Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Soil biogenic emissions of nitric oxide from arid and semi-arid ecosystems

The biogenic production of NO in the soil accounts for between 10% and 40% of the global total. A large degree of the uncertainty in the estimation of the biogenic emissions stems from a shortage of measurements in arid regions, which comprise 40% of the earth’s land surface area. This study examined the emission of NO from three ecosystems in southern Africa which cover an aridity gradient from semi-arid savannas in South Africa to the hyper-arid Namib Desert in Namibia. A laboratory method was used to determine the release of NO as a function of the soil moisture and the soil temperature. Various methods were used to up-scale the net potential NO emissions determined in the laboratory to the vegetation patch, landscape or regional level. The importance of landscape, vegetation and climatic characteristics is emphasized. The first study occurred in a semi-arid savanna region in South Africa, where soils were sampled from 4 landscape positions in the Kruger National Park. The maximum NO emission occurred at soil moisture contents of 10%-20% water filled pore space (WFPS). The highest net potential NO emissions came from the low lying landscape positions, which have the largest nitrogen (N) stocks and the largest input of N. Net potential NO fluxes obtained in the laboratory were converted in field fluxes for the period 2003-2005, for the four landscape positions, using soil moisture and temperature data obtained in situ at the Kruger National Park Flux Tower Site. The NO emissions ranged from 1.5-8.5 kg ha-1 a-1. The field fluxes were up-scaled to a regional basis using geographic information system (GIS) based techniques, this indicated that the highest NO emissions occurred from the Midslope positions due to their large geographical extent in the research area. Total emissions ranged from 20x103 kg in 2004 to 34x103 kg in 2003 for the 56000 ha Skukuza land type. The second study occurred in an arid savanna ecosystem in the Kalahari, Botswana. In this study I collected soils from four differing vegetation patch types including: Pan, Annual Grassland, Perennial Grassland and Bush Encroached patches. The maximum net potential NO fluxes ranged from 0.27 ng m-2 s-1 in the Pan patches to 2.95 ng m-2 s-1 in the Perennial Grassland patches. The net potential NO emissions were up-scaled for the year December 2005-November 2006. This was done using 1) the net potential NO emissions determined in the laboratory, 2) the vegetation patch distribution obtained from LANDSAT NDVI measurements 3) estimated soil moisture contents obtained from ENVISAT ASAR measurements and 4) soil surface temperature measurements using MODIS 8 day land surface temperature measurements. This up-scaling procedure gave NO fluxes which ranged from 1.8 g ha-1 month-1 in the winter months (June and July) to 323 g ha-1 month-1 in the summer months (January-March). Differences occurred between the vegetation patches where the highest NO fluxes occurred in the Perennial Grassland patches and the lowest in the Pan patches. Over the course of the year the mean up-scaled NO emission for the studied region was 0.54 kg ha-1 a-1 and accounts for a loss of approximately 7.4% of the estimated N input to the region. The third study occurred in the hyper-arid Namib Desert in Namibia. Soils were sampled from three ecosystems; Dunes, Gravel Plains and the Riparian zone of the Kuiseb River. The net potential NO flux measured in the laboratory was used to estimate the NO flux for the Namib Desert for 2006 using modelled soil moisture and temperature data from the European Centre for Medium Range Weather Forecasts (ECMWF) operational model on a 36km x 35km spatial resolution. The maximum net potential NO production occurred at low soil moisture contents (<10%WFPS) and the optimal temperature was 25°C in the Dune and Riparian ecosystems and 35°C in the Gravel Plain Ecosystems. The maximum net potential NO fluxes ranged from 3.0 ng m-2 s-1 in the Riparian ecosystem to 6.2 ng m-2 s-1 in the Gravel Plains ecosystem. Up-scaling the net potential NO flux gave NO fluxes of up to 0.062 kg ha-1 a-1 in the Dune ecosystem and 0.544 kg h-1 a-1 in the Gravel Plain ecosystem. From these studies it is shown that NO is emitted ubiquitously from terrestrial ecosystems, as such the NO emission potential from deserts and scrublands should be taken into account in the global NO models. The emission of NO is influenced by various factors such as landscape, vegetation and climate. This study looks at the potential emissions from certain arid and semi-arid environments in southern Africa and other parts of the world and discusses some of the important factors controlling the emission of NO from the soil.

Study of a wind-wave numerical model and its integration with ocean and oil-spill numerical models

The ability to represent the transport and fate of an oil slick at the sea surface is a formidable task. By using an accurate numerical representation of oil evolution and movement in seawater, the possibility to asses and reduce the oil-spill pollution risk can be greatly improved. The blowing of the wind on the sea surface generates ocean waves, which give rise to transport of pollutants by wave-induced velocities that are known as Stokes’ Drift velocities. The Stokes’ Drift transport associated to a random gravity wave field is a function of the wave Energy Spectra that statistically fully describe it and that can be provided by a wave numerical model. Therefore, in order to perform an accurate numerical simulation of the oil motion in seawater, a coupling of the oil-spill model with a wave forecasting model is needed. In this Thesis work, the coupling of the MEDSLIK-II oil-spill numerical model with the SWAN wind-wave numerical model has been performed and tested. In order to improve the knowledge of the wind-wave model and its numerical performances, a preliminary sensitivity study to different SWAN model configuration has been carried out. The SWAN model results have been compared with the ISPRA directional buoys located at Venezia, Ancona and Monopoli and the best model settings have been detected. Then, high resolution currents provided by a relocatable model (SURF) have been used to force both the wave and the oil-spill models and its coupling with the SWAN model has been tested. The trajectories of four drifters have been simulated by using JONSWAP parametric spectra or SWAN directional-frequency energy output spectra and results have been compared with the real paths traveled by the drifters.

Polygonal cracks in the seasonal semi-translucent CO2 ice layer in Martian polar areas

In this paper, we use morphological and numerical methods to test the hypothesis that seasonally formed fracture patterns in the Martian polar regions result from the brittle failure of seasonal CO2 slab ice. The observations by the High Resolution Imaging Science Experiment (HiRISE) of polar regions of Mars show very narrow dark elongated linear patterns that are observed during some periods of time in spring, disappear in summer and re-appear again in the following spring. They are repeatedly formed in the same areas but they do not repeat the exact pattern from year to year. This leads to the conclusion that they are cracks formed in the seasonal ice layer. Some of models of seasonal surface processes rely on the existence of a transparent form of CO2 ice, so-called slab ice. For the creation of the observed cracks the ice is required to be a continuous media, not an agglomeration of relatively separate particles like a firn. The best explanation for our observations is a slab ice with relatively high transparency in the visible wavelength range. This transparency allows a solid state green-house effect to act underneath the ice sheet raising the pressure by sublimation from below. The trapped gas creates overpressure and the ice sheet breaks at some point creating the observed cracks. We show that the times when the cracks appear are in agreement with the model calculation, providing one more piece of evidence that CO2 slab ice covers polar areas in spring.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

Locally Efficient Estimation in Censored Data Models: Theory and Examples

In many applications the observed data can be viewed as a censored high dimensional full data random variable X. By the curve of dimensionality it is typically not possible to construct estimators that are asymptotically efficient at every probability distribution in a semiparametric censored data model of such a high dimensional censored data structure. We provide a general method for construction of one-step estimators that are efficient at a chosen submodel of the full-data model, are still well behaved off this submodel and can be chosen to always improve on a given initial estimator. These one-step estimators rely on good estimators of the censoring mechanism and thus will require a parametric or semiparametric model for the censoring mechanism. We present a general theorem that provides a template for proving the desired asymptotic results. We illustrate the general one-step estimation methods by constructing locally efficient one-step estimators of marginal distributions and regression parameters with right-censored data, current status data and bivariate right-censored data, in all models allowing the presence of time-dependent covariates. The conditions of the asymptotics theorem are rigorously verified in one of the examples and the key condition of the general theorem is verified for all examples.

Semiparametric Estimation of Models for Natural Direct and Indirect Effects

In recent years, researchers in the health and social sciences have become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of an exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Natural direct and indirect effects are of particular interest as they generally combine to produce the total effect of the exposure and therefore provide insight on the mechanism by which it operates to produce the outcome. A semiparametric theory has recently been proposed to make inferences about marginal mean natural direct and indirect effects in observational studies (Tchetgen Tchetgen and Shpitser, 2011), which delivers multiply robust locally efficient estimators of the marginal direct and indirect effects, and thus generalizes previous results for total effects to the mediation setting. In this paper we extend the new theory to handle a setting in which a parametric model for the natural direct (indirect) effect within levels of pre-exposure variables is specified and the model for the observed data likelihood is otherwise unrestricted. We show that estimation is generally not feasible in this model because of the curse of dimensionality associated with the required estimation of auxiliary conditional densities or expectations, given high-dimensional covariates. We thus consider multiply robust estimation and propose a more general model which assumes a subset but not all of several working models holds.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

Parametric combustion modeling for ethanol-gasoline fuelled spark ignition engines

Ethanol-gasoline fuel blends are increasingly being used in spark ignition (SI) engines due to continued growth in renewable fuels as part of a growing renewable portfolio standard (RPS). This leads to the need for a simple and accurate ethanol-gasoline blends combustion model that is applicable to one-dimensional engine simulation. A parametric combustion model has been developed, integrated into an engine simulation tool, and validated using SI engine experimental data. The parametric combustion model was built inside a user compound in GT-Power. In this model, selected burn durations were computed using correlations as functions of physically based non-dimensional groups that have been developed using the experimental engine database over a wide range of ethanol-gasoline blends, engine geometries, and operating conditions. A coefficient of variance (COV) of gross indicated mean effective pressure (IMEP) correlation was also added to the parametric combustion model. This correlation enables the cycle combustion variation modeling as a function of engine geometry and operating conditions. The computed burn durations were then used to fit single and double Wiebe functions. The single-Wiebe parametric combustion compound used the least squares method to compute the single-Wiebe parameters, while the double-Wiebe parametric combustion compound used an analytical solution to compute the double-Wiebe parameters. These compounds were then integrated into the engine model in GT-Power through the multi-Wiebe combustion template in which the values of Wiebe parameters (single-Wiebe or double-Wiebe) were sensed via RLT-dependence. The parametric combustion models were validated by overlaying the simulated pressure trace from GT-Power on to experimentally measured pressure traces. A thermodynamic engine model was also developed to study the effect of fuel blends, engine geometries and operating conditions on both the burn durations and COV of gross IMEP simulation results.

Numerical investigation of effects of addition of ethanol to gasoline on Laminar Flame Speed (LFS), autoignition, and wall quenching

Since the advent of automobiles, alcohol has been considered a possible engine fuel1,2. With the recent increased concern about the high price of crude oil due to fluctuating supply and demand and environmental issues, interest in alcohol based fuels has increased2,3. However, using pure alcohols or blends with conventional fuels in high percentages requires changes to the engine and fuel system design2. This leads to the need for a simple and accurate conventional fuels-alcohol blends combustion models that can be used in developing parametric burn rate and knock combustion models for designing more efficient Spark Ignited (SI) engines. To contribute to this understanding, numerical simulations were performed to obtain detailed characteristics of Gasoline-Ethanol blends with respect to Laminar Flame Speed (LFS), autoignition and Flame-Wall interactions. The one-dimensional premixed flame code CHEMKIN® was applied to simulate the burning velocity and autoignition characteristics using the freely propagating model and closed homogeneous reactor model respectively. Computational Fluid Dynamics (CFD) was used to obtain detailed flow, temperature, and species fields for Flame-wall interactions. A semi-detailed validated chemical kinetic model for a gasoline surrogate fuel developed by Andrae and Head4 was used for the study of LFS and Autoignition. For the quenching study, a skeletal chemical kinetic mechanism of gasoline surrogate, having 50 species and 174 reactions was used. The surrogate fuel was defined as a mixture of pure n-heptane, isooctane, and toluene. For LFS study, the ethanol volume fraction was varied from 0 to 85%, initial pressure from 4 to 8 bar, initial temperature from 300 to 900K, and dilution from 0 to 32%. Whereas for Autoignition study, the ethanol volume fraction was varied between 0 to 85%, initial pressure was varied between 20 to 60 bar, initial temperature was varied between 800 to 1200K, and the dilution was varied between 0 to 32% at equivalence ratios of 0.5, 1.0 and 1.5 to represent the in-cylinder conditions of a SI engine. For quenching study three Ethanol blends, namely E0, E25 and E85 are described in detail at an initial pressure of 8 atm and 17 atm. Initial wall temperature was taken to be 400 K. Quenching thicknesses and heat fluxes to the wall were computed. The laminar flame speed was found to increase with ethanol concentration and temperature but decrease with pressure and dilution. The autoignition time was found to increase with ethanol concentration at lower temperatures but was found to decrease marginally at higher temperatures. The autoignition time was also found to decrease with pressure and equivalence ratio but increase with dilution. The average quenching thickness was found to decrease with an increase in Ethanol concentration in the blend. Heat flux to the wall increased with increase in ethanol percentage in the blend and at higher initial pressures. Whereas the wall heat flux decreased with an increase in dilution. Unburned Hydrocarbon (UHC) and CO % was also found to decrease with ethanol concentration in the blend.

Analytical Parametric Model Used to Study the Influence of Electrostatic Force on Surface Coverage During Electrospinning of Polymer Fibers

Electrospinning (ES) can readily produce polymer fibers with cross-sectional dimensions ranging from tens of nanometers to tens of microns. Qualitative estimates of surface area coverage are rather intuitive. However, quantitative analytical and numerical methods for predicting surface coverage during ES have not been covered in sufficient depth to be applied in the design of novel materials, surfaces, and devices from ES fibers. This article presents a modeling approach to ES surface coverage where an analytical model is derived for use in quantitative prediction of surface coverage of ES fibers. The analytical model is used to predict the diameter of circular deposition areas of constant field strength and constant electrostatic force. Experimental results of polyvinyl alcohol fibers are reported and compared to numerical models to supplement the analytical model derived. The analytical model provides scientists and engineers a method for estimating surface area coverage. Both applied voltage and capillary-to-collection-plate separation are treated as independent variables for the analysis. The electric field produced by the ES process was modeled using COMSOL Multiphysics software to determine a correlation between the applied field strength and the size of the deposition area of the ES fibers. MATLAB scripts were utilized to combine the numerical COMSOL results with derived analytical equations. Experimental results reinforce the parametric trends produced via modeling and lend credibility to the use of modeling techniques for the qualitative prediction of surface area coverage from ES. (Copyright: 2014 American Vacuum Society.)

Multimeric tyrosine sulfate acts as an endothelial cell protectant and prevents complement activation in xenotransplantation models

BACKGROUND: Activation of endothelial cells (EC) in xenotransplantation is mostly induced through binding of antibodies (Ab) and activation of the complement system. Activated EC lose their heparan sulfate proteoglycan (HSPG) layer and exhibit a procoagulant and pro-inflammatory cell surface. We have recently shown that the semi-synthetic proteoglycan analog dextran sulfate (DXS, MW 5000) blocks activation of the complement cascade and acts as an EC-protectant both in vitro and in vivo. However, DXS is a strong anticoagulant and systemic use of this substance in a clinical setting might therefore be compromised. It was the aim of this study to investigate a novel, fully synthetic EC-protectant with reduced inhibition of the coagulation system. METHOD: By screening with standard complement (CH50) and coagulation assays (activated partial thromboplastin time, aPTT), a conjugate of tyrosine sulfate to a polymer-backbone (sTyr-PAA) was identified as a candidate EC-protectant. The pathway-specificity of complement inhibition by sTyr-PAA was tested in hemolytic assays. To further characterize the substance, the effects of sTyr-PAA and DXS on complement deposition on pig cells were compared by flow cytometry and cytotoxicity assays. Using fluorescein-labeled sTyr-PAA (sTyr-PAA-Fluo), the binding of sTyr-PAA to cell surfaces was also investigated. RESULTS: Of all tested compounds, sTyr-PAA was the most effective substance in inhibiting all three pathways of complement activation. Its capacity to inhibit the coagulation cascade was significantly reduced as compared with DXS. sTyr-PAA also dose-dependently inhibited deposition of human complement on pig cells and this inhibition correlated with the binding of sTyr-PAA to the cells. Moreover, we were able to demonstrate that sTyr-PAA binds preferentially and dose-dependently to damaged EC. CONCLUSIONS: We could show that sTyr-PAA acts as an EC-protectant by binding to the cells and protecting them from complement-mediated damage. It has less effect on the coagulation system than DXS and may therefore have potential for in vivo application.

Water quality and health in a Sahelian semi-arid urban context: an integrated geographical approach in Nouakchott, Mauritania

Access to sufficient quantities of safe drinking water is a human right. Moreover, access to clean water is of public health relevance, particularly in semi-arid and Sahelian cities due to the risks of water contamination and transmission of water-borne diseases. We conducted a study in Nouakchott, the capital of Mauritania, to deepen the understanding of diarrhoeal incidence in space and time. We used an integrated geographical approach, combining socio-environmental, microbiological and epidemiological data from various sources, including spatially explicit surveys, laboratory analysis of water samples and reported diarrhoeal episodes. A geospatial technique was applied to determine the environmental and microbiological risk factors that govern diarrhoeal transmission. Statistical and cartographic analyses revealed concentration of unimproved sources of drinking water in the most densely populated areas of the city, coupled with a daily water allocation below the recommended standard of 20 l per person. Bacteriological analysis indicated that 93% of the non-piped water sources supplied at water points were contaminated with 10-80 coliform bacteria per 100 ml. Diarrhoea was the second most important disease reported at health centres, accounting for 12.8% of health care service consultations on average. Diarrhoeal episodes were concentrated in municipalities with the largest number of contaminated water sources. Environmental factors (e.g. lack of improved water sources) and bacteriological aspects (e.g. water contamination with coliform bacteria) are the main drivers explaining the spatio-temporal distribution of diarrhoea. We conclude that integrating environmental, microbiological and epidemiological variables with statistical regression models facilitates risk profiling of diarrhoeal diseases. Modes of water supply and water contamination were the main drivers of diarrhoea in this semi-arid urban context of Nouakchott, and hence require a strategy to improve water quality at the various levels of the supply chain.