dataesr/scientific-paragraphs-categorization huggingface dataset:
A 500 μl solution volume was used for all DLS measurements. It should be considered that during the analysis of DLS data, the solution viscosity varies depending on the denaturant concentration. This is simple and straightforward to correct using the Zetasizer software; by replacing the dispersant with a mixture of urea and water, available in the complex material options. Thus, all the radius distribution measurements in this study are adjusted to the actual solvent viscosity. It is well known that the concentration dependence of the apparent diffusion coefficient gives information about the protein interactions. The effect of the protein concentration is considered by extrapolating the value of the apparent diffusion coefficient, D app to that at zero concentration, D 0 , as described in  [23].
When the urea concentrations reach values lower than 4 M, the curves nearly collapse, reflecting the well-known behavior under conditions of BSA unfolding. By contrast, curves spread under higher denaturant concentrations, indicating an increase in the hydrodynamic radius. For each given value of the denaturant concentration, the experimental curve can be accurately fitted to the Eq 7 (Fig  3B ) using the volume fraction as a free parameter. Since the number of BSA molecules per unit of volume is known, the value of the hydrodynamic radius R H as a function of denaturant is directly obtained as 0 ¼ c Á N a 4 3 pR 3 H (with c in mM units). For the protein folded state, R Hf = 3. 3± 0. 1 nm is obtained, whereas for the unfolded state R Huf = 6. 7± 0. 1 nm is obtained. The entire dataset for the hydrodynamic radius are presented in Fig 4B , as a function of urea concentration, enabling further comparison with DLS data.
1 s to 1 min, depending of the protein and the respective disulfide bond  [26]. As a proof of concept, this study successfully followed the process of slow kinetic disulfide unfolding of a BSA protein, under the action of guanidine hydrochloride plus 2-mercaptoetanol. Moreover, water viscosity measurements were obtained through Sader's fit with 4% accuracy, in a trace of 10 ms in length at 1 MHz acquisition frequency, which is faster than any other previous data for viscosity assessments. It should be noted that in order to achieve a good definition of the first resonant mode of the cantilever (the value used to set the lower limit of frequency detection), a minimal condition is that the signal extends for at least 10 cycles. In this case, the cantilever's resonant frequency in water is in the order of KHz, which leads to a minimum of 10 ms acquisition time.
Acknowledgments Authors are very grateful to  J. Reyes  for help in collecting DLS data and  C. A. M. Wilson  for enlightening discussions. Valuable comments from anonymous referees greatly helped to improve the quality of our manuscript. The financial support provided by  Conicyt-Chile  through the  Fondecyt Program , under Grant:  1161010  and by  Universidad de Santiago de Chile through Proyecto Basal   USA1555  is kindly acknowledged. F. M. acknowledges the invitation as professor by the  E ´cole Normale Superieure de Lyon (ENS-Lyon ).
The EFSA Panel on Contaminants in the Food Chain (CONTAM) reviewed new studies on nivalenol since the previous opinion on nivalenol published in 2013, but as no new relevant data were identified the tolerable daily intake (TDI) for nivalenol (NIV) of 1. 2 lg/kg body weight (bw) established on bases of immuno-and haematotoxicity in rats was retained. An acute reference dose (ARfD) of 14 lg/kg bw was established based on acute emetic events in mink. The only phase I metabolite of NIV identified is de-epoxy-nivalenol (DE-NIV) and the only phase II metabolite is nivalenol-3-glucoside (NIV3Glc). DE-NIV is devoid of toxic activity and was thus not further considered. NIV3Glc can occur in cereals amounting up to about 50% of NIV. There are no toxicity data on NIV3Glc, but as it can be assumed that it is hydrolysed to NIV in the intestinal tract it should be included in a group TDI and in a group ARfD with NIV.
(bw) per day. Using the acute BMDL 10 of 0. 14 mg/kg bw for NIV and an uncertainty factor of 10 for intraspecies differences, an ARfD of 14 lg NIV/kg bw was established. No interspecies variability factor was applied because humans were not considered more sensitive than mink to the acute emetic effect of NIV, an assumption supported by studies on emesis with emetine showing that similar doses of the compound are effective both in humans and mink. There are no data on acute emetic effects of NIV3Glc but it is assumed that it will be hydrolysed in the lower intestine releasing NIV. Therefore, NIV3Glc should be included in a group ARfD with NIV with the same molar potency factor (i. e. 1). No new studies relevant for chronic hazard characterisation were identified since publication of the previous opinion on NIV in 2013.
Studies on analytical methods, chemistry and occurrence of the parent compound nivalenol only, however, were excluded because not considered of relevance for the present assessment. Date, search strings, databases used and numbers of publications retrieved and used for assessment are presented in detail in Appendix A. In total, 371 citations/abstracts were obtained. Those considered relevant by expert judgement were included in the present assessment. Only papers in English language were considered for inclusion in the assessment. During the development of the opinion, additional relevant studies published until January 2017 and not retrieved in the above-mentioned literature evaluation have been identified and considered for the assessment when relevant.
Previous assessments A comprehensive risk assessment of NIV in food and feed was published in 2013 (EFSA CONTAM Panel, 2013). In this opinion, immunotoxicity and haematotoxicity were identified as the critical effects of NIV for the allocation of a TDI. In an oral 90-day rat study, effects such as neutropenia, leucopenia, erythropenia and thrombocytopenia were observed. The decrease in white blood cell (WBC) count was identified as the most appropriate endpoint for benchmark dose (BMD) modelling. The 95% lower confidence limit for the benchmark dose response of 5% extra risk (BMDL 05 ) was calculated based on combined data for male and female animals and was 0. 35 mg NIV/kg body weight (bw) per day. Applying a default uncertainty factor (UF) of 100 for inter-and intraspecies differences and an additional UF of 3 for gaps in the database on NIV to the BMDL 05 , a TDI of 1.
The large dose spacing at the lower doses in these studies and the small number of animals used resulted in a BMDL 10 calculation associated with considerable uncertainty. In addition, the steepness of the dose-response curves between two dose groups add to the uncertainty. In the absence of in vivo toxicokinetic and toxicity data on NIV3Glc, the CONTAM Panel assumed the release of NIV upon ingestion and that therefore NIV3Glc should be included in the group HBGVs with the same molar potency as NIV (i. e. 1). The higher polarity of NIV compared with that of other trichothecenes leads to a more difficult analytical detection under the same chromatographic conditions. Further phase II metabolism would increase the polarity and thus conjugates would be even more difficult to analyse. It is, therefore, likely that there are other phase II metabolites of NIV not yet identified.
13. 7. Chronic dose-response analysis • Because no new relevant data for derivation of a chronic HBGV were available, no dose- response analysis was conducted and the BMDL 05 of 0. 35 mg NIV/kg bw per day based on a reduction in white blood cell counts in a 90-day rat study as set previously (EFSA CONTAM Panel, 2013) has been retained for the present assessment. • Using the acute BMDL 10 of 0. 14 mg/kg bw per day for NIV and an UF of 10, for intraspecies differences, an ARfD of 14 lg NIV/kg bw was established. No interspecies uncertainty factor was applied since it was assumed that humans are not more sensitive towards the effect than mink. • There are no data on acute emetic effects of NIV3Glc. It is assumed that it will be hydrolysed in the lower intestine releasing NIV. Therefore, the Panel found it appropriate to include it in a group ARfD with NIV with the same molar potency factor (i.
On the contrary, our body wave data could be affected by vertical anisotropy in the lithosphere and in D and therefore be partly SH. However, the body waves that we use have a quasi-vertical incidence so that they should not be very sensitive to the lithospheric radial anisotropy and we do not include waves traveling for a long distance in D such as S diff. We therefore consider that SEISGLOB2 is mostly an SV model. In what follows, we first present our data set and in particular the data processing and selection that have been performed before the inversion. Then, we introduce the inversion scheme that is applied to build SEISGLOB2. Finally, we present our model SEISGLOB2 and highlight the change in velocity pattern around 1000 km depth.
At 1000 km depth, in agreement with previous models (e. g. Grand 1994; Van der Voo et al. 1999; Li et al. 2008)  but even more strikingly, fast anomalies underline the closure of the Thethys (from under the Mediterranean sea to New Zealand) and the Farallon subduction (west of the Pacific-America border). Note that the maps between 670 and 1500 km contain shorter scale heterogeneities compared to the shallower and deeper maps. Finally, at the base of the mantle we find the LLSVPs below Pacific and Africa surrounded by a belt reminiscent of the positions of Mesozoic subductions  (Ricard et al. 1993). In Table  3 , we present the initial and final misfits for each kind of data. Our scaling of the different data sets favours slightly the  surface and body waves which are better fitted by SEISGLOB2 than the normal modes.
However, we show in Fig. 5  that SEISGLOB2 predicts accurately both surface waves and normal mode observations. This comforts us with the idea that our data weighting is a good compromise that captures the body wave information on in-termediate and short wavelengths, while remaining consistent with the long-wavelength structure constrained by the normal modes. Finally, even if we cannot entirely reject a dominance of even low degrees as we include more self-coupling than cross-coupling coefficients, we argue that the use of cross-coupling coefficients should , where s is the harmonic degree, x and A constants, which has been used to fit the spectrum of SEISGLOB2 (red), S40RTS (cyan) and SEMUCB-WM1 (blue). The red shaded area displays the zone ranging from 670 to 1500 km depth where the SEISGLOB2 spectrum is the flattest.
make SEISGLOB2 more reliable than previous models for the low and odd degrees. We also provide in Fig. S1  in the Supporting Information the correlations of SEISGLOB2 with other recent models [S40RTS  (Ritsema et al. 2011 ), S362WMANI+M  (Moulik & Ekström 2014) , SEMUCB-WM1  (French & Romanowicz 2014) ]. SEISGLOB2 shows a good agreement with other seismic models, as good as among these models. Correlations are above the 95 per cent confidence level for degrees up to 20 in the shallow upper mantle. In the transition zone and lower mantle, the agreement between global models generally ranges between the 66 per cent and 95 per cent confidence level up to at least degree 10. The correlation increases for degrees lower than 10 at the base of the mantle. S40RTS appears to be the model closest to SEISGLOB2, which is expected since they share the same theoretical framework and used data of the same kind.
However, there are many different steps in a global tomographic inversion and it is difficult to pinpoint exactly the origin of the discrepancies between two models using similar approaches. Moreover, even though they use similar data we include a larger self-coupling data set and cross-coupling measurements that are absent in S40RTS which can already lead to significant differences with S40RTS  (Durand et al. 2016). In Fig. S2  in the Supporting Information, we show our final 1-D model, those obtained by various tomographic studies and PREM. The main differences between these models are located in the upper mantle. Our final 1-D model is very close to PREM and includes the 220 km discontinuity, while S362WMANI+M and SEMUCB-WM1, which do not use PREM as a reference model, are continuous at 220 km.
On the existence of a velocity spectrum pattern change around 1000 km depth Fig. 6  displays the spectra computed for SEISGLOB1  (Durand et al. 2016) , SEISGLOB2, S40RTS  (Ritsema et al. 2011 ), S362WMANI+M  (Moulik & Ekström 2014) , and SEMUCB-WM1  (French & Romanowicz 2014). SEISGLOB1 and SEISGLOB2 share a parametrization in spherical harmonics and in splines, except that SEISGLOB1, only constrained by long-period surface waves and normal modes, is limited to degrees lower than 20. Fig. 6  shows all the information added mostly in the lower mantle thanks to the use of a large body wave data set in SEISGLOB2. Then, it shows that SEISGLOB2 has the highest amplitudes for larger harmonic degrees around 670-1200 km depth which suggests an accumulation of heterogeneities with horizontal wavelengths between about 4000 km (degree 10) and about 1300 km (degree 30).
It is noteworthy that we find an a posteriori exponent lower (x ranges from -0. 2 to -0. 6) than what was imposed in the a priori covariance matrix (x = -0. 1) meaning that the flattening of SEISGLOB2 spectrum does not come from our a priori choices and is rather imposed by the data. It can also be noted that SEISGLOB2 spectrum appears different from other models in the 250-660 km depth range. This difference is also seen in the correlations (Fig. S2 , Supporting Information). This originates from our surface wave data set and this point is discussed in  Debayle & Ricard (2012). These authors argued that it is due to the short radial correlation length required to fit the surface wave waveforms used to produce the phase velocity maps. We also provide in Fig. S3  in the Supporting Information the radial correlation functions of SEISGLOB2 and of the same three tomographic models.
The structure of the LLSVP and the behaviour of the slabs in the lower mantle We now propose to check whether this observation of a change in the velocity spectrum pattern around 1000 km depth affects mantle structures such as slabs, LLSVPs or mantle plumes. We thus present in Figs 7 and 8 some cross-sections obtained in SEISGLOB2 and compare them with the other recent tomographic models  (Ritsema et al. 2011; French & Romanowicz 2014; Moulik & Ekström 2014). Figs 7 and 8 reveal that SEISGLOB2 and S40RTS agree very well which certainly comes from the fact that they are built in a similar way and they are based on the same kind of data (see also Fig. S1 , Supporting Information). Even though SEMUCB-WM1 is based on a waveform inversion it also shares similar longwavelength patterns with SEISGLOB2.
A C K N O W L E D G E M E N T S This work was supported by the French ANR SEISGLOB ANR-11-BLANC-SIMI5-6-016-01, the European Research Council within the framework of the SP2-Ideas Program ERC-2013-CoG, under ERC grant agreement number 617588 and the German Deutsch Forschungsgemeinschaft under the DFG project HAADES DU1634/1-1. SEISGLOB2 can be downloaded on the website of S. Durand: http://earth. uni-muenster. de/∼durand.
Proteins identification using MALDI-TOF/TOF-MS/MS analysis The 2-DE gel spots corresponding to the T. pseudospiralis positive serum western blot spot were excised, and the proteins were digested in a gel with trypsin (Promega, Madison, USA). The samples mixed with an equivalent matrix solution (α-cyano-4-hydroxycinnamic acid) were applied for further MALDI-TOF/ TOF-MS/MS analysis using a fuzzy logic feedback control system (Ultraflex III TOF/TOF mass spectrometer, Bruker, Karlsruhe, Germany). MS spectral data were acquired from the samples, and an automatically generated MS/MS list was further analyzed. All mass spectra were recorded in positive reflector mode and generated by accumulating data from 1000 laser shots. The following threshold criteria and settings were used: mass range of 800-4000 Da for detection, UV wavelength of 355 nm, laser frequency of 50 Hz, repetition rate of 200 Hz and accelerated voltage of 20,000 V.
Peptide mass fingerprint (PMF) data were matched to the UniProt Trichinella and NCBInr T. pseudospiralis database using profound program under 50 ppm mass tolerance. Data were processed, and proteins were unambiguously identified searching against a comprehensive non-redundant sequence database by using the MASCOT software search engine (http://www. matrixscience. com). With the mascot search results, the protein probability score for the match, molecular weight (MW), isoelectric point (pI), number of peptide matches and percentage of the total sequence covered by the peptides were identified. Protein scores greater than 23 (P < 0. 05) were considered significant.
Functional categories of ES proteins from adult worms and NBL of T. pseudospiralis by gene ontology To further understand the functions of the ES proteins identified by early infection sera in this study, gene ontology annotation was performed. We utilize the UniProt database, and these 12 species-specific proteins were classified into a molecular function and biological process according to GO hierarchy (http://www. uniprot. org//). For the molecular function ontology, the proteins are related to peptidase activity acting on deoxyribonuclease II activity (GO: 0004531, 24%), serine-type endopeptidase activity (GO: 0004252, 16%), DNA binding (GO: 0003677, 12%), nuclease activity (GO: 0004518, 12%), protein kinase Experimental activity (GO: 0004672, 12%), ATP binding (GO:0005524, 12%), metal ion binding (GO: 0046872, 4%), phosphopyruvate hydratase activity (GO: 0004634, 4%) and magnesium ion binding (GO:0000287, 4%) (Fig.
In this study, we successfully employed 2-DE and western blotting combined with MALDI-TOF/TOF-MS/ MS to identify specific ES proteins of adult worms and NBL of T. pseudospiralis. Approximately 24 matched protein spots were recognized by PMF and were characterized to correlate with 12 different proteins. To further elucidate the functions of 12 different proteins, we utilized Quick GO online software, and these 12 proteins were categorized based on the GO annotation of biological process and molecular functions. Most of the molecular functions of ES proteins were DNase II activity and serine-type endopeptidase activity. Several proteins had multiple spots with different pI and MW, such as adult-specific DNase II-10, poly-cysteine and histidine tailed protein isoform 2, serine/threonine-protein kinase ULK3 and serine protease.
Abstract. As part of the ChArMEx-ADRIMED campaign (summer 2013), ground-based in situ observations were conducted at the Ersa site (northern tip of Corsica; 533 m a. s. l. ) to characterise the optical, physical and chemical properties of aerosols. During the observation period, a major influence of primary marine aerosols was detected (22-26 June), with a mass concentration reaching up to 6. 5 µg m -3 and representing more than 40 % of the total PM 10 mass concentration. Its relatively low ratio of chloride to sodium (average of 0. 57) indicates a fairly aged sea salt aerosol at Ersa. In this work, an original data set, obtained from online real-time instruments (ATOFMS, PILS-IC) has been used to characterise the ageing of primary marine aerosols (PMAs). During this PMA period, the mixing of fresh and aged PMAs was found to originate from both local and regional (Gulf of Lion) emissions, according to local wind measurements and FLEXPART back trajectories.
Analyses were performed in isocratic mode at 10 mM of KOH at a flow rate of 0. 25 mL min -1 , for the quantitative determination of the 5 anions (methanesulfonate, Cl -, NO - 3 , SO 2- 4 , oxalate) every 24 min. Based on these IC settings, the detection limit (2σ ) for anions was typically 0. 1 ppb, which corresponds to an atmospheric concentration of ∼ 1 ng m -3. Calibration was performed every 2 weeks for concentrations ranging from 10 to 800 ppb and showed a drift below 5 % for each anion between the beginning and the end of the campaign. To our best knowledge, this is the first time that PILS-IC measurements are reported in PM 10 , providing a unique opportunity to document water-soluble supermicron ions and sea salt in particular. Quality control of the PILC-IC data was successfully performed by comparison with PM 10 filter (Teflon)-based ion measurements taken in parallel on a 12 h time basis (Leckel, SEQ47/50 model running at 2.
Aerosol mass closure In order to assess the consistency of the chemical data set, we compared the TEOM PM 10 and PM 1 data with the on-line chemical concentration measurements taken in parallel. PM 10 mass concentrations were compared to the sum of chemical components obtained from the PILS-IC PM 10 data, the BC concentration from the MAAP instrument (PM 2. 5 ), and the organic matter (OM) concentration derived from the ACSM instrument (PM 1 ). The ratio of the reconstructed mass over the TEOM PM10 mass concentration averaged 0. 79 during the ADRIMED campaign. It is lower during the PMA period (0. 65 ± 0. 20) compared to the BBP period (0. 74 ± 0. 23). We did not measure TEOM PM10 during the dust period. We did not find any significant correlation between missing mass and TEOM PM10 mass, even though the missing mass is globally higher when the PM10 total mass is higher.
14c ). This is in contrast to July, when higher scattering coefficients (mean of 48 Mm -1 ± 24) associated with higher AE (AE July mean of 2. 1 ± 0. 2, AE June mean of 1. 6 ± 0. 5) are observed. During dust and PMA period, the scattering coefficient remains low (mean of 28 ± 11 and 28 ± 11 Mm -1 respectively). The PMA period is characterised by a relatively weak wavelength dependency (Fig. 14c ). While the mixing of dust with fine particles, previously shown by the AERONET volume size distribution, is shown here by a relatively high wavelength dependency (mean of 20 ± 9 Mm -1 ). On the contrary, during the BBP period, the wavelength dependency is highest (mean of 49 ± 15 Mm -1 between 450 and 700 nm), and the scattering coefficient reaches its highest values (up to 137 Mm -1 ). This clearly indicates that aerosols are smaller in size during this period, which is consistent with AERONET/PHOTONS data and PM 1 concentrations obtained at Ersa station.
During this period, the Ersa station was influenced by a westerly wind bringing air masses from the Gulf of Lion, the Mediterranean coasts of France and Spain and the Bay of Biscay. During this specific event, the concentration of PMA was relatively high, reaching 6. 5 µg m -3 , which represents 40 % of the total PM 10 mass concentration. Here, an original data set, obtained from ATOFMS and PILS-IC instruments has been used to study the ageing of PMA. By comparing the two instruments, we found that the majority of the time PMA had already undergone chemical reactions and so were not freshly emitted near Cap Corse, but rather advected from long-range transport. In particular, during the PMA period, based on FLEXPART simulations and local wind speed measurements, we distinguished the origin of fresh and aged PMA composing the mixing of PMA observed in the Ersa station.
Acknowledgements. This research was supported by  Direction Générale de l'Armement (DGA)  and  Météo-France. This research has received funding from the  French National Research Agency (ANR)  project  ADRIMED  (contract  ANR-11-BS56-0006 ). This work is part of the  ChArMEx  project supported by  ADEME ,  CEA ,  CNRS-INSU  and  Météo-France through the multidisciplinary programme MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ). The station at Ersa was partly supported by the  CORSiCA  project funded by the  Collectivité Territoriale de Corse  through the  Fonds Européen de Développement Régional  of the European Operational Program 2007-2013 and the  Contrat de Plan Etat-Région. We acknowledge the AERONET/PHOTONS sun photometer networks and the PI of the Ersa station and their staff for their work to produce the data set used in this study.