Vital Statistics of Iowa in Brief, 2003

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Pocket Digest Iowa Public Library Statistics, 1999

The pocket digest for public library statistics highlights pertinent information about public libraries in Iowa.

Pocket Digest Iowa Public Library Statistics, 1999

The pocket digest for public library statistics highlights pertinent information about public libraries in Iowa.

Pocket Digest Iowa Public Library Statistics, 2000

The pocket digest for public library statistics highlights pertinent information about public libraries in Iowa.

Iowa Public Library Statistics, 2004-2005

Public library statistics are taken from the annual survey. The statistics are used at the local, regional, state, and national levels to compare library performance, justify budget requests, track library data over time, assist in planning and evaluation, and provide valuable information for grants and other library programs. The annual survey collects current information from 543 public libraries about public service outlets, holdings, staffing, income, expenditures, circulation, services, and hours open. Furthermore, it helps provide a total picture of libraries on a state and nationwide basis. This report is authorized by law (Iowa Code 256.51 (H)). Each of the 50 states collects public library information according to guidelines established by the Federal State Cooperative System for public library data (FSCS). The information contained in the Iowa Public Library Statistics is based on definitions approved by FSCS. For additional information, contact Gerry Rowland, State Library, gerry.rowland@lib.state.ia.us; 1-800-248-4483.

Iowa Public Library Statistics, 2003-2004

Public library statistics are taken from the annual survey. The statistics are used at the local, regional, state, and national levels to compare library performance, justify budget requests, track library data over time, assist in planning and evaluation, and provide valuable information for grants and other library programs. The annual survey collects current information from 543 public libraries about public service outlets, holdings, staffing, income, expenditures, circulation, services, and hours open. Furthermore, it helps provide a total picture of libraries on a state and nationwide basis. This report is authorized by law (Iowa Code 256.51 (H)). Each of the 50 states collects public library information according to guidelines established by the Federal State Cooperative System for public library data (FSCS). The information contained in the Iowa Public Library Statistics is based on definitions approved by FSCS. For additional information, contact Gerry Rowland, State Library, gerry.rowland@lib.state.ia.us; 1-800-248-4483.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Meta-analysis of gene-level associations for rare variants based on single-variant statistics.

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Virus-like particles induce robust human T-helper cell responses.

Among synthetic vaccines, virus-like particles (VLPs) are used for their ability to induce strong humoral responses. Very little is reported on VLP-based-vaccine-induced CD4(+) T-cell responses, despite the requirement of helper T cells for antibody isotype switching. Further knowledge on helper T cells is also needed for optimization of CD8(+) T-cell vaccination. Here, we analysed human CD4(+) T-cell responses to vaccination with MelQbG10, which is a Qβ-VLP covalently linked to a long peptide derived from the melanoma self-antigen Melan-A. In all analysed patients, we found strong antibody responses of mainly IgG1 and IgG3 isotypes, and concomitant Th1-biased CD4(+) T-cell responses specific for Qβ. Although less strong, comparable B- and CD4(+) T-cell responses were also found specific for the Melan-A cargo peptide. Further optimization is required to shift the response more towards the cargo peptide. Nevertheless, the data demonstrate the high potential of VLPs for inducing humoral and cellular immune responses by mounting powerful CD4(+) T-cell help.

Vital Statistics of Iowa, 2005

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Vital Statistics of Iowa, 2004

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Vital Statistics of Iowa in Brief, 2005

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

A subsampling approach to estimating the distribution of diversing statistics with application to assessing financial market risks

In this paper we propose a subsampling estimator for the distribution ofstatistics diverging at either known rates when the underlying timeseries in strictly stationary abd strong mixing. Based on our results weprovide a detailed discussion how to estimate extreme order statisticswith dependent data and present two applications to assessing financialmarket risk. Our method performs well in estimating Value at Risk andprovides a superior alternative to Hill's estimator in operationalizingSafety First portofolio selection.