dataesr/scientific-paragraphs-categorization huggingface dataset:
Temperature was maintained at 30uC using a thermostated heater in an insulated box (Life Imaging Services). The system was controlled using Andor Revolution IQ software (version 2. 0). For dual color Cherry and GFP imaging, 3D stacks of 36 planes over 7 mm (0. 2 mm Z-step) were obtained at 400 ms and 200 ms acquisition time for mCherry and GFP respectively. Time lapse analysis of GFP foci was performed in 2D, acquiring stacks of 50 frames of 200 ms following HO induction. Stacks were acquired at 2 and 5 min intervals for yku and wt strains respectively starting at 8 minutes after HO induction. Controls were done in the overnight growth medium without addition of galactose.
Particle tracking, MSD calculation and fluorescence quantification Particle tracking experiments and MSD calculations were carried out as described previously,  [70]  with modifications of the Image J Particle Detector and Tracker plugin to the following settings: Radius = 4, CutOff = 0, percentile = 0,1, displacement 10. Only tracks of more than 15 consecutive frames were scored. Diffusion coefficients (D) were calculated from the slope of the average MSD curve at the first 2 seconds. D of the non-induced, uncut locus was set to 100%. Intensities of the fluorescent foci were obtained with the Nikon NIS 3. 2 AR element program using the intensity quantification line tool. Intensities of the pixels crossing the fluorescent focus were summed and normalized to the fluorescence intensity of the same number of pixels in a background region that does not contain the focus.
This work was supported by the  Institut Universitaire de France , the  Human Frontier Science Organisation  ( RGP0044/2011 ), the  Fondation Association de la Recherche contre le Cancer  ( ARC, fellowship  to FG and  SFI 2010 1201818 ), the  Fondation pour la Recherche Medicale (FRM, fellowship to MD) , the  Ministere de l'Education Nationale et de la Recherche  (PhD fellowship to HS) and the  Agence Nationale de Recherche  ( ANR-06-BLAN-0280-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
INTRODUCTION Pancreatic adenocarcinoma (PDAC) is the fourth leading cause of cancer death, with a median survival of 6 months and a dismal 5-year survival rate of less than 5%. Importantly, PDAC could move up to the second place as cause of cancer death as early as 2020 according to a new report from the Pancreatic Cancer Action Network. Moreover, using information from the Surveillance Epidemiology and End Results database, in 2030 the number of patients with a PDAC will represent more than a two-fold increase over the current rate in the occidental world  [1]. Based on these data the number of deaths from PDAC will exceed those from breast and colorectal cancer, and will be surpassed only by the loss of life from lung cancer. PDAC is one of the most intrinsically drugresistant tumors and resistance to chemotherapeutic agents is a major cause of treatment failure.
Sensitivity to 5-aza-dC in PDAC does not correlate with expression of Dnmt1, Dnmt3A or Dnmt3B Because, on one hand, the 5-aza-dC is an inhibitor of the DNMT1 activity and, on the other hand, it was proposed a relationship between sensitivity to the drug and DNMT1 expression in PDAC cells, we analyzed the correlation between DNMT1 as well as DNMT3Aand DNMT3B-related enzymes with the sensitivity of the cells to the treatment with 5-aza-dC. To this end we measured the expression of all this three transcripts in the RNA purified from the xenografts and we correlated these data with the DL50 obtained on the xenograft derived primary cultures. As shown in Figure  3 , the R 2 for DNMT1 is 0. 0555, for DNMT3A is 0. 0021 and for DNMT3B is 0. 0028 which are extremely low indicating no correlation. These results strongly suggest that the sensitivity to 5-aza-dC is independent of the DNMTs expression levels and therefore measuring their levels is clinically irrelevant.
PDAC samples and cell culture Consent's forms of informed patients were collected and registered in a central database. The tumor tissues used for xenograft development was deemed excess to that required for the patient's diagnosis. Two types of samples were obtained, namely Endoscopic Ultrasound-Guided Fine-Needle Aspiration (EUS-FNA) biopsies from patients with unresecable tumors, and tumor tissues from patients undergoing surgery. PDAC samples were mixed with 100 µl of Matrigel (BD Biosciences) and implanted with a trocar (10 Gauge, Innovative Research of America, Sarasota, FL) in the subcutaneous right upper flank of an anesthetized and disinfected mouse. When tumors reached 1 cm 3 , mice were sacrificed and tumors were removed. To obtain primary cell cultures of these tumors, xenografts were splited into several small pieces and processed in a biosafety chamber: after a fine mincing, they were treated with collagenase type V (ref C9263; Sigma) and trypsin/EDTA (ref 25200-056; Gibco, Life Technologies) and suspended in DMEM supplemented with 1% w/w Penicillin/Streptomycin (Gibco, Life Technologies) and 10% Fetal Bovine Serum (Lonza).
Gene expression microarrays RNAs extraction was performed according to Chirgwin's protocol  (Chirgwin et al. , 1979). Total RNA (1. 0 µg) was reverse transcribed for hybridization to the human oligonucleotide array Human Gene 2. 0 (Genechip, Affymetrix) as described previously  [26]. Briefly, arrays were processed using the Affymetrix GeneChip Fluidic www. impactjournals. com/oncotarget Station 450 (protocol EukGE-WS2v5_450) and scanned using a GeneChip Scanner 3000 G7 (Affymetrix). The GeneChip Operating Software (Affymetrix GCOS v1. 4) was used to obtain chip images and for quality control. Background substraction and normalization of probe set intensities were performed using the method of Robust Multiarray Analysis (RMA)  [27]. All array data are available at National Center for Biological Information (NCBI) Gene Expression Omnibus (GEO) omnibus GSE55513.
17 mM KCl, 0. 4 mM CaCl 2 , and 0. 16 mM MgSO 4 ). For bioluminescence imaging, embryos were immobilized in a 0. 3 % agarose chamber on a glass-bottom dish and CLZ (20 μM) was maintained during recording. Time-lapse images were acquired in a light-tight microscope (LV200, Olympus), with a ×20 oil-immersion obj e c t i v e ( N. A. 0. 8 5 ) , a n d a n E M -C C D c a m e r a (CascadeII:512, Photometrics) controlled by MetaMorph software (Molecular Devices). One brightfield image was acquired every 60 bioluminescence images. Recording of the tail contractions shown in Fig. 6d  was done in an embryo with the head embeded in 0. 3 % agar and a free tail, submerged in E3 medium. Camera was set to slow readout mode or to stream mode (20 fps; 10 MHz readout) for imaging contractions (Fig. 6d ). Image analysis was performed with ImageJ (NIH).
Acknowledgments This work was supported by grants from the  Spanish Ministry of Science and Innovation MICINN/FEDER   BFU2008-03288  and  MINECO/FEDER BFU2012-39548-C02-01. We thank  Pilar Alfaro ,  Carmen Cifuentes , and  Sandrine Picaud  for technical assistance. This article is dedicated to the memory of  Dr. Philippe Brûlet.
S. solfataricus cells preserve their membrane integrity at 45°C, but cells do not divide We first evaluated cell density, cell size, membrane integrity, and cell distribution according to DNA content in cultures at 45°C. We transferred cultures of exponentially growing cells at 80°C to 45°C and maintained the cultures at 45°C for three weeks. Cell density was determined both by measuring optical density at 600 nm (OD 600nm ) and by flow cytometry, and we confirmed that the OD 600nm accurately reflects the number of cells as counted by flow cytometry. Despite a small increase of OD 600nm during the first two days, the cell density remained roughly constant during the three weeks at 45°C (Figure  2A ). The same result was obtained when we changed the culture medium every week (data not shown), showing that this was not due to any modification of the growth medium composition over the long period at 45°C.
solfataricus crude extract was slightly slower, indicating a higher apparent molecular mass, than that of recombinant TopR2 (Figure  3B , lane 3 versus lane 2). This difference was also observed with antibodies raised against the same specific oligopeptides but produced in another rabbit (data not shown). We conclude that the protein in crude extracts recognized by the antibodies raised against the TopR2 peptides is indeed TopR2 which, like reverse gyrase  [46]  and numerous Sulfolobales proteins  [47, 48] , has post-translational modifications which remain to be elucidated. We used the TopR1-and TopR2-specific sera to determine the numbers of the two reverse gyrase molecules present in the different crude S. solfataricus cell extracts. We established a calibration curve with known amounts of purified recombinant proteins to allow determination of the absolute number of molecules of each reverse gyrase per cell (Additional file 1: Figure  S1 ).
The cells were washed with medium then centrifuged at 10000 × g for 6 min. Syto 9 and propidium iodide of the LIVE/DEAD BacLight bacterial viability kit (Molecular Probes) were diluted in the culture medium to a final concentration of 7 μM and 10 μM respectively and used to resuspend cell samples to a final concentration of 10 7 cells/μL. These samples were incubated for 15 minutes at room temperature in the darkness. The cells were observed in a three-dimensional deconvolution microscope (DMIRE2; Leica) equipped with an HCxPL APO 100 × oil CS objective, NA = 1. 40 (Leica). The images were captured on a 10-MHz Cool SNAP HQ 2 CCD camera (Roper Instruments), with a Z-optical spacing of 0. 2 μm. METAMORPH software (Universal Imaging Corp. ) was used to acquire Z-series, deconvoluted or not, and treat the images.
Acknowledgments This work was supported by  CNRS ,  EDF  ( RB-2003-16 ) and IFR 115. We thank  Magali Prigent  for her help with fluorescent microscopy and  Jean-Luc Ferat  for his help with flow cytometer experiments. We thank  Hélène Débat ,  Florence Constantinesco ,  Christiane Elie ,  Adrienne Kish  and  Martine Mathieu  for helpful discussions. We are indebted to  Patrick Forterre  for support during this work.
terval (∼10-20 years), which led to the concept of a "geomagnetic intensity spike"  (Ben-Yosef et al. , 2009). Two such events have been proposed so far, at ∼980 BC and ∼890 BC (Fig. 2(b) ; from Fig. 7  by  Shaar et al. , 2011). Fig. 2 (b) shows that regional intensity variations as extreme as ∼30 μT  (Shaar et al. , 2011, con-  sidering solely the data from archeological site Timna-30), even possibly ∼50 μT (Ben-Yosef et al. , 2009, further considering the data from site Khirbat en-Nahas, abbreviated in what follows to "KEN"), beyond the previously assumed intensity level (∼75 μT) would have occurred in approximately a decade. Figs. 2(c ) and 2(d) show smooth fits 2  through the data (orange curve: Timna-30 and KEN datasets, red: Timna-30 only); we present separate analyses as only the intensity recorded at Timna-30 is independently verified by a measurement of comparable magnitude nearby  (Ertepinar et al.
, 2009) a n dT i m n a -3 0( c l o s e dc i r c l e s ,  Shaar et al. , 2011). All data were reduced to the latitude of site Timna-30 (∼29. 8 • N). The age calibration is provided by 14 C dates and stratigraphic constraints. Two spikes in geomagnetic intensity were proposed by  Shaar et al. (2011)  around 980 BC and 890 BC (blue shaded zones). (c) as (b) but over-plotted with cubic-spline fits to 100 realisations of this dataset (thin blue lines), each constrained by a maximum intensity change of 10 μT/yr; orange shows the average curve, the red is the equivalent average restricted to the Timna-30 dataset. (d) The values of dF/dt for each curve in (c) as a function of time. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
b) is based upon the CALS10k1. b model  (Korte et al. , 2011) , which provides a set of spherical harmonic coefficients to degree 10 (with standard deviations) over the entire Holocene. We use only the more robustly determined coefficients up to degree 4 and extend to higher degree using a stochastic extrapolation technique as described below. The second model, ST_CHAOS, is based upon the The extrapolation is based on the expected spectra of the form A(2l + 1) -1. geomagnetic model CHAOS-4  (Olsen et al. , 2010) , providing the magnetic field at epoch 2010 (see Fig. 1 ). While the magnetic field has undoubtedly changed in the intervening 3010 years, the basic structure is much the same: a dominant dipole and high-latitude flux lobes  (Korte et al. , 2011). We exploit the fact that CHAOS-4 is known to a much greater accuracy since it is constructed from high-quality satellite data of almost uniform coverage, and use it as a tightly constrained proxy for the magnetic field in 1000 BC.
In each plot, 10 random representative realisations associated with purely-toroidal and unrestricted flows are shown. Figs. 7(c-d ) show the energy spectrum of the associated secular variation; the inset in (c) shows a zoomed version of the figure at very low degree. In all cases, the optimal flows are Fig. 7. Velocity power spectra of both unrestricted and purely-toroidal flows for 10 realisations of (a) ST_CHAOS and (b) ST_CALS magnetic models, for which dF/dt is optimised for Timna-30; both the energy per degree l and cumulative energy (up to degree l) are shown. Parts (c-d) show the energy spectrum for ST_CHAOS of the secular variation at the CMB and Earth's surface; the inset in (c) shows the data of the main plot at very low degree. large-scale (converging by approximately degree 10).
However, a fluxexpulsion event  (Bloxham, 1986 ) not only requires a non-stratified outer core, but it remains to be seen whether it can deliver the required change in intensity over ∼one decade, very short compared to a core-overturn time-scale of ∼one century. Modelling of these dynamics and whether or not such a mechanism can explain the spikes is a future extension of our analysis. Looking more broadly, further data from the Near East would of course be highly beneficial in order to add to (and perhaps to refute or corroborate) the existing studies of  Shaar et al. (2011) and Ben-Yosef et al. (2009). Moreover, it would be very useful to extend the analysis to directional data: the datasets that motivated this study contain only records of archeomagnetic intensity and not directional data, since the slag materials are, in most cases, displaced from their cooling position.
Acknowledgements We would like to acknowledge useful discussions with  Steve Hunter  and  Chris Davies ; comments by the deep-Earth research group at Leeds helped improve the manuscript. This study benefited from the support of the  International Space Science Institute (ISSI) , through the work of team 241. PWL was supported by  NERC  grant  NE/G014043/1. The contribution of AF and YG is IPGP contribution  3455. The numerical calculations were performed on the super-computer ARC1 at the  University of Leeds.
We generated 100 realisations (thin blue lines) of data bootstrapped from Shaar et al. (2011) and Ben-Yosef et al. (2009) , based on a uniform distribution of ages (within the bounds given) and normally distributed intensity (with the mean and standard deviation as given). We fitted uniformly-weighted cubic splines through the data using the Matlab routine SPAPS,w h e r et h et r a d e o f fb e t w e e nt h efi tt ot h e data and the smoothness was chosen so that the maximum |dF/dt| was at most 10 μT/yr, a factor of 100 higher than typical archeomagnetic intensity change. The solid orange line shows the average spline, the red shows the equivalent average restricted to the Timna-30 dataset.
BEHAVIORAL OBSERVATIONS Data were collected from the three trials of the third day of each session, which is sufficient to provide a reliable estimate of food searching behavior. For each trial we recorded birds' arrival order on the grid with the individual landing first being given the score 1 and last bird the score 4. For each bird we recorded the number of hops performed with the head pointed down and with the head pointed up, thought to reflect the investment into producer and scrounger tactic, respectively (more details provided in  David et al. , 2014 ; see also  Coolen et al. , 2001; Wu and Giraldeau, 2005). We quantified both the investment into a given tactic ("tactic investment") and the actual number of seeds obtained through this tactic ("tactic use"). The latter has been shown to be related to the number of full wells discovered or scrounged  (David et al.
Using this behavior we counted the number of seeds a bird ate from produced or scrounged wells. For each trial and each individual we derived the proportion of seeds produced from the number of seeds produced divided by the total number of seeds produced and scrounged. Then we calculated each individual's variance in the proportion of seeds produced among trials of the same session, as an index of its consistency in actual tactic use. As first arrivals on the foraging grid are left with no other options than producing, the possibility remains that a positive relationship between variance in the proportion of seeds produced and arrival order may result from a higher reliance on producer tactic use in the very first instants of a trial, before flock mates land on the grid. To rule out this potential "early effect," we separately collected the same data once all individuals had landed on the foraging grid.
STATISTICAL ANALYSES All statistical analyses were carried out using the statistical freeware R-2. 14. 1 (R Development Core Team, 2012). We investigated behavioral consistency at the population level by calculating the repeatability (R) of each behavior within and between sessions using the "rptR" package  (Nakagawa and Schielzeth, 2010). Between-session repeatability of "proportion of hops with the head down" were computed using the between-individual covariance of this behavior across sessions using additive overdispersion Generalized Linear Mixed Models (GLMMs) with Markov Chain Monte Carlo (MCMC) estimation  (Dingemanse and Dochtermann, 2013). These types of analyses provide a 95% Credible Interval (CrI) for the repeatability estimate, which we use to determine significance, but no p-value: if an estimate's CrI does not overlap zero we consider that repeatability estimate to be significantly different from zero.
ACKNOWLEDGMENTS Thanks to  François-Xavier Dechaume-Moncharmont  for statistical advice. We would also like to thank two referees for their valuable comments on the original manuscript. Morgan David  and the research were both financially supported through a  Natural Sciences and Engineering Research Council (Canada)   Discovery Grant  to  Luc-Alain Giraldeau. Morgan David received additional financial support from Fondation  UQAM , and from a  Fonds Québecois de la Recherche sur la Nature et les Technologies' Frontenac Scholarship.
GENOME SEQUENCE, ANNOTATION, AND METABOLIC NETWORK RECONSTRUCTION The genome sequence of "Ca. P. ectocarpi" was obtained in the course of the E. siliculosus genome project  (Cock et al. , 2010). It was assembled together with the algal genome and was available from the download section of the Ectocarpus genome portal as sctg_1 (http://bioinformatics. psb. ugent. be/orcae/overview/ Ectsi). Sctg_1 was identified as bacterial contaminant based on the lack of introns and its circularity, and removed from the published dataset. To identify possible plasmids belonging to the same genome TBLASTN searches using known plasmid replication initiators were carried out against the complete E. siliculosus genome database, but yielded no results. Scgt_1 was oriented according to the DnaA protein, and a first round of automatic annotations was generated using the RAST server  (Aziz et al.
, 2008). These annotations were used for functional comparisons between different bacteria with SEED viewer  (Overbeek et al. , 2005). The generated GenBank file with the automatic annotations was then used in Pathway Tools version 17. 5  (Karp et al. , 2010)  for metabolic network reconstruction including gap-filling and transporter prediction. Manual annotation was performed for selected metabolic pathways and gene families. Candidate genes were identified using bi-directional BLASTP searches with characterized protein sequences retrieved from the UniProt database. In addition, we used the transporter classification database (TCDB) as reference for transporters, and the carbohydrate active enzyme (CAZYme) database CAZY  (Lombard et al. , 2014)  as reference for CAZYmes. Finally, candidate sequences were compared to the genome of Zobellia galactanivorans Dsij T (accession FP476056), a genome of a marine bacterium for which all protein sequences were subject to expert annotation.
All of our manual annotations were incorporated both into the final genome release and the draft metabolic network. The resulting curated metabolic network is available in Pathway Tools via the SRI Registry of Pathway/Genome Databases and on the public Pathway Tools server of the Station Biologique de Roscoff (http://pwt. sb-roscoff. fr/). The manually annotated "Ca. P. ectocarpi" genome was deposited at the European Nucleotide Archive (ENA) under the accession number HG966617.
biotempo. univ-nantes. fr/cgi-bin/ portal. py#forms::memerge). In the context of this study, we refer to this merged network as the "holobiont network. " Following the procedure outlined on the EctoGEM website, we used Meneco 1. 4. 1 (https://pypi. python. org/pypi/meneco) to test the capacity of the holobiont network to produce 50 target metabolites that have previously been observed in xenic E. siliculosus cultures  (Gravot et al. , 2010; Dittami et al. , 2011)  from the nutrients found in the Provasoli culture medium as source metabolites. The exact list of target and source metabolites is available from the EctoGEM website. Results obtained for the holobiont network were also compared to EctoGEM 1. 0, the gap-filled and manually curated version of the E. siliculosus network, which we refer to as the "manually curated algal network" in this study.
TAXONOMIC POSITION AND DISTRIBUTION OF "CA. P. ECTOCARPI" Phylogenetic analyses with the predicted "Ca. P. ectocarpi" 16S rDNA sequence were carried out with selected representative sequences of known orders of Alphaproteobacteria. Sequences were aligned using MAFFT  (Katoh et al. , 2002) , and conserved positions manually selected in Jalview 2. 8  (Waterhouse et al. , 2009). The final alignment comprised 236 sequences with 1243 nucleotide positions. Neighbor-joining (NJ) analyses were carried out using MEGA5  (Tamura et al. , 2011) , the Kimura 2-parameter substitution model, and 500 bootstrap replicates. They were complemented by maximum likelihood (ML) analyses using RAXML 7. 4. 2 (Stamatakis, 2006)  and the GTR+G+I substitution model, which was estimated to be the most suitable for ML analyses of our dataset using MEGA5.
ML analyses were carried out with 100 bootstrap replicates. A second alignment comprising an extended set of 790 sequences was also generated and used in parallel. Results for this latter analysis and all sequence accessions are available in Data sheet 1. The tree topology obtained was compared with results from RDPclassifier  (Wang et al. , 2007). To explore the distribution of "Ca. Phaeomarinobacter," related sequences were searched for via BLAST in the NCBI nr, 16S rDNA, and EnvDB databases, in the megx. net databases version r6  (Kottmann et al. , 2010) , in the Global Ocean Survey database  (Parthasarathy et al. , 2007) , and in selected marine metagenome and metabarcoding experiments deposited in the NCBI and ENA short read archives.