DINeR: Database for Insect Neuropeptide Research

Neuropeptides are responsible for regulating a variety of functions, including development, metabolism, water and ion homeostasis, and as neuromodulators in circuits of the central nervous system. Numerous neuropeptides have been identified and characterized. However, both discovery and functional characterization of neuropeptides across the massive Class Insecta has been sporadic. To leverage advances in post-genomic technologies for this rapidly growing field, insect neuroendocrinology requires a consolidated, comprehensive and standardised resource for managing neuropeptide information. The Database for Insect Neuropeptide Research (DINeR) is a web-based database-application used for search and retrieval of neuropeptide information of various insect species detailing their isoform sequences, physiological functionality and images of their receptor-binding sites, in an intuitive, accessible and user-friendly format. The curated data includes representatives of 50 well described neuropeptide families from over 400 different insect species. Approximately 4700 FASTA formatted, neuropeptide isoform amino acid sequences and over 200 records of physiological functionality have been recorded based on published literature. Also available are images of neuropeptide receptor locations. In addition, the data include comprehensive summaries for each neuropeptide family, including their function, location, known functionality, as well as cladograms, sequence alignments and logos covering most insect orders. Moreover, we have adopted a standardized nomenclature to address inconsistent classification of neuropeptides

Colour appearance descriptors for image browsing and retrieval

In this paper, we focus on the development of whole-scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-existing global CIELAB colour histograms, firstly, we try to develop metrics for wholescene colour appearance: “colour strength”, “high/low lightness” and “multicoloured”. Secondly we propose methods using these metrics either alone or combined to classify whole-scene images into five categories of appearance: strong, pastel, dark, pale and multicoloured. Experiments show positive results and that the global colour histogram is actually useful and can be used for whole-scene colour appearance classification. We have also conducted a small-scale human evaluation test on whole-scene colour appearance. The results show, with suitable threshold settings, the proposed methods can describe the whole-scene colour appearance of images close to human classification. The descriptors were tested on thousands of images from various scenes: paintings, natural scenes, objects, photographs and documents. The colour appearance classifications are being integrated into an image browsing system which allows them to also be used to refine browsing

Sixth Annual Users' Conference

Conference papers and presentation outlines which address the use of the Transportable Applications Executive (TAE) and its various applications programs are compiled. Emphasis is given to the design of the user interface and image processing workstation in general. Alternate ports of TAE and TAE subsystems are also covered

MSUO Information Technology and Geographical Information Systems: Common Protocols & Procedures. Report to the Marine Safety Umbrella Operation

The Marine Safety Umbrella Operation (MSUO) facilitates the cooperation between Interreg funded Marine Safety Projects and maritime stakeholders. The main aim of MSUO is to permit efficient operation of new projects through Project Cooperation Initiatives, these include the review of the common protocols and procedures for Information Technology (IT) and Geographical Information Systems (GIS). This study carried out by CSA Group and the National Centre for Geocomputation (NCG) reviews current spatial information standards in Europe and the data management methodologies associated with different marine safety projects. International best practice was reviewed based on the combined experience of spatial data research at NCG and initiatives in the US, Canada and the UK relating to marine security service information and acquisition and integration of large marine datasets for ocean management purposes. This report identifies the most appropriate international data management practices that could be adopted for future MSUO projects

The SIMBAD astronomical database

Simbad is the reference database for identification and bibliography of astronomical objects. It contains identifications, `basic data', bibliography, and selected observational measurements for several million astronomical objects. Simbad is developed and maintained by CDS, Strasbourg. Building the database contents is achieved with the help of several contributing institutes. Scanning the bibliography is the result of the collaboration of CDS with bibliographers in Observatoire de Paris (DASGAL), Institut d'Astrophysique de Paris, and Observatoire de Bordeaux. When selecting catalogues and tables for inclusion, priority is given to optimal multi-wavelength coverage of the database, and to support of research developments linked to large projects. In parallel, the systematic scanning of the bibliography reflects the diversity and general trends of astronomical research. A WWW interface to Simbad is available at: http://simbad.u-strasbg.fr/SimbadComment: 14 pages, 5 Postscript figures; to be published in A&A

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis

BACKGROUND. Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. RESULTS. We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. CONCLUSION. Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research.Dana-Farber Cancer Institute; National Human Genome Research Institute (P50HG004233); National Science Foundation Integrative Graduate Education and Research Traineeship grant (DGE-0654108

Tracking dynamics of functional brain networks using dense EEG

International audienceCognition is formed from networks between functionally specific but distributed brain regions. A very challenging issue in cognition is how to precisely track brain networks at very short temporal scales (often very short <1s). So far, very few studies have addressed this problem as it requires high temporal and spatial resolution simultaneously. Due to its excellent temporal resolution, Electroencephalography (EEG) is a key neuroimaging technique to access real-time information flow among large scale neuronal networks. Here, we propose a new method based on EEG source connectivity to map large-scale networks at high temporal (in the order of ms) and spatial (~1000 regions of interest) resolution. We show clear evidence of the ability of EEG source connectivity to track brain networks with high time/space resolutions during picture naming task. Our results reveal that the cognitive process can be decomposed into a sequence of transiently-stable and partially-overlapping networks. Our qualitative and quantitative observations show that the identified brain networks are in accordance with results reported in the literature regarding involved brain areas during the analyzed task