dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors would like to thank  Jordi Merino  of  Massachusetts General Hospital  and  Chirag J. Patel  of  Harvard Medical School  for helpful comments on the analysis. KEW was supported by  NIDDK  Fellowship  2T32DK007028-46. HC and AKM are supported by  NHLBI   R01 HL145025. JCF is supported by  NHLBI   K24 HL157960. MSU is supported in part by  NIDDK   K23DK114551. JBC is supported by  NIDDK Pathway  to  Independence Award  ( K99DK127196 ). The WGHS is supported by the  National Heart, Lung, and Blood Institute  ( HL043851  and  HL080467 ) and the  National Cancer Institute  ( CA047988  and  UM1CA182913 ), with funding for genotyping provided by  Amgen. The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS.
RNA sequencing. Three RNA-seq libraries per population (three biological replicates) were prepared using the TruSeq Stranded mRNA sample Prep kit (Illumina) and sequenced on an Illumina GAIIx sequencer as 75 bp paired reads by Hybrigenics-Helixio (Clermont-Ferrand, France). Samples were quantified with Qubit 2. 0 Fluorometer (Thermo Fischer) and checked with Nanodrop ND-1000 (Thermo Fisher). Libraries were prepared with three kits: NEBNext Ultra II Directional Library prep Kit (New England Biolabs, E7760), NEBNext Poly(A) mRNA Isolation Module (New England Biolabs, E7490) and NEBNext Multiplex Oligos (New England Biolabs, E7140). Sequencing was done with NextSeq 500 (Illumina). Data were processed with Bcl2fastq (Illumina). Raw data were checked with FastQC (Babraham Institute). Reads were mapped on reference genome (GRCm38, release 91) with TopHat (https://ccb.
jhu. edu/software/tophat) and differential expression analysis is done with Cuffdiff (Cufflinks suite, https://github. com/cole-trapnell-lab/cufflinks). IT performs tests between supplied conditions. FPKM values were calculated for each transcript in each sample (geometric library normalization, pooled dispersion method). IT uses the reference annotation GTF file (GRCm38, release 91) and quantitates the expression values and differential regulation across BAM files. CummeRbund (https://bioconductor. org/packages/release/bioc/html/cummeRbund. html) R package is designed to process global statistics and quality control. Pie charts and heatmaps were generated using MATLAB 2020.
Coverslips were then mounted in FluorSave (Calbiochem, 345-789-20). Confocal images were acquired using a Zeiss LSM 780 Meta confocal laser scanning microscope (Carl Zeiss). Signals were sequentially visualized in the same section with acquisition performed in separated mode. Images were processed with Zen software (Carl Zeiss) and mounted using Adobe Photoshop 10. 0 (Adobe Systems). Total LC3-positive dots (GFP + RFP + and GFP -RFP + dots), early autophagosomes (GFP + RFP + dots) and autolysosomes (GFP -RFP + dots) were analyzed using ImageJ software and expressed as a number of positive dots per neuron per μm 2.
Surface Plasmon Resonance. All SPR binding experiments were performed on BIACORE T200 instrument (GE Healthcare) at 25°C. Human recombinant IGF1R at 20 μg/mL in 5 mM maleate buffer pH 5, was immobilized at 1000 RU by THIOL coupling procedure (according to recommendations and kits from GE Healthcare, BR-1005-57). PLAT/tPA actilyse, WT PLAT/tPA and ΔEGF PLAT/tPA were injected at 10, 5, 2. 5, 1. 25 and 0. 625 µM in a kinetic titration series (Single Cycle Kinetics) at a flow rate of 30 μL/min for a contact time of 120 s, followed by a dissociation time of 10 min. The regeneration between each SCK cycle has been realized by a 30-s injection of 1 M ethanolamine pH 8. All SPR data processing and analyses were performed using BiaEvaluation software 4. 1 (GE Healthcare). The SPR response as a function of time, namely the sensorgram, was then fitted globally or at the steady state by using a 1:1 binding model.
Magnetic resonance imaging. Experiments were carried out on a Pharmascan 7 T/12 cm system using surface coils (Bruker). T2-weighted images were acquired 24 h after cerebral ischemia using a multislice multiecho sequence: TE/TR 33 ms/2500 ms. Lesion sizes were quantified on these images using ImageJ software. T2*-weighted sequences were used to control if animals underwent hemorrhages events. Two-dimensional time-of-flight angiographies (TE/TR 12 ms/7 ms) were acquired and analyses of the MCA angiogram were performed to control the spontaneous recanalization of the arteries of the mice.
Functional recovery assessment. Functional recovery was assessed using the grip strength test (BIOSEB). Mice, holding by the tail, were dropped on a T-like-bar and gently removed by the experimenter. In this context of unilateral (right) ischemic lesion due to stroke, the individual strength of each forepaw was assessed with a wrench connected to the device, while a fake wrench (unconnected to the electronic device) was placed on the other side. To obtain the mean strength, 5 assays per mouse were performed to reduce variabilities with 1 min of rest between each assay. Data were assessed in grams. Measurements were picked up the day before MCAo surgery (baseline acquisition) and 24 h after MCAo surgery, ratio between the two values is calculated in order to normalize each mouse with its own baseline values.
Statistics. Statistical analyses were performed using GraphPad Prism 8. 0. 2 software. Results are represented as mean ± S. E. M. The distribution was assessed by the D'agostino Pearson normality test. When data were normally distributed, Student's t-tests were used to compare two conditions. When analysis of the data sets via parametric approaches turned out to be inappropriate due to violation of residual normality, non-parametric approaches where used (Mann-Whitney's U-tests for independent samples). An alpha level of p < 0. 05 was used for determination of significance in all statistical tests; all tests are two tailed. For KaHx experiments if the data were normally distributed, one-way ANOVA was done followed by a Tukey's multiple comparison test. If values were not normally distributed, the non-parametric equivalent Kruskal-Wallis test was used followed by a Dunn's multiple comparison test.
ACKNOWLEDGEMENTS This work was supported by the  INSERM (French National Institute for Health and Medical Research) , the  French Ministry for Research and upper Education , the  Caen-Normandy University , the  RHU  project  Marvelous  ( 16-RHUS-0009 ), the  ENTRAIN Marie Sklodowska-Curie Innovative Training Network ,  Fonds Européens de Développement Economique et Régional (FEDER) Normandie  and  AIRC  IG n. 15572 to PC. J. P. is supported by grants from the  Swiss National Science Foundation  ( 310030-163064  and  310030-182332 ).
pending the signature of a MTA approved by  Aix Marseille University. Funding: ES has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skodowska-Curie  grant agreement  No713750. Also, it has been carried out with the financial support of the  Regional Council of Provence-Alpes-Cote d'Azur  and with the financial support of the A � MIDEX (nÅ NR- 11-IDEX-0001-02 ), funded by the  "Investissements d'Avenir " project funded by the  French Government , managed by the  French National Research Agency (ANR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Microstructure Analysis The microstructure of the cutting edge was studied by EBSD using a field emission gun-scanning electron microscope (FEG-SEM, Zeiss Sigma 300 VP, Zeiss, Oberkochen, German) fitted with an Oxford Symmetry EBSD system, and the step size of EBSD analysis was 0. 5 µm. Then the EBSD data were analyzed with the help of EDAX OIM Analysis 7. 0 software. To study the distribution of deformation at the cutting edge of the punched sample, the kernel average misorientation (KAM), which calculates the average misorientation between the chosen pixel and its neighbors  [10] , was applied to analyze the local grain misorientation for studying the distribution of geometrically necessary dislocations (GNDs). During the KAM analysis of this study, the first neighbor was chosen to define the kernel size of calculation, and the maximum misorientation (or exclusion angle) was selected as 5 • to ensure the intra-substructure analysis instead of inter-substructure analysis.
From area 2 to area 3, the change in elongation direction of the grains was continuous. The distribution of plastic deformation at cutting edge was also studied by KAM distribution analysis after EBSD testing. As mentioned above, KAM analysis is based on the misorientation between pixels. For bent areas 2 and 3 of the cutting edge, there existed a change in coordinate axis directions of the sample system relative to the detector system, which must have led to the error in orientation for each pixel in these zones. Therefore, EBSD data in our case were corrected before KAM analysis. The orientation of each pixel in area 2 was corrected one by one with pixel rotation method (PRM)  [21]. However, since area 3 was an integrally bent region, EBSD data obtained in this region were corrected by overall reverse rotation of the orientations of all pixels.
Thus SymbRep(T \ ∆ P,R ) = SymbRep v (T \ ∆ P,R ). Then SymbRep(T \ ∆ P,R ) = SymbRep v (T \ ∆ P,R ) ⊆ SymbRep v (T). (⊆) Let w ∈ SymbRep v (∆ P,R ). Then w = lim →0 SymbRep(x + v) for some x ∈ ∆ P,R. We may extract a subsequence (SymbRep(x + n v)) n ∈ N with n ∈ R such that x + n v ∈ T \ ∆ P,R for all n ∈ N. This implies that w ∈ SymbRep(T \ ∆ P,R ). Therefore SymbRep v (∆ P,R ) ⊆ SymbRep(T \ ∆ P,R ). We obtain SymbRep v (T) = SymbRep v (T \ ∆ P,R ) ∪ SymbRep v (∆ P,R ) ⊆ SymbRep(T \ ∆ P,R ) which proves the first equality. Recall that the collection of all allowed patterns for P, R is the language L P,R. The set SymbRep(T \ ∆ P,R ) is a subshift and contains L P,R. Moreover the language of SymbRep(T \ ∆ P,R ) is contained in L P,R. The equality SymbRep(T \ ∆ P,R ) = X P,R follows since the symbolic dynamical system X P,R is the unique subshift whose language is L P,R.
The author acknowledges financial support from the  Laboratoire International Franco-Québécois de Recherche en Combinatoire (LIRCO) , the  Agence Nationale de la Recherche  through the project  CODYS  ( ANR-18-CE40-0007 ) and the  Horizon 2020 European Research Infrastructure project OpenDreamKit  ( 676541 ). Sébastien LABBÉ.
A spin lock field of 1. 5 kHz was used in the R 1ρ experiments. The decay of magnetization was sampled at 70, 130, 10, 90, 230, 30, 210, 50, 170, 1 ms with a repeat at 70 ms for the R 1ρ experiments, and at 0, 0. 6, 0. 08, 1. 6, 0. 4, 1. 8, 1. 04, 0. 8, 0. 2 s with a repeat at 0. 6 s for the R 1 experiments. Errors on the relaxation rates were estimated from Monte-Carlo simulations using the noise in each plane of the pseudo-3D spectra as errors on measured peak intensities. The concentration of MKK4 (200 µM) was kept constant, while varying the amount JNK1 kinase: 0%, 10%, 30% and 50% molar fraction for the R 1ρ experiments and 0%, 30% and 50% for the R 1 experiments. The measured 15 N R 1 rates are identical within experimental error for all molar fractions of JNK1, allowing us to use the most precise R 1 dataset (molar fraction of 0% JNK1) to correct for resonance offset  [37].
NMR Exchange Experiments The 15 N chemical exchange saturation transfer (CEST) experiments  [40]  were carried out on a sample of 250 µM MKK4 with 0 or 10% molar ratio of JNK1 at a 1 H frequency of 700 MHz or 850 MHz. A B 1 saturating field of 30 Hz (free MKK4) and 21 Hz (MKK4 with JNK1) was used during a constant period of 300 ms. The data were analyzed according to a two-site exchange model using ChemEx (https://github. com/gbouvignies/ChemEx accessed on 25 May 2021) as described in the main text. The NMR exchange experiments (CEST and CPMG relaxation dispersion) on MKK4 with the p38α kinase were previously acquired  [35]. Here, we used the dataset of MKK4 (247 µM) with 6% molar ratio of p38α to obtain the chemical shift differences between free and p38α-bound MKK4.
NMR Exchange Experiments The 15 N chemical exchange saturation transfer (CEST) experiments  [40]  were carried out on a sample of 250 μM MKK4 with 0 or 10% molar ratio of JNK1 at a 1 H frequency of 700 MHz or 850 MHz. A B1 saturating field of 30 Hz (free MKK4) and 21 Hz (MKK4 with JNK1) was used during a constant period of 300 ms. The data were analyzed according to a two-site exchange model using ChemEx (https://github. com/gbouvignies/ChemEx accessed on 25th May 2021) as described in the main text. The NMR exchange experiments (CEST and CPMG relaxation dispersion) on MKK4 with the p38α kinase were previously acquired  [35]. Here, we used the dataset of MKK4 (247 μM) with 6% molar ratio of p38α to obtain the chemical shift differences between free and p38α-bound MKK4.
Acknowledgments: We acknowledge platforms of the  Grenoble Instruct European Research Infrastructure Consortium (Integrated Structural Biology Grenoble ;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology. Platform access was supported by  French Infrastructure for Integrated Structural Biology  ( ANR-10-INSB-05-02 ) and the  Grenoble Alliance for Integrated Structural and Cell Biology, a project of the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The  Institut de Biologie Structurale acknowledges integration into the Interdisciplinary Research Institute of Grenoble.
Funding: This work was funded by the  French Agence Nationale de la Recherche  through  ANR T-ERC MAPKassembly  (to M. R. J. ), by a Ph. D. fellowship to J. K. from the  IDPbyNMR Marie Curie action of the European commission  (Contract no 264257) and by a Ph. D. fellowship to T. O. from the  Ecole Doctorale Chimie & Sciences du Vivant (EDCSV) of the University Grenoble Alpes (UGA).
Morphometric analysis of pulmonary fibrosis To further investigate pulmonary fibrosis, bronchiolar and alveolar fibrosis was quantitatively evaluated by morphometry. To measure fibrosis in the terminal bronchioles, photomicrographs were taken of trichrome-stained lung sections. Using lung sections stain with Masson's trichrome stain and ImageJ software (ImageJ (nih. gov)), measurements were (1) the area of collagen staining in the wall of the terminal bronchiole, (2) the total area of the region evaluated, and (3) the length of the basement membrane for the area measured. This enabled calculation of the area of fibrosis per micron of basement membrane (Fig. 4 ) and as percent area of fibrosis per area of the total field of view (Additional file 1: Fig. S3 ). Two distinct regions were systematically sampled in each lung section: (1) areas of minimal to no severity, or least affected terminal bronchioles, and (2) areas of most severe fibrosis, or most affected terminal bronchioles.
Bronchiolar fibrosis To assess the severity of bronchiolar fibrosis at 84 d post-exposure in lung sections stained with Masson's Trichrome stain, images of the broncho-alveolar duct junction in the most affected regions were taken using an Olympus BX63 microscope equipped with a DP73 camera and CellSens Dimension software (Olympus Corporation, Tokyo, Japan). For each lung section, a total of six images were taken. Three images were taken of junctions with minimal severity of bronchiolar fibrosis. Additionally, three images were taken of most affected junctions representing the most severe fibrosis. Only three images were taken of control animals as there were no "affected" regions. For each treatment, one section per mouse was taken for a total of n = 4-7 per treatment group. Bronchiolar fibrosis was quantified using ImageJ (National Institutes of Health, Bethesda, MD) as previously described  [52].
Statistics Data are presented as mean with standard error or standard deviation as indicated in the figure legends. Figures were prepared using SigmaPlot software (Systat Software, INC). Statistical analysis used include one-way analysis of variance (ANOVA), and two-way ANOVA (Dose by Treatment). Histopathological scoring were analyzed using Kruskal-Wallis tests and pairwise post-hoc comparisons were generated using Wilcoxon Rank-Sum tests. Differences were considered significant at p < 0. 05. This analysis was performed using SAS/STAT software, Version 9. 4 of the SAS system for Windows, and JMP version 12. 2 (SAS Institute, Cary, NC).
Numerical Modelling Details The FEA software Abaqus/CAE (ABAQUS Inc. , Johnston, RI, USA) was utilized to perform all simulations. All CCA samples were meshed using a mixture of hexahedral elements of type 'C3D8R' and wedge elements of type "C3D6 (both of linear geometric order, from the standard element library) to generate a regular symmetric mesh. However, the ACCA sample was meshed using only hexahedral elements. Independent Al and Cu parts were then assembled by merging the interfacial elements that satisfies the perfect interface assumption and allows the development of transverse stresses. All models were assigned a boundary condition of type "ZSYMM" (symmetry about a constant z-plane) on the fixed end. An arbitrary displacement of 0. 005 mm (0. 05% strain-within the reasonable range of elastic domain) was applied on all the CCA elastic models listed in Table  1.
To this end, we selected the most intense m/z features detected only in one or two monoculture or coculture samples from the 11 sample groups belonging to the experiment on NB medium after 4 days of growth (e. g. , Fig. S4 ). We performed tandem mass spectrometry (MS2) using the two remaining replicates (of eight total) belonging to each of the 11 coculture groups, resulting in successful acquisition of MS2 data for 80% to 90% of targeted parent (MS1) ions per sample. These spectra were submitted to the GNPS platform  (26) , which putatively identified 62 of the m/z features (Table  S3 ). We focused on four compounds of relatively high molecular weight because our targeted MS1 spectra in direct infusion were devoid of interfering peaks in these regions. Two putative identifications were the siderophores proferrioxamine G1t (m/z 520.
All statistical analyses were carried out in R (v4. 0. 2)  (31). In order to detect how many of the 26 known compounds had diffused through the system, we searched for 23 possible adducts (Table  S1 ) with a tolerance of 5 ppm using the function match. closest in the MALDIquant package  (32). A compound was considered detected if one or more adducts were identified in a well. Coculturing of soil isolates. The SubTap can be used to culture cells in a controlled environment with stimuli of interest (e. g. , drug candidates, antibiotics, extracts). We isolated five bacteria from the same soil sample and identified them as Streptomyces s18 (St. s18), Bacillus sp. (Ba. sp), Arthrobacter sp1 (Ar. sp1), Arthrobacter sp2 (Ar. sp2), and Aminobacter sp. (Am. sp) by sequencing their 16S rRNA gene (GenBank accession numbers MW041646 to MW041650).
Streptomyces coelicolor M145 (St. coe) was used as a positive control. MS1 data were collected for six replicates of monocultures of each strain as well as pairwise cocultures of strain St. s18 with Ba. sp, Ar. sp1, Ar. sp2, Am. sp, and St. coe. To explore the ability of the platform to detect small changes in the exometabolome at different time points, two replicate SubTap plates containing Luria-Bertani (25 g/liter) agar medium were incubated for 4 days and 6 days. Similarly, a third SubTap was made with nutrient broth (8 g/liter) agar and incubated 4 days to evaluate the impact of medium on the exometabolome. For each sample, 5 ml of cell suspension containing 10 6 cells per ml was added to both compartments of a well for monoculture, in separate compartments of a well for unmixed coculture, and 2.
The extract was directly injected into the MS through the Advion nanochip (5-mm nominal internal diameter nozzle chip). In order to detect problems with injection or acquisition, an external defined quality control (part number G1969-85000; Agilent), stored in position A1, was injected every 10 samples. MS data were acquired following the spectral-stitching method where data are measured in several overlapping m/z windows and subsequently "stitched" together to create a complete mass spectrum  (24). Full mass spectra were taken across the entire mass range and followed by a series of 100 m/z wide window scans. A 30-s wait period was established to allow for nESI spray stabilization prior to acquisition. The mass spectrometer was configured with the following parameters: mass resolution at 200 m/z of 100 K, full scans of 70 to 1,200 m/z, and 16 total window scans of 100 m/z selected ion monitoring (SIM) with 30 m/z overlap between windows, all with a 10 6 AGC target.
Window scans across the m/z range required ;15-s acquisition time. This process was repeated seven times per m/z window, the total time per injection was approximately 5 min. Processing of mass spectrometry data. Mass spectra were processed by scan stitching, noise removal, peak picking, and peak alignment using the Python (v2. 7. 15) package DIMSpy (v1. 3. 0) and its standard operating procedure  (24). First, SIM windows from 780 m/z to 1,200 m/z were discarded due to consistently low intensity. Then, the remaining SIM windows were stitched together, noise was removed with the noise_packets function using a minimum signal-to-noise ratio (SNR) of 3, and peaks were detected with a maximum mass tolerance of 3 ppm across replicate scans of the same sample. Finally, peaks were aligned across all samples with a maximum mass tolerance of 3 ppm in order to obtain a matrix of peak intensity by sample.
The result was used to reorder a heatmap of the observed intensities such that m/z features were grouped by the strain(s) to which they were attributed (Fig. 4 ). Acquisition and processing of tandem mass spectrometry data. Tandem mass spectrometry data were acquired for the experiment with NB medium at 4 days of growth. We selected m/z features present at high intensity in 100% of the six replicates of one or two samples and absent in all other samples (e. g. , see Fig. S5  in the supplemental material). This resulted in 11 lists of targeted m/z features for tandem mass spectrometry on the two remaining sample replicates of each monoculture and coculture. Extraction and injection in the mass spectrometer were identical to the procedure for acquiring MS1 data (see above), but the MS data acquisition method was modified to acquire MS2 between full spectral scans.
Specifically, an overall MS scan was collected and then another every seventh scan thereafter. For the interim six MS scans, collision-induced dissociation (CID) fragmentation was performed on the target peaks if it was detected in the full spectral scan (using a new target peak every cycle). The acquisition time per injection was 12 min. Tandem mass spectrometry data were processed under R with package xcms  (35) , after files were transformed into mzml format with ProteoWizard (v3)  (36). To control the percentage of targeted m/z features leading to the acquisition of fragmentation spectra, mzml files were processed with the.
ACKNOWLEDGMENTS We thank  Ralf Weber  for assistance with DIMPsy,  Travis de Wolfe  for providing the bacterial strains,  Ann Donnelly  for feedback on the manuscript, and  Eric Choi  for assistance with the design of the elevator plate. A portion of the research was performed at the  Environmental Molecular Sciences Laboratory , a  U. S. Department of Energy National User Facility  sponsored by the  DOE Office of Science, Office of Biological and Environmental Research , which is located at  Pacific Northwest National Laboratory  (EMSL proposal 50808). We acknowledge the  NIH  for financial support ( NIAID  grant  1DP2AI145058-01 ).