dataesr/scientific-paragraphs-categorization huggingface dataset:
3D Structured Illumination Microscopy (3DSIM) Instrumentation and imaging Samples were prepared as described above and mounted on the OMX acquisition stage. 3DSIM imaging was performed on an OMX V3 microscope (Applied Precision Inc. ) equipped with an X60/1. 42 oil PlanSApo objective (Olympus) and three emCCD cameras. 405 nm and 488 nm excitation lasers lines were used to excite DAPI and GFP, respectively. Dual color raw data were acquired sequentially. For each channel,a total of 255 images made of 17 different Zplanes separated by 125 nm each were acquired. Single color raw data contained 225 images for 15 Zplanes. Three different angles (60°, 0°and +60°) as well as five phase steps were used to reconstruct 3DSIM images using softWoRx v5. 0 (Applied Precision Inc. ). Final voxel size was 39.
5 nm in xy and 125 nm in z for a final 3D stack volume of 40 x 40 x 2 µm 3. Multicolor TetraSpeck beads (100 nm in diameter, Invitrogen) were used to measure x, y and z offsets, rotation about the zaxis and magnification differences between fluorescence channels. These corrections were applied to the reconstructed images. The same beads were used to validate the reconstruction process ensuring a final resolution of ~120 nm in xy and ~300 nm in z depending on the wavelength. Acquisition parameters were used as follows: DAPI, 510 ms exposure with 10% transmission of the 405 nm excitation line; HbsGFP, 10 ms exposure with 31. 3% transmission of the 488 nm excitation line. Analysis of 3DSIM data 3DSIM data were analyzed using homemade routines written in Matlab (Mathworks, Inc, US). Each individual nucleoid was automatically segmented using a 3D maximum entropy thresholding algorithm for binarization and minimum and maximum volume values for nucleoids detection.
3D Photoactivated localization microscopy (3DPALM) Instrumentation and imaging 3DPALM imaging was performed in a modified Nikon Eclipse TiS inverted microscope equipped with a 100x PlanApo Lambda oilimmersion objective (NA = 1. 45) equipped with two lasers (excitation wavelengths of 405nm and 561 nm) and an active feedback autofocus system as described previously  (Cattoni et al. , 2013; Fiche et al. , 2013). Acquisition software controlling lasers, filter wheels and camera were homemade using LabView 2012 (National Instruments TM ). To obtain 3D imaging conditions, a corrective MicAO 3DSR system (Imagine Optic TM , France) was inserted into the emission pathway between the microscope and the EMCCD camera (Andor Ixon 897, Ireland). The MicAO 3DSR adaptive optics device uses its deformable mirror to correct for the optical aberrations introduced in the imaging path, optimizing the photon budget and maximizing the lateral resolution for PALM experiment.
3DPALM image analysis and rendering Unless stated otherwise, all homemade software and routines were developed in Matlab. 3D singlemolecule localizations were obtained by using Rapid STORM  (Wolter et al. , 2012). For each sample, the calibration curves were generated by imaging a single 100nm TetraSpeck bead while scanning the sample stage along the optical axis (z) by steps of 50nm. For each position, the PSF was fitted with an elliptical Gaussian function and the xy widths (w x and w y ) were calculated in order to produce the calibration curves w x (z) and w y (z). Localization coordinates were further processed using PALMcbs, a homemade software written in Matlab. Fluorescent beads were used to correct for drift in all three directions: on agar pad, lateral and axial drifts were corrected with 10±5 nm and 25±10 nm precisions, respectively.
DNA was then precipitated with 800 µl of 3 M NaAcetate (pH 5. 2) and 8 ml isopropanol. After one hour at 80°C, the DNA was pelleted by centrifugation. Pellets were suspended in 500 µl 1X TE buffer and RNA degraded with a final concentration of 0. 03 mg/ml RNAse A and incubated for one hour at 37°C. DNA was transferred into 2 ml centrifuge tubes, extracted twice with 500 µl phenolchloroform pH 8. 0, precipitated, washed with 1 ml cold ethanol 70% and diluted in 30 µl 1X TE buffer. All tubes were pooled and the resulting 3C library was quantified on gel using QuantityOne software (BioRad).
Acknowledgements We thank  J. Bonnet  and  O. Espeli  for critical reading of the manuscript,  Marie Schaeffer  for her ideas on data interpretation,  A. Rappailles  for help in preparation of initial 3C/Hi-C libraries, and  A. Grossman  and  D. Rudner  for sharing strains. Research was supported by  European Research Council Starting Grants  to  R. K. ( ERC-Stg-260822 ) and M. N. ( ERC-Stg-260787 ), and by a  Royal Society University Research Fellowship  and a  BBSRC Research Grant  ( BB/K017527/1 ) to H. M. M. M. is the recipient of a fellowship from the  Association pour la Recherche sur le Cancer  (20100600373). 3D-SIM experiments were performed at Montpellier RIO imaging. We acknowledge support from  France-BioImaging  ( FBI ,  ANR-10-INSB-04 ), and  Imagine Optic.
Acknowledgements. This work was performed in the framework of the  ESA CCI program  supported by  ESA. It was also made possible thanks to the support of  CNES  for several years in altimetry data processing; in particular, thanks to the use of SSALTO/DUACS system developed in the framework of the  SALP  project  (Service d'Altimétrie et de Localisation Précise). We would also like to thank all contributors to this project who have participated actively in the  SL_cci  project, with special recognition to  S. Dinardo  and  B. M. Lucas  in support of  ESA , for their diligent reviewing of all the documents and data sets produced by the  SL_cci team. Edited by:  A. Schiller.
The production of ocean reanalyses (ORAs hereafter) is now an established activity in several research and operational centres. ORAs are revisited every so often, and new 'vintages' are produced at intervals of about five years, as improvements in ocean models, data assimilation methods, forcing fluxes or ocean observations become available. The previous vintage of ORAs  (produced around 2006)  has already been documented  (Stammer et al. 2010; Lee et al. 2009). A new vintage has recently been generated, which has come about through the availability of new surface forcing fluxes (from new atmospheric reanalyses), improved qualitycontrolled ocean datasets, including important corrections to the observations  (Lyman et al. 2010; Wijffels 2009) , as well as the steady improvement in the ocean models and data assimilation methods.
The correlation between SSL-density and SSL-residual is higher for EM-ORA (0. 84) than for any individual product (0. 77 at the maximum), and also higher than for EM-OO (0. , not shown). The latter is especially evident in areas where the in-situ observing network is poor and/ or where there is impact of deep and bottom waters. The bottom panels of Figure  2  show the difference of the anomaly correlation with respect to the validation dataset between the EM-ORA and the EM-OO for the full (left) and inter-annual (right) signals. The high correlation obtained by EM-ORA emphasizes the added value of the dynamical constraints and atmospheric forcing included in the ORAs. This is evident in the full fields (in the Southern Ocean, in the South Atlantic and just south of the Bering Strait), and especially noticeable for the interannual signal.
2013 ). The latter is a gridded dataset where the original altimeter data has been reprocessed with improved algorithms (orbit, wet tropospheric corrections, among others) and ancillary data (using improved atmospheric fields for instance) in order to produce consistent time series of sea level for climate studies. SLCCI has not been assimilated by any of the products in Table  2 , although many of these products assimilate along-track satellite altimetry (usually AVISO). Only two products (ECCO-v4 and LEGOS) use information from tide gauges. The tide gauges used for the evaluation are the same as the subset from the Global Sea Level Observing System [GLOSS, see http://www. gloss-sealevel. org/] chosen for evaluation of sea level reconstructions  (Meyssignac et al. 2012). Monthly means of sea-level anomaly at the tide gauge locations were created after removing the effects of tides and inverse barometer from the original tide gauge data.
In this case, ESACCI is used as validation data set. All products show a coherent interannual variability (Figure  3d ), even when altimeter data are not assimilated, and there is very small spread around EM-ORA (black). The variability is dominated by the El Niño 1997-98, and a significant negative trend of ~3-4 mm/y, consistent with the lack of Eastern Pacific ENSO in the last decade, and with the recently reported strengthening of the Pacific trade winds  (Balmaseda et al. 2013b; England et al. 2014; de Boisséson 2014). Figure  3c  shows the corresponding Taylor diagram for the different ORAs and OOs averaged over this region. In contrast with the tide-gauge evaluation, here the scores of the ORAs versus SLCCI are quite similar (sixteen of the nineteen products show correlations higher than 0.
Surface heat fluxes The purpose of this comparison is to assess the global heat closure in ORAs, the consistency of the seasonal cycle and interannual variability between the products, and to compare with other heat fluxes from a variety of sources (primarily satellite, ships, buoys and atmospheric reanalysis). These other sources are not completely independent (with the exception of satellite based radiative fluxes) because they may also use SST or near surface meteorological data to generate products. Nevertheless, they enable some assessment of the uncertainty introduced by the reanalysis methods themselves. Additional datasets include the OAFlux latent and sensible heat flux product  (Yu et al. 2008 ) combined with ISCCP satellite based radiation  (Zhang et al. 2004 ), the ship-based NOC2.
0 product  (Berry & Kent 2009) , the  Large and Yeager (2009)  hybrid flux dataset CORE. 2, and two atmospheric reanalysis products, the ECMWF ERAInterim reanalysis  (Dee et al. 2011 ) (referred to as ERAi) and the NCEP/DOE reanalysis R2 (referred to as NCEP-R2)  (Kanamitsu et al. 2002). Most ORAs are forced with bulk formulae using an atmospheric dataset taken from an atmospheric reanalysis product. In these cases, assimilation of sea surface temperature (SST) observations directly influences the net surface heat flux, as the turbulent latent and heat fluxes, computed from bulk formulae, and the outgoing long wave radiation, computed using the Stefan-Boltzmann Law, depend on the SSTs. The ORAs can also close their heat budget through the temperature assimilation increments, since the vertically integrated temperature assimilation increments, with the appropriate unit transformation, are equivalent to a heat flux  (Balmaseda et al.
2010) and deBoyer (deBoyer Montegut et al. 2004 ) datasets are used, also estimated from the individual TS profiles, following the definitions above. In particular, MILA-GPV uses only the Argo profiles without interpolation between grid points, although the spatio-temporal coverage of the dataset is limited. deBoyer provides the monthly climatological fields (MLDr003 and ILDt02). The MLD/ILD are calculated from monthly means of temperature-salinity (TS) fields on the individual native grids of three OO products (EN3v2a, ARMOR3D, WOA09) and 16 ORAs; these are then interpolated to the regular global longitude-latitude common grid. EM-ORA is estimated as the ensemble average of individual MLD/ ILD on the common grid (this will differ from the MLD/ ILD calculated from the ensemble mean of TS).
The MLD/ILD from the individual ORAs exhibit various biases in the mean fields depending on the diversity of model configurations and assimilation systems (not shown). Here the evaluation of EM-ORA is focussed on rather than on the detailed representations of the individual reanalysis fields, which will be described in future work. Figure  5  presents the zonal mean monthly MLD/ILD normalized differences of EN3v2a, ARMOR3D, deBoyer, WOA09 and EM-ORA with respect to the MILA-GPV as reference. Note that values averaged over the Argo-rich 2005-2011 period are plotted for MILA-GPV, EN3v2a, ARMOR3D and EM-ORA, while the climatological fields are provided by deBoyer and WOA. The differences between deBoyer and MILA-GPV (MLDr003 and ILDt02) are generally small, since MILA-GPV and deBoyer are comparable datasets that use individual TS profiles.
Acknowledgements This work has been partially funded by the  European Commission  funded projects MyOcean, MyOcean2 and COMBINE; by the  GEMINA  project -funded by the  Italian Ministry for Environment ; by the  NERC -funded  VALOR  project; by the  NERCfunded NCEO program ; by the  Research Program on Climate Change Adaptation of the Ministry of Education, Culture, Sports, Science and Technology of the Japanese government ; by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ); by  NASA 's  Modelling Analysis and Prediction Program  under  WBS 802678. 02. 17. 01. 25  and by the  NASA   Physical Oceanography Program ; by the  NOAA's Climate Observation Division (COD) ; by the  LEFE/GMMC French national program. The MILA-GPV dataset is provided for this study by  RCGC/JAMSTEC.
Study design Children were enrolled from the PREDICT long-term follow-up prospective study, which was conducted at 41 sites in 14 countries worldwide (NCT00699855; Merck Serono SA-Geneva). In the current study, we have analysed the data on first-year response to r-hGH from the PREDICT long-term follow-up study, which uses a pharmacogenomic approach to evaluate the association of single-nucleotide polymorphisms (SNPs) in growth and metabolism genes with long-term changes in growth whilst on r-hGH therapy. 12 he following data had been collected: location of the study centre, ethnicity, gender, birth weight, parental heights, peak serum GH response to stimulation testing, age, height, weight and r-hGH dose at baseline and height and weight after 1 year of treatment. Absolute latitude was assumed to be that of the study site.
For each patient, annualised HV (cm per year) at the first year of r-hGH therapy was assessed. To look at mechanisms underlying the latitude effect, the average number of daylight hours during summer at each centre was obtained using data obtained from the Geographical Information System and mesh climatic data. 13 he number of daylight hours depends both on date and latitude. At the sites in the Northern hemisphere, the three months with the longest days, in terms of daylight hours, were classed as summer (May 6 to August 6). 11 n the sites of the Southern hemisphere, summer period was considered to extend from November 6 to February 5. Patients were categorised into three groups based on the distribution of their locations and the corresponding summer daylight exposure (SDE) (as high (475th percentile of the cohort: ⩾ 15.
Statistical analysis All auxological data were expressed as median and inter-quartile ranges [median (Q1,Q3)]. Uncorrected P-values o0. 05 were considered statistically significant. Statistical analysis was performed using the Statistical Package for Social Science program, version 20. 0 software for Windows (SPSS, Chicago, IL, USA). Differences in continuous variables were examined for unpaired samples by the Kruskal-Wallis test, whereas differences in categorical variables were assessed by Fisher's exact test. Correlations between variables were assessed by Pearson's correlation coefficient. Partial least squares regression (PLSR) was applied to overcome multi-colinearity between variables. 18 By using PLSR, the 'variable important for projection' coefficients were computed and a value of o0.
Transcriptome analysis Gene expression profiling was performed at baseline on whole-blood RNA extracted centrally by qLAB (Edinburgh, UK) using the PAXgene 96 blood RNA kit (Qiagen, Crawley, UK). Reduction of globin messenger RNA was undertaken using the Ambion GLOBIN Clear Human Kit (Life Technologies, Paisley, UK). Complementary RNA was generated using the Two-Cycle Eukaryotic Target Labelling Kit (Affymetrix, Santa Clara, CA, USA) and a final quality check performed before hybridisation to Affymetrix GeneChip Human Genome U133 Plus 2. 0 Arrays. Arrays were then scanned on an Affymetrix GeneChip 7G scanner and assessed for quality against internal and hybridisation controls. All analyses were performed centrally by the Bioinformatics Group at Merck Serono. Processing and normalisation of gene expression data were performed using a Robust Multi-array Average background correction modified for probe sequence with quantile normalisation and median polish (Partek Genomics Suite, version 6.
Network analysis of transcriptomic data Network analysis is directed towards the identification and prioritisation of key functional elements within interactome models. An interactome model of all known protein-protein interactions between the differentially expressed genes as 'seeds' and their inferred immediate neighbours was calculated using BioGRID database (31. 2. 114). 20 Network processing was performed using Cytoscape 2. 8. 3. 21 he ModuLand plugin for Cytoscape 2. 8. 3 was used to determine overlapping modules within the network and to identify hierarchical structure within the model thus enabling the identification of key network elements and prioritise biological function. 22, 23 Network modules were prioritised for further investigation by their centrality property and the most central set of 10 genes within each module was used to assess associated biological pathways using the geneontology.
org database. 24 he network structure observed with community modelling in Moduland was confirmed by cluster analysis using the ClusterOne algorithm. 25 Cluster robustness was tested by random sample removal. 26 ausal Network Analysis was performed within the overlap of associated gene expression between SDE and HV. Causal Network Analysis identifies upstream molecules up to three steps distant that control the expression of the genes in the data set, and thus provides insight into information flow within the network. 27.
Network analysis of transcriptomic data The expression of 1868 and 4098 genes was correlated with SDE and HV, respectively, with no covariates; 397 genes were present in both data sets (overlap, P = 0. 0015) (Figure  5a ) [n = 60, Gene Expression Omnibus GSE72439]. The subset of common genes (143 unique genes with GH peak, r-hGH dose, BW SDS, baseline BMI SDS, distance to TH SDS, age and gender as covariates (Supplementary Table  S1 )) was used to generate an interactome model on which network analysis was performed (Figure  5b ). Network clusters were identified as markers of biological function  23, 28  and robustness was confirmed using random sample removal (Supplementary Figure  S1 ). The biological functions associated with the clusters were centred on chromatin remodelling (PHF21A), signal transduction (TLK2) and transcriptional regulation (MLLT10 and MSX1) (Supplementary Table  S2  and Figure  5b ).
3  Climatic variables of temperature, solar radiation and day length were analysed. It was shown that day length, but not other climate variables, was the primary predictor for the geographical gradient in body height. Differences in melatonin secretion due to variation in day length were proposed to explain Overlapping gene set correlated with SDE and HV using the same covariates as used for the partial least square regression (gender, GH peak, r-hGH dose, BW SDS, baseline age, BMI and distance to TH SDS) (Supplementary Table  S1 ) was used to generate an interactome model. Clusters of related genes were identified within the interactome model using the Moduland algorithm and a network of the cluster modules was generated (shown) where the different coloured octagons represent clusters and the gene name is the most central gene element within that cluster (Supplementary Table  S2 ).
(c) Biological pathways associated with the overlap between the clusters were identified using the Geneontology. org database (hypergeometric test with a Benjamini-Hochberg false discovery rate (FDR) modified P-value). This relates to the results from the generalised linear model, evaluating a carriage (carriage vs non-carriage of the SNP) and group (high vs intermediate vs low summer daylight exposure (SDE)) effect on 1-year growth velocity (cm per year). P* = significant ( o 0. 05). In (a) the ΔHV is greatest in the group with the highest number of SDE hours (for IGFBP-3, TGF-α and TP53). The difference in HV varies among SDE groups, ranging from 2. 4 to 1. 6 cm per year at higher to 0. 0-0. 7 cm per year at lower SDE. In (b) the ΔHV is greatest in the group with the lowest number of SDE hours (for GRB10 and CYP19A1).
12 We also showed that variation in the gene expression of GR pathway members relates to phase of growth in normal children. 45 It is likely that a complex interaction between melatonin, GR pathways and growth mechanisms is generating these seasonal differences. Gene expression (transcriptomic) data were studied on whole blood to explain biological mechanisms related to the interaction between SDE and HV. The transcriptome of whole blood has a substantial overlap with other human tissues. 45, 46 A number of studies have characterised mononuclear cells in whole blood as a growth-responsive tissue and an appropriate model to study GH action. 44, 47, 48 0] [51] [52]  We Figure  6. Causal analysis and mechanistic modelling of the subset of genes common to both HV and SDE. (a) Causal network analysis takes the genes with altered expression (examples numbered 1-5, green (low expression) and red (high expression)) and identifies upstream molecules up to three steps distant.
This approach provides insight into information flow within the network using the known literature to identify network edges linking to upstream regulators (a-c) and master regulators (A), for which there is statistical evidence (Fisher's exact test) to support a corresponding causal relationship (within Ingenuity Pathway Analysis software). The most significant causal edges between regulators are then used to construct networks downstream of a 'master' regulator to indicate possible mechanisms. (b) Regulators of gene expression with matched action in both HV and SDE were identified by causal network analysis and hierarchical clustering of results (Supplementary Table  S3 ). These data were mapped onto the clusters identified within the network model of the overlap of gene expression and implicated NANOG as a prime target of regulation.
ACKNOWLEDGMENTS The PREDICT study was supported by  Merck Serono SA-Geneva ,  Switzerland. Chiara De Leonibus  was supported by  ESPE   Research Fellowship , sponsored by  Novo Nordisk A/S. We also thank  Ekaterina Koledova ,  Olivier Morteau ,  John Raelson ,  Jerome Wojcik  and  Benoit Destenaves  (supported by  Merck Serono SA-Geneva, Switzerland)  for their assistance in the preparation of the manuscript and  Esther Dodge  and  Simon Lancaster  of inScience Communications, UK (supported by  Merck Serono S. A-Geneva, Switzerland)  for assistance with editing and formatting of the manuscript. This work was also supported by the  Manchester Biomedical Research centre. The PREDICT investigator group includes: Argentina:  A Belgorosky ,  Buenos Aires. Australia:  G Ambler ,  Westmead. Austria :  K Kapelari ,  Innsbruck.
Here, we report the sequence of the full-length genome of an indel strain isolated in the first PED case in northern France in December 2014. The outbreak occurred in a farrow-to-finish herd. The mortality rate ranged between 3. 3 and 5. 5% in the fattening building and reached 12% in piglets after 1 week and 25% at weaning in the farrowing building. The samples collected were from the jejunum of 3 affected animals that had died within the day. Nextgeneration sequencing and data analysis were performed on RNA extracted from the 3 jejunum samples in order to obtain the fulllength genome sequence. Trimmomatic and TMAP were used for cleaning and aligning reads (3) and SAMtools to create a consensus sequence (4). Alignment was realized using progressiveMauve  (5). The PEDV genome sequences obtained from jejunum samples were named PEDV FR/001/2014 to FR/003/2014.