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13 results on '"Jandt, U"'

1. Biodiversity monitoring in Europe: User and policy needs

Author

Moersberger, Hannah Alina, Valdez, J., Martin, J.G.C., Junker, J., Georgieva, I., Bauer, S., Beja, P., Breeze, T.D., Fernandez, M., Fernández, N., Brotons, L., Jandt, U., Bruelheide, H., Kissling, W.D., Langer, C., Liquete, C., Lumbierres, M., Lyche Solheim, A., Maes, J., Morán-Ordóñez, A., Moreira, F., Pe'er, Guy, Santana, J., Shamoun-Baranes, J., Smets, B., Capinha, C., McCallum, I., Pereira, H.M., Bonn, Aletta, Moersberger, Hannah Alina, Valdez, J., Martin, J.G.C., Junker, J., Georgieva, I., Bauer, S., Beja, P., Breeze, T.D., Fernandez, M., Fernández, N., Brotons, L., Jandt, U., Bruelheide, H., Kissling, W.D., Langer, C., Liquete, C., Lumbierres, M., Lyche Solheim, A., Maes, J., Morán-Ordóñez, A., Moreira, F., Pe'er, Guy, Santana, J., Shamoun-Baranes, J., Smets, B., Capinha, C., McCallum, I., Pereira, H.M., and Bonn, Aletta

Abstract

In this report, we present the analysis of the different available biodiversity data streams at the EU and national level, both baseline biodiversity data and monitoring data. We assess how these biodiversity data inform and trigger policy action and identify the related challenges the different European countries and relevant EU agencies face and the solutions to overcome them. To do this, we consulted with more than 350 expert stakeholders from policy, research and practice. The assessment identified a fragmented biodiversity data landscape that cannot currently easily answer all relevant policy questions. Quantity and quality of biodiversity baseline datasets differ for the different countries, ranging from non-existent biodiversity monitoring due to capacity issues, to regular monitoring of ecosystem processes and state. By engaging stakeholders and experts in both member states and non-member states and from several EU bodies, we identified key challenges and ways to address these with targeted solutions towards building a joint European Biodiversity Monitoring Network. Solutions include focussing on cooperation and coordination, enhanced data standardisation and sharing, as well as the use of models and new technologies. These solutions can however only be realised with dedicated funding and capacity building, in coordination with all stakeholders in partnership.

Published
2024

2. sPlotOpen – An environmentally balanced, open‐access, global dataset of vegetation plots

Author

Sabatini, F.M., Lenoir, J., Hattab, T., Arnst, E., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S.M., Jandt, U., Jansen, F., Jimenez‐Alfaro, B., Kattge, J., Levesley, A., Pillar, V.D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A.T.R., Agrillo, E., Álvarez, M., Apostolova, I., Arfin Khan, M.A.S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., Gasper, A.L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El‐Sheikh, M.A.El‐R.M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán‐de‐Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I.G., Grytnes, J‐A, Guerin, G.R., Gutiérrez, A.G., Haider, S., Hatim, M.Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J.A.M., Jedrzejek, B., Jentsch, A., Jürgens, N., Kącki, Z., Kapfer, J., Karger, D.N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H.S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D.V., Liu, H., Lysenko, T., Mahecha, M.D., Marcenò, C., Martynenko, V., Moeslund, J.E., Monteagudo Mendoza, A., Mucina, L., Müller, J.V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Pärtel, M., Pauchard, A., Peet, R.K., Penuelas, J., Pérez‐Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas‐Torres, G., Rodwell, J.S., Ruprecht, E., Rūsiņa, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M.G., Stančić, Z., Svenning, J‐C, Tang, Z., Tang, C.Q., Tsiripidis, I., Vanselow, K.A., Vásquez Martínez, R., Vassilev, K., Vélez‐Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., Wehrden, H., Wagner, V., Walker, D.A., Waller, D.M., Wang, H‐F, Wesche, K., Whitfeld, T.J.S., Willner, W., Wiser, S.K., Wohlgemuth, T., Yamalov, S., Zobel, M., Bruelheide, H., Bates, A., Sabatini, F.M., Lenoir, J., Hattab, T., Arnst, E., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S.M., Jandt, U., Jansen, F., Jimenez‐Alfaro, B., Kattge, J., Levesley, A., Pillar, V.D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A.T.R., Agrillo, E., Álvarez, M., Apostolova, I., Arfin Khan, M.A.S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., Gasper, A.L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El‐Sheikh, M.A.El‐R.M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán‐de‐Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I.G., Grytnes, J‐A, Guerin, G.R., Gutiérrez, A.G., Haider, S., Hatim, M.Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J.A.M., Jedrzejek, B., Jentsch, A., Jürgens, N., Kącki, Z., Kapfer, J., Karger, D.N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H.S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D.V., Liu, H., Lysenko, T., Mahecha, M.D., Marcenò, C., Martynenko, V., Moeslund, J.E., Monteagudo Mendoza, A., Mucina, L., Müller, J.V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Pärtel, M., Pauchard, A., Peet, R.K., Penuelas, J., Pérez‐Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas‐Torres, G., Rodwell, J.S., Ruprecht, E., Rūsiņa, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M.G., Stančić, Z., Svenning, J‐C, Tang, Z., Tang, C.Q., Tsiripidis, I., Vanselow, K.A., Vásquez Martínez, R., Vassilev, K., Vélez‐Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., Wehrden, H., Wagner, V., Walker, D.A., Waller, D.M., Wang, H‐F, Wesche, K., Whitfeld, T.J.S., Willner, W., Wiser, S.K., Wohlgemuth, T., Yamalov, S., Zobel, M., Bruelheide, H., and Bates, A.

Abstract

Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring.

Published
2021

3. EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats

Author

Chytrý, M., Tichý, L., Hennekens, S.M., Knollová, I., Janssen, J.A.M., Rodwell, J.S., Peterka, T., Marcenò, C., Landucci, F., Danihelka, J., Hájek, M., Dengler, J., Novák, P., Zukal, D., Jiménez‐Alfaro, B., Mucina, L., Abdulhak, S., Aćić, S., Agrillo, E., Attorre, F., Bergmeier, E., Biurrun, I., Boch, S., Bölöni, J., Bonari, G., Braslavskaya, T., Bruelheide, H., Campos, J.A., Čarni, A., Casella, L., Ćuk, M., Ćušterevska, R., De Bie, E., Delbosc, P., Demina, O., Didukh, Y., Dítě, D., Dziuba, T., Ewald, J., Gavilán, R.G., Gégout, J‐C, Giusso del Galdo, G.P., Golub, V., Goncharova, N., Goral, F., Graf, U., Indreica, A., Isermann, M., Jandt, U., Jansen, F., Jansen, J., Jašková, A., Jiroušek, M., Kącki, Z., Kalníková, V., Kavgacı, A., Khanina, L., Yu. Korolyuk, A., Kozhevnikova, M., Kuzemko, A., Küzmič, F., Kuznetsov, O.L., Laiviņš, M., Lavrinenko, I., Lavrinenko, O., Lebedeva, M., Lososová, Z., Lysenko, T., Maciejewski, L., Mardari, C., Marinšek, A., Napreenko, M.G., Onyshchenko, V., Pérez‐Haase, A., Pielech, R., Prokhorov, V., Rašomavičius, V., Rodríguez Rojo, M.P., Rūsiņa, S., Schrautzer, J., Šibík, J., Šilc, U., Škvorc, Ž., Smagin, V.A., Stančić, Z., Stanisci, A., Tikhonova, E., Tonteri, T., Uogintas, D., Valachovič, M., Vassilev, K., Vynokurov, D., Willner, W., Yamalov, S., Evans, D., Palitzsch Lund, M., Spyropoulou, R., Tryfon, E., Schaminée, J.H.J., Schmidtlein, S., Chytrý, M., Tichý, L., Hennekens, S.M., Knollová, I., Janssen, J.A.M., Rodwell, J.S., Peterka, T., Marcenò, C., Landucci, F., Danihelka, J., Hájek, M., Dengler, J., Novák, P., Zukal, D., Jiménez‐Alfaro, B., Mucina, L., Abdulhak, S., Aćić, S., Agrillo, E., Attorre, F., Bergmeier, E., Biurrun, I., Boch, S., Bölöni, J., Bonari, G., Braslavskaya, T., Bruelheide, H., Campos, J.A., Čarni, A., Casella, L., Ćuk, M., Ćušterevska, R., De Bie, E., Delbosc, P., Demina, O., Didukh, Y., Dítě, D., Dziuba, T., Ewald, J., Gavilán, R.G., Gégout, J‐C, Giusso del Galdo, G.P., Golub, V., Goncharova, N., Goral, F., Graf, U., Indreica, A., Isermann, M., Jandt, U., Jansen, F., Jansen, J., Jašková, A., Jiroušek, M., Kącki, Z., Kalníková, V., Kavgacı, A., Khanina, L., Yu. Korolyuk, A., Kozhevnikova, M., Kuzemko, A., Küzmič, F., Kuznetsov, O.L., Laiviņš, M., Lavrinenko, I., Lavrinenko, O., Lebedeva, M., Lososová, Z., Lysenko, T., Maciejewski, L., Mardari, C., Marinšek, A., Napreenko, M.G., Onyshchenko, V., Pérez‐Haase, A., Pielech, R., Prokhorov, V., Rašomavičius, V., Rodríguez Rojo, M.P., Rūsiņa, S., Schrautzer, J., Šibík, J., Šilc, U., Škvorc, Ž., Smagin, V.A., Stančić, Z., Stanisci, A., Tikhonova, E., Tonteri, T., Uogintas, D., Valachovič, M., Vassilev, K., Vynokurov, D., Willner, W., Yamalov, S., Evans, D., Palitzsch Lund, M., Spyropoulou, R., Tryfon, E., Schaminée, J.H.J., and Schmidtlein, S.

Abstract

Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert

Published
2020

11. CHO cells engineered for fluorescence read out of cell cycle and growth rate in real time.

Author

Fuge G, Hong Y, Riecken K, Zeng AP, and Jandt U

Subjects
Animals, Cricetinae, Cricetulus, Bioreactors, CHO Cells cytology, CHO Cells metabolism, Cell Cycle physiology, Cell Engineering methods, Fluorescent Dyes analysis
Abstract

For efficient production of recombinant proteins by mammalian cells in a bioreactor, optimal growth rates are required and represent the most important process parameter. We present the first successful attempt to monitor the growth behavior and cell cycle state of a mammalian production relevant cell line under bioreactor cultivation conditions up to 1.2 l, utilizing a fluorescent read-out without the need of additional staining or marking. For this purpose, we developed two new production relevant cell line derivatives (CHO-K1 FUCCI CM & CHO-K1 FUCCI CN) and corresponding analytical methods. The approach is easily scalable, applicable to mammalian recombinant protein production cell lines, and it allows for real-time monitoring using appropriate fluorescence probes. It is based on the Ubiquitination-based Cell Cycle Indicator (FUCCI) system developed by Miyawaki et al. CHO-K1 was chosen as a model cell line due to its close relationship to several production cell lines. 1 We defined a new process parameter i red , a quantitative and numerically robust representation of the cell cycle distribution, and demonstrate it to be linearly correlated with the cell cycle state and inversely related to the real time growth rate. Detection of growth rate limitations is possible earlier than using cell-count-based approaches. Analytics were compatible with bulk fluorescence methods, using a plate reader as well as a flow cytometer. For future real time applications in industry scale bioreactors we recommend the use of on-line or at-line fluorescence probes. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1408-1417, 2017., (© 2017 American Institute of Chemical Engineers.)

Published
2017
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12. Synchronized mammalian cell culture: part II--population ensemble modeling and analysis for development of reproducible processes.

Author

Jandt U, Barradas OP, Pörtner R, and Zeng AP

Subjects
Animals, Bioreactors, CHO Cells, Cell Line, Cricetinae, Cricetulus, Humans, Stochastic Processes, Cell Culture Techniques methods, Cell Cycle physiology, Models, Biological
Abstract

The consideration of inherent population inhomogeneities of mammalian cell cultures becomes increasingly important for systems biology study and for developing more stable and efficient processes. However, variations of cellular properties belonging to different sub-populations and their potential effects on cellular physiology and kinetics of culture productivity under bioproduction conditions have not yet been much in the focus of research. Culture heterogeneity is strongly determined by the advance of the cell cycle. The assignment of cell-cycle specific cellular variations to large-scale process conditions can be optimally determined based on the combination of (partially) synchronized cultivation under otherwise physiological conditions and subsequent population-resolved model adaptation. The first step has been achieved using the physical selection method of countercurrent flow centrifugal elutriation, recently established in our group for different mammalian cell lines which is presented in Part I of this paper series. In this second part, we demonstrate the successful adaptation and application of a cell-cycle dependent population balance ensemble model to describe and understand synchronized bioreactor cultivations performed with two model mammalian cell lines, AGE1.HNAAT and CHO-K1. Numerical adaptation of the model to experimental data allows for detection of phase-specific parameters and for determination of significant variations between different phases and different cell lines. It shows that special care must be taken with regard to the sampling frequency in such oscillation cultures to minimize phase shift (jitter) artifacts. Based on predictions of long-term oscillation behavior of a culture depending on its start conditions, optimal elutriation setup trade-offs between high cell yields and high synchronization efficiency are proposed., (© 2014 American Institute of Chemical Engineers.)

Published
2015
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13. Synchronized mammalian cell culture: part I--a physical strategy for synchronized cultivation under physiological conditions.

Author

Barradas OP, Jandt U, Becker M, Bahnemann J, Pörtner R, and Zeng AP

Subjects
Animals, CHO Cells, Cell Line, Cricetinae, Cricetulus, Humans, Metabolic Engineering, Bioreactors, Cell Culture Techniques methods, Cell Physiological Phenomena physiology, Models, Biological
Abstract

Conventional analysis and optimization procedures of mammalian cell culture processes mostly treat the culture as a homogeneous population. Hence, the focus is on cell physiology and metabolism, cell line development, and process control strategy. Impact on cultivations caused by potential variations in cellular properties between different subpopulations, however, has not yet been evaluated systematically. One main cause for the formation of such subpopulations is the progress of all cells through the cell cycle. The interaction of potential cell cycle specific variations in the cell behavior with large-scale process conditions can be optimally determined by means of (partially) synchronized cultivations, with subsequent population resolved model analysis. Therefore, it is desirable to synchronize a culture with minimal perturbation, which is possible with different yield and quality using physical selection methods, but not with frequently used chemical or whole-culture methods. Conventional nonsynchronizing methods with subsequent cell-specific, for example, flow cytometric analysis, can only resolve cell-limited effects of the cell cycle. In this work, we demonstrate countercurrent-flow centrifugal elutriation as a useful physical method to enrich mammalian cell populations within different phases of a cell cycle, which can be further cultivated for synchronized growth in bioreactors under physiological conditions. The presented combined approach contrasts with other physical selection methods especially with respect to the achievable yield, which makes it suitable for bioreactor scale cultivations. As shown with two industrial cell lines (CHO-K1 and human AGE1.HN), synchronous inocula can be obtained with overall synchrony degrees of up to 82% in the G1 phase, 53% in the S phase and 60% in the G2/M phase, with enrichment factors (Ysync) of 1.71, 1.79, and 4.24 respectively. Cells are able to grow with synchrony in bioreactors over several cell cycles. This strategy, combined with population-resolved model analysis and parameter extraction as described in the accompanying paper, offers new possibilities for studies of cell lines and processes at levels of cell cycle and population under physiological conditions., (© 2014 American Institute of Chemical Engineers.)

Published
2015
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