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Field Heterogeneities and Their Impact on Photocatalysis: Combining Optical and Kinetic Monte Carlo Simulations on the Nanoscale
No full textGaining insights into the working principles of photocatalysts on an atomic scale is a challenging task. The obviously high complexity of the reaction mechanism involving photo-excited electrons and holes is one reason. Another complicating aspect is that the electromagnetic field, driving photocatalysis, is not homogeneous on a nanoscale level for particle based catalysts as it is influenced by the particle’s shape and size.
We present a simple model, inspired by the CO2 reduction on titania anatase, which addresses the impact of these heterogeneities on the photocatalytic kinetics by combining kinetic Monte Carlo with electromagnetic wave simulations. We find that average activity and especially efficiency might differ significantly between different particles. Moreover, we find sizable variation of the catalytic activity on a single facet of a nanocrystal. Besides this quantitative heterogeneity, the coverage situation in general changes laterally on this facet and we observe a concomitant change of the rate-determining steps.
This heterogeneity on all levels of photocatalytic activity is masked in experimental studies, where only the spatially averaged activity can be addressed. Microkinetic models based on experimental findings might therefore not represent the true micro- scopic behavior, and mechanistic conclusion drawn from these need to be handled with care
Coarse-graining of non-reversible stochastic differential equations: quantitative results and connections to averaging
Get PDFThis work is concerned with model reduction of stochastic differential equations and builds on the idea of replacing drift and noise coefficients of preselected relevant, e.g. slow variables by their conditional expectations. We extend recent results by Legoll & Lelièvre [Nonlinearity 23, 2131, 2010] and Duong et al. [Nonlinearity 31, 4517, 2018] on effective reversible dynamics by conditional expectations to the setting of general non-reversible processes with non-constant diffusion coefficient. We prove relative entropy and Wasserstein error estimates for the difference between the time marginals of the effective and original dynamics as well as an entropy error bound for the corresponding path space measures. A comparison with the averaging principle for systems with time-scale separation reveals that, unlike in the reversible setting, the effective dynamics for a non-reversible system need not agree with the averaged equations. We present a thorough comparison for the Ornstein-Uhlenbeck process and make a conjecture about necessary and sufficient conditions for when averaged and effective dynamics agree for nonlinear non-reversible processes. The theoretical results are illustrated with suitable numerical examples
Vaquita-LR: A new bioinformatics tool for identifying structural variants using long and short reads. In: Abstracts from the 53rd European Society of Human Genetics (ESHG) Conference: Interactive e-Posters
No full textIntroduction:
The identification of structural variation in the genome is difficult due to the lack of a “perfect” sequencing technology. By combining short and long read sequencing data, Vaquita-LR is a novel bioinformatics approach to identify more variants with higher accuracy by utilizing the strengths of one method to overcome the weaknesses of the other.
Materials and Methods:
Vaquita-LR is an extension of Vaquita, a short read variant caller, which has been modified to identify potential variants in long reads in addition to short reads and merge them to provide a final set of variants. Vaquita-LR takes into consideration the read depth, read length, and other characteristics of the sequencing data to add weight to more confident calls. Additionally, it adapts techniques from Pilon to improve the accuracy of the long read data using relevant short read information.
Results:
By combining short and long reads, Vaquita-LR is able to outperform other structural variant callers, including meta callers which combine results from other callers. Importantly, supplying long reads is effective at sequencing depths as shallow as 1x. Using this combination allows Vaquita-LR to better filter out false positives while retaining true positives, providing a better list of possible causal variants for further investigation.
Conclusions:
With the abundance of data available today, it is important to consider how the data can be effectively merged to better serve our needs. Vaquita-LR is an initial step in showing the usefulness of integrating different sequencing data types when identifying structural variants
Model reduction of controlled Fokker-Planck and Liouville-von Neumann equations
No full textModel reduction methods for bilinear control systems are compared by means of practical examples of Liouville-von Neumann and Fokker-Planck type. Methods based on balancing generalized system Gramians and on minimizing an H2-type cost functional are considered. The focus is on the numerical implementation and a thorough comparison of the methods. Structure and stability preservation are investigated, and the competitiveness of the approaches is shown for practically relevant, large-scale examples
Empirical Analysis of Global-Scale Natural Data and Analogue Seismotectonic Modelling Data to Unravel the Seismic Behaviour of the Subduction Megathrust
Get PDFSubduction megathrusts host the Earth’s greatest earthquakes as the 1960 Valdivia (Mw9.5, Chile), the largest earthquake instrumentally recorded, and the recent 2004 Sumatra-Andaman (Mw9.2, Indonesia), 2010 Maule (Mw8.8, Chile), and 2011 Tohoku-Oki (Mw9.1,Japan) earthquakes triggering devastating tsunamis and representing a major hazard tosociety. Unravelling the spatio-temporal pattern of these events is thus a key for seismichazard assessment of subduction zones. This paper reviews the current state ofknowledge of two research areas–empirical analysis of global-scale natural data andexperimental data from an analogue seismotectonic modelling—devoted to study cause-effect relationships between subduction zone parameters and the megathrustseismogenic behavior. The combination of the two approaches overcomes theobservational bias and inherent sampling limitations of geological processes(i.e., shortness of instrumental and historical data, decreasing completeness andresolution with time into the past) and allows drawing appropriately from multipledisciplines with the aim of highlighting the geodynamic conditions that may favor theoccurrence of giant megathrust earthquakes
Particle-Continuum Coupling and its Scaling Regimes: Theory and Applications
No full textThis work is motivated by the goal of designing simulation software for technical devices that, at their functional core, rely on atomistic‐scale processes embedded in a larger‐scale fluid environment. The core of the problem is the conceptual and technical approach for coupling particle and continuum representations of a fluid. The state of the art for key aspects including physical modeling, mathematical formalization, computational implementation, and applications, is discussed and organized in a consistent picture across the relevant physical regimes
Portability of Scientific Workflows in NGS Data Analysis: A Case Study
No full textThe analysis of next-generation sequencing (NGS) data requires complex computational workflows consisting of dozens of autonomously developed yet interdependent processing steps. Whenever large amounts of data need to be processed, these workflows must be executed on a parallel and/or distributed systems to ensure reasonable runtime. Porting a workflow developed for a particular system on a particular hardware infrastructure to another system or to another infrastructure is non-trivial, which poses a major impediment to the scientific necessities of workflow reproducibility and workflow reusability. In this work, we describe our efforts to port a state-of-the-art workflow for the detection of specific variants in whole-exome sequencing of mice. The workflow originally was developed in the scientific workflow system snakemake for execution on a high-performance cluster controlled by Sun Grid Engine. In the project, we ported it to the scientific workflow system SaasFee that can execute workflows on (multi-core) stand-alone servers or on clusters of arbitrary sizes using the Hadoop. The purpose of this port was that also owners of low-cost hardware infrastructures, for which Hadoop was made for, become able to use the workflow. Although both the source and the target system are called scientific workflow systems, they differ in numerous aspects, ranging from the workflow languages to the scheduling mechanisms and the file access interfaces. These differences resulted in various problems, some expected and more unexpected, that had to be resolved before the workflow could be run with equal semantics. As a side-effect, we also report cost/runtime ratios for a state-of-the-art NGS workflow on very different hardware platforms: A comparably cheap stand-alone server (80 threads), a mid-cost, mid-sized cluster (552 threads), and a high-end HPC system (3784 threads)
Statistical models to capture protein-RNA interaction footprints from truncation-based CLIP-seq data
No full textProtein-RNA interactions play an important role in all post-transcriptional regulatory processes. High throughput detection of protein-RNA interactions has been facilitated by the emerging CLIP-seq (crosslinking and immunoprecipitation combined with high-throughput sequencing) techniques. Enrichments in mapped reads as well as base transitions or deletions at crosslink sites can be used to infer binding regions. Single-nucleotide resolution techniques (iCLIP and eCLIP) have been achieved by capturing high fractions of cDNAs which are truncated at protein-RNA crosslink sites. Increasing numbers of datasets and derivatives of these protocols have been published in recent years, requiring tailored computational analyses. Existing methods unfortunately do not explicitly model the specifics of truncation patterns and possible biases caused by background binding or crosslinking sequence preferences. We present PureCLIP, a hidden Markov model based approach, which simultaneously performs peak calling and individual crosslink site detection. It is capable of incorporating external data to correct for non-specific background signals and, for the first time, for the crosslinking biases. We devised a comprehensive evaluation based on three strategies. Firstly, we developed a workflow to simulate iCLIP data, which starts from real RNA-seq data and known binding regions and then mimics the experimental steps of the iCLIP protocol, including the generation of background signals. Secondly, we used experimental iCLIP and eCLIP datasets, using the proteins’ known predominant binding regions. And thirdly, we assessed the agreement of called sites between replicates, assuming target-specific signals are reproducible between replicates. On both simulated and real data, PureCLIP is consistently more precise in calling crosslink sites than other state-of-the-art methods. In particular when incorporating input control data and crosslink associated motifs (CL-motifs) PureCLIP is up to 13% more precise than other methods and we show that it has an up to 20% higher agreement across replicates. Moreover, our method can optionally merge called crosslink sites to binding regions based on their distance and we show that the resulting regions reflect the known binding regions with high-resolution. Additionally, we demonstrate that our method achieves a high precision robustly over a range of different settings and performs well for proteins with different binding characteristics. Lastly, we extended the method to include individual CLIP replicates and show that this can boost the precision even further. PureCLIP and its documenta- tion are publicly available at https://github.com/skrakau/PureCLIP
Development of 3-D Rift Heterogeneity Through Fault Network Evolution
No full textAbstract
Observations of rift and rifted margin architecture suggest that significant spatial and temporal structural heterogeneity develops during the multiphase evolution of continental rifting. Inheritance is often invoked to explain this heterogeneity, such as preexisting anisotropies in rock composition, rheology, and deformation. Here, we use high-resolution 3-D thermal-mechanical numerical models of continental extension to demonstrate that rift-parallel heterogeneity may develop solely through fault network evolution during the transition from distributed to localized deformation. In our models, the initial phase of distributed normal faulting is seeded through randomized initial strength perturbations in an otherwise laterally homogeneous lithosphere extending at a constant rate. Continued extension localizes deformation onto lithosphere-scale faults, which are laterally offset by tens of km and discontinuous along-strike. These results demonstrate that rift- and margin-parallel heterogeneity of large-scale fault patterns may in-part be a natural byproduct of fault network coalescence
Molecular interpretation of the non-Newtonian viscoelastic behavior of liquid water at high frequencies
No full textUsing classical as well as ab initio molecular dynamics simulations, we calculate the frequency-dependent shear viscosity of pure water and water–glycerol mixtures. In agreement with recent experiments, we find deviations from Newtonian-fluid behavior in the THz regime. Based on an extension of the Maxwell model, we introduce a viscoelastic model to describe the observed viscosity spectrum of pure water. We find four relaxation modes in the spectrum which we attribute to (i) hydrogen–bond network topology changes, (ii) hydrogen–bond stretch vibrations of water pairs, (iii) collective vibrations of water molecule triplets, and (iv) librational excitations of individual water molecules. Our model quantitatively describes the viscoelastic response of liquid water on short timescales, where the hydrodynamic description via a Newtonian-fluid model breaks down