dataesr/scientific-paragraphs-categorization huggingface dataset:
Che et al. (2011);association PSTJ 5 for its official host agreement, the Lagrange and OCA for computer server support. This research made use of the Jean-Marie Mariotti Center SearchCal service 6 codeveloped by Lagrange and IPAG, and of the CDS Astronomical Databases SIMBAD and VIZIER 7. This research made use of the AMBER data reduction package of the Jean-Marie Mariotti Center 8. Special thanks go to the project's grant ALMA-CONICYT N • 31150002 and the PI, Keiichi Ohnaka, who supported this work. Synspec Kurucz/Synspec Kurucz/Synspec Kurucz/Synspec ] [9500K, 4cm/s 2 ] [10500K, 4cm/s 2 ] ] [9500K, 4cm/s 2 ] [10500K, 4cm/s 2 to to to to ] [15000K, 4cm/s 2 ] [15000K, 4cm/s 2 ] ] [15000K, 4cm/s 2 ] [15000K, 4cm/s 2 Value 1. 57±0. 07 mas 1. 63±0. 04 mas 1. 57±0. 07 mas 1. 64±0. 09 mas 1. 35±0.
For example, optical mapping at the cellular level on the developing zebrafish heart during heart-looping stages in zebrafish has demonstrated that regional cell-function specificity is acquired concomitantly with structural patterning during heart maturation  [29]. Three-dimensional heart morphogenesis is particularly challenging to follow due to the heartbeat while developing and several approaches from microscopy to image analysis tools have been specifically developed to assess heart morphogenetic changes in living embryos  [20,30 -35]. A recurrent challenge has been to transform complex 3D datasets into two-dimensional (2D) projections in order to simplify the analysis. For example, transformation of 3D Cartesian coordinates projected into spherical coordinates and then into 2D coordinates has been used to study retina morphogenesis  [36].
Similarly, methodology developed to map cilia position and orientation on the inside surface of the spherically shaped zebrafish left -right organizer has enabled the creation of mathematical models to predict physical limits of flow sensing inside the organ  [37, 38]. Other approaches have focused on minimizing the perturbations introduced by long-term live-imaging and have developed methods to track cell migration on fixed tissue during follicle morphogenesis in Drosophila, achieved by tagging extracellular matrix proteins followed by image analysis that involves 'flattening' of the tissue from 3D to 2D thus revealing the trails left by migrating cells  [39, 40]. Other approaches have focused on reducing data dimensionality in order to deal with the processing of large datasets produced by fast-acquisition imaging methods such as light-sheet microscopy  [40, 41].
(e) Image analysis Image analysis was made via two major steps. For the first step, we used the commercially available software Imaris (Bitplane) while for the second we used custom-made Matlab (MathWorks) scripts. In the first step, the cell detection algorithm implemented in Imaris (spots function) was first used to automatically determine the 3D position of each EC nuclei in both the DA and the CV, and manual corrections were made as required. In the second step, the Matlab scripts read the 3D nuclei coordinates previously exported from Imaris, to precisely determine the embryonic axes (A -P, D -V and L -R axis) and re-orient the nuclei of each sample to a common reference system to allow for comparison and averaging of the different specimens acquired (figure  2  of the parametric position of each nucleus (figure  2 ).
Note that each cross-section, size, orientation (angle of the fitting ellipse), ellipticity and centre of mass change significantly along the vessel due to the irregular shape of the vessel along the anterior -posterior axis. The parametric surface allows mapping of the irregular morphology of the DA to a cylinder and average statistics on the different cross-sections along the same vessels and between different samples. This approach therefore overcomes the ambiguities intrinsic in the complexity of the actual geometry, allowing for a systematic analysis. In the remainder of this section, we describe the macro steps performed with the Matlab scripts in more depth. First, the DA and CV nuclei positions (3D coordinates) were imported into Matlab (figure  2a ). The nuclei positions of the DA were fitted with a least-square cylinder (using the Matlab routine lscylinder.
m, LSGE toolbox) (figure  2b ). The F-actin channel was then imported into Matlab and several planes, equally spaced (every 4. 4 mm; approx. 50 sections: length ¼ 221. 43 mm), were defined along the longitudinal (anterior-posterior) axis of the DA and orthogonal to it. The F-actin signal between each plane was then projected on the planes (cross-sections) and used to fit an ellipse (MathWorks file exchange; fit_ellipse by Ohad Gal, based on the Least-Squares criterion) to each cross-section in order to segment the DA (figures 2b and 5b,c). A similar approach can be done using a membrane or a cytoplasmic marker as long as the vessel walls are labelled. Later, the points of the ellipses were interpolated in space with a 3D spline (spline toolbox, Matlab), which provided the parametric surface describing the vessel wall.
Finally, a common reference system to all the datasets was generated such that the x, y and z axes match, respectively, the dorsal -ventral, the left -right and the anterior -posterior embryonic axes, and the origin of the reference system corresponds to the centre of the DA (figure  2e ). Registering all the samples to a common reference system permits clustering and averaging of the data from different samples while avoiding biases and mistakes that are introduced when orienting the specimens in the sample preparation step before imaging (e. g. LMP agarose embedding). Along the same line, our image analysis routine includes normalization of the data by the average segment length, the distance between two consecutive intersomitic vessels (ISVs). The ISV distance was previously obtained for each sample in Imaris using the 'measurement points' function and was then entered in Matlab.
The normalization allows for the comparison of different samples that might have different sizes, particularly when comparing control and experimental groups, such as mutants or drug-treated embryos. After the image registration and normalization steps, different parameters can be extracted from the processed datasets. For example, vessel calibre (cross-section area and perimeter) and shape (eccentricity) can be obtained from the ellipses describing the cross-sections of the aorta. Afterwards, four equally sized regions (dorsal, ventral, left and right) were defined and the DA nuclei were projected on it, and the number and percentage of nuclei in each region could be extracted (figures 2f and 3b). The subdivision into four regions was done such that the arch length of each region is the same on each cross-section and using the most dorsal point of the cross-section as reference.
All nuclei positions could be mapped on an average elliptical cross-section (figure  2f  ) or on a circle (figure  3a ), where each point is well defined by its angular position. The average cell nuclei distribution around the vessels perimeter (cross-section) could therefore be quantified and plotted as a function of the angular position (figure  3c ). This procedure is important for comparison between different developmental stages, experimental conditions (e. g. mutants or drug treatment) or to cluster samples of the same stage that may differ in vessel geometry (e. g. calibre). The described methodology was validated by repeating the analysis on several datasets artificially generated by arbitrary rotations of one of the acquired z-stacks in the AP direction (figure  4a ). The artefacts introduced by the analysis were estimated by measuring the distance between the nuclei from the different datasets in the common reference frame (figure  4b ), which should be zero.
(i) Instructions to run the Matlab script Here, we provide detailed instructions on how to run the Matlab script that refer to the critical steps and complement the guidelines provided inside the code. rstb. royalsocietypublishing. org Phil. Trans. R. Soc. B 373: 20170332 The method described above is implemented inside the script '3DcylindricMap. m', which will import the data needed for the analysis (i); segment, interpolate the DA and orient it on a common reference system (ii); analyse the vessel geometry and position of the nuclei in the tissue of interest (e. g. DA), in four equally sized regions (dorsal, ventral, left and right) and plot the number of nuclei as a function of their angular position (iii). As a preliminary step, download the functions needed to perform the analysis (keep them in a single folder) and create a folder where you will copy the files necessary to run the image analysis code.
These include the Matlab or Excel files containing the 3D coordinates of the nuclei in both the tissue of interest (e. g. the DA) as well as in the reference tissue (e. g. the CV in our case), allowing definition of one of the axes (the dorsal -ventral axis in our case). Make sure that, if Excel format is chosen, the coordinates of the nuclei of the tissue of interest are saved in Sheet1 and the coordinates of the nuclei of the reference tissue are saved in Sheet2. Please be consistent in the choice of the file format: you will have to specify if data are imported from Matlab or Excel files and you will not be able to switch while the code is running. In addition, copy a TIFF. file with the z-stack of the marker describing the vessel wall, which will be used to make the ellipse fitting on the cross-sections (e.
g. F-actin). An example of the data to run the code is provided together with the Matlab functions. 1. Open Matlab and choose as Matlab directory the folder where you stored the functions needed to perform the analysis. Open '3DcylindricMap. m' and in the 'import data' section ( part 1) indicate: -In 1. 1. , the path to the directory where all the files are saved (ID); if your data (nuclei coordinates) are in Excel or Matlab file format (respectively, excel ¼ true or false); -In 1. 2. and 1. 3. , the name of the Excel or Matlab file containing the 3D coordinates for all the tissues. Do not forget the file extension; -In 1. 4. , the name of the TIFF file with the z-stack (e. g. F-actin). Do not forget the file extension; -In 1. 5. , specify the average distance, in micrometre, between two ISVs to rescale and compare embryos (e.
g. L_ISV ¼ 85 in our example) and the TIFF file properties (voxel size: dx, dy and dz); 2. Save '3DcylindricMap. m' and run the code; 3. A window will pop-up in Matlab displaying the middle slice of your F-actin z-stack where you can draw a region of interest (ROI) around the tissue of interest (e. g. DA) by defining sequentially the delimitation points of the region (left click). Once you have drawn the ROI, double click the mouse left button and the ROI will be applied to all the slices in your z-stack (figure  5a ). It is important to be precise when defining the ROI, if made too large signal from adjacent vessels (e. g. ISVs) will be included in the ROI and negatively affect the automated ellipse fitting (next step), thus increasing the amount of manual correction necessary; 4. Another window will pop-up displaying an orthogonal view (cross-section) of the F-actin z-stack and a first ellipse will be automatically fitted to the F-actin signal of the tissue of interest, e.
The script will now run until the end and several results will appear in the workspace (under the names: 'data', 'data2' and 'data3') that contain, respectively, the results for the nuclei position, the cross-section area, perimeter and eccentricity of the individual ellipses as well as for the mean vessel calibre (cross-section area and perimeter) and shape (eccentricity) (see datasheet key in the electronic supplementary material). Plots of the number of nuclei as a function of their angular position and of the percentage of nuclei as a function of their angular position will also appear in figures 1 and 2, respectively. Finally, you will find, inside the folder created at the beginning of the analysis, three figures plotting area, perimeter and eccentricity of the cross-sections in a folder named 'results', as well as a 'matlab.
Concluding remarks The protocol described here provides an effective way to track EC nuclei distribution during DA formation. We are continuously improving our method and our goal is to optimize it to include automated cell contour segmentation and detection of cellular rearrangements. We also plan to develop the analysis of time-lapse datasets together with the possibility to study other features like the localization and number of dividing or extruding/apoptotic cells. In principle, this method could be applied generally to other tissues with tubular architecture, provided that the live-imaging is performed with appropriate temporal and spatial resolution. Comparative studies of the nuclei density changes within vascular tissues or in other tubular organs (such as the kidney, lungs, digestive tract) have the potential to provide new insights into the fundamental rules of tissue morphogenesis.
Authors' contributions. P. C. carried out the experiments and ran the image analysis protocol. P. C. , P. L. and F. B. designed the data analysis methodology. P. L. wrote the majority of the Matlab scripts based on earlier versions developed by F. B. P. C. and J. V. wrote the manuscript. Competing interests. We declare no competing interests. Funding. This work was supported by FRM (DEQ20140329553), the ANR (ANR-15-CE13-0015-01, ANR-12-ISV2-0001-01 and ANR-10-LABX-0030-INRT), the EMBO Young Investigator Program, the European Community, ERC CoG N8682938 Evalve and by the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir labelled ANR-10-IDEX-0002-02. P. C. was supported by FRM (FRM SPF20140129238), EMBO (ALTF 100-2014) and by the Foundation Lefoullon Delalande (2017).
Acknowledgements. We are grateful to  G. Charvin  for assisting with the design and 3D printing of the mould described in this protocol. We thank the entire staff of the IGBMC imaging center, in particular E. Guiot. We also thank the  IGBMC fish facility  staff:  S. Geschier  and  S. Pajot. We also thank  R. Chow  and  N. Hamilton  for help with proofreading. rstb. royalsocietypublishing. org Phil. Trans. R. Soc. B 373: 20170332.
These estimations were performed separately for the herb, shrub and tree layers, by assigning one of the abundance categories 1 (< 5 individuals), 2 (5 individuals; 30%) or 3 (> 30%) to each of the present plant species  [44]. We derived structural and functional properties at the plot level from the forest stand and vegetation data. To characterize stand structure, we determined stand height, tree density, basal area, tree slenderness coefficients and diameter distributions  [47]. To capture structural diversity, we calculated the coefficient of variation (for log-normal data) of the tree diameters and of the potential plant height of the herb layer, the latter derived from the TRY database  [48]. The coefficient of variation gives information about the dispersion of the diameters and plant heights and thus how different/diverse they are.
Functional traits that are related to the leaf economics spectrum (i. e. traits determining amongst others the decomposition of leaf-litter)  [49]  (Additional file 1: Table  S2 ) were derived from the vegetation abundance survey using the TRY  [48]  and LEDA  [50]  databases. For the herb species, growth and life forms, branching types and specific leaf area were determined (all defined in  [50] ), because they were assumed to influence the suitability of the herb layer as questing habitat for ticks. Moreover we determined the richness of different weight-classes of dispersules (lightweight: < 0. 1 g; medium: 0. 1-2 g; heavy: > 2 g, see  [38, 50] ) and the average overall dispersule mass, separately for all vegetation layers. This served as a proxy for the quality and amount of high energy food that is potentially available for different tick hosts, feeding on these dispersules  [51].
Climate Ambient microclimate was recorded at the same time as the tick/stand structure survey with Testo 175-H2 Data-Loggers (Lenzkirch, Germany; temperature precision of ± 0. 5 °C, relative humidity accuracy of ± 3%). Measurements were taken every minute for around half an hour in the plot center. Air temperature and relative humidity were measured at a height of both 5 and 130 cm. Soil temperature was measured at a depth of 5 cm. We calculated saturation deficit according to  [27] , based on values averaged between 5 and 130 cm height for both relative humidity and air temperature. Macroclimate data were extracted from the Global Summary of the Day (GSOD) dataset hosted on the webservers of NOAA's National Centers for Environmental Information (NCEI). We extracted climate metrics for the period from 1st of January 2013 to the day of sampling and for 30 days prior to tick sampling.
Statistical analyses All statistical work was carried out in R version 3. 3. 1  [57]. An explanatory factor analysis (EFA) was carried out, based on maximum likelihood, to derive factors of correlating variables (Additional file 4: Text 2, Additional file 5: Table  S6 ) within variable groups, with the 'psych' package  [58]. These correlation factors are assumed to represent the combined (and more general) influence of a set of correlating variables, which would not be significant separately because they might be too specific. The factors were then used in the same way as the other drivers for model building. We derived the letters indicating differences between patch averages per region (Fig. 3 ) with the multcomp-View package  [59]. We built linear mixed models (LMMs) to explore the effect of all environmental variables on the B.
The lmerTest package  [61]  was used to determine type-III ANOVA tables with the Wald F-test with Satterthwaite degrees of freedom for fixed effects. Response profiles based on partial residuals were determined with the visreg package  [62]  and plotted with ggplot  [63]. We derived the relative importance of the selected drivers based on partial eta 2 values (Additional file 8: Text 4)  [64]. Detailed R-code, which covers model building and preparation of graphs can be found on github (https://github. com/EhrmannS/2017_Parasit-Vec-tors_Habitat-properties-are-key-drivers-of ).
Acknowledgements The article has been supported by the TRY initiative on plant traits (http:// www. try-db. org). The TRY initiative and database is hosted, developed and maintained by  J. Kattge  and  G. Bönisch  ( Max Planck Institute for Biogeochemistry, Jena, Germany ). TRY is currently supported by  DIVERSITAS/ Future Earth  and the  German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We acknowledge  Katja Leischke ,  Peter Fräßdorf  and  Iris Gutierrez  for participating in field-work and  Lotta May  for preparing ticks for laboratory work.
Availability of data and materials The dataset utilized for this study is part of the overall smallFOREST database, which is currently still under analysis. Data will be publicly available on the smallFOREST project website (https://www. u-picardie. fr/smallforest/uk/), as soon as all the envisaged studies within this project are completed. Software and code utilized for analyzing the data can be found on github: https:// github. com/EhrmannS/2017_Parasit-Vectors_Habitat-properties-are-keydrivers-of.
2015). A recently-released open-source software platform (https://www. helsinki. fi/en/researchgroups/metapopu lation-research-centre/software), Robust Offsetting (RobOff), differs from the other systematic conservation planning tools, in that it is primarily 'action' rather than spatially-based. RobOff fills the niche, because it selects the best conservation actions given a set of goals through modelling of uncertainty around alternative conservation actions (e. g. tree planting vs. invasive alien plants [IAPs] control) have on different biodiversity features (e. g. species richness) in different environments  (Pouzols and Moilanen 2013). Systematic restoration planning studies done to guide restoration plans have accounted for either biodiversity features (e. g. species richness), ecosystem services provision (e.
Each action was assessed in terms of tree species richness, carbon stock and employment creation and compared with the current action (planting of at least 51 indigenous tree species). This was done through a series of workshops with 12 local restoration ecology and biodiversity experts (academics and practitioners). Experts were chosen based on their knowledge of local biodiversity, forest restoration management and cost, and the RobOff software. The do nothing (default in RobOff) and natural regeneration actions were proposed for the 'extant forest' while do nothing, natural regeneration action, current action, carbon action and biodiversity action were proposed for the 'former sugarcane fields' by the experts (Table  1 ). Restoration cost of each action within the habitats was estimated per ha, over a 20-year period (Table  S1  in Supplementary Material).
Biodiversity action To boost tree species richness and to increase ecosystem functioning. Planting of 80 indigenous trees species from the regional pool of scarp forest trees at 1000 trees per hectare and site maintenance. 4450 actions, because it included slow growing tree species with high wood density, thus requiring more intense site maintenance than the current and biodiversity actions (Table  S1  in Supplementary Material). The IAPs clearing cost was obtained from the South African National Parks' Working for Water Programme (IAPs clearing project, unpublished database) and from published scientific literature  (Marais and Wannenburgh 2008). The IAPs clearing method included uprooting of saplings and cutting of shrubs and trees. Restoration features response (referred to as restoration benefits in this study) to alternative restoration actions across habitats Three critical co-benefits from Buffelsdraai Landfill Site were chosen, namely, biodiversity, carbon stock and employment creation.
Data analyses The cost of each alternative restoration action per hectare was quantified. To assess the effect of budget availability on resource allocation, analyses were replicated under alternative budget levels (previous section). Effect of biodiversity, carbon stock and employment prioritization on allocation of resources to alternative restoration actions was also assessed. Seven weighting schemes (e. g. relative weight of 1. 0 for biodiversity, 1. 0 for carbon stock and 2. 0 for employment) were used to explore this effect. Prioritization of restoration benefits was done under seven permutations (i. e. seven prioritization scenarios) (Table  S5  in Supplementary Material). To assess the effects of prioritization of alternative restoration benefits, RobOff was run using the 50% and 100% budget scenarios over a 20-year period.
This article describes the latest stable release (version 2. 2) of the Atmospheric Radiative Transfer Simulator (ARTS), a public domain software for radiative transfer simulations in the thermal spectral range (microwave to infrared). The main feature of this release is a planetary toolbox that allows simulations for the planets Venus, Mars, and Jupiter, in addition to Earth. This required considerable model adaptations, most notably in the area of gaseous absorption calculations. Other new features are also described, notably radio link budgets (including the effect of Faraday rotation that changes the polarization state) and the treatment of Zeeman splitting for oxygen spectral lines. The latter is relevant, for example, for the various operational microwave satellite temperature sensors of the Advanced Microwave Sounding Unit (AMSU) family.