dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  A. Marcowith  for discussions. This work has been supported by  Univ. de Savoie , appel à projets: Diffusion from  Galactic High-Energy Sources  to the Earth (DIGHESE), by the  Programme National des Hautes Energies of CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES , and by  Villum Fonden  under project no. 18994. This work is partly supported by the  ANR  project  ANR-18-CE31-0006 , the national CNRS-INSU programmes PNHE and PNCG, and  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant  agreements  N • 690575  and  N • 674896.
App. B details results on the broken-down constraints from various low-energy datasets. The tension with ISOMAX data also reflects in the global fit (last column), which is pushed towards slightly larger L values, also preferred by AMS-02 Be/B data (second column). SLIM QUAINT Base & Be/B (AMS-02) L [kpc] 4. 96 +2. 97 -1. 76 5. 04 +3. 07 -1. 79 4. 79 +3. 19 -1. 77 χ 2 / n dof 233. 7 / 193 233. 1 / 195 235. 3 / 194 χ 2 nui / n nui 17. 4 / 20 17. 4 / 20 15. 8 / 20 Base & Be/B & 10 Be/Be & 10 Be/ 9 Be (all data) L [kpc] 4. 64 +1. 35 -0. 94 4. 66 +1. 35 -0. 97 4. 08 +1. 33 -0. 78 χ 2 / n dof 266. 3 / 251 265. 6 / 253 269. 0 / 252 χ 2 nui / n nui 25. 6 / 35 25. 4 / 35 25. 6 / 35 at once-.
We stress that pre-AMS-02 fit to B/C data (e. g. Maurin et al. 2001)  had larger V A. It was found that strong reacceleration gave rise to a prominent bump around 1 GeV in the predicted positron flux, which then easily overshoots the data, especially below a few GeV (Delahaye et al. 2009 , Lavalle et al. 2014 , and Boudaud et al. 2017). However, with milder reacceleration here, we see in the right panel that the flux predictions associated with QUAINT are not in that strong excess with respect to the data, and do not feature any significant bump.
Among the cell-based therapy investigations for COVID-19, some registered clinical trials aim to utilize extracellular vesicles (EVs) prepared from MSC-conditioned media rather than the cells themselves. MSC-EVs will be administered intravenously (ChiCTR2000030484) or by inhalation (NCT04276987, ChiCTR2000030261). The rationale for these approaches is based on a relatively small but growing number of investigations in pre-clinical lung injury and sepsis models in which MSC-EV preparations were described as being as safe and effective as-if not more than-their parent cells  [19, 40]. The approach is further supported by a growing body of literature on the therapeutic potential and mechanisms of EVs in a wide range of diseases, including recent positive results in a steroidrefractory graft-versus-host disease patient treated with MSC-EVs and in a single-center, randomized, placebo-controlled phase 2/3 clinical pilot study on chronic kidney disease patients treated with MSC-EVs  [14, 25].
Results Selection of cancer mutations in the Dyrk2 kinase. In the first layer of the multi-layered workflow (Fig. 1a ), we set out to identify and express a panel of functionally/structurally relevant Dyrk2 mutants. We used as a reference set the 119 mutations in the DYRK2 gene reported in the COSMIC database (11. 08. 2015)  12  , most of which are missense mutations (n = 72) located in the kinase domain (Supplementary Table  4 ). To select a suitable subset for this study, we considered the frequency of the mutations occurring in DYRK2 and a damage probability score provided by the structure-ppi algorithm  13  , which predicts the impact of missense mutations on proteins based on known structural information, the position of mutations in conserved and functionally relevant regions and the presence of mutations in tumor samples.
The Dyrk2-mutant constructs were genomically integrated into T-REx HEK293 cells using the Flp-In recombination system and expressed in an inducible manner by addition of doxycycline. As determined by western blot analysis, the generated Dyrk2 mutants were expressed at levels comparable to the wild-type (wt) Dyrk2 (Supplementary Fig. 1b ). A reference interaction network of the Dyrk2-EDVP complex. To our knowledge (i) the protein-protein interactions (PPIs) of all EDVP components have not yet been examined, (ii) no proximal interactome, which sheds light on transient interactors of this complex has been reported, and (iii) the overlap between the interactors reported to date for the four EDVP components is very low (n = 2, IID (Integrated Interactions Database)  18  ), probably due to the heterogeneity of the experimental methods used so far.
This represents a more than fourfold increase in identified interactors compared to database knowledge. Our data both recalled several known EDVP members and identified further proteins associated with the core complex. Among the most extensively characterized interaction modules, we confirmed the interaction of Dyrk2 with the EDVP complex components DDB1, VprBP, and Ubr5 by both methods (Fig. 2 , cytoscape graph and lower pie charts). Similarly, the interaction between DDB1 and the E3 CRL4 ligase machinery including several DCAF substrate receptors as well as the known association with the COP9 signalosome was represented in our data set  [19] [20] [21]. Furthermore, we found that Dyrk2 is prominently associated with members of the Set1A/COMPASS complex and several kinesins, which is in line with a putative role of Dyrk2 in cytokinesis and mitosis  5.
3d ). We further performed immunofluorescence microscopic analysis of GFPtagged wt Dyrk2 and its mutant variants. This demonstrated a clear nuclear translocation defect of Dyrk2 SX, resulting in an exclusive cytoplasmic localization of this mutant. The nuclear localization of Dyrk2 KR, on the other hand, was not affected (Supplementary Fig. 3e ). This finding suggests that the Y-complex-related interaction phenotype of Dyrk2 KR is associated with the loss of catalytic activity of Dyrk2 and not with a perturbed cellular localization indicating that specific phosphorylation events at the nuclear envelope could be required for the interaction. Overall, our data show significant and mutation-specific reorganization of the Dyrk2 interactome upon genetic perturbation of the kinase and a clear effect of some Dyrk2 mutants on the interactions with known CDPs.
5c ), form the first group and are exclusively positioned within the kinase domain. These cross-links could be mapped to the known crystal structure of Dyrk2 (PDB: 3K2L) and all fell within the expected DSS distance restraint of ~30 Å (Fig. 5b )  10, 28  , validating the correctness of the cross-linking procedure. The second group comprises the remaining ten high-confidence cross-links mapped to the Nterminal or C-terminal region of the kinase, for which no structural information is currently available (Fig. 5c ). This group of cross-links suggests that the potentially disordered N-terminal region of the protein (IUPRED; data not shown) is making contact with large portions of the protein, primarily the Cterminal region of the kinase domain. In both mutants the first group of cross-links remained largely unaltered, indicating that the structure of the kinase domain was not fundamentally reorganized.
We The statistical analysis and p-value calculation (adj. p-value (FDR)) was either performed within the mapDIA package (phosphodata) or with two-tailed unpaired Student's t-test and p-values were adjusted using the method of Benjamini-Hochberg 67 (interaction data). The network is based on reported interactions between the proteins (IID v. 2018-05; only experimentally validated interactions). The gene ontology analysis was performed with DAVID and significant terms (p-values < 0. 05) were merged in general terms ("Methods" section). The regulation of the interaction and phosphorylation of cancer driver proteins by Dyrk2 mutants is illustrated with a color code. For PCM1 the lower color code shows the regulation on interactome level, the upper color code shows the regulation on phosphorylation level.
The cell lysate was cleared by centrifugation (15,000×g, 20 min) and boiled for 5 min after addition of 3× Laemmli sample buffer. The denatured sample was loaded on NuPAGE 4-12% Bis-Tris SDS-PAGE gels (Invitrogen) for gel electrophoresis and then transferred onto nitrocellulose membranes (Trans-Blot Turbo, BioRad). The following primary antibodies were used: anti-Dyrk2 (HPA027230, Sigma; 1:200), anti-DDB1 (D4C8, #6998, Cell Signaling; 1:1000), anti-Ubr5 (D608Z, #65344, Cell Signaling; 1:1000), anti-VprBP (#14966, D5K5V, Cell Signaling; 1:1000), anti-FLAG (F3165, Sigma; 1:5000), anti-HA (HA. 11,901513, BioLegend; 1:5000), and anti-Actin (ab179467, Abcam; 1:1000). Proteins were detected by enhanced chemiluminescence (ECL, Amersham) using horseradish-peroxidase-coupled secondary antibodies (#7074, Cell Signaling (1:5000) and #115035003, Jackson ImmunoResearch (1:5000)).
Cell cycle analysis. Cell cycle analysis was performed with the Propidium Iodide Flow Cytometry Kit (Abcam, ab139418) according to the manufacturer's instructions. Briefly, T-REx-HeLa cells (#R71407, Invitrogen) were washed with PBS and then incubated with propidium iodide and RNase for 30 min at 37 °C. The stained cells were analyzed with the BD LSRFortessa (BD Biosciences) flow cytometer and data were analyzed with FlowJo v9. 7. 6. Apoptosis assay. MDA-MB-231 cells (ATCC HTB-26) were analyzed for apoptosis using the Annexin V-FITC Apoptosis Staining/Detection Kit (Abcam, ab14085) according to the manufacturer's instructions. Briefly, cells were harvested and resuspended in binding buffer. Then cells were incubated with Annexin V-FITC and propidium iodide for 5 min at room temperature followed by flow cytometry analysis with BD LSRFortessa (BD Biosciences) flow cytometer.
The data were analyzed with FlowJo v9. 7. 6. In vitro phosphorylation of NUP214. Commercial recombinant NUP214 (Aviva Systems Biology, OPCD05854) was incubated with 100 ng commercial recombinant Dyrk2 (Promega, V5090) in kinase reaction buffer (Promega, V9101) supplemented with ATP (0. 1 μM) and DTT (1 mM) for 1 h at 37 °C. The reaction was stopped by adding 3× Laemmli buffer and heating up at 95 °C for 5 min. In vitro ADP-Glo kinase assay. The in vitro kinase assay to estimate the activity of Dyrk2 mutants used in this study was performed with the ADP-Glo kinase assay kit (Promega, V9101) according to the manufacturer's instructions. Briefly, equal amounts of recombinant Dyrk2 variants purified from SF9 insect cells were incubated in kinase reaction buffer (Promega, V9101) together with ATP (10 uM), DTT (50 μM) and 3 μg substrate (DYRKtide) (Promega, V9101) for 1 h at 37 °C.
5 µg LysC (Wako) for 3 h, followed by dilution to 1 M urea and digestion by 0. 8 µg trypsin over night. The digestion was stopped by addition of 5% formic acid and the peptides were purified by C18 UltraMicroSpin columns and dried in a speedvac. The dried peptides were dissolved in 2% acetonitrile and 0. 1% formic acid. Data acquisition for interaction analysis. LC-MS/MS analysis was performed on an Orbitrap Elite mass spectrometer (AP-MS) (ThermoScientific) coupled to an Easy-nLC 1000 system (ThermoScientific) and LTQ Orbitrap XL mass spectrometer (BioID-MS) (ThermoScientific) with Xcalibur software (4. 1) (Thermo) coupled to an Easy-nLC II system (Proxeon). For samples derived from affinity purification peptides were separated on a Thermo PepMap RSLC column (15 cm length, 75 µm inner diameter) with a 60 min gradient from 5 to 35% acetonitrile at a flow rate of 300 nl/min whereas for BioID samples a gradient of 90 min (5-35% acetonitrile) was used.
Samples for total proteome analysis were measured on a Sciex TripleTOF 6600 instrument (AB Sciex Instruments) with SCIEX Analyst v1. 7 software equipped with a NanoLC Ultra 2D (Eksigent). Peptides were separated using a self packed C18 column (PicoTipTM Emitter, New Objective, Woburn, USA) (21 cm length, 75 µm inner diameter) with a 60 min gradient from 2 to 35% buffer B (98% acetonitrile and 0. 1% formic acid) at a flow rate of 300 nl/min. The mass spectrometer was operated in SWATH-mode using 64 variable windows between 400 and 1200 m/z (1 m/z overlap). The collision energy for each window was determined based on calculation for peptides with a charge state of 2+ adding a spread of 15 eV. For total proteome analysis, 1 µg peptides of the sample was injected. Data acquisition for phosphoproteomic analysis.
MS1 scan time was 300 ms over a mass to charge range of 360 to 1460, followed by 20 MS2 spectra measurements with 150 ms per spectra from 50 to 2000 m/z. For CID fragmentation, a collision energy spread of 15 eV was dynamically adjusted. The same phosphopeptide-enriched samples were submitted to measurements in SWATH-mode on the TripleTOF 6600 instrument. For SWATH measurements, the gradient was shortened to 90 min from 5% solvent A (2% acetonitrile and 0. 1% formic acid) to 35% solvent B (98% acetonitrile and 0,1% formic acid). Data were acquired in positive ion and high-sensitivity SWATH-mode, using 100 variable windows from 400 to 1250 m/z with 1 m/z overlap at the upper window boarder. A measurement time of 200 ms for MS1 precursor scans and 30 ms for each fragment ion scan was set, resulting in a 3.
2 s duty time per cycle. Data acquisition for cross-linking analysis. LC-MS/MS (DDA mode) was performed on Orbitrap Lumos Tribrid mass spectrometer (ThermoFischer) equipped with a Thermo easy-nLC1200 liquid chromatography system (ThermoFischer). Peptides were separated using reverse phase column (Acclaim PepMap RSLC C18 column, 2. 0 µm, 75 µm*250 mm) across 60 min linear gradient from 7 to 35% (buffer A: 0. 1% (v/v) formic acid, 2% (v/v) acetonitrile; buffer B: 0. 1% (v/v) formic acid, 98% (v/v) acetonitrile). The data acquisition mode (data-dependent acquisition) was set to perform a cycle of 3 s with high-resolution MS scan in the Orbitrap (120,000 at 400 m/z) and MS/MS spectra in the ion trap. Charge state lower than 3 and bigger than 7 were rejected. The dynamic exclusion window was set to 25 s.
Precursors with MS signal that exceeded a threshold of 5000 were allowed to be fragmented (CID, collision energy 35%). The ion accumulation time was set to 50 ms (MS) and 100 ms (MS/MS). LC-MS/MS (target mode) was performed on Orbitrap Lumos Tribrid mass spectrometer (ThermoFischer) equipped with a Thermo easy-nLC1200 liquid chromatography system (ThermoFischer) using the same configuration as described for DDA mode. The data acquisition mode (PRM) was set to perform a MS1 scan followed by time scheduled targeted PRM scans acquired at variable resolution (60,000 and 120,000) fragmented as in the DDA acquisition. The quadrupole isolation window for the PRM events was set to 1. 4 m/z units and the duration of the time scheduled windows were set to 2 min. Data analysis on interactomics data. Acquired MS/MS scans were searched against the UniProtKB/Swiss-Prot protein database (10.
05. 2018) with the Euler-Portal (ETH in-house software) workflow using the search engines X!TANDEM Jackhammer TPP (2013. 06. 15. 1-LabKey, Insilicos, ISB), Comet (2016. 01 rev. 3) and MyriMatch v2. 1. 138 considering a precursor mass tolerance of 15 ppm and a fragment mass error of 0. 4 Da. Tryptic peptides with a maximum number of two missed cleavages were considered for the peptide identification search. Carbamidomethyl on cysteine residues as static modification was added. High confident interactors of AP-MS experiments were determined by SAINTexpress  53  with default parameters using spectral counts obtained from EulerPortal. Eighteen Strep/HA-GFP pulldowns processed and measured in the same way like the samples were used as controls for SAINTexpress scoring. Proteins with a SAINT score > 0.
95 were filtered additionally against control runs of the CRAPome database (http://crapome. org/; 54 411 pulldowns). IDs identified in > 15% of the CRAPome control runs and in the SH-GFP pulldowns of this study were removed to obtain the final set of high confident interactors. High-confidence interactors of BioID-MS experiments were determined by SAINTexpress with default parameters as described. As control nine experiments with BirA*-GFP processed and measured in the same way like the samples were considered for the SAINT scoring. Furthermore, cells expressing the bait protein but not treated with biotin (nine experiments for Dyrk2, three experiments for Ubr5, DDB1, VprBP) were used as additional control for the SAINT scoring in order to identify and remove endogenously biotinylated proteins.
IDs assigned with a SAINT score = 1 were considered as high confident interactors. For the quantification of interactome changes in the AP-MS and BioID-MS network, LFQ MS1 intensities were determined by MaxQuant analysis v1. 5. 2. 8 using default parameters and the UniProtKB/Swiss-Prot protein database  25. Statistical analysis was performed by customized R scripts. Briefly, LFQ MS1 intensities were bait-normalized and missing values were imputed using random sampling from a distribution based on the 5th lowest quantile. In the following, fold changes and adjusted p-values were calculated. Network visualization and GO analysis. The general layout of protein-protein interaction networks (Figs. 2a  and 6a ) was generated using Cytoscape (v3. 6. 0)  55. To calculate the recall rate and represent already deposited interactions, we used as a reference database the Integrated Interaction Database (IID; release 2018.
p-value < 0. 05)). (ii) We mapped the known interactions among the selected proteins based on the integrated interactome database (IID v. 2018-05; http://iid. ophid. utoronto. ca/). Of all interactions reported in IID, only those annotated as experimentally validated and with associated PubMed ID(s) were considered. In the graph, isolated nodes are those for which no interaction was retrieved. (iii) We performed a gene ontology analysis using DAVID (https://david. ncifcrf. gov/). Specifically, we considered the Biological Process domain (as opposed to Cellular Component or Molecular Function domains), and the default "Direct" category with default Homo sapiens background. To group GO terms, we used (i) shared parent terms identified with amiGO2 v. 2. 5. 12 (which provides an inferred tree view of GO terms), (ii) and/or on shared assigned genes, (iii) and/or semantic similarity not captured in the amiGO2 inferred tree view.
Data analysis on total proteome data. DIA-SWATH data were analyzed with the OpenSWATH workflow  57  implemented on the in-house EulerPortal platform after conversion of the raw SWATH wiff files into mzXML format by ProteoWizard (3. 0. 8851). Spectra were extracted using the combined human assay library with a fragment ion m/z extraction window of 0. 05 Th and a retention time extraction window of 480 s. Detected features were aligned using TRIC with a target FDR of 0. 01. If no peak group was detected, the area was re-quantified as described  58. The OpenSWATH data output was further processed by the R package SWATH2stats  59. The data were filtered with a global m-score cut-off of 3. 9811E-5 resulting in a protein FDR of 0. 0284 using an estimated fraction of false targets (FFT) of 0. 46.
The resulting data matrix was further filtered for proteotypic peptides. The following quantitative and statistical analysis was carried out by mapDIA v1. 2. 1  60. The filtered data matrix was normalized using total intensity normalization and an independent study design with a minimum correlation of 0. 1 was chosen. For the calculation of fold changes and p-values, at least one peptide per protein and between 3-6 fragments per peptide were selected. Generation of a phosphopeptide SWATH-assay library. The 26 in DDA mode measured files were used to generate a phosphopeptide-specific SWATH library. The raw MS files were converted to the mzXML file format with ProteoWizard (3. 0. 8851)  61. For peptide identification, the data were searched using EulerPortal with the search engines X!TANDEM Jackhammer TPP (2013.
Phospho-SWATH data quantification. The extraction of quantitative phosphopeptide data was conducted with the OpenSWATH workflow 57 integrated in the EulerPortal tool and the Dyrk2-mutant-specific phospho-SWATH library generated for this project. The raw wiff files were converted to profile mzXML files with ProteoWizard (3. 0. 8851). All recorded phospho-SWATH maps were extracted with OpenSWATH applying an m/z fragment ion extraction window of 0. 05 Th around the expected mass of the fragment ions and a retention time window of ±300 s after iRT-alignment. Scoring of the peaks was conducted with PyProphet applying 10 different scores. Detected features were mapped between runs, and re-aligned using a regression with a local minimum spanning tree model, with a 0. 01 target FDR. If there was no feature identified within a run, the peptide and fragment signals were obtained by quantification of the respective background signal at the expected elution time of the peptide, allowing to boost the completeness of the resulting data matrix  58.
Next, the obtained intensities for all fragment features from Open-SWATH were processed with SWATH2stats 59 , allowing for elaborate filtering of the data matrix. Low quality features were removed with an m-score threshold of 0. 01 and additionally a fragment feature had to be detected in at least 30% of all phospho-SWATH measurements. These conservative filtering resulted in a data matrix of 2887 quantified unique phosphopeptides from 1193 uniprot IDs across all conditions. Data were normalized using TIS (total ion spectra) normalization within the mapDIA framework  60. Group comparison between each of the seven conditions against the wild-type Dyrk2 phospho signal was performed within mapDIA. This allowed us to obtain the log2FC values and to estimate the significance of change in intensity of phosphorylation of each given localized or nonlocalized phosphopeptide with a given adj.
p-value (or FDR). Analysis of cross-linking data. MS data were converted to mzXML format with msConvert and searched with xQuest/xProphet 28 against a database containing the fasta sequence of Dyrk2 and its decoy sequence. Cross-linked peptides with a minimal length of 5 amino acids and a xQuest ld (linear discriminant) score higher than 25 were considered and selected for further analysis. The selected cross-linked peptides were quantified across different condition with a targeted proteomics approach (PRM = Parallel Reaction Monitoring). To perform the targeted analysis, we generated a library with the elution time of the precursor, m/z value and the charge of the cross-linked peptides (heavy and light form) identified by xQuest. Data analysis of acquired targeted cross-linked peptides were performed in Skyline v.
Acknowledgements The work was supported by the  SystemsX. chproject PhosphoNetX PPM to R. A. , the  Swiss National Science Foundation  (Grant No. 3100A0-688 107679  to R. A. ), the  European Research Council  ( ERC 20140AdG 670821  to R. A. ), the  Innovative Medicines Initiative  project  ULTRA-DD  ( FP07/2007-2013 , Grant No. 115766  to R. A. and M. G. ). M. M. was supported by a  Long-Term Fellowship  from the  European Molecular Biology Organization  ( ALTF 928-2014 ). We thank  Marija Buljan  and members of the  Kutay lab  for very helpful discussions and critical input.
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  66  partner repository with the data set identifier PXD015687. The source data underlying Figs. 2, 3, 4, 5a, c-f, 6, 7, 8a-c and Supplementary Figs. 1b, 3c , e, 4a, 5b-g, 6a, 7a, b are provided as source data file. All other data are available from the corresponding authors on reasonable request. Source data are provided with this paper.