New radio observations of anomalous microwave emission in the HII region RCW175

We have observed the HII region RCW175 with the 64m Parkes telescope at 8.4GHz and 13.5GHz in total intensity, and at 21.5GHz in both total intensity and polarization. High angular resolution, high sensitivity, and polarization capability enable us to perform a detailed study of the different constituents of the HII region. For the first time, we resolve three distinct regions at microwave frequencies, two of which are part of the same annular diffuse structure. Our observations enable us to confirm the presence of anomalous microwave emission (AME) from RCW175. Fitting the integrated flux density across the entire region with the currently available spinning dust models, using physically motivated assumptions, indicates the presence of at least two spinning dust components: a warm component with a relatively large hydrogen number density n_H=26.3/cm^3 and a cold component with a hydrogen number density of n_H=150/cm^3. The present study is an example highlighting the potential of using high angular-resolution microwave data to break model parameter degeneracies. Thanks to our spectral coverage and angular resolution, we have been able to derive one of the first AME maps, at 13.5GHz, showing clear evidence that the bulk of the AME arises in particular from one of the source components, with some additional contribution from the diffuse structure. A cross-correlation analysis with thermal dust emission has shown a high degree of correlation with one of the regions within RCW175. In the center of RCW175, we find an average polarized emission at 21.5GHz of 2.2\pm0.2(rand.)\pm0.3(sys.)% of the total emission, where we have included both systematic and statistical uncertainties at 68% CL. This polarized emission could be due to sub-dominant synchrotron emission from the region and is thus consistent with very faint or non-polarized emission associated with AME.Comment: Accepted for publication in the Astrophysical Journa

Model Checking to Assess T-Helper Cell Plasticity

Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.LabEx MemoLife, Ecole Normale Supérieure, FCT grants: (PEst-OE/EEI/LA0021/2013, IF/01333/2013), Ph.D.program of the Agence National de Recherche sur Le Sida (ANRS), European Research Council consolidator grant

Fast wide-volume functional imaging of engineered in vitro brain tissues

The need for in vitro models that mimic the human brain to replace animal testing and allow high-throughput screening has driven scientists to develop new tools that reproduce tissue-like features on a chip. Three-dimensional (3D) in vitro cultures are emerging as an unmatched platform that preserves the complexity of cell-to-cell connections within a tissue, improves cell survival, and boosts neuronal differentiation. In this context, new and flexible imaging approaches are required to monitor the functional states of 3D networks. Herein, we propose an experimental model based on 3D neuronal networks in an alginate hydrogel, a tunable wide-volume imaging approach, and an efficient denoising algorithm to resolve, down to single cell resolution, the 3D activity of hundreds of neurons expressing the calcium sensor GCaMP6s. Furthermore, we implemented a 3D co-culture system mimicking the contiguous interfaces of distinct brain tissues such as the cortical-hippocampal interface. The analysis of the network activity of single and layered neuronal co-cultures revealed cell-type-specific activities and an organization of neuronal subpopulations that changed in the two culture configurations. Overall, our experimental platform represents a simple, powerful and cost-effective platform for developing and monitoring living 3D layered brain tissue on chip structures with high resolution and high throughput

From dynamics to links: a sparse reconstruction of the topology of a neural network

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity and how information processing occurs in the neural circuits. At the cellular and molecular level, mechanisms of signal transduction have been studied intensively and a better knowledge and understanding of some basic processes of information handling by neurons has been achieved. In contrast, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor electrical activity of a large number of neurons in real time. Then, the qualitative and quantitative analysis of the spiking activity of individual neurons is a very valuable tool for the study of the dynamics and architecture of the neural networks. Such activity is not due to the sole intrinsic properties of the individual neural cells but it is mostly the consequence of the direct influence of other neurons. The deduction of the effective connectivity between neurons, whose experimental spike trains are observed, is of crucial importance in neuroscience: first for the correct interpretation of the electro-physiological activity of the involved neurons and neural networks, and, for correctly relating the electrophysiological activity to the functional tasks accomplished by the network. In this work, we propose a novel method for the identification of connectivity of neural networks using recorded voltages. Our approach is based on the assumption that the network has a topology with sparse connections. After a brief description of our method, we will report the performances and compare it to the cross-correlation computed on the spike trains, which represents a gold standard method in the field

Cooperative development of logical modelling standards and tools with CoLoMoTo

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments. Contact: [email protected]

Validation of a questionnaire algorithm based on repeated open application testing with the constituents of fragrance mix I

Background In a European study on contact allergy in the general population, it was hypothesized that the combination of contact allergy to a fragrance together with a history indicating dermatitis at exposure, and thereafter subsequent avoidance of scented products, implied a diagnosis of allergic contact dermatitis. Objectives The primary aim of this study was to validate this hypothesis and algorithm. The secondary aim was to investigate whether there was any association between the outcome of the repeated open application test (ROAT) and the patch test reactivity. Methods In total, 109 patients with and without contact allergy to fragrance mix (FM) I were recruited. Volunteers from six European dermatology clinics participated in the study including a patch test and a ROAT. Results Positive ROAT reactions were noted in 26 of the 44 volunteers with contact allergy to FM I. None of the volunteers reacted to the vehicle (P <0 center dot 001). More individuals with a positive algorithm had positive ROATs than those with a negative algorithm. However, the difference was not statistically significant. The lower the patch test concentration eliciting a positive test reaction, the more likely a positive ROAT and the more likely that the positive ROAT appeared early during the investigative period. Conclusions The algorithm used in this study was not substantiated in this ROAT set-up. The stronger the patch test reactivity the more likely was a positive ROAT and the more likely it was that the positive ROAT appeared early during the application period. What's already known about this topic? To the best of our knowledge, a scientifically designed and conducted repeated open application test (ROAT) has never been performed before to validate a diagnosis of allergic contact dermatitis partly based on a questionnaire. What does this study add? This is the largest controlled, randomized and blinded ROAT performed to date. Higher patch test reactivity to fragrance mix I indicated a greater likelihood of a positive ROAT

Characterization of Reachable Attractors Using Petri Net Unfoldings

International audienceAttractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being %so far more generic since it applies to any safe Petri net

The Northern Cross fast radio burst project–I: overview and pilot observations at 408 MHz

Fast radio bursts (FRBs) remain one of the most enigmatic astrophysical sources. Observations have significantly progressed over the last few years, due to the capabilities of new radio telescopes and the refurbishment of existing ones. Here, we describe the upgrade of the Northern Cross radio telescope, operating in the 400–416 MHz frequency band, with the ultimate goal of turning the array into a dedicated instrument to survey the sky for FRBs

Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0

The logical formalism is well adapted to model large cellular networks, in particular when detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step toward the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network

Host phenotype characteristics and MC1R in relation to early-onset basal cell carcinoma.

Basal cell carcinoma (BCC) incidence is increasing, particularly among adults under the age of 40 years. Pigment-related characteristics are associated with BCC in older populations, but epidemiologic studies among younger individuals and analyses of phenotype-genotype interactions are limited. We examined self-reported phenotypes and melanocortin 1 receptor gene (MC1R) variants in relation to early-onset BCC. BCC cases (n=377) and controls with benign skin conditions (n=390) under the age of 40 years were identified through Yale's Dermatopathology database. Factors most strongly associated with early-onset BCC were skin reaction to first summer sun for 1 hour (severe sunburn vs. tan odds ratio (OR)=12.27, 95% confidence interval (CI)=4.08-36.94) and skin color (very fair vs. olive OR=11.06, 95% CI=5.90-20.74). Individuals with two or more MC1R non-synonymous variants were 3.59 times (95% CI=2.37-5.43) more likely to have BCC than those without non-synonymous variants. All host characteristics and MC1R were more strongly associated with multiple BCC case status (37% of cases) than a single BCC case status. MC1R, number of moles, skin reaction to first summer sun for 1 hour, and hair and skin color were independently associated with BCC. BCC risk conferred by MC1R tended to be stronger among those with darker pigment phenotypes, traditionally considered to be at low risk of skin cancer