dataesr/scientific-paragraphs-categorization huggingface dataset:
The prediction of orthologous genes is a difficult task because of non-uniform evolutionary rates, extensive gene duplication, gene loss and horizontal gene transfer  [6]. Over the last decades, a large number of methods and tools have been developed to perform orthologous gene prediction, and nowadays not less than 37 databases offer groups of orthologs  [7]. However, the results predicted by these various methods are often uncertain. In particular, users should be aware that the application of different methods on the same proteomes can lead to distinct orthology predictions  [6, 8, 9]. Accordingly to these results, it is particularly difficult to know which method or database will be the most appropriate. In addition, we might reasonably question the relevance of biological findings drawn from the orthology prediction obtained by any single method.
The method clusters the pairs into groups by using the MCL program  [35]. OrthoMCL was used with default parameters. Phylogeny  [34]  is based on the phylogenetic analysis of homologous genes. No species tree is required. Homologs detected by BLAST are grouped transitively. Homologous sequences are aligned using the MUSCLE program. These multiple alignments are processed with a maximum likelihood approach to reconstruct the phylogeny of the corresponding family, using the PhyML software. Group trees are rooted by using the program Retree from the Phylip package  [36]. The analysis of the rooted tree allows to identify duplication and speciation events and to distinguish orthologs and paralogs.
Evaluation on 70 reference ortholog groups Taking orthoBENCH  [37]  as a reference benchmark, we compared the results of the four initial methods, and those obtained by the meta-approach, to the reference ortholog groups (RefOGs). The orthoBENCH dataset involves 1519 proteins from 12 metazoan species divided into 70 manually curated ortholog groups. For our analysis, we downloaded the proteome version of Ensembl 72  [38]. As orthoBENCH is based on Ensembl 62, the proteins removed or added between the versions 60 and 72 of Ensembl were not taken into account. Moreover, if a gene has splice variants, When comparing the groups produced by the meta-approach or the individual methods with those of orthoBench, two types of errors were defined: group fissions (proteins of a RefOG are in two or more ortholog groups), and group fusions (more than 3 proteins have been added to a RefOG)  [37].
Functional annotation conservation test The Orthology Benchmark Service is a recent web server (http://orthology. benchmarkservice. org) allowing us to compare methods of orthologous gene prediction. This is based on a common set of 66 species (2011 quest for orthologs reference dataset)  [39, 40]. The benchmark service proposes two types of procedures for evaluating orthologous groups: phylogeny-based definition tests and functional annotation conservation test. In the Phylogeny based tests, orthologous groups are defined in such a way that every pair of genes in the group is either orthologous or inparalogous with respect to the last speciation event in their clade. However, we refer to a different and more recent definition of ortholog groups  [4]. Thus this test is not relevant for our purpose (see Additional file 1 for further details).
Comparing the results of the four initial methods with those of the meta-approach The results obtained for each of the four initial methods were compared with those obtained by the metaapproach (Table  1  and see Additional file 3) on the orthoBENCH dataset. First we observe that all the methods produce different numbers of ortholog groups (ranging from 14 771 for the meta-approach to 25 384 for BRH), and, in addition predict orthology relationships for different numbers of proteins. In order to measure the similarity between the groups obtained by each method, we computed the Jaccard coefficient by dividing the number of ortholog pairs in common between two methods by the total number of pairs of orthologs (|A ∩ B|/|A ∪ B|). The Jaccard coefficient value is expected to be between 0 (no or-tholog pairs in common) and 1 (all couples are identical).
Quality of ortholog groups Among the four input methods, BRH and Inparanoid give the highest level of accurately predicted groups (groups without fusion or fission events) (Figure  2A ). BRH presents the highest number of fissions and the smallest number of fusions (Figure  2B ). Inparanoid allows the detection of in-paralogs between each pair of proteomes and thus the number of fusions is higher than with BRH and Phylogeny. The Phylogeny approach presents the smallest number of groups impacted by fusions or fissions. The OrthoMCL method presents groups largely impacted by fusion events compared with the other three methods. The larger number of fusions is associated to a lower number of fissions. This result on orthoMCL is consistent with the results obtained by Dalquen et al  [6]  on a dataset of mammalian genomes.
The Pearson coefficient is not significant (-0. 471 and p-value 0. 06121 with the negative correlation hypothesis), showing that the result obtained with the meta-approach is not compatible with the linear regression. Furthermore, the point representing the meta-approach is above the linear regression curve (Figure  3B ), showing that the meta-approach outperforms the other methods on this dataset. Thus, the metaapproach increases both the average Schlicker similarity The black lines are the linear regression obtained on all methods except the meta-approach, the metaPhors and the BRH plus HMM profiles approach. Error bars for each method are in black. Fourteen methods were compared: •: Meta-approach. Δ: metaPhOrs  [18]. □: BRH  [33]  with complete link. □: BRH with complete link plus HMM steps.
Conclusions The meta-approach appears to be a reliable method of prediction of ortholog groups. Based on the combination of existing methods, the meta-approach finds a consensus of higher quality. Both ortholog group quality and consistence of group annotation have been positively tested. We showed with the orthoBench dataset  [37]  that, compared to the initial methods, the metaapproach reduce the number of incorrect groups as well as the number of fission and fusion events. Furthermore, the metaapproach presents the largest GO term similarity compared to twelve of the thirteen state-of-the-art methods on the protein reference dataset  [40]. Phylogeny's Schlicker similarity is larger than the metaapproach, but Phylogeny predicts many less pairs of annotated orthologs. All other methods present both a smaller Schlicker similarity and an smaller number of pairs of annotated orthologs.
, 2013; Schulp and Alkemade, 2011; Martin et al. , 2013; Poppenborg and Koellner, 2013). Remote sensing has been increasingly used to derive better LULC data for the past few decades  (Bartholomé and Belward, 2005; Loveland et al. , 2000; Loveland and Belward, 2010; Friedl et al. , 2010). Nevertheless, because available global land cover products are still limited thematically, continuous efforts to improve the LULC products are being made  (Blanco et al. , 2010; Colditz et al. , 2011; Fernandes et al. , 2004). Particularly for agricultural landscapes, detailed land cover information is often lacking  (Potgieter et al. , 2007; Pittman et al. , 2010; Fritz et al. , 2013). In fact, the most widely used land cover databases such as GlobCover or Moderate Resolution Imaging Spectroradiometer (MODIS) land cover only have a few crop-related classes  (Loveland et al.
Digitising the field records We digitised the field records into polygons and polylines with LULC type labels. The base map served as background information to complement the field records. In addition to LULC classes, we stored other descriptive information in an attribute table. In the corresponding columns, quality assurance (QA) was recorded as: "?" (questionable), "*" (unknown) and "/" (not valid). For instance, a question mark was assigned if we could not identify the crop reliably. Gap-filled data were also marked by a question mark. A forward slash indicates that the data were collected but was unreliable (e. g. incorrect identification). For further information, the reader is referred to the readme file of the data set at the Pangaea repository.
In other words, if a perennial crop was harvested after one growing season we classified it as "annual". We distinguished between the life forms "woody", "herbaceous" and "lichens/mosses", or a combination of them. Crop type patches were further subdivided into 12 different crop types (Supplement Table  S1  at Pangaea repository). We assigned mixed crop type values for patches where various crop/non-crop vegetation coexisted. In addition to the S1 scheme containing 67 classes, we reclassified the LULC information according to three simpler schemes. First, we generated a locally optimised scheme with 10 classes (called S2 in the following) that reflects the edaphic and socio-economic conditions in the study area. It consists of the classes "barren", "dry field", "forest", "greenhouse", "inland water", "orchard field", "paddy field", "seminatural" and "urban".
Then, based on the FAO-LCCS we regrouped the S1 classes into eight major types (Supplement Table  S2  at Pangaea repository). Two of the eight classes are relevant for crop distinction. Finally, we classified our data according to the International Geosphere-Biosphere Programme (IGBP) Discover land cover system which contains 17 classes, two of which are crop classes  (Loveland et al. , 2000; Loveland and Belward, 2010; Friedl et al. , 2010). Thus, the schemes S1, S2, FAO-LCCS and IGBP differ in the total number of classes and the number of crop classes (Table 2). These reclassified LULC data can be used together with global products such as MCD12Q1 or GlobCover that follow the IGBP and FAO-LCCS schemes, respectively. For the IGBP classes, we reclassified some of the perennial crops as non-crop types (forest or shrub) to be consistent with the IGBP system (e.
Software We processed the data in GNU R v3. 0. 2 (R Core Team, 2013) and provide the R code along with the data set in the repository Pangaea (doi:10. 1594/PANGAEA. 823677). For the reclassification, we used the package raster  (Bivand and Rundel, 2014). For the rasterisation, we used the geometry engine GEOS (Geometry Engine -Open Source) (GEOS Development Team, 2014) through the package rgeos  (Bivand and Rundel, 2014).
Local classification scheme S1 The field survey resulted in vector geographic information system (GIS) data with 67 LULC classes (S1). Overall, the study site can be characterised as an extremely heterogeneous agricultural landscape with a large number of LULC types in its central part (Fig. 1 ; proportions in the Supplement Table  S3  at Pangaea repository). We provide more details on the LULC types in the meta information of the data set (cf. Supplement to the data set at Pangaea repository). The data have 3377 polygons with an average size of 0. 019 km 2. Because in 2011 we only surveyed the northern half of the catchment, 12. 3 % of the values were lacking for this year. "Deciduous forest" at the steep hill slopes was stable during the studied period. It occupied more than half of the study area and was therefore the most dominant type (55.
Major changes in land use During the study period, dry fields and rice farming decreased and orchards and ginseng cultivation increased (Table 6 and Supplement Table  S3  at Pangaea repository). In fact, "Ginseng" almost doubled from 2009 to 2010 (1. 26 to 2. 48 %). It is consistent with the rapid ginseng expansion reported by  Jun and Kang (2010) , who suggested replacing annual dry crops by perennial crops to stabilise soils and thus prevent erosion. An expected reduction of soil erosion due to Additionally, fallow fields increased in 2010 (4. 8 %) compared to 2009 (1. 9 %) and replaced a large number of dry fields. We attribute these changes partially to the subsidy for fallow fields and partially to corporal regulations requiring at least 3 years of fallow or organic farming before ginseng farming could start.
Crop types We found 6 of the 12 FAO-LCCS crop types in the study area, namely "cereals and pseudocereals", "roots and tu- bers", "pulses and vegetables", "fruits and nuts", "fodder crops" and "industrial crops" (Supplement Table  S1  at Pangaea repository). We used combinations of them if multiple crop types were identified on the same patch. Occasionally, the class "mixed crops" was assigned when the combination was not precisely recorded. For some crops, the most suitable type was difficult to find. Indeed, the LCCS manual classifies "soybean" as an industrial crop, while in the region it is often used as a vegetable because the green part is popular in local cuisine. "Wild sesame" is another example of a crop with multiple purposes, namely "pulses and vegetables" and "industrial crops".
Comparison between MODIS land cover and the rasterised survey data After rasterisation, six IGBP classes were found in the survey data, namely "deciduous broadleaf forests", "mixed forests", "closed shrublands", "grasslands", "croplands" and "urban and built-up lands". "Urban and built-up lands" were missing in the MODIS data while "cropland/natural vegetation mosaics" does not exist in our data. Figure  7  shows the rasterised ground observations (in the middle row) and MCD12Q1 (in the bottom row). To compare the two maps, we derived confusion matrices, Cohen's κ and Pontius's Q and A for 2009 (Supplement Table  S3  at Pangaea repository) and 2010 (Supplement Table S4 at Pangaea repository). We excluded the year 2011 due to a lack of ground observations. The mean κ for the 2 years equals 0.
Data structure and data access The data set and its description are available at the Pangaea repository under the Creative Commons Attribution-NonCommercial 3. 0 Unported license. The data contain LULC observations and ancillary information in a single ESRI polygon shape file (ESRI Inc. , http://esri. com). The LULC type, QA, management and double-cropping and mixed-use information are provided on an annual basis. The definition of classes and the reclassification table are given separately in a legend table. For each polygon, LULC information for 3 years is given in separate columns (e. g. LULC2009, LULC2010 and LULC2011). Note that multiple entries in a LULC type column occur in cases when the polygon exhibited mixed land uses spatially or temporally. Note that the IGBP system does not distinguish the paddy field from a general cultivated zone.
Acknowledgements. We thank  Hamada Elsayed Ali ,  Sebastian Arnhold ,  Jaesung Eum  and  Ralf Geyer  for their help in the laboratory and during data collection. This research was supported by the  International Research Training Group of Germany and South Korea (DFG/KOSEF ,  Complex TERRain and ECOlogical Heterogeneity (TERRECO) ,  GRK 1565/1 ). MODIS data were obtained from https://lpdaac. usgs. gov,maintained by the  NASA   Land Processes Distributed Active Archive Center (LP DAAC ) at the  USGS/Earth Resources Observation and Science (EROS) Center. Edited by:  D. Carlson.
MRS Single-voxel MRS data were acquired from 30630630 mm voxels obliquely positioned by a radiologist (D. B. P. with 10 years of experience with liver imaging) on the Couinaud segment V that corresponded to the location of the biopsy. Localizer sequences at maximum inspiration, maximum expiration, and during free breathing were used to ensure the voxel was inside the liver during the entire respiratory cycle. Care was taken to avoid liver edges, large hepatic vessels, and the biliary tree. Water suppression was not performed and automated shimming generated water line widths of 40-50 Hz. Point -resolved spectroscopy sequences (PRESS) were used for MRS acquisition. For T2 correction, a multi-echo version was used and 8 spectra were collected at echo times (TE) of 40, 50, 60, 70, 80, 90, 100, and 110 ms.
Gradient echo image analysis All MR imaging results were interpreted by one radiologist (D. B. P. with 10 years of experience with liver imaging), who was blinded to spectroscopic results. Analysis was performed using a custom analysis package in Matlab (Mathworks, Natick, MA). The radiologist manually drew a region of interest (ROI) approximately 900 mm 2 in area and placed it at segment V of the liver, in the same region where MRS measurements and biopsies were performed (Figure  2 ). The fat fraction (including correction for T2* decay and noise bias) was obtained from average ROI values in magnitude images using a multipeak spectral model for fat detection  [29, 30].
MRS analysis MRS data were analyzed by a MR physicist (F. F. P. , 5 years of experience with MRS) who was blinded to imaging results. Analysis used the Advanced Method for Accurate, Robust, and Efficient Spectral (AMARES) algorithm included in jMRUI  [31]. At each echo time, the water (4. 7 ppm) and fat (0. 5-3 ppm) peaks were measured. Each measurable peak area was individually corrected for T2 decay using nonlinear least-square fits to determine their relative proton density. The relative proton densities of the fat peaks located underneath the water peaks were determined according to Hamilton et al. [32]. The total fat proton density was defined as the sum of all T2-corrected individual fat peaks. The proton density fat fraction (PDFF) was calculated as the ratio of the fat proton density to the sum of the fat and water proton densities.
Acknowledgments This project is funded in part by the  European Research Council  ( ERC-2011-AdG-295810  BOOTPHON), the  Agence Nationale pour la Recherche  ( ANR-10-LABX-0087   IEC ,  ANR-10-IDEX-0001-02 PSL*) , the  Fondation de France , the  Ecole de Neurosciences de Paris , and the  Région Ile de France  (DIM cerveau et pensée). We thank  Luc Boruta ,  Sanjeev Khudanpur ,  Isabelle Dautriche ,  Sharon Peperkamp  and  Benoit Crabbé  for highly useful discussions and contributions.
huxleyi in the NW Mediterranean Sea. We carried out weight measurements on E. huxleyi coccoliths from a sediment trap series deployed in the Gulf of Lions from 1993 to 2005, and compared them to environmental data in order to determine which environmental factors may have driven changes in coccolithophore calcification. We also investigated sediment records from the region representing the 20th century and the Holocene in order to test if coccolith weight has decreased since the beginning of the industrial age.
Splits of the original sediment trap samples were brought into homogenous suspension in water. An aliquot representing between 0. 2 and 2 mg of trap particles was taken from the suspension with a glass pipette and placed on a 47 mm diameter and 0. 45 µm pore size cellulose nitrate filter. The filter was oven dried at 50 • C over night and embedded in Canada balsam on a glass slide to make the filter transparent. All slides were analyzed with a polarizing microscope (Leica DMRBE) with a 2 megapixel Spot Insight camera. Routine checks on the assemblage showed that E. huxleyi dominated the assemblage throughout the record. 40 fields of view were taken automatically and analyzed with the automated recognition software SYRACO  (Beaufort and Dollfus, 2004). Image analysis was performed on the output files for Emiliania huxleyi to obtain length and weight of the individual coccoliths.
In total 35 602 coccoliths were identified. Only those data points within the inner fence of a box and whisker plot of coccolith length and weight were included in the analysis (values larger than 4. 22 µm length and 12. 28 pg weight were eliminated -1035 data points). For methodological details see previous studies  (Beaufort, 2005; Beaufort et al. , 2011 Beaufort et al. , , 2008)). The selected surface and the Holocene sediment samples were collected during RV Meteor cruise M40/4 (1998). Multicorer station #82 from the Gulf of Lions (42 • 18. 51 N, 3 • 46. 4 E, 1078 m water depth, 0-1 cm) spans approximately the last 40 to 80 years, assuming approximate sedimentation rates of 0. 1 to 0. 15 cm yr -1  (Miralles et al. , 2005)  and a bioturbation depth of 5-8 cm for the position. The gravity core station #87 taken near the Balearic Islands (38 • 59.
28 N, 4 • 1. 38 E, 1913 m water depth, 0-30 cm, sampled every cm) covers the time span of about 200 to 10 800 years BP  (Emeis et al. , 2003). Smear slides were prepared using standard protocols and coccolith measurements were carried out as described above. Environmental data covering the time span of the analyzed trap samples were collected from the DYFAMED database (http://www. obs-vlfr. fr/dyfBase/index. php), the Ocean LG: Lion Gyre, AIS: Atlantic-Ionian Stream, TC: Tyrrhenian Current, NBF: North Balearic Front). After  Millot (1987)  and  Pinardi and Masetti (2000). Productivity website (http://www. science. oregonstate. edu/ ocean. productivity/index. php) and the OceanColor website (http://oceancolor. gsfc. nasa. gov). Detailed carbonate system parameters, temperature, salinity and nutrient measurements are only available for the DYFAMED site, which is located about 200 km east of the trap position (43 • 25 N, 7 • 52 E), i.
e. , upstream with respect to the general E-W circulation of the Northern Current along the slope (Fig. 1 ). It has been shown before that this data can be used for comparison with the sediment trap data  (Heussner et al. , 2006). Carbonate system measurements (i. e. , alkalinity and DIC) are only available for the years 1998 to 2000 and 2003 to 2005. Data were obtained for the upper 200 m of the water column. Missing measurements were replaced with values obtained from linear regression of the measurements from above and below. [CO 2 ] and pH were calculated from the measured total alkalinity and dissolved inorganic carbon measurements with the CO 2 SYS macro (Lewis and Wallace, 1998) using the CO 2 dissociation constants by  Mehrbach et al. (1973) , refit by  Dickson and Millero (1987) , KHSO 4 by  Dickson (1990)  and the seawater scale for pH.
Monthly averages were calculated in those cases where traps were sampled twice in one month. Small gaps (less than 3 months) were filled by linear interpolation between the values before and after the gap. Longer gaps were filled using the monthly averages from all years, which were adjusted for the specific year. Gap filling methods are explained in detail in supplementary Fig. 1. The SSA decomposed the data into trends, seasonal signals and residuals. For analysis of the seasonality, the trends and 12-month frequencies were added and run through a principal component analysis (PCA) in order to determine which variables explain most of the variance in the data. Cross-correlation was calculated on the time-series and on the seasonal signals extracted by the SSA to determine if a lagged response of E.
4  and 5 ), whereas coccolith length shows no prominent seasonality. This lagged response may be due to the presence of diatoms at the beginning of the spring bloom. The general productivity pattern in the Gulf of Lions seems therefore to be responsible for an increase of E. huxleyi coccolith weight in February to May. If the P-limitation in summer has an effect, it is not strong enough to level out the seasonal signal in E. huxleyi weight, but may be one of the reasons for the reduced seasonal signal in E. huxleyi coccolith length. A less pronounced seasonality in nitrate and phosphate concentrations in 2001 and 2002 is paralleled by a reduced seasonal signal of E. huxleyi coccolith weight (Fig. 4 ), further indication of the importance of nutrient supply for E. huxleyi calcification. Interestingly, the PIC data does not follow the productivity and coccolith weight trends (Fig.