Linked Life Data

11544044

Source:http://linkedlifedata.com/resource/pubmed/id/11544044

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7-8
pubmed:dateCreated
2001-9-6
pubmed:abstractText
This paper describes two approaches for sensing changes in spiking cells when only a limited amount of spike data is available, i.e., dynamically constructed local expansion rates and spike area distributions. The two methods were tested on time series from cultured neuron cells that exhibit spiking both autonomously and in the presence of periodic stimulation. Our tested hypothesis was that minute concentrations of toxins could affect the local statistics of the dynamics. Short data sets having relatively few spikes were generated from experiments on cells before and after being treated with a small concentration of channel blocker. In spontaneous spiking cells, local expansion rates show a sensitivity that correlates with channel concentration level, while stimulated cells show no such correlation. Spike area distributions on the other hand showed measurable differences between control and treated conditions for both types of spiking, and a much higher degree of sensitivity. Because these methods are based on analysis of short time series analysis, they might provide novel means for cell drug and toxin detection.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0956-5663
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
503-12
pubmed:dateRevised
2009-7-14
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Methods for short time series analysis of cell-based biosensor data.
pubmed:affiliation
Naval Research Laboratory, Special Project in Nonlinear Science, Code 6700.3, Washington, DC 20375, USA. schwartz@nlschaos.nrl.navy.mil
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.