Müller-Eigner, A.; Sanz-moreno, A.; de Diego, I.; Venkateswaran Venkatasubramani, A.; Langhammer, M.; Gerlini, R.; Rathkolb, B.; Aguilar-Pimentel, A.; Klein-Rodewald, T.; Calzada-Wack, J.; Becker, L.; Palma Vera, S. E.; Gille, B.; Forne, I.; Imhof, A.; Meng, C.; Ludwig, A.; Koch, F.; Heiker, J.; Kuhla, A.; Caton, V.; Brenmoehl, J.; Reyer, H.; Schön, J.; Fuchs, H.; Gailus-Durner, V.; Höflich, A.; Hrabe de Angelis, M.; Peleg, S. (2022):
Dietary intervention improves health metrics and life expectancy of the genetically obese Titan mouse. COMMUN BIOL 5: 408, 1-17
https://doi.org/10.1038/s42003-022-03339-3
Klosa, J.; Simon, N.; Liebscher, V.; Wittenburg, D. (2022):
A fitted sparse-group lasso for genome-based evaluations [epublished ahead of print]. IEEE ACM T COMPUT BI
https://doi.org/10.1109/TCBB.2022.3156805
Ludwig, C.; Bohleber, S.; Rebl, A.; Wirth, E.; Venuto, M. T.; Langhammer, M.; Schweizer, U.; Weitzel, J. M.; Michaelis, M. (2022):
Endocrine and molecular factors of increased female reproductive performance in the Dummerstorf high-fertility mouse line FL1. J Mol Endocrinol 69 (1): 285-298
https://doi.org/10.1530/JME-22-0012
Calanni Pileri, M.; Weitzel, J. M.; Langhammer, M.; Wytrwat, E.; Michaelis, M. (2022):
Altered insulin, leptin and ghrelin hormone levels and atypical estrous cycle lengths in two highly fertile mouse lines. Reprod Domest Anim 57 (6): 577-586
https://doi.org/10.1111/rda.14097
Otten, W.; Heimbürge, S.; Tuchscherer, A.; Kanitz, E. (2022):
The age of hair matters – the incorporation of cortisol by external contamination is enhanced in distal hair segments of pigs and cattle. Animal 16 (4): 100495, 1-6
https://doi.org/10.1016/j.animal.2022.100495
de los Rios Pérez, L.; Druet, T.; Goldammer, T.; Wittenburg, D. (2022):
Analysis of autozygosity using whole-genome sequence data of full-sib families in pikeperch (Sander lucioperca). Front Genet 12: 786934, 1-9
https://doi.org/10.3389/fgene.2021.786934
Maier, G.; Delezie, J.; Westermark, P. O.; Santos, G.; Ritz, D.; Handschin, C. (2022):
Transcriptomic, proteomic and phosphoproteomic underpinnings of daily exercise performance and zeitgeber activity of training in mouse muscle. J Physiol-London 600 (4): 769-796
https://doi.org/10.1113/JP281535
Revskij, D.; Haubold, S. S.; Plinski, C.; Viergutz, T.; Tuchscherer, A.; Kröger-Koch, C.; Albrecht, E.; Günther, J.; Tröscher, A.; Hammon, H. M.; Schuberth, H.-J.; Mielenz, M. (2022):
Cellular detection of the chemokine receptor CXCR4 in bovine mammary glands and its distribution and regulation on bovine leukocytes. J Dairy Sci 105 (1): 866-876
https://doi.org/10.3168/jds.2021-20799
Petkov, S.; Brenmoehl, J.; Langhammer, M.; Höflich, A.; Röntgen, M. (2022):
Myogenic Precursor Cells Show Faster Activation and Enhanced Differentiation in a Male Mouse Model Selected for Advanced Endurance Exercise Performance. Cells-Basel 11 (6): 101, 1-20
https://doi.org/10.3390/cells11061001
Casanova-Vallve, N.; Duglan, D.; Vaughan, M.; Pariollaud, M.; Handzlik, M.; Fan, W.; Yu, R; Liddle, C.; Downes, M.; Delezie, J.; Mello, R.; Chan, A.; Westermark, P. O.; Metallo, C.; Evans, R.; Lamia, K. (2022):
Daily running enhances molecular and physiological circadian rhythms in skeletal muscle. Mol Metab 61: 101504, 1-15
https://doi.org/10.1016/j.molmet.2022.101504
Palma Vera, S. E.; Reyer, H.; Langhammer, M.; Reinsch, N.; Derezanin, L.; Fickel, J.; Qanbari, S.; Weitzel, J. M.; Franzenburg, S.; Hemmrich-Stanisak, G.; Schoen, J. (2022):
Author Correction: Genomic characterization of the world’s longest selection experiment in mouse reveals the complexity of polygenic traits. BMC BIOL 20: 238, 1-2
https://doi.org/10.1186/s12915-022-01439-4
Wittenburg, D. (2021):
Statistical perspectives on dependencies between genomic markers Rostock: 1-161
Brenmoehl, J.; Walz, C.; Caffier, C.; Brosig, E.; Walz, C.; Ohde, D.; Trakooljul, N.; Langhammer, M.; Ponsuksili, S.; Wimmers, K.; Zettl, U. K.; Höflich, A. (2021):
Central Suppression of the GH/IGF Axis and Abrogation of Exercise-Related mTORC1/2 Activation in the Muscle of Phenotype-Selected Male Marathon Mice (DUhTP). Cells-Basel 10 (12): 3418, 1-19
https://doi.org/10.3390/cells10123418
Mbuthia, J. M.; Mayer, M.; Reinsch, N. (2021):
A review of methods for improving resolution of milk production data and weather information for measuring heat stress in dairy cattle. LIVEST SCI 255: 104794, 1-11
https://doi.org/10.1016/j.livsci.2021.104794
Pett, P.J.; Westermark, P. O.; Herzel, H. (2021):
Simple kinetic models in molecular chronobiology. In: Circadian Clocks: Methods and Protocols (Steven A. Brown, Hrsg.) Springer, New York, NY (978-1-07-160381-9): 87-100
https://doi.org/10.1007/978-1-0716-0381-9_7
Melzer, N.; Foris, B.; Langbein, J. (2021):
Validation of a real-time location system for zone assignment and neighbor detection in dairy cow groups. Comput Electron Agr 187: 106280, 1-16
https://doi.org/10.1016/j.compag.2021.106280
vanAckern, I.; Wulf, A.; Dannenberger, D.; Tuchscherer, A.; Kuhla, B. (2021):
Effects of endocannabinoids on feed intake, stress response and whole‑body energy metabolism in dairy cows. Sci Rep-UK 11: 23657, 1-12
https://doi.org/10.1038/s41598-021-02970-0
Krause, A.; Kreiser, M.; Puppe, B.; Tuchscherer, A.; Düpjan, S. (2021):
The effect of age on discrimination learning and self-control in a marshmallow test for pigs. Sci Rep-UK 11: 18287, 1-10
https://doi.org/10.1038/s41598-021-97770-x
Tümmler, L.-M.; Derno, M.; Tuchscherer, A.; Kanitz, E.; Kuhla, B. (2021):
Effects of 2 liquid feeding rates over the first 3 months of life on whole-body energy metabolism and energy use efficiency of dairy calves up to 5 months. J Dairy Sci 104 (9): 10399-10414
https://doi.org/10.3168/jds.2021-20278
Goldammer, T.; Verleih, M.; Brunner, R. M.; Rebl, A.; Nguinkal, J. A.; de los Rios Pérez, L.; Schäfer, N.; Stüecken, M.; Swirplies, F.; Wittenburg, D. (2021):
Pikeperch genome data – basis for the smart farming in aquaculture. Mitteilungen der Landesforschungsanstalt für Landw (63): 125-133
https://www.landwirtschaft-mv.de/static/LFA/Dateien/Hefte/MdLFA_Heft63.pdf