11,491 research outputs found
Big Data and Analysis of Data Transfers for International Research Networks Using NetSage
Modern science is increasingly data-driven and collaborative in nature. Many scientific disciplines, including genomics, high-energy physics, astronomy, and atmospheric science, produce petabytes of data that must be shared with collaborators all over the world. The National Science Foundation-supported International Research Network Connection (IRNC) links have been essential to enabling this collaboration, but as data sharing has increased, so has the amount of information being collected to understand network performance. New capabilities to measure and analyze the performance of international wide-area networks are essential to ensure end-users are able to take full advantage of such infrastructure for their big data applications. NetSage is a project to develop a unified, open, privacy-aware network measurement, and visualization service to address the needs of monitoring today's high-speed international research networks. NetSage collects data on both backbone links and exchange points, which can be as much as 1Tb per month. This puts a significant strain on hardware, not only in terms storage needs to hold multi-year historical data, but also in terms of processor and memory needs to analyze the data to understand network behaviors. This paper addresses the basic NetSage architecture, its current data collection and archiving approach, and details the constraints of dealing with this big data problem of handling vast amounts of monitoring data, while providing useful, extensible visualization to end users
ImageJ2: ImageJ for the next generation of scientific image data
ImageJ is an image analysis program extensively used in the biological
sciences and beyond. Due to its ease of use, recordable macro language, and
extensible plug-in architecture, ImageJ enjoys contributions from
non-programmers, amateur programmers, and professional developers alike.
Enabling such a diversity of contributors has resulted in a large community
that spans the biological and physical sciences. However, a rapidly growing
user base, diverging plugin suites, and technical limitations have revealed a
clear need for a concerted software engineering effort to support emerging
imaging paradigms, to ensure the software's ability to handle the requirements
of modern science. Due to these new and emerging challenges in scientific
imaging, ImageJ is at a critical development crossroads.
We present ImageJ2, a total redesign of ImageJ offering a host of new
functionality. It separates concerns, fully decoupling the data model from the
user interface. It emphasizes integration with external applications to
maximize interoperability. Its robust new plugin framework allows everything
from image formats, to scripting languages, to visualization to be extended by
the community. The redesigned data model supports arbitrarily large,
N-dimensional datasets, which are increasingly common in modern image
acquisition. Despite the scope of these changes, backwards compatibility is
maintained such that this new functionality can be seamlessly integrated with
the classic ImageJ interface, allowing users and developers to migrate to these
new methods at their own pace. ImageJ2 provides a framework engineered for
flexibility, intended to support these requirements as well as accommodate
future needs
3D time series analysis of cell shape using Laplacian approaches
Background:
Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes.
Results:
We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells.
Conclusions:
The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations
The Scalable Brain Atlas: instant web-based access to public brain atlases and related content
The Scalable Brain Atlas (SBA) is a collection of web services that provide
unified access to a large collection of brain atlas templates for different
species. Its main component is an atlas viewer that displays brain atlas data
as a stack of slices in which stereotaxic coordinates and brain regions can be
selected. These are subsequently used to launch web queries to resources that
require coordinates or region names as input. It supports plugins which run
inside the viewer and respond when a new slice, coordinate or region is
selected. It contains 20 atlas templates in six species, and plugins to compute
coordinate transformations, display anatomical connectivity and fiducial
points, and retrieve properties, descriptions, definitions and 3d
reconstructions of brain regions. The ambition of SBA is to provide a unified
representation of all publicly available brain atlases directly in the web
browser, while remaining a responsive and light weight resource that
specializes in atlas comparisons, searches, coordinate transformations and
interactive displays.Comment: Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated
this project and played a crucial role in the design and quality assurance of
the Scalable Brain Atla
Challenges for the comprehensive management of cloud services in a PaaS framework
The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies
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