dataesr/scientific-paragraphs-categorization huggingface dataset:
7C , using the Michaelis Menten equation. Statistical differences between the kinetic curves were analyzed in relation to Dd2 using F-statistics and between kinetic parameters using the Holm-Sidak one way ANOVA test. * versus PfCRT Dd2 p = 0. 012, versus PfCRT S33D p = 0. 006 , according to Holm-Sidak one way ANOVA. Apparent K m (µM) Apparent V max (pmol oocyte -1 h -      A box plot analysis is overlaid over the individual data points (independent biological replicates), with the median, the mean (red line) and the 25% and 75% quartile ranges being shown. A size standard is indicated in kilodaltons. C, Kinetics of chloroquine uptake by oocytes expressing either PfCRT Dd2 (yellow circle), PfCRT S33A (orange triangle), PfCRT S33E (green triangle) or PfCRT S33D (blue square). Water-injected oocytes were investigated in parallel and the chloroquine uptake values were subtracted from those of PfCRT-expressing oocytes.
A box plot analysis is overlaid over the individual data points (independent biological replicates), with the median, the mean (red line) and the 25% and 75% quartile ranges being shown. A size standard is indicated in kilodaltons. C, Kinetics of chloroquine uptake by oocytes expressing either PfCRT Dd2 (yellow circle), PfCRT S33A (orange triangle), PfCRT S33E (green triangle) or PfCRT S33D (blue square). Water-injected oocytes were investigated in parallel and the chloroquine uptake values were subtracted from those of PfCRT-expressing oocytes. Chloroquine uptake is shown as the mean ± SEM of n=4-6 independent biological replicates, with N=10 oocytes per treatment and biological replicate. The Michaelis Menten equation was fitted to the data points and the kinetic parameters obtained were compiled in Table1.
A, CID mass spectrum of the triply charged tryptic phosphopeptide Y20-R34 (m/z 610. 64). After database searching, the peptide was identified as containing a single phosphorylation at residue Ser-33. B, CID mass spectrum of the triply charged tryptic phosphopeptide N405-R426 containing the residue Ser-411. The presence of fragments b10 and b11 confirmed that Ser-411 is phosphorylated. The phosphorylated residues are indicated by PH.
Acknowledgement The research leading to these results have received funding from the  joint French-German Research Initiative  sponsored by the  Agence Nationale de la Recherche (ANR)  and the  Deutsche Forschungsgemeinschaft (DFG)  ( EVOTRANSPORT / ANR-14-CE35-0012-01  and  LA 941/11-1 ) and the  PHC PROCOPE  ( 30995WE  and  ID 57051467 ). G. P. acknowledges the generous support by the  INSERM, CNRS , and the  Université Paris-Descartes. C. S. R thanks the  Proteomic French Infrastructure (ProFI  ;  ANR-10-INSB-08-03 ) for financial support. M. L. is a member of the research cluster of excellence CellNetworks. S. M. C. was a PhD student at the  HBIGS graduate school at Heidelberg University. We thank  Justine Schneider  for re-processing the MS/MS data and  Marina Müller  for excellent technical assistance.
MS/MS spectra of PfCRT Dd2 tryptic phosphopeptides. A, CID mass spectrum of the triply charged tryptic phosphopeptide Y20-R34 (m/z 610. 64). After database searching, the peptide was identified as containing a single phosphorylation at residue Ser-33. B, CID mass spectrum of the triply charged tryptic phosphopeptide N405-R426 containing the residue Ser-411. The presence of fragments b10 and b11 confirmed that Ser-411 is phosphorylated. The phosphorylated residues are indicated by PH. Figure 2.
Acquisition and Processing of Clinical MR Imaging and Histologic Data Patient Recruitment. We recruited patients with clinically suspected primary GBM undergoing preoperative stereotactic MR imaging for first-line surgical resection before any treatment, as per our institutional review board protocol at Barrow Neurological Institute. All patients provided written and informed consent before enrollment. The patient cohort presented here has also been described in previous studies. 3, 6 eoperative MR Imaging Acquisition Protocol. We acquired preoperative 3T MR imaging (Sigma HDx; GE Healthcare, Milwaukee, Wisconsin) within 1 day of stereotactic surgery, including T2-weighted, T1-weighted precontrast, and T1-weighted postcontrast (T1 ϩ C) sequences. 3, 6, 14 T1 ϩ C images were acquired after completion of dynamic susceptibility contrast perfusion MR imaging following a total Gd-DTPA dosage of 0.
3, 6 On average, we collected 5-6 tissue specimens from each tumor, selecting targets Ն1 cm apart from both T1 ϩ C enhancing regions and nonenhancing T2-weighted hyperintense regions (so called brain-around-tumor) in pseudorandom fashion from different poles of the enhancing lesion periphery while avoiding centrally necrotic regions on the basis of the clinical feasibility as per clinical protocol. Enhancing and nonenhancing regions were distinguished on the basis of visual assessment by the neurosurgeon at the time of surgical biopsy and/or resection, as per clinical protocol. The target volume of each biopsy sample was approximately 125 mg. The neurosurgeons recorded biopsy locations via screen capture to allow subsequent coregistration with multiparametric MR imaging datasets. The neurosurgeon visually validated stereotactic imaging locations with corresponding intracranial anatomic landmarks, such as vascular structures and ventricle margins, before recording specimen locations.
3, 6 This method served as a compromise between the resolution of TCD measurements and the precision of the neuropathologist's estimates. Image Signal Normalization, DSC and DTI Analysis, Image Coregistration, and ROI analysis. We normalized the signal for T1 ϩ C, T2-weighted, and EPIϩC image datasets using the Simple Insight Segmentation and Registration Toolkit (SimpleITK, Version 1. 0. 1; http://www. simpleitk. org/)  24 in Python (Version 3. 6. 2; https://www. python. org/). The CurvatureFlow ITK algorithm was applied to remove image noise,  25  and the N4ITK algorithm to correct for image-intensity nonuniformity bias that could be due to factors such as local magnetic field heterogeneity. 26 Following these corrections, the CSF of the lateral ventricles was used as a reference tissue to normalize the intensity distributions of each dataset using a previously described linear scaling process.
27 For DTI, we generated mean diffusivity and fractional anisotropy maps. 5 For DSC, we generated relative cerebral blood volume (rCBV) maps using IB Neuro (Imaging Biometrics, Elm Grove, Wisconsin), using leakage correction  28, 29  and white matter normalization. 3, 6, 14 We coregistered all images using ITK tools (https://itk. org/) and IB Suite (Imaging Biometrics), using the DTI B 0 anatomic volume as the coregistration target. 3, 6, 14 A board-certified neuroradiologist (L. S. H. ) placed ROIs (8 ϫ 8 voxels) for all coregistered multiparametric MR imaging datasets at the stereotactic biopsy locations. 3, 6, 14 We selected this ROI size (0. 93 ϫ 0. 93 cm) to encompass a broader area relative to the expected biopsy dimensions (125 mg). This selection helped to offset any potential minor shifts in biopsy localization (at the time of tissue sampling) that might otherwise result in misregistration.
5% BSA/2 mM EDTA. Afterwards cells were stained with the respective fluorochrome-conjugated mAbs (listed in Supplementary Table  5 ) for 30 min at room temperature. Following one wash step with 1 ml PBS/0. 5% BSA/2 mM EDTA, cells were resuspended in 200 µl buffer and 20 µl flow count fluorospheres (Beckman Coulter) were added. Samples were acquired on a Navios flow cytometer (Beckman Coulter). Resulting data were analyzed with Kaluza 1. 5a (Beckman Coulter). For cell-surface staining of PBMCs, cells were stained with fluorochromeconjugated mAbs (listed in Supplementary Table  5 ) in PBS/0. 5% BSA/2 mM EDTA at 4 °C for 30 min. For the identification of GrB and perforin expression, surfacelabeled cells were permeabilized with fixation/permeabilization solutions (BD Cytofix/Cytoperm™) followed by intracellular staining with fluorescence-labeled mAbs for the proteins (Supplementary Table  5 ).
To assess degranulation and intracellular cytokine production, cells were cultured for 4 h in the presence of 5 μg ml -1 allophycocyanin-labeled anti-CD107a antibody and Golgi Stop (BD). Cells were stimulated in vitro with phorbol myristate acetate (1 μg ml -1 , Sigma), ionomycin (1 μg ml -1 , Sigma), and Golgi Plug (BD) for 4 h, followed by intracellular staining for IFN-γ and TNF-α using BD Cytofix/Cytoperm. Mouse data were collected on a LSRII Fortessa flow cytometer (BD) and analyzed with the FlowJo software (Tree Star). (Quantitative) histopathology and immunofluorescent staining. Mice were perfused with PBS followed by 4% paraformaldehyde. Tissues were removed and embedded in paraffin. Immunohistochemical and immunofluorescence staining was performed on 3-5-µm-thick mouse or human serial sections using the primary antibodies indicated in Supplementary Table  5.
Scanning for Cy2 (488 nm), Cy3 (543 nm), and Cy5 (633 nm) was performed sequentially to rule out fluorescence bleed through. Quantification of immunohistochemistry data was performed by counting density of CD3 + cells in various regions of the brain. In each region, the number of T cells in an area of 2 mm 2 were counted by using a morphometric grid. Optical density of Ig leakage of total brain sections or individual cortical or hippocampal regions was analyzed using Image J. Quantification of immune cells of human immunohistochemistry data was performed by counting cells using a morphometric grid. Owing to the different sizes of the specimens, areas analyzed ranged from 5 to 48 mm 2 (on average 14 mm 2 ). Quantification of apoptosis of ECs was performed by analysis of nuclear condensation of CD34 + ECs.
TCR spectratyping. Spectratyping of the CDR3 sequences of TCR-Vβ-chains of CD4 + and CD8 + T cell subsets was performed by multiplex PCR using 24 Vβ forward primers and Cβ reverse primers "SpTy-b-out" and Cβ-R (provided by Metabion, Martinsried, Germany)  73, 74. Data analysis of the spectratypes was performed using the GeneMarker software (SoftGenetics, State College, PA, USA). Complexity score was calculated based on the number of peaks across all 24 spectratypes per individual. A lower complexity score is indicative of a more perturbed repertoire. Fragment length distribution classification was determined based on the number of observed peaks and the area under the curve for each peak in a spectratype. Gaussian distributed spectratypes were considered "normal"; "shifted" spectratypes displayed all peaks with shifted maxima, "skewed" spectratypes show reduced peak numbers and non-Gaussian distributions, and spectratypes with only 1-2 peaks that were twice the height of comparable peaks of a normally distributed spectratype were considered "oligoclonal.
" High-throughput TCR sequencing. TCRs of CD8 + and CD4 + T cells isolated from the blood of HD, SuS, and MS patients were profiled with high-throughput TCR sequencing. The ImmunoSEQ assay (Adaptive Biotechnologies), a multiplex PCR system, was used to amplify 54 expressed Vβ and 13 Jβ segments. The resulting amplicons span and sufficiently identify the VDJ region of each unique CDR3β  22. PCR bias was corrected using a baseline platform developed from a suite of synthetic templates to optimize primer concentrations and for computational corrections. The amplicons were deep sequenced using the Illumina HiSeq platform. The raw reads were annotated into V, D, and J gene definitions according to the IMGT (ImmunoGeneTics) database (http://www. imgt. org). The TCR reads were pre-processed and data were analyzed with the ImmunoSEQ Analyzer software.
25 dyn cm -2 for 5 min. If indicated, T cells were incubated with natalizumab (Tysabri, Biogen; 10 µg ml -1 ) for 10 min. Videos (×20 magnification) were recorded using a BZ-9000 BioRevo microscope (Keyence) and BZ II viewer software (Keyence). Adherent cells per field of view were determined using ImageJ (NIH). Transfer of HA-specific CTLs. On day 5 of differentiation of HA-specific CTLs, cells were collected by Ficoll density separation and 0. 3 × 10 7 or 3 × 10 7 CTLs were adoptively transferred IV to EC-HA + mice and EC-HA -littermate controls. Mice were assessed daily for weight and neurological signs. The motor skill and performance were assessed using a Rotarod device (Bioseb), with acceleration from 4 to 40 rpm over a 600-s period. First, the animals were trained before treatment to reach steady performance.
The 1-week training consisted of 2-3 trials of 10 min each with rest of 5 min between the trials. During the investigation period (days 0-28), the amount of time spent on the drum was measured daily for each mouse. 100% represents the day-0 value. Antibody treatment of mice. The anti-α4 integrin antibody (clone PS/2; BioXCell, West Lebanon, NH, USA) or a control rat IgG2b was injected into EC-HA + mice (250 µg per mouse i. p. ) at the indicated time points. Natalizumab treatment of patients. The humanized anti-α4 integrin antibody (natalizumab (TysabriTM), Biogen, Boston USA), which blocks VLA-4 from interacting with VCAM-1, was infused monthly at a fixed 300 mg dose of natalizumab every 28 days. Statistical analysis. Statistical analysis was performed using the GraphPad Prism 5. 0 software.
Acknowledgements We thank all patients included in this study for their participation. We also thank  Daniela Roostermann ,  Emilie Mauré ,  Gabriele Berens ,  Kirsten Weiß ,  Julia Sundermeier ,  Lena Schünemann ,  Aline Kiese ,  Arne Seeger ,  Alina Teich ,  Tobias Schilling ,  Ulrike Köck ,  Angela Kury ,  Marianne Leisser , and  Christiane Schulze-Weppel  for their excellent technical assistance. The authors also thank  Thomas Korn  for discussion and helpful suggestions regarding this manuscript as well as  Heike Blum  for designing Supplementary Fig. 10. This work was supported by the  European Susac Consortium (EuSaC) ; the  German Research Foundation  grant ( DFG) GR3946_3/1  " Susac syndrome (SuS)  as a paradigm of a CD8+ T cell mediated endotheliopathy" (to C. C. G. and I. K. ); the  Interdisciplinary Center for Clinical Studies (IZKF)  grant  Kl3/010/19  " Antigen-specific CD8 T cell responses in Neuromyelitis Optica and Susac syndrome " (to L.
Her work is funded by  Biogen ,  Novartis , the  German Ministry for Education and Research (BMBF ;  01Gl1603A ), the  German Research Foundation  (DFG,  GR3946/3-1 , SFB Transregio  128 A09 ), the  European Union  (Hor-izon2020, ReSToRE), the  Interdisciplinary Center for Clinical Studies (IZKF) , and the  IMF. I. K. received travel expenses for attending meetings from  Pfizer  and  CSL Behring. I. K. received speaker honoraria from  Daiichi Sankyo. J. B. 's work is funded by the  Austrian Science Fund  (FWF:  P26936-B27 ). A. S. -M. receives research support from  Novartis. T. K. received speaker honoraria from  Novartis  and  Excemed. Her  research is supported by the  DFG  ( SFB/Transregio 128 B07 ),  European Leukodystrophy Association  ( 2017-018C4A ),  National MS Society  ( RG-1801-30020 ), and  Progressive MS Alliance  ( PA-1604-08492 , VRAVEinMS).
Further, the high connectivity around Australia found by  Bernard et al. (2016)  was reflected in satellite tracking data  (Afonso et al. , 2017; Heithaus et al. , 2007; Kohler et al. , 1998). Nevertheless, contrary to the findings of  Bernard et al. (2016) ,  Holmes et al. (2017)  found no genetic differentiation between Hawaiian and Australian populations. In the present study, we used samples from the western Indian Ocean (233 individuals from four locations), the eastern Indian Ocean (nine individuals from one location), and the western Pacific (33 individuals from two locations), and 27 microsatellite loci and three mitochondrial genes (CR, COI, and cytb) to further investigate population structure and demographic parameters in the tiger shark. As we used samples in common with  Holmes et al.
(2015). For these individuals and the eight microsatellites in common between the present study and  Holmes et al. (2017) , the genotypes were compared and allele lengths calibrated. To ensure the allele frequency bins were uniform between the studies at each locus, alleles frequencies were plotted and compared for each sampling location (Appendix S2). We thus added genotypes of all the individuals from  Holmes et al. (2017)  (Figure  1 , Table  1 ) enlarging the geographic coverage of our sampling: northern and TA B L E 1 Summary of Galeocerdo cuvier sampling locations and number of individuals from this study,  Holmes et al. (2017)  and  Bernard et al. (2016) , as well as the molecular markers used in each study and the different datasets analyzed in the present study southeastern Australian coasts, Coral Sea, Hawaii, and Brazil.
We also expanded the number of individuals for some locations in common: western (AUS1) and northeastern (AUS2) Australian coasts, and New Caledonia (Figure  1 , Table  1 ). Moreover, for DAPC analyses (see below) we also used the microsatellite genotypes generated by  Bernard et al. (2016) , retaining only the eight microsatellites in common with our study  and Holmes et al. (2017). However, we could not analyze them together with our genotypes as we did not have in our possession samples from individuals in common to calibrate the electrophoregrams. In summary, four microsatellite datasets were used ( The mitochondrial DNA control region (CR) was amplified using the set of primers Gc-CR-F/Gc-CR-R  (Pirog et al. , 2016) , the cytochrome oxidase c subunit I (COI) using the primer cocktails C_ FishF1t1/C_FishR1t1  (Ivanova, Zemlak, Hanner, & Hebert, 2007; Ward & Myers, 2005)  as described in  Wong, Shivji, and Hanner (2009)  and the cytochrome b (cytb) using GluDG/C61121H  (Naylor, Ryburn, Fedrigo, & Lopez, 2005).
1. 0 (Kalinowski, 2005). This method accounts for differences in sample size by standardizing A r and A rp values across sampled locations by resampling the lowest number of genotypes available (i. e. , 16 haploid gene copies or eight diploid genotypes in Zanzibar) in each location. Mitochondrial sequences were quality checked and aligned using Geneious v. 8. 1. 2 (Kearse et al. , 2012). Alignments were performed using the MAFFT method  (Katoh, Misawa, Kuma, & Miyata, 2002) , first for each gene separately and then for the concatenated sequence CR-COI-cytb. Molecular diversity indices (i. e. , number of haplotypes, number of segregating sites, haplotype (h) and nucleotide (π) diversities) were calculated for each region separately and for the concatenated dataset (called hereafter CR-COI-cytb dataset), using DnaSP v.
5. 10. 1  (Librado & Rozas, 2009). Detection of partitioning schemes and substitution models within the concatenated sequence CR-COI-cytb was performed using partitionFinDer v. 2. 1. 1  (Guindon et al. , 2010; Lanfear, Frandsen, Wright, Senfeld, & Calcott, 2017). Beast v. 1. 8. 4  (Drummond, Suchard, Xie, & Rambaut, 2012)  was used to reconstruct phylogenetic relationships on the CR dataset, and on the CR-COI-cytb dataset. Bayesian Markov chain Monte Carlo (MCMC) analyses were performed assuming a HKY85 model of substitution as the latter was shown to best fit the data (see Section 3). Rate of variation among sites was modeled with a discrete gamma distribution with four rate categories. We assumed an uncorrelated lognormal relaxed clock to account for rate variation among lineages. To minimize prior assumptions about demographic history, we adopted an extended Bayesian skyline plot (EBSP) approach in order to integrate data over different coalescent histories.
Evolutionary model parameters were then estimated, with samples drawn from the posterior every 10 5 MCMC steps over a total of 10 8 steps from five independent runs. The first 10 7 steps were discarded as burn-in. Good mixing and convergence were assessed using tracer v. 1. 6 (Rambaut, Suchard, Xie, & Drummond, 2014)  and the best tree was selected using the maximum clade credibility option with treeannotator v. 1. 8. 4  (Drummond et al. , 2012)  and viewed with FiGtree v. 1. 4. 0 (http://tree. bio. ed. ac. uk/software/figtree/). To further evaluate phylogenetic relations among haplotypes, TCS statistical parsimony networks  (Clement, Posada, & Crandall, 2000)  were constructed using popart v. 1. 7  (Leigh & Bryant, 2015).
The analysis was also performed using the alternative ancestry prior as suggested in Wang (  2017 ), but it did not alter the results. For a given K, distinct modes were identified and, for each mode and each individual, the assignment probabilities to each cluster were averaged using cluMpak  (Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015). These analyses were performed on both 27msat and 8msat datasets. Secondly, a Discriminant Analysis of Principal Components (DAPC;  Jombart, Devillard, & Balloux, 2010) , which in contrast to structure does not rely on HWE or LD to identify clusters, was performed to check consistency between clustering methods based on different algorithms. This method transforms genotypes using PCA as a prior step to a discriminant analysis and defines clusters by minimizing variations within while maximizing differentiation among clusters.
DAPC was applied using the adegenet package  (Jombart, 2008)  for R (R Development Core Team 2017). We tested values of K ranging from 1 to 50 and visualized the Bayesian Information Criterion (BIC) values for increasing K using the find. clusters() function. We then used the dapc() function for values of K ranging from 1 to 10, retaining a number of principal components sufficient to explain ≥90% of total variance of the data  (Jombart et al. , 2010). This analysis was performed on the four microsatellite datasets (see Laboratory Procedures Assessing population differentiation between pairs of sampling locations, F ST  (Weir & Cockerham, 1984)  and D est  (Jost, 2008)  were estimated for the microsatellite data with arlequin v. 3. 5. 1. 2 (Excoffier & Lischer, 2010)  and DEMEtics v. 0. 8-7  (Gerlach, Jueterbock, Kraemer, Deppermann, & Harmand, 2010) , respectively, on both 27msat and 8msat datasets.
Contrary to F ST , which depends on within-population diversity and is affected by migration rates and effective population sizes, D est , based on the effective number of alleles strictly reflects the differentiation between populations. For the CR-COI-cytb dataset as well as the CR dataset, the metric Φ ST  (Slatkin, 1995)  was estimated using arlequin v. 3. 5. 1. 2 (Excoffier & Lischer, 2010). Significance of pairwise population differentiation indices was tested using 10,000 permutations.
It has indeed been shown that pooling individuals from different sampling locations, even with nonsignificant pairwise differentiation values may bias results  (Lombaert et al. , 2014). Nevertheless, the analysis was also run using only samples from New Caledonia (n = 23), to consolidate the results. For each scenario, 10 6 simulated datasets were run. To select the best scenario, posterior probabilities were computed via logistic regression on the 1% of simulated datasets closest to the empirical data  (Cornuet et al. , 2008). Summary statistics were transformed by linear discrimination analysis prior to logistic regression to reduce correlation among explanatory variables and provide conservative estimates of scenario discrimination  (Estoup et al. , 2012).
| 8-msat dataset The mean rarified allelic richness varied from 4. 63 ± 0. 73 in Brazil to 5. 89 ± 1. 11 in Coral Sea while H E and H O varied from 0. 66 ± 0. 04 in South Africa to 0. 72 ± 0. 07 in Brazil and from 0. 59 ± 0. 04 in South Africa to 0. 73 ± 0. 07 in Brazil, respectively (Table  2 ). No significant deviations from HWE were revealed. The mean rarified private allelic richness was rather constant among locations but was higher for Brazil, varying from 0. 07 ± 0. 04 in New Caledonia to 0. 49 ± 0. 17 in Brazil (Table  2 ).