dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of differentially expressed contigs and GO term enrichment Contigs with differences in expression among experimental modalities were identified via pairwise differential expression analysis using the DESeq package in the R statistical software  [82]. The following criteria were used to identify differentially expressed contigs: change in expression ≥ 2-fold and RPKM ≥ 1. 8 in at least one of the experimental modalities. The TopGO package  [83]  was subsequently used to identify the Gene Ontology (GO  [84] ) biological processes significantly enriched in contigs with a differential regulation among experimental modalities, considering contigs with a minimum length of 400 nucleotides, an RPKM value ≥ 1. 8 in at least one experimental modality and a difference in expression ≥ 2-fold between the experimental modalities compared.
Data access The raw reads have been deposited in the NCBI Sequence Read Archive (SRA) database (BioProject PRJNA234492, SRR1139294 to SRR1139349). The 65,558 contigs and the corresponding 32,138 predicted peptides sequences in ALOMYbase are available at https://iant. toulouse. inra. fr/ A. myosuroides2013?download=1.
52E-47 Up over the treated modalities Up over the treated modalities CYP72A LOLSS002187 d [19] Lolium sp. ALS Alomy016824 0. 00 Down until 24HAT, then up Stable CYP81B1 LOLSS010577 d [19] Lolium sp. ALS Alomy007987 0. 00 Stable but a peak at 24HAT Stable a CYP, cytochrome P450 [19] ne or candidate gene conferring NTSR to herbicides inhibiting acetyl-coenzyme A carboxylase (ACCase) or acetolactate synthase (ALS) c Accession in the Rice Genome Annotation Project database, http://rice. plantbiology. msu. edu/Accession in LOLbase[19].
While the influence of different LBC perturbation approaches has thus been partly studied experimentally in different regional NWP contexts, the purpose of the present paper is twofold. Firstly, while previous investigations have been mostly experimental, a theoretical development of underlying equations of errors and ensemble perturbations in the data assimilation cycle is presented here, in order to discuss the relative contributions of observation errors, LBC errors and model errors. Secondly, another purpose of the paper is to compare experimentally the spread associated to the three aforementioned options of LBC perturbations in the context of the ALADIN-France ensemble data assimilation system, for which the choice and effect of LBC perturbations is likely to vary at different steps of the ensemble data assimilation cycle.
The ALADIN-France LAM The ensemble experiments which will be described in Section 2. 3 have been performed using the ALADIN-France data assimilation and forecasting system. ALADIN-France is a high-resolution limited-area spectral model (e. g. Horanyi et al. , 1996)  with bi-periodic extension of the computation domain. It uses elliptical truncation of double-Fourier series, semi-implicit semi-Lagrangian integration scheme, fourth order horizontal diffusion and digital filter initialisation (DFI). It is run over a European domain centred over France, with a Lambert grid, a 7. 5-km horizontal resolution (in both x and y directions) and 70 vertical levels from the surface up to 0. 1 hPa. It is built on the basis of the global model IFS/ARPEGE and keeps the same dynamics, physics and vertical coordinate (a terrain-following pressure hybrid coordinate).
The one-way nesting is achieved by using a Davies relaxation scheme  (Davies, 1976 (Davies, , 1983;; Davies and Turner, 1977). Initial LBCs (i. e. specified at 0-h forecast range) are determined by the ALADIN-France 3D-Var analysis itself, while the LBC specified at 3-and 6-h forecast ranges correspond to 3-and 6-h forecasts of the global ARPEGE system, which are interpolated onto the ALADIN-France grid. Using analysis fields as initial LBC data in all experiments avoids generating spurious spin-up behaviour in the early forecast ranges, as shown in  Fischer and Auger (2011). This choice is also consistent with more recent experimental findings for the AROME assimilation at Me´te´o-France (Pierre Brousseau, personal communication). An incremental DFI is additionally applied to ALADIN-France initial conditions, following the study of Fischer and Auger, 2011.
Ensembles description Three regional ensemble data assimilation experiments are performed using the ALADIN-France model. Each ensemble consists of six ALADIN members, and it is run in the same way as described, for example, by  Houtekamer et al. (1996) ,  Fisher (2003)  and  Berre et al. (2006). For each member of each ensemble, and for each analysis step, observation perturbations E o are added to observation values, in order to simulate observation errors. These observation perturbations E o are provided by random draws of the observation error covariance matrix R: E o ¼ R 1 2 f, where z are random Gaussian draws which are spatially uncorrelated and which have zero mean and unit variance (i. e. f $ N ð0; IÞ, I being the identity matrix in observation space). In addition to these explicit perturbations of observations, the ensembles use implicit perturbations of the background, provided by the perturbation evolution during the 6-h data assimilation cycle.
Another purpose of interest is to examine the possibility to simulate LBC errors by using random draws of a specified error covariance model, as done in the PLBC ensemble. This will also include a sensitivity experiment with respect to the amplitude of such drawn perturbations: first, because it is likely to be a sensitive component of the PLBC ensemble, and second, because this is also a way to further document the effect of LBC perturbations in the LAM data assimilation ensemble.
Data collection and management During both periods, general data of patient flow are collected on a daily basis in each centre by the research team on a paper CRF, including: Total number of patients, Number of patients aged 18 to 64 not presenting secondary to HIV exposure, Number of patients aged 18 to 64 not presenting secondary to HIV exposure who are tested for HIV (either rapid test or Standard enzyme linked immunosorbent assay) as ordered by a physician, Number of patients whose HIV diagnosis is confirmed to be positive by serology, Number of patients newly diagnosed HIV positive presenting for a specialist consultation and their characteristics (CD4 counts, viral load, HIV, HBV, HCV and Syphilis serological status and their clinical presentation). These data are downloaded with the help of the laboratory personnel in each hospital and the infectious disease specialist.
1 ) and offering rate, acceptance rate and screening rate on the nurse page) on an e-CRF. The quality of these data is controlled on site by the monitoring team. Upon study completion, ED flow data, including patient baseline characteristics, degree of severity and time spent in the ED will be extracted from the electronic patient-level ED database. A data manager collects all study data on a secure password-protected server. A quality team, independent from the coordination team, will perform an internal audit of data completeness and will ensure the individual data matches the data collected on the e-CRF.
Additional analyses Factors associated with patient refusal of nurse-driven targeted HIV screening In half the centres, all patients who have completed the DICI-VIH questionnaire, are at risk of HIV exposure and eligible for nurse-driven targeted HIV screening are considered over randomly selected 32-h observation periods of ED activity during 7 consecutive days. The patient characteristics reported in the DICI-VIH questionnaire, history of HIV testing and perceived HIV risk will be compared between patients refusing to be tested and patients accepting the rapid test in the context of nurse-driven targeted HIV screening. A descriptive analysis of these data will be performed.
Patient perceptions of nurse-driven targeted HIV screening The respondents' perceptions of the DICI-VIH questionnaire, of being offered an HIV rapid test, of result disclosure and of HIV screening in general are collected over a week-long period in 4 of the centres. The study team interviews all the patients who fill out the DICI-VIH questionnaire using a face-to-face questionnaire. A descriptive analysis of these data will be performed. The patients refusing to fill the DICI-VIH questionnaire are not considered in this study; this is a limitation of the evaluation.
Ethical aspects The study was approved by the Ile-de-France XI Committee for Patient Protection (No. 13084, January 21, 2014, N°IDRCB: 2013-AO1569-36) and by the French Data Protection Authorities responsible for database security (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé (CCTIRS) and Commission Nationale de l'Informatique et des Libertés (CNIL)).
Funding/support The DICI-VIH study is funded by the Agence Nationale de Recherche sur le Sida et les Hépatites Virales (ANRS France Recherche Nord&sud Sida-hiv Hépatites), Paris, France. This work is supported by a grant from Assistance Publique -Hôpitaux de Paris (AP-HP), "Doctorat en recherche infirmière" programme, Paris, France. Rapid tests are provided free of charge by bioMérieux, Marcy l'Etoile, France. These 3 supporting entities have no involvement in the study design, data collection, analysis, interpretation, decision to publish, or preparation of the manuscript.
Acknowledgments The authors would like to express their gratitude to  C. Semaille ,  D. Pateron ,  F. Simon ,  K. d'Almeida ,  J. Côté ,  H. Piquet ,  M. Cachanado ,  L. Bérard  and all the clinical research nurses ( CRC-Est ) for contributing expertise and support to the conception of the study as well as  Espérie Burnet  for her help in re-reading of the manuscript. This research was supported through the authors' involvement in the European Science  Foundation Research Network Programme  ' REFLECTION' -09-RNP-049.
Acknowledgements We acknowledge both  ALISTORE-ERI  and  RS2E  institutions to fully support this work and  M. -L. Doublet ,  A. S. Prakash ,  K. Ramesha ,  P. Melin ,  V. Sarou-Kanian ,  M. Deschamps  and  J. Kurzman  for fruitful discussions. D. G. and H. V. thank  CNRS's infrastructure RENARD  ( FR3443 ) for EPR facilities.
Current estimates place the present-day total core-mantle boundary (CMB) heat flux in the range 10-16 TW  (Lay et al. , 2008; Wu et al. , 2011) , equivalent to 25-35% of the total mantle heat flux  (Jaupart et al. , 2007). Mantle heat transport is mostly advective, carried by the global-scale circulation associated with surface plate motions and by smaller scale motions such as thermal plumes  (Schubert et al. , 2001). Both of these elements of the mantle circulation affect the geodynamo by regulating the heat flux on the CMB, and are variable on time scales commensurate with the geomagnetic superchrons and their spacing. Phanerozoic examples of this variability include the change in mantle flow associated with the aggregation and breakup of supercontinent Pangaea  (Zhang et al. , 2010)  and the Cretaceous pulse in Large Igneous Province (LIP) activity  (Larson, 1991).
Lower Mantle Superplumes and CMB Heat Flux Seismic tomographic images of the lower mantle  (Dziewonski et al. , 2010)  reveal two large, low shear wave velocity provinces (called LLSVPs) in the D"-region above the CMB, one presently located below Africa and surrounding oceans, the other below the South Pacific. In this paper, these two LLSVPs are referred to as "superplumes" or "piles" for simplicity, although the term "superplume roots" is actually more appropriate, because these structures are located at the base of larger mantle anomalies and are thought to be stabilized by negative buoyancy due to compositional differences with respect to the rest of the lower mantle  (Lekic et al. , 2012). Hotspot reconstructions indicate that the two LLSVPs have existed in the lower mantle since the breakup of supercontinent Pangaea and possibly earlier  (Burke et al.
Although LLSVP locations may have changed little since Pangaea breakup, significant time variations in CMB heat flux result from variations in the height of the lower mantle superplumes associated with them. Figure  1  depicts schematically the growth and partial collapse of two lower mantle superplumes with fixed locations. Figure  1A  shows the superplume growth phase and Figures  1B, C  show a two-stage partial collapse event. Assuming steady state heat conduction across the dense mantle material that forms the superplume base in the D"-layer, both the average CMB heat flux q and its lateral variation δq are greater in the growth stage (Figure  1A ) compared to the collapsing stages (Figures  1B, C ), a consequence of the large thermal gradient and elevated CMB heat flux where the basal layer is thin, which more than compensates for the small thermal gradient and reduced CMB heat flux where the basal layer is thick beneath the superplumes  (Amit and Olson, 2015).
In most mantle GCMs the behavior of lower mantle superplumes are closely linked to the mantle global circulation and the surface plate motions. In contrast, in order for our model to be applicable, it is necessary that the lower mantle superplumes rise and partially collapse independently of the mantle global circulation. By "independently" we mean that the superplumes respond to major changes in the global circulation, for example, by collapsing after Pangaea assembly to produce the KRS and by growing after Pangaea breakup to produce hyper-reversing dynamo behavior in the Jurassic, but they also fluctuate at other times, collapsing to produce the CNS and the post-CNS pulse of LIP activity and re-building since that time. This additional freedom in superplume behavior is generally not seen in mantle GCMs in which the global circulation is strongly controlled by surface plate motions  (Zhang and Zhong, 2011).
Although our estimate of q s /q p = 0. 725 likely depends on our choice of dynamo model parameters, our qualitative inferences are more robust, in particular, our prediction of low CMB heat flux during the CNS and other superchrons. The longevity of the dual superplume structure in the lower mantle remains another controversial issue. Volcanic hotspot reconstructions  (Torsvik et al. , 2006)  point to the existence of two superplumes far back into the Paleozoic, whereas mantle GCMs predict a single superplume during Pangaea assembly prior to 330 Ma  (Zhang et al. , 2010)  and superchron conditions in the core during the transition to Pangaea breakup  (Olson et al. , 2013). It has been argued  (Courtillot and Olson, 2007)  that the KRS and CNS superchrons were terminated during the formation of plume heads that created, following 10-20 Myr of mantle ascent, the Siberian and Deccan Traps.
Oxygen profiles were measured using an oxygen minisensor OX 100 coupled to a picoammeter (PA 2000, UNISENSE) and a micro-manipulator using a profix data acquisition software (UNISENSE). After recovering on board, sediment cores were immediately transferred to a cold room (∼8 • C) for sub-sampling. Sediment cores were cut into 2 cm thick layers and then frozen at -80 • C for further nucleic acid extractions. Two cm 3 of sediments were collected from each layer and fixed for 4 h in PBS (Phosphate Buffered Saline)/formaldehyde (3% final) at 4 • C, washed twice with PBS and stored in an ethanol/PBS (1:1, vol/vol) buffer at -20 • C for fluorescence in situ hybridization (FISH).
ARISA An Automated fingerprinting method of Ribosomal Intergenic Spacer Analyses (ARISA) was carried out for rapid monitoring of microbial community composition in all sediment samples  (Fisher and Triplett, 1999)  using DNA 7500 R Chip on an Agilent 2100 Bioanalyzer R (Agilent Technology, Santa Clara, USA). ARISA-PCR was performed with primers targeting the archaeal 16S-23S intergenic spacer region (Table  1 ). PCR conditions were as described in  Vigneron et al. (2013). All data were recovered using the 2100 Expert R software  (Agilent Technology). Comparisons between samples were allowed by normalization of the data by the raw signal intensity before statistical analyses. Non-metric multidimensional scaling (NMDS)  (Kruskal, 1964)  was carried out based on the Bray-Curtis similarity index  (Bray and Curtis, 1957)  using the software package Vegan  (Oksanen et al.
These fusion primers were designed based on MIDs recommended by Roche and to minimize secondary structures as predicted by the OligoAnalyzer software  (Owczarzy et al. , 2008). The amplifications of the 16S rRNA genes were performed under the following conditions: 10 min at 95 • C, then 30 cycles for Bacteria and 35 cycles for Archaea including 30 s at 95 • C, 45 s at 58 • C and 45 s at 72 • C and a final step of 6 min at 72 • C. PCR amplifications were performed in independent replicates (X and Y duplicates) using the following reagents in a 25 µL reaction volume: 1X Brilliant III Ultra-Fast SYBR R Green QPCR Master Mix (Agilent Technologies, Palo Alto, CA), 0. 5 µM of each primer (Eurofins MWG Operon, Ebersberg, Germany) and 1 µL of purified DNA template. The final volume was adjusted to 25 µL with sterile water.
Pyrosequencing Data Analyses To minimize the effect of random sequencing errors  (Huse et al. , 2007; Kunin et al. , 2010) , pyrosequencing reads analyses and filtering were performed from the sff file using the Mothur pipeline  (Schloss et al. , 2009). We allowed for 1 mismatch to the barcode and 2 mismatches to the primer. We also removed sequences (i) shorter than 200 bp, (ii) containing homopolymers longer than 8 bp (iii) that aligned to the incorrect region within the 16S rRNA gene (Silva release 115, www. arb-silva. de), (iv) identified as chimeras using Uchime algorithm  (Edgar et al. , 2011; Schloss et al. , 2011) , and (v) affiliated to Archaea when Bacteria was targeted and inversely. Using the tag combinations, sequences were assigned to their respective sample. In order to minimize the impact of higher number of sequences per sample, all the following analyzes were performed on the same number of sequences for the bacterial (1013 sequences) and the archaeal (686 sequences) regions.
Taxonomic assignments of the reads were performed using the Mothur version of the "Bayesian" classifier  (Schloss et al. , 2009)  on both SILVA database (release 115, 418,497 sequences of Bacteria and 17,530 sequences of Archaea, www. arb-silva. de) and a personal database composed of deep-sea marine sediment sequences from reference publications (1677 bacterial and 786 archaeal sequences, as described in  Cruaud et al. , 2014). Results obtained from the two databases were then compared. If the results obtained for some microbial taxa were different depending on the database used, sequences affiliated to these taxa were extracted and BLAST analyzed against GenBank (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Consistencies of SILVA taxonomic affiliations were then checked by phylogenetic reconstructions with our reference database sequences and BLAST hits obtained.
Phylogenetic trees were estimated using a distance method. Pairwise nucleotide sequence divergences were calculated using a Kimura-2-parameter model of substitution  (Kimura, 1980)  and Neighbor-Joining trees  (Saitou and Nei, 1987)  were reconstructed using MEGA 4. 0. 2 (Tamura et al. , 2007). Robustness of topologies was assessed by bootstrap procedures using 100 replicates  (Felsenstein, 1985). Then, our reference database was updated and supplemented according to the results obtained. Taxonomic assignments of the reads were finally performed on our updated and supplemented database (2549 bacterial and 803 archaeal sequences, Table  S3 ). Only microbial groups retrieved in both PCR duplicates (X and Y) were conserved for further analyses. Non-metric multidimensional scaling (NMDS)  (Kruskal, 1964)  was carried out based on the Bray-Curtis similarity measure  (Bray and Curtis, 1957).
Analysis of similarity (ANOSIM) was used to determine significant differences between sampling locations based on a Bray-Curtis similarity measure. SIMPER (Similarity Percentages) analyses were used to determine the contribution of each microbial groups to the Bray-Curtis dissimilarity  (Clarke, 1993). These statistical analyses were performed using the software package Vegan  (Oksanen et al. , 2012)  based on the software environment R (v. 3. 0. 2). The raw sequencing data have been submitted to the NCBI database under SRA accession number SRP056234. ANME-1F Q-PCR ANME-1 GCT-TTC-AGG-GAA-TAC-TGC 200 60 1. 1 Lloyd et al. , 2011 ANME-1R TCG-CAG-TAA-TGC-CAA-CAC ANME-2cF Q-PCR ANME-2c TCG-TTT-ACG-GCT-GGG-ACT-AC 224 60 1. 1 Vigneron et al. , 2013 ANME-2cR TCC-TCT-GGG-AAA-TCT-GGT-TG ANME-3F Q-PCR ANME-3 GGA-TTG-GCA-TAA-CAC-CGG 234 60 1.
Archaeal Diversity Pyrosequencing was used to describe microbial community diversity and distribution in the selected sites. 32. 4% of total sequences were removed during quality control procedures (primer and barcode mismatches, long homopolymers, short sequences: 15%, sequences not aligned with the correct region: 15. 8% and chimeras: 1. 6%). After filtering, a total of 31,556 partial 16S rRNA gene sequences were used for archaeal diversity analyses. Taxonomic affiliation of these sequences highlighted various community structures according to sampling sites. Bray-Curtis similarities between samples are shown in Figure  3A  and percentages of the different archaeal lineages are reported on Figure  S2  (Results obtained with SILVA database are presented on Table  S5 ). Statistical analyses suggested that microbial communities from the reference sediments were significantly distinct from those observed in cold seep sediments (ANOSIM, p < 0.
Bacterial Diversity 24. 7% of total sequences were removed during quality control procedures (primer and barcode mismatches, long homopolymers, short sequences: 11. 7%, sequences not aligned with the correct region: 5% and chimeras: 8%). After filtering, a total of 46,598 partial sequences for the bacterial 16S rRNA gene were used for diversity analyses. Bray-Curtis similarities in bacterial diversity are shown in Figure  4A  and proportions for the different bacterial groups are reported on Figure  S3  (Results obtained with SILVA database are presented on Table  S5 ). Statistical analyses showed a clear dissimilarity between bacterial community compositions from the reference sediments and all other sites (ANOSIM, p < 0. 001, Figure  4A ). SIMPER analyses highlighted that number of reads affiliated to specific lineages of Gammaproteobacteria were mainly responsible for these differences.
Quantitative Real-time PCR In order to get a more comprehensive picture of microbial communities inhabiting the Sonora Margin sediments, we chose to combine our NGS results with two complementary approaches: FISH microscopy and Q-PCR analysis, as already suggested  (Cruaud et al. , 2014). Then, microbial groups highlighted by pyrosequencing and SIMPER analyses were also analyzed by Q-PCR. Thereby, relative abundances of ANME-1, ANME-2, ANME-3, and MBG-D for Archaea and DSS (Desulfosarcina-Desulfococcus group), DBB (Desulfobulbus group) and candidate division JS-1 for Bacteria were estimated by Q-PCR (Figure  5 ). Due to the very few data available on microorganisms from cold seeps, the number of 16S rRNA gene copy per cell remains uncertain. We thus considered a single 16S rRNA gene copy for each quantified populations, as previously suggested  (Vigneron et al.