Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

QUAL : A Provenance-Aware Quality Model

The research described here is supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.

GE models and algorithms for condensed phase equilibrium data regression in ternary systems: limitations and proposals

Phase equilibrium data regression is an unavoidable task necessary to obtain the appropriate values for any model to be used in separation equipment design for chemical process simulation and optimization. The accuracy of this process depends on different factors such as the experimental data quality, the selected model and the calculation algorithm. The present paper summarizes the results and conclusions achieved in our research on the capabilities and limitations of the existing GE models and about strategies that can be included in the correlation algorithms to improve the convergence and avoid inconsistencies. The NRTL model has been selected as a representative local composition model. New capabilities of this model, but also several relevant limitations, have been identified and some examples of the application of a modified NRTL equation have been discussed. Furthermore, a regression algorithm has been developed that allows for the advisable simultaneous regression of all the condensed phase equilibrium regions that are present in ternary systems at constant T and P. It includes specific strategies designed to avoid some of the pitfalls frequently found in commercial regression tools for phase equilibrium calculations. Most of the proposed strategies are based on the geometrical interpretation of the lowest common tangent plane equilibrium criterion, which allows an unambiguous comprehension of the behavior of the mixtures. The paper aims to show all the work as a whole in order to reveal the necessary efforts that must be devoted to overcome the difficulties that still exist in the phase equilibrium data regression problem.

Towards a reverse engineering approach for guiding user in applying data mining

Comunicación presentada en las XVI Jornadas de Ingeniería del Software y Bases de Datos, JISBD 2011, A Coruña, 5-7 septiembre 2011.

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

The design and implementation of a repository for the management of spatial data integrity constraints

This paper reviews the key features of an environment to support domain users in spatial information system (SIS) development. It presents a full design and prototype implementation of a repository system for the storage and management of metadata, focusing on a subset of spatial data integrity constraint classes. The system is designed to support spatial system development and customization by users within the domain that the system will operate.

Post-crystallization treatments for improving diffraction quality of protein crystals

X-ray crystallography is the most powerful method for determining the three-dimensional structure of biological macromolecules. One of the major obstacles in the process is the production of high-quality crystals for structure determination. All too often, crystals are produced that are of poor quality and are unsuitable for diffraction studies. This review provides a compilation of post-crystallization methods that can convert poorly diffracting crystals into data-quality crystals. Protocols for annealing, dehydration, soaking and cross-linking are outlined and examples of some spectacular changes in crystal quality are provided. The protocols are easily incorporated into the structure-determination pipeline and a practical guide is provided that shows how and when to use the different post-crystallization treatments for improving crystal quality.

Dissecting query performance in logical data models: Parsimony vs. greater ontological clarity

Even when data repositories exhibit near perfect data quality, users may formulate queries that do not correspond to the information requested. Users’ poor information retrieval performance may arise from either problems understanding of the data models that represent the real world systems, or their query skills. This research focuses on users’ understanding of the data structures, i.e., their ability to map the information request and the data model. The Bunge-Wand-Weber ontology was used to formulate three sets of hypotheses. Two laboratory experiments (one using a small data model and one using a larger data model) tested the effect of ontological clarity on users’ performance when undertaking component, record, and aggregate level tasks. The results indicate for the hypotheses associated with different representations but equivalent semantics that parsimonious data model participants performed better for component level tasks but that ontologically clearer data model participants performed better for record and aggregate level tasks.

Combination of altimetry data from different satellite missions

Substantial altimetry datasets collected by different satellites have only become available during the past five years, but the future will bring a variety of new altimetry missions, both parallel and consecutive in time. The characteristics of each produced dataset vary with the different orbital heights and inclinations of the spacecraft, as well as with the technical properties of the radar instrument. An integral analysis of datasets with different properties offers advantages both in terms of data quantity and data quality. This thesis is concerned with the development of the means for such integral analysis, in particular for dynamic solutions in which precise orbits for the satellites are computed simultaneously. The first half of the thesis discusses the theory and numerical implementation of dynamic multi-satellite altimetry analysis. The most important aspect of this analysis is the application of dual satellite altimetry crossover points as a bi-directional tracking data type in simultaneous orbit solutions. The central problem is that the spatial and temporal distributions of the crossovers are in conflict with the time-organised nature of traditional solution methods. Their application to the adjustment of the orbits of both satellites involved in a dual crossover therefore requires several fundamental changes of the classical least-squares prediction/correction methods. The second part of the thesis applies the developed numerical techniques to the problems of precise orbit computation and gravity field adjustment, using the altimetry datasets of ERS-1 and TOPEX/Poseidon. Although the two datasets can be considered less compatible that those of planned future satellite missions, the obtained results adequately illustrate the merits of a simultaneous solution technique. In particular, the geographically correlated orbit error is partially observable from a dataset consisting of crossover differences between two sufficiently different altimetry datasets, while being unobservable from the analysis of altimetry data of both satellites individually. This error signal, which has a substantial gravity-induced component, can be employed advantageously in simultaneous solutions for the two satellites in which also the harmonic coefficients of the gravity field model are estimated.

Rubric-Q:adding quality-related elements to the GEOSS clearinghouse datasets

Geospatial data have become a crucial input for the scientific community for understanding the environment and developing environmental management policies. The Global Earth Observation System of Systems (GEOSS) Clearinghouse is a catalogue and search engine that provides access to the Earth Observation metadata. However, metadata are often not easily understood by users, especially when presented in ISO XML encoding. Data quality included in the metadata is basic for users to select datasets suitable for them. This work aims to help users to understand the quality information held in metadata records and to provide the results to geospatial users in an understandable and comparable way. Thus, we have developed an enhanced tool (Rubric-Q) for visually assessing the metadata quality information and quantifying the degree of metadata population. Rubric-Q is an extension of a previous NOAA Rubric tool used as a metadata training and improvement instrument. The paper also presents a thorough assessment of the quality information by applying the Rubric-Q to all dataset metadata records available in the GEOSS Clearinghouse. The results reveal that just 8.7% of the datasets have some quality element described in the metadata, 63.4% have some lineage element documented, and merely 1.2% has some usage element described. © 2013 IEEE.

Teaching Statistics to Engineers: Learning from Experiential Data

The purpose of the work is to claim that engineers can be motivated to study statistical concepts by using the applications in their experience connected with Statistical ideas. The main idea is to choose a data from the manufacturing factility (for example, output from CMM machine) and explain that even if the parts used do not meet exact specifications they are used in production. By graphing the data one can show that the error is random but follows a distribution, that is, there is regularily in the data in statistical sense. As the error distribution is continuous, we advocate that the concept of randomness be introducted starting with continuous random variables with probabilities connected with areas under the density. The discrete random variables are then introduced in terms of decision connected with size of the errors before generalizing to abstract concept of probability. Using software, they can then be motivated to study statistical analysis of the data they encounter and the use of this analysis to make engineering and management decisions.

Information systems to support decision-making in *construction owner organizations: A data warehousing approach

Construction organizations typically deal with large volumes of project data containing valuable information. It is found that these organizations do not use these data effectively for planning and decision-making. There are two reasons. First, the information systems in construction organizations are designed to support day-to-day construction operations. The data stored in these systems are often non-validated, non-integrated and are available in a format that makes it difficult for decision makers to use in order to make timely decisions. Second, the organizational structure and the IT infrastructure are often not compatible with the information systems thereby resulting in higher operational costs and lower productivity. These two issues have been investigated in this research with the objective of developing systems that are structured for effective decision-making. ^ A framework was developed to guide storage and retrieval of validated and integrated data for timely decision-making and to enable construction organizations to redesign their organizational structure and IT infrastructure matched with information system capabilities. The research was focused on construction owner organizations that were continuously involved in multiple construction projects. Action research and Data warehousing techniques were used to develop the framework. ^ One hundred and sixty-three construction owner organizations were surveyed in order to assess their data needs, data management practices and extent of use of information systems in planning and decision-making. For in-depth analysis, Miami-Dade Transit (MDT) was selected which is in-charge of all transportation-related construction projects in the Miami-Dade county. A functional model and a prototype system were developed to test the framework. The results revealed significant improvements in data management and decision-support operations that were examined through various qualitative (ease in data access, data quality, response time, productivity improvement, etc.) and quantitative (time savings and operational cost savings) measures. The research results were first validated by MDT and then by a representative group of twenty construction owner organizations involved in various types of construction projects. ^

Information systems to support decision-making in construction owner organizations : a data warehousing approach

Construction organizations typically deal with large volumes of project data containing valuable information. It is found that these organizations do not use these data effectively for planning and decision-making. There are two reasons. First, the information systems in construction organizations are designed to support day-to-day construction operations. The data stored in these systems are often non-validated, nonintegrated and are available in a format that makes it difficult for decision makers to use in order to make timely decisions. Second, the organizational structure and the IT infrastructure are often not compatible with the information systems thereby resulting in higher operational costs and lower productivity. These two issues have been investigated in this research with the objective of developing systems that are structured for effective decision-making. A framework was developed to guide storage and retrieval of validated and integrated data for timely decision-making and to enable construction organizations to redesign their organizational structure and IT infrastructure matched with information system capabilities. The research was focused on construction owner organizations that were continuously involved in multiple construction projects. Action research and Data warehousing techniques were used to develop the framework. One hundred and sixty-three construction owner organizations were surveyed in order to assess their data needs, data management practices and extent of use of information systems in planning and decision-making. For in-depth analysis, Miami-Dade Transit (MDT) was selected which is in-charge of all transportation-related construction projects in the Miami-Dade county. A functional model and a prototype system were developed to test the framework. The results revealed significant improvements in data management and decision-support operations that were examined through various qualitative (ease in data access, data quality, response time, productivity improvement, etc.) and quantitative (time savings and operational cost savings) measures. The research results were first validated by MDT and then by a representative group of twenty construction owner organizations involved in various types of construction projects.

A novel near real-time quality-control procedure for radiometric profiles measured by Bio-Argo floats: protocols and performances

An array of Bio-Argo floats equipped with radiometric sensors has been recently deployed in various open ocean areas representative of the diversity of trophic and bio-optical conditions prevailing in the so-called Case 1 waters. Around solar noon and almost everyday, each float acquires 0-250 m vertical profiles of Photosynthetically Available Radiation and downward irradiance at three wavelengths (380, 412 and 490 nm). Up until now, more than 6500 profiles for each radiometric channel have been acquired. As these radiometric data are collected out of operator’s control and regardless of meteorological conditions, specific and automatic data processing protocols have to be developed. Here, we present a data quality-control procedure aimed at verifying profile shapes and providing near real-time data distribution. This procedure is specifically developed to: 1) identify main issues of measurements (i.e. dark signal, atmospheric clouds, spikes and wave-focusing occurrences); 2) validate the final data with a hierarchy of tests to ensure a scientific utilization. The procedure, adapted to each of the four radiometric channels, is designed to flag each profile in a way compliant with the data management procedure used by the Argo program. Main perturbations in the light field are identified by the new protocols with good performances over the whole dataset. This highlights its potential applicability at the global scale. Finally, the comparison with modeled surface irradiances allows assessing the accuracy of quality-controlled measured irradiance values and identifying any possible evolution over the float lifetime due to biofouling and instrumental drift.