Loading…
Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production
Two rapid vibrational spectroscopic approaches (diffuse reflectance–absorbance Fourier transform infrared [FT‐IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie‐point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, ind...
Saved in:
| Published in: | Biotechnology and bioengineering 2002-06, Vol.78 (5), p.527-538 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3 |
| container_end_page | 538 |
| container_issue | 5 |
| container_start_page | 527 |
| container_title | Biotechnology and bioengineering |
| container_volume | 78 |
| creator | McGovern, Aoife C. Broadhurst, David Taylor, Janet Kaderbhai, Naheed Winson, Michael K. Small, David A. Rowland, Jem J. Kell, Douglas B. Goodacre, Royston |
| description | Two rapid vibrational spectroscopic approaches (diffuse reflectance–absorbance Fourier transform infrared [FT‐IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie‐point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed‐batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information‐rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root‐mean‐square error for predictions of concentrations in an independent test set was |
| doi_str_mv | 10.1002/bit.10226 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_71899675</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>18424996</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3</originalsourceid><addsrcrecordid>eNqFkc1u1DAUhS0EokNhwQsgb0BiEWo7iR2zKxUMFQMIaQRLy3ZuiiGxU9sR7RvxmHg6A12hrvz3nXOsexB6SskrSgg7MS6XDWP8HlpRIkVFmCT30YoQwqu6lewIPUrpRzmKjvOH6IgySlvK2Ar9_hi8yyE6f4HDgG2Y5hGusPP9knJ0esTGhTkGCylBwkOIeIKsTRhdhoJ7Cz5HnV3wCS9pZzOFHqLHaQabY0g2zK4ote_xpO135wGPoKMv6Gt8Os-jszdynAO-cMZAhLHcYW1dj0tyv9jd82P0YNBjgieH9Rht373dnr2vNp_X52enm8q2rOaVIcJYAxSgZ1K0XNPeMsLadmiMaXhnOUAZlW2MaLXsByYZH2omDAcpB10foxd725J8uUDKanLJlh9pD2FJStBOSi7aO0Ema0FbTu4EadewpngW8OUetGVqKcKg5ugmHa8VJWrXsyo9q5ueC_vsYLqYCfpb8lBsAZ4fAJ2sHoeovXXplmtoJwjdcSd77pcb4fr_ierN-fZvdLVXuJTh6p9Cx5-Ki1q06tuntdqsBSVfPnxVvP4DWO_S9Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>18424996</pqid></control><display><type>article</type><title>Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production</title><source>Wiley</source><creator>McGovern, Aoife C. ; Broadhurst, David ; Taylor, Janet ; Kaderbhai, Naheed ; Winson, Michael K. ; Small, David A. ; Rowland, Jem J. ; Kell, Douglas B. ; Goodacre, Royston</creator><creatorcontrib>McGovern, Aoife C. ; Broadhurst, David ; Taylor, Janet ; Kaderbhai, Naheed ; Winson, Michael K. ; Small, David A. ; Rowland, Jem J. ; Kell, Douglas B. ; Goodacre, Royston</creatorcontrib><description>Two rapid vibrational spectroscopic approaches (diffuse reflectance–absorbance Fourier transform infrared [FT‐IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie‐point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed‐batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information‐rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root‐mean‐square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as “black box” methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation‐based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on‐line analysis. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 78: 527–538, 2002.</description><identifier>ISSN: 0006-3592</identifier><identifier>EISSN: 1097-0290</identifier><identifier>DOI: 10.1002/bit.10226</identifier><identifier>PMID: 12115122</identifier><identifier>CODEN: BIBIAU</identifier><language>eng</language><publisher>New York: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Algorithms ; Biological and medical sciences ; Biotechnology ; Cluster Analysis ; dispersive Raman spectroscopy ; evolutionary computing ; Expert Systems ; Feedback ; Fourier transform infrared spectroscopy ; Fundamental and applied biological sciences. Psychology ; Gibberella - metabolism ; Gibberellins - analysis ; Linear Models ; Mass Spectrometry - instrumentation ; Mass Spectrometry - methods ; Methods. Procedures. Technologies ; Microbial engineering. Fermentation and microbial culture technology ; Models, Biological ; Multivariate Analysis ; Others ; pyrolysis mass spectrometry ; Quality Control ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Fourier Transform Infrared - instrumentation ; Spectroscopy, Fourier Transform Infrared - methods ; Spectrum Analysis - instrumentation ; Spectrum Analysis - methods ; Spectrum Analysis, Raman - instrumentation ; Spectrum Analysis, Raman - methods ; Various methods and equipments</subject><ispartof>Biotechnology and bioengineering, 2002-06, Vol.78 (5), p.527-538</ispartof><rights>Copyright © 2002 Wiley Periodicals, Inc.</rights><rights>2002 INIST-CNRS</rights><rights>Copyright 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 78: 527-538, 2002.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3</citedby><cites>FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14187012$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/12115122$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McGovern, Aoife C.</creatorcontrib><creatorcontrib>Broadhurst, David</creatorcontrib><creatorcontrib>Taylor, Janet</creatorcontrib><creatorcontrib>Kaderbhai, Naheed</creatorcontrib><creatorcontrib>Winson, Michael K.</creatorcontrib><creatorcontrib>Small, David A.</creatorcontrib><creatorcontrib>Rowland, Jem J.</creatorcontrib><creatorcontrib>Kell, Douglas B.</creatorcontrib><creatorcontrib>Goodacre, Royston</creatorcontrib><title>Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production</title><title>Biotechnology and bioengineering</title><addtitle>Biotechnol. Bioeng</addtitle><description>Two rapid vibrational spectroscopic approaches (diffuse reflectance–absorbance Fourier transform infrared [FT‐IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie‐point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed‐batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information‐rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root‐mean‐square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as “black box” methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation‐based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on‐line analysis. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 78: 527–538, 2002.</description><subject>Algorithms</subject><subject>Biological and medical sciences</subject><subject>Biotechnology</subject><subject>Cluster Analysis</subject><subject>dispersive Raman spectroscopy</subject><subject>evolutionary computing</subject><subject>Expert Systems</subject><subject>Feedback</subject><subject>Fourier transform infrared spectroscopy</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Gibberella - metabolism</subject><subject>Gibberellins - analysis</subject><subject>Linear Models</subject><subject>Mass Spectrometry - instrumentation</subject><subject>Mass Spectrometry - methods</subject><subject>Methods. Procedures. Technologies</subject><subject>Microbial engineering. Fermentation and microbial culture technology</subject><subject>Models, Biological</subject><subject>Multivariate Analysis</subject><subject>Others</subject><subject>pyrolysis mass spectrometry</subject><subject>Quality Control</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Spectroscopy, Fourier Transform Infrared - instrumentation</subject><subject>Spectroscopy, Fourier Transform Infrared - methods</subject><subject>Spectrum Analysis - instrumentation</subject><subject>Spectrum Analysis - methods</subject><subject>Spectrum Analysis, Raman - instrumentation</subject><subject>Spectrum Analysis, Raman - methods</subject><subject>Various methods and equipments</subject><issn>0006-3592</issn><issn>1097-0290</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><recordid>eNqFkc1u1DAUhS0EokNhwQsgb0BiEWo7iR2zKxUMFQMIaQRLy3ZuiiGxU9sR7RvxmHg6A12hrvz3nXOsexB6SskrSgg7MS6XDWP8HlpRIkVFmCT30YoQwqu6lewIPUrpRzmKjvOH6IgySlvK2Ar9_hi8yyE6f4HDgG2Y5hGusPP9knJ0esTGhTkGCylBwkOIeIKsTRhdhoJ7Cz5HnV3wCS9pZzOFHqLHaQabY0g2zK4ote_xpO135wGPoKMv6Gt8Os-jszdynAO-cMZAhLHcYW1dj0tyv9jd82P0YNBjgieH9Rht373dnr2vNp_X52enm8q2rOaVIcJYAxSgZ1K0XNPeMsLadmiMaXhnOUAZlW2MaLXsByYZH2omDAcpB10foxd725J8uUDKanLJlh9pD2FJStBOSi7aO0Ema0FbTu4EadewpngW8OUetGVqKcKg5ugmHa8VJWrXsyo9q5ueC_vsYLqYCfpb8lBsAZ4fAJ2sHoeovXXplmtoJwjdcSd77pcb4fr_ierN-fZvdLVXuJTh6p9Cx5-Ki1q06tuntdqsBSVfPnxVvP4DWO_S9Q</recordid><startdate>20020605</startdate><enddate>20020605</enddate><creator>McGovern, Aoife C.</creator><creator>Broadhurst, David</creator><creator>Taylor, Janet</creator><creator>Kaderbhai, Naheed</creator><creator>Winson, Michael K.</creator><creator>Small, David A.</creator><creator>Rowland, Jem J.</creator><creator>Kell, Douglas B.</creator><creator>Goodacre, Royston</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley</general><scope>BSCLL</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>7TB</scope><scope>7X8</scope></search><sort><creationdate>20020605</creationdate><title>Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production</title><author>McGovern, Aoife C. ; Broadhurst, David ; Taylor, Janet ; Kaderbhai, Naheed ; Winson, Michael K. ; Small, David A. ; Rowland, Jem J. ; Kell, Douglas B. ; Goodacre, Royston</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Algorithms</topic><topic>Biological and medical sciences</topic><topic>Biotechnology</topic><topic>Cluster Analysis</topic><topic>dispersive Raman spectroscopy</topic><topic>evolutionary computing</topic><topic>Expert Systems</topic><topic>Feedback</topic><topic>Fourier transform infrared spectroscopy</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Gibberella - metabolism</topic><topic>Gibberellins - analysis</topic><topic>Linear Models</topic><topic>Mass Spectrometry - instrumentation</topic><topic>Mass Spectrometry - methods</topic><topic>Methods. Procedures. Technologies</topic><topic>Microbial engineering. Fermentation and microbial culture technology</topic><topic>Models, Biological</topic><topic>Multivariate Analysis</topic><topic>Others</topic><topic>pyrolysis mass spectrometry</topic><topic>Quality Control</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Spectroscopy, Fourier Transform Infrared - instrumentation</topic><topic>Spectroscopy, Fourier Transform Infrared - methods</topic><topic>Spectrum Analysis - instrumentation</topic><topic>Spectrum Analysis - methods</topic><topic>Spectrum Analysis, Raman - instrumentation</topic><topic>Spectrum Analysis, Raman - methods</topic><topic>Various methods and equipments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McGovern, Aoife C.</creatorcontrib><creatorcontrib>Broadhurst, David</creatorcontrib><creatorcontrib>Taylor, Janet</creatorcontrib><creatorcontrib>Kaderbhai, Naheed</creatorcontrib><creatorcontrib>Winson, Michael K.</creatorcontrib><creatorcontrib>Small, David A.</creatorcontrib><creatorcontrib>Rowland, Jem J.</creatorcontrib><creatorcontrib>Kell, Douglas B.</creatorcontrib><creatorcontrib>Goodacre, Royston</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Biotechnology and bioengineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McGovern, Aoife C.</au><au>Broadhurst, David</au><au>Taylor, Janet</au><au>Kaderbhai, Naheed</au><au>Winson, Michael K.</au><au>Small, David A.</au><au>Rowland, Jem J.</au><au>Kell, Douglas B.</au><au>Goodacre, Royston</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production</atitle><jtitle>Biotechnology and bioengineering</jtitle><addtitle>Biotechnol. Bioeng</addtitle><date>2002-06-05</date><risdate>2002</risdate><volume>78</volume><issue>5</issue><spage>527</spage><epage>538</epage><pages>527-538</pages><issn>0006-3592</issn><eissn>1097-0290</eissn><coden>BIBIAU</coden><abstract>Two rapid vibrational spectroscopic approaches (diffuse reflectance–absorbance Fourier transform infrared [FT‐IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie‐point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed‐batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information‐rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root‐mean‐square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as “black box” methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation‐based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on‐line analysis. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 78: 527–538, 2002.</abstract><cop>New York</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><pmid>12115122</pmid><doi>10.1002/bit.10226</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0006-3592 |
| ispartof | Biotechnology and bioengineering, 2002-06, Vol.78 (5), p.527-538 |
| issn | 0006-3592 1097-0290 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_71899675 |
| source | Wiley |
| subjects | Algorithms Biological and medical sciences Biotechnology Cluster Analysis dispersive Raman spectroscopy evolutionary computing Expert Systems Feedback Fourier transform infrared spectroscopy Fundamental and applied biological sciences. Psychology Gibberella - metabolism Gibberellins - analysis Linear Models Mass Spectrometry - instrumentation Mass Spectrometry - methods Methods. Procedures. Technologies Microbial engineering. Fermentation and microbial culture technology Models, Biological Multivariate Analysis Others pyrolysis mass spectrometry Quality Control Reproducibility of Results Sensitivity and Specificity Spectroscopy, Fourier Transform Infrared - instrumentation Spectroscopy, Fourier Transform Infrared - methods Spectrum Analysis - instrumentation Spectrum Analysis - methods Spectrum Analysis, Raman - instrumentation Spectrum Analysis, Raman - methods Various methods and equipments |
| title | Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T07%3A33%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Monitoring%20of%20complex%20industrial%20bioprocesses%20for%20metabolite%20concentrations%20using%20modern%20spectroscopies%20and%20machine%20learning:%20Application%20to%20gibberellic%20acid%20production&rft.jtitle=Biotechnology%20and%20bioengineering&rft.au=McGovern,%20Aoife%20C.&rft.date=2002-06-05&rft.volume=78&rft.issue=5&rft.spage=527&rft.epage=538&rft.pages=527-538&rft.issn=0006-3592&rft.eissn=1097-0290&rft.coden=BIBIAU&rft_id=info:doi/10.1002/bit.10226&rft_dat=%3Cproquest_cross%3E18424996%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c5236-b07bcbe1eed29756a1dc20255f4bb468c6ee102c4b75a9df2926f327b6e99fa3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=18424996&rft_id=info:pmid/12115122&rfr_iscdi=true |