Audit report on the American Recovery and Reinvestment Act (ARRA) - Program of Competitive Grants for Worker Training and Placement in High Growth and Emerging Industry Sectors program for the Iowa Green Renewable Electrical Energy Network Inc. (IGREEN) for the year ended June 30, 2012

Audit report on the American Recovery and Reinvestment Act (ARRA) - Program of Competitive Grants for Worker Training and Placement in High Growth and Emerging Industry Sectors program for the Iowa Green Renewable Electrical Energy Network Inc. (IGREEN) for the year ended June 30, 2012

Iowa Communications Network FY2013 Annual Report

The report includes a recap of the initiatives relating to the Network and highlights usage, including the increased need for bandwidth and access to high-speed Internet by ICN users.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

The role of the Fanconi anemia network in the response to DNA replication stress.

Fanconi anemia is a genetically heterogeneous disorder associated with chromosome instability and a highly elevated risk for developing cancer. The mutated genes encode proteins involved in the cellular response to DNA replication stress. Fanconi anemia proteins are extensively connected with DNA caretaker proteins, and appear to function as a hub for the coordination of DNA repair with DNA replication and cell cycle progression. At a molecular level, however, the raison d'être of Fanconi anemia proteins still remains largely elusive. The thirteen Fanconi anemia proteins identified to date have not been embraced into a single and defined biological process. To help put the Fanconi anemia puzzle into perspective, we begin this review with a summary of the strategies employed by prokaryotes and eukaryotes to tolerate obstacles to the progression of replication forks. We then summarize what we know about Fanconi anemia with an emphasis on biochemical aspects, and discuss how the Fanconi anemia network, a late acquisition in evolution, may function to permit the faithful and complete duplication of our very large vertebrate chromosomes.

An interaction network of the mammalian COP9 signalosome identifies Dda1 as a core subunit of multiple Cul4-based E3 ligases.

The COP9 signalosome (CSN) is an evolutionarily conserved macromolecular complex that interacts with cullin-RING E3 ligases (CRLs) and regulates their activity by hydrolyzing cullin-Nedd8 conjugates. The CSN sequesters inactive CRL4(Ddb2), which rapidly dissociates from the CSN upon DNA damage. Here we systematically define the protein interaction network of the mammalian CSN through mass spectrometric interrogation of the CSN subunits Csn1, Csn3, Csn4, Csn5, Csn6 and Csn7a. Notably, we identified a subset of CRL complexes that stably interact with the CSN and thus might similarly be activated by dissociation from the CSN in response to specific cues. In addition, we detected several new proteins in the CRL-CSN interactome, including Dda1, which we characterized as a chromatin-associated core subunit of multiple CRL4 proteins. Cells depleted of Dda1 spontaneously accumulated double-stranded DNA breaks in a similar way to Cul4A-, Cul4B- or Wdr23-depleted cells, indicating that Dda1 interacts physically and functionally with CRL4 complexes. This analysis identifies new components of the CRL family of E3 ligases and elaborates new connections between the CRL and CSN complexes.

Iowa Communications Network Performance Report, FY2004

Agency Performance Report

Iowa Communications Network Performance Report, FY2005

Agency Performance Report

Iowa Communications Network Performance Report, FY2006

Agency Performance Report

Iowa Communications Network Performance Report, FY2007

Agency Performance Report

Iowa Communications Network Performance Report, FY2008

Agency Performance Report

Iowa Communications Network Performance Report, FY2009

Agency Performance Report

Iowa Communications Network Performance Report, FY2010

Agency Performance Report

Iowa Communications Network Performance Report, FY2011

Agency Performance Report

Iowa Communications Network Performance Report, FY2011

Agency Performance Report

Iowa Communications Network Performance Plan, FY 2013

Agency Performance Plan, Iowa Communications Network