volume-segmentation-analysis-biological-materials-using-survos-super

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of ...

Diamond Light Source

Motor learning is thought to involve modulation of synaptic inputs in the cerebellar cortex, including granule neuron/Purkinje neuron contacts. During a complex motor task requiring mice to walk across irregularly spaced pegs, cerebellar granule neurons show a rapid and transient induction of mRNA for the extracellular protease tissue plasminogen activator (tPA). This induction of tPA mRNA is cerebellar specific, is not seen in the cerebella of exercised or stressed animals, and is distinct from simple performance phenomena. Knock-out mice lacking the tPA gene show a significant reduction in both rate and extent of learning. Furthermore, blocking tPA activity during training dramatically impaired motor learning. Thus, tPA plays an important role in motor learning, in which tPA may facilitate remodeling of the active synaptic zone.

Absence of tissue plasminogen activator gene or activity impairs mouse cerebellar motor learning.

This paper reports on the first user/application-driven multi-technology optical sub-wavelength network for intra/inter Data-Centre (DC) communications. Two DCs each with distinct sub-wavelength switching technologies, frame based synchronous TSON and packet based asynchronous OPST are interconnected by a WSON inter-DC communication. The intra/inter DC testbed demonstrates ultra-low latency (packet-delay <270 µs and packet-delay-variation (PDV)<10 µs) flexible data-rate traffic transfer by point-to-point, point-to-multipoint, and multipoint-to-(multi)point connectivity, highly suitable for cloud based applications and high performance computing (HPC). The extended GMPLS-PCE-SLAE based control-plane enables innovative application-driven end-to-end sub-wavelength path setup and resource reservation across the multi technology data-plane, which has been assessed for as many as 25 concurrent requests.

Demonstration of low latency Intra/Inter Data-Centre heterogeneous optical sub-wavelength network using extended GMPLS-PCE control-plane.

The focus in macromolecular crystallography is moving towards even more challenging target proteins that often crystallize on much smaller scales and are frequently mounted in opaque or highly refractive materials. It is therefore essential that X-ray beamline technology develops in parallel to accommodate such difficult samples. In this paper, the use of X-ray microradiography and microtomography is reported as a tool for crystal visualization, location and characterization on the macromolecular crystallography beamlines at the Diamond Light Source. The technique is particularly useful for microcrystals and for crystals mounted in opaque materials such as lipid cubic phase. X-ray diffraction raster scanning can be used in combination with radiography to allow informed decision-making at the beamline prior to diffraction data collection. It is demonstrated that the X-ray dose required for a full tomography measurement is similar to that for a diffraction grid-scan, but for sample location and shape estimation alone just a few radiographic projections may be required.

Visualization of membrane protein crystals in lipid cubic phase using X-ray imaging.

La mise au point en cristallographie macromol&eacute;culaire s&#39;oriente vers des prot&eacute;ines cibles encore plus difficiles qui souvent se cristallisent sur des &eacute;chelles beaucoup plus petites et sont souvent mont&eacute;s dans des mat&eacute;riaux opaques ou hautement r&eacute;fractives. Il est donc essentiel que les rayons x beamline technology d&eacute;veloppe en parall&egrave;le pour accueillir ces &eacute;chantillons difficiles. Dans cet article, l&#39;utilisation des rayons x microradiography et MICROTOMOGRAPHIE est signal&eacute;e comme un outil de visualisation de cristal, localisation et caract&eacute;risation sur les faisceaux de cristallographie macromol&eacute;culaire &agrave; la Source de lumi&egrave;re de diamant. La technique est particuli&egrave;rement utile de microcristaux et de cristaux, mont&eacute; dans les mat&eacute;riaux opaques tels que les cubes phase lipidique. Balayage de trame diffraction des rayons x peut &ecirc;tre utilis&eacute; en combinaison avec la radiographie pour permettre la prise de d&eacute;cisions &eacute;clair&eacute;e &agrave; la source de rayonnement pr&eacute;c&eacute;dant la collecte de donn&eacute;es de diffraction. Il est d&eacute;montr&eacute; que la dose de rayons x requise pour une mesure compl&egrave;te de la tomographie est similaire &agrave; celle d&#39;un grille de diffraction-scan, mais pour exemple emplacement et forme estimation seule quelques projections radiographiques peuvent &ecirc;tre n&eacute;cessaires.

Visualisation des cristaux de prot&eacute;ines membranaires en phase cubique de lipides &agrave; l&#39;aide d&#39;imagerie par rayons x.

I12 is the Joint Engineering, Environmental and Processing (JEEP) beamline, constructed during Phase II of the Diamond Light Source. I12 is located on a short (5 m) straight section of the Diamond storage ring and uses a 4.2 T superconducting wiggler to provide polychromatic and monochromatic X-rays in the energy range 50-150 keV. The beam energy enables good penetration through large or dense samples, combined with a large beam size (1 mrad horizontally × 0.3 mrad vertically). The beam characteristics permit the study of materials and processes inside environmental chambers without unacceptable attenuation of the beam and without the need to use sample sizes which are atypically small for the process under study. X-ray techniques available to users are radiography, tomography, energy-dispersive diffraction, monochromatic and white-beam two-dimensional diffraction/scattering and small-angle X-ray scattering. Since commencing operations in November 2009, I12 has established a broad user community in materials science and processing, chemical processing, biomedical engineering, civil engineering, environmental science, palaeontology and physics.

I12: the Joint Engineering, Environment and Processing (JEEP) beamline at Diamond Light Source.

Synchrotron light source facilities worldwide generate terabytes of data in numerous incompatible data formats from a wide range of experiment types. The Data Analysis WorkbeNch (DAWN) was developed to address the challenge of providing a single visualization and analysis platform for data from any synchrotron experiment (including single-crystal and powder diffraction, tomography and spectroscopy), whilst also being sufficiently extensible for new specific use case analysis environments to be incorporated (e.g. ARPES, PEEM). In this work, the history and current state of DAWN are presented, with two case studies to demonstrate specific functionality. The first is an example of a data processing and reduction problem using the generic tools, whilst the second shows how these tools can be targeted to a specific scientific area.

Data Analysis WorkbeNch (DAWN).

Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resolved studies, hundreds of tomographic datasets can be collected in sequence, yielding terabytes of data. Users of tomographic beamlines are drawn from various scientific disciplines, and many are keen to use tomographic reconstruction software that does not require a deep understanding of reconstruction principles. We have developed Savu, a reconstruction pipeline that enables users to rapidly reconstruct data to consistently create high-quality results. Savu is designed to work in an 'orthogonal' fashion, meaning that data can be converted between projection and sinogram space throughout the processing workflow as required. The Savu pipeline is modular and allows processing strategies to be optimized for users' purposes. In addition to the reconstruction algorithms themselves, it can include modules for identification of experimental problems, artefact correction, general image processing and data quality assessment. Savu is open source, open licensed and 'facility-independent': it can run on standard cluster infrastructure at any institution.

A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source.

With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source.

Automatic processing of multimodal tomography datasets.

Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the user's knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using Super-Regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets.

Mark Basham

Science Division,
Harwell Science and Innovation Campus,
Science Division, Harwell Science and Innovation Campus

Diamond Light Source

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Mark Basham

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Science Division,Harwell Science and Innovation Campus,Science Division, Harwell Science and Innovation Campus

Diamond Light Source

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Science Division,
Harwell Science and Innovation Campus,
Science Division, Harwell Science and Innovation Campus