dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  A Bianchi ,  AM Carr ,  JP Cooper ,  T Nakamura ,  P Nurse , and  M Sato  for sharing their strains and  J Lingner  and  T Wang  for sharing plasmid constructs. We are grateful to  K Tomita ,  L Jansen ,  R Carlos , and  A Sridhar  for reading the manuscript and to  MGF laboratory  for critical comments and discussions. This work was supported by the  Portuguese Fundação Ciência e tecnologia (FCT)  project number  PTDC/BEX-BCM/5179/14. JME is supported by  PTDC/BEX-BCM /.
The analysis yielded 12. 4 Gb of sequence fragments (415 bp) with error rate within quality specifications. Adapters were removed using the trimmomatic protocol  [43] , assembly of contigs was carried out using ea-utils  [44]  and demultiplexed using an in-house script by Fasteris (Geneva, Switzerland). The sequences that were obtained were aligned with Mothur v1. 41. 1 software  [45]. Chimeras were checked with Uchime  [46] , and remaining sequences were classified against the SILVA SSU database release 123  [47]. Cyanobacterial sequences are available in the National Center for Biotechnology Information database under the accession numbers MK968453-MK968520.
Only one alkalinity and one [Ca 2+ ] measurement were carried out because of the small sample volume and statistical analysis was therefore not possible. The calcite saturation state is given by the saturation index  [51] : SI calcite = log (IAP/K calcite ) (1) where IAP is ion activity product {Ca 2+ } × {CO 3 2-} and K calcite is the solubility of calcite  [49]. The saturation index (SI) was calculated with PHREEQC software  [52]. The temperature, pH, alkalinity and calcium concentrations were the variables used for this calculation.
), within the Phormidiaceae family of the Oscillatoriales order, and to the Oscillatoriaceae family Microcoleaceae genus (closely related to the Arthrospira sp. ) according to the latest version of the SILVA SSU database r132. A few sequences were classified within the Spirulina genus (Spirulina sp. ). Some pennate diatoms were observed in close proximity to cyanobacterial filaments (Figure  3D ). Presumably heterotrophic bacteria (500 nm-long, elongated cells) were often "trapped" in the EPS matrix (Figure  3E ), but their abundance was low compared to natural Mérantaise biofilms.
Acknowledgments: We wish to acknowledge the assistance of  Christine Stern Pôle Chimie Moléculaire, UBFC ) with FT-IR analyses and of  Maxime Guerineau  ( ICB, UBFC ) for his help with SEM observations. We appreciate the comments of four anonymous reviewers that helped improve the manuscript. Several research grants ( EC2CO-2016 ,  OSU-THETA-2019 ,  ISITE UB18037. BGS. IS ) funded this study.
Acknowledgements. This work has been funded by the  ANR GIPSE   ANR-14-CE33-0018. This work was supported by the  Programme National de Physique Stellaire (PNPS)  of the  CNRS/INSU , co-funded by the  CEA , the  CNES , and the  Programme National de Planétologie (PNP)  of the  CNRS/INSU , co-funded by the  CNES. We are very grateful to  Charlotte Norris , who has provided us with the plage contrasts used in this work prior to the publication of her thesis.
ACKNOWLEDGMENTS We thank  F. Orocú , his son  J. Orocú , and  P. Pimentel  for finding the described specimens and helping with the fieldwork logistics together with the elementary students from  Colegio Punta Burica and Escuela Primaria Caña Blanca in Chiriquí Province ;  L. G. Joly  from  Universidad Autónoma de Chiriquí , who initiated the first communications, for the recognition of the material;  D. Cárdenas ,  L. Mora ,  S. Gómez ,  D. Jimenez , and  L. Hincapié  ( CTPA, STRI ) for their efforts during fieldwork and geological prospecting;  S. Heckadon  from the  Smithsonian Tropical Research Institute , for help with finding accommodations in Limones;  O. Rodríguez ,  M. Carvalho ,  A. Rodríguez , and the CTPA lab people whose discussions about geosciences helped give focus to this study;  R.
Pérez  from the  Museo de Vertebrados (Universidad de Panamá) ,  E. A. Muñoz  from the  Museo de Biología Marina y Limnología (Universidad de Panamá) , and  K. Cárdenas  ( CTPA, STRI ), for their help with the repository logistics at these collections. This research was supported by the  Smithsonian Tropical Research Institute , the  Anders Foundation , the 1923 Fund, and  Gregory D. and  Jennifer Walston Johnson ; DC is funded by the  NSERC   CREATE 466283-2015. Three anonymous reviewers and PE editors provided constructive reviews that highly improved the manuscript.
We are able to demonstrate that this weak scattering arises from the inelastic scattering of phonons by examining the scattering observed in a single detector bank at a single orientation using the time-of-flight technique. Our first principles calculation of the phonon dispersion from Yb2Ti2O7 using the CASTEP code  28  , and the diffuse scattering arising from one-phonon excitations in our neutron single-crystal Laue diffraction using the approach described in Ref. [29], is in excellent agreement with the arc of scattering emerging from the (337) reflection, see Supplementary Figure  1. This demonstrates that annealing in oxygen completely eliminates the structural component of the diffuse scattering for Yb2Ti2O7, and proves that the dominant defects in our nominally stoichiometric samples are O(2) vacancies.
Structure determination. The average structures were determined by single-crystal x-ray diffraction using a Molybdenum source Oxford Diffraction diffractometer at Royal Holloway. A large CCD detector captures full reciprocal space coverage to a real space resolution of 0. 6Å. 3D profile analysis of each reflection is performed using the CrysAlisPro software  42  and refinements of the average structure, including anisotropic thermal parameters, are performed using the Jana2006 software  43. All refinements had R-factors less than 5%, exhibiting excellent fits to the data. Full details are available in the CIF files. The defect structures were studied by diffuse neutron scattering using the single-crystal diffractometer (SXD) at the ISIS pulsed neutron source at the Rutherford Appleton Laboratory.
The LZO dispersion was computed using density functional theory within the plane-wave pseudopotential approach as implemented in the CASTEP code  28. The local density approximation was used with the default CASTEP ultrasoft pseudopotentials (version C9)  51. The primitive electronic Brillouin zone was sampled with a Monkhorst-Pack grid 52 of 4 by 4 by 4 points with a plane-wave cutoff of 600 eV. The lattice and atomic positions were relaxed such that the residual stresses and forces were less than 0. 01GPa and 0. 005 eV/Å respectively with the quasi-Newton method  53. The phonon dispersion was then computed using the finitedisplacement, supercell method  54  with a single cubic unit cell as the supercell. Mass substitution was performed as a post processing step using the CASTEP utilities.
After the sample, the final polarization was analysed using an array of polarizing benders in front of the helium detectors. For each scattering vector, Q, the intensity was measured in both non-spin-flip (structural + magnetic correlations) and spin-flip (magnetic correlations only) channels. In the case of a ferromagnet in zero field, the formation of domains results in the depolarization of the neutron beam, but in this case the non-spin-flip and spin-flip intensities can be combined to give the total (unpolarised) cross section. The integrated intensities of the Bragg reflections were obtained by summing the data above the background level in the vicinity of the reciprocal lattice points, and these data were normalised to data integrated in the same way well above TC. Magnetic scattering: theory.
We used the canonical 16-spin cubic unit cell, and system sizes L=20 (main paper) and L=16 (Supplementary Figure  4 ). At every temperature step, the autocorrelation function was allowed to drop twice to 0. 01 to ensure equilibration before lowering the temperature further. Upon reaching the target temperature, an instance of the structure factor was computed, and the entire process was then repeated from high temperature (with a different random number seed) to generate statistically independent instances. All the figures in the paper were averaged over 1000 instances. Data availability. The x-ray datasets generated and analysed during the current study and the computer simulations are available from the corresponding author on reasonable request. The CIF files are available via Royal Holloway's Figshare repository from doi:10.
Acknowledgements We thank  G. Sala ,  T. J. Willis ,  S. T. Bramwell ,  M. R. Lees  and  L. Jaubert  for helpful discussions. D. F. B. acknowledges financial support from the  EPSRC  from their Doctoral Training Partnership. C. C. and D. P. acknowledge  EPSRC  grant  EP/K028960/1 , and D. P. and A. T. B. acknowledge  EPSRC  grant  EP/M020517/1.
A Distribution of the gene length (in bp) for LVGs (green) and HVGs (blue). The distribution of average (black) and 95% confidence interval (dotted grey) for the thousand random sets is also represented. B Distribution of the number of introns for LVG (green) and HVG (blue). The distribution of average (black) and 95% confidence interval (dotted grey) for the thousand random sets is also represented. C Distribution of the number of TFs targeting a gene, based on the DAP-seq available data set, for LVG (green) and HVG (blue). The distribution of average (black) and 95% confidence interval (dotted grey) for the thousand random sets is also represented. The large degree of gene expression variability revealed by our study will impact on our functional understanding of pathways as well as experimental design.
On top of genomic factors, we identified that HVGs and LVGs have distinct chromatin profiles, with HVGs being characterised by an enrichment in H3K27me1 and H3K27me3, which are repressive marks, and depleted in active marks such as H3K4me2, H3K4me3, H3K36me3, H2Bub or DNA methylation. The ChIP-seq data we used were obtained from bulk plant experiments, but in the future, it would be of interest to directly compare chromatin marks and expression levels by performing RNA-seq and ChIP-seq or BS-seq on the same individual seedling or cell. Although very challenging, recent advances on single-cell RNA-seq, ChIP-seq and BS-seq indicate that such types of experiment could be possible. Variability in DNA methylation was for example recently reported using singlecell approaches in human  (Ecker et al, 2017; Garg et al, 2018).
RNA-seq library preparation Sixteen 7-day-old Col-0 WT Arabidopsis seedlings were harvested individually and flash-frozen in dry ice every 2 h over a 24-h period. Total RNA was isolated from 1 ground seedling using the MagMAX TM -96 Total RNA Isolation Kit following manufacturer's recommendation. RNA quality and integrity were assessed on the Agilent 2200 TapeStation, and RNA concentration was assessed using Qubit RNA HS assay kit. Library preparation was performed using the TruSeq Stranded mRNA Library Preparation Kit (Illumina, RS-122-2101), for 1 lg of high-integrity total RNA (RIN > 8) into which 2 ll of diluted 1:100e ERCC RNA Spike-In Mix (Thermo Fisher, cat 4456740) was added. The libraries were sequenced on a NextSeq 500 using paired-end sequencing of 75 bp in length. RNA-seq mapping, identification of HVG and corrected CV 2 calculation The raw reads were analysed using a combination of publicly available software and in-house scripts.
We first assessed the quality of reads using FastQC (www. bioinformatics. babraham. ac. uk/projec ts/fastqc/). Potential adaptor contamination and low-quality trailing sequences were removed using Trimmomatic  (Bolger et al, 2014) , before aligned to the TAIR10 transcriptome using TopHat  (Trapnell et al, 2009). Potential optical duplicates resulting from library preparation were removed using the Picard tools (https://github. com/broadinstitute/picard). For each gene, raw reads and TPM (transcripts per million)  (Wagner et al, 2012)  were computed. TPMs, which correct for gene length and library size, were used for all analyses. Because of the high number of samples, RNA extraction and library preparation were performed in two batches, each batch containing half of the samples for each time-point.
9 between the corrected CV 2 calculated using 16 seedlings and the ones calculated with a least 12 seedlings  (Appendix Fig S1D). As our data set contains 14-16 seedlings for each time-point, we thus decided to use 14 seedlings in all cases to be able to compare the time-points. When more than 14 seedlings were available for one time-point, we removed the extra seedlings with the lowest number of reads. This is higher than what was done in plants until now, as Folta and colleagues  (Folta et al, 2014)  analysed inter-individual expression variability for eight genes using six seedlings, and Brennecke and colleagues  (Brennecke et al, 2013)  analysed gene expression variability using scRNA-seq for seven cells. Identification of HVGs was performed separately for each timepoint as described previously  (Brennecke et al, 2013) , using the code from M3Drop R package (https://github.
RT-qPCR Sixteen 7-day-old Col-0 WT Arabidopsis thaliana seedlings were harvested individually and flash-frozen in dry ice every 2 h over a 24-h period. Total RNA was isolated from 1 ground seedling. RNA concentration was assessed using Qubit RNA HS assay kit. cDNA synthesis was performed on 700 ng of DNAse-treated RNA using the Transcriptor First Strand cDNA Synthesis Kit. For RT-qPCR analysis, 0. 4 ll of cDNA was used as template in a 10 ll reaction performed in the LightCycler 480 instrument using LC480 SYBR Green I Master. Gene expression relative to two control genes (SandF and PP2A) was measured (See Table  EV7  for the list of primers used for RT-qPCR). Then, in order to directly compare RT-qPCR data with RNA-seq data, expression levels for each gene were normalised by the averaged expression level of that gene across all seedlings at all time-points.
ChIP-seq mapping and profiling The lists of genes being marked by the analysed chromatin marks were obtained from Roudier and colleagues  (Roudier et al, 2011)  and from Coleman-Derr and  Zilberman (Coleman-Derr & Zilberman, 2012). ChIP-seq data were downloaded from GSE101220 for H3K27me3  (Jiang & Berger, 2017) , from GSE79355 for H2A. Z and MNase  (Cortijo et al, 2017) , from GSE73972 for H3K4me3  (Chen et al, 2017)  and from GSE51304 for H3K23ac and H3  (Stroud et al, 2014). Sequenced ChIP-seq data were analysed in house, following the same quality control and pre-processing as in RNA-seq. The adaptortrimmed reads were mapped to the TAIR10 reference genome using Bowtie2  (Langmead et al, 2009). Potential optical duplicates were removed using Picard, as described earlier. Averaged profiles and heatmap of the ChIP-seq signal along the gene body from 500 bp upstream to the transcription start site (TSS) to 500 bp downstream of the transcription termination site (TTS) were generated using deepTools  (Ramirez et al, 2014 (Ramirez et al, , 2016)).
Acknowledgements We thank  Dr Hugo Tavares  ( Sainsbury Laboratory Cambridge University, UK ) for helping with the creation of the interactive web interface. We thank  Dr Katie Abley  ( Sainsbury Laboratory Cambridge University, UK ) and  Dr Varodom Charoensawan  ( Mahidol University, Thailand ) for critical reading of the manuscript draft. This research was made possible by a fellowship from the  Gatsby Foundation  ( GAT3272/GLC ). The work in the Locke laboratory is further supported by the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP/2007-2013)/ERC Grant Agreement 338060.
Data availability The data sets and computer code produced in this study are available in the following databases: • RNA-seq data: Gene Expression Omnibus GSE115583: https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE115583. • Graphical web interface: https://jlgroup. shinyapps. io/aranoisy/. • Computer codes used to analyse RNA-seq data: https://github. c om/scortijo/Scripts_noise_paper_MSB_2018. Expanded View for this article is available online.
Previously unassigned metazoan subunits were also clearly localized. For example, Med27, Med28, Med29 and Med30 make extensive contacts with the Head module while Med26 associates with the Middle module  [57]. Finally, a recombinant human 8-subunit Head module that additionally contained the metazoan subunit Med30 was successfully reconstituted in insect cells in 2014  [60]. The reconstitution of the recombinant human Head module was done in the context of an assembly of a functional 15-subunit human core Mediator complex that comprises the Head and From the top down, the depicted structures are (A) S. cerevisiae Med7C/Med21 complex (protein data bank (PDB) 1YKH  [42] ). The conserved flexible hinge is indicated. (B) S. cerevisiae Med8C/18/20 submodule (PDB 2HZS  [43] ), (C) S. cerevisiae Head module (PDB 3RJ1  [53] ).
The three major domains described initially in 2011 (fixed jaw, movable jaw and neck) are indicated. (D) S. pombe Head module (PDB 4H63  [55] ), (E) S. cerevisiae CDK8 kinase module (electron microscopy data bank (EMD)-5588  [99] ), (F) S. cerevisiae (EMD-2634) and Homo sapiens (EMD-2635)  [57]  Mediator complex, (G) S. cerevisiae RNA Pol II-core Mediator transcription initiation complex (EMD-2786  [48] ), (H) S. pombe core Mediator (PDB 5N9J  [47] ) and (I) Homo sapiens MED23 subunit (PDB 6H02  [72] ). For details, refer to the text. For a complete collection of structural data on Mediator, see Supplementary Table  S1  adapted from  [28]  with permission from Elsevier. Figures were prepared with PyMol  [116]  or UCSF Chimera  [117]. y, yeast; h, human; m, murine; Sc, Saccharomyces cerevisiae, Sp, Schizosaccharomyces pombe.
The OSSE set-up In this study, we follow the different OSSE steps and requirements as identified in  Timmermans et al. (2015). Figure  1  provides a schematic overview of the workflow in this study. As the observations under investigation were not available yet, we first had to produce a set of synthetic observations. We based production of the simulated observations on a so-called nature run (NR), which acts as the representation of reality. We converted the output from the NR into synthetic observations using information about the S4 and S5P instrument characteristics, as well as parameters that influence the observations, such as clouds and surface albedo. Next, the synthetic observations were ingested into the chemistry transport model (LOTOS-EUROS) using an ensemble Kalman filter data assimilation system.
Observation simulator We generate NO 2 tropospheric column synthetic observations using model profiles extracted from the NR model datasets. The observations are generated for the TROPOMI instrument on board the S5P satellite and the S4/UVN instrument on board the MTG-S satellite. The TROPOMI instrument is a spectrometer based on its predecessors OMI and SCIAMACHY  (Veefkind et al. , 2012). It measures in the ultraviolet (UV)-visible (270-500 nm), near infrared (NIR, 675-775 nm) and short-wave infrared (SWIR, 2305-2385 nm) wavelength ranges to enable the retrieval of several air quality data products, including ozone, NO 2 , formaldehyde, SO 2 , methane and CO. The instrument has a wide swath of 2600 km, allowing daily global coverage and was designed for a spatial resolution of 7 by 7 km.
Cloud, temperature and surface albedo information The satellite measurements of S4 and S5P, once available, will be used to retrieve cloud properties like cloud fraction and cloud pressure, using either the O 2 -A band  (Wang et al. , 2008)  or the O 2 -O 2 absorption feature around 477 nm  (Veefkind et al. , 2016). However, for an OSSE those need to   (Williams et al. , 2013). We use the simulated effective cloud fraction and cloud pressure in the synthetic retrievals. We convert the cloud fraction into a cloud radiance fraction (the fraction of the top-of-atmosphere radiance coming from the cloud-covered part of the scene) by computing radiances using the surface albedo map and assuming a cloud albedo of 0. 8. The surface albedo is taken from the 5-year OMI Lambertian reflectivity dataset, extended from the 3year dataset published by  Kleipool et al.
We assume the network of cur-rently existing stations for ozone is representative for the locations that will be available during the upcoming satellite missions. Therefore, the observation locations of the synthetic surface ozone observations have been drawn from the existing AirBase network (version 6, Simoens, 2012). We only use stations classified as "background", since these are representative for concentrations at the resolution of the models used in this study. Additionally, we omit stations above 700 m above sea level, considering the focus of the project on boundary layer concentrations and the difficulties models have reproducing conditions in mountainous areas. Figure  5  shows the location of the selected stations. During data assimilation experiments, it is common practice to leave a number of observations out of the assimilation procedure and use these for validation.
Generally, we base its estimate on comparison of the observations and model simulations. The only exception is determination of the measurement error, which is solely dependent on the instruments. However, in air quality databases like AirBase, it is not common practice to provide characteristics of instrumental errors (while retrieval uncertainties are provided for individual observations in the case of satellite measurements). Therefore, the error of ground-based observations should be set or parameterised explicitly (e. g. Thunis et al. , 2013)  To ensure realistic synthetic surface ozone observations, we mask the synthetic ground-based observations if the cor-responding real observations in AirBase are missing too. Secondly, we add a random number to each synthetic value to represent the instrumental error.
, 2010; Williams et al. , 2017). The same TM5 model runs are also used for the extension of the LOTOS-EUROS runs from its top at 3. 5 km to the tropopause. We base the anthropogenic emissions on the TNO-MACC II inventory  (Kuenen et al. , 2014). Note this is a different inventory version than used in the NR, supporting the requirement of significant differences between the models for the NR and the CR and AR. Biogenic emissions are calculated online as described in  Schaap et al. (2009). Fire emissions are taken from the MACC-II GFAS v1. 0 product  (Kaiser et al. , 2012). These emissions vary from day to day and are provided on a 0. 5 • resolution grid. Dynamically, we force the model by meteorological data from ECMWF. We choose a different meteorological forcing from the one used in the NR to meet the requirements on the differences between the NR and the CR and AR.
Evaluation of nature run and control run In the design of an OSSE, it is important to demonstrate that the NR exhibits the same statistical behaviour as the real atmosphere for aspects relevant to the observing system under study. To evaluate this, we compare the ozone and NO 2 concentrations from the NR to observations from the Air-Base network. We consider the sites falling into the first five classes, as defined in the objective classification method suggested by  Joly and Peuch (2012) , as we understand them to be representative for the resolution of the NR model. These first five classes mostly encompass sites classified as rural and urban background in the AirBase database but, especially for ozone, a small fraction of the urban traffic stations have also been assigned to one of the five lowest classes and are therefore considered representative for larger areas.