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This paper explores the many interesting implications for oscillator design, with optimized phase-noise performance, deriving from a newly proposed model based on the concept of oscillator conjugacy. For the case of 2-D (planar) oscillators, the model prominently predicts that only circuits producing a perfectly symmetric steady-state can have zero amplitude-to-phase (AM-PM) noise conversion, a so-called zero-state. Simulations on standard industry oscillator circuits verify all model predictions and, however, also show that these circuit classes cannot attain zero-states except in special limit-cases which are not practically relevant. Guided by the newly acquired design rules, we describe the synthesis of a novel 2-D reduced-order LC oscillator circuit which achieves several zero-states while operating at realistic output power levels. The potential future application of this developed theoretical framework for implementation of numerical algorithms aimed at optimizing oscillator phase-noise performance is briefly discussed.
Did the Federal Reserves’ Quantitative Easing (QE) in the aftermath of the financial crisis have macroeconomic effects? To answer this question, the authors estimate a large-scale DSGE model over the sample from 1998 to 2020, including data of the Fed’s balance sheet. The authors allow for QE to affect the economy via multiple channels that arise from several financial frictions. Their nonlinear Bayesian likelihood approach fully accounts for the zero lower bound on nominal interest rates. They find that between 2009 to 2015, QE increased output by about 1.2 percent. This reflects a net increase in investment of nearly 9 percent, that was accompanied by a 0.7 percent drop in aggregate consumption. Both, government bond and capital asset purchases were effective in improving financing conditions. Especially capital asset purchases significantly facilitated new investment and increased the production capacity. Against the backdrop of a fall in consumption, supply side effects dominated which led to a mild disinflationary effect of about 0.25 percent annually.
Nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) is an indolent lymphoma, but can transform into diffuse large B cell lymphoma (DLBCL), showing a more aggressive clinical behavior. Little is known about these cases on the molecular level. Therefore, the aim of the present study was to characterize DLBCL transformed from NLPHL (LP-DLBCL) by gene expression profiling (GEP). GEP revealed an inflammatory signature pinpointing to a specific host response. In a coculture model resembling this host response, DEV tumor cells showed an impaired growth behavior. Mechanisms involved in the reduced tumor cell proliferation included a downregulation of MYC and its target genes. Lack of MYC expression was also confirmed in 12/16 LP-DLBCL by immunohistochemistry. Furthermore, CD274/PD-L1 was upregulated in DEV tumor cells after coculture with T cells or monocytes and its expression was validated in 12/19 cases of LP-DLBCL. Thereby, our data provide new insights into the pathogenesis of LP-DLBCL and an explanation for the relatively low tumor cell content. Moreover, the findings suggest that treatment of these patients with immune checkpoint inhibitors may enhance an already ongoing host response in these patients.
Therapy evasion – and subsequent disease progression – is a major challenge in current oncology. An important role in this context seems to be played by various forms of cancer cell dormancy. For example, therapy-induced dormancy, over short timescales, can create serious obstacles to aggressive treatment approaches such as chemotherapy, and long-term dormancy may lead to relapses and metastases even many years after an initially successful treatment. The underlying dormancy-related mechanisms are complex and highly diverse, so that the analysis even of basic patterns of the population-level consequences of dormancy requires abstraction and idealization, as well as the identification of the relevant specific scenarios.
In this paper, we focus on a situation in which individual cancer cells may switch into and out of a dormant state both spontaneously as well as in response to treatment, and over relatively short time-spans. We introduce a mathematical ‘toy model’, based on stochastic agent-based interactions, for the dynamics of cancer cell populations involving individual short-term dormancy, and allow for a range of (multi-drug) therapy protocols. Our analysis shows that in our idealized model, even a small initial population of dormant cells can lead to therapy failure under classical (and in the absence of dormancy successful) single-drug treatments. We further investigate the effectiveness of several multidrug regimes (manipulating dormant cancer cells in specific ways) and provide some basic rules for the design of (multi-)drug treatment protocols depending on the types and parameters of dormancy mechanisms present in the population.
A stochastic model for the joint evaluation of burstiness and regularity in oscillatory spike trains
(2013)
The thesis provides a stochastic model to quantify and classify neuronal firing patterns of oscillatory spike trains. A spike train is a finite sequence of time points at which a neuron has an electric discharge (spike) which is recorded over a finite time interval. In this work, these spike times are analyzed regarding special firing patterns like the presence or absence of oscillatory activity and clusters (so called bursts). These bursts do not have a clear and unique definition in the literature. They are often fired in response to behaviorally relevant stimuli, e.g., an unexpected reward or a novel stimulus, but may also appear spontaneously. Oscillatory activity has been found to be related to complex information processing such as feature binding or figure ground segregation in the visual cortex. Thus, in the context of neurophysiology, it is important to quantify and classify these firing patterns and their change under certain experimental conditions like pharmacological treatment or genetical manipulation. In neuroscientific practice, the classification is often done by visual inspection criteria without giving reproducible results. Furthermore, descriptive methods are used for the quantification of spike trains without relating the extracted measures to properties of the underlying processes.
For that reason, a doubly stochastic point process model is proposed and termed 'Gaussian Locking to a free Oscillator' - GLO. The model has been developed on the basis of empirical observations in dopaminergic neurons and in cooperation with neurophysiologists. The GLO model uses as a first stage an unobservable oscillatory background rhythm which is represented by a stationary random walk whose increments are normally distributed. Two different model types are used to describe single spike firing or clusters of spikes. For both model types, the distribution of the random number of spikes per beat has different probability distributions (Bernoulli in the single spike case or Poisson in the cluster case). In the second stage, the random spike times are placed around their birth beat according to a normal distribution. These spike times represent the observed point process which has five easily interpretable parameters to describe the regularity and the burstiness of the firing patterns.
It turns out that the point process is stationary, simple and ergodic. It can be characterized as a cluster process and for the bursty firing mode as a Cox process. Furthermore, the distribution of the waiting times between spikes can be derived for some parameter combination. The conditional intensity function of the point process is derived which is also called autocorrelation function (ACF) in the neuroscience literature. This function arises by conditioning on a spike at time zero and measures the intensity of spikes x time units later. The autocorrelation histogram (ACH) is an estimate for the ACF. The parameters of the GLO are estimated by fitting the ACF to the ACH with a nonlinear least squares algorithm. This is a common procedure in neuroscientific practice and has the advantage that the GLO ACF can be computed for all parameter combinations and that its properties are closely related to the burstiness and regularity of the process. The precision of estimation is investigated for different scenarios using Monte-Carlo simulations and bootstrap methods.
The GLO provides the neuroscientist with objective and reproducible classification rules for the firing patterns on the basis of the model ACF. These rules are inspired by visual inspection criteria often used in neuroscientific practice and thus support and complement usual analysis of empirical spike trains. When applied to a sample data set, the model is able to detect significant changes in the regularity and burst behavior of the cells and provides confidence intervals for the parameter estimates.
A stochastic forward-looking model to assess the profitability and solvency of european insurers
(2016)
In this paper, we develop an analytical framework for conducting forward-looking assessments of profitability and solvency of the main euro area insurance sectors. We model the balance sheet of an insurance company encompassing both life and non-life business and we calibrate it using country level data to make it representative of the major euro area insurance markets. Then, we project this representative balance sheet forward under stochastic capital markets, stochastic mortality developments and stochastic claims. The model highlights the potential threats to insurers solvency and profitability stemming from a sustained period of low interest rates particularly in those markets which are largely exposed to reinvestment risks due to the relatively high guarantees and generous profit participation schemes. The model also proves how the resilience of insurers to adverse financial developments heavily depends on the diversification of their business mix. Finally, the model identifies potential negative spillovers between life and non-life business thorugh the redistribution of capital within groups.
A stochastic forward-looking model to assess the profitability and solvency of European insurers
(2016)
In this paper, we develop an analytical framework for conducting forward-looking assessments of profitability and solvency of the main euro area insurance sectors. We model the balance sheet of an insurance company encompassing both life and non-life business and we calibrate it using country level data to make it representative of the major euro area insurance markets. Then, we project this representative balance sheet forward under stochastic capital markets, stochastic mortality developments and stochastic claims. The model highlights the potential threats to insurers solvency and profitability stemming from a sustained period of low interest rates particularly in those markets which are largely exposed to reinvestment risks due to the relatively high guarantees and generous profit participation schemes. The model also proves how the resilience of insurers to adverse financial developments heavily depends on the diversification of their business mix. Finally, the model identifies potential negative spillovers between life and non-life business thorugh the redistribution of capital within groups.
Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data.
Results We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes.
Conclusions Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.
Highlights
• Single nucleotide variants (SNVs) may affect transcription factor (TF) binding
• Fast statistical approach to assess significance of differential TF binding for SNVs
• Validate new approach on in vitro and in vivo TF binding assays
• Applications on GWAS SNVs and large eQTL studies illustrate utility
Summary
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.