Huckemann, Stephan F.

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[["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.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.artnumber","e0126346"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Rehfeldt, Florian"],["dc.date.accessioned","2017-09-07T11:48:37Z"],["dc.date.available","2017-09-07T11:48:37Z"],["dc.date.issued","2015"],["dc.description.abstract","A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0126346"],["dc.identifier.gro","3146928"],["dc.identifier.pmid","25996921"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4710"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Wiechers, Henrik"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Mardia, Kanti V."],["dc.contributor.author","Huckemann, Stephan F."],["dc.date.accessioned","2022-05-05T06:46:35Z"],["dc.date.available","2022-05-05T06:46:35Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1101/2021.08.06.455406"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/107606"],["dc.language.iso","en"],["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.title","Learning torus PCA based classification for multiscale RNA backbone structure correction with application to SARS-CoV-2"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]