The Experts below are selected from a list of 19137 Experts worldwide ranked by ideXlab platform
Omar Abdelrahman - One of the best experts on this subject based on the ideXlab platform.
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outcomes of non metastatic colon cancer patients in relationship to socioeconomic status an analysis of seer census tract level socioeconomic Database
International Journal of Clinical Oncology, 2019Co-Authors: Omar AbdelrahmanAbstract:OBJECTIVE To evaluate the outcomes of non-metastatic colon cancer patients in relation to the socioeconomic status (SES) at diagnosis based on the Surveillance, Epidemiology, and End Results (SEER) census tract level-SES Database. METHODS SEER SES census tract level Database represents a specially designed Database to integrate different aspects of SES among cancer patients. It reports a composite SES index for each patient. Patients were then stratified into three SES groups. Patients with a non-metastatic colon cancer diagnosis, diagnosed (2004-2015), and who were included in this Specialized Database were included in the current study. Multivariate Cox regression analysis was used to assess the impact of SES index on colon cancer-specific survival. RESULTS A total of 80,121 patients with non-metastatic colon cancer were included in the current study. Comparing patients in the lower SES group with patients in the higher SES group, patients with lower SES were more likely to have a younger age at presentation (P < 0.001), black race (P < 0.001) and more advanced stage at presentation (P < 0.001). The impact of the SES on colon cancer-specific survival was evaluated through multivariate Cox regression analysis adjusted for age, sex, race, stage, and colon cancer side. Lower SES was associated with worse colon cancer-specific survival (hazard ratio for group 1 versus group 3: 1.257; 1.190-1.328; P < 0.001). Interaction testing between race (black race versus white race) and SES was non-significant (P = 0.932). CONCLUSIONS Lower SES is associated with worse colon cancer-specific survival among non-metastatic colon cancer patients.
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outcomes of non metastatic colon cancer patients in relationship to socioeconomic status an analysis of seer census tract level socioeconomic Database
International Journal of Clinical Oncology, 2019Co-Authors: Omar AbdelrahmanAbstract:To evaluate the outcomes of non-metastatic colon cancer patients in relation to the socioeconomic status (SES) at diagnosis based on the Surveillance, Epidemiology, and End Results (SEER) census tract level-SES Database. SEER SES census tract level Database represents a specially designed Database to integrate different aspects of SES among cancer patients. It reports a composite SES index for each patient. Patients were then stratified into three SES groups. Patients with a non-metastatic colon cancer diagnosis, diagnosed (2004–2015), and who were included in this Specialized Database were included in the current study. Multivariate Cox regression analysis was used to assess the impact of SES index on colon cancer-specific survival. A total of 80,121 patients with non-metastatic colon cancer were included in the current study. Comparing patients in the lower SES group with patients in the higher SES group, patients with lower SES were more likely to have a younger age at presentation (P < 0.001), black race (P < 0.001) and more advanced stage at presentation (P < 0.001). The impact of the SES on colon cancer-specific survival was evaluated through multivariate Cox regression analysis adjusted for age, sex, race, stage, and colon cancer side. Lower SES was associated with worse colon cancer-specific survival (hazard ratio for group 1 versus group 3: 1.257; 1.190–1.328; P < 0.001). Interaction testing between race (black race versus white race) and SES was non-significant (P = 0.932). Lower SES is associated with worse colon cancer-specific survival among non-metastatic colon cancer patients.
Susan B. Altenbach - One of the best experts on this subject based on the ideXlab platform.
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effect of cleavage enzyme search algorithm and decoy Database on mass spectrometric identification of wheat gluten proteins
Phytochemistry, 2011Co-Authors: William H. Vensel, Frances M Dupont, Stacia Sloane, Susan B. AltenbachAbstract:While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized Databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the Database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein Database content and size on the false discovery rate. We found that as Database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy Database influenced the number of proteins identified. Using three enzymes, two search algorithms and a Specialized Database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.
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analysis of expressed sequence tags from a single wheat cultivar facilitates interpretation of tandem mass spectrometry data and discrimination of gamma gliadin proteins that may play different functional roles in flour
BMC Plant Biology, 2010Co-Authors: Susan B. Altenbach, William H. Vensel, Frances M DupontAbstract:The gamma gliadins are a complex group of proteins that together with other gluten proteins determine the functional properties of wheat flour. The proteins have unusually high levels of glutamine and proline and contain large regions of repetitive sequences. While most gamma gliadins are monomeric proteins containing eight conserved cysteine residues, some contain an additional cysteine residue that enables them to be linked with other gluten proteins into large polymers that are critical for flour quality. The ability to differentiate among the gamma gliadins is important for studies of wheat flour quality because proteins with similar sequences can have different effects on functional properties. The complement of gamma gliadin genes expressed in the wheat cultivar Butte 86 was evaluated by analyzing publicly available expressed sequence tag (EST) data. Eleven contigs were assembled from 153 Butte 86 ESTs. Nine of the contigs encoded full-length proteins and four of the proteins contained nine cysteine residues. Only one of the encoded proteins was a perfect match with a sequence reported in NCBI. Contigs from four different publicly available EST assemblies encoded proteins that were perfect matches with some, but not all, of the Butte 86 gamma gliadins and the complement of identical proteins was different for each assembly. A Specialized Database that included the sequences of Butte 86 gamma gliadins was constructed for identification of flour proteins by tandem mass spectrometry (MS/MS). In a pilot experiment, proteins corresponding to six Butte 86 gamma gliadin contigs were distinguished by MS/MS, including one containing the extra cysteine residue. Two other proteins were identified as one of two closely related Butte 86 proteins but could not be distinguished unequivocally. Unique peptide tags specific for Butte 86 gamma gliadins are reported. Inclusion of cultivar-specific gamma gliadin sequences in Databases maximizes the number and quality of peptide identifications and increases sequence coverage of these gamma gliadins by MS/MS. This approach makes it possible to distinguish closely related proteins, to associate individual proteins with sequences of specific genes, and to evaluate proteomic data in a biological context to better address questions about wheat flour quality.
Xuesi Dong - One of the best experts on this subject based on the ideXlab platform.
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a mass spectrometry Database for identification of saponins in plants
Journal of Chromatography A, 2020Co-Authors: Fengqing Huang, Xuesi Dong, Wei ZhouAbstract:Abstract Saponins constitute an important class of secondary metabolites of the plant kingdom. Here, we present a mass spectrometry-based Database for rapid and easy identification of saponins henceforth referred to as saponin mass spectrometry Database (SMSD). With a total of 4196 saponins, 214 of which were obtained from commercial sources. Through liquid chromatography-tandem high-resolution/mass spectrometry (HR/MS) analysis under negative ion mode, the fragmentation behavior for all parent fragment ions almost conformed to successive losses of sugar moieties, α-dissociation and McLafferty rearrangement of aglycones in high-energy collision induced dissociation. The saccharide moieties produced sugar fragment ions from m/z (monosaccharide) to m/z (polysaccharides). The parent and sugar fragment ions of other saponins were predicted using the above mentioned fragmentation pattern. The SMSD is freely accessible at http://47.92.73.208:8082/ or http://cpu-smsd.com (preferrably using google). It provides three search modes (“CLASSIFY”, “SEARCH” and “METABOLITE”). Under the “CLASSIFY” function, saponins are classified with high predictive accuracies from all metabolites by establishment of logistic regression model through their mass data from HR/MS input as a csv file, where the first column is ID and the second column is mass. For the “SEARCH” function, saponins are searched against parent ions with certain mass tolerance in “MS Ion Search”. Then, daughter ions with certain mass tolerance are input into “MS/MS Ion Search”. The optimal candidates were screened out according to the match count and match rate values in comparison with fragment data in Database. Additionally, another logistic regression model completely differentiated between parent and sugar fragment ions. This function designed in front web is conducive to search and recheck. With the “METABOLITE” function, saponins are searched using their common names, where both full and partial name searches are supported. With these modes, saponins of diverse chemical composition can be explored, grouped and identified with a high degree of predictive accuracy. This Specialized Database would aid in the identification of saponins in complex matrices particular in the study of traditional Chinese medicines or plant metabolomics.
Frances M Dupont - One of the best experts on this subject based on the ideXlab platform.
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effect of cleavage enzyme search algorithm and decoy Database on mass spectrometric identification of wheat gluten proteins
Phytochemistry, 2011Co-Authors: William H. Vensel, Frances M Dupont, Stacia Sloane, Susan B. AltenbachAbstract:While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized Databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the Database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein Database content and size on the false discovery rate. We found that as Database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy Database influenced the number of proteins identified. Using three enzymes, two search algorithms and a Specialized Database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.
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analysis of expressed sequence tags from a single wheat cultivar facilitates interpretation of tandem mass spectrometry data and discrimination of gamma gliadin proteins that may play different functional roles in flour
BMC Plant Biology, 2010Co-Authors: Susan B. Altenbach, William H. Vensel, Frances M DupontAbstract:The gamma gliadins are a complex group of proteins that together with other gluten proteins determine the functional properties of wheat flour. The proteins have unusually high levels of glutamine and proline and contain large regions of repetitive sequences. While most gamma gliadins are monomeric proteins containing eight conserved cysteine residues, some contain an additional cysteine residue that enables them to be linked with other gluten proteins into large polymers that are critical for flour quality. The ability to differentiate among the gamma gliadins is important for studies of wheat flour quality because proteins with similar sequences can have different effects on functional properties. The complement of gamma gliadin genes expressed in the wheat cultivar Butte 86 was evaluated by analyzing publicly available expressed sequence tag (EST) data. Eleven contigs were assembled from 153 Butte 86 ESTs. Nine of the contigs encoded full-length proteins and four of the proteins contained nine cysteine residues. Only one of the encoded proteins was a perfect match with a sequence reported in NCBI. Contigs from four different publicly available EST assemblies encoded proteins that were perfect matches with some, but not all, of the Butte 86 gamma gliadins and the complement of identical proteins was different for each assembly. A Specialized Database that included the sequences of Butte 86 gamma gliadins was constructed for identification of flour proteins by tandem mass spectrometry (MS/MS). In a pilot experiment, proteins corresponding to six Butte 86 gamma gliadin contigs were distinguished by MS/MS, including one containing the extra cysteine residue. Two other proteins were identified as one of two closely related Butte 86 proteins but could not be distinguished unequivocally. Unique peptide tags specific for Butte 86 gamma gliadins are reported. Inclusion of cultivar-specific gamma gliadin sequences in Databases maximizes the number and quality of peptide identifications and increases sequence coverage of these gamma gliadins by MS/MS. This approach makes it possible to distinguish closely related proteins, to associate individual proteins with sequences of specific genes, and to evaluate proteomic data in a biological context to better address questions about wheat flour quality.
Rameshwar Sharma - One of the best experts on this subject based on the ideXlab platform.
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computer aided data acquisition tool for high throughput phenotyping of plant populations
Plant Methods, 2009Co-Authors: Raju Naik Vankadavath, Appibhai Jakir Hussain, Reddaiah Bodanapu, Eros Kharshiing, Pinjari Osman Basha, Soni Gupta, Yellamaraju Sreelakshmi, Rameshwar SharmaAbstract:The data generated during a course of a biological experiment/study can be sometimes be massive and its management becomes quite critical for the success of the investigation undertaken. The accumulation and analysis of such large datasets often becomes tedious for biologists and lab technicians. Most of the current phenotype data acquisition management systems do not cater to the Specialized needs of large-scale data analysis. The successful application of genomic tools/strategies to introduce desired traits in plants requires extensive and precise phenotyping of plant populations or gene bank material, thus necessitating an efficient data acquisition system. Here we describe newly developed software "PHENOME" for high-throughput phenotyping, which allows researchers to accumulate, categorize, and manage large volume of phenotypic data. In this study, a large number of individual tomato plants were phenotyped with the "PHENOME" application using a Personal Digital Assistant (PDA) with built-in barcode scanner in concert with customized Database specific for handling large populations. The phenotyping of large population of plants both in the laboratory and in the field is very efficiently managed using PDA. The data is transferred to a Specialized Database(s) where it can be further analyzed and catalogued. The "PHENOME" aids collection and analysis of data obtained in large-scale mutagenesis, assessing quantitative trait loci (QTLs), raising mapping population, sampling of several individuals in one or more ecological niches etc.