Biomarkers of angiogenesis for the development of antiangiogenic therapies in oncology: tools or decorations?

Since 2004, four antiangiogenic drugs have been approved for clinical use in patients with advanced solid cancers, on the basis of their capacity to improve survival in phase III clinical studies. These achievements validated the concept introduced by Judah Folkman that the inhibition of tumor angiogenesis could control tumor growth. It has been suggested that biomarkers of angiogenesis would greatly facilitate the clinical development of antiangiogenic therapies. For these four drugs, the pharmacodynamic effects observed in early clinical studies were important to corroborate activities, but were not essential for the continuation of clinical development and approval. Furthermore, no validated biomarkers of angiogenesis or antiangiogenesis are available for routine clinical use. Thus, the quest for biomarkers of angiogenesis and their successful use in the development of antiangiogenic therapies are challenges in clinical oncology and translational cancer research. We review critical points resulting from the successful clinical trials, review current biomarkers, and discuss their potential impact on improving the clinical use of available antiangiogenic drugs and the development of new ones.

Watershed Planning Advisory Council Annual Report January 2015

This report provides updates on the WPAC recommendations made in its 2013 legislative report, including actions taken on those recommendations and any follow-up recommendations from WPAC. Recommendations include documentation of activities, and the needs and challenges toward making progress in protecting Iowa’s water resources, identified by WPAC in coordination with all agencies and stakeholders in the management of the state’s water resources in a sustainable, fiscally responsible, and environmentally conscientious manner.

Watershed Planning Advisory Council Annual Report July 2013

This report provides updates on the WPAC recommendations made in its 2013 legislative report, including actions taken on those recommendations and any follow-up recommendations from WPAC. Recommendations include documentation of activities, and the needs and challenges toward making progress in protecting Iowa’s water resources, identified by WPAC in coordination with all agencies and stakeholders in the management of the state’s water resources in a sustainable, fiscally responsible, and environmentally conscientious manner.

Watershed Planning Advisory Council Annual Report January 31, 2012

This report provides updates on the WPAC recommendations to legislature, including actions taken on those recommendations and any follow-up recommendations from WPAC. Recommendations include documentation of activities, and the needs and challenges toward making progress in protecting Iowa’s water resources, identified by WPAC in coordination with all agencies and stakeholders in the management of the state’s water resources in a sustainable, fiscally responsible, and environmentally conscientious manner.

Watershed Planning Advisory Council Final Interim Report To The Iowa Legislature, December 1, 2010

This report provides updates on the WPAC recommendations to legislature, including actions taken on those recommendations and any follow-up recommendations from WPAC. Recommendations include documentation of activities, and the needs and challenges toward making progress in protecting Iowa’s water resources, identified by WPAC in coordination with all agencies and stakeholders in the management of the state’s water resources in a sustainable, fiscally responsible, and environmentally conscientious manner.

Watershed Planning Advisory Council Annual Report to the Iowa Legislature, January 2014

This report provides updates on the WPAC recommendations to legislature, including actions taken on those recommendations and any follow-up recommendations from WPAC. Recommendations include documentation of activities, and the needs and challenges toward making progress in protecting Iowa’s water resources, identified by WPAC in coordination with all agencies and stakeholders in the management of the state’s water resources in a sustainable, fiscally responsible, and environmentally conscientious manner.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Capitol Planning Commission 2013 Annual Report

This 2013 Annual Report further summarizes the work of the Commission during the last year and provides planning recommendations for the future of the Capitol Complex. Please note that Iowa Code Chapter 8A.373 provides that before any physical changes are made to the state capitol complex "it shall be the duty of the officers, commissions, and councils charged by law with the duty of determining such questions to call upon" the Capitol Planning Commission for advice. The Capitol Planning Commission members, as well as DAS Staff, welcome the opportunity to discuss future projects at the request of any legislator.

Capitol Planning Commission 2015 Annual Report

This 2015 Annual Report further summarizes the work of the Commission during the last year and provides planning recommendations for the future of the Capitol Complex. Please note that Iowa Code Chapter 8A.373 provides that before any physical changes are made to the state capitol complex "it shall be the duty of the officers, commissions, and councils charged by law with the duty of determining such questions to call upon" the Capitol Planning Commission for advice. The Capitol Planning Commission members, as well as DAS Staff, welcome the opportunity to discuss future projects at the request of any legislator.

Comprehensive Water Planning In Iowa: Past Efforts, February 2009, December 24, 2012

This document looks at some of Iowa’s more comprehensive, statewide water planning efforts that addressed all aspects of water or a major water issue such as water quality.

Iowa’s Water Planning History, 2009, December 26, 2012

Since 1978 when the Water Plan ’78 was published, there have been no truly comprehensive water planning efforts initiated. The ’85 Water Plan and the ’87 Groundwater Protection Strategy were significant efforts that resulted in real advancements in water resource protection but were not truly comprehensive in nature. Other efforts, such as the Section 208 (CWA) plans, the 2000 Nonpoint Source Management Plan, and various conservation and recreation planning efforts that involve various aspects of water have been completed but, like the ’85 Water Plan, were not comprehensive in nature.

Water Planning Law and Government, November 2008

Water planning efforts typically identify problems and needs. But simply calling attention to issues is usually not enough to spur action; the end result of many well-intentioned planning efforts is a report that ends up gathering dust on a shelf. Vague recommendations like “Water conservation measures should be implemented” usually accomplish little by themselves as they don’t assign responsibility to anyone. Success is more likely when an implementation strategy — who can and should do what — is developed as part of the planning process. The more detailed and specific the implementation strategy, the greater the chance that something will actually be done.

Planning for Water Quality, July 2012

The following document serves two purposes. First, the Environmental Protection Agency (EPA) requires a state to develop an approved Non-point Source Management Plan (NPSMP or Plan) that encompasses the nine key elements, described in full in Appendix A, to be eligible for federal Clean Water Act Section 319 funding. Second, the Plan serves as a representation of Iowa’s vision, goals, objectives and potential action steps to reduce non-point source pollution and improve water quality over the next five to ten years. This plan is not intended to be, nor should it be, limited to the Department of Natural Resources or Iowa’s Section 319 Program, but rather reflects the collective efforts and intents of the core partners and stakeholder groups that worked together to develop the goals identified herein and programmatic means of achieving those goals.