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1549 Publications visible to you, out of a total of 1549

Abstract (Expand)

Motivation: Genotype datasets typically contain a large number of single nucleotide polymorphisms for a comparatively small number of individuals. To identify similarities between individuals and to infer an individual’s origin or membership to a cultural group, dimensionality reduction techniques are routinely deployed. However, inherent (technical) difficulties such as missing or noisy data need to be accounted for when analyzing a lower dimensional representation of genotype data, and the uncertainty of such an analysis should be reported in all studies. However, to date, there exists no stability estimation technique for genotype data that can estimate this uncertainty. Results: Here, we present Pandora, a stability estimation framework for genotype data based on bootstrapping. Pandora computes an overall score to quantify the stability of the entire embedding, perindividual support values, and deploys a k-means clustering approach to assess the uncertainty of assignments to potential cultural groups. In addition to this bootstrap-based stability estimation, Pandora offers a sliding-window stability estimation for whole-genome data. Using published empirical and simulated datasets, we demonstrate the usage and utility of Pandora for studies that rely on dimensionality reduction techniques. Data and Code: Availability Pandora is available on GitHub https://github.com/tschuelia/Pandora. All Python scripts and data to reproduce our results are available on GitHub https://github.com/tschuelia/PandoraPaper.

Authors: Julia Haag, Alexander I. Jordan, Alexandros Stamatakis

Date Published: 15th Mar 2024

Publication Type: Journal

Abstract (Expand)

Estimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The most widely used method for calculating branch support on trees inferred under Maximum Likelihood (ML) is the Standard, non-parametric Felsenstein Bootstrap Support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the Rapid Bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira–Hasegawalike approximate Likelihood Ratio Test) or the UltraFast Bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or meaningless low branch support intervals (SH-aLRT). Here, we present the Educated Bootstrap Guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9.4 (σ = 5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can predict SBS support values on a phylogeny comprising 1654 SARS-CoV2 genome sequences within 3 hours on a mid-class laptop. EBG is available under GNU GPL3.

Authors: Julius Wiegert, Dimitri Höhler, Julia Haag, Alexandros Stamatakis

Date Published: 6th Mar 2024

Publication Type: Journal

Abstract (Expand)

We have computed a three-dimensional hydrodynamic simulation of the merger between a massive (0.4 M_⊙) helium white dwarf (He WD) and a low-mass (0.6 M_⊙) carbon-oxygen white dwarf (CO WD). Despite the low mass of the primary, the merger triggers a thermonuclear explosion as a result of a double detonation, producing a faint transient and leaving no remnant behind. This type of event could also take place during common-envelope mergers whenever the companion is a CO WD and the core of the giant star has a sufficiently large He mass. The spectra show strong Ca lines throughout the first few weeks after the explosion. The explosion only yields <0.01 M_⊙of ^56Ni, resulting in a low-luminosity SN Ia-like lightcurve that resembles the Ca-rich transients within this broad class of objects, with a peak magnitude of M_\mathrmbol ≈-15.7 mag and a rather slow decline rate of ∆m_15^\mathrmbol≈1.5 mag. Both, its lightcurve-shape and spectral appearance, resemble the appearance of Ca-rich transients, suggesting such mergers as a possible progenitor scenario for this class of events.

Authors: Javier Morán-Fraile, Alexander Holas, Friedrich K Röpke, Rüdiger Pakmor, Fabian R N Schneider

Date Published: 4th Mar 2024

Publication Type: Journal

Abstract

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Authors: Masaomi Ono, Takaya Nozawa, Shigehiro Nagataki, Alexandra Kozyreva, Salvatore Orlando, Marco Miceli, Ke-Jung Chen

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract (Expand)

Interfacial engineering has fueled recent development of p-i-n perovskite solar cells (PSCs), with self-assembled monolayer-based hole-transport layers (SAM-HTLs) enabling almost lossless contacts for solution-processed PSCs, resulting in the highest achieved power conversion efficiency (PCE) to date. Substrate interfaces are particularly crucial for the growth and quality of co-evaporated PSCs. However, adoption of SAM-HTLs for co-evaporated perovskite absorbers is complicated by the underexplored interaction of such perovskites with phosphonic acid functional groups. In this work, we highlight how exposed phosphonic acid functional groups impact the initial phase and final bulk crystal structures of co-evaporated perovskites and their resultant PCE. The explored surface interaction is mediated by hydrogen bonding with interfacial iodine, leading to increased formamidinium iodide adsorption, persistent changes in perovskite structure, and stabilization of bulk α-FAPbI3, hypothesized as being due to kinetic trapping. Our results highlight the potential of exploiting substrates to increase control of co-evaporated perovskite growth.

Authors: Thomas Feeney, Julian Petry, Abderrezak Torche, Dirk Hauschild, Benjamin Hacene, Constantin Wansorra, Alexander Diercks, Michelle Ernst, Lothar Weinhardt, Clemens Heske, Ganna Gryn’ova, Ulrich W. Paetzold, Paul Fassl

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract (Expand)

There is strong observational evidence that the convective cores of intermediate-mass and massive main sequence stars are substantially larger than those predicted by standard stellar-evolution models. However, it is unclear what physical processes cause this phenomenon or how to predict the extent and stratification of stellar convective boundary layers. Convective penetration is a thermal-timescale process that is likely to be particularly relevant during the slow evolution on the main sequence. We use our low-Mach-number SEVEN-LEAGUE HYDRO code to study this process in 2.5D and 3D geometries. Starting with a chemically homogeneous model of a 15  M⊙ zero-age main sequence star, we construct a series of simulations with the luminosity increased and opacity decreased by the same factor, ranging from 10^3 to 10^6. After reaching thermal equilibrium, all of our models show a clear penetration layer; its thickness becomes statistically constant in time and it is shown to converge upon grid refinement. The penetration layer becomes nearly adiabatic with a steep transition to a radiative stratification in simulations at the lower end of our luminosity range. This structure corresponds to the adiabatic ‘step overshoot’ model often employed in stellar-evolution calculations. The simulations with the highest and lowest luminosity differ by less than a factor of two in the penetration distance. The high computational cost of 3D simulations makes our current 3D data set rather sparse. Depending on how we extrapolate the 3D data to the actual luminosity of the initial stellar model, we obtain penetration distances ranging from 0.09 to 0.44 pressure scale heights, which is broadly compatible with observations.

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

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract

Not specified

Authors: Nina Horat, Sebastian Lerch

Date Published: 1st Mar 2024

Publication Type: Journal

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