{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44772"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44772","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Development and application of OmpT-based middle down proteomics","abstract":"\"Information at the protein level is essential to understand the functioning of a biological system. The goal of proteomics is to provide such information as protein identities, post-translational modifications (PTMs) on proteins, and ultimately, quantification of proteins or their PTM levels from given biological samples, mostly using mass spectrometry. Modern mass spectrometry-based proteomic analyses primarily fall into two categories in terms of general strategies -- the \"\"bottom-up\"\" and \"\"top-down\"\" approaches. Bottom-up proteomics relies upon enzymatic protein digestions (mostly by trypsin) prior to on-line liquid chromatography-coupled tandem mass spectrometry analysis (LC-MS/MS) of post-digestion peptides. Top-down proteomics omits the proteolysis step and directly focuses on the characterization of intact proteins and their post-translational modifications. While both approaches continue to mature, they each have limitations. In the bottom-up approach, tryptic peptides are the primary unit of measurement and are powerful in protein identification, but their relatively small size (typically ~8–25 residues long) raises potential issues such as significant sample complexity, the “protein inference problem” (i.e. the inability to assign an identified tryptic peptide to a specific protein, instead of a group of proteins sharing this tryptic peptide sequence), and loss of PTM and combinatorial PTM information. The top-down approach handles these issues by characterizing intact proteins, but becomes less successful as the protein size increases. Here, a hybrid approach based on restricted proteolysis to generate middle-sized peptides (i.e. 2–20 kDa) for mass spectrometry analysis could combine positive aspects of both bottom-up and top-down proteomics. Although various alternative methods to trypsin digestion have been investigated to explore restricted proteolysis options, these previous efforts only lead to the production of peptides marginally longer than tryptic peptides. Here, a digestion method using the outer membrane protease T (OmpT) derived from E. coli is developed to achieve efficient yet restricted proteolysis, thanks to its selective cleavage primarily between dibasic sites (K/R─K/R). In Chapter II, a robust OmpT expression and purification workflow with high enzyme yield and purity is depicted. One major obstacle in obtaining active OmpT enzyme is its autoproteolysis between Lys217 and Arg218, which causes the protease to become inactive. Several tactics are utilized to maintain the intact and active form of OmpT during purification and digestion. Thereafter, a set of OmpT digestion conditions are optimized using standard proteins as test substrates. In Chapter III, the initial demonstration of OmpT digestion on a complex proteome sample is described. First, OmpT-based proteolysis is integrated with a size-dependent protein fractionation technique, and established a robust middle-down proteomics pipeline. The platform is then applied to the analysis of prefractionated high-mass HeLa cell proteome (~20─100 kDa). From this initial trial, 3,697 unique OmpT peptides were identified from 1,038 unique proteins. These OmpT peptides were larger (>6.3 kDa on average) than those produced by traditional proteases, allowing differentiation of closely related protein isoforms and detection of PTMs or even multiple PTM combinations on a single OmpT peptide. This is the first report of a protease that is not commercially available, yet with insufficient previous knowledge about its protein chemistry, but is able to perform highly selective cleavage in an efficient way after extensive optimization. In Chapter IV, to further validate the efficacy of this novel protease-centered middle-down platform to deliver biologically meaningful proteome information, the same workflow is used to qualitatively compare mouse brain proteomes between two inbred mouse strains. After searching the middle-down data against strain-specific databases, 1,934 peptides (average size: 6.0 kDa) were identified from 714 proteins in the high-mass region of the mouse proteome from strain C57BL/6J; 1,855 peptides (average size: 6.0 kDa) were identified from 690 proteins in the same high-mass region from strain DBA/2J. Through the comparison of identified OmpT peptides, some interesting single nucleotide polymorphism (SNP) examples are found between the two strains. These findings verify the robustness and efficacy of the middle-down performance in analyzing complex tissue proteomes. With the creation of those strain-specific databases and the high-throughput interrogation of mouse brain proteomes using middle-down pipeline, a solid foundation has been built for future follow-up comparative studies on cross-strain differences. Chapter V describes the continuation work of the histone biology story initiated by Dr. Jihua Jiang. In her previous studies, a strong correlation between histone hyperacetylation state and higher survival rates was observed in histone deacetylase (HDAC) mutants than wild type yeast upon hydrogen peroxide-induced apoptosis due to DNA damage. Meanwhile, a global increase in the phosphorylation level at Ser129 on histone H2A in HDAC mutants was also observed after 200 min. hydrogen peroxide treatment. We hypothesized that the hyperacetylation in HDAC mutant may help to prevent chromatin condensation, allowing more time for Ser129 to be phosphorylated, which would recruit the DNA repair machinery to the DNA damage loci for DNA repair, and in turn lead to a higher survival rate. So I developed a protocol to image the condensed chromatin in yeast nuclei using transmission electron microscopy (TEM), which showed that in HDAC mutant cells less chromatin condensation was observed compared with wild type yeast. I also profiled the dynamic changes of all four core histones to illustrate the phosphorylation changes along with the acetylation states during the 200 min. time course after hydrogen peroxide induction to apoptosis. My contribution to this project provided further clues to uncover the resistance mechanism to H2O2-induced apoptosis in yeast.\"","abstract_html":""Information at the protein level is essential to understand the functioning of a biological system. The goal of proteomics is to provide such information as protein identities, post-translational modifications (PTMs) on proteins, and ultimately, quantification of proteins or their PTM levels from given biological samples, mostly using mass spectrometry. Modern mass spectrometry-based proteomic analyses primarily fall into two categories in terms of general strategies -- the ""bottom-up"" and ""top-down"" approaches. Bottom-up proteomics relies upon enzymatic protein digestions (mostly by trypsin) prior to on-line liquid chromatography-coupled tandem mass spectrometry analysis (LC-MS/MS) of post-digestion peptides. Top-down proteomics omits the proteolysis step and directly focuses on the characterization of intact proteins and their post-translational modifications. While both approaches continue to mature, they each have limitations. In the bottom-up approach, tryptic peptides are the primary unit of measurement and are powerful in protein identification, but their relatively small size (typically ~8–25 residues long) raises potential issues such as significant sample complexity, the “protein inference problem” (i.e. the inability to assign an identified tryptic peptide to a specific protein, instead of a group of proteins sharing this tryptic peptide sequence), and loss of PTM and combinatorial PTM information. The top-down approach handles these issues by characterizing intact proteins, but becomes less successful as the protein size increases. Here, a hybrid approach based on restricted proteolysis to generate middle-sized peptides (i.e. 2–20 kDa) for mass spectrometry analysis could combine positive aspects of both bottom-up and top-down proteomics. Although various alternative methods to trypsin digestion have been investigated to explore restricted proteolysis options, these previous efforts only lead to the production of peptides marginally longer than tryptic peptides. Here, a digestion method using the outer membrane protease T (OmpT) derived from E. coli is developed to achieve efficient yet restricted proteolysis, thanks to its selective cleavage primarily between dibasic sites (K/R─K/R). In Chapter II, a robust OmpT expression and purification workflow with high enzyme yield and purity is depicted. One major obstacle in obtaining active OmpT enzyme is its autoproteolysis between Lys217 and Arg218, which causes the protease to become inactive. Several tactics are utilized to maintain the intact and active form of OmpT during purification and digestion. Thereafter, a set of OmpT digestion conditions are optimized using standard proteins as test substrates. In Chapter III, the initial demonstration of OmpT digestion on a complex proteome sample is described. First, OmpT-based proteolysis is integrated with a size-dependent protein fractionation technique, and established a robust middle-down proteomics pipeline. The platform is then applied to the analysis of prefractionated high-mass HeLa cell proteome (~20─100 kDa). From this initial trial, 3,697 unique OmpT peptides were identified from 1,038 unique proteins. These OmpT peptides were larger (>6.3 kDa on average) than those produced by traditional proteases, allowing differentiation of closely related protein isoforms and detection of PTMs or even multiple PTM combinations on a single OmpT peptide. This is the first report of a protease that is not commercially available, yet with insufficient previous knowledge about its protein chemistry, but is able to perform highly selective cleavage in an efficient way after extensive optimization. In Chapter IV, to further validate the efficacy of this novel protease-centered middle-down platform to deliver biologically meaningful proteome information, the same workflow is used to qualitatively compare mouse brain proteomes between two inbred mouse strains. After searching the middle-down data against strain-specific databases, 1,934 peptides (average size: 6.0 kDa) were identified from 714 proteins in the high-mass region of the mouse proteome from strain C57BL/6J; 1,855 peptides (average size: 6.0 kDa) were identified from 690 proteins in the same high-mass region from strain DBA/2J. Through the comparison of identified OmpT peptides, some interesting single nucleotide polymorphism (SNP) examples are found between the two strains. These findings verify the robustness and efficacy of the middle-down performance in analyzing complex tissue proteomes. With the creation of those strain-specific databases and the high-throughput interrogation of mouse brain proteomes using middle-down pipeline, a solid foundation has been built for future follow-up comparative studies on cross-strain differences. Chapter V describes the continuation work of the histone biology story initiated by Dr. Jihua Jiang. In her previous studies, a strong correlation between histone hyperacetylation state and higher survival rates was observed in histone deacetylase (HDAC) mutants than wild type yeast upon hydrogen peroxide-induced apoptosis due to DNA damage. Meanwhile, a global increase in the phosphorylation level at Ser129 on histone H2A in HDAC mutants was also observed after 200 min. hydrogen peroxide treatment. We hypothesized that the hyperacetylation in HDAC mutant may help to prevent chromatin condensation, allowing more time for Ser129 to be phosphorylated, which would recruit the DNA repair machinery to the DNA damage loci for DNA repair, and in turn lead to a higher survival rate. So I developed a protocol to image the condensed chromatin in yeast nuclei using transmission electron microscopy (TEM), which showed that in HDAC mutant cells less chromatin condensation was observed compared with wild type yeast. I also profiled the dynamic changes of all four core histones to illustrate the phosphorylation changes along with the acetylation states during the 200 min. time course after hydrogen peroxide induction to apoptosis. My contribution to this project provided further clues to uncover the resistance mechanism to H2O2-induced apoptosis in yeast."","abstract_has_math":false,"creators":["Wu, Cong"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biochemistry","degree_department":null,"school":null,"contributors":["Kelleher, Neil L.","Hergenrother, Paul J.","Martinis, Susan A.","Sweedler, Jonathan V."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-28T19:19:18Z","date_published":"2013-05-28T19:19:18Z","updated_at":"2026-07-22T22:25:34Z","subjects":["Mass spectrometry","proteomics","Outer membrane protease T (OmpT)","restricted proteolysis","middle-down proteomics","mouse brain proteome comparison","yeast histone"],"languages":["en"],"rights":["Copyright 2013 Cong Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/44772","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kelleher, Neil L.","Hergenrother, Paul J.","Martinis, Susan A.","Sweedler, Jonathan V."]},{"key":"dc:creator","label":"Author","values":["Wu, Cong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-28T19:19:18Z","2015-05-28T10:02:31Z","2013-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biochemistry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mass spectrometry","proteomics","Outer membrane protease T (OmpT)","restricted proteolysis","middle-down proteomics","mouse brain proteome comparison","yeast histone"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Cong Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/44772"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"Information at the protein level is essential to understand the functioning of a biological system. The goal of proteomics is to provide such information as protein identities, post-translational modifications (PTMs) on proteins, and ultimately, quantification of proteins or their PTM levels from given biological samples, mostly using mass spectrometry. Modern mass spectrometry-based proteomic analyses primarily fall into two categories in terms of general strategies -- the \"\"bottom-up\"\" and \"\"top-down\"\" approaches. Bottom-up proteomics relies upon enzymatic protein digestions (mostly by trypsin) prior to on-line liquid chromatography-coupled tandem mass spectrometry analysis (LC-MS/MS) of post-digestion peptides. Top-down proteomics omits the proteolysis step and directly focuses on the characterization of intact proteins and their post-translational modifications. While both approaches continue to mature, they each have limitations. In the bottom-up approach, tryptic peptides are the primary unit of measurement and are powerful in protein identification, but their relatively small size (typically ~8–25 residues long) raises potential issues such as significant sample complexity, the “protein inference problem” (i.e. the inability to assign an identified tryptic peptide to a specific protein, instead of a group of proteins sharing this tryptic peptide sequence), and loss of PTM and combinatorial PTM information. The top-down approach handles these issues by characterizing intact proteins, but becomes less successful as the protein size increases. Here, a hybrid approach based on restricted proteolysis to generate middle-sized peptides (i.e. 2–20 kDa) for mass spectrometry analysis could combine positive aspects of both bottom-up and top-down proteomics. Although various alternative methods to trypsin digestion have been investigated to explore restricted proteolysis options, these previous efforts only lead to the production of peptides marginally longer than tryptic peptides. Here, a digestion method using the outer membrane protease T (OmpT) derived from E. coli is developed to achieve efficient yet restricted proteolysis, thanks to its selective cleavage primarily between dibasic sites (K/R─K/R). In Chapter II, a robust OmpT expression and purification workflow with high enzyme yield and purity is depicted. One major obstacle in obtaining active OmpT enzyme is its autoproteolysis between Lys217 and Arg218, which causes the protease to become inactive. Several tactics are utilized to maintain the intact and active form of OmpT during purification and digestion. Thereafter, a set of OmpT digestion conditions are optimized using standard proteins as test substrates. In Chapter III, the initial demonstration of OmpT digestion on a complex proteome sample is described. First, OmpT-based proteolysis is integrated with a size-dependent protein fractionation technique, and established a robust middle-down proteomics pipeline. The platform is then applied to the analysis of prefractionated high-mass HeLa cell proteome (~20─100 kDa). From this initial trial, 3,697 unique OmpT peptides were identified from 1,038 unique proteins. These OmpT peptides were larger (>6.3 kDa on average) than those produced by traditional proteases, allowing differentiation of closely related protein isoforms and detection of PTMs or even multiple PTM combinations on a single OmpT peptide. This is the first report of a protease that is not commercially available, yet with insufficient previous knowledge about its protein chemistry, but is able to perform highly selective cleavage in an efficient way after extensive optimization. In Chapter IV, to further validate the efficacy of this novel protease-centered middle-down platform to deliver biologically meaningful proteome information, the same workflow is used to qualitatively compare mouse brain proteomes between two inbred mouse strains. After searching the middle-down data against strain-specific databases, 1,934 peptides (average size: 6.0 kDa) were identified from 714 proteins in the high-mass region of the mouse proteome from strain C57BL/6J; 1,855 peptides (average size: 6.0 kDa) were identified from 690 proteins in the same high-mass region from strain DBA/2J. Through the comparison of identified OmpT peptides, some interesting single nucleotide polymorphism (SNP) examples are found between the two strains. These findings verify the robustness and efficacy of the middle-down performance in analyzing complex tissue proteomes. With the creation of those strain-specific databases and the high-throughput interrogation of mouse brain proteomes using middle-down pipeline, a solid foundation has been built for future follow-up comparative studies on cross-strain differences. Chapter V describes the continuation work of the histone biology story initiated by Dr. Jihua Jiang. In her previous studies, a strong correlation between histone hyperacetylation state and higher survival rates was observed in histone deacetylase (HDAC) mutants than wild type yeast upon hydrogen peroxide-induced apoptosis due to DNA damage. Meanwhile, a global increase in the phosphorylation level at Ser129 on histone H2A in HDAC mutants was also observed after 200 min. hydrogen peroxide treatment. We hypothesized that the hyperacetylation in HDAC mutant may help to prevent chromatin condensation, allowing more time for Ser129 to be phosphorylated, which would recruit the DNA repair machinery to the DNA damage loci for DNA repair, and in turn lead to a higher survival rate. So I developed a protocol to image the condensed chromatin in yeast nuclei using transmission electron microscopy (TEM), which showed that in HDAC mutant cells less chromatin condensation was observed compared with wild type yeast. I also profiled the dynamic changes of all four core histones to illustrate the phosphorylation changes along with the acetylation states during the 200 min. time course after hydrogen peroxide induction to apoptosis. My contribution to this project provided further clues to uncover the resistance mechanism to H2O2-induced apoptosis in yeast.\"","Item withdrawn by Katherine Eriksen (eriksen3@illinois.edu) on 2013-02-07T15:09:16Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 6 Ph.D. Thesis_Cong Wu.docx: 21578187 bytes, checksum: f2e5029baa0261c5f494d3251bf6ff06 (MD5) APPENDIX D.xlsx: 8675166 bytes, checksum: 465b9557faa692c1aa30b5e8d9131301 (MD5) APPENDIX C.xlsx: 4366332 bytes, checksum: 27db0c746f4bbff8438a2a7b35b8967d (MD5) APPENDIX B.xlsx: 670092 bytes, checksum: 4f13feeace8e2f227373e04b58891606 (MD5) APPENDIX A.xlsx: 2515830 bytes, checksum: 215591ed78301b9de5e6713f40a88bf5 (MD5) Wu_Cong.pdf: 3851867 bytes, checksum: 3d1b2e7ecc90e3ee4ed6f8dfda2e6b52 (MD5)","Made available in DSpace on 2013-05-28T19:19:18Z (GMT). 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The goal of proteomics is to provide such information as protein identities, post-translational modifications (PTMs) on proteins, and ultimately, quantification of proteins or their PTM levels from given biological samples, mostly using mass spectrometry. Modern mass spectrometry-based proteomic analyses primarily fall into two categories in terms of general strategies -- the \"\"bottom-up\"\" and \"\"top-down\"\" approaches. Bottom-up proteomics relies upon enzymatic protein digestions (mostly by trypsin) prior to on-line liquid chromatography-coupled tandem mass spectrometry analysis (LC-MS/MS) of post-digestion peptides. Top-down proteomics omits the proteolysis step and directly focuses on the characterization of intact proteins and their post-translational modifications. While both approaches continue to mature, they each have limitations. In the bottom-up approach, tryptic peptides are the primary unit of measurement and are powerful in protein identification, but their relatively small size (typically ~8–25 residues long) raises potential issues such as significant sample complexity, the “protein inference problem” (i.e. the inability to assign an identified tryptic peptide to a specific protein, instead of a group of proteins sharing this tryptic peptide sequence), and loss of PTM and combinatorial PTM information. The top-down approach handles these issues by characterizing intact proteins, but becomes less successful as the protein size increases. Here, a hybrid approach based on restricted proteolysis to generate middle-sized peptides (i.e. 2–20 kDa) for mass spectrometry analysis could combine positive aspects of both bottom-up and top-down proteomics. Although various alternative methods to trypsin digestion have been investigated to explore restricted proteolysis options, these previous efforts only lead to the production of peptides marginally longer than tryptic peptides. Here, a digestion method using the outer membrane protease T (OmpT) derived from E. coli is developed to achieve efficient yet restricted proteolysis, thanks to its selective cleavage primarily between dibasic sites (K/R─K/R). In Chapter II, a robust OmpT expression and purification workflow with high enzyme yield and purity is depicted. One major obstacle in obtaining active OmpT enzyme is its autoproteolysis between Lys217 and Arg218, which causes the protease to become inactive. Several tactics are utilized to maintain the intact and active form of OmpT during purification and digestion. Thereafter, a set of OmpT digestion conditions are optimized using standard proteins as test substrates. In Chapter III, the initial demonstration of OmpT digestion on a complex proteome sample is described. First, OmpT-based proteolysis is integrated with a size-dependent protein fractionation technique, and established a robust middle-down proteomics pipeline. The platform is then applied to the analysis of prefractionated high-mass HeLa cell proteome (~20─100 kDa). From this initial trial, 3,697 unique OmpT peptides were identified from 1,038 unique proteins. These OmpT peptides were larger (>6.3 kDa on average) than those produced by traditional proteases, allowing differentiation of closely related protein isoforms and detection of PTMs or even multiple PTM combinations on a single OmpT peptide. This is the first report of a protease that is not commercially available, yet with insufficient previous knowledge about its protein chemistry, but is able to perform highly selective cleavage in an efficient way after extensive optimization. In Chapter IV, to further validate the efficacy of this novel protease-centered middle-down platform to deliver biologically meaningful proteome information, the same workflow is used to qualitatively compare mouse brain proteomes between two inbred mouse strains. After searching the middle-down data against strain-specific databases, 1,934 peptides (average size: 6.0 kDa) were identified from 714 proteins in the high-mass region of the mouse proteome from strain C57BL/6J; 1,855 peptides (average size: 6.0 kDa) were identified from 690 proteins in the same high-mass region from strain DBA/2J. Through the comparison of identified OmpT peptides, some interesting single nucleotide polymorphism (SNP) examples are found between the two strains. These findings verify the robustness and efficacy of the middle-down performance in analyzing complex tissue proteomes. With the creation of those strain-specific databases and the high-throughput interrogation of mouse brain proteomes using middle-down pipeline, a solid foundation has been built for future follow-up comparative studies on cross-strain differences. Chapter V describes the continuation work of the histone biology story initiated by Dr. Jihua Jiang. In her previous studies, a strong correlation between histone hyperacetylation state and higher survival rates was observed in histone deacetylase (HDAC) mutants than wild type yeast upon hydrogen peroxide-induced apoptosis due to DNA damage. Meanwhile, a global increase in the phosphorylation level at Ser129 on histone H2A in HDAC mutants was also observed after 200 min. hydrogen peroxide treatment. We hypothesized that the hyperacetylation in HDAC mutant may help to prevent chromatin condensation, allowing more time for Ser129 to be phosphorylated, which would recruit the DNA repair machinery to the DNA damage loci for DNA repair, and in turn lead to a higher survival rate. So I developed a protocol to image the condensed chromatin in yeast nuclei using transmission electron microscopy (TEM), which showed that in HDAC mutant cells less chromatin condensation was observed compared with wild type yeast. I also profiled the dynamic changes of all four core histones to illustrate the phosphorylation changes along with the acetylation states during the 200 min. time course after hydrogen peroxide induction to apoptosis. My contribution to this project provided further clues to uncover the resistance mechanism to H2O2-induced apoptosis in yeast.\"","Item withdrawn by Katherine Eriksen (eriksen3@illinois.edu) on 2013-02-07T15:09:16Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 6 Ph.D. Thesis_Cong Wu.docx: 21578187 bytes, checksum: f2e5029baa0261c5f494d3251bf6ff06 (MD5) APPENDIX D.xlsx: 8675166 bytes, checksum: 465b9557faa692c1aa30b5e8d9131301 (MD5) APPENDIX C.xlsx: 4366332 bytes, checksum: 27db0c746f4bbff8438a2a7b35b8967d (MD5) APPENDIX B.xlsx: 670092 bytes, checksum: 4f13feeace8e2f227373e04b58891606 (MD5) APPENDIX A.xlsx: 2515830 bytes, checksum: 215591ed78301b9de5e6713f40a88bf5 (MD5) Wu_Cong.pdf: 3851867 bytes, checksum: 3d1b2e7ecc90e3ee4ed6f8dfda2e6b52 (MD5)","Made available in DSpace on 2013-05-28T19:19:18Z (GMT). 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