dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We found natural hybrids between T. pratensis and T. porrifolius in four populations in southeast England. At least one parental species was also found in each of these populations, and two sites that had previously been reported as containing hybrids were found to contain only plants with the morphology of the parental species. The hybrid plants had a morphology and fertility that fitted with the descriptions published by Linnaeus for this cross in 1760, and by numerous field and experimental botanists since (see Introduction). The F 1 hybrid status of these plants was strongly supported by the flow cytometry data and confirmed by Sanger sequencing of genomic DNA regions. All F 1 hybrids from which we sequenced plastid genes showed T. pratensis to be their maternal parent. None of the plants were polyploid.
Accession Numbers Herbarium samples of representative material are deposited with the British Museum Herbarium with accession numbers BM001139296 -BM001139307 [see Supporting Information-Table  S1 ]. The DNA sequences have been deposited in GenBank with accession numbers as follows: KT167073-KT167093 (ITS sequences), KT167094-KT167124 (ETS sequences), KT167125-KT167149 (plastid sequences) and KT167150-KT167161 (ADH sequences).
Acknowledgements We thank  Mark Spencer  and  Ian J. Kitching  of the  Natural History Museum, London ,  Geoffrey Kitchener ,  Sue Poyser ,  Kenneth Adams ,  Ann Sankey , of the  Botanical Society of the British Isles , and  Jonathan Turner  and  Dusty Gedge  for information on the location of sites containing Tragopogon hybrids. We would like to thank  Dr Malika Ainouche  ( Universitede Rennes, France ) for the help with collection and images of  Tragopogon from Museum national d'Histoire naturelle (France). We thank  Elaine Charwat  for research help at the  Linnean Society Library  and  Serena Marner  at the  Fielding-Druce Herbarium, Oxford.
allometric patterns) also differs between wild boar and domestic pigs (Table  1 ). Within wild boar, shape differences along the allometric axis (Figure  3 ) appear (at least to some extent) similar to the differences observed between wild and domestic groups (Figure  4 ). To exclude the possibility that allometry explains most of the shape differences between the two groups, we removed its effect from shape analyses using allometry-free residuals. Taxinomic prediction of specimens using the original shape datasets yield 88. 1% (M 2 ), 92. 5% (M 3 ), 91. 5% (M 2 ) and 92. 9% (M 3 ) of correct classification (similar to those obtained in  [33] ), whereas only 79. 7% (M 2 ), 87. 5% (M 3 ), 82. 1% (M 2 ) and 77. 4% (M 3 ) were correctly identified using the allometry-free residuals. In the four molars studied, we found no differences in the amount of size variation between wild and domestic pigs, whereas only three of the four teeth show no differences in molar shape variation (Table  2 ).
e. [85, 86] ). We then used the dimensionality reduction method proposed by  [85]  that recommends "select [ing] the number of retained components in each analysis so as to minimize the total cross-validated misclassification percentages" (  [85] , p. 152). For each analyses of variance and discriminant analyzes of shape, we therefore used the N first components of the PCA that maximize the variability between the a priori defined groups. As unbalanced design (differences in the number of specimen by groups) can affect crossvalidation results of the CVA (e. g. [33, 87] ), percentages of cross validation were obtained using 1000 resampled datasets following  [33]  and correspond to the upper 95 th percentiles of the distribution obtained for groups of same size. The overall phenotypic similarities between groups were depicted using neighbor-joining networks (unrooted trees) computed from the Mahalanobis' D 2 distances  [88].
The smallest distances will reflect a greater proximity between the group of interest and either the wild boar or the domestic pigs. The distance distributions were compared using a Wilcoxon's test. Due to the high number of statistical tests performed, the significance of the p values was assessed after adjustment for multiple comparisons following  [89]. All statistical and morphometric analyzes were performed using R v 2. 13. 1  [83] , with the 'ade4'  [90] , 'ape'  [91] , 'Rmorph'  [82]  libraries and newly designed R functions (available upon request). Centroid size, Procruste residuals and grouping factors have been deposited in the LabArchives database (doi: 10. 6070/H4ZK5DNC DOI:10. 6070/ H4ZK5DNC#doi,  [92] ).
Acknowledgements We thank the institutions and individuals that provided access to collections, especially the curators of the  Museum für Naturkunde, Berlin ;  Zoologische Staatssammlung ,  München ;  Muséum National d'Histoire Naturelle, Paris; Muséum d'Histoire Naturelle, Genève; National Museum of Natural History, Washington ; The  Field Museum, Chicago and The American Museum of Natural History, New-York. We also thank  Jean-Denis Vigne ,  Nelly Gidaszewski ,  Vincent Debat  and  Mathieu Joron  for fruitful discussions. This work was supported by a research grant from the  Natural Environment Research Council, UK  (grant number  NE/F003382/1 ).
Direct coupling analysis of repeat proteins We obtained sequences of single repeated units for the families listed in Additional file 1: Table  S1  from the PFAM database, version 27. 0  [32]. Since a repeat domain is typically formed with multiple tandem copies of repeated units  [6] , the minimal sequence that includes an interface between repeats is composed of two consecutive units. We thus constructed multiple sequence alignments (MSA) of pairs of consecutive repeats for each family. The sets of sequences were corrected for phylogenetic bias and finite-size sampling as described in the Methods. Direct information (DI) uses covariance in homologous protein sequences to deduce structural constraints. DI (eq 1) uncouples direct interactions from interactions mediated by a third residue on the complete sequence of the protein.
A particular case is represented in the analysis of PENTAPEPTIDE family, which has contacts between residues i and i + L 0 (Fig. 3d ). In this case the equalization DI id does not significantly reduce the detection of these pairs of positions as pairs with high correlation. In Additional file 1 the results obtained for all families are shown. For example the ARM family, where only 12 interactions correspond to contacts among the 31 predicted (Additional file 1: Figure  S3 ). In the case of the LRR family, few interactions appear as outliers in DI id distribution, and most of them have been observed to form close contacts between repeated units (Additional file 1: d PENTAPEPTIDE (PDB: 3DU1, chain X). On the upper triangle DI hits are marked in red crosses when they do not match a contact and on green circles when they do.
On Fig. 4a  we show the first 50 hits of DI (upper triangle) and DI id (lower triangle) overlaid on a contact map of three consecutive ANK repeats (PDB:1N11,A; resid 436 to 534). The necessity of the equalization becomes more evident when longer repeat arrays are considered. We observed that as the separation between repeats increases, the DI and DI id between repeats decay significantly (Fig. 4b ). True repeat pair interactions are less frequent, and this is reflected in the evolutionary couplings between units. While DI id decays to almost zero, there remains a fraction of DI hits between distant repeats, indicating the need for the equalization for similarity along the repeat array. The number of interactions between repeats decreases roughly exponentially with repeat separation, with a half-length of about 1.
Robustness and confidence of the analysis For a robust calculation of the DI one must have a sufficiently large number of effective sequences to approximate the marginal and joint probability distributions from the observed frequencies of occurrences of amino acids. Since there is no general principle indicating how many sequences are necessary and sufficient for robust estimation, we empirically quantified the minimum number of effective sequences in various repeat protein families. We constructed subsets of alignments by recurrently removing random groups of sequences from each dataset of repeat pairs, and calculated DCA  For well determined parameters we expect the true value will be better estimated as sampling increases. Examples of the robustness of the DI id assignments are shown in the panels of Fig.
Selection of repeat protein families We detected 159 PFAM accession numbers repeated more than once in a same PDB chain among all PDB entries classified as repeat proteins in the database RepeatsDB  [38]. We chose to analyze all the repetitive domains which appear in the structures catalogued in class III. We used HMMER  [39]  to get all the PFAM assignments  [40]  that match these structures. We kept only those repeats whose length is less than 70 residues, that are repeated at least once in the same protein, and which have an associated structure in the PFAM database  [40]. Families analyzed are listed in Additional file 1: Table  S1.
Multiple sequence alignments We obtained the MSA (multiple sequence alignment) for repeat units with NCBI data from the PFAM  [40]  database. For each MSA we ignored the columns that contain gaps in more than the 70 % of the members. The remaining number of residues in each case is referred as L 0 , the typical lenght of a repeat-unit. In order to reconstruct tandem arrays of repeats, we concatenated the sequences that belong to the same protein according to identifier in the PFAM's alignments, and for which the sequence separation is less than L 0 /3. We analysed MSAs which have a number of sequences larger than 1500. The alignment thus generated is referred as first neighbour alignment and has L = 2L 0 columns (positions) with M rows (sequences) for each of the prototypical families of repeat proteins listed in Additional file 1: Table  S1.
5 Fig. 5 55 Fig. 5  Robustness of the DI id procedure. Subsets of alignments were constructed by recurrently removing random groups of sequences from each dataset of repeat pairs. M eff is the number of effective sequences used in the alignment. a Particular examples of the stability of DI id assignments as sampling changes on the ANK family. The gray shadow delimits the 1 % fluctuation interval set as a convergence criteria. b Overall stability of the DI id assignments in several repeat protein families.
Introduction The high summertime ozone levels over the Mediterranean basin and especially in its eastern part have been the subject of many field studies on air pollution in the area (e. g. Varotsos et al. , 1993; Kalabokas et al. , 2000; Kourtidis et al. , 2002; Kouvarakis et al. , 2002; Lelieveld et al. , 2002; Zerefos et al. , 2002; Roelofs et al. , 2003; Kalabokas and Repapis, 2004) , as the frequent exceedance of the 60 ppb EU ozone standard for human health protection in the area is a serious problem regarding health and environmental policy. Among all the recorded profiles in mid-latitudes, the MOZAIC  (Marenco et al. , 1998)  database has revealed that this region is noteworthy by presenting differences up to 20 ppb between spring and summer seasonal profiles throughout the free troposphere, while such differences were not observed in other examined regions worldwide  (Zbinden et al.
Data Since 1994, five commercial airliners have been equipped within the framework of the MOZAIC programme  (Marenco et al. , 1998)  with instruments to measure ozone, water vapour, and (since 2002) carbon monoxide. Measure-ments are taken from take-off to landing so that vertical profiles are obtained near airports, in addition to the measurements at cruising altitude. The ozone measurement is based on dual-beam UV absorption (Thermo-Electron, Model 49-103). The accuracy is estimated at 9[2 ppbv'2%] for a 4 s response time  (Thouret et al. , 1998). The carbon monoxide measurement is based on an infrared analyser. The accuracy is estimated at 95 ppbv95% for a 30 s response time  (Nedelec et al. , 2003). The MOZAIC database (www. iagos. fr/web/) was screened for summer (JJA) flights at the airports of the eastern Mediterranean basin.
In total, 94 summer profiles over Cairo (30. 18N, 31. 28E) were analysed. The flights cover the period 1994Á2008 between June and August. Both ascent and descent data have been used. For the determination of the air-mass origin, the Lagrangian particle dispersion model FLEXPART (version 6. 2;  Stohl et al. , 1998 Stohl et al. , , 2005) )  was used in backward mode. FLEXPART was driven by data from the European Center for Medium-Range Weather Forecasts (ECMWF) provided at 60 vertical levels and with a temporal resolution of 3 h (analysis at 00:00, 06:00, 12:00, 18:00 UTC; 3 h forecasts at 03:00, 09:00, 15:00, 21:00 UTC). Horizontal resolution was globally 18)18. The model was initialised by release of 20 000 particles from grid boxes of 0. 58 )0. 58 horizontally and 100 m vertically centred on the MOZAIC profiles.
This work was supported by the Research Committee of the Academy of Athens and the Mariolopoulos-Kanaginis Foundation for the Environmental Sciences. The composite weather maps were provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at www. cdc. noaa. gov/. MOZAIC was funded by the European Commission. The authors acknowledge for the strong support of the European Commission, Airbus and the Airlines (Lufthansa, Austrian, Air France) who carry the MOZAIC equipment free of charge and have performed the maintenance since 1994. MOZAIC is presently funded by INSU-CNRS (France), Meteo-France and Forschungszentrum Juelich (Germany). The MOZAIC database is supported by ETHER (CNES and INSU-CNRS). Dr. J. Hjorth, JRC-Ispra, and Dr. H. Smit, FZ-Juelich, are also gratefully acknowledged for discussion and comments.
ChIP-chip and MeDP-chip ChIP-chip and MeDP-chip experiments were performed as previously described  (22). Briefly, chromatin fragments were precipitated with anti-MBD2 antibody (Covalab), and DNA fragments were precipitated using a His 6 -tagged recombinant protein containing four MBD domains cloned from MBD1  (39). The DNA fragments from the ChIP and MeDP experiments were amplified by random PCR. The samples were then labeled with the GeneChip WT Double-Stranded DNA Terminal Labeling Kit and hybridized to the human tiling arrays (Human Promoter 1. 0R Arrays), which were then washed and scanned by ProfileXpert service (Lyon, France) according to Affymetrix protocols. Microarray signals were analyzed using aroma. affymetrix package on the hg18 reference genome.
DNA methylation analysis Bisulfite sequencing, used to determine the CpG methylation patterns of tens regions (Supplementary Table  S2 ), was performed as described in  (40). Briefly, after a first amplification using sequence-specific primers, PCR fragments were tagged in a second amplification step and sequenced using the Roche/454 GS junior system according to the manufacturer's protocol (Roche emPCR Amplification Method Manual -Lib-A and Roche Sequencing Method Manual). The mean number of reads per sample was 475, see Supplementary Table  S2. Data were analyzed using Amplikyzer (https://pypi. python. org/pypi/ amplikyzer/0. 97).
RNAseq analysis Reads were aligned on the UCSC Homo sapiens hg19 genome using TopHat2  (41). Differential expression analysis was performed as described in  (42) , using the Bioconductor package edgeR  (43). Only genes with at least 1 read per million (RPM) in at least two of the samples were kept for subsequent analyses. Enriched Gene Ontology terms and KEGG pathway were identified using Gene Set Enrichment Analysis  (44)  with genes pre-ranked according to their fold change induced by siMBD2 treatment.
These data were compared with ChIP-chip and MeDP-chip data obtained using an array representing 25,500 promoter regions (Human Promoter 1. 0R array, Affymetrix). Methylated regions identified by MeDPseq were enriched in MeDP-chip experiments performed with recombinant proteins containing four MBD domains from MBD1  (22, 39)  (Supplementary Figure  S1A  and B ). Similarly, MBD2 peaks identified from ChIPseq were enriched in MBD2 ChIP-chip (Supplementary Figure  S1C  and D ). Next, a group of genomic regions sharing the same characteristics (MBD2 binding and DNA methylation) in both cell lines was analyzed by ChIP-qPCR (Figure  2A ). Data obtained indicated a good concordance between the two methods, qPCR (Figure  2A ) and determination of the distribution of MBD2 reads by ChIPseq (Figure  2B-H ).
The clustering of MeDP positive regions according to their MBD2 ChIPseq read densities suggested that up to a quarter of methylated DNA regions were bound by endogenous MBD2 (Figure  1D  and G ). Altogether, these data indicated that, in HMEC-hTERT and HMLER cells, the endogenous MBD2 proteins associated predominantly methylated DNA regions, as previously reported from ectopic expression of tagged-MBD2 proteins in other cellular models  (23, 24, 53). The relationship between DNA methylation and MBD2 binding was also observed at Alu sequences. Alu sequenced are classified into three main groups depending on the age of the sequence (Repeat-Masker table download from UCSC). The younger sequences, AluY, were found to be more methylated than the AluJ (older Alu sequences). The AluS, intermediate in age, exhibited an intermediate level of DNA methylation (Supplementary Figure  S4C ).
The distribution of MBD2 read densities on these Alu sequences was parallel to the DNA methylation profiles (Supplementary Figure  S4B ). When CpG islands (CGIs, UCSC database) were classified according to their length and plotted against MeDPreads, the shorter CGIs tended to exhibit an increased read density (Figure  1E  and H ). These short CGIs were also enriched in MBD2 reads (Figure  1E  and H ), showing that MBD2 proteins targeted CpG-rich sequences when methylated. The clustering of CGIs according to their MBD2 ChIPseq or MeDPseq read densities led to the identification of a CGI subgroup where MBD2 binding or DNA methylation were detectable mainly on the shore of the islands, while another subgroup shown a preferential enrichment at the core sequences (Supplementary Figure  S5A  and D ).
MBD2 is involved in the repression of genes downregulated in oncogenic HMLER cells Interestingly, genes downregulated during the transformation of HMEC-hTERT gained MBD2-reads and MeDPreads at TSS regions (Figure  4D  and E , red curves). Furthermore, more than 50% (1,168 of 2,164) of genes upregulated upon MBD2 depletion in HMLER cells were genes downregulated during the transformation process (Figure  4F  and G ). Read densities at the MBD2 peaks nearest to the annotated TSS of these 1,168 genes were visualized by heat maps showing read density from the MBD2reads and the MeDP-reads. At least half of these regions were enriched in MBD2 in HMLER cells when compared with HMEC-hTERT cells (Figure  4H ). Data obtained for TFPI2, LINC00842, OSR2, CLDN6 and TNFRSF12A genes illustrate this analysis (Figure  5E -  I and Supplementary Figure  S9A ).
The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0. 02 to 0. 06 m 3 /m 3 for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.
The main objectives of the current study are to: (i) asses the accuracy of CT model retrieved SM at higher resolution from BC time series, (ii) comparison of the performance of CT model with existing similar approaches (CD and DI model) in retrieving SM at higher resolution, (iii) evaluate the impact of number of images available on the SM retrieval accuracy, and (iv) study the impact of uncertainty in the BC (arising by ignoring the temporal variation of soil roughness and vegetation) on the retrieved SM at higher resolution. The models are applied in retrieving SM using 30 images of RADARSAT-2 spanning over four years in an experimental watershed in South India. A multi-scale validation is performed: at local scale by comparing the retrieved SM with the field data (SM measured in 50 plots) and at coarse scale by upscaling and comparing with the available SM products from SMOS.
It has Bandipur National park at its west boundary. The main three soil types in the area are comprising of black, red and rocky/weathered soils, as identified by the geophysical studies. These are representative of the soil types for granitic/gnessic lithology found in South India  [21]. The main land use classes encountered in the area are: dense/closed forest, scrub forest, land with scrub, Kharif (summer) crop, double crop and plantation. During Kharif (summer) and Rabi (winter) marigold, sunflower, finger millet, maize, garlic, sugarcane, sorghum, water-melon, lentils, and groundnut etc. , are grown  [22, 23]. Digital Elevation Model (DEM) obtained by processing the Cartosat-1 data at a spatial resolution of 30 m is available for entire India  [24]. The DEM for the study area is shown in Figure  1.
Field Data Set The SM is measured using the SM-300 theta probes (Delta-T devices; Delta-T Devices Ltd, Cambridge, UK), which measures SM averaged over 0 to 5 cm depth. Local calibration of the Delta-T device using 76 gravimetric measurements showed good performance with a correlation coefficient of 0. 91 and RMSE of 0. 031 m 3 /m 3. SM is measured manually within a window of 3 hours of the satellite pass at three locations in each field plot, and these measurements are averaged to get the plot averaged SM. The SM is measured in 50 field plots in the watershed, and location of the monitoring field plots inside the watershed is shown in the Figure  1. The fields are chosen to be representative of the spatial heterogeneity of soil and crop types while maintaining a reasonable accessibility. The size of field plots varied approximately from 0.
Satellite Data Set RADARSAT-2 is a C-band radar having a frequency of 5. 40 GHz (wavelength equal to 5. 6 cm). A total of 30 RADARSAT-2 images were acquired during the period from December 2009 to August 2013. Details (acquisition date, pixel/line spacing, pass direction, incidence angle) of the RADARSAT-2 images used in the study are given in Table  1. The local overpass time of ascending and descending direction is 6:30 PM and 6:30 AM respectively. All the images are fine quad polarization geocoded products. Incidence angle of the images varied between 21 to 25 degree. Lambert's law of optics is used to correct the BC for the incidence angle variation  [28] , which has been successfully used in previous studies (e. g. ,  [29] ). All the images are corrected to a reference incidence angle of 23 degree.
Throughout the article BC is presented in dB. For the computation of SM for entire study area, RADARSAT-2 images are aggregated to a grid size of 20 m. SMOS-L2 version 551 SM products from ascending and descending overpasses are used in the study. SMOS is a L-band radiometer operating at a frequency of 1. 4 GHz (wavelength equal to 21 cm)  [26]. The SM at a temporal resolution of 3 days and at a spatial resolution of approximately 40 km from SMOS is available from January 2010. The Discrete Global Grid (DGG) of the SMOS data products is Icosahedral Snyder Equal Area (ISEA) 15 km grid. The ID number of the DGG lying inside the study area is 3160498, which has the latitude of 11. 765 N and longitude of 76. 590 E. The SM data are filtered for Radio Frequency Interferences (RFI) by excluding data with RFI probability higher than 10%.