dataesr/scientific-paragraphs-categorization huggingface dataset:
CSP max and K D were used as fitting parameters in Xmgrace software (https://plasma-gate. weizmann. ac. il/Grace). For OR2 and N-term peptides containing respectively two and four OR motifs, the Hill-Langmuir equation, reflecting the occupancy of macromolecules, was applied: CSP i = CSP max Ã ðL i Þ n K D +ðL i Þ n (Equation 3) where n is the Hill coefficient, describing the cooperativity of ligand binding. CSP max , K D and n were used as fitting parameters in Xmgrace software. The plots report the average plus sample standard deviation of the K D and n values obtained fitting the well-resolved 1 H resonances during the titration.
Circular dichroism experiments and data analysis CD melting experiments were done in duplicate using a Jasco J-815CD spectrometer (0. 1 cm path length). CD melting curves (25 C-95 C, 1 C/min) of huPrP90-231 (8 mM) and huPrP23-231 (5 mM) in 10 mM Na 2 HPO 4 / NaH 2 PO 4 pH 7, with and without 15 molar equivalent of Zn(II)-BnPyP or Fe(III)-TMPyP, were collected at 222 nm. Duplicates were merged point to point, calculating the average CD signal and the standard error of the mean (SEM). The protein melting temperature (T m ) was obtained as in. 81.
Isothermal titration calorimetry Experiments were done at 25 C using a PEAQ-ITC MicroCalorimeter (MicroCal, Malvern Instruments Ltd. , Malvern, UK) following the general procedure. 82  A 24 mM huPrP23-231 solution was titrated with Zn(II)-BnPyP (1 mM) using injections of 2 mL. Protein and ligand were prepared and diluted with the same buffer (10 mM Na 2 HPO 4 /NaH 2 PO 4 pH 7). Calorimetric data were analyzed with the instrument software. Heat change peaks were integrated and normalized per mole of Zn(II)-BnPyP injected. The ITC binding curve was fitted to the one binding site model equation. 83.
ACKNOWLEDGMENTS We thank  Sylvain Lehmann  for the ScN2a-22L cells,  Emiliano Biasini  for the ScN2a-RML cells and the D18 antibody;  David A. Harris  for plasmids encoding PrPD23-89 and PrPD23-111;  Joel Watts  for plasmids encoding bvPrP;  Byron Caughey  for plasmid encoding huPrP90-231;  Ester Zito  for the anti-puromycin antibody;  Richard J. Kascsak  for the 3F4 and 6D11 antibodies;  Man Sun-Sy  for the 11G5 antibody;  Steven J. Collins  for the 8A4 antibody;  Olivier Andreoletti  for the Dawson scrapie strain;  Wenquan Zou ,  Vincent Beringue and  Glenn Telling  for the TgZWV, Tg338 and TgVole brains. We are grateful to  Rosanna Piccirillo  for participating in the initial phase of this project, and to  Stefano Fumagalli  for advice on super-resolution microscopy. We also thank the  BM29 BIOSAXS ,  ESRF  for provision of the beamtime and assistance during data collection, and  Massimo Degano  for access to the Italian block allocation group (BAG) for structural biology (proposal number MX-2291).
Given recent scientific advances, coastal flooding events can be modelled even in complex environments. However, such models are computationally expensive, preventing their use for forecasting. At the same time, metamodelling techniques have been explored for coastal hydrodynamics, showing promising results. Developing such techniques for predicting coastal flood information (e. g. , inland water depths) requires large enough learning datasets providing such inland information. However, detailed inland coastal flood observations are scarce and-when available-only correspond to a limited number of events. This paper aims at demonstrating how we can fill this gap by introducing a publicly available dataset, presenting its setup, and providing examples of use and recommendations. It has been built for the site of Gâvres (France), relying on the joint use of spectral wave (WW3) and non-hydrostatic wave-flow (SWASH) models, accounting for wave overtopping.
Metamodels are functions that aim to reproduce the behaviour of a "true" model (e. g. , a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Their main advantage is their reduced computation time, which may be of a few seconds to minutes instead of several days for the "true" model, even using high-performance computing  [10]. However, their setup requires the availability of learning datasets, datasets that are presently missing, especially when aiming to predict inland information (e. g. , flooded surface, map of inland water depth). To fill this gap, a method has been designed, allowing the construction of a simulated flood dataset (named CFMDG for Coastal Flood Modeling Dataset in Gâvres) for the site of Gâvres (Brittany, France) within the RISCOPE research project (see  [10]  for an overview).
It relies on the joint use of a spectral wave model named WW3  [11] , a non-hydrostatic wave-flow model named SWASH  [12] , historical data, and statistical methods. The objective of the current paper is threefold: (1) provide a detailed description of the developed method used to build this flood dataset for the sake of application to other sites; (2) provide an accessible dataset of use for method developments (metamodelling, forecast), site-specific process knowledge, and risk management purposes;  (3)  provide examples of use. The CFMDG dataset is publicly available at https://zenodo. org/record/7533336#. Y-3j8XbMKUk, accessed on 13 June 2023  [13]. It should be noted that previous non-publicly available versions of the CFMDG dataset have been already used to develop a forecast and early warning prototype, relying on metamodelling approaches  [10].
The Site and Dataset Gâvres is representative of towns presently subject to wave overtopping and presents the advantage to be a small territory (2 km 2 ) for exploration needs. Located on the French Atlantic coast (Figure  1a ), it has experienced five significant past flood events since 1900 (see  [3]  for a detailed analysis for the period from 1900-2010). The last major flood occurred on March 10th of 2008 (windstorm Johanna), in which approximately 120 houses were flooded (Figure  2a , purple dots). The CFMDG dataset compiles a set of 250 scenarios and related post-processed coastal flood simulations (Figure  1a ) for a digital elevation model representative of the 2018 coastal defence configuration. Each scenario is defined by a 6 h time series centred on high tide, with one time series per forcing variable.
The CFMDG dataset compiles a set of 250 scenarios and related post-processed coastal flood simulations (Figure  1a ) for a digital elevation model representative of the 2018 coastal defence configuration. Each scenario is defined by a 6 h time series centred on high tide, with one time series per forcing variable. The forcing variables are: local relative mean sea level (NM), tide (T), atmospheric storm surge (S), offshore wave characteristics (significant height, Hs; peak period, Tp; peak direction, Dp), and offshore wind (speed, U; direction, DU). These scenarios combine past real events with extreme statistics-based events and some complementary fictive events. The post-processed outputs include, for each scenario, the maximal flooded area (Smax) and the maximal water depth (Hmax) in each inland model grid point (grid cell size of 3 m).
The data described here were partly used in previous publications to develop metamodels, allowing the prediction of inland flood information, such as flooded areas  [14, 15]  or maps of inland water depths  [16] , and the construction of a forecast and early warning prototype  [10]. Table  1  lists the fields of the dataset for each scenario. For each scenario, the following complementary information is provided in the dataset: the type of scenario (past real event, variation around a past real event, statistical event, variation around a statistical event; when the type of scenario corresponds to a variation around an event, the n° of the scenario corresponding to the concerned event is given), the date of the high tide (UTC; for past real flood), the source of the data. A detailed description of the various types of scenarios is provided in Section 2.
Block 1: Coastal Flood Model Setup and Validation To produce the CFMDG dataset, we used the non-hydrostatic phase-resolving model SWASH  [12]  to model floods at the study site. The modelling chain was previously set up by Idier et al. [3, 4]. We briefly summarise the key elements here. First, the wave conditions Table  1  lists the fields of the dataset for each scenario. For each scenario, the following complementary information is provided in the dataset: the type of scenario (past real event, variation around a past real event, statistical event, variation around a statistical event; when the type of scenario corresponds to a variation around an event, the n • of the scenario corresponding to the concerned event is given), the date of the high tide (UTC; for past real flood), the source of the data.
Block 1: Coastal Flood Model Setup and Validation To produce the CFMDG dataset, we used the non-hydrostatic phase-resolving model SWASH  [12]  to model floods at the study site. The modelling chain was previously set up by Idier et al. [3, 4]. We briefly summarise the key elements here. First, the wave conditions offshore of Groix (Hs, Tp, Dp) are downscaled to the boundaries of the SWASH model (Figure  1 ) using the WW3 spectral wave model  [11]  by adopting the Jonswap wave spectrum with a directional spread of 30 • and taking into account the local wind (U, DU) and still water level (named ξ, with ξ = NM + T + S). The WW3 computational domain is shown in Figure  1a  (in black). SWASH is run with non-uniform wave boundary conditions and spatially uniform still water levels (ξ, resulting from spatially uniform mean sea level, tides, and atmospheric surges) and wind (U, DU).
The spatial and temporal resolutions of SWASH are 3 m and more than 10 Hz, respectively. In the vertical direction, a two-layer discretisation is used. The spatial variability in the bottom roughness (related to land use) is taken into account following the same approach as in  [2]. This model chain was validated in terms of the flooded area observed during the 10th March 2008 storm event  [3]. For this validation (Step 3, Figure  2 ), the model was run for a digital elevation model (DEM) representative of the 2008 period (denoted DEM2008) over a 6 h window (centred on high tide) using simulated water level data produced by Le Roy et al. [2]  and wave and wind conditions extracted from a hydro-meteorological dataset covering the 1900-2016 period and built using post-processing (correcting) surge, wave, and meteorological hindcasts ([3], Step 2 of the method, see Section 2.
From the available bathymetric surveys (provided by the French Hydrographic Hydrographic and Oceanographic Office (SHOM) and the Danish Hydraulic Institute (DHI)), LiDAR data (public RGE ALTI ® IGN product-https://www. data. gouv. fr/fr/datasets/rge-alti/, accessed on 13 June 2023; limited access data of Lorient Agglomeration), and Global Positioning System (GPS) survey of the coastal defences, Idier et al. [10]  have constructed a DEM (denoted DEM2018) that is representative of the 2015 bathymetry (winter) and 2018 coastal defences. All the simulations used to build the CFMDG dataset are performed with this DEM2018. It should be noted that Step 4 is optional, as it depends on the local evolution of the topo-bathymetry and coastal defences as well as the objective of the dataset. alti/ accessed date 13 June 2023; limited access data of Lorient Agglomeration), and Global Positioning System (GPS) survey of the coastal defences, Idier et al.
[10]  have constructed a DEM (denoted DEM2018) that is representative of the 2015 bathymetry (winter) and 2018 coastal defences. All the simulations used to build the CFMDG dataset are performed with this DEM2018. It should be noted that Step 4 is optional, as it depends on the local evolution of the topo-bathymetry and coastal defences as well as the objective of the dataset.
Block 2: X Data and Grid Experiment Design Table  2  summarizes the types of scenarios (forcing conditions X) included in the CFMDG dataset. As highlighted above and detailed in Section 2. 5, the computation time related to the hydrodynamic modelling limits the number of possible simulations. In the present work, the total number of simulations was initially limited to about 130. Analysing the preliminary results, other additional simulations were carried out to reach 250 scenarios. To design realistic scenarios, we require data covering past periods (ideally several decades). This is Step 2 of the method. In the present work, we rely on the database (named HM) of hydro-meteorological data (at the X locations, Figure  1a ) set up in  [3]  over the period from 1900-2016, with a 10 min time step resolution (Figure  4 ).
Here, we remind briefly the method used to set up this HM dataset. As there is no tide gauge measurement close enough to the site and covering the entire study period to estimate still water levels (ξ), each component of the still water level (NM, T, S) has been estimated over the 1900-2016 period based on different types of data. For the mean sea level (relative to the land), regional tide gauge data and the 3 nearest GPS stations data have been combined to account for the vertical land motion. For the tide, it has been reconstructed using the tidal component database FES2014  [18]. For the atmospheric storm surge, waves, and wind, datasets of different qualities have been combined, and a method has been applied to reduce the bias (based on a quantile-quantile method). Appendix A provides more details on the datasets and processing used to build the HM dataset.
S5 Past real event, outside the damage database of  [3]  68 183 to 250 To design realistic scenarios, we require data covering past periods (ideally several decades). This is Step 2 of the method. In the present work, we rely on the database (named HM) of hydro-meteorological data (at the X locations, Figure  1a ) set up in  [3]  over the period from 1900-2016, with a 10 min time step resolution (Figure  4 ). Here, we remind briefly the method used to set up this HM dataset. As there is no tide gauge measurement close enough to the site and covering the entire study period to estimate still water levels (ξ), each component of the still water level (NM, T, S) has been estimated over the 1900-2016 period based on different types of data. For the mean sea level (relative to the land), regional tide gauge data and the 3 nearest GPS stations data have been combined to account for the vertical land motion.
For the tide, it has been reconstructed using the tidal component database FES2014  [18]. For the atmospheric storm surge, waves, and wind, datasets of different qualities have been combined, and a method has been applied to reduce the bias (based on a quantile-quantile method). Appendix A provides more details on the datasets and processing used to build the HM dataset. Figure  4  shows the distribution of this continuous HM dataset over the 1900-2016 period. This dataset is used for two purposes: (1) to provide forcing conditions to the numerical model on past events for validation purposes (Step 3) and (  2 ) to design the X scenarios (Step 5). The first set of scenarios (Figure  2 , step 5) was built as follows. First, a combination of Monte Carlo random sampling and multivariate extreme value analysis is performed on the HM dataset to provide a large dataset of extreme conditions (here, ~150,000).
Then, we start by selecting a limited number of 100 offshore meteo-oceanic scalar conditions; XS = (ξ, Hs, Tp, Dp, U, DU), as was performed by Rohmer et al. [19]. This selection is designed by applying the clustering procedure described by Gouldby et al. [20]. This approach ensures that the selected XS input conditions are sufficiently extreme to lead to flooding but are also realistic. Then, using this first selection of XS conditions and the 1900-2016 hydrometeorological dataset  [3] , a probabilistic classifier is used to locate the time instants of The first set of scenarios (Figure  2 , step 5) was built as follows. First, a combination of Monte Carlo random sampling and multivariate extreme value analysis is performed on the HM dataset to provide a large dataset of extreme conditions (here, ~150,000).
Then, we start by selecting a limited number of 100 offshore meteo-oceanic scalar conditions; X S = (ξ, Hs, Tp, Dp, U, DU), as was performed by Rohmer et al. [19]. This selection is designed by applying the clustering procedure described by Gouldby et al. [20]. This approach ensures that the selected X S input conditions are sufficiently extreme to lead to flooding but are also realistic. Then, using this first selection of X S conditions and the 1900-2016 hydro-meteorological dataset  [3] , a probabilistic classifier is used to locate the time instants of these maximum values (based on quadratic discriminant analysis;  [21] ), and a multivariate truncated Gaussian-Monte-Carlo-based sampling procedure is applied to generate the 100 corresponding time series-i. e. , functional inputs X = (NM (t), T (t), S (t), Hs (t), Tp (t), Dp (t), U (t), DU (t)), over 6 h, with 10 min time steps.
Keeping in mind that SWASH is a research code, spurious numerical oscillations may sometimes affect the solutions in very tough applications and lead to simulation crashes. Thus, running the modelling chain for the 100 scenarios leads to a set of 94 usable model simulations (scenarios S1, Table  2 ). This first set of scenarios is completed with 21 historical forcing events (scenarios S2, Table  2 ) including the 9 known to have led to flooding at the study site (see  [3] ). It should be noted that two different scenarios-sc100 and sc116-correspond to the historical event Johanna 10/03/2008, the difference being related to the data source, leading finally to 22 scenarios pf type S2. The first set of scenario is also completed by 16 scenarios simulated from variations of the forcing conditions of the 9 historical flood events (part of scenarios S3, Table  2 ).
• Real past conditions not having led to flood or damage events (after  [3, 10] ), extracted from the 1900-2016 HM database of  [4] ) and from complementary source data (MARC/LOPS and Meteo-France for events occurring after 2016). This scenario type is denoted as S5 in Table  2. Finally, 250 scenarios are designed (summarized in Table  2  and Figure  5a ). For each X scenario, in addition to the information provided in Table  2 , the following complementary information is provided: data source and scenario of reference when the considered scenario corresponds to a variation around another scenario (e. g. , a past real event or a fictive event). first set of scenarios is completed with 21 historical forcing events (scenarios S2, Table  2 ) including the 9 known to have led to flooding at the study site (see  [3] ).
• Real past conditions not having led to flood or damage events (after  [3, 10] ), extracted from the 1900-2016 HM database of  [4] ) and from complementary source data (MARC/LOPS and Meteo-France for events occurring after 2016). This scenario type is denoted as S5 in Table  2. Finally, 250 scenarios are designed (summarized in Table  2  and Figure  5a ). For each X scenario, in addition to the information provided in Table  2 , the following complementary information is provided: data source and scenario of reference when the considered scenario corresponds to a variation around another scenario (e. g. , a past real event or a fictive event). 2 ) for each scenario, (b) time series of the forcing conditions for each scenario (250 curves for the 250 scenarios) from 3 h before high tide (H.
Block 3: Simulations and Post-Processing Regarding Step 6 (Figure  2 ), all the simulations used to build the Y dataset were performed on 2. 6 GHz MD Abu Dhabi and 2. 4 GHz AMD Interlagos processors. The entire modelling chain (WW3-SWASH) was run on 48 cores. A single scenario run requires a computation time of 3 days to simulate 6 h on 48 cores, with SWASH representing 97% of the computation. The total computation time to build the dataset represents about 2 years of computations (3 days × 250 scenarios) on 48 cores. Our simulations provide spatiotemporal grids of water depth over the entire SWASH domain (Figure  1a , pink box) at resolutions of 3 m and 1 min over a 6 h period. In step 7, these model outputs are post-processed in the following way: (1) computation of the maximum water depth (Hmax) in each pixel of the SWASH domain (i.
e. , every 3 m); (2) extraction of Hmax value and the centre of each grid cell over the inland domain of interest (Figure  1b , blue domain). This directly provides the Hmax outputs (i. e. , Hmax value in Np = 64,618 points, all located inland and distributed on a regular grid, see zoom in Figure  1b ). Smax is computed as the sum of inland points at which Hmax > 0, multiplied by the grid cell dimension (i. e. , 3 × 3 = 9 m 2 ). computation time of 3 days to simulate 6 h on 48 cores, with SWASH representing 97% of the computation. The total computation time to build the dataset represents about 2 years of computations (3 days × 250 scenarios) on 48 cores.
Results: Synthetic View of the Dataset This subsection provides a synthetic view of the built CFMDG dataset. The dataset is made of input data (X, with X = (NM, T, S, Hs, Tp, Dp, U, DU) (t)) and output data (Y, with Y = (Smax; Hmax(longitude, latitude) for the Np points)). Figures  5  and 6  show a synthetic view of the X time series and Y outputs for the 250 scenarios. To complete the synthetic view of the Hmax maps dataset, Figure  7  shows the distribution of the flood intensity (using Smax as an intensity indicator and considering 5 classes, from Smax = 0 to Smax larger than 30% of the studied area, Stot) as well as a map that shows, for each pixel, the ratio (rp) defined as the number of scenarios in which the pixel is flooded (npf ) to the total number of simulations (denoted npt, with here npt = 250).
To complete the synthetic view of the Hmax maps dataset, Figure  7  shows the distribution of the flood intensity (using Smax as an intensity indicator and considering 5 classes, from Smax = 0 to Smax larger than 30% of the studied area, Stot) as well as a map that shows, for each pixel, the ratio (rp) defined as the number of scenarios in which the pixel is flooded (npf) to the total number of simulations (denoted npt, with here npt = 250).
Present and Future Effect of Past Hydro-Meteorological Events Past flood events are often used as a reference for flood hazard assessment. However, the first issue when investigating historical flood events is to have access to the forcing conditions corresponding to these events. The present dataset allows access to these forcing conditions for the following nine known past flood events (five major and four minor):  The CFMDG dataset can also be used to analyse the maximum water depth (reached over each simulation in each location) depending on the classes: Figure  8  (second and third rows) shows the Hmax maps corresponding to the simulations that led to the minimum and maximum Smax values within each Smax class. The colour scale of Hmax is designed based on the needs of local users (see  [10] ; for instance, the threshold of 0.
Present and Future Effect of Past Hydro-Meteorological Events Past flood events are often used as a reference for flood hazard assessment. However, the first issue when investigating historical flood events is to have access to the forcing conditions corresponding to these events. The present dataset allows access to these forcing conditions for the following nine known past flood events (five major and four minor): 02/02/1904 04:00 (sc110), 09/01/1924 05:30 (sc95), 26/02/1978 05:30 (sc96), 10/01/2001 03:40 (sc98), 07/02/2001 15:20 (sc106), 27/10/2004 15:20 (sc99), 10/03/2008 05:20 (sc100 and sc116), 10/02/2009 04:00 (sc101), 28/02/2010 03:10 (sc108). For the 10/03/2008 event, there are two scenarios: sc100 is extracted from the continuous forcing conditions over the 1900-2016 period built by Idier et al.