TY - JOUR A1 - Prasad, Anup K. A1 - Tiwari, Chandni A1 - Ray, Sourav A1 - Holden, Stephanie A1 - Armstrong, David A. A1 - Rosengren, K. Johan A1 - Rodger, Alison A1 - Panwar, Ajay S. A1 - Martin, Lisandra L. T1 - Secondary Structure Transitions for a Family of Amyloidogenic, Antimircobial Uperin 3 Peptides in Contact with Sodium Dodecyl Sulfate JF - ChemPlusChem Y1 - 2022 U6 - https://doi.org/10.1002/cplu.202100408 VL - 87 SP - e202100408 ER - TY - JOUR A1 - Berg, Mascha A1 - Plöntzke, Julia A1 - Siebert, Heike A1 - Röblitz, Susanna T1 - Modelling Oscillatory Patterns in the Bovine Estrous Cycle with Boolean Delay Equations JF - Bulletin of Mathematical Biology N2 - Boolean delay equations (BDEs), with their relatively simple and intuitive mode of modelling, have been used in many research areas including, for example, climate dynamics and earthquake propagation. Their application to biological systems has been scarce and limited to the molecular level. Here, we derive and present two BDE models. One is directly derived from a previously published ordinary differential equation (ODE) model for the bovine estrous cycle, whereas the second model includes a modification of a particular biological mechanism. We not only compare the simulation results from the BDE models with the trajectories of the ODE model, but also validate the BDE models with two additional numerical experiments. One experiment induces a switch in the oscillatory pattern upon changes in the model parameters, and the other simulates the administration of a hormone that is known to shift the estrous cycle in time. The models presented here are the first BDE models for hormonal oscillators, and the first BDE models for drug administration. Even though automatic parameter estimation still remains challenging, our results support the role of BDEs as a framework for the systematic modelling of complex biological oscillators. Y1 - 2021 U6 - https://doi.org/10.1007/s11538-021-00942-z VL - 83 IS - 121 SP - 1 EP - 25 ER - TY - JOUR A1 - Djurdjevac, Ana T1 - Linear Parabolic Problems in Random Moving Domains N2 - We consider linear parabolic equations on a random non-cylindrical domain. Utilizing the domain mapping method, we write the problem as a partial differential equation with random coefficients on a cylindrical deterministic domain. Exploiting the deterministic results concerning equations on non-cylindrical domains, we state the necessary assumptions about the velocity filed and in addition, about the flow transformation that this field generates. In this paper we consider both cases, the uniformly bounded with respect to the sample and log-normal type transformation. In addition, we give an explicit example of a log-normal type transformation and prove that it does not satisfy the uniformly bounded condition. We define a general framework for considering linear parabolic problems on random non-cylindrical domains. As the first example, we consider the heat equation on a random tube domain and prove its well-posedness. Moreover, as the other example we consider the parabolic Stokes equation which illustrates the case when it is not enough just to study the plain-back transformation of the function, but instead to consider for example the Piola type transformation, in order to keep the divergence free property. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1137/19M1284889 VL - 9 IS - 2 SP - 848 EP - 879 ER - TY - JOUR A1 - Djurdjevac, Ana A1 - Gräser, Carsten A1 - Herbert, Philip J. T1 - Non-stationary incompressible linear fluid equations in a moving domain JF - arXiv N2 - This article considers non-stationary incompressible linear fluid equations in a moving domain. We demonstrate the existence and uniqueness of an appropriate weak formulation of the problem by making use of the theory of time-dependent Bochner spaces. It is not possible to directly apply established evolving Hilbert space theory due to the incompressibility constraint. After we have established the well-posedness, we derive and analyse a time discretisation of the system. Y1 - 2021 ER - TY - JOUR A1 - Colom, Remi A1 - Mikheeva, Elena A1 - Achouri, Karim A1 - Zuniga-Perez, Jesus A1 - Bonod, Nicolas A1 - Martin, Olivier J. F. A1 - Burger, Sven A1 - Genevet, Patrice T1 - Crossing of the branch cut: the topological origin of a universal 2π-phase retardation in non-Hermitian metasurfaces JF - Laser Photonics Rev. Y1 - 2023 U6 - https://doi.org/10.1002/lpor.202200976 VL - 17 SP - 2200976 ER - TY - JOUR A1 - Montefusco, Alberto A1 - Schütte, Christof A1 - Winkelmann, Stefanie T1 - A route to the hydrodynamic limit of a reaction-diffusion master equation using gradient structures JF - SIAM Journal on Applied Mathematics N2 - The reaction-diffusion master equation (RDME) is a lattice-based stochastic model for spatially resolved cellular processes. It is often interpreted as an approximation to spatially continuous reaction-diffusion models, which, in the limit of an infinitely large population, may be described by means of reaction-diffusion partial differential equations. Analyzing and understanding the relation between different mathematical models for reaction-diffusion dynamics is a research topic of steady interest. In this work, we explore a route to the hydrodynamic limit of the RDME which uses gradient structures. Specifically, we elaborate on a method introduced in [J. Maas and A. Mielke, J. Stat. Phys., 181 (2020), pp. 2257–2303] in the context of well-mixed reaction networks by showing that, once it is complemented with an appropriate limit procedure, it can be applied to spatially extended systems with diffusion. Under the assumption of detailed balance, we write down a gradient structure for the RDME and use the method in order to produce a gradient structure for its hydrodynamic limit, namely, for the corresponding RDPDE. Y1 - 2023 U6 - https://doi.org/10.1137/22M1488831 VL - 83 IS - 2 SP - 837 EP - 861 ER - TY - JOUR A1 - Dellnitz, Michael A1 - Hüllermeier, Eyke A1 - Lücke, Marvin A1 - Ober-Blöbaum, Sina A1 - Offen, Christian A1 - Peitz, Sebastian A1 - Pfannschmidt, Karlson T1 - Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning JF - SIAM Journal on Scientific Computing Y1 - 2023 U6 - https://doi.org/10.1137/21M1412682 VL - 45 IS - 2 SP - A579 EP - A595 ER - TY - JOUR A1 - Peppert, Felix A1 - von Kleist, Max A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks JF - IEEE Transactions on Neural Networks and Learning Systems N2 - Deep convolutional neural networks (DCNNs) are routinely used for image segmentation of biomedical data sets to obtain quantitative measurements of cellular structures like tissues. These cellular structures often contain gaps in their boundaries, leading to poor segmentation performance when using DCNNs like the U-Net. The gaps can usually be corrected by post-hoc computer vision (CV) steps, which are specific to the data set and require a disproportionate amount of work. As DCNNs are Universal Function Approximators, it is conceivable that the corrections should be obsolete by selecting the appropriate architecture for the DCNN. In this article, we present a novel theoretical framework for the gap-filling problem in DCNNs that allows the selection of architecture to circumvent the CV steps. Combining information-theoretic measures of the data set with a fundamental property of DCNNs, the size of their receptive field, allows us to formulate statements about the solvability of the gap-filling problem independent of the specifics of model training. In particular, we obtain mathematical proof showing that the maximum proficiency of filling a gap by a DCNN is achieved if its receptive field is larger than the gap length. We then demonstrate the consequence of this result using numerical experiments on a synthetic and real data set and compare the gap-filling ability of the ubiquitous U-Net architecture with variable depths. Our code is available at https://github.com/ai-biology/dcnn-gap-filling. Y1 - 2022 U6 - https://doi.org/10.1109/TNNLS.2021.3072746 VL - 33 IS - 11 SP - 6194 EP - 6205 ER - TY - JOUR A1 - Boeth, Heide A1 - Biesen, Robert A1 - Hollnagel, Jeremias A1 - Herrmann, Sandra A1 - Ehrig, Rainald A1 - Pelli, Lorenzo A1 - Taylor, William R. A1 - Duda, Georg A1 - Buttgereit, Frank T1 - Quantification of morning stiffness to assess disease activity and treatment effects in rheumatoid arthritis JF - Rheumatology N2 - Objectives The clinical parameter “morning stiffness” is widely used to assess the status of rheumatoid arthritis (RA), but its accurate quantitative assessment in a clinical setting has not yet been successful. This lack of individual quantification limits both personalized medication and efficacy evaluation in the treatment of RA. Methods We have developed a novel technology to assess passive resistance of the metacarpophalangeal (MCP) III joint (stiffness) and its Passive Range of Motion (PRoM). Within this pilot study, nineteen female postmenopausal RA patients and nine healthy controls were examined in the evening as well as in the morning of the following day. To verify the specificity of the biomechanical quantification, eleven patients with RA were assessed both prior to and ∼3 h after glucocorticoid therapy. Results While the healthy controls showed only minor changes between afternoon and morning, in RA patients mean±SD PRoM decreased significantly by 18 ± 22% and stiffness increased significantly by 20 ± 18% in the morning compared with the previous afternoon. We found a significant positive correlation between RA activity and biomechanical measures. Glucocorticoids significantly increased mean PRoM by 16 ± 11% and reduced mean stiffness by 23 ± 22%. Conclusion This technology allowed mechanical stiffness to be quantified in MCP joints, and has demonstrated high sensitivity in respect to disease status as well as medication effect in RA patients. Such non-invasive, low risk, and rapid assessment of biomechanical joint stiffness opens a novel avenue for judging therapy efficacy in patients with RA, and potentially also in other non-RA inflammatory joint diseases. Y1 - 2021 U6 - https://doi.org/10.1093/rheumatology/keab323 VL - 60 IS - 11 SP - 5282 EP - 5291 ER - TY - GEN A1 - Pflüger, Mika A1 - Kline, R Joseph A1 - Fernández Herrero, Analía A1 - Hammerschmidt, Martin A1 - Soltwisch, Victor A1 - Krumrey, Michael T1 - Extracting dimensional parameters of gratings produced with self-aligned multiple patterning using grazing-incidence small-angle x-ray scattering [Source Code] Y1 - 2020 U6 - https://doi.org/10.24433/CO.0953516.v2 ER - TY - JOUR A1 - Gao, Timm A1 - Rickert, Lucas A1 - Urban, Felix A1 - Große, Jan A1 - Srocka, Nicole A1 - Rodt, Sven A1 - Musial, Anna A1 - Zolnacz, Kinga A1 - Mergo, Pawel A1 - Dybka, Kamil A1 - Urbanczyk, Waclaw A1 - Sek, Grzegorz A1 - Burger, Sven A1 - Reitzenstein, Stephan A1 - Heindel, Tobias T1 - A quantum key distribution testbed using a plug&play telecom-wavelength single-photon source JF - Appl. Phys. Rev. Y1 - 2022 U6 - https://doi.org/10.1063/5.0070966 VL - 9 SP - 011412 ER - TY - JOUR A1 - Torun, Cem Güney A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Burger, Sven A1 - Munns, Joseph H.D. A1 - Schröder, Tim T1 - Optimized diamond inverted nanocones for enhanced color center to fiber coupling JF - Appl. Phys. Lett. Y1 - 2021 U6 - https://doi.org/10.1063/5.0050338 VL - 118 SP - 234002 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Çınaroğlu, Süleyman A1 - Fischer, Patrick D. A1 - Fackeldey, Konstantin A1 - Wagner, Gerhard A1 - Arthanari, Haribabu T1 - VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization JF - Special Issue Artificial Intelligence & Deep Learning Approaches for Structural Bioinformatics N2 - The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.3390/ijms22115807 VL - 22 IS - 11 SP - 5807 ER - TY - JOUR A1 - Ahiboz, Doguscan A1 - Andresen, Elina A1 - Manley, Phillip A1 - Resch-Genger, Ute A1 - Würth, Christian A1 - Becker, Christiane T1 - Metasurface-Enhanced Photon Upconversion upon 1550 nm Excitation JF - Adv. Opt. Mater. Y1 - 2021 U6 - https://doi.org/10.1002/adom.202101285 VL - 9 SP - 2101285 ER - TY - JOUR A1 - Martens, Kevin A1 - Funck, Timon A1 - Santiago, Eva Y. A1 - Govorov, Alexander O. A1 - Burger, Sven A1 - Liedl, Tim T1 - Onset of Chirality in Plasmonic Meta-Molecules and Dielectric Coupling JF - ACS Nano Y1 - 2022 U6 - https://doi.org/10.1021/acsnano.2c04729 VL - 16 SP - 16143 ER - TY - CHAP A1 - Perevoznik, Dmitrii A1 - Bose, Surajit A1 - Burger, Sven A1 - Demircan, Ayhan A1 - Morgner, Uwe T1 - Photonic components in polymers made by femtosecond pulses T2 - Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) Y1 - 2021 U6 - https://doi.org/10.1109/CLEO/Europe-EQEC52157.2021.9541906 SP - 1 ER - TY - CHAP A1 - Ahiboz, Doguscan A1 - Andresen, Elina A1 - Manley, Phillip A1 - Resch Genger, Ute A1 - Würth, Christian A1 - Becker, Christiane T1 - Enhanced Photon Upconversion Using Erbium-Doped Nanoparticles Interacting with Silicon Metasurfaces T2 - IEEE 48th Photovoltaic Specialists Conference (PVSC) Y1 - 2021 U6 - https://doi.org/10.1109/PVSC43889.2021.9518495 SP - 2008 EP - 2010 ER - TY - JOUR A1 - Bittracher, Andreas A1 - Mollenhauer, Mattes A1 - Koltai, Péter A1 - Schütte, Christof T1 - Optimal Reaction Coordinates: Variational Characterization and Sparse Computation JF - Multiscale Modelling & Simulation N2 - Reaction coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and general variational characterization of optimal RCs and provide conditions for their existence. Optimal RCs are minimizers of a certain loss function, and reduced models based on them guarantee a good approximation of the statistical long-term properties of the original high-dimensional process. We show that for slow-fast systems, metastable systems, and other systems with known good RCs, the novel theory reproduces previous insight. Remarkably, for reversible systems, the numerical effort required to evaluate the loss function scales only with the variability of the underlying, low-dimensional mechanism, and not with that of the full system. The theory provided lays the foundation for an efficient and data-sparse computation of RCs via modern machine learning techniques. Y1 - 2023 U6 - https://doi.org/10.1137/21M1448367 VL - 21 IS - 2 SP - 449 EP - 488 ER - TY - JOUR A1 - Mollenhauer, Mattes A1 - Klus, Stefan A1 - Schütte, Christof A1 - Koltai, Péter T1 - Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence JF - Journal of Machine Learning Research N2 - We consider autocovariance operators of a stationary stochastic process on a Polish space that is embedded into a reproducing kernel Hilbert space. We investigate how empirical estimates of these operators converge along realizations of the process under various conditions. In particular, we examine ergodic and strongly mixing processes and obtain several asymptotic results as well as finite sample error bounds. We provide applications of our theory in terms of consistency results for kernel PCA with dependent data and the conditional mean embedding of transition probabilities. Finally, we use our approach to examine the nonparametric estimation of Markov transition operators and highlight how our theory can give a consistency analysis for a large family of spectral analysis methods including kernel-based dynamic mode decomposition. Y1 - 2022 UR - https://jmlr.org/papers/v23/20-442.html VL - 23 IS - 327 SP - 1 EP - 34 ER - TY - CHAP A1 - Rickert, Lucas A1 - Gao, Timm A1 - Urban, Felix A1 - Große, Jan A1 - Srocka, Nicole A1 - Rodt, Sven A1 - Musial, Anna A1 - Zolnacz, Kinga A1 - Mergo, Pawel A1 - Dybka, Kamil A1 - Urbanczyk, Waclaw A1 - Sek, Grzegorz A1 - Burger, Sven A1 - Reitzenstein, Stephan A1 - Tobias, Heindel T1 - A Quantum Key Distribution Testbed Using Plug&Play Telecom-Wavelength Single-Photons T2 - Quantum Information and Measurement VI Y1 - 2021 U6 - https://doi.org/10.1364/QIM.2021.M2C.1 SP - M2C.1 ER -