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Dual-targeting triplebody 33-16-123 (SPM-2) mediates effective redirected lysis of primary blasts from patients with a broad range of AML subtypes in combination with natural killer cells.

Oncoimmunology

Braciak TA, Roskopf CC, Wildenhain S, Fenn NC, Schiller CB, Schubert IA, Jacob U, Honegger A, Krupka C, Subklewe M, Spiekermann K, Hopfner KP, Fey GH, Aigner M, Krause S, Mackensen A, Oduncu FS.
PMID: 30228941
Oncoimmunology. 2018 Jul 30;7(9):e1472195. doi: 10.1080/2162402X.2018.1472195. eCollection 2018.

A number of agents designed for immunotherapy of Acute Myeloid Leukemia (AML) are in preclinical and early clinical development. Most of them target a single antigen on the surface of AML cells. Here we describe the development and key...

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Braciak TA, Roskopf CC, Wildenhain S, et al. Dual-targeting triplebody 33-16-123 (SPM-2) mediates effective redirected lysis of primary blasts from patients with a broad range of AML subtypes in combination with natural killer cells. Oncoimmunology. 2018;7(9):e1472195doi: 10.1080/2162402X.2018.1472195.
Braciak, T. A., Roskopf, C. C., Wildenhain, S., Fenn, N. C., Schiller, C. B., Schubert, I. A., Jacob, U., Honegger, A., Krupka, C., Subklewe, M., Spiekermann, K., Hopfner, K. P., Fey, G. H., Aigner, M., Krause, S., Mackensen, A., & Oduncu, F. S. (2018). Dual-targeting triplebody 33-16-123 (SPM-2) mediates effective redirected lysis of primary blasts from patients with a broad range of AML subtypes in combination with natural killer cells. Oncoimmunology, 7(9), e1472195. https://doi.org/10.1080/2162402X.2018.1472195
Braciak, Todd A, et al. "Dual-targeting triplebody 33-16-123 (SPM-2) mediates effective redirected lysis of primary blasts from patients with a broad range of AML subtypes in combination with natural killer cells." Oncoimmunology vol. 7,9 (2018): e1472195. doi: https://doi.org/10.1080/2162402X.2018.1472195
Braciak TA, Roskopf CC, Wildenhain S, Fenn NC, Schiller CB, Schubert IA, Jacob U, Honegger A, Krupka C, Subklewe M, Spiekermann K, Hopfner KP, Fey GH, Aigner M, Krause S, Mackensen A, Oduncu FS. Dual-targeting triplebody 33-16-123 (SPM-2) mediates effective redirected lysis of primary blasts from patients with a broad range of AML subtypes in combination with natural killer cells. Oncoimmunology. 2018 Jul 30;7(9):e1472195. doi: 10.1080/2162402X.2018.1472195. eCollection 2018. PMID: 30228941; PMCID: PMC6140553.
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The evolutionarily conserved kinase SnRK1 orchestrates resource mobilization during Arabidopsis seedling establishment.

The Plant cell

Henninger M, Pedrotti L, Krischke M, Draken J, Wildenhain T, Fekete A, Rolland F, Müller MJ, Fröschel C, Weiste C, Dröge-Laser W.
PMID: 34755865
Plant Cell. 2021 Nov 10; doi: 10.1093/plcell/koab270. Epub 2021 Nov 10.

The onset of plant life is characterized by a major phase transition. During early heterotrophic seedling establishment, seed storage reserves fuel metabolic demands, allowing the plant to switch to autotrophic metabolism. Although metabolic pathways leading to storage compound mobilization...

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Henninger M, Pedrotti L, Krischke M, et al. The evolutionarily conserved kinase SnRK1 orchestrates resource mobilization during Arabidopsis seedling establishment. Plant Cell. 2021;doi: 10.1093/plcell/koab270.
Henninger, M., Pedrotti, L., Krischke, M., Draken, J., Wildenhain, T., Fekete, A., Rolland, F., Müller, M. J., Fröschel, C., Weiste, C., & Dröge-Laser, W. (2021). The evolutionarily conserved kinase SnRK1 orchestrates resource mobilization during Arabidopsis seedling establishment. The Plant cell, . https://doi.org/10.1093/plcell/koab270
Henninger, Markus, et al. "The evolutionarily conserved kinase SnRK1 orchestrates resource mobilization during Arabidopsis seedling establishment." The Plant cell vol. (2021). doi: https://doi.org/10.1093/plcell/koab270
Henninger M, Pedrotti L, Krischke M, Draken J, Wildenhain T, Fekete A, Rolland F, Müller MJ, Fröschel C, Weiste C, Dröge-Laser W. The evolutionarily conserved kinase SnRK1 orchestrates resource mobilization during Arabidopsis seedling establishment. Plant Cell. 2021 Nov 10; doi: 10.1093/plcell/koab270. Epub 2021 Nov 10. PMID: 34755865.
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The Russian-American gallium experiment (SAGE) Cr Neutrino source measurement.

Physical review letters

Abdurashitov JN, Bowles TJ, Cherry ML, Cleveland BT, Daily T, Davis R, Elliott SR, Gavrin VN, Girin SV, Gorbachev VV, Ibragimova TV, Kalikhov AV, Karaulov VN, Khairnasov NG, Khomyakov YS, Knodel TV, Kornoukhov VN, Lande K, Lee CK, Levitin VL, Maev VI, Mirmov IN, Nazarenko PI, Nico JS, Shikhin AA, Shkol'nik VS, Skorikov NV, Teasdale WA, Veretenkin EP, Vermul VM, Wark DL, Wildenhain PW, Wilkerson JF, Yants VE, Zatsepin GT, Zvonarev AV.
PMID: 10062611
Phys Rev Lett. 1996 Dec 02;77(23):4708-4711. doi: 10.1103/PhysRevLett.77.4708.

No abstract available.

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Abdurashitov JN, Bowles TJ, Cherry ML, et al. The Russian-American gallium experiment (SAGE) Cr Neutrino source measurement. Phys Rev Lett. 1996;77(23):4708-4711doi: 10.1103/PhysRevLett.77.4708.
Abdurashitov, J. N., Bowles, T. J., Cherry, M. L., Cleveland, B. T., Daily, T., Davis, R., Elliott, S. R., Gavrin, V. N., Girin, S. V., Gorbachev, V. V., Ibragimova, T. V., Kalikhov, A. V., Karaulov, V. N., Khairnasov, N. G., Khomyakov, Y. S., Knodel, T. V., Kornoukhov, V. N., Lande, K., Lee, C. K., Levitin, V. L., Maev, V. I., Mirmov, I. N., Nazarenko, P. I., Nico, J. S., Shikhin, A. A., Shkol'nik, V. S., Skorikov, N. V., Teasdale, W. A., Veretenkin, E. P., Vermul, V. M., Wark, D. L., Wildenhain, P. W., Wilkerson, J. F., Yants, V. E., Zatsepin, G. T., & Zvonarev, A. V. (1996). The Russian-American gallium experiment (SAGE) Cr Neutrino source measurement. Physical review letters, 77(23), 4708-4711. https://doi.org/10.1103/PhysRevLett.77.4708
Abdurashitov, et al. "The Russian-American gallium experiment (SAGE) Cr Neutrino source measurement." Physical review letters vol. 77,23 (1996): 4708-4711. doi: https://doi.org/10.1103/PhysRevLett.77.4708
Abdurashitov JN, Bowles TJ, Cherry ML, Cleveland BT, Daily T, Davis R, Elliott SR, Gavrin VN, Girin SV, Gorbachev VV, Ibragimova TV, Kalikhov AV, Karaulov VN, Khairnasov NG, Khomyakov YS, Knodel TV, Kornoukhov VN, Lande K, Lee CK, Levitin VL, Maev VI, Mirmov IN, Nazarenko PI, Nico JS, Shikhin AA, Shkol'nik VS, Skorikov NV, Teasdale WA, Veretenkin EP, Vermul VM, Wark DL, Wildenhain PW, Wilkerson JF, Yants VE, Zatsepin GT, Zvonarev AV. The Russian-American gallium experiment (SAGE) Cr Neutrino source measurement. Phys Rev Lett. 1996 Dec 02;77(23):4708-4711. doi: 10.1103/PhysRevLett.77.4708. PMID: 10062611.
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onlineFDR: an R package to control the false discovery rate for growing data repositories.

Bioinformatics (Oxford, England)

Robertson DS, Wildenhain J, Javanmard A, Karp NA.
PMID: 30873526
Bioinformatics. 2019 Oct 15;35(20):4196-4199. doi: 10.1093/bioinformatics/btz191.

SUMMARY: In many areas of biological research, hypotheses are tested in a sequential manner, without having access to future P-values or even the number of hypotheses to be tested. A key setting where this online hypothesis testing occurs is...

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Robertson DS, Wildenhain J, Javanmard A, et al. onlineFDR: an R package to control the false discovery rate for growing data repositories. Bioinformatics. 2019;35(20):4196-4199doi: 10.1093/bioinformatics/btz191.
Robertson, D. S., Wildenhain, J., Javanmard, A., & Karp, N. A. (2019). onlineFDR: an R package to control the false discovery rate for growing data repositories. Bioinformatics (Oxford, England), 35(20), 4196-4199. https://doi.org/10.1093/bioinformatics/btz191
Robertson, David S, et al. "onlineFDR: an R package to control the false discovery rate for growing data repositories." Bioinformatics (Oxford, England) vol. 35,20 (2019): 4196-4199. doi: https://doi.org/10.1093/bioinformatics/btz191
Robertson DS, Wildenhain J, Javanmard A, Karp NA. onlineFDR: an R package to control the false discovery rate for growing data repositories. Bioinformatics. 2019 Oct 15;35(20):4196-4199. doi: 10.1093/bioinformatics/btz191. PMID: 30873526; PMCID: PMC6792083.
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Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.

Cell systems

Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M.
PMID: 27136353
Cell Syst. 2015 Dec 23;1(6):383-95. doi: 10.1016/j.cels.2015.12.003. Epub 2015 Dec 23.

The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast...

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Wildenhain J, Spitzer M, Dolma S, et al. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst. 2015;1(6):383-95doi: 10.1016/j.cels.2015.12.003.
Wildenhain, J., Spitzer, M., Dolma, S., Jarvik, N., White, R., Roy, M., Griffiths, E., Bellows, D. S., Wright, G. D., & Tyers, M. (2015). Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell systems, 1(6), 383-95. https://doi.org/10.1016/j.cels.2015.12.003
Wildenhain, Jan, et al. "Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning." Cell systems vol. 1,6 (2015): 383-95. doi: https://doi.org/10.1016/j.cels.2015.12.003
Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst. 2015 Dec 23;1(6):383-95. doi: 10.1016/j.cels.2015.12.003. Epub 2015 Dec 23. PMID: 27136353; PMCID: PMC5998823.
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Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.

Journal of microscopy

Fishman D, Salumaa SO, Majoral D, Laasfeld T, Peel S, Wildenhain J, Schreiner A, Palo K, Parts L.
PMID: 34081320
J Microsc. 2021 Oct;284(1):12-24. doi: 10.1111/jmi.13038. Epub 2021 Jun 15.

Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence...

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Fishman D, Salumaa SO, Majoral D, et al. Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples. J Microsc. 2021;284(1):12-24doi: 10.1111/jmi.13038.
Fishman, D., Salumaa, S. O., Majoral, D., Laasfeld, T., Peel, S., Wildenhain, J., Schreiner, A., Palo, K., & Parts, L. (2021). Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples. Journal of microscopy, 284(1), 12-24. https://doi.org/10.1111/jmi.13038
Fishman, Dmytro, et al. "Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples." Journal of microscopy vol. 284,1 (2021): 12-24. doi: https://doi.org/10.1111/jmi.13038
Fishman D, Salumaa SO, Majoral D, Laasfeld T, Peel S, Wildenhain J, Schreiner A, Palo K, Parts L. Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples. J Microsc. 2021 Oct;284(1):12-24. doi: 10.1111/jmi.13038. Epub 2021 Jun 15. PMID: 34081320.
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Showing 1 to 6 of 6 entries