dataesr/scientific-paragraphs-categorization huggingface dataset:
It is important to note that the FP and FN classes are caused not only by wrong predictions of s2m, but also by errors in the tested datasets. Indeed, s2m allowed us to detect wrong annotations of monomeric structures or wrong SMILES for polymers or monomers. Figure  5  shows an example of a wrong annotation on Norine corrected by the analysis of the s2m results. Four of the five monomers are correctly mapped on the pentapetide. They represent 83 % of atoms correctly assigned over all the atoms of the polymer. The coverage rate is even higher: 96. 2 % of the atoms are within a mapped monomer. Because of a wrong SMILES in Norine, the gSer monomer is not recognized and the tool tries to fill in the uncovered region with smaller candidate monomers like NMe-Ser. With the correct SMILES for gSer, s2m correctly recognizes all the monomers (see Fig.
6 ). We are currently overviewing the obtained results to update the annotations stored in the Norine database. So far, the rates for TP classes have been underestimated and should increase while the remediation process progress. At the time of writing of the article, 291 of the 327 peptides (89 %) of Norine and 368 of the 378 compounds (97 %) of CCD are totally covered. Among the partially covered polymers, only 1 compound of CCD has a coverage of 0. 77, 2 peptides of Norine have a coverage of 0. 8, while 34 peptides and 9 compounds have a coverage of 0. 9. In total, the coverage reaches an average of 0. 996 for Norine and 0. 998 for CCD. So s2m succeeds in calculating monomeric structures that cover totally or at least 3 / 4 of the atomic structure of the tested polymers. Moreover, 230 peptides of the 327 (70 %) of Norine and 322 compounds of the 378 (85 %) of CCD are fully recognized (correctness of 1) by s2m.
The correctness reaches an average of 0. 934 for Norine and 0. 953 for CCD. The counts are illustrated in Fig. 7. This represents a recall (or sensitivity) of 0. 865 for Norine and 0. 970 for CCD (0. 919 for both sets) and a precision of 0. 790 for Norine, 0. 875 for CCD (0. 830 for both sets). As previously mentioned, these rates are underestimated as the FP and FN rates are partially caused by errors in the databases. processing (the images generation for example) parts, which use CDK library. First we discuss the algorithm execution time. The indexation of the monomer databases with our isomorphism algorithm coupled to an efficient and accurate tiling strategy ends with a fast tool. Indeed, the average algorithm execution time of s2m is 21 ms per polymer for Norine and only 7 ms per polymer for CCD.
Second, the complete reloading of the jvm and the complete loading of our data for each query increase the latency period. We are working on a solution using a memory cache for our data with a permanently loaded jvm to improve the performances. We hope to reduce the online delay to 1 s with all these technical features for the queries without image generation. The time measurements were computed on a desktop computer with an intel core i5-3470 CPU at 3. 20 GHz with 8 Gb of RAM dedicated to the Java Virtual Machine.
Conclusion and perspectives As demonstrated, s2m automatically creates de novo monomeric structures for polymers. Our tool, which implements Fig. 5  Gymnangiamide analyzed by s2m before SMILES corrections. The execution of s2m on the Norine database allows us to discover wrong annotations in the data. On this example the gSer SMILES was wrong so the software was not able to detect it. The NMe-Ser monomer find in the polymer is an artifact due to a smaller random match on the uncovered region let by gSer a smart algorithm, is efficient in terms of sensitivity. On average and per polymer for the two tested databases, the results are outputted in 2 s with an average coverage rate of 0. 997, and with an average correctness rate of 0. 943 (82 % of the polymers are correctly annotated). The source code is available under GNU AFFERO licence (https://github.
com/yoann-dufresne/Smiles2Monomers) and through a web server for the NRPs structure predictions. With the help of the tool we discovered wrong annotations or wrong chemical structures in the tested databases. So, we started the remediation of Norine data, with the help of the automatically generated monomeric structures and by going back to the literature. This proves that s2m is an efficient and accurate tool for automatic verification of already annotated polymers or for helping the annotation of newly discovered polymers. For example, s2m is included in MyNorine, the tool to submit new peptides in Norine (http://bioinfo. lifl. fr/norine/my/). It could be included in the remediation process of other databases such as CCD and BIRD  [3]. s2m already outputs adequate results, based on a given monomer database.
For the moment, the uncovered regions are partially covered by smaller monomers of the database. With the light matching, modifications as additional functional groups can already be detected (lose of an hydrogen replaced by a functional group). But, s2m depends on the completeness of the monomer database relating to the type of the target polymer. We are currently thinking of implementing a functionality that proposes unknown monomers by using local MCS against known monomers in the uncovered regions. As we generate residues from monomers by applying chemical Fig. 6  Gymnangiamide analysed by s2m after SMILES corrections. With the accurate SMILES for gSer, s2m was able to cover completely the polymer with the accurate monomers Fig. 7  s2m succeed to predict correctly more than 3 / 4 of the tested polymers.
Among the 327 polymers of Norine, 230 are correctly predicted (TP), 61 are totally covered but with few wrong monomers (FP) and 36 are not totally covered (FN). And among the 378 tested polymers of CCD, 322 were correctly predicted (TP), 46 aretotally covered but with few wrong monomers (FP) and 10 are not totally covered (FN) reaction rules, s2m can be extended to label the type of bonds between identified monomers. Finally, if the monomers and the bonds are clustered by polymer types (for example peptide or carbohydrate), once the monomeric structure is predicted, s2m can infer the polymer type. So, s2m can screen chemical databases (storing atomic structures) to generate the monomeric notation and assign the predicted polymer to its more probable type. This high-throughput technique will increase the fillingin of specialized databases such as Norine with external data and will help to add new and more reliable annotations to the original databases.
The need for defining reference conditions is more pronounced in highly populated areas, such as the coastal zones of the Mediterranean Sea, a semi-enclosed basin where human influence is so strong  [8]  that pristine areas do not exist anymore  [9]. In such situations, the availability of historical data  [10]  assumes greater importance, but quantitative information is extremely rare  [11]. On the other hand, descriptive information by early ecologists was collected most intensively along historically populated coastal areas  [12]. These early datasets can be combined to more recent quantitative snapshots to understand long-term ecosystem change. Coralligenous reefs are a deep biogenic habitat endemic to the Mediterranean, whose status urgently needs to be evaluated,  [13]. Coralligenous reefs are shaped by the dynamic equilibrium between bioconstruction (encrusting red algae, with an accessory contribution by serpulid polychaetes, bryozoans and scleractinian corals) and destruction processes (borer species and physical abrasion); its assemblages are characterised by high biodiversity  [14] , biomass and production, almost comparable to coral reefs also in terms of calcification rate, around 10 3 gCaCO 3 •m -2 •y -1  [15].
Such reefs develop on both rocky and biodetritic bottoms from about 20 m down to 120 m depth, in dim light conditions and in relatively constant conditions of temperature, currents and salinity  [16] , and are sensitive to natural and human disturbances  [17] [18]. The large bathymetric distribution of coralligenous is the cause for sampling constraints due to operative limitations imposed by scuba diving  [19]. As a consequence, coralligenous assemblages have been subjected to limited spatio-temporal investigations and historical information is seldom available  [20]. Rapid non-destructive underwater protocols for the assessment of coralligenous status have been implemented only recently  [21] [22] [23] , so that comparable quantitative historical information is virtually unavailable. In this study we analyse the oldest historical dataset existing in the Mediterranean Sea: the coralligenous assemblages of Mesco Reef (Ligurian Sea, NW Mediterranean).
Study area Mesco Point is a rocky headland located along the eastern Ligurian coast (NW Mediterranean Sea), near the town of Monterosso al Mare (Fig. 1A ). The headland points SE and is composed by sandstones (in contact with claystones and serpentinites) organized in strata, dipping SW as a plunging cliff. The rocky reef immediately off the point, for which only approximate descriptions existed  [24] [25] , has been thoroughly investigated in 2008, using multibeam data and scuba surveys. Fieldwork have been authorised by the Cinque Terre Marine Protected Area authority and consisted in photographic surveys, without any direct manipulation of the organisms. Three different groups of rocky outcrops can be recognised according to their depth: 1) outcrops at the cliff toe, between 8-12 m depth, which elevate 3-4 m on an irregular seabed made by rockfall and toppling deposits from the cliff; 2) outcrops at 15-25 m depth, which are 7-9 m high on coarse, partly biodetritic sand, with a relevant extension and a general NW-SE direction; 3) outcrops at 40-55 m depth, which elevate 2-5 m on fine, partly biodetritic sand.
A three-dimensional Digital Elevation Model (DEM), with a cell size of 0. 25m, was extracted from the multibeam data (Fig. 1B ). From the DEM, a bathymetric map of the study area (Fig. 1C ) was derived using the v. surf. rst. command of GRASS GIS software. This command performs a spatial approximation and a topographic analysis using regularized spline curves to obtain a raster dataset from points with latitude, longitude and third dimension properties  [26]. For a complete description of the script, the reader is referred to http://grass. osgeo. org/grass64/manuals/v. surf. rst. html.
As regards quantitative information, species were classified on the basis of their change in cover over time as 'losers' if they decreased or disappeared between 1961 and 2008, or 'winners' if they conversely increased  [55]. The category 'commuters' was introduced to represent those species that showed a major change in the 1990s, with no obvious difference between 1961 and 2008. Species that showed little or no change in cover over time were named 'constants'. A single data matrix (photoquadrats × time) × species was organised on the basis of available cover data for 1961-62  [44] , 1990  [47] , 1996  [48] , and 2008  [51]. Data collected in 1988 on anthozoans only  [46]  were excluded. For each site and for each year, data from three photoquadrats, considered as site replicates, were compiled.
We used multivariate analysis of historical quantitative data  [89]  together with qualitative observations arranged into a chronological chain of events  [90] , to reconstruct the ecological history of the coralligenous assemblage of Mesco Reef. Adopting approaches more typical of historical ecology may prove to be the key to understand change and to provide information for environmental management  [91]. The accumulation of observations and data for over 70 years represents a precious heritage to be used for conservation purposes, after the establishment of the Cinque Terre MPA in 1997. As for other sites, many old descriptions of the marine habitats are often buried in publications and reports that are not readily available to the international scientific community  [92] : we recommend MPA administrators and environmental managers to promote efforts aimed at resuming and reorganising such fundamental information.
Supporting Information S1 Fig. Left  panel-Multidecadal trends of annual mean sea surface temperature (SST) and air temperature in the Ligurian Sea. SST data have been derived from NOAA satellite time series (http://www. esrl. noaa. gov/psd/cgi-bin/data/timeseries/timeseries1. pl), corrected with the ENEA CRAM oceanographic historical data bank MOIS, Mediterranean Oceanographic Information System (http://www. santateresa. enea. it/wwwste/siamen/home. htm); air temperature data come from the Meteorological Observatory of the University of Genoa. (http://www. distav. unige. it/rsni/meteo_sito/main. htm). Right panel-Change in water transparency (Secchi disk depth). Data for the 1950s and the 1990s were taken from Morri and Bianchi  [29] , those for the 2000s from Attolini and Coppo  [33]. (PDF).
Acknowledgments This paper is dedicated to the memory of  Gianni Roghi  ( 1927Roghi ( -1967 )), pioneer in scuba diving and UW photography (http://www. gianniroghi. it). We thank his granddaughter  Fiorenza  for her help. Luigi Piazzi  (Pisa) identified Womersleyella setacea,  Franco Martini  (Genova) and  Federico De Strobel  ( La Spezia ) assisted in the search for historical information,  Giorgio Bavestrello  (Genoa),  Riccardo Cattaneo Vietti (Ancona), Paolo Povero (Genoa) and Eva Salvati (Rome)  provide information and/or advice. We wish to acknowledge  Stefano Coppo and Corinna Artom of Regione Liguria  for giving us the multibeam data and the access to the archive of perspective photos of the shoreline. The " Cinque Terre" Marine Protected Area authorised fieldwork at Mesco Reef.
TbMCP14 has an unusual domain organization and is unique to trypanosomatids While the N-terminal half of TbMCP14 (amino acids 1 to 161) is devoid of known motifs, the C-terminal half (amino acids 162 to 402) carries mitochondrial carrier protein (MCP) motifs of about 100 amino acids each, which are typical for transporters of the inner mitochondrial membrane (Pfam entry PF00153). Mitochondrial carriers usually possess three such motifs, whereas TbMCP14 only has two. A profile search with PF00153 against SwissProt, the manually curated section of UniProt  [47] , returned 1347 hits of E-value <10 -10 , of which only 24 (1. 8%) possessed two MC motifs while 1302 (97%) had three. Blastp similarity searches  [48]  with TbMCP14 as the query returned highly significant hits (expectancy E<10 -12 ) from trypanosomatids only, followed by many hits from the viridiplantae.
Metabolomic analysis Parasites (5 × 10 7 cells) collected from cultures grown to mid log phase were harvested and quenched and metabolites extracted in 100 μl of chloroform/methanol/water (1:3:1, by vol) as previously described in  [60]. HPLC using a ZIC-pHILIC column (150 mm × 4. 6 mm, 5 μm column, Merck Sequant and a Dionex UltiMate 3000 RSLC system (Thermo, Hemel Hempstead, UK) with metabolite masses identified using a Thermo Orbitrap Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) operated in polarity switching mode with lock-mass correction applied to enhance calibration stability. XCMS software  [61]  was used for untargeted peak detection and mzMatch. R  [62]  for peak matching and annotation of related peaks. The IDEOM software package  [63]  was used to identify metabolites either through matching accurate masses and retention times of authentic standards (Metabolomics Standards Initiative confidence level 1) or using predicted retention times using a previously validated model  [64]  (Metabolomics Standards Initiative confidence level 2) if authentic standards were not available.
Bioinformatic analyses Profile searches were performed with the command line version of HMMer 3. 01  [65]  and the results were parsed with ad hoc Perl scripts. SwissProt release 2014_05 was downloaded from ftp. uniprot. org. Blast searches were carried out on blast. ncbi. nlm. nih. gov. Muscle multiple alignments and Neighbor-Joining trees were done with amino acid sequences on Mega5  [66] , using default parameters and the JTT substitution model. GenBank accession numbers of the (full-length) sequences of S5.
Acknowledgments We thank  M. Rauch  and  J. Jelk  for technical assistance during part of the study and  C. Clayton  and members of the  Sinergia  project for stimulating discussions. In addition, we thank  D. Rentsch  and  E. Sigel  for attempting to express TbMCP14 in yeast and Xenopus oocytes, respectively. We are grateful to  Karl Burgess  at  Glasgow Polyomics  for mass spectrometry and to  Frank Voncken  ( University of Hull, U. K. ) for providing a plasmid encoding TbMCP5. J. P. M. thanks  A. Gonzalez  and  L. C. Alves  for helpful discussions and support,  J. Gertsch  for expert technical advice with the flow cytometry measurements and  D. Rentsch  for kind cooperation and support to allow completion of the project. P. B. thanks  R. Tedder ,  R. Plant  and  A. K. Menon  for valuable input.
Red dots: Y. lipolytica RS genes. Red circles: KanMX. Small blue dots: all others. (b) Comparison of mutant BYAT583-345 with a non-amplified strain (BYAT581-0). Same legend as above. The 24 amplicon-embedded genes (2 not tested) are overexpressed 16. 2 times on average (standard deviation 7. 7) in strain BYAT583-345 (22-24 amplicon copies) compared with strain BYAT581-0 (2 amplicon copies). because some of these genes were known to be toxic to cells when overexpressed  41, 42. Normalized data for Fig. 5  and Supplementary Fig. 3  are available in GEO under the accession code GSE64431.
Sequence data were analysed according to the pipeline illustrated by Supplementary Fig. 12. All reads were first submitted to FastQC v0. 10. 1 Babraham Bioinformatocs (http://www. bioinformatics. babraham. ac. uk/projects/ fastqc/) and regions of problematic base calling (deviation from Chargaff's rules) were trimmed off using Fastx Toolkit v0. 0. 13 (http://hannonlab. cshl. edu/fastx_ toolkit/). Trimmed reads were then aligned along the 16 chromosomes of S. cerevisiae S288c (GenBank NC_001133 to NC_001148, PLN 06-DEC-2008), plus the 2 RS genes of Y. lipolytica (YALI0E05005g and YALI0F16291g) using single-end mapping mode of BWA v0. 6. 2 (ref. 55) with default parameters. Output SAM files were converted to BAM files using SAMtools v0. 1. 18 (ref. 56). For each yeast strain, sequencing coverage along chromosome maps was computed by BEDtools v2.
lipolytica Asn-RS and Lys-RS genes (upstream and downstream parts of YALI0E05005g and YALI0F16291g, respectively) and 2 probes for the KanMX marker. The arrays also contained triplicates of 427 probes for S. cerevisiae spliceosomal introns, 1,233 probes for Cryptic Unstable Transcripts (CUTs)  62  , 1,304 probes for other noncoding RNAs and 23 probes for mitochondrial DNA. Comparative hybridizations (with dye swap) were scanned on AXON GENEPIX 4200AL scanner. Data were analysed using GenePix Pro 7 (Molecular Devices). Data drawings represent MA plot log2 ratios (R 'arrays' package Bioconductor R:2. 13(3. 0. 1), http:// www. bioconductor. org).
Acknowledgements We thank  Gwenae ¨lle Badis  for help with micro-array hybridization analyses,  Sergi Leon-Bernabeu  for sharing unpublished data,  Christian Marck  for expert advices on tRNAs,  Romain Koszul  for critical reading of the manuscript, and our colleagues of the Unitede Genetique moleculaire des levures for fruitful discussions. This work was supported in part by contract  DYGEVO  from  ANR  Blanc  2011 SVSE6.
For instance, the resolution used by Micheli et al. [41]  within the Mediterranean and Black sea is 1 km² pixels and no data is associated with the first pixels close to the coast, where most anthropogenic pressures are concentrated. Interested in data that could be of real use to local managers and stakeholders, we sampled data from a homogeneous environmental policy context and thus focused on a unique country: France with its 1700 km of Mediterranean coastline, including Corsica. Our goals were to (1) provide the first complete marine coastal habitat map of the French Mediterranean coast (including Corsica), and (2) to quantify and map cumulative impacts to provide the data needed (one pixel = 20 x 20 m) to help the development of an effective marine policy. On these bases, we identified the most and least impacted areas (water bodies), the top threats affecting coastal waters, and the areas representing top priorities for ecosystem-based management and conservation efforts.
Marine habitats The study considers the entire French Mediterranean coastline (including Corsica) included within the 46 water bodies of homogeneous water according to the Water Framework Directive (WFD,2000/60/EC)  [43]. Interested in costal-based impacts, we particularly focus on the shallow part: between 0 and -80 m. After a bibliographic synthesis, we gathered and homogenized data on habitat maps; these data were collected by Andromède Océanologie, Agence Gaps were completed with the program DONIA  [44]  with a fine scale (1:10 000 map) between 0 and -80 m and a lower resolution (1:25 000) beyond (S1-S3 Figs). Campaigns were led between 2010 and 2014 using first aerial or satellite photography (in order to measure the spatial extent of habitats in shallow waters) and a multi-beam echo-sounder GeoSwath Plus (Kongsberg Geoacoustics LTD) survey (to draw the bathymetry).
Then, a side-scan sonar survey (used in more turbid and deeper (< -15 m) waters) was led. By ensonifying a swath of seabed and measuring the amplitude of the backscattered return signals, an image of the seabed was built up with information on the morphology and substrate content. We used a Klein System 3900 with a frequency comprised between 445 and 900 kHz. After that, sonar information was post-treated to determine the potential presence and coverage of underwater habitat representation. All of these data allow achieving a preliminary cartography of benthic habitats. Numerous uncertainties still remained after this preliminary cartography work. Direct observations ("ground-truth points") were thus needed through diving sessions (around 1600 dives between 0 and -80 m all along the coastline between 2010 and 2014).
Field work was organized in cooperation with the French water agency (public authority) which gave permission to conduct the work. Field work was also declared to the authorities responsible of the concerned marine parks. The field studies did not involve endangered or protected species. A final continuous habitat map (scale = 1:10 000 between 0 and-80 m and 1:25 000 beyond in the case of deeper water bodies) was realized comprising eleven habitat classes: Cymodocea nodosa seagrass, Zostera marina and noltii. seagrass, Posidonia oceanica seagrass, dead matte association, infralittoral shingle association, infralittoral soft bottoms, photophilous algae association, coralligenous assemblages, circalittoral soft bottoms, artificial habitats, offshore rocks. Ecosystem data were finally converted into presence/absence 20 x 20 m pixel layers (in order to be adapted to the pixel size related to the anthropogenic pressures, see below); the habitat corresponding to each pixel was defined by the major habitat observed within the grid (percent cover > 50%).
Even if continuous pressures (e. g. wastewater) are generally distinguished from discrete pressures (e. g a groyne building), low resilience of marine ecosystems (especially Posidonia oceanica beds and coralligenous reefs;  [48, 49] ) allow the combination of both pressures within the same methodology. Data concerning the origin and intensity of these pressures are available in published databases: MEDAM  [50] , CORINE land cover  [51] , INSEE [52], MEDOBS data  [53]  but were also provided by Agence de l'Eau RMC and Ifremer completed with an analysis of satellite-aerial pictures and unpublished data (Andromède Océanologie). Models of the spatial extent of the pressures were built using ArcGIS 10 (ESRI) with a 20-m distance matrix. We applied a pressure curve (type y = ae-bx) considering the distance to the source with a negative exponential shape ranging between 100% (origin) and 0% (no more impact) to each type of pressure.