dataesr/scientific-paragraphs-categorization huggingface dataset:
Areas of the cells were determined from the vertices with the function 'polyarea' , the local density in each data point was defined as the inverse value of the area of the corresponding Voronoi polygon. To have a smooth appearance that can be used for visualization (Fig. 1C-E ) or segmentation (Fig. 2E, F ) the values of the local density were interpolated to a regular grid (pixels) using the 'griddata' function and the 'natural' interpolation method  32. The spacing of the grid corresponds to the desired pixel size (in range of 2 to 20 nm in our case, it is indicated in the figure legends; at least 3-4 times smaller than the expected resolution to satisfy the Nyquist theorem). For the comparison of visualization techniques, we used a Gaussian distribution of points arranged as two parallel lines, with a distance of 40 nm between the centers of the distributions and the standard deviation of each distribution σ = 10 nm in the X direction (Fig.
1A-D ). The linear density of localization in each line was 0. 5 nm -1. To this dataset we added localizations with random (x, y) coordinates, with overall density of 400 μm -2 , comparable to background density at experimental data. The photon counts for all events were distributed with Gaussian distribution around the mean value of 1000 photons with standard deviation of 300. This is similar to a procedure described by Baddeley et al. 4  with following modifications: linear densities were chosen to be similar to those of typical experimental data, noise was added, and single line profiles (Fig. S1D ) were used along with projections (Fig. 1D ; even though projections tend to not reflect discontinuities in the data, i. e. they generate back the underlying line structure). The Gaussian mode image was built representing each event as a Gaussian filter kernel with σ = A/(N ph ) 1/2 , where A = 240 nm (experimentally determined value for our system), and N ph is the number of photons.
The FRC curves were calculated in 90 concentric rings (resulting in 90 frequency values in the FRC graphs) using the corresponding type of image representation for half-datasets  3. For statistics, the FRC curves and the resolution values were calculated 50 times for each localization table, using different random separations of the dataset on two parts. Standard deviations of the obtained values are shown in error bar for each frequency point. The FRC curves for histogram, Gaussian and Voronoi representations were also calculated by the FSC program (Image Science Software GmbH)  33  confirming the results obtained with our method. The simulated cluster data on Fig. 2  were generated as randomly distributed points in circles with a radius of 50 nm. The density of points in the clusters was 3 • 10 -3.
The positions of clusters and of low-density (4 • 10 -4 ) background points were distributed randomly in the field of view (FOV). The distributions of the sizes of the Voronoi cells were built as histograms of the areas of the cells, for a range from 0 to 4 times the median value of the area, using 2N 1/3 bins, where N is the number of the polygons, excluding infinity-sized polygons at the boundaries of the ROI. For Monte-Carlo simulations, random coordinates were generated throughout the ROI using the 'rand' function. To obtain the confidence envelope, the distributions were generated for 50 different random sets of points, the boundaries of the envelope were determined as < n> ± 1. 96 σ for each bin of the histogram, where < n> is the average number of cells within the range of the bin and σ is the standard deviation of n, calculated from the 50 random datasets.
The abscissas of the first and the second intersections between the curves of the experimental and the mean value of the randomized distributions were determined from the two points around the intersections in the linear approximation. The Voronoi cells smaller than the area corresponding to the first intersection were kept, and all touching cells (those that have at least one common vertex) were combined together yielding vertices of clusters. The clusters were rasterized with the 'poly2mask' function using a pixel size of 1 nm. For the nuclear pore data a threshold was used to set the minimum number of events to remove small, spurious clusters. In our current implementation calculations for medium-sized datasets, e. g. Monte-Carlo simulations of 5⋅ 10 4 localizations, take around a minute; calculations may be parallelized in future software versions.
The simulated data on Figs S3-S6 were generated as 10 × 10 clusters with radius of 50 nm through a FOV of 4 × 4 μm. The positions of the centers of the clusters were distributed regularly in the FOV. The points inside the clusters and in background were placed randomly with the indicated average density. For Fig. S3  the positions of the points were picked from the normal distribution using the mean defined at the previous step and the standard deviation of σ loc. For Figs S4 and S5, n (number of re-localizations) points were picked for every original point using the normal distribution with the standard deviation σ loc = 10 nm and the mean value at the position of the original point. For Monte-Carlo simulations on Fig. S5  the randomized datasets were formed using N/n sim seeds at spatially random positions and distributing n sim points by Gaussian distribution with σ loc = 10 nm around the seeds; N is the number of points in the original dataset, n sim is the number of re-localizations used in the Monte-Carlo simulations.
For segmented multi-color images, the co-localization value for a given species is defined by the ratio of the superposed area S between two colors relative to the total area of clusters of a given species. To compare the obtained values with overlapping area S rand of randomly distributed clusters we shuffled the experimentallydetermined clusters into randomized positions through the same field of view, independently for each color. Firstly, the list of cluster polygons was permutated randomly using Matlab's 'randperm' function. Then, we determined the centroids of the clusters by formulas: ; where C x , C y are the (x, y) coordinates of the centroid of a given polygon, x i , y i are the coordinates of the vertices, n is the number of the vertices, and the vertex i = 0 equals to the vertex i = n.
A is the signed area of the polygon: = ∑ -= -+ + A xy x y ( ) i n i i i i 0 1 1 1. After subtraction of the centroid coordinates from coordinates of the vertices of the polygons, the new coordinates were determined as a random number situated in the FOV of the initial size. To avoid overlapping of newly placed clusters, we iteratively checked for each cluster being placed at a random spot if it was not overlapping with the previously placed clusters of the same color, in which case another random coordinate was chosen. The shuffling procedure was repeated 50 times with different random positions, and the 95% confidence range for the co-localization in the random case was obtained as mean ± 1. 96 σ of the corresponding ratios of the surfaces. Pair distribution functions were calculated using Matlab's 'pdist2'.
(E) Interpolation of the local densities to pixel grid produces local density map. (F) Clusters, obtained by thresholding of the density map. The simulated dataset contained 48 clusters with a radius of 50 nm consisting of ~27 events each. After segmentation, the Voronoi tessellation allows quantification of the clusters (35 were detected, excluding small clusters that contained only one localization event, with 29 ± 16 events each and with equivalent radius of 49 ± 11 nm). The blue frames in (C,E,F) correspond to the region shown in panel (B).
106; random: S rand /S Pol = 0. 096 ± 0. 013, S rand /S H2B = 0. 113 ± 0. 015) revealing no correlation between the two distributions of clusters. (E) Voronoi polygon distributions of the clustered dataset (blue) and mean density distribution of the random datasets (green) with confidence envelopes (red) obtained from Monte-Carlo simulations for the RNA Pol II and H2B data. Scale bars 300 nm (A), 2 μm (B), 500 nm (D); panel (D) corresponds to the frame in panel (B) and (C) to that in panel (D).
Acknowledgements We thank  Alexander Myasnikov  for discussions and  Remy Fritz  and  Jonathan Michalon  for IT support. This work was supported by the  European Research Council  ( ERC Starting Grant   N_243296 TRANSLATIONMACHINERY ), the  Centre Nationale pour la Recherche Scientifique (CNRS) ,  Association pour la Recherche sur le Cancer (ARC) ,  Université de Strasbourg and Investissement d' Avenir (IDEX)  and  Institut National du Cancer (INCa). The super-resolution microscope setup is supported by the Alsace Region and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI).
090, S/S H2B = 0. 106; random: S rand /S Pol = 0. 096 ± 0. 013, S rand /S H2B = 0. 113 ± 0. 015) revealing no correlation between the two distributions of clusters. (E) Voronoi polygon distributions of the clustered dataset (blue) and mean density distribution of the random datasets (green) with confidence envelopes (red) obtained from Monte-Carlo simulations for the RNA Pol II and H2B data. Scale bars 300 nm (A), 2 μm (B), 500 nm (D); panel (D) corresponds to the frame in panel (B) and (C) to that in panel (D).
The primary objective of SensiPath, thus, is to en-large the number of detectable compounds for synthetic biology applications. The algorithms we implemented to simulate biochemical reactions are derived from the welltested RetroPath  (18). It notably allows to take advantage of enzymatic promiscuity, i. e. the ability that enzymes have to process structurally similar substrates, thus yielding more results. SensiPath is built from a comprehensive list of more than 100 000 compounds and 87 000 reactions from four metabolic databases, covering most of the known metabolism. We also collected a large dataset of more than 500 detectable compounds for which intracellular biosensors exist from several gene expression regulation databases, focusing our search on allosteric transcription factors.
MATERIALS AND METHODS Figure  1  shows an overview of how SensiPath works, the details are exposed in the following subsections. SensiPath is based on a comprehensive internal database of biochemical reactions and compounds encoded as chemical signatures. Once a compound query is submitted, it performs a search in order to find a match against all the enzymatic reactions that we have collected in our database. The search is carried out in order to predict reachable compounds from the target. This search generates a metabolic graph at up to two enzymatic steps away from the target, in which nodes are compounds and edges are reactions. Detectable compounds are identified and annotated by a score of similarity based on searching against the list of known detectable compounds in the database. For later reference, all SensiPath sources in its current online version are available on FigShare (https://dx.
Source databases SensiPath predictions are based on imported data from metabolic and gene expression regulation databases. We gathered data from multiple sources to cover most of available knowledge in current databases. Reactions. Known biochemical reactions were extracted from main common reaction databases (Rhea (v66, http: //www. rhea-db. org) (19) , MetaCyc (v19. 1, http://metacyc. org)  (20) , BRENDA (v15. 2, http://www. brenda-enzymes. info)  (21)  as well as from a more specialized database, the Biocatalysis/Biodegradation Database (http://eawag-bbd. ethz. ch, accessed in December 2015)  (22). We considered only reactions for which structures of all reactants were available, fully defined and valid. Overall, we collected more than 100 000 compounds and 87 000 reactions with references to external databases.
Detectable compounds. We gathered a list of 504 putative detectable compounds focusing our search on effectors of allosteric transcription factors from prokaryotes. Data were collected from several gene expression regulation databases: RegulonDB (v9. 0, http://regulondb. ccg. unam. mx) (23) , RegPrecise (v4, http://regprecise. lbl. gov) (24) , RegTransBase (v7, http://regtransbase. lbl. gov) (25)  and BioNemo (v6. 0, http://bionemo. bioinfo. cnio. es) (26).
Matching algorithm After integrating reaction signatures in our database, we can predict on-the-fly if a compound can act as substrate of a reaction by using a new implementation of the RetroPath forward algorithm  (18). If a compound C has a list of fragments (atomic signatures and their respective occurrence) embedding the substrate fragments contained in a reaction signature R (i. e. the negative part of reaction signature), then the compound is said to match the reaction. The sum of the signatures of compound C and those of the reaction R generates a new list of (positive) fragments P, representing the putative products generated by the reaction signature acting upon compound C. If we can retrieve a set of known compounds from those fragments, then the reaction is accepted and C is considered a valid substrate for R to produce P.
Similarity search In order to annotate compounds structurally similar to detectable compounds in predicted metabolic graphs, we precomputed the similarities between all compounds and detectable ones. Indeed, promiscuous detection of structurally similar compounds may not be reported in databases and should be checked in the literature if no suitable detectable compound is found by SensiPath. Similarity was evaluated with RDKit python library (http://www. rdkit. org/), representing compounds with RD-Kit's ECFP4 fingerprint implementation and a Jaccard-Tanimoto index  (32). A Tanimoto of one is a perfect match.
Input Users query SensiPath with the compound they wish to detect (Figure  1 , left panel), either as an identifier from an external database (e. g. ChEBI available at https://www. ebi. ac. uk/chebi/) or as a standard InChi (http://www. inchi-trust. org/). InChi is a IUPAC string representation of compounds and can be easily obtained from compound databases. Users can choose to search for detectable compounds that are at one or two enzymatic steps away from their target.
CASE STUDIES Five examples of SEMPs were characterized experimentally in Escherichia coli by our group to validate SEMP concept. For a model bacteria such as E. coli, in vivo implementation of SEMPs only requires basic molecular biology knowledge. As an example, we describe here the design steps required to build a strain of E. coli able to detect the drug cocaine, and a strain able to detect the pollutant parathion with the help of SensiPath. A detailed in vivo characterization for these examples is described elsewhere  (17). We refer to 'the metabolic module' as the genetic parts providing the enzymatic transformations and to 'the sensing module' as the gene circuit consisting of the transcription factor, its responsive promoter and the reporter gene.
Clicking on an edge of the graph provides a link to databases providing information on each reaction. One of these compounds has a green border indicating that a biosensor is known to interact with an identical or highly similar chemical structure. This suggests that the information of the presence of cocaine in the medium can be transferred to the genetic layer and thus constitutes a putative SEMP. All found SEMPs are summarized on the Pathway view (Figure  2A ). In the present case study, cocaine can be hydrolysed and forms the detectable molecule benzoate. Clicking on the arrow that represents the enzymatic transformation will display cross reference links to external databases of enzymatic transformations. It is strongly recommended to carefully check the bibliography that motivated the annotation of the reaction in the database, since important results might be omitted or misrepresented due to an incorrect curation process.
In the case of cocaine hydrolysis, several publications confirm the benzoate conversion and databases such as Rhea and MetaCyc provide a direct link to Uniprot or GenBank where the sequence coding for the enzyme can be found (GenBank AF173165. 1). This sequence can be synthesized and cloned into an expression vector of choice to constitute the metabolic module part of the SEMP. In parallel, a query on BioNemo or RegTransBase for benzoate (or benzoic acid), the compound reported by Sen-siPath as having a biosensor, leads to several potential transcription factors that are known to interact with this compound (BenM, BenR, CbdS, PcaR, TcbR, CatR, BadR and XylS). In our experimental implementation, we chose the couple composed of BenR and its responsive promoter pBen from Pseudomonas putida KT2440 after a quick assessment of the available literature.
We have experimentally validated this SEMP with a metabolic module based on the PTE coding sequence coupled with the sensing mod-ule made up of the transcription factor DmpR and its responsive promoter Pu from Pseudomonas sp. CF600. However, both PTE enzyme and DmpR promoter are known to be promiscuous, and other pollutants harbouring phenolic structures could activate DmpR. As this could impair applications requiring a high specificity, alternative SEMPs were also explored. Interestingly, with a 2-steps query, SensiPath's Pathway view shows that 4-nitrophenol can be an intermediate compound to another SEMP based on nitrite detection. Indeed a second enzymatic step mediated by a monooxygenase  (38)  is able to further transform 4-nitrophenol into nitrite, which is known to interact with regulators such as NarL from E.
Other potential limitations of the method hold with regards to the choice of the biosensor. Although some information about the degree of promiscuity of transcription factors may be available from databases and literature, this aspect should be carefully considered in each application, especially if the final application requires a high level of specificity. The choice of the biosensor should also take into account dose response parameters such as the dynamic range and linear range of detection. SEMP's properties will depend on the actual proper-ties of the biosensor, an information that therefore should be considered and retrieved from the available literature. In addition, promoter sequences responding to transcription factors may not be always found in databases, often requiring an investigation of associated references.
FUNDING Structure et Dynamique des Systèmes Vivants doctoral school ,  Université Paris Saclay ;  Direction Générale de l'Armement (DGA-MRIS Scholarship) ;  Biotechnology and Biological Sciences Research Council ,  Centre for synthetic biology of fine and speciality chemicals  [ BB/M017702/1 ]; GIP Genopole;  Toulouse White Biotech  (TWB); and  French National Research Agency  [ ANR-15-CE1-0008 ]. Funding for open access charge:  Biotechnology and Biological Sciences Research Council  [ BB/M017702/1 ].
More over, a preliminary communication  (Busset et al. , 2015)  indicates that this tool is of great interest for the international research corn munity in the field of Biorefinery and industrial companies. The DSS developed in this context aims at achieving progress in the complex issue of assessing the environmental impact of alternative Biorefinery systems. The presented DSS has the following functionalities. (i) lt is able to annotate, store and maintain potentially incom plete or imprecise data extracted from the scientific litera ture, in dedicated databases containing biorefinery system characteristics ( e. g. glucose yield, mass balance in terms of water, chemical reagents) and process parameters ( e. g. milling rotation speed, treatment duration, temperature, pres sure); (ii) lt computes environmental impact indicators ( called Efactor in the following) integrating data reliability aspects; (iii) lt ranks the different Biorefinery processes according to their environmental impact.
Relation extraction methods are in general based on those three independent steps. In our context, arguments of the n ary relation can be implicitly expressed in the text and usually appear in several sentences. Therefore, state of the art methods, which make the hypothesis of the presence of a trigger word, are not directly usable. Let us now discuss automatic extraction of relevant information in tables. State of the art methods and tools  (Knoblock et al. , 2012; Buche et al. , 2013; Tian et al. , 2013; Zhang, 2014)  make the assumption that data tables are organized in the same way than in relational databases: a data table is composed of vertical col umns (each column corresponding to a single feature, for example Biomass, temperature, etc. ), themselves composed of cells. Unfor tunately, this assumption is not always valid for data tables pub lished in scientific articles.
Functional specifications of the system Since the DSS functional specifications depend on the users, the first step was therefore to identify the potential users. They were identified in the 3BCAR network of researchers. Then, the func tional specifications were determined during the IC2ACV project's meetings, gathering several experts of the 3BCAR network. Finally, the functional specifications were refined during annual meetings of 3BCAR about IC2ACV results. The following functional specifications are implemented in the IC2ACV DSS: 1. gathering, integrating and structuring heterogeneous data available in the scientific literature about biomass transforma tion processes; 2. defining a simple and generic ontological model of the informa tion which must be identified and annotated in the scientific papers.
Architecture of the decision support system This section details the three steps of the data treatment pipe line. In the first step, experimental data published in scientific papers are annotated thanks to an ontology implemented in OWL 5  2/DL and assessed in terms of their source reliability. Annotated data are stored in a RDF database and available in open access via permalinks, a SPARQL 6  end point and a dedicated query ing system guided by the ontology. The second step consists in extracting annotated data from the RDF database to compute Efactor indicators and data reliability scores. This extraction is done using SPARQL queries generated by the dedicated querying system guided by the ontology. Process yields, Efactor indicators and data reliability scores can be visualized in the third step as graphical maps.
Heterogeneous experimental data integration (step 1) To facilitate integration of scientific data coming from heteroge neous sources, one of the relevant solutions is to use ontologies  (Noy, 2004; Doan et al. , 2012). An ontology defines a set of primi tives to model a domain of interest: classes, attributes (or proper ties) and relations between members of the classes  (Guarino et al. , 2009). The ontology is used to create and/or reuse standardized vocabularies and to index data sources with those vocabularies in order to allow data source interoperability. Our system uses @Web to capitalize experimental data extracted from scientific documents found on the Web. Here are its main components. @Web implements a complete workflow (see Fig. 4 ) to manage experimental data: extraction and semantic annotation of data from scientific documents, data source reliability assessment and uniform querying of the collected data stored in a database opened on the Web.