dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlating LM and EM datasets A critical step in the correlation of LM and EM data is the re-identification of structures of interest. Near-infrared branding is one of the approaches used to overcome this problem  (Bishop et al. , 2011; Lees et al. , 2017)  as it allows permanent marks or fiducials to be etched in the sample close to the region of interest. However, this method requires specialised equipment (laser) and is destructive. Polymer beads can also be used  (Kukulski et al. , 2012) , however this also results in obscuring regions of interest. A more desirable approach is to use relocation coordinates etched into the base of gridded dishes. However, a common challenge surfaces when analysing structures within dense beds of multi-cellular cultures/ (D) Graph comparing g-ratios estimated from data represented in A-C, calculated using diameter measurements.
Each point represents one g-ratio measurement. N=3 axons, N=100 line scans. Note that due to excessive 'noise' created by out-of-focus light, some measurements could not be obtained from matching regions found in confocal images (N=3 axons, N=45 line scans). (E) A bar graph comparing mean g-ratio measurements acquired using diameter, cross sectional area or volume (from 3D modelling data). Bars denote standard error of the mean. Statistical significance between datasets was registered using an analysis of variance (ANOVA). n. s. , not significant, **P<0. 01. Scale bars: (A) 4 µm and (B) 2 µm. tissues, which masks the coordinates, essentially camouflaging the position of targets in both X-Y and Z planes. This issue may be avoided when analysing simple cultures consisting of monolayers of well-dispersed cells  (Booth et al.
Accuracy of correlation between LM and SBF-SEM data It is important to note that the degree of correlation between LM and EM datasets is rarely perfect, using any format of CLEM. This is likely to be a compound effect of numerous factors, including; (1) processing artefacts, (2) differences in the thickness of optical and physical sections and (3) disparities in how the sample is seated, with reference to imaging using a light or electron microscope. The latter can be influenced by the sample to pin-mounting angle and also the approach angle of the diamond knife. We demonstrated that LM to EM registration can be improved by digital re-sectioning of the datasets and using projections to account for section thickness. Any remaining differences are likely due to processing artefacts. Our system also allowed the presence of anisotropic shrinkage to be determined by measuring the distance from origin (the centre of the scatter plot) and the angle of each point relative to the centre line.
These values, retrieved from the SBF-SEM datasets, fall within the empirical range of g-ratios (0. 55<d/D>0. 68) reported for normal peripheral nerve fibres of the sciatic nerve  (Chomiak and Hu, 2009) , thus suggesting that the myelinated axons in our cultures are structurally matured. There is a general acceptance that g-ratios can be derived reliably only from EM images and not from LM images  (Dyck and Thomas, 2005). It is surprising that this has never been stringently tested. We exploited our imaging pipeline to directly compare g-ratios derived from different modes of microscopy to those obtained from EM analysis and as might be expected most measurements acquired fell below the empirical range. However longitudinal g-ratio measurements acquired using the Airyscan were just within the empirical range, suggesting that this mode of imaging may have sufficient resolution to attain accurate g-ratios.
Additionally, the volume-based g-ratio value corresponds exactly to the value predicted by theory [0. 6-0. 62; (Deutsch, 1969; Rushton, 1951) ] further reinforcing the suggestion that indeed these myelin fibres are structurally mature. As for the usefulness and versatility of SuperCLEM for other applications, we have been able to identify and image the axon initial segment (AIS) (Fig. S6A-E , Movie 2) of a sensory neuron. These images showed the typical arrangement of satellite glial cells flanking the AIS (arrows). Other suitable and actively studied organelles could include mitochondria. Dyes such as MitoTracker (Thermo Fisher Scientific) are well characterized and could be readily incorporated into the SuperCLEM pipeline. Further developments of this imaging pipeline will include the incorporation of STED super-resolution microscopy to further narrow the resolution gap between light and electron microscopic datasets.
The final approach of the block face to the knife was achieved using the automatic approach on Digital Micrograph (Gatan) at 200 nm. Progressive low magnification 'survey' images (at 600× magnification; 200 nm sections) were first acquired from the block with continued reference to optical images. Once a suitable landmark or ROI had been identified, appropriate section thickness and acquisition settings were established (kV 2. 3, image size, 3180×6954; dwell time, 15μs; magnification 1972×; final pixel size, 13. 8 nm; section thickness, 80 nm). EM datasets were batch-converted into tiff files in preparation for modelling in Amira™ (FEI). CLEM registration was performed using primary (EM) and secondary (LM) overlays with the 'multiplanar' tool, to confirm successful correlation of images.
Shift analysis (Fiji) To account for differences in section thickness (between LM and EM datasets), projections were generated from any sections that contained axons. The optical stack projection consisted of 9×300 nm sections (2. 7 µm in total). The EM projection consisted of 33×80 nm sections (2. 64 µm in total). A grid of concentric rings ('concentric circles', 6. 5 µm spacing) was placed over both projections, centred on a clear landmark observable in both projections. Next, the position of structures/objects clearly identifiable in both projections (myelin, Fluoromyelin Green; nuclei, DAPI) were marked. The resulting coordinates were mapped onto a 2D-scatter plot and the 'shift' distance for X and Y, between LM and EM, was measured for each point. Using the same dataset both the distance of data points from origin (the landmark used to centre the shift analysis), and directional location (presented as the sin -1 angle of origin) of data points were also measured.
3D volume rendering of EM data using Amira™ Semi-automated segmentation of EM data was performed using the blow tool, allowing parameters such as volume and surface area to be measured. The blow tool acts as a suitable compromise between automation and accuracy. For example, all segmentation could be done totally manually, however this would be time consuming and subject to bias. Using tools that are totally automated removes all bias but is often poor at sufficiently segmenting fine structural details. The blow tool is a 'drag-and-drop' tool that uses a polygon expansion based on contrast gradient. In the case of myelin (which is very dark), this tool works very well in most cases. A more comprehensive guide is provided below (also refer to Fig. S1G -numbers refer to the numbered arrows in the figure).
EM stacks were imported into Amira™ and the appropriate voxel dimensions inputted when prompted. 1. Select the 'Segmentation' tab (arrow 1) to begin annotating the EM data (Fig. 4Ci ). 2. Create a 'new material' and label it appropriately (e. g. Myelin) and choose a render colour. 3. Choose an optimal brightness/contrast using the virtual slider under 'Display Control'. If comparative analyses of datasets are expected, make a note of the brightness/contrast values to provide consistency between datasets. 4. Select the blow tool. [Note: other segmentation tools (lasso, magic wand and threshold) can be used with varying degrees of success and levels of manual input. Our preference for segmenting myelin is the semiautomated blow tool. Once appropriate criteria have been established (for example adjustment of brightness/contrast and selection of structures of interest) we feel that the blow tool provides suitably unbiased (machine led) segmentation that is largely automated and relatively quick.
Study Design and Sampling The fourth COSI data collection round was conducted in 2015/2017 in 35 countries from the WHO European Region. Nationally representative samples of children were drawn in all countries except for the Russian Federation, where data collection was carried out only in the city of Moscow. Thirteen countries were not able to apply the voluntary COSI family record form; thus, of the countries participating in the COSI, 22 collected data on breastfeeding and characteristics at birth and were included in this study (Fig. 1 ). Thirteen of these 22 countries used two-stage cluster samples, with primary school as the primary sampling unit and school classes as the secondary sampling unit. Five countries adopted a cluster design with classes (Croatia and Italy), schools (Denmark and Latvia) and paediatric units (Czechia) as sampling units.
Three countries targeted two age groups (7-to 8-year-olds in France and 8-to 9-year-olds in Italy and San Marino), while Spain targeted all age groups. In most of the countries that adopted a sampling approach, the number of measured children per targeted age group was equal to or above the minimum sample size suggested by the Protocol (i. e. 2,800 children per age group), while in 7 countries it was considerably lower (Czechia and Ireland) or slightly lower (Denmark, Montenegro, Lithuania and the Russian Federation). Data on children's body weight, height, sex and age were collected by trained examiners in the Child's Record Form. Children's consent was always obtained prior to the anthropometric measurements. Children's weight and height were measured by fieldworkers who were trained in taking measurements following WHO standardized techniques  [36, 37].
Measurements in each country were taken during the data collection period defined for round 4 (2015/2017). Taking into account local arrangements and available budgets, countries chose the most appropriate professionals to measure the children's weight and height (e. g. physical education teachers, national or regional health professionals). The voluntary family record form included questions on a child's birth weight, breastfeeding and its duration, and other environmental and socioeconomic characteristics of the family and was completed by parents or caregivers  [37]. Inclusion criteria were: (1) children aged between 6 and 9 years (irrespective of whether or not they belonged to the target age groups);  (2)  having available data on sex, age, height and adjusted body weight following the procedures explained elsewhere  [34, 36] ; and (3) having a family record form filled in by the mother, with complete information about breastfeeding and birth weight.
The Russian Federation was also excluded, because of the lack of information about maternal education. Maternal weight was not assessed in Ireland, and thus the regression analysis for this country was carried out without considering this variable. As for the pooled models, only countries with available information on the abovementioned covariates were included. More specifically, 16 countries were included in the regression model with general breastfeeding among the explanatory variables (namely Albania, Bulgaria, Croatia, Czechia, France, Georgia, Kazakhstan, Lithuania, Latvia, Malta, Montenegro, Poland, Portugal, Romania, Spain and Turkmenistan). Italy and San Marino were excluded because the data on duration of general breastfeeding were not compatible with the other country measures. With one exception (Ireland), the pooled model with exclusive breastfeeding included all 8 countries that collected this information: Bulgaria, Georgia, Kazakhstan, Lithuania, Latvia, Poland, Portugal and Spain.
b All children with complete information on sex, whose age was between 6 and 9 years, whose weight and height were measured, whose BMI/A z-scores were within the normal range (≥-5 to ≤+5), whose mothers had filled in the family record form and with complete information about breastfeeding practice. c Total figures were calculated including only countries with available information about the number of children invited to participate in the surveillance. d For Croatia, only data on 8-year-olds were available for comparison at the European level. Children's and families' participation in the survey was calculated for the whole sample (not only 8-year-olds). For an explanation of the country abbreviations, see Table  1. Q1, first quartile; Q3, third quartile. a For Italy and San Marino, the figures about breastfeeding duration refer to the following categories: "never," "up to 6 months" and "more than 6 months.
Acknowledgements We gratefully acknowledge all participating children, their parents, and the school teachers and principals for kindly volunteering to participate in the study. We also thank the examiners and regional and local supervisors/coordinators who collected the data in each country. Furthermore, we would like to acknowledge the contribution of other researchers and/or principal investigators in this study, namely:  Quenia Santos , consultant at the  WHO Regional Office for Europe ;  Radka Taxova Braunerova  from the  Obesity Management Centre -Institute of Endocrinology, Czechia ;  Paola Nardone  from the  Istituto Superiore di Sanità, Italy ;  Boban Mugoša  from the  Institute of Public Health of Montenegro and Natasa Terzic from the Center for Health System Development -Institute of Public Health of Montenegro ;  Alexandra Cucu  from the  Department of Public Health and Management of the University of Medicine and Pharmacy, Romania ;  Elena Sacchini  from the  Health Authority San Marino ;  María Ángeles Dal Re Saavedra  from the  Spanish Agency for Food Safety & Nutrition (AESAN) ;  Mukhtarova Parvina  from the  Ministry of Health and Social Protection of Population of the Republic of Tajikistan ; and  Guljemal Ovezmyradova  and  Laura Vremis  from the  WHO Country Office in Turkmenistan.
The Pontgibaud fault zone, located in the La Sioule licence (Fig. 1 ) corresponds to a well-suited case study since numerous data are available (structural geology, lithology, topography, springs geochemistry, thermal properties, magnetotellurics, gravimetry, ambient noise tomography, and seismicity)  (Bellanger 2017). Thanks to this complete dataset, and with new additional petrophysical data, 2D numerical modeling can help to better understand the links between fluid flow and permeability. The possible presence Fig. 1  Simplified geological map showing the "La Sioule" licence area (black contour) and a brief overview of the major stratigraphic units in the study area. French Massif Central (FMC). Coordinate system: WGS84 Pseudo-Mercator EPSG:3857 (After the 1/50,000th geological map published by BRGM) of convection cells is here examined to understand the establishment of positive or negative thermal anomalies in the fault system.
Surface heat flow and heat production Part of the Pontgibaud fault zone was intersected by core drilling undertaken by the BRGM (French Geological Survey) in the 1970s. The purpose of these drillings was to characterize the mining potential of these structures. As part of this study, one of these boreholes (Peyrouses 1, location in Fig. 2 ) remains accessible and has been used in this study. In the past, temperature measurements have been carried out between 55 and 120 m. From these measurements, a geothermal gradient of 41 °C/km has been inferred and a heat flux of 110 mW m -2 was estimated (IHFC database; http://www. datap ages. com/gis-map-publi shing -progr am/gis-open-files /globa l-frame work/globa l-heat-flowdatab ase). This relatively high heat flow value can be explained by an elevated mantle heat flow and/or a large crustal heat production.
Field observations This approach aims to characterize the intensity of the brittle deformation as well as the general geometry of the Pontgibaud fault zone. Structural data (fault orientations and dips) collected since 2014 by TLS-Geothermics have been compiled to assess the intensity of deformation. These observations are key to improve the geometry and scaling of the simplified numerical model. The microstructural analysis of the samples collected within this hydrothermal zone ("Peyrouses 1" borehole). Figure  2  provides an understanding of how fluids flow within this deformed zone.
A three-dimensional reconstruction of the object is performed using this series of images. However, the density contrast that may exist between dense mineral phases (pyrite) and low-density mineral phases (feldspar) can bias the information. Beam-Hardening-Correction (BHC) limits this loss of information. This imaging technique makes it possible to obtain voxel resolutions between 25 and 4 μm. Once rebuilt, the three-dimensional volume analysis and processing can be performed using the Volume Graphics VG Studio Max software (https ://www. volum egrap hics. com/en/produ cts/vgstu dio-max. html). The connected porosities and permeabilities of selected samples were measured at the Ecole et Observatoire des Sciences de la Terre (EOST) in Strasbourg (France). Measurements were performed on the seven samples for which the porosity was visualized using X-ray micro-tomography (including fractured samples, intact samples, altered samples, and altered and fractured samples).
Numerical approach In order to quantify the effect of fault zone dip and the impact of variable permeability on fluid circulation in the fault zone, a series of numerical simulations using Comsol Multiphysics ™ software was performed. Heat equations and Darcy's law were coupled in a similar way to the  Guillou-Frottier et al. (2013). The numerical approach was organized in two successive stages. The first stage was dedicated to the study of a theoretical fault zone and focused on the influence of fault dip and permeability on fluid flow in order to understand the processes governing deep fluid circulation. The second stage was dedicated to the Pontgibaud hydrothermal system, in order to verify the legitimacy of the digital crustal-scale model. Finally, the obtained results, and in particular the geothermal gradient and surface heat flow, were compared with field data, including the resistivity data from Ars et al.
Acknowledgements The financial support was provided by  TLS-Geothermics  and supported by  ISTO  and  BRGM. Funding for open access was generously offered by the  Helmholtz Centre for Environmental Research-UFZ, Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences , the  Karlsruhe Institute of Technology (KIT) , and  LABEX  Grant  ANR-11-LABX-0050_G-EAU-THERMIE-PROFONDE  (this research therefore benefited from state funding managed by the  Agence National de la Recherche (ANR)  as part of the  "Investissements d'avenir" program )".
The final concentration of AEBSF (protease inhibitor in cocktail of proteases) was 1 mM in order to prevent proteolysis of Aβ peptides. The human Aβ42 standard was diluted in the same standard diluent buffer of samples. Plates were incubated with detection antibody overnight at 4˚C. After washing, plates were incubated with HRP anti-rabbit antibody for 30 min at RT, then they were washed and stabilized chromogen was added in each well for 20 min in a dark chamber at RT. After stopping the reaction, the absorbance of plates was read at 450 nm using the Multiskan spectrum spectrophotometer. The standard curves were established using a range of concentrations (15. 63-1,000 pg/mL) of a synthetic Aβ 42 peptide. Data are expressed as pg of total Aβ 42 /mg of proteins.
Donkey anti-mouse-Alexa 488 (green channel) and donkey anti-rabbit-RRX or donkey anti-goat RRX (red channel) were used as secondary antibodies, respectively. Nuclei were stained with DAPI (blue channel). Scale bars: 50 μm. On each image, a magnification (X10) of the amyloid plaque delineated by a white frame was added. Signal of W02 throughout frontal and parietotemporal cortex and only in amyloid deposits, GFAP and IBA-1 signals were quantified by using image J software 1. 47 V and raw integrated densities were represented in panels E, F, G and H, respectively. The line represents the mean of 4 to 9 mice in each group, expressed as percentage of control (rounds represent females, triangles represent males). To compare values between untreated APPswePS1dE9 mice and APPswePS1dE9 mice treated with trans ε-viniferin, Mann-Whitney test was used.
Nuclei were stained with DAPI (blue channel). Scale bars: 50 μm. On each image, a magnification (X10) of the amyloid plaque delineated by a white frame was added. Quantifications of global signal of W02 throughout CA1 and dentate gyrus hippocampal regions and only in amyloid deposits, GFAP and IBA-1 signals were performed by using image J software 1. 47 V and raw integrated densities were represented respectively in panels E, F, G and H. The line represents the mean of 4 to 9 mice in each group, expressed as percentage of control (rounds represent females, triangles represent males). To compare values between untreated APPswePS1dE9 mice and APPswePS1dE9 mice treated with trans ε-viniferin, Mann-Whitney test was used. � p < 0. 05, compared to respective controls. https://doi. org/10. 1371/journal.
Donkey antimouse-Alexa 488 (green channel) and donkey anti-rabbit-RRX or donkey anti-goat RRX (red channel) were used as secondary antibodies, respectively. Nuclei were stained with DAPI (blue channel). Scale bars: 50 μm. On each image, a magnification (X10) of the amyloid plaque delineated by a white frame has been added. Quantifications of global signal of W02 throughout CA1 and dentate gyrus hippocampal regions and only in amyloid deposits, GFAP and IBA-1 signals were performed by using image J software 1. 47 V and raw integrated densities were represented respectively in panels E, F, G and H. The line represents the mean of 4 to 6 mice in each group, expressed as percentage of control (rounds represent females, triangles represent males). To compare values between untreated APPswePS1dE9 mice and APPswePS1dE9 mice treated with trans ε-viniferin, Mann-Whitney test was used but no statistical difference was observed.
Nevertheless, data and knowledge storage and integration enriched with the bibliographic data is of utmost importance for sustainable process development. To address data and knowledge integration, a relevant solution is the use of an ontology  (Doan, Halevy, & Ives, 2012). Ontology is defined as a formal common vocabulary of a given domain, shared by the domain experts. Ontology is designed to represent the knowledge from one domain in terms of concepts, relationships among these concepts and instances of these concepts  (Guarino, Oberle, & Staab, 2009). Moreover, publishing ontologies on the Linked Open Data (LOD) cloud (http://linkeddata. org) and building networks of interconnected ontologies  (Suárez-Figueroa, Gómez-Pérez, & Fernández-López, 2012)  facilitate data integration and data sharing, such as giving access to data from specific disciplines or data produced within specific geographic regions  (Bizer, 2013).
With this objective, our collaborative network gathering scientists in food process, oral physiology and sensory perception, eco-design and computer science, built a first ontology for the eco-design of transformation processes  (Dibie, Dervaux, Doriot, Ibanescu, & Pénicaud, 2016). This ontology has been extended to PO², a process and observation ontology in food science (available at http://agroportal. lirmm. fr/ontologies/PO2/), to integrate data for the formulation of dairy products, taking into account nutritional and sensory properties  (Ibanescu, Dibie, Dervaux, Guichard, & Raad, 2016). A database, called BaGaTel, structured by the PO² ontology has been built, hosted by the PLASTIC platform of INRA (http://www. pfl-cepia. inra. fr). Data from different sources (e. g. , experiments, papers, databases) are manually annotated with concepts and relations from PO² ontology and are thus stored into specific EXCEL files that were previously designed according to the structure of PO².