Analyzing ion distributions around DNA: sequence-dependence of potassium ion distributions from microsecond molecular dynamics

Microsecond molecular dynamics simulations of B-DNA oligomers carried out in an aqueous environment with a physiological salt concentration enable us to perform a detailed analysis of how potassium ions interact with the double helix. The oligomers studied contain all 136 distinct tetranucleotides and we are thus able to make a comprehensive analysis of base sequence effects. Using a recently developed curvilinear helicoidal coordinate method we are able to analyze the details of ion populations and densities within the major and minor grooves and in the space surrounding DNA. The results show higher ion populations than have typically been observed in earlier studies and sequence effects that go beyond the nature of individual base pairs or base pair steps. We also show that, in some special cases, ion distributions converge very slowly and, on a microsecond timescale, do not reflect the symmetry of the corresponding base sequenc

Analyzing ion distributions around DNA

We present a new method for analyzing ion, or molecule, distributions around helical nucleic acids and illustrate the approach by analyzing data derived from molecular dynamics simulations. The analysis is based on the use of curvilinear helicoidal coordinates and leads to highly localized ion densities compared to those obtained by simply superposing molecular dynamics snapshots in Cartesian space. The results identify highly populated and sequence-dependent regions where ions strongly interact with the nucleic and are coupled to its conformational fluctuations. The data from this approach is presented as ion populations or ion densities (in units of molarity) and can be analyzed in radial, angular and longitudinal coordinates using 1D or 2D graphics. It is also possible to regenerate 3D densities in Cartesian space. This approach makes it easy to understand and compare ion distributions and also allows the calculation of average ion populations in any desired zone surrounding a nucleic acid without requiring references to its constituent atoms. The method is illustrated using microsecond molecular dynamics simulations for two different DNA oligomers in the presence of 0.15 M potassium chloride. We discuss the results in terms of convergence, sequence-specific ion binding and coupling with DNA conformatio

Exploring the role of ICT-Enabled Social Innovation to support the modernisation of EU Social Protection Systems: findings and insights from analysis of case studies in fourteen Member States

This report presents the results of the analysis of case studies on how ICT-enabled social innovations promoting social investment can contribute to the modernisation of social protection systems in the EU. The case studies are drawn from 14 different Member States and address diverse social services and policy domains. Evidence from the analysis points out to the strong potential of using new approaches based on ICT-enabled social innovation to support public authorities, at various governance levels, in their efforts to improve the effectiveness and impact of social services delivery mechanisms and outreach. The analysis makes a first attempt to assess the relationship between different typologies of ICT-enabled social innovation and the broader social protection system in which they are embedded in. However, more research is needed to better understand the potential impact these initiatives could have on enhancing the adequacy and sustainability of welfare systems in the EU.JRC.B.4-Human Capital and Employmen

A Limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods

In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event. The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered. For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts. High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set

The 8 and 9 September 2002 flash flood event in France: a model intercomparison

Within the framework of the European Interreg IIIb Medocc program, the HYDROPTIMET project aims at the optimization of the hydrometeorological forecasting tools in the context of intense precipitation within complex topography. Therefore, some meteorological forecast models and hydrological models were tested on four Mediterranean flash-flood events. One of them occured in France where the South-eastern ridge of the French “Massif Central”, the Gard region, experienced a devastating flood on 8 and 9 September 2002. 24 people were killed during this event and the economic damage was estimated at 1.2 billion euros. To built the next generation of the hydrometeorological forecasting chain that will be able to capture such localized and fast events and the resulting discharges, the forecasted rain fields might be improved to be relevant for hydrological purposes. In such context, this paper presents the results of the evaluation methodology proposed by Yates et al. (2005) that highlights the relevant hydrological scales of a simulated rain field. Simulated rain fields of 7 meteorological model runs concerning with the French event are therefore evaluated for different accumulation times. The dynamics of these models are either based on non-hydrostatic or hydrostatic equation systems. Moreover, these models were run under different configurations (resolution, initial conditions). The classical score analysis and the areal evaluation of the simulated rain fields are then performed in order to put forward the main simulation characteristics that improve the quantitative precipitation forecast. The conclusions draw some recommendations on the value of the quantitative precipitation forecasts and way to use it for quantitative discharge forecasts within mountainous areas

Personalization in BERT with Adapter Modules and Topic Modelling

As a result of the widespread use of intelligent assistants, personalization in dialogue systems has become a hot topic in both research and industry. Typically, training such systems is computationally expensive, especially when using recent large language models. To address this challenge, we develop an approach to personalize dialogue systems using adapter layers and topic modelling. Our implementation enables the model to incorporate user-specific information, achieving promising results by training only a small fraction of parameters

Health Misinformation Detection in Web Content via Web2Vec: A Structural-, Content-based, and Context-aware Approach based on Web2Vec

In recent years, we have witnessed the proliferation of large amounts of online content generated directly by users with virtually no form of external control, leading to the possible spread of misinformation. The search for effective solutions to this problem is still ongoing, and covers different areas of application, from opinion spam to fake news detection. A more recently investigated scenario, despite the serious risks that incurring disinformation could entail, is that of the online dissemination of health information. Early approaches in this area focused primarily on user-based studies applied to Web page content. More recently, automated approaches have been developed for both Web pages and social media content, particularly with the advent of the COVID-19 pandemic. These approaches are primarily based on handcrafted features extracted from online content in association with Machine Learning. In this scenario, we focus on Web page content, where there is still room for research to study structural-, content- and context-based features to assess the credibility of Web pages. Therefore, this work aims to study the effectiveness of such features in association with a deep learning model, starting from an embedded representation of Web pages that has been recently proposed in the context of phishing Web page detection, i.e., Web2Vec