Rapport livrable ResiWater D3.3: Intégration d'informations hydrauliques pour la détection d'évènements anormaux

The aim of work package 3 is the development of an enhanced self-learning monitoring and event detection module. In summary the principal items of work package 3 are: 1) Development of a data analysis platform for the integration of the heterogeneous sensor measurements 2) Development of self-learning monitoring and event detection algorithms. These algorithms will take into account the spatial distribution of the measurements. 3) Integration of online plausibility checks for the results of the algorithms 4) Deployment of tools for the launch of the enhanced event detection module. This deliverable describes the integration of hydraulic information into the event detection module with the aim to reduce the rate of false positive alarms. Three main concepts are proposed: - Use of backward transit time between two sensors; - Knowledge of source provenance; - Aggregation of clusters of similar water quality for spatial segmentation

Rapport livrable ResiWater D1.2 : Investigation de cas d'étude en utilisant des réseaux sécurisés de capteurs et des outils de surveillance par auto-apprentissage

The core of Resiwater project is the development of new sensors and secured sensor networks, self- learning monitoring tools, robust simulation models and vulnerability and resilience assessment tools. To ensure realistic and relevant application on field for water suppliers the project is end user oriented. Increasing the resilience of water distribution systems is a strong target of the project. Consequently the benefits of developed tools have to be investigated and assessed by the perspective of realistic water distribution system failures. Therefore, WP1 aims to specify the use cases with scenarios for each end user (WP1.1) to evaluate vulnerability and resilience of a Water Distribution System (WDS) for the use cases (WP1.3) and to evaluate new tools of the project by the mean of the use cases (WP1.2) as described in the present report. First, the test results for online water quality measurement at the TZW scale network are reported. Then, the self-learning methods and tools are tested for different configurations at the ResiWater three water utility

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

Background!#!The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.!##!Methods!#!In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.!##!Results!#!Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.!##!Conclusions!#!Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity