dataesr/scientific-paragraphs-categorization huggingface dataset:
Follow-up and outcome measures All patients were systematically followed for 6 months after the procedure. Post-procedural data included length of hospital stay, delayed adverse events, symptoms and Eckardt score, symptoms of gastro-oesophageal reflux disease (GERD), medication (e. g. proton pump inhibitors) at 3 and 6 months and manometry data at 6 months. The main study endpoint was the comparison of the efficacy of POEM as a treatment of achalasia between the POEM1 and POEM2 groups. The main outcome measure was the proportion of clinical success at 6 months, defined as an Eckardt score of ≤ 3  [15] , in the two groups. The secondary endpoints were the technical success and procedural, post-procedural and late POEM-related morbidity. The secondary outcome measures were the comparison between the two groups for completeness of the procedure, defined as a successful myotomy and tunnel closure, duration of the procedure adverse events that occurred intraprocedure, post-procedure or late after the procedure, and length of hospital stay.
Statistical analyses Quantitative variables were presented as the median and percentile (interquartile range: 25-75%). Categorical variables were presented as the number and percent of the cohort. The non-parametric Mann-Whitney test was used for continuous variables and non-parametric Pearson Chi-squared test or Fisher exact test were used for categorical variables. A p value < 0. 05 was considered to be statistically significant. Statistical analyses were performed using JMP Pro Software, version 13. 0. 0 (SAS Institute Inc. , Cary, NC, USA).
Dysphagia was the main symptom in both groups. The proportion of patients with type III achalasia tended to be higher amongst POEM2 patients but the difference was not significant (6% versus 19%; p = 0. 07). The gastroscope could not cross the esogastric junction in 23 (22%) patients and the oesophagus lumen appeared sigmoid-like or dilated in 8 (8%) patients. Oesophageal biopsy data were available for 71 (68%) patients and none exhibited eosinophilic oesophagitis. CT scans were performed for 23 patients (22%), and no abnormalities were detected other than a sigmoid or dilated oesophagus.
Other hypotheses can explain our mixed results. Firstly, all consecutive patients were included regardless of age, comorbidities or the severity of the disease, providing real-life conditions. In our series, 17 patients (16%) over 70 years of age were included, whilst it has been reported that the rate of adverse events is slightly higher in elderly patients  [31]. In addition, the proportion of associated diseases, such as cancer (9%) and autoimmune disease (15%), was high in our study. Secondly, all POEM cases performed by the operator, including the first cases, were reported. This might have affected the overall results, although the distribution of adverse events and the distribution of patients by group were homogeneous over time in our study (data not shown). Indeed, in the literature the results on the efficacy and morbidity depend on the number of interventions performed with an estimated number of procedures required to achieve proficiency varying between 15  [32, 33]  and 100  [34].
With the key scientific driver of this kind of interferometry being high-contrast detections close to the stellar host, a large and time-varying photometric leakage is undesirable. Various numerical self-calibration methods  [12, 546, 548, 557]  have been demonstrated to retrieve the astrophysical null in the presence of wavefront residuals, by exploiting the statistical distribution properties of the signal and various noise sources. However, these approaches have some limitations, with the model-fitting being fairly complex and scaling dramatically with more inputs, and the need for large datasets to obtain enough measurements to build accurate statistical models. Non-nulling interferometry typically makes extensive use of the production of self-calibrated observables, like CP  [558, 559] , and their generalized form, kernel phases [560], to sidestep the limitations brought about by wavefront residuals.
Acknowledgments This work was supported by the  National Science Foundation  under Grant No. 2109232  and support for  P Gatkine  was provided by  NASA  through the  NASA   Hubble Fellowship  Grant  HST-HF2-51478. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  Contract  NAS5-26555.
Acknowledgments We thank  R J Harris  for the fruitful discussions on the topic. P H is supported by the  Deutsche Forschungsgemeinschaft (DFG)  through Project  326946494 , ' Novel Astronomical Instrumentation through Photonic Reformatting '. O G acknowledges support from  NASA  Grant  80NSSC19K0336  and  JSPS  Grant  21H04998  supporting development of advanced WFS techniques including photonic devices. O G and R J-C acknowledge support from the  Heising-Simons foundation.
Acknowledgments B R M N acknowledges support from the  Australian Research Council ,  Discovery Early Career Researcher Award  ( DE210100953 ). R J H would like to thank the  Deutsche Forschungsgemeinschaft (DFG)  for their support through Project  326946494 , ' Novel Astronomical Instrumentation through photonic Reformatting '. A N D acknowledges support by the  Bundesministerium für Bildung und Forschung (BMBF)  through the Project  03Z22AN11. This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-2034835. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  National Science Foundation. This work was also supported by the  National Science Foundation  under Grant No.
Acknowledgments Support for  P Gatkine  was provided by  NASA  through the  NASA   Hubble Fellowship  Grant  HST-HF2-51478. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  Contract  NAS5-26555. P Gatkine  would further like to acknowledge support from the  Wilf Family Discovery Fund in Space and Planetary Science , funded by the  Wilf Family Foundation , as well as the support from  Keck Institute for Space Studies at Caltech. Some of this research was carried out at  Caltech  and the  Jet Propulsion Laboratory  and funded through the  President 's and Director's  Research & Development Fund program. Y Zhang  is supported by  National Science Foundation (NSF)  under Grant  1711377  and the  National Aeronautics and Space Administration (NASA)  under Grant  16-APRA 16-0064.
Acknowledgments Support for  P Gatkine  was provided by  NASA  through the  NASA   Hubble Fellowship  Grant  HST-HF2-51478. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  Contract  NAS5-26555. Some of this research was carried out at the  California Institute of Technology  and the  Jet Propulsion Laboratory  and funded through the President's and Director's  Research & Development Fund  and the  Center for Innovation Fund  programs. Acknowledgments L L acknowledges support from the  German Ministry of Education and Research (BMBF ,  ALSI  Project  05A14PK2 ) and the  DFG  ( NAIR  Project, Grant  326946494 ) in the field of mid-infrared astrophotonics. S G acknowledges funding by the  Australian Research Council  ( FT200100590 ).
Acknowledgments A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). A E was supported by the  European Union  ( ERC Consolidator Grant  No. 101043486 TIFUUN ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the  European Union  nor the granting authority can be held responsible for them.
Acknowledgments Support for SYH was provided by  NASA  through the  NASA   Hubble Fellowship  Grant # HST-HF2-51436. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  Contract  NAS5-26555. Acknowledgments  S Diddams  acknowledges support from  NIST ,  JPL , and  NSF  Grants  AST-1310875  and  AST-2009982. T Herr  acknowledges support by the  Helmholtz YIG   VH-NG-1404  and the  EU   ERC StG 853564.
Acknowledgments The authors acknowledge the  Keck Institute for   Space Studies  for funding and  BRIGHT Photonics  for their design and simulation efforts of the prototype devices presented herein. The research was carried out in part at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). We would like to acknowledge technical contributions by  Pradip Gatkine ,  Boquang Shen ,  Maodong Gao ,  Nick Cvetojevic ,  Jeffery Jewell  and  Gautam Vasisht  during the development of the first generation all-photonic flattener described above.
Acknowledgments GRAVITY has been developed in a collaboration by the  Max Planck Institute for Extraterrestrial Physics, LESIA of the Paris Observatory  and  IPAG of Université Grenoble Alpes/CNRS , the  Max Planck Institute for Astronomy , the  University of Cologne , the  Centro Multidisciplinar de Astrofisica Lisbon  and Porto, and the  European Southern Observatory. We acknowledge the funding support of  CNRS/INSU ,  Agence Nationale de la Recherche  Contract # ANR-06-BLAN-0421 ,  LabEx OSUG @2020  ( Investissements d'avenir   ANR10LABX56 ),  LabEx   FOCUS ANR-11-LABX-0013 ,  Action Spécifique ASHRA of CNRS/INSU  and  CNES , and  Observatoire des Sciences de l'Univers de Grenoble. SPICA-FT has been developed thanks to fundings from  CNRS/INSU ,  OCA ,  UCA ,  Idex Jedi , and  Région Sud.
This work has been partially supported by the  National Research Agency (ANR)  through the  French' Recherche Technologique de Base' Programme. The  FIRST  project has received support from  Action Spécifique Haute Résolution Angulaire (ASHRA) of CNRS/INSU  co-funded by  CNES  and from the  French National Research Agency  ( ANR-21-CE31-0005 ). Acknowledgments A S N acknowledges the combined support from  Deutsche Forschungsgemeinschaft  ( 326946494 ) and  Bundesministerium für Bildung und Forschung  ( 03Z22AN11 ). S K acknowledges support from an  European Research Council (ERC)   Consolidator Grant  (Grant Agreement  ID 101003096 ). The authors would like to thank  Dr Aline Dinkelaker  for her valuable comments that improved the clarity of the paper.
Acknowledgments This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement  CoG-866070 ) and from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 101004719. Acknowledgments The authors acknowledge the funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement  CoG-683029 ). Acknowledgments This work was supported by the  National Science Foundation  under Grant No. 2109232 , and by the  National Science Foundation Graduate Research Fellowship  under Grant No. 1122374. Part of this work was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  NASA  ( 80NM0018D0004 ).
This strategy can be adopted only in those scintillator regions which are thermally stable: in such a way the scintillator convective currents, which could move out-of-equilibrium 210 Po, are strongly suppressed. With respect to the previous data release, the CNO analysis has been improved on multiple sides. The exposure of the final Phase-III dataset (Jan 2017 -Oct 2021), which is considered for this analysis, is 33. 5% larger with respect to the previous analysis one. Most importantly, the final Phase-III dataset is characterized by a better thermal stability: as a consequence, the 210 Po convective motions within the fiducial volume are furtherly attenuated. These features led to the extraction of an enhanced 210 Bi constraint, directly impacting on the CNO neutrino rate precision. In addition the treatment of the time evolution of the PMTs effective quantum efficiencies, based on the 14 C data, has been improved.
The analysis ROI extends from 320 keV to 2640 keV, while the reconstructed events are selected within a software-cut defined 71. 3 tonne fiducial volume. The analysis relies on a Monte Carlo based multivariate fit, performed on two energy spectra and the radial distribution of events passing the selection cuts. The Three-Fold Coincidence (TFC), an algorithm for the cosmogenic event reduction, is applied to create the two complementary datasets on which the fit is performed: one depleted (TFCsubtracted) and one enriched (TFC-tagged) in 11 C. The spectral fit of the final Phase-III TFC-subtracted dataset is reported in Fig. 2. By including the Borexino results in the global analysis of solar neutrinos data, along with the KamLAND reactor data, the resulting solar neutrino fluxes are found in good agreement with the high metallicity SSM B16-GS98  [14].
The directional angle α is defined for each PMT hit as the angle between the known solar direction and the photon direction, given by the reconstructed event vertex of the recoil electron and the hit PMT position. An example of the angular correlation of photon hits is shown in Fig. 4. The dominant scintillation light is emitted isotropically and is uncorrelated with the Sun position. For this reason, the analysis is restricted to the first and second hits as they are characterized by the highest Cherenkov over scintillation ratio. In this analysis, as shown in Fig. 5 , the cos α first hit distribution of solar neutrinos is expected to have a signature Cherenkov peak at ∼ 0. 7, while the one for radioactive background should be almost flat. The considered dataset is Phase-I one (2007-2010) with a 0.
Assessment for diseases and serological tests for these participants were done in 22 assessment centres during the same period. Data collection included a self-report questionnaire, physical measures, and blood, urine, and saliva sample collection. Analyses in the UK Biobank were conducted under the data application number 25205. Participants in both EPIC-Norfolk and UK Biobank gave signed and informed consent for their participation in these respective studies. Furthermore, UK Biobank has been approved by the North West Multi-Center Research Committee. A total of 438 260 UK Biobank participants and Lp(a) measurements available were included in the current analysis. Patients with CAVS at baseline (self-reported and/ or who met the diagnostic criteria for CAVS) in EPIC-Norfolk and UK Biobank were excluded from the present study (Figure  1 ).
The design and rationale of the ASTRONOMER trial (NCT00800800) has previously been described. 21 This study included 269 participants between 18 and 82 years of age with mild-to-moderate CAVS at baseline [peak aortic jet velocity (V peak ) between 2. 5 and 4. 0 m/s]. Participants were recruited between 2002 and 2005 at 23 Canadian sites. Patients diagnosed with severe or symptomatic AS, severe aortic regurgitation, symptomatic CAD, significant mitral valve disease (stenosis or regurgitation), congestive heart failure, low-density lipoprotein cholesterol (LDL-C)-lowering therapy, or diabetes were excluded from the trial. Participants were randomly assigned to either rosuvastatin 40 mg or a placebo in a study protocol approved by the institutional review boards of all participating centres. Participants gave written signed informed consent for the study.
Outcomes ascertainment Cases of CAVS in the EPIC-Norfolk study were identified by hospital admission codes through their unique NHS number linked with ENCORE (East Norfolk Health Authority Database). Hospital admission codes were determined by trained professionals in accordance with the International Classification of Diseases version 10 (ICD-10) guidelines. CAVS cases were identified by patient hospitalizations or deaths with CAVS as an underlying cause. A total of 406 CAVS cases were reported according to this outcome definition within EPIC-Norfolk, whereas 17 820 participants did not develop CAVS over a median follow-up time of 16 years. In the UK Biobank, the CAVS outcome was defined using ICD-10 identification codes. Such cases were defined as patients with ICD codes I35. 0 or I35. 2.
Patients with codes related to rheumatic fever or rheumatic heart disease, as defined by ICD-10 codes I00, I02, I05, and I09, were excluded from the CAVS outcome. All other patients except for those with self-reports of CAVS were included in the analysis. This definition of CAVS allowed for the inclusion of 4582 CAVS cases and 433 678 without CAVS from the UK Biobank. Median follow-up time for participants in the UK Biobank was of 12. 5 years. The CAVS outcome in the ASTRONOMER trial was the progression rate of aortic valve stenosis as measured by the annual change of peak aortic jet velocity (V peak ) over a median follow-up of 3. 5 years, an established measure of CAVS progression. 28 Measurement methods employed for clinical and Doppler echocardiographic data in the ASTRONOMER study were described previously  29  and allowed to measure V peak and calculate aortic valve area.
Statistical analyses Continuous variables were reported as mean and SD values for normally distributed variables and as median and interquartile range for non-normally distributed variables. Normality was assessed through visual examination of graphical representation of the data. Categorical data were expressed as percentages of the total study cohort. Participants were categorised according to their blood Lp(a) levels in EPIC-Norfolk, the UK Biobank, and ASTRONOMER. A threshold of 50 mg/dL was set in EPIC-Norfolk and patient were classified as having a circulating Lp(a) level either superior or equal or lower than this threshold. This same stratification was done using a threshold of 125 nmol/L in the UK Biobank. In ASTRONOMER, patients were stratified into tertiles of the Lp(a) distribution and comparison was done between patients from the 3rd tertile (Lp(a) ≥ 58.
Two-way analyses of variance followed by Tukey and Dunnett post-hoc tests were used to assess the effect of Lp(a) levels on annualised changes in V peak in ASTRONOMER. All statistical analyses were performed using the computational software R version 4. 1. 3 (R Foundation for Statistical Computing) along with the packages 'survival', 'rstatix', and 'multcomp'. Figures were created using the 'ggplot2' and 'ggsignif' packages.
, 2011; Cramwinckel et al. , 2020; Herold et al. , 2014; Sauermilch et al. , 2021). We used an ensemble of model simulations of the EECO climate assuming different atmospheric CO 2 levels, performed as part of the Deep-Time Model Intercomparison Project (DeepMIP;  Lunt et al. , 2021 Lunt et al. , , 2017)). Hence, we analyzed the global temperature relationships under fixed non-CO 2 climatic factors (such as continental configuration and astronomical conditions) and variable CO 2 level. Most of the DeepMIP model simulations have a run-time of 4,500 yr or more, allowing us to study the long-term relationship between global DST, SST, and SAT. In addition to this model-based analysis, we performed a model-data comparison, building on the analysis by  Lunt et al. (2021). They found that the DeepMIP simulations with CO 2 levels of 1,120 and 1,680 ppm (four and six times preindustrial) are consistent with paleo-proxies of both global SAT and CO 2 level during the EECO.
Estimation of Model-Based Global Temperatures The global DST per model simulation was estimated using the monthly mean potential sea water temperature data (Table  2 ). First, the annual mean temperature was calculated as the average over the 12 months for all models (except NorESM). We defined the deep-sea as the part of the ocean ranging from a depth of 2,000 m to the ocean floor. This demarcation corresponds with the study of deep ocean warming by  Desbruyères et al. (2016)  and the assessment of ocean heat uptake in IPCC AR6 in which the ocean below 2,000 m is considered separately from the upper layers  (Gulev et al. , 2021). As most model depth levels do not exactly coincide with this boundary, we used a model-specific deep-sea definition starting at the depth level closest to 2,000 m (Table  S1  in Supporting Information S1).
First, there are small outlier sections in some EECO model simulations, where the DST values were several °C higher or lower than those of the surrounding area. These sections span a couple of grid cells in the Arctic gateway east of Greenland for COSMOS, HadCM High Resolution, INMCM, and MIROC, and between the Australian and Antarctic continents for COSMOS and IPSL. The outliers were manually removed from the data. Second, we chose to exclude the Arctic Ocean from the calculations of global DST and SST. Inspection of the model ocean bathymetry showed that the Arctic was poorly connected with the rest of the ocean during EECO, which limits deep-water exchange. For example, in the CESM simulations the bulk of the gateway has a depth of about 50 m (Figure  1a ). The Arctic was removed from the data by excluding the surface grid cells with latitudes above 65°N.
This can be clearly seen in the simulations of CESM, MIROC, and NorESM. The Southern Ocean is generally the coldest ocean basin in the DeepMIP EECO simulations. However, several models, such as COSMOS, GFDL, HadCM, and IPSL, show a relatively large temperature difference between the deep-sea south and east of the Australian continent. The spatial DST distribution of these relatively warm and cold regions appears to be model-dependent and persists for different increasing CO 2 levels within the same model. This suggests that the spatial DST distribution is an inherent characteristic of the model and likely related to the bathymetry and nature of the Tasman Seaway between Antarctica and Australia limiting throughflow  (Zhang et al. , 2022). Proxy data as well as other modeling studies support such a limited flow through the Tasman Seaway.
Data Availability Statement Access to the Deep-Time Model Intercomparison Project (DeepMIP) model database can be requested following the steps outlined on https://www. deepmip. org/data-eocene/. The data set of local SST and SAT proxy estimates was obtained from the Supporting Information of  Inglis et al. (2020) , and the data set of DST proxy estimates was obtained from the Supporting Information of  Meckler et al. (2022). The data sets on modern ocean temperatures were obtained from E. U. Copernicus Marine Service Information (2021aInformation ( , 2021bInformation ( , 2021c)). Hersbach et al. (2019)  was downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store.
This minimises the risk of underestimating the anti-factor Xa activity  [8]. Our data indicate that for LMWH monitoring, blood samples collected in citrate and CTAD tubes are stable for at least 6 h (WB and plasma). This is in line with previous recommendations  [8]  based on a single study that used a dextran sulfate-containing reagent  [11]. Our study also showed that anti-factor Xa activity is stable for 6 h even when using a reagent without dextran sulfate (Stago). A summary of the literature data on LMWH stability in samples collected in citrate-containing tubes is in Supplemental material Table  3.