Analysis of chlordecone by LC/MS-MS in surface and wastewaters

International audienceChlordecone (also known as Kepone) was used extensively in the French West Indies until 1993. This persistent pollution raises the question of the faith of this pesticide through water treatment plants and its eventual release in the environment. To address this issue, a two-step methodology is herein proposed. First, a complete description of the analysis of \CLD\ is given using liquid chromatography with mass spectrometry (LC/MS-MS). The reliability of this analytical methodology was demonstrated in ultrapure water as well as in the presence of organic and/or inorganic compounds (groundwater, river water and nutritive solutions). The limits of quantification were decreased to 1.5 μg L−1. In a second part, the removal of \CLD\ is considered via the sorption onto activated sludge. Kinetics and isotherms of sorption were determined. Very short times (less than 5 min) were observed to reach the equilibrium. Moreover, a linear relationship was determined for the sorption equilibrium, which led to the conclusion that the solid/liquid partition coefficient was 7600 L kg−1, i.e. log KOC of 3.88, very close to the values encountered for the sorption of \CLD\ in soils

Conflict Resilience

Although large-scale wars and interstate conflicts have almost disappeared, intrastate conflicts remain widespread and result in a high number of victims. During the last ten years, the number of fatalities was substantially higher than in the previous decade. Though these conflicts take place outside the borders of the EU, they can generate important direct and indirect effects. Moreover, they are connected to climate change, can lead to various disasters, geopolitical effects, or material supply disruptions. The concept of resilience has recently gained ground as a framework for addressing contemporary global threats. It has also become the key principle in the EU’s external action. One of its key building blocks is the modelling and monitoring of conflict risk to allow early action. Conflict resilience refers to the capacity of a state to resist a drift towards violence contrary to the structural conditions prevailing (pre-conflict resilience). It also includes the response of a state in the presence of a conflict (post-conflict resilience). Evaluating the pre-conflict resilience of states can provide insights into conflict aversion or enable a warning for the eruption of violence. On the other hand, the study of postconflict resilience may unveil the adaptive and transformative mechanisms that can be followed by other war-torn countries. Climate change and conflicts are closely related. For example, climate change exacerbates current conflict drivers like food insecurity, competition for water and land resources, poverty and internal displacement of people. Adaptation and mitigation policies may lead to new regulations or infrastructures (like new hydropower reservoirs) which can generate tensions and eventually conflicts. Finally, conflict-torn countries are unable to invest in adaptation strategies, which makes them even more vulnerable to climate change effects.JRC.E.1-Disaster Risk Managemen

The Global Conflict Risk Index: Artificial intelligence for conflict prevention

The Global Conflict Risk Index (GCRI), which was designed by the European Commission’s Joint Research Centre (JRC), is the quantitative starting point of the EU’s conflict Early Warning System. Taking into consideration the needs of policy-makers to prioritize actions towards conflict prevention, the GCRI expresses the statistical risk of violent conflict in a given country in the upcoming one to four years. It is based on open source data and grounded in the assumption that the occurrence of conflict is linked to structural conditions, which are used to compute the probability and intensity of conflicts. While the initial GCRI model was estimated by means of linear and logistic regression models, this report presents a new GCRI model based on the Artificial Intelligence (AI) random forest (RF) approach. The models’ hyperparameters are optimized using a ten-fold cross validation. Overall, it is demonstrated that the random forest GCRI models are internally stable, not overfitting, and have a good predictive power. The precision and accuracy metrics are above 98%, both for the national power and subnational power conflict models. The AI GCRI, as a supplementary modelling method for the European conflict prevention policy agenda, is scientifically robust as a baseline quantitative evaluation of armed conflict risk additional to the linear and logistic regression GCRI.JRC.E.1-Disaster Risk Managemen

Dynamic Global Conflict Risk Index

This report presents a dynamic model of the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union’s (EU) conflict prevention strategies developed by the Joint Research Centre (JRC) of the European Commission (EC) in collaboration with an expert panel of researchers and policy-makers. While most studies as well as the regression GCRI measure conflict intensity by counting the number of causalities, the proposed dynamic GCRI integrates and identifies every stage of the conflict development or de-escalation in its entire complexity. The emergence of conflict related event data sets offers researchers new ways to quantify and predict conflicts through big data. Using country-level actor-based event data sets that signal potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence, the model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. Three potential datasets are tested in this report following a political event coding classification: (i) the Global Data on Events Location and Tone (GDELT) project, (ii) the Integrated Crisis Early Warning System (ICEWS) Dataverse dataset and (iii) the Phoenix - Open Event Data Alliance (OEDA)-Phoenix Dataset. The Artificial Intelligence (AI) methodology adopted to model the dynamic GCRI is built upon a Long-Short Term Memory (LSTM) Cell Recurrent Neural Network (RNN). These models are well-suited to classify, process and make predictions based on time series data and forecast near future events. Besides this AI model, we have set up an early warning alarm system to signal abnormal social unrest upheavals. The dynamic GCRI, through the AI and early warning alarm, seems to be able to predict the materialization of a conflict on a monthly basis. This new tool gives policy makers the possibility to observe the situation in a country on a monthly base, taking into consideration both the current and the predicted available information, and to implement preventive actions more rapidly to mitigate conflict exacerbations at an earlier stage of the conflict development cycle.JRC.E.1-Disaster Risk Managemen

Tumor-activated lymph node fibroblasts suppress T cell function in diffuse large B cell lymphoma

Recent transcriptomic-based analysis of diffuse large B cell lymphoma (DLBCL) has highlighted the clinical relevance of LN fibroblast and tumor-infiltrating lymphocyte (TIL) signatures within the tumor microenvironment (TME). However, the immunomodulatory role of fibroblasts in lymphoma remains unclear. Here, by studying human and mouse DLBCL-LNs, we identified the presence of an aberrantly remodeled fibroblastic reticular cell (FRC) network expressing elevated fibroblast-activated protein (FAP). RNA-Seq analyses revealed that exposure to DLBCL reprogrammed key immunoregulatory pathways in FRCs, including a switch from homeostatic to inflammatory chemokine expression and elevated antigen-presentation molecules. Functional assays showed that DLBCL-activated FRCs (DLBCL-FRCs) hindered optimal TIL and chimeric antigen receptor (CAR) T cell migration. Moreover, DLBCL-FRCs inhibited CD8+ TIL cytotoxicity in an antigen-specific manner. Notably, the interrogation of patient LNs with imaging mass cytometry identified distinct environments differing in their CD8+ TIL-FRC composition and spatial organization that associated with survival outcomes. We further demonstrated the potential to target inhibitory FRCs to rejuvenate interacting TILs. Cotreating organotypic cultures with FAP-targeted immunostimulatory drugs and a bispecific antibody (glofitamab) augmented antilymphoma TIL cytotoxicity. Our study reveals an immunosuppressive role of FRCs in DLBCL, with implications for immune evasion, disease pathogenesis, and optimizing immunotherapy for patients

Matière critique. Entretien avec Marie Papazoglou

Entretien sur l'exposition de photographie "Matière critique", qui ouvre à l'ISELP en janvier 202

The Global Conflict Risk Index : A quantitative tool for policy support on conflict prevention

In an effort to bridge the gap between academic and governmental initiatives on quantitative conflict modelling, this article presents, validates and discusses the Global Conflict Risk Index (GCRI), the quantitative starting point of the European Union Conflict Early Warning System. Based on open-source data of five risk areas representing the structural conditions characterising a given country (political, economic, social, environmental and security areas), it evaluates the risk of violent conflict in the next one to four years. Using logistic regression, the GCRI calculates the probability of national and subnational conflict risk. Several model design decisions, including definition of the dependent variable, predictor variable selection, data imputation, and probability threshold definition, are tested and discussed in light of the model's direct application in the EU policy support on conflict prevention. While the GCRI remains firmly rooted by its conception and development in the European conflict prevention policy agenda, it is validated as a scientifically robust and rigorous method for a baseline quantitative evaluation of armed conflict risk. Despite its standard, simple methodology, the model predicts better than six other published quantitative conflict early warning systems for ten out of twelve reported performance metrics. Thereby, this article aims to contribute to a cross-fertilisation of academic and governmental efforts in quantitative conflict risk modelling

Limited Ca2+ and PKA-pathway dependent neurogenic differentiation of human adult mesenchymal stem cells as compared to fetal neuronal stem cells

The ability of mesenchymal stem cells to generate functional neurons in culture is still a matter of controversy. In order to assess this issue, we performed a functional comparison between neuronal differentiation of human MSCs and fetal-derived neural stem cells (NSCs) based on morphological, immunocytochemical, and electrophysiological criteria. Furthermore, possible biochemical mechanisms involved in this process were presented. NF200 immunostaining was used to quantify the yield of differentiated cells after exposure to cAMP. The addition of a PKA inhibitor and Ca(2+) blockers to the differentiation medium significantly reduced the yield of differentiated cells. Activation of CREB was also observed on MSCs during maturation. Na(+)-, K(+)-, and Ca(2+)-voltage-dependent currents were recorded from MSCs-derived cells. In contrast, significantly larger Na(+) currents, firing activity, and spontaneous synaptic currents were recorded from NSCs. Our results indicate that the initial neuronal differentiation of MSCs is induced by cAMP and seems to be dependent upon Ca(2+) and the PKA pathway. However, compared to fetal neural stem cells, adult mesenchymal counterparts are limited in their neurogenic potential. Despite the similar yield of neuronal cells, NSCs achieved a more mature functional state. Description of the underlying mechanisms that govern MSCs' differentiation toward a stable neuronal phenotype and their limitations provides a unique opportunity to enhance our understanding of stem cell plasticity