dataesr/scientific-paragraphs-categorization huggingface dataset:
Manufacturing process The food enzyme is manufactured according to the Food Hygiene Regulation (EC) No 852/2004  4 and in accordance with current Good Manufacturing Practice (GMP). A dataset related to the manufacturing process including a list of raw materials used and a production flow process from fermentation and downstream processes was provided. The food enzyme is produced by a pure culture in a contained, submerged, fermentation system with conventional process controls in place. After completion of the fermentation, the solid biomass is removed from the fermentation broth by filtration. The filtrate containing the enzyme is then further purified and concentrated by ultrafiltration. The production strain could not be detected in three different batches of the food enzyme. From each batch, before formulation, a Recombinant DNA was detected in three enzyme batches, tested in triplicate, by polymerase chain reaction (PCR) amplifying a fragment of about of the xylanase coding sequence.
EFSA Comprehensive European Food Consumption Database Since 2010, the EFSA Comprehensive European Food Consumption Database (hereafter the EFSA Comprehensive Database 6  ) has been populated with detailed national data on food consumption. Competent authorities in European countries provide EFSA with data regarding the level of food consumption by individual consumers, as taken from the most recent national dietary survey in their country (EFSA, 2011a). The food consumption data gathered by EFSA were collected using different methodologies and thus direct country-to-country comparisons should be made with caution. Depending on the food category and the level of detail used in exposure calculations, uncertainties might be introduced owing to subjects possibly underreporting and/or misreporting of consumption amounts.
e. flour, via the application of conversion factors (Appendix B). For example, consumption of 100 g of bread was converted into an intake of 70 g flour (recipe fraction of 0. 7) and then multiplied by 29 mg TOS/kg flour, as provided by the applicant, to arrive at an exposure of 2. 03 mg TOS/100 g bread. Exposure to the food enzyme-TOS was calculated by multiplying values reported for each food category by their respective consumption amount per kilogram of body weight (kg bw) separately for each individual in the database. Table  2  provides an overview of the derived exposure estimates. The average and 95th percentile exposure to the food enzyme-TOS per age class, country and survey are reported in Appendix C -Table  C. 1. The contribution of the food enzyme-TOS from each FoodEx category to the total dietary exposure is indicated in Appendix C -Table  C.
CMIP5 data and observations The models evaluation is performed on monthly precipitation (pr) and monthly air surface temperature (tas) derived from historical long-term and decadal simulations. The long-term simulations extend from 1850 to 2005, i. e. from the beginning of the industrial revolution to the near present. The period 1901-2005 is evaluated since the observations are available from the beginning of the 20th Century. The 30-year hindcasts of decadal simulations start in 1961. We evaluate the 1961-2010 period by coupling the time series of the 30-year hindcasts  initialized in 1961 and 1981. A common-time period 1971-2000  is selected to study climatological features (annual cycles and spatial distribution of annual means) of the present climate for both long-term and decadal simulations.
This period is chosen as it is the last normal (30-year) period available on both simulations. The observational data set of temperature and precipitation used in this study is the CRU TS 3. 20 from the Climate Research Unit -University of East Anglia  (Harris et al. , 2014). CRU TS 3. 20 consists of monthly gridded data with a 0. 5 ∘ × 0. 5 ∘ spacing extending from January 1901 to December 2011. Other gridded data sets are available, such as the Global Precipitation Climatology Centre data set  (GPCC, Schneider et al. , 2011)  and the University of Delaware Air Temperature and Precipitation database (UDEL;  Matsuura and Willmott, 2009). All gridded products for southeastern South America face the problem of sparse gauge coverage at the beginning of the 20th century (e. g. Barreiro et al. , 2014).
For reference, similar pictures are shown for the two single models that exhibit the best evaluation metrics, the CCSM4 and CESM1-BGC. Figures  3(a ) and (b) show that the ensembles achieve a low dispersion around the linear regression line (R 2 = 0. 93). The dispersion of the data is slightly higher in cold months than in warm months. The regression line in Figure  3 (a) shows that the 25-model ensemble overestimates temperature on warm months. In the 9-model ensemble (Figure  3 (b)), the regression line fits the 1 : 1 line better than the 25-model ensemble reaching a slope value m = 1. 03. Although the ensembles fit well the data, their variability is reduced given that the ensembles diminish the dispersion of simulated temperatures keeping their monthly values in a narrow band when compared with observations.
Conclusions This paper evaluated the ability of CMIP5 GCMs to simulate the observed spatio-temporal behaviour of the climate in northeastern Argentina. The GCMs were selected from the two main core sets of CMIP5 simulations: long-term  and decadal. Monthly simulations of precipitation and temperature were evaluated with the observed CRU TS 3. 20 data set. The simulations of each GCM and the multi-model ensembles were evaluated through the MBE, the MAE, the RMSE, the coefficient of NSE, and the Pearson correlation. These metrics were computed for both areal-averaged time series and spatial fields. We also determined the ability of GCMs to simulate the annual mean fields and the mean annual cycle of temperature and precipitation for the normal 30-year period 1971-2000. Inspection of the results allowed selection and evaluation of model subsets that represent better the regional climate.
Acknowledgements The thoughtful questions and recommendations of the anonymous reviewers are greatly appreciated. This e1173 research was carried out with the support of Projects  CRN3035  and  CRN3095  of the  Inter-American Institute for Global Change Research (IAI) , which is supported by the  US National Science Foundation. NOAA  Grant  NA14NES4320003  and a  UNL  Project  C. A. I. + D. 2011 35/180  are also acknowledged. Miguel Lovino  and  Omar Müller  were supported by PhD grants from  CONICET ,  Argentina.
Emboli were identified using characteristic short audible sound (range 10-100 ms, intensity threshold above 7 dB) and spectral appearance using the International Consensus Group microembolus identification criteria and assisted by an automated Embolus Detection Software (Spencer Technologies, USA)  [44]. The Doppler wave forms were reassessed to exclude artefact and confirm the presence of true emboli. The mean of maximal, mean and end-diastolic flow velocities were determined from the mean of measurements obtained over ten cardiac cycles.
Statistical analysis Continuous variables were expressed as mean ± standard deviation for normally distributed data, and categorical variables were expressed as total and percentage. To quantify intra-observer repeatability and reproducibility of imaging measurements, the intra-class correlation co-efficient (ICC) was calculated and Bland-Altman analysis undertaken. Statistical significance was taken as a two-sided P < 0. 05. Statistical analyses were performed with the use of IBM SPSS Statistics for Mac, version 23 (Armonk, New York, IBM Corp, USA).
Hence, the maximum error has been estimated to be less than 15%  [34, 35]. Finally, individual variability of the middle cerebral artery size, length and tortuosity are also contributing to the scattering of the velocity measurements  [27, 29, 37]. In general, cerebral flow velocity decreases with age in a bimodal pattern with a first decline above the age of 40 years and a further reduction above 60 years of age  [22, 26, 27, 54]. Unsurprisingly, our data demonstrate similar results with lower velocity values in patients cohort when compared with the healthy volunteers. Overall the obtained ICC values in our study represent a good repeatability and reproducibility in both cohorts. However, the peak and mean ICC repeatability values recorded in patients group reached an excellent agreement (ICC > 0.
Elastic core and peripheral zone We could introduce a second category, which distinguishes a spot-weld core and a peripheral zone, but eventually characterized by different behaviors. For example the "ACM2" model developed by (Heiserer et Al) for the convenience of numerical implementation is made of a solid elastic hexahedral element to characterize the spot-weld core and is connected to the rest of the sheet through rigid body elements. The MSC Cweld model developed by NASTRAN software enters this category as well. It is an elastic beam connected to the two sheets through patches with local kinematical constraint.
We present a dataset made of 3D digital brain templates and of an atlas of the gray mouse lemur (Microcebus murinus), a small prosimian primate of growing interest for studies of primate biology and evolution. A template image was constructed from in vivo magnetic resonance imaging (MRI) data of 34 animals. This template was then manually segmented into 40 cortical, 74 subcortical and 6 cerebrospinal fluid (CSF) regions. Additionally, the dataset contains probability maps of gray matter, white matter and CSF. The template, manual segmentation and probability maps can be downloaded in NIfTI-1 format at https://www. nitrc. org/projects/mouselemuratlas. Further construction and validation details are given in "A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy" (Nadkarni et al.
Data MR images of the brain of 34 healthy young adult mouse lemurs (Table  1 ) were acquired in a 7 T scanner. 3D images of the whole brain were mutually registered to create a template (Fig. 1A ). This template was used for manual segmentation (Fig. 1 , Table  2 ) and to create probabilistic gray matter, white matter and CSF templates of the brain (Fig. 2 ). The templates and atlas are available as NIfTI volumes in an NITRC repository (https://www. nitrc. org/projects/mouselemuratlas). The dataset can be freely used for academic work upon citing this paper and  [1].
Creation of the template MR images from the 34 mouse lemurs were upsampled to 115 mm isotropic resolution. The template was generated using the function anats_to_common available within the sammba-mri python module (https://sammba-mri. github. io/generated/sammba. registration. anats_to_common. html#sammba. registra tion. anats_to_common). Most steps used tools from freely available AFNI software (https://afni. nimh. nih. gov/  [2] , except for brain extraction which was done with RATS  [3, 4]. First, head images were bias corrected. In a second step the brains were extracted and individual brain extracted image centers were shifted to the brain center of mass. Brains were then all rigid body aligned to a previous histological atlas of the mouse lemur brain  [5]  and the transform was then applied to the original heads.
Neuroscience More specific subject area Mouse lemur (Microcebus murinus) brain, MRI atlas Type of data Template, atlas and probabilistic maps for the mouse lemur brain Figure of the brain template and atlas. Figure of probabilistic maps for the mouse lemur brain Table of animals used for template creation Table with the list of segmented regions How data was acquired in vivo 7T MRI (Agilent, Santa Clara, CA, USA) Template created with Sammba-MRI (https://sammba-mri. github. io).
Neuroscience More specific subject area Mouse lemur (Microcebus murinus) brain, MRI atlas Type of data Template, atlas and probabilistic maps for the mouse lemur brain Figure of the brain template and atlas. Figure of probabilistic maps for the mouse lemur brain Table of animals used for template creation Table with the list of segmented regions How data was acquired in vivo 7T MRI (Agilent, Santa Clara, CA, USA) Template created with Sammba-MRI (https://sammba-mri. github. io). Atlas created using ITK-SNAP (http://www. itksnap. org) Probabilistic atlas created using SPM8 (www. fil. ion. ucl. ac. uk/spm) with the SPMMouse toolbox (http://spmmouse. org) Data format Analyzed (NIfTI-1 format) Experimental factors 34 mouse lemurs (22 males and 12 females; age range 15-58 months) Experimental features 1.
DSF-based fluorescent assays Thermal shift/stability assays (TSAs) were performed using a StepOnePlus Real-Time PCR machine (Life Technologies) using SYPRO-Orange dye (emission maximum 570 nm, Invitrogen), with thermal ramping between 20 and 95°C in 0. 3°C step intervals per data point to induce denaturation in the presence or absence of test biochemicals or small-molecule inhibitors, as previously described  [48]. HS2ST was assayed at a final concentration of 5 μM in 50 mM Tris-Cl ( pH 7. 4) and 100 mM NaCl. Final DMSO concentration in the presence or absence of the indicated concentrations of ligand was no higher than 4% (v/v). Normalised data were processed using the Boltzmann equation to generate sigmoidal denaturation curves, and average T m /ΔT m values were calculated as described using the GraphPad Prism software  [48].
Pressure and voltage settings were adjusted manually to afford optimal separation of the sulfated product and non-sulfated hexasaccharide substrate, with a sample (sip) volume of 20 nl, and total assay times appropriate for the experiment. Individual sulfation assays were assembled in a 384-well plate in a volume of 80 μl in the presence of the indicated concentration of PAPS or various test compounds, 50 mM HEPES ( pH 7. 4), 0. 015% (v/v) Brij-35 and 5 mM MgCl 2 (unless specified otherwise). The degree of oligosaccharide sulfation was directly calculated using the EZ Reader software by measuring the sulfo oligosaccharide : oligosaccharide ratio at each time-point. The activity of HS2ST enzymes in the presence of biochemicals and small-molecule inhibitors was quantified in 'kinetic mode' by monitoring the amount of sulfated glycan generated over the assay time, relative to control assay with no additional inhibitor molecule (DMSO control).
NMR-based oligosaccharide sulfation analysis For NMR experiments, fluorescein-labelled hexasaccharide L-IdoA substrate and the HS2ST-catalysed sulfation product (10 mM) dissolved in 50 mM HEPES, pH 7. 4, 5 mM MgCl 2 and 0. 002% (v/v) Brij-35 were lyophilised overnight and re-dissolved in an equivalent amount of D 2 O. NMR experiments were performed at 25°C on a Bruker Avance III 800 MHz spectrometers equipped with a TCI CryoProbe. 1D and 2D proton and TOCSY spectra (mixing time 80 ms) were measured using standard pulse sequences provided by the manufacturer. Spectra were processed and analysed using TopSpin 3. 4 software (Bruker).
Docking studies Docking models for rottlerin, suramin and GW407323A were built using Spartan16 (https://www. wavefun. com) and energy minimised using the Merck molecular forcefield. GOLD 5. 2 (CCDC Software) was used to dock molecules  [53] , with the binding site defined as 10 Å around the 5 0 phosphorous atom of PAP, using co-ordinates from chicken MBP-HS2ST PDB ID: 4NDZ  [20]. A generic algorithm with ChemPLP as the fitness function  [54]  was used to generate 10 binding modes per ligand in HS2ST. Protons were added to the protein. Default settings were retained for the 'ligand flexibility' and 'fitness and search options'; however, 'GA settings' were changed to 200%.
Docking analysis of HS2ST ligands The X-ray structure (PDB ID: 4NDZ) of trimeric chicken MBP-HS2ST fusion protein bound to non-sulfated PAP (adenosine-3 0 -5 0 -diphosphate, a potent HS2ST inhibitor that was identified in the present study) and a polymeric oligosaccharide have previously been reported  [20, 28]. We employed a 3. 45 Å structural dataset to dock rottlerin, suramin and the most potent oxindole-based 'hit' from the screen (GW407323A, see Figure  4B ) into the extended enzyme active site. As shown in Figure  5A , HS2ST possesses substrate-binding features that accommodates an extended oligosaccharide that place it in close proximity to the desulfated PAP end-product, which substitutes for the endogenous PAPS cofactor during crystallisation. The 3 0 -phosphoadenine moiety of PAP also helps anchor the nucleotide in an appropriate position.
Funding This work was funded by a  Biotechnology and Biotechnological Sciences Research Council ( part of UK Research and Innovation) Tools  and  Resources Development Grant  [ BB/N021703/1 ] and a  Royal Society   Research Grant  (to P. A. E. ), a  European Commission FET-OPEN  grant [ArrestAD no. 737390 ] to D. P. G. , S. C. , D. G. F. , E. A. Y. and P. A. E. ,  North West Cancer Research (NWCR)  grants  CR1088  and  CR1097 , and a NWCR endowment (to D. G. F. ). V. P. is supported by  NIH Small Business Innovation Research  Contract  HHSN261201500019C. The SGC is a registered charity (number  1097737 ) that receives funds from  AbbVie ,  Bayer Pharma AG ,  Boehringer Ingelheim ,  Canada Foundation for Innovation ,  Eshelman Institute for Innovation ,  Genome Canada ,  Innovative Medicines Initiative  ( EU/EFPIA) [ULTRA-DD  grant no.
X-ray crystallography. Powder diffraction The sample was mounted in a top-loaded sample holder and investigated by X-ray powder diffraction. Powder X-ray data were collected at room temperature using a SIEMENS D5000 diffractometer equipped with a Cu X-ray source employed (k1 = 1. 5406 A ˚) at 40 kV and 40 mA. In order to limit the axial divergence, 0. 02 rad primary and secondary soller slits were inserted.
Single-crystal X-ray crystallography The crystal data collection and structure refinement, at room temperature, procedures are given in Table  1. Total reflections were collected with a Bruker APEX II CCD diffractometer, correction was made by Lorentz-Polarization effects and absorption. Unit cell parameters were refined using sets of 949. 9 reflections. The crystal data of the complex were collected at 293(2) K. Intensity data were collected in the w-2h scan mode using graphite monochromatic Mo Ka radiation (0. 71073 A ˚). The crystal structure was solved in the monoclinic symmetry, space group P2 1 /c. WR 2 ¼ P w jFOj 2 ÀjFCj 2 ð Þ ½ 2 P w jFOj 2 ð Þ ½ 2 1 2. The positions of the tellurium atoms were determined through a three dimensional Patterson synthesis. Bromine, carbon, nitrogen and hydrogen atoms were located by a three-dimensional Fourier function.
Structure solution and refinement were carried out using SHELX programs  [33, 34]. The non-hydrogen atoms were refined anisotropically. The hydrogen atoms were attributed to isotropic thermal factors close to those of the atoms to which they are linked. All the hydrogen positions of the protonated cation were placed geometrically and held in the riding mode (the C-H and O-H bonds were fixed at 0. 93 A ˚and 0. 82 A ˚respectively). The H-atom of the N is located by difference Fourier synthesis. The atomic coordinates and equivalent isotropic and anisotropic thermal agitation factors are listed in tables (see ESI). The structural graphics of the asymmetric unit were realized with ORTEP  [35]  and Mercury  [36]  and the remaining figures were issued by the DIAMOND program  [37]. CCDC 1450634 contains the supplementary crystallography data for [(C 7 H 6 NO 4 ) 2 TeBr 6 Á4H 2 O].
Sea water samples collection and environmental data Sea water samples were collected at two stations: one close to the coast (4. 1 km from the coastline) located off Cabo Silleiro (RAIA station; 42 ) and chlorophyll a (Chl a) concentrations. For coccolithophore analyses, a seawater volume between 2 and 5 L was filtered through a cellulose filter of 0. 45 µm pore size by using a vacuum pump. Then, each filter was stored in plastic Petri dishes and dried. Additionally, diatom abundance was determined at 5 m water depth by depositing between 10 and 50 mL in composite sedimentation chambers for observation through an inverted microscope. Sample preparation and analyses for diatom abundance determination are described in detail in  Zúñiga et al. (2017). Inorganic nutrients were measured by segmented flow analysis with Futura-Alliances autoanalysers following  Hansen and Grassoff (1983)  and pigment extract fluorescence was performed to estimate final Chl a concentrations with a Turner Designs fluorometer calibrated using pure Chl a (Sigma) (for details, see  Zúñiga et al.
Statistical analysis Evaluation of ecological similarities among coccolithophore species and their relationship with the environmental conditions was performed using a canonical correspondence analysis (CCA). The analysis was only implemented for the CALIBERIA station data set given that RAIA station was affected by resuspension of coccoliths from the underlying sediments, which may also have affected coccolithophore distribution in the water column (see Sect. 5. 1. 1). Ecological relationships were assessed as a function of the distance among the species and the variables on an ordination graph. Akaike's information criterion (AIC) was applied in prior ordination analysis to identify the model with the minimum number of environmental variables that, being statistically significant, explained the maximum inertia (i.
CCA The first canonical axis (CCA1) of the CCA CALIBERIA model explained 51 % of the constrained inertia (i. e. variance) in the coccosphere data and was mostly related to the negative gradient of irradiance, and to the positive gradients of salinity and both Douro River and Minho River discharges (Fig. 8 ). The second canonical axis (CCA2) explained 22 % of the constrained variance and showed a positive gradient with temperature and a negative relationship with HPO 2- B. Ausín et al. : Spatial and temporal variability in coccolithophore abundance with temperature. The small Gephyrocapsa group was also related to the upwelling regime during March but, unlike E. huxleyi, linked to higher nutrient content. F. profunda was associated with the downwelling regime samples and presented a negative relationship with irradiance.
e. November-January and May), counting on many more taxa than the typical bloom-forming E. huxleyi and small Gephyrocapsa group (Fig. 6 ) recurrently dominating in productive conditions  (Tyrrell and Merico, 2004). Simultaneously, large increases in wave height were observed (Fig. 2b ), that is, high-energy wave-driven processes could have favoured resuspension of coccoliths from the underlying sediments. Our data are in agreement with  Zúñiga et al. (2016) , who studied downward CaCO 3 fluxes at the same location and concluded that calcareous shelled organisms had to be remobilized from surface sediments during the highly hydrodynamic downwelling periods. As a consequence, discernment between autochthonous and allochthonous coccolithophore signals in the water column was not possible for these periods, preventing application of CCA to the RAIA data set.
Coccolithophore ecology and potential as paleoenvironmental indicators Both the coccosphere and coccolith major assemblages were mainly represented by E. huxleyi, the small Gephyrocapsa group, G. oceanica, F. profunda, Syracosphaera spp. , C. mediterranea, and C. leptoporus at both stations. Given that resuspension events were found to mask the composition of the coccolith assemblage, and therefore to possibly influence the coccosphere assemblage, in the water column of RAIA station, discussion on coccolithophore ecology will be uniquely based on the coccosphere data set from CAL-IBERIA station. E. huxleyi was the dominant species throughout the studied period regardless of the environmental conditions (Fig. 7 ). Higher cell densities of E. huxleyi, as well as the small Gephyrocapsa group, appeared highly related to the upwelling regime, in agreement with other studies carried out in the central Iberian Margin and other coastal upwelling regions  (Ziveri et al.