dataesr/scientific-paragraphs-categorization huggingface dataset:
69 Software tools that bypass deconvolution procedures and perform direct matching of isotopic envelopes of multiply charged product ions to simulated profiles included TDValidator, LcMsSpectator (data viewer in Informed Proteomics suite), and AutoVectis. A detailed description of the data processing and analysis tools and parameters employed by participants can be found in Table  S10  (Supplementary Material). Notably, mass tolerance for product ion annotation in MS/MS experiments was typically 10 ppm, and sometimes lowered to 5 ppm. Precursor mass tolerances of up to 50 ppm and 10 Da were reported (Table  S10 , Supplementary Material). For the determination of mass accuracy or mass measurement errors (expressed in parts per million, ppm) from deconvolved full MS measurements, results submitted by the participating laboratories were compared to a single set of calculated masses (determined for both intact mAbs and their subunits).
Such masses were calculated by the use of monoisotopic and average atomic masses, as well as abundances, as employed in ChemCalc isotopic calculator algorithm and described elsewhere (Table  S4 , Supplementary Material). 70 The respective mAb sequences (Table  S1 , Supplementary Material) were used to generate molecular formulae further employed for monoisotopic and average mass calculations of mAbs and their subunits (see examples provided by Tables S5-S7, Supplementary Material, for data on SiLuLite mAb, NIST mAb, and trastuzumab, correspondingly). These computational results were achieved either with the web-version of ChemCalc (www. chemcalc. org) or via the desktop version of the FTMS Isotopic Simulator (part of Peak-by-Peak software, Spectroswiss). Note, that Tables S5-S7 (Supplementary Material) include all major proteoforms identified by participants along with unmodified and de-glycosylated proteoforms, some of which were not present in the samples but are given here for didactic and self-controlling reasons.
The latter is justified by the errors in molecular weight calculations and proteoform misassignment demonstrated in some reports. Results of the statistical analysis, in the form of box-and-whisker plots, containing errors (accuracy) for MU and intact mass measurements (Figures example in a comprehensive report on NIST mAb glycosylation profiling. 71 For tandem MS results, sequence coverage values were obtained directly from the participants' reports, without data re-processing. S1-S13.
A compilation of sample introduction methods, MS and MS/MS instrumentation and allied approaches employed for the study, as reported by the participants, is depicted in Figure  2  and in allied Tables  S11  and S12  (Supplementary Material). The following brief observations could be made (Figure  2  and Tables  S11  and S12 , Supplementary Material): (i) most groups employed LC for on-line sample purification and separation; (ii) about half of the employed instruments were Orbitraps; (iii) most of the currently available MS/MS methods were employed, from a traditional CID to the less frequent but up-and-coming UVPD and MALDI ISD; (iv) most data processing approaches utilized instrument vendor provided software (with some packages requiring additional licenses); and (v) in most cases sequence maps were visualized using additional freeware such as the ProSight Lite tool.
The wide range in the reported sequence coverages is due to a few reports that described a sequence coverage as low as 5% from, for example, MALDI ISD. On the other hand, other groups reported substantially higher sequence coverages using MALDI ISD, with the maximum   Despite the pronounced simplification of product ion distributions achieved by the PTR approach, which reduces the charge of the product ions to enhance their m/z separation, Figure  7  demonstrates a well-resolved, but still complex pattern of overlapping isotopic distributions of product ions. , 68, 76, 89 Examples of the sequence maps generated with TD/MD MS approaches in the current study are provided in Figures  S60-S66  (Supplementary Material). The total sequence coverage obtained for the ETD/PTR MS/MS data depicted in Figure  7  is one of the highest in the study, Figure  8.
Here, notation "zn+1" refers to one of the possible product ion types in ETD MS/MS. Similar observations were reported for MALDI ISD (zn+2 type ions) sequence and for ETD MS/MS measurements performed by FT-ICR MS. 82  The "sequence validation percentage", or SVP, approach employed here by group #1 was originally introduced to improve the interpretation of TD/MD sequencing data compared to sequence coverage alone. 33 It uses parameterized conditions to obtain a metric for the percentage of the analyzed sequence that could be validated based on the available data. For example, the SVP method considers sequence gaps as validated if: (i) sequence readout starts before or at the 10th (or another user-defined number) amino acid residue from the N-or Cterminus of a given chain, (ii) internal gaps are attributed to Pro-containing moieties, such as …PX… or …PPX…; or (iii) other internal gaps, say, two amino acid residues long, are immediately followed by an at least equally long sequence tag.
The analysis of the trastuzumab, SiLuLite, and NIST mAb Fc/2 subunits yielded 75/77/77% sequence coverage, respectively, in addition to Fd' 58/62/71%, and Lc 77/87/77 %. Thus, an average sequence coverage of 74 +/-8% was observed in the three analyses from LC-MALDI ISD as reported by Group #1. For example, as a result of LC-MALDI ISD sequencing of the NIST mAb IdeS-derived subunits, up to 80-100 residues from both the N-and Ctermini toward the subunit center were confirmed (Figure  10 ). Generally, ~7-10 terminal residues were not directly observed in the low-mass background region in MALDI ISD mass spectra. The match was considered as valid (match rules were parameterized in BioPharma Compass software)  33   CDRs on the Lc and 2 CDRs on the Fd' domain (Figure  11 ). In summary, together with the achievable low ppm mass accuracy for mass measurements of mAb subunits (IdeS digestion followed by disulfide bond reduction), the combination of both MD and BU approaches leaves little uncertainty about the correctness of the given sequence in general and of CDRs in particular.
Acknowledgments We greatly appreciate the support of the  Pilot  Project by the  Consortium for Top-Down Proteomics  and its supporting organizations,  Thermo Fisher Scientific Inc. , Bruker Corp. and Genovis AB. We also acknowledge  Genovis  for providing enzymes (IdeS and KGP) and  Millipore Sigma  for donation of their mAb standard (SiLuLite mAb). JAL acknowledges support from the  US National Institutes of Health  ( R01GM103479 ,  S10RR028893 ,  S10 OD018504 );  YOT and JCR from European Horizon 2020 research and innovation program  under grant agreement No  829157  (TopSpec); JSB from the  Welch Foundation  ( F-1155 ) and the  National Science Foundation  ( CHE1402753 ); and DFH from the  NIH  grant  GM037537. LPT, JBS, and MZ acknowledge the support from the  High Resolution and Mass Accuracy Capability  development project at EMSL, a  DOE Office of Science User Facility  sponsored by the  Office of Biological and Environmental Research  and located at the  Pacific Northwest National Laboratory in Richland, Washington, USA.
CLH, LCA, LH, AGM acknowledge support for work performed at the  National High Magnetic Field Laboratory , which is supported by  National Science Foundation  Cooperative Agreement No. DMR-1644779  and the  State of Florida. This research was carried out in collaboration with the  National Resource for Translational and Developmental Proteomics  under Grant  P41 GM108569  from the  National Institute of General Medical Sciences, National Institutes of Health.
Misassignment can increase the mass errors by multiples of about +/-1 Da. 45  Furthermore, peak interferences due to partially reduced mAb subunits are common and should be prevented by thorough reduction methods and/or considered during data analysis. 33 The molecular weight determination of 25 kDa mAb subunits can drastically reduce this shortcoming, as seen with the IdeS or KGP approach (with disulfide bond reduction) and the disulfide-bond reduced mAb dataset analysis. However, even monoisotopic mass measurements of 25 kDa based on state-of-the-art MS instruments can provide mass measurement errors exceeding 10 ppm (Figure  S7 , Supplementary Material). Among the origins for these errors are: (i) incomplete reduction of disulfide bonds of intact mAbs or acid (Glu) and glutamine (Gln). In the mAbs targeted in the present study, SiLuLite mAb contains both Gln on the N-terminus of the Lc and Glu on the N-terminus of the Hc; whereas NIST mAb has a Gln on the N-terminus of the Hc and trastuzumab has a Glu on its Hc (Table  S1 , Supplementary Material).
Data and methods We have used the outputs of a global hindcast performed in the frame of the DRAKKAR project  (Barnier et al. , 2006 (Barnier et al. , , 2007) )  at the eddy-resolving resolution of 1/12°with the ORCA12 configuration described here (https://github. com/meom-configurations/ ORCA12. L46-MJM189). This hindcast is based on the NEMO ocean / sea ice GCM numerical code  (Madec and NEMO-team, 2016 ). In the past, similar simulations based on OPA 8. 1 were performed during the CLIPPER experiments and compared to Eddy Kinetic Energy (EKE) satellite observations by  Penduff et al. (2004)  whereby concluded to a reasonable agreement. In addition, such simulations showed a good agreement with altimetry when considering Rossby waves phase occurence and speeds  (Lecointre et al. , 2008) , which is a prerequisite for our study.
2 (based on a rescaling of ERA-Interim ECMWF atmospheric re-analysis)  (Dussin et al. , 2018). The relevant parametrisations for this study A running average on 30 days and 28 km is applied. are: linear filtered free surface technique to avoid fast barotropic waves, biharmonic horizontal lateral viscosity, and isopycnal lateral diffusivity. Vertical mixing is controlled by the TKE scheme  (Blanke and Delecluse, 1993; Madec et al. , 1998). The basic data set consists of the SSH fields (5-day average) model output, for the period 1970-2015, extracted from the global simulation on the NA (0-65°N). These fields are available at https://doi. org/10. 5281/zenodo. 3968801 between 38 and 40°N  (Molines, 2020). The chosen period allows for a 13-year spin-up of the simulation. We decided to focus on the latitude 39°N which was considered representative of the average GS path from our SST fields in  Watelet et al.
(2017). Here, indeed, we only examine the zonal component of the Rossby waves travelling across the NA. Regarding longitudes, we chose to analyse propagating signals from 30 to 70°W, i. e. between the approximate center of the NA at 39°N where we can expect the NAO signal to be carried to the ocean through wind forcing and the western limit of the GS region as defined in  Watelet et al. (2017). These SSH fields, originally provided at a horizontal resolution of 1/12°(ORCA12 grid), were interpolated bilinearly to a constant 1/12. 5°or 0. 08°grid in order to avoid truncation issues with repeating decimals. From there, we removed the climatological annual cycle as well as the 1970-2015 trend. The data set is available at https://doi. org/10. 5281/zenodo. 3968885 (Watelet, 2020). Figure  1  shows these detrended SSH anomalies as an Hovmöller (or lon-time) diagram.
Immunohistochemical staining of multiple slides from serially sectioned lungs was carried out to identify progenitors and terminally differentiated cells that were, and were not, EdU+. The percentage of EdU+ progenitors was therefore used to infer self-renewal capacity, and the percentage of EdU+ terminally differentiated cells was used to infer differentiation capacity of their upstream progenitors. In the alveolar region, the number of Lamp3+ Type II progenitor cells and terminally differentiated Hopx+ Type I cells was counted and normalised to tissue density (tissue density measurements based on data in Fig. 1c ). The normalised data therefore reflect cellular density within a unit area of the alveolar walls. Importantly, Lamp3 co-localized with another Type II cell marker, Sftpc, whilst Hopx co-localized with another Type I cell marker, T1α, in young and old lungs, demonstrating that these markers are sufficiently specific for our analysis (Supp Fig.
Aging lung epithelial cells display changes in their gene expression profile. CD45-CD31- EpCAM+ epithelial cells were sorted from lungs of six 3-and six 22-month old mice (Fig. 5a ). RNA was isolated and amplified, and microarray analysis carried out. Cutoffs of p < 0. 05 and fold change >1. 5 were applied (Full dataset supplied in Supplementary Table  1 ). This yielded 92 genes that were downregulated with aging and 56 genes that were upregulated (Fig. 5b ). Samples clustered by gene expression pattern according to their age group (Fig. 5c ). Ingenuity Pathway Analysis revealed multiple pathways and regulators that are linked to directional gene expression patterns observed in this dataset (Tables  1  and 2 ). There was upregulation of a cell death pathway, and downregulation of multiple proliferation and viability pathways (Table  1 ).
Known regulator of airway cell differentiation, Myb,  33  is predicted to be the most strongly inhibited, and indeed its expression was downregulated at the RNA level within the microarray experiment. Other upstream regulators predicted to be inhibited include Tp53, Nfkbia, Prdm1 and Creb1, which regulate cellular activities such as proliferation, migration and inflammation in the context of lung cancer  [34] [35] [36] [37]. Many of the regulators and pathways listed above centred on a module of common genes that were differentially expressed in our microarray data. To confirm their differential expression, we performed RT-PCR using commercially validated TaqMan assays on 96-well array plates (ThermoFisher, primer AssayIDs shown in Supplementary Table  2 ). 5 housekeeping genes were included (18 S rRNA, Tbp, Ppia, Rab7 and Ubc) that are commonly used for normalisation of RT-PCR data from young adult mice.
Discussion In this study, we asked how lung epithelial progenitor activities change with aging, and how this correlates with changes in gene expression profile and overall tissue architecture. Our aim was to understand the baseline changes that occur with healthy aging over the natural lifespan. Aging is thought to directly contribute to the pathogenesis of IPF, COPD and lung cancer along with multiple other factors. Our data will help to disentangle the specific contribution of aging to disease pathogenesis. We carried out our morphometric studies using high-throughput, automated analysis of whole H&E stained sections to eliminate investigator bias. Previous studies have demonstrated age-related airspace enlargement by measuring an increase in the mean linear intercept, mean alveolar volume, or ductal airspace volume  [2] [3] [4] [5] 40, 41.
The inferred position of the basement membrane was measure by drawing a line on ImageJ using the "freehand line" feature, and measuring its length. Microscopy and automated image analysis. H&E stained slides were scanned on an Axio Scan Z. 1 (Zeiss, Germany) and digital images of full lung sections were recorded at x20 magnification with a pixel size of 0. 452 µm. Fully automated quantification of morphological changes of lung tissue was carried out by Biocellvia (Marseille, France) using their validated proprietary software programs dedicated to respiratory diseases  24, 25. Dedicated algorithms were developed to automatically discriminate bronchioles of which the Feret diameter (the largest diameter) ranged between 100 and 500 µm. Then, automated delineation of the inner and outer limits of the bronchiolar epithelium was carried out, in order to delineate the lumen and the basal membrane (Supp Fig.
Remaining contaminant CD31/45+ cells were excluded, and EpCAM+ singlet cells were sorted on an Aria cell sorter. RNA isolation, microarray analysis and RT-PCR confirmation. RNA was isolated from lung epithelial cells immediately after they were isolated, using the MirVana kit (AM1560, ThermoFisher). RNA quantity and quality was checked on a Nanodrop 1000 (ThermoFisher) and a BioAnalyzer (Agilent), respectively. RNA was amplified and biotinylated sense strand cDNA generated using the GeneChip WT Pico Reagent Kit (Affymetrix). cDNA was hybridised to GeneChip Mouse Transcriptome Assay 1. 10 microarrays, using a GeneChip Fluidics Station (Affymetrix). Arrays were scanned using a GeneChip Scanner system (Affymetrix). Microarray data quality control was carried out using Qlucore software. Differential expression values were generated using the Affymetrix Transcriptome Analysis Console, and Ingenuity Pathway Analysis (Mar 2018 Release) was used for further data analysis.
Before conducting analyses, the initial task of the OSCAR project consisted of mapping the distribution of ticks, pathogens and the principal domestic (cattle) and wild (small mammals and roe deer) hosts, along a gradient of landscape fragmentation, from forest cores and edges to more or less isolated hedges, all bordering meadows. This paper describes the collected datasets (Fig. 1 ) (1) on questing tick and small mammal densities, (2) on local environmental conditions (habitat, vegetation and livestock densities) of sampled transects and (3) on pathogen prevalence in ticks and small mammals. Due to time and manpower constraints, we restricted our assessment of tick host species to small mammals, livestock and roe deer, the principal reservoir hosts implicated in disease for production animals.
Sampling description: Recording local environmental conditions Georeferencing of sampling locations of ticks (Table  1 ) and small mammals (Table  2 ) was obtained in the field using a Trimble GNSS GeoExplorer XT 6000 receiver. A differential correction in post-processing made it possible to obtain decimetric precision. The points obtained were exported in a shape (shp) format and inserted into Geographic Information System (ArcGIS) software. Drawings of the sampling lines were performed on maps by the operators during sampling and were corrected with the GIS database with the help of orthophotos (BD ORTHO®, resolution 50 cm x 50 cm, IGN). During sampling, local environmental conditions were recorded for the questing tick transect-lines, the tick subtransects and the small mammal trap-lines.
The following variables were recorded in the field during tick sampling (Fig. 4 ) and small mammal sampling (Fig. 5 ): date and time of the day, habitat type, vegetation type and characteristics, slope, traces of use by livestock. In the VG site, livestock occurrence and abundance were also recorded each week along each tick transect. The livestock survey was only performed in the VG site in association with other research projects and these data were not collected in the ZA site. The data were entered into specific tables of the database (Tables  3, 4, 5, 6 ). Text (5 c. ).
Ticks from small mammals were counted immediately after being euthanised in VG, but in ZA, due to the high number of captured mammals, dead animals were frozen and ticks were collected later during dissections. All collected ticks were stored in 70% ethanol for later identification and use for molecular analyses. The animals captured in spring 2014 were not euthanised, but were released at least 500 m away from the capture site to avoid recapture and ticks were quickly collected on these individuals. Table  7. Field description of the dataset concerning the analyses of tick DNA for infectious agents. c. : characters.
DataBase All the data of Tables 1, 2, 3, 4, 5, 6, 7, 8, 9 were united in a single Access database. The relationship between the tables is given in Figs 8, 9. Table  9. Field description for livestock dataset. c. , characters. Heads. day refers to the number of individual animals that were counted in a pasture on a given day. The data presented in this dataset are detailed by campaign and by site in Table  10. Relational model for small mammals: relationships between tables concerning small mammal sampling and analyses. Similar colour corresponds to similar data present in two tables. Key is primary key. ECHLM_*, Identifier code for small mammal trap-line; MAM_*, Identifier code for captured small mammal.
The data  (2011) (2012) (2013) (2014)  are available upon request to the corresponding author. Additional variables were calculated to measure landscape heterogeneity around the sampling locations. These data and their production (ecotone length between wooded habitat and meadows, proportion of woodland cover, grassland cover and crops, mean distance between wooded patches, perimeter-area ratio of wooded patches, connectivity of wooded habitat patch) are presented in  Perez et al. (2016)  and  Perez et al. (2020).
Description: The data concerning small mammal sampling are presented in the 3 following tables. Table  5. Field description of the characteristics of the small mammal trap-lines in the dataset. (Associated file: SmallMammalsTrapLineData. tab) Table  6. Field description of the dataset concerning small mammal sampling and identification (Associated file: SmallMammalsSamplingData. tab) Table  8. Field description of the dataset concerning the analyses of small mammal DNA for infectious agents (Associated file: SmallMammalsPathogenData. tab) The date format ISO 8601 (YYYY-MM-DD) was used. Fig. 10  presents the density of I. ricinus nymphs, according to landscape type and field campaign. Densities were generally higher in the ZA site than in the VG site, regardless of the campaign or landscape type.
Acknowledgements We thank the  French National Research Agency , which funded the  OSCAR  project ( ANR-11-AGRO-001-04 ). The  OSCAR  project website can be accessed at: https:// www6. inrae. fr/oscar. This study benefited from the help of the " Zone Atelier Armorique " and the  Zone Atelier "Vallées et Coteaux de Gascogne". We are grateful to  Dominique L'Hostis ,  Sylvie Cocaud ,  Nathalie Morcrette ,  Esther Dzalé ,  Anne-Sophie Martel , members of the pole DigitaIST (INRAE) and  Nathalie Gandon ,  Unité Ingénierie Numérique en Recherche (INRAE) , for their help preparing this data paper and all the colleagues who have participated to the  OSCAR project (see the "Personnel" mentioned in the section "Project description "). Publications, using the presented data, are available on the OSCAR project website: https:/ /www6.
Mesoscale oceanic eddies have a visible signature on Sea Surface Temperature (SST) satellite images, portraying diverse patterns of coherent vortices, temperature gradients and swirling filaments. However, learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image. We introduce a novel Deep Learning approach to classify sea temperature eddy signatures, even if they are corrupted by strong cloud coverage. A large dataset of SST image patches is automatically retained and used to train a CNN-based classifier. Classification is performed with very high accuracy on coherent eddy signatures and is robust to a high level of cloud coverage, surpassing human expert efficiency on this task.
Similarly in  [34]  the OW detection method is used to label training data derived from the velocity field. These studies while successfully exploring novel methods for eddy detection application, stumble upon the inherent limitation of the gridded altimetric products, on which the learning dataset is based. The measurement error will therefore propagate throughout the whole training process. In visible imagery, Deep Learning has been employed by  [35] to classify eddy signatures on SAR images. Here, following  [36] , we employ Convolutional Neural Networks to build a Sea Surface Temperature eddy signature classifier, a tool which can serve for validating and correcting altimetry eddy detections. This study provides contributions in automatically retaining a large dataset of SST patches with eddy signatures and constructing a CNN-based classifier of sea temperature eddy signatures.