dataesr/scientific-paragraphs-categorization huggingface dataset:
01), and between Philips and Siemens (p-adjusted < 0. 01). Some outliers have notable impacts on the standard deviations: nottwil03, nottwil04, pavia05. These outliers are likely caused by poor image quality due to participant motion on the MT0 scan (see the full reports on the Github issue https://github. com/spine-generic/ data-multi-subject/issues/36). Interestingly, these participants did not produce such outliers on the MTR results (which is computed from the MT1 and MT0 scans), and the T1w scan looked visually normal. We therefore decided to keep these participants on the figure in order to highlight possible implications about the reliability of the MTsat measures as a myelin biomarker (see discussion). We also decided to keep the stanford site (removed for MTR computation), because the T1 recovery effect induced by the different TR compared to other sites is supposed to be taken into account by the additional GRE-T1w scan when computing the MTsat metric, as is indeed confirmed in the figure (average MTsat for this site falls within the 1σ-2σ interval).
42 +/-0. 04) mm 2 /s and 10. 31% for GE, (0. 48 +/-0. 06) mm 2 /s and 12. 25% for Philips, and (0. 52 +/-0. 05) mm 2 /s and 8. 91% for Siemens. Differences in qMRI results between manufacturers. Before discussing differences across and within manufacturers, we would like to stress that results presented here will become further refined with time because, as for any neuroimaging analysis pipeline, the algorithms evolve. Moreover, visual QC and manual corrections are prone to human error. We therefore encourage users of this living database to provide feedback. As it is an open source project, contributions are welcome. Also, as future participants are added, the statistics will be updated. Siemens Philips GE 0. 9 x 0. 9 x 5 mm 3 Fig. 9  Results of the single subject study for the DWI protocol.
Users of our proposed protocols have the option of deriving the SC CSA  from T1w or T2w images. While the two contrasts did lead to different SC CSA values, these have been modeled for each manufacturer. This means that when users compare SC CSA values that were obtained from different contrasts, they can account for differences between them by either acquiring sufficient data themselves, using our protocol and modeling the relationship between T1w and T2w SC CSA, or by using our estimated regression coefficients linking T1w CSA with T2w CSA (Fig. 12 ). Gray matter CSA. In terms of the GM CSA, for the multi-subject dataset, GM CSAs showed a maximum inter-site COV of 5. 6% (3. 5% for the single subject dataset), which is highly encouraging, especially considering the small size of the GM, making CSA measures very sensitive to segmentation errors.
Usage Notes BIDS convention. We recommend that researchers planning to contribute to the spine generic database or creating other databases check the validity of the json sidecars associated with BIDS datasets. This will help assess how well protocols are followed by different centers. For json files to contain the relevant information, it is necessary that (i) DICOM fields include the relevant fields themselves, including the obvious (TR, TE, flip angle) as well as lesser known parameters that can have a strong impact on the computed metrics (water excitation, fat saturation, monopolar vs. bipolar readout, etc. ), and (ii) that these fields are populated in the json files. Checking these parameters as well as the files and folder names can be automatized via continuous integration (e. g. , GitHub Actions, as used in the present study).
Concluding remarks and future directions. To the best of our knowledge, this study features the first "large-scale" multi-center SC qMRI datasets ever acquired and made public. These datasets are shared according to the 'Findable, Accessible, Interoperable and Reusable' (FAIR) principles  34. The normative values from the multi-subject dataset could serve as age-matched healthy control references. More generally, these datasets will be useful for developing new image processing tools dedicated to the SC, and the fact that they are public and version-tracked with git-annex technology makes it possible for researchers to compare tools with the same data. Lastly, important efforts were deployed to make the data analysis methods fully transparent and the results reproducible. The analysis is fully automated -aside from minor manual corrections when necessary-, minimizing user bias and facilitating large multi-center studies.
We hope this analysis framework can serve as an example for future studies and we encourage researchers to use it. The SC MRI community has initiated a forum (https:// forum. spinalcordmri. org/) to encourage discussions about these open-access datasets, and to pitch new ideas for subsequent analyses and acquisitions. In a time where reproducibility of scientific results is a major concern  35  , we believe a consensus acquisition protocol along with publicly-shared datasets and a transparent analysis pipeline provide a solid foundation for the field of SC qMRI so that, in the future, inclusion of the SC in neuroimaging protocols will become a "no-brainer".
acknowledgements We thank  Gerald Moran  and  Bart Schraa  ( Siemens Healthcare ),  Suchandrima Banerjee  and  Naoyuki Takei  ( GE Healthcare ) for sharing proprietary information and helping with setting up manufacturer-specific protocols,  Nicholas Guenther  and  Alexandru Jora  for setting up the git-annex server and procedure for hosting the public database,  Charley Gros  for helping with the analysis script,  Paul Bautin  for helping with manual corrections,  Noémie Roberge  for helping with the interactive plots,  Carollyn Hurst ,  André Cyr ,  Arnaud Boré  and  Pierre Bellec  ( Functional Neuroimaging Unit), Charles Tremblay (Polytechnique Montreal ),  Antonys Melek  and  Habib Benali  ( PERFORM center, Concordia University ),  Ives Levesque  ( McGill University ),  Carol Lien  ( University of Minnesota ) for helping with data acquisitions,  Compute Ontario  (https://computeontario.
ca/) and  Compute Canada  (www. computecanada. ca) for providing the supercomputer infrastructure and all the volunteers who participated in the Spinal Cord MRI Public Database. This work was funded by the  Canada Research Chair in Quantitative Magnetic Resonance Imaging  [ 950-230815 ], the  Canadian Institute of Health Research [CIHR   FDN-143263 ], the  Canada Foundation for Innovation  [32454, 34824], the  Fonds de Recherche du Québec -Santé  [28826], the  Fonds de Recherche du Québec -Nature et Technologies  [ 2015-PR-182754 ], the  Natural Sciences and Engineering Research Council of Canada  [ 435897-2013 ], the  Canada First Research Excellence Fund (IVADO and TransMedTech) , the  Quebec BioImaging Network  [5886],  Spinal Research (UK), Wings for Life (Austria , # 169111 ) and  Craig H.
0137 ;  BMBF  ( 01EW1711A  & B) in the framework of  ERA-NET NEURON , the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 634541 , the  Engineering and Physical Sciences Research Council  ( R006032/1 ,  M020533/1 ) and  Rosetrees Trust (UK) ,  UK Multiple Sclerosis Society  ( 892/08 ,  77/2017 ),  NIHR Biomedical Research Centres, UCLH , the  Italian Ministry of Health   Young Researcher Grant 2013  ( GR-2013-02358177 ), the  FISR  Project " Tecnopolo di nanotecnologia e fotonica per la medicina di precisione "(funded by  MIUR/CNR ,  CUP B83B17000010001 ),  TECNOMED  project (funded by  Regione Puglia ,  CUP B84I18000540002 ),  Million Dollar Bike Ride  from the  University of Pennsylvania  ( MDBR-17-123-MPS ), investigator-initiated PREdICT study at the  Vall d'Hebron Institute of Oncology (Barcelona)  funded by  AstraZeneca  and  CRIS Cancer Foundation , the  Wellcome Trust (UK)  ( 203139/Z/16/Z ),  Systems, Technologies and Applications for Radiofrequency and Communications (STARaCOM)  and  Swiss National Science Foundation  ( PCEFP3_181362/1 ).
Code availability Data were processed using Python and shell scripts contained in the spine-generic package (https://github. com/ spine-generic/spine-generic/releases/tag/v2. 6), which is distributed under the MIT license. A comprehensive procedure is described in the "Analysis pipeline" section of the spine generic website (https://spine-generic. rtfd. io/). This procedure includes the list of dependent software packages to install, a step-by-step analysis procedure with a list of commands to run, a procedure for quality control and for manual correction of intermediate outputs (e. g. cord segmentation and vertebral labeling). The procedure includes embedded video tutorials and has been tested by external users. The analysis documentation also includes a section on how to generate the static figures that are shown in this article (in PNG format) as well as the interactive figures embedded in the spine-generic website.
Notable software used in this study include: the Spinal Cord Toolbox v5. 0. 1 (https:// spinalcordtoolbox. com) to analyse the MRI data, pandas  36  to perform statistics, plotly v4. 12. 0 (https://plotly. com) to display the interactive plots, brainsprite v0. 13. 3 (https://brainsprite. github. io/) for embedding in the online documentation an interactive visualization of example datasets, pybids  37  for checking the acquisition parameters on the BIDS datasets, FSLeyes v0. 34. 0 (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSLeyes) for manually-correcting the segmentations.
The main metric of interest is the uniaxial compressive strength (i. e. the maximum axial stress the sample sustained before macroscopic failure), but the mechanical data from these tests can also be used to provide values of, for example, static Young's modulus and Poisson's ratio. Axial force is monitored using a load cell and displacement (axial and radial) by linear variable differential transducer(s) (LVDT), strain gauge(s) glued onto the sample, optical/laser displacement transducer(s), and/or by a circumferential strain belt. A recent paper also found that near-infrared spectroscopy can be used as a non-contact technique for measuring strain  (Butcher et al. 2019). Force and displacement (corrected for the displacement of the loading chain) are then converted to axial stress and axial strain using the sample dimensions.
Factors that influence the uniaxial compressive strength of volcanic rocks Due to its first-order importance, uniaxial compressive strength datasets are often plotted as a function of sample porosity. Experimental studies have shown that the uniaxial compressive strength of volcanic rocks decreases as a function of increasing porosity (e. g. Al-Harthi et al. 1999; Pola et al. 2014; Heap et al. 2014b; Schaefer et al. 2015; Mordensky et al. 2018; Coats et al. 2018) , as observed for porous sedimentary rocks (e. g. Baud et al. 2014). Figure  5  shows uniaxial compressive strength as a function of porosity for a compilation of volcanic rocks. This compilation includes data for andesite, basalt, dacite, and pyroclastic rocks (e. g. tuffs and welded block-and-ash flow (BAF) deposits). The compilation shown in Fig.
6, strength varies from a couple of MPa to ~50 MPa (Fig. 5 ). In order to better understand the data, and the variability in the data, presented in Fig. 5 , we plot uniaxial compressive strength as a function of porosity for the four main rock types in our compilation: andesite, basalt, dacite, and pyroclastic rocks (Fig. 6 ). These plots show that, for all rock types, uniaxial compressive strength is reduced nonlinearly as a function of increasing porosity (Fig. 6 ). As for the combined dataset (Fig. 5 ), we observe that the range in strength is high at low-porosity and low at high-porosity for the two most represented rock types, andesite (Fig. 6a ) and basalt (Fig. 6b ). The trend for dacite, the dataset with the fewest data, is obscured by the paucity of data at low (< 0. 1) and high (> 0.
3) porosity (Fig. 6c ). The data for pyroclastic rocks show two main trends, one defined by the variably welded block-andash flow deposits from Mt. Meager (Canada)  (Heap et al. 2015a ) and one defined by the variably altered Ohakuri ignimbrites (New Zealand)  (Heap et al. 2020c ) (Fig. 6d ). It is clear from Fig. 6  that the scatter in the compiled dataset (Fig. 5 ) cannot be explained by differences in rock type. The reasons for this are twofold. Although porosity exerts a firstorder control on the uniaxial compressive strength of volcanic rocks (Figs. 5  and 6 ), (1) it does not consider differences in factors such as hydrothermal alteration, crystal content, and groundmass crystallinity and (2) porosity is a scalar and does not consider the nature of the void space: the proportion of pores and microcracks and their geometrical properties (e.
However, since the largest variation in values of K IC is likely related to rock type, it is perhaps sensible to restrict this type of analysis to a single rock type. For example, the compiled data for andesite can be bracketed between K IC = ffiffiffiffiffi πr p values of 3. 5 and 40 MPa (Fig. 12b ). Possible ranges for values of K IC for andesite, assuming a minimum and maximum macropore radius of 5 and 500 μm, are 0. 014 and 0. 159 MPa. m 0. 5 and 0. 139 and 1. 59 MPa. m 0. 5 , respectively. Alternatively, if we assume the relevant K IC is that of the mineral constituents (0. 7 and 0. 3 MPa. m 0. 5 for glass and feldspar, respectively), we can use the available data to provide a range of pore radii that can describe the dataset. If we assume the microcracks are propagating through glass (K IC = 0.
2006 ) and limestones (e. g. Vajdova et al. 2004). Hydrostatic experiments on volcanic rocks are very rare, and are biased towards porous pyroclastic rocks. We compile   4. The effective pressure required for P * is plotted as a function of porosity for volcanic rocks in Fig. 16a  (data in Table  4 ). These data show that the pressure required for P * decreases non-linearly as a function of increasing porosity, as observed for porous sedimentary rocks  (Wong and Baud 2012). For the data shown in Fig. 16a , the reduction in P * as porosity is increased from 0. 07 to 0. 35 is large, and reductions as porosity increases above 0. 35 are small. Although the same non-linear trend is observed for sedimentary rocks  (Wong and Baud 2012) , we note here that, for this dataset of volcanic rocks, this porosity also marks the transition from lavas to granular volcanic rocks (Fig.
23a  critical stress data for a range of different volcanic rocks with different porosities and microstructural attributes: trachyandesite from the Açores  (Loaiza et al. 2012) , two andesites from Volcán de Colima (porosity 0. 17 and 0. 24;  Heap et al. 2015b) , dacite from Mt St Helens (porosity 0. 32;  Heap et al. 2016b) , and tuffs from Whakaari volcano (porosity 0. 29;  Heap et al. 2015c)  and Alban Hills (porosity 0. 32;  Zhu et al. 2011). Because no data exist for porous rhyolite, we supplement these data with new data for rhyolite from Tarawera (porosity 0. 39; Taupō Volcanic Zone, New Zealand; data unique to this study; Table  6 ), data which also act as a high porosity end-member. The dataset is also supplemented by new data for trachyandesite from Volvic (porosity 0. 21; data unique to this review; Table  5 ).
Acknowledgements This review would not have been possible without the many discussions the first author has had with  Patrick Baud , as well as conversations with many other scientists over the years. Ben Kennedy  is thanked for providing the rhyolite from Tarawera. We also thank  Thierry Reuschlé ,  Manon Lincker , and  Bertrand Renaudié. Figures  were prepared using open source software  R Studio , ggplot, and  Inkscape. Marie Violay  acknowledges support from  European Research Council (ERC)  grant  BEFINE (757290 ). We thank two reviewers and the associate editor for comments that helped improve this manuscript. Author's contribution  M. J. Heap  wrote the first draft of the manuscript and prepared the figures. M. E. S. Violay compiled the frictional property data; all other data were compiled by  M.
DISCUSSION AND CONCUSIONS In this work, we used petrography, mineralogy, and whole-rock major and trace-element chemistry to classify the Tintigny meteorite. In addition, we have undertaken oxygen isotope analysis which has confirmed that Tintigny is a member of the HED clan. Based on our studies we conclude that Tintigny is a non-cumulate eucrite, specifically a polymict basaltic eucrite. Eucrites, together with howardites and diogenites, form the HED clan that constitutes 74. 6% by number of all achondrites, and 3. 6% by number of all meteorites in meteorite collections worldwide (Meteoritical Bulletin Database (https://www. lpi. usra. edu/meteor/metbull. php), accessed February 2021). Based on the spectral data obtained in the laboratory and their comparison with data measured by ground-based observatories and the results from the NASA Dawn mission, HEDs are thought to originate from differentiated asteroids with V-type spectra, and in particular 4-Vesta  (McSween et al.
According to their textural and compositional characteristics eucrites can be further divided into basaltic rocks (mostly monomict breccias), cumulate gabbros, and polymict eucrites made of different eucritic textures (without diogenitic clasts) like Tintigny  (Vollmer et al. , 2020). Barrat et al. (2003)  also divides them to cumulate and noncumulate eucrites. Howardites formed as the products of impact events on the parent body surface, occurring as breccias made from different percentages of eucritic and diogenitic materials. Meteoritical Bulletin Database (https://www. lpi. usra. edu/meteor/metbull; August 2021) lists 2447 HED meteorites encompassing 1516 eucrites, 399 howardite, and 532 diogenites. Out of this number, only 69 of the HED are falls, i. e. , meteorites that have been observed falling and have been collected soon after their impact, avoiding the detrimental effects of terrestrial weathering, which are common in meteorite finds (meteorites without any fall record) and even in some cases in falls if not collected immediately  (Walker et al.
Acknowledgments We all warmly thank the  Schmitz  family for their donation of the meteorite to the  RBINS. We thank  W. Debouge ,  S. Cauchies , and  J. de Jong  for their support in the clean labs and analysis facilities at the  Laboratoire G-Time. We appreciate constructive comments by the two anonymous reviewers and editorial handing by  Dr. A. P. Rossi. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  801505. VD thanks the  ERC StG "ISoSyC" and FRS-FNRS  for funding. SG, SD and VD thank the  Brain-be Belspo  projects BAMM and DESIRED for funding.
RNA Analysis RNA samples were taken at 0 and 6 hours post I-SceI induction in triplicate. RNA was extracted from 50 ml of culture at 1 x 10 6 cells / ml. Briefly, polyadenylated transcripts were enriched using poly-dT beads and reverse-transcribed before sequencing. RNA-seq was carried out on a BGISeq platform at The Beijing Genome Institute (BGI). Reads were mapped to a hybrid genome assembly consisting of the T. brucei 427 reference genome plus the bloodstream VSG-ESs  [10, 33, 43]. Bowtie 2-mapping was used in the default mode with the parameters-very-sensitive-no-discordant-phred33. Alignment files were manipulated with SAMtools  [44]. Per-gene read counts were derived using the Artemis genome browser  [45] ; MapQ, 0. Read counts were normalised using edgeR and differential expression was determined with classic edgeR.
qPCR and RT-qPCR analysis To determine the dynamics of cleavage we followed a published qPCR protocol  [25]. Briefly, 1 x 10 6 cells were collected at 3 hours intervals between 0-24 hours post I-SceI induction. Genomic DNA was extracted using the Qiagen Blood and Tissue kit and quantified using a Nanodrop. The DNA was diluted to 0. 2 ng. μl -1 and 1 ng was analysed by qPCR using Luna Universal qPCR MasterMix (NEB) with 500nM of primers. For each pair of primers (below), samples were run in triplicate (Hard-shell PCR Plates 96 well, thin wall; Bio-Rad), which were sealed with Microseal 'B' Seals (BioRad). All experiments were run on a CFX96 Touch Realtime Detection system with a C1000 Touch Thermal cycler (Bio-Rad), using the following PCR cycling conditions: 95˚C for 3 min, followed by 40 cycles of 95˚C for 15 sec and 60˚C for 1 min (fluorescence intensity data collected at the end of the last step).
Data was then analysed by relative quantification using the ΔΔCt method (CFX Maestro software-Bio-Rad). Primer pairs CACAACGAGGACTACACCATC and CGGCCTATTACCCTGTTATCC (ESAG1 sce , Pseudo sce , 70int sce ), or GTTGTGAGTGTGTGCTTACC and ATCTAGAGGATCTGGGA CCC (VSG up )  [25]. The expression levels of ESAG6/7 and VSG2 following induction of a break were determined using the following protocol. RNA was extracted using a Qiagen RNeasy Kit and the samples were treated with DNase 1 for 1 hour according to manufactures instructions and eluted in 30 μl of RNase free water. The samples were quantified using a Nanodrop (ThermoFisher). cDNA was prepared using SuperScript IV (ThermoFisher) following the supplier instructions from 1-2 μg RNA with a polyT primer. For each pair of primers (used at 500nM), triplicates of each sample were run per plate (Hard-shell PCR Plates 96 well, thin wall; Bio-Rad), which were sealed with Microseal 'B' Seals (BioRad).
All experiments were run on a CFX96 Touch Real-time Detection system with a C1000 Touch Thermal cycler (Bio-Rad), using the following PCR cycling conditions: 95˚C for 3 min, followed by 40 cycles of 95˚C for 15 sec and 60˚C for 1 min (fluorescence intensity data collected at the end of the last step). Data was then analysed by relative quantification using the ΔΔCt method (CFX Maestro software-Bio-Rad) and Cq determination regression was used. Primer pairs VSG2F (AGCTA GACGACCAACCGAAGG) and VSG2R (CGCTGGTGCCGCTCTCCTTTG)  [48]  or ESAG6/ 7 F (ACTGTGGATGAATTGGCGAA) ESAG6/7 R (ACTGCTACTGTGTTGGACCC). In all cases, product abundance was determined relative to an actin control locus, which was amplified with primer pairs actin F (GTACCACTGGCATTGTTCTCG) and actin R (CTTCATGAGATATTCCGTCAGGTC).
From our data we conclude that an I-SceI induced DSB that is flanked by 70-bp repeats and distal to the VSG gene is not toxic to bloodstream form trypanosomes. To assess the DNA damage response following a DSB in the BES we used the G2 cell cycle checkpoint and γH2A focal accumulation  [11, 41]. The number of cells in G2 and the number of nuclei with γH2A foci increased in all four cell lines between 0-12 hours following PLOS PATHOGENS induction (Fig 1D  and 1E ). In the ESAG1 sce , Pseudo sce , 70int sce and VSG up cells lines we saw an increase in cells in G2 between 6 to 12 hours following induction (Fig  1D ). For the γH2A focal accumulation, we saw an increase in the number of foci at 12 hours post DSB induction.
Our results from the Pseudo sce cell line broadly agree with previously published data  [13] , where a DSB in this position has been shown to exhibit a low switching frequency  [12]. The slight discrepancy between the two studies may be due to technical variations. In contrast all the VSG up clones were VSG2 negative as previously published (Fig  2B )  [13, 37]. We next assessed DNA rearrangements following repair in the cloned survivors. Using primers specific to genes in BES1 or the RFP:PAC cassette we used their presence or absence to infer the mechanisms of repair. In the ESAG1 sce cell line,  23   A weak band was present in clone 1 that is approximately the same size as the parental clones. This clone was puromycin sensitive and no cells grew out of the treated culture suggesting they had all been cut.