Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks

The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is hence governed by two parameters: (i) The type of test, and (ii) its significance level. These parameters are usually set to values recommended by an expert. Nevertheless, such an approach can suffer from human bias, leading to suboptimal reconstruction results. In this paper we consider a more principled approach for choosing these parameters in an automatic way. For this we optimize a reconstruction score evaluated on a set of different Gaussian Bayesian networks. This objective is expensive to evaluate and lacks a closed-form expression, which means that Bayesian optimization (BO) is a natural choice. BO methods use a model to guide the search and are hence able to exploit smoothness properties of the objective surface. We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation. Importantly, we have found that an often overlooked statistical test provides the best over-all reconstruction results

Ship wave patterns on floating ice sheets

This paper aims to explore the response of a floating icesheet to a load moving in a curved path. We investigate the effect of turning on the wave patterns and strain distribution, and explore scenarios where turning increases the wave amplitude and strain in the ice, possibly leading to crack formation, fracturing and eventual ice failure. The mathematical model used here is the linearized system of differential equations introduced in Dinvay et al. (J. Fluid Mech. 876:122–149, 2019). The equations are solved using the Fourier transform in space, and the Laplace transform in time. The model is tested against existing results for comparison, and several cases of load trajectories involving turning and decelerating are tested

Alcohol exposure impairs trophoblast survival and alters subtype-specific gene expression in vitro

Maternal alcohol consumption is common prior to pregnancy recognition and in the rat results in altered placental development and fetal growth restriction. To assess the effect of ethanol (EtOH) exposure on the differentiation of trophoblast stem (TS) cells, mouse TS lines were differentiated in vitro for 6 days in 0%, 0.2% or 1% EtOH. This reduced both trophoblast survival and expression of labyrinth and junctional zone trophoblast subtype-specific genes. This suggests that fetal growth restriction and altered placental development associated with maternal alcohol consumption in the periconceptional period could be mediated in part by direct effects on trophoblast development. (C) 2016 Elsevier Ltd. All rights reserved

Uniform random generation of large acyclic digraphs

Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of the structure itself is of great interest. As well as for the assessment of different structure learning algorithms in simulation studies, a uniform sample from the space of directed acyclic graphs is required to evaluate the prevalence of certain structural features. Here we analyse how to sample acyclic digraphs uniformly at random through recursive enumeration, an approach previously thought too computationally involved. Based on complexity considerations, we discuss in particular how the enumeration directly provides an exact method, which avoids the convergence issues of the alternative Markov chain methods and is actually computationally much faster. The limiting behaviour of the distribution of acyclic digraphs then allows us to sample arbitrarily large graphs. Building on the ideas of recursive enumeration based sampling we also introduce a novel hybrid Markov chain with much faster convergence than current alternatives while still being easy to adapt to various restrictions. Finally we discuss how to include such restrictions in the combinatorial enumeration and the new hybrid Markov chain method for efficient uniform sampling of the corresponding graphs.Comment: 15 pages, 2 figures. To appear in Statistics and Computin

Corticosteroid status influences the volume of the rat cingulate cortex: a magnetic resonance imaging study

Imbalances in the corticosteroid milieu result in reductions in hippocampal volume in humans and experimental rodents. The functional correlates of these changes include deficits in cognitive performance and regulation of the hypothalamic–pituitary–adrenal axis. Since other limbic structures which are intricately connected with the hippocampal formation, also play an important role in behavioural and neuroendocrine functions, we here used magnetic resonance imaging (MRI) to analyse how two of these areas, the anterior cingulate and retrosplenial cortex, respond to chronic alterations of adrenocortical status: hypocortisolism (induced by adrenalectomy, ADX), normocortisolism (ADX with low-dose corticosterone replacement), and hypercortisolism (ADX with high-dose dexamethasone supplementation). Hypercortisolism was associated with a significant reduction in the volume (absolute and normalized) of the left anterior cingulate gyrus as measured by MRI and confirmed using classical histological methods; a similar trend was observed in the right anterior cingulate region. In contrast, hypercortisolism did not influence the volume of the adjacent retrosplenial cortex. The volumes of the anterior cingulate gyrus and retrosplenial cortex were unaffected by the absence of adrenocortical hormones. These findings are the first to suggest that corticosteroid influences on the structure of the limbic system extend beyond the hippocampal formation, i.e., to fronto-limbic areas also

Fully Dispersive Models for Moving Loads on Ice Sheets

The response of a floating elastic plate to the motion of a moving load is studied using a fully dispersive weakly nonlinear system of equations. The system allows for accurate description of waves across the whole spectrum of wavelengths and also incorporates nonlinearity, forcing and damping. The flexural-gravity waves described by the system are time-dependent responses to a forcing with a described weight distribution, moving at a time-dependent velocity. The model is versatile enough to allow the study of a wide range of situations including the motion of a combination of point loads and loads of arbitrary shape. Numerical solutions of the system are compared to data from a number of field campaigns on ice-covered lakes, and good agreement between the deflectometer records and the numerical simulations is observed in most cases. Consideration is also given to waves generated by an accelerating or decelerating load, and it is shown that a decelerating load may trigger a wave response with a far greater amplitude than a load moving at constant celerity

Understanding human functioning using graphical models

<p>Abstract</p> <p>Background</p> <p>Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disability and Health (ICF) on the one hand and recent developments in graphical modeling on the other hand might be combined and open the door to a more comprehensive understanding of human functioning. The objective of our paper therefore is to explore how graphical models can be used in the study of ICF data for a range of applications.</p> <p>Methods</p> <p>We show the applicability of graphical models on ICF data for different tasks: Visualization of the dependence structure of the data set, dimension reduction and comparison of subpopulations. Moreover, we further developed and applied recent findings in causal inference using graphical models to estimate bounds on intervention effects in an observational study with many variables and without knowing the underlying causal structure.</p> <p>Results</p> <p>In each field, graphical models could be applied giving results of high face-validity. In particular, graphical models could be used for visualization of functioning in patients with spinal cord injury. The resulting graph consisted of several connected components which can be used for dimension reduction. Moreover, we found that the differences in the dependence structures between subpopulations were relevant and could be systematically analyzed using graphical models. Finally, when estimating bounds on causal effects of ICF categories on general health perceptions among patients with chronic health conditions, we found that the five ICF categories that showed the strongest effect were plausible.</p> <p>Conclusions</p> <p>Graphical Models are a flexible tool and lend themselves for a wide range of applications. In particular, studies involving ICF data seem to be suited for analysis using graphical models.</p

p-Adic Mathematical Physics

A brief review of some selected topics in p-adic mathematical physics is presented.Comment: 36 page

Insights into the Role of a Cardiomyopathy-Causing Genetic Variant in ACTN2

Pathogenic variants in ACTN2, coding for alpha-actinin 2, are known to be rare causes of Hyper-trophic Cardiomyopathy. However, little is known about the underlying disease mechanisms. Adult heterozygous mice carrying the Actn2 p.Met228Thr variant were phenotyped by echocar-diography. For homozygous mice, viable E15.5 embryonic hearts were analysed by High Reso-lution Episcopic Microscopy and wholemount staining, complemented by unbiased proteomics, qPCR and Western blotting. Heterozygous Actn2 p.Met228Thr mice have no overt phenotype. Only mature males show molecular parameters indicative of cardiomyopathy. By contrast, the variant is embryonically lethal in the homozygous setting and E15.5 hearts show multiple morphological abnormalities. Molecular analyses, including unbiased proteomics, identified quantitative abnormalities in sarcomeric parameters, cell cycle defects and mitochondrial dys-function. The mutant alpha-actinin protein is found to be destabilised, associated with increased activity of the ubiquitin-proteosomal system. This missense variant in alpha-actinin renders the protein less stable. In response, the ubiquitin-proteosomal system is activated; a mechanism which has been implicated in cardiomyopathies previously. In parallel, lack of functional al-pha-actinin is thought to cause energetic defects through mitochondrial dysfunction. This seems, together with cell cycle defects, the likely cause of death of the embryos. The defects also have wide-ranging morphological consequences