dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell samples were labeled with L-[ 35 S]-methionine during the exponential growth phase (control, OD 500nm 0. 4) and 30 min after the addition of NaCl. Dual channel images were created from the 2D-gels with the Delta 2D software (Decodon GmbH, Greifswald, Germany). doi: 10. 1371/journal. pone. 0080956. g003 biosynthetic enzymes in response to high osmolarity strongly suggested that this Bacillus species should be able to synthesize glycine betaine though the oxidation of choline (Figure  5A ). To test this directly, we performed a metabolome analysis and monitored the cellular pools of choline and glycine betaine in cells that were grown in the presence or absence of choline (1 mM) and either at low or high (1 M NaCl) osmolarity. Cells grown at low osmolarity did not accumulate choline nor contained glycine betaine.
Microarrays were scanned using the Agilent scanner Type G2565CA combined with the high-resolution upgrade G2539A and the software Scan Control 8. 4. 1 (Agilent Technologies; Santa Clara, CA; USA). Data were extracted from scanned images using Agilent's Feature Extraction Software (version 10. 5. 1. 1) (Agilent Technologies; Santa Clara, CA; USA) using default settings. Gene expression data were loaded into the Rosetta Resolver® Gene Expression Analysis System 7. 2. (Rosetta Inpharmatics c/o Ceiba Solutions; Boston, MA; USA). A common reference type of design was employed, and data from three biological replicate hybridizations were combined using an error-weighted average (Rosetta Resolver error model  [108] ). Genes showing significant differences in expression were identified by error-weighted ANOVA analysis, with a Benjamini-Hochberg FDR (false discovery rate) multiple test correction.
[21]. Proteins, which were found to be upregulated in response to salt-stress were cut out from preparative Coomassie-blue-stained gels and identified by mass spectrometry according to Liedert et al. [110]. The identification of proteins down-regulated in response to salt stress was conducted by consulting a B. licheniformis proteome reference map established by Voigt et al. [23]. Spot quantification and calculation of synthesis rates of proteins were performed with the Delta 2D software (Decodon GmbH, Greifswald, Germany). The protein-labeling experiments were repeated twice and the synthesis rates (Table  S1 ) were calculated from these two independent experiments. Furthermore, technical replicates were included for the quantification of proteins and their synthesis rates.
Data evaluation and label-free quantification For data processing, protein identification and label free quantification, raw data were imported into ProteinLynx Global Server 2. 4 (PLGS) (Waters, Milford, MA; USA) and processed via the Apex3D algorithm with the following parameters. Chromatographic peak width: automatic; MS ToF resolution: automatic; lock mass for charge 2: 785. 8426 Da/e; lock mass window: 0. 25 Da; low energy threshold: 250 cts; elevated energy threshold: 30 cts; retention time window: automatic; intensity threshold of precursor/fragment ion cluster: 1000 cts. Searches for peptides were carried out by the ion accounting algorithm  [111]  against a randomized protein database predicted from the B. licheniformis DSM 13 T genome sequence  [13]  with added common laboratory contaminants (8 478 entries) using the following parameters.
Peptide tolerance: automatic; protein tolerance: automatic; minimal fragment ions matches per peptide: 3; minimal fragment ions matches per protein: 7; minimal peptide matches per protein: 2; primary digest reagent: trypsin; missed cleavages: 2; variable modifications: carbamidomethylation C (+57. 0215), deamidation N, Q (+0. 9840), oxidation M (+15. 9949), pyrrolidone carboxylacid N-TERM (-27. 9949); false discovery rate (FDR): 5 %; calibration protein: yeast ADH1. Label-free relative quantification was carried out by Expression E software included in PLGS 2. 4 (Waters, Manchester, UK). The quantification was achieved on basis of the observed peptide ion peaks intensities. Normalization was performed on all identified peptide signals using the autonormalization function. Proteins were taken as significantly changed in their amount if regulation probability was below 0.
In silico analysis of the B. licheniformis genome sequence and of the folding of the proBA and proI 5'-UTR mRNA sequences The mining of the B. licheniformis DSM 13 T genome  [13]  for genes involved in osmostress responses were done using the web-server available from the Joint Genomic Institute homepage (http://img. jgi. doe. gov/cgi-bin/w/main. cgi) using the "Find genes-by BLAST" and the "gene neighborhood analysis" tools. The structures of the 5'-untranslated regions (UTR) of the B. licheniformis proI and proBA genes were predicted using the Mfold algorithm  [115]. The mRNA secondary structures suggested by the program (http://mfold. rna. albany. edu/) corresponding to the T-box control element  [76]  were then further manually adjusted based on phylogenetic considerations of other T-box regulated genes  [72, 116].
Acknowledgements We are very greatful to  Jana Pribe ,  Susanne Paprotny  and  Jutta Gade  for excellent technical assistance. We greatly appreciate the kind help of  Vickie Koogle  in the language editing of our manuscript and thank  Wolfgang Schumann  for providing us with the pX cloning vector. E. B. greatly appreciated the hospitality and kind support of  Tom Silhavy  during a sabbatical at the  Department of Molecular Biology of Princeton University  (Princeton, NJ,  USA ).
Follow-up Clinical and biological data were obtained from initial gastrointestinal symptoms to the end of the follow-up at Week 10 after initiation of eculizumab. Treatment of STEC-HUS Plasma exchange. TPE with 60 mL/kg/body weight plasma volume was performed daily by plasma filtration (Prismaflex ® system, Hospal) and substitution with solvent detergent-processed frozen plasma. Heparin or citrate was used as an anticoagulant for the extracorporeal circuit. Eculizumab. Eculizumab was administrated according to the following protocol: for adults, eculizumab 900 mg once per week for 4 weeks, 1200 mg Weeks 5, 7 and 9. For children from 10 to 20 kg of body weight, eculizumab 600 mg at first infusion (Week 1) then 300 mg at Weeks 2, 4, 6 and 8. All patients received a quadrivalent meningococcal (A, C, Y and W-135) vaccine (Mencevax ® , GlaxoSmithKline) prior to receiving first eculizumab infusion.
Ethics The first case of STEC-HUS was admitted to the University Hospital of Bordeaux on 21 June 2011. We obtained approval from the Regional Ethics Committee on 29 June, for a study aimed to gain knowledge on pathophysiology, prognosis and treatment of STEC-HUS. This study was approved by the AFSSAPS on 13 July and was registered in ClinicalTrials NCT01406288. All patients, or their representatives, signed informed consent to participate in the study.
Hospitalization Data. The French programme for hospital information system (PMSI) is implemented since 1994 in public hospitals and since 1997 in private hospitals. A complete hospitalization database is available since 1998. It is a medical economic database based on the diagnosis-related group (DRG) method. Each hospitalization is registered in a local database grouped in a national database. Since 2004, a patient identification number is included to identify patients and hospital stays related to each patient. The national hospitalization database held by the PMSI provided hospitalization data for the whole region. Hospital stays included in the analysis were selected over the study period 2004-2007 based on several selection criteria. On the first step, we excluded stays without patient identification number and stays for patient that moved outside or inside the study area between 2004 and 2007.
We used the adjacency-based weights where   = 1 if district  is adjacent to district ,   = 0 otherwise are used. We have taken Gamma prior distributions for the precision parameters (reciprocal of the variance) of the heterogeneity and spatial terms. For both we have taken the noninformative Γ (0. 5, 0. 0005). The Γ(, ) denotes the Gamma distribution with expectation equal to /. Non-informative priors were taken for the other parameters, that is, the intercept and the regression coefficients. In a Bayesian context, we defined the credible interval at the 5%, that is, the probability that the parameter belongs to is 95%. Analysis was done by age (children 0-14 years, adults over 15 years) and by sex for the adults with the software R and WinBUGS.
Oxygen stable isotopes (δ 18 O) are among the most useful tools in palaeoclimatology/palaeoceanography. Simulation of oxygen stable isotopes allows testing how the past variability of these isotopes in water can be interpreted. By modelling the proxy directly in the model, the results can also be directly compared with the data. Water isotopes have been implemented in the global three-dimensional model of intermediate complexity iLOVECLIM, allowing fully coupled atmosphere-ocean simulations. In this study, we present the validation of the model results for presentday climate against the global database for oxygen stable isotopes in carbonates. The limitation of the model together with the processes operating in the natural environment reveal the complexity of use the continental calcite-δ 18 O signal of speleothems for a global quantitative data-model comparison exercise.
On the contrary, the reconstructed surface ocean calcite-δ 18 O signal in iLOVECLIM does show a very good agreement with the late Holocene database (foraminifers) at the global and regional scales. Our results indicate that temperature and the isotopic composition of the seawater are the main control on the fossil-δ 18 O signal recorded in foraminifer shells when all species are grouped together. Depth habitat, seasonality and other ecological effects play a more significant role when individual species are considered. We argue that a data-model comparison for surface ocean calcite δ 18 O in past climates, such as the Last Glacial Maximum (≈ 21 000 yr), could constitute an interesting tool for mapping the potential shifts of the frontal systems and circulation changes throughout time. Similarly, the potential changes in intermediate oceanic circulation systems in the past could be documented by a data (benthic foraminifers)model comparison exercise whereas future investigations are necessary in order to quantitatively compare the results with data for the deep ocean.
Among them, the majority has been used exclusively to simulate water isotopes separately in the atmosphere and ocean components. Oxygen isotopes have been implemented in the three-dimensional model of intermediate complexity called iLOVECLIM (version 1. 0), allowing fully coupled atmosphere-ocean simulations. The detailed implementations of oxygen isotopes in iLOVECLIM can be found in  Roche (2013). The present work aims at showing preindustrial experiment results in comparison to different global datasets from marine and continental realms. We mainly focus our discussion on carbonate archives that are extensively used in climate studies. The validation of the model results against water isotope observations can be found in  Roche and Caley (2013). The benefits, caveats, agreements and problems from this data-model comparison for oxygen stable isotopes in carbonates will be discussed to determine the potential and the interest of using iLOVECLIM for palaeoclimatic studies.
Global datasets Global oxygen isotopic datasets for the atmosphere and ocean components have been compiled from published late Holocene measurements to allow comparison and discussion with iLOVECLIM results. Table  S1  is a compilation of 74 δ 18 O calcite measurements and drip water data of speleothems from the literature. The δ 18 O of the calcite are reported for the last 1000 years of each record with the associated 2σ error when it was possible to calculate it. Table S1 also includes a compilation of 19 δ 18 O data of ice   S1 ). cores from the literature. The δ 18 O of the ice are reported for the last 1000 yr of each record with the associated 2σ error when it was possible to calculate it. For the EPICA Dome C, Vostok and Illimani site, the δD values were converted in δ 18 O values using the global meteoric water line δD = 8.
2×δ 18 O + 11. 27  (Rozanski et al. , 1993). To compare global oceanic isotopic datasets with iLOVECLIM results, we used the late Holocene planktonic foraminifer's δ 18 O database of  Waelbroeck et al. (2005)  and extend it with 62 data averaging over the last 3000 yr (Table  S2 ). Similarly, for the intermediate and deep ocean, we used the published Atlantic benthic foraminifer's stable isotopic database of  Marchal and Curry (2008)  and extend it with 73 data covering the global ocean and averaged over the last 3000 yr (Table  S2 ).
The main differences for the δ 18 O sw are in the Atlantic and Indian subtropical Ocean and the region offshore from   S2 ). Note that the legend is non-linear. California  (Roche and Caley, 2013). These regions are all marked by warmer SST in the model (Fig. 4 ). The δ 18 O sw in the model is slightly depleted in the Atlantic Ocean and even more depleted in the North Indian Ocean in comparison to data  (Roche and Caley, 2013). These two effects (warmer SST and more depleted δ 18 O sw ) produce both a weak supplementary decrease of the δ 18 O calcite signal but no compensation effect. Concerning the region offshore from California, the warmer SST and more enriched δ 18 O sw could compensate each other but the modelled signal is compared to only two calcite-δ 18 O points of our dataset (Fig.
5 ). Therefore, the δ 18 O calcite signal of the model can be compared with data with a good accuracy because slight errors in the modelled temperature and water isotope distributions do not compensate each other. Also note that the problem of biased absolute SST values may not apply when anomalies with respect to past climates are calculated (for example the last glacial maximum). Combining the δ 18 O sw and oceanic temperature and using the equation of  Shackleton (1974) , we are able to reconstruct the calcite δ 18 O in the model and to compare it with late Holocene δ 18 O planktonic foraminifers' data (Fig. 5 ). We choose specifically the interval 0-50 m as the different species of foraminifers that constitute the global dataset (G. Ruber pink, G. Ruber white, G. sacculifer, G. bulloides, N.
, 2002; Simstich, 2003)  (Table  1 ). In first approximation, a depth habitat of 0-50 m seems to be suitable to compare the model results with a global and varied dataset. Indeed, for this depth habitat, the relationship between model results and data exhibit a very strong correlation (R 2 = 0. 85, with a slope of 0. 92 and an intercept of 0. 15) (Fig. 6 ). For a depth habitat of 0-20 m, the slope of the relation is 0. 89 and the intercept is 0. 27, suggesting that the depth habitat is not the main control of the calcite-δ 18 O signal. We obtain a very good quantitative agreement for calcite δ 18 O with highdepleted values in the tropical regions (around -3 ‰) and more enriched values in high latitude regions (around 4 ‰), reflecting the combination of both δ 18 O sw and temperature changes with latitudes (Fig.
Acknowledgements. T. Caley is supported by  NWO  through the  VIDI/AC 2   ME  project no  864. 09. 013. D. M. Roche  is supported by  NWO  through the  VIDI/AC 2   ME  project no  864. 09. 013  and by  CNRS-INSU. The authors wish to thank  D. Genty  for useful discussion on equilibrium conditions during speleothem calcite growth. Institut Pierre Simon Laplace  is gratefully acknowledged for hosting the iLOVECLIM model code under the LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus). This is NWO/AC 2 ME contribution number  03. Edited by:  R. Marsh.
Patients and data collection Consecutive patients diagnosed with GCA, who met the American College of Rheumatology criteria, entered our prospective cohort study between 1984 and 2009. The study took place in the department of internal medicine, University Hospital of Limoges, France. All the patients gave their informed, written consent. Using a comprehensive questionnaire, a senior internist recorded baseline clinical and laboratory parameters, and also prospectively assessed the outcome data (Table  1 ).
Statistical analyses We conducted the data analyses with XLSTAT © software, version Mac 2012 (Addinsoft, Paris, France). The means from continuously distributed data were tested for statistical differences between groups using the two-tailed Mann-Whitney U-test. To transfer continuous data into discrete units, we determined the cut-off points for GC dosage at different endpoints (3, 6, 12 months), for basal cortisol level, treatment duration and cumulative GC dose, using the nested-average method. For each parameter, the cut-off points were taken from the intersection of the curves between the responders and nonresponders. This method gave more accurate thresholds than using median values. Multivariate analyses (logistic regression) were performed on variables that reached a 25% significance level in univariate analysis; p<0.
For this purpose [Ni(intz) 6 ](BF 4 ) 2 with n = 3 and 4 was prepared using a 40% aqueous solution of Ni(BF 4 ) 2 and the appropriate molar ratios of the respective ligands in ethanolic solution. This yielded the desired good single crystals in the shape of hexagonal violet plates by slow solvent evaporation at room temperature in small crystallisation dishes. Single crystal X-ray data of [Ni(i3tz) 6 ](BF 4 ) 2 and [Ni(i4tz) 6 ](BF 4 ) 2 were collected at T = 100 K on a Bruker Smart CCD 3-axis diffractometer using graphite-monochromated Mo Ka radiation (k = 0. 71073 Å) and x-scan frames covering complete spheres of the reciprocal space up to h max = 30°. After data integration with program SAINT, corrections for absorption and k/2-effects were applied with program SADABS  [18]. The structures were solved with Patterson methods and were then refined on F 2 with the program package SHELX97  [19].
3  and 4 ). In order to verify that the two iron complexes are homeotypic with the Ni complexes (vide supra), X-ray powder diffraction was used. The measurements were carried out on a Panalytical X'Pert diffractometer in Bragg-Brentano geometry using Cu Ka 1,2 radiation, a X'Celerator linear detector with a Ni-filter, sample spinning with backloading sample holders and 2h = 5-70°, T = 24 °C. Using the structural data of the Ni-complexes, Rietveld refinements were carried out with program TOPAS using the fundamental parameter approach  [27]. Isotropic equivalents of the anisotropic displacement parameters of the Ni-compounds combined with an overall temperature factor were applied, but atomic coordinates were not refined. Apart from unit cell dimensions, instrumental parameters, background polynomial coefficient, a texture parameter was refined.
Magnetic susceptibility measurements Temperature dependent magnetic susceptibility measurements were performed in an applied field of B = 0. 5 T in a SQUID. The magnetic susceptibility measurement for [Fe(i4tz) 6 ](BF 4 ) 2 reveals (see Fig. 6 , left) an almost complete spin transition between T = 170-250 K; the estimate for the transition temperature is T ½ % 220 K. The low-temperature tail of the magnetic susceptibility proves that some paramagnetic impurity (probably high-spin Fe(III)) is present in the sample. Different heating/cooling rates have no effect to this sample. The bulk magnetic susceptibility data have been directly fitted to the function consisting of the low-spin (LS) and high-spin (HS) fractions along with the paramagnetic impurity (PI): v mol ¼ x LS v LS þ x HS v HS þ x PI v PI ð1Þ using the normalisation of the mole fractions to unity: 1 ¼ x LS þ x HS þ x PI.
Variable temperature UV-Vis-NIR spectroscopy To support the magnetic and 57 Fe-Mößbauer data electronic spectra have been recorded using a thermostatable solid state diffuse reflectance accessory between 110 and 300 K. The undiluted sample was measured versus a BaSO 4 background between 300 and 1200 nm after thermostating the sample for 10 min. According to the magnetic data of [Fe(i3tz) 6 ](BF 4 ) 2 a change of the pure HS sample is expected around 140 K upon cooling, which is reflected in the gradual raising of the typical iron(II) low-spin d-d transitions at 540 nm ( 1 A 1 ? 1 T 1 ) and 380 nm ( 1 A 1 ? 1 T 2 ) in the UV-Vis-NIR spectra for T = 130-110 K (see Fig. 9 ). As only   approximately 13% of the iron(II) centres switch from high spin to low spin within the reachable temperature range of the thermostating unit of the spectrometer the dominating species at T = 110 K is high spin iron(II).
Acknowledgements Thanks for financial support are due to the '' Fonds zur Förderung der Wissenschaftlichen Forschung in Österreich '' (Project  19335-N17 ) as well as COST  D35/0014/05  for granting STSM missions of  Mr. Nader Hassan  to Toulouse. Furthermore, we want to warmly acknowledge technical support of  Lionel Rechignant  ( LCC/CNRS Toulouse ). Grant Agencies (Slovakia:  VEGA 1/0213/08 ,  APVV 0006-07 ,  COST-0006-06 ,  VVCE 0004-07 ) are acknowledged for the financial support.
1 D, left ). Epidermal CFP + and YFP + LCs were not randomly distributed in the epidermal skin, but assembled in monocolored foci (Fig. 1 D, right ). To confirm this visual impression, we quantified the clustering index of CFP + LCs that represented only 20% of all LCs (Fig. 1 E ). To achieve this, we developed the analysis software "ClusterQuant," in which the different cell types (CFP + and YFP + LCs) are manually labeled within microscopic images. Subsequently, automatic computation of the corresponding Voronoi diagrams was combined with Monte Carlo simulations to obtain a quantitative statistical measure of the spatial distribution (i. e. , cluster formation or diffuse cell spreading) of the LCs (see Materials and methods and Fig. S1  for technical details). ClusterQuant is specifically designed for the statistical analysis of spatial cell distributions and can be applied to any multicolor fate mapping system.
In brief, the minimal vector for Red/ET cloning (2. 7 kb) was amplified using high-fidelity Taq polymerase from Roche and a pair of specific primers flanking the 10-kb promoter region of UBC gene: on standard hardware running Microsoft Windows 7 64-bit. In the first module, the different LC classes are defined using the Region-of-Interest plugin (ROImanager) of ClusterQuant/CognitionMaster, and the respective cells are labeled manually to obtain the cell center coordinate information for the subsequent processing step, in which the second module computes the two-dimensional Voronoi mesh based on the manually labeled cell centers. The resulting tessellation establishes the neighborhood relationships between the cells, which is used to evaluate the number of cells (of the same type) assembled in clusters and the respective cluster sizes.
Although this step provides a robust and objective means of cell cluster quantification, it does not yet allow for an assessment of the (non)randomness of the spatial cell distribution/cluster formation. To achieve this, in the final step, we use Monte Carlo simulations based on the cell numbers present in the corresponding experimental image data. In the 10,000 simulation runs we perform for each experimental dataset, the same numbers of cells as in the real images are randomly distributed over the same image areas, and for each simulation run, the spatial statistics (Voronoi mesh, neighborhood, and cell cluster features) are computed in same way as for the experimentally obtained images. After all simulation runs are completed, we compare the results of the spatial statistics of the real and simulated data.