dataesr/scientific-paragraphs-categorization huggingface dataset:
We report the discovery of two planetary systems, namely G 264-012, an M4. 0 dwarf with two terrestrial planets (M b sin i = 2. 50 +0. 29  -0. 30 M ⊕ and M c sin i = 3. 75 +0. 48  -0. 47 M ⊕ ), and Gl 393, a bright M2. 0 dwarf with one terrestrial planet (M b sin i = 1. 71 ± 0. 24 M ⊕ ). Although both stars were proposed to belong to young stellar kinematic groups, we estimate their ages to be older than about 700 Ma. The two planets around G 264-012 were discovered using only radial-velocity (RV) data from the CARMENES exoplanet survey, with estimated orbital periods of 2. 30 d and 8. 05 d, respectively. Photometric monitoring and analysis of activity indicators reveal a third signal present in the RV measurements, at about 100 d, caused by stellar rotation. The planet Gl 393 b was discovered in the RV data from the HARPS, CARMENES, and HIRES instruments.
High-resolution spectroscopic data G 264-012 was monitored only with CARMENES, whereas Gl 393 was monitored also with another two instruments, namely HARPS and HIRES. The RV time series of the two stars and their uncertainties are listed in Tables B. 1 and B. 2. Table  2  shows a summary of the RVs used in this work and their overall quality, quantified by the mean formal RV uncertainties σ RV and the root-mean-square (rms) scatter of the RVs around the mean. CARMENES is located at the 3. 5 m telescope of the Calar Alto Observatory in Almería, Spain, and consists of two independent spectrographs, the visible channel (VIS; 5200-9600 Å, R ≈ 94 600) and the near-infrared channel (NIR; 9600-17 100 Å, R ≈ 80 400). Details on the instrument and its performance were given by, for instance,  Quirrenbach et al.
We excluded the first 8 NIR spectra in the time series for the same reason as above. All CARMENES raw spectra were processed using caracal  (Caballero et al. 2016; Zechmeister et al. 2014) , which performs the basic spectral reduction process to obtain calibrated 1D spectra (wavelength calibration using Th-Ne, U-Ne, U-Ar, and Fabry-Pérot exposures,  Bauer et al. 2015 ; simultaneous drift monitoring using Fabry-Pérot lamps,  Schäfer et al. 2018 ; as well as spectrum extraction,  Zechmeister et al. 2014). The RVs were obtained with serval  (Zechmeister et al. 2018) , a python code that uses a least-square-matching approach between each individual spectrum and a high S/N template, as described by  Anglada-Escudé & Butler (2012). They were corrected for barycentric motion, secular perspective acceleration, instrumental drift, and nightly zero-point variations  (Trifonov et al.
Gl 393 The high-quality Kepler K2 light curve (see Sect. 4. 5. 2) was analysed in order to determine the rotation period of Gl 393. The light curve displays flares, as expected from a moderately active star, on top of significant quasi-periodic variations with time scales of tens of days and a long-term linear trend, probably of instrumental origin. Hence, we first linearly detrended the light curve and removed flares with a 3σ-clipping filter. Our frequency analysis, with the interactive computer package Period04  (Lenz & Breger 2005) , which is based on algorithms similar to the Lomb-Scargle periodograms, resulted in a main periodicity of P ∼ 34. 0 ± 0. 1 d. The amplitude of this variabil-Article number, page 11 of 24 A&A proofs: manuscript no. Amado Fig. 8. Same as Fig. 6 , but for Gl 393 and the period of 34.
Acknowledgements. CARMENES is an instrument for the  Centro Astronómico Hispano-Alemán (CAHA) at Calar Alto (Almería, Spain ), operated jointly by the  Junta de Andalucía  and the  Instituto de Astrofísica de Andalucía (CSIC). CARMENES was funded by the  Max-Planck-Gesellschaft (MPG) , the  Consejo Superior de Investigaciones Científicas (CSIC) , the  Ministerio de Economía y Competitividad (MINECO)  and the  European Regional Development Fund (ERDF)  through projects  FICTS-2011-02 ,  ICTS-2017-07-CAHA-4 , and  CAHA16-CE-3978 , and the members of the  CARMENES Consortium (Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Andalucía, Landessternwarte Königstuhl, Institut de Ciències de l'Espai, Institut für Astrophysik Göttingen ,  Universidad Complutense de Madrid ,  Thüringer Landessternwarte Tautenburg ,  Instituto de Astrofísica de Canarias ,  Hamburger Sternwarte ,  Centro de Astrobiología  and  Centro Astronómico Hispano-Alemán ), with additional contributions by the  MINECO , the  Deutsche Forschungsgemeinschaft  through the  Major Research Instrumentation Programme and Research Unit FOR2544 "Blue Planets around Red Stars ", the  Klaus Tschira Stiftung , the states of  Baden-Württemberg  and  Niedersachsen , and by the  Junta de Andalucía.
This publication made use of the SIMBAD database, the Aladin sky atlas, and the VizieR catalogue access tool developed at  CDS, Strasbourg Observatory, France , the  Python libraries Matplotlib, NumPy, and SciPy , the collection of software packages AstroPy and topfplotter, and data from the CARMENES data archive at  CAB (CSIC-INTA) , the  ESO Science Archive Facility  under request number vperdelw-552557, the TESS mission, obtained from the  MAST  data archive at the  Space Telescope Science Institute (STScI) , and the K2 mission.
Results Unsupervised cluster analysis of whole-blood transcriptomes reveals five distinct subgroups of H/IPAH. Whole blood samples from patients with H/IPAH (n = 359) were processed for RNA-sequencing as previously described  13. Samples from 359 patients and 21 samples collected from a second time point underwent RNAseq data processing to reduce noise, and gene filtering to remove gender bias as sex chromosomes produced the highest variation in gene expression during clustering (Supplementary Fig. 1 ). Sample collection site did not produce any discernible effect on clustering (Supplementary Figs. 2  and 3 ). Simultaneously, the 300 genes that produced the most stable expression dataset were utilised to identify unique subgroups of gene expression profiles and describe the biological and clinical descriptors of these subgroups (Fig.
1 ). A clustering algorithm for selection and majority voting of multiple internal validation indexes (Supplementary Data 1) allowed us to identify as statistically optimal five distinct and stable subgroups of patients' profiles (Fig. 2a ) while retaining the maximum heterogeneity information found in our dataset. The largest of the patient clusters identified was subgroup I (n = 129), which had poorer survival (53%, five-year median survival from sampling; Fig. 2b ). The second largest, subgroup II (n = 112), demonstrated the best survival (78%, 5 years from sampling; Fig. 2b ). Subgroup V (n = 89) demonstrated a mixed gene expression pattern and average survival outcome compared to subgroups I and II (Fig. 2a, b ). Subgroups III (n = 19) and IV (n = 10) also demonstrated distinct gene expression patterns, with subgroup III most similar to subgroup II, and subgroup IV similar to subgroup I both in terms of gene expression level and survival outcomes.
Estimates of cell counts in whole blood revealed elevated levels of lymphocytes, in particular T cells, and lower levels of inflammatory markers in the better prognosis subgroup. We further generated classifiers based on associated clinical features of these new RNA subgroups and used it to identify subgroups that differed in survival outcome in an independent cohort. The most striking difference between the best and worst surviving subgroups was in immunoglobulin transcription. The upregulation of transcripts coding for the variable domain of immunoglobulin light chains (IGLV and IGKV genes) that participate in antigen recognition were markers of subgroup II, while their downregulation were markers of subgroup I. Differential levels of IGVL and IGKV gene transcripts, as seen in subgroup I, may control self-reactivity of human antibodies  27  , and the reduction in the diversity of light chains has been associated with several autoimmune diseases, including systemic lupus erythematosis (SLE), type 1 diabetes, and myasthenia gravis  28, 29.
Methods Study design. The Cohort study of idiopathic and heritable PAH is an observational, prospective and longitudinal study of patients with idiopathic and heritable PAH (clinicaltrials. gov NCT019072950). The Sheffield Teaching Hospitals Observational Study of Pulmonary Hypertension, Cardiovascular and other Respiratory Disease (UK REC Ref 18/YH/0441) is a longitudinal study of patients with suspected pulmonary hypertension or an associated cardiovascular or respiratory condition. Follow-up information is collected as a part of routine clinical care every 6 months. The study allows recruitment of both incident and prevalent cases. Patients consented to the study agreed to have blood taken for next-generation sequencing and other omics studies. Healthy adult controls were recruited for comparison studies.
Ethics Following diagnosis, subsequent treatments and follow-ups were at the discretion of the treating physician, according to the contemporary guidelines. In most centres, patients were seen every 3-6 months with an assessment of functional status and exercise capacity. Right heart catheterisation was repeated when considered necessary by the responsible clinician. Study visits were performed every 6 months. Healthy controls had been sampled only once and had clinical information recorded from the time of sampling. Clinical data capture, processing and quality control. Pseudonymised results of routinely performed clinical tests reported in either clinical case notes or electronic medical records (EMR) were stored in web-based OpenClinica (OC) data capture system (Community edition). Twenty electronic Clinical Case Report Forms (eCRFs) distributed across seven events (Diagnostic, Continuous data, Follow-up, Epidemiology questionnaire, Suspension, Relatives, Unrelated healthy control) were constructed to accommodate routinely available clinical information.
Details regarding data verification procedures were previously described in detail  54. Information about participants' status was collected every 6 months (via National Health System Digital Spine portal or an equivalent local system). Current analysis was performed on the census performed on 31 January 2020. Two risk assessment strategies were applied to the data. Reveal risk score  4  and abbreviated ERS risk scores  55  were calculated in all patients who had the necessary minimum phenotypic information available. Patients who died or were transplanted were suspended on the day of the event, patients who withdrew from the study were censored on the date of the last visit, the reason for withdrawal was recorded. Missingness assessment and imputation. Missingness rates, patterns and causes were assessed per individual, variable and centre and visualised with vim package v5.
1. 1R (Supplementary Fig. 12 ). Multiple imputation by the chain equations method was used to impute missing data (mice v3. 8. 0 package R)  56. The imputation model included all variables that were necessary in the analysis model, including cumulative baseline hazard function and variables that predicted both the incomplete variable and if the incomplete variable was missing like the centre and whether the case was incident or prevalent. Quality of predictors was assessed using outflux-influx plot. Numerical data were imputed with predictive mean matching (pmm), factors with two levels were imputed using logistic regression, factors with more than two levels with multinomial logit model and ordered factors with more than two levels with the ordered logit model. Transformed variables (BMI, ratios, score sums) were imputed as just another variable as well as passively with good concordance.
RNA data preprocessing. A number of preprocessing steps were required to prepare the raw sequencing data for unsupervised machine learning. Highthroughput sequencing generated raw pair-end counts of 205,259 transcripts across 508 samples that belong to GenCode Release 28 (GRCh38. p12). Consequently, Salmon (https://combine-lab. github. io/salmon/) was used to estimate the relative abundance of the transcripts (TPM, units of transcripts per million) which were then mapped to genes (n = 60,144) using the tximport R package. Only genes with more than two reads (in a transcript level) in at least 95% of control and patient samples were considered and 11 additional male genes were removed (n = 25,955). Hyperbolic arcsine transformation (package base v3. 6. 0) was applied to the final RNAseq TPM matrix.
Further information on quality control of samples and genes can be found in the Supplementary Methods. The RNA-sequencing and clinical data of healthy controls were not used in the main pipeline of this study. A secondary clustering with all patient and healthy samples was implemented to demonstrate the lack of pure patient and healthy subgroups within our cohort (Supplementary Fig. 6b ). Principal component analysis of expression profiles from samples with a second replicate clustered together according to the first four principal components (Supplementary Fig. 13 ). Spectral clustering: gene expression subgroup identification. We performed cluster analysis to partition IPAH patients to distinct RNA-based groups. The spectral clustering model (package kernlab v0. 9-29) was selected as the most suitable unsupervised learning algorithm based on the highest partitional consistency when comparing multiple dissimilar algorithms (Supplementary Table  6 ).
We elected to investigate k = 5 subgroups, since in clustering contexts it is safer to overestimate than underestimate the number of subgroups to prevent loss of information. However, k = 3 subgroups were voted from the vast majority of methods and we expect them to be the main subgroups. Further information on the selection of clustering algorithms and parameters can be found in the Supplementary Methods. Analysis of subgroup differences. Survival analysis was performed (R package survival 3. 1-7) on the main (Supplementary Fig. 17 ) and validation cohorts to identify the survival differences between subgroups. Kaplan-Meier survival curves from diagnosis and sampling were calculated for the main patient cohort (per spectral subgroup) as well as the validation cohort (per predicted subgroup).
Subsequently, two multivariate Cox models were fitted and Hazard ratios calculated on the main cohort once adjusting for gender and once adjusting for the composite clinical signature discovered by supervised machine learning. Gene signatures for each subgroup were identified using LASSO regression models with cross-validation (package glmnet 3. 0-1). The variables with the 5% highest coefficients for each class were highlighted (Supplementary Fig. 18 ), and the full list of non-zero coefficients for each class can be found in Supplementary Data 3. The pathfinder R package was used to highlight enriched gene pathways between subgroups and differential expression analysis using the DESeq2 package  59  was performed on genes associated with the subgroups (Supplementary Fig. 19 ). Gene expression differences across subgroups are presented in Supplementary Fig.
20. All statistical tests between subgroups were two-sided and Bonferonni adjusted for multiple testing. Identifying clinical signatures of subgroups. The dataset was initially cleaned and filtered on 119 features that were identified by a domain expert from the original 887 features that described the dataset. Subsequently, any feature that had more than 5% missing data was dropped, and categorical features numerically encoded. All ML tasks were carried out using Scikit-learn  60  ML framework version 0. 23. 2 in a Python 3 environment. As machine learning classifiers, we used Logistic Regression (LR), support vector machines (SVM), Random Forest (RF) and knearest neighbour (kNN). RF is a powerful ensemble learning technique especially for high-dimensional classification tasks. Further details about classifier training and feature selection can be found in the Supplementary Methods.
Classification of new patients using signatures. Each clinical signature was used to develop a classification model trained on the discovery cohort to classify new patients into the RNA-based subgroups. Classification models were built using SVM  61  , RF  62  , LR  63  and KNN  64. The candidate signature that obtained the best performance was selected. This process was repeated for all signature sizes, s = 1 to s = 20, for subgroups I, II and V. A final signature for each subgroup was selected based on a compromise between the fewest number of features (s = 1 to s = 20) and classification performance. Final selected signatures for each of the subgroups were pooled to create a composite signature, which was then used in a multi-class classification model. The model was trained on the discovery dataset to discriminate between subgroups I, II and V, used to predict subgroup membership of an unseen validation dataset.
The predicted subgroup membership was then used to calculate survival of predicted subgroups. Survival of the predicted subgroups was compared to known survival of subgroups in the discovery dataset for validation purposes. qPCR on validation cohort. Frozen Tempus tubes collected from patients in the validation cohort, collected under the UK National Cohort study, were obtained; RNA was extracted using Maxwell® 16 LEV simplyRNA Blood Kit (Cat. # AS1310) as described in the manufacturer's instructions on the Maxwell® 16 Instrument (Cat. # AS2000). Extracted RNA was transcribed using the High-Capacity-RNA-to-cDNA kit (Thermo Fisher Cat. # 437406) following the manufacturer's instructions. Resultant cDNA was analysed using custom TaqMan array cards (Thermo Fisher Cat. #4342249) with Fast Advanced Mastermix (Thermo Fisher Cat.
Clinical variable and gene correlations. We calculated correlations between the clinical and gene signatures we generated in previous steps of this study. For discovery and validation cohorts we used the rcorr function of R package Hmisc (version 4. 5-0). For the external validation we used the values found in ref. 26. cohortcoordination@medschl. cam. ac. uk. You will receive an application form within 30 days. The 'UK National PAH Cohort Study Data Access Committee' will review requests within 3 months of receipt of the completed application form and if approved, provide details for access to the RNAseq data stored at the EGA. All requesters must agree to the data access conditions found in EGA. The data used to generate statistics, plots and figures are accessible through our interactive portal found in https://sheffielduniversity.
All UK samples were obtained following informed consent into the UK National Cohort Study of Idiopathic and Heritable Pulmonary Arterial Hypertension (clinicaltrials. gov NCT01907295; UK REC Ref. 13/EE/0203) and/or the Sheffield Teaching Hospitals Observational Study of Pulmonary Hypertension, Cardiovascular and other Respiratory Disease (UK REC Ref 18/YH/0441). Data were obtained from samples collected at the University of Arizona Pulmonary Hypertension clinic between 2012 and 2015 following institutional guidelines and following informed consent.
Study approval. Study approval for the use of sample and data were obtained from the UK National PAH Cohort Study Data Access Committee (clinicaltrials. gov NCT01907295; UK REC Ref 13/EE/0203), and the Sheffield Teaching Hospitals Observational Study of Pulmonary Hypertension, Cardiovascular and other Respiratory Diseases Scientific Advisory Board (UK REC Ref 18/YH/0441).
Acknowledgements The  UK National Cohort of Idiopathic and Heritable PAH  is supported by grants from the  British Heart Foundation  ( SP/12 /12/29836 & SP/18/10/33975 ) and the  UK Medical Research Council  ( MR/K020919/1 ). Additional samples from the  Sheffield Teaching Hospitals Observational Study of Pulmonary Hypertension, Cardiovascular  and other Respiratory Diseases were supported by  British Heart Foundation  ( PG/11/116/29288 ). We gratefully acknowledge financial support from the  UK Department of Health  via the  NIHR comprehensive Biomedical Research Centre  award to  Imperial College Healthcare NHS Trust, Cambridge Biomedical Research Centre , and  Guy's and St Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust  and the  NIHR Imperial Clinical Research Facility.
Data availability The transcriptomic and clinical data used in this study have been deposited in the EGA (the European Genome-phenome Archive) database under accession code EGAS00001005532  65. In compliance with the Ethics under which these data and samples have been collected, the transcriptomic data are available through restricted access for approved researchers who agree to the conditions of use, i. e. keeping it secure and only using it for approved purposes. To apply for access please contact Author contributions SK, EJ and EMS contributed equally. All authors made substantial contributions to the conception or design and data acquisition of the work. S. K. , E. J. , E. M. S. , J. A. P. , C. J. R. , P. O. , J. W. , J. I. , M. J. D. , D. P. , T. S. M. , N. E. , A. A. R. T. , C. E. R.
8. qCT was used to assess BMD within the region of the previously identified tibial tunnel. 36  The qCT scans were acquired with a CT scanner protocol (Somatom Definition SD; Seimens Healthcare, Forcheim, Germany) set at collimation of 64 Â 0. 6, pitch of 1 mm, kernel of B70s, and reconstruction increment of 0. 5 mm. The cadaveric tibias were placed on top of a 6level hydroxyapatite phantom (Model 3 CT Phantom; Mindways Software, Inc. , San Francisco, CA) and were visualized within the same field of view. 37  The region of interest (RoI) was a 3-mm thick ring between a 9 mm and 15 mm circle per axial slice centered over the guide pin tract (Fig  2A). Custom software was used to stack the ring RoIs in series along the trajectory of the guide pin tract to provide a cylindrical RoI within the peripheral bone of a potential 9-mm tibial tunnel, at the site of graftebone or screwebone interface.
Statistical Analysis All analyses were performed with Excel (version 14. 0, Microsoft, Redmond, WA) and JMP Pro (version 14. 1. 0; SAS Institute Inc. , Cary, NC), with sample size considered for all calculations. General statistics (mean, standard deviation, median, range, and frequencies) were performed on the subject's demographic, radiographic, and clinical information when applicable. The Wilcoxon rank-sum test was used to compare means of continuous variables, and the Fisher exact and c 2 tests were used to compare nominal variables, when appropriate. Spearman's rank correlation coefficient was used to test for significant correlation between demographic and radiologic information and outcome scores. P values <. 05 were considered statistically significant.
Limitations There are several limitations to this study. First, there is potential for variability despite an attempt to standardize the technique with a single surgeon performing all the procedures and a single postimaging analysis technician performing all the processing. The authors aimed to reduce this with a testeretest demonstrating acceptable intraobserver reliability. Second, the authors used a commercially available qCT software with a stacked "ring" technique centered along a guide pin tract on axial images to create a reproducible method of tibial tunnel BMD assessment on any in-vitro specimen. However, the in vivo BMD analysis required custom software development to allow the user to define the trajectory of a standard ACL reconstruction tibial tunnel upon which the stacked "ring" technique could be aligned.
Outcomes The prespecified primary outcome in this analysis was the time to first occurrence of composite of cardiac death or MI. Secondary outcomes included the time to first occurrence of all-cause death, cardiac death, MI, target-vessel revascularization (TVR), and definite stent thrombosis. Outcomes were analyzed at the longest available follow-up in the primary analysis, as well as at 5-and 1-year follow-up and with a 30-day and 1year landmark. End point definitions in each trial are reported in Data S1.