42 research outputs found

    Genomic RNA Elements Drive Phase Separation of the SARS-CoV-2 Nucleocapsid

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    We report that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with viral RNA. N-protein condenses with specific RNA genomic elements under physiological buffer conditions and condensation is enhanced at human body temperatures (33°C and 37°C) and reduced at room temperature (22°C). RNA sequence and structure in specific genomic regions regulate N-protein condensation while other genomic regions promote condensate dissolution, potentially preventing aggregation of the large genome. At low concentrations, N-protein preferentially crosslinks to specific regions characterized by single-stranded RNA flanked by structured elements and these features specify the location, number, and strength of N-protein binding sites (valency). Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is RNA sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules, and therefore presents a screenable process for identifying antiviral compounds effective against SARS-CoV-2

    Analyse de la compatibilité institutionnelle des politiques publiques

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    International audiencePolicy assessment from an institutional perspective follows the concept of institutions for sustainability, which is defined as the necessary institutional structure capable of delivering economic, social, and environmental sustainability objectives. Thus, the effectiveness of a policy and the cost-effectiveness of its implementation depend to a large extent on the degree of compatibility between this policy option and the respective institutional context. However, not least because institutions usually relate to a great diversity of situations, the state-of-theart in institutional economics offers hardly any standardized procedures for institutional analysis that can easily be combined with environmental and agricultural models widely used for policy impact assessment. To assess the compatibility between policy options and various institutional contexts a standardized methodology has been developed within the SEAMLESS-Integrated Project that provides for an institutional dimension in modelling: the Procedure for Institutional Compatibility Assessment' (PICA)

    L'analyse institutionnelle ex ante des politiques publiques : une procédure d'analyse de la compatibilité institutionnelle

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    AutresEx-ante impact assessment of agricultural, environmental, and rural policies has become an integral part of political decision making processes in the EU. While there is a large variety of agri-environmental modelling tools available to analyse likely social, economic, and environmental impacts of these policies, scientifically well-founded ex-ante policy assessment tools capturing institutional dimensions are still missing. In this paper, we introduce a formalised procedure for modelling - ex-ante - institutional aspects for policy implementation: the 'Procedure for Institutional Compatibility Assessment' (PICA). It has recently been developed within the SEAMLESS project as a component of an integrative modelling framework for ex-ante assessment of policy impacts on sustainable development. PICA is based on the assumption that the effectiveness of a policy and the cost-effectiveness of its implementation largely depend on the degree of compatibility between this policy and the institutional context in the respective countries and regions. It has been designed as an explorative and flexible, yet formalised methodology that enables policy makers to identify at an early stage potential institutional incompatibilities. After providing a brief overview of relevant approaches for policy assessment we elaborate on the four distinct steps of PICA and use a core element of the EU Nitrate Directive to illustrate its function

    SystÚme de Modélisation en Environnement et Agronomie reliant Science et Société ( SEAMLESS): rapport sur les attributs des systÚmes pour lesquels des indicateurs and valeurs-seuils ont été développés (PD 1.3.1). Rapport spécifiant les indicateurs et méthodes pour des analyses qualitatives en pre et post-modélisation (PD 1.3.5).

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    The systems that SEAMLESS aims to assess the impact of different policy options are today seen as more complex and it was not only one but several systems that the project will have to assess. Consequently the initial framework that was promised to be produced in WP 1 PD 1.2.1 could not be produced at that early point. In chapter 2 several general components of these systems has been identified Based on the ideas forwarded in WP1 and on literature reviews WP 2 has also developed two suggestions for how a SEAMLESS framework could look like. One is a framework based on themes and issues. This is a well known approach by policy makers but it risks creating a long list of indicators and have problems with the aggregation between the different dimensions of SD. The second proposal is a systems approach. This approach is more complex and less known by policy maker but it can serve as a basis for aggregation between SD dimensions. In chapter 3 the main methodologies that WP2 will be using for the purpose of specifying indicators, indicator calculation, calculation and qualification is outlined. This chapter also discusses the steps that have to be taken in order to translate a policy question into an indicator. The development of different methodologies for the specification of qualitative and quantitative indicators for Seamless will need a continued input form the scientific side. However as pointed out throughout the chapter the continuous interaction with stakeholders and end-users will be crucial for the success of the tool. In chapter 4 the PICA model is discussed. PICA is the institutional ingredient in the Seamless-IF. PICA is a model which will serve as a tool for analysing the plausibility of implementing a policy from data (pre modelling) and from output from the quantitative models (post-modelling). Pre-modelling analysis will be used to test whether a certain policy will be implemented or whether the institutional constraints will result in prohibitive transaction costs making it less probable that the policy will reach its objectives. In this case the analysis will be based on data i.e, there is no need to run, e.g., bio-physical farm models. The PICA model can also function as a post-model analysis for other models. In that sense, it facilitates the linking of models. In brief, PICA is a flexible tool. The results can serve for a qualitative pre-and post model analysis. The PICA model itself can have different places within the model chain depending on the issue under scrutiny. This PD has shown that WP2 has an accumulated knowledge related to indicator frameworks, indicator specification, user selection of indicators and methodologies for qualitative pre and post model analysis. However to make this knowledge useful for the SEAMLESS project is important that this knowledge is adjusted to the specific context of SEAMLESS. Throughout the PDs it has been concluded that to proceed in the development of these methodologies interaction with stakeholders is crucial. For the success of the developed tool it is important to define at which points and to which degrees interaction is needed. WP 7 has provided the methodologies for such an interaction and PD 2.6.1 as well as this PD has listed issues on which interaction with stakeholders is important. Preferably the issues for interaction should be divided in two stages, the pre-modelling stage and the post modelling stage. Two suggestions for future steps are to; 1. Produce a chronological list of these issues which is consulted and reduced by WP3- WP4-WP5-WP2 based on their needs and limitations 2. Build up a close cooperation between WP2-6-7 to develop the content of the written material that should be the basis for the stakeholder consultation as well as the time schedule of this consultation. Moreover, a few issues which seems to have fallen outside the responsibility of a WP or that have been disregarded have been identified: 1) The visualisation of indicators 2) The development of policy scenarios (which are relevant for the area of assessment of SEAMLESS). 3) The flexibility of the selection of indicators 4) The role and place of qualitative models in SEAMLESS-IF The suggestion for point 1 and 2 is that a special task force with members form relevant WP is created and that the points is taken into consideration when writing the new action plan. As for point 3 a discussion is necessary throughout the project. WP 2 suggests that this discussion is held relatively soon based on the existing draft of D 2.1.1. The suggestion for point 4 is that slightly more attention is given to the qualitative aspects of SEAMLESS IF. It is natural at this stage in the project when the first Prototype soon is to be delivered that more attention is given to SEAMFRAME. A discussion of the qualitative aspects of SEAMLESS IF would probably also clarify the possibilities as well as limitations of the quantitative models in SEAMFRAME in relation to the goal of the final SEAMLESS tool to take into consideration all dimensions of SD
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