ConnecSenS, a Versatile IoT Platform for Environment Monitoring: Bring Water to Cloud.

Climate change is having an increasingly rapid impact on ecosystems and particularly on the issue of water resources. The Internet of Things and communication technologies have now reached a level of maturity that allows sensors to be deployed more easily on sites to monitor them. The communicating node based on LoRaWAN technology presented in this article is open and allows the interfacing of numerous sensors for designing long-term environmental monitoring systems of isolated sites. The data integration in the cloud is ensured by a workflow driving the storage and indexing of data, allowing a simple and efficient use of the data for different users (scientists, administration, citizens) through specific dashboards and extractions. This article presents this infrastructure through environmental monitoring use cases related to water resources.

CEBA: A Data Lake for Data Sharing and Environmental Monitoring.

This article presents a platform for environmental data named "Environmental Cloud for the Benefit of Agriculture" (CEBA). The CEBA should fill the gap of a regional institutional platform to share, search, store and visualize heterogeneous scientific data related to the environment and agricultural researches. One of the main features of this tool is its ease of use and the accessibility of all types of data. To answer the question of data description, a scientific consensus has been established around the qualification of data with at least the information "when" (time), "where" (geographical coordinates) and "what" (metadata). The development of an on-premise solution using the data lake concept to provide a cloud service for end-users with institutional authentication and for open data access has been completed. Compared to other platforms, CEBA fully supports the management of geographic coordinates at every stage of data management. A comprehensive JavaScript Objet Notation (JSON) architecture has been designed, among other things, to facilitate multi-stage data enrichment. Data from the wireless network are queried and accessed in near real-time, using a distributed JSON-based search engine.

From Sensor to Cloud: An IoT Network of Radon Outdoor Probes to Monitor Active Volcanoes.

While radon in soil gases has been identified for decades as a potential precursor of volcanic eruptions, there has been a recent interest for monitoring radon in air on active volcanoes. We present here the first network of outdoor air radon sensors that was installed successfully on Mt. Etna volcano, Sicily, Italy in September 2019. Small radon sensors designed for workers and home dosimetry were tropicalized in order to be operated continuously in harsh volcanic conditions with an autonomy of several months. Two stations have been installed on the south flank of the volcano at ~3000 m of elevation. A private network has been deployed in order to transfer the measurements from the stations directly to a server located in France, using a low-power wide-area transmission technology from Internet of Things (IoT) called LoRaWAN. Data finally feed a data lake, allowing flexibility in data management and sharing. A first analysis of the radon datasets confirms previous observations, while adding temporal information never accessed before. The observed performances confirm IoT solutions are very adapted to active volcano monitoring in terms of range, autonomy, and data loss.

Reducing the ionizing radiation background does not significantly affect the evolution of Escherichia coli populations over 500 generations.

Over millennia, life has been exposed to ionizing radiation from cosmic rays and natural radioisotopes. Biological experiments in underground laboratories have recently demonstrated that the contemporary terrestrial radiation background impacts the physiology of living organisms, yet the evolutionary consequences of this biological stress have not been investigated. Explaining the mechanisms that give rise to the results of underground biological experiments remains difficult, and it has been speculated that hereditary mechanisms may be involved. Here, we have used evolution experiments in standard and very low-radiation backgrounds to demonstrate that environmental ionizing radiation does not significantly impact the evolutionary trajectories of E. coli bacterial populations in a 500 generations evolution experiment.

Evaluation of early radiation DNA damage in a fractal cell nucleus model using Geant4-DNA.

The advancement of multidisciplinary research fields dealing with ionising radiation induced biological damage - radiobiology, radiation physics, radiation protection and, in particular, medical physics - requires a clear mechanistic understanding of how cellular damage is induced by ionising radiation. Monte Carlo (MC) simulations provide a promising approach for the mechanistic simulation of radiation transport and radiation chemistry, towards the in silico simulation of early biological damage. We have recently developed a fully integrated MC simulation that calculates early single strand breaks (SSBs) and double strand breaks (DSBs) in a fractal chromatin based human cell nucleus model. The results of this simulation are almost equivalent to past MC simulations when considering direct/indirect strand break fraction, DSB yields and fragment distribution. The simulation results agree with experimental data on DSB yields within 13.6% on average and fragment distributions agree within an average of 34.8%.

Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry.

Mechanistic modelling of DNA damage in Monte Carlo simulations is highly sensitive to the parameters that define DNA damage. In this work, we use a simple testing geometry to investigate how different choices of physics models and damage model parameters can change the estimation of DNA damage in a mechanistic DNA damage simulation built in Geant4-DNA. The choice of physics model can lead to variations by up to a factor of two in the yield of physically induced strand breaks, and the parameters that determine scavenging, and physical and chemical single strand break induction can have even larger consequences. Using low energy electrons as primary particles, a variety of parameters are tested in this geometry in order to arrive at a parameter set consistent with past simulation studies. We find that the modelling of scavenging can play an important role in determining results, and speculate that high-scavenging regimes, where only chemical radicals within 1 nm of DNA are simulated, could provide a good means of testing mechanistic DNA simulations.

Mechanistic DNA damage simulations in Geant4-DNA Part 2: Electron and proton damage in a bacterial cell.

We extended a generic Geant4 application for mechanistic DNA damage simulations to an Escherichia coli cell geometry, finding electron damage yields and proton damage yields largely in line with experimental results. Depending on the simulation of radical scavenging, electrons double strand breaks (DSBs) yields range from 0.004 to 0.010 DSB Gy-1 Mbp-1, while protons have yields ranging from 0.004 DSB Gy-1 Mbp-1 at low LETs and with strict assumptions concerning scavenging, up to 0.020 DSB Gy-1 Mbp-1 at high LETs and when scavenging is weakest. Mechanistic DNA damage simulations can provide important limits on the extent to which physical processes can impact biology in low background experiments. We demonstrate the utility of these studies for low dose radiation biology calculating that in E. coli, the median rate at which the radiation background induces double strand breaks is 2.8 × 10-8 DSB day-1, significantly less than the mutation rate per generation measured in E. coli, which is on the order of 10-3.

Understanding low radiation background biology through controlled evolution experiments.

Biological experiments conducted in underground laboratories over the last decade have shown that life can respond to relatively small changes in the radiation background in unconventional ways. Rapid changes in cell growth, indicative of hormetic behaviour and long-term inheritable changes in antioxidant regulation have been observed in response to changes in the radiation background that should be almost undetectable to cells. Here, we summarize the recent body of underground experiments conducted to date, and outline potential mechanisms (such as cell signalling, DNA repair and antioxidant regulation) that could mediate the response of cells to low radiation backgrounds. We highlight how multigenerational studies drawing on methods well established in studying evolutionary biology are well suited for elucidating these mechanisms, especially given these changes may be mediated by epigenetic pathways. Controlled evolution experiments with model organisms, conducted in underground laboratories, can highlight the short- and long-term differences in how extremely low-dose radiation environments affect living systems, shining light on the extent to which epimutations caused by the radiation background propagate through the population. Such studies can provide a baseline for understanding the evolutionary responses of microorganisms to ionizing radiation, and provide clues for understanding the higher radiation environments around uranium mines and nuclear disaster zones, as well as those inside nuclear reactors.

Simulating the Impact of the Natural Radiation Background on Bacterial Systems: Implications for Very Low Radiation Biological Experiments.

At very low radiation dose rates, the effects of energy depositions in cells by ionizing radiation is best understood stochastically, as ionizing particles deposit energy along tracks separated by distances often much larger than the size of cells. We present a thorough analysis of the stochastic impact of the natural radiative background on cells, focusing our attention on E. coli grown as part of a long term evolution experiment in both underground and surface laboratories. The chance per day that a particle track interacts with a cell in the surface laboratory was found to be 6 × 10-5 day-1, 100 times less than the expected daily mutation rate for E. coli under our experimental conditions. In order for the chance cells are hit to approach the mutation rate, a gamma background dose rate of 20 µGy hr-1 is predicted to be required.

Sentinel e-health network on grid: developments and challenges.

Grid technologies have proven their capabilities to settle challenging problems of medical data access. The grid ability to access distributed databases in a secure and reliable way while preserving data ownership opened new perspectives in medical data sharing and disease surveillance. This paper focuses on the implementation challenges of grid-powered sentinel networks within the e-sentinelle project. This initiative aims to create a lightweight grid dedicated to cancer data exchange and enable automatic disease surveillance according to definition of epidemiological alarms. Particularly, issues related to security, patient identification, databases integration, data representation and medical record linkage are discussed.

Grid-based International Network for Flu observation (g-INFO).

The 2009 H1N1 outbreak has demonstrated that continuing vigilance, planning, and strong public health research capability are essential defenses against emerging health threats. Molecular epidemiology of influenza virus strains provides scientists with clues about the temporal and geographic evolution of the virus. In the present paper, researchers from France and Vietnam are proposing a global surveillance network based on grid technology: the goal is to federate influenza data servers and deploy automatically molecular epidemiology studies. A first prototype based on AMGA and the WISDOM Production Environment extracts daily from NCBI influenza H1N1 sequence data which are processed through a phylogenetic analysis pipeline deployed on EGEE and AuverGrid e-infrastructures. The analysis results are displayed on a web portal (http://g-info.healthgrid.org) for epidemiologists to monitor H1N1 pandemics.

Grid-enabled sentinel network for cancer surveillance.

Recent developments of grid services for secured distributed data management open new perspectives for disease surveillance. In this paper, we report on our initiative to develop a surveillance network for breast cancer in the Auvergne region. The network gathers cytopathology laboratories, structures in charge of cancer screening and institutes in charge of cancer epidemiology. Data stored in cytopathology laboratories are queried through the grid for the purpose of second diagnosis and to produce statistical indicators. The paper describes the network goal and design and discusses specific issues related to patient identification and security.

Innovative in silico approaches to address avian flu using grid technology.

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper proposes new approaches for the integration of existing data sources towards a global surveillance network for molecular epidemiology and in silico drug discovery.