In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

Portal levels of latent transforming growth factor-β are related to liver function in patients with liver cirrhosis

Transforming growth factor-β1 (TGFβ1) is a short-lived immune suppressive and profibrotic protein. Its latent precursor is relatively stable and may even protect from fibrosis. Latent TGFβ1 is synthesized by various tissues including the liver and portal, hepatic, and systemic concentrations of latent TGFβ1 were determined in patients with liver cirrhosis and patients with normal liver function to find out whether circulating levels are affected by liver disease.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Modeling the Volatility of Real GDP Growth: The Case of Japan Revisited

Previous studies (e.g., Hamori, 2000; Ho and Tsui, 2003; Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perronâs (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Molecular dynamics modeling and simulation of void growth in two dimensions

The mechanisms of growth of a circular void by plastic deformation were studied by means of molecular dynamics in two dimensions (2D). While previous molecular dynamics (MD) simulations in three dimensions (3D) have been limited to small voids (up to ≈10 nm in radius), this strategy allows us to study the behavior of voids of up to 100 nm in radius. MD simulations showed that plastic deformation was triggered by the nucleation of dislocations at the atomic steps of the void surface in the whole range of void sizes studied. The yield stress, defined as stress necessary to nucleate stable dislocations, decreased with temperature, but the void growth rate was not very sensitive to this parameter. Simulations under uniaxial tension, uniaxial deformation and biaxial deformation showed that the void growth rate increased very rapidly with multiaxiality but it did not depend on the initial void radius. These results were compared with previous 3D MD and 2D dislocation dynamics simulations to establish a map of mechanisms and size effects for plastic void growth in crystalline solids.

Gene therapy in allergic encephalomyelitis using myelin basic protein-specific T cells engineered to express latent transforming growth factor-β1

A myelin basic protein (MBP)-specific BALB/c T helper 1 (Th1) clone was transduced with cDNA for murine latent transforming growth factor-β1 (TGF-β1) by coculture with fibroblasts producing a genetically engineered retrovirus. When SJL x BALB/c F1 mice, immunized 12–15 days earlier with proteolipid protein in complete Freund’s adjuvant, were injected with 3 × 106 cells from MBP-activated untransduced cloned Th1 cells, the severity of experimental allergic encephalomyelitis (EAE) was slightly increased. In contrast, MBP-activated (but not resting) latent TGF-β1-transduced T cells significantly delayed and ameliorated EAE development. This protective effect was negated by simultaneously injected anti-TGF-β1. The transduced cells secreted 2–4 ng/ml of latent TGF-β1 into their culture medium, whereas control cells secreted barely detectable amounts. mRNA profiles for tumor necrosis factor, lymphotoxin, and interferon-γ were similar before and after transduction; interleukin-4 and -10 were absent. TGF-β1-transduced and antigen-activated BALB/c Th1 clones, specific for hemocyanin or ovalbumin, did not ameliorate EAE. Spinal cords from mice, taken 12 days after receiving TGF-β1-transduced, antigen-activated cells, contained detectable amounts of TGF-β1 cDNA. We conclude that latent TGF-β1-transduced, self-reactive T cell clones may be useful in the therapy of autoimmune diseases.

The Epstein–Barr virus oncogene product latent membrane protein 1 engages the tumor necrosis factor receptor-associated death domain protein to mediate B lymphocyte growth transformation and activate NF-κB

The Epstein–Barr virus latent membrane protein 1 (LMP1) is essential for the transformation of B lymphocytes into lymphoblastoid cell lines. Previous data are consistent with a model that LMP1 is a constitutively activated receptor that transduces signals for transformation through its carboxyl-terminal cytoplasmic tail. One transformation effector site (TES1), located within the membrane proximal 45 residues of the cytoplasmic tail, constitutively engages tumor necrosis factor receptor-associated factors. Signals from TES1 are sufficient to drive initial proliferation of infected resting B lymphocytes, but most lymphoblastoid cells infected with a virus that does not express the 155 residues beyond TES1 fail to grow as long-term cell lines. We now find that mutating two tyrosines to an isoleucine at the carboxyl end of the cytoplasmic tail cripples the ability of EBV to cause lymphoblastoid cell outgrowth, thereby marking a second transformation effector site, TES2. A yeast two-hybrid screen identified TES2 interacting proteins, including the tumor necrosis factor receptor-associated death domain protein (TRADD). TRADD was the only protein that interacted with wild-type TES2 and not with isoleucine-mutated TES2. TRADD associated with wild-type LMP1 but not with isoleucine-mutated LMP1 in mammalian cells, and TRADD constitutively associated with LMP1 in EBV-transformed cells. In transfection assays, TRADD and TES2 synergistically mediated high-level NF-κB activation. These results indicate that LMP1 appropriates TRADD to enable efficient long-term lymphoblastoid cell outgrowth. High-level NF-κB activation also appears to be a critical component of long-term outgrowth.

Interactions between Growth Factors and Integrins: Latent Forms of Transforming Growth Factor-β Are Ligands for the Integrin αvβ1

The multipotential cytokine transforming growth factor-β (TGF-β) is secreted in a latent form. Latency results from the noncovalent association of TGF-β with its processed propeptide dimer, called the latency-associated peptide (LAP); the complex of the two proteins is termed the small latent complex. Disulfide bonding between LAP and latent TGF-β–binding protein (LTBP) produces the most common form of latent TGF-β, the large latent complex. The extracellular matrix (ECM) modulates the activity of TGF-β. LTBP and the LAP propeptides of TGF-β (isoforms 1 and 3), like many ECM proteins, contain the common integrin-binding sequence RGD. To increase our understanding of latent TGF-β function in the ECM, we determined whether latent TGF-β1 interacts with integrins. A549 cells adhered and spread on plastic coated with LAP, small latent complex, and large latent complex but not on LTBP-coated plastic. Adhesion was blocked by an RGD peptide, and cells were unable to attach to a mutant form of recombinant LAP lacking the RGD sequence. Adhesion was also blocked by mAbs to integrin subunits αv and β1. We purified LAP-binding integrins from extracts of A549 cells using LAP bound to Sepharose. αvβ1 eluted with EDTA. After purification in the presence of Mn2+, a small amount of αvβ5 was also detected. A549 cells migrated equally on fibronectin- and LAP-coated surfaces; migration on LAP was αvβ1 dependent. These results establish αvβ1 as a LAP-β1 receptor. Interactions between latent TGF-β and αvβ1 may localize latent TGF-β to the surface of specific cells and may allow the TGF-β1 gene product to initiate signals by both TGF-β receptor and integrin pathways.

Induction of cyclooxygenase-2 by Epstein–Barr virus latent membrane protein 1 is involved in vascular endothelial growth factor production in nasopharyngeal carcinoma cells

Cyclooxygenase-2 (COX-2) is an inducible form of COX and is overexpressed in diverse tumors, raising the possibility of a role for COX-2 in carcinogenesis. In addition, COX-2 contributes to angiogenesis. The Epstein–Barr virus (EBV) oncoprotein, latent membrane protein 1 (LMP1), is detected in at least 70% of nasopharyngeal carcinoma (NPC) and all EBV-infected preinvasive nasopharyngeal lesions. We found that in specimens of LMP1-positive NPC, COX-2 is frequently expressed, whereas LMP1-negative NPC rarely express the enzyme. We next found that expression of LMP1 in EBV-negative nasopharyngeal epithelial cells induced COX-2 expression. Coexpression of IκBα(S32A/S36A), which is not phosphorylated and prevents NF-κB activation, with LMP1 showed that NF-κB is essential for induction of COX-2 by LMP1. We also demonstrate that NF-κB is involved in LMP1-induced cox-2 promoter activity with the use of reporter assays. Two major regions of LMP1, designated CTAR1 and CTAR2, are signal-transducing domains of LMP1. Constructs expressing either CTAR1 or CTAR2 induce COX-2 but to a lesser extent than wild-type LMP1, consistent with the ability of both regions to activate NF-κB. Furthermore, we demonstrate that LMP1-induced COX-2 is functional because LMP1 increased production of prostaglandin E2 in a COX-2-dependent manner. Finally, we demonstrate that LMP1 increased production of vascular endothelial growth factor (VEGF). Treatment of LMP1-expressing cells with the COX-2-specific inhibitor (NS-398) dramatically decreased production of VEGF, suggesting that LMP1-induced VEGF production is mediated, at least in part, by COX-2. These results suggest that COX-2 induction by LMP1 may play a role in angiogenesis in NPC.

Modeling of domain growth activity in polycrystalline ferrites.

"Supported in part by Contract No. U.S. AEC AT(11-1)-1018."

Examining Gender Differences in Writing Skill with Latent Factor Modeling

Thesis (Master's)--University of Washington, 2016-06