dataesr/scientific-paragraphs-categorization huggingface dataset:
Combined CpG island, shores and shelves pattern analysis CGI+SS patterns were compared using dynamic time warping (DTW)  [46]  as it is less sensitive to small variations than the Fréchet distance  [47]. Dynamic time warping was originally applied as a speech signal similarity measure and has been applied with success in several other fields including computer vision  [48] , protein structure matching  [49]  and time series analysis  [50]. A CGI+SS profile i can be represented as a couple of vector (X i , Y i ) = ((x i 1 , y i 1 ),. , (x i n , y i n )) where x i k represents the position of the k th CpG associated with the CGI+SS and y i k ∈ [ 0; 1] represents the mean methylation level for this probe accross a given dataset. For two CGI+SS profiles with respectively m and n probes, we compute the distance between the two profiles as: The algorithm is applied for each pair of CGI+SS patterns to obtain a dissimilarity matrix.
Computing gene expression susceptibility to DNA methylation changes We apply ridge  [54]  and LASSO  [55]  multivariate regression methods to predict gene expression using the full CGI+SS methylation profiles as well as univariate least square regression when using only the averaged methylation from the whole CGI+SS profile. Following Acharjee et al. [56] , we assess the predictive power of the methylation using the predictive goodness of fit R 2 which represents the squared Pearson correlation between observed and fitted values on an independent dataset. The estimation of the predictive power for each gene is obtained through 3-fold cross-validation averaged over 100 repeats. Parameters for both lasso and ridge regression methods were obtained by minimizing the mean squared error function using nested 3-fold cross-validation on the training dataset.
The use of the predictive goodness of fit instead of the classic mean squared error as a score allows to compute a comparable score between different predictions. In particular, the mean squared error is highly affected by the absolute level of gene expression while the R 2 is invariant to scaling. It is also important to note that in this case the R 2 computed for least square regression is a prediction R 2 and not just a goodness-of-fit of the given dataset and therefore provides confidence on the generalization of the score on independent datasets. R code for analysis is available upon request.
Acknowledgements This study was financially supported by " La Ligue Nationale Contre le Cancer"  (to M. M), the  European Research Council  ( SMAC-ERC-280032  to J-P. V). The authors would like to acknowledge the  Cancer Genome Atlas , the  UCSF 1a  , the Walter Reed 1a , the Christiana Healthcare 1a,2a,3a,3b , Indivumed 1a,2a,3a,2b , Cureline 1a,3a , the  Memorial Sloan Kettering Cancer Center   1a  ,  UNC 2a , 3a , 3b  ,  Mayo 1a  ,  Duke 1a  , the  University of Pittsburgh 1a ,1b , ILSBio 1a , Greater Poland Cancer Center 1a , Roswell Park 1a,1b,3a,3b , Washington University 3a , Johns Hopkins 3a,3b , the  International Genomics Consortium 3a , 3b  ,  Fox Chase 3a  ,  St Joseph 's Medical Center 3a for the distribution of patients data (as specified below). Patients data legend:.
Genomes Most of sequences were retrieved from NCBI annotated database (nr and EST, http://www. ncbi. nlm. nih. gov), using NCBI PHI-BLAST as well as BLAST and Annotation search tools available in the Geneious 7. 1. 5 package (Biomatters, http://www. geneious. com/). For specific searches, additional genome browsers were used as follows: Vertebrate and chordate genomes were searched using keyword or BLAST/BLAT search tools available in Ensembl (http://www. ensembl. org/, [Flicek et al. 2014].
Sequence Alignments Amino acid sequences were aligned using MAFFT v7. 017  (Katoh et al. 2002)  or MUSCLE  (Edgar 2004 ) programs, available in the Geneious 7. 1. 5 package (Biomatters, http://www. geneious. com/, last accessed April 28, 2015). Multiple sequence alignments (MSA) were manually edited and processed by Gblocks at http://molevol. cmima. csic. es/castresana/ Gblocks_server. html (last accessed April 28, 2015)  (Talavera and Castresana 2007)  to remove poorly aligned and divergent regions, except for PA28 for which BMGE (block mapping and gathering with entropy; Criscuolo and Gribaldo 2010) with a 0. 6 cut-off value was used instead. Nucleotide MSAs were performed using the translation align tool implemented in Geneious. For the detection of the arrays of HEAT-like repeats in PA200 sequences, a sequence profile method  (Bucher et al.
2C), 1145. 25 (fig. 3C), 2710. 73 (fig. 4E), 1973. 24 (fig. 5C), and 1166. 98 (fig. 6B). Tree samples were summarized by computing a 50% majority-rule consensus tree with associated clade PPs. Trees were visualized and exported as PDF files with FigTree (v1. 4. 2, http://tree. bio. ed. ac. uk/software/figtree/, last accessed April 28, 2015) then assembled in Adobe Illustrator. Divergence times between taxa presented in figure  7  were collected from the TimeTree database  (Hedges et al. 2006 ) (http://www. timetree. org/index. php, last accessed April 28, 2015).
These two clades also show a lower conservation of lid subunits, which may reflect a synapomorphic trait because they both belong to Metamonads. In most cases, orthology was not only supported by highly significant BLAST scores (E-value below e À100 ) but also by the presence of conserved functional domains: a PH-like domain (Pleckstrin Homology; PF04683) in ADMR1/Rpn13, a PCI domain (Proteasome Cop Initiation factor; PF01399) in PSMD3, 6, 8, 11, 12, 13, a RPN domain (Proteasome Regulatory subunit C-terminal; PF08375) in PSMD3, a JAB/ MPN (Mpr1/Pad1 N-terminal; PF01398) in PSMD7/Rpn8 and PSMD14/Rpn11, and a von Willebrand Factor A domain (VWA; PF00092) responsible for the binding of PSMD4/ Rpn10 to the ubiquitin-like modifier (UbL) FAT10 and possibly other UbLs  (Rani et al. 2012 ) (supplementary fig.
Deutsch et al. (2007)  and  Eugster and Gruber (2012)  found that the rates of N 2 fixation were higher in the Pacific Ocean than the Atlantic. Luo et al. (2012)  compiled a global database of diazotroph abundances and N 2 -fixation rates and estimated the global pelagic (open ocean) N 2 -fixation rate to be 140 ± 9. 2 Tg N yr -1 (arithmetic mean ± 1 standard error). One possible limitation of this approach is that 99 % of the data were collected within the range of 40 • S to 55 • N, and if substantial N 2 fixation is found to occur outside of this range,   Luo et al. (2012)  note that the most common method for field measurements of N 2 fixation has recently been found to underestimate the rates for Trichodesmium by 62 %  (Großkopf et al. , 2012). Extrapolating from the differences found between the 15 N 2 tracer bubble-addition and dissolution methods,  Großkopf et al.
Combining the increases in anthropogenic activity expected up to 2100 according to the RCP8. 5  (Lamarque et al. , 2011) , with the estimated effect of climate warming on emissions, gives an overall estimate of NH 3 emissions for 2100 of 135 (89-179) Tg N yr -1. As  Sutton et al. (2013b, Supplement)  point out, this value is nearly a factor of 3 higher than that included in the currently mapped EDGAR database, which is a consequence of including (a) additional sources (including oceans, see further below), (b) the effect of the climate change feed-.
The flux is calculated by dividing the ocean surface into 5 degree grid squares, and determining the gas transfer velocity and the Henry's law constant for NH 3 within each grid square, using temperature, pH, and wind speed. The resulting estimated NH 3 flux for 2005 is 5. 7 Tg N yr -1 from the ocean to the atmosphere. Atmospheric concentrations of NH 3 were obtained from STOCHEM  (Lamarque et al. , 2013; Derwent et al. , 2003)  model output. Surface ocean NH 3 and NH + 4 concentrations were obtained from the British Oceanographic Data Centre. These recent analyses suggest that the global NH 3 emission estimate of 8. 2 Tg N yr -1 of  Bouwman et al. (1997) , which was incorporated into the global emissions estimates of  Sutton et al. (2013b) , summarized above, may be overestimated. If so, the estimates of  Sutton et al.
ABCA1 determination in ligatured carotids Ligatured carotids first extracted with Chloroform:Methanol, were solubilized in NAOH 0. 1N (100μL/carotid). The solution is briefly sonicated and centrifuged at 15,000 x g for 10 minutes. The protein concentration was determined with Bradford assay and 40μg of sample were loaded on SDS-PAGE. The ABCA1 expression (ab7360 from Abcam-dilution 1/1000) was quantified using imageJ software. Calnexin expression was used as a loading control for each sample (ab10286 from Abcam-dilution 1/1000). The different samples were quantified and normalized to the same reference sample loaded each time on the SDS-PAGE.
The total dataset provides the basis for a complex set of hierarchy-based keys across all levels (orders, suborders, superfamilies, families, and genera) and the recognition of the class Archaeocyatha as a monophyletic taxon  (Pierre Kruse, personal communication, April 2008). In terms of hierarchy, the features that appear ontogenetically earlier have the higher taxonomic rank: orders are based on the architecture of the cup, suborders are represented by first-order intervallar structures, superfamilies are recognized from outer wall elements, families are determined using inner wall structures, and genera are based on certain variants of wall and intervallar structures. The archaeocyaths are recognized as the first group of hypercalcified sponges to display a modular habit, apparently developing this tendency as a part of their colonization of more active reef habitats during the Early Cambrian  (wood, zHuravlev, & debrenne, 1992).
ACKNOWLEDGMENTS In preparing these Revised Volumes 4 and 5 of the Treatise, Part E on the Porifera, I have been assisted by a number of specialists, virtually all of them invited in the early years of the project (late 1980s to early 1990s) by the previous Treatise Director and Editor, the late Roger L. Kaesler. At the outset, it was our intention only to survey the stromatoporoids, chaetetids, and related living hypercalcified sponge taxa (members of classes Demospongiae and Calcarea with spicules and hypercalcified calcareous basal skeletons), with a team comprising Jean Vacelet and the late Willard Hartman for the living forms, Ron West for the fossil chaetetids, Rachel Wood for the Mesozoic stromatoporoids, and Colin Stearn, Barry Webby, Heldur Nestor, Carl Stock, and Stephen Kershaw for the Paleozoic stromatoporoids.
SPEcIES-GROuP nAmES Detailed consideration of valid emendation of specific and subspecific names is unnecessary here, both because the topic is well understood and relatively inconsequential and because the Treatise deals with genus-group names and higher categories. When the form of adjectival specific names is changed to agree with the gender of a generic name in transferring a species from one genus to another, one need never label the changed name as nomen correctum. Similarly, trans literation of a letter accompanied by a diacritical mark in the manner now called for by the Code, as in changing originally bröggeri to broeggeri, or eliminating a hyphen, as in changing originally published cornu-oryx to cornuoryx, does not require the designation nomen correctum. Of course, in this age of computers and electronic databases, such changes of name, which are perfectly valid for the purposes of scholarship, run counter to the requirements of nomenclatorial stability upon which the preparation of massive, electronic databases is predicated.
I am particularly indebted to the Coordinating Author of this volume,  Barry Webby , for his sage advice, continuous support, and careful attention to detail. Unless otherwise noted, the many SEM images are due to the technical expertise and photographic skills of George R. Clark II. I am greatly indebted to the excellent and timely assistance of the staff of the  Interlibrary Loan Department of Hale Library at Kansas State University. Financial assistance from the  Petroleum Research Fund of the American Chemical Society , the  National Science Foundation , the  Kansas Geological Survey , and the  Bureau of General Research at Kansas State University  are gratefully acknowledged. I am especially grateful for the financial support for research in Japan and China, which was provided by the  Japanese Society for the Promotion of Science and the Nanjing Institute of Geology and Paleontology , respectively.
ACKNOWLEDGMENTS The financial support by the  German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)  allowed  B. Senowbari-Daryan  (Project Se  416/14 ) to stay some time in the United States and made it possible to complete this manuscript. We appreciate the support and assistance of officers and staff of the  Department of Geology, Brigham Young University , during preparation of the manuscript, in particular  Jeffrey Keith ,  Scott Ritter ,  Kris Mortenson , and  Kirsten Thompson. Christopher Scotese  kindly gave permission for use of his paleogeographic maps (1990, 2001) as bases upon which distributions of the sphinctozoan and inozoan sponges discussed herein were plotted.
d) initial mass function: As for different cosmologies and halo mass definitions, the assumption of different IMFs can be compensated for when calibrating the model: the observational data set used for the calibration (and the initial mass function assumed in its preparation) will determine the values of the model parameterswhatever the assumption for the model IMF. However, the model's stellar masses (and other quantities not studied here such as the recycled fraction of gas, the amount of energy available for supernovae, chemical enrichment, etc. ) will certainly be affected by the IMF choice. We can confirm that this has an effect but is not the primary source of the variations between models. e) tuning: This is by far the most decisive factor for the scatter  (Henriques et al. 2009; Mutch et al.
Model origin The parameters used were calibrated using Monte Carlo trees built using the algorithm of  Parkinson et al. (2008). Modifications to the supplied data When importing merger trees for this project, GALACTICUS aims to make minimal changes to the tree structure. Two small modifications are required to ensure consistency of the merger trees. First, where halo A is indicated as being the host of halo B, but A is not present in the merger trees (i. e. is not listed as the progenitor of any other halo), then A is assumed to be a progenitor of the same halo of which B is a progenitor. Second, if two haloes are mutual hosts (i. e. A is the host of B, while B is the host of A), GALACTICUS resolves this inconsistency by reassigning the more massive of the two haloes to be unhosted (i. e. to no longer be classified as a subhalo).
ACKNOWLEDGMENTS The authors would like to express special thanks to the  Instituto de Fisica Teorica (IFT-UAM/CSIC in Madrid)  for its hospitality and support, via the  Centro de Excelencia Severo Ochoa Program  under Grant No. SEV-2012-0249 , during the three week workshop 'nIFTy Cosmology' where this work developed. We further acknowledge the financial support of the 2014  University of Western Australia Research Collaboration Award for 'Fast Approximate Synthetic Universes  for the SKA', the  ARC Centre of Excellence for All Sky Astrophysics (CAASTRO)  grant number  CE110001020 , and the two ARC Discovery Projects  DP130100117  and  DP140100198. We also recognize support from the  Universidad Autonoma de Madrid (UAM)  for the workshop infrastructure. AK is supported by the  Ministerio de Economía y Competitividad (MINECO) in Spain  through grant  AYA2012-31101  as well as the  Consolider-Ingenio 2010 Programme  of the  Spanish Ministerio de Ciencia e Innovación (MICINN)  under grant MultiDark  CSD2009-00064.
VGP acknowledges support from a  European Research Council   Starting Grant  ( DEGAS-259586 ). This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure. The work of BH was supported by  Advanced  Grant  246797 GALFORMOD  from the  European Research Council. MH acknowledges financial support from the  European Research Council  via an  Advanced Grant  under grant agreement no. 321323  NEOGAL. PM has been supported by a  FRA2012 grant  of the  University of Trieste ,  PRIN2010-2011  ( J91J12000450001 ) from  MIUR , and  Consorzio per la Fisica di Trieste.
NDP was supported by  BASAL   PFB-06   CATA , and Fondecyt  1150300. Part of the calculations presented here were run using the Geryon cluster at the  Center for Astro-Engineering at U. Catolica , which received funding from  QUIMAL 130008  and Fondequip AIC-57. CP acknowledges support of the  Australian Research Council (ARC)  through  Future Fellowship FT130100041 and Discovery Project  DP140100198. WC and CP acknowledge support of  ARC   DP130100117. AP was supported by beca  FI  and  2009-SGR-1398  from  Generalitat de Catalunya  and project  AYA2012-39620  from  MICINN. RAS acknowledges support from the  NSF  grant  AST-1055081. RSS thanks the Downsbrough family for their generous support. SKY acknowledges support from the  National Research Foundation of Korea  (Doyak 2014003730). Numerical simulations were performed using the KISTI supercomputer under the programme of KSC-2013- C3-015.
A representative panel of mutations which occur in CDKs and Cyclins may be found in the catalogue of Cosmic Mutations in Cancer (COSMIC database-http://www. sanger. ac. uk/genetics/CGP/cosmic/)  [135] , which integrates all mutations identified through sequencing of human cancer tissue samples. Figure  4A  schematizes how alterations in CDK/Cyclin function contribute to establishment of cancer phenotypes. Table  2  summarizes the different mutations and dysregulations in CDK and Cyclin expression associated with human cancers.
Cell Cycle CDKs The mitotic kinase CDK1/Cyclin B being essential for viability, and capable of recapitulating the functions of the other cyclin-dependent kinases in regulating cell cycle transitions in mammalian cells is not surprisingly one of the least mutated kinases in human cancers. One single missense mutation of CDK1 has been reported in the COSMIC database in ovary carcinoma at amino acid position 73 (aspartic acid to histidine)  [135]. CDK1 overexpression has been documented in lymphoma, advanced melanoma and lung cancer, and loss of cytoplasmic CDK1 predicts poor survival and confers chemotherapeutic resistance in the latter  [136] [137] [138]. Cyclin B1 overexpression and/or mislocalization has been described in several primary cancers including breast, colon, gastric, prostate, thyroid carcinoma and non small-cell lung cancer (NSCLC)  [139] [140] [141] [142] [143].
CDK2 overexpression has been reported in laryngeal squamous cell cancer, advanced melanoma and breast cancer  [137, [144] [145] [146]. Moreover, 33 simple coding mutations have been reported in the COSMIC database for CDK2 in a wide variety of cancer tissues, most of which are missense mutations in the Nterminal lobe (amino acid  2, 13, 20, 34, 45, 68, 84) , one silent mutation at amino acid 45 and one deletion frameshift at amino acid 79  [135]. But CDK2 hyperactivation in human cancers is most often associated with amplification and/or overexpression of its partner cyclins A and E in a wide variety of human cancers, but in particular in breast cancer, ovarian and endometrial carcinomas, lung and thyroid carcinoma, melanoma and osteosarcoma  [129, 131, 143, [147] [148] [149] [150] [151] [152] [153] [154] [155] [156] [157] [158].
The COSMIC database reports on 38 simple coding mutations in CDK4, with 25 missense mutations within its N-terminal lobe (residues 2, 9, 10, 23, 24, 31, 35, 43) and 12 synonymous mutations encoding silent substitutions at positions 2 and 18  [135]. CDK4 hyperactivity is further often associated with Cyclin D overexpression, genetic amplification, polymorphism, translocation or alternative splicing  [134, 238]. High levels of cyclin D1 are frequently encountered in breast cancers either through genetic amplification or overexpression  [243]. Genetic aberrations of cyclin D1 (CCND1), including overexpression, have been reported in neuroblastoma  [240]. Cyclin D1 overexpression alone is not sufficient to drive oncogenic transformation, but its nuclear accumulation is clearly associated with neoplastic development  [133].
CDK6 gene amplification and overexpression have been described in lymphomas, leukemias, squamous cell carcinoma, gliomas and medulloblastoma  [186, 187, 191]. This overexpression can lead to translocation of CDK6 in some leukemias, and may link the TP53 and RB1 tumor suppressor pathways to medulloblastoma pathomechanisms  [191]. The COSMIC database reports on 33 simple coding mutations in CDK6, with 1 nonsense substitution at position 157, 18 missense mutations (amino acids 18, 84, 87, 113, 118 and 139) and 11 synonymous mutations that encode silent substitutions at positions 65 and 148  [135].
Acknowledgments Research in M. C. Morris group is supported by the  CNRS  ( Centre National de la Recherche Scientifique ) and grants from the  Association de Recherche contre le Cancer  ( ARC-SFI20111203923 ), the  Region Languedoc-Roussillon  (Subvention " Chercheuse d'Avenir "  DGA4/DESR 2010 Q-013 ),  Institut National du Cancer  ( INCA PL-BIO-10-058693 ) and  Agence Nationale de Recherche  ( ANR-13-BS10-0003-01 ). MP and CP are supported by fellowships from CBS2-University Montpellier. We thank  J. Saarbach  for his assistance in figure preparation.
They provided ambulatory care in opiate maintenance. This group was formed using the 2008 Alcohol Treatment care structures Directory (6 th edition) which comprehensively inventories addictive disorders care establishments treating addiction (2 Universitary hospital, 7 general hospital and 7 ambulatory addictive disorders care centers). The target consisted of all specialized care establishments in the Pays de la Loire region. Questionnaire. The Family Medicine department, the addiction networks and the Addiction Psychiatry department, working as a team, constructed the questionnaire. This questionnaire was divided into three parts: 1. The first part determined the prescriber's profile through socio-demographic data (age, sex,. ), professional data (specialty, type of practice: independent-/hospital/network), BHD prescription habits data (having prescribed BHD as a first-time prescription, number of patients followed-up on BHD) and data on the perceived relationship with opioid dependent patients, and the perceived quality of information received on OSTs.
ACKNOWLEDGMENTS We thank  D. Ray-Gallet ,  Z. A. Gurard-Levin ,  L. Prendergast ,  G. A. Orsi ,  J. -P. Quivy , and  R. C. Rancourt  for discussions and proof-reading of the manuscript. We thank  Dr. E. Soutoglou  for lacO-tetO U2OS cells,  Drs. D. Cleveland  ( University of California, San Diego ) and  I. Cheeseman  ( MIT ) for the CENP-T antibodies,  Dr. B. Earnshaw  ( University of Edinburgh ) for the human CENP-C cDNA, and  Dr. D. Foltz  ( University of Virginia ) for the HJURP antibody. We also thank  Drs. D. Pan  and  C. Smith  ( Max Planck ) for technical assistance, and  Drs. H. Kurumizaka  and  Y. Arimura  ( Waseda University ) for sharing the reconstituted human CenH3 CENP-A -H4 complex. We thank  G. Ossolengo  at the  Antibody Facility of IFOM-IEO Campus (Milan ) for antibody production.