State of Iowa Space Utilization and Building Study, October 9, 2000

Executive Summary Purposes of this Report: • Recommend the most logical and economical options to address state governmental space needs in the Polk County metropolitan area to the year 2010. • Include building size, location, phasing, financing, method of project delivery and estimated cost. • Develop a software tool to compare costs of leasing vs. ownership of space. Methodology: Identify: 1. Current amount and location of owned and leased space, by agency; 2. Types of space and whether best located on or off of the Capitol Complex; 3. Utilization of space, noting over-crowding and under-utilization; 4. Current number of workstations for full and part time employees, Personnel Employment Organization (PEO) workers, contractors, interns, etc.; and, 5. History of staff levels to assist in the prediction of staff growth. Scope: This report focuses on 10 state-owned buildings located on the Capitol Complex and 48 leased spaces in the Polk County metropolitan area. (See Figures 1 and 2.) • Due to a separate space study under way by the Legislature, implications of area and staff for the State Capitol building are included only for the Governor, Lieutenant Governor, Treasurer, Secretary of State, Auditor and the Department of Management. • Because it is largely a museum building that does not have office space available for other agencies, the area and staff of the Historical Building are not fully addressed. • Only the parking implications of the new Judicial Building are included in this study because the building space is under the jurisdiction of the Judicial Branch and not available for other agencies. Several state-owned buildings are not included in the scope of this report, generally because they have highly focused purposes, and their space is not available for assignment to other agencies. Several leased locations are not included for similar reasons, including leases that do not fall within the authority of the Department of General Services.

Use of human MAR elements to improve retroviral vector production.

Retroviral vectors have many favorable properties for gene therapies, but their use remains limited by safety concerns and/or by relatively lower titers for some of the safer self-inactivating (SIN) derivatives. In this study, we evaluated whether increased production of SIN retroviral vectors can be achieved from the use of matrix attachment region (MAR) epigenetic regulators. Two MAR elements of human origin were found to increase and to stabilize the expression of the green fluorescent protein transgene in stably transfected HEK-293 packaging cells. Introduction of one of these MAR elements in retroviral vector-producing plasmids yielded higher expression of the viral vector RNA. Consistently, viral titers obtained from transient transfection of MAR-containing plasmids were increased up to sixfold as compared with the parental construct, when evaluated in different packaging cell systems and transfection conditions. Thus, use of MAR elements opens new perspectives for the efficient generation of gene therapy vectors.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Molecular basis of selective PPARgamma modulation for the treatment of Type 2 diabetes.

Peroxisome proliferator-activated receptors (PPARs) (alpha, beta/delta and gamma) are lipid sensors capable of adapting gene expression to integrate various lipid signals. As such, PPARs are also very important pharmaceutical targets, and specific synthetic ligands exist for the different isotypes and are either currently used or hold promises in the treatment of major metabolic disorders. In particular, compounds of the class of the thiazolinediones (TZDs) are PPARgamma agonists and potent insulin-sensitizers. The specific but still broad expression patterns of PPARgamma, as well as its implication in numerous pathways, constitutes also a disadvantage regarding drug administration, since this potentially increases the chance to generate side-effects through the activation of the receptor in tissues or cells not affected by the disease. Actually, numerous side effects associated with the administration of TZDs have been reported. Today, a new generation of PPARgamma modulators is being actively developed to activate the receptor more specifically, in a cell and time-dependent manner, in order to induce a specific subset of target genes only and modulate a restricted number of metabolic pathways. We will discuss here why and how the development of such selective PPARgamma modulators is possible, and summarize the results obtained with the published molecules.

Gauge transformations in the Lagrangian and Hamilton formalisms of generally covariant theories

We study spacetime diffeomorphisms in the Hamiltonian and Lagrangian formalisms of generally covariant systems. We show that the gauge group for such a system is characterized by having generators which are projectable under the Legendre map. The gauge group is found to be much larger than the original group of spacetime diffeomorphisms, since its generators must depend on the lapse function and shift vector of the spacetime metric in a given coordinate patch. Our results are generalizations of earlier results by Salisbury and Sundermeyer. They arise in a natural way from using the requirement of equivalence between Lagrangian and Hamiltonian formulations of the system, and they are new in that the symmetries are realized on the full set of phase space variables. The generators are displayed explicitly and are applied to the relativistic string and to general relativity.

The immune space: a concept and template for rationalizing vaccine development.

Abstract Empirical testing of candidate vaccines has led to the successful development of a number of lifesaving vaccines. The advent of new tools to manipulate antigens and new methods and vectors for vaccine delivery has led to a veritable explosion of potential vaccine designs. As a result, selection of candidate vaccines suitable for large-scale efficacy testing has become more challenging. This is especially true for diseases such as dengue, HIV, and tuberculosis where there is no validated animal model or correlate of immune protection. Establishing guidelines for the selection of vaccine candidates for advanced testing has become a necessity. A number of factors could be considered in making these decisions, including, for example, safety in animal and human studies, immune profile, protection in animal studies, production processes with product quality and stability, availability of resources, and estimated cost of goods. The "immune space template" proposed here provides a standardized approach by which the quality, level, and durability of immune responses elicited in early human trials by a candidate vaccine can be described. The immune response profile will demonstrate if and how the candidate is unique relative to other candidates, especially those that have preceded it into efficacy testing and, thus, what new information concerning potential immune correlates could be learned from an efficacy trial. A thorough characterization of immune responses should also provide insight into a developer's rationale for the vaccine's proposed mechanism of action. HIV vaccine researchers plan to include this general approach in up-selecting candidates for the next large efficacy trial. This "immune space" approach may also be applicable to other vaccine development endeavors where correlates of vaccine-induced immune protection remain unknown.

Nonlinear filtering in object and Fourier space in a joint transform optical correlator: comparison and experimental realization

The use of different kinds of nonlinear filtering in a joint transform correlator are studied and compared. The study is divided into two parts, one corresponding to object space and the second to the Fourier domain of the joint power spectrum. In the first part, phase and inverse filters are computed; their inverse Fourier transforms are also computed, thereby becoming the reference in the object space. In the Fourier space, the binarization of the power spectrum is realized and compared with a new procedure for removing the spatial envelope. All cases are simulated and experimentally implemented by a compact joint transform correlator.

Massless minimally coupled fields in de Sitter space: O(4)-symmetric states versus de Sitter invariant vacuum

The issue of de Sitter invariance for a massless minimally coupled scalar field is examined. Formally, it is possible to construct a de Sitterinvariant state for this case provided that the zero mode of the field is quantized properly. Here we take the point of view that this state is physically acceptable, in the sense that physical observables can be computed and have a reasonable interpretation. In particular, we use this vacuum to derive a new result: that the squared difference between the field at two points along a geodesic observers spacetime path grows linearly with the observers proper time for a quantum state that does not break de Sitter invariance. Also, we use the Hadamard formalism to compute the renormalized expectation value of the energy-momentum tensor, both in the O(4)-invariant states introduced by Allen and Follaci, and in the de Sitterinvariant vacuum. We find that the vacuum energy density in the O(4)-invariant case is larger than in the de Sitterinvariant case.

Generalized Weyl solutions

It was shown by Weyl that the general static axisymmetric solution of the vacuum Einstein equations in four dimensions is given in terms of a single axisymmetric solution of the Laplace equation in three-dimensional flat space. Weyls construction is generalized here to arbitrary dimension D>~4. The general solution of the D-dimensional vacuum Einstein equations that admits D-2 orthogonal commuting non-null Killing vector fields is given either in terms of D-3 independent axisymmetric solutions of Laplaces equation in three-dimensional flat space or by D-4 independent solutions of Laplaces equation in two-dimensional flat space. Explicit examples of new solutions are given. These include a five-dimensional asymptotically flat black ring with an event horizon of topology S1S2 held in equilibrium by a conical singularity in the form of a disk.

New horizons for black holes and branes

We initiate a systematic scan of the landscape of black holes in any spacetime dimension using the recently proposed blackfold effective worldvolume theory. We focus primarily on asymptotically flat stationary vacuum solutions, where we uncover large classes of new black holes. These include helical black strings and black rings, black odd-spheres, for which the horizon is a product of a large and a small sphere, and non-uniform black cylinders. More exotic possibilities are also outlined. The blackfold description recovers correctly the ultraspinning Myers-Perry black holes as ellipsoidal even-ball configurations where the velocity field approaches the speed of light at the boundary of the ball. Helical black ring solutions provide the first instance of asymptotically flat black holes in more than four dimensions with a single spatial U(1) isometry. They also imply infinite rational non-uniqueness in ultraspinning regimes, where they maximize the entropy among all stationary single-horizon solutions. Moreover, static blackfolds are possible with the geometry of minimal surfaces. The absence of compact embedded minimal surfaces in Euclidean space is consistent with the uniqueness theorem of static black holes

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Nouvelles options dans le traitement de l'incontinence ano-rectale [New options in the treatment of ano-rectal incontinence]

Ano-rectal incontinence is known to affect about 2% of the population. Main risk factors are traumatic delivery and previous anal surgery. All patients should have a trial of conservative treatment. Patients with major external anal sphincter defect have a 70 to 80% improvement of their symptoms after an overlap sphincter repair Unfortunately, these results deteriorate over time. Sacral nerve modulation improves continence and quality of life in 75 to 100% of patients with various aetiologies. In case of idiopathic internal sphincter degeneration, sphincter augmentation with bulking agents seems to be the least expensive option.

Lipid binding by the unique and SH3 domains of c-Src suggests a new regulation mechanism

c-Src is a non-receptor tyrosine kinase involved in numerous signal transduction pathways. The kinase,SH3 and SH2 domains of c-Src are attached to the membrane-anchoring SH4 domain through the flexible Unique domain. Here we show intra- and intermolecular interactions involving the Unique and SH3 domains suggesting the presence of a previously unrecognized additional regulation layer in c-Src. We have characterized lipid binding by the Unique and SH3 domains, their intramolecular interaction and its allosteric modulation by a SH3-binding peptide or by Calcium-loaded calmodulin binding to the Unique domain. We also show reduced lipid binding following phosphorylation at conserved sites of the Unique domain. Finally, we show that injection of full-length c-Src with mutations that abolish lipid binding by the Unique domain causes a strong in vivo phenotype distinct from that of wild-type c-Src in a Xenopus oocyte model system, confirming the functional role of the Unique domain in c-Src regulation.

Poxvirus vector-based HIV vaccines.

PURPOSE OF REVIEW: In this review, we will provide the scientific rationale for the use of poxvirus vectors in the field of HIV vaccines, the immunological profile of the vaccine-induced immune responses, an update on the current use of poxvirus vector-based vaccines in HIV vaccine clinical trials, and the development of new modified poxvirus vectors with improved immunological profile. RECENT FINDINGS: An Ad5-HIV vaccine was tested in a phase IIb clinical trial (known as the Step trial). Vaccinations in the Step trial were discontinued because the vaccine did not show any effect on acquisition of infection and on viral load. After the disappointing failure of the Step trial, the field of HIV vaccine has regained enthusiasm and vigour due to the promising protective effect observed in the phase III efficacy trial (known as RV-144) performed in Thailand which has tested a poxvirus-gp120 combination. SUMMARY: The RV-144 phase III has provided for the first time evidence that an HIV vaccine can prevent HIV infection. The results from the RV-144 trial are providing the scientific rationale for the future development of the HIV vaccine field and for designing future efficacy trials.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.