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The solid solution series (GeTe)x(LiSbTe2)2 (1 ≤ x ≤ 11) and the thermoelectric properties of (GeTe)11(LiSbTe2)2.

Inorganic chemistry

Schröder T, Schwarzmüller S, Stiewe C, de Boor J, Hölzel M, Oeckler O.
PMID: 24093486
Inorg Chem. 2013 Oct 07;52(19):11288-94. doi: 10.1021/ic401516m. Epub 2013 Sep 16.

Exchanging one Ge(2+) with two Li(+) per formula unit in (GeTe)n(Sb2Te3) (n = 1, 2, 3, ...) eliminates cation vacancies, because it leads to an equal number of cations and anions. This substitution results in the solid solution (GeTe)x(LiSbTe2)2...

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Schröder T, Schwarzmüller S, Stiewe C, et al. The solid solution series (GeTe)x(LiSbTe2)2 (1 ≤ x ≤ 11) and the thermoelectric properties of (GeTe)11(LiSbTe2)2. Inorg Chem. 2013;52(19):11288-94doi: 10.1021/ic401516m.
Schröder, T., Schwarzmüller, S., Stiewe, C., de Boor, J., Hölzel, M., & Oeckler, O. (2013). The solid solution series (GeTe)x(LiSbTe2)2 (1 ≤ x ≤ 11) and the thermoelectric properties of (GeTe)11(LiSbTe2)2. Inorganic chemistry, 52(19), 11288-94. https://doi.org/10.1021/ic401516m
Schröder, Thorsten, et al. "The solid solution series (GeTe)x(LiSbTe2)2 (1 ≤ x ≤ 11) and the thermoelectric properties of (GeTe)11(LiSbTe2)2." Inorganic chemistry vol. 52,19 (2013): 11288-94. doi: https://doi.org/10.1021/ic401516m
Schröder T, Schwarzmüller S, Stiewe C, de Boor J, Hölzel M, Oeckler O. The solid solution series (GeTe)x(LiSbTe2)2 (1 ≤ x ≤ 11) and the thermoelectric properties of (GeTe)11(LiSbTe2)2. Inorg Chem. 2013 Oct 07;52(19):11288-94. doi: 10.1021/ic401516m. Epub 2013 Sep 16. PMID: 24093486.
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CIGS thin-film solar module processing: case of high-speed laser scribing.

Scientific reports

Gečys P, Markauskas E, Nishiwaki S, Buecheler S, De Loor R, Burn A, Romano V, Račiukaitis G.
PMID: 28084403
Sci Rep. 2017 Jan 13;7:40502. doi: 10.1038/srep40502.

In this paper, we investigate the laser processing of the CIGS thin-film solar cells in the case of the high-speed regime. The modern ultra-short pulsed laser was used exhibiting the pulse repetition rate of 1 MHz. Two main P3...

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Gečys P, Markauskas E, Nishiwaki S, et al. CIGS thin-film solar module processing: case of high-speed laser scribing. Sci Rep. 2017;7:40502doi: 10.1038/srep40502.
Gečys, P., Markauskas, E., Nishiwaki, S., Buecheler, S., De Loor, R., Burn, A., Romano, V., & Račiukaitis, G. (2017). CIGS thin-film solar module processing: case of high-speed laser scribing. Scientific reports, 740502. https://doi.org/10.1038/srep40502
Gečys, Paulius, et al. "CIGS thin-film solar module processing: case of high-speed laser scribing." Scientific reports vol. 7 (2017): 40502. doi: https://doi.org/10.1038/srep40502
Gečys P, Markauskas E, Nishiwaki S, Buecheler S, De Loor R, Burn A, Romano V, Račiukaitis G. CIGS thin-film solar module processing: case of high-speed laser scribing. Sci Rep. 2017 Jan 13;7:40502. doi: 10.1038/srep40502. PMID: 28084403; PMCID: PMC5233978.
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Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium.

Behavior genetics

van den Berg SM, de Moor MH, Verweij KJ, Krueger RF, Luciano M, Arias Vasquez A, Matteson LK, Derringer J, Esko T, Amin N, Gordon SD, Hansell NK, Hart AB, Seppälä I, Huffman JE, Konte B, Lahti J, Lee M, Miller M, Nutile T, Tanaka T, Teumer A, Viktorin A, Wedenoja J, Abdellaoui A, Abecasis GR, Adkins DE, Agrawal A, Allik J, Appel K, Bigdeli TB, Busonero F, Campbell H, Costa PT, Smith GD, Davies G, de Wit H, Ding J, Engelhardt BE, Eriksson JG, Fedko IO, Ferrucci L, Franke B, Giegling I, Grucza R, Hartmann AM, Heath AC, Heinonen K, Henders AK, Homuth G, Hottenga JJ, Iacono WG, Janzing J, Jokela M, Karlsson R, Kemp JP, Kirkpatrick MG, Latvala A, Lehtimäki T, Liewald DC, Madden PA, Magri C, Magnusson PK, Marten J, Maschio A, Mbarek H, Medland SE, Mihailov E, Milaneschi Y, Montgomery GW, Nauck M, Nivard MG, Ouwens KG, Palotie A, Pettersson E, Polasek O, Qian Y, Pulkki-Råback L, Raitakari OT, Realo A, Rose RJ, Ruggiero D, Schmidt CO, Slutske WS, Sorice R, Starr JM, St Pourcain B, Sutin AR, Timpson NJ, Trochet H, Vermeulen S, Vuoksimaa E, Widen E, Wouda J, Wright MJ, Zgaga L, Porteous D, Minelli A, Palmer AA, Rujescu D, Ciullo M, Hayward C, Rudan I, Metspalu A, Kaprio J, Deary IJ, Räikkönen K, Wilson JF, Keltikangas-Järvinen L, Bierut LJ, Hettema JM, Grabe HJ, Penninx BW, van Duijn CM, Evans DM, Schlessinger D, Pedersen NL, Terracciano A, McGue M, Martin NG, Boomsma DI.
PMID: 26362575
Behav Genet. 2016 Mar;46(2):170-82. doi: 10.1007/s10519-015-9735-5. Epub 2015 Sep 11.

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively...

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van den Berg SM, de Moor MH, Verweij KJ, et al. Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behav Genet. 2015;46(2):170-82doi: 10.1007/s10519-015-9735-5.
van den Berg, S. M., de Moor, M. H., Verweij, K. J., Krueger, R. F., Luciano, M., Arias Vasquez, A., Matteson, L. K., Derringer, J., Esko, T., Amin, N., Gordon, S. D., Hansell, N. K., Hart, A. B., Seppälä, I., Huffman, J. E., Konte, B., Lahti, J., Lee, M., Miller, M., Nutile, T., Tanaka, T., Teumer, A., Viktorin, A., Wedenoja, J., Abdellaoui, A., Abecasis, G. R., Adkins, D. E., Agrawal, A., Allik, J., Appel, K., Bigdeli, T. B., Busonero, F., Campbell, H., Costa, P. T., Smith, G. D., Davies, G., de Wit, H., Ding, J., Engelhardt, B. E., Eriksson, J. G., Fedko, I. O., Ferrucci, L., Franke, B., Giegling, I., Grucza, R., Hartmann, A. M., Heath, A. C., Heinonen, K., Henders, A. K., Homuth, G., Hottenga, J. J., Iacono, W. G., Janzing, J., Jokela, M., Karlsson, R., Kemp, J. P., Kirkpatrick, M. G., Latvala, A., Lehtimäki, T., Liewald, D. C., Madden, P. A., Magri, C., Magnusson, P. K., Marten, J., Maschio, A., Mbarek, H., Medland, S. E., Mihailov, E., Milaneschi, Y., Montgomery, G. W., Nauck, M., Nivard, M. G., Ouwens, K. G., Palotie, A., Pettersson, E., Polasek, O., Qian, Y., Pulkki-Råback, L., Raitakari, O. T., Realo, A., Rose, R. J., Ruggiero, D., Schmidt, C. O., Slutske, W. S., Sorice, R., Starr, J. M., St Pourcain, B., Sutin, A. R., Timpson, N. J., Trochet, H., Vermeulen, S., Vuoksimaa, E., Widen, E., Wouda, J., Wright, M. J., Zgaga, L., Porteous, D., Minelli, A., Palmer, A. A., Rujescu, D., Ciullo, M., Hayward, C., Rudan, I., Metspalu, A., Kaprio, J., Deary, I. J., Räikkönen, K., Wilson, J. F., Keltikangas-Järvinen, L., Bierut, L. J., Hettema, J. M., Grabe, H. J., Penninx, B. W., van Duijn, C. M., Evans, D. M., Schlessinger, D., Pedersen, N. L., Terracciano, A., McGue, M., Martin, N. G., & Boomsma, D. I. (2016). Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behavior genetics, 46(2), 170-82. https://doi.org/10.1007/s10519-015-9735-5
van den Berg, Stéphanie M, et al. "Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium." Behavior genetics vol. 46,2 (2016): 170-82. doi: https://doi.org/10.1007/s10519-015-9735-5
van den Berg SM, de Moor MH, Verweij KJ, Krueger RF, Luciano M, Arias Vasquez A, Matteson LK, Derringer J, Esko T, Amin N, Gordon SD, Hansell NK, Hart AB, Seppälä I, Huffman JE, Konte B, Lahti J, Lee M, Miller M, Nutile T, Tanaka T, Teumer A, Viktorin A, Wedenoja J, Abdellaoui A, Abecasis GR, Adkins DE, Agrawal A, Allik J, Appel K, Bigdeli TB, Busonero F, Campbell H, Costa PT, Smith GD, Davies G, de Wit H, Ding J, Engelhardt BE, Eriksson JG, Fedko IO, Ferrucci L, Franke B, Giegling I, Grucza R, Hartmann AM, Heath AC, Heinonen K, Henders AK, Homuth G, Hottenga JJ, Iacono WG, Janzing J, Jokela M, Karlsson R, Kemp JP, Kirkpatrick MG, Latvala A, Lehtimäki T, Liewald DC, Madden PA, Magri C, Magnusson PK, Marten J, Maschio A, Mbarek H, Medland SE, Mihailov E, Milaneschi Y, Montgomery GW, Nauck M, Nivard MG, Ouwens KG, Palotie A, Pettersson E, Polasek O, Qian Y, Pulkki-Råback L, Raitakari OT, Realo A, Rose RJ, Ruggiero D, Schmidt CO, Slutske WS, Sorice R, Starr JM, St Pourcain B, Sutin AR, Timpson NJ, Trochet H, Vermeulen S, Vuoksimaa E, Widen E, Wouda J, Wright MJ, Zgaga L, Porteous D, Minelli A, Palmer AA, Rujescu D, Ciullo M, Hayward C, Rudan I, Metspalu A, Kaprio J, Deary IJ, Räikkönen K, Wilson JF, Keltikangas-Järvinen L, Bierut LJ, Hettema JM, Grabe HJ, Penninx BW, van Duijn CM, Evans DM, Schlessinger D, Pedersen NL, Terracciano A, McGue M, Martin NG, Boomsma DI. Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behav Genet. 2016 Mar;46(2):170-82. doi: 10.1007/s10519-015-9735-5. Epub 2015 Sep 11. PMID: 26362575; PMCID: PMC4751159.
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Prioritization of .

Analytical chemistry

Smets T, Waelkens E, De Moor B.
PMID: 32168446
Anal Chem. 2020 Apr 07;92(7):5240-5248. doi: 10.1021/acs.analchem.9b05764. Epub 2020 Mar 20.

Mass spectrometry imaging (MSI) is a promising technique to assess the spatial distribution of molecules in a tissue sample. Nonlinear dimensionality reduction methods such as Uniform Manifold Approximation and Projection (UMAP) can be very valuable for the visualization of...

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Smets T, Waelkens E, De Moor B. Prioritization of . Anal Chem. 2020;92(7):5240-5248doi: 10.1021/acs.analchem.9b05764.
Smets, T., Waelkens, E., & De Moor, B. (2020). Prioritization of . Analytical chemistry, 92(7), 5240-5248. https://doi.org/10.1021/acs.analchem.9b05764
Smets, Tina, et al. "Prioritization of ." Analytical chemistry vol. 92,7 (2020): 5240-5248. doi: https://doi.org/10.1021/acs.analchem.9b05764
Smets T, Waelkens E, De Moor B. Prioritization of . Anal Chem. 2020 Apr 07;92(7):5240-5248. doi: 10.1021/acs.analchem.9b05764. Epub 2020 Mar 20. PMID: 32168446.
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Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS).

Frontiers in neurology

Mowry EM, Bermel RA, Williams JR, Benzinger TLS, de Moor C, Fisher E, Hersh CM, Hyland MH, Izbudak I, Jones SE, Kieseier BC, Kitzler HH, Krupp L, Lui YW, Montalban X, Naismith RT, Nicholas JA, Pellegrini F, Rovira A, Schulze M, Tackenberg B, Tintore M, Tivarus ME, Ziemssen T, Rudick RA.
PMID: 32849170
Front Neurol. 2020 Aug 07;11:632. doi: 10.3389/fneur.2020.00632. eCollection 2020.

No abstract available.

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Mowry EM, Bermel RA, Williams JR, et al. Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS). Front Neurol. 2020;11:632doi: 10.3389/fneur.2020.00632.
Mowry, E. M., Bermel, R. A., Williams, J. R., Benzinger, T. L. S., de Moor, C., Fisher, E., Hersh, C. M., Hyland, M. H., Izbudak, I., Jones, S. E., Kieseier, B. C., Kitzler, H. H., Krupp, L., Lui, Y. W., Montalban, X., Naismith, R. T., Nicholas, J. A., Pellegrini, F., Rovira, A., Schulze, M., Tackenberg, B., Tintore, M., Tivarus, M. E., Ziemssen, T., & Rudick, R. A. (2020). Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS). Frontiers in neurology, 11632. https://doi.org/10.3389/fneur.2020.00632
Mowry, Ellen M, et al. "Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)." Frontiers in neurology vol. 11 (2020): 632. doi: https://doi.org/10.3389/fneur.2020.00632
Mowry EM, Bermel RA, Williams JR, Benzinger TLS, de Moor C, Fisher E, Hersh CM, Hyland MH, Izbudak I, Jones SE, Kieseier BC, Kitzler HH, Krupp L, Lui YW, Montalban X, Naismith RT, Nicholas JA, Pellegrini F, Rovira A, Schulze M, Tackenberg B, Tintore M, Tivarus ME, Ziemssen T, Rudick RA. Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS). Front Neurol. 2020 Aug 07;11:632. doi: 10.3389/fneur.2020.00632. eCollection 2020. PMID: 32849170; PMCID: PMC7426489.
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High .

Proceedings of the National Academy of Sciences of the United States of America

Bekaert DV, Gazel E, Turner S, Behn MD, de Moor JM, Zahirovic S, Manea VC, Hoernle K, Fischer TP, Hammerstrom A, Seltzer AM, Kulongoski JT, Patel BS, Schrenk MO, Halldórsson SA, Nakagawa M, Ramírez CJ, Krantz JA, Yücel M, Ballentine CJ, Giovannelli D, Lloyd KG, Barry PH.
PMID: 34799449
Proc Natl Acad Sci U S A. 2021 Nov 23;118(47). doi: 10.1073/pnas.2110997118.

It is well established that mantle plumes are the main conduits for upwelling geochemically enriched material from Earth's deep interior. The fashion and extent to which lateral flow processes at shallow depths may disperse enriched mantle material far (>1,000...

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Bekaert DV, Gazel E, Turner S, et al. High . Proc Natl Acad Sci U S A. 2021;118(47)doi: 10.1073/pnas.2110997118.
Bekaert, D. V., Gazel, E., Turner, S., Behn, M. D., de Moor, J. M., Zahirovic, S., Manea, V. C., Hoernle, K., Fischer, T. P., Hammerstrom, A., Seltzer, A. M., Kulongoski, J. T., Patel, B. S., Schrenk, M. O., Halldórsson, S. A., Nakagawa, M., Ramírez, C. J., Krantz, J. A., Yücel, M., Ballentine, C. J., Giovannelli, D., Lloyd, K. G., & Barry, P. H. (2021). High . Proceedings of the National Academy of Sciences of the United States of America, 118(47), . https://doi.org/10.1073/pnas.2110997118
Bekaert, David V, et al. "High ." Proceedings of the National Academy of Sciences of the United States of America vol. 118,47 (2021). doi: https://doi.org/10.1073/pnas.2110997118
Bekaert DV, Gazel E, Turner S, Behn MD, de Moor JM, Zahirovic S, Manea VC, Hoernle K, Fischer TP, Hammerstrom A, Seltzer AM, Kulongoski JT, Patel BS, Schrenk MO, Halldórsson SA, Nakagawa M, Ramírez CJ, Krantz JA, Yücel M, Ballentine CJ, Giovannelli D, Lloyd KG, Barry PH. High . Proc Natl Acad Sci U S A. 2021 Nov 23;118(47). doi: 10.1073/pnas.2110997118. PMID: 34799449; PMCID: PMC8617460.
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Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model.

JAMA network open

Van Loon E, Zhang W, Coemans M, De Vos M, Emonds MP, Scheffner I, Gwinner W, Kuypers D, Senev A, Tinel C, Van Craenenbroeck AH, De Moor B, Naesens M.
PMID: 34967877
JAMA Netw Open. 2021 Dec 01;4(12):e2141617. doi: 10.1001/jamanetworkopen.2021.41617.

IMPORTANCE: Like other clinical biomarkers, trajectories of estimated glomerular filtration rate (eGFR) after kidney transplant are characterized by intra-individual variability. These fluctuations hamper the distinction between alarming graft functional deterioration or harmless fluctuation within the patient-specific expected reference range...

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Van Loon E, Zhang W, Coemans M, et al. Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model. JAMA Netw Open. 2021;4(12):e2141617doi: 10.1001/jamanetworkopen.2021.41617.
Van Loon, E., Zhang, W., Coemans, M., De Vos, M., Emonds, M. P., Scheffner, I., Gwinner, W., Kuypers, D., Senev, A., Tinel, C., Van Craenenbroeck, A. H., De Moor, B., & Naesens, M. (2021). Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model. JAMA network open, 4(12), e2141617. https://doi.org/10.1001/jamanetworkopen.2021.41617
Van Loon, Elisabet, et al. "Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model." JAMA network open vol. 4,12 (2021): e2141617. doi: https://doi.org/10.1001/jamanetworkopen.2021.41617
Van Loon E, Zhang W, Coemans M, De Vos M, Emonds MP, Scheffner I, Gwinner W, Kuypers D, Senev A, Tinel C, Van Craenenbroeck AH, De Moor B, Naesens M. Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model. JAMA Netw Open. 2021 Dec 01;4(12):e2141617. doi: 10.1001/jamanetworkopen.2021.41617. PMID: 34967877.
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Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes.

Frontiers in psychology

Pastrana JL, Reigal RE, Morales-Sánchez V, Morillo-Baro JP, de Mier RJ, Alves J, Hernández-Mendo A.
PMID: 31866896
Front Psychol. 2019 Dec 05;10:2675. doi: 10.3389/fpsyg.2019.02675. eCollection 2019.

Data mining is seen as a set of techniques and technologies allowing to extract, automatically or semi-automatically, a lot of useful information, models, and tendencies from a big set of data. Techniques like "clustering," "classification," "association," and "regression"; statistics...

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Pastrana JL, Reigal RE, Morales-Sánchez V, et al. Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes. Front Psychol. 2019;10:2675doi: 10.3389/fpsyg.2019.02675.
Pastrana, J. L., Reigal, R. E., Morales-Sánchez, V., Morillo-Baro, J. P., de Mier, R. J., Alves, J., & Hernández-Mendo, A. (2019). Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes. Frontiers in psychology, 102675. https://doi.org/10.3389/fpsyg.2019.02675
Pastrana, José L, et al. "Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes." Frontiers in psychology vol. 10 (2019): 2675. doi: https://doi.org/10.3389/fpsyg.2019.02675
Pastrana JL, Reigal RE, Morales-Sánchez V, Morillo-Baro JP, de Mier RJ, Alves J, Hernández-Mendo A. Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes. Front Psychol. 2019 Dec 05;10:2675. doi: 10.3389/fpsyg.2019.02675. eCollection 2019. PMID: 31866896; PMCID: PMC6906179.
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Discrepancy between Constant Properties Model and Temperature-Dependent Material Properties for Performance Estimation of Thermoelectric Generators.

Entropy (Basel, Switzerland)

Ponnusamy P, de Boor J, Müller E.
PMID: 33286897
Entropy (Basel). 2020 Oct 04;22(10). doi: 10.3390/e22101128.

The efficiency of a thermoelectric (TE) generator for the conversion of thermal energy into electrical energy can be easily but roughly estimated using a constant properties model (CPM) developed by Ioffe. However, material properties are, in general, temperature (T)-dependent...

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Ponnusamy P, de Boor J, Müller E. Discrepancy between Constant Properties Model and Temperature-Dependent Material Properties for Performance Estimation of Thermoelectric Generators. Entropy (Basel). 2020;22(10)doi: 10.3390/e22101128.
Ponnusamy, P., de Boor, J., & Müller, E. (2020). Discrepancy between Constant Properties Model and Temperature-Dependent Material Properties for Performance Estimation of Thermoelectric Generators. Entropy (Basel, Switzerland), 22(10), . https://doi.org/10.3390/e22101128
Ponnusamy, Prasanna, et al. "Discrepancy between Constant Properties Model and Temperature-Dependent Material Properties for Performance Estimation of Thermoelectric Generators." Entropy (Basel, Switzerland) vol. 22,10 (2020). doi: https://doi.org/10.3390/e22101128
Ponnusamy P, de Boor J, Müller E. Discrepancy between Constant Properties Model and Temperature-Dependent Material Properties for Performance Estimation of Thermoelectric Generators. Entropy (Basel). 2020 Oct 04;22(10). doi: 10.3390/e22101128. PMID: 33286897; PMCID: PMC7597267.
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Kinetic state diagrams for a highly asymmetric block copolymer assembled in solution.

Soft matter

Vena MP, de Moor D, Ianiro A, Tuinier R, Patterson JP.
PMID: 33289775
Soft Matter. 2021 Jan 28;17(4):1084-1090. doi: 10.1039/d0sm01596b. Epub 2020 Dec 08.

Polymer self-assembly is used to form nanomaterials with a wide range of structures. While self-assembly of polymers in bulk has been thoroughly explored, the same process in solution remains widely used but partially unresolved, due to the formation of...

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Vena MP, de Moor D, Ianiro A, et al. Kinetic state diagrams for a highly asymmetric block copolymer assembled in solution. Soft Matter. 2020;17(4):1084-1090doi: 10.1039/d0sm01596b.
Vena, M. P., de Moor, D., Ianiro, A., Tuinier, R., & Patterson, J. P. (2021). Kinetic state diagrams for a highly asymmetric block copolymer assembled in solution. Soft matter, 17(4), 1084-1090. https://doi.org/10.1039/d0sm01596b
Vena, M Paula, et al. "Kinetic state diagrams for a highly asymmetric block copolymer assembled in solution." Soft matter vol. 17,4 (2021): 1084-1090. doi: https://doi.org/10.1039/d0sm01596b
Vena MP, de Moor D, Ianiro A, Tuinier R, Patterson JP. Kinetic state diagrams for a highly asymmetric block copolymer assembled in solution. Soft Matter. 2021 Jan 28;17(4):1084-1090. doi: 10.1039/d0sm01596b. Epub 2020 Dec 08. PMID: 33289775.
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Tragedy revisited.

Science (New York, N.Y.)

Boyd R, Richerson PJ, Meinzen-Dick R, De Moor T, Jackson MO, Gjerde KM, Harden-Davies H, Frischmann BM, Madison MJ, Strandburg KJ, McLean AR, Dye C.
PMID: 30545871
Science. 2018 Dec 14;362(6420):1236-1241. doi: 10.1126/science.aaw0911. Epub 2018 Dec 13.

No abstract available.

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Boyd R, Richerson PJ, Meinzen-Dick R, et al. Tragedy revisited. Science. 2018;362(6420):1236-1241doi: 10.1126/science.aaw0911.
Boyd, R., Richerson, P. J., Meinzen-Dick, R., De Moor, T., Jackson, M. O., Gjerde, K. M., Harden-Davies, H., Frischmann, B. M., Madison, M. J., Strandburg, K. J., McLean, A. R., & Dye, C. (2018). Tragedy revisited. Science (New York, N.Y.), 362(6420), 1236-1241. https://doi.org/10.1126/science.aaw0911
Boyd, Robert, et al. "Tragedy revisited." Science (New York, N.Y.) vol. 362,6420 (2018): 1236-1241. doi: https://doi.org/10.1126/science.aaw0911
Boyd R, Richerson PJ, Meinzen-Dick R, De Moor T, Jackson MO, Gjerde KM, Harden-Davies H, Frischmann BM, Madison MJ, Strandburg KJ, McLean AR, Dye C. Tragedy revisited. Science. 2018 Dec 14;362(6420):1236-1241. doi: 10.1126/science.aaw0911. Epub 2018 Dec 13. PMID: 30545871.
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Information Sources Used by Oncologists to Understand Multi-marker Tumor Panel Tests for Cancer Patients.

Journal of cancer education : the official journal of the American Association for Cancer Education

Buckenmaier SS, Mollica MA, Freedman A, Kent EE, de Moor JS.
PMID: 32415436
J Cancer Educ. 2021 Oct;36(5):1105-1114. doi: 10.1007/s13187-020-01742-0.

Precision medicine using multi-marker tumor panel (MMTP) testing can help inform cancer treatment decisions. Oncologists' knowledge of these tests and their ability to find up-to-date information about their application in clinical care is essential. This study aimed to (1)...

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Buckenmaier SS, Mollica MA, Freedman A, et al. Information Sources Used by Oncologists to Understand Multi-marker Tumor Panel Tests for Cancer Patients. J Cancer Educ. 2021;36(5):1105-1114doi: 10.1007/s13187-020-01742-0.
Buckenmaier, S. S., Mollica, M. A., Freedman, A., Kent, E. E., & de Moor, J. S. (2021). Information Sources Used by Oncologists to Understand Multi-marker Tumor Panel Tests for Cancer Patients. Journal of cancer education : the official journal of the American Association for Cancer Education, 36(5), 1105-1114. https://doi.org/10.1007/s13187-020-01742-0
Buckenmaier, Susan S, et al. "Information Sources Used by Oncologists to Understand Multi-marker Tumor Panel Tests for Cancer Patients." Journal of cancer education : the official journal of the American Association for Cancer Education vol. 36,5 (2021): 1105-1114. doi: https://doi.org/10.1007/s13187-020-01742-0
Buckenmaier SS, Mollica MA, Freedman A, Kent EE, de Moor JS. Information Sources Used by Oncologists to Understand Multi-marker Tumor Panel Tests for Cancer Patients. J Cancer Educ. 2021 Oct;36(5):1105-1114. doi: 10.1007/s13187-020-01742-0. PMID: 32415436.
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