State of Iowa Transportation Map, 1997

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 1998

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 1999

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2000

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2001

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2002

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2003

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2004

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2005

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2006

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

State of Iowa Transportation Map, 2007

This document is the State Map of Iowa, both front and back of the year in the title. All maps were are in pdf format and can be used as a historical reference.

Maternal interactive behaviour as a predictor of preschoolers' attachment representations among full term and premature samples.

BACKGROUND: Associations between maternal sensitivity and child attachment have been established in many samples, but the strength of the association varies across populations. The sensitivity-attachment link has never been examined at the level of representations nor among premature samples. OBJECTIVE: The present study is aimed at exploring associations between maternal interactive behaviour and children's attachment representations in a population of preterm and full-term infants. METHOD: Maternal interactive behaviour was assessed at 6 and 18 months (Ainsworth Sensitivity Scale & Care Index) and children's attachment representations were measured at 42 months (Attachment Story Completion Task) in a sample of preterm (N=48) and full-term (N=23) infants. RESULTS: Maternal unresponsiveness at 6 months and sensitivity at 18 months explained 54% of the variance of disorganized attachment representations in the full-term group but was not significantly related to attachment patterns in the preterm group. CONCLUSION: These results corroborate previous work on the causes of disorganized attachment and also point to the need to consider the development of attachment differently for children evolving in specific developmental contexts. They especially stress the importance of distinguishing between risk factors associated with the mother as opposed to the child.

Integration of ground-penetrating radar and microgravimetric methods to map shallow caves

Ground-penetrating radar (GPR) and microgravimetric surveys have been conducted in the southern Jura mountains of western Switzerland in order to map subsurface karstic features. The study site, La Grande Rolaz cave, is an extensive system in which many portions have been mapped. By using small station spacing and careful processing for the geophysical data, and by modeling these data with topographic information from within the cave, accurate interpretations have been achieved. The constraints on the interpreted geologic models are better when combining the geophysical methods than when using only one of the methods, despite the general limitations of two-dimensional (2D) profiling. For example, microgravimetry can complement GPR methods for accurately delineating a shallow cave section approximately 10 X 10 mt in size. Conversely, GPR methods can be complementary in determining cavity depths and in verifying the presence of off-line features and numerous areas of small cavities and fractures, which may be difficult to resolve in microgravimetric data.

ExpressionView--an interactive viewer for modules identified in gene expression data.

SUMMARY: ExpressionView is an R package that provides an interactive graphical environment to explore transcription modules identified in gene expression data. A sophisticated ordering algorithm is used to present the modules with the expression in a visually appealing layout that provides an intuitive summary of the results. From this overview, the user can select individual modules and access biologically relevant metadata associated with them. AVAILABILITY: http://www.unil.ch/cbg/ExpressionView. Screenshots, tutorials and sample data sets can be found on the ExpressionView web site.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.