dataesr/scientific-paragraphs-categorization huggingface dataset:
acKnOWleDgMenTs The European Synchrotron Radiation Facility (ESRF) is greatly acknowledged for beam time allocation. The authors would like to thank Eleni Douni and George Kollias [both from the Institute of Immunology of the Biomedical Sciences Research Center in Vari (Greece)] for testing the Nanobodies in the Tg197 model and finally Peter Casteels, Patrick Stanssens, and Frank Landolt (all from Ablynx) for retrieving in vitro and in vivo data sets.
, 2011)  via RAMPART  (Mapleson et al. , 2015). The completeness of the genome was evaluated using CEGMA with the set of 248 core eukaryotic genes (CEGs)  (Parra et al. , 2007). The assembly (accession number PRJEB9419) can be visualized at http://apollo. tgac. ac. uk/Pseud o-nitzschia_multistriata_V1_4_browser/sequences (username and password: pnitzschia).
Gene prediction and annotation Protein-coding genes were predicted using a workflow incorporating RNA-seq reads, homologous proteins from P. tricornutum, T. pseudonana and a de novo P. multistriata transcriptome assembly. RNA-seq reads were combined from four different libraries (samples: B856, libraries HCUO and HCUH; B857, libraries HCUN and HATT; available at http://genomeportal. jgi. doe. gov/pages/dynamicOrganismDownload. jsf?organism=Psenittra phaseII) and assembled de novo. The transcripts generated were used as training data for AUGUSTUS  (Stanke et al. , 2006). The model built on the training data was applied to the entire repeat masked assembly, together with external support from homologous proteins aligned using EXONERATE  (Slater & Birney, 2005). The predicted gene models were annotated using ANNOCRIPT  (Musacchia et al.
, 2015). Repeats were identified using REPET. The TEDENOVO pipeline  (Flutre et al. , 2011)  was used to build a library of consensus sequences of repetitive elements in the genome assembly. The TEANNOT pipeline  (Quesneville et al. , 2005)  was employed with default settings using the sequences from the filtered combined library as probes to perform genome annotation. Full-length complete long terminal repeats (LTRs) were identified using LTRHARVEST and LTRDIGEST  (Gremme et al. , 2013). The relative age of LTR insertion was estimated using the method proposed in previous studies  (Kimura, 1980). The statistics for the genomic features in Table  1  were extracted from the GFF files using shell scripts and the BEDTOOLS package  (Quinlan, 2014). The genome size, N50 value and GC content were taken from the respective publications  (Armbrust et al.
Identification of CNEs The public genomes of sequenced diatoms were aligned pairwise against the reference P. multistriata genome with LASTZ. Utilities from the University of California Santa Cruz (UCSC) genome browser source code tree  (Speir et al. , 2016)  were used to generate NET alignments from the raw pairwise alignments. The pairwise NET alignments in MAF format were combined into a single diatom NET alignment file using the roast binary from the MULTIZ package  (Blanchette et al. , 2004)  with P. multistriata as reference. Custom PERL scripts were used to scan the diatom NET alignment to identify conserved intergenic blocks (window, 20 bp; step, 10 bp) which do not overlap gene/expressed sequence tags in the species conserved. Searches for transcription factor binding sites were performed using JASPAR 2014  (Mathelier et al.
, 2014). (Wilson et al. , 2009). The comparison was performed using Perl scripts provided by the SUPERFAMILY database (http://supfam. cs. bris. ac. uk/SUPER FAMILY/howto_use_models. html) and the hmmscan binary from HMMER3  (Eddy, 1995). For each SUPERFAMILY present in P. multistriata, a Z-score was calculated using the following formula (no. of SUPERFAMILY genes in P. multist riatamean no. of SUPERFAMILY genes in all proteomes)/ SD of SUPERFAMILY genes in all proteomes.
Identification of gene families by clustering of protein sequences An All vs All BLASTp search was performed on the combined FASTA file of proteomes from bacteria, archaea and 50 eukaryotes. The results of the BLASTp search were provided to the orthAgogue software  (Ekseth et al. , 2014)  for the estimation of homology between the protein sequences. The 'abc' format output from ORTHAGOGUE was given to the MCL software www. newphytologist. com (Enright et al. , 2002)  for clustering of the proteins into homologous groups.
Protein clusters with at least one member from any stramenopile species were identified to obtain 28 927 clusters. For each stramenopile species, a binary code was established stating the presence or absence of the species in each cluster. The binary file, together with the maximum likelihood tree, was subjected to DOLLO parsimony analysis using the PHYLIP package  (Felsenstein, 1989). The topologies of the maximum likelihood and Bayesian trees were compared with treedist from the PHYLIP software, indicating an identical topology. Identification of genes acquired from red algae and by HGT from bacteria in P. multistriata Identification of HGT events in P. multistriata was performed with the following steps: identification of protein clusters containing at least one P. multistriata protein; building of a multiple alignment for each cluster using MAFFT  (Katoh & Standley, 2013) ; trimming of columns with ≥ 95% gaps in the alignment generated using TRIMAL  (Capella-Guti errez et al.
Identification of homologous genes and Ka : Ks analysis The analysed data included 12 152 and 19 703 coding DNA sequences (CDSs) of P. multistriata and Pseudo-nitzschia multiseries (Psemu1, downloaded from the Joint Genome Institute (JGI)), respectively. As a first step, a reciprocal best BLAST hit (RBH) was used to identify P. multistriata and P. multiseries orthologues. Only alignments covering at least 30% of P. multistriata sequences were retained. The analysis identified 7128 reciprocal best BLAST hits. Next, each pair of sequences was aligned with PRANK (L€ oytynoja, 2014) using the empirical codon model, and the alignments were refined using TRIMAL  (Capella-Guti errez et al. , 2009). Of the 7128 alignments, 6066 were suitable for Ka : Ks calculation. Ka : Ks calculation was performed with KaKs_Calculator  (Wang et al.
High-resolution phylogenomic analyses define gene family expansions, gene gains and HGT in diatoms Lineage-specific gene duplications, losses and pseudogenization, together with genome rearrangements and horizontal transfer of genes between species, have paved the way for the evolution of diversity  (Koonin, 2010). In order to identify gene family expansions in diatoms, we compared the proteomes of 13 Stramenopiles (including five diatoms) against the collection of gene families from the SUPERFAMILY database. In total, 11 families showed expansion within the diatom lineage (Fig. 3 ; Table  S5 ); none was P. multistriata specific. We confirmed an expansion of a gene family (pseudouridine synthase) (Fig. 3a ) reported to be expanded in the P. tricornutum genome  (Bowler et al. , 2008)  and found specific expansion events within the order Bacillariales (Fig.
For the correspondence between protein IDs used in this tree and GenBank IDs, see Supporting Information Methods S1. Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust New Phytologist (2017) 215: 140-156 www. newphytologist. com organisms spanning the tree of life (50 eukaryotes, 1000 bacteria, 170 archaea; Table  S6 ) to infer, at an unprecedented resolution, gene gains/losses in diatoms. Phylogenetic clustering analysis generated c. 240 000 clusters of putative homologous proteins, 8113 of which contained 9122 P. multistriata proteins. Approximately 26% (2886 singlets + 241 genes in 109 clusters) of the P. multistriata proteome was predicted to be orphan (P. multistriata-specific) (Fig. S4 ), which is slightly less than that reported for other diatoms (P. tricornutum, 35%, Bowler et al.
Finally, 20 differentially expressed genes are derived from bacteria by HGT in diatoms (Table  S16 ); an example (nitrate/nitrite transporter, PSNMU-V1. 4_AUG-EV-PASAV3_0048930. 1) is shown in Fig. S12. A schematic summary of the regulated pathways and functions in a P. multistriata cell responding to sexual cues is shown in Fig. 6. These data provide markers for data mining of metatranscriptomic datasets and will improve our ability to understand and monitor toxic Pseudo-nitzschia blooms. Methods S1 Supplementary methods. Table  S1  Validation of a selected subset of genes differentially expressed during sexual reproduction in Pseudo-nitzschia multistriata by quantitative PCR Table  S2  Genomic coordinates of the Pseudo-nitzschia multistriata conserved noncoding elements, together with coordinates in other diatom species, where each element remains conserved Table  S3  The core, plant and fungal transcription factor families which show enrichment of binding sites on the Pseudo-nitzschia multistriata conserved noncoding elements  New Phytologist is an electronic (online-only) journal owned by the New Phytologist Trust, a not-for-profit organization dedicated to the promotion of plant science, facilitating projects from symposia to free access for our Tansley reviews.
Insertion period estimation of complete long terminal repeats (LTRs) identified in diatom genomes Table S5 Number of proteins from stramenopile genomes represented by different superfamilies from the SUPERFAMILY database Table S6 Details of eukaryotic and prokaryotic organisms considered for the generation of the protein clusters for the Pseudonitzschia multistriata proteome Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust New Phytologist (2017) 215: 140-156 www. newphytologist. com Table S7 Annotation for the Pseudo-nitzschia/diatom/stramenopile/SAR (Stramenopile, Alveolates and Rhizaria)-specific genes of bacterial origin Table S8 Annotation for diatom genes of red algal origin Table S9 Summary statistics of RNA-seq read mapping results for Pseudo-nitzschia multistriata samples Table S10 Differential expression analyses of all sexualized samples vs all control samples, MT+ sexualized samples against MT+ controls, and MTÀ sexualized samples against MTÀ controls, at two different time points Table S11 Log FC (fold change) and false discovery rate (FDR) values for all Pseudo-nitzschia multistriata transcripts for the same conditions as in Table S10.
Acknowledgements This work was supported by  Marie Curie   FP7-PEOPLE-2011-CIG (GyPSy , grant no. 293887 ) to M. I. F. and the  RITMARE Italian Flagship Project. The work conducted by the  US Department of Energy Joint Genome Institute  is supported by the  Office of Science of the US Department of Energy  under contract no. DE-AC02-05CH11231. S. P. and L. V. were supported by a  SZN Open University  PhD fellowship from  Stazione Zoologica Anton Dohrn. This research was partly supported by the  NBIP Computing infrastructure for Science (CiS). The authors wish to thank  Cecilia Balestra  for technical help,  Anil Thanki  for the TGAC browser,  Riccardo Aiese Cigliano  and  Walter Sanseverino  ( Sequentia Biotech , www. sequentiabiotech. com), the  International Centre for Theoretical Physics  for cooperation, the  EMBL GeneCore service  and the  SZN Molecular Biology and Bioinformatics Unit.
Relative efficiency as a function of π + π -mass The relative efficiency factors in Eq. 1 are obtained from simulation. Fully simulated π + π -mass spectra from D + → π + π + π -decays for Run 1 are divided by the generated spectra to give the relative efficiency as a function of the π + π -mass. The efficiency is highest at large π + π -masses, mainly due to the effects of the hardware and software triggers. The relative efficiencies in Run 1 data are found to be (η) = 0. 85 ± 0. 01 and (η ) = 1. 01 ± 0. 01, where the uncertainties come from the simulation sample size. The relative efficiencies for Run 2 are found to be statistically compatible with those for Run 1, through a comparison of the π + π -mass spectra from the D + and D + s signal candidates in the data. An additional systematic uncertainty of 2% is assigned to the Run 2 relative efficiencies, corresponding to the maximum difference between the mass spectra.
022524 N, 5. 206978 W, 1,178 masl (meters above sea level)]. A detailed pollen record has already been published  (Cheddadi et al. , 2016). BEK is located in the Western part of the Rif Mountains ca. 8 km SW of the closest mixed Atlas cedar/Moroccan fir [Abies pinsapo var. marocana (Trab. ) Ceballos and Martín Bol. ] forest in the Talassemtane National Park (TNP). The lowest-elevation Atlas cedar stands in the TNP lie at ca. 1,650 masl. Another 6. 5 m sediment core was obtained from the Aanasser (ANS) peat bog (35. 017629 N, 4. 992503 W, 1,342 masl); located at the foot of Jbel Tizirène. Preliminary data of ANS are published  (Cheddadi et al. , 2015) , but here we provide a more detailed pollen record. The ANS site harbors today a small isolated Atlas cedar forest (ca. 250 hectares) between 1,700 and 2,200 masl.
A third 5 m core was collected from M'Had marsh (MHD) (35. 128395 N, 5. 438668 W, 754 masl), which was located SW of the Bou Hachem natural Park (BNP). MHD lay ca. 30 km west of the Atlas cedars in the TNP. Preliminary data of MHD have also been published  (Cheddadi et al. , 2015). All three cores were collected using a Russian corer. We extracted the pollen grains from these three records using a standard chemical procedure (HCL, KOH, ZnCL 2 , acetolysis). The pollen percentages were computed on a sum that excludes aquatic plants (Figures  2A-C ).
Chronological Frame The three sedimentary sequences were dated using AMS 14 C dates  (Cheddadi et al. , 2015 (Cheddadi et al. , , 2016) )  and calibrated using CALIB 7. 0 software  (Stuiver et al. , 2013)  using the IntCal13 calibration dataset  (Reimer et al. , 2013). To establish a chronology for the three records we interpolated the calibrated ages over the sample depths using a linear fit between neighboring calibrated ages. The time spans covered by BEK, MHD, and ANS were 9. 2 cal ka BP (thousands of calendar years Before Present; hereafter cal ka BP), 5. 7 and 3. 5 cal ka BP, respectively.
Modern Range and Basic Climate Requirements of Atlas Cedar C. atlantica is an endemic species in Algeria and Morocco with its densest populations in the Middle Atlas, Morocco. The overall range of Atlas cedar in the Rif Mountains covers ca. 12,000 ha. We have refined the mapping of the range of the species via GPS data that we collected in the field. Atlas cedars in the Rif Mountains (Figure  1 ) occur in six main scattered populations (Jbel Kelti, Talassemtane, Jbel Tizirene, Issaguen, Oursane, and Jbel Tidighine) between ca. 1,400 and 2,300 masl, where the annual precipitation ranges roughly between ca. 500 and 900 mm (Figure  3 ). The minimum and the mean temperature of the coldest month (January) varies between ca. -1 and 4 • C, and ca. 4 and 9 • C, respectively (Figure  3 ). In a study of seedling regeneration in natural forest areas at high altitudes (over 2,000 masl), field observations show that the germination process is often inhibited by both the water deficit and temperatures below -5 • C  (Ezzahiri and Belghazi, 2000).
This method allows us to minimize the effect of some specific taxa that are either over-or underrepresented or may have a strong variation throughout the record. The method was written using R software version 3. 4. 0 (2017-04-21) (R Core Team, 2014) with the following libraries: akima  (Akima and Gebhardt, 2016) , RMySQL  (Ooms et al. , 2016) , and stats which is part of R. Our modern plant database includes species distributions from Flora Europaea  (Jalas and Suominen, 1973 , 1979 , 1980)  and additional data from GBIF (data. gbif. org). The modern climate variables used to define the climate range of the modern plant species, were obtained from the WORLDCLIM database  (Hijmans et al. , 2005)  and interpolated onto the species georeferenced occurrences.
Vegetation Model Simulations A transient simulation of the geographical distribution of C. atlantica was performed using, as  Cheddadi et al. (2009) , the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model. CARAIB is a physically-based model developed to study the role of vegetation dynamics in the carbon cycle at the global, continental or regional scale  (Gérard et al. , 1999; Dury et al. , 2011; Fontaine et al. , 2014). CARAIB includes coupled hydrological, biogeochemical and biogeographical modules, respectively describing the exchange of water between the atmosphere, the vegetation and the soil, the evolution of biospheric carbon stocks and fluxes, and the establishment, growth, competition, mortality, seed production, and regeneration of plants groups or individual species  (Laurent et al.
, 2008; Raghunathan et al. , 2015). In this study, only one tree species (C. atlantica) is considered in the overstorey. The understorey is assumed to consist of C3 herbs. CARAIB requires the input of several climatic variables: mean air temperature, precipitation, diurnal temperature range, air relative humidity, cloud cover (converted into percentage of sunshine hours), and surface horizontal wind speed. We used CRU (Climate Research Unit, http://www. cru. uea. ac. uk/) monthly climatic anomalies (0. 5 • dataset,  Harris et al. , 2014)  combined with WORLDCLIM 1951-2000 climatology (30 arc sec dataset,  Hijmans et al. , 2005)  to obtain high-resolution (∼1 km) transient climatic inputs over the period 1901-2010. Atmospheric CO 2 concentrations are prescribed from time series of global values  (Meinshausen et al.
, 2011; Dlugokencky et al. , 2014). The soil texture was obtained from the global HWSD v1. 21 (30 arc sec) database [FAO/IIASA/ISRIC/ISS-CAS/JRC, Harmonized World Soil Database (version 1. 2). FAO, 2012]. In our simulation we have assumed a homogeneous soil layer 1. 2 m in depth. This value is not based on field data, but was obtained by model calibration. It is slightly lower than the standard rooting depth of Mediterranean woodlands in CARAIB (1. 70 m, following  Schenk and Jackson, 2002). The CARAIB simulations are performed at 30 arc sec resolution, which represents a significant improvement compared with the work performed with the same model at 10 arc min resolution by  Cheddadi et al. (2009). The improved spatial scale more realistically captures the distribution of C. atlantica relative to topographic variability than prior models.
Other differences with this previous study are that the current simulations are transient model runs over the period 1901-2010 (compared with steady-state simulations using mean climatological values in  Cheddadi et al. , 2009)  and that the new version of the model contains a fire module  (Dury et al. , 2011)  which impacts tree mortality. CARAIB requires a set of parameters describing climate tolerances of the simulated species, which are controlling plant stress and germination. These factors were obtained by superimposing the above CRU/ WORLDCLIM gridded climatology interpolated at a 30 arc sec resolution with the observed distribution of C. atlantica (based on GPS data collected on the field), and then selecting a given quantile in the distribution of the climatic variables. The potential distribution of C.
ACKNOWLEDGMENTS This work is a contribution to the  Belmont Forum  funded project  VULPES (Project   ID :  ANR-15-MASC-0003 ). We acknowledge support for this research from  F. R. S. -FNRS  under research grant  FRS-FNRS X. 3041. 17-VULPES-ULg. We thank  Juanma Rubiales ,  Henry Lamb  and  William Fletcher  whose comments helped improve and clarify this manuscript.
Separating emissivity and temperature from TIR multispectral data consists of estimating waveband emissivity for a given channel to next retrieve all the waveband emissivities and the radiometric temperature. Normalized emissivity method (NEM) aims to accurately quantify emissivity variations, by arbitrarily setting the maximum emissivity  (Coll et al. , 2002) , whereas adjusted NEM (ANEM) makes use of vegetation cover fraction to improve the setting of maximum emissivity  (Coll et al. , 2003). The temperature emissivity separation (TES) approach consists of estimating minimum emissivity from the emissivity spectral contrast captured with multispectral measurements  (Gillespie et al. , 1998; Schmugge et al. , 1998). As compared to NEM and ANEM, TES does not rely neither on arbitrary setting of emissivity nor on ancillary information, but on an empirical relationship between minimum emissivity ε-min and emissivity spectral contrast.
We investigated two critical issues applying TES over vegetation canopies. We first addressed the calibration/validation of the ε-min -MMD relationship over the library of simulated spectra, by addressing the spectral configurations of recent and forthcoming sensors. Then, we evaluated the interest of using the SAIL-Thermique simulated dataset of emissivity spectra, by comparing the corresponding calibration/validation results with those obtained in former studies based on single or linearly mixed samples of soil/leaf spectra. Section 2 presents the TES concept. Section 3 presents the methodological strategy, including the set-up of an emissivity spectral library by using the SAIL-Thermique model (Section 3. 1), the set-up of a library of surface outgoing radiance (Section 3. 2), the computation of the waveband quantities for different spectral configurations corresponding to various sensors (Section 3.
Detailed descriptions can be found in  Gillespie et al. (1998) ;  Schmugge et al. (1998)  and  Schmugge et al. (2002). Hereafter, emissivity and radiance values correspond to waveband values over channels. TES was originally designed to make use of the emissivity spectral contrast captured from multispectral remote sensing data over the TIR spectral domain. The undetermined problem related to the recovering of N + 1 unknowns (N emissivities and radiometric temperature) from N equations (measurements of surface outgoing radiance over N channels) is solved by empirically relating the minimum emissivity εmin to the emissivity spectral contrast captured over the N channels. In order to reduce experimental noises that stem from instrumental and atmospheric perturbations, emissivity spectral contrast is characterized using the maximum minimum difference (MMD).
The first part, referred to as calibration dataset hereafter, was used to calibrate the TES empirical relationship, i. e. to adjust the (A, B, C) coefficients of the ε-min -MMD relationship. The second part, referred to as validation dataset hereafter, was used to (1) apply TES on waveband values of surface outgoing radiance, and (2) compare TES retrievals of radiometric temperature and emissivities against original prescriptions. The robustness of the coefficient estimation by means of randomization was a posteriori confirmed, since the variability of the calibrated coefficients was about 1-2% relative when repeating the procedure 100 times. We detail hereafter the different methodological steps, where emissivity and radiance values correspond to waveband values for given channels, except when indicated for a specific wavelength.
SAIL-Thermique inputs: spectral libraries of soil and leaf optical properties Only few leaf data including reflectance and transmittance spectra were available. We used data from five soil spectral libraries and one leaf spectral library. These spectra were obtained from laboratory measurements over soil and leaf samples, using TIR spectrometry combined with an integrating sphere. All reflectance and transmittance values to be considered hereafter are directional-hemispherical quantities. For soil samples, we used several spectral libraries, to be listed below. Since we focused on vegetation canopies, we excluded spectra collected over rock samples. 1. The ASTER spectral library version 2 was set up in the context of the EOS/ASTER mission  (Baldridge et al. , 2009). It included 41 reflectance spectra over the [0.