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Find video protocols related to scientific articles indexed in Pubmed.
Estimation of clustering parameters using gaussian process regression.
PLoS ONE
PUBLISHED: 01-01-2014
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We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information the distribution is modelled as a Gaussian process. The clustering parameters are then estimated numerically by fitting to the covariance structure of the model. It is shown that the proposed method is resilient to any sampling regime. The method is applied to simulated two-dimensional clustered populations and the results are compared to a related method from the literature.
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Two sisters resembling Gorlin-Chaudhry-Moss syndrome.
Am. J. Med. Genet. A
PUBLISHED: 01-18-2011
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The Gorlin-Chaudhry-Moss syndrome (GCMS), was describe initially by Gorlin et al. [Gorlin et al. (1960)] in two sisters with craniosynostosis, hypertrichosis, hypoplastic labia majora, dental defects, eye anomalies, patent ductus arteriosus, and normal intelligence. Two other sporadic instances have been documented. Here, we report on two sisters with a condition with some similarities to GCMS as well as some differences, which could represent either previously unreported variability in GCMS, or it may represent a novel disorder.
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Spatial structure and activity of sedimentary microbial communities underlying a Beggiatoa spp. mat in a Gulf of Mexico hydrocarbon seep.
PLoS ONE
PUBLISHED: 01-15-2010
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Subsurface fluids from deep-sea hydrocarbon seeps undergo methane- and sulfur-cycling microbial transformations near the sediment surface. Hydrocarbon seep habitats are naturally patchy, with a mosaic of active seep sediments and non-seep sediments. Microbial community shifts and changing activity patterns on small spatial scales from seep to non-seep sediment remain to be examined in a comprehensive habitat study.
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Multi-scale measures of rugosity, slope and aspect from benthic stereo image reconstructions.
PLoS ONE
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This paper demonstrates how multi-scale measures of rugosity, slope and aspect can be derived from fine-scale bathymetric reconstructions created from geo-referenced stereo imagery. We generate three-dimensional reconstructions over large spatial scales using data collected by Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), manned submersibles and diver-held imaging systems. We propose a new method for calculating rugosity in a Delaunay triangulated surface mesh by projecting areas onto the plane of best fit using Principal Component Analysis (PCA). Slope and aspect can be calculated with very little extra effort, and fitting a plane serves to decouple rugosity from slope. We compare the results of the virtual terrain complexity calculations with experimental results using conventional in-situ measurement methods. We show that performing calculations over a digital terrain reconstruction is more flexible, robust and easily repeatable. In addition, the method is non-contact and provides much less environmental impact compared to traditional survey techniques. For diver-based surveys, the time underwater needed to collect rugosity data is significantly reduced and, being a technique based on images, it is possible to use robotic platforms that can operate beyond diver depths. Measurements can be calculated exhaustively at multiple scales for surveys with tens of thousands of images covering thousands of square metres. The technique is demonstrated on data gathered by a diver-rig and an AUV, on small single-transect surveys and on a larger, dense survey that covers over [Formula: see text]. Stereo images provide 3D structure as well as visual appearance, which could potentially feed into automated classification techniques. Our multi-scale rugosity, slope and aspect measures have already been adopted in a number of marine science studies. This paper presents a detailed description of the method and thoroughly validates it against traditional in-situ measurements.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.