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.

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

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.

Building a Social Accounting Matrix within the ESA95 Framework: Obtaining a Dataset for Applied General Equilibrium Modelling

This research provides a description of the process followed in order to assemble a "Social Accounting Matrix" for Spain corresponding to the year 2000 (SAMSP00). As argued in the paper, this process attempts to reconcile ESA95 conventions with requirements of applied general equilibrium modelling. Particularly, problems related to the level of aggregation of net taxation data, and to the valuation system used for expressing the monetary value of input-output transactions have deserved special attention. Since the adoption of ESA95 conventions, input-output transactions have been preferably valued at basic prices, which impose additional difficulties on modellers interested in computing applied general equilibrium models. This paper addresses these difficulties by developing a procedure that allows SAM-builders to change the valuation system of input-output transactions conveniently. In addition, this procedure produces new data related to net taxation information.

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

Kunnostusojitustarpeen ennustaminen ojitusalueilla