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- Alocci, D., et al.
(author)
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GlyConnect: Glycoproteomics Goes Visual, Interactive, and Analytical
- 2019
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 18:2, s. 664-677
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Journal article (peer-reviewed)abstract
- Knowledge of glycoproteins, their site-specific glycosylation patterns, and the glycan structures that they present to their recognition partners in health and disease is gradually being built on using a range of experimental approaches. The data from these analyses are increasingly being standardized and presented in various sources, from supplemental tables in publications to localized servers in investigator laboratories. Bioinformatics tools are now needed to collect these data and enable the user to search, display, and connect glycomics and glycoproteomics to other sources of related proteomics, genomics, and interactomics information. We here introduce GlyConnect (https://glyconnect.expasy.org/), the central platform of the Glycomics@ExPASy portal for glycoinformatics. GlyConnect has been developed to gather, monitor, integrate, and visualize data in a user-friendly way to facilitate the interpretation of collected glycoscience data. GlyConnect is designed to accommodate and integrate multiple data types as they are increasingly produced.
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- Bajorath, J., et al.
(author)
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Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds
- 2022
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In: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 14:1
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Journal article (peer-reviewed)abstract
- We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/.
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- Latenstein, Anouk E. J., et al.
(author)
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Clinical Outcomes After Total Pancreatectomy A Prospective Multicenter Pan-European Snapshot Study
- 2022
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In: Annals of Surgery. - : LIPPINCOTT WILLIAMS & WILKINS. - 0003-4932 .- 1528-1140. ; 276:5, s. E536-E543
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Journal article (peer-reviewed)abstract
- Objective: To assess outcomes among patients undergoing total pancreatectomy (TP) including predictors for complications and in-hospital mortality. Background: Current studies on TP mostly originate from high-volume centers and span long time periods and therefore may not reflect daily practice. Methods: This prospective pan-European snapshot study included patients who underwent elective (primary or completion) TP in 43 centers in 16 European countries (June 2018-June 2019). Subgroup analysis included cutoff values for annual volume of pancreatoduodenectomies (<60 vs >= 60). Predictors for major complications and in-hospital mortality were assessed in multivariable logistic regression. Results: In total, 277 patients underwent TP, mostly for malignant disease (73%). Major postoperative complications occurred in 70 patients (25%). Median hospital stay was 12 days (IQR 9-18) and 40 patients were readmitted (15%). In-hospital mortality was 5% and 90-day mortality 8%. In the subgroup analysis, in-hospital mortality was lower in patients operated in centers with >= 60 pancreatoduodenectomies compared <60 (4% vs 10%, P = 0.046). In multivariable analysis, annual volume <60 pancreatoduodenectomies (OR 3.78, 95% CI 1.18-12.16, P = 0.026), age (OR 1.07, 95% CI 1.01-1.14, P = 0.046), and estimated blood loss >= 2L (OR 11.89, 95% CI 2.64-53.61, P = 0.001) were associated with in-hospital mortality. ASA >= 3 (OR 2.87, 95% CI 1.56-5.26, P = 0.001) and estimated blood loss >= 2L (OR 3.52, 95% CI 1.25-9.90, P = 0.017) were associated with major complications. Conclusion: This pan-European prospective snapshot study found a 5% inhospital mortality after TP. The identified predictors for mortality, including low-volume centers, age, and increased blood loss, may be used to improve outcomes.
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- Mariethoz, J., et al.
(author)
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Glycomics@ExPASy: Bridging the gap
- 2018
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In: Molecular and Cellular Proteomics. - 1535-9476. ; 17:11, s. 2164-2176
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Journal article (peer-reviewed)abstract
- Glycomics@ExPASy (https://www.expasy.org/glycomics) is the glycomics tab of ExPASy, the server of SIB Swiss Institute of Bioinformatics. It was created in 2016 to centralize web-based glycoinformatics resources developed within an international network of glycoscientists. The hosted collection currently includes mainly databases and tools created and maintained at SIB but also links to a range of reference resources popular in the glycomics community. The philosophy of our toolbox is that it should be {glycoscientist AND protein scientist}-friendly with the aim of (1) popularizing the use of bioinformatics in glycobiology and (2) emphasizing the relationship between glycobiology and protein-oriented bioinformatics resources. The scarcity of data bridging these two disciplines led us to design tools as interactive as possible based on database connectivity to facilitate data exploration and support hypothesis building. Glycomics@ExPASy was designed, and is developed, with a long-term vision in close collaboration with glycoscientists to meet as closely as possible the growing needs of the community for glycoinformatics. © 2018 Mariethoz et al.
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- Zhang, Shidong, et al.
(author)
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Simple and complex polymer electrolyte fuel cell stack models : A comparison
- 2018. - 13
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In: ECS Transactions. - : The Electrochemical Society. - 1938-6737 .- 1938-5862. - 9781607685395 ; 86, s. 287-300
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Conference paper (peer-reviewed)abstract
- In this paper, two distinct polymer electrolyte fuel cell stack models are constructed: a detailed numerical model (DNM) employing a fine-scale computational mesh and a coarse-mesh approach based on a distributed resistance analogy (DRA) where diffusion terms in the transport equations are replaced by rate terms. Both methods are applied to a 5-cell, high-temperature polymer electrolyte fuel cell stack with an active area of 200 cm2 per cell. The polarization curve and local current density distributions from both the DRA and DNM are compared with experimental data, finding good agreement. Temperature, pressure, Nernst potential, and species distributions are also exhibited. The DNM displays details of fine-scale local extrema not captured by the DRA; however, the latter requires orders of magnitude less computer processor power and memory for execution. Both methods provide much finer-scale results than present experimental techniques.
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