dataesr/scientific-paragraphs-categorization huggingface dataset:
STOCHASTIC SIMULATION METHODS We used the Open Source project Cytosim in 1D (www. github. com/nedelec/cytosim). The top (resp. bottom) microtubule is represented by an ordinate p (resp. p ) and a direction d = +1 (resp. d = -1). The location of the heads are recorded by their distance from the minus-end, a. k. a the abscissa x i , such that the position in space is p+d x i. An array of boolean values T is used for each microtubule to represent lattice occupancy, where T [i] corresponds to abscissas in [ai, a(i+1)]. The system is evolved using a time step of τ = 10 -5 s. Hopping to neighboring sites are stochastic events, simulated using a random number generator: a rate R is simulated by testing θ < 1 -e -Rτ at every time step. Hopping is forbidden if the lattice is occupied, and the lattice is updated at each molecular binding, unbinding or displacement.
HST/STIS Spectroscopic Data The STIS spectroscopic observations (PID: 14742, PI: Nguyen) were taken on 2017 September 22-23 for NGC 205, 2017 June 01 for NGC 5102, and 2017 July 21-22 for NGC 5206. All data was taken with the G430L grating and the 52 0 × 0. 1 slit. This provides spectra over a wavelength range of 2900 Å to 5700 Å with a pixel size of 2. 73 Å, and spectral resolving power of R ∼ 530 -1040. The specific exposure times per frame and total exposure times for each galaxy is detailed in Table  1. However, only nine exposures of NGC 5102 are used in this analysis, as the last two are unusable due to dithering off the chip. For each target, the source was dithered along the slit and centered near the E1 aperture position to minimize charge-transfer inefficiency losses. Reduced and rectified spectroscopic exposures (x2d files) were downloaded from HST /MAST.
Details of the data reduction follow N17. First, median frames were constructed from the undithered combined data in each galaxy and subtracted to remove hot pixels. Each dither was separated by 30 (NGC 205), 13 (NGC 5102), and 20 (NGC 5206) pixels, thus, some background galaxy light will be included in their median images. However, this effect is very small; the maximum row-averaged fluxes of their median images are <10% (NGC 205), <5% (NGC 5102), and <9% (NGC 5206) of that at the outermost radii we analyze for each galaxy. After median combining, we combine the dithered images including rejection of cosmic rays and bad pixel masking. We examine the quality of the reduced spectroscopic data by looking at the signal-to-noise (S/N) of the central pixel in the combined spectra at 3700 Å and 5000 Å for each galaxy and get S/N (3700 Å, 5000 Å) = (73, 94) for  NGC 205, (178, 200)  for  NGC 5102, and (52, 79)  for NGC 5206.
3) to fit Jeans models to these three galaxies, and present the improved and more accurate estimates of their central BH masses in Section 5. 1. We note that the dynamical modeling requires the astrometric alignment of these IFU data to their corresponding HST images as well. As was done with the NGC 205 and NGC 5206, our new data was aligned to the F814W image. These alignments are quite trivial due to the fact that our galaxies have minimal dust and internal extinction in the area around the nucleus as seen in Figure  1. The PSFs of the IFU are presented in Section 2. 3 and Table  3 of N18. 3. MASS-TO-LIGHT RATIO VARIATIONS Significant color variations are seen in all three nuclei (see Figure  1  and also Section 4, N18). These color variations suggest spatial variations in stellar populations (or extinction) that will create a variable M/L.
Here, we combine HST imaging and STIS spectroscopic data to   ). These color maps are made from HST imaging, cross-convolved to match the PSFs. The contours show the F814W surface brightness at µF814W of (13. 0, 15. 0, 16. 5, 17. 5) mag arcsec -2 for NGC 205,  (12. 2, 13. 0, 13. 3, 13. 7, 14. 0, 15. 4, 16. 0, 16. 5, 17. 0) mag arcsec -2 for NGC 5102, and (15. 0, 16. 0, 17. 0, 18. 0, 18. 5, 19. 0) mag arcsec -2 for NGC 5206. The centers of the nuclei of NGC 205, NGC 5102, and NGC 5206 are represented as (0 , 0 ) on these maps (their real Equatorial J2000 coordinates are presented in columns 2 and 3 of Table  1 ).
Green data points are the fractional residuals ((Data-Model)/Data) between data and model. The spectrum is normalized at a wavelength of λ = 5500 Å. PSFs. Second, we estimate the background level on each image in an annulus at the maximum radius available in each observation, which varies in the range of 10. 0-12. 0 away from the nucleus and subtract it off. We then create color images using the Vega-based zero points and correct for foreground extinction as listed in Column 12 of Table  1. Figure  1  shows these color maps within a field-ofview (FOV) of 14. 0. The color maps of NGC 205 and NGC 5102 show the signatures of two distinct and dominant stellar populations with young stars concentrated in the nucleus and older populations at larger radii  (Valluri et al. 2005; Davidge 2015; Mitzkus et al.
Jeans Anisotropic Models In this section, we present our JAM dynamical modeling using the new mass constraints for the central SMBHs in NGC 205, NGC 5102, and NGC 5206, and compare these to our previous results (N18). The two main improvements are: (1) the new mass models presented in Section 3. 2 and (2) the use of a Markov chain Monte Carlo (MCMC) sampler to fit the BH masses with JAM 4 , along with the emcee code 5 (Foreman-Mackey et al. 2013), which utilizes an affine-invariant ensemble sampler  (Goodman & Weare 2010)  to explore the parameter space with a set of walkers. At each step,     2017). Using MCMC is also more computationally effi-cient for comparing JAM models with data than a grid search when the number of free parameters is ≥3. The ingredients fed into the JAM models are: (1) The new stellar mass MGE models derived from using our HST /STIS spectroscopic color-M/L eff relations in Section 3.
However, because we are fitting azimuthally symmetric MGE models to these data we do not expect that this will significantly impact our MGE models. We test this by applying two approaches: (1) we exclude the obvious stars in the central 1 in mass model and interpolate over them; and (2) we find the ellipse averaged color from the color maps. The JAM model derives a BH of M BH = 7. 3 × 10 3 M and β z = -0. 38 for the former mass MGE model, while the latter mass MGE model gives a BH of M BH = 8. 0 × 10 3 M and β z = -0. 82. These differences are not significant, although the β z values are low, with values that are further from isotropic. Since we are using a new MCMC method, we verify that our original mass model gives similar results with the MCMC approach. Specifically, we take the N18 mass and light MGEs and run our MCMC code on them with identical priors to our original run; the results are shown in the Figure  19.
JAM for the BHs in NGC 5102 and NGC 5206 The BH masses in both NGC 5102 and NGC 5206 remain essentially unchanged from the results presented in N18. We examine their new best-fit models here. The best-fit Jeans model of NGC 5102 gives M BH = 9. 12 +1. 84 -1. 53 × 10 5 M , β z = 0. 06 +0. 14 -0. 08 , Γ F814W = 0. 96 +0. 14 -0. 12 (M/L F814W,dyn. = 0. 48 +0. 07 -0. 06 (M /L )), and i = 70 •. 0 +20. 0 -30. 0 (see Figure  14 ). Our new Jeans model changes the best-fit parameters (M BH , β z , Γ, i) by (+3. 5%, -6. 0%, -16. 5%, -1. 5%) compared to the results of N18. The largest change is the decrease of Γ, and our new measurement brings the dynamical mass estimate into better agreement with our stellar population estimates as well as those of  (Mitzkus et al. 2017). We note that this may be partly due to our improved WFC3 data; the previously available data was challenging to model due to the saturation of the center in some of the images (see appendix A of N18).
ACKNOWLEDGEMENTS The authors thank the anonymous referee for careful reading and useful comments which greatly improved this paper. We would also like to thank the  Physics and Astronomy Department, University of Utah  for supporting this work. D. D. N. and A. C. S. acknowledge financial support from  NSF  grant  AST-1350389  and a part of the financial support from the  HST  grant  GO-14742. D. D. N. also delivers his gratitude to the Willard L. and  Ruth P. Eccles Foundation  for their Eccles Fellowship during the 2017-2018 academic year at the  University of Utah. M. C. acknowledges the support from a  Royal Society University Research Fellowship. J. S. acknowledges support from  NSF  grant  AST-1514763  and the  Packard Fellowship. E. T. acknowledges financial support from the  UnivEarthS Labex program of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ).
We performed structural and sequence analyses to evaluate this hypothesis (see "Experimental procedures"). Protein structural analysis indicated that cysteine 570, which is at the boundary of the transmembrane and cytoplasmic domains, is the less likely to form disulfide bonds. To determine the putative cysteine involved in such bonds we studied the evolution of this cytoplasmic region using multiple sequence analyses. We performed a mining of the UniProt/Swiss-Prot database using PSI-BLAST and found more than 400 conserved sequences with appropriate similarity. Interestingly, cysteine 570 was found in all mammals but not in some reptiles. The two other cysteines, cysteine 578 and cysteine 579, were found in all mammals with complete conservation. In the reptile species, it was difficult to distinguish cysteine 578 from cysteine 579 as the local similarity decreased.
Cell culture, transfection, and flow cytometry MDCK (ATCC), HEK 293T (ATCC) and Caco-2 (DSMZ) cells were grown in DMEM GlutaMAX I supplemented with 10% (MDCK, HEK 293T) or 20% (Caco-2) FCS, antibiotic/antimycotic and 0. 1 mM nonessential amino acids. Cells were grown in a humidified atmosphere at 37 °C with 5% CO 2. Stable MDCK and Caco-2 cells expressing WT or mutant Lu/BCAM were obtained as described  (32). Cell surface expression of Lu/BCAM was analyzed using anti-Lu/BCAM F241 mAb followed by a phycoerythrinconjugated anti-mouse IgG (HϩL) (Beckman Coulter); a BD FACSCanto II flow cytometer (Becton Dickinson) with FACSDiva software (version 6. 1. 2) for acquisition and FlowJo v10 software for data analysis. Expression of Lu WT and dimerization mutant forms in Lu-positive MDCK cells fell within the same range except for LuS557V (Fig.
After a washing step with PBS, ligation and amplification steps were performed, cells were washed, and Duolink® In Situ Mounting Medium with DAPI was added in the channels. Cells were imaged using a laser scanning confocal microscope (LSM700, Carl Zeiss). Acquired images were then processed using ImageJ (National Institutes of Health) to count the number of spots per field of acquisition using the following steps: maximum intensity projection along z axis, subtract background, smooth and find maxima to count the number of spots. The number of nuclei per field of view was determined manually. Data were processed using Excel software (Microsoft) to calculate the ratio "number of spots/nucleus" for each experimental condition.
Cell migration assays For cell migration on coated laminin, untreated -Slide VI 0. 4 chambers (ibidi) were coated with human recombinant LN-521 (Biolamina) at 2 g/cm 2 and blocked with 1% BSA. MDCK cells were seeded at 2. 25 ϫ 10 3 cells/30 l/channel to ensure a surface with sparse cells and grown for 3 h in complete medium. Cells were then incubated at 37 °C in FCS-depleted DMEM GlutaMAX I medium for 1 h in a CO 2 microscope stage incubator before monitoring cell migration by time-lapse microscopy, without medium change. Bright field pictures composed of 2 ϫ 2 mosaics were taken with 10-min intervals for 12 h at 37 °C and 5% CO 2 using an AxioObserver Z1 microscope (Objective 10ϫ) and AxioVision 4 software (Carl Zeiss). For the inhibition assays, cells were treated with the PKA inhibitor H89 (Sigma 19 -141, 10 M), the Akt inhibitor Akti (Sigma A6730, 2.
5 M) in depleted medium and bright field pictures were taken for 5 h at 37 °C and 5% CO 2 using a Celldiscoverer 7 microscope and Zen Blue software (Carl Zeiss). Images were processed with ZEN lite (blue edition) software (Carl Zeiss), and the position of nuclei was tracked using the MTrackJ plugin  (47)  of Fiji (48) to quantify cell motility; velocities were expressed in m/min. For each condition, cells were tracked from three different fields (12-15 cells/field). Cells undergoing mitosis were excluded. Randomly selected cells' paths were plotted with SigmaPlot software (Systat Software Inc. ) for illustrative purposes.
Protein sequence analyses Using the transmembrane and the cytoplasmic regions, a mining was done with PSI-BLAST (http://blast. ncbi. nlm. nih. gov/) 4 classic approach (51) on UniProt/Swiss-Prot database  (52). The selected sequences were aligned using Clustal Omega (http://www. ebi. ac. uk/Tools/msa/clustalo/) 4 webserver  (53, 58). The sequences were conserved when they had an e-value higher than 10 Ϫ4 and a coverage higher than 80%.
Statistical analyses Descriptive analyses and statistical hypothesis testing were performed using R software on RStudio  (54, 55). For FRET and PLA analyses, Kruskal-Wallis one-way ANOVA on ranks test was performed, followed by Dunn's multiple comparison procedure in case of differences at a 0. 05 significance level. For the migration assays, normal, log-transformed data were used to fit a two-factor (transfectant and experiment or treatment and experiment) linear model. The most influential points (according to the Cook's distance), all of them classified as potential outliers (with absolute studentized residuals values Ͼ2. 5) were removed to achieve homoscedasticity (p Ͼ 0. 05 on Bartlett's test). This led to removal of 6, 5, and 1 outliers for each analysis (Figs. 4, 5B, and 5D, respectively).
Acknowledgments-We acknowledge the Indo-French Centre for the Promotion of Advanced Research/CEFIPRA for Collaborative Grant 5302-2. This work was supported by institutional funding to the Inserm unit 1134, Institut National de la Transfusion Sanguine, Agence Nationale de la Recherche (SCADHESION 2007), and Région Ile-de-France  (SESAME 2007 no. F-08-1104/R ).
This study was supported by the  French National Research Agency  through the  "Investissements d'avenir" Program ,  France-BioImaging ,  ANR-10-INBS-04  and by  Laboratory of Excellence GR-Ex Grant , reference  ANR-11-LABX-0051. The labex GR-Ex is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. The authors declare that they have no conflicts of interest with the contents of this article. This article contains Figs. S1-S3 and Videos  S1-S9. 1 These authors contributed equally to this work.
Quantification of C3, CATS, MMP1 and MMP7 in a subset of the INCA population. To assess the relevance of the results obtained by the SOMAscan assay, we were only able to quantify C3, CATS, MMP1 and MMP7 in the plasma of patients from a subset of INCA patients by conventional assays: C3, MMP1 and MMP7 were measured using Luminex technology, while CATS was quantified by enzyme-linked immunosorbent assay (ELISA). We did not find any adequate and specific assays for MAPK5 and F107B. As shown in Supplementary Fig. S1 , we obtained consistent data between the SOMAscan assay (left panels) and conventional assays (middle   S4 ; ‡ Information provided by GO (Gene Ontology, http:// geneontology. org/). panels), except for CATS (Supplementary Fig. S1d ). C3 was significantly decreased in the plasma of patients who died of CV causes compared to patients who were alive after 3 years of follow-up (P = 0.
The resulting fluorescence is quantified and reflects protein levels. The data were then processed for SOMAscan ™ standardization to correct for systematic effects in data intro- duced during the hybridization process, and 12 hybridization control sequences were introduced into each clinical sample. There was a predetermined global reference RFU for each hybridization control based on independently run assays. A ratio was determined by this global RFU for each control/measured RFU of each hybridization control. The median of the ratios determined the sample-based hybridization scale factor. Each sample was multiplied by its own scale factor. Median normalization was performed to remove sample or assay biases that may have been due to differences between samples in overall protein concentration, pipetting variation, variation in reagent concentrations, assay timing, and any other source of systematic variability within a single plate run.
Data analysis. To identify significantly modulated proteins between patients who died of CV causes after 3 years and the patients who were alive, the Mann-Whitney-Wilcoxon test was performed and adjusted for multiple testing using the Benjamini-Hochberg false discovery rate (FDR)-controlling procedure  44. Then, as shown in Fig. 1 , we used 2 strategies for analysing our high-throughput proteomic profiling. Molecular INCA network building. A molecular network was constructed using the proteins that were significantly modulated in the plasma of INCA patients, i. e. , proteins with an adjusted p-value < 0. 05 and an absolute log2FC > 0. 25. The 203 significantly changed proteins could be mapped to 211 nodes, which were used as "seed nodes" to construct the network model. To build the INCA network, we used the knowledge platform EdgeBox (EdgeLeap's proprietary knowledge platform), which contained 13 public databases on molecular interactions (Supplementary Table  S2 ): ChBI (http://www.
ebi. ac. uk/chebi), ChEMBL (http://www. ebi. ac. uk/chembl), ENCODE (http://encodenets. gersteinlab. org), Ensembl Genes (http://www. ensembl. org), Microcosm (http:// www. ebi. ac. uk/enright-srv/microcosm), miRBase (http://www. mirbase. org), miRecords (http://c1. accurascience. com/miRecords), miRTarBase (http://mirtarbase. mbc. nctu. edu. tw), Reactome (http://www. reactome. org), STRING (http://string-db. org), STITCH (http://stitch. embl. de), TFe (http://www. cisreg. ca/cgi-bin/tfe/home. pl), and WikiPathways (www. wikipathways. org). The INCA network was built by including nodes and edges satisfying the following criteria: 1. all molecule nodes that were direct neighbours of at least two seed nodes; 2. all seed nodes that had at least one interaction with another seed node or a node satisfying criterion 1; 3.
the edges connected two seed nodes or connected a seed node to a node satisfying criterion 1. "Molecular nodes" refers to genes, miRNAs, metabolites and metabolites (type "protein coding", "miRNA" or "metabolite"). All the INCA network model visualizations were performed using Cytoscape version 3. 2. 1  45. Topological-based cluster analysis. The InfoMap algorithm  46  as implemented in the igraph R package (version 1. 0. 1)  47 was used to perform network clustering (http://igraph. org/r/). The InfoMap algorithm assigned each node in the network to a cluster. Clusters with less than 5 nodes and clusters with only one or fewer edges per node within the cluster were excluded. Then, clusters were annotated to biological pathways by performing overrepresentation analysis on biological pathways using Fisher's exact test with the Gene Ontology database (http:// geneontology.
org/) and the Reactome database (version 56). Pathways with fewer than 10 proteins or more than 500 proteins were excluded. Betweenness centrality analysis. For each node in the network, the betweenness centrality was calculated using the igraph R package. The betweenness centrality of a node represents the number of shortest paths between all other nodes in the network that cross the node, normalized by the number of all possible shortest paths. A high betweenness centrality suggests a crucial role of the molecule in the physiopathological process, while a low betweenness centrality indicates a more peripheral role. Penalized regression analysis. To select a few proteins that could explain the early death of patients, a framework based on penalized logistic regressions was used. Our study sample size was mainly determined by the size of the initial cohort and the proteomic profiling assay performed.
To limit the influence of potential extreme individuals, 168 adaptive LASSOs were performed with the pre-defined regularization parameters and weights calculated above, each time removing one individual among the 168. We only kept the proteins whose frequency of selection by the adaptive LASSO was higher than 0. 90 among the 168 different training subsets. The entire procedure was implemented in R, using the R package glmnet (version 2. 0. 16) for penalized regressions  51. CATS, MMP1, MMP7 and C3 measurements. CATS was quantified using the human cathepsin S ELISA (Abcam ab155427) according to the manufacturer's instructions. Plasma samples were diluted 1/1000, and CATS concentration was determined by calculating the mean absorbance for each standard and sample and subtracting the average zero standard optical density.
All samples were analysed with the Bio-Plex system (Bio-Rad Laboratories, Hercules, CA) following the manufacturer's instructions. For MMP1 and MMP7, plasma samples were diluted 1/4 and for C3, 1/40000. The detection limit was 0. 3-1. 1 pg/mL for MMP1, 1. 3-6. 6 pg/mL for MMP7 and 0. 08 ng/mL for C3. Experimental data were analysed by fitting a four-parameter logistic curve to the standard analyte curves, except for MMP7. Materials, data and associated protocols are promptly available to readers without undue qualifications in material transfer agreements upon request.
List of the 6 candidate proteins selected by LASSO analysis. *Full name, protein symbol and UniProt ID are provided from the UniProtKB database (https://www. uniprot. org/);**Dataare expressed in relative fluorescence units; ≠ P value was calculated by the Mann-Whitney-Wilcoxon test; ‡ Frequency of selection after the 168 adaptative LASSOs; ≈ Information provided from the INCA network (for more details, see Supplementary Fig. S1, and TableS4); ≡ Information provided by GO (Gene Ontology, http://geneontology.
Acknowledgements This work was supported by grants from the  French Clinical Research Infrastructure Network INI-CRCT (Investigation Network Initiative-Cardiovascular and Renal Clinical Trialists) , the  E. U. FP7 HOMAGE (305507) ,  "Agence Nationale de Recherche "  15-CEA-U16 /" Direction Générale de l'Offre de Soins " ( 5-013 ) and  CHRU de Lille  ( Bonus H2018 ). We acknowledge  Somalogic Inc. as the provider of the proteomics analysis and EdgeLeap B. V. for the systems biology analysis.
Aims: DNA methylation has been found to regulate microRNAs (miRNAs) expression, but the prognostic value of miRNA-related DNA methylation aberration remained largely elusive in cancers including glioblastomas (GBMs). This study aimed to investigate the clinical and biological feature of miRNA methylation in GBMs of non-glioma-CpG island methylator phenotype (non-G-CIMP). Methods: Prognostic miRNA methylation loci were analyzed, with TCGA and Rennes cohort as training sets, and independent datasets of GBMs and low-grade gliomas (LGGs) were obtained as validation sets. Different statistical and bioinformatic analysis and experimental validations were performed to clinically and biologically characterize the signature.
| GBM datasets Rennes cohort of 77 newly diagnosed non-G-CIMP GBMs with clinical and genome-wide DNA methylation microarray data by Infinium HumanMethylation450k BeadChip (Illumina Inc, San Diego, CA, USA) was obtained from the ArrayExpress under the accession number "E-MTAB-4969. "  9 All patients received standard adjuvant treatment of radiotherapy (RT) and concurrent temozolomide (TMZ). Public DNA methylation datasets of non-G-CIMP GBM samples were also downloaded from The Cancer Genome Atlas (TCGA) data portal,  10  and Gene Expression Omnibus (GEO) under the accession number "GSE60274. "  11 (Detailed clinical data of and relative CpG information are listed in the Supporting Information S1) We also obtained clinical and DNA methylation data of LGGs from TCGA  12  and GSE48462. 13 ong the heterogeneous datasets, only those with age over 18 years old and a molecular diagnosis of non-G-CIMP tumors were included in this study.