The Experts below are selected from a list of 83850 Experts worldwide ranked by ideXlab platform
Fiona S L Brinkman - One of the best experts on this subject based on the ideXlab platform.
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enhanced annotations and features for comparing thousands of pseudomonas genomes in the pseudomonas genome database
Nucleic Acids Research, 2016Co-Authors: Geoffrey L Winsor, Emma Griffiths, Bhavjinder K Dhillon, Julie A Shay, Fiona S L BrinkmanAbstract:The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of Community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using Community-based approaches including a major new Pseudomonas Community Initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches.
Geoffrey L Winsor - One of the best experts on this subject based on the ideXlab platform.
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enhanced annotations and features for comparing thousands of pseudomonas genomes in the pseudomonas genome database
Nucleic Acids Research, 2016Co-Authors: Geoffrey L Winsor, Emma Griffiths, Bhavjinder K Dhillon, Julie A Shay, Fiona S L BrinkmanAbstract:The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of Community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using Community-based approaches including a major new Pseudomonas Community Initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches.
Greg Arling - One of the best experts on this subject based on the ideXlab platform.
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do residents participating in minnesota s return to Community Initiative experience similar postdischarge outcomes to their peers
Medical Care, 2020Co-Authors: Zachary Hass, Mark Woodhouse, Greg ArlingAbstract:OBJECTIVE The objective of this study was to evaluate the impact of Minnesota's Return to Community Initiative (RTCI) on postdischarge outcomes for nursing home residents transitioned through the program. DATA SOURCES Secondary data were from the Minimum Data Set and RTCI staff (January 2015 to December 2016), state Medicaid eligibility files and death records. The sample consisted of 29,201 nursing home discharges in Minnesota occurring in 2015. RESEARCH DESIGN Cox proportional hazard models were used to compare 1-year postdischarge outcomes of nursing home readmission, mortality, and Medicaid conversion for RTCI assisted Community discharges and a propensity-matched sample of unassisted Community discharges. RESULTS The majority (60%) of RTCI assisted discharges remained alive, in the Community and not having converted at Medicaid at 1 year after discharge. Time to mortality was significantly lower for the assisted group than the unassisted group, but time to readmission and Medicaid conversion were similar. CONCLUSION The RTCI assisted residents fared well postdischarge in their time to mortality, nursing home readmission, and Medicaid conversion; they lived longer than a propensity-matched sample of their peers.
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assessing the impact of minnesota s return to Community Initiative for newly admitted nursing home residents
Health Services Research, 2019Co-Authors: Zachary Hass, Mark Woodhouse, David C. Grabowski, Greg ArlingAbstract:OBJECTIVE To evaluate Minnesota's Return to Community Initiative's (RTCI) impact on Community discharges from nursing homes. DATA SOURCES Secondary data were from the Minimum Data Set and RTCI staff (April 2014 - December 2016). The sample consisted of 18 444 non-Medicaid nursing home admissions in Minnesota remaining for at least 45 days, with high predicted probability of Community discharge. STUDY DESIGN The RTCI facilitates Community discharge for non-Medicaid nursing home residents by assisting with discharge planning, transitioning to the Community, and postdischarge follow-up. A key evaluation question is how many of those transitions were directly attributable to the program. Return to Community Initiative was implemented statewide without a control group. Program impact was measured using regression discontinuity, a quasi-experimental design approach that leverages the programs targeting model. PRINCIPAL FINDINGS Return to Community Initiative increased Community discharge rates by an estimated 11 percent (P < 0.05) for the targeted population. The program effect was robust to time and increased with level of facility participation in RTCI. CONCLUSIONS The RTCI had a modest yet significant impact on the Community discharge rates for its targeted population. Findings have been applied in strengthening the RTCI's targeting approach and transitioning process.
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Assessing the impact of Minnesota's return to Community Initiative for newly admitted nursing home residents.
Health Services Research, 2019Co-Authors: Zachary Hass, Mark Woodhouse, David C. Grabowski, Greg ArlingAbstract:OBJECTIVE To evaluate Minnesota's Return to Community Initiative's (RTCI) impact on Community discharges from nursing homes. DATA SOURCES Secondary data were from the Minimum Data Set and RTCI staff (April 2014 - December 2016). The sample consisted of 18 444 non-Medicaid nursing home admissions in Minnesota remaining for at least 45 days, with high predicted probability of Community discharge. STUDY DESIGN The RTCI facilitates Community discharge for non-Medicaid nursing home residents by assisting with discharge planning, transitioning to the Community, and postdischarge follow-up. A key evaluation question is how many of those transitions were directly attributable to the program. Return to Community Initiative was implemented statewide without a control group. Program impact was measured using regression discontinuity, a quasi-experimental design approach that leverages the programs targeting model. PRINCIPAL FINDINGS Return to Community Initiative increased Community discharge rates by an estimated 11 percent (P
Julie A Shay - One of the best experts on this subject based on the ideXlab platform.
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enhanced annotations and features for comparing thousands of pseudomonas genomes in the pseudomonas genome database
Nucleic Acids Research, 2016Co-Authors: Geoffrey L Winsor, Emma Griffiths, Bhavjinder K Dhillon, Julie A Shay, Fiona S L BrinkmanAbstract:The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of Community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using Community-based approaches including a major new Pseudomonas Community Initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches.
Bhavjinder K Dhillon - One of the best experts on this subject based on the ideXlab platform.
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enhanced annotations and features for comparing thousands of pseudomonas genomes in the pseudomonas genome database
Nucleic Acids Research, 2016Co-Authors: Geoffrey L Winsor, Emma Griffiths, Bhavjinder K Dhillon, Julie A Shay, Fiona S L BrinkmanAbstract:The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of Community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using Community-based approaches including a major new Pseudomonas Community Initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches.