Agency Fact Sheet, Iowa Communications Network , FY2005

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Artificial zinc finger fusions targeting Sp1-binding sites and the trans-activator-responsive element potently repress transcription and replication of HIV-1.

Tat activates transcription by interacting with Sp1, NF-kappaB, positive transcription elongation factor b, and trans-activator-responsive element (TAR). Tat and Sp1 play major roles in transcription by protein-protein interactions at human immunodeficiency virus, type 1 (HIV-1) long terminal repeat. Sp1 activates transcription by interacting with cyclin T1 in the absence of Tat. To disrupt the transcription activation by Tat and Sp1, we fused Sp1-inhibiting polypeptides, zinc finger polypeptide, and the TAR-binding mutant Tat (TatdMt) together. A designed or natural zinc finger and Tat mutant fusion was used to target the fusion to the key regulatory sites (GC box and TAR) on the long terminal repeat and nascent short transcripts to disrupt the molecular interaction that normally result in robust transcription. The designed zinc finger and TatdMt fusions were targeted to the TAR, and they potently repressed both transcription and replication of HIV-1. The Sp1-inhibiting POZ domain, TatdMt, and zinc fingers are key functional domains important in repression of transcription and replication. The designed artificial zinc fingers were targeted to the high affinity Sp1-binding site, and by being fused with TatdMt and POZ domain, they strongly block both Sp1-cyclin T1-dependent transcription and Tat-dependent transcription, even in the presence of excess expressed Tat.

Agency Fact Sheet, Iowa Commutations Network, FY2006

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Early stage primary bone lymphoma - a retrospective, multicenter rare cancer network (rcn) study

Purpose: Primary bone lymphoma (PBL) accounts for less than 1% of all malignant lymphomas, and 4-5% of all extra-nodal lymphomas. In this study, the disease profile, outcome, and prognostic factors were assessed in patients with stage I and II PBL.Patients and Methods: Thirteen Rare Cancer Network (RCN) institutions enrolled 116 consecutive patients with PBL treated between 1987 and 2008 in this study. Inclusion criteria were age > 16 years, stage I and II, minimum 6 months follow-up and a biopsy-proven confirmation of non-Hodgkin's lymphoma (NHL). Eighty-seven patients underwent chemoradiotherapy (CXRT), 15 radiotherapy (RT) without (13) or with (2) surgery, 14 chemotherapy (CXT) without (9) or with (5) surgery. Median RT dose was 40 Gy (range: 4-60). The median number of CXT cycles was 6 (range: 2-8). Median follow-up was 41 months (range: 6-242).Results: The overall response rate at the end of treatment was 91% (CR 74%, PR 17%). Local recurrence or progression was observed in 12 (10%) patients, and systemic recurrence in 17 (15%). Causes of death included disease progression in 21, unrelated in 5, CXT-related toxicity in 1, and second primary cancer in 2 patients. The 5-yr overall survival (OS), lymphoma-specific survival (LSS), and local control (LC) were 76%, 78% and 92%, respectively. In univariate analyses (log-rank test), favorable prognostic factors for OS were age <50 years (P=0.008), international prognostic index (IPI) score ≤1 (P=0.009), high grade histology (P=0.04), CXRT (P=0.05), CXT (P=0,0004), complete response (CR) (P<0.0001), number of CXT cycles ( ≥6 ) (P=0.01), and RT dose > 40 Gy (P=0.005). All above-mentioned parameters were also significant for LSS except for age and number of chemotherapy cycles. For LC, only CR and stage I were favorable factors. In multivariate analysis, IPI score, RT dose, complete response, and chemotherapy were independently influencing the outcome (OS and LSS). Complete response at the end of treatment was the only predicting factor for LC. Six patients developed grade 3 or more toxicities, according to Common Terminology Criteria for Adverse Events (CTCAE) V3.0.Conclusion: This large multicenter study confirms the relatively good prognosis of early stage PBL treated with combined CXRT. Local control was excellent, while systemic failures were rare. An adequate dose of RT (40 Gy or more) and complete CXT regime (≥ 6 cycles) were associated with better outcome.

Optimal network topologies for local search with congestion

The problem of searchability in decentralized complex networks is of great importance in computer science, economy, and sociology. We present a formalism that is able to cope simultaneously with the problem of search and the congestion effects that arise when parallel searches are performed, and we obtain expressions for the average search cost both in the presence and the absence of congestion. This formalism is used to obtain optimal network structures for a system using a local search algorithm. It is found that only two classes of networks can be optimal: starlike configurations, when the number of parallel searches is small, and homogeneous-isotropic configurations, when it is large.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Self-similar community structure in a network of human interactions

We propose a procedure for analyzing and characterizing complex networks. We apply this to the social network as constructed from email communications within a medium sized university with about 1700 employees. Email networks provide an accurate and nonintrusive description of the flow of information within human organizations. Our results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar. This suggests that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

Hierarchical neural network with high storage capacity

A recent method used to optimize biased neural networks with low levels of activity is applied to a hierarchical model. As a consequence, the performance of the system is strongly enhanced. The steps to achieve optimization are analyzed in detail.

Modelling diffusion of innovations in a social network

A simple model of diffusion of innovations in a social network with upgrading costs is introduced. Agents are characterized by a single real variable, their technological level. According to local information, agents decide whether to upgrade their level or not, balancing their possible benefit with the upgrading cost. A critical point where technological avalanches display a power-law behavior is also found. This critical point is characterized by a macroscopic observable that turns out to optimize technological growth in the stationary state. Analytical results supporting our findings are found for the globally coupled case.

Exact long-time behavior of a network of oscillators under random fields

We present an exact solution for the order parameters that characterize the stationary behavior of a population of Kuramotos phase oscillators under random external fields [Y. Kuramoto, in International Symposium on Mathematical Problems in Theoretical Physics, Lecture Notes in Physics, Vol. 39 (Springer, Berlin, 1975), p. 420]. From these results it is possible to generate the phase diagram of models with an arbitrary distribution of random frequencies and random fields.

Remote Synchronization Reveals Network Symmetries and Functional Modules

We study a Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result, and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

Comparative genomics of the vertebrate insulin/TOR signal transduction pathway: A network-level analysis of selective pressures

Complexity of biological function relies on large networks of interacting molecules. However, the evolutionary properties of these networks are not fully understood. It has been shown that selective pressures depend on the position of genes in the network. We have previously shown that in the Drosophila insulin/target of rapamycin (TOR) signal transduction pathway there is a correlation between the pathway position and the strength of purifying selection, with the downstream genes being most constrained. In this study, we investigated the evolutionary dynamics of this well-characterized pathway in vertebrates. More specifically, we determined the impact of natural selection on the evolution of 72 genes of this pathway. We found that in vertebrates there is a similar gradient of selective constraint in the insulin/TOR pathway to that found in Drosophila. This feature is neither the result of a polarity in the impact of positive selection nor of a series of factors affecting selective constraint levels (gene expression level and breadth, codon bias, protein length, and connectivity). We also found that pathway genes encoding physically interacting proteins tend to evolve under similar selective constraints. The results indicate that the architecture of the vertebrate insulin/TOR pathway constrains the molecular evolution of its components. Therefore, the polarity detected in Drosophila is neither specific nor incidental of this genus. Hence, although the underlying biological mechanisms remain unclear, these may be similar in both vertebrates and Drosophila.

Diffusion Spectrum Imaging after stroke shows structural changes in the contra-lateral motor network correlating with functional recovery

There is growing interest in understanding the role of the non-injured contra-lateral hemisphere in stroke recovery. In the experimental field, histological evidence has been reported that structural changes occur in the contra-lateral connectivity and circuits during stroke recovery. In humans, some recent imaging studies indicated that contra-lateral sub-cortical pathways and functional and structural cortical networks are remodeling, after stroke. Structural changes in the contra-lateral networks, however, have never been correlated to clinical recovery in patients. To determine the importance of the contra-lateral structural changes in post-stroke recovery, we selected a population of patients with motor deficits after stroke affecting the motor cortex and/or sub-cortical motor white matter. We explored i) the presence of Generalized Fractional Anisotropy (GFA) changes indicating structural alterations in the motor network of patientsâeuro? contra-lateral hemisphere as well as their longitudinal evolution ii) the correlation of GFA changes with patientsâeuro? clinical scores, stroke size and demographics data iii) and a predictive model.