Automatic Algorithm Selection for Complex Simulation Problems

To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. The thesis consists of three parts. The first part surveys existing approaches to solve the algorithm selection problem and discusses techniques to analyze simulation algorithm performance.The second part introduces a software framework for automatic simulation algorithm selection, which is evaluated in the third part.Die Auswahl des passendsten Simulationsalgorithmus für eine bestimmte Aufgabe ist oftmals schwierig. Dies liegt an der komplexen Interaktion zwischen Modelleigenschaften, Implementierungsdetails und Laufzeitumgebung. Die Arbeit ist in drei Teile gegliedert. Der erste Teil befasst sich eingehend mit Vorarbeiten zur automatischen Algorithmenauswahl, sowie mit der Leistungsanalyse von Simulationsalgorithmen. Der zweite Teil der Arbeit stellt ein Rahmenwerk zur automatischen Auswahl von Simulationsalgorithmen vor, welches dann im dritten Teil evaluiert wird

Visualizing Mortality Dynamics in the Lexis Diagram

The goal of this book is simple: We would like to show how mortality dynamics can be visualized in the so-called Lexis diagram. To appeal to as many potential readers as possible, we do not require any specialist knowledge. This approach may be disappointing: Demographers may have liked more information about the mathematical underpinnings of population dynamics on the Lexis surface as demonstrated, for instance, by Arthur and Vaupel in 1984. Statisticians would have probably preferred more information about the underlying smoothing methods that were used. Epidemiologists likewise might miss discussions about the etiology of diseases. Sociologists would have probably expected that our results were more embedded into theoretical frameworks...

Sex differences in the functional connectivity of the amygdalae in association with cortisol

Human amygdalae are involved in various behavioral functions such as affective and stress processing. For these behavioral functions, as well as for psychophysiological arousal including cortisol release, sex differences are reported.Here, we assessed cortisol levels and resting-state functional connectivity (rsFC) of left and right amygdalae in 81 healthy participants (42 women) to investigate potential modulation of amygdala rsFC by sex and cortisol concentration.Our analyses revealed that rsFC of the left amygdala significantly differed between women and men: Women showed stronger rsFC than men between the left amygdala and left middle temporal gyrus, inferior frontal gyrus, postcentral gyrus and hippocampus, regions involved in face processing, inner-speech, fear and pain processing. No stronger connections were detected for men and no sex difference emerged for right amygdala rsFC. Also, an interaction of sex and cortisol appeared: In women, cortisol was negatively associated with rsFC of the amygdalae with striatal regions, mid-orbital frontal gyrus, anterior cingulate gyrus, middle and superior frontal gyri, supplementary motor area and the parietal–occipital sulcus. Contrarily in men, positive associations of cortisol with rsFC of the left amygdala and these structures were observed. Functional decoding analyses revealed an association of the amygdalae and these regions with emotion, reward and memory processing, as well as action execution.Our results suggest that functional connectivity of the amygdalae as well as the regulatory effect of cortisol on brain networks differs between women and men. These sex-differences and the mediating and sex-dependent effect of cortisol on brain communication systems should be taken into account in affective and stress-related neuroimaging research. Thus, more studies including both sexes are required

A FLEXIBLE AND SCALABLE EXPERIMENTATION LAYER

Modeling and simulation frameworks for use in different application domains, throughout the complete development process, and in different hardware environments need to be highly scalable. For achieving an efficient execution, different simulation algorithms and data structures must be provided to compute a concrete model on a concrete platform efficiently. The support of parallel simulation techniques becomes increasingly important in this context, which is due to the growing availability of multi-core processors and network-based computers. This leads to more complex simulation systems that are harder to configure correctly. We present an experimentation layer for the modeling and simulation framework JAMES II. It greatly facilitates the configuration and usage of the system for a user and supports distributed optimization, on-demand observation, and various distributed and non-distributed scenarios.

Stochastic Simulation of Process Calculi for Biology

Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

Highly sensitive liquid biopsy Duplex sequencing complements tissue biopsy to enhance detection of clinically relevant genetic variants

BackgroundLiquid biopsy (LB) is a promising complement to tissue biopsy for detection of clinically relevant genetic variants in cancer and mosaic diseases. A combined workflow to enable parallel tissue and LB analysis is required to maximize diagnostic yield for patients.MethodsWe developed and validated a cost-efficient combined next-generation sequencing (NGS) workflow for both tissue and LB samples, and applied Duplex sequencing technology for highly accurate detection of low frequency variants in plasma. Clinically relevant cutoffs for variant reporting and quantification were established.ResultsWe investigated assay performance characteristics for very low amounts of clinically relevant variants. In plasma, the assay achieved 100% sensitivity and 92.3% positive predictive value (PPV) for single nucleotide variants (SNVs) and 91.7% sensitivity and 100% PPV for insertions and deletions (InDel) in clinically relevant hotspots with 0.5-5% variant allele frequencies (VAFs). We further established a cutoff for reporting variants (i.e. Limit of Blank, LOB) at 0.25% VAF and a cutoff for quantification (i.e. Limit of Quantification, LOQ) at 5% VAF in plasma for accurate clinical interpretation of analysis results. With our LB approach, we were able to identify the molecular cause of a clinically confirmed asymmetric overgrowth syndrome in a 10-year old child that would have remained undetected with tissue analysis as well as other molecular diagnostic approaches.ConclusionOur flexible and cost-efficient workflow allows analysis of both tissue and LB samples and provides clinically relevant cutoffs for variant reporting and precise quantification. Complementing tissue analysis by LB is likely to increase diagnostic yield for patients with molecular diseases

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Probabilistic mortality forecasting with varying age-specific survival improvements

Abstract Many mortality forecasting approaches extrapolate past trends. Their predictions of the future development can be quite precise as long as turning points and/or age-shifts of mortality decline are not present. To account even for such mortality dynamics, we propose a model that combines recently developed ideas in a single framework. It (1) uses rates of mortality improvement to model the aging of mortality decline, and it (2) optionally combines the mortality trends of multiple countries to catch anticipated turning points. We use simulation-based Bayesian inference to estimate and run this model that also provides prediction intervals to quantify forecast uncertainty. Validating mortality forecasts for British and Danish women from 1991 to 2011 suggest that our model can forecast regular and irregular mortality developments and that it can perform at least as well as other widely accepted approaches like, for instance, the Lee-Carter model or the UN Bayesian approach. Moreover, prospective mortality forecasts from 2012 to 2050 suggest gradual increases for British and Danish life expectancy at birth