistulae represent the most frequent congenital anomalies of the coronary arteries, but remain a relatively uncommon clinical problem. Moreover, multiple fistulae originating from both the left and the right coronary arteries and draining into the left ventricular chamber are a rare condition. Due to the low prevalence of these anomalies, the appropriate management of patients with symptomatic coronary artery fistulae is controversial. Transcatheter closure approaches have emerged as a less invasive strategy and are nowadays considered a valuable alternative to surgical correction with similar effectiveness, morbidity and mortality. The percutaneous management, however, is mainly limited by the individual anatomic features of the fistula and an appropriate patient's selection is considered as a key determining factor to achieve complete occlusion. Thus, success rates of transcatheter closure techniques reported in the literature are extremely variable and highly dependent upon the nature of the follow up, which, at present, is not standardized. The optimal management of symptomatic patients with multiple coronary artery fistulae still remains a challenging problem and has been traditionally considered as an indication for cardiac surgery. We report here the case of a patient with double bilateral congenital coronary artery fistulae arising from both the left and right coronary arteries and draining individually into the left ventricular chamber. This patient underwent successful transcatheter anterograde closure of both fistulae using a microcoil embolization technique.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

Genetic variation for carbon isotope composition in Juglans regia L.: relationships with growth, phenology and climate of origin

Among the traits of breeding interest for the common walnut tree Juglans regia L., characteristics such as timing of budbreak and leaf fall, water-use efficiency and growth performance are regarded as being of utmost relevance in Mediterranean conditions. The authors evaluated intraspecific variation in $\delta$13C (carbon isotope composition, surrogate of intrinsic water-use efficiency, WUE$_{\rm i}$) for 22 J. regia families grown in a progeny test under supplementary irrigation, and investigated whether such variation correlated with climatic indicators of native habitats. The genetic relationships between $\delta$13C, growth and phenology were also assessed during two consecutive years. Overall, the most water-use-efficient families (i.e. with higher $\delta$13C), which originated mainly from drought-prone provenance regions which have a high vapour pressure deficit and low rainfall, exhibited less height growth and smaller DBH. Using a stepwise regression procedure, $\delta$13C was included as the main explanatory variable of genotypic variation in growth traits, together with growing season duration (for DBH in both years) and flushing (for height in 2007). It was concluded that WUE$_{\rm i}$ is largely unconnected to phenology effects in the explanation of growth performance for J. regia, therefore suggesting the opportunity of simultaneously selecting for low WUE$_{\rm i}$ and extended growing period to maximise productivity in non-water-limited environments.

Imaging of prolonged BOLD response in the somatosensory cortex of the rat.

Blood oxygenation level-dependent (BOLD) functional MRI is a widely employed methodology in experimental and clinical neuroscience, although its nature is not fully understood. To gain insights into BOLD mechanisms and take advantage of the new functional methods, it is of interest to investigate prolonged paradigms of activation suitable for long experimental protocols and to observe any long-term modifications induced by these functional challenges. While different types of sustained stimulation paradigm have been explored in human studies, the BOLD response is typically limited to a few minutes in animal models, due to fatigue, anesthesia effects and physiological instability. In the present study, the rat forepaw was electrically stimulated for 2 h, which resulted in a prolonged and localized cortical BOLD response over that period. The stimulation paradigm, including an inter-stimulus interval (ISI) of 10 s, that is 25% of the total time, was applied at constant or variable frequency over 2 h. The steady-state level of the BOLD response was reached after 15-20 min of stimulation and was maintained until the end of the stimulation. On average, no substantial loss in activated volume was observed at the end of the stimulation, but less variability in the fraction of remaining activated volume and higher steady-state BOLD amplitude were observed when stimulation frequency was varied between 2 and 3 Hz every 5 min. We conclude that the combination of ISI and variable stimulus frequency reproducibly results in robust, prolonged and localized BOLD activation. Copyright © 2015 John Wiley & Sons, Ltd.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Impaired aldehyde dehydrogenase 1 subfamily member 2A-dependent retinoic acid signaling is related with a mesenchymal-like phenotype and an unfavorable prognosis of head and neck squamous cell carcinoma.

BACKGROUND: An inverse correlation between expression of the aldehyde dehydrogenase 1 subfamily A2 (ALDH1A2) and gene promoter methylation has been identified as a common feature of oropharyngeal squamous cell carcinoma (OPSCC). Moreover, low ALDH1A2 expression was associated with an unfavorable prognosis of OPSCC patients, however the causal link between reduced ALDH1A2 function and treatment failure has not been addressed so far. METHODS: Serial sections from tissue microarrays of patients with primary OPSCC (n = 101) were stained by immunohistochemistry for key regulators of retinoic acid (RA) signaling, including ALDH1A2. Survival with respect to these regulators was investigated by univariate Kaplan-Meier analysis and multivariate Cox regression proportional hazard models. The impact of ALDH1A2-RAR signaling on tumor-relevant processes was addressed in established tumor cell lines and in an orthotopic mouse xenograft model. RESULTS: Immunohistochemical analysis showed an improved prognosis of ALDH1A2(high) OPSCC only in the presence of CRABP2, an intracellular RA transporter. Moreover, an ALDH1A2(high)CRABP2(high) staining pattern served as an independent predictor for progression-free (HR: 0.395, p = 0.007) and overall survival (HR: 0.303, p = 0.002), suggesting a critical impact of RA metabolism and signaling on clinical outcome. Functionally, ALDH1A2 expression and activity in tumor cell lines were related to RA levels. While administration of retinoids inhibited clonogenic growth and proliferation, the pharmacological inhibition of ALDH1A2-RAR signaling resulted in loss of cell-cell adhesion and a mesenchymal-like phenotype. Xenograft tumors derived from FaDu cells with stable silencing of ALDH1A2 and primary tumors from OPSCC patients with low ALDH1A2 expression exhibited a mesenchymal-like phenotype characterized by vimentin expression. CONCLUSIONS: This study has unraveled a critical role of ALDH1A2-RAR signaling in the pathogenesis of head and neck cancer and our data implicate that patients with ALDH1A2(low) tumors might benefit from adjuvant treatment with retinoids.

The environmental effects of changing speed limits: a quantile regression approach

Two speed management policies were implemented in the metropolitan area of Barcelona aimed at reducing air pollution concentration levels. In 2008, the maximum speed limit was reduced to 80 km/h and, in 2009, a variable speed system was introduced on some metropolitan motorways. This paper evaluates whether such policies have been successful in promoting cleaner air, not only in terms of mean pollutant levels but also during high and low pollution episodes. We use a quantile regression approach for fixed effect panel data. We find that the variable speed system improves air quality with regard to the two pollutants considered here, being most effective when nitrogen oxide levels are not too low and when particulate matter concentrations are below extremely high levels. However, reducing the maximum speed limit from 120/100 km/h to 80 km/h has no effect – or even a slightly increasing effect –on the two pollutants, depending on the pollution scenario. Length: 32 pages

Least squares regression with errors in both variables: case studies

Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS). In this work, the use of orthogonal distance regression (ODR) is discussed as an alternative approach in order to take into account the error in the x variable. Four examples are presented to illustrate deviation between the results from both regression methods. The examples studied show that, in some situations, ODR coefficients must substitute for those of OLS, and, in other situations, the difference is not significant.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Adjust of regression models to estimate the rectal temperature of broilers for the first 14 days of life

The broiler rectal temperature (t rectal) is one of the most important physiological responses to classify the animal thermal comfort. Therefore, the aim of this study was to adjust regression models in order to predict the rectal temperature (t rectal) of broiler chickens under different thermal conditions based on age (A) and a meteorological variable (air temperature - t air) or a thermal comfort index (temperature and humidity index -THI or black globe humidity index - BGHI) or a physical quantity enthalpy (H). In addition, through the inversion of these models and the expected t rectal intervals for each age, the comfort limits of t air, THI, BGHI and H for the chicks in the heating phase were determined, aiding in the validation of the equations and the preliminary limits for H. The experimental data used to adjust the mathematical models were collected in two commercial poultry farms, with Cobb chicks, from 1 to 14 days of age. It was possible to predict the t rectal of conditions from the expected t rectal and determine the lower and superior comfort thresholds of broilers satisfactorily by applying the four models adjusted; as well as to invert the models for prediction of the environmental H for the chicks first 14 days of life.

Energy-efficient control strategies for variable speed driven parallel pumping systems based on pump operation point monitoring with frequency converters

The pumping processes requiring wide range of flow are often equipped with parallelconnected centrifugal pumps. In parallel pumping systems, the use of variable speed control allows that the required output for the process can be delivered with a varying number of operated pump units and selected rotational speed references. However, the optimization of the parallel-connected rotational speed controlled pump units often requires adaptive modelling of both parallel pump characteristics and the surrounding system in varying operation conditions. The available information required for the system modelling in typical parallel pumping applications such as waste water treatment and various cooling and water delivery pumping tasks can be limited, and the lack of real-time operation point monitoring often sets limits for accurate energy efficiency optimization. Hence, alternatives for easily implementable control strategies which can be adopted with minimum system data are necessary. This doctoral thesis concentrates on the methods that allow the energy efficient use of variable speed controlled parallel pumps in system scenarios in which the parallel pump units consist of a centrifugal pump, an electric motor, and a frequency converter. Firstly, the suitable operation conditions for variable speed controlled parallel pumps are studied. Secondly, methods for determining the output of each parallel pump unit using characteristic curve-based operation point estimation with frequency converter are discussed. Thirdly, the implementation of the control strategy based on real-time pump operation point estimation and sub-optimization of each parallel pump unit is studied. The findings of the thesis support the idea that the energy efficiency of the pumping can be increased without the installation of new, more efficient components in the systems by simply adopting suitable control strategies. An easily implementable and adaptive control strategy for variable speed controlled parallel pumping systems can be created by utilizing the pump operation point estimation available in modern frequency converters. Hence, additional real-time flow metering, start-up measurements, and detailed system model are unnecessary, and the pumping task can be fulfilled by determining a speed reference for each parallel-pump unit which suggests the energy efficient operation of the pumping system.

Reference values for lung function tests: III. Carbon monoxide diffusing capacity (transfer factor)

Carbon monoxide diffusing capacity (DLCO) or transfer factor (TLCO) is a particularly useful test of the appropriateness of gas exchange across the lung alveolocapillary membrane. With the purpose of establishing predictive equations for DLCO using a non-smoking sample of the adult Brazilian population, we prospectively evaluated 100 subjects (50 males and 50 females aged 20 to 80 years), randomly selected from more than 8,000 individuals. Gender-specific linear prediction equations were developed by multiple regression analysis with single breath (SB) absolute and volume-corrected (VA) DLCO values as dependent variables. In the prediction equations, age (years) and height (cm) had opposite effects on DLCOSB (ml min-1 mmHg-1), independent of gender (-0.13 (age) + 0.32 (height) - 13.07 in males and -0.075 (age) + 0.18 (height) + 0.20 in females). On the other hand, height had a positive effect on DLCOSB but a negative one on DLCOSB/VA (P<0.01). We found that the predictive values from the most cited studies using predominantly Caucasian samples were significantly different from the actually measured values (P<0.05). Furthermore, oxygen uptake at maximal exercise (VO2max) correlated highly to DLCOSB (R = 0.71, P<0.001); this variable, however, did not maintain an independent role to explain the VO2max variability in the multiple regression analysis (P>0.05). Our results therefore provide an original frame of reference for either DLCOSB or DLCOSB/VA in Brazilian males and females aged 20 to 80 years, obtained from the standardized single-breath technique.

Limited-sampling strategy models for estimating the pharmacokinetic parameters of 4-methylaminoantipyrine, an active metabolite of dipyrone

Bioanalytical data from a bioequivalence study were used to develop limited-sampling strategy (LSS) models for estimating the area under the plasma concentration versus time curve (AUC) and the peak plasma concentration (Cmax) of 4-methylaminoantipyrine (MAA), an active metabolite of dipyrone. Twelve healthy adult male volunteers received single 600 mg oral doses of dipyrone in two formulations at a 7-day interval in a randomized, crossover protocol. Plasma concentrations of MAA (N = 336), measured by HPLC, were used to develop LSS models. Linear regression analysis and a "jack-knife" validation procedure revealed that the AUC0-¥ and the Cmax of MAA can be accurately predicted (R²>0.95, bias <1.5%, precision between 3.1 and 8.3%) by LSS models based on two sampling times. Validation tests indicate that the most informative 2-point LSS models developed for one formulation provide good estimates (R²>0.85) of the AUC0-¥ or Cmax for the other formulation. LSS models based on three sampling points (1.5, 4 and 24 h), but using different coefficients for AUC0-¥ and Cmax, predicted the individual values of both parameters for the enrolled volunteers (R²>0.88, bias = -0.65 and -0.37%, precision = 4.3 and 7.4%) as well as for plasma concentration data sets generated by simulation (R²>0.88, bias = -1.9 and 8.5%, precision = 5.2 and 8.7%). Bioequivalence assessment of the dipyrone formulations based on the 90% confidence interval of log-transformed AUC0-¥ and Cmax provided similar results when either the best-estimated or the LSS-derived metrics were used.