HUG model: an interaction point process for Bayesian detection of multiple sources in groundwaters from hydrochemical data

This paper presents a new interaction point process that integrates geological knowledge for the purpose of automatic sources detection of multiple sources in groundwaters from hydrochemical data. The observations are considered as spatial data, that is a point cloud in a multi-dimensional space of hydrogeochemical parameters. The key hypothesis of this approach is to assume the unknown sources to be the realisation of a point process. The probability density describing the sources distribution is built in order to take into account the multi-dimensional character of the data and specific physical rules. These rules induce a source configuration able to explain the observations. This distribution is completed with prior knowledge regarding the model parameters distributions. The composition of the sources is estimated by the configuration maximising the joint proposed probability density. The method was first calibrated on synthetic data and then tested on real data from hydrothermal systems

Ethanol Concentration Effect on the Extraction of Phenolic Compounds from Ribes nigrum Assessed by Spectrophotometric and HPLC-DAD Methods

The purpose of this study was to assess the phenolic compounds extraction from black currants (Ribes nigrum) by analyzing the effect of ethanol concentration (40-80 % v/v

Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures

International audienceHydrogeochemical data may be seen as a point cloud in a multi-dimensional space. Each dimension of this space represents a hydrogeochemical parameter (i.e. salinity, solute concentration, concentration ratio, isotopic composition...). While the composition of many geological fluids is controlled by mixing between multiple sources, a key question related to hydrogeochemical data set is the detection of the sources. By looking at the hydrogeochemical data as spatial data, this paper presents a new solution to the source detection problem that is based on point processes. Results are shown on simulated and real data from geothermal fluids

Evaluating the ecotoxicity of different pharmaceuticals using Aliivibrio fischeri bioassays

An endless list of companies have produced a large amount of pharmaceutical compounds in a year-on-year growth trend. Due to the excessive consumption of these substances and the inappropriate disposal, the environment was contaminated, especially aquatic ecosystems, with quantities of pharmaceuticals (PHACs) so that they have affected the living organisms, leading to decreased biodiversity and ecological degradation. Many studies on PHACs environmental presence and toxic effects were performed, but unfortunately, no limit was establish for discharging into environment, especially into the aquatic systems. The aim of this study was to use the bioluminescence of Aliivibrio fischeri bacteria as an indicator of toxically effect of different PHACs in simulated marine medium. The Microtox® bioassay is based on the PHACs inhibitory effect on the metabolism of bacteria which induced changes in their bacterial bioluminescence. The test organisms were exposed to analgesics and anti-inflammatories such as Diclofenac, Ketoprofen, Naproxen and Ibuprofen. The results showed that based on EC50 values, Naproxen had a very low toxicity but Diclofenac, Ketoprofen and Ibuprofen had a harmful effect on the aquatic organisms

Metformin and Its Benefits in Improving Gut Microbiota Disturbances in Diabetes Patients

The human gastrointestinal tract presents a vastly population of microorganisms, called the microbiota. The presence of these microorganisms offers many benefits to the host, through a range of physiological functions. However, there is a potential for these mechanisms to be disrupted condition, known as dysbiosis. Recent results are showing important associations between diabetes and the gut microbiota and how the intestinal flora can influence the prognosis of this illness. Microbial intestinal imbalance has been linked to alterations in insulin sensitivity and in glucose metabolism and may play an important role in the development of diabetes. Metformin is one of the most important and widely used first-line medications for the management of type 2 diabetes (T2D). It is a complex drug with multiple sites of action and multiple molecular mechanisms. In recent years, attention has been directed to other modes of action, other than the classic ones, with increasing evidence of a major key role of the intestine. By analysing the effects of metformin on the homeostasis of the microbiota of diabetes patients, our present topic becomes one of the major importance in understanding how metformin therapy can improve gut microbiota dysbiosis and thus provide a better outcome for this illness

National identity predicts public health support during a global pandemic

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.publishedVersio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Peer reviewe