Eltzner, Benjamin

[["dc.bibliographiccitation.firstpage","263"],["dc.bibliographiccitation.lastpage","282"],["dc.bibliographiccitation.seriesnr","134"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Hauke, Lara"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Rehfeldt, Florian"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.editor","Salditt, Tim"],["dc.contributor.editor","Egner, Alexander"],["dc.contributor.editor","Luke, D. Russell"],["dc.date.accessioned","2021-04-21T11:15:39Z"],["dc.date.available","2021-04-21T11:15:39Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/978-3-030-34413-9_10"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/84280"],["dc.relation","SFB 755: Nanoscale Photonic Imaging"],["dc.relation.crisseries","Topics in Applied Physics"],["dc.relation.doi","10.1007/978-3-030-34413-9"],["dc.relation.eisbn","978-3-030-34413-9"],["dc.relation.isbn","978-3-030-34412-2"],["dc.relation.ispartof","Nanoscale Photonic Imaging"],["dc.relation.ispartofseries","Topics in Applied Physics; 134"],["dc.relation.orgunit","Institut für Röntgenphysik"],["dc.subject.gro","SFB 755"],["dc.title","A Statistical and Biophysical Toolbox to Elucidate Structure and Formation of Stress Fibers"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","013012"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","New Journal of Physics"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Bernhardt, M."],["dc.contributor.author","Nicolas, J. D."],["dc.contributor.author","Eckermann, M."],["dc.contributor.author","Eltzner, B."],["dc.contributor.author","Rehfeldt, F."],["dc.contributor.author","Salditt, Tim"],["dc.date.accessioned","2019-07-09T11:43:26Z"],["dc.date.available","2019-07-09T11:43:26Z"],["dc.date.issued","2017"],["dc.description.abstract","X-ray diffraction from biomolecular assemblies is a powerful technique which can provide structural information about complex architectures such as the locomotor systems underlying muscle contraction. However, in its conventional form, macromolecular diffraction averages over large ensembles. Progress in x-ray optics has now enabled to probe structures on sub-cellular scales, with the beam confined to a distinct organelle. Here, we use scanning small angle x-ray scattering (scanning SAXS) to probe the diffraction from cytoskeleton networks in cardiac tissue cells. In particular, we focus on actin-myosin composites, which we identify as the dominating contribution to the anisotropic diffraction patterns, by correlation with optical fluorescence microscopy. To this end, we use a principal component analysis approach to quantify direction, degree of orientation, nematic order, and the second moment of the scattering distribution in each scan point.Wecompare the fiber orientation from micrographs of fluorescently labeled actin fibers to the structure orientation of the x-ray dataset and thus correlate signals of two different measurements: the native electron density distribution of the local probing area versus specifically labeled constituents of the sample. Further, we develop a robust and automated fitting approach based on a power law expansion, in order to describe the local structure factor in each scan point over a broad range of the momentum transfer qr. Finally, we demonstrate how the methodology shown for freeze dried cells in the first part of the paper can be translated to alive cell recordings."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2017"],["dc.identifier.doi","10.1088/1367-2630/19/1/013012"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14526"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58886"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1367-2630"],["dc.relation.orgunit","Institut für Röntgenphysik"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.workinggroup","RG Salditt (Structure of Biomolecular Assemblies and X-Ray Physics)"],["dc.rights","CC BY 3.0"],["dc.subject.ddc","530"],["dc.subject.gro","x-ray scattering"],["dc.title","Anisotropic x-ray scattering and orientation fields in cardiac tissue cells"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","submitted_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","22"],["dc.bibliographiccitation.lastpage","29"],["dc.bibliographiccitation.seriesnr","9389"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Jung, Sungkyu"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.editor","Nielsen, Frank"],["dc.date.accessioned","2017-09-07T11:50:29Z"],["dc.date.available","2017-09-07T11:50:29Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1007/978-3-319-25040-3_3"],["dc.identifier.gro","3147625"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5092"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer"],["dc.publisher.place","Cham [u.a.]"],["dc.relation.conference","Second International SEE Conference on \"Geometric Science of Information\""],["dc.relation.crisseries","Lecture Notes in Computer Science"],["dc.relation.doi","10.1007/978-3-319-25040-3"],["dc.relation.eventend","2015-10-30"],["dc.relation.eventlocation","Palaiseau"],["dc.relation.eventstart","2015-10-28"],["dc.relation.isbn","978-3-319-25039-7"],["dc.relation.ispartof","Geometric science of information"],["dc.relation.ispartofseries","Lecture Notes in Computer Science; 9389"],["dc.relation.issn","0302-9743"],["dc.title","Dimension Reduction on Polyspheres with Application to Skeletal Representations"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Mardia, Kanti V."],["dc.date.accessioned","2017-09-07T11:47:59Z"],["dc.date.available","2017-09-07T11:47:59Z"],["dc.date.issued","2015"],["dc.description.abstract","There are several cutting edge applications needing PCA methods for data on tori and we propose a novel torus-PCA method with important properties that can be generally applied. There are two existing general methods: tangent space PCA and geodesic PCA. However, unlike tangent space PCA, our torus-PCA honors the cyclic topology of the data space whereas, unlike geodesic PCA, our torus-PCA produces a variety of non-winding, non-dense descriptors. This is achieved by deforming tori into spheres and then using a variant of the recently developed principle nested spheres analysis. This PCA analysis involves a step of small sphere fitting and we provide an improved test to avoid overfitting. However, deforming tori into spheres creates singularities. We introduce a data-adaptive pre-clustering technique to keep the singularities away from the data. For the frequently encountered case that the residual variance around the PCA main component is small, we use a post-mode hunting technique for more fine-grained clustering. Thus in general, there are three successive interrelated key steps of torus-PCA in practice: pre-clustering, deformation, and post-mode hunting. We illustrate our method with two recently studied RNA structure (tori) data sets: one is a small RNA data set which is established as the benchmark for PCA and we validate our method through this data. Another is a large RNA data set (containing the small RNA data set) for which we show that our method provides interpretable principal components as well as giving further insight into its structure."],["dc.identifier.arxiv","1511.04993"],["dc.identifier.gro","3146823"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4627"],["dc.notes.intern","mathe"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.title","Torus Principal Component Analysis with an Application to RNA Structures"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Applied Magnetic Resonance"],["dc.contributor.author","Hiller, M."],["dc.contributor.author","Tkach, I."],["dc.contributor.author","Wiechers, H."],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Huckemann, Stephan F."],["dc.contributor.author","Pokern, Y."],["dc.contributor.author","Bennati, Marina"],["dc.date.accessioned","2021-09-03T12:57:30Z"],["dc.date.available","2021-09-03T12:57:30Z"],["dc.date.issued","2021-08-26"],["dc.description.abstract","H ENDOR spectra of tyrosyl radicals (Y∙) have been the subject of numerous EPR spectroscopic studies due to their importance in biology. Nevertheless, assignment of all internal 1H hyperfine couplings has been challenging because of substantial spectral overlap. Recently, using 263 GHz ENDOR in conjunction with statistical analysis, we could identify the signature of the Hβ2 coupling in the essential Y122 radical of Escherichia coli ribonucleotide reductase, and modeled it with a distribution of radical conformations. Here, we demonstrate that this analysis can be extended to the full-width 1H ENDOR spectra that contain the larger Hβ1 coupling. The Hβ2 and Hβ1 couplings are related to each other through the ring dihedral and report on the amino acid conformation. The 263 GHz ENDOR data, acquired in batches instead of averaging, and data processing by a new “drift model” allow reconstructing the ENDOR spectra with statistically meaningful confidence intervals and separating them from baseline distortions. Spectral simulations using a distribution of ring dihedral angles confirm the presence of a conformational distribution, consistent with the previous analysis of the Hβ2 coupling. The analysis was corroborated by 94 GHz 2H ENDOR of deuterated Y∙122. These studies provide a starting point to investigate low populated states of tyrosyl radicals in greater detail."],["dc.identifier.doi","10.1007/s00723-021-01411-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89260"],["dc.language.iso","en"],["dc.relation","SFB 1456 | Cluster A | A01: Geometric and Bayesian statistics to reconstruct protein radical structures from ENDOR spectroscopy"],["dc.relation","SFB 1456 | Cluster A: Data with Geometric Nonlinearities"],["dc.relation","SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften"],["dc.relation.issn","0937-9347"],["dc.relation.issn","1613-7507"],["dc.relation.orgunit","Max-Planck-Institut für biophysikalische Chemie"],["dc.relation.orgunit","Fakultät für Chemie"],["dc.relation.orgunit","Institut für Mathematische Stochastik"],["dc.rights","CC BY 4.0"],["dc.title","Distribution of H$^\\beta$ Hyperfine Couplings in a Tyrosyl Radical Revealed by 263 GHz ENDOR Spectroscopy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","1332"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","The Annals of Applied Statistics"],["dc.bibliographiccitation.lastpage","1359"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Mardia, Kanti V."],["dc.date.accessioned","2020-12-10T18:41:47Z"],["dc.date.available","2020-12-10T18:41:47Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1214/17-AOAS1115"],["dc.identifier.issn","1932-6157"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77678"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Torus principal component analysis with applications to RNA structure"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0047259X21001408"],["dc.bibliographiccitation.firstpage","104862"],["dc.bibliographiccitation.journal","Journal of Multivariate Analysis"],["dc.bibliographiccitation.volume","188"],["dc.contributor.author","Mardia, Kanti V."],["dc.contributor.author","Wiechers, Henrik"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Huckemann, Stephan F."],["dc.date.accessioned","2022-04-01T10:00:56Z"],["dc.date.available","2022-04-01T10:00:56Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.jmva.2021.104862"],["dc.identifier.pii","S0047259X21001408"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105554"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation","SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften"],["dc.relation","SFB 1456 | Cluster A | A01: Geometric and Bayesian statistics to reconstruct protein radical structures from ENDOR spectroscopy"],["dc.relation","SFB 1456 | Cluster B | B02: Ensemble inference – new sampling algorithms and applications in structural biology"],["dc.relation.issn","0047-259X"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Principal component analysis and clustering on manifolds"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","25"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Image Analysis & Stereology"],["dc.bibliographiccitation.volume","36"],["dc.contributor.author","Benes, Viktor"],["dc.contributor.author","Vecera, Jakub"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.author","Rehfeldt, Florian"],["dc.contributor.author","Kralova, Veronika"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:50:29Z"],["dc.date.available","2017-09-07T11:50:29Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.5566/ias.1627"],["dc.identifier.gro","3147619"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14778"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5090"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","chake"],["dc.publisher","Slovenian Society for Stereology and Quantitative Image Analysis"],["dc.relation.issn","1854-5165"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0/"],["dc.title","ESTIMATION OF PARAMETERS IN A PLANAR SEGMENT PROCESS WITH A BIOLOGICAL APPLICATION"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","187"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","European Biophysics Journal"],["dc.bibliographiccitation.lastpage","209"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Pein, Florian"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2021-06-01T09:42:49Z"],["dc.date.available","2021-06-01T09:42:49Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Analysis of patchclamp recordings is often a challenging issue. We give practical guidance how such recordings can be analyzed using the model-free multiscale idealization methodology JSMURF, JULES, and HILDE. We provide an operational manual how to use the accompanying software available as an R-package and as a graphical user interface. This includes selection of the right approach and tuning of parameters. We also discuss advantages and disadvantages of model-free approaches in comparison to hidden Markov model approaches and explain how they complement each other."],["dc.identifier.doi","10.1007/s00249-021-01506-8"],["dc.identifier.pmid","33837454"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85363"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/272"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.eissn","1432-1017"],["dc.relation.issn","0175-7571"],["dc.relation.workinggroup","RG Munk"],["dc.rights","CC BY 4.0"],["dc.title","Analysis of patchclamp recordings: model-free multiscale methods and software"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]