Amiloride is an ineffective conditioned stimulus in taste aversion learning in C57BL/6J and DBA/2J mice

The epithelial sodium channel (ENaC) blocker amiloride has been shown to increase the behaviorally measured NaCl detection threshold in mice. In this study, a conditioned taste aversion (CTA) paradigm was used to examine whether 100 microM amiloride has a perceptible taste that could contribute to this observed decrease in behavioral responsiveness. Eighty-four C57BL/6J (B6) and 64 DBA/2J (D2) mice were divided into eight groups (n=8-12 per group), in which half received an injection of 0.15 M LiCl (2 mEq/kg) and the other half an equivalent saline injection, in three conditioning trials. The four conditioned stimuli were 100 microM amiloride hydrochloride, water, 0.1 and 0.3 M NaCl. Neither strain demonstrated acquisition of a CTA to amiloride in a brief-access (BA) taste test (5 s trials in the gustometer). Although 0.3 M NaCl is inherently aversive, its pairing with LiCl led to significantly further decreases in licking during the BA test on salt trials in both strains. The D2 strain clearly avoided 0.1 M NaCl, whereas avoidance of this stimulus was more equivocal in B6 mice. The inefficacy of amiloride to serve as a conditioned stimulus in taste aversion learning involving three LiCl pairings suggests that the effects of this ENaC blocker on taste-related behavioral responses to NaCl are likely due to its pharmacological interference with sodium taste transduction.     

Precise periodic components estimation for chronobiological signals through Bayesian Inference with sparsity enforcing prior

The toxicity and efficacy of more than 30 anticancer agents present very high variations, depending on the dosing time. Therefore, the biologists studying the circadian rhythm require a very precise method for estimating the periodic component (PC) vector of chronobiological signals. Moreover, in recent developments, not only the dominant period or the PC vector present a crucial interest but also their stability or variability. In cancer treatment experiments, the recorded signals corresponding to different phases of treatment are short, from 7 days for the synchronization segment to 2 or 3 days for the after-treatment segment. When studying the stability of the dominant period, we have to consider very short length signals relative to the prior knowledge of the dominant period, placed in the circadian domain. The classical approaches, based on Fourier transform (FT) methods are inefficient (i.e., lack of precision) considering the particularities of the data (i.e., the short length). Another particularity of the signals considered in such experiments is the level of noise: such signals are very noisy and establishing the periodic components that are associated with the biological phenomena and distinguishing them from the ones associated with the noise are difficult tasks. In this paper, we propose a new method for the estimation of the PC vector of biomedical signals, using the biological prior informations and considering a model that accounts for the noise. The experiments developed in cancer treatment context are recording signals expressing a limited number of periods. This is a prior information that can be translated as the sparsity of the PC vector. The proposed method considers the PC vector estimation as an Inverse Problem (IP) using the general Bayesian inference in order to infer the unknown of our model, i.e. the PC vector but also the hyperparameters (i.e the variances). The sparsity prior information is modeled using a sparsity enforcing prior law. In this paper, we propose a Student’s t distribution, viewed as the marginal distribution of a bivariate normal-inverse gamma distribution. We build a general infinite Gaussian scale mixture (IGSM) hierarchical model where we assign prior distributions also for the hyperparameters. The expression of the joint posterior law of the unknown PC vector and hyperparameters is obtained via Bayes rule, and then, the unknowns are estimated via joint maximum a posteriori (JMAP) or posterior mean (PM). For the PM estimator, the expression of the posterior distribution is approximated by a separable one, via variational Bayesian approximation (VBA), using the Kullback-Leibler (KL) divergence. For the PM estimation, two possibilities are considered: an approximation with a partially separable distribution and an approximation with a fully separable one. Both resulting algorithms corresponding to the PM estimation and the one corresponding to the JMAP estimation are iterative algorithms. The algorithms are presented in detail and are compared with the ones corresponding to the Gaussian model. We examine the convergency of the algorithms and give simulation results to compare their performances. Finally, we show simulation results on synthetic and real data in cancer treatment applications. The real data considered in this paper examines the rest-activity patterns of KI/KI Per2::luc mouse, aged 10 weeks, singly housed in RealTime Biolumicorder (RT-BIO).     

Science vs Conspiracy: Collective Narratives in the Age of Misinformation

The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.     

MicroDSC study of Staphylococcus epidermidis growth

Background  

A microcalorimetric study was carried out using a Staphylococcus epidermidis population to determine the reproducibility of bacterial growth and the variability of the results within certain experimental parameters (temperature, bacterial concentration, sample thermal history). Reproducibility tests were performed as series of experiments within the same conditions using either freshly prepared populations or samples kept in cold storage. In both cases, the samples were obtained by serial dilution from a concentrated TSB bacterial inoculum incubated overnight.