dataesr/scientific-paragraphs-categorization huggingface dataset:
Surfaces are represented by an oriented triangular mesh, which is typically extracted from the surface of an object, and thus represents the outermost tissue layer (Figure  1A ,C,D), or the boundaries of individual 3D objects (e. g. , cells) in the case of full 3D segmentation (Figure  1B ). Once processed, surfaces and associated data can be exported in a variety of file formats suitable for loading into modeling or analysis softwares, allowing the direct use of sample Box 1. All resources for MorphoGraphX, including the user manual, the latest software downloads and the source code, can be found on www. MorphoGraphX. org. The latest version of the documentation is also distributed with MorphoGraphX itself, and is available from the 'Help' menu (see also Supplementary file 1). We encourage users to develop their own plugins to extend MorphoGraphX for new research tasks.
Feature extraction on curved surfaces A key strength of MorphoGraphX is the ability to summarize 3D data as a curved surface image. After extracting the shape of an organ, 3D data just below the surface can be projected onto it, creating a curved image of the outer layer of cells (Figure  2 ). This enables the extraction of precise cell outlines without the distortions associated with a flat 2D projection (Figure  2 -figure supplement 1). We have found that many algorithms designed for 2 and 3D image processing can be adapted to our curved surface images. Feature extraction in MorphoGraphX typically follows a pattern: (i) volumetric data (often a cell outline marker) is pre-processed to remove noise or obstructions; (ii) the object of interest is turned into a mask (binary image); (iii) the object is extracted as a surface mesh; (iv) volumetric data is projected onto the surface; (v) the projection is used for segmentation of the surface into cells (Figure  2 , Video 2).
Interaction with Bezier surfaces MorphoGraphX allows user-defined surfaces to interact with volumetric data both for visualization and feature extraction. The researcher can interactively define Bezier surfaces to visualize curved slices through an object. By manipulating the Bezier control points it is possible to fit almost any shape to a surface of interest within the sample. An extreme example of this is shown in Figure  2H  where the surface has been shaped to display the cortical cells of a mature Arabidopsis embryo. The Bezier surface can be converted to a triangular mesh, and segmented into cells with the same procedure used for Figure  2A-E. The extracted tissue geometry can be then used, for example, as template for simulations  (Santuari et al. , 2011).
Signal quantification Once a surface is segmented into cells, data collected simultaneously on a different channel, such as a GFP fusion to a protein of interest, can then be projected onto the segmented surface (Figure  3 ). This allows the quantification of genetic expression and protein localization at the cellular, or sub-cellular scale. As with the cell outlines, the projection creates a curved image of the data that can be processed in a similar way as a planar 2D image. Many tools commonly used for the analysis of flat images (for example in softwares such as Adobe Photoshop, Gimp and ImageJ) have been adapted for use on curved surfaces in MorphoGraphX. This includes Gaussian blur, erosion, dilation, morphological closing, local minima detection, normalization, etc. The flexibility of this approach is demonstrated by our implementation of more complex algorithms, such as the watershed transform for cell segmentation and our adaptation of an algorithm based on signal gradients to compute the orientation of microtubules (Figure  3A , Figure  3 -figure supplement 3) that was previously implemented in 2D  (Boudaoud et al.
The process can be used to quantify what portion of a tagged protein, for example, the auxin efflux carrier PIN1, is localized to the plasma membrane or internalized  (Nakayama et al. , 2012). Projection of the signal on the surface allows to summarize essential information from several channels of a large confocal data set into a very compact form. For example, the global shape of the sample can be extracted from an autofluorescence signal, while the cell wall or membrane marker collected in another channel is used to segment cells and obtain their geometry. The expression level of a protein from a third channel may then be quantified at the cellular level based on the segmentation. Finally, several samples in a time lapse experiment can be compared to obtain information about the temporal evolution of shape and gene expression.
Time lapse analysis In addition to data from single image stacks, MorphoGraphX is able to process and compare multiple time points. This enables the analysis of stacks before and after experimental treatments, or timelapse data. This capability relies on an efficient method to co-segment samples from two time points. One approach is to segment both stacks separately and then to use an automated algorithm to match the points  (Fernandez et al. , 2010). However, automatic segmentation and matching can be prone to errors that have to be checked and corrected by hand, which can be very time-consuming depending on the error rate. For this we have developed a user-friendly interface in MorphoGraphX to manually identify cell lineages on curved surfaces representing the tissue at different time points (Video 4).
Another possibility is to combine confocal stacks acquired from different angles  (Fernandez et al. , 2010). Currently it is possible to assemble data from multiple angle acquisition within MorphoGraphX. MorphoGraphX uses the auto-seeded, morphological watershed algorithm available in the Insight Segmentation and Registration Toolkit  (Yoo et al. , 2002)  (ITK) for 3D segmentation. We have developed a collection of user-friendly 3D voxel editing tools allowing the researcher to correct segmentation errors. Alternatively, cells and other objects which are not in contact with each other can be segmented by extracting the surface of the individual objects (Figure  1B , Video1). As with 2D surfaces of cells, geometrical properties (surface area, wall length, volume) and fluorescent signal (e. g.
Discussion A key strength of our MorphoGraphX software is the ability to accurately extract curved surface meshes from 3D volumetric data and perform image processing on the resulting curved (2. 5D) surface images. This has wide application, since many biological processes happen on surfaces, and the method has been proven in both animal  (Aegerter-Wilmsen et al. , 2012)  and plant  (Santuari et al. , 2011; Chitwood et al. , 2012; Kierzkowski et al. , 2012; Nakayama et al. , 2012; De Rybel et al. , 2013; Kierzkowski et al. , 2013; Wabnik et al. , 2013; Sampathkumar et al. , 2014; Vlad et al. , 2014; Yoshida et al. , 2014)  systems, in embryonic as well as mature tissues. The method is especially powerful for time-lapse confocal imaging, where laser exposure has to be kept to a minimum, limiting penetration to the outermost layers of the sample.
In addition to curved surface image processing, MorphoGraphX provides an intuitive and user-friendly interface for the visualization and editing of 3D volumetric data, making it possible to digitally remove obstructing objects from the surface of expansion over the first 11 hr interval. Principal directions of growth (PDGs) are indicated for cells displaying an anisotropy above 15%, with expansion in white and shrinkage in red. Note the rapid anisotropic expansion of the developing primordium P1 and of the peripheral zone close to the incipient primordium I1, while cells in the boundary between P1 and the meristem contract in one direction (red lines). Arrows indicate the correspondence between top and side views. Raw confocal data from  (Kierzkowski et al. , 2012). Scale bars 50 μm. DOI: 10.
7554/eLife. 05864. 016 The following figure supplements are available for figure  4 : interest, such as the peripodial membrane overlying the Drosophila wing disc  (Aegerter-Wilmsen et al. , 2012). We have also included a range of standard 3D image processing tools, similar to those available in many other softwares  (Fernandez et al. , 2010; Peng et al. , 2010; Sommer et al. , 2011; Federici et al. , 2012; Mosaliganti et al. , 2012; Schmidt et al. , 2014). These can be used for 3D segmentation  (De Rybel et al. , 2013; Bassel et al. , 2014; Yoshida et al. , 2014) , or to pre-process data before surface extraction. The modular design of MorphoGraphX allows the integration of existing libraries and the creation of custom processing 'pipelines', going from the raw microscopy image to feature extraction and fluorescence quantification.
Fully automatic tools are complemented with intuitive methods for interactive correction  (Peng et al. , 2011)  and validation, greatly increasing the utility of new and existing algorithms. Streamlined data exchange with modeling tools allows cell geometry and gene expression data to be used as model inputs, and facilitates the validation of simulation results. These features combine to make MorphoGraphX a significant step towards an interdisciplinary computational morphodynamics platform to study the interactions between growth, mechanics and gene expression. 5A ) were acquired using SP8 or SP5 Leica confocal microscopes, as described previously  (Kierzkowski et al. , 2012; Vlad et al. , 2014). After dissection samples were stained with 0. 1% propidium iodide (PI) and grown in vitro on medium  (Bayer et al.
Raw data acquisition Mature A. thaliana embryos (Figure  2H ) were fixed and stained as previously described  (Bassel et al. , 2014)  and imaged using a Zeiss LSM710 confocal microscope with a 25× oil immersion lens. Confocal stacks of microtubule marker line TUA6-GFP  (Ueda et al. , 1999)  in live Cardamine hirsuta fruits (Figure  3A ) were acquired using a SP2 Leica microscope, with a 40× long working distance water immersion objective and an argon laser emitting at 488 nm. GFP signal was collected at 495-545 nm. The z step between stack slices was 0. 2 μm. The sequential replica method  (Williams and Green, 1988)  was used to acquire a stereopair of SEM images from an Arabidospsis leaf surface (Figure  1D ) as described in  (Elsner et al. , 2012). Stereoscopic reconstruction  (Routier-Kierzkowska and Kwiatkowska, 2008)  was then performed for the stereo pair and converted into a triangular mesh using a custom MorphoGraphX module.
Software design and data representation MorphoGraphX is written in C++ and has been developed on GNU/Linux. For GPU processing, MorphoGraphX uses CUDA (https://developer. nvidia. com/cuda-zone) via the Thrust template library (http://thrust. github. io). Multi-threaded host processing is done using OpenMP (http://openmp. org/wp/). CUDA requires a compatible nVidia (http://www. nvidia. com) graphics card. The user interface is designed in Qt4 (http://qt-project. org/), and OpenGL is used for 3D rendering (http://www. opengl. org). MorphoGraphX can be extended using either C++ modules or Python scripts. C++ modules can be loaded at the start of MorphoGraphX through a plug-in system, inspired by the shared library loading architecture of Lpfg in VLab  (Federl and Prusinkiewicz, 1999). C++ processes can access all the internal data structures used in MorphoGraphX and modify them as needed.
They can also call other processes or query for their existence, and get parameter values in a uniform way from the graphical user interface. The last parameter values used for each process are stored in the project (. mgxv) file for future reference. All process calls and their parameters are logged to a re-playable python script log file created in the current directory. Each process is represented a light C++ object defining the name, parameters and code of the process and is bundled in shared libraries for easy distribution. The shared library is placed into a system or user process folder, and the processes it contains are loaded upon startup. Python scripts can also be written and executed within MorphoGraphX using the Python Script process. This option offers a more limited interaction with MorphoGraphX as a script is only able to launch other processes and not directly interact with the data structure.
However, it allows use of the wealth of modules existing for Python 2. 7 for file interactions and data analysis. Most data analysis processes import/export their data as CSV files to facilitate the writing of Python modules for complex or ad-hoc data analysis. Surfaces are represented by vertex-vertex systems  (Smith et al. , 2004) , which implement graph rotation systems. Properties can be stored in the mesh, such as the label attributed to an individual vertex, the normal associated to it or a value for the projected signal. The rendering uses a modified front-to-back depth peeling technique  (Everitt, 2001)  interweaving the volumetric rendering between peels of translucent surfaces. The volumetric rendering itself is done using volume ray casting  (Levoy, 1990) , using the depth of the successive pair of peels to limit the ray casting to the region currently being rendered.
This method allows for correct polygon-polygon and polygon-volume intersections. Combined with occlusion detection, we implemented early ray termination when the total opacity of the current fragment becomes too high for subsequent peels to be visible. MorphoGraphX can be easily extended to import and export voxel and triangle mesh data in various formats. For voxel data, MorphoGraphX can read and write the tiff format compatible with ImageJ or Fiji  (Schindelin et al. , 2012). 3D data can also be loaded from series of 2D images using any of the various image formats supported by the C++ Template Image Processing Toolkit (CImg)  (Tschumperl é, 2012). The Visualization Toolkit (VTK)  (Wills, 2012)  is used to import and export VTK triangle meshes. Various other formats, such as the Stanford Polygon File format (.
Acknowledgements This work was funded by the  SystemsX. chRTDs Plant Growth 1 & 2 (RSS and CK), the  SystemsX. chRTD WingX (PK and KB) , the  SystemsX. chRTD SyBIT (IX) , the  Swiss National Science Foundation  interdisciplinary project grants  CR32I3_132586  and  CR32I3_143833  (RSS), the  Swiss National Science Foundation  international short research visit (AB and RSS),  Human Frontier Science Program  grant  RGP0008/2013  (AHKR and RSS),  European Research Council Advanced grant (PK) , the  Biotechnology and Biological Sciences Research Council  grant  BB/L010232/1  (GWB),  University of Birmingham Research Fellowship (GWB) ,  Forschungskredit of UZH (TA-W) , the  Polish National Science Centre  MAESTRO research grant No  2011/02/A/NZ3/00079 (DK) ,  Deutsche Forschungsgemeinschaft  grant  SFB 680  (MT),  Human Frontier Science Program  grant  RGY0087/2011  (AH), and  Max Planck Society   W2 Minerva program  grant (AH).
Sequence alignment A Variant Call Format (VCF) file was generated from each of the Illumina short read data sets listed in Table  1. The fastx_barcode_splitter. pl script from the fastx_toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) was used to separate reads in multiplexed F. vesca accessions ('Pawtuckaway', 'Yellow Wonder' and 'Baron Solemacher'). Adaptor sequences and low-quality ends were removed from the raw reads using cutadapt with filtering set at a phred compatible score of 20 and a minimum length of 26 to keep the read  [49]. The Burrows-Wheeler aligner (BWA), with default arguments  [50] , was used to align the resulting reads to the F. vesca 'Hawaii 4' v1. 1 reference genome  [41, 51, 52]. The BWA aln command was used with both forward and reverse reads from each panel member to produce separate forward and reverse sequence alignment index (.
Variant discovery and filtration panels Variant discovery was conducted separately in the octoploid germplasm panel versus in diploid F. iinumae hybrid F1D. For variant discovery at the octoploid level, the VCF files from all members of the GDP were employed. As noted in Additional file 2, HolKor 2637 was sequenced twice and was therefore represented by two VCF files, so the GDP of 19 octoploid accessions was represented by 20 VCF files. For subsequent filtration procedures, two GDP subpanels were defined. The HD-16 subpanel consisted of the ten data sets with a minimum of 16× genome coverage (Table  1 ), and as such included both of the HolKor 2637 samples. The HD-20 subpanel consisted of the eight data sets with a minimum of 20× coverage (Table  1 ), and therefore excluded the lower-coverage HolKor 2637 sample.
Genotyping in octoploid strawberry DNA was isolated and quantified as described above for the octoploid strawberry validation set (Table  2 ). Up to 50 μL of DNA (≥20 ng/μL) from each sample was submitted to Affymetrix for genotyping. The Axiom assay was performed on four 96-sample Axiom arrays using the Affymetrix GeneTitan® system according to the procedure described by Affymetrix (http:// media. affymetrix. com/support/downloads/manuals/ axiom_2_assay_auto_workflow_user_guide. pdf ). Next, cell intensity files (. CEL) generated by the GeneTitan instrument were converted to genotype calls using the Axiom Genotyping Algorithm version 1 (Axiom GT1) available through Affymetrix Power Tools or Genotyping Console™v4. 1 software package. The procedure is documented by Affymetrix (http://media.
Genotyping in diploid strawberry The Axiom assay was used initially to process all 27 diploid strawberry samples, and these samples were genotyped with the AxiomGT1 algorithm. All samples passed QC. As with the analysis of octoploid samples, the SNPolisher software was used to classify the SNPs into six quality classes according to their clustering performance. However, for these samples, the default diploid settings were used. Subsequently, the three F. vesca samples were excluded from the analysis, and genotype calling was repeated using only the 24 F. iinumae samples.
Diploid-like Tetraploid-like Fixed BB subgenome Octoploid Fixed BB subgenome software JoinMap 4. 1  [55]  with the multipoint maximum likelihood mapping algorithm approach for cross pollinators  [56]  and the Haldane mapping function using all preset default settings for the calculation options. For linkage group 6D (LG6D), quality of genotype calls and of the presented maps was evaluated through a graphical genotyping approach  [57]  using Excel. Single data points causing double recombination events were checked by examination of the corresponding cluster plots. Linkage groups were identified by integration of the SSR data of Van Dijk et al. [38]  with the current SNP data. Once the SNP markers were grouped according to linkage groups, these SSR data were excluded from the generation of linkage maps in order to avoid issues of data consistency that may easily arise among markers from different experiments and genotyping platforms.
Inheritance-based SNP validation in the F. iinumae F2D population Genotype calls were obtained for the two crossing parents (J17 and J4), for F 1 progeny plant F1D, and for 21 individuals of the derived F 2 population (F2D). To be employed for mapping in the F2D population, SNPs that displayed segregation in the F2D population also had to be heterozygous in F1D, and the J17 and J4 parents could not share the same homozygous genotype. A genetic linkage map was constructed using the software JoinMap 4. 1  [55]  following the same procedures described above for 'Holiday' × 'Korona'.
Analyses of SNP data Minor allele frequency (MAF) was calculated in 65 strawberry cultivars representing breeding selections and founders from Europe and across U. S. breeding programs in Florida, eastern U. S. , California and the Pacific Northwest (Diversity set in Additional file 1) with an R script available in plantbreeding R software  [58]. Distribution of SNPs was also drawn with plantbreeding R  [58]  by minor allele frequency according to physical location on the F. vesca 'Hawaii 4' v1. 0 reference genome in 65 diverse strawberry accessions (Additional file 1).
The presence of some genotyping problems was revealed during the mapping process. Of the 353 HK- polymorphic OTV SNPs, a large group of 121 SNPs showed a similar pattern of non-concordance for parentprogeny genotypes that could not be solely explained by the introduction of null alleles. For the HK progeny, these OTV SNPs showed a clear 1:1 segregation for the AA and BB clusters whereas the parents were genotyped as homozygous AA and BB or BB and AA. The 1:1 segregation indicated segregation from just a single parent. To examine their performance in mapping, these SNP data were scored as two alternatives, either as a maternal or as a paternal marker. The alternative that integrated well into the PHR framework map was maintained. In this way, three such markers could be well integrated into LG6D (See OTV-4, -5, and -6 SNPs of Figure  10 ).
Acknowledgements This is Scientific Contribution Number  2565  from the  New Hampshire Agricultural Experiment Station. This study was partially funded by the  USDA's National Institute of Food and Agriculture -Specialty Crop Research Initiative  project, " RosBREED: Enabling marker-assisted breeding in Rosaceae " ( 2009-51181-05808 ). Partial funding was also provided by the  New Hampshire Agricultural Experiment Station  (Projects  NH00433  and  NH00588  -to TMD), and from  USDA-CSREES National Research Initiative (NRI)   Plant Genome Grant   2008-35300-04411  (to TMD). Technological Top Institute Green Genetics , project number  1C004RP , is acknowledged for financial support. Financial support was also provided by  AGL2010-21414  from the  Spanish Ministry of Science  (to AM); from the  USDA-ARS  CRIS number  5358-21000-044-00D  (to NVB).
This study presents a comprehensive comparison of radar altimetry signatures at Ka-, Ku-, C-, and S-bands using SARAL, ENVISAT and Jason-2 data over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, with an emphasis on the new information at Ka-band provided by the recently launched SARAL-Altika mission. Spatio-temporal variations of the radar altimetry responses were related to changes in surface roughness, land cover and soil wetness. Analysis of time series of backscattering coefficients along the West African bioclimatic gradient shows that radar echoes at nadir incidence are well correlated to soil moisture in semi-arid savannah environments. Radar altimeters are able to detect the presence of water even under a dense canopy cover at all frequencies. But only measurements at Ka-band are able to penetrate underneath the canopy of non-inundated tropical evergreen forests.
Radar altimetry data 2. 2. 1. SARAL SARAL is a CNES-ISRO joint-mission that was launched on February 25th 2013. Its payload is composed of the AltiKa radar altimeter and bi-frequency radiometer, and a triple system for precise orbit determination: the real-time tracking system DIODE of DORIS instrument, a Laser Retroflector Array (LRA), and the Advanced Research and Global Observation Satellite (ARGOS-3). SARAL orbits at an average altitude of 790 km, with an inclination of 98. 54°, on a sun-synchronous orbit with a 35-day repeat cycle. It provides observations of the Earth surface (ocean and land) from 82. 4°latitude North to 82. 4°latitude South. This orbit was formerly used by ERS-1 and 2 and ENVISAT missions, with an equatorial ground-track spacing of about 85 km. The first four cycles of SARAL do not follow precisely the ENVISAT orbit.