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Rearing water microbiomes in white leg shrimp (<i>Litopenaeus vannamei</i>) larviculture assemble stochastically and are influenced by the microbiomes of live feed products. <i>Environ. Microbiol. 23(1)</i>: 281-298. <a href=\"https://hdl.handle.net/10.1111/1462-2920.15310\" target=\"_blank\">https://hdl.handle.net/10.1111/1462-2920.15310</a>","AutID":266531,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":348868,"RR":"<b>Heyse, J.; Schattenberg, F.; Rubbens, P.; Müller, S.; Waegeman, W.; Boon, N.; Props, R.</b> (2021). Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting. <i>mSystems 6(5)</i>: e00551-21. <a href=\"https://dx.doi.org/10.1128/msystems.00551-21\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00551-21</a>","AutID":477143,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":332287,"RR":"<b>Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W.</b> (2021). Cytometric fingerprints of gut microbiota predict Crohn’s disease state. <i>ISME J. 15(1)</i>: 354-358. <a href=\"https://dx.doi.org/10.1038/s41396-020-00762-4\" target=\"_blank\">https://dx.doi.org/10.1038/s41396-020-00762-4</a>","AutID":266541,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":348873,"RR":"<b>Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W.</b> (2021). PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes inmicrobial community structure. <i>mSphere 6(1)</i>: e00530-20. <a href=\"https://dx.doi.org/10.1128/msphere.00530-20\" target=\"_blank\">https://dx.doi.org/10.1128/msphere.00530-20</a>","AutID":266541,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":334823,"RR":"<b>Rubbens, P.; Props, R.</b> (2021). Computational analysis of microbial flow cytometry data. <i>mSystems 6(1)</i>: e00895-20. <a href=\"https://dx.doi.org/10.1128/msystems.00895-20\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00895-20</a>","AutID":436454,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321078,"RR":"<b>García-Timermans, C.; Rubbens, P.; Heyse, J.; Kerckhof, F.-M.; Props, R.; Skirtach, A.G.; Waegeman, W.; Boon, N.</b> (2020). Discriminating bacterial phenotypes at the population and single‐cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting. <i>Cytometry A 97(7)</i>: 713-726. <a href=\"https://dx.doi.org/10.1002/cyto.a.23952\" target=\"_blank\">https://dx.doi.org/10.1002/cyto.a.23952</a>","AutID":400841,"MonDate":null,"AnaDate":2020,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":321910,"RR":"<b>Czechowska, Kamila; Lannigan, Joanne; Wang, Lili; Arcidiacono, Judith; Ashhurst, Thomas M.; Barnard, Ruth M.; Bauer, Steven; Bispo, Cláudia; Bonilla, Diana L.; Brinkman, Ryan R.; Cabanski, Maciej; Chang, Hyun‐Dong; Chakrabarti, Lina; Chojnowski, Grace; Cotleur, Bunny; Degheidy, Heba; Dela Cruz, Gelo V.; Eck, Steven; Elliott, John; Errington, Rachel; Filby, Andy; Gagnon, Dominic; Gardner, Rui; Green, Cherie; Gregory, Michael; Groves, Christopher J.; Hall, Christopher; Hammes, Frederik; Hedrick, Michael; Hoffman, Robert; Icha, Jaroslav; Ivaska, Johanna; Jenner, Dominic C.; Jones, Derek; Kerckhof, Frederiek‐Maarten; Kukat, Christian; Lanham, David; Leavesley, Silas; Lee, Michael; Lin‐Gibson, Sheng; Litwin, Virginia; Liu, Yanli; Molloy, Jenny; Moore, Jonni S.; Müller, Susann; Nedbal, Jakub; Niesner, Raluca; Nitta, Nao; Ohlsson‐Wilhelm, Betsy; Paul, Nicole E.; Perfetto, Stephen; Portat, Ziv; Props, Ruben; Radtke, Stefan; Rayanki, Radhika; Rieger, Aja; Rogers, Samson; Rubbens, Peter; Salomon, Robert; Schiemann, Matthias; Sharpe, John; Sonder, Soren Ulrik; Stewart, Jennifer J.; Sun, Yongliang; Ulrich, Henning; Van Isterdael, Gert; Vitaliti, Alessandra; Vreden, Caryn; Weber, Michael; Zimmermann, Jakob; Vacca, Giacomo; Wallace, Paul; Tárnok, Attila</b> (2019). Cyt‐Geist: current and future challenges in cytometry: reports of the CYTO 2018 conference workshops. <i>Cytometry A 95(6)</i>: 598-644. <a href=\"https://dx.doi.org/10.1002/cyto.a.23777\" target=\"_blank\">https://dx.doi.org/10.1002/cyto.a.23777</a>","AutID":403582,"MonDate":null,"AnaDate":2019,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":321900,"RR":"<b>Heyse, J.; Buysschaert, B.; Props, R.; Rubbens, P.; Skirtach, A.G.; Waegeman, W.; Boon, N.</b> (2019). Coculturing bacteria leads to reduced phenotypic heterogeneities. <i>Appl. Environ. Microbiol. 85(8)</i>: e02814-18. <a href=\"https://dx.doi.org/10.1128/aem.02814-18\" target=\"_blank\">https://dx.doi.org/10.1128/aem.02814-18</a>","AutID":403558,"MonDate":null,"AnaDate":2019,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321916,"RR":"<b>Rubbens, P.; Schmidt, M.L.; Props, R.; Biddanda, B.A.; Boon, N.; Waegeman, W.; Denef, V.J.</b> (2019). Randomized Lasso links microbial taxa with aquatic functional groups inferred from flow cytometry. <i>mSystems 4(5)</i>: e00093-19. <a href=\"https://dx.doi.org/10.1128/msystems.00093-19\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00093-19</a>","AutID":400841,"MonDate":null,"AnaDate":2019,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321911,"RR":"<b>Props, R.; Rubbens, P.; Besmer, M.; Buysschaert, B.; Sigrist, J.; Weilenmann, H.; Waegeman, W.; Boon, N.; Hammes, F.</b> (2018). Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data. <i>Wat. Res. 145</i>: 73-82. <a href=\"https://dx.doi.org/10.1016/j.watres.2018.08.013\" target=\"_blank\">https://dx.doi.org/10.1016/j.watres.2018.08.013</a>","AutID":266541,"MonDate":null,"AnaDate":2018,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":288222,"RR":"<b>Props, R.; Kerckhof, F.-M.; Rubbens, P.; De Vrieze, J.; Hernandez Sanabria, E.; Waegeman, W.; Monsieurs, P.; Hammes, F.; Boon, N.</b> (2017). Absolute quantification of microbial taxon abundances. <i>ISME J. 11(2)</i>: 584-587. <a href=\"https://dx.doi.org/10.1038/ismej.2016.117\" target=\"_blank\">https://dx.doi.org/10.1038/ismej.2016.117</a>","AutID":266531,"MonDate":null,"AnaDate":2017,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321913,"RR":"<b>Rubbens, P.; Props, R.; Boon, N.; Waegeman, W.</b> (2017). Flow cytometric single-cell identification of populations in synthetic bacterial communities. <i>PLoS One 12(1)</i>: e0169754. <a href=\"https://dx.doi.org/10.1371/journal.pone.0169754\" target=\"_blank\">https://dx.doi.org/10.1371/journal.pone.0169754</a>","AutID":266541,"MonDate":null,"AnaDate":2017,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321917,"RR":"<b>Rubbens, P.; Props, R.; Garcia-Timermans, C.; Boon, N.; Waegeman, W.</b> (2017). Stripping flow cytometry: how many detectors do we need for bacterial identification? <i>Cytometry A 91(12)</i>: 1184-1191. <a href=\"https://dx.doi.org/10.1002/cyto.a.23284\" target=\"_blank\">https://dx.doi.org/10.1002/cyto.a.23284</a>","AutID":266541,"MonDate":null,"AnaDate":2017,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":288223,"RR":"<b>Props, R.; Monsieurs, P.; Mysara, M.; Clement, L.; Boon, N.</b> (2016). Measuring the biodiversity of microbial communities by flow cytometry. <i>Methods Ecol. 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