A wireless sensor network to measure the health care workers exposure to tuberculosis

International audienceIn parallel to the advances in modern medicine, health sciences and public health policy, epidemic models aided by computer simulations and information technologies offer an important alternative for the understanding of transmission dynamics and epidemic patterns. In this paper, we focus on the first steps that may lead towards the design of epidemic models, i.e. the measure and analysis of interactions within a closed socio-professional context. More precisely, this project was motivated by the study of the Health Care Workers (HCWs) exposure to tuberculosis in their work environment. Despite the progresses in treatment and prevention, tuberculosis remains a disease in expansion and represents the third cause of death by infectious pathologies in the world. In the health care context, if the transmission is globally controlled, the HCWs exposure remains obscure. Individual factors associated to the contamination of HCWs in their work environment are not precisely known. Our study focus on the evaluation of the intensity and the frequency of contacts between tuberculosis infected patients and HCWs. To gather this information, classical methods consist in performing audits, consulting medical and administrative files or using self-reports of conversations and trusting HCW souvenirs. All these methods are time-consuming, subject to memory failures and interpretations. As an alternate method, we have chosen to dedicate a Wireless Sensor Network (WSN) to gather these interactions inside a Service of Infectious and Tropical Diseases (Bichat-Claude Bernard Hospital, Paris) and a Service of Pneumology (La Piti ´ e Salp ´ etri ` ere Hospital, Paris). Within the two services, each room has been equipped with a sensor node and each HCW carries an autonomous sensor during his presence in the service. An important characteristic of this measurement campaign is that it was performed in a closed environment, over a closed population and during a large continuous period of time. That is, the presence of all HCWs of the units was monitored in all patient rooms, 24/7 during a three months period. In addition to the experimental measure system description, this paper main contributions are the analysis and characterization of this huge and unique data set describing a complex dynamic interaction network, and the impact study of the measurement process bias on the network dynamic. The analyze of large dynamic in situ interaction networks provides an opportunity to study dynamical processes occurring on dynamical networks, such as spreading or epidemical processes, taking into account the dynamics both on and of the network structure

Reconstructing Social Interactions Using an unreliable Wireless Sensor Network

International audienceIn the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study. While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns. In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of Health Care Workers' exposure to tuberculosis infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a 3 month period. We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phe- nomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture

EM Monitoring and classification of IEMI and protocol-based attacks on IEEE 802.11n communication networks

International audienceThe development of connected devices and their daily use are today at the origin of the omnipresence of Wi-Fi wireless networks. However, these Wi-Fi networks are often vulnerable, and can be used by malicious people to disturb services, intercept sensitive data or to gain access to system. In railways, trains are now equipped with wireless communication systems for operational purposes or for passenger services. In both cases, defense strategies have to be developed to prevent misuses of the networks. The first objective of this study is to propose a monitoring solution, which is independent of the communication networks, to detect the occurrence of attacks. The second objective is to develop a method able to classify attacks of different types: the intentional electromagnetic interference (IEMI), i.e., jamming attacks, and the protocol-based attacks. This study focuses on the IEEE 802.11n Wi-Fi protocol. To perform these analyses, we propose to monitor and to analyze electromagnetic (EM) signals received by a monitoring antenna and a receiver collecting EM spectra. After that, we build a classification protocol following two steps: the first consists in the construction of a Support Vector Machine (SVM) classification model using the collected spectra and the second step uses this SVM model to predict the class of the attack (if any). A time-based correction of this prediction using the nearest neighbors is also included in this second step

Platelets selectively regulate the release of BDNF, but not that of its precursor protein, proBDNF

Background: Brain-derived neurotrophic factor (BDNF) plays a role in synaptic plasticity and neuroprotection. BDNF has well-established pro-survival effects, whereas its precursor protein, proBDNF, induces apoptosis. Thus, it has been suggested that the proBDNF/BDNF ratio could be an indicator of neuronal health. Access to neurons is, understandably, limited. Because of their similarities, platelets have been put forward as a non-invasive biomarker of neuronal health; indeed, they store large quantities of BDNF and can release it into circulation upon activation, similarly to neurons. However, whether platelets also express the precursor proBDNF protein remains unknown. We therefore sought to characterize proBDNF levels in human platelets and plasma. Methods: The presence of proBDNF was assessed by immunoblotting, cell fractionation, flow cytometry, and confocal microscopy in washed platelets from 10 healthy volunteers. Platelets from 20 independent healthy volunteers were activated with several classical agonists and the release of BDNF and proBDNF into plasma was quantified by ELISA. Results: Platelets expressed detectable levels of proBDNF (21 ± 13 fmol/250 x 106 platelets). ProBDNF expression was mainly localized in the intracellular compartment. The proBDNF to BDNF molar ratio was ~1:5 in platelets and 10:1 in plasma. In stark contrast to the release of BDNF during platelet activation, intraplatelet and plasma concentrations of proBDNF remained stable following stimulation with classical platelet agonists, consistent with non-granular expression. Conclusions: Platelets express both the mature and the precursor form of BDNF. Whether the intraplatelet proBDNF to BDNF ratio could be used as a non-invasive biomarker of cognitive health warrants further investigation

Cyber Security of the Railway wireless system: detection, decision and Human-in-the-Loop

TRA 2018, 7th Transport Research Arena, Vienne, AUTRICHE, 16-/04/2018 - 19/04/2018The networks used in the Railway domain are usually heterogeneous, not enough protected and not fitted to the usual Cyber Security requirements in terms of sustainability, protection and attack detection. Furthermore, the quick evolution of the telecommunication means, the threats and the sustainability aspects have to be taken into account in order to protect the Railway system. The paper presents the first contributions on Cyber Security for railways that can be divided into three main aspects dealing with the Cyber Security of the wireless part of the railway communication system: detection, decision and Human-in-the-Loop. Part of the work will be devoted to the development of an Open Pluggable Framework (OPF). The OPF is a software framework based on automation principles. It monitors the environment, then some algorithms detect abnormal behaviours, and next, OPF decides which reaction to take and finally apply this action (e.g. an alarm or a reconfiguration). The last part 'human in the loop' aims at answering the questions: what happens if the automatic countermeasures fail and how the driver can cope with the attack consequences. It consists in placing professional drivers and Central Traffic Control operators in a realistic simulator and playing scenarios involving attacks and observing the reactions of the professional drivers. A preliminary methodology is proposed and discussed through a concrete case study

QseC controls biofilm formation of non-typeable Haemophilus influenzae in addition to an AI-2-dependent mechanism.

Non-typeable Haemophilus influenzae (NTHi) is a common pathogen associated with diseases such as acute otitis media or exacerbations in patients with chronic obstructive pulmonary disease. The biofilm-forming capability substantially contributes to the persistence of NTHi. However, the regulation of biofilm formation is not completely understood. Quorum sensing regulated by autoinducer-2 produced by luxS is until now the only described regulatory mechanism. In this study, we show that the two-component signalling system QseB/C is involved in the biofilm formation of NTHi in vitro. An isogenic NTHi mutant of qseC (Hi3655KR2) showed a significant decrease in biofilm formation under static and semi-static conditions as assessed by crystal violet staining. In addition, under constant flow conditions, Hi3655KR2 formed less biofilm after 48h. The biofilm defects were irrespective of autoinducer-2 levels. Hence, here we suggest for the first time a regulatory circuit in NTHi, which controls biofilm formation by mechanisms other than or in addition to luxS-dependent factors

PEPOP: Computational design of immunogenic peptides

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