dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , Cytoscape 20 , https://cytoscape. org/). Refer to the documentation on how to run FBMN within GNPS and multiple mass spectrometry data processing tools. https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking/ For IIMN, refer to the related part of the GNPS documentation. https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin/.
Acknowledgments We thank the  German Chemical Industry Fund  ( FCI, Fonds der Chemischen Industrie ) for a Ph. D. scholarship and travel support to RS. We thank the  Deutsche Forschungsgemeinschaft  for support to DP ( PE 2600/1-1 ) and to SB and KD ( BO 1910/20 ). AB thanks  FAPESP  fellowship ( 2018/24865-4 ). COG thanks  FAPESP scholarship  ( 2019/06061-8 ). PCD was supported by the  Gordon and Betty Moore Foundation  ( GBMF7622 ), the  US National Institutes of Health  for the Center ( P41 GM103484 ,  R03 CA211211 ,  R01 GM107550 ). LFN was supported by the  US National Institutes of Health  ( R01 GM107550 ), and the  European Union 's  Horizon 2020 program  ( MSCA-GF, 704786 ). AMCR and PCD were supported by the  National Sciences Foundation  grant  IOS-1656481. LIA was supported by the  National Science Foundation  grant  OCE-1736656.
MR is supported by  Public Health Service  Grants  AI126277 ,  AI114625 ,  AI145325 , by the  Chiba University-UCSD Center for Mucosal Immunology, Allergy , and  Vaccines  and an Investigator in the  Pathogenesis of Infectious Disease Award  from the  Burroughs Wellcome Fund. DP, MAP and KIP were supported by the  National Science Foundation's Center for Aerosol Impacts on the Chemistry of the Environment (CAICE)  under grant number  CHE1801971. KLM was supported by the  Gordon and Betty Moore Foundation  ( GBMF6920 ) and the  US National Institutes of Health.
Code availability The IIMN workflow is available as an interface on the GNPS web platform (https://gnpsquickstart. ucsd. edu/featurebasednetworking). The workflow code is open source and available on GitHub (https://github. com/CCMS-UCSD/GNPS_Workflows). It is released under the license of The Regents of the University of California and free for non-profit research (https://github. com/CCMS-UCSD/GNPS_Workflows/blob/master/LICENSE). The workflow was.
Download and visualize the results in a network analysis software (e. g. , Cytoscape 20 , https://cytoscape. org/). Refer to the documentation on how to run FBMN within GNPS and multiple mass spectrometry data processing tools. https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking/ For IIMN, refer to the related part of the GNPS documentation. https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin/.
Image processing pipeline We used a single-atlas based segmentation followed by LDDMM registration and label propagation to automatically extract subcortical shape displacements. More details about this image processing pipeline can be found in  Khan et al. (2019) , and the full scripts have been made available by the authors (https://github. com/khanlab/diffparc-sumo). The original version of this method was proposed by  Khan et al. (2008)  and shown a high degree of robustness for the segmentation of the caudate nucleus and putamen with a Dice overlap measure of 81% and 83%. As the reliability of this method has already been proven many times  (Garg et al. , 2015; Khan et al. , 2019; Khan et al. , 2008; Garg et al. , 2014; Wang et al. , 2009; Ansari, 2010) , we did not perform any extensive segmentation quality check at this stage.
Cohort description Data for this article come from two databases. The first database consists of PD patients from the cohort of DBS patients recruited at Rennes University Hospital (from now refered as 'DBS PD'). These patients are all candidates for DBS, indicating that they experience sufficiently advanced motor symptoms to warrant such an intervention and are thus in an advanced phase of the disease. All patients of this cohort  Table  1  shows the UPDRS mean and standard deviation of each cohort. From this, one can observe a natural progression of the disease in terms of motor symptoms: first healthy, then prodromal stage, then early-stage Parkinson and finally late-stage Parkinson. One can also note that the standard deviation of each cohorts is quite high, indicating that this information alone is not optimal and underscoring the importance of finding other biomarkers.
Image acquisition All patients from the Rennes database had one preoperative 3T T1weighted MRI scan (1 mm × 1 mm × 1 mm, Philips Medical Systems). All sequences were acquired prior to DBS electrode placement. For the PPMI database, all T1-weighted sequences (e. g. MPRAGE or SPGR) were required to have a total scan time between 20 and 30 min and to have a slice thickness of 1. 5 mm or less with no interslice gap.
Classification performance between problems In order to develop a reference of the relative difficulty between the problems, a preliminary classification using only the clinical symptomatology was performed. Table  3  presents the classification metrics obtained on the training base for the 6 different binary classification problems, based solely on the MDS-UPDRS3 using a naive Bayes classifier. It is to note that the dataset is heavily class unbalanced between the DBS cohort and the others, as in the PPMI program the MDS-UPDRS3 is assessed at multiple visits, thus explaining the low f1 scores and sensitivity of the last three rows of the Table. The accuracy is expected to be higher using clinical symptomatology than via the use of morphological information as clinical symptomatology is currently relied upon for the diagnosis and staging of PD  (Mahlknecht et al.
Phenotypic diversity of innate myeloid cells after two doses of MVA 2 weeks apart We stained whole blood with a 35-antibody panel detailed in Fig. 3a  and Supplementary Table  3  and used an analysis pipeline consisting of three successive clustering steps (Fig. 3b ). The SPADE algorithm was applied to identify leukocyte clusters sharing similar phenotypes within the entire dataset, as previously done in the longer vaccine schedule study  15, 16. The whole dataset was composed of fixed leukocytes from the five macaques immunized with MVA at 2 weeks interval and collected at 13 timepoints (Fig. 1a ). The parametrization of the SPADE algorithm was optimized and we identified 800 clusters based on the following 28 clustering markers: CADM1, CCR5, CCR7, CD1c, CD3, CD4, CD8, CD11a, CD11b, CD11c, CD14, CD16, CD20, CD23, CD32, CD39, CD45, CD62L, CD64, CD66, CD86, CD123, CD125, CD141, CD172a, CXCR4, FcεRI, and HLA-DR.
Unsupervised phenotypic comparisons between the current shortened vaccine schedule study and a previously published longer vaccine schedule study  15  were performed and correlations between innate myeloid responses and antibody responses were assessed. c The SPADE tree generated using all samples of the shortened vaccine schedule dataset and annotated based on the expression of 15 markers (Supplementary Fig. 3 ). Granulocyte clusters were defined as neutrophils (CD66 + CD125 -), eosinophils (CD66 + CD125 + ), and basophils (CD66 -CD123 + HLA-DR -). Monocyte-DC clusters were defined as monocytes (CD14 + HLA-DR + ), cDCs (CD14 -HLA-DR + CD11c + CD16 + ), inflammatory cDCs/nonclassical monocytes (CD14 + HLA-DR + CD11c + CD16 + ), pDCs (CD123 + HLA-DR + ), other CADM1 + APCs (HLA-DR + CD3 -CD8 -CD14 -CD11c -CD16 -CD20 -CADM1 + ), and other CD141 + CADM1 -APCs (HLA-DR + CD3 -CD8 -CD14 -CD11c - CD16 -CD20 -CD141 + CADM1 -).
Similar innate myeloid cell responses after a first and a second MVA injection shortly afterwards Beyond the phenotypic diversity within the dataset, we were primarily interested in the behavior of these cells over time after the prime and second boost 2 weeks later. We thus grouped the granulocyte (Supplementary Fig. 4 ) and monocyte/DC (Supplementary Fig. 5 ) phenotypic families sharing the same kinetics into so-called kinetic families (randomly numbered from I to XIII in roman numerals) (Fig. 5 ) to analyze the dynamics of the innate immune responses to MVA short interval injections (Supplementary Table  4 ). Among these 13 kinetic families, six (families III, IX, X, XI, XII, and XIII) showed no response or heterogeneous responses to MVA injections, because of high variability between animals at baseline or post immunization (no statistically significant p value for comparison of cell counts with baseline) (Fig.
The cross-validation could only build a model with a very high error rate for class prediction. The minimal mean square error (MSE) was 0. 76, which was approximately four times less accurate than the model built for the longer vaccine schedule (MSE = 0. 24) (Supplementary Fig. 7b ). This analysis firmly establishes that the response to prime and early second injection cannot be distinguished from each other, contrary to the response to prime and delayed second injection. Missing activated/mature innate myeloid cells in the shortened vaccine schedule dataset We then formally associated the cellular phenotypes identified in the shortened and longer vaccine schedule datasets to identify those present in both datasets and those retrieved in only one. The comparison was challenging because we used different mass cytometers, different Ab panels and Ab batches, and different staining protocols.
However, eosinophils did not match. This was expected because eosinophils were characterized only in the shortened vaccine schedule (superfamily A and phenotypic family 5), whereas they had been removed from the analysis of the longer vaccine schedule study due to nonspecific staining  20. We observed an additional discordance, consisting of the lack of association of CD14 + neutrophils between the accelerated vaccine schedule dataset and the delayed vaccine schedule dataset (phenotypic family 13 in both regimens). CD14 + neutrophils from the prime/boost at 2 weeks dataset associated with intermediately activated neutrophils from the prime/boost at 2 months dataset instead (superfamily B). This may have been related to the differential expression of other markers (CD66 and CD11b) by CD14 + neutrophils from both studies.
4 ) and monocyte-DC (Supplementary Fig. 5 ) phenotypic families sharing similar dynamics. Kinetic families were further regrouped based on their kinetic patterns (Supplementary Table  5 ) as indicated in gray, (a) heterogeneous or no response, (b) similar response to prime and boost, (c) distinct response to prime and boost. The immunizations are indicated by red arrows. The scale of the Y-axis is specific to each kinetic family. Most cDCs and inflammatory cDCs/nonclassical monocytes (superfamily E), as well as uncharacterized APCs (superfamily F), from the shortened vaccine schedule study matched their counterparts in the longer vaccine schedule boost study (Fig. 6b ). Surprisingly, this was not the case for pDCs (phenotypic family 28 in both schedules), which were not associated between the two datasets, likely due to the higher expression of CD20 by pDCs in the shortened vaccine schedule study than in the longer vaccine schedule study.
Most monocytes from the shortened vaccine schedule dataset (superfamily G) also associated with monocytes from the longer vaccine schedule dataset (superfamily H), except phenotypic families 19 and 32 from the accelerated vaccine regimen dataset. They corresponded to HLA-DR low and CCR5 mid CXCR4 mid monocytes, respectively, and were not linked to phenotypic families 22 or 36 (HLA-DR low monocytes), or 25 (CCR5 mid CXCR4 mid monocytes) from the the delayed vaccine regimen dataset. This discrepancy may have been due to the differential expression of other markers (CD32, CD11b, CD11a, and CD14) between HLA-DR low monocytes and CCR5 mid CXCR4 mid monocytes in the two studies. Although most phenotypic families of the shortened vaccine schedule study associated with their counterparts of the longer vaccine schedule study, the inverse was not true.
Indeed, most phenotypic families from the injections 2 months apart dataset previously classified as characteristic of the response to boost by LDA (which discriminated responses to the first and second immunization 2 months later) were absent from the shortened vaccine schedule dataset, such as highly activated neutrophils and CD4 + CD23 + CD11c + neutrophils  15. This was more pronounced for granulocytes than monocytes and DCs, with seven nonassociated phenotypic families among the 11 that defined the boost at 2 months signature in the LDA model for granulocytes versus one among six for monocytes-DCs. Overall, these results strengthen the body of evidence demonstrating that early second immunization did not result in an innate myeloid cell immune response different from prime, in contrast to a later second immunization, which involved more mature/activated cells.
7a ). We did not use highly activated neutrophils or poorly activated monocytes for this multivariate correlation analysis, because they were not present in both datasets. We found associations between monocytes and cDCs and early and late nAb titers and late IgG binding to FcγRIIIa, as well as between neutrophils and early and late IgG and IgA titers and early IgG binding to FcγRIIIa (Fig. 7a ). Post-boost poorly activated neutrophil counts inversely correlated with peak IgA and nAb titers and IgG/FcγRIIIa binding, whereas post-boost highly activated monocytes and cDCs positively correlated with peak nAb titers and IgG/FcγRIIIa binding and peak and long-term nAb titers, respectively (Fig. 7b ). In contrast, intermediately activated neutrophils did not correlate with any variable, possibly because they responded to both first and second immunization.
For the shortened vaccine schedule dataset, the color code for the phenotypic families is identical for the circular graph (Fig. 6 ) and heatmaps (Fig. 4 ), whereas it has been modified for the previously published longer vaccine schedule  15  to match identical annotations in both datasets. Phenotypic family annotations are displayed. For the longer vaccine schedule dataset, phenotypic families that were selected using the LASSO-LDA approach to distinguish prime and boost responses  15  are represented with a blue (prime) or red (boost) color band. response for longer-lasting protective immunity. Indeed, by comparing macaques immunized with MVA according to two different schedules, shortened or longer, and phenotyping their blood innate myeloid cells over time by mass cytometry, we reported that, conversely to a second immunization at 2 months, a second immunization at 2 weeks did not induce an innate myeloid response different from that of prime.
Vaccine schedule and blood sampling Five cynomolgus macaques (males and adults imported from a breeding facility in Mauritius), identified as BC554D, BT145, CBL004, CC840, and CCB116, were immunized subcutaneously 2 weeks apart with the ANRS recombinant MVA HIV-B vaccine (MVATG17401; Transgene, Illkirch-Graffenstaden, France) at 4 × 10 8 plaque-forming units (Fig. 1 ). As previously described  [14] [15] [16]  , this vaccine encodes the full-length gag sequence (amino acids 1-512), fragments of the pol sequence (amino acids 172-219, 325-383, and 461-519), and fragments of the nef sequence (amino acids 66-147 and 182-206) from the Bru/Lai isolate (Los Alamos database accession number K02013). Blood samples were longitudinally collected in lithium-heparin for soluble plasma protein quantification and single-cell mass cytometry profiling, and in ethylenediaminetetraacetic acid (EDTA) for complete blood counts.
For subclasses, anti-rhesus IgG1, IgG2, or IgG3 Abs (NHP Bioresources) were biotinylated (Thermo Scientific, Waltham, USA) according to the manufacturer's instruction. The biotinylated antibody diluted 1:500, 1:100, or 1:100, respectively, were used and incubated for 1 h at RT. Plates were washed five times and incubated with 1:1000 diluted streptavidin-biotin (Thermo Scientific, Waltham, USA) with PBS + 1% BSA for 1 h at RT. All plates were washed five times and 100 µL 3,3′,5,5′-tetramethylbenzidine (Thermo Scientific, Waltham, USA) was added and incubated for 20 min at RT in the dark. The reaction was stopped by adding 100 µL 2 N H 2 SO 4. Absorbance was measured at 492 nm using a Spark spectrophotometer and data were analyzed using Magellan software (both from Tecan, Lyon, France). Ab titers were calculated by extrapolation from the OD as a function of a serum dilution curve (five-parameter logistic curve) and defined as the dilution of the test serum reaching 2× OD of the corresponding preimmune serum tested at a dilution of 1:50.
Quantification of cytokines, chemokines, and growth factors Cytokines, chemokines, and growth factors present in plasma and PBMC culture supernatants were quantified as duplicates and using a multiplex immunoassay (MILLIPLEX MAP non-human primate cytokine magnetic bead panel, Millipore), and using a BioPlex 200 system as reader and the Bio-Plex data manager to analyze data (BioRad, Hercules, California, USA), except for IP-10. The IP-10 concentration was assessed by ELISA (human CXCR10/IP-10, R&D Systems).
Mass cytometry data preprocessing and leukocyte gating To avoid bias in density estimation by the Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm  58  , zero values of mean signal intensities (MSI) generated on the Helios were randomized between -1 and 0. Data were then normalized using the MATLAB normalizer from Finck et al. 59. Samples were debarcoded using the Debarcoder software (Fluidigm, San Francisco, USA), following user guide instructions. Initial gating was performed using Cytobank (Mountain View, California, USA) as previously described  15, 55  , and included the definition of singlets (based on Ir191/Cell length), intact cells (Ir191/Ir193), no beads (Ce140/ Gd155), and exclusion of CD3 + CD66 + cells. Note that although the use of heparin strongly reduced the nonspecific staining of eosinophils, some CD3 + CD66 + cells were still present and excluded at that step (approximately 0.
Automatic identification of cell populations The SPADE algorithm was used to automatically identify cell populations as previously described  15, 16. Briefly, uniform predownsampling was used to select 95,000 cells from each sample (corresponding to the number of cells contained in the smallest sample-Supplementary Table  6 ). Cell clusters (groups of cells with similar phenotypic patterns) were identified using SPADE, and applied to the whole set of samples (all macaques and all timepoints). Upsampling was eventually performed. The optimal SPADE settings for this particular dataset were determined using the SPADEVizR package  60. The optimal SPADE analysis was obtained using 28 clustering markers (CADM1, CCR5, CCR7, CD1c, CD3, CD4, CD8, CD11a, CD11b, CD11c, CD14, CD16, CD20, CD23, CD32, CD39, CD45, CD62L, CD64, CD66, CD86, CD123, CD125, CD141, CD172a, CXCR4, FcεRI, and HLA-DR), a density-based downsampling of 10%, an outlier density parameter of 0.
Statistical tests Blood protein concentrations and cell abundances were compared between timepoints using the two-sided permutation test available in the "exactRankTests" R package (available at https://cran. r-project. org/ web/packages/exactRankTests/index. html). The AUC corresponds to the sum over time of all plasma soluble factor concentrations (cumulated concentration) or cell abundances (cumulated abundance) between H6PP and D14PP and H6PB and D14PB. AUC were compared using a permutation test.
Phenotypic comparison of cell clusters from the two vaccine schedules Cell cluster phenotypes from this study (shortened vaccine schedule) and our previous study (longer vaccine schedule)  15  were compared using the Manhattan distance and visualized using the CytoCompare package  61. Distances were computed based on the heatmap expression categories of the 27 markers shared between both panels (CCR5, CCR7, CD3, CD4, CD8, CD11a, CD11b, CD11c, CD14, CD16, CD20, CD23, CD32, CD45, CD64, CD66, CD86, CD123, CXCR4, HLA-DR, IFNα, IL-1α, IL-4, IL-6, IL-8, IL-12, and IP-10). Distances consisted of the sum of the absolute value of the difference between the categorical value of each cluster for each marker. Two clusters were considered to be associated if this sum, calculated for the 27 markers, was ≤9 and no term was >2 (if one term was >2, the distance was penalized to be >10).
ACKNOWLEDGEMENTS This work was supported by the " Investissements d'Avenir " programs managed by the  ANR  under reference  ANR-10-LABX-77-01 , funding by the  Vaccine Research Institute (VRI) ,  Créteil  ( ImMemory research program ),  ANR-11-INBS-0008 , funding by the  Infectious Disease Models and Innovative Therapies  ( IDMIT, Fontenay-aux-Roses, France) infrastructure , and  ANR-10-EQPX-02-01 , funding by the  FlowCyTech facility  ( IDMIT, Fontenay-aux-Roses, France ) and by the  European Union , EVHA  H2020  project (Grant no. 681032 ), and  TRANSVAC2 H2020  (Grant no. 730964 ). N. T. held a fellowship from the  ANRS  ( France Recherche Nord & Sud Sida-HIV Hépatites ). The authors thank  Bruce Wines  and  Mark Hogarth  ( Centre for Biomedical Research, Burnet Institute, Melbourne, Australia ), and  Stephen J.
Library of real astronomical images In an effort to have the most realistic dataset, we choose to use real data as much as possible and take advantage of the private archive of wide-field images gathered for the COSMIC-DANCE survey  (Bouy et al. 2013). The COSMIC-DANCE library offers several advantages. First, it includes images from many past and present optical and near-infrared wide-field cameras. Images cover a broad range of detector types and groundbased observing sites, ensuring that our dataset is representative of most modern astronomical wide-field instruments. Table  1  gives an overview of the properties of the cameras used to build the image database. Second, most problematic exposures featuring tracking/guiding loss, defocusing or strong fringing were already identified by the COSMIC-DANCE pipeline, providing an invaluable sample of real problematic images.
In all cases except for Megacam, DECam, UKIRT and HSC exposures, the raw data and associated calibration frames were downloaded and processed using standard procedures with an updated version of Alambic  (Vandame 2002) , a software suite developed and optimized for the processing of large multi-chip imagers. In the case of Megacam, the exposures processed and calibrated with the Elixir pipeline were retrieved from the CADC archive  (Magnier & Cuillandre 2004 ). In the case of DECam, the exposures processed with the community pipeline were retrieved from the NOAO public archive  (Valdes et al. 2014). UKIRT ex-   Dalton et al. (2006)  posures processed by the Cambridge Astronomical Survey Unit were retrieved from the WFCAM Science Archive. Finally, the HSC raw images were processed using the official HSC pipeline  (Bosch et al.
Non-contaminated images None of the exposures in our library are defect-free. The first step to create the non-contaminated dataset to be used as "reference" images consists in identifying the cleanest possible subset of exposures. CFHT-Megacam (u, r, i, z bands), CTIO-DECam (g, r, i, z, Y bands) and Subaru-HSC (g, r, i, z, y bands) exposures are found to have the best cosmetics and are selected to create the non-contaminated dataset. The defects inevitably present in these images are handled as follows. First, dead pixels and columns are identified from flatfield images and inpainted using Gaussian interpolation (e. g. ,  Williams 1998). Then, the vast majority of cosmic rays are detected using the Astro-SCRAPPY Python implementation  (Mc-Cully et al. 2018 ) of LA Cosmic (van Dokkum 2001)  and also inpainted using Gaussian interpolation.