dataesr/scientific-paragraphs-categorization huggingface dataset:
Gesture recognition comparison using three different camera positions from the SW dataset The accuracy in the Table  6  is 100%, meaning that the recognizer works perfectly for the gestures of the SW dataset. We think that the difference between the accuracy of the two datasets is mostly due two main reasons: 1. the top mounted view used in the TVA dataset and 2. the fact that in a number of frames the user is not captured in the TVA dataset, thus there is no any pose estimation. In addition, the three gestures made in the SW dataset have a greater variance in space than in the TVA dataset.
Comparison mixing gestures and data from the TVA and the SW datasets The last experiment we tried is mixing gestures and data from the two datasets. In Table  7 , we calculate the precision, recall and f-score for each gesture   6 : Gesture recognition using all joints and 2 dimensions on Silk Weaving dataset 2J-2D G4 G5 G7 Pr(%) HMM1 183 0 0 100 HMM2 0 166 0 100 HMM3 0 0 181 100 Rc(%) 100 100 100 100 ± 0 when we train the gesture recognition engine with samples from both datasets. As a general conclusion, we notice that there is a decrease of accuracy for every gesture. Nevertheless, this decrease is not important given the fact that we have many users, thus a high variance in the way the gestures are executed, and different camera positions.
Acknowledgement The research leading to these results has received funding by the  EU   Horizon 2020 Research and Innovation Programme  under grant agreement No. 820767 ,  CoLLaboratE  project, and No. 822336 ,  Mingei  project. We acknowledge also the  Arelik factory  and the  Museum Haus der Seidenkultur  for providing as with the use-cases as well as the  Foundation for Research and Technology Hellas  for contributing to the motion capturing.
Pulmonary arterial hypertension cohorts and genotyping We did two genomewide association studies and a meta analysis on pulmonary arterial hypertension. Pulmonary arterial hypertension was defined by haemodynamic criteria according to international guidelines. 2  Unrelated individuals with idiopathic, heritable, or anorexigen associ ated pulmonary arterial hypertension were inclu ded. Individuals with evidence of other known causes of pulmonary arterial hypertension were excluded; there fore, no patients were known to have pulmonary arterial hyper tension associated with clinically dia gnosable autoimmune diseases (appendix pp 2-3). All enrolled individuals provided written informed consent from their respective institutions or were included as anonymous controls under the DNA databank at Vanderbilt University, BioVU, optout policy (appendix p 2).
Annotation and functional assessment of the locus near SOX17 The locus near SOX17 was assessed against publicly available functional annotation datasets (including ENCODE, Factorbook Motifs, and Blueprint). The locus was investigated using CRISPRmediated repression in human pulmonary artery endothelial cells (hPAECs; PromoCell GmbH, Heidelberg, Germany) by trans duction with a lentivirus containing a plasmid en coding the nucleasedeficient Cas9 (dCas9) fused to the repressor KRAB and a 20 bp guide RNA (appendix pp 6-7). Cells were harvested following blasticidin selection, and the ex pression of SOX17 as well as neighbouring MRPL15 and TMEM68 was assessed by quantitative PCR. Invitro enhancer activity of the loci and variants near SOX17 was investigated using a luciferase reporter assay. Specifically, genomic DNA was isolated from endothelial progenitor cells (also known as blood outgrowth endothelial cells) derived from a patient with pulmonary arterial hypertension who was heterozygous for the lead SNP at SOX17 and used to clone 100 bp putative enhancer regions containing the SOX17 pulmonary arterial hypertension variants.
Statistical analysis Loci associated with pulmonary arterial hypertension were tested for associations with clinical variables (appendix pp 17-18). Allcause mortality was the primary endpoint in survival analyses using KaplanMeier estimates and Cox regression in the survival package in R, version 3. 3. 0. 12 Survival was calculated from diagnosis to date of death or censoring (Oct 31, 2016, for NIHRBR; Aug 1, 2017, for PAHB; Sept 27, 2017, for PHAAR; Oct 12, 2017, for BHFPAH), with left truncation using date of genetic con sent, and patients were censored at lung or heartandlung transplantation. Age and sex were covariates to correct for their association with prognosis. 2 NIHRBR and PAHB were analysed separately, and PHAAR and BHFPAH were combined before analysis because of their smaller sample sizes.
Cox regression re sults from these three analyses were then metaanalysed using the default randomeffects model restricted maximumlikelihood estimator method implemented in the metafor package in R, version 3. 3. 0. 13 ll cohorts were combined for KaplanMeier analysis. We did sensitivity analyses excluding pathogenic BMPR2 variant carriers, all pathogenic rare variant carriers, and patients diagnosed in previous decades who might have been exposed to different treat ment regimens. HLA alleles and amino acids totalling 1873 features were determined by imputation from genotyped and high quality imputed variants in the HLA region using the SNP2HLA software and the type 1 diabetes genetics consortium reference database. 14 HLA alleles and amino acids were tested for association with the novel lead variants or casecontrol status by χ² test with false discovery rate correction.
We detected no further loci at genomewide significance. Allele frequencies in the different control groups were similar between studies and to nonFinnish Europeans in the public database gnomAD (table  1 ). The conditional analysis confirmed that the HLA-DPA1/ DPB1 locus contained a single signal of asso ciation, but showed that the SOX17 locus was composed of two independent signals; signal 1 is 100-103 kb upstream of SOX17 (p conditional =9•82 × 10 -⁹) and signal 2 is 106-200 kb upstream of SOX17 (p conditional =4•16 × 10 -¹¹; figure  2 ; appendix p 15). The lead SNPs for the two signals in the SOX17 locus were rs13266183 (for signal 1, odds ratio 1•36 [1•25-1•48], p=1•69 × 10 -¹²) and rs10103692 (signal 2). A Bayesian credible set analysis to narrow the variants in these loci to those 99% likely to be causal (appendix p 15) showed that the HLA-DPA1/DPB1 locus included nine SNPs (all p<9•1 × 10 -¹⁸), SOX17 signal 1 included four SNPs 100-103 kb upstream of SOX17 (all p<3•3 × 10 -⁸), and SOX17 signal 2 included 31 SNPs 106-142 kb upstream of SOX17 (all p<5•7 × 10 -¹⁰).
Patie ts with the C allele at rs2856830 presented at a significantly younger age than those with the T allele, but the association of the HLA-DPB1 SNP with survival remains significant after correction for both age and sex. The somewhat conflicting observation that the C/C genotype is associated with earlier, more frequent presentation but improved survival compared with the C/T or T/T genotypes perhaps suggests that there could be two different mechanisms involved; one that affects initial disease pathogenesis and another that alters the adaptation to the established disease state. A parallel in pulmonary arterial hyper tension is the paradox of female prevalence contrasted with poorer outcomes for male patients, 2 although the mechanisms for this still remain unclear. Furth r evaluation of this survival association in independent datasets would help to define how clinical HLA typing or rs2856830 genotyping could improve risk stratification in clinical practice and in clinical trials, in which overrepresentation of the C/C genotype in one treatment group could significantly affect outcomes.
Contributors All authors were involved in study design, data collection, and constructive criticism of the manuscript. CJR, KB, MB, MHa, LSo, MG, MWP, JA, LH, IP, RK, SGr, WCN, RCT, AAD, NWM, and MRW were involved in data interpretation and writing of the manuscript. Data were analysed by CJR, KB, MB, MHa, LSo, MG, MWP, CH, JA, AA, KBH, JHK, MK, AU, LH, EMS, and SGr.
Acknowledgments We gratefully acknowledge the participation of patients recruited to the  UK National Institute of Health Research BioResource (NIHRBR) Rare Diseases study. We thank the  NIHRBR  staff and coordination teams at the  University of Cambridge (Cambridge, UK) , and the research nurses and coordinators at the specialist pulmonary hypertension centres involved in this study. We are also grateful to  Jenny Thomson  and  Caroline Langman  for invaluable assistance in patient recruitment for the  British Heart Foundation Pulmonary Arterial Hypertension (BHFPAH)  study. The  UK National Cohort of Idiopathic and Heritable PAH  is supported by the  NIHRBR ; the  BHF  ( SP/12/12/29836 ); the  BHF Cambridge Centre of Cardiovascular Research Excellence ; the  UK Medical Research Council  ( MR/K020919/1 ); the  Dinosaur Trust ;  BHF Programme grants  to  RCT  ( RG/08/006/25302 ),  NWM  ( RG/13/4/30107 ), and  MRW  ( RG/10/16/28575 ).
The genotyping of the VESPA samples was supported by  RC2GM092618. The authors acknowledge use of  BRC Core Facilities  provided by financial support from the  UK Department of Health  via the  NIHR comprehensive Biomedical Research Centre  award to  Cambridge Biomedical Research Centre ,  Imperial College Healthcare NHS Trust , and  Guy's and St Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust. NWM is a  British Heart Foundation Professor and NIHR Senior Investigator. CH is a  NIHR Rare Disease Translational Research Collaboration Clinical PhD Fellow. CJR is supported by a  BHF   Intermediate Basic Science Research fellowship  ( FS/15/59/31839 ). LH and JA are the recipients of  ERS  and joint  ERS/EMBO LongTerm Research Fellowships  ( LTRF 2016-6884  and  LTRF 20170100072 ).
AL is supported by a  BHF Senior Basic Science Research fellowship  ( FS/13/48/30453 ). LSo is supported by the  Wellcome Trust Institutional Strategic Support Fund  ( 204809/Z/16/Z ) awarded to  St George's, University of London (London, UK). IP is supported by the  Wellcome Trust  ( WT205915 ), and the  EU H2020  programme (DYNAhealth, project number  633595 ). Funding for the PAH Biobank is provided by  NIH/NHLBI  ( HL105333 ). WCN and MWP are supported by  NIH/NHLBI  ( HL105333 ). JHK receives support from the  American Heart Association  ( 16SDG29090005 ) and the  American College of Clinical Pharmacy Research Institute  ( Futures Grant ). AAD receives support from  NIH/NHLBI  ( R01HL136603 ). JF is supported by the  Wellcome Trust  ( WT101033 ). JH is supported by eMERGE  U01  ( NHGRI U01 HG008666 ).
We acknowledge the support of the  Imperial NIHR Clinical Research Facility and Biomedical Research Centre ,  Netherlands CardioVascular Research Initiative , the  Dutch Heart Foundation ,  Dutch Federation of University Medical Centres , the  Netherlands Organisation for Health Research and Development , and the  Royal Netherlands Academy of Sciences. This work was supported in part by the  Assistance PubliqueHôpitaux de Paris, INSERM, Université ParisSud , and  Agence Nationale de la Recherche  ( Département HospitaloUniversitaire Thorax Innovation ;  LabEx LERMIT ,  ANR10LABX0033 ; and  RHU BIOART LUNG 2020 ,  ANR15RHUS0002 ). MRW and HAG receive funding from  German Research Foundation (DFG)   SFB1213 , project  A09. The popgen 2. 0 network is supported by a grant from the  German Ministry for Education and Research  ( 01EY1103 ).
gnomAD is the Genome Aggregation Database, which provides information including allele frequencies in different populations. HLA-DPA1 and HLA-DPB1 are collectively referred to as HLA-DPA1/DPB1 in this Article. BHFPAH=British Heart Foundation Pulmonary Arterial Hypertension study. n eff =number of individuals that would make up an equally powered study with a 1:1 case:control ratio (appendix p 2). NIHRBR=National Institute for Health Research BioResource study. PAH=pulmonary arterial hypertension. PAHB=PAH Biobank study. PHAAR=Pulmonary Hypertension Allele-Associated Risk study. SNP=single-nucleotide polymorphism. *Significant. †This is the most significant SOX17 SNP after combining the three genotyping studies (not including NIHRBR) and forms part of signal 2.
In May 2017 he underwent surgery. An abscess around the left iliac part of the graft was found intraoperatively. The graft was replaced by a new graft made of silver-triclosan knitted collagen-coated polyester. The patient was treated postoperatively with meropenem, linezolid, caspofungin and amikacin. All of the intraoperative samples were negative. Only one sample from the right iliac part of the graft was positive for Staphylococcus pasteuri, which was considered to be a contaminant. Coxiella burnetii serology was negative. No multi-drug resistant bacterium was isolated and antimicrobial therapy was changed to piperacillin-tazobactam. Since cultures of graft specimens were negative, a 16S rRNA gene-based PCR with subsequent sequencing was performed. 5 The Bioinformatics Bacterial Identification tool (BIBI)  6  positioned the primer-less 16S rRNA sequence (GenBank accession number: MH230166) among the Bartonella species cluster.
The identification at the species level was then performed using specific PCRs targeting groEL/hsp60, rpoB and gltA genes since these genes have the best discriminating power among the Bartonella species. The BIBI positioned both groEL/hsp60 and rpoB primer-less sequences (GenBank accession numbers: MH230168 and MH230167, respectively) close to B. alsatica. The identification was confirmed by gltA sequence analysis (GenBank accession numbers: MH230169) that presented 100% identity in a 337-nucleotide overlap using the NCBI Blast program. Accordingly, indirect immunofluorescence serological analyses (Bartonella IFA-Focus Diagnostics, Diasorin kit) were positive for B. henselae and B. quintana (IgG titer 1/1280 and 1/640, respectively, thresholds 1/320). Nevertheless, three different specific PCRs identified B.
Therefore, it is challenging to find a method that could exploit all the information of the cardiac cycle and inherently dispose discriminatory information for cardiac motion characterization without the need of segmentation of the LV myocardium (that remains a time-consuming task) and with few user interaction. A recent classification technique that is widely exploited in natural images is the Sparse Representation based Classification (SRC)  (Wright et al. , 2009)  which involves the use of different algorithms to learn dictionaries from labeled training dataset and then use the features of the sparse decomposition of the testing signal for classification. This algorithm has achieved competitive performance on face recognition. However the performance of SRC has been improved by using discriminative dictionaries based on Dictionary Learning (DL) in which, the dictionary is inferred from a set of input signals  (Rubinstein et al.
Population and Image Acquisition The experimental procedures involving human subjects described in this paper were approved by the ethics committee of the CHU-Pontchaillou in Rennes, and were conducted in accordance with the Declaration of Helsinki. The shortaxis cine-MRI databases used in these experiments were collected from three groups: two groups of data patients candidates for cardiac resynchronization therapy, that were acquired from clinical protocols in national and international research projects composed of 3 and 6 patients, respectively (group G1 and G2); and one control group (G3) formed by 9 healthy patients obtained from an online database  (Tobon-Gomez et al. , 2013). For the experimental part on these data, all the acquisitions were obtained with a Philips Medical System Achieva 3T MRI scanner with, 30 phases over the cardiac cycle for the first group of data patients (G1), 40 phases for the second group (G2) and 30 phases for the control group (G3).
Assessment of radial spatio-temporal profiles with echocardiography 2D-STE In the group of pathological patients, radial strain tracings from 2D-STE were available only at the mid-cavity plane. For these patients a cardiologist, expert in echocardiography, performed Trans-thoracic echocardiography TTE US acquisitions. STE traces and regional strain curves were computed and exported using an echoPAC clinical workstation software (GE). Only SAX images have been used in this work. In order to perform the evaluation of the proposed method, the experiments and results presented in this paper correspond only by considering the medial plane in MRI. Previous works have demonstrated the relation between myocardial strain and strain rate calculated from two dimensional echocardiography versus regional LV function defined by cardiac cine-MRI for the assessment of regional left ventricular function  (Becker et al.
Robustness study To evaluate the generalization capability of the proposed approach, we take several subsets of samples from the original database. Each subset is composed of training and testing groups. Most specifically, we take randomly 75% of the radial spatio-temporal profiles cataloged as abnormal to conform one half of the training group. The other half has the same number taken randomly from the group of normal profiles. This is, an initial dictionary is composed of 414 profiles where 207 correspond to abnormal profiles (75% of the 267 abnormal profiles) and 207 to normal profiles. The rest of profiles (234 profiles, 168 normal and 66 abnormal) that are not taken in the training stage are selected to test the methods. We repeat this procedure during 50 iterations. Then, we calculate the average classification accuracy of the LC-KSVD and FD-DL algorithms by reducing the number of atoms in the initial dictionary.
Validation was performed using an intracorrelation segment criteria. In  (Afshin et al. , 2014) , each myocardial segment was marked following a binary score, either normal or abnormal. Starting with the delineation of the endo-and epicardial boundaries at the first frame, the method used the Bhattacharyya measure of similarity as a statistical feature related to the proportion of blood within each segment characterizing segmental contraction. The local ground truth (per segment) was built by three experienced radiologists, each of whom annotated a different portion of the data set. Among the 928 segments identified in this study, 579 segments were marked as normal and 349 as abnormal. Results were reported using a leave-onethrid-subject-out validation criteria. In  (Punithakumar et al. , 2013) , among the 480 myocardial segments identified in the study population, 389 segments were marked as normal and 91 as abnormal by expert visual assessment.
Acknowledgment This work was supported in part by the  French project  " Utility of medical imaging for the optimization of the implantation of implantable cardiac devices" (IMOP ):  ANR CIC-IT n. 04 187-188-189-190 , and the  European  project  eu-Heart: European Community 's  Seventh Framework Programme   FP7 ICT 2007-2(2008-12 ) under grant agreement n. 224495. Authors would like to thank the  University of Táchira, Venezuela  for the scholarship of the PhD program. Authors would also like to thank the  ECOS-NORD -FONACIT  grant  PI-2010000299  for their financial support and  Prof. Ruben Medina  for the proofreading and thorough discussions. Prof. Paredes  wants to thank  CDCHTA-ULA  for supporting his research under the project  I-1336-12-02-B.
Pan-STARRS 1 3π Our analysis includes imaging from the PS1 3π survey, in particular the Processing Version 2 catalog available in a local Desktop Virtual Observatory (DVO) database (released January 2015). The overall survey is described in  Chambers et al. (2016) ; see  Magnier et al. (2016c,b,a) ;  Waters et al. (2016) ;  Flewelling et al. (2016)  for a description of the data analysis and products. PS1 observations were made with a 1. 8m telescope equipped with a 1. 4-gigapixel camera. Over the course of 4 years of the 3π survey, up to four exposures per year in 5 bands, g P1 , r P1 , i P1 , z P1 , y P1 have been taken across the full δ > -30 • sky. Each nightly observation consists of a pair of exposures 15 min apart to search for moving objects. For each exposure, the PS1 3π survey has a typical 5σ depth of 22.
83, (iv) no BOSS spectrum, and (v) a current g magnitude at least 1 mag dimmer or brighter compared to the SDSS spectral epoch. For (v), the photometric data point nearest in time to the earliest SDSS spectrum is compared to the most recent PS1 data point. After visual vetting of the combined SDSS/PS1/CRTS light curves and spectra, we arrive at 262 CLQ candidates, listed in Table  2. Table  1  summarizes each stage of filtering. We prioritize these candidates based on a number of factors to most efficiently use telescope time. These include: their morphology to minimize overlap with the sample from the Time Domain Spectroscopy Survey (TDSS) "HYPQSO" program, which is obtaining few-epoch-spectroscopy for hypervariable quasars  (Morganson et al. 2015; MacLeod et al. 2018) , but only those with point-source morphology in SDSS; X-ray detections from the second release of the Chandra Source Catalog (CSC;  Evans et al.
SPECTROSCOPY AND ANALYSIS Starting with a spectroscopic quasar catalog from SDSS, we target highly variable quasars for new optical spectroscopy. We describe the SDSS and follow-up spectroscopic data sets in § 3. 1, and describe our spectral analysis in § 3. 2. A complete log of the spectroscopic observations and other relevant information can be found in Table  2. 3. 1. Spectroscopic Data 3. 1. 1. SDSS/BOSS The final spectroscopically confirmed quasar catalog from SDSS-I/II, based on the Seventh Data Release of SDSS (DR7;  Abazajian et al. 2009) , is presented in  Schneider et al. (2010). This catalog contains 105,783 quasars that have luminosities larger than M i = -22. 0. These quasars form our parent sample, and are hereafter referred to as the DR7Q catalog. As described by  Richards et al.
0 and nominal total coverage of ∼ 3100-10600 Å (but effectively further limited by the atmosphere). Typically, calibration images were taken at the start of each night including bias frames, lamp flats and CuNe/Ar arc lamp images. Spectroscopic standard stars were observed at ∼2h intervals throughout the night though this cadence was not always possible. The slit was oriented at the parallactic angle. Exposures were taken in 1800s increments and the number of shots on target was adjusted based on the latest PS1 photometry. WHT data were reduced using custom pyraf scripts and standard techniques. For analysis of follow-up spectra from WHT and other telescopes described below, we correct for telluric absorption where needed by using a standard star observation at similar airmass and the empirical method described in  Wade & Horne (1988)  and  Osterbrock et al.
MMT Observations of a fainter set of targets were made with the Blue Channel Spectrograph on the 6. 5m MMT situated on Mount Hopkins, Arizona. Observations were carried out over several dates in 2016-2018 (see Table  2  for exact dates). Here, the 300 mm -1 grating was used with a clear filter and 2× binning in the spatial direction. The central wavelength setting ranged from 5835 Å to 6335 Å (mostly at 5900 Å). To reduce MMT data, we used the pydis software adapted for use with Blue Channel data. 2 We use three exposures to remove cosmic rays by taking the median, and we compute the error in flux as rms/ √ N. When three exposures were not available, we use the errors output by pydis and remove cosmic rays by filtering large deviations from the background regions on the smoothed two-dimensional spectrum.