Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling

Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach can be used for latent variable estimation through an undirected graphical models. For instance, non-parametric artificial neural networks. In this study, we explore the use of generative non-parametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation

IREEL: remote experimentation with real protocols and applications over emulated network

This paper presents a novel e-learning platform called IREEL. IREEL is a virtual laboratory allowing students to drive experiments with real Internet applications and end-to-end protocols in the context of networking courses. This platform consists in a remote network emulator offering a set of predefined applications and protocol mechanisms. Experimenters configure and control the emulation and the end-systems behavior in order to perform tests, measurements and observations on protocols or applications operating under controlled specific networking conditions. A set of end-to-end mechanisms, mainly focusing on transport and application level protocols, are currently available. IREEL is scalable and easy to use thanks to an ergonomic web interface

Electrochemically switchable platform for the micro-patterning and release of heterotypic cell sheets

This article describes a dynamic platform in which the biointerfacial properties of micro-patterned domains can be switched electrochemically through the spatio-temporally controlled dissolution and adsorption of polyelectrolyte coatings. Insulating SU-8 micro-patterns created on a transparent indium tin oxide electrode by photolithography allowed for the local control over the electrochemical dissolution of polyelectrolyte mono- and multilayers, with polyelectrolytes shielded from the electrochemical treatment by the underlying photoresist stencil. The platform allowed for the creation of micro-patterned cell co-cultures through the electrochemical removal of a non-fouling polyelectrolyte coating and the localized adsorption of a cell adhesive one after attachment of the first cell population. In addition, the use of weak adhesive polyelectrolyte coatings on the photoresist domains allowed for the detachment of a contiguous heterotypic cell sheet upon electrochemical trigger. Cells grown on the ITO domains peeled off upon electrochemical dissolution of the sacrificial polyelectrolyte substrate, whereas adjacent cell areas on the insulated weakly adhesive substrate easily detached through the contractile force generated by neighboring cells. This electrochemical strategy for the micro-patterning and detachment of heterotypic cell sheets combines simplicity, precision and versatility, and presents great prospects for the creation of cellular constructs which mimic the cellular complexity of native tissue

Experimental Infection of Squirrel Monkeys with Nipah Virus

We infected squirrel monkeys (Saimiri sciureus) with Nipah virus to determine the monkeys’ suitability for use as primate models in preclinical testing of preventive and therapeutic treatments. Infection of squirrel monkeys through intravenous injection was followed by high death rates associated with acute neurologic and respiratory illness and viral RNA and antigen production

Classifying DME vs Normal SD-OCT volumes: A review

International audienceThis article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison

Lethal Nipah Virus Infection Induces Rapid Overexpression of CXCL10

Nipah virus (NiV) is a recently emerged zoonotic Paramyxovirus that causes regular outbreaks in East Asia with mortality rate exceeding 75%. Major cellular targets of NiV infection are endothelial cells and neurons. To better understand virus-host interaction, we analyzed the transcriptome profile of NiV infection in primary human umbilical vein endothelial cells. We further assessed some of the obtained results by in vitro and in vivo methods in a hamster model and in brain samples from NiV-infected patients. We found that NiV infection strongly induces genes involved in interferon response in endothelial cells. Among the top ten upregulated genes, we identified the chemokine CXCL10 (interferon-induced protein 10, IP-10), an important chemoattractant involved in the generation of inflammatory immune response and neurotoxicity. In NiV-infected hamsters, which develop pathology similar to what is seen in humans, expression of CXCL10 mRNA was induced in different organs with kinetics that followed NiV replication. Finally, we showed intense staining for CXCL10 in the brain of patients who succumbed to lethal NiV infection during the outbreak in Malaysia, confirming induction of this chemokine in fatal human infections. This study sheds new light on NiV pathogenesis, indicating the role of CXCL10 during the course of infection and suggests that this chemokine may serve as a potential new marker for lethal NiV encephalitis

R-loop formation during S phase is restricted by PrimPol-mediated repriming

During DNA replication conflicts with ongoing transcription are frequent and require careful management to avoid genetic instability. R-loops, three stranded nucleic acid structures comprising a DNA:RNA hybrid and displaced single stranded DNA, are important drivers of damage arising from such conflicts. How R-loops stall replication and the mechanisms that restrain their formation during S phase are incompletely understood. Here we show in vivo how R-loop formation drives a short purine-rich repeat, (GAA)10, to become a replication impediment that engages the repriming activity of the primase-polymerase PrimPol. Further, the absence of PrimPol leads to significantly increased R-loop formation around this repeat during S phase. We extend this observation by showing that PrimPol suppresses R-loop formation in genes harbouring secondary structure-forming sequences, exemplified by G quadruplex and H-DNA motifs, across the genome in both avian and human cells. Thus, R- loops promote the creation of replication blocks at susceptible structure-forming sequences, while PrimPol-dependent repriming limits the extent of unscheduled R-loop formation at these sequences, mitigating their impact on replication

Classification of SD-OCT Volumes using Local Binary Patterns: Experimental Validation for DME Detection

International audienceThis paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of pre-processing is inconsistent with respect to different classifiers and feature configurations