Statistical methods for multi-marker testing in genetic association studies

As the development of genotyping and next-generation sequencing technologies, multi-marker testing in genome-wide association study and rare variant association study became active research areas in statistical genetics. This dissertation contains three methodologies for association study by exploring different genetic data features and demonstrates how to use those methods to test genetic association hypothesis. The methods can be categorized into in three scenarios: 1) multi-marker testing for strong Linkage Disequilibrium regions, 2) multi-marker testing for family-based association studies, 3) multi-marker testing for rare variant association study. I also discussed the advantage of using these methods and demonstrated its power by simulation studies and applications to real genetic data.

Design, Simulation und Erprobung einer aktiven Sitzaufhängung für Nutzfahrzeuge

Nutzfahrzeuge müssen oft durch sehr unebenes Gelände gefahren werden. In diesem Fall wird der Fahrer starken Vibrationen ausgesetzt, die von der Fahrzeugkarosserie durch die Sitzaufhängung auf ihn wirken. Um diese Schwingungen zu verringern, werden die Sitzaufhängungen in der Regel mit Feder-Dämpfer-Systemen ausgerüstet. Jedoch erreichen die passiven Systeme vor allem bei niederfrequenten Schwingungen ihre physikalischen Grenzen. Eine wesentliche Verbesserung des Sitzkomforts kann unter solchen Anregungsbedingungen nur mit einer aktiven Sitzaufhängung erreicht werden. In diesem Beitrag wird ein neuartiges aktives System für die Sitzaufhängung auf Basis von elektrorheologischen Flüssigkeiten vorgestellt. Außerdem werden die theoretischen Grundlagen für die Modellierung der beschriebenen aktiven Sitzaufhängung dargestellt. Anschließend werden die Simulationsergebnisse mit den Messergebnissen unter realen Betriebsbedingungen verglichen. Die Repräsentation der Ergebnisse mit Hilfe der im Bereich der Sitztechnik weit verbreiteten SEAT-Werten (Seat Effective Amplitude Transmissibility) zeigt das Potenzial des entwickelten Systems zur aktiven Reduktion der Schwingungsbelastung des Fahrers und ermöglicht seine objektive Bewertung.

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

A simulation study of the effects of small center enrollment in multi -center clinical trials

Multi-center clinical trials are very common in the development of new drugs and devices. One concern in such trials, is the effect of individual investigational sites enrolling small numbers of patients on the overall result. Can the presence of small centers cause an ineffective treatment to appear effective when treatment-by-center interaction is not statistically significant?^ In this research, simulations are used to study the effect that centers enrolling few patients may have on the analysis of clinical trial data. A multi-center clinical trial with 20 sites is simulated to investigate the effect of a new treatment in comparison to a placebo treatment. Twelve of these 20 investigational sites are considered small, each enrolling less than four patients per treatment group. Three clinical trials are simulated with sample sizes of 100, 170 and 300. The simulated data is generated with various characteristics, one in which treatment should be considered effective and another where treatment is not effective. Qualitative interactions are also produced within the small sites to further investigate the effect of small centers under various conditions.^ Standard analysis of variance methods and the "sometimes-pool" testing procedure are applied to the simulated data. One model investigates treatment and center effect and treatment-by-center interaction. Another model investigates treatment effect alone. These analyses are used to determine the power to detect treatment-by-center interactions, and the probability of type I error.^ We find it is difficult to detect treatment-by-center interactions when only a few investigational sites enrolling a limited number of patients participate in the interaction. However, we find no increased risk of type I error in these situations. In a pooled analysis, when the treatment is not effective, the probability of finding a significant treatment effect in the absence of significant treatment-by-center interaction is well within standard limits of type I error. ^

An open-source simulation tool of grid-connected PV systems.

This paper will present an open-source simulation tool, which is being developed in the frame of an European research project1. The tool, whose final version will be freely available through a website, allows the modelling and the design of different types of grid-connected PV systems, such as large grid-connected plants and building-integrated installations. The tool is based on previous software developed by the IES-UPM2, whose models and energy losses scenarios have been validated in the commissioning of PV projects3 carried out in Spain, Portugal, France and Italy, whose aggregated capacity is nearly 300MW. This link between design and commissioning is one of the key points of tool presented here, which is not usually addressed by present commercial software. The tool provides, among other simulation results, the energy yield, the analysis and breakdown of energy losses, and the estimations of financial returns adapted to the legal and financial frameworks of each European country. Besides, educational facilities will be developed and integrated in the tool, not only devoted to learn how to use this software, but also to train the users on the best design PV systems practices. The tool will also include the recommendation of several PV community experts, which have been invited to identify present necessities in the field of PV systems simulation. For example, the possibility of using meteorological forecasts as input data, or modelling the integration of large energy storage systems, such as vanadium redox or lithium-ion batteries. Finally, it is worth mentioning that during the verification and testing stages of this software development, it will be also open to the suggestions received from the different actors of the PV community, such as promoters, installers, consultants, etc.

Multiscale Modeling of Composites: Toward Virtual Testing ... and Beyond

Recent developments in the area of multiscale modeling of fiber-reinforced polymers are presented. The overall strategy takes advantage of the separa-tion of length scales between different entities (ply, laminate, and component) found in composite structures. This allows us to carry out multiscale modeling by computing the properties of one entity (e.g., individual plies) at the relevant length scale, homogenizing the results into a constitutive model, and passing this information to the next length scale to determine the mechanical behavior of the larger entity (e.g., laminate). As a result, high-fidelity numerical sim-ulations of the mechanical behavior of composite coupons and small compo-nents are nowadays feasible starting from the matrix, fiber, and interface properties and spatial distribution. Finally, the roadmap is outlined for extending the current strategy to include functional properties and processing into the simulation scheme.

Software tool for simulation of aircraft trajectories

New concepts in air navigation have been introduced recently. Among others, are the concepts of trajectory optimization, 4D trajectories, RBT (Reference Business Trajectory), TBO (trajectory based operations), CDA (Continuous Descent Approach) and ACDA (Advanced CDA), conflict resolution, arrival time (AMAN), introduction of new aircraft (UAVs, UASs) in air space, etc. Although some of these concepts are new, the future Air Traffic Management will maintain the four ATM key performance areas such as Safety, Capacity, Efficiency, and Environmental impact. So much, the performance of the ATM system is directly related to the accuracy with which the future evolution of the traffic can be predicted. In this sense, future air traffic management will require a variety of support tools to provide suitable help to users and engineers involved in the air space management. Most of these tools are based on an appropriate trajectory prediction module as main component. Therefore, the purposes of these tools are related with testing and evaluation of any air navigation concept before they become fully operative. The aim of this paper is to provide an overview to the design of a software tool useful to estimate aircraft trajectories adapted to air navigation concepts. Other usage of the tool, like controller design, vertical navigation assessment, procedures validation and hardware and software in the loop are available in the software tool. The paper will show the process followed to design the tool, the software modules needed to perform accurately and the process followed to validate the output data.

Design, development and construction of an outdoor testing facility for semi-transparent photovoltaic modules

Building-integrated Photovoltaics (BIPV) is one of the most promising technologies enabling buildings to generate on-site part of their electricity needs while performing architectural functionalities. A clear example of BIPV products consists of semi-transparent photovoltaic modules (STPV), designed to replace the conventional glazing solutions in building façades. Accordingly, the active building envelope is required to perform multiple requirements such as provide solar shading to avoid overheating, supply solar gains and thermal insulation to reduce heat loads and improve daylight utilization. To date, various studies into STPV systems have focused on their energy performance based on existing simulation programs, or on the modelling, normally validated by limited experimental data, of the STPV modules thermal behaviour. Taking into account that very limited experimental research has been conducted on the energy performance of STPV elements and that the characterization in real operation conditions is necessary to promote an energetically efficient integration of this technology in the building envelope, an outdoor testing facility has been designed, developed and built at the Solar Energy Institute of the Technical University of Madrid. In this work, the methodology used in the definition of the testing facility, its capability and limitations are presented and discussed.

A simulation environment for bio-inspired heterogeneous chained modular robots

This paper presents a new simulation environment aimed at heterogeneous chained modular robots. This simulator allows testing the feasibility of the design, checking how modules are going to perform in the field and verifying hardware, electronics and communication designs before the prototype is built, saving time and resources. The paper shows how the simulator is built and how it can be set up to adapt to new designs. It also gives some examples of its use showing different heterogeneous modular robots running in different environments.

Development of the infrastructure for a multi-agent traffic management system simulation

This document contains detailed description of the design and the implementation of a multi-agent application controlling traffic lights in a city together with a system for simulating traffic and testing. The goal of this thesis is to design and build a simplified intelligent and distributed solution to the problem with the traffic in the big cities following different good practices in order to allow future refining of the model of the real world. The problem of the traffic in the big cities is still a problem that cannot be solved. Not only is the increasing number of cars a reason for the traffic jams, but also the way the traffic is organized. Usually, the intersections with traffic lights are replaced by roundabouts or interchanges to increase the number of cars that can cross the intersection in certain time. But still there are places where the infrastructure cannot be changed and the traffic light semaphores are the only way to control the car flows. In real life, the traffic lights have a predefined plan for change or they receive information from a centralized system when and how they have to change. But what if the traffic lights can cooperate and decide on their own when and how to change? Using this problem, the purpose of the thesis is to explore different agent-based software engineering approaches to design and build a non-conventional distributed system. From the software engineering point of view, the goal of the thesis is to apply the knowledge and use the skills, acquired during the various courses of the master program in Software Engineering, while solving a practical and complex problem such as the traffic in the cities.

Testing Independence in High Dimensions & Identifiability of Graphical Models

Thesis (Ph.D.)--University of Washington, 2016-06

Is cannabis a gateway drug? Testing hypotheses about the relationship between cannabis use and the use of other illicit drugs

We outline and evaluate competing explanations of three relationships that have consistently been found between cannabis use and the use of other illicit drugs, namely, ( 1) that cannabis use typically precedes the use of other illicit drugs; and that ( 2) the earlier cannabis is used, and ( 3) the more regularly it is used, the more likely a young person is to use other illicit drugs. We consider three major competing explanations of these patterns: ( 1) that the relationship is due to the fact that there is a shared illicit market for cannabis and other drugs which makes it more likely that other illicit drugs will be used if cannabis is used; ( 2) that they are explained by the characteristics of those who use cannabis; and ( 3) that they reflect a causal relationship in which the pharmacological effects of cannabis on brain function increase the likelihood of using other illicit drugs. These explanations are evaluated in the light of evidence from longitudinal epidemiological studies, simulation studies, discordant twin studies and animal studies. The available evidence indicates that the association reflects in part but is not wholly explained by: ( 1) the selective recruitment to heavy cannabis use of persons with pre-existing traits ( that may be in part genetic) that predispose to the use of a variety of different drugs; ( 2) the affiliation of cannabis users with drug using peers in settings that provide more opportunities to use other illicit drugs at an earlier age; ( 3) supported by socialisation into an illicit drug subculture with favourable attitudes towards the use of other illicit drugs. Animal studies have raised the possibility that regular cannabis use may have pharmacological effects on brain function that increase the likelihood of using other drugs. We conclude with suggestions for the type of research studies that will enable a decision to be made about the relative contributions that social context, individual characteristics, and drug effects make to the relationship between cannabis use and the use of other drugs.