dataesr/scientific-paragraphs-categorization huggingface dataset:
The sequences were aligned using the default settings in Clustal Omega v. 1. 1. 0 [82] , and the best-fit nucleotide substitution model for the alignment was determined by the Aikake Information Criterion as implemented in jModeltest v. 0. 1. 1  [83, 84]. MrBayes v. 3. 1. 2 [85]  was used to carry out Bayesian phylogenetic inference under the GTR+I+C substitution model. All parameters were estimated during two independent runs of six Markov Monte Carlo chains, both of which were run for 4,000,000 generations (longer runs gave identical results). Phylogenetic trees were sampled every 4,000 th generation, and a consensus phylogeny was built from the 751 trees remaining after the first 250 were discarded as burn-in. Nexus formatted alignments including the commands used in MrBayes are available from the Dryad Digital Repository: http//dx.
doi. org/10. 5061/dryad. mq5ct [86]. The branch-site model test for positive selection at codon sites was carried out using the CODEML program in the PAML 4. 4 package  [87]. The tree (Figure  2B ) was obtained using the PHYML  [83]  with default settings as implemented in Geneious v. 5. 4. 6  [75]. Foreground branches for the branch-site model were assumed to be those in which LaLal2 evolved separately from related sequences in Figure  2B.
Analysis of Synonymous and Nonsynonymous Substitution To determine whether sequence evolution of Lal2 associated with S locus evolution in this group was concentrated into particular protein domains, we compared the sequence of the a1-1 haplotype with that of the phylogenetically closest SRK sequence (allele SRK15 from Arabidopsis halleri). Estimates of synonymous and nonsynonymous substitution and their ratios were obtained by maximum likelihood using the program CODEML in the PAML package  [87]. Estimated parameters for each major protein domain were compared by constraining them to be equal and carrying out the log likelihood ratio test.
Sequence Data Sequences unique to this study were deposited in GenBank. Figure  S3  Phylogenetic reconstruction of the relationships among Lal2, Lal2-like, ARK, and SRK for different portions of the sequence. Bayesian 50% consensus phylogeny for the Sdomain (A) and the transmembrane and kinase domains (B) of Lal2, Lal2-like, ARK, and SRK sequences used in this study. Posterior probabilities for each bifurcation are indicated at the nodes. Lal2 sequences form a clade separate and distinct from ARK and SRK sequences (vertical bars). The outgroup in each tree is identified by its NCBI gene ID number. (PDF).
Acknowledgments Eef Harmsen  and  Alexa Patoine  provided logistical support. The plant growth work was conducted in the  McGill University Phytotron. We thank  Ehab Abouheif ,  Jeremiah Busch ,  Vincent Castric ,  Deborah Charlesworth ,  Daphne Goring ,  Stephen Wright ,  Sally Otto ,  Rajee Rajakumar , and three anonymous reviewers for discussing with us the results presented in this study. We also thank  Richard Clark  and  Joshua Steffen  for access to the Arabidopsis lyrata RNAseq data used in this article,  Daphne Goring  and  Emily Indriolo  for providing an A. lyrata plant used in RT-PCR analysis,  Barbara Mable  for providing A. lyrata genomic DNA samples used for AlLal2 polymorphism studies, and  Vincent Castric  for help with expression analyses.
This work was supported by the  Natural Sciences and Engineering Research Council (NSERC)  through a  Discovery Grant entitled  ' The Evolution of Genetic Systems ', by an  NSERC   Strategic Network Grant  to the  Canadian Pollination Initiative , and by  Genome Canada  and  Genome Quebec  through their funding of Value-directed  Evolutionary Genomics Initiative , led by  Thomas Bureau  and  Stephen Wright. DJS thanks UniversiteLille 1 for a visiting grant to the  GEPV lab in Lille. The work of XV is supported by the  French Agence Nationale de la Recherche  ( ANR-11-BSV7-013-03 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Classification and features 16S rRNA gene sequence analysis The single genomic 16S rRNA gene sequence of L. illini 3055 T was compared using NCBI BLAST  [18, 19]  under default settings (e. g. , considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database  [20]  and the relative frequencies of taxa and keywords (reduced to their stem  [21] ) were determined, weighted by BLAST scores. The most frequently occurring genera were Leptospira (53. 4%), Anaeromyxobacter (31. 6%), Leptonema (11. 5%), Turneriella (1. 3%) and Desulfomonile (0. 8%) (96 hits in total). Regarding the three hits to sequences from members of the species, the average identity within HSPs was 99. 7%, whereas the average coverage by HSPs was 97. 4%. Among all other species, the one yielding the highest score was Leptospira wolbachii (AY631890), which corresponded to an identity of 86.
4% and an HSP coverage of 76. 8%. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification. ) The highest-scoring environmental sequence was EF648066 (Greengenes short name 'dynamics during produced water treatment aerobic activated sludge clone HB63'), which showed an identity of 99. 2% and an HSP coverage of 98. 4%. The most frequently occurring keywords within the labels of all environmental samples which yielded hits were 'microbi' (5. 2%), 'soil' (2. 3%), 'anaerob' (2. 3%), 'industri' (2. 0%) and 'ecolog' (1. 4%) (154 hits in total). The most frequently occurring keywords within the labels of those environmental samples which yielded hits of a higher score than the highest scoring species were 'microbi' (4.
Genome project history This organism was selected for sequencing on the basis of its phylogenetic position  [50] , and is part of the Genomic Encyclopedia of Bacteria and Archaea project  [51]. The genome project is deposited in the Genomes OnLine Database  [28]  and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI) using state of the art sequencing technology  [52]. A summary of the project information is shown in Table  2. Table  1. Classification and general features of L. illini 3055 T according to the MIGS recommendations  [36].
Genome sequencing and assembly The genome was sequenced using a combination of Illumina and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website [54]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler assembly consisting of 140 contigs in tree scaffolds was converted into a phrap  [55]  assembly by making fake reads from the consensus, to collect the read pairs in the 454 paired end library. Illumina GAii sequencing data (5,940 Standards in Genomic Sciences Mb) was assembled with Velvet  [56]  and the consensus sequences were shredded into 1. 5 kb overlapped fake reads and assembled together with the 454 data. The 454 draft assembly was based on 179 Mb 454 draft data and all of the 454 paired end data.
Genome annotation Genes were identified using Prodigal  [59]  as part of the DOE-JGI genome annotation pipeline  [60] , followed by a round of manual curation using the JGI GenePRIMP pipeline  [61]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) non-redundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes -Expert Review (IMG-ER) platform  [62].
Acknowledgements We would like to gratefully acknowledge the help of  Sabine Welnitz  for growing L. illini cultures, and  Evelyne-Marie Brambilla  for DNA extraction and quality control (both at DSMZ). This work was performed under the auspices of the  US Department of Energy Office of Science, Biological and Environmental Research Program , and by the  University of California ,  Lawrence Berkeley National Laboratory  under contract No. DE-AC02-05CH11231 ,  Lawrence Livermore National Laboratory  under Contract No. DE-AC52-07NA27344 , and  Los Alamos National Laboratory  under contract No. DE-AC02-06NA25396 ,  UT-Battelle  and  Oak Ridge National Laboratory  under contract  DE-AC05-00OR22725.
Results Increased Expression of DEPDC1A Gene in Multiple Myeloma Cells Compared to Normal Bone Marrow Plasma Cells in Association with a Poor Prognosis DEPDC1A gene expression was significantly increased (P =. 001) in MMCs of previously-untreated patients compared to normal BMPCs and in HMCLs compared to primary MMCs (Figure  1A ). Affymetrix data (probe set 222958_s_at) were validated by real time RT-PCR (P =. 005, Supplementary Figure  S1A ). A high DEPDC1A expression could predict for shorter overall survival in 2 independent large cohorts of previously-untreated patients. Using Maxstat R function, 22% of the patients of UAMS-TT2 cohort with the highest DEPDC1A expression had an overall survival of 56 months versus not reached in the remaining patients (Figure  1B , P,. 001). Twelve % of the patients of the HM cohort with high DEPDC1A expression had an overall survival of 42.
DEPDC1A Knockdown Increases Expression of Markers of Mature Plasma Cells Genes whose expression was regulated by DEPDC1A knockdown were identified using Affymetrix U133 plus 2. 0 microarrays performing 3 independent experiments for each of the 3 HMCLs. Wild-type TP53 XG7 and XG19 cells were treated for 5 days with or without dox and mutated TP53 XG2 cells for 3 days before apoptosis occurrence. Statistical analysis was done with Affymetrix GCOS (GeneChip Operating Software) software and the P-value are adjusted with Benjamini-Hochberg multiple testing correction. Crossing the gene lists obtained in the 3 independent experiments for each HMCL, 34 genes/ESTs were upregulated (fold change $1. 5, P#. 05) and 125 downregulated (fold change #0. 67, P#. 05) by DEPDC1A knockdown in XG7 HMCL (supplementary Table  S3 ), 56 genes/ESTs upregulated (fold change $1.
Patients' characteristics are available in Supplementary Table  S1. Gene expression profiling (GEP) of purified MMCs was assayed using Affymetrix U133 2. 0 plus microarrays as described  [39]  and data were normalized using the MAS5 Affymetrix algorithm with a scaling factor of 100. The. CEL and MAS5 files are deposited in the ArrayExpress public database (http://www. ebi. ac. uk/arrayexpress/), under accession number E-MTAB-362. Interphase-FISH-analysis was performed according to our previously reported protocol  [40]. We also used publicly available Affymetrix data (http://www. ncbi. nlm. nih. gov/geo, GSE2658) of a cohort of 345 purified MMCs from previously untreated patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR). These patients were treated with total therapy 2  [41]  and this cohort is termed in the following UAMS-TT2 cohort.
XG7-TR-shD1 and XG19-TR-shD1 cells were cultured with or without doxycycline for 5 days. Genes whose expression is deregulated by DEPDC1A knockdown were identified using Affymetrix U133 plus 2. 0 microarrays. Data are the common genes significantly (P#. 05) upregulated ($1. 5 fold) or downregulated (#0. 67 fold) by DEPDC1A knockdown in the 2 human multiple myeloma cell lines. doi:10. 1371/journal. pone. 0062752. t002.
As a control for protein loading, we used a mouse monoclonal anti-bactin antibody (Sigma, St Louis, MO, USA). The primary antibodies were visualized with goat anti-rabbit (Sigma) or goat anti-mouse (Bio-Rad, Hercules, CA, USA) peroxidase-conjugated antibodies by an enhanced chemiluminescence detection system. Blots were quantified by densitometry using NIH ImageJ software (National Institutes of Health, Bethesda, MD, USA) and protein expression normalized according to b-actin expression.
Cell Growth, Cell Cycle, Apoptosis and Immunophenotypic Analysis HMCLs were cultured for 4 days in 96-well flat-bottom microtiter plates in complete culture medium with IL-6 and cell growth was evaluated by quantifying intracellular ATP amount with a Cell Titer Glo Luminescent Assay (Promega, Madison, WI) with a Centro LB 960 luminometer (Berthold Technologies, Bad Wildbad, Germany). In some experiments, doxycycline (1 mg/ml) was added to induce shDEPDC1A expression. The cell cycle was assessed using 49,6-diamidino-2-phenylindole (DAPI) staining (Sigma Aldrich) and ModFit analysis (Verity Software House, Topsham, ME). Cells in the S phase were quantified using incubation with bromodeoxyuridine (BrdU) for 1 hour, and labelling with an anti-BrdU antibody (APC BrdU flow kit, BD Pharmingen, Le Pont De Claix, France) as indicated  [43].
Statistical Analysis Gene expression data were normalized with the MAS5 algorithm and analyzed with our bioinformatics platform -RAGE, http:// The table indicates the mean ratio of DMFI between dox-treated and dox-untreated XG19 myeloma cells in 3 independent experiments. *indicates a significant increase in CXCR4, IL6R or CD38 expression using a paired t-test (P,. 05). doi:10. 1371/journal. pone. 0062752. g007 rage. montp. inserm. fr/ [44]  and Amazonia, http://amazonia. montp. inserm. fr/) [45]  -and the SAM (Significance Analysis of Microarrays) software (www-stat. stanford. edu/,tibs/SAM/). Statistical comparisons were done with Mann-Whitney, Chi-square, or unpaired or paired Student's t-tests. The change in gene expression induced by shDEPDC1A in HMCLs was evaluated with Affymetrix GCOS (GeneChip Operating Software) software using Wilcoxon test and crossing, for each cell line, the gene lists of the 3 independent experiments.
The advantage of Affymetrix GCOS software is to use the information provided by the 11 perfect-match (PM) and the 11 mismatch (MM) probes designed to build a given probe set signal, and avoids losing most of information given we have only 3 replicates and stringent multiple testing correction. Briefly, The analysis compares the difference values (PM-MM) of each probe pair in the baseline array to its matching probe pair on the experiment array. For each probe set a change p-value and an associated fold change is generated using Wilcoxon test. The p-value is then adjusted using Benjamini-Hochberg multiple testing correction. gene was overexpressed 8 fold in XG7 cells by lentiviral delivery of DEPDC1A coding sequence. DEPDC1A RNA was quantified by real time RT-PCR and results are mean values of 3 representative experiments.
Common genes deregulated by DEPDC1A knockdown in the 2 wild-type TP53 XG19 and XG7 human myeloma cell lines and in the mutated TP53 XG2 human myeloma cell line. TR-shD1 cells were cultured with or without doxycycline for 3 days and XG7-TR-shD1 and XG19-TR-shD1 cells for 5 days. Genes whose expression is deregulated by DEPDC1A knockdown were identified using Affymetrix U133 plus 2. 0 microarrays. Data are the common gene significantly significantly (P#. 05) upregulated ($1. 5 fold) or downregulated (#0. 67 fold) by DEPDC1A knockdown in the 3 human multiple myeloma cell lines. doi:10. 1371/journal. pone. 0062752. t003.
This work was supported by grants from  ARC  ( SL220110603450 ,  Paris France) , the  European Community  ( FP7-OVERMYR ), the  Tumorzentrum Heidelberg/Mannheim, Germany , and the  Deutsche Krebshilfe, Bonn, Germany , the  Deutsche Forschungsgemeinschaft , Transregio  TRR79 ,  Bonn, Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We investigate global temperature data produced by the Climate Research Unit at the University of East Anglia (CRU) and the Berkeley Earth Surface Temperature consortium (BEST). We first fit the 1850-2010 data with polynomials of degrees 1 to 9. A significant ~60-yr oscillation is accounted for as soon as degree 4 is reached. This oscillation is even better modeled as a broken line, more precisely a series of ~30-yr long linear segments, with slope breaks (singularities) in ~1904, ~1940, and ~1974 (±3 yr), and a possible recent occurrence at the turn of the 20th century. Oceanic indices PDO (Pacific Decadal Oscillation) and AMO (Atlantic Multidecadal Oscillation) have undergone major changes (respectively of sign and slope) roughly at the same times as the temperature slope breaks. This can be interpreted with a system of oceanic non-linear coupled oscillators with abrupt mode shifts.
Introduction Global surface temperatures are one of the parameters most commonly used to discuss the evolution of climate. Databases of instrumental temperatures that cover the past century and a half have been compiled by four main groups (Climate Research Unit at the University of East Anglia-CRU, NASA Goddard Institute for Space Studies-GISS, National Oceanic and Atmospheric Administration-NOAA, Berkeley Earth Surface Temperature consortium-BEST). This is a huge, difficult task and these databases are necessarily faced with a number of limitations: the geographical distribution of stations is far from uniform and varies with time; also, there may be fundamental difficulties in establishing meaningful global temperatures for the Earth  [1]. Climate is generally defined as the ~30 year average of weather and since only 100 to 150 years of instrumental data are available, this places severe limitations on the significance of global temperature trends and multi-decadal oscillations.
A monotonic (low degree) trend can be fit to all global tem-perature data sets over the period from 1850 to the present. This trend amounts to a secular rise of ~1 K over the period. Once this trend is removed, a ~60 yr oscillation stands out in most records. Such an oscillation has been discussed for instance by  [2]  and  [3]. Lean  and Rind  [4, 5]  have built a model curve for 1889-2006 monthly mean global temperatures, in which they distinguish contributions from oceans (using the El Nino Southern Oscillation ENSO), volcanic aerosols, solar irradiance and anthropogenic forcing. Both observations and their empirical curve display the secular rise of approximately 1 K. But data are lower than model by some 0. 2 K around 1910 and higher by 0. 2 K around 1940: this is because of the significant ~60 year oscillation identified by previous authors, which is not accounted for by  [4].
Polynomial Fits to the "~60-yr Oscillation" in Global Temperatures We have used monthly temperature anomaly data from the CRU and BEST databases. We chose to use the "raw data" rather than other data-derived products that have been "homogenized" and have thus undergone rather extensive and not always transparent/reproducible data polishing. The CRUTem4 database is found at http://www. cru. uea. ac. uk/cru/data/temperature/. We have analyzed the most recent variance-adjusted (version 4) CRU data from 1850 through 2010 for the whole globe (CRUTem4vGL). We have also analyzed hemispheric and global series CRUTem4-SH, CRUTem4-NH, CRU-Tem4vSH, HadSST2GL, HadCRU3GL, and CRUTEM-3vGL (these series are described in http://www. metoffice. gov. uk/hadobs/crutem4/ and  [7] , and http://www. cru. uea. ac.
uk/cru/data/temperature/crutem4v gl. txt), and the Berkeley Earth Surface Temperature (BE-ST) monthly global averages (found at http://berkeleyearth. org/dataset/, description in  [8] ). All yield similar results and mainly results based on CRU-Tem4vGL are shown in this paper. We note in passing differences between the long-term linear or low-degree trends obtained in the various temperature anomaly databases (notwithstanding a possible overall difference in baseline due to the definition of the period with respect to which the anomalies are calculated); the CRU and GISS curves are about 0. 3 K below the NOAA and BEST curves in the 2000-2010 decade, whereas all agree to better than 0. 1 K between 1920 and 1970  [8]. We return to this in Section 5. In order to check the significance of the "60-yr oscillation", we have very simply fit the data sets of monthly mean temperature anomalies (using least squares) with polynomials of increasing degree (Figure  1 ).
less than ~4. In order to quantify the goodness of fit, we have calculated the adjusted R-squared values not only for polynomial fits to the data with degrees 1 up to 9 but also for an exponential fit. The adjusted R-squared  [9]  is a statistically unbiased version (i. e. corrected for the number of degrees of freedom) of the R 2 "coefficient of determination", whose main purpose is the prediction of future outcomes on the basis of prior information. R 2 itself is 1 minus the ratio of variance of errors to variance of observations. The adjusted R-squared values take into consideration the number of free parameters of the model (the adjusted R-squared values may be negative when the parameters of the model do not improve the fit compared to the simple average value of the data, which happened in the case of one data set, CRUTem4vSH, that could not be adequately fit by an exponential).
We point out the puzzling fact that an earlier CRU global temperature dataset, HadCRUT3GL, displayed the alternating segments better than the latest version CRU-Tem4vGL (Figures  4(a) vs (b) ). The annual variability of the monthly averages increased by a factor of 2 in the recent revision. So did the amplitude of the temperature anomaly since 1975, that changed from 0. 5 K to about 1. 0 K. The segment with negative slope prior to ~1910-1920, and more importantly the segment with flat or even negative slope after 2000, are particularly conspicuous on HadCRUT3GL (Figure  4(a) ). [8]  discuss the fact that the four available global temperature anomaly curves start diverging in the past two decades (their Figure  8 ): there is close agreement from 1900 to the late 1980s, but after that GISS and HadCRU display a plateau, whereas the NOAA and BEST curves continue to rise to values that are 0.
we have resumed the same computations as in Figure 2(a) (5 segments, 4 nodes) for all 21 datasets that can be obtained from the CRU site http://www. cru. uea. ac. uk/cru/data/temperature/#datter (i. e. land air temperature anomalies CRUTem3, CRU-Tem3v, CRUTem4 and CRUTem4v, sea surface temperature anomalies HadSST2 and combined land and marine temperatures HadCRUT3 and HadCRU3v, each with hemispheric means for the northern and southern hemispheres and combined global series), and we have stacked all corresponding histograms, resulting in Figure 3. There is a prominent bimodal cluster of nodes at 1938-1940 and 1945-1946 and a single mode cluster at 1975-1976, and two lesser bimodal clusters around 1904/1908 and 2006/ 2010. Altogether, most data series are well fit by a sequence of 3 linear segments between the early 1900s and the early 2000s, with suggestions for an earlier and a later segment that remain to be confirmed.
2 Ces établissements ont depuis longtemps attiré l'attention de chercheurs s'intéressant à différents domaines : histoire des idées, des sciences, des sciences de l'éducation, histoire intellectuelle, histoire sociale ou économique. Cependant, les ouvrages généraux qui font encore autorité sont assez anciens, et l'on ressent cruellement le manque d'une histoire générale des Académies dissidentes fondée sur une historiographie moderne. Un vaste projet collectif, le Dissenting Academies Project, mis en oeuvre sous la direction d'Isabel Rivers et de David Wykes depuis 2006, a entrepris de remédier à ce problème  6. Au coeur du projet, les deux bases de données relationnelles qui constituent l'ensemble Dissenting Academies Online (rendu public en juin 2011, bien qu'il soit encore inachevé  7  ) jouent un rôle primordial.