dataesr/scientific-paragraphs-categorization huggingface dataset:
The model From the previously described database, we build a network of interactions which can be coded into a simple bipartite matrix, K, whose elements K i, j 2 [0, 1] indicate whether or not the seller i and the buyer j have interacted during the selected period, and the weighted bipartite matrix, B, of elements B i;j 2 N which reports the number of contacts during the period. For this work, we have chosen not to include a weighting for the links, which would take into account information about the prices or the quantities exchanged. This is certainly a simplifying hypothesis, but it also serves another purpose. As this market is very heterogeneous (with big and small buyers and sellers) this assumption is needed to prevent the information about social interactions from being blurred by the different buying/selling capacities of the agents involved.
Discussion We have performed a data-based study of the interaction networks of the two coexisting submarkets (auction and bilateral negotiation) of the Boulogne-sur-Mer Fish Market, searching for stylized facts characterizing agents' behavior and market structure. By applying tools drawn from the study of mutualistic ecosystems, we observe that both sub-markets are nested, although typical market densities are much higher than those observed in mutualistic ecosystems. It is noteworthy that the densities of contacts corresponding to the whole period and to daily data are quite different. The density is quite high for both markets when the whole period database is analyzed (50% for the bilateral market and 40% for auctions), but it is much lower, and with a reverse tendency, for daily matrices (10% and 20% respectively), thus affecting the nesting indices.
doliolus and Salpingella sp. , respectively. The sequencing of a specific genetic marker within the ribosomal DNA (rDNA) gene loci, the short V9 region of the 18S rDNA gene, was performed to interrogate the Tara Oceans V9 metabarcoding data set. This revealed the biogeography of the organisms, thus highlighting nine stations in which sequence matches of both partners were high. We further used various microscopy methods and DNA sequencing of multiple genetic markers to analyze similar and new consortia within these nine other Tara Oceans stations. Additionally, the V4 region was sequenced because this marker resolves more accurately diatom diversity  [31] , and for similar reasons sequences of the ITS loci were generated for the tintinnids  [32, 33]. Overall, a continuous sequencing from the beginning of 18S to D1-28S was completed to obtain highly resolved phylogenetic information.
Detection in Tara Oceans stations Sequencing results were assembled and trimmed to retain the V9 18S fragment and interrogated against the Tara Oceans ribotype database using BLAST  [38]. The Tara Oceans nucleotide sequences are available at the European Nucleotide Archive (ENA) under the project PRJEB402 and PRJEB6610. Diatom isolated V9 sequences matched at 100% identity with the ribotype "f2f8b" assigned to "Raphid-pennate_X+sp". Tintinnid isolated V9 sequences matched with three different tintinnid ribotypes and in majority with "a7cbc" and "b61a7" assigned to "Choreotrichia_X+sp. " (see Table  S2  for complete barcode identifier). The search for these four sequences in the global metabarcoding data set revealed that they were widely distributed in various size fractions  [39]  and were positively correlated (Spearman rho = 0.
Group-specific primers were designed and inspired from Bachy et al. [32]  and McDonald et al. [40]  and are shown in Table  S4. For amplification protocol, see Supplementary Information. Amplicon sequences of both the diatom and the tintinnid were cleaned, trimmed, and assembled using Sequencher (version 5. 4) and accession numbers are available in Table  S1. Summaries of available imaging and molecular data for each station and consortia are available in Tables  1  and 2.
Phylogenetic analysis For the tintinnids, contigs of the amplicons were obtained, and two matrices of 18S rDNA and ITS+5. 8S+28S rDNA were built, including reference and outgroup sequences from Bachy et al. [32]. For diatoms, similar matrices were built including reference sequences from BLAST top hits in GenBank and reference sequences from Theriot et al. [41]. Sequences were aligned using MAFFT version 7  [42]  and trimmed with Gblocks  [43]  before applying JmodelTest  [44]  to determine the best model of nucleotide substitution for each matrix. The general time-reversible model with gamma distribution of rate variation (GTR+G) was selected for the diatom ITS+5. 8S+28S tree and the tintinnid 18S tree, which are, respectively, the best markers to anchor species at the genus level. Phylogenetic and Bayesian inferences were performed using PhyML 3.
Partial least square analysis We investigated correlations between environmental parameters collected during Tara Oceans (doi: 10. 1594/PAN-GAEA. 853810) and the read abundance of the two partners. As variables were shown to be multicolinear, partial least square regression analysis was conducted on rangetransformed oceanographic data as predictors and Hellingertransformed read abundance data of corresponding barcodes as responses. Samples from all size fractions in surface depth were selected to increase statistical signal. Analysis was performed using the « plsdepot » package in R version 3. 3. 0. Predictors and responses used in the analysis as well as resulting regression coefficients are available in Table  S6.
[6]. In light of the extended Tara Oceans data set  [39] , encompassing 126 Tara Oceans stations, f2f8b is the second most prevalent Fragilariopsis sequence with a total abundance of 414,113 reads. It is found throughout all Tara Oceans stations, with higher abundances reported in the southern hemisphere (0°S-40°S) and occurrences both in coastal and open ocean stations (Fig. 5a ). By comparison, one diatom V9 matched at 100% identity the barcode "53cf4" that totaled 853 reads across the whole Tara Oceans metabarcoding data set and displayed a distribution similar to that of f2f8b (Figure  S3b ). The other three complete V9 sequences from isolated diatom cells were not found in the Tara Oceans metabarcode data set. It is possible that the V9 microdiversity was not captured in the Tara Oceans data due to sequencing biases or filtering of the metabarcode database, which was performed by removal of metabarcodes present in less than three reads and two distinct samples  [38].
Abundances were extracted samples of microplankton (20-180 μm size fraction) collected in sub-surface waters  [34]. Regions framed with a gray dashed line in the Mediterranean Sea, Red Sea, and South Pacific Ocean have been magnified on the right for more clarification. Absolute abundances were transformed according to the log(Abundance+1) formula. b Circle of correlations. The abundance of the diatom (red) and tintinnid (blue) V9 rDNA barcodes were predicted using the Tara Oceans metabarcoding and environmental parameters (green) data sets from surface water samples. The most prevalent tintinnid barcode a7cbc is more abundant in high density samples, whereas the main abiotic driver for the second tintinnid barcode b61a7 is chlorophyll. Regression coefficients are available in Table  S6  characterized by small lorica oral diameters suggesting that small prey items of pico-and nano-sizes are likely to be their preferred prey  [57]  and could potentially benefit from increased abundance of bacteria and nanoflagellate bacteriovores in the diatom phycosphere.
Acknowledgements We thank the commitment of the following people and sponsors who made the Tara Oceans project possible:  Centre National de la Recherche Scientifique  and the  Groupement de Recherche   GDR3280 ,  European Molecular Biology Laboratory, Génoscope/Commissariat à l'Energie Atomique , the  French Government "Investissements d'Avenir" programs OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ),  Paris Sciences et Lettres Research University  ( ANR-11-IDEX-0001-02 ), the  European Union   Framework Programme 7  (MicroB3/No. 287589 ),Agnès b. , the  Veolia Environment Foundation ,  Region Bretagne ,  World Courier ,  Illumina, Cap l'Orient , the  Electricité de France Foundation EDF Diversiterre, Fondation pour la Recherche sur la Biodiversité , the  Prince Albert II de Monaco Foundation ,  Etienne Bourgois , and the Tara schooner, its captains and crew.
006 -139. 206 South Pacific Ocean 864 KY782378 TARA_124 -9. 1463 -140. 515 South Pacific Ocean 807 D f2f8b KY782362 TARA_124 -9. 1463 -140. 515 South Pacific Ocean 808 D f2f8b b KY782363 TARA_124 -9. 1463 -140. 515 South Pacific Ocean 811 D No match KY782365 TARA_124 -9. 1463 -140. 515 South Pacific Ocean 813 D f2f8b KY782366 TARA_124 -9. 1463 -140. 515 South Pacific Ocean 814 D, T f2f8b KY782367 KY782340 TARA_124 -9. 1463 -140. 515 South Pacific Ocean 815 T b61a7 KY782341 Each isolated consortium has a unique identifier starting with "TI_8##". For single cell barcoding, we indicate whether complete or partial (italic) V9 sequences were obtained for the D or the T and searched for the equivalent matching metabarcode sequence in Tara Oceans with 100% identity. Genbank accession numbers indicate contigs of sequenced molecular markers (partial 18S, ITS1 and 2, 5.
Due to high numerical aperture of the objectives, resolution of the device was in the range of 0. 4-0. 6 μm. Contact angle values were determined using the Contact Angles Measurement System-101 from KSV (Finland). The device is equipped with a high resolution optical camera, a high intensity red lamp and an objective stage. Droplets of distilled water were deposited onto different places of the sample and average wetting angle values were calculated using KSV-Cam100 software (4-5 measurements per sample). Bilayers for AFM studies were transferred onto high quality and freshly cleaved mica. These studies were conducted on a Multimode V (Veeco, USA) microscope, equipped with a Nanoscope IIIa controller and TESP brand AFM tips (standard geometry, needle height 10 μm, point radius 5 nm). All measurements were done in tapping mode.
Fatty acid composition Before analysis, lipids containing fatty acids (FAs) were converted to fatty acid methyl esters (FAMEs) by shaking for 25 min a solution of 60 mg oil and 3 mL of hexane with 0. 3 mL of 2N methanolic potassium hydroxide. FAMEs were analyzed by gas chromatography using a Varian CP-3800 (Varian Inc. ) chromatograph equipped with a FID and a CP-Wax 52CB column (30 m Â 0. 25 mm id. ; Varian Inc. , Middelburg, The Netherlands). The carrier gas was helium and the flow rate was 1 mL/min. The initial and final column temperatures were 170 °C and 230 °C, respectively, and the temperature was increased at a rate of 4 °C/min. The injector and detector temperature was 230 °C. Data were processed using a Varian Star Workstation v 6. 30 (Varian Inc. , Walnut Creek, CA, USA). Results were expressed as the relative percentage of each individual FA present in the sample.
Sterol composition After isolation from the thin layer chromatography plate, the sterol fraction was prepared by trimethylsilylation according to the standard EN ISO 6799. Sterol composition was determined by gas chromatography using a Varian 3800 instrument equipped with a VF-1 ms column (30 m & 0. 25 mm id. ) and using helium (flow rate 1. 6 mL/min) as carrier gas. Column temperature was isothermal at 270 °C, injector and detector temperature was 300 °C. Injected volume was 1 mL. Data were processed using Varian Star Workstation v 6. 30 (Varian Inc. , Walnut Creek, CA, USA).
Determination of ovarian cycles Vaginal lavages were performed daily on female nontransgenic (NTG) and hAPP mice. The vaginal opening was flushed 2-3 times with 50l normal saline using a transfer pipet and collected on a slide. Slides were manually read at 5x magnification and classified (Fig. 1A ) as proestrus (nucleated epithelial cells), estrus (cornified squamous epithelial cells), metestrus (mix of all 3 cell types), or diestrus (leukocytes) by an investigator blinded to the genotype of the mouse. Vaginal cytology was assessed each morning for a period of ϳ3 weeks (17-24 d). Number and length of cycles was quantified and a cycle was counted each time proestrus was followed by a successive estrus. Cycle stage for 1-3 sporadic days per mouse was extrapolated based on the preceding and succeeding days of the cycle; analysis of only sampled data did not alter results (data not shown).
Immunoblotting Twenty micrograms of protein were loaded into each well of a 4 -12% Bis-Tris gradient SDS-PAGE gel. Gels were transferred to nitrocellulose membranes and immunoblotted with antibodies against human amyloid precursor protein (8E5, 1:5000; Elan Pharmaceuticals), total tau (EP2456Y, 1:20,000; Millipore), phosphorylated-tau (PHF-1, 1:2000; Peter Davies, Albert Einstein College of Medicine), and actin (A2066, 1:3000; Sigma-Aldrich). Gels were read using the Odyssey CLx Imaging System (LI-COR Biosciences) and quantified using Image Studio Lite software. Actin was used as a loading control and quantified independently to ensure there were no differences between groups (data not shown).
A standard curve was prepared by serial dilution of human A␤ 1-42. Standards and diluted samples (100 l each) were loaded in duplicate onto a plate precoated with affinity-purified anti-human A␤ (38-42) rabbit IgG antibody, and incubated overnight at 4°C. The plate was washed 7 times with washing buffer, then 100 l of HRP conjugated antihuman A␤(N) rabbit IgG affinity-purified antibody was added to each well. Following incubation for 1 h at 4°C the plate was washed nine times, and the reaction was visualized by the addition of 100 l of chromogenic substrate (TMB) for 30 min at RT in the dark. The reaction was stopped by the addition of 100 l 1 N H 2 SO 4 , and absor- bance at 450 nm was measured using a SpectraMax Plus 384 plate reader (Molecular Devices). A␤ 1-42 levels were calculated with SoftMax Pro software.
Quantitative reverse-transcription PCR RNeasy Mini kits (Qiagen) were used to extract total RNA from hippocampus. Total RNA was reverse transcribed using TaqMan Reverse Transcription Reagents (Applied Biosystems). The expression level of hAPP mRNA was determined using SYBR Green PCR Master Mix and an ABI PRISM 7900HT sequence detection system (Applied Biosystems). Analysis of dissociation curves, standard curve slopes, and reverse-transcription-negative reactions were used to verify the quality of primers and amplification reactions. Expression levels of GAPDH mRNA were used as a control to ensure that levels did not vary between groups (data not shown). Primer sequences: hAPP (forward, 5=-GAGGAGGATGACTCGGATGTCT-3=; reverse, 5=-AGCCACTTCTTCCTCCTCTGCTA-3=); mouse GAPDH (forward, 5=-GGGAAGCCCATCACCATCTT-3=; reverse, 5=-GCCTTCTCCATGGTGGTGAA-3=).
Statistics All experimenters were blinded to genotypes and hormonal status of mice. Statistical analyses were performed with GraphPad Prism v5. 0 for t tests, two-way ANOVAs, and linear regression, and Stata v13. 0 for mixed-model ANOVAs and 2 tests. R (R Foundation for Statistical Computing, 2013) was used to correct for multiple comparisons. Post hoc comparisons were corrected for using the Bonferroni-Holm method. Differences among multiple means for unpaired data were assessed by two-way (genotype and ovarian status/treatment), between-subject ANOVAs. A mixed-model ANOVA was used for analyses of spikes (factors: ovarian status and time) and seizure latency (factors: genotype, severity, and treatment.
, and with lesions to regions of the hypothalamus involved Proestrus and estrus, High E/P cycle stages, worsen locomotor hyperactivity in female hAPP but not NTG mice as detected in the context of an open field. NTG and hAPP female mice were cycled to determine ovarian cycle stage (Pro, Est, Met, Di) and another group was Gnx. All groups underwent testing in the open-field apparatus. Total number of movements during exploration of an open field in NTG and hAPP female mice by ovarian status: High E/P (proestrus/estrus), Low E/P (metestrus/diestrus), and Gnx. Two-way ANOVA: ovarian status effect, p Ͻ 0. 05 k2 ; genotype effect, p Ͻ 0. 0001 k3. ‫ء‬p Ͻ 0. 05 as indicated by bracket k4. (n ϭ 3-11 mice per group, age 2. 5-4 months). Data are mean Ϯ SEM.
, and with lesions to regions of the hypothalamus involved Proestrus and estrus, High E/P cycle stages, worsen locomotor hyperactivity in female hAPP but not NTG mice as detected in the context of an open field. NTG and hAPP female mice were cycled to determine ovarian cycle stage (Pro, Est, Met, Di) and another group was Gnx. All groups underwent testing in the open-field apparatus. Total number of movements during exploration of an open field in NTG and hAPP female mice by ovarian status: High E/P (proestrus/estrus), Low E/P (metestrus/diestrus), and Gnx. Two-way ANOVA: ovarian status effect, p Ͻ 0. 05 k2 ; genotype effect, p Ͻ 0. 0001 k3. ‫ء‬p Ͻ 0. 05 as indicated by bracket k4. (n ϭ 3-11 mice per group, age 2. 5-4 months). Data are mean Ϯ SEM. 3000 * Total Activity 1000 2000 0 NTG hAPP NTG hAPP NTG hAPP High E/P Low E/P Gnx Figure 5.
Characterization of land cover change in the past is fundamental to understand the evolution and present state of the Earth system, the amount of carbon and nutrient stocks in terrestrial ecosystems, and the role played by land-atmosphere interactions in influencing climate. The estimation of land cover changes using palynology is a mature field, as thousands of sites in Europe have been investigated over the last century. Nonetheless, a quantitative land cover reconstruction at a continental scale has been largely missing. Here, we present a series of maps detailing the evolution of European forest cover during last 12,000 years. Our reconstructions are based on the Modern Analog Technique (MAT): a calibration dataset is built by coupling modern pollen samples with the corresponding satellite-based forest-cover data.
Therefore, given two pollen samples -a modern and a fossil one -composed by a similar mixture of taxa, the environmental parameters of the first (e. g. , forest cover, climate variables) can be directly transferred to the latter. The relationship between pollen assemblages and forest cover in the Williams method is established using a calibration dataset of modern pollen samples where the forest cover around the pollen site is estimated using satellite remote sensing from the Advanced Very High-Resolution Radiometer (AVHRR). Past forest cover is then reconstructed by assigning to each fossil sample the average forest cover of its closest modern analogs from amongst the modern samples in the calibration dataset. This approach was applied to Northern Eurasia  (Tarasov et al. , 2007; Kleinen et al.
, 2011)  and to the whole forest-tundra ecotone of the Northern Hemisphere  (Williams et al. , 2011). Its application in Europe has been so far limited to point reconstructions for a few selected time windows  (Williams et al. , 2011). In the present study, we test the capabilities of the MAT for continuous forest-cover reconstructions at a continental scale and from the Pleistocene/Holocene transition to the present day. We follow the methods used by  Tarasov et al. (2007)  and  Williams et al. (2011)  but with improved modern calibration and fossil datasets and spatially continuous mapping. The size of modern and fossil pollen data sets have been increased by 80 and 50%, respectively, compared to the European data used by  Williams et al. (2011) , while the quality of the metadata and chronological controls for the fossil data have also been greatly improved  (Fyfe et al.
, 2009; Davis et al. , 2013; Giesecke et al. , 2013). The AVHRRbased forest-cover data (1 km resolution) used in all previous MAT applications has been upgraded with a 30-m resolution dataset  (Hansen et al. , 2013)  based on LANDSAT. Our results are presented here as both maps for the whole of Europe at 1,000-year intervals, and as regional area-average time-series. The full set of 49 maps, each one covering an ∼250-year interval, is available as Supplementary Material. Each map displays interpolated forest-cover data with continental coverage at a resolution of five arc-minutes. The predictive ability of our model was assessed though standard statistical indicators based on analysis of the modern training set [r 2 and Root Mean Square Error of Prediction (RMSEP) from crossvalidation exercises].
Fossil and Modern Pollen Data The fossil pollen dataset used in our analysis is the same as presented in  Mauri et al. (2015)  and based largely on the European Pollen Database (EPD) with some additional data coming from the PANGAEA data archive 1 and  Collins et al. (2012)  (Supplementary Figure  S1 ). All age-depth models for all sites used calibrated 14 C chronologies, with those for EPD sites based on the latest available chronologies from  Giesecke et al. (2013). 1 www. pangaea. de The European Modern Pollen Database (EMPD;  Davis et al. , 2013)  was selected as the source for modern palynological data (Supplementary Figure  S2 ). The EMPD includes nearly 5,000 samples covering Eurasia and the circum-Mediterranean, which were filtered based on quality-control criteria (see section "Quality Filtering").
Pollen spectra in both the modern and fossil datasets were converted into PFTs following the approach developed by  Prentice et al. (1996)  and refined by  Peyron et al. (1998). Pollen taxa are grouped into PFTs following combinations of plant habit (e. g. , woody, herbaceous), phenology (e. g. , evergreen, deciduous), leaf form (e. g. , broad-leaved, needle-leaved), and climatic range  (Prentice, 1985). Differential pollen productivity and dispersal are not accounted for during PFT assignment. The main steps within the taxa-to-PFTs algorithm are presented in Supplementary Tables  S1-S3. The full procedure is described in  Peyron et al. (1998). The capabilities of PFTs in pollenbased climatic and ecological reconstructions have been tested in European contexts through the Holocene  (Collins et al.
Remote Sensing Data Modern forest-cover values were extracted from the Global Forest Change dataset  (Hansen et al. , 2013) , which includes estimates of forest-cover fraction for the year 2000. The forest-cover dataset produced by  Hansen et al. (2013)  was preferred over the AVHRR-based maps from  DeFries et al. (2000)  used in previous studies  (Tarasov et al. , 2007; Williams et al. , 2011). While  DeFries et al. (2000)  offer a wider range of maps covering different vegetation categories, the dataset from  Hansen et al. (2013)  offers a much higher resolution and a wider range of values. DeFries et al. (2000)  provide separate quantitative data on broadleaf, deciduous, evergreen, needle leaf, and total forest cover at a resolution of 1-km. Pixel values are continuous in the range 10-80% of forest cover (woody fraction), with specific codes to mark non-vegetated areas and areas with forest cover lower than 10%.
In contrast,  Hansen et al. (2013)  offer a resolution of 1 arcsecond, equivalent to around 30-m at the equator, and a full range of pixel values from 0 to 100% canopy closure. The average forest-cover values for the modern pollen data set were extracted by placing a search window around the location of each sample. We applied a general search radius of 50 km to approach a more consistent comparison between MAT and REVEALS estimates, because the REVEALS method suggests that its reconstructions are representative of a 50-km radius around the sites in question (see section "Comparison between MAT and REVEALS Estimates of Past forest cover"). A circular search window was preferred over a square one  (used, e. g. , in Williams et al. , 2011) , thus assuming a pollen source area with equal contributions from vegetation from all directions.