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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Abusarah, J., Khodayarian, F., El-Hachem, N., Salame, N., Olivier, M., Balood, M., Roversi, K., Talbot, S., Bikorimana, J.-P., Chen, J., Jolicoeur, M., Trudeau, L.-É., Kamyabiazar, S., Annabi, B., Robert, F., Pelletier, J., El-Kadiry, A. E.-H., Shammaa, R., & Rafei, M. (2021). Engineering immunoproteasome-expressing mesenchymal stromal cells: A potent cellular vaccine for lymphoma and melanoma in mice. Cell Reports Medicine, 2(12), 100455 (27 pages). External link
Arnold, E., Hammami, I., Chen, J., Gupte, S., Durocher, Y., & Jolicoeur, M. (2016). Overexpression of G6PDH does not affect the behavior of HEK-293 clones stably expressing interferon-α2b. AIMS Bioengineering, 3(3), 319-336. Available
Aucoin, M. G., McMurray-Beaulieu, V., Poulin, F., Boivin, E. B., Chen, J., Ardelean, F. M., Cloutier, M., Choi, Y. J., Míguez, C. B., & Jolicoeur, M. (2006). Identifying conditions for inducible protein production in E. coli: combining a fed-batch and multiple induction approach. Microbial Cell Factories, 5(1). Available
Bardyn, M., Chen, J., Dussiot, M., Crettaz, D., Schmid, L., Längst, E., Amireault, P., Tissot, J.-D., Jolicoeur, M., & Prudent, M. (2020). Restoration of physiological levels of uric acid and ascorbic acid reroutes the metabolism of stored red blood cells. Metabolites, 10(6), 226 (18 pages). Available
Claeyssen, É., Dorion, S., Clendenning, A., He, J. Z., Wally, O., Chen, J., Auslender, E. L., Moisan, M.-C., Jolicoeur, M., & Rivoal, J. (2013). The futile cycling of hexose phosphates could account for the fact that hexokinase exerts a high control on glucose phosphorylation but not on glycolytic rate in transgenic potato (solanum tuberosum) roots. PLOS One, 8(1), e53898. Available
Chen, J. (2004). In vivo ³¹P-NMR study of phosphate metabolism for Eschscholtzia californica using a small-scale perfused bioreactor [Master's thesis, École Polytechnique de Montréal]. Available
Ghorbaniaghdam, A., Chen, J., Henry, O., & Jolicoeur, M. (2014). Analyzing Clonal Variation of Monoclonal Antibody-Producing CHO Cell Lines Using an In Silico Metabolomic Platform. PLOS One, 9(3). Available
Hammami, I., Chen, J., Murschel, F., Bronte, V., De Crescenzo, G., & Jolicoeur, M. (2012). Immunosuppressive activity enhances central carbon metabolism and bioenergetics in myeloid-derived suppressor cells in vitro models. BMC Cell Biology, 13(1). Available
Hammami, I., Bertrand, M., Chen, J., Bronte, V., De Crescenzo, G., & Jolicoeur, M. (2012). Nitric Oxide Affects Immune Cells Bioenergetics: Long-Term Effects of Nitric-Oxide Derivatives on Leukaemic Jurkat Cell Metabolism. Immunobiology, 217(8), 808-815. External link
Hammami, I., Chen, J., Bronte, V., De Crescenzo, G., & Jolicoeur, M. (2011). Myeloid-derived suppressor cells exhibit two bioenergetic steady-states in vitro. Journal of Biotechnology, 152(1-2), 43-48. External link
Laflaquière, B., Leclercq, G., Choey, C., Chen, J., Peres, S., Ito, C., & Jolicoeur, M. (2018). Identifying biomarkers of Wharton's Jelly mesenchymal stromal cells using a dynamic metabolic model: the cell passage effect. Metabolites, 8(1), 18. Available
Poliquin, P. O., Chen, J., Cloutier, M., Trudeau, L.-É., & Jolicoeur, M. (2013). Metabolomics and in-silico analysis reveal critical energy deregulations in animal models of Parkinson's disease. PLOS One, 8(7). Available
Ren, X., Chen, J., Deschênes, J.-S., Tremblay, R., & Jolicoeur, M. (2016). Glucose feeding recalibrates carbon flux distribution and favours lipid accumulation in Chlorella protothecoides through cell energetic management. Algal Research-Biomass Biofuels and Bioproducts, 14, 83-91. External link
Robitaille, J., Chen, J., & Jolicoeur, M. (2015). A Single Dynamic Metabolic Model Can Describe mAb Producing CHO Cell Batch and Fed-Batch Cultures on Different Culture Media. PLOS One, 10(9). Available
Zhao, X., Kasbi, M., Chen, J., Pérès, S., & Jolicoeur, M. (2017). A dynamic metabolic flux analysis of ABE (acetone-butanol-ethanol) fermentation by Clostridium acetobutylicum ATCC 824, with riboflavin as a by-product. Biotechnology and Bioengineering, 114(12), 2907-2919. External link
Zhao, X., Condruz, S., Chen, J., & Jolicoeur, M. (2016). A quantitative metabolomics study of high sodium response in Clostridium acetobutylicum ATCC 824 acetone-butanol-ethanol (ABE) fermentation. Scientific Reports, 6(1), 1-13. Available
