Extreme prematurity and intra uterine growth restriction effects in brain network topology at school age

Higher risk for long-term behavioral and emotional sequelae, with attentional problems (with or without hyperactivity) is now becoming one of the hallmarks of extreme premature (EP) birth and birth after pregancy conditions leading to poor intra uterine growth restriction (IUGR) [1,2]. However, little is know so far about the neurostructural basis of these complexe brain functional abnormalities that seem to have their origins in early critical periods of brain development. The development of cortical axonal pathways happens in a series of sequential events. The preterm phase (24-36 post conecptional weeks PCW) is known for being crucial for growth of the thalamocortical fiber bundles as well as for the development of long projectional, commisural and projectional fibers [3]. Is it logical to expect, thus, that being exposed to altered intrauterine environment (altered nutrition) or to extrauterine environment earlier that expected, lead to alterations in the structural organization and, consequently, alter the underlying white matter (WM) structure. Understanding rate and variability of normal brain development, and detect differences from typical development may offer insight into the neurodevelopmental anomalies that can be imaged at later stages. Due to its unique ability to non-invasively visualize and quantify in vivo white matter tracts in the brain, in this study we used diffusion MRI (dMRI) tractography to derive brain graphs [4,5,6]. This relatively simple way of modeling the brain enable us to use graph theory to study topological properties of brain graphs in order to study the effects of EP and IUGR on childrens brain connectivity at age 6 years old.

Network-based scoring system for genome-scale metabolic reconstructions

Background: Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental evidence or model-based likelihood. Results: Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results. Conclusions: We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.

Traumatic Brain Injury in Iowa: Iowa Brain Injury Resource Network Outcome Evaluation 2007-2009, August 2011

Termed the “silent epidemic,” traumatic brain injury (TBI) is the most debilitating outcome of injury, and is characterized by the irreversibility of its damages, long-term effects on quality of life and healthcare costs. The latest data available from the CDC estimate that nationally, 52,000 people die each year from TBI2. In Iowa, TBI is a major public health problem. The numbers and rates of hospitalizations and emergency department (ED) visits due to TBIs are steadily increasing. From 2006 to 2008, there were on average 545 injury deaths per year. Among the injured Iowans, TBI constituted nearly 30 percent (545) of all injury deaths, ten percent (1,591) of people hospitalized and seven percent (17,696) of ED visitors. 3 The state of Iowa has been supporting secondary prevention services to TBI survivors for several years. An Iowa organization that has made a significant effort in assisting TBI survivors is the Brain Injury Association of Iowa (BIAIA). The BIAIA administers the IBIRN program in cooperation with the Iowa Department of Public Health (IDPH) through HRSA TBI Implementation grant funding and state appropriations.

Early childhood network, 2005.

This brochure states the mission of the Early Childhood Network along with contact names and addresses of the specialists and consultants in the Iowa Department of Education and the Iowa Area Education Agencies for the years 2005/2006.

Predicting the need for ditch network maintenance in drained peatland sites in Finland

Kunnostusojitustarpeen ennustaminen ojitusalueilla

Treatment results and prognostic factors in primary thyroid lymphoma patients: a rare cancer network study.

Background: This study analyzed prognostic factors and treatment outcomes of primary thyroid lymphoma. Patients and Methods: Data were retrospectively collected for 87 patients (53 stage I and 34 stage II) with median age 65 years. Fifty-two patients were treated with single modality (31 with chemotherapy alone and 21 with radiotherapy alone) and 35 with combined modality treatment. Median follow-up was 51 months. Results: Sixty patients had aggressive lymphoma and 27 had indolent lymphoma. The 5- and 10-year overall survival (OS) rates were 74% and 71%, respectively, and the disease-free survival (DFS) rates were 68% and 64%. Univariate analysis revealed that age, tumor size, stage, lymph node involvement, B symptoms, and treatment modality were prognostic factors for OS, DFS, and local control (LC). Patients with thyroiditis had significantly better LC rates. In multivariate analysis, OS was influenced by age, B symptoms, lymph node involvement, and tumor size, whereas DFS and LC were influenced by B symptoms and tumor size. Compared with single modality treatment, patients treated with combined modality had better 5-year OS, DFS, and LC. Conclusions: Combined modality leads to an excellent prognosis for patients with aggressive lymphoma but does not improve OS and LC in patients with indolent lymphoma.

Report on the Iowa Communications Network (ICN) for the year ended June 30, 2012

Report on the Iowa Communications Network (ICN) for the year ended June 30, 2012

Early Stage Primary Bone Lymphoma: A Retrospective, Multicenter Rare Cancer Network Study

PURPOSE/OBJECTIVE(S): Primary bone lymphoma (PBL) represents less than 1% of all malignant lymphomas, and 4-5% of all extranodal lymphomas. In this study, we assessed the disease profile, outcome, and prognostic factors in patients with stage I and II PBL. MATERIALS/METHODS: Between 1987 and 2008, 116 consecutive patients with PBL treated in 13 RCNinstitutions were included in this study. Inclusion criteriawere: age.17 yrs, PBLin stage I and II, andminimum6months follow-up. The median agewas 51 yrs (range: 17-93).Diagnosticwork-up included plain boneXray (74%of patients), scintigraphy (62%), CT-scan (65%),MRI (58%), PET (18%), and bone-marrow biopsy (84%).All patients had biopsy-proven confirmation of non-Hodgkin's lymphoma (NHL). The histopathological type was predominantly diffuse large B-cell lymphoma (78%) and follicular lymphoma (6%), according to theWHOclassification. One hundred patients had a high-grade, 7 intermediate and 9 low-gradeNHL. Ninety-three patients had anAnn-Arbor stage I, and 23 had a stage II. Seventy-seven patients underwent chemoradiotherapy (CXRT), 12 radiotherapy (RT) alone, 10 chemotherapy alone (CXT), 9 surgery followed by CXRT, 5 surgery followed by CXT, and 2 surgery followed by RT. One patient died before treatment.Median RT dosewas 40Gy (range: 4-60).Themedian number ofCXTcycleswas 6 (range, : 2-8).Median follow-upwas 41months (range: 6-242). RESULTS: Following treatment, the overall response rate was 91% (CR 74%, PR 17%). Local recurrence was observed in 12 (10%) patients, and systemic recurrence in 17 (15%) patients. Causes of death included disease progression in 16, unrelated disease in 6, CXT-related toxicity in 1, and secondary cancer in 2 patients. The 5-yr overall survival (OS), disease-free survival (DFS), lymphoma- specific survival (LSS), and local control (LC) were 76%, 69%, 78%, and 92%, respectively. In univariate analyses (log-rank test), favorable prognostic factors for survival were: age\50 years (p = 0.008), IPI score #1 (p = 0.009), complete response (p\0.001), CXT (p = 0.008), number of CXT cycles $6 (p = 0.007), and RT dose . 40 Gy (p = 0.005). In multivariate analysis age, RT dose, complete response, and absence of B symptoms were independent factors influencing the outcome. There were 3 patients developing grade 3 or more (CTCAE.V3.0) toxicities. CONCLUSIONS: This large multicenter study, confirms the relatively good prognosis of early stage PBL, treated with combined CXRT. Local control was excellent, and systemic failure occurred infrequently. A sufficient dose of RT (. 40 Gy) and complete CXT regime (. 6 cycles) were associated with a better outcome. Combined modality appears to be the treatment of choice.

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.

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.