Effects of an aging vascular model on healthy and diseased hearts

The vitamin D(3) and nicotine (VDN) model is a model of isolated systolic hypertension (ISH) due to arterial calcification raising arterial stiffness and vascular impedance similar to an aged and stiffened arterial tree. We therefore analyzed the impact of this aging model on normal and diseased hearts with myocardial infarction (MI). Wistar rats were treated with VDN (n = 9), subjected to MI by coronary ligation (n = 10), or subjected to a combination of both MI and VDN treatment (VDN/MI, n = 14). A sham-treated group served as control (Ctrl, n = 10). Transthoracic echocardiography was performed every 2 wk, whereas invasive indexes were obtained at week 8 before death. Calcium, collagen, and protein contents were measured in the heart and the aorta. Systolic blood pressure, pulse pressure, thoracic aortic calcium, and end-systolic elastance as an index of myocardial contractility were highest in the aging model group compared with MI and Ctrl groups (P(VDN) < 0.05, 2-way ANOVA). Left ventricular wall stress and brain natriuretic peptide (P(VDNxMI) = not significant) were highest, while ejection fraction, stroke volume, and cardiac output were lowest in the combined group versus all other groups (P(VDNxMI) < 0.05). The combination of ISH due to this aging model and MI demonstrates significant alterations in cardiac function. This model mimics several clinical phenomena of cardiovascular aging and may thus serve to further study novel therapies.

Discrete element methods for asphalt concrete : development and application of user-defined microstructural models and a viscoelastic micromechanical model

As an important Civil Engineering material, asphalt concrete (AC) is commonly used to build road surfaces, airports, and parking lots. With traditional laboratory tests and theoretical equations, it is a challenge to fully understand such a random composite material. Based on the discrete element method (DEM), this research seeks to develop and implement computer models as research approaches for improving understandings of AC microstructure-based mechanics. In this research, three categories of approaches were developed or employed to simulate microstructures of AC materials, namely the randomly-generated models, the idealized models, and image-based models. The image-based models were recommended for accurately predicting AC performance, while the other models were recommended as research tools to obtain deep insight into the AC microstructure-based mechanics. A viscoelastic micromechanical model was developed to capture viscoelastic interactions within the AC microstructure. Four types of constitutive models were built to address the four categories of interactions within an AC specimen. Each of the constitutive models consists of three parts which represent three different interaction behaviors: a stiffness model (force-displace relation), a bonding model (shear and tensile strengths), and a slip model (frictional property). Three techniques were developed to reduce the computational time for AC viscoelastic simulations. It was found that the computational time was significantly reduced to days or hours from years or months for typical three-dimensional models. Dynamic modulus and creep stiffness tests were simulated and methodologies were developed to determine the viscoelastic parameters. It was found that the DE models could successfully predict dynamic modulus, phase angles, and creep stiffness in a wide range of frequencies, temperatures, and time spans. Mineral aggregate morphology characteristics (sphericity, orientation, and angularity) were studied to investigate their impacts on AC creep stiffness. It was found that aggregate characteristics significantly impact creep stiffness. Pavement responses and pavement-vehicle interactions were investigated by simulating pavement sections under a rolling wheel. It was found that wheel acceleration, steadily moving, and deceleration significantly impact contact forces. Additionally, summary and recommendations were provided in the last chapter and part of computer programming codes wree provided in the appendixes.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

Laboratory Model Shaking Table; An Investigation of Defects and Suggested Improvements

The laboratory model is considered in this thesis. Information gained from this investigation has not been trans­ferred to the larger industrial machines. Some of the factors noted concerning the efficiency of the laboratory shaking table are inherent in this small scale model only.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by process-based modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under http://www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws. We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10 m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25 m resolution.

Beyond the tree of texts: Building an empirical model of scribal variation through graph analysis of texts and stemmata

Stemmatology, or the reconstruction of the transmission history of texts, is a field that stands particularly to gain from digital methods. Many scholars already take stemmatic approaches that rely heavily on computational analysis of the collated text (e.g. Robinson and O’Hara 1996; Salemans 2000; Heikkilä 2005; Windram et al. 2008 among many others). Although there is great value in computationally assisted stemmatology, providing as it does a reproducible result and allowing access to the relevant methodological process in related fields such as evolutionary biology, computational stemmatics is not without its critics. The current state-of-the-art effectively forces scholars to choose between a preconceived judgment of the significance of textual differences (the Lachmannian or neo-Lachmannian approach, and the weighted phylogenetic approach) or to make no judgment at all (the unweighted phylogenetic approach). Some basis for judgment of the significance of variation is sorely needed for medieval text criticism in particular. By this, we mean that there is a need for a statistical empirical profile of the text-genealogical significance of the different sorts of variation in different sorts of medieval texts. The rules that apply to copies of Greek and Latin classics may not apply to copies of medieval Dutch story collections; the practices of copying authoritative texts such as the Bible will most likely have been different from the practices of copying the Lives of local saints and other commonly adapted texts. It is nevertheless imperative that we have a consistent, flexible, and analytically tractable model for capturing these phenomena of transmission. In this article, we present a computational model that captures most of the phenomena of text variation, and a method for analysis of one or more stemma hypotheses against the variation model. We apply this method to three ‘artificial traditions’ (i.e. texts copied under laboratory conditions by scholars to study the properties of text variation) and four genuine medieval traditions whose transmission history is known or deduced in varying degrees. Although our findings are necessarily limited by the small number of texts at our disposal, we demonstrate here some of the wide variety of calculations that can be made using our model. Certain of our results call sharply into question the utility of excluding ‘trivial’ variation such as orthographic and spelling changes from stemmatic analysis.

One size fits all? Managing trust and privacy on social networking sites in Russia and Germany

When it comes to platform sustainability, mitigating user privacy concerns and enhancing trust represent two major tasks providers of Social Networking Sites (SNSs) are facing today. State-of-the-art research advocates reliance on the justice-based measures as possible means to address these challenges. However, as providers are increasingly expanding into foreign markets, the effectiveness of these measures in a cross-cultural setting is questioned. In an attempt to address this set of issues, in this study we build on the existing model to examine the impact of culture on the robustness of four justice-based means in mitigating privacy concerns and ensuring trust. Survey responses from German and Russian SNS members are used to evaluate the two structural equation models, which are then compared. We find that perceptions regarding Procedural and Informational Justice are universally important and hence should be addressed as part of the basic strategy by the SNS provider. When expanding to collectivistic countries like Russia, measures enhancing perceptions of Distributive and Interpersonal Justice can be additionally applied. Beyond practical implications, our study makes a significant contribution to the theoretical discourse on the role of culture in determining individual perceptions and behavior.

Investigating the sensitivity of hurricane intensity and trajectory to sea surface temperatures using the regional model WRF

The influence of sea surface temperature (SST) anomalies on the hurricane characteristics are investigated in a set of sensitivity experiments employing the Weather Research and Forecasting (WRF) model. The idealised experiments are performed for the case of Hurricane Katrina in 2005. The first set of sensitivity experiments with basin-wide changes of the SST magnitude shows that the intensity goes along with changes in the SST, i.e., an increase in SST leads to an intensification of Katrina. Additionally, the trajectory is shifted to the west (east), with increasing (decreasing) SSTs. The main reason is a strengthening of the background flow. The second set of experiments investigates the influence of Loop Current eddies idealised by localised SST anomalies. The intensity of Hurricane Katrina is enhanced with increasing SSTs close to the core of a tropical cyclone. Negative nearby SST anomalies reduce the intensity. The trajectory only changes if positive SST anomalies are located west or north of the hurricane centre. In this case the hurricane is attracted by the SST anomaly which causes an additional moisture source and increased vertical winds.

Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis

The responses of carbon dioxide (CO2) and other climate variables to an emission pulse of CO2 into the atmosphere are often used to compute the Global Warming Potential (GWP) and Global Temperature change Potential (GTP), to characterize the response timescales of Earth System models, and to build reduced-form models. In this carbon cycle-climate model intercomparison project, which spans the full model hierarchy, we quantify responses to emission pulses of different magnitudes injected under different conditions. The CO2 response shows the known rapid decline in the first few decades followed by a millennium-scale tail. For a 100 Gt-C emission pulse added to a constant CO2 concentration of 389 ppm, 25 ± 9% is still found in the atmosphere after 1000 yr; the ocean has absorbed 59 ± 12% and the land the remainder (16 ± 14%). The response in global mean surface air temperature is an increase by 0.20 ± 0.12 °C within the first twenty years; thereafter and until year 1000, temperature decreases only slightly, whereas ocean heat content and sea level continue to rise. Our best estimate for the Absolute Global Warming Potential, given by the time-integrated response in CO2 at year 100 multiplied by its radiative efficiency, is 92.5 × 10−15 yr W m−2 per kg-CO2. This value very likely (5 to 95% confidence) lies within the range of (68 to 117) × 10−15 yr W m−2 per kg-CO2. Estimates for time-integrated response in CO2 published in the IPCC First, Second, and Fourth Assessment and our multi-model best estimate all agree within 15% during the first 100 yr. The integrated CO2 response, normalized by the pulse size, is lower for pre-industrial conditions, compared to present day, and lower for smaller pulses than larger pulses. In contrast, the response in temperature, sea level and ocean heat content is less sensitive to these choices. Although, choices in pulse size, background concentration, and model lead to uncertainties, the most important and subjective choice to determine AGWP of CO2 and GWP is the time horizon.

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

A model-data comparison of Holocene timberline changes in the Swiss Alps reveals past and future drivers of mountain forest dynamics

Mountain vegetation is strongly affected by temperature and is expected to shift upwards with climate change. Dynamic vegetation models are often used to assess the impact of climate on vegetation and model output can be compared with paleobotanical data as a reality check. Recent paleoecological studies have revealed regional variation in the upward shift of timberlines in the Northern and Central European Alps in response to rapid warming at the Younger Dryas/Preboreal transition ca. 11700years ago, probably caused by a climatic gradient across the Alps. This contrasts with previous studies that successfully simulated the early Holocene afforestation in the (warmer) Central Alps with a chironomid-inferred temperature reconstruction from the (colder) Northern Alps. We use LandClim, a dynamic landscape vegetation model to simulate mountain forests under different temperature, soil and precipitation scenarios around Iffigsee (2065m a.s.l.) a lake in the Northwestern Swiss Alps, and compare the model output with the paleobotanical records. The model clearly overestimates the upward shift of timberline in a climate scenario that applies chironomid-inferred July-temperature anomalies to all months. However, forest establishment at 9800 cal. BP at Iffigsee is successfully simulated with lower moisture availability and monthly temperatures corrected for stronger seasonality during the early Holocene. The model-data comparison reveals a contraction in the realized niche of Abies alba due to the prominent role of anthropogenic disturbance after ca. 5000 cal. BP, which has important implications for species distribution models (SDMs) that rely on equilibrium with climate and niche stability. Under future climate projections, LandClim indicates a rapid upward shift of mountain vegetation belts by ca. 500m and treeline positions of ca. 2500m a.s.l. by the end of this century. Resulting biodiversity losses in the alpine vegetation belt might be mitigated with low-impact pastoralism to preserve species-rich alpine meadows.

Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model

This study examines how different microphysical parameterization schemes influence orographically induced precipitation and the distributions of hydrometeors and water vapour for midlatitude summer conditions in the Weather Research and Forecasting (WRF) model. A high-resolution two-dimensional idealized simulation is used to assess the differences between the schemes in which a moist air flow is interacting with a bell-shaped 2 km high mountain. Periodic lateral boundary conditions are chosen to recirculate atmospheric water in the domain. It is found that the 13 selected microphysical schemes conserve the water in the model domain. The gain or loss of water is less than 0.81% over a simulation time interval of 61 days. The differences of the microphysical schemes in terms of the distributions of water vapour, hydrometeors and accumulated precipitation are presented and discussed. The Kessler scheme, the only scheme without ice-phase processes, shows final values of cloud liquid water 14 times greater than the other schemes. The differences among the other schemes are not as extreme, but still they differ up to 79% in water vapour, up to 10 times in hydrometeors and up to 64% in accumulated precipitation at the end of the simulation. The microphysical schemes also differ in the surface evaporation rate. The WRF single-moment 3-class scheme has the highest surface evaporation rate compensated by the highest precipitation rate. The different distributions of hydrometeors and water vapour of the microphysical schemes induce differences up to 49 W m−2 in the downwelling shortwave radiation and up to 33 W m−2 in the downwelling longwave radiation.

Development and characterization of a scaffold-free 3D spheroid model of iPSC-derived human cardiomyocytes

Purpose: Cardiomyocytes are terminally differentiated cells in the adult heart and ischemia and cardiotoxic compounds can lead to cell death and irreversible decline of cardiac function. As testing platforms, isolated organs and primary cells from rodents have been the standard in research and toxicology, but there is a need for better models that more faithfully recapitulate native human biology. Hence, a new in vitro model comprising the advantages of 3D cell culture and the availability of induced pluripotent stem cells (iPSC) from human origin was developed and characterized. Methods: Human cardiomyocytes (CMs) derived from induced pluripotent stem cells (iPSCs) were studied in standard 2D culture and as cardiac microtissues (MTs) formed in hanging drops. 2D cultures were examined using immunofluorescence microscopy and Western blotting while the cardiac MTs were subjected to immunofluorescence, contractility, and pharmacological investigations. Results: iPSC-derived CMs in 2D culture showed well-formed myofibrils, cell-cell contacts positive for connexin-43, and other typical cardiac proteins. The cells reacted to pro-hypertrophic growth factors with a substantial increase in myofibrils and sarcomeric proteins. In hanging drop cultures, iPSC-derived cardiomyocytes formed spheroidal MTs within 4 days showing a homogeneous tissue structure with well-developed myofibrils extending throughout the whole spheroid without a necrotic core. MTs showed spontaneous contractions for more than 4 weeks that were recorded by optical motion tracking, sensitive to temperature, and responsive to electrical pacing. Contractile pharmacology was tested with several agents known to modulate cardiac rate and viability. Calcium-transients underlay the contractile activity and were also responsive to electrical stimulation, caffeine-induced Ca2+-release, extracellular calcium levels. Conclusions: 3D culture using iPSC-derived human cardiomyocytes provides an organoid human-based cellular platform that is free of necrosis and recapitulates vital cardiac functionality, thereby providing new and promising relevant model for the evaluation and development of new therapies and detection of cardiotoxicity.

Atmospheric water parameters measured by a ground-based microwave radiometer and compared with the WRF model

The microwave radiometer TROWARA measures integrated water vapour (IWV) and integrated cloud liquid water (ILW) at Bern since 1994 with a time resolution of 7 s. In this study, we compare TROWARA measurements with a simulation of summer 2012 in Switzerland performed with the Weather Research and Forecasting (WRF) model. It is found that the WRF model agrees very well with TROWARA’s IWV variations with a mean bias of only 0.7 mm. The ILW distribution of the WRF model, although similar in shape to TROWARA’s distribution, overestimates the fraction of clear sky periods (83% compared to 60%).

Comparison of a traditional and a multilevel Cox Proportional Hazards model

Hierarchically clustered populations are often encountered in public health research, but the traditional methods used in analyzing this type of data are not always adequate. In the case of survival time data, more appropriate methods have only begun to surface in the last couple of decades. Such methods include multilevel statistical techniques which, although more complicated to implement than traditional methods, are more appropriate. ^ One population that is known to exhibit a hierarchical structure is that of patients who utilize the health care system of the Department of Veterans Affairs where patients are grouped not only by hospital, but also by geographic network (VISN). This project analyzes survival time data sets housed at the Houston Veterans Affairs Medical Center Research Department using two different Cox Proportional Hazards regression models, a traditional model and a multilevel model. VISNs that exhibit significantly higher or lower survival rates than the rest are identified separately for each model. ^ In this particular case, although there are differences in the results of the two models, it is not enough to warrant using the more complex multilevel technique. This is shown by the small estimates of variance associated with levels two and three in the multilevel Cox analysis. Much of the differences that are exhibited in identification of VISNs with high or low survival rates is attributable to computer hardware difficulties rather than to any significant improvements in the model. ^