Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Mapping the increasing risk of human alveolar echinococcosis in Limburg, The Netherlands

The parasite Echinococcus multilocularis was first detected in The Netherlands in 1996 and repeated studies have shown that the parasite subsequently spread in the local population of foxes in the province of Limburg. It was not possible to quantify the human risk of alveolar echinococcosis because no relationship between the amount of parasite eggs in the environment and the probability of infection in humans was known. Here, we used the spread of the parasite in The Netherlands as a predictor, together with recently published historical records of the epidemiology of alveolar echinococcosis in Switzerland, to achieve a relative quantification of the risk. Based on these analyses, the human risk in Limburg was simulated and up to three human cases are predicted by 2018. We conclude that the epidemiology of alveolar echinococcosis in The Netherlands might have changed from a period of negligible risk in the past to a period of increasing risk in the forthcoming years.

A qualitative risk assessment approach for Swiss dairy products: opportunities and limitations

Switzerland implemented a risk-based monitoring of Swiss dairy products in 2002 based on a risk assessment (RA) that considered the probability of exceeding a microbiological limit value set by law. A new RA was launched in 2007 to review and further develop the previous assessment, and to make recommendations for future risk-based monitoring according to current risks. The resulting qualitative RA was designed to ascertain the risk to human health from the consumption of Swiss dairy products. The products and microbial hazards to be considered in the RA were determined based on a risk profile. The hazards included Campylobacter spp., Listeria monocytogenes, Salmonella spp., Shiga toxin-producing Escherichia coli, coagulase-positive staphylococci and Staphylococcus aureus enterotoxin. The release assessment considered the prevalence of the hazards in bulk milk samples, the influence of the process parameters on the microorganisms, and the influence of the type of dairy. The exposure assessment was linked to the production volume. An overall probability was estimated combining the probabilities of release and exposure for each combination of hazard, dairy product and type of dairy. This overall probability represents the likelihood of a product from a certain type of dairy exceeding the microbiological limit value and being passed on to the consumer. The consequences could not be fully assessed due to lack of detailed information on the number of disease cases caused by the consumption of dairy products. The results were expressed as a ranking of overall probabilities. Finally, recommendations for the design of the risk-based monitoring programme and for filling the identified data gaps were given. The aims of this work were (i) to present the qualitative RA approach for Swiss dairy products, which could be adapted to other settings and (ii) to discuss the opportunities and limitations of the qualitative method.

Performance of Interleukin-6 and Interleukin-8 serum levels in pediatric oncology patients with neutropenia and fever for the assessment of low-risk

BACKGROUND: Patients with chemotherapy-related neutropenia and fever are usually hospitalized and treated on empirical intravenous broad-spectrum antibiotic regimens. Early diagnosis of sepsis in children with febrile neutropenia remains difficult due to non-specific clinical and laboratory signs of infection. We aimed to analyze whether IL-6 and IL-8 could define a group of patients at low risk of septicemia. METHODS: A prospective study was performed to assess the potential value of IL-6, IL-8 and C-reactive protein serum levels to predict severe bacterial infection or bacteremia in febrile neutropenic children with cancer during chemotherapy. Statistical test used: Friedman test, Wilcoxon-Test, Kruskal-Wallis H test, Mann-Whitney U-Test and Receiver Operating Characteristics. RESULTS: The analysis of cytokine levels measured at the onset of fever indicated that IL-6 and IL-8 are useful to define a possible group of patients with low risk of sepsis. In predicting bacteremia or severe bacterial infection, IL-6 was the best predictor with the optimum IL-6 cut-off level of 42 pg/ml showing a high sensitivity (90%) and specificity (85%). CONCLUSION: These findings may have clinical implications for risk-based antimicrobial treatment strategies.

Computational simulations of latent heat thermal energy storage systems - with innovative and first-principles based simulation for the underlying unsteady melting (and solidification) processes

This thesis develops an effective modeling and simulation procedure for a specific thermal energy storage system commonly used and recommended for various applications (such as an auxiliary energy storage system for solar heating based Rankine cycle power plant). This thermal energy storage system transfers heat from a hot fluid (termed as heat transfer fluid - HTF) flowing in a tube to the surrounding phase change material (PCM). Through unsteady melting or freezing process, the PCM absorbs or releases thermal energy in the form of latent heat. Both scientific and engineering information is obtained by the proposed first-principle based modeling and simulation procedure. On the scientific side, the approach accurately tracks the moving melt-front (modeled as a sharp liquid-solid interface) and provides all necessary information about the time-varying heat-flow rates, temperature profiles, stored thermal energy, etc. On the engineering side, the proposed approach is unique in its ability to accurately solve – both individually and collectively – all the conjugate unsteady heat transfer problems for each of the components of the thermal storage system. This yields critical system level information on the various time-varying effectiveness and efficiency parameters for the thermal storage system.

Energy and conductance state modeling of power electronic converters for DC microgrids

This document will demonstrate the methodology used to create an energy and conductance based model for power electronic converters. The work is intended to be a replacement for voltage and current based models which have limited applicability to the network nodal equations. Using conductance-based modeling allows direct application of load differential equations to the bus admittance matrix (Y-bus) with a unified approach. When applied directly to the Y-bus, the system becomes much easier to simulate since the state variables do not need to be transformed. The proposed transformation applies to loads, sources, and energy storage systems and is useful for DC microgrids. Transformed state models of a complete microgrid are compared to experimental results and show the models accurately reflect the system dynamic behavior.

Spatiotemporal dynamics: the need for an innovative approach in mountain hazard risk management

Starting with an overview on losses due to mountain hazards in the Russian Federation and the European Alps, the question is raised why a substantial number of events still are recorded—despite considerable efforts in hazard mitigation and risk reduction. The main reason for this paradox lies in a missing dynamic risk-based approach, and it is shown that these dynamics have different roots: firstly, neglecting climate change and systems dynamics, the development of hazard scenarios is based on the static approach of design events. Secondly, due to economic development and population dynamics, the elements at risk exposed are subject to spatial and temporal changes. These issues are discussed with respect to temporal and spatial demands. As a result, it is shown how risk is dynamic on a long-term and short-term scale, which has to be acknowledged in the risk concept if this concept is targeted at a sustainable development of mountain regions. A conceptual model is presented that can be used for dynamical risk assessment, and it is shown by different management strategies how this model may be converted into practice. Furthermore, the interconnectedness and interaction between hazard and risk are addressed in order to enhance prevention, the level of protection and the degree of preparedness.

A pilot test of the new Swiss regulatory procedure for categorizing clinical trials by risk: A randomized controlled trial

BACKGROUND/AIMS Several countries are working to adapt clinical trial regulations to align the approval process to the level of risk for trial participants. The optimal framework to categorize clinical trials according to risk remains unclear, however. Switzerland is the first European country to adopt a risk-based categorization procedure in January 2014. We assessed how accurately and consistently clinical trials are categorized using two different approaches: an approach using criteria set forth in the new law (concept) or an intuitive approach (ad hoc). METHODS This was a randomized controlled trial with a method-comparison study nested in each arm. We used clinical trial protocols from eight Swiss ethics committees approved between 2010 and 2011. Protocols were randomly assigned to be categorized in one of three risk categories using the concept or the ad hoc approach. Each protocol was independently categorized by the trial's sponsor, a group of experts and the approving ethics committee. The primary outcome was the difference in categorization agreement between the expert group and sponsors across arms. Linear weighted kappa was used to quantify agreements, with the difference between kappas being the primary effect measure. RESULTS We included 142 of 231 protocols in the final analysis (concept = 78; ad hoc = 64). Raw agreement between the expert group and sponsors was 0.74 in the concept and 0.78 in the ad hoc arm. Chance-corrected agreement was higher in the ad hoc (kappa: 0.34 (95% confidence interval = 0.10-0.58)) than in the concept arm (0.27 (0.06-0.50)), but the difference was not significant (p = 0.67). LIMITATIONS The main limitation was the large number of protocols excluded from the analysis mostly because they did not fit with the clinical trial definition of the new law. CONCLUSION A structured risk categorization approach was not better than an ad hoc approach. Laws introducing risk-based approaches should provide guidelines, examples and templates to ensure correct application.

Temporal variability of damage potential on roads as a conceptual contribution towards a short-term avalanche risk simulation

The fatality risk caused by avalanches on road networks can be analysed using a long-term approach, resulting in a mean value of risk, and with emphasis on short-term fluctuations due to the temporal variability of both, the hazard potential and the damage potential. In this study, the approach for analysing the long-term fatality risk has been adapted by modelling the highly variable short-term risk. The emphasis was on the temporal variability of the damage potential and the related risk peaks. For defined hazard scenarios resulting from classified amounts of snow accumulation, the fatality risk was calculated by modelling the hazard potential and observing the traffic volume. The avalanche occurrence probability was calculated using a statistical relationship between new snow height and observed avalanche releases. The number of persons at risk was determined from the recorded traffic density. The method resulted in a value for the fatality risk within the observed time frame for the studied road segment. The long-term fatality risk due to snow avalanches as well as the short-term fatality risk was compared to the average fatality risk due to traffic accidents. The application of the method had shown that the long-term avalanche risk is lower than the fatality risk due to traffic accidents. The analyses of short-term avalanche-induced fatality risk provided risk peaks that were 50 times higher than the statistical accident risk. Apart from situations with high hazard level and high traffic density, risk peaks result from both, a high hazard level combined with a low traffic density and a high traffic density combined with a low hazard level. This provided evidence for the importance of the temporal variability of the damage potential for risk simulations on road networks. The assumed dependence of the risk calculation on the sum of precipitation within three days is a simplified model. Thus, further research is needed for an improved determination of the diurnal avalanche probability. Nevertheless, the presented approach may contribute as a conceptual step towards a risk-based decision-making in risk management.

Model for ranking freshwater fish farms according to their risk of infection and illustration for viral haemorrhagic septicaemia

We developed a model to calculate a quantitative risk score for individual aquaculture sites. The score indicates the risk of the site being infected with a specific fish pathogen (viral haemorrhagic septicaemia virus (VHSV); infectious haematopoietic necrosis virus, Koi herpes virus), and is intended to be used for risk ranking sites to support surveillance for demonstration of zone or member state freedom from these pathogens. The inputs to the model include a range of quantitative and qualitative estimates of risk factors organised into five risk themes (1) Live fish and egg movements; (2) Exposure via water; (3) On-site processing; (4) Short-distance mechanical transmission; (5) Distance-independent mechanical transmission. The calculated risk score for an individual aquaculture site is a value between zero and one and is intended to indicate the risk of a site relative to the risk of other sites (thereby allowing ranking). The model was applied to evaluate 76 rainbow trout farms in 3 countries (42 from England, 32 from Italy and 2 from Switzerland) with the aim to establish their risk of being infected with VHSV. Risk scores for farms in England and Italy showed great variation, clearly enabling ranking. Scores ranged from 0.002 to 0.254 (mean score 0.080) in England and 0.011 to 0.778 (mean of 0.130) for Italy, reflecting the diversity of infection status of farms in these countries. Requirements for broader application of the model are discussed. Cost efficient farm data collection is important to realise the benefits from a risk-based approach.

Risk Factors for Whole Carcass Condemnations in the Swiss Slaughter Cattle Population

We used meat-inspection data collected over a period of three years in Switzerland to evaluate slaughterhouse-level, farm-level and animal-level factors that may be associated with whole carcass condemnation (WCC) in cattle after slaughter. The objective of this study was to identify WCC risk factors so they can be communicated to, and managed by, the slaughter industry and veterinary services. During meat inspection, there were three main important predictors of the risk of WCC; the slaughtered animal's sex, age, and the size of the slaughterhouse it was processed in. WCC for injuries and significant weight loss (visible welfare indicators) were almost exclusive to smaller slaughterhouses. Cattle exhibiting clinical syndromes that were not externally visible (e.g. pneumonia lesions) and that are associated with fattening of cattle, end up in larger slaughterhouses. For this reason, it is important for animal health surveillance to collect data from both types of slaughterhouses. Other important risk factors for WCC were on-farm mortality rate and the number of cattle on the farm of origin. This study highlights the fact that the many risk factors for WCC are as complex as the production system itself, with risk factors interacting with one another in ways which are sometimes difficult to interpret biologically. Risk-based surveillance aimed at farms with reoccurring health problems (e.g. a history of above average condemnation rates) may be more appropriate than the selection, of higher-risk animals arriving at slaughter. In Switzerland, the introduction of a benchmarking system that would provide feedback to the farmer with information on condemnation reasons, and his/her performance compared to the national/regional average could be a first step towards improving herd-management and financial returns for producers.

ENVIRONMENTAL EXPOSURES AND LUNG CANCER RISK AMONG WOMEN IN HARRIS COUNTY, TEXAS, 1977-1980 (OCCUPATION, SMOKING, BYSTANDER)

Excessively high, accelerating lung cancer rates among women in Harris County, Texas, prompted this case-comparison study. Objectives were to compare patterns of employment, indirect exposures, and sociodemographic variables of lung cancer cases with comparison subjects (compeers) after standardizing for possible confounders, such as age and cigarette smoking. Lung cancer cases were microscopically confirmed, white, Harris County residents. Compeers, chosen from Medicare records and Texas Department of Public Safety records, were matched on gender, race, age, resident and vital status. Personal interviews were conducted with study subjects or next-of-kin. Industries and occupations were categorized as high risk, based on previous studies.^ Almost all cases (95.0%) and 60.0% of compeers smoked cigarettes. The odds ratio for lung cancer and smoking is 13.9. Stopping smoking between ages 30-50 years carries a lower risk than stopping at age 58 or more years. Women's employment in a high risk industry or occupation results in consistently elevated, smoking-adjusted odds ratios. Frequency and duration of employment demonstrate a moderate dose-response effect. A temporal association exists with employment in a high risk occupation during 1940-1949.^ No increased risk appeared with passive smoking. Husband's employment in a construction industry or a structural occupation significantly increased the smoking-adjusted odds ratios among cases and compeers (O.R. = 2.9, 2.2). Smoking-adjusted odds ratios increased significantly when women had resided with persons employed in cement (O.R. = 3.2) or insulation (O.R. = 5.5) manufacturing, or a high rise construction industry (O.R. = 2.4). A family history of lung cancer resulted in a two-fold increase in smoking-adjusted odds ratios. Vital status of compeers affected the odds ratios.^ Work-related exposures appear to increase the risk of lung cancer in women although cigarette smoking has the single highest odds ratio. Indirect exposure to certain employment also plays a significant role in lung cancer in women. Investigations of specific direct and indirect hazardous exposures in the workplace and home are needed. Cigarette smoking is as hazardous for women as for men. Smoking should be prevented and eliminated. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

EPANET Input Files of New York tunnels and Pacific City used in a metamodel-based optimization study

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.