dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation We implemented algorithms nCSC, hCSC, and saCSC as the program CSC. Given one of the three methods, two sequences x and y in (Multi)FASTA format, the number β of blocks, and the length q of the q-grams, CSC finds the rotation of x (or an approximation of it) that minimizes its β-blockwise q-gram distance from y. The imple- mentation is distributed under the GNU General Public License (GPL), and it is available freely at http://www. github. com/solonas13/csc. diff j x ′ i + jm β = diff j x ′ i + jm β + 1 diff j x ′ i + (j + 1)m β = diff j x ′ i + (j + 1)m β -1, δ i+1,j =    δ i,j -1, if diff j � x ′ [i + jm β ] � ≤ 0 δ i,j + 1, if diff j � x ′ [i + jm β ] � > 0 δ i+1,j =    δ i+1,j -1, if diff j � x ′ [i + (j+1)m β ] � ≥ 0 δ i+1,j + 1, if diff j � x ′ [i + (j+1)m β ] � < 0.
Experimental results The following experiments were conducted on a desktop computer using one core of Intel ® Core TM i7-2600 CPU at 3. 4GHz and 12GB of RAM under 64-bit GNU/Linux. All programs were compiled with gcc version 4. 7. 3. We used both synthetic data and real data. All input datasets referred to in this section are publicly maintained at the same web-site. First, in "Accuracy"-"Time performance" sections, we establish the quality (accuracy and performance) of our methods. Then, in "Application to synthetic data"-"Application to real data" sections, we show applications of our methods.
Accuracy We began with simulating three DNA sequence datasets using INDELible  [42] , with each dataset consisting of 12 sequences (denoted by α), each of length approximately 2500 bp (denoted by γ). INDELible produces linear sequences with substitutions, insertions, and deletions at rates defined by the user. Three unique substitution rates (denoted by θ) were set, per dataset, using the substitu- tion model JC69 (Jukes-Cantor, 69): 5, 20, and 35 %. The insertion and deletion rates were set, respectively, to 4 and 6 % (denoted by κ and ω), relative to substitution rate of one, similar to those observed in MtDNA in primates and mammals  [25]. We refer to these datasets as Original. To allow for comparison of the performance of the algorithms in realigning randomly rotated sequences, which should be similar to those obtained from sequencing circular DNA structures, such as MtDNA, one random rotation was generated in each sequence in all datasets, creating new datasets which will be referred to as Random.
Using the three Random datasets allowed us to test the accuracy of hCSC and saCSC; notice that nCSC and saCSC always return the same rotation. For each Random dataset, an all-against-all sequence comparison was performed. That is, all 66 possible pairs of sequences in each dataset were given as input to both hCSC and saCSC. β was set to ⌈ √ m⌉ = 50 and q was set to ⌈log | | m⌉ = 6. The resultant re-rotated sequences were aligned using EMBOSS Needle (default parameters) and the similarity scores were compared to those of the Original and Random datasets, which were input directly to EMBOSS Needle (default parameters). The results can be found in Fig. 1. The results show that: (a) hCSC and saCSC yield significantly improved similarity scores compared to those obtained from giving Random datasets as input directly to EMBOSS Needle; and (b) hCSC and saCSC yield similarity scores that are identical or almost identicalnotice that the black (Original), green (hCSC), and blue (nCSC/saCSC) points coincide-to those obtained from giving Original datasets as input directly to EMBOSS Needle.
Time performance We then compared the time performance of the algorithms. Each algorithm was given a pair of randomly generated sequences starting from m = n = 50 bp and doubling 8 times to a length of m = n = 12, 800 bp. It was expected that the slowest algorithm would be cNW which runs in time O(nm 2 ). Then it would be algorithm nCSC which runs in time O(m(m + n)), then algorithm hCSC, which runs in time O β(m + n) + m(m+n) β , and lastly algorithm saCSC, which runs in time O(βm + n). Initially, β was set to ⌈ √ m⌉ and q was set to ⌈log | | m⌉. The results in Fig. 2  demonstrate orders-of-magnitude superiority of saCSC compared to cNW and nCSC, confirming our theoretical findings. Algorithm hCSC is the second fastest. Although β was set to ⌈ √ m⌉, saCSC clearly outperforms hCSC, due to the use of a highly optimized implementation of the suffix-array construction  [43] , thus highlighting the importance of suitably implemented data structures such as suffix arrays.
Application to synthetic data For evaluating the proposed methods for circular sequence comparison in some relevant application, we also implemented the following pipeline for distancebased phylogenetic reconstruction of a dataset with N circular sequences. 1. For each pair (x, y) of the N sequences, we use one method for circular sequence comparison to compute the best rotation x i. 2. A similarity score for (x i , y) is then computed using EMBOSS Needle (default parameters) and stored in cell [x, y] of an N × N similarity score matrix. 3. The similarity score matrix is transformed into a distance matrix by converting each score into a distance relative to the maximum score in the similarity score matrix. 4. Neighbour joining clustering is performed on the distance matrix, using NINJA  [44] , to produce a phylogenetic tree.
Phylogenetic trees were constructed by NINJA  [44] , for the aforementioned Random datasets, using output from the following algorithms: cNW (EMBOSS default parameters), hSW (see introduction of "Implementation" section, EMBOSS default parameters), and saCSCr (β = 50, q = 5, and p = 1). Notice that, output from cNW should be the same as from EMBOSS Needle (default parameters) with the Original datasets as input. In terms of accuracy, the Robinson-Foulds (RF) distance metric  [45, 46]  was used to compare the three resultant phylogenetic trees with the tree resulting from EMBOSS Needle (default parameters) on the Original and Random datasets, denoted by NW(o) and NW(r), respectively. RF distance can be defined as the number of operations required to transform one tree in to another. If two isomorphic trees share the same labelling then they have an RF distance of 0.
The results displayed in Table  3  clearly show that saCSCr and cNW produce the most accurate results with these nine datasets. As also shown in  [11] , hSW followed by EMBOSS Needle (default parameters) can often result in sub-optimal global alignments. In terms of time performance, the elapsed time required for each method to process each dataset was recorded and the results are displayed in Table  4. It is clear, from the results presented heretofore, that saCSCr outperforms all other algorithms by at least one order of magnitude.
DNA sequences Pairwise sequence comparison. As the input dataset, we used two real sequences from GenBank: human    5. The convergence of the results after the additional step of refinement (see Table  5  in italics) demonstrates the convenience and necessity of saCSCr. For clarity of presentation hereafter, instead of using β , we denote by ℓ the length of the block chosen in algo- rithm saCSCr. We repeated this experiment with the human and gorilla (NC_011120) MtDNA sequences. The MtDNA genome size for gorilla is 16,412 bp. Their pairwise sequence alignment using EMBOSS Needle (default parameters) gives a similarity of 83. 5 %. After using saC- SCr to rotate sequence NC_001807 (ℓ = 50, q = 5, and p = 1), EMBOSS Needle (default parameters) gave a sig- nificantly improved similarity of 88.
4 %. Finally, note that the experiments which used saCSC and saCSCr each took a fraction of a second to run. Distance-based phylogenetic reconstruction Three datasets of 16 primate, 12 mammalian and 19 mixed mammalian and primate MtDNA sequences, of average length 16,500 bp, were obtained from GenBank. We followed the same pipeline as described in "Application to synthetic data" section. The RF distance between the trees produced by cNW (EMBOSS default parameters), and the trees produced by saCSCr (ℓ = ⌈ √ m⌉ = 129, q = 5, and p = 1) followed by EMBOSS Needle (default parameters), was 0.
RNA sequences Eighteen viroid sequences were obtained from RefSeq, a database of curated molecular biological sequences  [47]. Their lengths and target hosts vary, ranging from 348 to 371 bp and infecting peppers and citrus fruits, respectively. We followed the same pipeline as described in "Application to synthetic data" section. The RF distance between the tree produced by cNW (EMBOSS default parameters), and the tree produced by saCSCr (ℓ = ⌈ √ m⌉ = 19, q = ⌈log | | m⌉ = 5 , and p = 1) fol- lowed by EMBOSS Needle (default parameters), was 0.
Protein sequences Linear, circularly-permuted protein sequences Eight sequences of proteins, of average length 950 amino acids, belonging to β-glucosidase family  [48]  were obtained from the UniProt protein database  [49]. We followed the same pipeline as described in "Application to synthetic data" section. The RF distance between the tree produced by cNW (EMBOSS default parameters), and the tree produced by saCSCr (ℓ = ⌈ √ m⌉ = 31, q = ⌈log | | m⌉ = 5, and p = 1) followed by EMBOSS Needle (default parameters), was 0. Naturally-occurring circular proteins Ten bacteriocin protein sequences, of average length 20 amino acids, were obtained from Cybase  [50] , a database of cyclical protein sequences. We followed the same pipeline as described in "Application to synthetic data" section. The RF distance between the tree produced by cNW (EMBOSS default parameters), and the tree produced by saCSCr (ℓ = 2⌈ √ m⌉ = 10, q = 2⌈log | | m⌉ = 6, and p = 1) fol- lowed by EMBOSS Needle (default parameters), was 0.
Acknowledgements The publication costs for this article were funded by the  Open Access funding scheme of King's College London. RG and NP have been partially supported by the  Italian Ministry of Education, Universities, and Research (MIUR)  under  PRIN 2012C4E3KT  national research project  AMANDA-Algorithmics  for Massive and Networked Data, and by the  University of Pisa  under  PRA  2015 project Computational Methods for Personalized Medicine. RM is supported by the P. R. I. M. E. programme of DAAD co-funded by  BMBF  and the  EU 's  7th Framework Programme  (# 605728 ), and the Newton International Fellowship co-funded by the  Royal Society  and the  British Academy. FV is supported by an  EPSRC  Grant (Doctoral Training Grant # EP/M506357/1 ).
Introduction The retrieval of tropospheric ozone is a major issue for air quality studies. Ground-level ozone is a priority air pollutant, causing approximately 22 000 excess deaths per year in Europe  (Amann et al. , 2005). Current (e. g. Meteorological Operational, MetOp; Earth Observing System -Aura, EOS-Aura) and future (e. g. EUMETSAT Polar System -Second Generation, EPS-SG; Meteosat Third Generation Sounder, Published by Copernicus Publications on behalf of the European Geosciences Union. MTG-S) satellite observation systems offer great potential for monitoring air quality on a daily and global basis. Because of their global coverage every day, they can be used in synergy with global or regional chemical transport models (CTMs) for full data assimilation (e. g. Coman et al. , 2012)  or intervalidation (e.
This is particularly true for parameters such as cloud fraction, surface temperature and temperature profiles. The consistency of pseudo-reality and POS with respect to real data and existing sensor products is analysed and validated in terms of absolute magnitude and spatial variability of a number of atmospheric variables (ozone concentration and temperature profiles, skin temperature) and diagnostic parameters (degree of freedom and altitude of maximum sensitivity of the retrieval algorithm). In the following paragraphs, we present the methodology used to set up parts of the simulation experiment: the nature run, the forward radiative transfer calculations and retrieval scheme of the OS simulator. Then, we carry out a statisti-   cal analysis of MOCAGE pseudo-reality with respect to real IASI+GOME-2 measurements and quantify retrieval errors and sensitivity for IASI+GOME-2 and IASI-NG+UVNS.
Acknowledgements. The authors are grateful for the financial support given by the  Centre National des Études Spatiales (CNES , the  French Space Agency) , the  Université Paris Est Créteil (UPEC) , the  Centre National des Recherches Scientifiques -Institut National des Sciences de l'Univers (CNRS-INSU). This study was financed by the project  SURVEYOZON-TOSCA  ( Terre, Oceìan, Surfaces Continentales, Atmosphère ) from  CNES. We acknowledge the support by the data center ETHER (http://www. pole-ether. fr), ICARE  (http://www. icare. univ-lille1. fr),and  NOAA  (https://www. class. ncdc. noaa. )for providing respectively data sets from IASI (level  1C , originally supplied by  EU-METSAT  through the EUMETCast system distribution, http://www. eumetsat. int)and GOME-2 (level  1B ). Meteorological reanalyses are produced by  ECMWF  and supplied by CLIMSERV (http://climserv.
As a consequence, FGSG_05404 was discarded from any further analyses. From the eight remaining possible GPCR targets, only FGSG_02655, predicted to code for a class I pheromone receptor, was retained for entering into the molecular  modeling pipeline. This protein contains a mating type pheromone receptor domain (PF02116). In fungi, one of the first steps in sexual reproduction is sexual pheromone reception by a mating type receptor. As sexual reproduction is a fundamental part of the FG disease cycle, FGSG_02655 is therefore considered a good choice for developing as a fungicide target  [27]. Using a reverse genetics approach, FGSG_02655 has already been shown to be required for sexual mating and virulence in F. graminearum. Single gene deletion strains exhibit reduce female fertility and fewer mature perithecia were produced when strains were selfed  [28, 29].
Then, a large compound library was used with a docking program to find putative inhibitors. All calculations were performed on a 64 cores computer. Step 1: GPCR identification in FG proteome. The Fusarium graminearum genome was firstly published in 2007  [33]. The complete proteome of Fusarium graminearum PH-1 assembly FG3 (13,321 proteins) was downloaded from the BROAD Institute Fusarium comparative database  [34]. 6  Root mean square deviation (RMSD) map showing the conformational behavior of the protein during the 100 ns MD. The color scale is given below, the black color corresponding to dissimilar conformations (RMSD > 3. 5 Å) and white corresponds to identical conformations (RMSD = 0 Å). The two conformational families 1 and 2 found as stable structural states are highlighted by the white squares Fig.
7  Structural alignment of the conformational families. The first conformation is in yellow and the second in red. The center of the binding pocket is in blue FG putative GPCRs were firstly detected using the GPCRpipe program  [24]. Two methods in GPCRpipe were used for the detection of GPCR. Based on Hidden Markov Model, the first step was designed for the detection of GPCR. The second one is a library that consists of 39 Pfam profile HMMs (specific to different families of GPCR). We used the GPCRpipe "AND" method, meaning that a GPCR was predicted only if the two methods confirmed the prediction. This choice resulted in a reduced number of GPCRs predictions and limited the number of false positive predictions. The next step, to validate the GPCRpipe predictions, involved using three transmembrane prediction softwares namely HMMtop  [35] , TMHMM  [36]  and Phobius  [37].
Then, our models were constructed by aligning transmembrane helices instead of similar amino acids. Moreover, crystal structures of several GPCRs, as well as experimental evidence, have shown the presence of a disulfide bond linking transmembrane helix 3 (TM3) to the second extracellular loop (ECL2)  [60]  and we used this additional information positioning this cysteine residue at the top of TM3. Finally, the position of the conserved motifs was also used for our selection process  [61]. The homology modeling task was performed using the MODELLER program with its default settings  [62]. Additionally, the automatic loop refinement method available in MODELLER was used. The DOPE score from MODELLER was used to estimate model quality.
Lipids overlapping with the protein complex were deleted, leaving a bilayer consisting of 357 POPC molecules. To ascertain that the cytoplasmic and extracellular loops did not interact, an amount of 35,479 water molecules was added, as well as 10 counterions to make the whole system-neutral, thus making a total number of atoms equal to 159,461. The complete system, represented in Fig. 3  was replicated periodically in the three directions of space, with a repeat distance of ∼ 120 Å. The MD simulations were carried out in the isobaricisothermal ensemble, maintaining the pressure and the temperature of 1. 0 atm and 300. 0 K, respectively, using Langevin dynamics and the Langevin piston approach. The MD program NAMD  [63]  was employed in conjunction with the CHARMM27 force field  [69]  to describe the receptor, the lipid bilayer, and the water molecules.
RMSD maps were built using a previously developed in-house Tool Command Language (TCL) script. Step 4: Virtual screening Ligand library The chemical libraries used for the virtual screening were the GPCR Targeted Libraries (11,571 compounds) from Life chemicals. These libraries contained compounds for sixteen types of GPCRs. Pocket detection and analysis For each stable conformer detected thanks to the MD simulation, the coordinates of the binding pocket center were identified using LigSite  [71].
Ensemble docking For the docking, we used the docking program GOLD  [72]  which has been considered as one of the best docking software  [73]. Because several stable conformations were identified, we used the ensemble docking possibility available in GOLD. The use of such conformational ensembles was considered as an improved strategy in structure-based docking calculations  [74]. For each docking, 100 starting ligand conformers were used in GOLD. All target conformers used were aligned in a common reference system and the center of the pocket cavity is an average of the individual centers found in each conformation. A sphere of 15 Å was selected to define the binding region around this center. Toxicity In order to remove probable toxic molecules, the side effects of the finally identified compounds were detected using toxicity predictors such as PAINSremover, Badapple and Protox webservers  [75] [76] [77].
This paper proposes a graph-based Named Entity Linking (NEL) algorithm named REDEN for the disambiguation of authors' names in French literary criticism texts and scientific essays from the 19th and early 20th centuries. The algorithm is described and evaluated according to the two phases of NEL as reported in current state of the art, namely, candidate retrieval and candidate selection. REDEN leverages knowledge from different Linked Data sources in order to select candidates for each author mention, subsequently crawls data from other Linked Data sets using equivalence links (e. g. , owl:sameAs), and, finally, fuses graphs of homologous individuals into a non-redundant graph well-suited for graph centrality calculation; the resulting graph is used for choosing the best referent. The REDEN algorithm is distributed in open-source and follows current standards in digital editions (TEI) and semantic Web (RDF).
idref. fr/027835995>, pointing to the French identity reference catalogue entry for Jules de Goncourt. Proper linking can only be achieved by choosing the second strategy, and adding an external link to each mention. Ideally, the link should also point to a source containing additional and machine readable information on this author (e. g. , birth date, birth place, authored works, etc. ). Linked Data (LD)  [4]  is a standardised way of publishing knowledge in the Semantic Web and many of the available data sets are of great interest for the DH  [3]. LD principles such as interlinking and vocabulary reuse facilitate manipulation of data from heterogeneous sources. The formalised knowledge published in the form of LD can provide the background information required to disambiguate NEs in a given context by means of reasoning.
In addition, such external knowledge enriches the text by remaining available in the annotation as a reference, and can be accessed at later stages for further processing; for instance, further connections can be found for the links by using information discovery systems such as in  [5]. The increasing volume and availability of Linked Data brings new opportunities to build LD-based tools. In this context, LD sets serve frequently as external KBs to NLP tasks such as NEL  3. The quality of the LD sets used as reference bases -in particular the completeness in terms of entities and the richness of relations between them -is crucial in NEL (see  [7]  for a discussion of LD quality and the formalisation of metrics adapted to NEL). Indeed, better datasets can help the algorithm in choosing the right referent, at the same time, they will better serve the final purpose of discovering and connecting information in the annotated texts.
In particular, NEL is similar to what the authors of  [11]  refer to as Disambiguate to Wikipedia (D2W), with a Text and a Set of mentions as input and a Set of relevant annotations (links to Wikipedia) for each mention as output. In our case though, the reference base is rather a linked data set, such as DBpedia or other domain specific ones, as we shall later see. A similar task to D2W and NEL is Word Sense Disambiguation (WSD) more generally, namely, the task of identifying the sense of polysemous words. A crucial difference is that in WSD we assume that the KB used for disambiguation, often a computational lexicon that lists senses for words, is complete, whereas in D2W, as in NEL, this assumption does not hold  [13] ; generally NEL algorithms should assign a null link to entities without a referent in the KB.
Both these methods are unsupervised, and they do not rely on pre-annotated corpora for training. The best known tool of the first group is DBpedia Spotlight  [15] , which performs NER and DBpedia linking at the same time. Spotlight identifies the candidates for each mention by performing string similarity between the mention and the DBpedia labels, then it decides which entry is the most likely the sought one by comparing the text surrounding the mention with the textual description of each candidate. The referent whose description is more similar to the context of the mention in terms of TF/IDF is chosen. This method is known to be very efficient, but it can only provide linking towards resources such as DBpedia, whose entries come with a description in the form of unstructured text. Other knowledge bases do not provide a textual description for their entries, such is the case of the bibliographical databases that constitute the ideal linking for mentions of authors.
As for the KBs, generalistic ones such as DBpedia or Yago are the most cited ones, whereas experiments with domain specific KBs are less frequent. To this respect,  [18]  rightly insist that a NEL algorithm should ideally be agnostic as to the type of KB used, but in the most of tools, such as DBSL, it is very difficult to replace DBpedia with a custom or domain specific KB. Indeed, the authors developed their own tool, AGDISTIS, that allows for the use of any linked data resource provided with a SPARQL endpoint. However, this tool, as the aforementioned ones, does not take advantage of the main strength of Linked Data, which is the possibility to access more LD sets available through equivalence links and thereby to enrich the graph of candidates. To this respect, a special mention should be given to Babelfy  [14]  a graph based tool that performs WSD and NEL at the same time.