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1. A multidomain lifestyle intervention to maintain optimal cognitive functioning in Dutch older adults—study design and baseline characteristics of the FINGER-NL randomized controlled trial

Author

Deckers, Kay, Zwan, Marissa D., Soons, Lion M., Waterink, Lisa, Beers, Sonja, van Houdt, Sofie, Stiensma, Berrit, Kwant, Judy Z., Wimmers, Sophie C. P. M., Heutz, Rachel A. M., Claassen, Jurgen A. H. R., Oosterman, Joukje M., de Heus, Rianne A. A., van de Rest, Ondine, Vermeiren, Yannick, Voshaar, Richard C. Oude, Smidt, Nynke, Broersen, Laus M., Sikkes, Sietske A. M., Aarts, Esther, Köhler, Sebastian, and van der Flier, Wiesje M.

Published
2024
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2. Temporal patterns control carbon balance in forest and agricultural tropical peatlands in North Selangor, Malaysia

Author

Vijayanathan J, Ishak MF, Parlan I, Omar H, Ahmed OH, Lion M, Hassan MG, Jong RM, and Abu Samah AK

Subjects
Peat Characteristics, Carbon Storage, Carbon Dioxide Fluxes, Cash Crop Cultivation, Seasonal Variations, Forestry, SD1-669.5
Abstract

Tropical peat swamp forests can sequester significant amount of carbon (C). However, there is dearth of understanding on the tropical soils’ C stocks and emissions because of the changes in peatland use, land use policies, and micro-climate. The objective of this study was to determine the C stocks and fluxes of two peat swamp forests and a peatland under mixed cropping in Selangor, Malaysia. Standard procedures were used to determine aboveground biomass, belowground biomass, selected peat soil physical, chemical, and biological properties, and environmental variables that are related to peat soil respirations. The mean C stocks for the peat swamp forest and mixed cropping sites were 1788.79 Mg C ha-1 and 1023.57 Mg C ha-1, respectively. The carbon dioxide emission rates of peat swamp forest and mixed cropping sites ranged from 7.20 to 73.13 tCO2 ha-1 year-1 and 26.50 to 43.43 tCO2 ha-1 year-1, respectively. These emissions are related to seasonal changes because the relative humidity, soil temperature, and ground water of the experimental sites had significant effects on soil respiration. Unlike the mixed cropping sites, the fluxes of the peat swamp forest were significantly higher in the dry season compared with the wet season. These findings suggest that peat soil respiration is controlled by relative humidity, temperature, and the changes in ground water table. Continued monitoring and conservation efforts to preserve stored C in peatlands are essential.

Published
2021
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5. Umbrella review and Delphi study on modifiable factors for dementia risk reduction.

Author

Rosenau, Colin, Köhler, Sebastian, Soons, Lion M., Anstey, Kaarin J., Brayne, Carol, Brodaty, Henry, Engedal, Knut, Farina, Francesca R., Ganguli, Mary, Livingston, Gill, Lyketsos, Constantine G., Mangialasche, Francesca, Middleton, Laura E., Rikkert, Marcel G. M. Olde, Peters, Ruth, Sachdev, Perminder S., Scarmeas, Nikolaos, Salbæk, Geir, van Boxtel, Martin P. J., and Deckers, Kay

Abstract

A 2013 systematic review and Delphi consensus study identified 12 modifiable risk and protective factors for dementia, which were subsequently merged into the "LIfestyle for BRAin health" (LIBRA) score. We systematically evaluated whether LIBRA requires revision based on new evidence. To identify modifiable risk and protective factors suitable for dementia risk reduction, we combined an umbrella review of systematic reviews and meta‐analyses with a two‐round Delphi consensus study. The review of 608 unique primary studies and opinions of 18 experts prioritized six modifiable factors: hearing impairment, social contact, sleep, life course inequalities, atrial fibrillation, and psychological stress. Based on expert ranking, hearing impairment, social contact, and sleep were considered the most suitable candidates for inclusion in updated dementia risk scores. As such, the current study shows that dementia risk scores need systematic updates based on emerging evidence. Future studies will validate the updated LIBRA score in different cohorts. Highlights: An umbrella review was combined with opinions of 18 dementia experts.Various candidate targets for dementia risk reduction were identified.Experts prioritized hearing impairment, social contact, and sleep.Re‐assessment of dementia risk scores is encouraged.Future work should evaluate the predictive validity of updated risk scores. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Interpreting discordant indirect and multiple treatment comparison meta-analyses: an evaluation of direct acting antivirals for chronic hepatitis C infection

Author

Druyts E, Thorlund K, Humphreys S, Lion M, Cooper CL, and Mills EJ

Subjects
Infectious and parasitic diseases, RC109-216
Abstract

Eric Druyts,1 Kristian Thorlund,2,3 Samantha Humphreys,4 Michaela Lion,4 Curtis L Cooper,5 Edward J Mills1,31Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada; 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada; 3Stanford Prevention Research Centre, Department of Medicine, Stanford University, Palo Alto, CA, USA; 4Merck Sharp and Dohme Ltd, UK; 5Division of Infectious Diseases, Department of Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, CanadaAbstract: Indirect treatment comparison (ITC) and multiple treatment comparison (MTC) meta-analyses are increasingly being used to estimate the comparative effectiveness of interventions when head-to-head data do not exist. ITC meta-analyses can be conducted using simple methodology to compare two interventions. MTC meta-analyses can be conducted using more complex methodology, often employing Bayesian approaches, to compare multiple interventions. As the number of ITC and MTC meta-analyses increase, it is common to find multiple analyses evaluating the same interventions in similar therapeutic areas. Depending on the choice of the methodological approach, the conclusions about relative treatment efficacy may differ. Such situations create uncertainty for decision makers. An illustration of this is provided by four ITC and MTC meta-analyses assessing the efficacy of boceprevir and telaprevir for chronic hepatitis C virus infection. This paper examines why these evaluations provide discordant results by examining specific methodological issues that can strengthen or weaken inferences.Keywords: indirect treatment comparison, multiple treatment comparison, meta-analysis, hepatitis C virus

Published
2013

10. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (vol 7, 225, 2020)

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

Abstract

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Published
2021

11. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

Abstract

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

Published
2021

14. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Published
2020

15. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, G. (Gilberto), Trotta, C. (Carlo), Canfora, E. (Eleonora), Chu, H. (Housen), Christianson, D. (Danielle), Cheah, Y.-W. (You-Wei), Poindexter, C. (Cristina), Chen, J. (Jiquan), Elbashandy, A. (Abdelrahman), Humphrey, M. (Marty), Isaac, P. (Peter), Polidori, D. (Diego), Ribeca, A. (Alessio), van Ingen, C. (Catharine), Zhang, L. (Leiming), Amiro, B. (Brian), Ammann, C. (Christof), Arain, M. A. (M. Altaf), Ardo, J. (Jonas), Arkebauer, T. (Timothy), Arndt, S. K. (Stefan K.), Arriga, N. (Nicola), Aubinet, M. (Marc), Aurela, M. (Mika), Baldocchi, D. (Dennis), Barr, A. (Alan), Beamesderfer, E. (Eric), Marchesini, L. B. (Luca Belelli), Bergeron, O. (Onil), Beringer, J. (Jason), Bernhofer, C. (Christian), Berveiller, D. (Daniel), Billesbach, D. (Dave), Black, T. A. (Thomas Andrew), Blanken, P. D. (Peter D.), Bohrer, G. (Gil), Boike, J. (Julia), Bolstad, P. V. (Paul V.), Bonal, D. (Damien), Bonnefond, J.-M. (Jean-Marc), Bowling, D. R. (David R.), Bracho, R. (Rosvel), Brodeur, J. (Jason), Bruemmer, C. (Christian), Buchmann, N. (Nina), Burban, B. (Benoit), Burns, S. P. (Sean P.), Buysse, P. (Pauline), Cale, P. (Peter), Cavagna, M. (Mauro), Cellier, P. (Pierre), Chen, S. (Shiping), Chini, I. (Isaac), Christensen, T. R. (Torben R.), Cleverly, J. (James), Collalti, A. (Alessio), Consalvo, C. (Claudia), Cook, B. D. (Bruce D.), Cook, D. (David), Coursolle, C. (Carole), Cremonese, E. (Edoardo), Curtis, P. S. (Peter S.), D'Andrea, E. (Ettore), da Rocha, H. (Humberto), Dai, X. (Xiaoqin), Davis, K. J. (Kenneth J.), De Cinti, B. (Bruno), de Grandcourt, A. (Agnes), De Ligne, A. (Anne), De Oliveira, R. C. (Raimundo C.), Delpierre, N. (Nicolas), Desai, A. R. (Ankur R.), Di Bella, C. M. (Carlos Marcelo), di Tommasi, P. (Paul), Dolman, H. (Han), Domingo, F. (Francisco), Dong, G. (Gang), Dore, S. (Sabina), Duce, P. (Pierpaolo), Dufrene, E. (Eric), Dunn, A. (Allison), Dusek, J. (Jiri), Eamus, D. (Derek), Eichelmann, U. (Uwe), ElKhidir, H. A. (Hatim Abdalla M.), Eugster, W. (Werner), Ewenz, C. M. (Cacilia M.), Ewers, B. (Brent), Famulari, D. (Daniela), Fares, S. (Silvano), Feigenwinter, I. (Iris), Feitz, A. (Andrew), Fensholt, R. (Rasmus), Filippa, G. (Gianluca), Fischer, M. (Marc), Frank, J. (John), Galvagno, M. (Marta), Gharun, M. (Mana), Gianelle, D. (Damiano), Gielen, B. (Bert), Gioli, B. (Beniamino), Gitelson, A. (Anatoly), Goded, I. (Ignacio), Goeckede, M. (Mathias), Goldstein, A. H. (Allen H.), Gough, C. M. (Christopher M.), Goulden, M. L. (Michael L.), Graf, A. (Alexander), Griebel, A. (Anne), Gruening, C. (Carsten), Gruenwald, T. (Thomas), Hammerle, A. (Albin), Han, S. (Shijie), Han, X. (Xingguo), Hansen, B. U. (Birger Ulf), Hanson, C. (Chad), Hatakka, J. (Juha), He, Y. (Yongtao), Hehn, M. (Markus), Heinesch, B. (Bernard), Hinko-Najera, N. (Nina), Hoertnagl, L. (Lukas), Hutley, L. (Lindsay), Ibrom, A. (Andreas), Ikawa, H. (Hiroki), Jackowicz-Korczynski, M. (Marcin), Janous, D. (Dalibor), Jans, W. (Wilma), Jassal, R. (Rachhpal), Jiang, S. (Shicheng), Kato, T. (Tomomichi), Khomik, M. (Myroslava), Klatt, J. (Janina), Knohl, A. (Alexander), Knox, S. (Sara), Kobayashi, H. (Hideki), Koerber, G. (Georgia), Kolle, O. (Olaf), Kosugi, Y. (Yoshiko), Kotani, A. (Ayumi), Kowalski, A. (Andrew), Kruijt, B. (Bart), Kurbatova, J. (Julia), Kutsch, W. L. (Werner L.), Kwon, H. (Hyojung), Launiainen, S. (Samuli), Laurila, T. (Tuomas), Law, B. (Bev), Leuning, R. (Ray), Li, Y. (Yingnian), Liddell, M. (Michael), Limousin, J.-M. (Jean-Marc), Lion, M. (Marryanna), Liska, A. J. (Adam J.), Lohila, A. (Annalea), Lopez-Ballesteros, A. (Ana), Lopez-Blanco, E. (Efren), Loubet, B. (Benjamin), Loustau, D. (Denis), Lucas-Moffat, A. (Antje), Lueers, J. (Johannes), Ma, S. (Siyan), Macfarlane, C. (Craig), Magliulo, V. (Vincenzo), Maier, R. (Regine), Mammarella, I. (Ivan), Manca, G. (Giovanni), Marcolla, B. (Barbara), Margolis, H. A. (Hank A.), Marras, S. (Serena), Massman, W. (William), Mastepanov, M. (Mikhail), Matamala, R. (Roser), Matthes, J. H. (Jaclyn Hatala), Mazzenga, F. (Francesco), McCaughey, H. (Harry), McHugh, I. (Ian), McMillan, A. M. (Andrew M. S.), Merbold, L. (Lutz), Meyer, W. (Wayne), Meyers, T. (Tilden), Miller, S. D. (Scott D.), Minerbi, S. (Stefano), Moderow, U. (Uta), Monson, R. K. (Russell K.), Montagnani, L. (Leonardo), Moore, C. E. (Caitlin E.), Moors, E. (Eddy), Moreaux, V. (Virginie), Moureaux, C. (Christine), Munger, J. W. (J. William), Nakai, T. (Taro), Neirynck, J. (Johan), Nesic, Z. (Zoran), Nicolini, G. (Giacomo), Noormets, A. (Asko), Northwood, M. (Matthew), Nosetto, M. (Marcelo), Nouvellon, Y. (Yann), Novick, K. (Kimberly), Oechel, W. (Walter), Olesen, J. E. (Jorgen Eivind), Ourcival, J.-M. (Jean-Marc), Papuga, S. A. (Shirley A.), Parmentier, F.-J. (Frans-Jan), Paul-Limoges, E. (Eugenie), Pavelka, M. (Marian), Peichl, M. (Matthias), Pendall, E. (Elise), Phillips, R. P. (Richard P.), Pilegaard, K. (Kim), Pirk, N. (Norbert), Posse, G. (Gabriela), Powell, T. (Thomas), Prasse, H. (Heiko), Prober, S. M. (Suzanne M.), Rambal, S. (Serge), Rannik, U. (Ullar), Raz-Yaseef, N. (Naama), Reed, D. (David), de Dios, V. R. (Victor Resco), Restrepo-Coupe, N. (Natalia), Reverter, B. R. (Borja R.), Roland, M. (Marilyn), Sabbatini, S. (Simone), Sachs, T. (Torsten), Saleska, S. R. (Scott R.), Sanchez-Canete, E. P. (Enrique P.), Sanchez-Mejia, Z. M. (Zulia M.), Schmid, H. P. (Hans Peter), Schmidt, M. (Marius), Schneider, K. (Karl), Schrader, F. (Frederik), Schroder, I. (Ivan), Scott, R. L. (Russell L.), Sedlak, P. (Pavel), Serrano-Ortiz, P. (Penelope), Shao, C. (Changliang), Shi, P. (Peili), Shironya, I. (Ivan), Siebicke, L. (Lukas), Sigut, L. (Ladislav), Silberstein, R. (Richard), Sirca, C. (Costantino), Spano, D. (Donatella), Steinbrecher, R. (Rainer), Stevens, R. M. (Robert M.), Sturtevant, C. (Cove), Suyker, A. (Andy), Tagesson, T. (Torbern), Takanashi, S. (Satoru), Tang, Y. (Yanhong), Tapper, N. (Nigel), Thom, J. (Jonathan), Tiedemann, F. (Frank), Tomassucci, M. (Michele), Tuovinen, J.-P. (Juha-Pekka), Urbanski, S. (Shawn), Valentini, R. (Riccardo), van der Molen, M. (Michiel), van Gorsel, E. (Eva), van Huissteden, K. (Ko), Varlagin, A. (Andrej), Verfaillie, J. (Joseph), Vesala, T. (Timo), Vincke, C. (Caroline), Vitale, D. (Domenico), Vygodskaya, N. (Natalia), Walker, J. P. (Jeffrey P.), Walter-Shea, E. (Elizabeth), Wang, H. (Huimin), Weber, R. (Robin), Westermann, S. (Sebastian), Wille, C. (Christian), Wofsy, S. (Steven), Wohlfahrt, G. (Georg), Wolf, S. (Sebastian), Woodgate, W. (William), Li, Y. (Yuelin), Zampedri, R. (Roberto), Zhang, J. (Junhui), Zhou, G. (Guoyi), Zona, D. (Donatella), Agarwal, D. (Deb), Biraud, S. (Sebastien), Torn, M. (Margaret), Papale, D. (Dario), Pastorello, G. (Gilberto), Trotta, C. (Carlo), Canfora, E. (Eleonora), Chu, H. (Housen), Christianson, D. (Danielle), Cheah, Y.-W. (You-Wei), Poindexter, C. (Cristina), Chen, J. (Jiquan), Elbashandy, A. (Abdelrahman), Humphrey, M. (Marty), Isaac, P. (Peter), Polidori, D. (Diego), Ribeca, A. (Alessio), van Ingen, C. (Catharine), Zhang, L. (Leiming), Amiro, B. (Brian), Ammann, C. (Christof), Arain, M. A. (M. Altaf), Ardo, J. (Jonas), Arkebauer, T. (Timothy), Arndt, S. K. (Stefan K.), Arriga, N. (Nicola), Aubinet, M. (Marc), Aurela, M. (Mika), Baldocchi, D. (Dennis), Barr, A. (Alan), Beamesderfer, E. (Eric), Marchesini, L. B. (Luca Belelli), Bergeron, O. (Onil), Beringer, J. (Jason), Bernhofer, C. (Christian), Berveiller, D. (Daniel), Billesbach, D. (Dave), Black, T. A. (Thomas Andrew), Blanken, P. D. (Peter D.), Bohrer, G. (Gil), Boike, J. (Julia), Bolstad, P. V. (Paul V.), Bonal, D. (Damien), Bonnefond, J.-M. (Jean-Marc), Bowling, D. R. (David R.), Bracho, R. (Rosvel), Brodeur, J. (Jason), Bruemmer, C. (Christian), Buchmann, N. (Nina), Burban, B. (Benoit), Burns, S. P. (Sean P.), Buysse, P. (Pauline), Cale, P. (Peter), Cavagna, M. (Mauro), Cellier, P. (Pierre), Chen, S. (Shiping), Chini, I. (Isaac), Christensen, T. R. (Torben R.), Cleverly, J. (James), Collalti, A. (Alessio), Consalvo, C. (Claudia), Cook, B. D. (Bruce D.), Cook, D. (David), Coursolle, C. (Carole), Cremonese, E. (Edoardo), Curtis, P. S. (Peter S.), D'Andrea, E. (Ettore), da Rocha, H. (Humberto), Dai, X. (Xiaoqin), Davis, K. J. (Kenneth J.), De Cinti, B. (Bruno), de Grandcourt, A. (Agnes), De Ligne, A. (Anne), De Oliveira, R. C. (Raimundo C.), Delpierre, N. (Nicolas), Desai, A. R. (Ankur R.), Di Bella, C. M. (Carlos Marcelo), di Tommasi, P. (Paul), Dolman, H. (Han), Domingo, F. (Francisco), Dong, G. (Gang), Dore, S. (Sabina), Duce, P. (Pierpaolo), Dufrene, E. (Eric), Dunn, A. (Allison), Dusek, J. (Jiri), Eamus, D. (Derek), Eichelmann, U. (Uwe), ElKhidir, H. A. (Hatim Abdalla M.), Eugster, W. (Werner), Ewenz, C. M. (Cacilia M.), Ewers, B. (Brent), Famulari, D. (Daniela), Fares, S. (Silvano), Feigenwinter, I. (Iris), Feitz, A. (Andrew), Fensholt, R. (Rasmus), Filippa, G. (Gianluca), Fischer, M. (Marc), Frank, J. (John), Galvagno, M. (Marta), Gharun, M. (Mana), Gianelle, D. (Damiano), Gielen, B. (Bert), Gioli, B. (Beniamino), Gitelson, A. (Anatoly), Goded, I. (Ignacio), Goeckede, M. (Mathias), Goldstein, A. H. (Allen H.), Gough, C. M. (Christopher M.), Goulden, M. L. (Michael L.), Graf, A. (Alexander), Griebel, A. (Anne), Gruening, C. (Carsten), Gruenwald, T. (Thomas), Hammerle, A. (Albin), Han, S. (Shijie), Han, X. (Xingguo), Hansen, B. U. (Birger Ulf), Hanson, C. (Chad), Hatakka, J. (Juha), He, Y. (Yongtao), Hehn, M. (Markus), Heinesch, B. (Bernard), Hinko-Najera, N. (Nina), Hoertnagl, L. (Lukas), Hutley, L. (Lindsay), Ibrom, A. (Andreas), Ikawa, H. (Hiroki), Jackowicz-Korczynski, M. (Marcin), Janous, D. (Dalibor), Jans, W. (Wilma), Jassal, R. (Rachhpal), Jiang, S. (Shicheng), Kato, T. (Tomomichi), Khomik, M. (Myroslava), Klatt, J. (Janina), Knohl, A. (Alexander), Knox, S. (Sara), Kobayashi, H. (Hideki), Koerber, G. (Georgia), Kolle, O. (Olaf), Kosugi, Y. (Yoshiko), Kotani, A. (Ayumi), Kowalski, A. (Andrew), Kruijt, B. (Bart), Kurbatova, J. (Julia), Kutsch, W. L. (Werner L.), Kwon, H. (Hyojung), Launiainen, S. (Samuli), Laurila, T. (Tuomas), Law, B. (Bev), Leuning, R. (Ray), Li, Y. (Yingnian), Liddell, M. (Michael), Limousin, J.-M. (Jean-Marc), Lion, M. (Marryanna), Liska, A. J. (Adam J.), Lohila, A. (Annalea), Lopez-Ballesteros, A. (Ana), Lopez-Blanco, E. (Efren), Loubet, B. (Benjamin), Loustau, D. (Denis), Lucas-Moffat, A. (Antje), Lueers, J. (Johannes), Ma, S. (Siyan), Macfarlane, C. (Craig), Magliulo, V. (Vincenzo), Maier, R. (Regine), Mammarella, I. (Ivan), Manca, G. (Giovanni), Marcolla, B. (Barbara), Margolis, H. A. (Hank A.), Marras, S. (Serena), Massman, W. (William), Mastepanov, M. (Mikhail), Matamala, R. (Roser), Matthes, J. H. (Jaclyn Hatala), Mazzenga, F. (Francesco), McCaughey, H. (Harry), McHugh, I. (Ian), McMillan, A. M. (Andrew M. S.), Merbold, L. (Lutz), Meyer, W. (Wayne), Meyers, T. (Tilden), Miller, S. D. (Scott D.), Minerbi, S. (Stefano), Moderow, U. (Uta), Monson, R. K. (Russell K.), Montagnani, L. (Leonardo), Moore, C. E. (Caitlin E.), Moors, E. (Eddy), Moreaux, V. (Virginie), Moureaux, C. (Christine), Munger, J. W. (J. William), Nakai, T. (Taro), Neirynck, J. (Johan), Nesic, Z. (Zoran), Nicolini, G. (Giacomo), Noormets, A. (Asko), Northwood, M. (Matthew), Nosetto, M. (Marcelo), Nouvellon, Y. (Yann), Novick, K. (Kimberly), Oechel, W. (Walter), Olesen, J. E. (Jorgen Eivind), Ourcival, J.-M. (Jean-Marc), Papuga, S. A. (Shirley A.), Parmentier, F.-J. (Frans-Jan), Paul-Limoges, E. (Eugenie), Pavelka, M. (Marian), Peichl, M. (Matthias), Pendall, E. (Elise), Phillips, R. P. (Richard P.), Pilegaard, K. (Kim), Pirk, N. (Norbert), Posse, G. (Gabriela), Powell, T. (Thomas), Prasse, H. (Heiko), Prober, S. M. (Suzanne M.), Rambal, S. (Serge), Rannik, U. (Ullar), Raz-Yaseef, N. (Naama), Reed, D. (David), de Dios, V. R. (Victor Resco), Restrepo-Coupe, N. (Natalia), Reverter, B. R. (Borja R.), Roland, M. (Marilyn), Sabbatini, S. (Simone), Sachs, T. (Torsten), Saleska, S. R. (Scott R.), Sanchez-Canete, E. P. (Enrique P.), Sanchez-Mejia, Z. M. (Zulia M.), Schmid, H. P. (Hans Peter), Schmidt, M. (Marius), Schneider, K. (Karl), Schrader, F. (Frederik), Schroder, I. (Ivan), Scott, R. L. (Russell L.), Sedlak, P. (Pavel), Serrano-Ortiz, P. (Penelope), Shao, C. (Changliang), Shi, P. (Peili), Shironya, I. (Ivan), Siebicke, L. (Lukas), Sigut, L. (Ladislav), Silberstein, R. (Richard), Sirca, C. (Costantino), Spano, D. (Donatella), Steinbrecher, R. (Rainer), Stevens, R. M. (Robert M.), Sturtevant, C. (Cove), Suyker, A. (Andy), Tagesson, T. (Torbern), Takanashi, S. (Satoru), Tang, Y. (Yanhong), Tapper, N. (Nigel), Thom, J. (Jonathan), Tiedemann, F. (Frank), Tomassucci, M. (Michele), Tuovinen, J.-P. (Juha-Pekka), Urbanski, S. (Shawn), Valentini, R. (Riccardo), van der Molen, M. (Michiel), van Gorsel, E. (Eva), van Huissteden, K. (Ko), Varlagin, A. (Andrej), Verfaillie, J. (Joseph), Vesala, T. (Timo), Vincke, C. (Caroline), Vitale, D. (Domenico), Vygodskaya, N. (Natalia), Walker, J. P. (Jeffrey P.), Walter-Shea, E. (Elizabeth), Wang, H. (Huimin), Weber, R. (Robin), Westermann, S. (Sebastian), Wille, C. (Christian), Wofsy, S. (Steven), Wohlfahrt, G. (Georg), Wolf, S. (Sebastian), Woodgate, W. (William), Li, Y. (Yuelin), Zampedri, R. (Roberto), Zhang, J. (Junhui), Zhou, G. (Guoyi), Zona, D. (Donatella), Agarwal, D. (Deb), Biraud, S. (Sebastien), Torn, M. (Margaret), and Papale, D. (Dario)

Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Published
2020

16. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author

Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D, Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, and Papale D

Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Published
2020

19. Examples of feedback, experimental and theoretical approaches for concrete durability assessment

Author

Toutlemonde F., Le Pape Y., Lion M., and Jeanpierre A.

Subjects
Physics, QC1-999
Abstract

This paper presents some experimental data obtained from UHPFRC (Ultra-High Performance Fibre-Reinforced Concrete) being exposed for 10 years in a cooling tower and a high slag content concrete being exposed for 30 years in a marine environment. Experimental data are then used for assessing concrete durability through a theoretical approach, namely performance-based analysis. The results from the application of this approach are consistent with the penetration depth of aggressive agents measured from core samples. Finally a simulation method currently being developed by EDF is presented, which has great relevance to durability assessment.

Published
2011
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22. HAB-MAPS of toxic marine microalgae in the 'Cono Sur¿ of South America

Author

Akselman, R., Reguera, B. (Beatriz), Lion, M., and Moestrup, Ø.

Abstract

Geo-referenced distributions of potentially toxic microalgal species in coastal and shelf waters of South America have been created as part of the HAB-MAP project of the International Society for the Study of Harmful Algae (ISSHA). A total of 40 potentially toxic species - 9 diatoms, 23 dinoflagellates, 3 haptophytes and 5 raphidophytes - were recorded. The total number of toxic species could be greater than the apparent one because of dubious taxonomic identifications of some taxa, and low frequency of sampling in large areas of South America Versión del editor

Published
2008

24. Investors call for strong corporate governance

Author

Lion, M.

Subjects
Corporate governance -- Surveys -- Forecasts and trends, Institutional investments -- Surveys -- Forecasts and trends, Financial institutions -- Investments, Investors -- Surveys -- Beliefs, opinions and attitudes, Banking, finance and accounting industries, Business, Market trend/market analysis, Beliefs, opinions and attitudes, Surveys, Forecasts and trends
Abstract

INSTITUTIONAL INVESTORS ACROSS THE GLOBE ARE increasingly focused on better corporate governance, according to a recent study by Institutional Shareholders Services (ISS), a Rockville, Md.-based research and consulting firm. Representing [...]

Published
2006

25. Abstract P1-08-27: Quantitative analysis of tumor expression of phosphoproteins from PI3-kinase and MAP-kinase signaling pathways as biomarkers of the biological and clinical activity of trastuzumab and everolimus in breast cancer: Unicancer RADHER phase II trial results

Author

Merlin, J-L, primary, Lion, M, additional, Wong, J, additional, Bachelot, T, additional, André, F, additional, Treilleux, I, additional, Loussouarn, D, additional, Bonneterre, J, additional, Rios, M, additional, Diéras, V, additional, Jimenez, M, additional, Leroux, A, additional, and Campone, M, additional

Published
2013
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32. Comparison of Hole and Electron Emission from InAs Quantum Dots

Author

TECHNISCHE UNIV BERLIN (GERMANY F R), Kapteyn, C. M., Lion, M., Heitz, R., Bimberg, D., Brunkov, P. N., TECHNISCHE UNIV BERLIN (GERMANY F R), Kapteyn, C. M., Lion, M., Heitz, R., Bimberg, D., and Brunkov, P. N.

Abstract

Carrier escape processes from self-organized InAs quantum dots (QDs) embedded in GaAs are investigated by time-resolved capacitance spectroscopy. Electron emission is found to be dominated by tunneling processes. In addition to tunneling from the ground state, we find thermally activated tunneling involving excited QD states with an activation energy of 82 meV. For boles, the tunnel contribution is negligible and thermal activation from the QD ground state to the GaAs valence band with an activation energy of 164 meV dominates. Extrapolation to room temperature yields an emission time constant of 5 ps for holes, which is an order of magnitude larger than for electrons. The measured activation energies agree well with theoretically predicted QD levels., This article is from Nanostructures: Physics and Technology Intl Symposium (8th), p. 375-378 This article is from ADA407315 Nanostructures: Physics and Technology International Symposium (8th) Held in St. Petersburg, Russia on June 19-23, 2000 Proceedings

Published
2000

35. M & L Jaargang 1/3

Author

Delepierre, A. M., Lion, M., Ramakers, M., Ostyn, Guido, Schepper, Jo De, Stynen, Herman, and Roose, P.

Abstract

Redactioneel, A.M. Delepierre – Het arrondissement Veurne: een globale benadering. [The district of Veurne: a comprehensive approach of historic building inventory.], M. Lion en M. Ramakers – De kustarchitectuur in de Westhoek of het bouwen aan de rand van de zeefascinatie. [Coastal architecture in the ‘Westhoek’, or Building on the Verge of Sea-fascination.], Guido Ostyn – De Frans-Belgische Moeren. [The French-Belgian ‘Moeren’.], Jo De Schepper – De Franse-Belgische Moeren: enkele gegevens aangaande de windgemalen. [The French-Belgian ‘Moeren’: some data about the wind-pumpingengines.], Herman Stynen – Jozef Viérin (1872-1949). [Jozef Viérin (1872-1949), profile of an architect.], P. Roose – Het Van Peteghem-orgel (1838) in het voormalig Sint-Jans-Gasthuis (thans Bisschoppelijk College) te Veurne. [The Van Peteghem organ (1838) in the former Saint John’s hospital (now Episcopal College) in Veurne.], Summary, M&L Binnenkrant

Published
1982

43. Transcription factors form a ternary complex with NIPBL/MAU2 to localize cohesin at enhancers.

Author

Fettweis G, Wagh K, Stavreva DA, Jiménez-Panizo A, Kim S, Lion M, Alegre-Martí A, Rinaldi L, Johnson TA, Krishnamurthy M, Wang L, Ball DA, Karpova TS, Upadhyaya A, Vertommen D, Recio JF, Estébanez-Perpiñá E, Dequiedt F, and Hager GL

Abstract

While the cohesin complex is a key player in genome architecture, how it localizes to specific chromatin sites is not understood. Recently, we and others have proposed that direct interactions with transcription factors lead to the localization of the cohesin-loader complex (NIPBL/MAU2) within enhancers. Here, we identify two clusters of LxxLL motifs within the NIPBL sequence that regulate NIPBL dynamics, interactome, and NIPBL-dependent transcriptional programs. One of these clusters interacts with MAU2 and is necessary for the maintenance of the NIPBL-MAU2 heterodimer. The second cluster binds specifically to the ligand-binding domains of steroid receptors. For the glucocorticoid receptor (GR), we examine in detail its interaction surfaces with NIPBL and MAU2. Using AlphaFold2 and molecular docking algorithms, we uncover a GR-NIPBL-MAU2 ternary complex and describe its importance in GR-dependent gene regulation. Finally, we show that multiple transcription factors interact with NIPBL-MAU2, likely using interfaces other than those characterized for GR.

Published
2025
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44. Evaluation of efficiency and effectiveness of different recruitment strategies for the FINGER-NL multidomain lifestyle intervention trial via the Dutch Brain Research Registry.

Author

Waterink L, Sikkes SAM, Soons LM, Beers S, Meijer-Krommenhoek Y, van de Rest O, Nynke S, Oosterman JM, Scherder E, Deckers K, Vermeiren Y, de Heus RAA, Köhler S, van der Flier WM, and Zwan MD

Abstract

Introduction: Recruitment of participants for intervention studies is challenging. We evaluated the effectiveness and efficiency of a participant recruitment campaign through an online registry for the FINGER-NL study, a multi-domain lifestyle intervention trial targeting cognitively healthy individuals aged 60-79 with dementia prevention potential. Additionally, we explored which recruitment strategy successfully reached individuals from underrepresented groups in research., Methods: The campaign entailed seven recruitment strategies referring to The Dutch Brain Research Registry (DBRR): (1) Facebook advertisements, (2) appearance on national television, (3) newspaper articles, (4) researcher outreach, (5) patient organizations, (6) search engines, and (7) other. For each strategy, we describe the number of individuals (a) registered, (b) potentially eligible, and (c) included in FINGER-NL. Subsequently, the efficiency, defined by the eligibility ratio (eligible/registered), and effectiveness, defined by the inclusion ratio (included/registered) were calculated. Associations between recruitment strategies and sociodemographic factors of underrepresented groups were tested with binomial logistic regressions., Results: The campaign resulted in 13,795 new DBRR registrants, of which n  = 3475 were eligible (eligibility ratio = 0.25) and n  = 1008 were included (inclusion ratio = 0.07). The Facebook advertisements and television appearance resulted in the highest numbers of registrants ( n  = 4678 and n  = 2182) which translated to the highest number of inclusions ( n  = 288 and n  = 262). The appearance on national television (eligibility ratio = 0.35), newspaper articles (0.26), and Facebook campaigns (0.26) were the most efficient strategies. The national television appearance (inclusion ratio = 0.13) was the most effective strategy. The Facebook campaign and appearance on national television performed relatively better in recruiting individuals from underrepresented groups., Discussion: A multipronged recruitment campaign via a national online recruitment registry is efficient and effective in recruiting and prescreening an adequate number of individuals aged 60-79 years with prevention potential for a multi-site intervention trial within a limited time frame of 15 months. Social media advertisements and television are preferred strategies to recruit individuals from underrepresented groups., Highlights: An online brain research registry recruited eligible participants successfully.Mass media recruitment strategies are efficient for reaching large numbers.Direct recruitment through researchers and patient organizations seems more effective.Online registries offer automated prescreening and alternatives for screen-failures.Tailored strategies are needed to reach underrepresented groups to improve diversity., Competing Interests: S.A.M.S. provided consultancy services to Prothena Biosciences, Aribio, and Biogen, and she is part of the Scientific Advisory Board of Cogstate. All funds are paid to the institution. W.M.F. has performed contract research for Biogen MA Inc, and Boehringer Ingelheim. W.M.F. has been an invited speaker at Biogen MAInc, Danone, Eisai, Novonordisk, Web MD Neurology (Medscape), Springer Healthcare, European Brain Council. W.M.F. is consultant to Oxford Health Policy Forum CIC, Roche, Eisai, and Biogen MA Inc. W.M.F. participated on advisory boards of Biogen MAI inc, Roche, and EliLilly. All funding is paid to her institution. W.M.F. is a member of the steering committee of PAVE, and Think Brain Health. W.M.F. was associate editor of Alzheimer, Research & Therapy in 2020/2021. W.M.F. is associate editor at Brain. M.D.Z. is site coordinator of the phase 1/2 ASPIRE‐FTD clinical trial (NCT06064890) sponsored by AviadoBio. L.W., L.M.S., S.B., Y.M., O.R., N.S., J.M.O., E.S., K.D., Y.V.,R.A.A.H., and S.K. report no conflicts of interest. Author disclosures are available in the Supporting Information., (© 2025 The Author(s). Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Published
2025
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45. Umbrella review and Delphi study on modifiable factors for dementia risk reduction.

Author

Rosenau C, Köhler S, Soons LM, Anstey KJ, Brayne C, Brodaty H, Engedal K, Farina FR, Ganguli M, Livingston G, Lyketsos CG, Mangialasche F, Middleton LE, Rikkert MGMO, Peters R, Sachdev PS, Scarmeas N, Salbaek G, van Boxtel MPJ, and Deckers K

Subjects
Humans, Risk Factors, Life Style, Hearing Loss, Sleep physiology, Dementia epidemiology, Dementia prevention & control, Delphi Technique, Risk Reduction Behavior
Abstract

A 2013 systematic review and Delphi consensus study identified 12 modifiable risk and protective factors for dementia, which were subsequently merged into the "LIfestyle for BRAin health" (LIBRA) score. We systematically evaluated whether LIBRA requires revision based on new evidence. To identify modifiable risk and protective factors suitable for dementia risk reduction, we combined an umbrella review of systematic reviews and meta-analyses with a two-round Delphi consensus study. The review of 608 unique primary studies and opinions of 18 experts prioritized six modifiable factors: hearing impairment, social contact, sleep, life course inequalities, atrial fibrillation, and psychological stress. Based on expert ranking, hearing impairment, social contact, and sleep were considered the most suitable candidates for inclusion in updated dementia risk scores. As such, the current study shows that dementia risk scores need systematic updates based on emerging evidence. Future studies will validate the updated LIBRA score in different cohorts. HIGHLIGHTS: An umbrella review was combined with opinions of 18 dementia experts. Various candidate targets for dementia risk reduction were identified. Experts prioritized hearing impairment, social contact, and sleep. Re-assessment of dementia risk scores is encouraged. Future work should evaluate the predictive validity of updated risk scores., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Published
2024
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46. Endothelial extracellular vesicles promote tumour growth by tumour-associated macrophage reprogramming.

Author

Njock MS, O'Grady T, Nivelles O, Lion M, Jacques S, Cambier M, Herkenne S, Muller F, Christian A, Remacle C, Guiot J, Rahmouni S, Dequiedt F, and Struman I

Subjects
Animals, Disease Models, Animal, Endothelial Cells, Mice, Tumor Microenvironment, Tumor-Associated Macrophages, Extracellular Vesicles genetics, MicroRNAs genetics, Neoplasms
Abstract

Tumour-derived extracellular vesicles (EVs) participate in tumour progression by deregulating various physiological processes including angiogenesis and inflammation. Here we report that EVs released by endothelial cells in a mammary tumour environment participate in the recruitment of macrophages within the tumour, leading to an immunomodulatory phenotype permissive for tumour growth. Using RNA-Seq approaches, we identified several microRNAs (miRNAs) found in endothelial EVs sharing common targets involved in the regulation of the immune system. To further study the impact of these miRNAs in a mouse tumour model, we focused on three miRNAs that are conserved between humans and mouse, that is, miR-142-5p, miR-183-5p and miR-222-3p. These miRNAs are released from endothelial cells in a tumour microenvironment and are transferred via EVs to macrophages. In mouse mammary tumour models, treatment with EVs enriched in these miRNAs leads to a polarization of macrophages toward an M2-like phenotype, which in turn promotes tumour growth., (© 2022 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.)

Published
2022
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47. Sorting and packaging of RNA into extracellular vesicles shape intracellular transcript levels.

Author

O'Grady T, Njock MS, Lion M, Bruyr J, Mariavelle E, Galvan B, Boeckx A, Struman I, and Dequiedt F

Subjects
Cell Movement, RNA, Messenger genetics, RNA, Messenger metabolism, Extracellular Vesicles genetics, Extracellular Vesicles metabolism, MicroRNAs metabolism, RNA, Long Noncoding genetics, RNA, Long Noncoding metabolism
Abstract

Background: Extracellular vesicles (EVs) are released by nearly every cell type and have attracted much attention for their ability to transfer protein and diverse RNA species from donor to recipient cells. Much attention has been given so far to the features of EV short RNAs such as miRNAs. However, while the presence of mRNA and long noncoding RNA (lncRNA) transcripts in EVs has also been reported by multiple different groups, the properties and function of these longer transcripts have been less thoroughly explored than EV miRNA. Additionally, the impact of EV export on the transcriptome of exporting cells has remained almost completely unexamined. Here, we globally investigate mRNA and lncRNA transcripts in endothelial EVs in multiple different conditions., Results: In basal conditions, long RNA transcripts enriched in EVs have longer than average half-lives and distinctive stability-related sequence and structure characteristics including shorter transcript length, higher exon density, and fewer 3' UTR A/U-rich elements. EV-enriched long RNA transcripts are also enriched in HNRNPA2B1 binding motifs and are impacted by HNRNPA2B1 depletion, implicating this RNA-binding protein in the sorting of long RNA to EVs. After signaling-dependent modification of the cellular transcriptome, we observed that, unexpectedly, the rate of EV enrichment relative to cells was altered for many mRNA and lncRNA transcripts. This change in EV enrichment was negatively correlated with intracellular abundance, with transcripts whose export to EVs increased showing decreased abundance in cells and vice versa. Correspondingly, after treatment with inhibitors of EV secretion, levels of mRNA and lncRNA transcripts that are normally highly exported to EVs increased in cells, indicating a measurable impact of EV export on the long RNA transcriptome of the exporting cells. Compounds with different mechanisms of inhibition of EV secretion affected the cellular transcriptome differently, suggesting the existence of multiple EV subtypes with different long RNA profiles., Conclusions: We present evidence for an impact of EV physiology on the characteristics of EV-producing cell transcriptomes. Our work suggests a new paradigm in which the sorting and packaging of transcripts into EVs participate, together with transcription and RNA decay, in controlling RNA homeostasis and shape the cellular long RNA abundance profile., (© 2022. The Author(s).)

Published
2022
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48. Implementation and evaluation of a multivariate abstraction-based, interval-based dynamic time-warping method as a similarity measure for longitudinal medical records.

Author

Lion M and Shahar Y

Subjects
Humans, Electronic Health Records, Time Factors, Diabetes Mellitus, Knowledge Bases
Abstract

Objectives: A common prerequisite for tasks such as classification, prediction, clustering and retrieval of longitudinal medical records is a clinically meaningful similarity measure that considers both [multiple] variable (concept) values and their time. Currently, most similarity measures focus on raw, time-stamped data as these are stored in a medical record. However, clinicians think in terms of clinically meaningful temporal abstractions, such as "decreasing renal functions", enabling them to ignore minor time and value variations and focus on similarities among the clinical trajectories of different patients. Our objective was to define an abstraction- and interval-based methodology for matching longitudinal, multivariate medical records, and rigorously assess its value, versus the option of using just the raw, time-stamped data., Methods: We have developed a new methodology for determination of the relative distance between a pair of longitudinal records, by extending the known dynamic time warping (DTW) method into an interval-based dynamic time warping (iDTW) methodology. The iDTW methodology includes (A): A three-steps interval-based representation (iRep) method: [1] abstracting the raw, time-stamped data of the longitudinal records into clinically meaningful interval-based abstractions, using a domain-specific knowledge base, [2] scoping the period of comparison of the records, [3] creating from the intervals a symbolic time series, by partitioning them into a predetermined temporal granularity; (B) An interval-based matching (iMatch) method to match each relevant pair of multivariate longitudinal records, each represented as multiple series of short symbolic intervals in the determined temporal granularity, using a modified DTW version., Evaluation: Three classification or prediction tasks were defined: (1) classifying 161 records of oncology patients as having had autologous versus allogenic bone-marrow transplantation; (2) classifying the longitudinal records of 125 hepatitis patients as having B or C hepatitis; and (3) predicting micro- or macro-albuminuria in the second year, for 151 diabetes patients who were followed for five years. The raw, time-stamped, multivariate data within each medical record, for one, two, or three concepts out of four or five concepts judged as relevant in each medical domain, were abstracted into clinically meaningful intervals using the Knowledge-Based Temporal-Abstraction method, using previously acquired knowledge. We focused on two temporal-abstraction types: (1) State abstractions, which discretize a concept's raw value into a predetermined range (e.g., LOW or HIGH Hemoglobin); and (2) Gradient abstractions, which indicate the trend of the concept's value (e.g., INCREASING, DECREASING Hemoglobin value). We created all of the combinations of either uni-dimensional (State or Gradient) or multi-dimensional (State and Gradient) abstractions, of all of the concepts used. Classification of a record was determined by using a majority of the k-Nearest-Neighbors (KNN) of the given record, k ranging over the odd numbers (to break ties) from 1 to N, N being the size of the training set. We have experimented with all possible configurations of the parameters that our method uses. Overall, a total of 75,936 experiments were performed: 33,600 in the Oncology domain, 28,800 in the Hepatitis domain, and 13,536 in the Diabetes domain. Each experiment involved the performance of a 10-fold Cross Validation to compute the mean performance of a particular iDTW method-configuration set of settings, for a specific subset of one, two, or three concepts out of all of the domain-specific concepts relevant to the classification or prediction task on which the experiment focuses. We measured for each such experimental combination the Area Under the Curve (AUC) and the optimal Specificity/Sensitivity ratio using Youden's Index. We then aggregated the experiments by the types of unidimensional or multidimensional abstractions used in them (including the use of only raw concepts as a special case); for example, two state abstractions of different concepts, and one gradient abstraction of a third concept. We compared the mean AUC when using each such feature representation, or combination of abstractions, across all possible method-setting configurations, to the mean AUC when using as a feature representation, for the same task, only raw concepts, also across all possible method-setting configurations. Finally, we applied a paired t-test, to determine whether the mean difference between the accuracy of each temporal-abstraction representation, across all concept and configuration combinations, and the respective raw-concept combinations, across all concept subset and configuration combinations, is significant (P < 0.05)., Results: The mean performance of the classification and prediction tasks when using, as a feature representation, the various temporal-abstraction combinations, was significantly higher than that performance when using only raw data. Furthermore, in each domain and task, there existed at least one representation using interval-based abstractions whose use led, on average (over all concept subset combinations and method configurations) to a significantly better performance than the use of only subsets of the raw time-stamped data. In seven of nine combinations of domain type (out of three) and number of concepts used (one, two, or three), the variance of the AUCs (for all representations and configurations) was considerably higher across all raw-concept subsets, compared to all abstract combinations. Increasing the number of features used by the matching task enhanced performance. Using multi-dimensional abstractions of the same concept further enhanced the performance. When using only raw data, increasing the number of neighbors monotonically increased the mean performance (over all concept combinations and method configurations) until reaching an optimal saddle-point aroundN; when using abstractions, however, optimal mean performance was often reached after matching only five nearest neighbors., Conclusions: Using multivariate and multidimensional interval-based, abstraction-based similarity measures is feasible, and consistently and significantly improved the mean classification and prediction performance in time-oriented domains, using DTW-inspired methods, compared to the use of only raw, time-stamped data. It also made the KNN classification more effective. Nevertheless, although the mean performance for the abstract representations was higher than the mean performance when using only raw-data concepts, the actual optimal classification performance in each domain and task depends on the choice of the specific raw or abstract concepts used as features., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Published
2021
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49. Predicting insect outbreaks using machine learning: A mountain pine beetle case study.

Author

Ramazi P, Kunegel-Lion M, Greiner R, and Lewis MA

Abstract

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate-term future, e.g., 5-year. Machine-learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance-measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1-, 3-, 5- and 7-year infestations. We train nine machine-learning models, including two generalized boosted regression trees (GBM) that predict future 1- and 3-year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5- and 7-year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year-based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k -nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year-based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history-length increases, particularly for future 1- and 3-year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies., Competing Interests: The authors declare no conflict of interest., (© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Published
2021
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50. Core Histones Are Constituents of the Perinuclear Theca of Murid Spermatozoa: An Assessment of Their Synthesis and Assembly during Spermiogenesis and Function after Gametic Fusion.

Author

Hamilton LE, Lion M, Aguila L, Suzuki J, Acteau G, Protopapas N, Xu W, Sutovsky P, Baker M, and Oko R

Subjects
Animals, Cell Nucleus metabolism, Chromatography, Liquid methods, Female, Fertilization physiology, Male, Mice, Mice, Inbred C57BL, Rats, Rats, Sprague-Dawley, Sperm Injections, Intracytoplasmic, Tandem Mass Spectrometry methods, Histones biosynthesis, Sperm Head metabolism, Spermatids metabolism, Spermatogenesis physiology
Abstract

The perinuclear theca (PT) of the eutherian sperm head is a cytoskeletal-like structure that houses proteins involved in important cellular processes during spermiogenesis and fertilization. Building upon our novel discovery of non-nuclear histones in the bovine PT, we sought to investigate whether this PT localization was a conserved feature of eutherian sperm. Employing cell fractionation, immunodetection, mass spectrometry, qPCR, and intracytoplasmic sperm injections (ICSI), we examined the localization, developmental origin, and functional potential of histones from the murid PT. Immunodetection localized histones to the post-acrosomal sheath (PAS) and the perforatorium (PERF) of the PT but showed an absence in the sperm nucleus. MS/MS analysis of selectively extracted PT histones indicated that predominately core histones (i.e., H3, H3.3, H2B, H2A, H2AX, and H4) populate the murid PT. These core histones appear to be de novo -synthesized in round spermatids and assembled via the manchette during spermatid elongation. Mouse ICSI results suggest that early embryonic development is delayed in the absence of PT-derived core histones. Here, we provide evidence that core histones are de novo -synthesized prior to PT assembly and deposited in PT sub-compartments for subsequent involvement in chromatin remodeling of the male pronucleus post-fertilization.

Published
2021
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