@incollection{GlowatzHaufsBrusbergSchmidt2019, author = {Glowatz, Christoph and Haufs-Brusberg, Peter and Schmidt, Holger}, title = {Unternehmensorganisation und Informationssicherheit: Einf{\"u}hrung und Grundlagen (Kap. F1)}, series = {Cybersicherheit f{\"u}r vernetzte Anwendungen in der Industrie 4.0}, booktitle = {Cybersicherheit f{\"u}r vernetzte Anwendungen in der Industrie 4.0}, editor = {Schulz, Thomas}, publisher = {Vogel Communications Group}, address = {W{\"u}rzburg}, isbn = {978-3834334244}, pages = {315 -- 334}, year = {2019}, language = {de} } @techreport{Duckardt2022, author = {Duckardt, Alina}, title = {Anwendung von Learning Analytics in Schule und Hochschule}, volume = {Arbeitspapier des Lehrgebiets Datenbanken und E-Business, Nr. 1/2022}, address = {D{\"u}sseldorf}, organization = {Hochschule D{\"u}sseldorf}, doi = {10.20385/opus4-3652}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-36521}, pages = {39}, year = {2022}, subject = {Ethik}, language = {de} } @article{MiguelesRowlandsHuberetal.2019, author = {Migueles, Jairo H. and Rowlands, Alex V. and Huber, Florian and Sabia, S{\´e}verine and van Hees, Vincent T.}, title = {GGIR: A Research Community-Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data}, series = {Journal for the Measurement of Physical Behaviour}, volume = {2}, journal = {Journal for the Measurement of Physical Behaviour}, number = {3}, publisher = {Human Kinetics}, issn = {2575-6605}, doi = {10.1123/jmpb.2018-0063}, pages = {188 -- 196}, year = {2019}, language = {en} } @article{SchornVerhoevenRidderetal.2021, author = {Schorn, Michelle A. and Verhoeven, Stefan and Ridder, Lars and Huber, Florian and Acharya, Deepa D. and Aksenov, Alexander A. and Aleti, Gajender and Moghaddam, Jamshid Amiri and Aron, Allegra T. and Aziz, Saefuddin and Bauermeister, Anelize and Bauman, Katherine D. and Baunach, Martin and Beemelmanns, Christine and Beman, J. Michael and Berlanga-Clavero, Mar{\´i}a Victoria and Blacutt, Alex A. and Bode, Helge B. and Boullie, Anne and Brejnrod, Asker and Bugni, Tim S. and Calteau, Alexandra and Cao, Liu and Carri{\´o}n, V{\´i}ctor J. and Castelo-Branco, Raquel and Chanana, Shaurya and Chase, Alexander B. and Chevrette, Marc G. and Costa-Lotufo, Leticia V. and Crawford, Jason M. and Currie, Cameron R. and Cuypers, Bart and Dang, Tam and de Rond, Tristan and Demko, Alyssa M. and Dittmann, Elke and Du, Chao and Drozd, Christopher and Dujardin, Jean-Claude and Dutton, Rachel J. and Edlund, Anna and Fewer, David P. and Garg, Neha and Gauglitz, Julia M. and Gentry, Emily C. and Gerwick, Lena and Glukhov, Evgenia and Gross, Harald and Gugger, Muriel and Guill{\´e}n Matus, Dulce G. and Helfrich, Eric J. N. and Hempel, Benjamin-Florian and Hur, Jae-Seoun and Iorio, Marianna and Jensen, Paul R. and Kang, Kyo Bin and Kaysser, Leonard and Kelleher, Neil L. and Kim, Chung Sub and Kim, Ki Hyun and Koester, Irina and K{\"o}nig, Gabriele M. and Leao, Tiago and Lee, Seoung Rak and Lee, Yi-Yuan and Li, Xuanji and Little, Jessica C. and Maloney, Katherine N. and M{\"a}nnle, Daniel and Martin H, Christian and McAvoy, Andrew C. and Metcalf, Willam W. and Mohimani, Hosein and Molina-Santiago, Carlos and Moore, Bradley S. and Mullowney, Michael W. and Muskat, Mitchell and Nothias, Louis-F{\´e}lix and O'Neill, Ellis C. and Parkinson, Elizabeth I. and Petras, Daniel and Piel, J{\"o}rn and Pierce, Emily C. and Pires, Karine and Reher, Raphael and Romero, Diego and Roper, M. Caroline and Rust, Michael and Saad, Hamada and Saenz, Carmen and Sanchez, Laura M. and S{\o}rensen, S{\o}ren Johannes and Sosio, Margherita and S{\"u}ssmuth, Roderich D. and Sweeney, Douglas and Tahlan, Kapil and Thomson, Regan J. and Tobias, Nicholas J. and Trindade-Silva, Amaro E. and van Wezel, Gilles P. and Wang, Mingxun and Weldon, Kelly C. and Zhang, Fan and Ziemert, Nadine and Duncan, Katherine R. and Cr{\"u}semann, Max and Rogers, Simon and Dorrestein, Pieter C. and Medema, Marnix H. and van der Hooft, Justin J. J.}, title = {A community resource for paired genomic and metabolomic data mining}, series = {Nature Chemical Biology}, volume = {17}, journal = {Nature Chemical Biology}, number = {4}, publisher = {Nature}, issn = {1552-4469}, doi = {10.1038/s41589-020-00724-z}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34708}, pages = {363 -- 368}, year = {2021}, language = {en} } @article{BeniddirKangGentaJouveetal.2021, author = {Beniddir, Mehdi A. and Kang, Kyo Bin and Genta-Jouve, Gr{\´e}gory and Huber, Florian and Rogers, Simon and van der Hooft, Justin J. J.}, title = {Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches}, series = {Natural Product Reports}, volume = {38}, journal = {Natural Product Reports}, number = {11}, publisher = {The Royal Society of Chemistry}, issn = {1460-4752}, doi = {10.1039/D1NP00023C}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34772}, pages = {1967 -- 1993}, year = {2021}, language = {en} } @article{HuberBoireLopezetal.2015, author = {Huber, Florian and Boire, Adeline and L{\´o}pez, Magdalena Preciado and Koenderink, Gijsje H.}, title = {Cytoskeletal crosstalk: when three different personalities team up}, series = {Current Opinion in Cell Biology}, volume = {32}, journal = {Current Opinion in Cell Biology}, publisher = {Elsevier}, issn = {1879-0410}, doi = {10.1016/j.ceb.2014.10.005}, pages = {39 -- 47}, year = {2015}, language = {en} } @article{HuberKaes2011, author = {Huber, Florian and K{\"a}s, Josef}, title = {Self-regulative organization of the cytoskeleton}, series = {Cytoskeleton}, volume = {68}, journal = {Cytoskeleton}, number = {5}, publisher = {Wiley}, issn = {1949-3592}, doi = {10.1002/cm.20509}, pages = {259 -- 265}, year = {2011}, abstract = {Despite its impressive complexity the cytoskeleton succeeds to persistently organize itself and thus the cells' interior. In contrast to classical man-made machines, much of the cellular organization originates from inherent self-assembly and self-organization allowing a high degree of autonomy for various functional units. Recent experimental and theoretical studies revealed numerous examples of cytoskeleton components that arrange and organize in a self-regulative way. In the present review we want to shortly summarize some of the principle mechanisms that are able to inherently trigger and regulate the cytoskeleton organization. Although taken individually most of these regulative principles are rather simple with intuitively predictable consequences, combinations of two or more of these mechanisms can quickly give rise to very complex, unexpected behavior and might even be able to explain the formation of different functional units out of a common pool of available building blocks.}, language = {en} } @article{HuberKaesStuhrmann2008, author = {Huber, Florian and K{\"a}s, Josef and Stuhrmann, Bj{\"o}rn}, title = {Growing actin networks form lamellipodium and lamellum by self-assembly}, series = {Biophysical Journal}, volume = {95}, journal = {Biophysical Journal}, number = {12}, publisher = {Biophysical Society}, issn = {1542-0086}, doi = {10.1529/biophysj.108.134817}, pages = {5508 -- 5523}, year = {2008}, abstract = {Many different cell types are able to migrate by formation of a thin actin-based cytoskeletal extension. Recently, it became evident that this extension consists of two distinct substructures, designated lamellipodium and lamellum, which differ significantly in their kinetic and kinematic properties as well as their biochemical composition. We developed a stochastic two-dimensional computer simulation that includes chemical reaction kinetics, G-actin diffusion, and filament transport to investigate the formation of growing actin networks in migrating cells. Model parameters were chosen based on experimental data or theoretical considerations. In this work, we demonstrate the system's ability to form two distinct networks by self-organization. We found a characteristic transition in mean filament length as well as a distinct maximum in depolymerization flux, both within the first 1-2 microm. The separation into two distinct substructures was found to be extremely robust with respect to initial conditions and variation of model parameters. We quantitatively investigated the complex interplay between ADF/cofilin and tropomyosin and propose a plausible mechanism that leads to spatial separation of, respectively, ADF/cofilin- or tropomyosin-dominated compartments. Tropomyosin was found to play an important role in stabilizing the lamellar actin network. Furthermore, the influence of filament severing and annealing on the network properties is explored, and simulation data are compared to existing experimental data.}, language = {en} } @article{HuberSchnaussRoenickeetal.2013, author = {Huber, Florian and Schnauß, J{\"o}rg and R{\"o}nicke, S. and Rauch, P. and M{\"u}ller, K. and F{\"u}tterer, C. and K{\"a}s, Josef}, title = {Emergent complexity of the cytoskeleton: from single filaments to tissue}, series = {Advances in Physics}, volume = {62}, journal = {Advances in Physics}, number = {1}, publisher = {Taylor \& Francis}, issn = {1460-6976}, doi = {10.1080/00018732.2013.771509}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34812}, pages = {1 -- 112}, year = {2013}, abstract = {Despite their overwhelming complexity, living cells display a high degree of internal mechanical and functional organization which can largely be attributed to the intracellular biopolymer scaffold, the cytoskeleton. Being a very complex system far from thermodynamic equilibrium, the cytoskeleton's ability to organize is at the same time challenging and fascinating. The extensive amounts of frequently interacting cellular building blocks and their inherent multifunctionality permits highly adaptive behavior and obstructs a purely reductionist approach. Nevertheless (and despite the field's relative novelty), the physics approach has already proved to be extremely successful in revealing very fundamental concepts of cytoskeleton organization and behavior. This review aims at introducing the physics of the cytoskeleton ranging from single biopolymer filaments to multicellular organisms. Throughout this wide range of phenomena, the focus is set on the intertwined nature of the different physical scales (levels of complexity) that give rise to numerous emergent properties by means of self-organization or self-assembly.}, language = {en} } @article{HuberStrehleKaes2012, author = {Huber, Florian and Strehle, Dan and Kaes, Josef}, title = {Counterion-induced formation of regular actin bundle networks}, series = {Soft Matter}, volume = {8}, journal = {Soft Matter}, number = {4}, publisher = {Royal Society of Chemistry}, issn = {1744-6848}, pages = {931 -- 936}, year = {2012}, language = {en} } @article{HuberStrehleSchnaussetal.2015, author = {Huber, Florian and Strehle, Dan and Schnauß, J{\"o}rg and K{\"a}s, Josef}, title = {Formation of regularly spaced networks as a general feature of actin bundle condensation by entropic forces}, series = {New Journal of Physics}, volume = {17}, journal = {New Journal of Physics}, number = {4}, publisher = {IOP Publishing}, issn = {1367-2630}, doi = {10.1088/1367-2630/17/4/043029}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34833}, year = {2015}, language = {en} } @article{HubervanderBurgvanderHooftetal.2021, author = {Huber, Florian and van der Burg, Sven and van der Hooft, Justin J. J. and Ridder, Lars}, title = {MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, number = {1}, publisher = {Cold Spring Harbor Laboratory}, issn = {1758-2946}, doi = {10.1186/s13321-021-00558-4}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34847}, pages = {84}, year = {2021}, abstract = {Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.}, language = {en} }