Fear expression and return of fear following threat instruction with or without direct contingency experience

Prior research showed that mere instructions about the contingency between a conditioned stimulus (CS) and an unconditioned stimulus (US) can generate fear reactions to the CS. Little is known, however, about the extent to which actual CS US contingency experience adds anything beyond the effect of contingency instructions. Our results extend previous studies on this topic in that it included fear potentiated startle as an additional dependent variable and examined return of fear (ROF) following reinstatement. We observed that CS US pairings can enhance fear reactions beyond the effect of contingency instructions. Moreover, for all measures of fear, instructions elicited immediate fear reactions that could not be completely overridden by subsequent situational safety information. Finally, ROF following reinstatement for instructed CS+s was unaffected by actual experience. In summary, our results demonstrate the power of contingency instructions and reveal the additional impact of actual experience of CS US pairings

Transverse instability of gravity–capillary solitary waves on deep water in the presence of constant vorticity

International audienc

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

On Discrimination Discovery and Removal in Ranked Data using Causal Graph

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.Comment: 9 page

Remark on a paper of Kalisch

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32200/1/0000259.pd

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

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

Membrane-transferring regions of gp41 as targets for HIV-1 fusion inhibition and viral neutralization

12 páginas, 4 figurasThe fusogenic function of HIV-1 gp41 transmembrane Env subunit relies on two different kinds of structural elements: i) a collapsible ectodomain structure (the hairpin or six-helix bundle) that opens and closes, and ii) two membrane- transferring regions (MTRs), the fusion peptide (FP) and the membrane-proximal external region (MPER), which ensure coupling of hairpin closure to apposition and fusion of cell and viral membranes. The isolation of naturally produced short peptides and neutralizing IgG-s, that interact with FP and MPER, respectively, and block viral infection, suggests that these conserved regions might represent useful targets for clinical intervention. Furthermore, MTR-derived peptides have been shown to be membrane-active. Here, it is discussed the potential use of these molecules and how the analysis of their membrane activity in vitro could contribute to the development of HIV fusion inhibitors and effective immunogensThe authors wish to thank financial support obtained from Spanish MICINN (BIO2008- 00772) (JLN) and University of the Basque Country (GIU 06/42 and DIPE08/12) (NH and JLN).Peer reviewe