Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

P value and the theory of hypothesis testing: an explanation for new researchers.

In the 1920s, Ronald Fisher developed the theory behind the p value and Jerzy Neyman and Egon Pearson developed the theory of hypothesis testing. These distinct theories have provided researchers important quantitative tools to confirm or refute their hypotheses. The p value is the probability to obtain an effect equal to or more extreme than the one observed presuming the null hypothesis of no effect is true; it gives researchers a measure of the strength of evidence against the null hypothesis. As commonly used, investigators will select a threshold p value below which they will reject the null hypothesis. The theory of hypothesis testing allows researchers to reject a null hypothesis in favor of an alternative hypothesis of some effect. As commonly used, investigators choose Type I error (rejecting the null hypothesis when it is true) and Type II error (accepting the null hypothesis when it is false) levels and determine some critical region. If the test statistic falls into that critical region, the null hypothesis is rejected in favor of the alternative hypothesis. Despite similarities between the two, the p value and the theory of hypothesis testing are different theories that often are misunderstood and confused, leading researchers to improper conclusions. Perhaps the most common misconception is to consider the p value as the probability that the null hypothesis is true rather than the probability of obtaining the difference observed, or one that is more extreme, considering the null is true. Another concern is the risk that an important proportion of statistically significant results are falsely significant. Researchers should have a minimum understanding of these two theories so that they are better able to plan, conduct, interpret, and report scientific experiments.

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

Iowa Communications Network Performance Plan, FY2014

Agency Performance Plan, Iowa Communications Network