Mapping the Membrane Topology of Hepatitis C Virus Nonstructural Protein 4B

BACKGROUND: Nonstructural protein 4B (NS4B) plays an essential role in the formation of the hepatitis C virus (HCV) replication complex. It is an integral membrane protein that has only poorly been characterized to date. In particular, a precise membrane topology is thus far elusive. Here, we explored a novel strategy to map the membrane topology of HCV NS4B. METHODS: Selective permeabilization of the plasma membrane, maleimide-polyethyleneglycol (mPEG) labeling of natural or engineered cysteine residues and immunoblot analyses were combined to map the membrane topology of NS4B. Cysteine substitutions were introduced at carefully selected positions within NS4B and their impact on HCV RNA replication and infectious virus production analyzed in cell culture. RESULTS: We established a panel of viable HCV mutants with cysteine substitutions at strategic positions within NS4B. These mutants are infectious and replicate to high levels in cell culture. In parallel, we adapted and optimized the selective permeabilization and mPEG labeling techniques to Huh-7 human hepatocellular carcinoma cells which can support HCV infection and replication. CONCLUSIONS: The newly established experimental tools and techniques should allow us to refine the membrane topology of HCV NS4B in a physiological context. The expected results should enhance our understanding of the functional architecture of the HCV replication complex and may provide new opportunities for antiviral intervention in the future.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Indian Creek Channel: the evolution & significance of an historic public works administration flood control facility, Council Bluffs, Iowa, 2010

Plagued for nearly a century by the perennial flooding of Indian Creek, the City begins construction on a massive channelization project designed to confine the creek to its banks. Funded largely through a grant from the recently established Public Works Administration (PWA), the Indian Creek Channel, upon its completion two years later, would become the largest PWA undertaking in the State of Iowa. Though it did not completely end flooding in Council Bluffs, construction of the Indian Creek Channel did substantially reduce both the number and severity of the city's subsequent floods. It also profoundly impacted the residential and commercial development of Council Bluffs, as well as the city's sanitary conditions. The effects of the Indian Creek channelization, both practical and historical, are still realized today. In 2009, plans for a City road and bridge construction project at the intersection of North Broadway Street and Kanesville Boulevard proposed to replace a 221-foot-long segment of the Indian Creek Channel with a concrete box culvert. In compliance with the National Historic Preservation Act, a cultural resources study was conducted at the proposed construction site, the findings of which concluded that the historic character of the Indian Creek Channel would be compromised by the impending construction. As a means of mitigating these damages, an agreement was reached among the City, the Iowa State Historic Preservation Office, and the Federal Highway Administration that resulted in detailed research and documentation of the historical significance of the Indian Creek Channel. The findings of that study are summarized in this publication.

Dementoituneiden asiakkaiden hoidon laadun havainnointi- pilottitutkimus Dementia Care Mapping -menetelmällä

Summary: Assessment of the quality of care of people with dementia - Dementia Care Mapping pilot

Mapping of quantitative trait loci for thermosensitive genic male sterility in indica rice

The objective of this work was to select and use microsatellite markers, to map genomic regions associated with the genetic control of thermosensitive genic male sterility (TGMS) in rice. An F2 population, derived from the cross between fertile and TGMS indica lines, was used to construct a microsatellite-based genetic map of rice. The TGMS phenotype showed a continuous variation in the segregant population. A low level of segregation distortion was detected in the F2 (14.65%), whose cause was found to be zygotic selection. There was no evidence suggesting a cause-effect relationship between zygotic selection and the control of TGMS in this cross. A linkage map comprising 1,213.3 cM was constructed based on the segregation data of the F2 population. Ninety-five out of 116 microsatellite polymorphic markers were assembled into 11 linkage groups, with an average of 12.77 cM between two adjacent marker loci. The phenotypic and genotypic data allowed for the identification of three new quantitative trait loci (QTL) for thermosensitive genic male sterility in indica rice. Two of the QTL were mapped on chromosomes that, so far, have not been associated with the genetic control of the TGMS trait (chromosomes 1 and 12). The third QTL was mapped on chromosome 7, where a TGMS locus (tms2) has recently been mapped. Allelic tests will have to be developed, in order to clarify if the two regions are the same or not.

Issue review : flood recovery at the University of Iowa.

In June 2008, the University of Iowa (UI) campus experienced severe flooding and major damage to campus facilities. This report summarizes information provided by the UI on flood recovery as of August 2012.

Determination of Flood Dischard Characteristics of Small Drainage Areas, HR-3, Progress Report, 1960

Project HR-3 of the Iowa Highway Research Board has been active since October 1, 1950. The project objective is the determination of flood discharge characteristics of small drainage areas. Funds for the project amount to $10,000 per year of which, by cooperative agreement, the Highway Commission and the U. S. Geological Survey each furnish $5,000. Previous reports have explained the set-up of the project and these explanations will not be repeated in this report.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Techniques for Estimating Flood-Frequency Discharges for Streams in Iowa, HR-395A, 2001

A statewide study was conducted to develop regression equations for estimating flood-frequency discharges for ungaged stream sites in Iowa. Thirty-eight selected basin characteristics were quantified and flood-frequency analyses were computed for 291 streamflow-gaging stations in Iowa and adjacent States. A generalized-skew-coefficient analysis was conducted to determine whether generalized skew coefficients could be improved for Iowa. Station skew coefficients were computed for 239 gaging stations in Iowa and adjacent States, and an isoline map of generalized-skew-coefficient values was developed for Iowa using variogram modeling and kriging methods. The skew map provided the lowest mean square error for the generalized-skew- coefficient analysis and was used to revise generalized skew coefficients for flood-frequency analyses for gaging stations in Iowa. Regional regression analysis, using generalized least-squares regression and data from 241 gaging stations, was used to develop equations for three hydrologic regions defined for the State. The regression equations can be used to estimate flood discharges that have recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for ungaged stream sites in Iowa. One-variable equations were developed for each of the three regions and multi-variable equations were developed for two of the regions. Two sets of equations are presented for two of the regions because one-variable equations are considered easy for users to apply and the predictive accuracies of multi-variable equations are greater. Standard error of prediction for the one-variable equations ranges from about 34 to 45 percent and for the multi-variable equations range from about 31 to 42 percent. A region-of-influence regression method was also investigated for estimating flood-frequency discharges for ungaged stream sites in Iowa. A comparison of regional and region-of-influence regression methods, based on ease of application and root mean square errors, determined the regional regression method to be the better estimation method for Iowa. Techniques for estimating flood-frequency discharges for streams in Iowa are presented for determining ( 1) regional regression estimates for ungaged sites on ungaged streams; (2) weighted estimates for gaged sites; and (3) weighted estimates for ungaged sites on gaged streams. The technique for determining regional regression estimates for ungaged sites on ungaged streams requires determining which of four possible examples applies to the location of the stream site and its basin. Illustrations for determining which example applies to an ungaged stream site and for applying both the one-variable and multi-variable regression equations are provided for the estimation techniques.

The link between land-use management and fluvial flood risk : a chaotic conception?

There is much policy interest in the possible linkages that might exist between land use and downstream fluvial flood risk. On the one hand, this position is sustained by observations from plot- and field-scale studies that suggest land management does affect runoff. On the other, upscaling these effects to show that land-management activities impact upon flood risk at larger catchment scales has proved to be elusive. This review considers the reasons for why this upscaling is problematic. We argue that, rather than it reflecting methodological challenges associated with the difficulties of modelling hydrological processes over very large areas and during extreme runoff events, it reflects the fact that any linkage between land management and flood risk cannot be generalized and taken out of its specific spatial (catchment) and temporal (flood event) context. We use Sayer's (1992) notion of a `chaotic conception' to describe the belief that there is a simple and general association between land management and downstream flood risk rather than the impacts of land management being spatially and temporally contingent in relation to the particular geographical location, time period and scale being considered. Our argument has important practical consequences because it implies that land-management activities to reduce downstream flood risk will be different to traditional flood-reduction interventions such as levees. The purpose of demonstration projects then needs careful consideration such that conclusions made for one project are not transferred uncritically to other scales of analysis or geographical locations.

Evaluation of Design Flood Frequency Methods for Iowa Streams, TR-533, 2009

The objective of this project was to assess the predictive accuracy of flood frequency estimation for small Iowa streams based on the Rational Method, the NRCS curve number approach, and the Iowa Runoff Chart. The evaluation was based on comparisons of flood frequency estimates at sites with sufficiently long streamgage records in the Midwest, and selected urban sites throughout the United States. The predictive accuracy and systematic biases (under- or over-estimation) of the approaches was evaluated based on forty-six Midwest sites and twenty-one urban sites. The sensitivity of several watershed characteristics such as soil properties, slope, and land use classification was also explored. Recommendations on needed changes or refinements for applications to Iowa streams are made.

Quantitative Mapping of Waterways Characteristics at Bridge Sites, TR-569, 2008

Iowa state, county, and city engineering offices expend considerable effort monitoring the state’s approximately 25,000 bridges, most of which span small waterways. In fact, the need for monitoring is actually greater for bridges over small waterways because scour processes are exacerbated by the close proximity of abutments, piers, channel banks, approach embankments, and other local obstructions. The bridges are customarily inspected biennially by the county’s road department bridge inspectors. It is extremely time consuming and difficult to obtain consistent, reliable, and timely information on bridge-waterway conditions for so many bridges. Moreover, the current approaches to gather survey information is not uniform, complete, and quantitative. The methodology and associated software (DIGIMAP) developed through the present project enable a non-intrusive means to conduct fast, efficient, and accurate inspection of the waterways in the vicinity of the bridges and culverts using one technique. The technique combines algorithms image of registration and velocimetry using images acquired with conventional devices at the inspection site. The comparison of the current bridge inspection and monitoring methods with the DIGIMAP methodology enables to conclude that the new procedure assembles quantitative information on the waterway hydrodynamic and morphologic features with considerable reduced effort, time, and cost. It also improves the safety of the bridge and culvert inspections conducted during normal and extreme hydrologic events. The data and information are recorded in a digital format, enabling immediate and convenient tracking of the waterway changes over short or long time intervals.