dataesr/scientific-paragraphs-categorization huggingface dataset:
To generate BAM files, more than 50 million reads (length of 73 bp) for sample were mapped against the C. elegans worm version WS236 following the GEMTools pipeline (http://gemtools. github. io/). These BAM files were analyzed using the SeqSolve software, which use Cufflinks/ Cuffdiff for differential gene/transcript expression analyses  (Trapnell et al. 2012). A full 98. 7% of the reads from the control gfp(RNAi) sample mapped in exons. The sequence data for the 11 transcriptomes analyzed in this study are available at the NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE72952.
ACKNOWLEDGMENTS We thank  Dr. Juan Valcarcel  and  Dr. Ana Méndez  for critical reading of the manuscript. Some strains were provided by the  CGC (Caenorhabditis Genetics Center) , which is funded by the  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD010440 ). J. C. is a Miguel Servet Researcher (ISCIII). K. R. -P. has a  FINCyT PhD fellowship  from  the government of Peru. This study was supported by  La Marató  de TV3 foundation (Ref. 100910) and by a grant from the  Instituto de Salud Carlos III (ISCIII)  (Exp. PI12/01554 ), which is cofunded by  FEDER funds/ European Regional Development Fund (ERDF) -a way to build Europe. Received July 23, 2015; accepted September 19, 2015.
Durvalumab monotherapy has shown durable responses in a NSCLC cohort from a Phase I/II study in heavily pretreated patients with solid tumors[1] and durvalumab plus tremelimumab has shown encouraging clinical activity and a manageable safety profile in a Phase Ib study in patients with NSCLC[2]. A comprehensive clinical development program of durvalumab +/-tremelimumab in NSCLC is underway. MYSTIC (NCT02453282) is a global Phase III study to determine the efficacy and safety of durvalumab plus tremelimumab combination therapy or durvalumab monotherapy versus SoC platinum-based doublets in the first-line treatment of patients with advanced or metastatic NSCLC.
Background: Amelotin (AMTN) is an ameloblast-secreted protein that belongs to the secretory calcium-binding phosphoprotein (SCPP) family, which originated in early vertebrates. In rodents, AMTN is expressed during the maturation stage of amelogenesis only. This expression pattern strongly differs from the spatiotemporal expression of other ameloblast-secreted SCPPs, such as the enamel matrix proteins (EMPs). Furthermore, AMTN was characterized in rodents only. In this study, we applied various approaches, including in silico screening of databases, PCRs and transcriptome sequencing to characterize AMTN sequences in sauropsids and amphibians, and compared them to available mammalian and coelacanth sequences. Results: We showed that (i) AMTN is tooth (enamel) specific and underwent pseudogenization in toothless turtles and birds, and (ii) the AMTN structure changed during tetrapod evolution.
Methods Amelotin sequences from publically available databases AMTN sequences were obtained from databases (Ensembl [28] and NCBI  [29] ). See Additional file 1 for detailed information. (i) Sequences of six representative mammalian lineages were used for comparison with non-mammalian AMTN: the published sequences from Homo sapiens (human, Primates) and Mus musculus (house mouse, Rodentia), and four sequences used in our previous study  [17] : Bos taurus (cow, Laurasiatheria), Loxodonta africana (African elephant, Afrotheria), Monodelphis domestica (gray shorttail opossum, Marsupiala), and Ornithorhynchus anatinus (platypus, Monotremata). (ii) Computer-predicted sequence of the lizard Anolis carolinensis (green anole, Squamata) was used as template to find AMTN with BLAST (Basic Local Alignment Search Tool) in three additional sauropsid genomes available in the Whole Genome Shotgun (WGS) database: Alligator mississippiensis (American alligator, Crocodilia) and two snakes, Ophiophagus hannah (king cobra, Squamata) and Python molurus (Indian python, Squamata).
(iii) No hit was obtained when blasting the following genomes with sauropsid and mammalian AMTN sequences: the bird Anas platyrhynchos (mallard duck, Anseriformes), the turtle Chelonia mydas (green sea turtle, Testudines), the anuran Xenopus (Silurana) tropicalis (clawed frog, Amphibia) and the African coelacanth Latimeria chalumnae (Coelacanthiformes). However, as AMTN is located immediately upstream AMBN in mammalian and reptilian genomes, we used a synteny-based approach. We extracted 100 kb of genomic DNA (gDNA) upstream AMBN in these four genomes and explored this region with UniDPlot [30], a software designed to screen DNA regions showing weak sequence similarity  [31]. The search was performed using UniDPlot with mammalian and sauropsid AMTN sequences and provided a predicted AMTN sequence in these four species.
RNA extraction and PCR amplification of Amelotin transcripts The lower jaw of a juvenile specimen of the following species was used for RNA extraction: X. tropicalis, A. carolinensis, Python regius (royal python, Squamata) and Takydromus sexlineatus (long-tailed lizard, Squamata). The primers were designed using Primer3 v. 0. 4. 0  [33, 34]  taking into account the AMTN sequences of X. tropicalis, A. carolinensis and P. molurus obtained in silico (Additional file 3). Immediately after dissection, the jaws were immersed in RNAlater (Qiagen). Total RNAs were extracted and purified (RNeasy Midi Kit; Qiagen, France), and converted into cDNA (RevertAid™ H Minus First Strand cDNA Synthesis Kit; Fermentas, France) using an oligo (dT)18 primer. AMTN transcripts were recovered by PCR amplification using GoTaq DNA polymerase (Promega, France), as previously described  [15].
Transcriptome screening The sequenced jaw transcriptomes (see below "Jaw transcriptome sequencing") of the caudate amphibian, Pleurodeles waltl (sharp-ribbed salamander, Amphibia), and two sauropsids, Caiman crocodilus (spectacled caiman, Crocodilia) and Tarentola mauritanica (common wall gecko, Squamata) were assembled at ISEM-Montpellier 2 (France) and screened for AMTN transcripts with BLAST using the Montpellier Bioinformatics Biodiversity (MBB) platform  [36].
Histological analyses One adult P. waltl, and several young adults A. carolinensis (3 month-to 2 year-old) were used. Immediately after dissection, the lower jaw quadrants were fixed in a mixture of glutaraldehyde (1. 5%) and paraformaldehyde (1. 5%) in PBS for 2 h, at room temperature. They were demineralized for three weeks, under gentle agitation at 4°C, in the same fixative to which 5% EDTA was added. The solution was changed every two days. After washing overnight in PBS, tissues were post-fixed for 2 h in 1% osmium tetroxide, rinsed in PBS, dehydrated through a graded series of ethanol then immersed in propylene oxide prior embedding in Epon 812 (EMS). 2 μm-thick sections were obtained using a Reichert OMU-3 ultramicrotome, deposited on a glass slide, stained with toluidine blue, and photographed with an Olympus BX61 microscope equipped with a QImaging camera using Image Pro Plus software (Media Cybernetics, Bethesda, MD).
Amelotin gene structure in sarcopterygians A total of 17 AMTN sequences of sarcopterygian species were obtained using in silico screening of public databases, RT-PCR on jaw cDNA, and screening of assembled jaw transcriptomes: six species representative of mammalian lineages (Homo sapiens, Mus musculus, Bos taurus, Loxodonta africana, Monodelphis domestica and Ornithorhynchus anatinus), eight sauropsids (two crocodiles: Alligator mississippiensis and Caiman crocodilus; three lizards: Anolis carolinensis -AMTN referred to as [GenBank gene ID: LOC100554538] -, Tarentola mauritanica and Takydromus sexlineatus; three snakes: Ophiophagus hannah, Python molurus and P. regius), two amphibians (one frog: Xenopus tropicalis; one salamander: Pleurodeles waltl), and one coelacanth (Latimeria chalumnae).
Genbank accession numbers of AMTN sequences obtained from cDNA are indicated in Additional file 1. Most sequences included either complete or partial 5′ and 3′ untranslated regions (UTRs). No alternative splicing of AMTN was detected in the PCR products obtained from the cDNA of all non-mammalian species used in this study. In the gDNA sequences analyzed in GenBank, AMTN was always found upstream AMBN, from 5 kb in X. tropicalis to 73 kb in B. taurus. The exon-intron boundaries of nine non-mammalian AMTN sequences were inferred either from the comparison of cDNA and gDNA sequences obtained for the same species (A. carolinensis and X. tropicalis) or from sequence comparison of related species (P. molurus/P. regius/O. hannah, A. mississippiensis/C. crocodilus, and T. sexlineatus/T. mauritanica/A.
Particular features of Amelotin in sarcopterygians A single signal peptide (SP) encoded by exon 2a was detected in the N-terminal region of all AMTN obtained in this study (Figure  2 ). The SP cleavage site was predicted to occur between position 16 (either Ala, Cys, Gly or Ser, depending on the species) and 17 (Leu, Lys or Figure  1  Amelotin structure in the main sarcopterygian lineages. The origin of the data is indicated below the species names. cDNA means that the sequences were obtained from PCR sequencing and RNA-seq means that the sequences result from transcriptome sequencing. The exon-intron boundaries were defined using comparison of cDNA and gDNA sequences (mouse, crocodile, lizard and clawed frog), or only cDNA (salamander), or only gDNA (opossum, platypus and coelacanth) sequences.
Acknowledgements We are grateful to  Samuel Martin , Director of  "La ferme des crocodiles", Pierrelatte, France , for giving us a young Caiman crocodilus; to  K. Daouès , director of  "La ferme tropicale", Paris, France , for giving us a juvenile Python regius, a juvenile Tachydromus sexlineatus, and several Anolis carolinensis. This study was financially supported by grants from  CNRS ,  Université Pierre & Marie Curie , and from the  ANR-12-BSV7-020 , project "Jaws". We thank  Marie-Claire Lajarille  for histological preparations.
DATA SET The proposed algorithms were mostly tested on the ROSIS VNIR reflectance hyperspectral Pavia Center data set 1. Its spectral domain ranges from 430nm to 860nm. Its associated land cover ground truth includes the next classes : "water", "trees", "meadows", "self blocking bricks", "bare soil", "asphalt", "roofing bitumen" , "roofing tiles" and "shadows". They were also tested on the VNIR-SWIR AVIRIS Indian Pines and Salinas scenes, captured over rural areas.
Results Obtained results on Pavia data set are presented on figure  5  : 5 merged bands (as in  (Le Bris et al. , 2014) ) were selected at each level of the hierarchy of merged bands. It can be seen that the positions of the selected merged bands don't change a lot when climbing the hierarchy, except when reaching the lowest spectral resolution configurations. It can also be noticed that at some level of the hierarchy the position of some selected merged bands can move and then come back to its initial position when climbing the hierarchy. Thus, it can be possible to use the selected bands at a level l to initialize the algorithm at next level l + 1. This modified method will be presented in section 5. The merged band subsets selected at the different levels of the hierarchy were evaluated according to a classification quality measure.
As in previous section, the Kappa coefficient reached by a rbf SVM was considered. Results for Pavia and Indian Pines data sets can be seen on figure  6. At each level of the hierarchy, 5 bands were selected for Pavia, and 10 bands for Indian Pines. It can be seen that these accuracies remain very close to each other whatever the band merging criterion used, and no band merging criterion tend to really be better than the other ones. Results obtained using merged bands are generally better than using the original bands. Figure  6 : Kappa (in %) reached for rbf SVM classification for merged band subsets selected at the different levels of the hierarchy for Pavia and Indian Pines data sets using the greedy FS algorithm (x-axis = number of merged bands in the spectral configuration corresponding to the hierarchy level).
Results Obtained results on Pavia scene for the band merging criterion "spectra piece-wise approximation error" are presented on figure  8  : 5 merged bands were selected at each level of the hierarchy, starting from an initial solution obtained at the bottom level of the hierarchy. As for previous experiments, obtained results were evaluated both for Pavia (5 selected bands) and Indian Pines (10 selected bands) data sets. Kappa reached for rbf SVM classification for merged band subsets selected at the different levels of the hierarchy (built for band merging criterion "spectra piece-wise approximation error") can be seen both for the greedy FS algorithm and for the hierarchy aware one on figure  9  : obtained results remain very close, whatever the optimization algorithm. It can be said from table 7 that both algorithms lead to equivalent results considering classification performance while the proposed hierarchy aware algorithm is really faster.
CONCLUSION In this paper, a method was proposed to extract optimal spectral band subsets out of hyperspectral data sets. A hierarchy of merged bands was first built according to a band merging criterion. It was then used to explore the solution space for band extraction : band selection was then performed at each level of the hierarchy, either using a greedy approach or an adapted hierarchy aware approach. Classification results tend to be slightly improved when using merged bands, compared to a direct use of the original bands. Besides, in the context of band optimization for sensor design, it can also be a way to get more photons. Further work will investigate band optimization aiming at selecting merged bands at different levels of the hierarchy. This method will also be applied to a specific sensor design band optimization problem : optimizing spectral bands for urban material classification within the French ANR HYEP ANR 14-CE22-0016-01 project.
Imaging and quantification of the area of invasion Samples were fixed at T0 and T2 and costained with fluorescent phalloidin to label F-actin and DAPI. Images were taken with a confocal LSM 510 (Zeiss) microscope with a 5× dry objective, collecting stacks of images along the Z-axis with 10-µm intervals between optical sections. Quantification of invasion was done with ImageJ software (http:// rsb. info. nih. gov/ij) by estimating the diameter of spheroids at T0 and T2 as described  (Rey et al. 2011 ). These values were averaged and used to calculate the mean invasion area (πr 2 ). The mean invasion area at T2 was normalized to the mean invasion area at T0.
IHC quantification IHC images were first processed with the ImageJ Colour Deconvolution plug-in (http://www. mecourse. com/landinig/software/ cdeconv/cdeconv. html) in order to separate HE and DAB signals. For each tissue microarray (TMA), signal intensity of DAB signal over HE signal was then calculated. Alternatively, when the level of background was too high (i. e. , for EZH2, showing areas of nonspecific staining), staining intensity was scored on a scale of 0 to 3 in a blind fashion. Importantly, both software-assisted and visual scoring yielded highly correlated results.
ChIP-seq analysis Chip-seq data for H3K27me3 were generated in duplicates by the next-generation sequencing (NGS) platform at the Institut Curie (T. Rio Frio). From ∼50 million SOLiD 5500 75-bp reads per sample, 50% were uniquely mapped (minimum mapQ = 10) onto the mouse reference genome (mm9) using Bowtie (parameters: -S -C -p 8 -q -y -col-keepends -l 28 -n 2 -e 70 -a -best -strata -m 1). From 50 million Illumina HiSeq 2500 100-bp reads per sample, ∼98% were uniquely mapped onto the mouse reference genome (mm9) using Bowtie 2, allowing one mismatch in the seed (22 bp long). Given the high proportion of mapped read duplicates observed (until 22%), the duplicates were removed using the rmdup function of SAMTools. The mapped read data were then normalized to the total number of reads (25 million) by performing a down-sampling with Picard, and a coefficient factor was applied in some conditions (Ezh2 -/-: 0.
05; Ezh1 rescue: 0. 25) according to the amount of immunoprecipitated DNA obtained during the ChIP preparation. Peak calling for each sample was done with MACS 2 (2. 0. 10) with default parameters for histone analysis by specifying a fragment size of 150 bp and the input DNA as control. From the significant peak regions, the differential binding analysis was performed with the R package DiffBind. Only reproducible peaks between replicates were kept for read counting. Significant differential peaks were identified with a maximum false discovery rate (FDR) of 1% and a minimum value of fold change equal to 2 for each comparison. Peaks were quantified using HOMER.
Transcriptome data analysis Microarray data were generated in duplicates by the microarray core facility of the Institut Curie (D. Gentien) using Affymetrix Mouse Gene 1. 1 ST arrays (targeting 21041 genes). Raw data were normalized with the Robust Multiarray Average (RMA) method available in the Bioconductor R package oligo and the "pd. mogene. 1. 1. st. v1" annotation package. For rescue experiments, given the observed batch bias between the first and second replicates, the data were then batch-corrected using a linear model (Limma R package). Differential gene expression analysis was done using the RankProduct R package, and significantly underexpressed or overexpressed genes were identified with a minimum adjusted P-value of 15% and a minimum value of fold change equal to 2. Hierarchical clustering analysis was performed using Cluster 3.
Gene expression, copy number, and survival analysis of breast primary tumors This study makes use of data generated by METABRIC (first described in  Curtis et al. (2012). Funding for the project was provided by Cancer Research UK and the British Columbia Cancer Agency Branch. Upon access request, single-nucleotide polymorphism (SNP) 6. 0 copy number and Illumina HT-12 expression data for nearly 2000 primary breast tumors were available through the European Genome-Phenome Archive (http://www. ebi. ac. uk/ega) under accession number EGAS00000000083. For survival analyses, disease-specific survival was used as the end point. Follow-up time was defined as time from diagnosis until death from breast cancer or time of last follow-up if the patient was not known to have died. Kaplan-Meier and ROC analyses were performed using Prism 6.
Acknowledgments We thank all members of the  Margueron laboratory  for helpful discussions, and  Marc-Henry Stern ,  Pascale Gilardi ,  Elphège Nora , and  Edith Heard  for critical reading of the manuscript. The  ATIP-Avenir program , the  Fondation pour la Recherche Médicale (FRM) , the  European Research Council (ERC-Stg, REPOD-DID) , the  Labex DEEP , and the  Institut Curie  supported work in R. M. 's laboratory. M. W. was a recipient of a post-doctoral fellowship from the  Association pour la Recherche contre le Cancer (ARC). High-throughput sequencing was performed by the  NGS platform of the Institut Curie , supported by grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de le Recherche (Investissements d'Avenir)  and by the  Canceropôle Ile-de-France. High-throughput qPCR was carried out on the qPCR- HD-Genomic Paris Centre platform , supported by grants from the  Region Ile-de-France.
Biodiversity distribution data We used data on 275 mammals, 429 breeding birds and 102 amphibians that were compiled from Maiorano and colleagues (  [16] ) and cover the European sub-continent, including Turkey. For mammals and amphibians, the primary data were extent of occurrences (EOO) collected from the IUCN Global Mammal and Amphibian Assessments (http://www. iucnredlist. org/ initiatives/mammals and http://www. iucnredlist. org/initiatives/amphibians, available on the IUCN website). For bird species, data on EOOs available from Hagemeijer and Blair (  [17] ) were combined with those available from the BWPi2. 0. 1 DVD-ROM (Birds of the Western Palearctic interactive 2006, version 2. 0. 1). For each taxon, EOOs were refined using species' habitat preferences (based on both expert knowledge and literature).
Habitat preferences of each species was expressed as a suitability score (0: unsuitable, 1: secondary, 2: primary habitat) assigned to each GlobCover land-use land-cover class. Following scores, EOOs were filtered out at a high resolution (i. e. 300m) to remove false presence data (no presence data added). Then, filtered EOOs were up-scaled to a lower resolution (i. e. 10'), more suitable to biogeographical studies. Indeed, the up-scaling step limits the fine-scale signal of land-cover on biodiversity data and enables further comparisons with outcomes from Maiorano et al. [16]. A species was considered present in a 10' cell as long as it occurred at least in one of the overlapped 300m cells. The reliability of filtered EOOs of 37% of the amphibians, 71. 4% of the birds and 33. 8% of the mammals from the final species list, were also evaluated against field data.
Third, the learning rate (lr), which is a shrinkage parameter determining the contribution of each single decision tree within the entire BRT model. And fourth, the bag fraction, which defines the percentage of input data that is withheld while fitting the model to be used for testing  [26] [27]. After testing for parameter sensitivity, we set tree complexity to 2, learning rate to 0. 01, bag fraction to 0. 5 and used a Gaussian error distribution. The final number of trees kept in each BRT model was determined using a stepwise procedure as implemented in the gbm. step routine provided by the dismo package  [29]  in R  [24]. The performance of the model was assessed using the percent of explained deviance  [26]. The explained deviance of the BRT model was calculated as: 1 -(residual deviance/total deviance).
Axis elongation measurements Variation of the distance between a formed somite and the node was used to determine the velocity of body axis elongation. The coordinates of the different points were determined on bright-field images of the time-lapse experiments using the cellular tracking option of ImageJ. ImageJ is a public domain, Java-based image processing program developed at the National Institutes of Health. For wild-type embryo measurements, axis elongation velocity was measured between 1 and 3 somites (n = 8), between 5 and 7 somites (n = 8), between 9 and 11 somites (n = 6), between 15 and 17 somites (n = 5), between 20 and 22 somites (n = 6), and between 25 and 27 somites (n = 8). For 15-17 somites measurements, embryos were cultured starting at 13 somites and imaged until 18 somites.
Finally, raw data (. CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip Command Console (AGCC) version 3. 1. CEL files were further processed with MAS5 and RMA algorithms using the Bioconductor package (version 2. 8) available through R (version 2. 12. 1). Probe sets were filtered based on their expression intensity value (MAS5 value). Probe sets with an intensity value under 100 were discarded. Probe sets were ranked based on fold change between the intensity value of the control condition and the Hoxa13 over-expression condition. The microarrays raw data are available on the GEO website (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE38107).
Acknowledgements The authors thank  V Wilson ,  D Wellik ,  L Selleri , and members of the  Pourqui é laboratory  for comments. The authors acknowledge P Francois,  P Moncuquet ,  C Ebel , and  O Tassy  for help. This research was supported by the  Howard Hughes Medical Institute , the  Stowers Institute for Medical Research , a  NIH  grant  R02 HD043158  and a Chaire d'excellence of the  Agence Nationale pour la Recherche (ANR)  as well as the  European Research Council.