dataesr/scientific-paragraphs-categorization huggingface dataset:
Steed et al. ( To determine the new selection criteria, we use a training set of crowdsourced detections between January 2016 and May 2019 including 708 events triggered by the EMSC website traffic, 782 events triggered by the LastQuake app, and 648 events triggered by TED. Note that the same earthquake may initiate several triggers and the data set represents 2,138 unique events. To validate the selection criteria, we use the data set between 10 October 2019 and 12 December 2019 that were not used in the creation of the training data set. We consider only those events that produced a location at the last, 10th iteration. The validation data set contains 288 events of which 123 events triggered by the EMSC web-site traffic, 97 events triggered by the LastQuake app, and 68 events triggered by TED.
(2019). The selection criteria allow us to reduce the EMSC publication delay after the event origin time by as much as 4 min on average and publish 75% of the events within 2 min after their occurrence. The performance of the CsLoc services depends on both population and station density as well as information timeliness. To further improve the CsLoc services we plan to improve the network coverage by complementing the actual real time seismic phases obtained from the GEOFON Program with more openly accessible stations, without significantly increasing the data latency.
We developed a multi-level model of the French COVID-19 epidemic at the regional level. We rely on a global extended Susceptible-Exposed-Infectious-Recovered (SEIR) mechanistic model as a simplified representation of the average epidemic process, with the addition of region specific random effects. Combining several French public datasets on the early dynamics of the epidemic, we estimate regionspecific key parameters conditionally on this mechanistic model through Stochastic Approximation Expectation Maximization (SAEM) optimization using Monolix software. We thus estimate the basic reproductive numbers by region before lockdown (with a national average at 2. 81 with 95% Confidence Interval [2. 58; 3. 07]), attack rates (i. e. percentages of infected people) over time per region which range between 1.
Data sources Open-data regarding the French COVID-19 epidemic is currently scarce, as the epidemic is still unfolding. Santé Publique France (SPF) in coordination with the French regional health agencies (Agences Régionales de Santé -ARS) has been reporting a number of aggregated statistics at various geographical resolutions since the beginning of the epidemic. During the first weeks of the epidemic in France, SPF was reporting the cumulative number of confirmed COVID-19 cases with a positive PCR test. Other French surveillance resources such as the Réseau Sentinelles  (Valleron et al. , 1986)  or the SI-VIC (Système d'information pour le suivi des victimes, maintained by the ANS -Agence du Numérique en Santé, formerly named ASIP) database quickly shifted their focus towards COVID-19, leveraging existing tools to monitor the ongoing epidemic in real time, making as much data available as possible (whith respect to data privacy regulation).
In this study, we combined data from three different data sources: i) the daily release from SPF, from which we computed the daily incident number of confirmed COVID-19 cases (i. e. with a positive PCR test) in each region; ii) the SI-VIC database that started recording COVID-19 patients hospitalizations in French public and private hospitals on March, 13 th , from which we computed the daily incident number of COVID-19 hospitalization in each region. All cases are either biologically confirmed or present with a computed tomographic image highly suggestive of SARS-CoV-2 infection; iii) the Réseau Sentinelles which started estimating the weekly incidence of COVID-19 in each French region on March 16 th. We used the Réseau Sentinelles network's weekly incidence estimates of symptomatic cases (including non confirmed cases) to set the ratio between ascertained and unascertained cases in each region (later denoted as r).
Estimation Region-specific model parameters Based on the results from the theoretical identifiability analysis of the structural model of the epidemic from Equation (1) (see Section S1 of Web Appendix 1), we estimate the parameters (b i , D q i ) as well as the initial state (E 0i ) and intervention effects (β w1i , β w2i , β w3i , ans β w4i ) when the epidemic begins being reported in each region i. We used the Monolix software version 2019R2 (Lixoft SAS, 2019) to estimate those seven parameters by maximizing the likelihood of the data given the model and the other fixed parameters. This software relies on a frequentist version of the Stochastic Approximation Expectation Maximization (SAEM) algorithm  (Delyon et al. , 1999)  and standard errors are calculated via estimation of the Fisher Information Matrix  (Kuhn and Lavielle, 2005) , which is derived from the second derivative of the log-likelihood evaluated by importance sampling.
In addition, we use profile-likelihood to confirm that no further information can be gained from the data at hand on parameters α, D e and D I by running the SAEM algorithm multiple times while setting these parameters to different values and obtaining similar maximum likelihood values (meaning more data would be needed to be able to estimate those parameters). During inference, practical identifiability of the model is evaluated by the ratio of the minimum and maximum eigenvalues of the Fisher Information Matrix, that will be referred as "convergence ratio" in the reminder of the manuscript. Convergence of the SAEM algorithm was assessed by running multiple SAEM chains and checking that they all mix around similar probability distributions.
gouv. fr web portal (https://www. data. gouv. fr/fr/ organizations/sante-publique-france/) as well as on the GEODES platform (https://geodes. santepubliquefrance. fr/), hence allowing us to work immediately on this topic. Furthermore, we have made our code fully available on GitHub www. github. com/sistm/SEIRcovid19 for facilitating its dissemination and re-use. In conclusion, the lockdown has clearly helped controlling the epidemics in France in every region. The number of infected people varies from one region to the other because of the variations in the epidemic start in these regions (both in terms of timing and size). Hence, the predicted proportion of infected people as of May 11 varies, but stays below 10 % everywhere. It is clear from this model, as in other published models  (Di Domenico et al.
Acknowledgements The authors thank  Romain Griffier  for his time in discussing the aggregated features of COVID-19 patients care at the  Bordeaux University Hospital. The authors thank the opencovid-19 initiative for their contribution in opening the data used in this article. BPH thanks  Vincent Pey  for discussions about the clinical characteristics of the COVID-19 infection. We thank  Lixoft SAS  for their support. Numerical computations were in part carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr/). This work is supported in part by  Inria Mission   COVID19 , project  GESTEPID.
Availability statement The data from the SurSaUD R database regarding COVID-19 is available from the data. gouv French government platform at https://www. data. gouv. fr/fr/datasets/donnees-des-urgences-hospitalieres-et-de-sosmedecins-relatives-a-lepidemie-de-covid-19 alternatively on the GEODES platform by Santé Publique France (https://geodes. santepubliquefrance. fr). The source code used for this work is available on GitHub at www. github. com/sistm/SEIRcovid19. The Kalman implementation used in Section 2 of Web Appendix 1 is available upon request.
Finally, sensaas provides an alignment and evaluates the molecular similarity by using Tversky coefficients. To assess the efficiency of this approach, we tested its ability to reproduce the superimposition of X-ray structures of the benchmarking AstraZeneca (AZ) data set and, compared its results with those generated by the two shape-alignment approaches shaep and shafts. We also illustrated submatching properties of our method with respect to few substructures and bioisosteric fragments.
Point set registration is a fundamental problem in many domains: pattern recognition, computer vision or data reconstruction. 30] Registration is often considered as a hard optimization problem because perfect point-to-point correspondences rarely exist: the point clouds might partially overlap only, or the underlying objects may have particular local geometrical features. Nevertheless, we show in this paper that 3D point set registration can be particularly attractive to align molecular shapes and sub-shapes. The resulting approach is called sensaas, for sensitive surface as a shape. The following sections detail the sensaas workflow and show its ability to reproduce experimentally verified superimpositions of dissimilar molecules of the benchmarking AstraZeneca (AZ) data set. [31] Originally, this data set was created for validating pharmacophore programs but it also provides an appropriate benchmarking data set for evaluating the general applicability of a molecular alignment method.
Choice of the Voxel Size (VS) To be efficient in a context of molecular surfaces, we had to tune the voxel size (VS) for global and color. All the functions used in global and color are implemented with functions of the open-source library Open3D0. 7. 0. [42] As described in the workflow description, the VS influences the number of points in the reduced point cloud. It is important to select a value consistent with the density of the point cloud. In our study, points are uniformly distributed on the vdW surface and the distance between two adjacent points is ~0. 3 Å (NSC program (version 2. 0) from Eisenhaber et al. [33]  ). Table  1  shows the average percentage of remaining points for various VSs. As expected, a VS � 0. 3 does not significantly lower the number of points since more than (5 of 14) 2000081 70 % of initial points are kept.
Computing Time All experiments have been carried out on a Dell PowerEdge R940 (Intel Xeon Gold 5220). sensaas is running using the Debian version 5. 3. 15-1 of Linux. The processing time for molecules used in present tests takes between 3 to 25 seconds per run (Figure  4 ). Computation times of 25 seconds occur when a molecule is aligned on a shifted version of itself, for example in self-matching tests. However, aligning a molecule on itself is a rare and specific experiment, per se. In our study, we only used it as a control. No optimization of the code was carried out so far but an optimization of the point cloud preprocessing and the use of Python threads over voxel size could bring a substantial improvement in computation times.
Benchmarking AZ Data Set Tests involving self-and substructure matchings are prerequisites but are not broad enough to evaluate the general applicability of a molecular alignment method. The only experimental evidence of alignments between different molecules is their co-crystallization with the same protein. Therefore, the performance of sensaas was assessed by comparing its alignments using the 121 experimental overlays of the AZ test set available at the CCDC Center. [31] This data set contains 1465 co-crystallized ligands extracted from the Protein Data Bank (PDB). The authors annotated overlays according to shape, feature and 2D fingerprint similarities between ligands and, finally classified overlays as either easy (22 cases), moderate (73 cases), hard (18 cases) or unfeasible (8 cases) to predict.
We then assessed the ability of sensaas to generate the expected alignment when a different conformation of the Source is used. Figure  S2a  in the Supporting Information shows the best alignment of such a conformer of the Source Imatinib-part3 that has a RMSD of 1. 99 Å with the original substructure conformation. This alignment shows the expected matching of the aromatic and piperazine rings, but an inversion of the amide function. This is caused by the conformer that differs from the original Imatinib structure, precluding a perfect matching. Therefore, moderate variations in conformation do not prevent the identification of the correct alignment by sensaas. We further investigated the capacity of sensaas to identify the best Imatinib-part3 conformer from a library of 74 conformers generated with RDKit Open-Source Cheminformatics Software (http://rdkit.
Benchmarking AZ Data Set As the only experimental evidence of alignments between different molecules is their co-crystallization with the same protein, we tested the ability of sensaas to reproduce the superimposition of X-ray structures of the benchmarking AZ data set. Then, to assess its performance, we compared results with those generated with shaep  [6]  and shafts. [7] In this experiment, sensaas was run ten times and the best ranked solution, i. e. best gfit + hfit value, was retained. shaep and shafts, being deterministic methods, were run once with the default settings and with partial charges calculated with cxcalc calculator. [49] Contrary to shaep and shafts, sensaas does not require the calculation of partial charges. Figure  5  shows the distribution of the lowest RMSD obtained for the 1465 molecules.
[19] Our algorithm, in contrast, uses all atoms and dense point-based surfaces calculated by the program NSC (600 dots per atomic sphere). [33] The advantages of using dense point clouds are that a property is not associated with a single point in the space (the center of a patch for example), and that finer variations of physico-chemical properties can be mapped onto the surface. In the current setting, points are colored according to the pharmacophore feature of their closest atoms: polar, apolar, aromatic and other, but this could easily evolve towards more complexity: additional classes and colors, computed molecular properties such as the partial charge, potential energy… Evaluating the impact of such properties on alignments need to be investigated and assessed. The present results show that sensaas is able to identify the correct alignment of similar structures (self-and substructure matchings) and is able to rank them first.
The general applicability of our alignment method was assessed by using the benchmarking AZ data set. Results showed that sensaas provides accuracy performance equivalent to that of the reference methods shaep and shafts, but also that it generates more accurate alignments in the first precision interval (RMSD � 0. 5 Å). The stochastic nature of point set registration is known. In some usage cases, we would prefer a deterministic approach to always produce the same result, but, we can also take advantage of this stochastic property to suggest several realistic solutions when searching for bioisosteres or scaffold-hopping, for instance. The example of the alignment of the tetrazole fragment on the drug Valsartan shows that the fragment can match itself but also the carboxylate function as a bioisostere, which was intended.
As a result of this study, the applicability of sensaas is in the field of lead optimization where scaffold hopping and bioisosteric replacement properties of a method are out of importance to identify promising compounds. Indeed, the execution speed of sensaas in its current implementation is prohibitive for virtual screening of large chemical databases. However, even if sensaas is not yet completely optimized and is not fully competitive with established Gaussian-shape methods in speed, we think that this approach provides a relevant contribution to shape-based alignment methods.
Conclusions Molecular similarity is a central concept in drug discovery. A wide range of methods have been developed to describe molecules and to assess similarity by using representations such as the chemical graph in 2D or 3D, or the molecular shape. In this study, we investigated the use of 3D point set registration methods to identify similarities between colored point-based molecular surfaces. The resulting workflow, sensaas, was evaluated and validated against several examples, including the benchmarking AZ data set. It shows potential for molecular similarity evaluation, scaffold hopping and bioisosteric replacement. In particular, as it uses open-source libraries and programs, it can be easily deployed for other pairwise comparison of shapes such as peptides, proteins, cavities (the negative image of the protein surface), or to search for shape complementarity.
Acknowledgements The authors would like to thank  Dr Antonini ,  M. ( Université Côte d'Azur, CNRS, I3S ) for fruitful discussions on the project. Authors D. Douguet  and  F. Payan  received funding from the  French National Research Agency (ANR)  under the project ' Investissements d'Avenir' UCA JEDI  with the reference number  ANR-15-IDEX-01.
One well-known forest data inventory is FAO's "Forest Resource Assessments" (FRAs)  [15] , which provides spatial data on forest statistics. Although valuable, the spatial data provided by FRAs are at best at the country scale, hence unsuitable for high-resolution mapping for use in developing local deployment strategies. The data countries supply to the FAO for the FRA typically stem from National Forest Inventories (NFIs), which provide comprehensive data on forestry  [16]  at sub-regional, regional, or country scales. Despite major harmonization efforts, the aggregation of these national data in FRA reports still present severe discrepancies regarding nomenclature, baseline variables, etc. [17] [18] [19]. For example, the Swiss NFI only measures trees with the diameter at breast height (d.
b. h. ) ≥ 12 cm for quantifying growing stock  [20]. In contrast, this threshold is 0 cm (hence including the complete stem top) in the reference definition of Cost Action E43  [21] , a landmark in Europe for harmonized NFI definitions  [17]. Furthermore, one vital consideration for spatial quantification is the scale and spatial resolution that is being used. For geographic information system (GIS) applications, it is a common practice to merge datasets through the use of different functions, such as map overlay. But if datasets do not have harmonized spatial resolutions, the result of this operation becomes erroneous. In simple words, spatial scale refers to the size of a land area or geographic distance studied. However, there are various categories of scale, such as geographic, operational, measurement, and cartographic, and it is essential that a clear distinction is made amongst them.
The definition of these terminologies is provided in Supplementary Table  1. Most studies on spatial quantification do not consider spatial scale as an important parameter; instead, in general, the studies directly use certain spatial data related to the available methods without questioning the uncertainties associated with them. For example, the use of local forest inventory statistics on a global or continental assessment would lead to a large amount of uncertainty in the analysis due to inconsistencies in geometry, attributes, and semantics owing to scale differences  [22]. In fact, when it comes to high-resolution mapping of residual biomass resources at country scales, there is no real discussion on what resolution can be considered as useful for bioeconomic purposes. Globally, very few studies have attempted to spatially quantify the potential of residual biomass from forestry and none of them report the uncertainty associated with the results.
• Fails to include a land cover or forest cover map for precise mapping (unless the spatially-explicit dataset is available for tree diameter and height). Fails to incorporate regions specific parameters. • Useful only for assessment involving different species. • Discretization at different scales not possible. [26]  Based on remote sensing, forest descriptive statistics and site-specific silvicultural model. Uses automated forest segmentation algorithm developed by Wells  [27]  Regional/Sub-regional (USA) / 2016 1. Remote sensing measurement (Aerial digital imagery) 2. Stand characteristics, basal area, tree density, and above-ground biomass. • Precise mapping even at sub-regional scale. • Very high spatial resolution. • Very high temporal resolution of 2 to 5 years. • Not replicable for use in different areas.
The method developed in  [13]  is an approach to spatially estimate the quantity of logging residues by using remote sensing products. In their study, a set of multi-year harvest maps and a set of forest attribute maps along with inventory data from NFI were used, and finally, this data was aggregated per hectare of forest area. As reported by the researchers  [13] , the advantage of using this approach is to have an estimate on the future availability of the residues at a country scale. However, there are several constraints for the application of this method in different regions of the world such as, availability of long-term (decadal) harvest maps, availability for specialized products like "CanLaD" (a comprehensive dedicated regional database), etc. , which limits the reproducibility of their approach in other regions.
Even with a constraint like limited availability of inventory statistics, this method can be used to estimate the quantity of PFRs based entirely on remote sensing data products (Forest cover map from CORINE Land Cover and Net Primary Productivity data from MODIS) with a certain degree of accuracy. Additionally, due to the discrete and flexible nature of the BEE approach, uncertainty accounting can easily be introduced in the intermediate steps of calculation. This would allow a significant improvement of the current method by generating final quantification results with a confidence interval. Ergo, the present study fully acknowledges and builds upon the efforts and knowledge developed within the BEE method. From a theoretical viewpoint, spatial estimations are representations, and all representations of reality have their complexities that cannot be measured directly, which may be termed as potential error or uncertainty.
SW_NAI = FA × NAI_reg Eq. (  2 ) Here, SW_NAI is the raster of average stemwood NAI per pixel (m 3 year -1 ), and FA is a raster of forest area (ha), obtained from forest maps. The forest map of the area under assessment can be obtained from land-cover data, NFIs, or earth observation data. The forest map provides an understanding of how many hectares of land is under forest with their exact location. Vis and Van den Berg  [31]  recommend using the forest data with the best possible spatial resolution to get the highest accuracy. Apart from NFIs, this data can be derived from other data platforms such as Global Land Survey (GLS)  [38] , Climate Change Initiative (CCI) Land Cover V2  [39] , GlobeLand30  [40] , CORINE Land Cover (CLC)  [41] , etc. , unless finer regional data can be obtained.
This ultimately provides the uncertainty that gets propagated in the spatial quantification of aboveground forestry residues. Here, there are two non-constant statistical input variables of forestry from which uncertainty propagates, viz. , GAI_reg and NL_reg (or NAI_reg if NAI is directly available). Ideally, the standard deviation associated with these values will be provided (or the raw measurement datasets to derive it), and this can be used as the value of "u" in Eqs. 5-6. In cases where no indications of the standard deviation are provided, techniques are available to derive an uncertainty. Notably, the pedigree matrix technique  [55]  was developed in the field of life cycle assessment, where such a situation is common.