dataesr/scientific-paragraphs-categorization huggingface dataset:
IT species pairs We searched for reports of interspecific territorial aggression involving wood-warblers using the Birds of North America Online (BNA; Poole 2005), Web of Science, BIOSIS and Zoological Record databases (Thomson Reuters, New York, NY). We considered chases, attacks, aggressive displays and songs directed at heterospecifics to be interspecific territorial aggression, unless they occurred exclusively in the immediate vicinity of food or nests. We did not consider nest defense and dominance interactions in foraging aggregations to be territorial aggression. Some authors restrict the term interspecific territoriality to cases in which the species defend exclusive, non-overlapping territories. In this paper, we consider territorial aggression between species to be interspecific territoriality, whether it results in exclusive space use or not.
Other data collected from the literature We also used the BBS to measure the degree to which each species pair is syntopic  (Rivas 1964 ) in the region where interspecific territoriality was reported. To measure the degree of syntopy, we found all BBS routes within a 250 km radius of the relevant study location(s). One BBS count route comprises 50 stops (a standardized 3-minute point count is performed at each stop). Within each route, we tallied the number of stops occupied by species 1, by species 2, and by both species. To obtain a regional measure of syntopy, we divided the observed number of stops where both species were found by the expected number of stops where both species would be found if their distributions were independent (e. g. , if species 1 and 2 are found at proportions p and q, respectively, out of n possible stops, then the expected number of stops with both species is npq).
This metric equals 0 if the species were never found together at the same stop, 1 if the species were found together as often as expected by chance, and >1 if the species were found together more often than expected by chance. Data on hybridization in parulids were obtained from  McCarthy (2006)  and we also searched for reports of hybridization since 2004 (we disregarded hybridization in captivity and anecdotal reports of hybridization in the wild that McCarthy (2006) classified as doubtful). We used the BNA habitat descriptions to assign each species a habitat complexity score on a threepoint scale: 1, simple, such as tundra or grassland; 2, intermediate, such as chaparral or forest edge; and 3, complex, such as coniferous and deciduous forest. The main rationale for this habitat classification is that forests offer more opportunities for vertical stratification of niches, which has long been considered to be relevant for birds  (MacArthur 1958; Orians & Willson 1964).
We based foraging guilds on de Graaf et al. 's (1985)  classification of North American birds on three niche axes: food type, feeding substrate, and method of food collection. We measured foraging guild overlap for a given species pair as the number of axes on which the species were classified in the same way, and we also categorized species pairs according to whether they overlapped on all three axes or not. We obtained mass and bill length (exposed culmen length) data from the BNA  (Poole 2005) , CRC handbook  (Dunning 2008) , and primary sources. When possible, we used only measurements of breeding season males from within the polygon delimited by the locations where interspecific territoriality was reported; if multiple sources were available, we calculated sample-size weighted averages.
To assess the repeatability of the mean plumage and song dissimilarity scores, we calculated Spearman correlations between the means of different groups of observers that rated the same species pairs (a randomly selected subset of species pairs was repeated across sets expressly for this purpose). Repeatability was high for both plumage (ρ = 0. 72, N = 21) and song (ρ = 0. 80, N = 37). The SPCC method involves sliding two spectrograms relative to each other and using the maximum cross-correlation value as a measure of song similarity  (Clark et al. 1987 ). We used the default settings for SPCC (biased normalized spectrogram correlation, no filter) in RavenPro v. 1. 4 (Bioacoustics Research Program 2011). The mean SPCC for a given species pair across song exemplars was used in data analyses.
2014a ). This is a mathematically different approach than the evolutionary simulations described above and the comparative reliability (i. e. , type I and type II error rates) of two approaches has yet to be established. We fitted a logistic PLMM in MCMCglmm  (Hadfield 2010 ) to the same dataset as used in the MLR using the "categorical" model, with random effects specifying the phylogeny, the focal species and the species to which the focal species is compared, and an inverse gamma prior distribution. We included species in the 'focal' and 'comparison' categories an equal number of times where possible. To account for variation in the time since divergence, we also included patristic distance in the model. Adding interactions between patristic distance and the other predictor variables did not improve the overall fit of the model as measured by DIC.
Wood-warbler species with more similar territorial songs are more likely to hybridize  (Willis et al. 2014). Three of the four wood-warbler species pairs that hybridize extensively in the wild  (Willis et al. 2014 ) are interspecifically territorial, and two of those three IT species pairs are the most similar in song out of all 305 species pairs in our dataset (upper left points in Figure  2 ). Thus, territorial song, plumage, hybridization, interspecific territoriality and phylogenetic relatedness are closely entwined in wood-warblers -perhaps too much so to sort out causal relationships between these variables statistically. Interspecific territoriality, and indeed interference competition in general, is a complex and neglected subject  (Grether et al. 2013). Our results suggest that interspecific territoriality is an important selective force acting over evolutionary timescales, rather than simply a transient or accidental byproduct of intraspecific territoriality.
Acknowledgements. We thank  I. J. Lovette  for providing phylogenetic data,  T. Garland ,  Jr. for suggesting the phylogenetic simulation approach,  C. K. Cornwallis  for assistance with the PLMMs and  N. Burroughs  for help with data collection. For comments on the manuscript, we thank  E. Lewitus ,  R. D. Cooper  and  A. Gonzalez-Karlsson. This research was supported by  NSF   Graduate Research Fellowships  to N. L. and J. P. D. and by  NSF  grants  DEB-1020586  and  DEB-1457844  to G. F. G.
, 2012). Besides, combining all these missions with the reference missions allowed a better spatial resolution of sea-level gridded products (AVISO,  Dibarboure et al. , 2011 , SL_cci project, Ablain et al. , 2015). The GMSL time series are therefore only based on TOPEX/Poseidon, Jason-1 and Jason-2 (denoted "reference missions" in the paper) by several prominent groups: AVISO (aviso. altimetry. fr/msl), Colorado University (sealevel. colorado. edu/), GSFC (http://climate. nasa. gov/ vital-signs/sea-level/), NOAA (http://www. star. nesdis. noaa. gov/sod/lsa/SeaLevelRise/), etc. The main advantage of using these continuous data records with three missions on the same orbit comes from the "calibration phases" between TOPEX and Jason-1 (about 7 months in 2002) as well as between Jason-1 and Jason-2 (about 7 months in 2008).
During this period, both satellites measured the same sea levelspaced about 1 min apart -on the same ground track. This calibration phase is essential to computing accurate sea level relative biases between two missions in order to link their global and regional MSL time series  (Leuliette et al. , 2004; Dorandeu et al. ,2003). This error of the GMSL relative bias estimate between TOPEX and Jason-1 or Jason-1 and Jason-2 has been estimated to be close to 0. 5-1 mm  (Ablain et al. , 2009). For comparison, the error to link TOPEX-A and TOPEX-B (February 1999) -where no overlapping data ex-Published by Copernicus Publications on behalf of the European Geosciences Union. L. Zawadzki and M. Ablain: Accuracy of the mean sea level continuous record ist between both TOPEX phases -goes up to 2 mm. Although these linking errors seem a priori low, the impact on the GMSL trend has been estimated close to 0.
Method to estimate the relative MSL bias uncertainty The method to estimate the relative bias between two mean sea level time series is based on the AVISO processing (see www. aviso. altimetry. fr/msl) to link TOPEX-Poseidon (TP) with Jason-1 or Jason-1 with Jason-2. The same method can be applied in the case of Jason-2/Jason-3 to take advantage of the calibration phase between both missions: a common period of nine cycles is selected within the calibration phase, centered on the cycle chosen to switch from Jason-2 data to Jason-3 (usually in the middle of the period). The bias is then computed as the difference of both MSL series averaged over this common period. This method is prevalently used by the MSL groups (AVISO, CU, NOAA, GSFC) with minor differences concerning the choice of the window within the calibration phase (see  Masters et al.
Hence, the correlation between Jason-2 and Jason-3 global MSL time series over their calibration phase is strong. In the cases of TP/Jason-1 and Jason-1/Jason-2 calibration phases, we performed an analysis of the MSL time series, showing their correlation was close to 0. 8 after removing annual and semiannual signals. In this study, the main objective is to estimate the relative bias uncertainty it induces. A reliable statistical method has been developed based on simulated Jason-2 and Jason-3 MSL data over the calibration phase. The Mercator-Ocean Global Oceanic Reanalysis (GLORYS2v1) has been used. It provides model-based weekly 1/4 • grids of sea level anomaly (SLA) over the altimetry era. Jason-2 and Jason-3 simulated MSL data have been generated by interpolating GLORYS SLA bi-linearly (in time and space) on Jason-2 and Jason-3 ground tracks, which -in this paper -are considered identical during the calibration phase.
Then, a Jason-2 and a Jason-3 global MSL time series have been computed (based on AVISO MSL method, see www. aviso. altimetry. fr/msl). GLORYS-based simulated MSL data contain however fewer high-frequency signals than real altimetric MSL. In order to compute a rigorous estimation of the relative bias uncertainty between Jason-2 and Jason-3, a realistic high-frequency correlated random noise has been designed and added to Jason-2 and Jason-3 simulated MSL series. The choice to add this noise directly on the MSL series and not along-track allows us to greatly simplify its design and is sufficient for an accurate relative bias estimation. This noise has two purposes: (i) it reproduces partly the high-frequency signal missing in the original GLORYS-based simulated MSL data, partly the SSH errors, and (ii) it is designed to ensure a high and realistic correlation (close to 0.
Acknowledgements. This work was performed in the framework of the  ESA CCI program  supported by  ESA. This work was also made possible thanks to the support of  CNES  for several years in altimetry data processing, in particular in the framework of the  SALP  project  (Service d'Altimétrie et de Localisation Précise). We would also like to thank  J. Benveniste  and  N. Picot  for their diligent review of this study. GLORYS reanalysis received support from  INSU-CNRS ,  Mercator Océan , Groupe Mission Mercator Coriolis and the  European Community 's  Seventh Framework Programme   FP7/2007-2013  under grant agreement no. 218812  (MyOcean) Edited by:  J. M. Huthnance.
Differentiation of immortalized clonal lines follows the normal myogenic cascade To study the behavior of genes involved in the myogenic cascade in immortalized and primary cell lines, we created a consensus set of genes that were differentially expressed in four previously published studies of myoblast differentiation (Additional file 1: Table  S1 ). These studies included human and mouse primary myoblasts, and C2C12 myoblasts, and we retained genes that were strongly up or downregulated after five or more days of differentiation in at least three of the four datasets. For the consensus downregulated genes, a heatmap showing their relative expression levels across our samples is shown: note the rows near the top of the figure that indicate the cell_type and clonal_state (Fig. 3 ). Hierarchical clustering analysis of the samples is presented on the same plot (branch lines at top of figure) and shows that these genes neatly separated myoblasts (cyan in the cell_type row) from myotubes (lilac in the cell_type row) with no effect of immortalization (green and yellow in the clonal_state row)-myoblast clones showed similar expression values to the myoblast primary populations, and myotube clones showed similar expression values to the myotube primary populations.
These lists were obtained from more than 100 studies, and included the 4 in vitro differentiation datasets from which our consensus set was derived as described above. When immortalized clones of myoblasts or myotubes were compared to their respective primary cells, neither showed any significantly enriched rank distributions with Fig. 1  Representative images of primary and immortalized human myotubes. Myogenic cells were purified by magnetic bead sorting of CD56 expression then differentiated for 5 days. Examples here are from primary (a) and immortalized (b) populations of a healthy subject (CHQ). Myotubes were immunostained for desmin (green) to determine the percentage of myogenic purity (>94% for all samples). Magnified region shows the structure of desmin filaments in both primary and immortalized cells.
Nuclei are DAPI-stained (blue). Scale = 100 μm FDR q val < 0. 05 for any of the 393 muscle gene sets. Muscle gene sets represent an ensemble of genes that are identified empirically to respond at the expression level to muscle perturbations, so their lack of change in immortalized v primary cells is indicative that immortalization has little effect on these lines. For instance, it is informative to contrast those results with the following: when genes of our dataset were ranked according to their change in myotubes v myoblasts, GSEA on the muscle gene sets specifically identified strong overlap with 35 of the 393 muscle gene sets with a very highly stringent statistical cut-off of FDR < 0. 001: these highly significant muscle gene sets are visualized using an enrichment map-this shows gene sets that were downregulated (in myotubes v myoblasts) in blue with color intensity proportional to the enrichment score (none of these highly significant gene sets were upregulated in this case), and grey connections represent the sharing of genes between gene sets (i.
Discussion Immortalization does not confer any general dysregulation of normal myogenic processes, nor of any canonical pathways or gene ontology terms. While senescence shuts down the cell cycle of primary human myogenic isolates after a number of divisions in tissue culture, hTERT/cdk4 immortalized lines are protected, retaining the characteristics of non-senescent primary lines. Indeed, the single outlier group in our dataset was a population of primary myoblasts-whereas immortalized clones derived from that primary population remained similar to the other myoblast lines. In the search for therapeutic strategies, it is important to have models that are representative of the in vivo nature of a pathology but that also allow close dissection of the mechanistic details of the pathology. When care is taken to understand the differences between humans and animal models  [36] , these models can be used to gain insight into the full complexity of the human condition such as in the cross-talk between cell and tissue types, and the relationship of disease progression to developmental changes and age.
Primary antibody anti-desmin (D33, IgG1, 1:100, Dako) and secondary antibody goat antimouse IgG1 AlexaFluor 488 (1:400, Invitrogen™) were used. Culture dishes were washed three times with PBS, counter-stained with 1 ug/ml DAPI for 1 min, RT, washed three times with PBS, and mounted with ibidi mounting medium (ibidi®). Five non-overlapping images were acquired with an Olympus IX70 and an Olympus UPlan FI 10×/0. 30 Ph1 objective equipped with a Photomatics CoolSNAP™ HQ camera. Images were acquired using Metavue 7. 5. 6. 0 software. The percentage of desminpositive cells (desmin to DAPI cells) was calculated. Sorted cells were found to be of equivalent of higher purity in each case and subsequently used in cell cultures.
Transcriptomic raw data treatment Raw intensity values (Illumina idat files) of 94 samples were passed to the R lumidat library (https://github. com/ drmjc/lumidat) to create an EListRaw class object compatible with further downstream analyses in R/Bioconductor. Two samples were excluded at this stage due to having zero proportion of expressed probes (all other samples had more than 40% of probes expressed). The limma library  [39]  was then used to carry out neqc normalization-this involves background correction followed by quantile normalization, using negative control probes for background correction and both negative and positive controls for normalization  [40]. Probes were excluded if: (1) they had low bead numbers in at least one sample; (2) they were not expressed (expression p value < 0.
Limma's arrayWeights function was used to weight each sample according to its estimated reliability by measuring how well the expression values for that array followed the linear model  [42] , limma's duplicateCorrelation function was used to estimate the correlation between technical replicates (samples from the same subject, cell type, and clonal state, were considered as technical replicates)  [43] , and limma's eBayes function was used to rank genes in order of evidence for differential expression, based on an empirical Bayes method  [44]. The data are deposited in NCBI's Gene Expression Omnibus and are accessible through GEO series accession number GSE79263.
Functional analyses of transcriptomic data Rank-based gene set enrichment tests were carried out using GSEA  [45]  on the 15342 quality-filtered, normalized, non-log 2 , gene expression values described above, applying default settings (e. g. , permutations on phenotype, collapse genes to max of probesets) except that minimum overlap with gene sets was changed from 15 to 8 to allow for the small sizes of some muscle gene sets. Gene sets were taken from the muscle gene sets homepage (Muscle Gene Sets v2) http://sys-myo. rhcloud. com/muscle_gene_sets. php or from the Molecular Signatures Database (http://software. broadinstitute. org/ gsea/msigdb)  [45]. Gene set enrichment mapping was generated using Cytoscape  [46]  and Enrichment Map  [47]. Enrichment tests were carried out on the most differentially expressed 800 genes by fold-change after filtering for FDR < 0.
05, using Enrichr  [48]. Heatmaps were created using the pheatmap R function (Raivo Kolde, 2015). For the consensus set of genes that were differentially expressed in previously published studies of myoblast differentiation, we downloaded publicly available gene expression data from the Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo) database. Four studies were chosen that included murine and human myoblasts and myotubes at five or more days of differentiation: GEO series GSE10424, GSE11415, GSE24811, and GSE26145. For each study, signals were converted to expression levels using robust multi-array averaging  [49]  and custom chip definition files based on Entrez genes from the BrainArray resource  [50]  at http:// brainarray. mbni. med. umich. edu (mgu74av2mmentrezgcdf 17.
1. 0 for GSE10424, mouse4302mmentrezgcdf 17. 1. 0 for GSE11415, mogene10stmmentrezgcdf 17. 1. 0 for GSE24811 and huex10stv2hsrefseqcdf 17. 1. 0 for GSE26145). Specifically, raw CEL files were downloaded and fluorescence signals were background adjusted, normalized using quantile normalisation and log 2 expression values were calculated using median polish summarization. To identify differentially expressed genes for each study, the gene expression matrices were analysed with statistical analysis of microarray method (SAM)  [51]. For each comparison, a two-class procedure was applied, and the percentage false discovery rate (FDR) was calculated. The FDR threshold was set to 0. 05, and the 300 most upregulated genes in both groups in the comparison were selected for further analyses. All data analyses were performed using R program version 3.
Acknowledgements We thank the  Biobank of the London MRC Neuromuscular Translational Research Centre , the  Biomedical Research Centre of Great Ormond Street Hospital for Children in London , and the platform for immortalization of human cells of the  Institute of Myology. This work was supported by the  French Muscular Dystrophy Association  (AFM; http://www. afm-telethon. fr). MT was supported by  University Pierre and Marie Curie (UPMC)  through the  MyoGrad International Graduate School for Myology  (http:// www. muskelforschung. de/en/startseite-myograd/myograd). We thank  Laura Le Gall  for technical assistance.
It was based in Cagliari, Sardinia. The LNG lidar has been mainly used with a downward-looking mode. Two tracks have been made in late June during the passage of the BB plume over the western Mediterranean: a transect between Cagliari and Minorca on 27 June 2013 and a loop around Sicily on 28 June. Only the loop on 28 June is considered in this work because the 27 June data will be discussed in a future paper by  Pelon et al. (2016)  on the airborne observations during ChArMEx. The ground-based lidars are located in Minorca (40 • N, 4 • E),  Barcelona (41. 4  • N, 2 • E) and Lampedusa (35. 5 • N, 12. 5 • E). The Minorca lidar works at 355 nm, while the Barcelona and Lampedusa lidars measure the atmospheric backscatter signal at 532 nm. The ground-based lidar systems are respectively described in  Chazette et al.
Several ground-based lidar observations have also identified aerosol plumes possibly related to the transatlantic transport. The characteristics of the aerosol layers are summarized in Table  3. The Minorca lidar data are discussed in a companion paper by  Chazette et al. (2016). An aerosol layer between 3 and 5 km seen in Minorca is quite similar to the CALIOP observations on 28 June. A second layer between 5 and 7 km is also seen in Minorca with a noticeable depolarization (δ 355 > 12 ± 1 %). The upper layer is not seen by CALIOP because it is expected at latitudes higher than 40 • N and is masked by overlaying clouds. In Minorca the vertical profiles of the water vapor mixing ratio were also measured during the night  (Chazette et al. , 2014)  showing elevated mixing ratio > 1 g kg -1 above 5 km and values near 0.
10 ), i. e. , a depolarization between 10 and 15 % and LR between 50 and 55 sr. The LNG airborne lidar data obtained during ChArMEx will be thoroughly discussed in a forthcoming paper by  Pelon et al. (2016). Here we will only consider the vertical structure of the aerosol layers observed on 28 June 2013 along the loop shown in The spatial distribution of the aerosol layers seen by the LNG lidar corresponds quite well to the position of North American BB plumes and the expected latitudinal extent of the Saharan dust calculated with the FLEXPART simulation in Sect. 3. Indeed layer A is related to the tail of the Canadian BB plume. Layer B is also in the latitude range of the Canadian BB plume, possibly mixed with the Colorado BB plume present between Gibraltar and Messina (see Fig. 4 ). Layers A and B seen by the LNG airborne lidar on 28 June are also consistent with the superposition of two different aerosol layers seen above Minorca 24 h before.