Datler J, Hansen J, Thader A, et al. Multi-modal cryo-EM reveals trimers of protein10.1038/s41594-023-01201-6__to form the palisade layer in poxvirus cores. Nature Structural &; Molecular Biology. 2024. doi:10.1038/s41594-023-01201-6
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DOI: 10.1038/s41594-023-01201-6
PMID: 38316877

Zeller P, Yeung J, Viñas Gaza H, et al. Single-cell sortChIC identifies hierarchical chromatin dynamics during hematopoiesis. Nature Genetics. 2023;55:333-345. doi:10.1038/s41588-022-01260-3
LibreCat: https://research-explorer.ista.ac.at/record/12158
DOI: 10.1038/s41588-022-01260-3

Elefante S, Stadlbauer S, Alexander MF, Schlögl A. Cryo-EM software packages: A sys-admins point of view. In: ASHP - Austrian-Slovenian HPC Meeting 2023. EuroCC; :42-42.
LibreCat: https://research-explorer.ista.ac.at/record/13162

Schlögl A, Elefante S, Hodirnau V-V. Running Windows-applications on a Linux HPC cluster using WINE. In: ASHP - Austrian-Slovenian HPC Meeting 2023. EuroCC; :59-59.
LibreCat: https://research-explorer.ista.ac.at/record/13161

Yeung J, Florescu M, Zeller P, De Barbanson BA, Wellenstein MD, Van Oudenaarden A. scChIX-seq infers dynamic relationships between histone modifications in single cells. Nature Biotechnology. 2023;41:813–823. doi:10.1038/s41587-022-01560-3
LibreCat: https://research-explorer.ista.ac.at/record/12106
DOI: 10.1038/s41587-022-01560-3
WoS: 000909067600003

D’Elia D, Truu J, Lahti L, et al. Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action. Frontiers in Microbiology. 2023;14. doi:10.3389/fmicb.2023.1257002
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DOI: 10.3389/fmicb.2023.1257002
WoS: 001080536000001
PMID: 37808321

Schlögl A, Hornoiu A, Elefante S, Stadlbauer S. Where is the sweet spot? A procurement story of general purpose compute nodes. In: ASHP - Austrian-Slovenian HPC Meeting 2022. EuroCC Austria c/o Universität Wien; 2022:7. doi:10.25365/phaidra.337
LibreCat: https://research-explorer.ista.ac.at/record/12894
DOI: 10.25365/phaidra.337

Schlögl A, Elefante S, Hornoiu A, Stadlbauer S. Managing software on a heterogenous HPC cluster. In: ASHP – Austrian-Slovenian HPC Meeting 2021. University of Ljubljana; 2021:5. doi:10.3359/2021hpc
LibreCat: https://research-explorer.ista.ac.at/record/12909
DOI: 10.3359/2021hpc

Zhang X, Schlögl A, Vandael DH, Jonas PM. MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo. Journal of Neuroscience Methods. 2021;357(6). doi:10.1016/j.jneumeth.2021.109125
LibreCat: https://research-explorer.ista.ac.at/record/9329
DOI: 10.1016/j.jneumeth.2021.109125
WoS: 000661088500005

Rubel P, Fayn J, Macfarlane PW, Pani D, Schlögl A, Värri A. The history and challenges of SCP-ECG: The standard communication protocol for computer-assisted electrocardiography. Hearts. 2021;2(3):384-409. doi:10.3390/hearts2030031
LibreCat: https://research-explorer.ista.ac.at/record/15285
DOI: 10.3390/hearts2030031

Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–C network. 2021. doi:10.15479/AT:ISTA:10110
LibreCat: https://research-explorer.ista.ac.at/record/10110
DOI: 10.15479/AT:ISTA:10110

Schlögl A, Kiss J, Elefante S, eds. Austrian High-Performance-Computing Meeting (AH). Klosterneuburg, Austria: IST Austria; 2020. doi:10.15479/AT:ISTA:7474
LibreCat: https://research-explorer.ista.ac.at/record/7474
DOI: 10.15479/AT:ISTA:7474

Zhang X, Schlögl A, Jonas PM. Selective routing of spatial information flow from input to output in hippocampal granule cells. Neuron. 2020;107(6):1212-1225. doi:10.1016/j.neuron.2020.07.006
LibreCat: https://research-explorer.ista.ac.at/record/8261
DOI: 10.1016/j.neuron.2020.07.006
WoS: 000579698700009
PMID: 32763145

Schlögl A, Kiss J, Elefante S. Is Debian suitable for running an HPC Cluster? In: AHP - Austrian HPC Meeting 2019 . Institut für Mathematik und wissenschaftliches Rechnen der Universität Graz; 2019:25.
LibreCat: https://research-explorer.ista.ac.at/record/12901

Sauermann S, David V, Schlögl A, et al. Biosignals standards and FHIR: The way to go. In: Vol 236. IOS Press; 2017:356-362. doi:10.3233/978-1-61499-759-7-356
LibreCat: https://research-explorer.ista.ac.at/record/630
DOI: 10.3233/978-1-61499-759-7-356

Schlögl A, Kiss J. Scientific Computing at IST Austria. In: AHP – Austrian HPC Meeting 2017. FSP Scientific Computing; 2017:28.
LibreCat: https://research-explorer.ista.ac.at/record/12905

Guzmán J, Schlögl A, Frotscher M, Jonas PM. Synaptic mechanisms of pattern completion in the hippocampal C network. Science. 2016;353(6304):1117-1123. doi:10.1126/science.aaf1836
LibreCat: https://research-explorer.ista.ac.at/record/1350
DOI: 10.1126/science.aaf1836

Schlögl A, Stadlbauer S. High performance computing at IST Austria: Modelling the human hippocampus. In: AHP - Austrian HPC Meeting 2016. VSC - Vienna Scientific Cluster; 2016:37.
LibreCat: https://research-explorer.ista.ac.at/record/12903

Körner C, Braunstein V, Stangl M, Schlögl A, Neuper C, Ischebeck A. Sequential effects in continued visual search: Using fixation-related potentials to compare distractor processing before and after target detection. Psychophysiology. 2014;51(4):385-395. doi:10.1111/psyp.12062
LibreCat: https://research-explorer.ista.ac.at/record/1890
DOI: 10.1111/psyp.12062

Guzmán J, Schlögl A, Schmidt Hieber C. Stimfit: Quantifying electrophysiological data with Python. Frontiers in Neuroinformatics. 2014;8(FEB). doi:10.3389/fninf.2014.00016
LibreCat: https://research-explorer.ista.ac.at/record/2230
DOI: 10.3389/fninf.2014.00016