EuroPhenome: a repository for high-throughput mouse phenotyping data.

The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress.har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies.

Geostatistical prediction of clay percentage based on soil survey data

Selostus: Viljelymaiden savespitoisuuden alueellistaminen geostatistiikan ja pistemäisen tiedon avulla

Construcción de un índice de calidad ocupacional (ICO) para el análisis de la inserción profesional de los graduados universitarios = Building an employment quality index (EQI) to analyse graduates’ access to the labour market

En este trabajo se propone la construcción de un índice de calidad ocupacional (ICO) a partir de los datos de la Encuesta de Inserción Laboral de los Graduados de las Universidades Catalanas realizada por la Agencia para la Calidad del Sistema Universitario de Catalunya (AQU), que ha de permitir un mejor análisis de la información que proporciona la encuesta y facilitar su comparación con estudios similares. La encuesta se realiza tres años después de la graduación. En este artículo, se utiliza la segunda encuesta realizada el año 2005 entre 11.456 graduados (52,63%) de la promoción 2001 (AQU, 2005, Serra-Ramoneda, 2007). El índice se ha elaborado a partir de los indicadores objetivos ‘tipo y duración del contrato laboral’, ‘retribución económica’, ‘adecuación entre la formación universitaria y el empleo’ a los que se otorga una puntuación ponderada según las respuestas dadas por los graduados. La suma de las puntuaciones se matiza con un coeficiente derivado del indicador subjetivo ‘satisfacción con el trabajo en general’. A partir de la información proporcionada por el índice, se realiza un análisis comparativo del nivel de calidad ocupacional que han logrado los graduados de áreas de conocimiento, ámbitos de trabajo, ramas de actividad y ubicaciones territoriales del empleo diferentes. Los resultados obtenidos permiten observar que entre los graduados catalanes los siguientes hechos son buenos predictores de la calidad de la ocupación: haber estudiado una carrera que no sea de Humanidades, ser un hombre, haber desempeñado durante la carrera un trabajo relacionado con los estudios, estar ocupado en la construcción, en instituciones financieras o en servicios a empresas, haber tenido algún tipo de movilidad por motivos de trabajo, trabajar fuera de Cataluña y hacerlo en empresas grandes, especialmente con más de 500 trabajadores. Finalmente, se presentan algunas reflexiones y propuestas que pueden resultar de interés para la orientación de los estudiantes y la planificación universitaria

Automatic filtering and substantiation of drug safety signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions

Metrical strength and contradiction in turkish Makam music

In this paper we investigate how note onsets in Turkish Makam music compositions are distributed, and in how far this distribution supports or contradicts the metrical structure of the pieces, the usul. We use MIDI data to derive the distributions in the form of onset histograms, and comparethem with metrical weights that are applied to describe the usul in theory. We compute correlation and syncopation values to estimate the degrees of support and contradiction, respectively. While the concept of syncopation is rarelymentioned in the context of this music, we can gain interesting insight into the structure of a piece using such a measure.We show that metrical contradiction is systematically applied in some metrical structures. We will compare thedifferences between Western music and Turkish Makam music regarding metrical support and contradiction. Such a study can help avoiding pitfalls in later attempts to perform audio processing tasks such as beat tracking or rhythmic similarity measurements.

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools

PURPOSE: Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in-depth analysis of adverse drug reactions risks.METHODS: The EU-ADR Web Platform exploits the wealth of data collected within a large-scale European initiative, the EU-ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug-event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data-mining and statistical analysis tasks. This permits obtaining a ranked drug-event list, removing spurious entries and highlighting relationships with high risk potential.RESULTS: The EU-ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug-event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug-event pairs can be substantiated and statistically analysed within the platform's innovative working environment.CONCLUSIONS: A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU-ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/. Copyright © 2012 John Wiley & Sons, Ltd.

CleanEx: a database of heterogeneous gene expression data based on a consistent gene nomenclature.

The main goal of CleanEx is to provide access to public gene expression data via unique gene names. A second objective is to represent heterogeneous expression data produced by different technologies in a way that facilitates joint analysis and cross-data set comparisons. A consistent and up-to-date gene nomenclature is achieved by associating each single experiment with a permanent target identifier consisting of a physical description of the targeted RNA population or the hybridization reagent used. These targets are then mapped at regular intervals to the growing and evolving catalogues of human genes and genes from model organisms. The completely automatic mapping procedure relies partly on external genome information resources such as UniGene and RefSeq. The central part of CleanEx is a weekly built gene index containing cross-references to all public expression data already incorporated into the system. In addition, the expression target database of CleanEx provides gene mapping and quality control information for various types of experimental resource, such as cDNA clones or Affymetrix probe sets. The web-based query interfaces offer access to individual entries via text string searches or quantitative expression criteria. CleanEx is accessible at: http://www.cleanex.isb-sib.ch/.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Effect of data acquisition accuracy on timing of stand harvests and expected net present value

Abstract

High Priority Corridors for Access Management Near Large Urban Areas in Iowa, 2002

This research project was intended to produce a strategy for addressing current and future access management problems on state highway routes located just outside urban areas that serve as major routes for commuting into and out of major employment centers in Iowa. There were two basic goals: (1) to develop a ranking system for identifying high-priority segments for access management treatments on primary highways outside metro and urban areas and (2) to focus efforts on routes that are major commuting routes at present and in the future. The project focused on four-lane expressways and two-lane arterials most likely to serve extensive commuter traffic. Available spatial and statistical data were used to identify existing and possible future problem corridors with respect to access management. The research team developed a scheme for ranking commuter routes based on their need for attention to access management. This project was able to produce rankings for corridors based on a variety of factors, including proportion of crashes that appear to be access-related, severity of those crashes, and potential for improvement along corridors. Frequency and loss were found to be highly rank correlated; because of this, these indicators were not used together in developing final priority rankings. Most of the highest ranked routes are on two-lane rural cross sections, but a few are four-lane expressways with at-grade private driveways and public road intersections. The most important conclusion of the ranking system is that many of the poor-performing corridors are located in a single Iowa Department of Transportation district near two urban areas--Des Moines and Ames. A comprehensive approach to managing access along commuting corridors should be developed first in this district since the potential benefits would be highest in that region.

Reliable selfing rate estimates from imperfect population genetic data.

Genotypic frequencies at codominant marker loci in population samples convey information on mating systems. A classical way to extract this information is to measure heterozygote deficiencies (FIS) and obtain the selfing rate s from FIS = s/(2 - s), assuming inbreeding equilibrium. A major drawback is that heterozygote deficiencies are often present without selfing, owing largely to technical artefacts such as null alleles or partial dominance. We show here that, in the absence of gametic disequilibrium, the multilocus structure can be used to derive estimates of s independent of FIS and free of technical biases. Their statistical power and precision are comparable to those of FIS, although they are sensitive to certain types of gametic disequilibria, a bias shared with progeny-array methods but not FIS. We analyse four real data sets spanning a range of mating systems. In two examples, we obtain s = 0 despite positive FIS, strongly suggesting that the latter are artefactual. In the remaining examples, all estimates are consistent. All the computations have been implemented in a open-access and user-friendly software called rmes (robust multilocus estimate of selfing) available at http://ftp.cefe.cnrs.fr, and can be used on any multilocus data. Being able to extract the reliable information from imperfect data, our method opens the way to make use of the ever-growing number of published population genetic studies, in addition to the more demanding progeny-array approaches, to investigate selfing rates.

Iowa Department of Transportation Office of Research & Analytics Guidance on Data Management Plan (DMP) Requirements for Federally Funded Research Projects

The Iowa Department of Transportation Office of Research & Analytics has created this Guide to help researchers and contractors of the Iowa DOT attain compliance with Federal and Iowa DOT Public Access Policies for transportation-related research publications and datasets. This guide provides direction for filling out the data management plan template (also attached to this record) that will help satisfy Iowa DOT and U.S. DOT requirements.

Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment

Abstract