dataesr/scientific-paragraphs-categorization huggingface dataset:
Transmission Electron Microscopy Analysis of Sperm Cells Mouse spermatozoa from three different males were prepared as described above (in vitro fertilization) and fixed by incubation in PBS supplemented with 2% glutaraldehyde (Grade I, Sigma) for 2 h at room temperature. Samples were washed twice in PBS and post-fixed by incubation with 1% osmium tetroxide (Electron Microscopy Sciences), after which they were dehydrated by immersion in a graded series of alcohol solutions and embedded in Epon resin (Polysciences Inc. , Warrington, PA, USA). Ultra-thin sections (90 nm) were cut with a Reichert Ultracut S ultramicrotome (Reichert-Jung AG, Wien, Austria) and were then stained with uranyl acetate and lead citrate. Sections were analyzed with a JEOL 1011 microscope and digital images were acquired with a Gatan Erlangshen CCD camera and Digital Micrograph software.
Digital and Bibliographic Tools Used to Shorten the List of Candidate Genes Databases were searched for the presence of genic sequences including UCSC GenomeBrowser (genome. ucsc. edu), Ensembl (www. ensembl. org), and NCBI Gene (www. ncbi. nlm. nih. gov/gene). The nature of these sequences was reported as protein coding gene, processed transcript, pseudogene, or non coding RNA, although some discrepancies exist between databases. The RNA expression level was searched in the BioGPS (biogps. org) and NCBI Gene databases, in mouse and in human when available. Results from antibody labelling on human tissues were obtained from ProteinAtlas (proteinatlas. org). Data relative to mouse knock-out models and human diseases were collected from the Mouse Genome Informatics website (www. informatics.
How to Participate The participation includes three steps: ψ s,i = -100 at r = r root    q r = q root , if ψ s > -15, 000 ψ s = -15, 000 else , q r | r=rout = 0 Analytical solution, Equations (  11 ) and (  12 ) Sand, loam, clay (Table  3 ), scenarios 1-3: q root = 0. 1, scenarios 4-6: q root =0. 05 C1. 2: RWU, static RSA static 8-days old root system, soil: l×w×d = 8x8x15,  Shows an image of a maize root system growing on filter paper (5 days old). All images were analyzed using the semi-automated root image analysis software SmartRoot  (Lobet et al. , 2011) , colors distinguish different root orders. The RSML files containing the full information about the root systems are provided on the github repository in the folder "M1. 1 RSA calibration\M1. 1 Reference data. " t sim = 3 ψ s,i = -659.
8-z    Q x | collar = 6. 4 if ψ x | collar > -15, 290 ψ s = -15, 290 else , Q x | tips = 0, ψ s,i = -200    Q x = 0. 5 • rel LAI if ψ x > -15, 000 ψ x | collar = -15, 000 else , Q x | tips = 0, (1) Registration: Any interested researcher is welcome to contact the corresponding author of this paper, Andrea Schnepf, with the following information: Name, affiliation, name or reference to the participating simulator. Upon signing a letter of agreement confirming that results of other participants will not be published without consent, researchers will be accepted as participants and enabled to include their individual simulation results to the github repository of this benchmark initiative, https://github. com/ RSA-benchmarks/collaborative-comparison. (2) Simulation: Each participant implements all or a selected number of benchmark problems in their respective simulator and makes the results in the prescribed formats available to the github repository through pull requests.
Requested formats include the Root System Markup Language, RSML  (Lobet et al. , 2015)  for root architectures and the Visualization Toolkit, VTK  (Schroeder et al. , 2006)  for 3D and 1D simulation outputs. Python scripts to read and write RSML files will be provided on the github repository. Packages to read and write VTK files are for example available at https://pypi. org/project/vtk/. (3) Analysis and publication: The analysis of results and computation of relevant metrics, such as root mean square error, coefficient of determination or Nash-Sutcliffe efficiency, will be done by the code implemented in the Jupyter Notebooks for each benchmark problem. The final goal is to jointly publish the results.
The particular challenge to benchmark RSA models is to include the stochastic nature of these models. We propose to perform the benchmarking of those models in four steps: (1) Parameterizing the root architecture models based on the provided experimental data, (2) Simulating a set of root systems for a dicotyledonous (Lupinus albus) and a monocotyledonous (Zea mays) plant species following two benchmark scenarios (M1. 1 and M1. 2), (3) Export and store the simulated root systems as Root System Markup Language (RSML) files  (Lobet et al. , 2015) , and (4) Compare the simulation results using the data analysis pipelines available in the associated Jupyter Notebooks. The analysis pipelines are explained below and illustrated in Figure  1. In particular, we include persistent homology as an approach that augments purely trait-based comparisons, i.
This is the reason why, in this benchmark, we do not prescribe a parameter set as in e. g. , M2 or M3, but we let each participating model derive its respective model parameters based on a reference dataset. In this first benchmark (M1. 1), modelers simulate root systems for the same duration as the age of the root systems in the reference dataset. 2. 2. 1. 1. 1. Reference data set. Although the parameterization of 3D models using a set of parameters derived from 2D images has some limitations, it has been shown to be a simple and efficient strategy allowing the simulation of realistic 3D root systems  (Landl et al. , 2018). Our reference dataset contains two distinct sets of images: (1) images of lupin roots grown for 11 days in an aeroponic setup  (Lobet et al. , 2011) , and (2) images of maize roots grown for 8 days on filter papers  (Hund et al.
, 2009). All images were analyzed using the semi-automated root image analysis software SmartRoot  (Lobet et al. , 2011)  and root tracings were saved as RSML files for further analysis (Figure  1 ). These RSML files were then processed using functions of the R package archiDART developed to compute root system-and single root-level metrics  (Delory et al. , 2016 (Delory et al. , , 2018)). These metrics have been made open-access (https://github. com/RSA-benchmarks/ collaborative-comparison/tree/master/root_architecture/data) and should help modelers to parameterize their respective RSA model. Schnepf et al. (2018a). This motivated the creation of two data analysis pipelines for the first benchmark (M1. 1) that will be used to compare simulation outputs with reference experimental data (reference root systems) (Figure  2A ).
These two data analysis pipelines are implemented in the Jupyter Notebook RSA calibration. ipynb that can be found on the github repository that contains code that will automatically include every model output in the analysis that is available in the prescribed folder. The analysis relies on the functions available in the R package archiDART  (Delory et al. , 2016 (Delory et al. , , 2018)). In the first pipeline, traits computed at the root system level (e. g. , total root system length, number of roots per branching order) are compared between all simulated and reference root systems. This comparison takes place in three steps: (1) identifying the key morphological, architectural, and topological  (Fitter indices, Fitter, 1987; Fitter and Stickland, 1991 ) traits explaining differences between simulated and reference root systems using multivariate data analysis techniques (e.
2. 2. 1. 2. 1. Required output. The following results are to be uploaded via pull requests to this path on the github repository: M1 Root architecture development/M1. 2 RSA simulation/M1. 2 Numerical results. θres is the residual water content, θsat is the saturated water content, α and n are the van Genuchten parameters, Ksat is the saturated soil hydraulic conductivity and λ is the van Genuchten-Mualem parameter. 2. 2. 1. 2. 2. Analysis pipeline for M1. 2. For the second benchmark (M1. 2), three data analysis pipelines are used to compare simulation outputs given by different root architecture models. For this benchmark, the reference experimental data cannot be used as a reference as data of 60 days old plants is not available. The first two data analysis pipelines for M1. 2 are very similar to the ones described earlier for the M1.
C1. 2: Water Uptake by a Root System From Drying Soil This benchmark scenario considers water uptake by a static 8days-old lupine root system given in the public data set  (Koch, 2019)  as RSML or DGF. The root is the same as the one in benchmark M3. 2, only younger, in order to reduce the computational cost for the reference scenario. The root system has been segmented from MRI measurements. The lupine is embedded in a soil box of l × w × d = 8 × 8 × 15 cm filled with loam (soil hydraulic properties given in Table  3 ). The benchmark is to evaluate the accuracy of root water uptake models under conditions of drying soil. To this end, the soil has an initial water content of θ top = 0. 129, corresponding to a pressure head ψ s,top = -659. 8 cm at the soil surface (z = 0). The pressure head in the rest of the domain initially follows a hydrostatic distribution ψ s,i = ψ s,top -z, (16) where z (in cm) denotes the vertical position (upward-pointing axis, zero at soil surface).
Automated Comparison Within All Benchmark Problems Each benchmark folder on the github repository contains a Jupyter Notebook named "Automated comparison. " It provides the analytical solution of the respective benchmark and in addition includes Python code that automatically loads all the outputs of participating models that are provided in the "Numerical results" folder of that benchmark. As soon as new outputs are provided, they are automatically included in the analysis. Currently, different model outputs are already available. We envision more participating models' outputs to be provided in this way. Future analysis will include graphical and quantitative approaches.
CONCLUSIONS Functional-structural root architecture models have been compared qualitatively (e. g. ,  Dunbabin et al. , 2013) , but until now no quantitative benchmarking existed. In other communities, benchmarking has been done or is ongoing, e. g. , AgMIP  (Porter et al. , 2014)  for crop models, CMIP  (Eyring et al. , 2016)  for climate models, subsurface reactive transport models  (Steefel et al. , 2015). With this paper, we propose a framework for collaborative benchmarking of functional-structural root architecture models that allows quantitative comparison of the outputs of different simulators with reference solutions and with each other. This framework is presented using Jupyter Notebooks. Behind every "module" benchmark, there is a working code that explains and implements the reference solution or analysis of reference data.
For both, "module" and "coupled" benchmarks, Jupyter Notebooks facilitate the automated comparison of simulator simulation outputs that are stored in specified folders of a public github repository. In this way, new numerical simulators that may be developed in the future may still be added to the automated comparison. All the analysis that is done in the Jupyter Notebooks is freely available so that the comparisons and analysis of uploaded model outputs will be transparent and repeatable. Future efforts will aim at extending the benchmarks from water flow in root and soil systems to further processes, such as solute transport, rhizodeposition, etc. We expect that this benchmarking will result in a better understanding of the different models and contribute toward improved models, with which we can simulate various scenarios with greater confidence.
ACKNOWLEDGMENTS AS acknowledges funding by the  German Research Foundation  (grant number  SCHN 1361/3-1 ). VC was supported by the  Belgian Fonds National de la Recherche Scientifique (FNRS , grant  FC84104 ). VS acknowledges funding by the  German Research Foundation  (grant number  SCHM 997/33-1 ). This research was institutionally funded by the  Helmholtz Association (POF III Program-Research Fields Key Technologies for the Bioeconomy and Terrestrial Environment ). CB acknowledges funding by the  Advanced Research Projects Agency - Energy (ARPA-E) ,  U. S. Department of Energy , under Award Number  DE-AR0000821. This manuscript has been released as a pre-print at bioRxiv (Schnepf et al. , 2019).
All paths are relative to the github repository https://github. com/RSA-benchmarks/collaborative-comparison. git. For other abbreviations and units see Table1. Example of root images used for the benchmarking dataset. (A) Shows an image of lupin root systems, 11 days old, growing in an aeroponic setup. (B) Frontiers in Plant Science | www. frontiersin. org FIGURE 1 | no-flux at all soil faces No reference solution, comparison amongst models Loam (Table 3), k x =0. 0432, k r =1. 73×10 -4 , RSA parameters from M1. 1, rel LAI scales the potential transpiration a.
The RXLR motif is often associated with a downstream EER motif, both of which have been associated with secretion and/or translocation of effectors into the plant cell  [14, 15]. For some RXLR effectors, such as Avr3a, the RXLR motif has been shown to be cleaved just prior to the EER sequence and therefore plays a role in targeting the protein for translocation rather than directly being involved in uptake into the host cell  [16]. The RXLR motif is similar in sequence to the PEXEL motif (RXLX[EDQ]) of the distantly related malaria pathogen (Plasmodium falciparum)  [17]  and the TEXEL motif of Toxoplasma gondii (RRLXX)  [18] , both of which are required for proteolytic modification in the endoplasmic reticulum and targeting effector proteins for specialized export out of the cell  [19]. The sequences of ORFs from the oomycete genomes used for effector prediction, along with scripts used for their analysis, can be found on Github at https:// github.
A region in the C-terminus of BSW04p is responsible for cell death on L. sativa cv. Ninja To determine the region of the BSW04p sequence responsible for cell death on Ninja, we generated four domain-swapped chimeric proteins between BSW04m (which does not cause cell death on Ninja, despite being expressed (S6  Candidate effectors were expressed in lettuce leaves using Agroinfiltration and leaves were scored for their reaction four to seven days post-infiltration. Qualitative scores were converted into numeric scores for data analysis: 0 = no reaction (green), 1 = mild chlorosis (light green), 2 = chlorosis (yellow), 3 = mild necrosis or mixture of chlorosis and necrosis (pink), and 4 = full necrosis (magenta). The figure shows average scores for each effector on each genotype; only accessions and effectors that had a necrotic or chlorotic response are shown.
virosa and other Lactuca spp. The lettuce lines ViAE and ViCQ contain introgressed disease resistance from L. virosa, which was achieved using embryo rescue followed by extensive backcrossing to L. sativa  [49]. We have shown that ViAE and ViCQ recognize five WY domain containing effectors from B. lactucae and are therefore expected to be highly resistant to B. lactucae in the field; this is consistent with resistance of ViAE to all 162 isolates tested from California (https://bremia. ucdavis. edu/bremia_database. php). For ViAE, we show that these effector recognitions are genetically linked to each other, and importantly, completely genetically cosegregate with resistance to B. lactucae. Experiments are underway to determine the molecular basis of this resistance. The non-RXLR, WY containing effector BSW04p is a candidate for Avr11 due to recognition by multiple lettuce lines containing Dm11.
Sequence comparison To generate a neighbor-joining tree of the WY effector candidates, whole protein amino acid alignments were performed using MUSCLE 3. 8. 425 [69]  implemented in Geneious 11. 0. 5 (http://www. geneious. com) with default settings. A UPGMA tree was built in Geneious with bootstrap resampling (100 replicates). The resulting tree was annotated using the Interactive Tree of Life  [70]  and stylistically refined in the GNU Image Manipulation Program (http:// www. gimp. org). To build a sequence logo for the WY domain from B. lactucae, a multiple sequence alignment of WY domains was built using MUSCLE 3. 8. 425 [69] , manually corrected for alignment errors, and the sequence logo generated using Weblogo (http://weblogo. berkeley. edu/logo. cgi).
Prediction and characterization of WY domain effectors in the oomycete pathogen Bremia lactucae PLOS Pathogens | https://doi. org/10. 1371/journal. ppat. 1009012 October 26, 2020 2 / 29 reads of lettuce cv. Cobham Green infected with B. lactucae isolate SF5 is available at NCBI SRA BioProject PRJNA523226. Sequences of effector proteins are available in File S1. All data for interactions between effectors and individual lines of Lactuca spp. are available at http://bremia. ucdavis. edu/BIL/BIL_interaction. php.
Acknowledgments We thank  Keri Cavanaugh ,  Alyssa Schweickert ,  Catherine Lopez ,  Amber Robbins ,  Natalie Hamada ,  Jesus Cardenas ,  Pauline Sanders , and  Sebastian Reyes Chin Wo  (all  UC Davis ) for wet lab and computational assistance. We thank  Anne Giesbers  for the plasmid pl2B-KAN-LjUB1-GW-tHSP. We thank  Gitta Coaker  ( UC Davis ) for assistance with the ROS assay. We thank  Ales Lebeda  ( Palacky ´University, Olomouc, Czech Republic ) and  Alex Beharav  (The  Hebrew University, Tel Aviv, Israel ) for lines of L. saligna. We thank  Lien Bertier ,  Anne Giesbers , and  Elizabeth Georgian  for helpful comments on earlier versions of this manuscript.
Data Analysis Using the Data Viewer software (SR Research), four regions of interest (ROI) were defined surrounding each of the four written words on the display. Participants' fixations durations and locations on the display were summarized using the same software. Thus, fixations to the critical printed word (colour competitors and target) could be individualized, as well as those fixations to the distractor printed words. We analysed the first 1000 ms after the onset of the critical word for each sentence on each condition. For each time step of one ms in this time window, a value of 1 was given to the ROI that the participant was looking at, while all other ROIs received a 0. If no ROI was fixated in a given time window, a 0 was assigned to all four ROIs. This procedure was conducted per participant per trial.
The procedure for the experiment and the data analysis approach in Experiment 2 were identical to Experiment 1, except for the number of factors in the GCA analysis. Experiment 2 included (in addition to the object and trial type) the colour manipulation (coloured vs. greyscale) as a fixed effect as well as the corresponding interactions with the other predictors. All other aspects of the analysis were kept the same. Data files and script are available at https://osf. io/853nk/.
Materials and design, procedure and data analysis Language and visual materials were kept the same as in Experiment 2, as well as the experimental design, thus, colour and greyscale trials were intertwined within experimental lists. The procedure was also identical, except for the stimuli presentation timing. Unlike the previous experiments, where the visual display and the spoken sentences were presented concurrently, in Experiment 3 the visual context was presented for 3000 ms and then disappeared with the onset of corresponding sentences. Consequently, the eye record reflects looks to an empty white screen were critical objects used to be. The data analysis approach is the same as that in the previous experiments. Data files and script are available at https://osf. io/853nk/.
Acknowledgements We thank  Christoph Scheepers  and an anonymous reviewer for their constructive comments on a previous of this paper. Funding for these studies was provided by the  Max Planck Society (FH). Additional support was provided by  ANID/PIA/Basal Funds for Centers of Excellence   FB0003  to EG and AH. EG and AH are currently funded by Fondecyt individual grants  N° 11171074  and  N°11180334 , respectively, also by  ANID.
Resource use efficiencies The definition of resource use efficiencies is explained in Sect. 4. 2. Observation-based light use efficiency (LUE) was calculated using MTE GPP, downward shortwave radiation from CRUJRA, and the fraction of absorbed photosynthetically active radiation (fAPAR) from the Global SeaWiFS Level-3 data  (Gobron et al. , 2006a, b). Uncertainty was derived from 25 ensemble members of MTE GPP. Observationbased water use efficiency (WUE) was calculated as the ratio between MTE GPP and MTE ET (Table  1 ); its uncertainties were calculated using a Monte Carlo resampling procedure in which 25 different members of GPP and ET were randomly selected. Observation-based carbon use efficiency (CUE) was calculated from the ratio of MODIS NPP to MODIS GPP. It should be noted that MODIS NPP is based on a calibrated version of the BIOME-BGC model  (Turner et al.
This paper is organised as follows. The multiwavelength observations of MS 0451 upon which we base our analysis are summarised in Section 2. Our methods for measuring gravitational lensing and reconstructing the distribution of mass are described in Section 3, while our X-ray data analysis is presented in Section 4. Our measurements of the main cluster halo and surrounding large-scale structures are the subject of Section 5. We discuss the cluster's dynamical state in Section 6, before presenting our conclusions in Section 7. Throughout this paper, we adopt a ΛCDM cosmology with Ω m = 0. 27, Ω Λ = 0. 73, and H 0 = 70 km s -1 Mpc -1 ; 1 corresponds to 6. 49 kpc at the redshift of the cluster. All magnitudes are quoted in the AB system.
Imaging Data Multicolour imaging in the B,V,R c ,I c and z passbands was obtained with the 8. 3 m Subaru telescope's wide-field Suprime-Cam camera for 1440 s, 2160 s, 3240 s, 1800 s, and 1620 s, respectively. Observations were performed on  December 21, 2006 (z ) , December 11, 2001 (R c ,I c ), and January 23, 2009 (B,V). Near-UV imaging in the u * passband was obtained with the 3. 6 m CFHT's MegaPrime camera for 6162 s on November 27, 2006 (ID: 06BH34, PI: H. Ebeling). Near-infrared observations in the J and K S passbands were performed with CFHT's Wide-field InfraRed Camera (WIRCam) on  November 8, 2008 and October 25, 2007 , respectively (ID: 08BH63, 07BH98, PI: C. -J. Ma). All observations were dithered to facilitate the removal of cosmic rays and minimise the impact of pixel defects and chip gaps; all data were reduced using standard procedures  (Donovan 2007).
We use these data to measure photometric redshifts and thereby identify galaxies within, in front of, or behind the cluster. In order to allow a robust estimate of the spectral energy distribution (SED) to be obtained for all objects within the field of view, data from different passbands were seeing-matched using the technique described in  Kartaltepe et al. (2008). The object catalogue was then created with the SExtractor photometry package  (Bertin & Arnouts 1996)  in 'dual-image mode', with the R C -band image as the reference detection image. Photometric redshifts for galaxies with magnitude R C < 24 were subsequently computed using the adaptive SED-fitting code Le Phare  (Arnouts et al. 1999; Ilbert et al. 2009). For more details of this procedure, see  Ma et al. (2008).