dataesr/scientific-paragraphs-categorization huggingface dataset:
The 5' and 3' genome ends were obtained using 5'RACE and Smart™ Long Distance-RT-PCR  [18] , respectively. The completed sequences have been deposited under accession numbers KT893293-KT893296 in the GenBank database. In addition, the genome sequences of an additional APV2 isolate (Ta Tao 25 source) and of three additional APV3 isolates (two from the Ta Tao 23 source and one from the Ta Tao 25 source) were also obtained during the assembly process. Their 3' genome end was completed as described above but no specific effort was made to complete the 5' genome end, thus, depending on the isolate, between 395 to 745 nucleotides were missing. These sequences have been deposited under accession numbers KT893297 to KT893300 in the GenBank database.
Acknowledgments The authors would like to thank  Dr. A. Hadidi  ( USDA Beltsville, USA ),  Dr. J. B. Quiot  ( INRA Montpellier, France ), and  Dr. V. Decroocq  and  D. Tricon  ( INRA, Villenave d'Ornon, France ) for providing some of the APV Prunus sources. The  GeT-PlaGe platform (INRA, Toulouse, France)  is also thanked for the 454 pyrosequencing.
Gene expression studies Total RNA was extracted with TRIzol reagent (Invitrogen). Transcriptomic profiles were obtained using Agilent Whole Mouse Genome microarrays (4×44k). Microarray data and experimental details are available in the Gene Expression Omnibus (GEO) database (accession number GSE73298 and GSE73299). For real-time quantitative PCR (qPCR), 2 mg RNA samples were reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Online supplementary file 2 presents the SYBR Green assay primers. Amplifications were performed using an ABI Prism 7300 Real-Time PCR System (Applied Biosystems). qPCR data were normalised to TATA-box-binding protein mRNA levels, and analysed with LinRegPCR. v2015. 3.
Statistical analysis Data were analysed using R (http://www. r-project. org). Microarray data were processed using bioconductor packages (http://www. bioconductor. org) as described in GEO entry GSE38083. Genes with a q value of <0. 001 were considered differentially expressed between genotypes. Gene Ontology (GO) Biological Process enrichment was evaluated using conditional hypergeometric tests (GOstats package). For nonmicroarray data, differential effects were analysed by analysis of variance followed by Student's t-tests with a pooled variance estimate. A p value <0. 05 was considered significant.
Acknowledgements We thank all members of the EZOP staff, particularly  Colette Bétoulières  for her careful and outstanding help from the early start of this project. We thank  Aurore Dequesnes  and  Laurent Monbrun  for their excellent work on plasma biochemistry. We thank  Christine Salon  and  Florence Capilla  for their excellent work on histology. We thank the staff from the  Genotoul: Anexplo ,  Get-TriX  and  Metatoul-Lipidomic  facilities. The authors wish to thank  Professor Daniel Metzger ,  Professor Pierre Chambon  ( IGBMC, Illkirch, France ) and the staff of the  Mouse Clinical Institute (Illkirch, France)  for their critical support in this project. We thank  Professor Didier Trono  ( EPFL, Lausanne, Switzerland ) for providing us with the Albumin-cre mice. We thank  Professor David Mangelsdorf  ( Howard Hughes Medical Institute, Dallas, TX ) and  Professor Steven Kliewer  ( UT Southwestern, Dallas, TX ) for providing us with the FGF21-deficient mice.
Funding This work was funded by grants from the  Human Frontier Science Program (HFSP)  (WW), by  Start-Up Grants  from the  Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore  (to WW), by  SFN  (to HG), by  ANRs 'Crisalis'  (to CP and HG), by 'Obelip' (to DL, CP, AM and HG). DL is a member of the  Institut Universitaire de France. AM, DL, WW and HG were supported by  Région Midi-Pyrénées.
Reviewing the literature To determine how published research has been focused towards informing sea turtle management and conservation, we evaluated the peer-reviewed literature. We undertook a systematic review using Web of  Science (v. 5. 22. 3; 21 June 2016). In order to ensure capture of all relevant texts we used the topic search 'sea turtle  ' or 'marine turtle' or 'loggerhead turtle' or 'green turtle' or 'leatherback tur-tle' or 'olive ridley' or 'Kemps ridley' or 'hawksbill turtle' or 'flatback turtle' in ' All databases' for the 2 most recent complete years  (2014 and 2015) , which re sulted in a list of 707 articles. We then removed duplicates, spurious hits, generic texts (such as references to entire proceedings) and non-peerreviewed 'grey' literature based on title and abstract, or main text if relevance was unclear from the abstract.
2008 , Sims et al. 2008 , Girondot 2010 , Whiting et al. 2013 , 2014a). A time-series of hatchling production is essential for determining the change in the productivity of a population (e. g. Brost et al. 2015). In recent years, genetics, satellite tele metry and ultrasonography have been used to generate improved estimates of clutch frequency, the number of nesting females, and effective population size  (Tucker 2010 , Blanco et al. 2012 , Frey et al. 2014). Somatic growth is determined through long-term capture-recapture studies (e. g. Hawkes et al. 2014 , Colman et al. 2015). A combination of skeletochronological and stable isotope analyses can be used to estimate growth rates and age at maturity (e. g. Avens et al. 2013 Avens et al. , 2015;; see Q7). Data collection at foraging grounds is essential because multiple stage classes, both sexes and turtles originating from different rookeries can be sampled at the same time.
To estimate demographic parameters at a foraging ground, distance sampling and CMR methods can be used. Distance sampling is useful for estimating density and abundance as a snapshot (e. g. Lauriano et al. 2011 , Seminoff et al. 2014) , whereas CMR methods are useful for estimating various demographic para meters with long-time series of data (e. g. Sasso et al. 2007 , Patrício et al. 2014 , Stewart et al. 2014). Al though a CMR study using both nesting beaches and foraging grounds is needed to obtain rookery-specific estimates on survival and movement rates within a population, this kind of approach has not been implemented because of the logistical difficulty in collecting sufficient data at nesting beaches and foraging grounds simultaneously. The sex ratio is a poorly understood demographic characteristic of sea turtle populations (see Q2).
Q17. How can we effectively determine the conservation status of sea turtle populations? This can be divided into 2 sub-questions: (1) What is the appropriate conservation unit and (  2 ) What are the appropriate methodological approaches to assess the extinction risk and type of data required? (1) The IUCN Red List assessments are the most cited. Significant advancement has been made from the original global scale at species level to a new scale: the RMU  (Wallace et al. 2010). Thus far, the RMU approach has been applied to leatherback and loggerhead sea turtles (www. iucnredlist. org), while others are underway. Although an improvement, an RMU may include several biological units whose individual fate is not reflected by the overall risk assessment. As genetic markers improve, smaller biological units are recognised  (Shamblin et al.
2011 , Valverde et al. 2012 , Lagueux et al. 2014) , or globally  (Humber et al. 2014). These datasets provide necessary information for improved understanding of what kind of directed take exists, and help establish a baseline against which to evaluate future trends. Beyond this, focused research on what level of harvest is sustainable to the target population is relatively rare. Nearly all recent research on harvest has focused on stage class survivorship and population trends of turtle rookeries exposed to directed take of either eggs or later life stages, and these studies tend to suggest that any harvest will cause population decline (e. g. Campbell & Lagueux 2005 , Santidrián Tomillo et al. 2008 , Macrae & Whiting 2014 , Senko et al. 2014a , Bourjea et al. 2015b ), even if there has been no documented decline in all the study populations.
Acknowledgements. Zara Botterell helped with project administration and we acknowledge the support and insight of 3 anonymous referees. K. R. W-S is supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1252521. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Mention of products and trade names do not imply endorsement by the US Government. LITERATURE CITED Abd Mutalib AH, Fadzly N (2015) Assessing hatchery management as a conservation tool for sea turtles: a case study in Setiu, Terengganu. Ocean Coast Manage 113:47-53 Abella Perez E, Marco A, Martins S, Hawkes LA (2016) Is this what a climate change-resilient population of marine turtles looks like? Biol Conserv 193:124-132 Abreu-Grobois A, Plotkin P (2008) (IUCN SSC Marine Turtle Specialist Group).
Ecol Inform 20:13-26 Fujioka E, Soldevilla MS, Read AJ, Halpin PN (2014b) Integration of passive acoustic monitoring data into OBIS-SEAMAP, a global biogeographic database, to advance spatially-explicit ecological assessments. Ecol Inform 21:59-73 Fujisaki I, Lamont MM (2016) The effects of large beach debris on nesting sea turtles. J Exp Mar Biol Ecol 482:33-37 Fukuoka T, Narazaki T, Sato K (2015) Summer-restricted migration of green turtles Chelonia mydas to a temperate habitat of the northwest Pacific Ocean. Endang Species Res 28:1-10 Fuller W, Godley B, Hodgson D, Reece SE, Witt M, Broderick A (2013) Importance of spatio-temporal data for predicting the effects of climate change on marine turtle sex ratios. Mar Ecol Prog Ser 488:267-274 Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units.
Rainfall The TRMM3B42 (Tropical Rainfall Measuring Mission)  [31]  product is available for the 50 ˝N-50 ˝S latitude band, at a spatial resolution of 0. 25 ˝ˆ0. 25 ˝and a time-step of three hours. The derived rainfall is based mainly on microwave measurements from the TRMM mission (Microwave Imager TMI, Precipitation Radar PR), complemented with measurements from other platforms (Special Sensor Microwave/Imager (SSMI) on the Defense Meteorological Satellite Program (DMSP) satellite series, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on AQUA, and the Advanced Microwave Sounding Unit-B (AMSU-B) on the National Oceanic and Atmospheric Administration (NOAA) satellites). In addition, infrared data from geostationary satellites provide information on the precipitation cloud life, obtaining full coverage of the tropical latitudes.
Data from the years 2000 to 2010 are used in this study. The RFE 2. 0 (African Rainfall Estimates Version 2. 0) product  [32]  has a spatial resolution of 0. 1 ˝ˆ0. 1 ˝and a daily time-step. It covers from 40 ˝S to 40 ˝N and from 20 ˝W to 55 ˝E-the entire African continent. RFE 2. 0 data are based on the combination of daily Global Telecommunication System (GTS) rain gauge data that consist of about 1000 rain gauges in Africa, as well as Advanced Microwave Sounding Unit (AMSU) and Special Sensor Microwave/Imager (SSM/I) satellite rainfall estimates. RFE 2. 0 uses additional techniques to better estimate precipitation while continuing the use of cloud top temperature and station rainfall data, which formed the basis of RFE 1. 0. Meteosat 7 geostationary satellite infrared data and are acquired at 30-min intervals, and areas depicting cloud top temperatures of less than 235 K are used to estimate convective rainfall.
Both estimates are acquired at six-hour intervals and have a resolution of 0. 25 degrees. Finally, daily rainfall is estimated by combining all satellite data using a maximum likelihood estimation method. GTS station data are then used to remove bias. Warm cloud precipitation estimates are not included in RFE 2. 0. The database starts in October 2000, so all applications of this product in this study are for the period 2001-2010. Both TRMM3B42 and the RFE 2. 0 products can be used to evaluate the quality of the rainy season in the Sahel, as for example by  Samimi et al. (2012)   [33]. The major difference between them is that RFE 2. 0 incorporates data from rain gauge measurements  [34] , whereas TRMM3B42 does not. These two daily products have been used in many previous studies across Africa analyzing precipitation  [35] , and TRMM3B42 data have been used to study the response of vegetation phenology to rainfall  [36].
Soil Moisture SM is defined as the water present in the unsaturated part of the soil profile, i. e. , between the soil surface and the water table (e. g. ,  [39, 40] ). Several studies have stressed the important role of SM in the water cycle (e. g. ,  [41] ) and in vegetation development (e. g. ,  [42, 43] ). The SM dataset used in this study is derived from microwave measurements, with both active and passive sensors, SMMR, SSM/I, TMI and ASMR-E (for the passive products) and the ERS and ASCAT scatterometers (for the active products)  [44] [45] [46]. The signals from microwave sensors are related to SM in the upper few centimeters of the soil but the relationships between SM at the surface and in the root zone are well established (e. g. ,  [45] ). The SM product, in volumetric (m 3 ¨m´3 ) units, has a spatial resolution of 0.
This threshold may be defined differently according to the scientific discipline (meteorologist, agronomist or hydrologist) and the data source (local data or satellite data). Here, we define dry spells as occurring when the rainfall is less than or equal to 0. 8 mm per day for TRMM3B42 and 0. 5 mm per day for RFE 2. 0 during a period of at least seven consecutive days. Although the mean maps present a picture of overall agreement (Figure  3a, c, e ), the spatiotemporal variations (Figures  3b,d,f  and 4 ) show that the TRMM3B42 product displays much higher variation, particularly in the southeast. TRMM3B42 and RFE 2. 0 standard deviations differ by a factor of approximately two. Despite an observed intensity difference, with higher maxima in the southern VSZs for TRMM3B42 (Figure  4 ), the two products are well correlated when comparing the yearly cumulated rainfalls of all VSZs over 10 years (Figure  5b ).
On average, over all VSZs, the rainy season onset dates agree to within one day (around Day 158). The dates of maximum rain are close for both rain products (difference, on average, of less than three days), but maximum SM occurs 10 days later, after loss through infiltration, runoff and evapotranspiration (Figure  6b ). Finally, the number of dry spells longer than one week, estimated from the three datasets, agree to within one spell (between two and three dry spells for RFE 2. 0 and SM, between three and four for TRMM3B42), and the duration of the longest dry spell per year is, on average, 11 days (12 days for TRMM3B42). Following the rainfall spatial variations, the dry spells are shorter in the south than in the north. There is thus good agreement between the seasonal and intra-seasonal characteristics of rain and SM (Figure  6 ) and the CCI-SM product seems to properly depict the effects of the major rainfall variations within the season.
Negative Anomalies (Rainfall or SM below the Mean) The negative rainfall or SM anomalies are better correlated to LAI negative anomalies than positive ones: the correlation coefficient is larger with a difference of +0. 1, on average, for the two rain datasets and SM. We note that a significant correlation is missing only in TSvS-L1, and only for TRMM3B42. Compared to positive anomalies, negative anomalies have a stronger effect on the vegetation, as they induce water stress. The correlation coefficients between negative anomalies are almost the same when using the two rainfall products but they are higher in the case of SM. For both anomaly types, the correlations with SM are slightly larger than with rainfall on most of the VSZs. The vegetation response is faster for SM compared to rainfall with an average lag of between one to two weeks and two to three weeks, respectively, over all areas.
50 and ´0. 70, whereas they are not significant for the others. Similar results were found when correlating the anomaly intensity (cumulated over all dry spells, or only the longest one) with the maximum LAI. Again, the correlation is the largest with the SM anomaly amplitude compared with the anomalies derived from the two rainfall datasets. However, in addition to the soil type, the spatial distribution of dry spells is different (Figure  6 ), since the longest dry spells are localized in the northern part of Ferlo and have more impact in this sub-region than in the south.
Significant Information from Rainfall and SM Products The analysis shows that rainfall and SM have similar spatial variations, but with less temporal variability for SM. This can be expected since SM is an integrated variable, resulting from the surface partitioning of rainfall into evapotranspiration, surface runoff and infiltration. It has a slow temporal evolution. Although TRMM3B42 and RFE 2. 0 are direct estimates of the daily rainfall, the uncertainty in their variations, as seen when comparing the two databases, results in a similar uncertainty in the rainy season patterns at the scale of each VSZ. Despite the unknown source of these errors, SM properly depicts the overall rainfall seasonal variations and anomalies. An advantage of SM is that its relationship with the microwave radiometer measurement (variations of the surface emission linked to soil dielectric properties) is more direct than that for precipitation (conversion of instantaneous emission from droplets and crystals in the atmosphere into a cumulated surface amount).
SM product, although the mean variations over the rainy season are smoother than the rainfall ones. The three databases provide intra-seasonal variations, which were found to correlate with the variations in vegetation phenology. However, correlations with RFE 2. 0 were found to be higher than with TRMM3B42; SM variations were found to be significantly more correlated to LAI (anomalies and maximum value) than rainfall variations. SM thus appears as the most relevant parameter for evaluating the impact of rainfall anomalies on vegetation growth. The within-season rainfall and SM anomalies are followed three weeks later (on average) by correlated anomalies in LAI. Dry spells are correlated with the maximum LAI. However, a significant response of the LAI is observed only when the absence of rainfall lasts more than seven days, whereas a three-day deficit of SM leads to a significant correlation.
Acknowledgments: This work was supported by Institut de Recherche pour le Développement (IRD), Service de Coopération et Action Culturelle (SCAC) of the French Embassy in Senegal and the Centre National de le Recherche Scientifique (CNRS). The authors acknowledge Ecological Monitoring Centre (Centre de Suivi Écologique, CSE) colleagues in Dakar for providing information about the Ferlo vegetation, Gaëlle De Coëtlogon from the Laboratoire Amosphéres, Milieux etObservations spatiales (LATMOS) and Fabienne Maignan from the Laboratoire des Sciences du Climat et de l'Environnement (LSCE) for their help with data processing.
We successfully apply this to the Grotte du Renne Châtelperronian. We obtain a direct hominin radiocarbon date at the site, thereby directly addressing the chronostratigraphic context of this specimen in relation to the hypothesis regarding movement of the hominin specimens from the underlying Mousterian into the Châtelperronian layers and provide biomolecular data (palaeoproteomics, aDNA) on the genetic ancestry of the Grotte du Renne Châtelperronian hominins. Although proteomic data on Pleistocene hominin bone specimens has been presented before  (17, 18) , the phylogenetic and physiological implications of such datasets has, so far, not been fully explored. Here we use the potential of error-tolerant MS/MS database searches in relation to the biological questions associated with the Châtelperronian.
Results ZooMS Screening. We screened 196 taxonomically unidentifiable or morphologically dubious bone specimens (commonly <20 mg of bone) using ZooMS from the areas of the Grotte du Renne that had previously yielded hominin remains  (20). This required us to construct a collagen type I (COL1) sequence database including at least one species of each medium or larger-sized genus in existence in Western Europe during the Late Pleistocene  (19)  (Dataset S1), and from this derived a ZooMS peptide marker library (Dataset S2). ZooMS uses differences in tryptic peptide masses from COL1α1 and COL1α2 amino acid chains to taxonomically identify bone and tooth specimens  (15). The peptide marker library combines newly obtained and published COL1 sequences with published ZooMS peptide markers  (15, 21)  and enabled us to confidently identify 28 bone fragments within the extant Pan-Homo clade to the exclusion of other Hominidae (SI Appendix, Figs.
S1 and S2 and Table  S1 ). Together with other studies, this confirms the suitability of ZooMS as a screening technique to identify hominin specimens among unidentified fragmentary bone specimens  (17, 22). We confirmed these identifications for samples AR-7, AR-16, and AR-30 by analyzing the same extracts using shotgun proteomics and spectra assignment against our COL1 sequence database (SI Appendix, Fig. S3 ). In each case, taxonomic assignment to the genus Homo had the highest score (SI Appendix, ZooMS Screening). Molecular contamination is an important issue when studying ancient biomolecules, especially when it concerns ancient hominins. Extraction blanks were included throughout all analysis stages to monitor the introduction of potential contamination, although such controls only provide insight into contamination introduced during the laboratory analysis.
Error-tolerant liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of the protein content of the ZooMS extracts of AR-7, AR-16, and AR-30 and two additional palaeoproteomic extractions performed on AR-30 resulted in the identification of 73 proteins (Table  1  and SI Appendix, Table  S2 ). We base our assessment of endogenous and possibly exogenous proteins on four lines of evidence. First, we analyzed our extraction blanks by LC-MS/MS analysis. This allowed us to identify several proteins introduced as contaminants during the analysis (human keratins, histones, and HBB) (Table  1  and SI   Table  S2 ), and matches to these proteins for AR-7, AR-16, and AR-30 were excluded from further in-depth analysis. Second, we searched our data against the complete UniProt database, which contains additional nonhuman and nonvertebrate proteins from various sources that could have contaminated our extracts.
These proteins have been identified in nonhominin palaeoproteomes as well and are consistently incorporated into the mineralized bone matrix  (25, 26). Error-tolerant analysis of MS/MS spectra has the ability to identify amino acid variants not present in protein databases or reference genomes, potentially revealing relevant phylogenetic information. We used an error-tolerant search engine (PEAKS) against the human reference proteome and compared our protein sequence data against available amino acid sequence variations known through genomic research for modern humans  (32) , a Denisovan genome  (33) , and the coding regions of three Neandertals  (34). Using this approach, we confidently identify five proteins that contain a total of seven amino acid positions with nonsynonymous SNPs with both alleles at frequencies ≥1.
All biomolecular extractions were performed in dedicated facilities at the Max Planck Institute for Evolutionary Anthropology. Extended methods can be found in SI Appendix. COL1 database samples can be accessed via ProteomeXchange with identifier PXD003190, and LC-MS/MS data for AR-7, AR-16, and AR-30 via PXD003208. The DNA sequences analyzed in this study were deposited in the European Nucleotide Archive under accession number PRJEB14504.