Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
We demonstrate a proof of concept for detecting heterogeneities and estimating lifetimes in time-correlated single-photon-counting (TCSPC) data when photon counts per molecule are low. In this approach photons are classified as either prompt or delayed according to their arrival times relative to an arbitrarily chosen time gate. Under conditions in which the maximum likelihood (ML) methods fail to distinguish between heterogeneous and homogeneous data sets, histograms of the number of prompt photons from many molecules are analyzed to identify heterogeneities, estimate the contributing fluorescence lifetimes, and determine the relative amplitudes of the fluorescence, scatter, and background components of the signal. The uncertainty of the lifetime estimate is calculated to be larger than but comparable to the uncertainty in ML estimates of single lifetime data made with similar total photon counts. Increased uncertainty and systematic errors in lifetime estimates are observed when the temporal profile of the lifetime decay is similar to either the background or scatter signals, primarily due to error in estimating the amplitudes of the various signal components. Unlike ML methods, which can fail to converge on a solution for a given molecule, this approach does not discard any data, thus reducing the potential for introducing a bias into the results.
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Source |
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http://dx.doi.org/10.1366/10-06147 | DOI Listing |
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