dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007). All other manipulations were performed in the Spyder interface for Python 3. 4 using the NumPy and SciPy packages  (Jones et al. , 2001). The scripts used are available at https://github. com/ maaeli/IEC. As the background scattering is dependent on the buffer composition, the gradient or stepwise buffer changes during elution must be accounted for in the subtraction process. We present three methods to find the optimal background subtractions, which are explained for the individual cases in x3. Known BSA crystal structures were compared with the experimental data using CRYSOL  (Svergun et al. , 1995). Default fitting parameters were used, which allow the hydration shell to be adjusted to optimize the fit.
2a ). We then ran the same sample online on the BM29 beamline at ESRF, as described in x2, and continuously acquired SAXS data from the eluent at a rate of 1 Hz. In order to improve the resolution of the peaks and to ensure a sufficiently high sampling rate, the flow rate for the online experiments was reduced to 1 ml min À1. The UV absorption signal at 280 nm and the total scattering elution profiles show the expected three peaks (Fig. 1b , top), whereas the scattering at higher angles increases linearly (Fig. 1b , bottom). The matching background was found to be slightly shifted with respect to the buffer run (compare the bottom graph in Fig. 1b  with Fig. 1c ). It is therefore necessary to subtract not the directly corresponding frames but slightly shifted ones. Possible reasons for this shift are a nonperfect synchronization between the ion-exchange run and the SAXS data acquisition or the co-elution of ions bound to the column.
, 2014; Zhang et al. , 2007)   adjusted p-value in a CORMAP test  (Franke et al. , 2015)  being 0. 16 (Figs. 3b  and 3c ). This suggests that the two subpeaks do not represent different conformations of BSA. 169 frames were averaged in the region of interest shown (grey) in Fig. 3(a). The resulting curve (Fig. 3d ) gives a radius of gyration of 2. 7 AE 0. 1 nm (Fig. 3e ) and a Porod volume of 116 AE 5 nm 3 , corresponding to a molecular weight of about 68 kDa  (Petoukhov et al. , 2007;  see Table  1  for further details). Both of these values correspond well to the expected size (2. 77 nm; PDB entry 3v03;  Majorek et al. , 2012)  and molecular weight (66. 5 kDa) of monomeric BSA. Additional comparison to the monomeric crystal structure (PDB entry 3v03;  Majorek et al. , 2012)  shows a similarly good match as the SEC-SAXS data for BSA ( 2 = 1.
, 2015). The background-correction method here for D5 323-785 , based on averages, can be performed using any software package for SAXS data analysis, such as PRIMUS  (Konarev et al. , 2003)  or SCA ˚TTER, assuming that the possible buffer range has been well determined experimentally. For IEC, buffer conditions, such as salt concentrations or pH, are primarily determined by the biochemical characteristics of the protein of interest, such as its isoelectric point and solubility  (Yigzaw et al. , 2009). However, one should aim to minimize the amounts of all additives in order to maximize the X-ray contrast between the protein and the buffer in a SAXS experiment. As with any SAS data, IEC-SAXS results need to be carefully validated. Special care should be paid to sample monodispersity, which cannot be directly assessed by IEC-SAXS, and correct background subtraction, especially in the case of flexible proteins  (Jacques et al.
Acknowledgements We thank the  ESRF  for SAXS beam time and support. We further wish to acknowledge  Wim Burmeister  from  Universite Ǵrenoble Alpes  for fruitful discussions and financial support. This work was supported by an  ANR  grant ( REPLIPOX ,  ANR-13-BSV8-0014 ). We would like to thank  Petra Pernot  and  Matthew Bowler  for proofreading the manuscript.
Lowpressure (<1 bar) adsorption measurements for C 2 hydrocarbons were performed using an Autosorb 1-MP instrument (Quantachrome Instruments). Ultra-high-purity grade C 2 H 2 , C 2 H 4 , and C 2 H 6 were used for adsorption measurements. The temperature-dependent adsorption data were analyzed using the virial equation: 13 = + + + n p A A n A n ln( / ). 0 1 2 2 where p is pressure, n is the amount adsorbed, and A 0 , A 1 , etc. are virial coefficients. The Henry's law constant (K H ) is equal to exp(A 0 ), and the selectivity can be derived from the ratio of the constants K H for different gases. Synthesis of H 4 L IV. 2′,2″,5′,5″-Tetramethyl-[1,1′:4′,1″:4″,1‴quaterphenyl]-3,3‴,5,5‴-tetracarboxylate Tetramethyl Ester (1). A mixture of 5-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)benzene-1,3dicarboxylate dimethyl ester (1.
Anal. Calcd (%) for C 61 H 91 Cu 2 N 7 O 21 : C, 52. 88; H, 6. 62; N, 7. 08. Found (%): C, 52. 48; H, 5. 88; N, 7. 45. X-ray Crystallographic Analyses. Single-crystal diffraction data for MFM-130 were collected at 150(2) K on a Bruker SMART APEX CCD area detector using graphite-monochromated Mo Kα radiation. Data for MFM-131 were collected using synchrotron radiation at Beamline I19 at Diamond Light Source. The details for data collection are included as part of the Crystallographic Information File (CIF) in the Supporting Information. The structures were solved by the direct method and refined by full-matrix least-squares methods on F 2 using SHELXL-2013. 14 Hydrogen atoms on the ligands were placed geometrically and refined using a riding model; the hydrogen atoms of the coordinated water molecules could not be located but are included in the formula.
Gene expression analysis Total RNAs were extracted from plantlets grown on half-strength Murashige and Skoog (MS/2) medium for 8 days using the RNeasy Plant Minikit (Qiagen) and quantified by nanodrop after DNase I treatment (Invitrogen). First-strand cDNAs were synthesized with Super-Script III reverse transcriptase (Invitrogen), according to the manufacturer's instructions. Oligonucleotides for gene-specific amplification were designed using PRI-MER3 software (http://frodo. wi. mit. edu/primer3/). The primer pair AtNUP62-F (5′-GCAGAGTGGGATA-AGCGGAT-3′) and reverse primer of pair B (5′-TCTCTTTCTACTAGCTCAGACTG-3′) spans intron IV and V, according to AtNUP62 gene structure (Fig. 2 ), the 5 th intron containing the T-DNA insertion (Fig. 2a ). For normalization, the PDF2 reference gene (At1g13320) was selected on the basis of its expression stability in roots and leaves under our conditions (primers PDF2-F (5′-TAACGTGGCCAAAATGATGC-3′) and PDF2-R (5′-GTTCTCCACAACCGCTTGGT-3′)).
PCR reactions were performed on a LightCycler 480 (ROCHE Applied Science) in triplicate with two independent biological samples. Absence of genomic DNA and primer dimers was confirmed by analysis of minus-RT and water control samples, and by examination of melting curves. Baseline data were collected between cycles 3 and 15. All amplification plots were analysed with an Rn (normalized reporter) threshold of 0. 2 to obtain CT values. Data were analyzed using Roche LightCycler software and to derive relative expression levels, the comparative CT method (DDCT) was used as described in Cuéllar et al. [47]. Selection of atnup62 mutant plants harbouring the DR5::GUS construct Atnup62-1 homozygous mutant plants were crossed with plants homozygous for the DR5::GUS insertion  [20]. The presence of SALK T-DNA insertion in AtNUP62 gene and DR5::GUS integration were confirmed respectively by PCR and ß-glucuronidase activity, and homozygotes were selected at the next generation by checking the absence of AtNUP62 PCR product and the presence of GUS activity in the progeny (100 % of GUS positive plantlets).
Acknowledgements We wish to thank  Rémy Gibrat  for pictures of plants on vertical plates,  Nadine Paris  for Fig. 3c,  Geoffrey Duby  for Fig. 1k,  Hugues Baudot ,  Thierry Dessup  and  José Garcia  for taking care of greenhouse plants. We also thank the  Salk Institute Genomic Analysis Laboratory  for providing the sequence-indexed Arabidopsis T-DNA insertion mutants, and the  Nottingham Arabidopsis Stock Center (NASC)  for their distribution. This work was partly funded by the  GABI-Genoplante  project  "Functional genomics of membrane transporters " ( AF2001065 ).
analysis The samples were analyzed by agarose gel electrophoresis as detailed elsewhere. 10, 29 After migration at room temperature, the gel was stained with ethidium bromide and exposed to ultraviolet light (312 nm). The plasmid fluorescence was recorded with a charged coupled device camera. The densitometry was performed using the Quantity One software package (Bio-Rad Laboratories Inc. , Hercules, CA, USA). The yields of SSBs and DSBs were determined as described elsewhere. 10, 29 Briefly, considering that ethidium bromide binds 1. 47 times less efficient to supercoiled plasmids (S) than in relaxed (R) and linear (L) forms, we calculated the norm N S R L = × + + 1 47. and then SSB = - × In 1 1 47 L N S N. and DSB = - L N L N 1. No significant artifact due to the binding of NPs to plasmids was found.
Different PcG subunits have non-redundant roles in repressing particular gene sets via H3K27me3 To compare roles of different PcGs, we used chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) to characterize the genome-wide profiles of H3K27me3 in Col-0, clf-29, tfl2-2, atbmi1a,b, and atring1a,b (S2A Table ). In Col-0, 5,055 read enriched H3K27me3 regions (peaks) were identified (S2B  Thus, we used the pipeline of MAnorm, a software package specifically designed for quantitative comparison of ChIP-seq datasets  [33]. MAnorm derives its power from definition of M value, a statistic characterizing the strength of differential binding, the higher the absolute M value, the larger the difference, with the sign (+/-) representing higher intensity in PcG mutants or Col-0.
All of the raw data files were searched against Mascot Daemon software (Version 2. 3. 0, Matrix Science, London, UK) based on the Mascot algorithm. The database used was Swiss-Prot (Taxonomy: Arabidopsis Thaliana; release 2012_12_28, with 11571 entries). To reduce false positive identification results, a decoy database containing the reverse sequences was appended. The searching parameters were set up as following: full trypsin (KR) cleavage with two missed cleavage sites was considered. Oxidation on methionine and acetylation of the protein N-terminus were set as variable modifications. The peptide mass tolerance was 20 ppm and the fragment ion tolerance was 1. 0 Da. Peptides with Percolater scores exceeding 13 were accepted as correct matches, the FDR is 0. 01 (refer to S1 Text for details).
Raw data comprise 50 bp of single-end sequences for ChIP-seq and 100 bp of pair-end sequences for RNA-seq. ChIP-seq, ChIP-chip and RNA-seq data analysis. We started by cleaning the sequencing reads, including removing bases with low quality score (<20) and irregular GC content, cutting sequencing adaptor followed by filtering short reads. As a result, 12-36 million reads with MAPQ>20 were obtained for further analysis (detailed statistics summarized in S2A Table ). The cleaned reads were mapped to Arabidopsis genome (TAIR10) using BWA 0. 7. 5a-r405  [74]  for DNA sequencing and TOPHATv2. 0. 8  [75]  for RNA sequencing, both with default settings. Tracks for all sequencing data can be visualized through http://bioinfo. sibs. ac. cn/gb2/gbrowse/tair10/. The genotype for each sample was validated by RNA-seq data (S12 Fig).
ChIP-seq results were verified for selected sites by qPCR (S13 Fig). MACS1. 4  [76]  was used to identify read enriched regions (peaks). Next, MAnorm, a software designed for quantitative comparison of ChIP-seq datasets  [33] , was applied to characterize the change of genome-wide H3K27me3 profile in PcG mutants as compared to that of Col-0, including atbmi1a,b, atring1a,b, clf-29 and tfl2-2. 3,289 regions with differential binding in at least one of the three comparisons were selected based on the following criteria: P value <1e-3 and |M|>1, where M represents the log2 fold change of binding intensity. K-means clustering was performed to partition these sites according to their M values. The target gene of each peak was defined as the genes closest to a given peak localized around the gene body (from 1 kb upstream of transcription start site (TSS) to transcription end site (TES)), The gene annotation file was downloaded from the TAIR homepage (http://www.
arabidopsis. org). Published data sets including FIE ChIP-seq and LHP1 ChIP-chip data were downloaded from Gene Expression Omnibus (GEO) http://www. ncbi. nlm. nih. gov/geo/ with accession numbers GSE48857  [40]  and GSE8169  [15] , respectively. Peaks for ChIP-seq data were identified using MACS14  [76] , and ChIP-chip peaks were identified using TileMap implemented in CisGenome  [77]. For IGV viewing  [78] , we normalized H3K27me3 samples such that the numbers of reads in common peak regions between Col-0 and PcG mutants are the same. For input sample, we fitted Poisson distribution model for read in both input and H3K27me3 peak free regions in Col-0, and made these two Poisson distributions the sample λ value (λ = mean and variance of Poisson distribution). Permutation test was used to test the significance of peak overlap between LHP1 binding sites and regions with reduced H3K27me3 in tfl2-2.
The clf-29swn-21 double mutant has effect on much larger range of genes, and only the expression change profile for 2,438 genes are shown here. Transcriptomic data for different tissues were downloaded from GENEVESTIGA-TOR  [80]. Gene Set Enrichment Analysis was performed with default settings  [43]. The replicates of ChIP-seq and RNA-seq data show good correlation in terms of log 2 (foldchange) of read intensity between Col-0 and PcG mutants (S14-S16 Figs). Robust Index was calculated for each genomic region or gene to measure the repeatability between replicates. Robust Index ¼ jlogðFC1ÞÀlogðFC2Þj logðFC1ÞþlogðFC2Þ. FC1 and FC2 represent fold change in replicated dataset 1 and dataset 2, respectively. Genomic regions or genes with lower Robust Index are more credible in terms of the change of binding (i.
e. M value) or expression (S3 and S4 Tables). Motif and TF binding enrichment analyses in H3K27me3 peaks. To detect TF binding motifs enriched in H3K27me3 peak regions, we downloaded the position weight matrixes (PWMs) of 269 motifs identified from both mammals and plants collected by JASPAR database  [81] , and performed motif scan  [82]  applied to a 1,000 bp window at the peak center. For each motif M, the raw motif matching score at each peak P was calculated as: max SP log PðSjMÞ PðSjBÞ h i , in which S is a sequence fragment of the same length as the motif, and B is the background frequency of four types of nucleotides (A, C, G, T) estimated from the genome. The enrichment of motif M in a peak list was defined as the ratio of the motif occurrence in the peak list as compared to its occurrence in random genomic regions.
Fisher's exact test was used to calculate the enrichment P value. Motifs enriched with an enrichment P value of 0. 01 were presented in a heat map. To identify TFs whose binding enriched in different peak sets, we collected ChIP-seq data sets from 695 publicly available studies (till May 2015) from Gene Expression Omnibus (GEO http://www. ncbi. nlm. nih. gov/geo/) and processed the data to peak lists as described above. Next, we calculated the enrichment of the overlap between peak set I or peak set II with each of these peak lists using Fisher's exact test, and the top enriched TF bindings are shown in Fig 6C. The GEO accession numbers, the enrichment statistics, as well as overlapping regions for enriched TFs are listed in S7 Table.
For example,  Kang et al. (2015)  clearly improved their mapping resolution when they compared a 44 K SNP dataset and a 700 K SNP dataset for the analysis of a same resistance locus reducing their target zone from 220 kb to 20 kb. To increase our detection power, we also need to work with a larger number of accessions, which would enable to capture more low-frequency resistant alleles  (Zuk et al. 2014). Multiple alleles may exist at single resistance loci within populations with a broad genetic base, as we observed at the Pi33 locus in our indica panel. It may therefore be useful to complement GWAS based on single biallelic markers with GWAS based on haplotypes  (Lorenz et al. 2010 ). In the context of an autogamous plant species such as rice, this should be straightforward as it would not involve the complication of inferring the linkage phase of the markers.
Broad-sense heritability (h 2 ) was calculated from variance components obtained using the REML option of SAS PROC VARCOMP (SAS institute Inc. ) where all factors were considered to be random (SAS, 2011) as: h 2 = σ 2 c /(σ 2 c + (σ 2 e /r)) where σ 2 c and σ 2 e are the variety and error variances, respectively, and r is the number of replications.
Genotyping Genomic DNA was extracted from the leaf tissues of a single plant from each accession using the MATAB method described in  Risterucci et al. (2000)  and then diluted to 100 ng/μl. Genotyping was conducted at Diversity Arrays Technology Pty Ltd. (DArT P/L), Australia, using a method of GBS combining Diversity Arrays Technology (DArT) and a next-generation sequencing technique called DArTseq™. The full methodology for marker production is described in  Courtois et al. (2013). Missing data were estimated using Beagle v3. 3, which enables the inference of haplotypes and imputation of sporadic missing data in large-scale genotype datasets  (Courtois et al. 2013). For the indica panel, we used 14,187 markers with no missing data and with a minimum frequency of minor allele of 4. 2 % (8 out of 190).
Linkage disequilibrium and marker trait association analysis We used Tassel software to perform linkage disequilibrium and association analysis  (Bradbury et al. 2007 ). These analyses were performed within each of the two sub-specific panels (i. e. indica and tropical japonica) separately. The R 2 value of linkage disequilibrium was calculated for all possible marker pairs within each chromosome. We used the mixed linear model (MLM) with control of structure (Q) and kinship (K) to avoid spurious associations  (Yu et al. 2006). The structure was taken into account through a principal component analysis (PCA) of the genotypic data  (Price et al. 2006 ) computed with Tassel. We retained the first five principal components to build the Q matrix. The kinship matrix.
Antibodies Immunofluorescence experiments were conducted with antibodies raised against the C-terminal peptide sequence of CDK13  [16]  at a working concentration of 0. 1 mg/ml. Antibodies against HA and Sam68 were mouse monoclonal antibodies (sc-7392 and sc-1238 respectively) purchased from Santa Cruz Biotechnology and used at working dilution 1:50. Mouse monoclonal antibodies against SRSF2 (S4045) were purchased from Sigma and used at 1:5000. Mouse monoclonal antibodies against nucleolin (7G2) (gift of Dr. Pinol Roma) and fibrillarin (a gift of Dr. Hernandez-Verdun) were used respectively at 1:1000 and 1:500. Mouse monoclonal antibodies against PTB (gift of C. Gooding), polyclonal anti-cyclin K (Sigma HPA000645), anticyclin L1 and L2 (gift of P. Loyer) were used at 1:50. Secondary antibodies were purchased from Sigma.
This work was funded by grants from  Cancer Research foundations, "Association pour la Recherche sur le Cancer " ( ARC 5290 ), and  "Ligue contre le cancer" Languedoc-Roussillon (LR). Y. Even  received short-term fellowships from the  "Fondation pour la Recherche Médicale ", "Ligue contre le cancer" LR and Soroptimist. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data collection Sampling was conducted in March 2005. A total of nine stations were sampled, at three sites (A, B and C) and three depths (6, 12 and 20 m) on the outer reef slope around the atoll (Fig. 1 ). Sites were selected as a function of their geomorphology and relative live coral abundance. At each station, three transects were sampled. For each transect, a visual census (2×25 m) was performed in order to record fish species richness, abundance (number of individuals) and size. Fish were classified into 13 sizes at intervals of 5 cm, from 5 cm to >100 cm. The biomass of each species was estimated as fish size and abundance, using the formula B=aL b , where B is biomass (g m -2 ), L is the weighted average height, and a and b are constants for the length-weight relationship. The constants a and b were obtained from Fishbase  (Froese and Pauly 2015).
ACKNOWLEDGEMENTS This survey was conducted during the French Expedition Clipperton organized by  Jean-Louis Etienne  with the R/V  Rara Avis , in collaboration with the  École Pratique des Hautes Études, University of Perpignan , France. We thank staff members and colleagues for their help during field work. M. Adjeroud was supported by grants from  World Wildlife Fund, France. This is a contribution of the LABEX Corail. Also, this work was a collaboration among the academic groups  Recursos Marinos Tropicales  ( UADY-CA-95 ) of the  Universidad Autónoma de Yucatán  and  Ecosistemas Marinos y Pesquerías  ( UDU-CA-46 ) and  Investigaciones Costeras  ( UDG-CA-304 ) of the  Universidad de Guadalajara.
, 2002; Pettorelli et al. , 2005). The Enhanced Vegetation Index (EVI) dataset from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor enables analysis of spatio-temporal vegetation patterns, while being less susceptible to the limitations of some VI, such as atmospheric aerosols influence and invariance to vegetation condition due to saturation of the chlorophyll signal in densely vegetated canopies  (Cao, Cova, Dennison, & Dearing, 2011; Pettorelli et al. , 2005). EVI is formulated as: EVI ¼ G Ã N-R ð Þ= N þ C 1 R-C 2 B þ L ð Þ ð Þ where N (near-infrared), R (red), and B (blue) are atmosphericallycorrected (or partially atmospherically-corrected) surface reflectances, G is a gain factor, C 1 , and C 2 are coefficients to correct for atmospheric condition, and L is a soil-adjustment factor.
, 2002) , and tick (Acari: Ixodidae) distribution in Spain  (Estrada-Pena, Quilez, & Sanchez Acedo, 2004). In an epidemiological context multi-temporal satellite VI datasets have been used to relate disease distribution and dynamics to environmental conditions for Rift Valley Fever  (Linthicum et al. , 1999) , malaria  (Thomson et al. , 1999) , and hantavirus  (Cao et al. , 2011; Glass, Shields, Cai, Yates, & Parmenter, 2007).
MODIS time series imaging processing MODIS EVI 16-day composite 250 m resolution (MOD13Q1) imagery providing complete temporal coverage from 2001 to 2013 (299 images in total) was acquired and reprojected into UTM WGS84 Zone 47 N projection. A de-noising procedure using inverse Fourier Transform  (Wayant et al. , 2010)  was applied to the time-series EVI dataset using IDRISI 17. 02 to minimise potential sources of data error. The de-noised EVI values were extracted for the complete time-series for each transect interval. Mean EVI values were calculated for twenty three sixteen-day MODIS 16-day compositing periods (CP) across the whole time series (CP dates shown in Table  1 ) for every transect interval. To quantify the vegetation seasonal cycle, a series of phenological metrics were extracted  (Reed et al.
, 1994)  (Table  2 ) using the Timesat software package  (Jönsson & Eklundh, 2002 , 2004). To identify which compositing period and phenological metrics are of greatest importance in relation to Ochotona spp. presence, random forest analysis was performed (environmental variables = 37 (comprised of the 23 compositing periods (Table  1 ) and the 14 phenological metrics (Table  2 )), number of trees = 5000, variables tried at each split = 12). Random forests (RF) is an ensemble learning technique developed by  Breiman (2001)  based on a combination of a large set of classification and regression trees. This method is well-suited to handling large datasets with correlated predictor variables which can be non-parametric, comprise a variety of data types, and where no assumption of independence concerning the data being analysed is made (Perdiguero-Alonso, Montero,  Kostadinova, Raga, & Barrett, 2008).