Rebuild Iowa Office Quarterly Report, January 2011

The RIO’S January 2011 Quarterly Report details the economic recovery strategy in housing; business; workforce development; infrastructure investments; individual services and guidance; local economic recovery; smart planning; mitigation planning; floodplain and watershed management; floodplain mapping and quality of life. The report also includes an update of the flow of federal and state disaster recovery funding to the state, counties, cities and individuals affected by the 2008 disasters.

Rebuild Iowa Office Quarterly Report, April 2011

The RIO’S April 2011 Quarterly Report is the Office's final report and details the economic recovery strategy in housing; business; workforce development; infrastructure investments; individual services and guidance; local economic recovery; smart planning; mitigation planning; floodplain and watershed management; floodplain mapping and quality of life. The report also includes an update of the flow of federal and state disaster recovery funding to the state, counties, cities and individuals affected by the 2008 disasters.

Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor.

The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression.

Consumer Advisory Bulletin, How to Avoid Buying a Salvage, Damaged or Flood Vehicle, August 2011

The Attorney General’s Consumer Protection Division receives hundreds of calls and consumer complaints every year. Follow these tips to avoid unexpected expense and disappointments. This record is about: How to Avoid Buying a Salvage, Damaged or Flood Vehicle

Mitigation of methane and nitrous oxide emissions from flood-irrigated rice by no incorporation of winter crop residues into the soil

Winter cover crops are sources of C and N in flooded rice production systems, but very little is known about the effect of crop residue management and quality on soil methane (CH4) and nitrous oxide (N2O) emissions. This study was conducted in pots in a greenhouse to evaluate the influence of crop residue management (incorporated into the soil or left on the soil surface) and the type of cover-crop residues (ryegrass and serradella) on CH4 and N2O emissions from a flooded Albaqualf soil cultivated with rice (Oryza sativa L.). The closed chamber technique was used for air sampling and the CH4 and N2O concentrations were analyzed by gas chromatography. Soil solution was sampled at two soil depths (2 and 20 cm), simultaneously to air sampling, and the contents of dissolved organic C (DOC), NO3-, NH4+, Mn2+, and Fe2+ were analyzed. Methane and N2O emissions from the soil where crop residues had been left on the surface were lower than from soil with incorporated residues. The type of crop residue had no effect on the CH4 emissions, while higher N2O emissions were observed from serradella (leguminous) than from ryegrass, but only when the residues were left on the soil surface. The more intense soil reduction verified in the deeper soil layer (20 cm), as evidenced by higher contents of reduced metal species (Mn2+ and Fe2+), and the close relationship between CH4 emission and the DOC contents in the deeper layer indicated that the sub-surface layer was the main CH4 source of the flooded soil with incorporated crop residues. The adoption of management strategies in which crop residues are left on the soil surface is crucial to minimize soil CH4 and N2O emissions from irrigated rice fields. In these production systems, CH4 accounts for more than 90 % of the partial global warming potential (CH4+N2O) and, thus, should be the main focus of research.

Main-Channel Slopes of Selected Streams in Iowa for Estimation of Flood-Frequency Discharges, November 2004

This report describes a statewide study conducted to develop main-channel slope (MCS) curves for 138 selected streams in Iowa with drainage areas greater than 100 square miles. MCS values determined from the curves can be used in regression equations for estimating flood frequency discharges. Multi-variable regression equations previously developed for two of the three hydrologic regions defined for Iowa require the measurement of MCS. Main-channel slope is a difficult measurement to obtain for large streams using 1:24,000-scale topographic maps. The curves developed in this report provide a simplified method for determining MCS values for sites located along large streams in Iowa within hydrologic Regions 2 and 3. The curves were developed using MCS values quantified for 2,058 selected sites along 138 selected streams in Iowa. A geographic information system (GIS) technique and 1:24,000-scale topographic data were used to quantify MCS values for the stream sites. The sites were selected at about 5-mile intervals along the streams. River miles were quantified for each stream site using a GIS program. Data points for river-mile and MCS values were plotted and a best-fit curve was developed for each stream. An adjustment was applied to all 138 curves to compensate for differences in MCS values between manual measurements and GIS quantification. The multi-variable equations for Regions 2 and 3 were developed using manual measurements of MCS. A comparison of manual measurements and GIS quantification of MCS indicates that manual measurements typically produce greater values of MCS compared to GIS quantification. Median differences between manual measurements and GIS quantification of MCS are 14.8 and 17.7 percent for Regions 2 and 3, respectively. Comparisons of percentage differences between flood-frequency discharges calculated using MCS values of manual measurements and GIS quantification indicate that use of GIS values of MCS for Region 3 substantially underestimate flood discharges. Mean and median percentage differences for 2- to 500-year recurrence-interval flood discharges ranged from 5.0 to 5.3 and 4.3 to 4.5 percent, respectively, for Region 2 and ranged from 18.3 to 27.1 and 12.3 to 17.3 percent for Region 3. The MCS curves developed from GIS quantification were adjusted by 14.8 percent for streams located in Region 2 and by 17.7 percent for streams located in Region 3. Comparisons of percentage differences between flood discharges calculated using MCS values of manual measurements and adjusted-GIS quantification for Regions 2 and 3 indicate that the flood-discharge estimates are comparable. For Region 2, mean percentage differences for 2- to 500-year recurrence-interval flood discharges ranged between 0.6 and 0.8 percent and median differences were 0.0 percent. For Region 3, mean and median differences ranged between 5.4 to 8.4 and 0.0 to 0.3 percent, respectively. A list of selected stream sites presented with each curve provides information about the sites including river miles, drainage areas, the location of U.S. Geological Survey stream flowgage stations, and the location of streams Abstract crossing hydro logic region boundaries or the Des Moines Lobe landforms region boundary. Two examples are presented for determining river-mile and MCS values, and two techniques are presented for computing flood-frequency discharges.

Mapping, organic matter mass and water volume of a peatland in Serra do Espinhaço Meridional

Peatlands form in areas where net primary of organic matter production exceeds losses due to the decomposition, leaching or disturbance. Due to their chemical and physical characteristics, bogs can influence water dynamics because they can store large volumes of water in the rainy season and gradually release this water during the other months of the year. In Diamantina, Minas Gerais, Brazil, a peatland in the environmental protection area of Pau-de-Fruta ensures the water supply of 40,000 inhabitants. The hypothesis of this study is that the peat bogs in Pau-de-Fruta act as an environment for carbon storage and a regulator of water flow in the Córrego das Pedras basin. The objective of this study was to estimate the water volume and organic matter mass in this peatland and to study the influence of this environment on the water flow in the Córrego das Pedras basin. The peatland was mapped using 57 transects, at intervals of 100 m. Along all transects, the depth of the peat bog, the Universal Transverse Mercator (UTM) coordinates and altitude were recorded every 20 m and used to calculate the area and volume of the peatland. The water volume was estimated, using a method developed in this study, and the mass of organic matter based on samples from 106 profiles. The peatland covered 81.7 hectares (ha), and stored 497,767 m³ of water, representing 83.7 % of the total volume of the peat bog. The total amount of organic matter (OM) was 45,148 t, corresponding to 552 t ha-1 of OM. The peat bog occupies 11.9 % of the area covered by the Córrego das Pedras basin and stores 77.6 % of the annual water surplus, thus controlling the water flow in the basin and consequently regulating the water course.

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Surface mapping, organic matter and water stocks in peatlands of the Serra do Espinhaço meridional - Brazil

Peatlands are soil environments that store carbon and large amounts of water, due to their composition (90 % water), low hydraulic conductivity and a sponge-like behavior. It is estimated that peat bogs cover approximately 4.2 % of the Earth's surface and stock 28.4 % of the soil carbon of the planet. Approximately 612 000 ha of peatlands have been mapped in Brazil, but the peat bogs in the Serra do Espinhaço Meridional (SdEM) were not included. The objective of this study was to map the peat bogs of the northern part of the SdEM and estimate the organic matter pools and water volume they stock. The peat bogs were pre-identified and mapped by GIS and remote sensing techniques, using ArcGIS 9.3, ENVI 4.5 and GPS Track Maker Pro software and the maps validated in the field. Six peat bogs were mapped in detail (1:20,000 and 1:5,000) by transects spaced 100 m and each transect were determined every 20 m, the UTM (Universal Transverse Mercator) coordinates, depth and samples collected for characterization and determination of organic matter, according to the Brazilian System of Soil Classification. In the northern part of SdEM, 14,287.55 ha of peatlands were mapped, distributed over 1,180,109 ha, representing 1.2 % of the total area. These peatlands have an average volume of 170,021,845.00 m³ and stock 6,120,167 t (428.36 t ha-1) of organic matter and 142,138,262 m³ (9,948 m³ ha-1) of water. In the peat bogs of the Serra do Espinhaço Meridional, advanced stages of decomposing (sapric) organic matter predominate, followed by the intermediate stage (hemic). The vertical growth rate of the peatlands ranged between 0.04 and 0.43 mm year-1, while the carbon accumulation rate varied between 6.59 and 37.66 g m-2 year-1. The peat bogs of the SdEM contain the headwaters of important water bodies in the basins of the Jequitinhonha and San Francisco Rivers and store large amounts of organic carbon and water, which is the reason why the protection and preservation of these soil environments is such an urgent and increasing need.

Mapping the development of the human connectome

From birth to early adulthood the brain undergoes dramatic modifications resulting in network development and optimization. In the present study we investigate the development of the human connectome but measuring myelination trajectories of individual connections over the entire brain structural network using high b-value diffusion imaging and tractography. We found significant changes in several network measures that support increased integration and efficiency. We also observe that the network doesn't myelinate at a uniform rate but with different myelination speeds dependant on the type of cortex.