dataesr/scientific-paragraphs-categorization huggingface dataset:
FEM modelling Our FEM computations are performed using Cast3M opensource software (http://www-cast3m. cea. fr), a code developed by the French Atomic Research Center  (Laborderie and Jeanvoine, 1994) , and employed in previous studies on 1 × 10 8 N m -3 [10 5 -10 8 N m -3 ] Normal stiffness, K n 1 × 10 8 N m -3 [10 5 -10 8 N m -3 ] Cohesion, c [0. 5-2. 5 kPa, 2. 8 kPa] Angle of friction, φ [10-75 • ] Boundary Inclination 0, 25, 35 • Oscillations (max amplitude) Horizontal (16. 5 mm) snow avalanche release  (Gaume et al. , 2011 (Gaume et al. , , 2012 (Gaume et al. , , 2013;; Gaume, 2012). The code (Education and Research Release of 2010) employs an implicit time integration scheme; governing equations are solved incrementally, thus enabling nonlinear computations, and taking into account dynamic effects.
Acknowledgements. The research leading to these results has received funding from the  International Affairs Directorate of IRSTEA, INTERREG ALCOTRA  ( MAP3 ), from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement # 298672  ( FP7-PEOPLE-2011-IIF, "TRIME "), and from  LabEx OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). EAP is grateful for all the support, also to  E. A. Hardwick ,  N. Hadda ,  P. Hagenmuller ,  T. Faug , and  M. Schneebeli  for help and for comments on initial version of the manuscript. The quality of the paper was improved thanks to remarks by  A. van Herwijnen  and the two anonymous reviewers. Edited by: P. Bartelt Reviewed by:  A. van Herwijnen  and two anonymous referees.
Five other morphologically and genetically indistinguishable isolates were recovered from five other O. sonrai ticks from the same batch. The 16S rRNA nucleotide sequence (GenBank accession number GU937608) of Occidentia massiliensis strain OS18T was 93. 7% similar to Orientia tsutsugamushi strain Gilliam (GenBank accession number D38622), the phylogenetically closest species, but formed a separate, well-supported (bootstrap value 98%) sister branch to the O. tsutsugamushi species (Figure  1 ). This value was lower than the 95% 16S rRNA gene sequence threshold recommended by Stackebrandt and Elbers to delineate a new genus without carrying out DNA-DNA hybridization  [21]. Growth was attempted at 28°C in an aerobic atmosphere, which were the culture conditions required for the BME/ CTVM2 cell line, and at 37°C in an aerobic atmosphere in L929 and XTC cell lines.
028 ± 0. 007 μm). Figure  1  A consensus phylogenetic tree showing the position of Occidentia massiliensis strain OS18 T relative to other strains within the family Rickettsiaceae, based on 16S rDNA sequence comparison. GenBank accession numbers are indicated in parentheses. Sequences were aligned using CLUSTALW, and phylogenetic inferences obtained using the Bayesian phylogenetic analysis  [10]  with the TOPALi 2. 5 software (Biomathematics and Statistics Scotland, Edinburgh, UK) within the integrated MrBayes application  [11] , using the HKY85 + Г substitution model. Numbers at the nodes are percentages of bootstrap values from 100 replicates. Anaplasma ovis was used as the outgroup. The scale bar represents a 5% nucleotide sequence divergence.
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position and 16S rRNA similarity to members of the family Rickettsiaceae. Nucleotide sequence similarity levels of these genes suggested that strain OS18 T represents a new genus within the family Rickettsiaceae. It was the first genome of Occidentia massiliensis gen. nov. , sp. nov. The Genbank accession number is CANJ00000000 and consists of 47 large contigs (>1. 5 kb) in 18 scaffolds. Table  2  shows the project information and its association with MIGS version 2. 0 compliance  [14].
Genome annotation Open reading frames were predicted using PRODIGAL with default parameters  [23] , but predicted ORFs were excluded if they spanned a sequencing gap region. The functional annotation of protein sequences was performed using BLASTP against the GenBank and Clusters of Orthologous Groups (COG) databases  [24]. The    prediction of tRNAs and rRNAs was carried out using the tRNAScan-SE  [25]  and RNAmmer  [26]  tools, respectively. Lipoprotein signal peptides and numbers of transmembrane helices were predicted using SignalP  [27]  and TMHMM  [28] , respectively. ORFans were identified if their BLASTP E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. Such parameter thresholds have already been used in previous works to define ORFans.
To estimate the mean level of nucleotide sequence similarity at the genome level between O. massiliensis and another 4 members of the family Rickettsiaceae (Table  3 ), we used the Average Genomic Identity Of gene Sequences (AGIOS) home-made software  [29]. Briefly, this software combines the Proteinortho software  [30]  for detecting orthologous proteins in pairwise comparisons of genomes, then retrieves the corresponding genes and termines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
Occidentia massiliensis (mas. si. li. en'sis. L. gen. fem. n. massiliensis, of Massilia, the Latin name of Marseille, France, where strain OS18 T was first grown, identified and characterized). Gimenez positive and weakly Gram-negative rods. Strictly intracellular. Non-spore-forming. Grows in BME/CTVM2 tick cells at 28°C. The bacteria multiply freely in the cytoplasm, but not the nucleus, of host cells. Monotrichous flagellum. The mean length and width of the bacteria are 1. 23 ± 0. 19 μm and 0. 42 ± 0. 06 μm, respectively. Bacteria exhibit a large periplasmic space of 0. 028 ± 0. 007 μm. The genome is 1,469,252-bp long and contains 1,502 proteincoding and 41 RNA genes. The 16S rRNA and genomic sequences are deposited in GenBank under accession numbers GU937608 and CANJ00000000, respectively.
Bacterial genomes used for the genomic comparisonGenomic comparison of O. massiliensis and other members of the family RickettsiaceaeWe compared the genome sequence of O. massiliensis strain OS18 T to those of O. tsutsugamushi strains Boryong (GenBank accession number NC_009488) and Ikeda (NC_ 010793), and R. prowazekii strain Madrid E (NC_000963) and R. bellii strain RML369-C (NC_007940). Occidentia massiliensis strain OS18 T had a much smaller genome (1,469,252 bp, 2,127,051 bp and 2,008,987 bp, respectively), fewer genes (1,670, 2,216 and 2,005 genes, respectively) and a lower G + C content (29. 05%, 30. 5%, 30. 5% than O. tsutsugamushi strains Boryong and Ikeda (Table6). However, when compared to Rickettsia species, O. massiliensis had a larger genome than R. prowazekii (1,469,252 bp and, 1,111,523 bp, respectively), but smaller than R.
Acknowledgements We are grateful to  Marie-Laure Birg ,  Nathalie Duclos ,  Audrey Borg  and  Denis Pyak  for their technical help. The present work was funded by the  Agence Nationale de Recherche  grant  2010   MALEMAF  (research on emergent pathogens in Africa) and the  Mediterranée-Infection Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Simulated samples of signal and background events are produced using various Monte Carlo (MC) event generators, with the CMS detector response modeled with GEANT4  [17]. The Higgs boson signal samples are produced using the POWHEG  [18]  event generator. The MADGRAPH 5. 1  [19]  generator is used for the diboson, W+jets, Z+jets, and tt samples. The single-top-quark samples, including the tW-, t-, and s-channel processes, are produced with POWHEG and the QCD multijet samples with PYTHIA 6. 4  [20]. The production cross sections for the diboson and tt samples are rescaled to the cross sections from the next-to-leading-order (NLO) MCFM generator  [21] , while the cross sections for the W+jets and Z+jets samples are rescaled to nextto-next-to-leading order (NNLO) cross sections calculated using the FEWZ program  [22] [23] [24].
Triggers Several triggers are used to collect events consistent with the signal hypothesis in the six channels under consideration. For the W(µν)H and W(eν)H channels, the trigger paths consist of several single-lepton triggers with tight lepton identification. Leptons are also required to be isolated from other tracks and calorimeter energy deposits to maintain an acceptable trigger rate. For the W(µν)H channel and for the 2011 data, the trigger thresholds for the muon transverse momentum, p T , are in the range of 17 to 24 GeV. The higher thresholds are used for the periods of higher instantaneous luminosity. For the 2012 data the muon trigger p T threshold for the single-isolated-muon trigger is set at 24 GeV. For both the 2011 and 2012 data, a single-muon trigger with a 40 GeV p T threshold, but without any isolation requirements, is also used for this channel.
To mitigate these effects, all charged hadrons that do not originate from the primary interaction are identified by a particle-flow-based algorithm and removed from consideration in the event. In addition, the average neutral energy density from pileup interactions is evaluated from particle-flow objects and subtracted from the reconstructed jets in the event and from the summed energy in the isolation cones used for leptons, described below  [32]. These pileup-mitigation procedures are applied on an event-by-event basis. Jets are reconstructed from particle-flow objects using the anti-k T clustering algorithm  [33] , with a distance parameter of 0. 5, as implemented in the FASTJET package  [34, 35]. Each jet is required to lie within |η| < 2. 5, to have at least two tracks associated with it, and to have electromagnetic and hadronic energy fractions of at least 1%.
The CSV btagging algorithm  [40]  is used to identify such jets. The algorithm combines the information about track impact parameters and secondary vertices within jets in a likelihood discriminant to provide separation between b jets and jets originating from light quarks, gluons, or charm quarks. The output of this CSV discriminant has values between zero and one; a jet with a CSV value above a certain threshold is referred to as being "b tagged". The efficiency to tag b jets and the rate of misidentification of non-b jets depend on the threshold chosen, and are typically parameterized as a function of the p T and η of the jets. These performance measurements are obtained directly from data in samples that can be enriched in b jets, such as tt and multijet events (where, for example, requiring the presence of a muon in the jets enhances the heavyflavor content of the events).
Uncertainties The systematic uncertainties that affect the results presented in this article are listed in Table  8  and are described in more detail below. The uncertainty in the CMS luminosity measurement is estimated to be 2. 2% for the 2011 data  [49]  and 2. 6% for the 2012 data  [50]. Muon and electron trigger, reconstruction, and identification efficiencies are determined in data from samples of leptonic Z-boson decays. The uncertainty on the event yields resulting from the trigger efficiency estimate is 2% per lepton        7. Top left: Dijet p T distribution in the Z+jets control region for the Z(ee)H channel. Top right: p T distribution in the tt control region for the W(µν)H channel. Bottom left: CSV min distribution for the W+HF high-boost control region for the Z(νν)H channel.
Bottom right: Z+HF high-boost control region for the Z(νν)H channel. The bottom inset in each figure shows the ratio of the number of events observed in data to that of the Monte Carlo prediction for signal and backgrounds. The impact of the jet energy scale uncertainty is determined by recomputing the BDT output distribution after shifting the energy scale up and down by its uncertainty. Similarly, the impact of the jet energy resolution is determined by recomputing the BDT output distribution after increasing or decreasing the jet energy resolution. An uncertainty of 3% is assigned to the event yields of all processes in the W( ν)H and Z(νν)H channels due to the uncertainty related to the missing transverse energy estimate. Data/MC b-tagging scale factors are measured in heavy-flavor enhanced samples of jets that contain muons and are applied consistently to jets in signal and background events.
Diboson signal extraction As a validation of the multivariate technique, BDT discriminants are trained using the diboson sample as signal, and all other processes, including VH production (at the predicted standard model rate for a 125 GeV Higgs mass), as background. This is done for the 8 TeV dataset only. The observed excess of events for the combined WZ and ZZ processes, with Z → bb, differs by over 7 standard deviations from the event yield expectation from the background-only hypothesis. The corresponding signal strength, relative to the prediction from the diboson MADGRAPH generator mentioned in Section 2, and rescaled to the cross section from the NLO MCFM generator, is measured to be µ VV = 1. 19 +0. 28  -0. 23.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the  Austrian Federal Ministry of Science and Research and the Austrian Science Fund ; the  Belgian Fonds de la Recherche Scientifique , and  Fonds voor Wetenschappelijk Onderzoek ; the  Brazilian Funding Agencies (CNPq, CAPES ,  FAPERJ , and  FAPESP) ; the  Bulgarian Ministry of Education and Science ; CERN; the  Chinese Academy of Sciences, Ministry of Science and Technology , and  National Natural Science Foundation of China ; the  Colombian Funding Agency (COLCIENCIAS) ; the  Croatian Ministry of Science, Education and Sport ; the  Research Promotion Foundation , Cyprus; the  Ministry of Education and Research, Recurrent  financing contract  SF0690030s09  and  European Regional Development Fund , Estonia; the  Academy of Finland ,  Finnish Ministry of Education and Culture , and  Helsinki Institute of Physics ; the  Institut National de Physique Nucléaire  et.
The budding yeast transcriptome of mitotic growth and meiotic differentiation has been the focus of numerous studies based upon microarrays typically covering entire genes or their 3 -regions  (4, 5) , tiling arrays covering the complete genome on both strands  (27, 28)  and RNA-sequencing (RNA-Seq). This recent method for RNA profiling employs ultra high-throughput DNA sequencing  (15, 19, 29). As genomics technology improved, it became feasible to determine transcript-splicing patterns, and to identify multiple transcript isoforms from single genes  (15, 20, 30). High-throughput DNA sequencing and RNA profiling technologies spawned the development of bioinformatics tools useful for finding biologically relevant regulatory motifs. The TRANSFAC database provides information on DNA binding transcription factor (TF) target sites represented by position weight matrices (PWMs), which help gain insight into the regulatory composition of promoters  (9, 10, (31) (32) (33) (34).
A PWM is built by aligning the sequences of all known binding sites a given TF interacts with, and logtransforming the number of observations of each nucleotide at each position  (35, 36). This method is therefore employed to predict a range of sequence motifs that likely interact with DNA binding TFs of interest, such as Ume6 (for more details, see the ScerTF database at http://stormo. wustl. edu/ ScerTF  (37) ). The present study is designed to identify, classify and validate developmentally regulated isoforms showing increased UTR length, and, importantly, to gain insight into the transcriptional mechanism that controls meiosis-specific isoforms containing extended 5 -UTRs. We investigated RNA concentration and transcript architecture in a comprehensive sample set comprising synchronously growing hap-loid MATa cells, fermenting or respiring MATa/␣ cells, a starving meiosis-deficient MAT␣/␣ strain and sporulating MATa/␣ cells  (27, 28).
Our genomic analysis is based on tiling arrays covering both strands of the entire yeast genome, and was confirmed using RNA-Seq data (Becker et al. , in preparation), and molecular biological methods. We found that predicted URS1 motifs bound by Ume6 are statistically significantly enriched in promoters that mediate the expression of distinct transcript isoforms. This led to our discovery of a direct role for the conserved HDAC Rpd3 and its DNA binding interactor Ume6 in repressing meiotic transcript isoforms during vegetative growth.
Regulatory motif enrichment analysis The average size of a yeast promoter is ∼300 bases and the median of 5 -mUTR size distribution is 241 bases. We therefore investigated the enrichment of predicted URS1 sites in a region restricted to 100 bases upstream of 5 -mUTRs, using PWMs M01503 and M01898 provided by the TRANSFAC Professional database release 2011. 4 (42). The Promoter Analysis Protocol (PAP)  (43)  was executed using the TRANSFAC minSUM profile. Predicted URS1 motifs were filtered using a minCSS (core score similarity) cut-off set at 0. 9, and a minMSS (matrix score similarity) cut-off set at 0. 7. In addition, motif enrichment was estimated using the algorithms implemented in MEME  (44) , DREME  (45) , Tomtom  (46)  and Clover (47) software. In the case of Clover, enrichment was estimated as compared to regions of 100 bp upstream of the cognate protein-coding genes.
Labelled DNA was precipitated and its specific activity was determined with a Bioscan Counter (Packard, USA) in 3 ml of scintillation buffer (Ultima-Gold-MV, USA). The membrane was prehybridized at 65 • C and hybridized overnight in 25 ml buffer (6× SSC, 5× Denhardt's, 10 mM PO 4 buffer, 0. 5% sodium dodecyl sulphate (SDS)) containing 500 l salmon sperm DNA (7 mg/ml) and 200 l of radioactive probe denatured at 100 • C for 10 min and 5 min at 4 • C. The membrane was washed in 1× SCC, 0. 1% SDS at 65 • C three times for 15 min and exposed to a Storage Phosphor Screen for 30 h. Data were produced with the Storm Imager 825 (General Electric, USA) at the default settings. Image files were processed and band intensities were computed using ACT1 as an internal control with the ImageQuantTL 7. 0 software (General Electric, USA).
Reverse transcriptase-PCR (RT-PCR) assays To design RT-PCR oligonucleotide primers using the Primer3 online tool (simgene. com/Primer3; (48)) we retrieved the target gene ORFs from Saccharomyces Genome Database (SGD; yeastgenome. org), and then aligned it with the SK1 genome using the online Saccharomyces Genome Resequencing Project Browser software (sanger. ac. uk/research/projects/genomeinformatics/sgrp. html; (49) ). Similarly, DNA sequences corresponding to newly annotated mUTRs were retrieved from SGD based on their genome coordinates and aligned to the SK1 genome. RT-PCR reactions were done with 2 g of RNA that was reverse transcribed with RT (High Capacity cDNA Reverse Transcription kit; Life Technologies, USA) and amplified using Taq Polymerase (Qiagen, France) at 60 o C for 26 cycles.
Becker et al. , in preparation). Many of the transient 5 -mUTR expression patterns were broadly reminiscent of early meiotic genes that are repressed by the Ume6/Rpd3/Sin3 complex in mitosis. Thus, we asked if the Ume6 DNA binding target motif URS1 was enriched in the immediate upstream regions of our target genes. To this end, we used the Promoter Analysis Protocol (see Materials and Methods) based on two PWMs (M01503, M01898) for URS1 provided by the TRANSFAC database  (34). This approach complements and vastly extends information about the classical URS1 motif reported 18 years ago  (56). We found that the algorithm implemented in Clover identified predicted URS1 motifs as being signif- icantly enriched (M01503 P-value 0. 001, M01898 P-value 0. 008; see Materials and Methods). A total of 21 to 61 out of 92 UTRs (23-66%) contain a URS1 depending on how stringent the filtration criteria were set (Supplementary Additional file 1, see Materials and Methods).
We then explored the biological relevance of predicted URS1 motifs in the target gene promoters. First, we further examined target transcript architecture by RT-PCR using primers that detect the mitotic isoform (CFT2, RTT10), and two combinations of primer pairs that reveal the meiotic isoform in both SK1 and JHY222 WT strains (mCFT2/lmCFT2 and mRTT10/lmRTT10; Figure  4A  and B ; quantified band intensities are shown for mUTRs in Supplementary Additional file 3). RT-PCR data reproduced the expression patterns observed with tiling arrays; the weak PCR signal we detect in respiring cells (YPA) might be due to the assay's high sensitivity, promoter leakage or premature entry into meiosis of a small fraction of cells in YPA pre-sporulation medium. Next, we monitored the expression of meiotic isoforms during vegetative growth in the absence of a functional Rpd3/Ume6 repressor complex.
ACKNOWLEDGEMENTS We are grateful to  Angelika Amon  for her critical reading, and  Aaron Mitchell  and  Nancy Kleckner  for providing Nucleic Acids Research, 2015, Vol. 43, No. 1 127 yeast strains. We thank  Olivier Collin  for providing us with access to the  GenOuest bioinformatics infrastructure , and  Olivier Sallou  for system administration.
As reference strains for Box-PCR and ARDRA, five fast growing rhizobial strains were used: Ensifer meliloti (LMG 85217), Ensifer terangae (USDA 4894), Ensifer saheli (USDA 4893), Rhizobium etli (CFN 42), and Rhizobium leguminosarum bv. trifolii (LMG 8820). These reference strains were obtained from the diazotrophic bacteria culture collection at Embrapa Agrobiologia. The gel images were analyzed with the BioNumerics 7. 0 software (Applied Maths, Kortrijk, Belgium) followed by clustering analysis to generate similarity dendrograms with the unweighted pair group method with arithmetic mean (UPGMA) and the Jaccard index.
Basically, if permanent-plot data are available, the tree status (cut or alive) can be seen as a binary variable. The individual probability of being harvested can then be modelled through a logistic regression as a function of some tree and plot features such as diameter at breast height (dbh: 1. 3 m), tree vigour and quality, plot basal area and density, etc. Whereas the harvest algorithm is most often designed on the basis of theoretical harvest rules, the statistical harvest model is fitted to empirical observations. In the case of departures from the rules, the statistical harvest model can be expected to better represent the way the forest is really harvested. Thurnher et al. (2011)  recently fitted such a harvest model to be used with the Austrian growth simulator, MOSES. Regardless of the nature of the harvest procedure, algorithm or statistical model, a major limitation remains the compatibility with growth predictions.
Database The data that were used to fit the harvest models were taken from the provincial control survey. In the province of Quebec, harvesting on public lands has long been carried out by private companies. To avoid high grading, Quebec's Ministry of Natural Resources (MNR) has set some good practice standards for a large array of silvicultural treatments. Control surveys are carried out by the MNR in cut blocks to check that harvesting was properly done according to these standards. The survey consists in establishing variable-radius permanent plots using a 2-m 2 ha -1 basal area factor wedge prism in the cut block a few weeks before harvesting. All trees with a dbh of at least 9. 1 cm are tallied from the plot centre in a clockwork direction. For each tree, the species is recorded and the dbh is measured in 2-cm diameter classes.