dataesr/scientific-paragraphs-categorization huggingface dataset:
CALR, MPL); and (4) available next-generation sequencing (NGS) result or archived DNA for NGS analysis. The database of SVT patients referred for MPN suspicion at H ôpital Saint-Louis (n 5 284) and the NGS database of MPN patients routinely followed (n 5 1371) were cross-checked to identify these patients. Clinical data were extracted from medical records and the French Network for Vascular Disorders of the Liver database, approved by the institutional review board (Comit é de Protection des Personnes [CPP] Ile de France IV, Paris, France; number 2003/ 21). Demographic data including age, sex, MPN diagnosis, SVT type, blood counts at diagnosis, spleen size at diagnosis, associated thrombophilia (including factor V, factor II, C677T methylenetetrahydrofolate reductase mutations, protein C, protein S, and antithrombin deficiencies, antiphospholipid antibodies, and paroxysmal nocturnal hemoglobinuria determined by flow cytometry), and cytoreductive and anticoagulant therapy were recorded.
Acknowledgments The authors thank the  French Intergroup of Myeloproliferative Neoplasms (FIM)  and the  French National Network for Vascular Liver Diseases  for helpful discussion; in particular, the authors thank  Kamal Zekrini  for help in database management. N. M. was supported by a Poste d'accueil grant of the  Institut National Du Cancer (INCa). The Varese group was supported by grants from the  Fondazione Regionale Ricerca Biomedica (Milan, Italy ;  FRRB  project no. 2015-0042 ); the  Ministero della Salute (Rome, Italy ;  Finalizzata 2018 ,  NET-2018-12365935 ); the  Ministero dell'Istruzione, dell'Universit à e della Ricerca (Roma, Italy ;  PRIN 2017 ,  2017WXR7ZT );  Fondazione Matarelli  (Milan, Italy); and  AIL-Varese ONLUS (Varese, Italy).
Co-immunofluorescence Formalin fixed and paraffin embedded human brain cortical sections (5 µm thick) from spAD, rpAD and age matched controls were analysed by co-immunofluorescence using protocols validated previously  [42, 86]. Imaging was performed with confocal laser scanning microscope TCS-SPE (Leica, Germany; 543 and 633 nm helium-neon and 488 nm argon excitation wavelengths). All images were separately analysed for colocalization using ImageJ (WCIF plugin) and FIJI (coloc 2) software. Threshold Mander's overlap coefficients (tM1 & tM2) and Pearson's linear correlation coefficient (rP) were calculated to measure the strength and direction of linear correlations between two fluorescence channels. In addition, intensity correlation analysis (ICA) was also performed  [43].
Statistical analysis All the data in the present study was obtained after performing at least three independent experiments. All the results are described as mean ± SEM (standard error of the mean). The Fig. 1  Identification of RNAbinding proteins by RNA pull-down assay and mass spectrometry analysis. a Total RNA was isolated from the human brain frontal cortical region of 20 cases (spAD, rpAD, sCJD-MM1, sCJD-VV2 as well as controls). Bead-only control was used for non-specific binding. Isolated protein complexes were identified quantitatively using MS/MS analysis. In total, 1091 proteins were identified and quantified at a minimum peptide count of 2 and a protein threshold of 99%. b Target selection from proteomic investigation and their pathological characterization in the post-mortem human brains: a combination of bioinformatic and computational approaches was used to find out significant hits from the proteomic study, including differential enrichment analysis of MS data, hierarchical clustering analysis to visualize global proteome profile, comparative RBPome analysis, Gene Ontology (GO) functional enrichment analysis, database search for identification of bona-fide and novel/putative RBP candidates, and prion-like domain scanning with PLAAC database.
Target candidates prioritized from proteomic study were pathologically characterized in the postmortem human brain, using various techniques including immunoblotting, qRT-PCR and immunohistochemical analysis 1 3 densitometry analysis of immunoblots was performed with Image Lab software (version 3. 0. 1). Statistical tests were applied using GraphPad Prism 6. 01. The data from mass spectrometry analysis were analysed using the Perseus software. Hierarchical clustering analysis was also performed with Perseus software. Two-dimensional interaction plots were plotted in R (version 3. 4. 3), followed by editing using Inkscape (version 0. 92). For comparisons between two groups, the student's t-tests or Welch's t-tests were used. For comparisons between three or more groups, a one-way ANOVA followed by the Tukey post-hoc analysis was used unless otherwise stated in the text.
Prion-like domain (PLD) prediction Prion-like domains (PLD) are often present in the RBPs that carry out protein aggregation in neurodegenerative diseases, e. g. amyotrophic lateral sclerosis (ALS) and, more recently, in Alzheimer's disease  [40, 86, 87]. To evaluate the presence and biological significance of PLD-containing proteins in our dataset, identified RBP candidates were analysed using highly-stringent computational algorithms  [81, 92]. We used an updated web-based PLAAC (Prion Like Amino Acid Composition) database to identify proteins with prion-like amino acid composition in our data-set. Criteria for a protein to have a PLD consisted of four requirements: it had to (1) be rich in Q/N-sequences, (2) exhibit disorderliness (according to PAPA algorithm)  [60] , (3) have compositional similarity with yeast prion domains, and (4) 1 3 contain short amyloidogenic stretches able to carry out their self-assembly into an amyloidogenic state.
The data points above the dashed lines represent proteins having a p-value < 0. 05 and FC > ± 1. 5 as significant hits and are depicted in red (for enriched) and blue (for depleted). g The heatmap (hierarchical clustering analysis) showing significantly-enriched proteins. The Log2-transformed expression values of proteins were normalized by Z-score for each biological replicate. Horizontal-axis indicates the differentially-enriched proteins, and the vertical axis shows the biological replicates from all the groups. Blue denotes depleted proteins, red represents enriched proteins 1 3 AD  [4] , although it was not detected in our MS dataset (most likely due to hydrophobic nature of the protein). In order to explore specific mechanisms by which our selected target candidates (particularly SFPQ) are involved in the disease pathogenesis, and to further ascertain whether our selected approaches were appropriate for target selection, we began to analyze these proteins in more detail.
a Venn diagram representing unique and shared RBP candidates in all groups. b Functional classification of MS results: RBP candidates identified in each group were analysed for associated Gene ontology functional-terms in two domains "biological process and molecular function''. The protein counts associated with GO-terms from each group were uploaded to Perseus software, to prepare heatmap showing relative enrichment of different functional categories across all the groups. The variation in each term across the groups was calculated by Z-score. The columns of heatmap are representing disease groups (vertical-axis) and rows are displaying functional terms (x-axis), with red indicating an enriched category as compared with other groups, and green indicating depleted terms  1 3  as differentiation of SG reactivity from lipofuscin can be quite challenging  [46, 86].
TIA-1 immunoreactivity was also observed in control cases and a partial colocalization was also observed with SFPQ (Fig. 5f , zoom-in section). In spAD cases, a moderate colocalization was observed. For rpAD cases, a significant colocalization was identified between SFPQ and TIA-1 in the perinuclear/cytoplasmic area (Fig. 5f , zoom-in panel). Multiple methods were used to provide a quantitative measure of the extent of colocalization between SFPQ and TIA-1, using FIJI (Coloc2) and image J (WCIF plugin) software. We examined the colocalization between SFPQ and TIA-1 using Pearson's correlation coefficient (rP), Threshold Mander's coefficients (tM1, tM2)  [14] , and intensity correlation analysis (ICA). ICA (PDM plots) showed highest degree of colocalization between SFPQ and TIA-1 in the rpAD brains, followed by spAD and controls (Fig.
Control (untreated) cells showed a positive signal for total-tau (tau-5) predominantly in the cytoplasm (suppl. Figure  3a , online resource). For p-tau (S199), a positive signal was detected in the cytosol and nucleus (suppl. Figure  3b ,, online resource), with predominance in the nucleus, which is in-line with previous studies conducted in HeLa cells  [36, 73]. Such a signal was increased upon oxidative stress induction. Stress treatment induced formation of both tau and p-tau granules, which showed colocalization with TIA-1 cytoplasmic SGs (Fig. 7b, c , zoom-in sections). The average number of SGs in each cell was estimated with FIJI-software. We identified a higher number of p-tau positive SGs, compared with tau-positive The cells were co-immunostained with primary antibodies specific for total-tau, p-tau and TIA-1.
d The cells positive for SGs were calculated with FIJI software. More than 80% cells were identified positive for SGs after stress treatment. e Tau and p-tau-positive SGs per cell (average no. of SGs) were calculated using FIJI software. Significance was calculated by t-test, *p < 0. 05. f-i Stress induced increase in tau-phosphorylation and TIA-1 levels. Representative immunoblots for TIA-1, total-tau and p-tau in control (untreated) and stress (arsenite treated) cells. Statistical significance was calculated with t-tests **p < 0. 01, ***p < 0. 001  1 3  granules (Fig. 7e ). Immunoblotting analysis revealed a significant increase in the intensity levels of TIA-1 in stress (arsenite treatment) cells, compared with controls (Fig. 7f, g ). There were no significant changes observed for total-tau levels (Fig.
employed a quantitative proteomics approach (SWATH-MS), which has emerged as a powerful technology for investigation of cellular signalling pathways  [35, 44]. SWATH-MS identified 3597 proteins quantitatively at a critical FDR of 1%. Bioinformatics analysis was performed using Perseus software to identify differentially expressed proteins (DEPs). Proteins showing a corrected Benjamini Hochberg corrected p-value < 0. 05 were considered differentially expressed (Fig. 10a, b ). DEPs (314) were divided into two sets: the first consisted of 63 up-regulated proteins, and the second consisted of 251 down-regulated proteins (suppl. Tables  7  and 8 , online resource).
Proteomic changes associated with SFPQ downregulation after human tau-expression Human-tau expression induced SFPQ downregulation in vitro, in the present study. To investigate the combinatorial effect of tau-toxicity and SFPQ-reduction hallmarks at 48 h post transfection, we A number of bioinformatics tools were used to examine enriched functional categories and pathways after human tau expression. We performed hierarchical clustering analysis and Fisher's exact test by Perseus software. As shown in Fig. 10c , DEPs were clustered hierarchically across all the samples (columns). The main branch point in the columns separated the tau-expressing cells (Fig. 10c , Lanes 1-12) from controls (mock-transfected or non-transfected controls) (Fig. 10c , Lanes 13-24). The DEPs (rows) were grouped into two main clusters.
The second most-enriched network was-cell morphology, cellular assembly and organization, DNA replication, recombination, and repair network-consisting of 20 focus molecules from DEPs from proteomic dataset. Proteomics investigation with functional characterization has provided a comprehensive overview of major alterations associated with tau toxicity and downregulation of SFPQ. Analysis of global proteomic alterations revealed two major themes--RNA metabolism and protein synthesis, and -DNA homeostasis-related processes-that were significantly altered after human tau-expression.
GH model development All individual GH profiles were analyzed with a deconvolution analysis in which the baseline secretion, the elimination rate, and the pulsatile secretion events were extracted from an individual profile. The pulsatile secretion events were assumed to follow a Gaussian shape and the optimal number, the location of secretion events, and the Gaussian pulse width of an individual were determined. This methodology has been shown to have high sensitivity and specificity in the identification of pulses in endogenous pulsatile hormonal profiles  [15]. The individual deconvolution analysis of GH profiles was performed in AutoDecon (developed by  Johnson et al. )  which requires regularly spaced observations, an initial pulse secretion, and an initial half-life as input  [15]. Therefore, a data transformation was performed to maintain the required regularly spaced 20 min sampling interval.
First, modelling started with the estimation of the population parameters in placebo treated individuals. Interindividual variability (IIV) in the population parameters and between-occasion variability (BOV) between day 7 and day 12 was included following a bottom-up inclusion procedure. Then, the estimated population parameters and variance distributions were fixed to the placebo estimates and model development continued with the full dataset containing both placebo and BIM23B065 treated individuals  [17]. Multiple PK/PD relationships, linear and (sigmoidal) maximal effect (E MAX ), driven by the plasma PK of BIM23B065 or via an effect compartment, were tested for significance on the baseline secretion and pulse amplitude parameters during model development  [18]. The E MAX relationship in which the hill coefficient (c) was estimated or fixed to 1, and where the E MAX parameter was estimated or fixed to -100% on the pulse amplitude (assuming a full inhibition of GH secretion) was explored.
PRL model development The data from this study did not include any information on the concentrations of inhibitory feedback hormones for PRL. Furthermore, no dose administrations with short consecutive dosing intervals were administered, which complicated the estimation of feedback mechanisms on PRL secretion and therefore model development focused on the quantification of a pool model, as proposed by Movin-Osswald et al. [14]. As a circadian component in the release of PRL secretion was expected  [19] , all time points in the dataset were normalized to 6 a. m. and the model included a 24 h initialization period. The periodicity of the circadian rhythm of PRL secretion was explored by implementing a cosine function (Eq. 1) in the structural model while only including data from the placebo treated individuals.
Software Data transformation and graphical analysis was performed in R (V3. 4. 0)  [21]. Deconvolution analysis was performed using AutoDecon (V20090124)  [15]. NLME modeling was performed in NONMEM V7. 3  [16]  in conjunction with Perl-speaks-NONMEM V4. 6. 0  [22]. All models were estimated with the first-order conditional estimation with interaction (FOCEI) method applying user-written ordinary differential equations in ADVAN 9 or 13. The non-parametric bootstrap was performed using the 'bootstrap' command in Perl-speaks-NONMEM.
Hence, no observations at the maximum effect (a fully inhibited pulse) are available. If the pulse interval between two identified pulses was higher than 300 min, a [ 95% probability of an unidentified pulse was expected. Therefore, an additional pulse location in the middle of these two identified pulses was added to account for this. This was done for both placebo and treated individuals to prevent a selection bias. As a result, a total of 34 new pulse locations were included (4 placebo-, 30 BIM23B065-treated pulses) in the dataset. In the placebo model, significant IIV was estimated on the elimination rate constant (DOFV = -264. Placebo parameters were estimated with low RSEs (\ 10%) with high levels of inter-pulse variability in the pulse amplitude (CV = 555%). No structural bias could be identified in the CWRESI versus time which showed the majority of the points between the [-2, 2] interval, the population model predictions showed a broad scatter at the lowest regions, indicating a wide distribution in the model fit at the baseline levels (Fig.
In order to account for the underestimation of the 'true' number of pulses in the deconvolution analysis, a pulse location was added if an interval was larger than the 95%percentile of the placebo data. The implementation of these pulses in the dataset informed the concentration-effect curve at the section of the maximal effect (informing on the parameter estimate of E MAX ) and enabled the quantification of the inhibitory BIM23B065 effects with high accuracy in the parameter estimates. However, the high proportion of data below the LLOQ limited the precise estimation of the actual baseline GH secretion in the BIM23B065-treated individuals. The clear increase in the percentage of data below the LLOQ in the BIM23B065-treated versus placebo-treated individuals indicate that BIM23B065 may have had an effect on this baseline secretion, which could not be quantified in the current PK/PD model.
NanoLC-MS/MS data analysis NanoLC-MS/MS raw data were converted into mgf data files using the MS Data Converter software (Peakview, ABSciex). Sequence coverage of the proteins was determined through database searching using the Mascot search engine (Matrix Science; version 2. 6) with the SwissProt mammalian database (Swis-sProt_2018_10 database release) together with a decoy database search. Only peptides with a mascot score above 20 were considered. Inter-molecular cross-linked peptides between aSyn and chaperones were searched using the STAVROX software (version 3. 6. 6. 6),  [51]. For GA, which exists as a mixture of monomers (free aldehyde or cyclic hemiacetal forms) or different polymers  [52] , we set the mass modification to 64. 031 or 82. 042 to search for interprotein peptides cross-linked by a free aldehyde or a cyclic hemiacetal GA, respectively, on a list of potentially reactive amino acids for sites 1 and 2 as reported in the literature, or as indicated by their physico-chemical properties (Lysine, Serine, Threonine, Tyrosine, Arginine, Tryptophan, Phenylalanine, Proline, and N-terminus).
S2A ). When aSyn-aBc complex was cross-linked with GA, a unique and specific protein band appears at around 40 kDa (arrow). Western blot analyses with antibodies directed against aSyn and aBc confirms that this specific band contains both proteins and thus corresponds to a covalent aSyn-aBc complex. Its apparent molecular mass of 40 kDa suggests it is formed of one aSyn and one aBc monomer. The band of interest corresponding to the aSyn-aBc complex was thus cut off the gel and subjected to trypsin digestion. The resulting peptides were analyzed by nanoliquid chromatography coupled to tandem mass spectrometry (nanoLC-MS/MS). For the aSyn-aBc complex, using database searching as described in Materials & Methods, we obtained a sequence coverage of 100% for aSyn and 97% for aBc. Using the Stavrox software  [51] , we identified one aSyn-aBc inter-protein cross-linked peptide that was absent from control bands (monomeric or dimeric aSyn or aBc with or without GA cross-linking) and was validated manually.
The band of interest corresponding to the aSyn-CHIP covalent complex was cut off the gel and in-gel digested using trypsin and the resulting peptides were further analyzed by nanoLC-MS/MS as described above. By database search as described in Material and Methods, we obtained a sequence coverage of 100% for aSyn and 85% for CHIP. Using the Stavrox software  [51] , we identified one aSyn-CHIP inter-protein cross-linked peptide that was absent from control bands (monomeric or dimeric aSyn or CHIP with or without EDC cross-linking) and was validated manually. This peptide was identified as a quadruple charged ion with a mass-to-charge of 616. 136 (Fig. 4B ). Its fragmentation spectrum presented in Fig. 4B  unambiguously identified the cross-linked peptides and sites. The y and b fragments ions, reported in Fig.
Acknowledgements We thank  David Cornu  for expert assistance in MS and  Jeffrey L. Brodsky  for the CHIP plasmid. This work was supported by the  Centre National de la Recherche Scientifique , the  Institut National de la Sant e et de la Recherche M edicale , the  R egion Ile de France through DIM Cerveau et Pens ee  and the  EC Joint Programme on Neurodegenerative Diseases and Agence Nationale pour la Recherche  ( TransPathND ,  ANR-17-JPCD-0002-02  and  Protest-70 ,  ANR-17-JPCD-0005-01 ). This work also received support from the  EU/EFPIA/Innovative Medicines Initiative 2 Joint Undertaking  ( IMPRiND grant  No. 116060  and  PD-MitoQUANT  grant No. 821522 ). This work benefited from the electron microscopy facility  Imagerie-Gif  and the proteomic facility SICaPS.
Mitochondrial and cellular analysis For mitochondrial ROS, mitochondrial mass, and DJm measurements, MEFs were incubated with MitoSOX (5 mM), Mitotracker Green (100 nM), or Mitotracker Red (100 nM; ThermoFisher Scientific), respectively, before assessment in a FACSCanto II (BD Biosciences) of the total population (10,000 cells). Data were analyzed using FlowJo software and expressed as MFI (mean fluorescence intensity), which referred to the fluorescence intensity of each event on average. Additionally, lysed 15 Â 10 3 WT and AIF -/Y MEFs or 1 Â 10 4 cells from embryos dissociated in trypsin were tested for ATP content with a luciferin-luciferase kit (Abcam) and expressed as an ATP/ADP ratio or RLU (relative light units). In some experiments, MEFs were pretreated with oligomycin (10 mM) For lactate measurement in MEFs, cells were cultured for 2 h in a serum-free medium and centrifuged.
Immunohistochemistry Brains of WT and AIF þ/-HC females were fixed in 4% PFA. After decalcification, free-floating brain sections (100 mm) were fixed in PFA before performing the immunostaining with anti-Iba1 (1:100 Dako). We used a long incubation process (24 h) after deterging the lipid with high concentration triton X (1%). Nuclei were counterstained with the nuclear dye Hoechst 33342. Slides were imaged by using an LSM 710 Zeiss confocal microscope and analyzed by using the ImageJ software. The activities of cyclooxygenase (COX) and succinate dehydrogenase (SDH) were visualized to explore respiratory dysfunction in embryos  [40]. Briefly, E9. 5 WT and AIF -/Y embryos were dissected, placed in a plastic mold containing cold optimal cutting temperature (OCT) compound, and snap-frozen in liquid nitrogen.
Statistics The results, displayed as mean AE SEM, were statistically analyzed by the ManneWhitney or student t tests by using GraphPad Prism software. Significant p values were indicated as follows: *p 0. 05, **p 0. 01, and ***p 0. 001. All experiments were independently repeated at least 3 times. Chemicals and reagents were purchased from SigmaeAldrich except where otherwise noted.
ACKNOWLEDGMENTS We thank the animal facility staff ( CEF Cordeliers, Paris, France ) for mice housing support,  Danielle Chateau  for help in electron microscopy,  Pascal Ezan  for help in protein extraction,  Patricia Flamant  and  Patrick Avé  for help in brain and embryo immunohistochemistry (Human histopathology and animal models unit,  Pasteur Institute ),  Marie-Annick Thomas  for advice on brain assessments, and  M. Segade  for proofreading. This work was supported by  Fondation ARC  ( PJA20151203407  and  PJA20171206551 ),  Fondation pour la Recherche Médicale ,  French National Research Agency  ( ANR-09-BLAN-0247 ), and the  SIRIC-CURAMUS  ( INCA-DGOS-Inserm_12560 ). L. C. received PhD fellowship support from  ENS-Cachan ,  Société Française d'Hématologie , and  Fondation ARC. N. M.