dataesr/scientific-paragraphs-categorization huggingface dataset:
Pollen beetles Adult pollen beetles cause most damage to oilseed rape plants when they feed on pollen in the buds early in the season, affecting developing racemes and causing bud abortion  (Alford et al. 2003). To assess pollen beetle damage, we calculated the percentage of podless stalks in relation to total number of pods on 10 randomly chosen plants per sampling point (100 plants in total per field) at GS (growth stages) 77-79 (70-100% pods having reached final size, cf. Lancashire et al. 1991). Since podless stalks can be caused by other factors, we correlated percent podless stalks with the mean pollen beetle density and found high correlation (Pearson's product-moment correlation = 0. 35, P < 0. 001). Pollen beetle density was estimated using data from visual counts of adults. Pollen beetle counts were conducted weekly on ten randomly chosen plants per sampling point, three times in total during the season from oilseed rape green bud stage until flowering.
In contrast,  Zaller et al. (2008b)  found negative pod midge effects on oilseed rape yield. The difference between our findings and theirs may be explained by the different yield measures. We estimated average seed weight per plant, while Zaller et al. measured seed weight per area (ha), a measure that depends on plant density. Pod midge damage was strongly negatively correlated with plant density in our study (data not shown, see also  Valantin-Morison et al. 2007 ) and consequently to the area-based yield measure (seed yield multiplied by plant density per m 2 ). Hence, the scale of the yield measure, that is per plant versus per unit area, might influence conclusions of the herbivore impact on yield, since plant density can affect both pest pressure and seed yield measure. Furthermore, if one pest facilitates a second, as has been commonly reported with seed weevil and pod midge, plant performance can be reduced by their combined attack.
Data Compilation There are a few differences (Figures 9 and 10) from a recent compilation  [Lang and Wolff, 2011] , on which we have built. First, a few additional marine records of SST have been included, allowing better, geographical coverage. There are now 17 SST records (of which two fall short of the full 800 ka period). Antarctic temperature is still represented by only one record (EPICA Dome C): although a second long ice core record, covering 720 ka from Dome F, distant from Dome C on the East Antarctic plateau, exists, the data are as yet unpublished. However, we note that the shorter published record  [Kawamura et al. , 2007; Sime et al. , 2009]  suggests that the relative intensity of different interglacials at Dome F is similar (though not identical) to that at Dome C and that this likely represents an Antarctic-wide pattern.
Acknowledgments This paper arose as a result of a succession of workshops of the Past Interglacials Group (PIGS), sponsored by the  Past Global Changes Project (PAGES). The authors acknowledge the contributions of all participants at those workshops, of whom the listed authors are only a subset. Numerous funding agencies have contributed to the work of this paper including  NSF (USA) ,  NERC  and  The Royal Society (UK) ,  F. R. S-FNRS (Belgium) , and  SNF (Switzerland). Most data described in this paper are available through relevant data repositories, http://www. ncdc. noaa. gov/data-access/paleoclimatology-data and www. pangaea. de in particular. In addition, the data sets from which Figures 9 and 10 were derived have been compiled into a spreadsheet in the supporting information. Insolation data for Figure 5 can be calculated using programs available at ftp://ftp.
Sample and data The XXL Survey, described in detail by  Pierre et al. (2016, Paper I) , is a 50 deg 2 XMM-Newton survey with a sensitivity of ∼5 × 10 -15 erg s -1 cm -2 in the [0. 5-2] keV band for pointsources. This survey is an extension of the 11 deg 2 XMM-LSS survey  (Pierre et al. 2004 ) and consists of two regions of 25 deg 2 each, XXL-North and XXL-South. The main aim of XXL is to provide a well-defined galaxy cluster sample for studies of precision cosmology, galaxy evolution, and active galactic nuclei. Within the XXL Survey, the bright XXL 100 cluster sample (XXL-100-GC 3  and via the XMM XXL DataBase 4  ) is defined as a flux-limited sample based on the 100 brightest clusters (more details available in  Pacaud et al. (2016, hereafter Paper II). Some of these clusters were previously studied as part of the XMM-LSS and XMM-BCS surveys  (Clerc et al.
Optical and near-infrared imaging This paper uses ugriz imaging data from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS 6  ) in addition to CFHT WIRCam K s -band data over a subset of the CFHTLS W1 field. The WIRCam camera consists of four HAWAII2-RG detectors, each containing 2048 × 2048 pixels. The four detectors image an area of 20 × 20 with a pixel scale of 0. 3 /pixel (the cross-shaped gap between the four detectors is 45 wide). A total of 151 WIRCam pointings were observed, each for a total of 1050 s with observations acquired as two sequences of 21 spatially dithered exposures of 25 s. The WIRCam data is part of the MIRACLES survey and is reduced subsequently at CFHT and TERAPIX 7. This pipeline makes extensive use of the software from the Astromatic webpage 8  and is similar to the one used to process the CFHT-WIRDS  (Bielby et al.
Each exposure was finally delivered with an initial astrometric and photometric calibration in two flavours: the detrended exposures, and the detrended images with the sky removed. TERAPIX finally used QualityFITS on detrended sky subtracted exposures to produce weight maps, object catalogues, and overviews of individual image qualities (e. g. seeing, depth). The production of the weight maps was possible via the WeightWatcher software  (Marmo & Bertin 2008) , while astrometric and photometric calibration was performed using SCAMP  (Bertin 2006 ) with the Two Micron All Sky Survey (2MASS) taken as the astrometric reference catalogue. Source extraction and photometry were performed on the ugrizK s images using SExtractor v2. 5. 0  (Bertin & Arnouts 1996)  in dual image mode with the K s -band as the detection image in each case.
This work is based on 20 clusters in the XXL Survey. In order to have more robust estimates of the quantities explored so far, we need to increase our sample. In a future work, we plan to include clusters observed by the VIDEO 11  and UKIDSS 12  surveys to improve statistical precision, using all weak-lensing masses presented in Paper IV. Moreover, we will expand our sample of clusters with accurate weak-lensing masses as more weaklensing data becomes available for both the northern and southern fields in XXL. Finally, the upcoming analysis of 50 clusters from LoCuSS  (Mulroy et al. , in prep. )  will allow us to extend the sample to high masses, yielding a more robust estimate of the fit parameters of the L K -M WL relation.
Acknowledgements. We thank the anonymous referee for the constructive comments. XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼5 × 10-15 erg cm -2 s -1 in the [0. 5-2] keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. Multi-band information and spectroscopic follow-up of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. We thank the  CFHT  staff and all the PIs that have contributed to the success of this work ( K. Thanjavur ,  G. Morrison ,  L. VanWaerbeke ,  J. P. Willis  and  S. Arnouts ). Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
In addition, measuring systolic performance tracing all along endocardial end-diastolic and end-systolic borders (EF B-mode and FAC; Figure  1 -source data 1) excluded a left ventricular abnormal regional remodeling. Finally, heart diastolic performance assessed by measuring left ventricle filling waves in standard 4 cavities view. (Figure  1 -figure supplement 2D and E/A ratio in Figure 1-source data 1) did not show any difference. Thus, PLAT CRE ::Meis1 LoxP/LoxP mice present no cardiac malformations and exhibit normal hemodynamic parameters suggesting that the outflow tract is unaffected. Altogether, these explorations revealed that PLAT CRE ::Meis1 LoxP/LoxP mice present profound alterations in the sympatho-vagal regulation of cardiac functions, affecting specifically the sympathetic component whereas the parasympathetic component is unaffected.
Quantifications For the calculation of innervation density, the total TH/PGP9. 5 fibers areas were measured with ImageJ software  (Schneider et al. , 2012) , using the "'analyze particles' option after adjusting the threshold with the max entropy method. In TEM experiments, the number of endocytic structures per axons and the area ratio (area of endocytic structures per axons/area of axons) were measured on at least 5 pictures randomly taken in each samples. Samples were obtained from 3 WT and 2 PLAT CRE ::Meis1 LoxP/LoxP sympathetic nerves.
ECG recording and HRV (Heart Rate Variability) analysis Electrocardiogram (ECG) monitoring (2000 Hz) were performed by telemetry system using Physio-TelÒETAF20 teletransmitter (DSI, USA) and IOX software (EMKA Technologies, France). Continuous 24 hr ECGs were analyzed with ECG-auto software (EMKA Technologies, France), in respect of circadian light/dark cycles (12 hr/12 hr). The mean RR interval and the mean PR, QRS, QTc durations were calculated. The QT interval was defined as the time between the first deviation from an isoelectric PR interval until the return of the ventricular repolarization to the isoelectric TP baseline from lead II ECGs. The QT correction was performed with the adapted Bazett's formula of Mitchell  (Mitchell et al. , 1998). Arrhythmia were detected and counted by hand, according to the Lambeth conventions guidelines  (Walker et al.
The Meis1/2/3 antibody has been previously validated for ChIP experiment or co-immunoprecipitation  (Shim et al. , 2007; Pfeufer et al. , 2010). The DNA was then purified using the iPure kit (Diagenode, C03010012, Belgium) according to the manufacturer's instructions. Sequencing was realized by MGX platform (Montpellier Genomix, Montpellier) using a HiSeq 2000 sequencing system (Illumina, San Diego, California, USA). Data were analyzed using the CA-SAVA software for alignment and the MACS software for statistical analyses. Signals were annotated by the name of the genes flanking the 20 kb to the precipitated sequence. Bibliographical search was realized using PubMed. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE54144 (http://www.
Acknowledgements This work was supported by  Inserm Avenir grant ,  AFE grant  ( Association France-Ekbom ) and  Fondation de France  ( Synaptocard  project, N  ˚2013-00038586 ). FM, SR, JT hold CNRS positions and FB was supported by the  University of Montpellier2. We thank  S Dufour  for generous gift of the PLAT-CRE strain,  JF Brunet  for the Phox2b antibody,  MS Featherstone  for the mouse Meis1 cDNA,  A Pattyn  for the Hand2, Gata3, DBH probes vectors, CD Deppmann for Ngf KO embryos,  C Cazevieille  for help with TEM,  S Salinas  for Rab5-GFP plasmid and  L Journot  and the  MGX (Montpellier Genomix ) platform for advices on the ChIP-seq experiments. We also thank  P de Santa Barbara ,  C Raoul ,  P Carroll  and  A Pattyn  for helpful discussions and critical reading of the manuscript.
2004; Armanios et al. 2005; Marrone et al. 2005). It is technically not feasible to keep the genome heterozygous during long-term propagation. Fortunately, the ability to computationally estimate telomere length allowed us to further validate our approach using data from the Million Mutation Project (MMP)  (Thompson et al. 2013 ) and examine whether the equivalent of a mutant screen for telomere length would provide insight into our result examining wild isolate genomes. The MMP generated over two thousand mutagenized strains using the laboratory N2 background. After each strain was passaged by self-mating of hermaphrodites for ten generations, the strains were whole-genome sequenced to identify and to predict the effects of induced mutations. The MMP data set can be used to identify correlations of phenotype and mutant genes in the laboratory strain background.
We obtained wholegenome sequence data from 1,936 mutagenized N2 strains, each of which has a unique collection of mutations. Importantly, ten generations of self-propagation of these mutagenized strains prior to sequencing likely allowed telomere lengths to stabilize in response to mutations in genes that regulate telomere length, enabling us to observe differences. TelSeq returned telomere length estimates for this population, which had a right long-tailed distribution (Figure  4A ). We classified 39 of 1936 strains within the population as long-telomere strains with telomere lengths greater than 6. 41 kb (98th percentile). Reasoning that certain mutant genes would be overrepresented in these 39 strains compared to the others, we performed a hypergeometric test to identify if enrichment for particular genes in long-telomere strains existed.
Alignment, variant calling, and filtering FASTQ sequence data has been deposited under NCBI Bioproject accession PRJNA318647. Sequences were aligned to WS245 (http://www. wormbase. org) using BWA (version 0. 7. 8-r455)  (Li and Durbin 2009). Optical/PCR duplicates were marked with PICARD (version 1. 111). BAM and CRAM files are available at www. elegansvariation. org/Data. To determine which type of SNV caller would perform best on our dataset and to set appropriate filters, we simulated variation in the N2 background. We used bamsurgeon (github. com/adamewing/bamsurgeon), which modifies base calls to simulate variants at specific positions within aligned reads and then realigns reads to the reference genome using BWA. We simulated 100,000 SNVs in 10 independent simulation sets. Of the 100,000 sites chosen in each simulation set, bamsurgeon successfully inserted an average of 95,172.
elegans). The value of  is a constant representing the length of 100 bp windows within the reference genome where GC content is equal to the GC content of the telomeric repeat divided by the total number of telomere ends. By default, TelSeq provides length estimates applicable to humans. We found the number of 100 bp windows with a 50% GC content in the WS245 reference genome to be 58,087. We calculated  for C. elegans as 58,087  / 12  = 484. This value was used to transform human length estimates to length estimates appropriate to C. elegans. Notably, telomere length estimates are averaged across all chromosomes, as no specific data about any one particular telomere is determined. To assess how well the TTAGGC hexamer distinguishes telomeric reads from non-telomeric reads, we examined the frequencies of non-cyclical permutations of the C.
Quantitative PCR assays for telomere-length measurements Telomere lengths were measured by qPCR as described previously with some modifications  (Cawthon 2009). Primer sequences were modified from the vertebrate telomere repeat (TTAGGG) to use the C. elegans telomere repeat (TTAGGC): telG: 5'-ACACTAAGCTTTGGCTTTGGCTTTGGCTTTGGCTTAGTCT-3' telC: 5'-TGTTAGGTATGCCTATGCCTATGCCTATGCCTATGCCTAAGA-3' The internal control, act-1, was amplified using the following primer pair: forward: 5'-GTCGGTATGGGACAGAAGGA-3' reverse: 5'-GCTTCAGTGAGGAGGACTGG-3' Two primer pairs were amplified separately (singleplex qPCR). All the samples were run in triplicate. qPCR was performed using iQ SYBR green supermix (BIORAD) with iCycler iQ real-time PCR detection system (BIORAD). After thermal cycling, C t (cycle thresholds) values were exported from BIORAD iQ5 software.
The samples were denatured on a heat block at 85℃ for three minutes. After overnight incubation at 37℃, the samples were washed in the following order: 1X PBST once for five minutes at room temperature, 2X SSC (0. 3 M sodium chloride, 30 mM sodium citrate) in 50% formamide once for 30 minutes at 37℃, 1X PBST three times for 10 minutes each at room temperature. The samples were incubated in DAPI and mounted in anti-bleaching solution (Vectashield). The samples were imaged with a confocal microscope (LSM700, Zeiss). Telomere spots were quantified with TFL-TELO software (Dr. Peter Lansdorp, Terry Fox Laboratory, Vancouver)  (Poon SS1, Martens UM, Ward RK 1999).
Genome-wide association (GWA) mapping GWA mapping was performed on marker genotype data and telomere-length estimates using the rrBLUP package (version 4. 3) (Endelman 2011) and GWAS function. rrBLUP requires a kinship matrix and a SNV set to perform GWA. We generated a kinship matrix using our imputed SNV set with the A. mat function within rrBLUP. Genomic regions of interest were determined empirically from simulating a QTL that explained 20% of the phenotypic variance at each marker in our mapping data set. All simulated QTL were mapped within 100 markers (50 markers to the left and 50 markers to the right) of the simulated marker position. To generate a SNV set for mapping, we again used our imputed SNV set. However, we filtered the number of SNVs to a set of 38,688 markers. This set was generated by lifting over (from WS210 to WS245) a set of 41,888 SNVs previously used for GWA mapping  (Andersen et al.
Million Mutation Project analysis Whole-genome sequence data from mutagenized strains within the Million Mutation Project (MMP) was obtained from the sequence read archive (SRA, project accession number SRP018046). We removed 59 strains that were contaminated with other strains. We also were unable to locate the sequence data for 12 MMP strains on SRA, leaving us with a total of 1,936 mutagenized strains. Within the MMP project, read lengths varied among sequencing runs, being either 75 bp or 100 bp. We ran TelSeq on all sequencing runs assuming 100 bp reads. To utilize 75 bp sequencing runs, we took the 448 strains that were sequenced at both 75 and 100 bp lengths and used those estimates to develop a linear model. Then, this model was used to transform 75 bp length estimates to 100 bp estimates (Supplementary Figure  15 ).
We then used the weighted average of telomere length estimates for all runs of a given strain based on total reads to calculate telomere-length estimates. Because telomeric reads resemble PCR duplicates, TelSeq utilizes them in calculating telomere length. However, we observed very low PCR and optical duplicate rates among MMP sequence data likely due to differences in library preparation in contrast to wild isolate sequence data. These differences likely account for shorter telomere estimates from the MMP sequence data. Long-telomere strains from the MMP were classified as strains with telomere lengths greater than the 98 th quantile of all MMP strains (6. 41 kb). Mutation data was obtained from the MMP website (http://genome. sfu. ca/mmp/mmp_mut_strains_data_Mar14. txt). A hypergeometric test was performed to identify which genes were enriched for mutations from long-telomere strains (Supplementary File 11) using the phyper function in R (R Core Team 2013).
High-throughput fecundity assays Assays were performed similar to previously reported  (Andersen et al. 2015)  with the following differences. Animals were bleached, synchronized, and grown to L4 larvae in 96-well plates. From the L1 to L4 stage, animals were fed 5 mg/mL of a large-scale production HB101 lysate in K medium  (Boyd et al. 2010)  to provide a stereotyped and constant food source. Then, three L4 larvae from each of the 152 genotypes were dispensed using a COPAS BIOSORT instrument to wells containing 10 mg/mL HB101 lysate in K medium and progeny were counted 96 hours later. Fecundity data were calculated using 12 samples -triplicate technical replicates from four biological replicates. The data were processed using COPASutils  (Shimko and Andersen 2014)  and statistically analyzed using custom R scripts.
Acknowledgements We would like to thank  Joshua Bloom  and members of the  Andersen laboratory  for critical comments on this manuscript. We also thank  M. Barkoulas ,  T. Bélicard ,  D. Bourc 'his,  N. Callemeyn-Torre ,  S. Carvalho ,  J. Dumont ,  L. Frézal ,  C. -Y. Kao ,  L. Lokmane ,  I. Ly ,  K. Ly ,  A. Paaby ,  J. Riksen ,  G. Wang  for isolating new wild C. elegans.
These noise levels impact on the ocean scales we can observe. In winter, when the mixed layers are deepest and the submesoscale is energetic, all of the altimeter missions can observe wavelengths down to 40-50 km (individual feature diameters of 20-25 km). In summer when the submesoscales are weaker, SARAL can detect ocean scales down to 35 km wavelength, whereas the higher noise from Jason-2 and CryoSat-2 blocks the observation of scales less than 50-55 km wavelength. This statistical analysis is completed by individual case studies, where filtered along-track altimeter data are compared with co-located glider and high-frequency (HF) radar data. The glider comparisons work well for larger ocean structures, but observations of the smaller, rapidly moving dynamics are difficult to co-locate in space and time (gliders cover 200 km in a few days, altimetry in 30 s).
, 2017)  in collaboration with oceanographers to improve the waveform retracking over the ocean and provide adequate corrections for ocean observations. CryoSat-2 is in low-resolution mode over most of the global ocean but has synthetic aperture radar (SAR) mode observations available over a few regions, including the Mediterranean Sea, with improved along-track sampling down to 300 m and reduced noise. However, certain ocean corrections are less accurate than on Jason-2 or SARAL, including the radiometer correction and the mean sea surface estimate, since CryoSat-2 is on a geodetic orbit. These three altimeter missions with different technologies and data processing will provide an ideal data set to test the improved observational capabilities in the NW Mediterranean Sea. Previous studies have analysed the altimetric capabilities in the NW Mediterranean Sea from conventional along-track data  (Bouffard et al.
Altimeter data Along-track SSH observations from the most recent altimetry missions (Jason-2, CryoSat-2 and SARAL/AltiKa) are analysed over the NW Mediterranean Sea (Fig. 1 ) and over different periods (Table  1 ). The data are made available from AVISO/CNES. Jason-2 is a conventional pulse-width limited altimeter operating in Ku-band  (Lambin et al. , 2010)  and provides the longest time series: we use data over the 6. 8-year period from July 2008 to February 2015. SARAL/AltiKa, with its 40 Hz Ka-band emitting frequency, its wider bandwidth, lower orbit, increased pulse repetitivity frequency and reduced antenna beamwidth, provides a smaller footprint and lower noise than the Ku-band altimeters  (Verron et al. , 2015). We use data from the nearly 2-year period from March 2013 to January 2015.
HF radar data As part of the MOOSE observing system, a HF radar system has been installed near Toulon (http://hfradar. univ-tln. fr/HFRADAR) to monitor the Northern Current, with gridded data available since 2012. HF radars measure the reflected radar signal from the ocean surface at a given lateral incidence angle. The surface currents are obtained after subtracting the surface wave speed, which is estimated from the measured frequency of the wave energy peak and the known frequency of the emitted radar signal. Two radars orientated with different angles allow the determination of the current direction. The Toulon HF radar system uses two WERA radars that provide surface current vectors over a region extending 80-100 km offshore, with a spatial resolution of 3 km and an angular resolution of 2 •.