Finding faint HI structure in and around galaxies: scraping the barrel

Soon to be operational HI survey instruments such as APERTIF and ASKAP will produce large datasets. These surveys will provide information about the HI in and around hundreds of galaxies with a typical signal-to-noise ratio of $\sim$ 10 in the inner regions and $\sim$ 1 in the outer regions. In addition, such surveys will make it possible to probe faint HI structures, typically located in the vicinity of galaxies, such as extra-planar-gas, tails and filaments. These structures are crucial for understanding galaxy evolution, particularly when they are studied in relation to the local environment. Our aim is to find optimized kernels for the discovery of faint and morphologically complex HI structures. Therefore, using HI data from a variety of galaxies, we explore state-of-the-art filtering algorithms. We show that the intensity-driven gradient filter, due to its adaptive characteristics, is the optimal choice. In fact, this filter requires only minimal tuning of the input parameters to enhance the signal-to-noise ratio of faint components. In addition, it does not degrade the resolution of the high signal-to-noise component of a source. The filtering process must be fast and be embedded in an interactive visualization tool in order to support fast inspection of a large number of sources. To achieve such interactive exploration, we implemented a multi-core CPU (OpenMP) and a GPU (OpenGL) version of this filter in a 3D visualization environment ($\tt{SlicerAstro}$).Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte

3-D interactive visualisation tools for HI spectral line imaging

Upcoming HI surveys will deliver such large datasets that automated processing using the full 3-D information to find and characterize HI objects is unavoidable. Full 3-D visualization is an essential tool for enabling qualitative and quantitative inspection and analysis of the 3-D data, which is often complex in nature. Here we present $\tt{SlicerAstro}$, an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing, which we developed for the inspection and analysis of HI spectral line data. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparative modelling

SlicerAstro: a 3-D interactive visual analytics tool for HI data

SKA precursors are capable of detecting hundreds of galaxies in HI in a single 12 hours pointing. In deeper surveys one will probe more easily faint HI structures, typically located in the vicinity of galaxies, such as tails, filaments, and extraplanar gas. The importance of interactive visualization has proven to be fundamental for the exploration of such data as it helps users to receive immediate feedback when manipulating the data. We have developed SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on traditional 2-D input/output hardware. These capabilities enhance the data inspection, allowing faster analysis of complex sources than with traditional tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing. We demonstrate the capabilities of the current stable binary release of SlicerAstro, which offers the following features: i) handling of FITS files and astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii) interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D modeling. In addition, SlicerAstro has been designed with a strong, stable and modular C++ core, and its classes are also accessible via Python scripting, allowing great flexibility for user-customized visualization and analysis tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing. SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr

MOTIFATOR: detection and characterization of regulatory motifs using prokaryote transcriptome data

Summary: Unraveling regulatory mechanisms (e.g. identification of motifs in cis-regulatory regions) remains a major challenge in the analysis of transcriptome experiments. Existing applications identify putative motifs from gene lists obtained at rather arbitrary cutoff and require additional manual processing steps. Our standalone application MOTIFATOR identifies the most optimal parameters for motif discovery and creates an interactive visualization of the results. Discovered putative motifs are functionally characterized, thereby providing valuable insight in the biological processes that could be controlled by the motif.

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time-annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks

A morphological algorithm for improving radio-frequency interference detection

A technique is described that is used to improve the detection of radio-frequency interference in astronomical radio observatories. It is applied on a two-dimensional interference mask after regular detection in the time-frequency domain with existing techniques. The scale-invariant rank (SIR) operator is defined, which is a one-dimensional mathematical morphology technique that can be used to find adjacent intervals in the time or frequency domain that are likely to be affected by RFI. The technique might also be applicable in other areas in which morphological scale-invariant behaviour is desired, such as source detection. A new algorithm is described, that is shown to perform quite well, has linear time complexity and is fast enough to be applied in modern high resolution observatories. It is used in the default pipeline of the LOFAR observatory.Comment: Accepted for publication in A&

SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks

Summary: SpotXplore is a plugin for Cytoscape for extraction and visualization of differentially expressed subnetworks (hotspots) from gene networks. The hotspot-based visualization approach enables interactive exploration of regulatory interactions in differentially expressed gene sets, and it allows a researcher to explore gene expression in direct relation to the affected cellular gene network. The hotspots provide a view beyond the commonly used metabolic pathways and gene ontologies

Scientific Visualisation of Extremely Large Distributed Astronomical Surveys

Interactive real-time visualisation of large data sets plays an important role in scientific research. It is even more relevant for astronomy where new cutting edge large telescopes will generate tens of petabytes sky surveys. We describe our solution, developed in context of the Euclid space mission whose large astronomical imaging data will be distributed over several heterogeneous Science Data Centres (SDCs) across the world. In our visualisation architecture for distributed data, millions of survey images (HiPS) distributed over SDCs are efficiently transported and combined to deliver image(s) of interest at the desired resolution (up to pixel level) to the user. This is achieved by optimally utilising a combination of several modern tools consisting of http servers, a Front-End Node and load-balancer (FEN), reverse proxies, PHP/Python scripts, MySQL databases, including on the fly image generation/combination which all feed (only) the required information to the Aladin interactive visualisation tool at the remote user's Personal Computer (PC). It has potential applications for large projects (e.g., Square Kilometre Array) having data distributed across several locations