dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with the InterPro reference dataset reveals that two thirds (65) of these one-to-one associations are novel compared to InterPro. Interestingly, we confirmed in our source datasets that all of these associations involve single-domain proteins. Thus, these unambiguous associations constitute the most reliable novel associations calculated by ECDomainMiner. The complete list of one-to-one EC-Pfam associations found by ECDomainMiner may be downloaded from the ECDomainMiner web site. Interestingly 14 of these associations (8 Gold, of which 2 match InterPro reference associations, and 6 Silver) concern "DUF" (domain of unknown function) or "UPF" (uncharacterised protein family) Pfam entries. These are listed in part (A) of Table  2  in order of decreasing confidence score. These examples demonstrate that ECDomainMiner can be used to enrich domain annotation.
Clearly, minor and unpredictable spelling differences impair the automatic retrieval of such similar but nonidentical EC and Pfam names. Nonetheless, while these associations could be found by clever text matching, we emphasise that ECDomainMiner's confidence scores and p-values provide a level of support for each association that would be very difficult to obtain from text mining alone. The multi-partner associations calculated by ECDo-mainMiner provide many more complex EC-Pfam associations. As a first analysis of such multiple associations, we looked for obligate pairs or tuples of Pfam domains that are always associated with a given EC number. Briefly, for any pair of Pfam domains, (d 1 , d 2 ), associated with the same EC number, ec, (i) we reject those pairs for which at least one ec-annotated CID (in any source dataset) occurs in relation with d 1 and not d 2 or with d 2 and not d 1 , (ii) for all other pairs we calculate for each source dataset the ratio of the number of ec-annotated CIDs related to d 1 and d 2 , to the total number of ec-annotated CIDs.
A support ratio of 1 means that all CIDs annotated with ec in a dataset are also related to d 1 and d 2. A similar algorithm was used for triplets and quadruples of Pfam domains. For a support ratio of 1 in at least one source dataset, we found 907, 191 and 47 obligate associations between an EC number and a pair, a triplet or a quadruplet of Pfam domains. These associations are available from the ECDo-mainMiner website. Two examples are given in part (C) of Table  2. Interestingly, filtering the names of the Pfam domains with the expressions "N-terminal" and "C-terminal" yielded 58 obligate pairs containing both a N-terminal and a C-terminal domain of the same function. This indicates that our approach is finding enzymes in which the catalytic function is provided by the interface between two consecutive Pfam domains.
Annotating PDB chains with EC numbers Our analysis of the December 2015 release of the SIFTS database reveals that about 45% of PDB entries lack an EC number annotation. Of course, such an annotation is not expected to be present in all PDB entries because not all proteins have enzymatic activity. Nonetheless, it is interesting to use ECDomainMiner to analyse the number of PDB entries that contain Pfam domains which are present in EC-Pfam associations. Table  3  shows that a total of 58,722 PDB chains lacking EC annotations in SIFTS include at least one of the 3542 Pfam domains present in ECDomainMiner. Overall, we calculated that these chains map to a total of 24,995 PDB entries that could benefit from the additional annotations inferred by ECDomainMiner. For those chains lacking EC annotations, ECDomainMiner finds Gold, Silver, and Bronze EC-Pfam associations for 41,246, 44,406 and 34,820 PDB chains, respectively.
The ECDomainMiner web server The ECDomainMiner web server may be queried by EC number or Pfam domain. Thus, if one wishes to search for associations for a protein chain that currently lacks any EC annotation in the PDB (e. g. chain 2q7xA), one first needs to retrieve from the PDB the Pfam domain(s) that it contains (in this example, PF01933). Then, querying the ECDomainMiner server with each Pfam domain identifier will show the associated EC numbers (in this example, 2. 7. 8. 28), along with the associated filtering scores and quality classes. In this example, ECDomainMiner finds a Gold quality association between PF01933, present in PDB chain 2q7xA, and EC number 2. 7. 8. 28 (2-phospho-L-lactate transferase) which consequently can be associated with PDB entry 2q7x. Interestingly, PDB entry 2q7x is described as a putative phospho transferase from streptococcus pneumoniae tigr4, which is consistent with  the enzymatic activity found by ECDomainMiner, and which could not be deduced from the Pfam domain name (UPF0052).
A) Fourteen one-to-one EC-Pfam associations found by ECDomainMiner and involving domains of unknown function, (B) an example of one-to-one EC-Pfam association with very similar EC and Pfam descriptions, and (C) two examples of obligate Pfam pairs associated with an EC numberThe 'PDBs (SIFTS)' column contains 3 counts of PDB chains containing the mentioned Pfam domain and having either the same EC annotation in SIFTS as calculated by ECDomainMiner (first position), or different EC annotations between SIFTS and ECDomainMiner (second position), or no EC annotations in SIFTS (third position). Complete lists of PDB identifiers may be retrieved from the ECDomainMiner web server.
1. 99. 5 PF11266 0. 944 Aldehyde oxygenase (deformylating) Protein of unknown function Gold 18/0/0 DUF3066 2. 1. 1. 286 PF11968 0. 889 25S rRNA (adenine(2142)-N(1))- Putative methyltransferase Gold 0/0/0 methyltransferase DUF3321 1. 13. 99. 1 PF05153 0. 667 Inositol oxygenase Family of unknown function Gold 4/0/0 DUF706 2. 4. 1. 155 PF15027 0. 611 Alpha-1,6-mannosyl-glycoprotein Domain of unknown function Gold 0/0/0 6-beta-N-acetylglucosaminyltransferase DUF4525 4. 2. 3. 130 PF10776 0. 611 Tetraprenyl-beta-curcumene synthase Protein of unknown function Gold 0/0/0 DUF2600 2. 3. 1. 78 PF07786 0. 609 Heparan-alpha-glucosaminide Protein of unknown function Gold 0/0/0 N-acetyltransferase DUF1624 3. 1. 4. 45 PF09992 0. 584 N-acetylglucosamine-1-phosphodiester Predicted periplasmic protein Gold 0/0/1 alpha-N-acetylglucosaminidase DUF2233 1.
13. 12. 20 PF08592 0. 556 Noranthrone monooxygenase Domain of unknown function Gold 0/0/0 DUF1772 2. 1. 1. 312 PF11312 0. 556 25S rRNA (uracil(2843)-N(3))- Protein of unknown function Gold 0/0/0 methyltransferase. DUF3115 2. 1. 1. 313 PF10354 0. 556 25S rRNA (uracil(2634)-N(3))- Domain of unknown function Gold 0/0/0 methyltransferase DUF2431 2. 5. 1. 128 PF01861 0. 556 N4-bis(aminopropyl) spermidine Protein of unknown function Gold 0/0/1 synthase DUF43 5. 2. 1. 14 PF13225 0. 556 Beta-carotene isomerase Domain of unknown function Gold 0/0/0 DUF4033 1. 14. 99. 29 PF04248 0. 333 Deoxyhypusine monooxygenase Domain of unknown function Silver 0/0/5 DUF427 B 6. 3. 2. 25 PF03133 0. 610 Tubulin-tyrosine ligase Tubulin-tyrosine ligase family Gold 0/2/21 C PF00370 0. 847 FGGY family of carbohydrate Gold 85/32/9 2.
7. 1. 30 Glycerol kinase kinases, N-terminal domain PF02782 0. 828 FGGY family of carbohydrate Gold 85/32/7 kinases, C-terminal domain PF06973 0. 997 Formate-phosphoribosyl-amino- DUF1297 Gold 16/3/0 6. 3. 4. 23 imidazol PF06849 0. 997 carboxamide ligase DUF1246 Gold 16/3/0.
Model approach Modelling is a valuable tool in the systematic and transparent synthesis of evidence to support policy decisions. With a series of numbers and mathematical and statistical relationships, modelling creates a representation of realworld events. 15 To assess the clinical, social and economic benefits of TARGIT-IORT over the current practice of whole-breast irradiation, we constructed a decision analytic model based on outcome probabilities from the published TARGIT-A trial data (prepathology cohort) and costs from the INTRABEAM manufacturer and UK NHS tariffs. 7 Utility values for the model health states were drawn from the published literature. 16 A CE Markov model was developed using TreeAge Pro V. 2015 (TreeAge Software, Williamstown, Massachusetts, USA) to capture the costs and outcomes of the two breast cancer radiation therapy options, namely: conventional whole-breast radiation as reference strategy and TARGIT-IORT using INTRABEAM as new innovative strategy.
Model description The decision analytic model compared two competing breast cancer radiation strategies in a hypothetical cohort of patients with early breast cancer. Treatment outcomes were simulated for both strategies for a time horizon of 10 years. We used the TARGIT-A trial as an evidence to inform the model structure and incorporated disease progression as various model health states. 7 Currently, these clinical effectiveness data published in the Lancet are the only level 1 randomised evidence available for the TARGIT-IORT. The TARGIT-A trial was conducted as a pragmatic risk-adapted design reflecting a real-world situation. 7 ur model uses five distinct health states: disease free; local recurrence; distant recurrence; death from breast cancer; and non-breast cancer death (figure  1 ).
dIscussIOn We used published data from the TARGIT-A trial to investigate the CE of TARGIT-IORT in patients with early breast cancer. The findings suggest that in comparison to the EBRT which involves delivering whole-breast radiations in multiple sessions, individuals treated with TARGIT-IORT, during the surgery performed to remove the breast cancer, had higher mean health gain (QALYs) at a lower mean cost. The model outputs indicate definite cost savings by the use of TARGIT-IORT within a risk-adapted strategy rather than using EBRT in call cases. The model runs for 10 years which is very conservative as most events related to breast cancer occur in the first 5 years. 21 These findings, based on extrapolation of the relevant outcomes obtained from the analysis of complete trial data, were generally found to be robust to uncertainty surrounding various model parameter inputs and assumptions.
Software implementations We implemented BGN2, which have been presented and briefly discussed above. In this cryptosystem, the homomorphic multiplication asks to compute pairings. We chose to compute an optimal Ate pairing over an elliptic curve in the Barreto-Naehrig curve family  [BN05]  using a program called DCLXVI [NNS10]. This work is not yet published, but will be available soon both as an article and as a software. Concerning lattice-based (S/F)HE schemes, several implementations have emerged since their introduction in 2009. Due to the pace of evolution in the theoretical field, some are now outdated but served as good proof of concept in the early days of (S/F)HE (  [Bre,Cor,Lep] ). Other libraries ([DGBL + 15,Hal,Cry16,LCP16]) implement the latest techniques described in [SV10,FV12,BGV12,BLLN13].
Introduction Points of Interest (POIs) describe places of interest for any user of geographic information, such as touristic places, amenities, or shops, and can be easily crowdsourced by citizens. As a consequence, they are often a major part of the contributions of volunteered geographic information (VGI) projects; see for instance OpenStreetMap (OSM) and Wikimapia. However, representing a geographic place that can have an extent as large as a building using the point primitive is not a straightforward process for contributors or even for professional surveyors: what is the best location for a point representing a school that might be composed of multiple buildings? Moreover, crowdsourced data might be subject to frequent, minor or major, changes until a consensus is reached among volunteers.
Methodology and Datasets Used In order to assess the quality of crowdsourced POIs, we used two general approaches: (i) use intrinsic characteristics and measurements derived from the crowdsourced datasets at hand, and (ii) evaluate crowdsourced POIs using an external dataset, that can either be an authoritative reference or another crowdsourced dataset. More specifically, the data under evaluation are the OSM POIs for the Paris region. For the first approach, two methods are proposed for quality evaluation: using the history of each feature alone (Section 3) and analyzing the spatial and topological relationships of the OSM features (Section 4). For the second case, we examined the quality of OSM points against authoritative ones from IGN (The French Mapping Agency) by using a data matching algorithm (see  Section 5)  and by using geo-tagged photographs from Flickr (see  Section 6).
OpenStreetMap Dataset The OSM project is one of the prime examples of crowdsourcing GI. Since 2004, the OSM project has mobilized contributors from around the globe in order to create a freely available geographic database. Today, there are more than three million registered contributors in OSM and despite the fact that the vast majority are occasional contributors and thus only a small fraction of them is consistently contributing, on a monthly basis, there are more than 20,000 active contributors (at the time of writing); this constitutes a huge workforce. The initial aim was to enable volunteers to use their local knowledge to capture geographic data for areas they know. This has since expanded and volunteers are now mapping areas worldwide due to technological advances and the open availability of very high resolution satellite and aerial imagery.
This development has boosted the expansion of OSM with the tradeoff that the influence of local knowledge is diminishing in OSM data. Nevertheless, OSM is still the most up-to-date crowdsourced global geographic database today and the data are used in diverse applications from aiding humanitarian efforts  [19]  to use in governmental projects  [20]. The OSM data model consists of three vector-based geometric primitives. The first are nodes, which are point-based and are used to capture either individual points (e. g. , landmarks) or are part of the second primitive, i. e. , a way, which is used to capture either linear or polygon entities. Finally, relations are used to the capture logical collection of nodes, ways, or other relations. For each of these primitives, the OSM data model allows attributes (commonly referred to as tags) to be attached using a key:value form.
While a fundamental OSM principle is that contributors are free to choose whatever attributes they want, in practice there is a wiki-based specification for the OSM project that provides suggestions and best practices about the combinations that should be used in each case. Another important element of the OSM data model is that the geographic database itself is created in a wiki-based manner. This means that all contributors can edit all existing features and that all the previous versions of a feature are available, along with details for each version (e. g. , when it was created, by which user, which tags were assigned to the feature, etc. ). OSM provides an Application Programming Interface (API) that enables access to the geographic data, data about the contributors, and data about the individual features and their previous versions.
For example, the entire history of the OSM feature with OSM_ID 26691437, can be accessed using the API request: http://api. openstreetmap. org/api/0. 6/ node/26691437/history/. The API response is an XML file that includes all the versions of the feature with OSM_ID 26691437. Through an iterative process, all the versions of each OSM features in scope were downloaded and stored in a PostgreSQL/PostGIS database. This method provided a complete timeline of the OSM edits made in the area for the data of interest.
Reference Dataset The reference data were extracted from the BD TOPO database produced by IGN. BD TOPO is a topographic dataset with a positional accuracy below 1 m. Its scope does not cover all POIs that are captured in OSM (e. g. , there are no shops or restaurants), but the POI layer covers education, administration, transportation, religion, health, sports, and hydrography, which gives a sufficient overlap with OSM POIs for comparison purposes. The attribute values, originally in French, have been translated to English to enable semantic matching with OSM. The IGN POI dataset contains 6202 features. The change in IGN features is due to updates (e. g. , a change in the real world, errors correction), changes in specifications or changes due to partnerships that provide data to IGN (e. g.
Flickr Dataset The development of Web 2. 0  [21]  has led to a bi-directional Web where the aim of many web-based applications is to provide platforms that enable users to create and publish their own content and share this with other users. This development has led to the emergence of social networking websites. One of the first examples of such websites have been photograph sharing applications, e. g. , Flickr, Picasa Web, or the more recent Instagram, which urges users to share their photographs along with titles, comments, keywords (known as tags), and their location (known as geo-tagging), and then to use them as means of networking. While geography or the location of the content is not the prime feature of such applications, they can still be implicit sources of GI since newly developed GI retrieval methods can transform implicit information into geospatial content  [22].
g. , supermarket or stations), the contributors might disagree on where the best place is to position the point that will represent the physical features since the area of the feature might be quite large (see the next sections for more discussion of this point). For other cases such as café, bakery, hotel, and bank, which are characterized by small and precise geographic extents, the maximum distance shows that there have been some gross errors made by contributors that have been corrected in a later version (or vice versa). However, the notion of gross error is somehow elusive and certainly not uniform in these datasets. For example, a maximum displacement of 339 m for a point that represents a hotel might be a valid movement since both points could be inside the spatial footprint of the hotel.
Thus, we also computed the Voronoï regions around the amenity points (Figure  5b ) and intersected the buildings with the Voronoï regions (Figure  5c ). Then, we also computed the distance to the center and to the boundary for these new regions. We selected four types of amenities (around 6500 POIs in the Paris dataset): • very small amenities: gift shops, i. e. , the features tagged with "amenity = gifts"; • medium amenities: bars ("amenity = bar"), cafes ("amenity = café"), and restaurants ("amenity = restaurant"); • large amenities: cinemas ("amenity = cinema"); • very stable amenities: hairdressers ("amenity = hairdressers") that do not move often, according to  [17]. Figure  5a  shows that the building center is not always a good approximation of the amenity center, as several amenities can share a building.
Thus, we also computed the Voronoï regions around the amenity points (Figure  5b ) and intersected the buildings with the Voronoï regions (Figure  5c ). Then, we also computed the distance to the center and to the boundary for these new regions. We selected four types of amenities (around 6500 POIs in the Paris dataset): • very small amenities: gift shops, i. e. , the features tagged with "amenity = gifts"; • medium amenities: bars ("amenity = bar"), cafes ("amenity = café"), and restaurants ("amenity = restaurant"); • large amenities: cinemas ("amenity = cinema"); • very stable amenities: hairdressers ("amenity = hairdressers") that do not move often, according to  [17]. The results, summarized in Table  3 , show that precision is somehow related to the size of the amenity, which is logical as there is more room to locate a POI inside large amenities.
ATM POIs In the city of Paris, most ATMs are located on the walls of banks and only a minority are located inside a bank building. Thus, a logical positioning of the ATM POIs would be just on the boundary of the building polygons. We used the same methodology as that used for amenities to compute the building center, and we measured the distance to the center and to the boundary (Table  5 ). There were 347 ATMs in the dataset and 6. 6% of points are outside any building: these points clearly are poor quality because the building layer has good completeness, which was confirmed by a visual inspection of those points. Sixty-five percent of the ATMs lie just on the boundary of buildings, so there is a vast majority of points with a good quality. There are an additional 6% that are both far from the center and from the boundary, so they are considered randomly and badly located.
School POIs Schools are complex functional entities composed of buildings, schoolyards, sports fields, libraries, and even chapels  [30]. Ideally, we need to know the complete extent of the school and its components to assess the quality of the school POI position: in Figure  7 , the POI is located in the schoolyard, so not inside any building, but it is close to the school extent centroid. Unfortunately, extent polygons are not available for all school POIs in this dataset: only 23% of the 438 schools are captured with both a POI and a polygon. The difficulty of assessing school POI quality due to the complex structures of schools is highlighted in  [31] , the authors of which tried to match several school POI datasets from different VGI or reference datasets. The results, summarized in Table  4 , show good homogeneity inside a building.
School POIs Schools are complex functional entities composed of buildings, schoolyards, sports fields, libraries, and even chapels  [30]. Ideally, we need to know the complete extent of the school and its components to assess the quality of the school POI position: in Figure  7 , the POI is located in the schoolyard, so not inside any building, but it is close to the school extent centroid. Unfortunately, extent polygons are not available for all school POIs in this dataset: only 23% of the 438 schools are captured with both a POI and a polygon. The difficulty of assessing school POI quality due to the complex structures of schools is highlighted in  [31] , the authors of which tried to match several school POI datasets from different VGI or reference datasets. When a polygon extent was available for the POI, we measured the distance to the center of this polygon and to its boundary; when none was available, we used the building that contained the POI, as we did for amenities and ATMs.
In parallel, as large roads are captured with several lanes in Parisian OSM data, we used 10 m as a threshold for being too far from any road. Table  7  shows the results obtained on the 2022 bus stop points in the dataset. Of these, 13. 5% of the bus stops are either too close to a road or a building (since they cannot be both), and are considered to be badly located. Only 0. 4% are too far from any road. The results show consistent locations in the remaining bus stop points, which can be explained by the fact that bus stops are entities with a small geographic extent, and are easy to spot from high-resolution satellite imagery.
Assessment based on Data Matching with Reference Data A number of research papers dealing with VGI data quality assessment have compared crowdsourced data with reference data to infer their quality  [2, 3, 33]. The comparison of two datasets supposes that homologous features (i. e. , features representing the same object in the real world) are first identified. This process is referred to as data matching. In this paper, we propose using a data matching algorithm defined by  [34]  to automatically detect homologous features between OSM and IGN POIs. The method, which uses belief theory, combines different criteria based on geometry, thematic and semantic properties.