dataesr/scientific-paragraphs-categorization huggingface dataset:
56 × 10 -3 m -1 (Fig. 4b ). These AMT22 data were collected in surface waters that were not sampled in the AMT26 dataset used to parameterise the coated-sphere model. These waters were generally rich in large cells such as diatoms, as confirmed by the two-time higher relative pigment contribution than observed along the AMT26 transect (Supplementary Fig. 8 ). Algal populations encountered on AMT26 were mainly characterised by cyanobacteria and picoeukaryotes mixed with some coccolithophores, cryptophytes and other nanoeukaryotes (Supplementary Fig. 9 ). Thus, the observed vertical and regional differences predicted by the coated-sphere model suggest that oceanic b bp might be better estimated by parameterising the thickness and composition of the coat according to depth and trophic regime.
e. , <1 µm) to optical scattering measurements. A total of 22 additional stations were sampled, at the same time as the ship's CTD casts, in flow-through mode from the 5-m underway ship's seawater supply (Supplementary Fig. 1 ). Optical measurements were taken, and seawater for PSD analysis collected, before and after filtration through a prerinsed (with ultra-pure water) large surface-area cartridge filter (Cole Parmer) with nominal pore size of 1 µm (i. e. , AMT26_1µm). Filter back-flushing using ultra-pure water before filtrations minimised filter clogging. Filtering in flowthrough mode minimised particle aggregation and precipitation. During filtration, the flow rate of the underway system was set to 2 l min -1. AMT26 data collected at the ocean surface (5 m) and at the DCM were used to test the homogeneous-sphere assumption for marine particles and parameterise the coated-sphere model.
AMT22 and AMT26_1µm samples were used as independent datasets to validate model results. AMT26 samples collected in the mesopelagic region of the Atlantic Ocean were used to discuss coated-sphere model advantages and limitations. Particle size distribution (PSD). During AMT22, a total of 134 PSDs were measured on board, in multiple 2-ml replicates (from 2 to 26), through a Multisizer III Coulter counter (Beckman Coulter) fitted with a 70-μm aperture. The measured size range of equivalent spherical diameters (ESDs) was 1. 4-42 μm and distributed in 200 logarithmically spaced size bins. During AMT26, seawater samples (N = 134 from casts + 44 from underway) were directly collected in 500-ml acid-washed amber bottles and immediately prepared under a vertical flow hood for the analysis. Samples were kept in a cold and dark place during the analysis.
We expect the results of this modelling analysis to be more sensitive to the structural complexity of the spheres than to the impact of light absorbing material on b bp  38, 39  , and thus to be representative also of other portions of the visible spectrum. Look-Up-Tables of Q c (D, n) for homogeneous and coated spheres were generated by using the scattnlay software  70  , for angles between 0 and 180°(0. 1°i ncrements) and logarithmically spaced ESDs between 0. 4 and 70 µm. The Q c (D, n) values both for homogeneous and coated spheres were corrected for the acceptance angle effect to be consistent with WETLabs ac-9 and ac-s in-situ measurements  67. The values of Q b b ðD; nÞ were computed as: Q b b ðD; nÞ ¼ Q b ðD; nÞ where Q b is the dimensionless efficiency factor for the total scattering (output of the freely available scattnlay software  70  ) and S 11 ðθ; D; nÞ is the top left component (i.
Acknowledgements The authors would like to thank the captains, chief scientists, officers and crew of the  RRS James Cook (UK National Oceanography Centre ) and the  RRS James Clark Ross  ( British Antarctic Survey ) for the help they provided during  AMT22  and  AMT26  field expeditions. We thank  Kristen Reifel  and  Virginie van Dongen-Vogels  for helping in data collection during AMT22. We thank  Hervé Claustre  for insightful conversations. This study has been conducted in the frame of the  REOPTIMIZE-REmineralisation, OPTIcs and Marine partIcle siZE project. This  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  706781  (assigned to  E. Organelli  and the  Plymouth Marine Laboratory ).
This study is a contribution to the international  IMBeR  project and was supported by the  UK Natural Environment Research Council National Capability  funding to  Plymouth Marine Laboratory  and the  National Oceanography Centre, Southampton. This is contribution number  326  of the AMT programme. Optics data collection by G. D. and R. J. W. B. during AMT26 was supported by the  European Space Agency (ESA)  contract: Copernicus Sentinel Atlantic Meridional Transect Fiducial Reference Measurements Campaign (AMT4SentinelFRM; grant  ESRIN/RFQ/3-14457/ 16/I-BG  to  Gavin Tilstone  and the  Plymouth Marine Laboratory ). Data collection by G. D. during AMT22 was funded by  NASA  (grant  NNX10AT70G  to  Mike Behrenfeld  and the  Oregon State University ). E. B. was supported by  NASA  (Grant  NNX15AC08G ).
Data availability This study uses data from the Atlantic Meridional Transect Consortium (NER/0/5/ 2001/00680), provided by the British Oceanographic Data Centre and supported by the Natural Environment Research Council. Particle size distribution data collected during the AMT26 cruise can be downloaded at https://doi. org/10. 5285/79103bda-8913-39f3-e053-6c86abc0567a. All other data are freely available at https://www. bodc. ac. uk/projects/uk/amt/ and https://www. bodc. ac. uk/data/bodc_database/ nodb/data_collection/207/, or from the authors upon request.
Normalized VSFðθ; nÞ ¼ M 11 ðθ; nÞ 2π R π 0 M 11 ðθ; nÞ sin θdθð10ÞFinally, to compare the polarised components with existing datasets, we normalised each component of the scattering matrix by M 11 ðθ; nÞ as follows: P ij θ; n ð Þ¼ M ij ðθ; nÞ M 11 ðθ; nÞ.
■ ACKNOWLEDGMENTS We thank  T. Jarrosson  and  F. Serein-Spirau  for the synthesis of 3TBT molecules. C. S. and R. H. gratefully acknowledge financial support from the  Deutsche Forschungsgemeinschaft  ( GRK1640 ). R. H. is grateful for support through  Elitenetzwerk Bayern (ENB) Macromolecular Science. L. S. acknowledges the  Agence Nationale de la Recherche (ANR)  through  Grant TRANSFILSEN  ( ANR-09-BLAN-196 ). G. R. acknowledges support through the  International Research Training group IRTG-1642 "Soft Matter Science". This publication was funded by the  German Research Foundation (DFG)  and the  University of Bayreuth  through the  funding program Open Access Publishing.
3D Hierarchical Structure of the Vocal Folds Larynx and vocal fold geometries. Using large voxel sizes of 45  3  and 25 3 μm 3 , the obtained 3D images allow the definition of the outer surface of the larynx and the vocal folds (Figs  1  and 2 ). Using phase retrieval imaging combined with ethanol immersion, important measurements of the inner and layered structure of vocal folds can be made (see next subsections). In addition, the database of samples scanned in this work comprises four laryngeal samples (L 5 to L 8 ) scanned at rest and while being subjected to a 3D deformation resulting from cricothyroid tilt (Supplementary Figs  S7  and S8 ). Thanks to the glass beads stuck inside the samples onto the vocal folds (see Fig. 2(a) ), the longitudinal elongation λ of vocal folds after the larynx deformations can be estimated: depending on the tested samples, λ ranges from 1.
These structural data are very helpful to understand and model the mechanics of vocal folds. Towards mechanical tests with 3D in situ observations. At "medium" spatial resolution, a preliminary collection of 3D images was acquired for larynges subjected to anatomical placements close to those occurring during a typical phonatory cycle. This constitutes an important database which can be used either to study the larynx and vocal fold anatomy  31, 55  or to simulate the mechanics of vocal folds  56, 57. For example, the vocal fold elongation λ which was measured after the laryngeal tilt is consistent with the recent measurements obtained by Lagier et al. 55. Using 10 excised human larynges prepared and deformed in close conditions (body ages from 71 to 92, freezing in 0. 9% saline, double adduction), these authors reported λ-values ranging from 1.
2 camera (SN 62000031, 2048 × 2048 pixel 2 chip, no binning, FFM mode) were used, allowing fast and highly contrasted imaging. The acquisition parameters were characterised by a 19 keV beam energy, an undulator gap of 21 mm, a sample-to-detector distance x c = 40 mm in average, and a beam exposure time of 20 ms per radiograph. Several acquisitions were taken sequentially to cover the full height and width of the vocal-fold sample, with a field of view displaced by 1 mm-step in longitudinal and transversal directions, yielding to an overlap of 300 μm. In the end, the scan duration was within 1-2 min and the total acquisition time about 1 h 35 min per sample. Image processing. After reconstruction of the scans using Paganin's method for phase retrieval coupled with filtered back-projection  26  and removal of tomographic artefacts such as rings, the resulting 3D and greyscale images were analysed using Fiji ® , Avizo ® and Matlab ® routines.
Some of them were automatically segmented using standard smoothing and thresholding algorithms implemented in these software, to get meaningful 3D views (Figs 2(a), 3(c) and 4(d)) and to analyse the vocal-fold structure in detail (Figs 5(b), 6 and 7). In some cases, this was not possible. Thus, to pursue the quantitative analysis, manual image thresholding was carried out using Fiji ® and a graphical tab (Wacom Cintiq 22HD touch). For example, the geometries of two vocal folds and the sublayers of their lamina propria were tracked on images acquired at "medium" spatial resolution (voxel size of 13 3 μm 3 ), as shown in Fig. 5 (a). Similarly, some individual muscle fibres and ECM fibre bundles were extracted from the "high" resolution images (voxel size of 0. 65 3 μm 3 ), to analyse their geometry (Figs 6(a) and 7(a)).
Whatever the tissue and spatial resolution, the following processing route was used for each considered Region Of Interest (ROI)see Supplementary Figs S5 and S6: First, to enhance the grey-level gradients of the fibrous phases, 3D images were subjected to a coarse thresholding process, and a series of 3D smoothing and morphological operations (median filter, hole filling, opening/closure sequences). Second, the 3D Euclidean distance map was calculated in the considered ROI. Therewith, a home-made Matlab ® code was used (i) to compute the gradients of grey levels in the as-treated ROI with a centred finite difference scheme, and (ii) to build from these gradients 3D structure tensors  68  for the N voxels i of the ROI. For that purpose, the lateral size of the Gaussian-shaped windows used to compute structure tensors was adapted to the size of the objects to be characterised: 21 voxels (273 3 μm 3 ) for the muscle fibres of Fig.
Thereby, the spatial periods λ uv (respectively λ uw ) as well as the magnitudes R 0uv (respectively R 0uw ) of the quasi-periodic shape of fibres were estimated. This was done by manually clicking at least 50 data per descriptor. Sections of muscle fibres. Two methods were used to analyse the size and shape of the sections of muscle fibres. Both of them used "high" resolution 3D images of the vocalis (voxel size of 0. 65 3 μm 3 ). The first method consisted in analysing slices perpendicular to the mean orientation of muscle fibres. As depicted in the inset of Fig. 6 (b), the edges of muscle fibres were detected using an edge detection subroutine (Matlab ® ). Then, the length P of each fibre's edges was estimated, together with the surface A of the closed area they defined, the equivalent fibre diameter π = d A 4 / e , the roundness of fibre ξ π = A P 4 / 2 and the position of their centre of mass x G.
Acknowledgements This work was supported by the  ESRF  proposal MD-957 and shift IN-996 (ID19 beamline), the  LabEx Tec 21  ( Investissements d' Avenir -grant  agreement n°  ANR-11-LABX-0030 ), the AGIR-PEPS 2015 FIBROSCILLE ( UGA-CNRS)  and the  INSIS PEPS 2016 MICROPLI (CNRS). We would like to thank  Philippe Masson ,  Clare Peine ,  Stéphane Priem ,  Véronique Curri ,  Guillaume Bravais ,  Maxime Terrien ,  Carole Neuville ,  Emilie Mathieu ,  Florian Martoïa ,  Vincent Fernandez  and  Edward Andò  for their helpful assistance.
Motivation The European project TIARA (Test Infrastructure and Accelerator Research Area) [1] which was funded under the 7th framework (2011-2015) had among its goal to make a survey of accelerator training in Europe. The results of this survey have been published  [2]  and were followed by recommendations  [3]. The first recommendation of that report was to establish An 'e-learning' course, 'Introduction to Accelerator Science and Technology', primarily aimed 1234567890 ''"" 9th International Particle Accelerator Conference, IPAC18 IOP Publishing IOP Conf. Series: Journal of Physics: Conf. Series 1067 (2018) 092004 doi :10. 1088/1742-6596/1067/9/092004 at physics and engineering students at the undergraduate level, but potentially accessible to any interested person. This recommendation is now addressed by the European project ARIES (Accelerator Research and Innovation for European Science and Society)  [4]  funded by the European Union H2020 program.
Accelerator Research Area)  project funded by the  European Union  7th framework programme made a survey of provision of education and training in accelerator science in Europe. This survey highlighted the need for more training opportunities targeting undergraduate-level students. This need is now being addressed by the  European Union   H2020  project  ARIES  ( Accelerator Research and Innovation for European Science and Society)  via the preparation of a  Massive Open Online Course (MOOC)  on particle accelerator science and engineering. We present here the current status of this project, the main elements of the syllabus, how it will be delivered, and the schedule for providing the course.
This paper presents recent progress of the IAEA MODARIA WG4 in updating the freshwater Kd dataset. The imporved dataset can be used to derive statistical distributions of freshwater Kd values which can be applied to assess solid/liquid fractionation of chemical elements associated with radionuclides released or observed in freshwater environments. In these environments, the solid/liquid fractionation of elements is a key process as it directly affects the bioavailability of elements via their transfer pathways. It depends on numerous interactions specific to elements (including radionuclides), properties of solid particles (eg. size, nature, origin) and geochemical conditions (eg. pH, temperature, conductivity)  (Sigg et al. , 2000; Eguchi, 2017). These conditions influence the complexation  (Xu et al.
g. figure, table ). For eight of these 15 elements (Ag, Am, Co, Cs, I, Mn, Pu and Sr) the datasets were large enough (defined by  Durrieu et al. (2006)  as at least ten data values originating from more than five different references) to allow the calculation of conditional log-normal distributions (geometric mean (GM) and geometric standard deviation (GSD)) in three exchange conditions: adsorption, desorption and field  (IAEA, 2010). The notion of field condition was introduced for two reasons: 1) it is difficult to clearly identify in the field if the solid/liquid exchange conditions are equilibriated and correspond to sorption or desorption whereas they are well controlled and identified in laboratories and 2) the temporal and spatial scales associated with Kd in the field are significantly larger than those associated with Kd obtained in the laboratory.
For example, sorption/desorption experiments in the laboratory can cover several days or weeks, but rarely several months or years as is the case in the environment. For these reasons, Kd in the field are sometimes called "apparent Kd" and hereafter have been termed here as Kd(a). For seven elements (Ba, Be, Ce, Ra, Ru, Sb and Th), the datasets were only sufficient (less than five references and/or less than ten Kd values) to determine non-conditional log-normal distributions. For a final third group of ten elements (Cr, Fe, Zn, Zr, Tc, Pm, Eu, U, Np and Cm), mean, maximum and minimum Kd values were suggested on the basis of a single publication  (Onishi et al, 1991)  or expert judgment. To demonstrate the requirement to update and complete these previous compilations, Table  1  collates the 64 elements for which reference Kd values are required by the ERICA Tool for wildlife assessment  (Brown et al.
Ag 3 Gd 0 Ra 3 Am 3 H 1 Rb 0 As 1 Hg 1 Rh 1 At 1 I 3 Rn 0 Au 0 In 1 Ru 3 Ba 3 Ir 0 S 1 Be 3 La 0 Sb 3 Bi 1 Mn 3 Se 1 Br 0 Mo 1 SS 1 C 1 Na 1 Sn 1 Ca 1 Nb 1 Sr 3 Cd 0 Nd 0 Tc 1 Ce 3 Ni 1 Te 1 Cf 0 Np 2 Th 3 Cl 1 P 0 Tl 1 Cm 2 Pa 0 U 2 Co 3 Pb 1 W 0 The aim of freshwater Kd related activities within WG4 of the MODARIA program was to enhance the EMRAS I compilations of Kd in freshwater environments to allow an update of Kd distributions published in TRS 472  (IAEA, 2010). The article is divided into three sections presenting: 1) an updated and completed dataset of freshwater Kd, 2) the analysis methodology of the enhanced dataset and 3) analysis of Kd distributions as a function of suspended and deposited sediments, suspended load and conditions of solid-liquid exchange.
ENHANCEMENT OF THE FRESHWATER Kd DATASET Ciffroy kindly supplied the freshwater Kd dataset, from which the Kd distributions published in TRS 472 (IAEA, 2010) were derived to MODARIA WG4. The dataset was then expanded giving priority to elements for which there were few or no data. Major stable elements inputs included were from large datasets published by the US Geological Survey (USGS) for Colorado River (http://pubs. usgs. gov/ds/614/contents/), the Geochemical Atlas of Europe (http://weppi. gtk. fi/publ/foregsatlas/) and water quality data from 26 Ribble and Wyre river basin sites in North West England  (Neal et al. , 1997; Neal, 2007; Neal et al. , 2011). New Kd values were also derived from peer reviewed publications for both radionuclides and stable elements. Over 2300 new Kd values were compiled for 27 new elements (Al, As, B, Ca, Cd, Cu, Dy, Er, Gd, Hf, Ho, K, La, Li, Mg, Mo, Na, Ni, Pb, Pr, Rb, S, Se, Si, Sn, Ti and V) which were added to those in the TRS 472 dataset.
A total of 270 new Kd values were also added for the 25 elements already included in the TRS 472 dataset (Ag, Am, Ba, Be, Ce, Cm, Co, Cr, Cs, Eu, Fe, I, Mn, Np, Pm, Pu, Ra, Ru, Sb, Sr, Tc, Th, U, Zn, Zr). In TRS 472, ten elements have too few data values to derive lognormal distributions. The MODARIA dataset now provides adequate data to derive lognormal distributions for Cm, Cr, Eu, Fe, U and Zn. The values for the other four elements (Np, Pm, Tc and Zr) were not updated and remain the same as those in TRS 472  (IAEA, 2010). In freshwater environments, the Kd approach can be applied to both suspended and/or deposited sediments. Reference documents such as TRS 472 do not distinguish Kd distributions between these two components. However, there is evidence, discussed below, that this omission needs to be addressed.
METHOD OF ANALYSIS The dataset of each chemical element was sub-divided according the type of sediment (suspended, deposited) and the solid-liquid exchange conditions (sorption, desorption, field). The analysis of each combination (element × component × condition) depended on the number of values, N.  If N < 10, the distribution was not determined and the Kd value was provided for indication only and without statistical information. Two cases were possible: o N = 1: only one value was given. o N > 1: the geometric mean, the minimum and the maximum values of the dataset was reported.  If N ≥ 10, it was assumed that the dataset followed a lognormal distribution which was considered as the most appropriate approach when the data range was over several orders of magnitude, as it is often the case for Kd  (Sheppard, 2011).
CONFIDENCE INDICATORS An empirical confidence indicator (CI) was associated to each Kd distribution to provide users with an indication of the level of confidence they could have for the reported values. The CI incorporated different criteria based on (i) the statistical properties of the datasets, (ii) some empirical assumptions about Kd behavior which were checked by comparing the statistical Kd distributions with the statistical Student t-test and (iii) comparisons with reference Kd values already published in previous documents. For each distribution, CI was initiated at 1 and was increased by 1 if N ≥ 10 and by 1 again if the KS-test was validated. The CI of the distributions checking the KS-test were incremented as a function of several empirical assumptions as follows: 1. For each condition of liquid -solid exchange (adsorption, desorption and field), it was assumed that the Kd distributions were greater for suspended sediments than for deposited sediments.
RESULTS Table  2  reports the GM, GSD, maximum and minimum values, the 5 th and 95 th percentiles, the size of the dataset, the number of reference, the result of the KS-test and the CI value for 49 chemical elements. For the 49 elements in table 2, Kd distributions should be available for all 294 combinations (49 elements x 2 components x 3 conditions). Including all new data, improved the number of elements and the assessment of the representativeness of the data. However, Kd data were still missing for 63% (186) of the possible combinations. For 7% (21) of the combinations, the datasets comprised less than 10 Kd data and so only screening values were reported. On a more positive note, 30% (87) of the combinations were associated to Kd datasets with more than 10 Kd values and their Kd distributions were reported.
For Kd values of SS in the field, only those values were considered that were associated with datasets with a CI above or equal to 0. 4 and to a suspended load value. With these criterion, this analysis could be carried out for Am, As, B, Ba, Be, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, K, La, Mg, Mn, Mo, Na, Ni, Pb, Po, Pr, Pu, Ra, Rb, S, Sb, Se, Si, Sn, Sr, Th, Ti, U, V and Zn. For each element, the change in Kd as a function of [SS] was fitted with the following relationship  (He and Walling, 1996) : • Equation 3 An inverse behaviour between Kd and [SS] is obtained when b is negative as shown in Figure  4  for Cs, Am, Pu and Sr. The parameter b is negative for 86. 5% of the listed elements and it is positive for only five elements (Ce, Th, La, Pr and Ra). However, the relationship was not significant for As, Be, Cu, Mn, Sn and Th for which the p-value of the parameter b was greater than to 0.
05 and the determination coefficient was lower than 0. 2. For B, Ba, Ca, Cd, K, Mo, Pb, Po, Rb, S, Se, Si and U, the sensitivity of the Kd values in the field to [SS] explained more than 50% of the dataset variability and [SS] appeared to be a useful cofactor that significantly reduced the variability of Kd distributions. The interpretation of these results required consideration of the feature that the Kd datasets for SS in the field combined Kd data associated with both anthropogenic radionuclides and/or natural elements. The interest in data for natural elements is to permit filling of Kd data gaps for those radionuclides which are poorly characterised or where no data has been identified. However, these data require specific attention because their apparent Kd values can include the partial contribution of some of the element which is naturally present inside the solid particles.