dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study was sponsored by  Allergan plc, Dublin, Ireland. Writing and editorial assistance was provided to the authors by  Helena Cant , MChemPhys, of  Complete HealthVizion, Inc. , Chicago, IL,  USA  and funded by  Allergan plc, Dublin, Ireland. All authors met the International Committee of Medical Journal Editors (ICMJE) authorship criteria. Neither honoraria nor payments were made for authorship. The sponsor and authors would like to acknowledge the contributions of  Dr. Jean Paul Nicandro , formerly of Allergan plc, to the design of the AURORA study and to the development of this manuscript. They would also like to express their gratitude to  Dr. Eric Lefebvre , formerly of Allergan plc, and acknowledge his contributions to the clinical development of cenicriviroc and to the design of the AURORA study.
Acknowledgments: We thank  Alastair McEwen  for his critical reading of the manuscript. TDM received an allocation from the  Fondation pour la Recherche Médicale  (FRM; https://www. frm. org/). This work was supported by grants from the  Ligue Contre le Cancer (Conférence de Coordination Interrégionale du Grand Est ; https://www. ligue-cancer. net) and the  Institut National Du Cancer  ( INCA_9269 ; www. ecancer. fr). The authors also acknowledge funds from the  Institut National de Santé et de Recherche Médicale  (http://www. inserm. fr/), the  Centre National de la Recherche Scientifique  (http://www. cnrs. fr/), the  Université de Strasbourg  (http://www. unistra. fr),and 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   ANR-10-IDEX-0002-02.
Furthermore,  Sutherland et al. (2012)  documented a 0. 53 MPa fluid pressure difference across the principal slip zone (PSZ) of the fault, which suggests that the fault core has significantly lower permeability than the surrounding cataclasite units. It is therefore interpreted as acting as a fault seal that limits fluid circulation within its hanging wall  (Sutherland et al. , 2012). Permeability variations like this are closely associated with the porosity evolution of fault cores and thus are likely to affect the fault strength and seismic properties  (Sibson, 1990; Renard et al. , 2000; Gratier and Gueydan, 2007). In this study, we investigate the porosity distribution in rocks from the Alpine Fault core and consider the potential effects of this porosity on fault strength. We have measured open pore spaces in these rocks from X-ray computed tomography (XCT) datasets and examined pore morphology by implementing quantitative shape analyses.
X-ray computed tomography (XCT) We imaged the samples using X-ray absorption tomography, where the signal intensity depends on how electron density and bulk density attenuate a monochromatic X-ray along its path through the material (e. g. ,  Fusseis et al. , 2014). We acquired the X-ray microtomography data for this study at the 2-BM beamline of the Advanced Photon Source, Argonne National Laboratories, USA, in December 2012. The noncylindrical samples of ∼ 7 mm height and ∼ 4 mm diameter were mostly drilled parallel to the foliation, mounted on a rotary stage, and imaged with a beam energy of 22. 5 keV. A charge-couple device camera collected images at 0. 25 • rotation steps over 180 •. A sample-detector distance of 70 mm yielded a field of view of 2. 81 mm. The voxel size (i. e. , spatial sampling) was 1.
Analyses of XCT datasets Data analyses and image processing were performed using the commercial software Avizo 9. 1™ (Fig. 2 ). Initially, the datasets were rescaled to 8 bit grayscale volumes for enhanced computer performance. In addition, small volumes of interest were cropped from the whole volume before a nonlocal means filter was applied to reduce noise  (Buades et al. , 2005). For each voxel, this filter compares the value of this voxel with all neighboring voxels in a given search window. A similarity between the neighbors determines a correction applied to each voxel (e. g. ,  Thomson et al. , 2018). On the filtered grayscale images, pores were identified as disconnected materials of the darkest grayscale range (Figs. 2a and S1 in the Supplement). The corresponding grayscale values were thresholded, and the datasets were converted into binary form.
This step is called segmentation. Several segmentation techniques exist, from thresholding at a given grayscale value (e. g. ,  Ianossov et al. , 2009; Andrew et al. , 2013)  to deep-learning algorithms  (Ma et al. , 2020). It is up to the user to choose the segmentation technique that is most appropriate to analyze a given dataset. To our knowledge, no single segmentation technique can be generalized and universally used independently of the nature of the samples. In the present study, we have chosen a simple segmentation technique by applying a threshold to the grayscale images to separate the void space from the solid. This technique has been used in many studies in the last 2 decades to characterize porosity in rocks, including some very recent studies in rock physics  (Macente et al. , 2019; Renard et al.
, 2019). The segmented porosity volume depends strongly on the choice of the threshold and some studies have demonstrated that the final porosity estimated by different segmentation methods can vary by 20 %  (Andrä et al. , 2013). However, when the level of noise in the data is low, the differences in porosities estimated by different segmentation techniques are negligible  (Andrew, 2018). Our data were acquired at a synchrotron where the parallel beam and high photon flux ensured a low level of the noise in the images. In addition, application of a non-local-means filter applied to our data reduced the noise level. For these reasons, we consider that it was robust to apply a simple thresholding technique to this dataset but acknowledge that the porosity values we estimate could differ by < 20 % from the "true" porosity of the rock (see  Andrä et al.
, 2013; Hapca et al. , 2013). However, our segmentation procedure also captured cracks within a sample, which are likely to result from depressurization during core recovery (Figs. 2b and S1 in the Supplement). To omit the cracks, we utilized the morphological operation "connected components" available in the software Avizo 9. 1, which allows volumes larger than the selected number of connected voxels to be excluded from the binary label images. To each sample we applied upper limits of 20 (43. 94 µm 3 ), 50 (109. 85 µm 3 ), 100 (219. 7 µm 3 ), and 200 (439. 4 µm 3 ) face-connected voxels. Total porosity estimates based on these operations are presented as percentages of the sample volume in Table  S1  in the Supplement. Unfortunately, this methodology results in either loss of larger pores or inclusion of small cracks depending on the implemented limit of connected components, and thus the calculated porosities include significant bias.
Therefore, the operation "connected components" was used only for visualization purposes, and clusters of 200 face-connected voxels were created to show the 3D volumes of segmented pore spaces (Fig. 2c ) Instead, the volumes and shape characteristics of segmented materials (including cracks, i. e. , without any data limitation) were exported from the Avizo software in numerical format, and volume distributions within a sample were plotted on a logarithmic scale (Fig. 3 ). Data up to a specific volume size were fit to a polynomial curve, and then the curve was extrapolated to the x-axis intercept, which is the expected maximum pore size (Fig. 3 ). For each sample the total porosity was then estimated by integrating the curve, which excludes all volumes on the right side of the curve. To- tal porosities are presented as a percentage of the whole sample volume (Fig.
3 ). The implemented equations are given in the Supplement. Pore shapes were analyzed on bivariate histograms plotted by using the numerical pore characteristics, previously extracted from the Avizo software. Only pore volumes between 21. 97 µm 3 (10 voxels) and 878. 8 µm 3 (400 voxels) were included to avoid bias in the data due to an insufficient voxel count and the presence of cracks, respectively. Individual pores in our dataset are separated (Fig. 2c ). The covariance matrix of each pore was calculated, and the three eigenvalues of this covariance matrix were extracted. These three values correspond to the three main orthogonal directions in each pore (i. e. , the longest, medium, and shortest axes), and we use them as proxies to describe pore geometry. Thus, their amplitudes provide information on the spatial extension of a given pore and its shape.
Characteristics of porosity within the Alpine Fault core Porosity analyses of samples from or in close proximity to the two PSZs encountered in the DFDP-1B drill core reveal total pore volumes between 0. 1 % and 0. 24 % (Fig. 3 ). These values are significantly lower than the porosity estimates from other active faults in the world, such as 0. 2 to 5. 7 % total porosity in the core of the Nojima Fault, Japan  (Surma et al. , 2003)  and 0 % to 18 % in the San Andreas Fault core  (Blackburn et al. , 2009). The Alpine Fault core contains total pore space volumes comparable only with the lower porosities in these previous studies. It should be noted that the smallest pore spaces captured in the XCT datasets are 1. 3 µm in size due to acquisition constraints, whereas nanoscale porosity was identified on the TEM images.
Therefore, the estimated total porosities from XCT data represent only minimum values of the open pore spaces in the Alpine Fault core. TEM images presented here mainly focus on nanoscale materials (Fig. 9a, c, d ) but were also used to describe the distribution of micro-porosity in these rocks (Fig. 9b ). The pores visible on grain and phase boundaries in Fig. 9b  have similar sizes to the pores segmented on XCT images (> 1. 3 µm in diameter); thus we conclude that this is the typical habit of both nano-and micro-pores within the Alpine Fault core (Fig. 9 ). In addition, both quantitative micro-porosity shape analyses (Figs. 4, 5,  and 6 ) and nano-pores identified on TEM images (Fig. 9 ) reveal that a significant population of pores is predominantly non-spherical with elongated, flat shapes.
Conclusions Analyses of XCT datasets and TEM images of borehole samples from the core of the Alpine Fault reveal micro-and nanoscale pores, distributed along grain boundaries of the constituent mineral phases, especially clay minerals. The tendency of these pores to ornament clays defines their predominantly non-spherical, elongated, flat shapes and the bipolar distribution of pore orientations. The documented extremely low total porosities (in the range 0. 1 %-0. 24 %) in these rocks suggest effective porosity reduction and fault healing. Microstructural observations presented here and documented in previous studies indicate that pressure solution processes were the dominant healing mechanism and that fluids were present in these rocks. Therefore, fluid-filled pores may be places where elevated pore fluid pressures develop, due to further mineral precipitation that decreases the already critically low total porosities.
Alternatively, these pores may also facilitate the deposition of weak mineral phases (such as clay minerals and graphite) that may very effectively weaken the fault. We conclude that the current state of the fault core porosity is possibly a controlling factor in the mechanical behavior of the Alpine Fault and will likely play a key role in the initiation of the next fault rupture. Data availability. Avizo screenshots, total porosity estimates, Matlab script, and numerical data of pore volumes can be found in the Supplement. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-11-2425-2020-supplement. Author contributions. MK reconstructed, processed, and analyzed the XCT datasets presented here, interpreted the TEM data, and prepared the paper.
Acknowledgements. This research used resources of the  Advanced Photon Source , a  US Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. We thank  Sherry Mayo  for helping with the reconstruction process of XCT data and  Andrew Squelch  for providing use of the Avizo workstation, located at  CSIRO , Perth, Australia during the initial data analyses. Special thanks to  Reed Debaets  for assistance with the development of Matlab code. Klaus Gessner publishes with permission of the Executive Director,  Geological Survey of Western Australia. Financial support. This research has been supported by the  Advanced Photon Source, Chicago  (grant no. GUP 31177 ),  Nvidia Corporation ,  Royal Society of New Zealand ' s Rutherford Fellowship  (grant no.
16-UOO-001 ), the  Ministry of Business and Innovation's Endeavor Fund, New Zealand  (grant no. C05X1605/GNS-MBIE00056 ), the  Tectonics and Structure of Zealandia Program at GNS Science  (grant no. GNS-DCF00020 ), and publishing bursary funding provided by the  University of Otago. This open-access publication was funded by  Johannes Gutenberg University Mainz. Review statement. This paper was edited by  Florian Fusseis  and reviewed by  James Gilgannon  and  Michel Bestmann.
The number of hMuStem cells was estimated using an Alu-based technique for detecting genomic human DNA (hDNA)  43. Noninfarcted hearts retained 16,600-139,390 cells, depending on the rat, corresponding to 1%-5% of the total number of cells originally transplanted. By contrast, samples from the liver, lung, spleen, brain, kidney, and skeletal muscle were all negative for hDNA (data not shown). Analyses were performed to rule out the presence of electrical, structural, or contractile cardiac dysfunction associated with the persistence of hMuStem cells in injected hearts. ECG analysis revealed no electrical abnormalities (Figure  2D ; Tables  1  and S1 ). Similarly, the presence of hMuStem cells had no effect on heart rate, atrial (P-wave duration), atrio-ventricular (PR interval), or ventricular (QRS complex duration) conduction or ventricular repolarization (QT interval).
Fluorescent coimmunolabeling for human lamin A/C and the fast skeletal myosin heavy chain (MHC) isoform, the slow skeletal troponin T1 (TNNT1) isoform, or cardiac troponin I (TNNI) showed that a proportion of the transplanted hMuStem cells contributed to the formation of fast or slow skeletal muscle fibers, as well as cardiac fibers. As observed in HES-stained sections, these human nuclei-containing fibers colonized the connective tissue with occasional clusters of skeletal muscle fibers. By contrast, human TNNI + fibers appeared isolated in the fibrotic zone. Fibers positive for skeletal MHC and TNNT1 isoforms accounted for 18% and 19% of fibers, respectively, whereas 5% expressed the TNNI isoform. Even though the Cx43 protein is detected in cultured hMuStem cells, none of the human TNNI + fibers expressed it, indicating an absence of electrical cell-to-cell coupling with host CMs (data not shown).
Our data therefore demonstrate the absence of arrhythmia genesis following injection or implantation of hMuStem cells. Ultrasound studies were performed 1, 3, and 4 weeks postinfarction. As expected, MI induced an increase in LV structural remodeling characterized by an increase in LV chamber dimensions (LVEDD), leading to a decrease in systolic function, as evidenced by EF values (Figure  6B ; Tables 2 and S5). Diastolic diameter was increased to 108. 93% ± 3. 74%, 122. 13% ± 5. 81%, and 115. 20% ± 3. 70% of control values, respectively, 1, 3, and 4 weeks postinfarction in rats from the mock group as compared with 102. 23% ± 2. 39%, 110. 29% ± 4. 59%, and 105. 64% ± 4. 01% of control values in rats from the treated group (Figure  6C ; Videos S1, S2, S3, and S4). Although not significant, these differences suggest that the presence of hMuStem cells results in greater preservation of LVEDD as compared with the mock group.
Flow Cytometry Cultured hMuStem cell samples (n = 4; P5 corresponding to 10. 4-12. 7 population doublings) were resuspended in cold phosphate-buffered saline (PBS)/2% human serum, and 1. 0 Â 10 5 cells were incubated (30 min, 4 C in darkness) with fluorochrome-conjugated Abs against the appropriate cell-surface marker at a saturating concentration (Table  S6 ). Isotype-matched Abs and fluorescence minus control samples were used as negative controls for gating and analyses. Where applicable, 7-amino-actinomycin D (7-AAD; BD Biosciences, Franklin Lakes, NJ, USA) was added to evaluate cell viability. Samples were acquired using a FACSAria flow cytometer (BD Biosciences), and data were analyzed using FlowJo software (FlowJo, Ashland, OR, USA). For each labeling experiment, at least 15 Â 10 3 viable cells were considered.
Reverse Transcription PCR Total RNA was extracted from dry pellets of 10 6 cells using the RNeasy mini kit following the manufacturer's instructions (QIAGEN, Santa Clara, CA, USA). After DNase treatment (Ambion, Austin, TX, USA), RNA was quantified using a NanoDrop spectrophotometer (Labtech, Wilmington, DE, USA) and processed for reverse transcriptase, as previously described. 32 Quantitative real-time PCR reactions were performed with specific Taq-Man primers and TaqMan universal PCR Master Mix (Life Technologies, Carlsbad, CA, USA) on the CFX96 PCR System (Bio-Rad, Marnes-la-Coquette, France). Data were normalized using HPRT1 as an internal control, and differential expression was calculated using the DCt method. The oligonucleotide primers used are listed in Table  S7.
LV end-diastolic and endsystolic volumes (LVEDV and LVESV, respectively) were calculated from bi-dimensional short-axis parasternal views, taken through the infarcted area by means of the single-plane area-length method. These indexes were calculated in accordance with the standard, widely accepted formulas. 73 Transmitral flow measurements of ventricle filling velocity were obtained with pulsed Doppler, using an apical 4-chamber orientation. Doppler-derived mitral DT, isovolumic relaxation time, E wave, A wave, and the E/A ratio were determined to assess diastolic dysfunction. To avoid bias, experiments were performed and the data analyzed by investigators blind to treatment. Echocardiography measurements were performed 1 day before induction of MI (baseline) to rule out any initial functional differences, after which, the MI was induced.
Immunohistochemistry The frozen sections were first permeabilized (30 min, room temperature) using 0. 1% Triton X-100 (Sigma-Aldrich), incubated (30 min, room temperature) in blocking buffer (10% goat serum in 0. 1 M PBS; Sigma-Aldrich), and incubated overnight at 4 C with the following primary Abs: mouse monoclonal IgG2ak anti-PCNA (1:1,000, M0879; Dako, Glostrup, Denmark); Alexa Red 555-conjugated WGA (1:500, W32464; Invitrogen); mouse monoclonal IgG2b anti-lamin A/C (1:100, MA3-1000; Invitrogen); mouse monoclonal IgG1 anti-skeletal fast myosin (1:400, M4276; Sigma-Aldrich); rabbit polyclonal anti-TNNT1 (1:500, HPA058448; Ozyme SAS, Saint-Quentin-en-Yvelines, France); rabbit monoclonal IgG2b anti-cardiac TNNI (1:250, Ab52852; Abcam, Cambridge, UK); and rabbit polyclonal anti-vWF (1:1,000, Ab6994; Abcam).
Next, the appropriate Alexa Red 555 or Green 488 secondary Abs (1:300, A11008; A21127; A21434; Invitrogen, Carlsbad, CA, USA) were added for 1 h at room temperature, after which, the nuclei were counterstained with DAPI. For immunoperoxidase revelation, sections were incubated with biotinylated secondary Abs (1:300, E433; E432; Dako) in PBS with 5% rat serum for 1 h. Bound Abs were detected with streptavidin (P397; Dako) and 3,3 0 -diaminobenzidine (DAB) liquid substrate (Dako). Acquisitions were performed under a confocal laser microscope (LSM 780; Zeiss, Oberkochen, Germany) using ZenBlack software (Zeiss) or under a slide scanner (Zeiss Axio Scan Z1; Zeiss). To assess apoptosis in the infarcted heart, TUNEL staining was performed using the Kit ClickiT Plus TUNEL Assay (Abcam, Cambridge, UK), following the manufacturer's instructions.
Histomorphometry Morphometric analysis was performed using a digital camera (Nikon DXM 1200; Nikon Instruments, Badhoevedorp, the Netherlands) combined with image-analysis software (NIS; Nikon). The quantification and morphometric analyses of the immunolabeled sections were performed blindly at 20Â magnification. To evaluate anisocytosis, the MinFeret of the transversal CMs was determined manually. The CM perimeter was identified manually to prevent potential errors. Fiber size was documented by measuring the MinFeret of at least 100 fibers per sample in at least 2 randomly selected microscopic fields in HES-stained sections. The same operator acquired measurements 5 times in the same sample to test repeatability. Intra-assay variation coefficients were always lower than 10%. Fibrosis was evaluated by determining the ratio of collagen-rich areas (identified using specific Picrosirius staining) to the total muscle area in the overall cross-section.
PCR Detection of hDNA The Alu sequence remains the marker of choice when assessing the biodistribution of transplanted cells in xenogeneic models, owing to its genomic repetition and species specificity. The hAlu sequence can be amplified and quantified by qPCR from gDNA with a high degree of accuracy. gDNA was isolated from frozen samples using the NucleoSpin tissue kit (Macherey-Nagel, Hoerdt, France), following the manufacturer's instructions. PCR reactions were carried out on 0. 5 mg of input gDNA. hAlu-specific oligonucleotide primers were designed using Oligo Primer Analysis Software, v. 7 (Molecular Biology Insights, Colorado Springs, CO, USA) and synthesized by MWG Operon (Eurofins, Ebersberg, Germany): forward primer, 5 0 -CATGGTGAAACCCCGTCTCTA-3 0 ; reverse primer, 5 0 -GCCTCAGCCTCCCGAGTAG-3 0 ; probe, 5 0 -ATTAGCCGGG CGTGGTGGCG-3 0.
ACKNOWLEDGMENTS The authors thank  Cindy Schleder  and  Samuel Fréhel  ( INRA/Oniris UMR 0703, Nantes, France ) for their respective contributions to the qPCR and morphometric assays and  Blandine Lieubeau  ( IECM, INRA, Oniris, UBL, F-44307, Nantes, France ) for the cytometry experiments. We also thank the technical staff of the  UTE IRS1-UN Animal Facility (Nantes, France ) and the  Therassay Core Facility (Nantes, France)  for providing the ECG recording platform and the ultrasound scanner. We are grateful to  Nathalie Gaborit  ( l'Institut du Thorax, INSERM, CNRS, Nantes, France ) and  Jean-Thomas Vilquin  ( UMRS 974 Sorbonne Université, INSERM, AIM, Paris, France ) for helpful discussion that allowed us to improve the manuscript. We also thank  Chantal Thorin  ( NP3 Unit, Nutrition, Physiopathologie et Pharmacologie, Oniris, Nantes, France ) for assistance with the statistical analyses.
This work was supported by grant " AAP Pari Scientifique 2016 " from the  Région Pays de la Loire, France  and the  Bioregate Research/Education/Innovation Program  (project title MuSCO; reference number  34000650 );  LabCom SOURIRAT Project  ( ANR-14-LAB5-0008 ); '' TEFOR'' Investissements d'Avenir  ( AN-RIIINSB-0014 ); and  Labex IGO Project Investissements d'Avenir  ( ANR-11-LABX-0016-01 ). The study was also financed by  INRA, France  (K. R. 's team allocation) and the research unit's own funds ( 2016-2019 ) and by the annual INSERM, France allocation to l' Institut du Thorax  (F. C. 's team allocation). A. R. 's academic thesis was financed by the  Bioregate program.
The present article is an extended version of one presented at the conference INSCI 2019 [23]; with respect to that version, we added the report on the  Rome experience  and the part on online science cafés. Acknowledgments: The  Florence Science Shop "La bottega della Scienza e della Sostenibilità a Firenze"  was born thanks to the  EU project InSPIRES, "Ingenious Science shops to promote Participatory Innovation, Research and Equity in Science".