Cloud computing allows access to a collection of computing resources that can be easily provisioned, configured as well as released on-demand with minimum cost and effort. OpenStack is an open source cloud management platform aimed at providing public or private IaaS cloud on standard hardware. Since, deploying OpenStack manually is tedious and time-consuming, several tools that automate the deployment of OpenStack are available. Usually, cloud administrators choose a tool based on its level of automation, ease of use or interoperability with existing tools used by them. However, another desired factor while choosing a deployment tool is its deployment speed. Cloud admins cannot select based on this factor since, there is no previous work done on the comparison of deployment tools based on deployment time. This thesis aims to address this issue. The main aim of the thesis is to evaluate the performance of OpenStack deployment tools with respect to operating system provisioning and OpenStack deployment time, on physical servers. Furthermore, the effect of varying number of nodes, OpenStack architecture deployed and resources (cores and RAM) provided to deployment node on provisioning and deployment times, is also analyzed. Also, the tools are classified based on stages of deployment and method of deploying OpenStack services. In this thesis we evaluate the performance of MAAS, Foreman, Mirantis Fuel and Canonical Autopilot. The performance of the tools is measured via experimental research method. Operating system provisioning time and OpenStack deployment times are measured while varying the number of nodes/ OpenStack architecture and resources provided to deployment node i.e. cores and RAM. Results show that provisioning time of MAAS is less than Mirantis Fuel which is less than Foreman for all node scenarios and resources cases considered. Furthermore, for all 3 tools as number of nodes increases provisioning time increases. However, the amount of increase is lowest for MAAS than Mirantis Fuel and Foreman. Similarly, results for bare metal OpenStack deployment time show that, Canonical Autopilot outperforms Mirantis Fuel by a significant difference for all OpenStack scenarios and resources cases considered. Furthermore, as number of nodes in an OpenStack scenario as well as its complexity increases, the deployment time for both the tools increases. From the research, it is concluded that MAAS and Canonical Autopilot perform better as provisioning and bare metal OpenStack deployment tool respectively, than other tools that have been analyzed. Furthermore, from the analysis it can be concluded that increase in number of nodes/ OpenStack architecture, leads to an increase in both provisioning time and OpenStack deployment time for all the tools. Finally, after analyzing the results the tools are classified based on the method of deploying OpenStack services i.e. parallel or role-wise parallel.

The TAK1 and canonical IKK complexes are the two master protein kinases of the innate immune system that control the production of inflammatory mediators, but the mechanisms by which they are activated in this system are still unclear. In this thesis, I present the research I have carried out to solve these problems. The IKKb component of the canonical IKK complex is required to activate the transcription factors NF-kB and IRF5 and the protein kinase Tpl2, but how IKKβ itself is activated in vivo is still unclear. It was found to require phosphorylation by one or more ‘upstream’ protein kinases in some reports, but by autophosphorylation in others. In the first part of this thesis, I describe my work that has resolved this controversy by demonstrating that the activation of IKKb induced by IL-1 (interleukin-1) or TNF (tumour necrosis factor) in embryonic fibroblasts, or by ligands that activate Toll-like receptors in macrophages, requires two distinct phosphorylation events: first, the TAK1 catalysed phosphorylation of Ser177 and, secondly, the IKKb-catalysed autophosphorylation of Ser181. The phosphorylation of Ser177 by TAK1 is a priming event required for the subsequent autophosphorylation of Ser181, which enables IKKk to phosphorylate exogenous substrates. I also present genetic evidence which indicates that the IL-1-stimulated, LUBAC (linear ubiquitin chain assembly complex)-catalysed formation of Met1-linked/linear ubiquitin (Met1-Ub) chains and their interaction with the NEMO (NF-kB essential modulator) component of the canonical IKK complex permits the TAK1-catalysed priming phosphorylation of IKKb at Ser177 and IKKa at Ser176. These findings may be of general significance for the activation of other protein kinases. The activation of the TAK1 complex by inflammatory stimuli is thought to be triggered by the binding of Lys63-linked ubiquitin chains to the TAB2 or TAB3 components of the TAB1-TAK1-TAB2 and TAB1-TAK1-TAB3 complexes. In the second part of the thesis I tested whether this broadly accepted model was correct by knocking out the genes encoding TAK1 and its regulatory subunits TAB1, TAB2 and TAB3 by CRISPR/Cas9 gene-editing technology, alone and in combination, in an IL-1 receptor expressing human cell line. These genetic studies led me to discover that the IL-1-dependent activation of TAK1 occurs by two different mechanisms. The first, involves the previously described interaction of Lys63-linked ubiquitin chains with TAB2 and TAB3, while the second can take place in the complete absence of TAB2 and TAB3. The second mechanism, which involves activation of the TAB1-TAK1 heterodimer is more transient than the first, but is sufficient for the IL-1-dependent transcription of immediate early genes (A20, IkBa). I show that the activation of the TAB1-TAK1 complex requires the expression of the E3 ubiquitin ligase TRAF6 and the TRAF6-generated formation of Lys63-linked ubiquitin chains, which leads to the phosphorylation of TAK1 at Thr187 and activation. However, neither TAB1 nor TAK1 bind directly to Lys63-linked ubiquitin chains. I identify one novel IL-1-dependent phosphorylation site on TAB1 and two on TAK1 and propose that Lys63-linked ubiquitin chains activate an as yet unidentified protein kinase, which phosphorylates one or more of the novel phosphorylation sites on the TAB1-TAK1 heterodimer inducing a conformational change that permits TAK1 to autophosphorylate Thr187.

The Laguna Madre is a hypersaline lagoon. Despite harsh conditions, the upper Laguna Madre (ULM) is a highly productive ecosystem and a popular sportfishing area, especially for spotted seatrout and red drum. It is also the most important Texas bay for commercial fishing of black drum. TPWD’s Coastal Fisheries division began conducting routine monitoring of coastal fishery resources in 1977 to guide management. The goal of the present study was to improve understanding of spatiotemporal trends in relative abundance of selected fish and macro-invertebrate species in the upper Laguna Madre. I used TPWD’s bag-seine and water-quality data from the years 1985-2004 to examine variation in species’ relative abundances and relationships to several environmental factors. I hypothesized that one or more of these variables, alone or in combination, were related to spatial and temporal trends in community composition. I used detrended correspondence analysis (DCA) to measure species turnover (beta diversity) and to determine which model (linear or unimodal) of species response along a gradient to apply. I used canonical correspondence analysis to relate species abundances directly to explanatory variables. The explanatory variables were tested for significance and the variance partitioned among three groupings: temporal, spatial and environmental. DCA indicated complete species turnover along two dimensions: seasonal and spatial. It also indicated that a unimodal method such as CCA was appropriate for further analysis. The CCA model included 39 variables. The included variables explained 14% of the variation in species abundance in the data set. Since the first four axes explained 67% of the variation contained in the first two DCA axes, the chosen explanatory variables were sufficient to explain the majority of the tractable variation in species abundance. The variance partitioning procedure indicated that temporal effects were the most important in explaining species variation in the Upper Laguna, followed by the spatial component. The pure environmental component explained the least amount of variation. In this study, much of the variability in species abundance was due to the spawning patterns of estuary-dependent species, most of which spawn in the spring and summer months, leading to higher abundances from spring through fall.

Canonical Wnt signaling is involved in many aspects of development including axis specification and anterior-posterior neuroectoderm formation during vertebrate embryogenesis. Axin2, a homologue of Axin1, is thought to have a similar regulatory role within the cell, but differences in their expression and binding partners suggest Axin2 is not completely redundant with Axin1. To better understand Axin2 in canonical Wnt signaling, I utilized several approaches to explore its expression and function. In the zebrafish embryo, I found Axin2 is expressed in known active domains of Wnt signaling, suggesting an inducible regulatory role. Additionally, canonical Wnt signaling was sufficient and necessary to induce Axin2 expression and Axin2 was sufficient and necessary to inhibit Wnt signaling. As Wnt signaling is important in development and its dysregulation has been implicated in diseases such as colorectal cancer, this study helps advance our understanding of how Wnt signaling regulates itself through the use of negative feedback inhibitors, such as Axin2.

No description available.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1990. / Includes bibliographical references (leaves 94-95) / Microfiche. / vii, 95 leaves, bound 29 cm

Determining protein structure and function experimentally is both costly and time consuming. Transferring function-related protein annotations based on homology-based methods is relatively straightforward for proteins that have sequence identity of more than 40%. However, there are many proteins in the "twilight zone" where sequence similarity with any other protein is very weak, while being structurally similar to several. Such cases require methods that are capable of using and exploiting both sequence and structural similarity. To understand ways of how such methods can and should be designed is the focus of this study. In this thesis, models that use both sequence and structure features are applied on two protein prediction problems that are particularly challenging when relying on sequence alone. Enzyme classification benefits from both kinds of features because on one hand, enzymes can have identical function with limited sequence similarity while on the other hand, proteins with similar fold may have disparate enzyme class annotation. This thesis shows that the full integration of protein sequence and structure-related features (via the use of kernels) automatically places proteins with similar biological properties closer together, leading to superior classification accuracy using Support Vector Machines. Disulfide-bonds link residues in a protein structure, but may appear distant in sequence. Sequence similarity reflecting such structural properties is thus very hard to detect. It is sufficient for the structure to be similar for accurate prediction of disulfide-bonds, but such information is very scarce and predictors that rely on protein structure are not nearly as useful as those operating on sequence alone. This thesis proposes a novel approach based on Kernel Canonical Correlation Analysis that uses structural features during training only. It does so by finding sequence representations that correlate with structural features that are essential for a disulfide bond. The resulting representations enable high prediction accuracy for a range of disulfide-bond problems. The proposed model thus taps the advantage of structural features without requiring protein structure to be available in the prediction process. The merits of this approach should apply to a number of open protein structure prediction problems.

The purpose of the current study was to explore the relationships between Attention-Deficit/Hyperactivity Disorder, depression, and memory impairment in children. It was hypothesized that level of inattention would negatively correlated with performance on measures of visual-spatial short-term memory and verbal memory. Children with greater levels of depressive symptoms were predicted to perform more poorly than less depressed peers on effortful measures of verbal and visual short-term memory, measures of verbal working memory, and measures of verbal long-term memory recall. Results indicated that impaired performance on one measure of visual-spatial short-term memory was related to increased levels of inattention and depression. Impairments were found on measures of verbal long-term memory recall and recognition related to greater attention problems, hyperactivity, and depressive symptoms. These deficits remained significantly related to inattention and hyperactivity beyond a deficit in encoding verbal material.

abstract: Estimating cointegrating relationships requires specific techniques. Canonical correlations are used to determine the rank and space of the cointegrating matrix. The vectors used to transform the data into canonical variables have an eigenvector representation, and the associated canonical correlations have an eigenvalue representation. The number of cointegrating relations is chosen based upon a theoretical difference in the convergence rates of the eignevalues. The number of cointegrating relations is consistently estimated using a threshold function which places a lower bound on the eigenvalues associated with cointegrating relations and an upper bound on the eigenvalues on the eigenvalues not associated with cointegrating relations. The proposed estimator performs better with a large number of cross-sectional observations and moderate time series length. / Dissertation/Thesis / Ph.D. Economics 2012

This paper presents the R package anacor for the computation of simple and canonical correspondence analysis with missing values. The canonical correspondence analysis is specified in a rather general way by imposing covariates on the rows and/or the columns of the two-dimensional frequency table. The package allows for scaling methods such as standard, Benzécri, centroid, and Goodman scaling. In addition, along with well-known two- and three-dimensional joint plots including confidence ellipsoids, it offers alternative plotting possibilities in terms of transformation plots, Benzécri plots, and regression plots.