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1703 Publications visible to you, out of a total of 1703
Approximating Phylogenetic Tree Distributions with Distance-Based Methods

Abstract (Expand)

Accurately reconstructing the evolutionary history of a group of organism is a complex task. Current state-of-the-art tools produce phylogenetic tree distributions with Markov chain Monte-Carlo (MCMC) … methods by sampling the posterior tree distribution under a given model to reflect uncertainties in the underlying models and data. While these distributions offer very good insight into the phylogenetic history, they are very compute intensive. In this thesis we present and evaluate multiple heuristics to approximate these distributions with distance-based methods. To judge the quality of our heuristics, we compare our distribution against a reference MCMC-based distribution with split and frequency-based metrics. We show that our method works well for some types of data, but not all, compared to other tools, and that further information about the data needs to be incorporated to make this viable in practice. Our most successful method is characterized by the use of pair-wise distance distributions to apply likelihood-supported perturbation to the input distances for the Neighbor Joining algorithm. Because this ignores the interdependencies between distances, we need to add parsimony filtering as a post-processing step to eliminate unlikely trees from our distributions, which significantly improves the results. Finally, we also discuss the shortcomings and future potential of our heuristics to more accurately estimate pair-wise distances and their interdependencies, which should lead to more competitive results.

Authors: Noah Wahl, Benoit Morel, Alexandros Stamatakis

Date Published: 1st Dec 2023

Publication Type: Master's Thesis

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

Abstract (Expand)

tive reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely … published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Authors: Elisabeth K. Brockhaus, Daniel Wolffram, Tanja Stadler, Michael Osthege, Tanmay Mitra, Jonas M. Littek, Ekaterina Krymova, Anna J. Klesen, Jana S. Huisman, Stefan Heyder, Laura M. Helleckes, Matthias an der Heiden, Sebastian Funk, Sam Abbott, Johannes Bracher

Date Published: 27th Nov 2023

Publication Type: Journal

Wird KI neue Medikamente gegen Herzkrankheiten hervorbringen?

Abstract (Expand)

Zusammenfassung Angesichts der umwälzenden Auswirkungen, die künstliche Intelligenz (KI) auf Wissenschaft, Medizin und darüber hinaus hat, betrachten wir hier das Potenzial von KI für die Entdeckung …enzial von KI für die Entdeckung neuer Medikamente gegen Herzkrankheiten. Wir definieren KI im weitesten Sinne als den Einsatz von maschinellem Lernen, einschließlich Statistik und Deep Learning, um Muster in Datensätzen zu erkennen, die für Vorhersagen genutzt werden können. Jüngste Durchbrüche in der Fähigkeit, sehr große Datenmengen zu berücksichtigen, haben einen Boom in der KI-gestützten Arzneimittelentdeckung sowohl in der Wissenschaft als auch in der Industrie ausgelöst. Viele neue Unternehmen verfügen bereits über Arzneimittel-Pipelines, die bis in die klinische Erprobung reichen, aber noch keine Medikamente gegen Herzkrankheiten enthalten. Wir beschreiben hier den Einsatz von KI für die Entdeckung von niedermolekularen Medikamenten und Biologika, einschließlich therapeutischer Peptide, sowie für die Vorhersage von Wirkungen wie Kardiotoxizität. Der konzertierte Einsatz von KI zusammen mit physikbasierten Simulationen und experimentellen Rückkopplungsschleifen wird notwendig sein, um das Potenzial der KI für die Arzneimittelentdeckung und die Entwicklung von Präzisionsarzneimitteln für Herzkrankheiten voll auszuschöpfen.

Authors: Manuel Glaser, Julia Ritterhof, Patrick Most, Rebecca C. Wade

Date Published: 20th Nov 2023

Publication Type: Journal

Computational Models of Hen Egg White Lysozyme Physisorption to Silica- and Mica-like Surfaces

Abstract (Expand)

Protein-surface adsorption phenomena play a crucial role in a variety of fields, including medicine, molecular and cell biology, biotechnology, phar- maceutical sciences, and biophysics. It is therefore … desirable to develop accu- rate models to describe them. Hen-Egg-White-Lysozyme (HEWL) adsorption to silica- and mica-like surfaces entails minimal conformational changes, rendering it an ideal system for rigid-body Brownian dynamics simulations. Experimen- tal studies revealed that the fluorescence attached to HEWL exhibits a sharp overshoot followed by a trough as a function of time. This work identifies two inconsistencies in the explanation previously used by the authors to interpret the experiment. A reorientation does not seem to be the main driving factor for the overshoot. Furthermore, I examine previously used electrostatic potential models based on the Poisson-Boltzmann equation (PBE) used by Romanowska et al. and Reinhardt et al.. It is found that even though different orientations of HEWL- FITC on the surface are possible, the required amount of adsorbed proteins and corresponding reorientation is attained only with the linearized PBE-potential. The study provides a possible explanation why reaching the critical overshoot value with non-linearized PBE-based models poses difficulties. Overcoming this problem is an indispensable step in developing Brownian-dynamics-based atomic- detail multi-molecule models for protein-surface adsorption.

Author: Jakob Nießner

Date Published: 14th Nov 2023

Publication Type: Master's Thesis

High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.

Abstract (Expand)

Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening … program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.

Authors: P. Linciano, A. Quotadamo, R. Luciani, M. Santucci, K. M. Zorn, D. H. Foil, T. R. Lane, A. Cordeiro da Silva, N. Santarem, C. B Moraes, L. Freitas-Junior, U. Wittig, W. Mueller, M. Tonelli, S. Ferrari, A. Venturelli, S. Gul, M. Kuzikov, B. Ellinger, J. Reinshagen, S. Ekins, M. P. Costi

Date Published: 3rd Nov 2023

Publication Type: Journal

Turbulent dynamo action and its effects on the mixing at the convective boundary of an idealized oxygen-burning shell

Abstract (Expand)

Convection is one of the most important mixing processes in stellar interiors. Hydrodynamic mass entrainment can bring fresh fuel from neighboring stable layers into a convection zone, modifying the …convection zone, modifying the structure and evolution of the star. Because flows in stellar convection zones are highly turbulent, multidimensional hydrodynamic simulations are fundamental to accurately capture the physics of mixing processes. Under some conditions, strong magnetic fields can be sustained by the action of a turbulent dynamo, adding another layer of complexity and possibly altering the dynamics in the convection zone and at its boundaries. In this study, we used our fully compressible S EVEN -L EAGUE H YDRO code to run detailed and highly resolved three-dimensional magnetohydrodynamic simulations of turbulent convection, dynamo amplification, and convective boundary mixing in a simplified setup whose stratification is similar to that of an oxygen-burning shell in a star with an initial mass of 25 M ⊙ . We find that the random stretching of magnetic field lines by fluid motions in the inertial range of the turbulent spectrum (i.e., a small-scale dynamo) naturally amplifies the seed field by several orders of magnitude in a few convective turnover timescales. During the subsequent saturated regime, the magnetic-to-kinetic energy ratio inside the convective shell reaches values as high as 0.33, and the average magnetic field strength is ∼10 10 G. Such strong fields efficiently suppress shear instabilities, which feed the turbulent cascade of kinetic energy, on a wide range of spatial scales. The resulting convective flows are characterized by thread-like structures that extend over a large fraction of the convective shell. The reduced flow speeds and the presence of magnetic fields with strengths up to 60% of the equipartition value at the upper convective boundary diminish the rate of mass entrainment from the stable layer by ≈20% as compared to the purely hydrodynamic case.

Authors: G. Leidi, R. Andrassy, J. Higl, P. V. F. Edelmann, F. K. Röpke

Date Published: 1st Nov 2023

Publication Type: Journal

New red giants in NGC 6791 and NGC 6819 using Kepler superstamps

Abstract (Expand)

Context: Stars that are members of stellar clusters are assumed to be formed at the same time and place from material with the same initial chemical composition. These additional constraints on the … ensemble of cluster stars make these stars suitable as benchmarks. Aims: We aimed (1) to identify previously unknown red giants in the open clusters NGC 6791 and NGC 6819, (2) to extract their asteroseismic parameters, and (3) to determine their cluster membership. Methods: We followed a dedicated method based on difference imaging to extract the light curves of potential red giants in NGC 6791 and NGC 6819 from Kepler superstamp data. We extracted the asteroseismic parameters of the stars that showed solar-like oscillations. We performed an asteroseismic membership study to identify which of these stars are likely to be cluster members. Results: We found 149 red giant stars within the Kepler superstamps, 93 of which are likely cluster members. We were able to find 29 red giants that are not primary targets of Kepler, and therefore, their light curves had not been released previously. Five of these previously unknown red giants have a cluster membership probability greater than 95%.

Authors: A. Covelo-Paz, N. Themeßl, F. Espinoza-Rojas, S. Hekker

Date Published: 1st Nov 2023

Publication Type: Journal

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