dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteins were separated on a 15 cm X 100 m diameter i. d. PLRP-S column with a linear gradient of ACN from 5 to 100% and a flow rate of 300 nL/min. 10 l of the samples were also injected and separated using a 3-h gradient. Data were acquired on a Q-Exactive mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with a nanoESI source (Proxeon, Thermo Fisher Scientific, Bremen, Germany). 1. 6 kV was applied on the PicoTip nanospray emitter (New Objective) and the spectra were acquired in data-dependent mode using a top 3 strategy. Full scans were acquired by averaging 4 microscans at 70,000 resolution (at m/z 400) within a m/z range of 800 -2000 with an AGC target of 1 ϫ 10 6 and a maximum accumulation time of 200 ms. The three most abundant ions with charge states superior than ϩ3 or unassigned were selected for fragmentation.
Precursors were selected within an m/z selection window of 15 by the quadrupole and fragmented by averaging two microscans at a resolution of 70,000 with a Normalized Collision Energy (NCE) of 25. The AGC target was set to 1 ϫ 10 6 with a maximum accumulation time of 500 ms. Dynamic exclusion was set to 20 s. Data Analysis-RAW files were processed with ProSight PC 3. 0 or 4. 0 (Thermo Fisher Scientifique, Bremen Germany). Spectral data were deisotoped using the cRAWler algorithm and searched against the complex Rattus norvegicus ProSightPC database version 2014_07. Using a similar approach, a second search was performed to detect alternative protein products, by interrogating RAW files with a concatenated custom database containing every reference proteins and their isoforms. These were generated from an in-silico transcriptome-wide translated database that contains every possible reference and alternative protein products from the Ensembl Rnor 6.
0 transcripts sequence database with at least 30 amino acids  (36). For alternative protein identification, it was verified that the ID was coming from a specific precursor that was not identified during the reference protein search. Files were searched using a two-step search tree containing a 1-kDa precursor tolerant search ("Absolute") and a "Biomarker" search and MS/MS spectra were matched with sequences within a 15-ppm mass tolerance. Proteins were considered identified when one of the two steps gave expected values (E-value) inferior to 1 ϫ 10 Ϫ4. Likewise, data from PMID 27512083 (42) were interrogated using the same search strategy with the concatenated database to identify alternative proteins that were not interrogated in the original publication. As ProsightPC's "Absolute" search mode adds multiple identifications for a single spectrum, output files were filtered using a custom R script.
For each identified spectrum, 1) the one with the best E-Value and (2) identification that had the closest experimental mass compared with ProsightPC database was selected, which were concatenated in a single table. In this table, the ProsightPC PTMs were considered true if this PTM matches both its theoretical and experimental masses. On the other hand, mass shifts that matched known shifts were annotated accordingly (e. g. ϩ80 for phosphorylation, ϩ42 for acetylation) whereas undescribed shifts were automatically marked as unmodified (supplemental Data S1). Finally, a nonredun-dant identification file was generated (supplemental Data S2) containing information about identifications, methods, ROIs, found modifications, E-values, best P-score, and spectral-count. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (43) partner repository with the data set identifier PXD005424.
Subnetwork Enrichment Pathway Analyses and Statistical Testing-Elsevier's Pathway Studio version 10. 0 (Ariadne Genomics/ Elsevier) was used to deduce relationships among differentially expressed proteomics protein candidates using the Ariadne ResNet database  (44, 45). "Subnetwork Enrichment Analysis" (SNEA) algorithm was selected to extract statistically significant altered biological and functional pathways pertaining to each identified set of protein hits among the different groups. SNEA utilizes Fisher's statistical test set to determine if there are nonrandom associations between two categorical variables organized by specific relationships. Integrated Venn diagram analysis was performed using "the InteractiVenn": a web-based tool for the analysis of complex data sets  (46). See supplemental Data S3 and S4 for the listed differential pathways.
MALDI-MSI-DHB matrix (50 mg/ml in 6:4 v/v MeOH/0. 1% TFA in water) was manually sprayed using a syringe pump connected to an electrospray nebulizer at a flow rate of 300 l/h under nitrogen gas flow. The nebulizer was moved uniformly across the entire tissue until crystallization was sufficient to ensure optimal lipid detection. The tissue was then analyzed using an UltraFlex II MALDI-TOF/TOF mass spectrometer equipped with a Smartbeam Nd-YAG 355 nm laser and controlled by FlexControl software (Bruker Daltonics, Bremen, Germany). Acquisition was performed in positive reflector mode with an m/z range of 50 to 900 and a spatial resolution of 300 m. Each image pixel was obtained by averaging 300 laser shots at a rate of 200 Hz. External calibration was performed using the Peptide calibration standard mix 6 (LaserBio Labs).
Lipid ion distributions were generated using FlexImaging software version 3. 0 (Bruker Daltonics). For intact protein imaging, SA and HCCA liquid ionic matrices were used. These were prepared by dissolving the matrices in 7:3 v/v ACN/0. 1% TFA in water containing 7. 2 l of aniline at a concentration of 15 and 10 mg/ml, respectively. The matrices were deposited on the tissue sections using ImagePrep (Bruker Daltonics). Images were acquired using the UltraFlex II instrument in positive linear mode with an m/z range of 3000 -25000 and 2000 -25000, respectively, at 50 m resolution with the laser size set using "Medium" setting. Each image pixel was obtained by accumulating 500 laser shots at a rate of 200 Hz. External calibration was performed using the Protein Calibration standard I (Bruker Daltonics).
Image files were processed using SCiLS Software (version 2015b, SCiLS GmbH, Bremen, Germany). Baseline removal was performed by applying the tophat filter, and normalization was done based on total ion count (TIC). Peak detection was performed by orthogonal matching pursuit, and the peaks were aligned to the mean spectrum by centroid matching. The m/z intervals were set to Ϯ 5 Da. Spatial segmentation was made using the bisecting k-means algorithm using Manhattan distance calculation. After analysis, the ROIs were determined by selecting regions where the correlation distances were significantly distant from one another. The ion images of the individual peaks were plotted following medium denoising and automatic hotspot removal. For back-correlation between protein MALDI-MS and top-down proteomics identification, spectra underwent realignment after m/z intervals were defined at Ϯ 5Da for both HCCA and SA images using SCiLS.
The maxima of the m/z intervals obtained after peak detection (Observed M avg ) were individually matched with the average masses (M avgs ) of top-down-identified proteins derived from their measured monoisotopic masses (M mono. ). Matching was performed with ⌬M avgs Յ 6 Da all throughout the measured mass range and by considering that MALDI MS mass deviations tends to increase with high molecular weight. When available, tissue brain in situ hybridization images from Allen Brain Atlas  (47)  were added to analysis (supplemental Data S7). Tissue Immunofluorescence-Immunofluorescence was performed on 10-m sagittal rat brain sections (supplemental Data S9). The sections were immersed in blocking buffer (PBS 1ϫ containing 1% bovine serum albumin, 1% ovalbumin, 2% Triton, 1% NDS, and 0. 1 M Glycine) for 1 h.
By combining the two approaches, 15 specific nonredundant proteins were identified from the corpus callosum, 17 from medulla oblongata, and 24 from hippocampus (Fig. 1E  and 1F , supplemental Data S1 and S2). Thirty-five are common to the 3 brain regions; 16 are shared between corpus callosum and medulla oblongata, 8 between corpus callosum and hippocampus, and 8 between medulla oblongata and hippocampus. Most identified spectra exhibited a mass shift close to 0 Da (Fig. 1G , inset). The mass tolerant identification approach allowed characterization of modified forms of proteins, which can either be truncated compared with database prediction or modified (Fig. 1G ) in a similar fashion to what is described by Chick et al. (48). Systems Biology Analyses of the Identified Proteins-Functional enrichment analysis using Search Tool for Recurring Instances of Neighboring Genes (STRING, (49)) identified 4 GO terms associated with Molecular function: Hydrogen ions transmembrane transport (GO 0015078), Cytochrome-c oxidase activity (GO: 0004129), Ion transmembrane transporter activity (GO: 0015075), and Oxidoreductase activity (GO: 0016491).
We then enlarged this study by re-analyzing previous data obtained using whole rat brain sections (PMID: 27512083)  (42). Reanalysis of this dataset allowed the identification of 5 more alternative proteins (Table  III , supplemental Data S6). These alternative proteins are translated from sequences located in mRNAs 3ЈUTR (AltSstr3, AltKcnq5, Al-tLdlr), 5ЈUTR regions (AltZbtb8a) of mRNAs and from a putative noncoding RNA (AltRn50_X_0580. 1). Back Correlation to Localization by MALDI-MSI-Intact protein MSI experiments were performed to show ion distributions of the proteins identified by top-down MS. To this end, two images were acquired; the first section was prepared with HCCA/aniline matrix and the second one with SA/aniline. The images were acquired only on the three ROIs specified in the previous imaging experiment.
Peaks obtained from these images were then matched with the M avg derived from the top-down MS analysis performed on the entire rat brain tissue section. Thirty-five protein IDs obtained from the reference proteome were assigned to peaks obtained from both images with a ⌬M avgs cutoff Յ 6 Da (Fig. 5A -5D and supplemental Data S7). This includes five proteins previously matched also with top-down MS data, namely PEP-19 (Pcp4, Fig. 5D ), ubiquitin (Ubc), thymosin ␤-4 (Tmsb4x), thymosin ␤-10 (Tmsb10), and calmodulin (Calm1)  (34).
Furthermore, its detection by MSI and assignment of N-acetylation by top-down is in accord with the MSI database reported by Maier et al. (52). Fig. 6C  shows the mapping of m/z 5180 assigned as the C-terminal fragment of ␣-synuclein (observed as the [Mϩ4H] 4ϩ charge state in topdown, Fig. 6C ), showing its particularly intense distribution along the hippocampal dentate gyrus. Its distribution in the cerebral cortex observed in the ROI that includes the corpus callosum, was also detected in both MSI and spatially-resolved top-down proteomics. To verify the specific formation of this fragment, the putative protease cleavage sites found in the full amino acid sequence of ␣-synuclein was mapped using the PROtease Specificity Prediction servER (PROSPER,  (53) , https://prosper. erc. monash. edu.
The signal is markedly absent in the corpus callosum and surrounding region (GFAP Ϫ ), and is slightly present in the medulla oblongata region (GFAP ϩ/Ϫ ) as evidenced by immunoreactivity of several astrocyte processes projecting in different directions. The specific localization of GFAP-positive astrocytes in the hippocampus was confirmed after gathering z-series of images at varying focal planes throughout the entire tissue thickness of 10 m. Likewise, GFAP was detected only in the hippocampus in MALDI-MSI and spatiallyresolved top-down proteomics experiments. Results for the other proteins tested are shown in supplemental Data S9.
All the three cameras are fixed on an adjustable tripod to master precisely the elevation and horizontal tilt. The visible database is used for the 3-D reconstruction of the gas plume with the geomodeler gOcad ® (Paradigm ® ). This software has led the industry for over 20 years in providing the most advanced capabilities for seismic, geological and reservoir modeling  [30] [31] [32]. Among the main topics coming from the oil industry, the gOcad ® software offers a maximum number of facilities to work with various types of 3-D data and a high workability that allow, when necessary, specific and manual construction and adjustment of 3-D models. The geometrical model of the gas plume, represented by a 3-D surface envelope, allows definitions of local plume thicknesses that are used to quantitatively analyze gas concentrations in ppm.
The vertical and horizontal tilts of the GoPro optical axes are measured, in degrees, with an accurate digital inclinometer at a resolution of 0. 1 degrees. The GeDetekt (BRUKER) software is related to a user-defined measurement window (see Table  2 ) that directly controls the location of the SIGIS infrared system. The positioning parameters are summarized in Table  3  and the main atmospheric parameters during the measurements in Table  4. The Global Positioning System (GPS) parameters are recorded by a Magellan ® eXplorist ® 610 (MiTAC Digital Corp, Taipei, Taiwan). For the 3-D modeling approach, it has been assumed that the visible atmospheric dispersion is the total of all gases emitted at the same time by a chimney. The visible trace of the gas plume, mainly due to H 2 O vapor, delimits the entire envelope of all the gases emitted with various NH 3 concentrations.
The 3-D reconstruction is based on the combination of visible GoPro cameras and SIGIS images that are precisely relocated. The XYZ georeferencing of the images is realized using the geometrical and optical criteria of the different cameras (see Table  3 ). Figure  3  schematically shows all the geometrical and angular data, respecting the viewing angles, the heights of the cameras, the XYZ location of the cameras and the point of view of the shot. 3  and 4. Only three pixels (Pixel i ), of the infrared measurement grid have been shown as an example. A rectangular surface is defined and constrained by these angular data and the target of the shot: the NOVACARB chimney (bottom located at X 4 , Y 4 , Z 4 ). The visible image is then precisely located, adapted and draped onto this surface using gOcad ® abilities  [34].
The slight overestimation of NH 3 concentration compared to the environmental tolerances set by government regulation can be explained by the vicinity of channeled and diffuse emissions. Such an approach can then be used as an inexpensive monitoring method for gas plumes having a visible trace. For gas emissions without visible traces, visible cameras can be replaced by other thermic or infrared remote-sensing systems, for example. The cost of the monitoring will, necessarily, increase. The information provided by such experimental 3-D modeling is very important for the human and environmental safety of all types of emissive sites under normal conditions or in a crisis situation. acquisition. She was also involved in the IR data treatments (path concentrations and concentrations). J. S. was in charge of the elaboration of the interpolation algorithms using gOcad ® software.
Acknowledgments: The authors would like to thank  NOVACARB Society (Laneuveville-devant-Nancy, France)  for giving them access to their industrial site and Renaud DESCAMPS (NOVACARB) for his scientific contribution. The authors also associate the  National French Agency (ANR ,  Program  N •  ANR-07-PCO2-007-08 ,  SENTINELLE ) and the  European Fund of Regional Development (FEDER)  for their financial support in the acquisition of the SIGIS remote-sensing system used in this study. They also thank the industrial and academic members of the  gOcad ® Consortium and ASGA  for their support, especially  Paradigm Geophysical  for the supply of gOcad ® software.
MaTerials anD MeThODs reagents and antibodies The following reagents were used: BAY61-3606 (Merck Millipore), Stattic (Torcis), Q-VD-Oph (Sigma), MG-132 (Calbiochem), and cycloheximide (Sigma). We used the following primary Abs: hCD19-APD-Cy-7 (SJ25C1; BD Biosciences Cat# 557791 RRID:AB_396873), hCD38-BV421 (HIT2; BD Biosciences Cat# 562445 RRID:AB_11153870) hsIgD-Pe-Cy7 (IA6-2; BD Biosciences Cat# 561314 RRID:AB_10642457), hCD80-BV605 (L307. 4; BD Biosciences Cat# 563315), hPhospho-SYK-AF488 (C87C1; Cell Signaling Technology Cat# 4349), and hMcl-1-FITC (Biorbyt Cat# orb15956 RRID:AB_10747574) for flow cytometry. Anti-Mcl-1 (S-19; Santa Cruz Biotechnology Cat# sc-819 RRID:AB_2144105), anti-Bcl-2 (C-2; Santa Cruz Biotechnology Cat# sc-819 RRID:AB_2144105), anti-Bcl-xS/L (S-18; Santa Cruz Biotechnology Cat# sc-819 RRID:AB_2144105), anti-GAPDH (Sigma-Aldrich Cat# G9545 RRID:AB_796208), and anti-PARP (Cell Signaling Technology Cat# 9542 also 9542S, 9542L, 9542P RRID:AB_2160739) antibodies were used for immunoblotting and anti-hCD19 (HIB19; BD Biosciences Cat# 555409 RRID:AB_395809), anti-hMcl-1 (Sigma-Aldrich Cat# HPA008455 RRID:AB_1079334), and anti-STAT3 (K-15; Santa Cruz Biotechnology Cat# sc-483 RRID:AB_632441) Abs were used for immunohistology.
5% FCS and 0. 1% sodium azide, and cells were then incubated with fluorescent Abs. Intracellular staining for non-phosphorylated proteins was performed as follows: cells were fixed in 4% paraformaldehyde (PFA, Alfa Aesar), then incubated with NH4Cl (100 mM) for quenching, permeabilized with 0. 15% saponin (VWR), and incubated with conjugated Abs in the presence of saponin. For intracellular staining for phosphorylation analysis, saponin was replaced with 0. 1% Triton X-100 (Sigma) and 50% methanol was added, and the cells were then left on ice for permeabilization. The cells were then incubated with fluorochrome-conjugated Abs and analyzed in a BD LSRFortessa flow cytometer (BD Biosciences). All flow cytometry data were analyzed with FlowJo tm (FlowJo Treestar, RRID:SCR_008520) software.
Materials and Methods 2. 1. Patient and Sample Collection. Two hundred and three consecutive patients, fulfilling the 2013 American College of Rheumatology/European League Against Rheumatism classification criteria for SSc  [16] , were recruited from the Rheumatology Department of Cochin Hospital, Paris Descartes University. The control group consisted of 47 age-and sexmatched patients with noninflammatory diseases, being osteoporosis for the very large majority. Clinical and laboratory data were collected longitudinally from medical records since the time of serum and urinary samples were taken. Patients were classified as having diffuse cutaneous SSc (DcSSc) or limited cutaneous SSc (LcSSc) based on subset classification criteria  [17]. Disease activity was measured through the European Scleroderma Study Group activity index (EScSG-AI)  [18].
The following clinical data were collected for all patients: age, sex, disease duration (date of the first non-Raynaud symptom), skin involvement according to the modified Rodnan skin score (mRSS)  [19] , presence of interstitial lung disease (ILD) on high-resolution computed tomography (HRCT), pulmonary hypertension confirmed by right heart catheterization, digital ulcers, and treatment received. Pulmonary function was assessed based on measurements of forced vital capacity (FVC) and diffusing capacity of the lungs (DLCO) and expressed as percentage of values predicted. Laboratory data included Westergren erythrocyte sedimentation rate (ESR), C-reactive protein level (CRP), serum and urinary creatinine levels, and tests for anticentromere antibodies, antitopoisomerase I antibodies, and antipolymerase III antibodies.
Statistical analysis The β-galactosidase activity was analyzed with linear mixed effect models using the R package lme4  (Bates et al. , 2014). As two technical replicates were measured for each pool, the model including the pools fitted as a random effect and age, photoperiod or temperature, and sex and their two-way interactions as fixed factors. Linear models with Gaussian distribution were fitted to analyze variation in the cell size and viability. For the cell density, a linear model of the cell number (log-transformed,  (Ives and Freckleton Robert, 2015)  was fitted. Survival in different conditions of temperature and photoperiod was analyzed with generalized models (with a binomial error). All results were obtained by stepwise backward selection model. The effect size of temperature and photoperiod on the β-galactosidase activity and immune parameters was measured by rescaling and standardized slopes were reported as a measure of effect size  (Schielzeth, 2010).
The lower limb was placed in a single thermoformed shell adaptable to any leg length, with the hip in a free position; the arms were along the body. The knee was locked in the shell with a patellar support placed against the inferior patella margin. The ankle was fixed in a moveable boot leaving the foot free to rotate without a predefined position. The linear pusher allowed two preset levels of PL, 134 N or 250 N  [5, 11] , on the proximo-posterior part of the calf. These forces were only applied in the sagittal plane. A displacement sensor (the accuracy given by the manufacturer is 0. 1 mm; TR50 NOVO-TECHNIK, FGP Instrumentation, Garches, France) recorded the translation of the anterior tibial tuberosity from its initial position. The recorded data determined the displacement curves for each PL.
The diagnostic value of the tests was evaluated by the area under the curve (AUC) of ROC, determining five classification levels: null (AUC < 0. 5), poorly informative (0. 5 ≤ AUC < 0. 7), fairly informative (0. 7 ≤ AUC < 0. 9), highly informative (0. 9 ≤ AUC < 1), and complete (AUC = 1)  [27]. Statistical tests were performed using SPSS software (version 17, SPSS Inc. , Chicago, IL, USA). For all statistical analyses, significance was accepted at p < 0. 05.
To determine how these genes are expressed during WT DC development, we compared their levels of expression in Ik L/L CDPs with those in WT progenitors and mature DC populations, as reported by the ImmGen Compendium (GSE15907), using unsupervised clustering  [7]. Interestingly, this revealed that, among the genes up-regulated in Ik L/L CDPs (Fig 6B ), the large majority (>70%) were related to mature cDC genes, and not pDCs. The remainder of the genes were DC progenitor-related. In contrast, among the genes down-regulated in Ik L/L CDPs (S4A Fig), most were related to DC progenitor (CMP, MDP, CDP) genes. Further, gene set enrichment analyses (GSEA) indicated that both the up-and down-regulated genes in the Ik L/L CDPs correlated with those normally expressed in WT cDCs (S4B Fig). Thus, Ikaros is required to repress the premature expression of cDC-associated genes in CDPs.
(C) K-means clustering of 70 genes differentially expressed between WT and Ik L/L CDPs, and deregulated by GSI (FC>1. 5). (D) Top 5 putative upstream regulators related to the 70 genes from (C), as identified by the Ingenuity Pathways Analysis software. (E) GSEA enrichment plots of genes specifically down-regulated [clusters I and III in (A)] and (F) up-regulated (clusters II and IV) in Ik L/L CDPs. The ranked gene list corresponds to TGFβ1-regulated genes in CDPs, as identified by Felker et al (2010). NES: normalized enrichment score; FDR: false discovery rate. (G) Genome browser tracks showing Ikaros binding to loci associated with TGFβ activation in pre-B cells (BH1-Ik1-ER-Bcl2 cell line) and immature DN3 thymocytes (GEO GSE114629 and GSE61148 accession numbers). https://doi. org/10. 1371/journal.
TGFβ1 signaling during pDC development in Ik L/L CDPs. (A) Genome browser tracks showing Ikaros binding in the Eng locus in pre-B cells and DN3 thymocytes (GEO GSE114629 and GSE61148 accession numbers). (B) Total numbers of cells after 8 days of Flt3L-supplemented cultures of WT and Ik L/L BM cells treated with SB431542 and/or GSI. See experiments shown in Fig 7C and 7D. Representative of 4 independent experiments; 2 mice per genotype per experiment; p values were obtained by a Student's t-test. Ã p 0. 05; ÃÃÃ p 0. 001. (EPS) S1 Table. FC of the 70 genes deregulated in Ik L/L CDPs vs. WT cells, and sensitive to GSI treatment. (EPS).