Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 144
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 144
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 212
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3106
Function: getPubMedXML
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
Conducting research on lipid vesicles is very convenient, since they provide a stable and controllable environment for in vitro observations. Their resemblance to biological cell membranes allows biologists to assess hazardous potential of nanoparticles by exposing the vesicles instead of live organisms. When considering behavior of vesicles during incubation with nanoparticles, majority of existing research focus on observing single vesicles only. Our approach provides an ability to observe thousands of lipid vesicles for more representative behavior estimation. We developed an efficient algorithm to transform video sequences acquired with video microscopy into quantitative data. This includes steps required to filter noise, use multiple frames for more precise content presentation, detection of regions of interest, and segmentation of circular and non-primitively shaped vesicles. Presented work is a crucial step towards the creation of an automated computer analysis for lipid vesicles behavior assessment.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1109/IEMBS.2010.5626223 | DOI Listing |
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