dataesr/scientific-paragraphs-categorization huggingface dataset:
This strategy led to the selection of a subsample (referred thereafter to as the ANN dataset) of 592 patients (497 DVT and 95 PE)  whose proteomics/biological entered the ANN analysis. Visual inspection of the proteomics and biological data in the selected ANN sample is shown in Supplementary Fig. 2. A two hidden-layers ANN was then built from the ANN dataset with a training set of 576 patients (487 DVT and 89 PE) and a testing set of 16 patients (10 DVT and 6 PE). This allocation was chosen so that the number of PE cases used for training was sufficiently large. Because the training set presented with a strong imbalance with respect to the DVT/PE classes with ~ 5 times more DVT than PE patients, the ANN was trained iteratively as described in the Materials and Methods section. By completion of the iterative algorithm, the final ANN obtained an area under the operative curve (AUC) of 0.
Among these 4 individuals, only two (Individuals 11 and 15) have been sequenced for their whole genome. Sequence data of these two individuals were then scrutinized for candidate rare variants that could explain the VTE phenotype. Individual 11 is a woman that experienced PE under oral contraceptives (OC) at age 45. Of note, her ten closest neighbors inferred from HPA data were all DVT patients which would likely explain why the derived ANN predicted her a DVT outcome instead of PE. She was not found to harbor any candidate variation in known VTE genes but presented in her genome with 61 very rare coding variants with strong predicted deleteriousness that could be good candidates responsible for the PE event (Supplementary Table  7 ). Individual 15 is a woman that had experienced PE at age 53 also under OC.
Nine out of 10 of her closest proteomics based neighbors were DVT patients which may also explain why this PE patient was incorrectly predicted to be DVT. This patient was found to carry a very rare nonsynonymous variation (rs121918154; PROC:NM_000312:exon9:c. C814T:p. R272C) in the VTE-associated PROC gene. This variation has a minor allele frequency of 0. 005% in public database (https:// www. ncbi. nlm. nih. gov/ snp/ rs121 918154), is predicted to be deleterious by several bioinformatics tools and have been previously reported in VTE patients with protein C deficiency  47, 48. This variation is located in the last exon of the gene and is predicted to alter splicing regulatory elements  [49] [50] [51]  , which could lead to a deletion of a part of the peptidase S1 domain that is responsible for the cleavage activity of the protein.
PLXNA4 codes for Plexin A4, which is part of a receptor complex involved in signal transduction of semaphorin 3 signals linked to cytoskeletal rearrangement, inhibiting integrin adhesion  54, 55. It has a role in axon guidance in nervous system development, and genetic variants in PLXN4 have been linked to risk of Alzheimer disease  56, 57. Based on RNA seq data from HPA, FANTOM and GTEx datasets, PLXNA4 is expressed at medium/ high levels in central nervous system, adipose, breast and female reproductive tract tissues, and low levels in a broad range of other tissues (https:// www. prote inatl as. org/ ENSG0 00002 21866-PLXNA4/ tissue), indicating roles outside the nervous system. Despite found in most tissues, based on more recently available data from an integrated analysis of single cell RNA seq data available in public repositories (https:// www.
The dataset encompassed a total of 26. 5 Gb raw data from mature embryo and endosperm tissues transcriptomes. The raw and mapped reads data on build SL4. 0 and annotation ITAG4. 0 are available under accession GSE158641 at NCBI Gene Expression Omnibus (GEO) database. Data on seed vigor characteristics are presented together with the differentially expressed gene transcripts. GO and Mapman annota-tions were generated on ITAG4. 0 to analyse this dataset and are provided for datamining future datasets. © 2020 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Value of the Data • This is a tissue-specific seed transcriptome dataset for tomato obtained from fruits that were ripened ex planta under four environmental conditions (high/standard temperature and standard/low light intensity). • These data are a useful resource for the scientific community studying the developmental programs of various seed tissues and working on the effect of maternal environments on seed vigor. Annotation files of Gene Ontology or Mapman format are provided for the recently published SL4. 0 genome version and can be used for enrichment analysis and data mining. • These data provide new insights on tissue-specific molecular processes affected by heat and light leading to defects in seed vigor. They allow the identification of candidate genes as well as molecular markers that might predict seed vigor on tomato.
RNA analysis and functional annotation After quality control of fastq files using FastQC  [2]  , high-quality reads were mapped onto the reference tomato transcriptome build SL4. 0  [1]  and transcript abundances were quantified with Salmon algorithm (version 0. 14. 1) [3]  using the quasi-mapping mode and the '-validateMappings' and '-seqBias' options. Before mapping, the reference genome was indexed with Salmon using k-mers of length 31. Coverage estimates and statistics of the reads mapping are presented in Table  2. Differential expression of transcripts were calculated via DESeq2  [4]. Transcripts were considered differentially expressed if log 2 fold change (FC) was above 1 or below -1 and if Benjamini-Hochberg adjusted p -value threshold was below 0. 05. Data on total counts and differential gene expression can be found at https://data.
mendeley. com/datasets/ 6h44fvz8x9/1. Gene Set Enrichment Analysis (GSEA) on GO Terms were performed with hypergeometric test using clusterProfiler package (v3. 10. 1) in R  [5]. GO Terms were considered as enriched if Bonferroni adjusted p-value threshold was below 0. 05. Gene Ontology (GO) annotation on SL4. 0 was generated using OmicsBox ( https://www. biobam. com/omicsbox/ ,  [6]  ) (Table  S1 ) and Mapman annotation was generated using Mercator v4  [7]  (Table  S2 ). The total gene enrichment analysis can be found at https://data. mendeley. com/datasets/6h44fvz8x9/1.
Acknowledgments We thank  D. Lalanne  and  D. Sochard  for technical help in the laboratory and greenhouse, respectively. The Phenotic and ANAN platforms are acknowledged for the access to equipment. This research was conducted in the framework of the regional programme " Objectif Végétal, Research, Education and innovation in Pays de la Loire ", supported by the  French Region Pays de la Loire ,  Angers Loire Métropole  and the  European Regional Development Fund.
Search Strategy and Data Extraction A comprehensive search strategy was designed to retrieve all articles published up to March 2021 by combining (corneal suture OR corneal suturing OR corneal transplantation AND training OR residents OR novice surgeons OR assessment OR skill assessment OR quality assessment) in the electronic bibliographic database of PubMed and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. All studies proposing an English abstract were reviewed. Inclusion criteria involved the study design (i. e. , prospective comparative studies with two or more groups or prospective studies with before/after comparison [simple or crossover] in one single group), the type of population studied (i. e. , ophthalmology residents or novice surgeons), and the study outcome (i.
e. , assessment of residents' surgical performance in corneal suturing during specific training sessions). All studies presenting with the following criteria were excluded from this review: inappropriate study type (i. e. , retrospective studies, observational studies, clinical guidelines, consensus documents, reviews, systematic reviews, and conference proceedings), studies dealing with surgical procedures other than corneal suturing or with populations other than residents or novice surgeons or with another condition than training session (i. e. , OR assessments) and, finally, studies reporting molecular aspects (i. e. , artificial cornea design). The search results were compiled using EndNote X9. 3. 3 software. Two experienced reviewers screened the retrieved articles and extracted data from each eligible study using a standardized data extraction sheet following presence or absence of inclusion and exclusion criteria.
Results The database identified 175 articles. After removing 23 duplicates, 152 articles were reviewed based on the title and abstract. Of these, 148 were excluded based on study type (i. e. , retrospective, observational, clinical guidelines, consensus documents, reviews, systematic reviews, and conference proceedings), surgical procedures other than corneal suturing, participants other than residents, surgical conditions other than training, and studies only reporting molecular aspects of corneal models. Four articles were assessed for the systematic review (►Fig. 1). One was excluded after a full-text review as it reported the same training session from the same team as another already included article, the difference being the results focused exclusively on ophthalmic surgeries other than corneal suturing.
Study 1 Study 1 was conducted in 2006 by Saleh et al. 16  This prospective and comparative study included 30 surgeons assigned to three different groups according to their level of experience in corneal suturing (defined by the number of stitches previously performed). The surgeons were asked to perform a 10-0 nylon suture pass using a 3-1-1 pattern to close a wound on an artificial cornea under a microscope. Evaluation of the suture pass was performed using the Imperial College Surgical Assessment Device (ICSAD). Briefly, this method uses a 10-mm electromagnetic tracker attached to the surgeon's index finger of each hand from which threedimensional records are created using a magnetic field. The data were then analyzed using a custom-made computer software program which assessed the number of movements, the path length of these movements, and the time taken to complete the procedure.
Characterizations The structural analysis of all the samples were performed, using STOE Powder X-Ray diffractometer θ-θ between 2θ values ranging from 20 • to 80 • , at room temperature, using Cu Kα (λ = 1. 5406 Å) radiation. The patterns were evaluated by X'pert Highscore software and plotted, using Origin software. For the investigation and confirmation of required functional groups in the synthesized samples, FTIR transmission spectra were taken, using PerkinElmer, SpectrumTM100 in a range from 300 to 1000 cm -1 for NPs and from 400 to 4000 cm -1 for emulsions. For morphological and elemental analysis, Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) were performed, using Vega3 Tescan microscope (Czech Republic) connected with EDS apparatus of Oxford instrument.
5 h, at 4500 rpm, by adding ethanol. The supernatant was then used for UV-Vis analysis. The drug encapsulation efficiency and loading capacity was calculated by using Equations (  1 ) and (2)  [60] , respectively, as given below. Diffusion and dissolution of drug encapsulated within the polymer matrix was monitored and different kinetic models, i. e. , Zero Order kinetics, First Order kinetics, Higuchi kinetics and Korsemeyer-Peppas kinetics models, were applied, using a software named "DD Solver 1. 0" to analyze drug release  [60]. The best fitted model was then selected depending upon the correlation coefficient (R 2 ), which is a statistical measure of closeness of predicted data to experimental ones under the same conditions.
Magnetic Resonance Imaging (MRI) Analysis The performance of the synthesized samples as MRI agents was evaluated by using clinical MRI system GE Optima MR450w 3. 0T. For this purpose, the samples were prepared at different concentrations by using different TE and TR sequence. For T 2 contrast, TR sequence of 5000 ms with varying TE of 14. 27, 42. 8, 76, 108, 131, 164 ms and for T 1 , fixed TE value of 10. 64 ms with TR of 101 and 685 ms was used. Two glass vials, one containing water and one containing commercial Gd sample, respectively, were used as references for comparing the imaging ability of the formulated emulsions with that of water and Gd. After taking MRI images, a software named "Kpacs" was used for calculating the mean intensities of the samples by using mean Region of Interest (ROI) values of the respective samples and the relativity values of the samples were calculated, as well  [61, 62].
Methods. We study stars with measured chemical and seismic properties from the APOGEE spectroscopic survey and the Kepler satellite, respectively. In addition, astrometry from the Gaia satellite is available for the majority of the sample. We separate the [α/Fe]-[Fe/H] diagram into three stellar populations: the thin disc, the high-α metal-poor thick disc, and the high-α metal-rich thick disc and characterise each of these in the age-chemo-kinematics parameter space. Because of the model-dependent nature of the ages inferred from asteroseismology, and because they depend on the quality of the input spectroscopic information, we compare results obtained from different APOGEE data releases (DR14 and DR16). We also use age determinations from two recent works in the literature. In addition, we use the Besançon stellar populations synthesis model to highlight selection biases and mechanisms (such as mergers and secular evolution) not included in the model.
Seismic sample based on -Apparent magnitude range of 7<H<11; -Stars with an effective temperature (T eff ) lower than 5300 K to select only giant stars; -Stars within the observed domains of the large separation (∆ν) and the frequency of maximum power (ν max ) (0. 4 < ∆ν < 20 µHz ; 2 < ν max < 250 µHz); -Binaries identified by the radial velocity variation are rejected using VSCATTER<1 km s - 1  -We adopt 'ASPCAPFLAG' 1 to select the best well-defined spectroscopic determinations  (Holtzman et al. 2018 ); -The most accurate seismic determinations only, using the information in the Notes column indicated in  Pinsonneault et al. (2018). In the APOKASC catalogue  (Pinsonneault et al. 2018) , ages are determined using a modified version of the grid-based modelling as implemented in the BeSPP code  (Serenelli et al.
Chemical properties We use the spectroscopic parameters from APOKASC which are taken from APOGEE DR14. Ahumada et al. (2020)  published the sixteenth data release of APOGEE. For this data release, entirely new synthetic grids were created based on MARCS stellar atmospheres  (Gustafsson et al. 2008). The metallicity and [α/Fe] abundances differ from the DR14 to the DR16, implying a difference in [Fe/H] and in [α/Fe] of less than 0. 1 dex. These differences are illustrated in Fig. 3. To investigate all possible parameters that could change the conclusions of the present work, we performed the same study (in parallel) using also the [Fe/H] and [α/Fe] abundances derived from DR16. We investigate the effect of these changes between DR14 and DR16 on the relations between kinematics and metallicity or [α/Fe] in Section 5.
APOKASC sample To compare the distributions before and after the observational selections and to reveal whether or not these cuts change the overall distributions, we ran a simulation of the Kepler field. We then applied the same selections as for the data, as described in Section 2. We compare our BGM simulation with the APOKASC data including the H magnitude and T eff selections using a Kolmogorov-Smirnov (KS) test of the magnitude distributions in different bands (H, J, K). As the sample is not complete at higher and lower values of H magnitude, the KS test returns a low p-value, indicating that the synthetic stellar sample and the observed one do not come from the same parent distribution. For that reason, we prefer to restrict our observed sample to 7. 4≤ H<10. 8 in this study. Using this selection in the H magnitude, we obtain a KS test that shows better agreement (for H band p-value=0.
1 ). Moreover, the M21 age distribution shows a crest between 2 and 5. 5 Gyr that is compatible with the BGM simulations. Although  Miglio et al. (2021)  showed that low ages are strongly dependent on hydrodynamical processes included in stellar evolution models during the main sequence (such as overshoot and rotation-induced mixing), several previous studies pointed out an increase in star formation in the period between 2 and 3 Gyr which is probably reflected in the M21 distribution. More data are needed to statistically confirm this feature. Vergely et al. (2002)  showed a peak of star formation 1. 6 Gyr ago using a sample from the Hipparcos catalogue (Perryman 2002). Also using Hipparcos data, but with a different approach,  Cignoni et al. (2006)  found maximum star formation activity to be about 3 Gyr ago in the solar neighbourhood.
g. Fuhrmann 2011;  Haywood et al. 2013; Delgado Mena et al. 2017; Anders et al. 2018; Nissen et al. 2020; Grieves et al. 2018; Silva Aguirre et al. 2018; Miglio et al. 2021 ) who used different datasets and methods of age determinations. The hαmp thick disc age distribution is particularly extended in the APOKASC data. Approximately 80% of the hαmp stars are found to be older than 6 Gyr. The sample of younger hαmp stars needs to be increased for this result to be confirmed. Regarding the hαmr thick-disc population (Figure  7 , middle panels), its age distribution ranges from 1 to 14 Gyr with the majority of stars having an age of 7 to 14 Gyr. This is very similar to the hαmp thick disc population in both samples (KS tests done between 7 and 14 Gyr give p-values of ∼0. 99 and 0. 92 for APOKASC and M21, respectively), with a higher contribution in the younger tail (Fig.
Although the [α/Fe] abundances are usually used as a proxy for age, the behaviours of the velocity dispersion of the hαmp thick-disc stars are completely different as a function of age and [α/Fe]. The trends are the same when DR14 or DR16 APOGEE data are considered. Bottom panels of Fig. 15  present the velocity dispersions with stellar age derived from APOKASC (solid lines) and M21 (dashed lines) catalogues. It is important to recall that the M21 sample excludes the low-mass red clump stars in order to avoid the effect of mass loss on the age determination. Although the mass-age relations are quite robust for giants, stars can gain and lose mass in different events (e. g. binarity, mass-loss, accretion), inducing a significant age bias: stars appear younger or older than they are. Also, with the more robust ages of M21, the ages of hαmp thick-disc stars do not extend to younger ages (see discussion about overmassive stars in  Miglio et al.
15. Dispersion of the velocities V R (left panels), V ϕ (middle panels), V Z (right panels) as a function of [Fe/H] (top panels), [α/Fe] (middle panels), and age (bottom panels) for the thin-disc, hαmp, and hαmr thick-disc populations (orange, blue, and magenta symbols, respectively). Solid lines represent velocity dispersions computed using chemistry and stellar ages from the APOKASC catalogue, while dotted lines represent velocity dispersions computed using abundances derived from DR16 APOGEE data (top and middle panels) and using stellar ages derived by M21 (bottom panels). Error bars on the velocity dispersions are also shown. following section). Therefore, we believe that these behaviours are not induced by sample selection but are real features.
Acknowledgements. N. L. and C. R. acknowledges financial support from " Programme National de Physique Stellaire" (PNPS). N. L. , C. R. , A. R. and O. B. acknowledges financial support from the  "Programme National Cosmology et Galaxies (PNCG) " of  CNRS/INSU, France. Simulations have been executed on computers from the  Utinam Institute of the Université de Franche-Comté , supported by the  Région de Franche-Comté and Institut des Sciences de l'Univers (INSU). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
F. F. , A. F. , R. M. , M. R. ,T. A. acknowledge supported by the  Spanish Ministry of Science, Innovation and University (MI-CIU/FEDER, UE)  through grant  RTI2018-095076-B-C21 , the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia "María de Maeztu")  through grant  CEX2019-000918-M , the  Ramon y Cajal Fellowship   RYC2018-025968-I. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 800502. AM acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 772293project ASTEROCHRONOMETRY, https://www. asterochronometry. eu. We also acknowledge the  International Space Science Institute, Bern, Switzerland  for providing financial support and meeting facilities.
Study population and design The Cholcoeur Study (Clinical Trial N°NCT02517944) is an observational prospective case-control monocentric study. One-hundred and three subjects (60 men, 43 women) aged between 40 and 60 years (mean age 49¢24 §4¢66) were consecutively enrolled between October 2014 and June 2017 at the Cardiovascular Prevention Unit of Piti e-Salpêtri ere Hospital in Paris, France according to the following inclusion criteria: genetically confirmed HeFH, no symptoms or electrocardiographic signs of ischemia, no personal history of CHD. Exclusion criteria were the absence of health insurance, refusal after informed consent, contra-indication to CMR, pregnancy, diabetes mellitus, uncontrolled hypertension. Control subjects were matched for age, body mass index (BMI), smoking habits.
CMR data were analysed using QMass v. 6 (Medis, the Netherlands) by the same operator blinded to study groups and to clinical data. Left ventricular (LV) end-diastolic and end-systolic volumes as well as LV mass were measured after semi-automated tracing of myocardial contours on all contiguous cine SSFP short axis slices. LV ejection fraction, stroke volume and mass-to-volume ratio were appropriately calculated. Similarly, right ventricular (RV) end-diastolic and end-systolic volumes, ejection fraction as well as stroke volume were measured. The same cine images were analysed to visually assess regional LV myocardial function as normokinetic, hypokinetic, akinetic or dyskinetic reported according to the standardized American Heart Association segmentation  [14]. Left ventricle longitudinal global strain and strain rate were calculated as the average of 4 and 2 chambers view using CMR feature-tracking, as previously described  [15].
Proteomic Analysis Three independent biological replicates of exo-proteomes from the studied strains were prepared as described for enzymatic analysis. Culture supernatants were concentrated using lyophilization, mixed with trichloroacetic acid (10% final) and incubated overnight at 4 • C. The precipitated proteins were collected by centrifugation (14,000 g, 4 • C, 20 min), washed three times with glacial acetone, suspended in Tris-HCl buffer and quantified using Qubit analysis (Thermofisher). Laemmli buffer was added to 10 µg of sample proteins before separation by SDS-PAGE 10%. After a short migration, proteins were visualized by staining with InstantBlue (Expedeon) and scanning with a ChemiDoc XRS camera (Bio-Rad). The proteins were quantified with Image Lab Software v5. 0 (Bio-Rad) before shotgun analysis.