dataesr/scientific-paragraphs-categorization huggingface dataset:
Progresses foreseen within NURESAFE NURESIM and NURISP paved the way for arriving at a European platform that will allow progress towards higher-fidelity reactor simulation in an incremental manner. Well established and validated codes covering different domains of the reactor analysis are coupled after the NURISP project: several transient core    dynamics codes are coupled with a core-thermal-hydraulics code that offers sub-channel capability, using the SALOME coupling software. It allows for quite general mapping between the calculated fields of exchanged variables and represents a necessary key feature for multi-physics. Mixing phenomena occurring in the large volumes of the RPV are analyzed with the help of CFDcodes and these codes are interfaced through SALOME with the NURESIM system-behavior codes.
This evaluation includes thermohydraulics parameters, cross-section uncertainties being excluded, This simulation will be based on the Oskarshamm-2 NPP benchmark. The transient simulated is the Oskarshamm-2 1999 stability event. This event was initiated by a loss of feedwater preheaters and a control system failure that drove to diverging power oscillations. This problem is challenging to neutron kinetics and core thermal-hydraulics coupling. The work program calls for an objective estimation of the PDF's of the uncertain thermal-hydraulic parameters using a procedure developed during NURISP and part of the URANIE module. The error propagation using Monte Carlo sampling (currently a standard approach) will then establish the uncertainty for the key parameters of the reactor. An important aspect is the consideration of the uncertainty induced by the coupling schemes, not usually considered in such analysis.
The NURESAFE program of work is organized in the simulation of some accidental scenarios named ''situation targets'' relevant for LWR reactors safety. In order to fulfill the individual codes and models validation, ''situation targets'' modeling include reference calculations, validation against experiments and plant data. The challenging selected ''situation targets'' have been selected with respect to their potential for two-way coupling: PWR main steam line break (MSLB). U PWR application. U VVER application. Boiling water reactor anticipated transient without scram (BWR ATWS). Loss of coolant accident (LOCA) in PWR.
VALIDATION The validation of the ''situation target'' models will be done by using experiments, reference plant data and quantitative deterministic and statistical sensitivity and uncertainty analyses with the methods developed within NURISP in URANIE software. Therefore, each situation target includes a specific S&U and validation task. In order to avoid duplication, the NEA and IAEA databanks will be used to contribute to the validation. Concerning codes and models, the NURESAFE project will of course benefit from the validation tasks of core physics and thermal-hydraulics codes achieved at the end of the NURISP project. Validation of coupled schemes is always difficult because of a lack of sufficiently detailed and representative experiments performed on real reactors. Therefore, validation work will focus on some specific features of the simulated situation targets.
With regard to the MSLB, one challenging problem is the validation of the core inlet flow mixing matrix. As relevant to this problem, we will use the experimental ROCOM dataset representative of a vessel of a German PWR in order to compare simulations against measurements. Another validation task will be based on the Kozlodui-6 transient representing a steam generator isolation experiment from a steady power state. In addition, these models need to be tested using uncertainty quantification methods. For this purpose, UQ evaluation will be performed on a BWR transient including propagation of uncertainties on all the physics.
The results show high concentrations of secondary inorganic aerosols in the most polluted regions being Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emissions reduction in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the model global outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement in all geographical regions with MODIS AOD retrievals when introducing SIA. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA gives generally a better agreement for secondary inorganic aerosols precursors (nitric acid, sulfur dioxide, ammonia) in particular with a reduction of the Modified Normalised Mean Bias (MNMB).
At the regional scale, over Europe, the model simulation with SIA are compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained with the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants being particulate matter, ozone and nitrogen dioxide concentrations. The introduction of the SIA in MOCAGE provides a reduction of the PM 2. 5 MNMB of 0. 44 on a yearly basis and even 0. 52 on a three spring months period (March, April, May) when SIA are maximum.
Current aerosol module The model in its current state is able to represent primary aerosols  (Martet et al. , 2009; Sič et al. , 2015). The latest version of the primary aerosol scheme in MOCAGE has been evaluated by  Sič et al. (2015). Sič et al. (2015)  checked aerosols physical parameterization and proposed improvements. Based on simulations including only primary aerosols, they checked the consistency and validates the dry and wet deposition, the sedimentation and the emission processes. MOCAGE uses the sectional approach with six size bins per type of aerosols, especially chosen to fit the different characteristics of each aerosol. Primary aerosols in MOCAGE are composed of four species: desert dust, sea salts, primary organic carbon and black carbon. Desert dust emissions are dynamically managed using  Marticorena and Bergametti (1995)  and divided into the bins using 3 modes of mean number diameters r 1 = 0.
New developments of the aerosol module In  Sič et al. (2015) , each type of aerosols uses specific size bins. Here we assume aerosols internal mixing. Therefore we use the same bin sizes for all types of aerosols, ranging from 2 nm to 50 µm with size bins limits: 2, 10, 100 nm, 1, 2. 5, 10 and 50 µm. These new bin limits have been tested on a one year global simulation only with primary aerosols and compared to a simulation similar but with the aerosol specific size bins following  Sič et al. (2015). The use of these new size bins gives similar results as using the aerosol dependent ones with a difference of less than 5 % on the forecast of PM 10 and PM 2. 5 burden on a annual mean on the global scale. From this basis, it was possible to introduce secondary inorganic aerosols in MOCAGE. SIA results from a partition between the gaseous phase and the aerosols.
Two simulations, with and without secondary inorganic aerosols, are conducted for the year 2010 and are compared to the EMEP measurement dataset. Both simulations have the global domain at 2 • lon×2 • lat, and a nested European domain at 0. 5 • lon×0. 5 • lat resolution. The latter domain covers the western part of the European continent between 16 • W to 36 • E and 32 to 72.
Observations for global simulation evaluation MODIS daily mean AOD were used to evaluate to the model simulations. For this purpose, we use the daily MODIS data level 3 (L3, collection 5. 1) for the year 2005 and perform an additional quality control and screening as presented in  Sič et al. (2015). This processing is done to minimize the number of observations that are cloud contaminated and those with statistically low confidence which often artificially increase AOD  (Zhang et al. , 2005; Koren et al. , 2007; Remer et al. , 2008). Moreover,  Ruiz-Arias et al. (2013)  showed there is a rapid increase of the relative underestimation when the MODIS' L3 AODs are below 0. 1. We then perform an additional screening by rejecting all AOD values below 0. 05. Below this value, underestimation of AOD leads to a mean relative error higher than 50 %  (Ruiz-Arias et al.
, 2013). Aerosol Optical Depth (AOD) in MOCAGE are calculated at 550 nm using Mie theory with refractive indices taken from Global Aerosol Data Set  (Köpke et al. , 1997)   For the model evaluation, we also use the HTAP observation database. It includes data from several measurement networks: EMEP, IMPROVE, NAtChem, EANET, CRE-ATE, EUSAAR, NILU and the WMO-PCSAG global assessment precipitation dataset (http://www. htap. org/). Observations used are gaseous concentrations (nitric acid, nitric oxides, sulfur dioxide, ammonia), and the particulate matter composition (sulfate, nitrate, ammonium). The release used here is dated from 1 April 2014. Daily observations and weekly observations are used separately in order to consider comparisons at the same temporal scale. Daily observations cover both Europe countries and Canada.
EMEP database The European Monitoring and Evaluation Programme (EMEP) is a scientifically based and policy driven programme under the Convention on Long-range Transboundary Air Pollution (CLRTAP) for international co-operation to solve transboundary air pollution problems (http://www. emep. int). Observations were downloaded through the EBAS repository (http://ebas. nilu. no). Daily observations are used to evaluate secondary inorganic aerosol composition (sulfate, nitrate, ammonium) over Europe. Figure  2.
AIRBASE database To make a complementary evaluation, and because SIA directly affects major regulated air pollutants, we also make AIRBASE measuring stations are located on various sites: urban, periurban, rural, etc. In order to be able to compare model simulation at the 0. 5 • longitude × 0. 5 • latitude resolution, we select the stations which are representative of the model resolution. Following  Joly and Peuch (2012) , each station is characterised with a class between 1 and 10 according to its statistical characteristics, 1 corresponding to a fully rural behaviour and 10 to a highly polluted station. The selection of station is done following  Lacressonnière et al. (2012)  who conducted an evaluation of MOCAGE at the regional scale over several years. Only the stations corresponding to 1 to 5 classes are kept for ozone.
For gaseous compounds, statistics are not presented here because there are only between 16 and 28 stations depending on the parameter and there are no nitrogen dioxide measures. Nevertheless the behaviour on this limited number of stations is similar to that of the daily observations. Figure  1  presents the location of the HTAP stations used in this study. By looking at the weekly station localisation, one can see that there are two main groups of stations, one in North America and one in Asia. By splitting the dataset between Asian and American stations, there are 29 stations for the Asian area and 156 for the American one. The results are presented in Table  9. Sulfates, based on total sulfate data, has a MNMB similar in both zones, but the correlation is better in North America (0. 67) than in Asia (0.
Results and evaluation of the regional simulation The first set of simulations, showed the model was able to simulate correctly SIA on the global scale. The next step is to check the behaviour of the model on a regional domain, Europe, with a better resolution and different emission inventories. This section presents results on the second set of simulation over the year 2010 including two nested domains: the global one (at 2 • lon × 2 • lat) and a regional one (at 0. 5 • lon × 0. 5 • lat). As we already looked at model results on a global scale (see Sect. 4), the focus in this section is put on the regional European domain. Firstly we analyse the results before comparing them with measurements from EMEP database for secondary inorganic aerosols components. Then we make a comparison to AIR-BASE measurements from an air quality point of view.
Feedback on the gaseous chemistry Figure  11  represents the annual mean concentrations of surface ozone for the RACM and the RACMSIA experiments. One can see a significant decrease of surface ozone, especially over oceans, between 5 and 10 ppbv. On land, concentrations are nearly the same. By being absorbed into the aerosol phase, nitric acid is not available for forming NO x again and then the ozone equilibrium is displaced. The effect is less important over the land because of the proximity of NO x sources which drive the nitrous oxides concentrations. Table  12  presents the statistics for ozone against hourly observations from the AIR-BASE database. The statistics are very similar between the two experiments, only the MNMB is slightly better for the RACMSIA experiment. It is linked with the ozone maps showing a decrease over the ocean while the field is similar over land.
Conclusions In this study we developed a secondary inorganic aerosol module into the CTM MOCAGE. We showed that the model is able to represent secondary inorganic aerosols on both the global scale and the European regional scale. The different constituents of the secondary inorganic aerosols being sulfate, nitrate and ammonium simulated by the model fit well against the different observational datasets used: HTAP and EMEP. These databases and the AIRBASE database were also used to assess gaseous species concentrations. Comparisons show a neutral impact of SIA on SO 2 and NO 2 , a mixed impact on HNO 3 (with a much better MNMB but slightly worse FGE and correlation) and a large improvement of NH 3. Simulations with SIA does not show a significant improvement on statistical scores for ozone.
Nevertheless, there is an impact on ozone fields at surface over sea that is important but very little change over land as reflected by the scores. The comparison with satellite AODs shows that the global aerosol budget is significantly better when SIA are used in the model. Finally, the model is able to perform generally very well at reproducing daily variations of SIA as illustrated by the comparison between MOCAGE and observations at a station in Ireland. By comparing the MOCAGE model results to the AIRBASE dataset over Europe in terms of particulate matter concentration, we showed that the model performs also better with the introduction of secondary inorganic aerosols. Especially in spring (March, April, May), the MNMB of the PM 2. 5 is improved 0. 52 rising from -0. 55 to -0. 03. Over the full year of simulation, there is still a negative bias in PM 2.
Concerning ammonia and ammonium, they have both positive bias that can at least be partly explained by the large uncertainties in ammonia emissions of about 50 %  (Kuenen et al. , 2014). In the implementation, we made choices for representing phenomena favouring computational efficiency over a very detailed representation while keeping a good accuracy. One of the final goals is to integrate this module for operational forecasts into the Prev'Air and the COPERNICUS programs. The model MOCAGE will also be used to make research studies including long run simulations for instance for the CCMI program (Chemistry-Climate Model initiative) and the analysis of the aerosol budget in the Mediterranean area. Introduction   Optics, 19, 1505 Optics, 19, -1509 Optics, 19, , 1980. 3604. 3604  Xu, L. and Penner, J.
comparisons with air quality indicators monitored over Europe. For this we use AIRBASE, which is a dense measurement network used for air quality issues. It is managed by the European Topic Centre on Air Pollution and Climate Change Mitigation on behalf of the European Environment Agency. For this study, we use the latest version (version 8) of AIRBASE database (http://acm. eionet. europa. eu/databases/airbase). AIRBASE data are used in this study to evaluate the performance of the model on PM 10 , PM 2. 5 , ozone and nitrogen dioxide forecast. For 2010, a total of 38 countries, including the 27 European Union countries have provided air quality data.
Background: Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands. Results: We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one.
Existing methods and algorithms typically use evolutionary, geometrical, probe-mapping or energy-based principles for BS identification. Evolutionary methods  [6] [7] [8]  make use of structure and/or sequence alignments to identify BSs. They assume that conserved residues among one group of functionally related proteins would vary across different groups  [9]  so they constitute an "evolutionary trace" of BSs. These approaches are limited by the fact that conserved features may not be correlated to protein activity but rather to stability or folding  [10]. Moreover, as a consequence of a low degree of sequence similarity or identity within a working dataset for a given protein query, the obtained results may be poor  [10]. Purely geometric methods  [1, [11] [12] [13] [14] [15]  have the advantage of being fast.
In a first step, we calibrated our method on two challenging targets from the "Database of Useful Decoys -Enhanced" (DUD-E)  [42]. The DUD-E database provides dedicated ligand libraries for each protein target to benchmark docking approaches, one with the known effectors, and one with a series of decoy compounds. We also used an additional "generic" library, the Enamine Golden Fragments (EGF) collection (www. enamine. net). It contains a moderate number of entities, 1500 fragments, presenting a wide chemical diversity (see Additional file 1: Figure  S1 ). The use of this library permits us to cover a large chemical space with a limited computational effort. Blind dockings of these databases were performed with two free software packages that have been extensively used and evaluated by others  [43] [44] [45] [46] [47] ; Dock  [48]  and AutoDock Vina (ADvina)  [49] , and are based on different searching algorithms and scoring functions.
We identified the combination of docking program and ligand library giving the best prediction rates in this analysis. Then, we used this combination to assess the method accuracy in BS identification on all targets in the DUD-E (102 proteins). Docking results were analyzed with an in-house version of the Self-Organizing Map (SOM) applied directly to the ligand atomic coordinates as descriptors. The resulting SOMs provide a simple and intuitive representation of the spatial distribution of the docking poses. BSs can be identified as zones of high docking pose density and homogeneity. In addition, we tested whether the proposed approach could give some "a priori" information on the chemical nature of potential ligands. For that, we analyzed the chemical structure of the docked compounds from the naive fragment library with Morgan fingerprints, which provide a decomposition of the molecules into a set of "chemical features"  [50, 51].
Protein targets and ligand libraries The DUD-E database  [42, 55]  provides 102 targets ready for docking in pdb format. For each target, a definition of the active site (AS) is provided by means of the co-crystal 3D structure of the target with an active ligand. Prior to the assessment of the method's accuracy in BS identification, we tuned the parameters of the approach to obtain the most accurate predictions on two specific targets. The first target, the HIV-1 reverse-transcriptase, is a heterodimer with two structurally distinct subunits, p51 (429 AA) and p66 (553 AA)  [56]. The docking target site defined in the DUD-E is a sub-domain (272 AA) derived from the 3LAN PDB entry  [57]. It is composed of a part of p66 and a small portion of p51 and contains the active site, an allosteric site and many other pockets and cavities (Figure  1 ).
The DUD-E provides 338 active molecules and 18880 decoys for HIV-RT. The second target is a sub-domain (221 AA) of the human tyrosine-protein kinase ABL1 (1130 AA) as defined in the DUD-E (PDB entry: 2HZI)  [58]. Similarly to the first target, this sub-domain contains the active site, a secondary BS and many other cavities (Figure  1 ). A library of 182 actives molecules and 10745 decoys is provided for ABL1. In addition to the specific libraries of active and nonactive molecules provided by the DUD-E, we docked the Enamine Golden Fragment (EGF) library, composed of 1500 fragments, (www. enamine. net). The EGF collection follows the "Rule of Three"  [59]  with range of values in slightly tighter intervals. In practice, these ranges are: (i) molecular weight within [ 150, 300] Da; (ii) clogP within [ -2, 3]; (iii) less than 3 Hbond acceptors; (iv) less than 3 Hbond donors; (v) less than 3 rotatable bonds and (vi) polar surface area inferior to 60Å 2.
Cavity identification An in-house software based on the Lee and Richards solvent accessible surface calculation algorithm  [60] , called mkgrid  [61]  was used to detect cavities embedded in both protein targets. The method discretizes space on a 0. 5 Å grid and calculates the solvent accessible volume with a 1. 4 Å radius probe sphere (also accessing interior cavities). Bulk solvent is defined with a 10 Å radius probe sphere. Cavities are defined as the volume accessible to the solvent, but not to bulk solvent. Remaining void grid points are clustered by connectivity and labeled according to their cluster number to identify individual cavities. Clusters having less than 96 points (12 Å 3 , about the volume of a water molecule) are discarded. The cavities were graphically inspected.
SOM analysis and BS identification The SOM algorithm reveals docking hotspots by the presence of areas with low U-values and generally high neuron densities. Areas between docking hotspots appear as low density regions on the SOM, and associated with high U-values. We defined a cutoff (t U ) on the U-values to distinguish between potential BSs (consensual binding regions with U-values ≤ t U ) from barriers between BSs (regions with U-values > t U ). To automate the definition of t U , we fitted a Gaussian mixture model (GMM) to the distribution of the U-values with an algorithm implemented in the scikitlearn python package  [63]. The number of Gaussians to fit was defined by making use of the Bayesian information criterion (BIC)  [64]. The components which had the largest Gaussian weight were selected.
HIV-RT The SOM analyzes for HIV-RT are shown in Figure  2  for ADVina, and Figure  3  for Dock. U-matrices are shown in the top line (a-c) and docking score projections in the bottom line (d-f ). For ADvina, the U-values for the three libraries, DUD-E decoys, DUD-E active molecules and EGF fragments, shown in Figure  2 (a) to (c), are quite similar, with the same areas showing low U-values. One, labeled (2), contains the co-crystallized ligand, and thus fits the AS as described in the literature (PDB entry: 3LAN). The second area, labeled (3), corresponds to an allosteric site of HIV-RT  [66]  and will be referred to as the second binding site (BS2) of HIV-RT. For the EGF library, a higher neuron consensus (lower U-values) is observed at the AS than with the DUD-E active molecules. Inversely, the fragments gave lower neuron consensus than the DUD-E active compounds at the BS2.