dataesr/scientific-paragraphs-categorization huggingface dataset:
These mountain sites offer facilities to sample clouds under optimal conditions and to conserve quality homogeneity of chemical analysis on a long-term basis. These stations have a relatively high altitude, i. e. , hundreds of meters above the surrounding plains, where cloud formation occurs, optimizing collection efficiency and sampling air masses from varied origins, throughout the year. The present study aims at analyzing a long-term dataset of the chemical composition of cloud water samples at PUY. This remote site is influenced by long-range transport  [42, 43]  and the proposed study has the objective to provide information on the physicochemical variability of air masses on a regional scale. For this, cloud water chemical composition is used to constrain a multivariate statistical analysis and propose a chemical classification of the sampled clouds.
The top of the mountain is frequently in cloudy conditions, on average 30% of the time per year, with higher occurrences during winter and autumn  [50]. This makes PUY a reference site to study and sample cloud properties. The cloud sampling dataset used in this study covered the period 2001-2018, with an average sampling time of 3 h and an average sampling volume of 75 mL. Non-precipitating cloud droplets were sampled using cloud collectors, as described previously for PUY cloud studies  [46]. Cloud droplets, larger than 7 μm (cut-off diameter)  [51] , were collected by impaction onto a rectangular aluminum plate. Most of the time, droplets were collected directly as a liquid, and more rarely, they froze upon impaction (supercooled conditions). The water was transferred at room temperature, either directly or after a short melting period into glass vials.
PuyCloud Database The PuyCloud observation system deals with the monitoring of the biological, microphysical, and chemical properties of clouds. Biological and chemical analyses are performed in collaboration with the ICCF (Institute of chemistry of Clermont-Ferrand). It is part of the French CO-PDD (Cézeaux-Aulnat-Opme-Puy De Dôme) multisite platform fully described by  Baray et al. [39]  in terms of instrumentations and data availability and widely employed  [22, 25, [52] [53] [54] [55]. The meteorological parameters that are monitored at PUY include the following: wind speed and direction, temperature, pressure, and relative humidity. Cloud microphysical properties, i. e. , liquid water content (LWC) and effective droplets radius (re), are measured with a Gerber particle volume monitor, model 100 (PVM-100).
Statistical Analysis A principal component analysis (PCA) was performed using the concentrations of both organic and inorganic ions (Ca 2+ , K + , Mg 2+ , Na + , NH4 + , Cl -, NO3 -, SO4 2-, acetate, formate, malonate, oxalate, and succinate). The aim was to determine the most relevant variables to establish chemical categories (categories which would, then, be put in parallel with microphysical parameters or air mass history matrices). However, some chemical analyses were lacking and some samples presented missing values. Missing values were not replaced by the mean values to fully represent the variability of the dataset and to avoid overfitting  [66]. Thus, samples, that were not fully characterized, were not considered in the statistical analysis. Then, we performed numerous PCAs, and the maximum of information, gathered on the first two factors, was obtained with three ions with predominant marine sources (Cl -, Mg 2+ , and Na + ) and three ions with major sources from the continental surface (mostly anthropogenic ones) (NH4 + , SO4 2-, and NO3 -).
Among the nine cloud events in "polluted", eight are clustered in "polluted_01-11". In summary, the "marine" category slightly increases in percentage, as the mean ion concentration of the "continental" and "polluted" categories dwindle, in particular for the anthropogenic ions. "Highly marine" is the category that has expanded the most. This trend is not fully explained by the minor statistical processing adjustments (see Section 2. 4). We compared (not shown) the two methods on the first 2001-2011 dataset, without observing any significant difference. We performed a Mann-Whitney test (Figure  5 ) on the clouds sampled from 2001 to 2011 (period covered by our previous work  [46] ) and since then. It appears that NH4 + , NO3 -, and SO4    [46] , and 88 clouds sampled from 2012 to 2018. We compare, for both periods, NH4 + , NO3 -, and SO4 2-concentrations.
Funding: This research received no external funding. Acknowledgments: This work on the long-term analysis of the cloud water chemical composition was supported by the  French Ministry and CNRS-INSU. Authors acknowledge additional financial support from the  Observatoire de Physique du globe de Clermont-Ferrand (OPGC) , from the  Regional Council of Auvergne , and from the  Fédération de Recherche en Environnement  through the  CPE Environnement  founded by égion  Auvergne-ho ne-Alpes , the  French ministry , and the  FEDER  from the  European community. This work was supported by  CEA/CNRS  contracts. The authors thank  Jean-Marc Pichon  and  Mickaël Ribeiro  for technical support and discussions.
, 2015)  conducted a "Replay" run, meaning the general circulation model computes its own meteorological fields for a 3 h simulation period, then calculates the increment necessary to match a pre-existing reanalysis dataset, in this case the Modern-Era Retrospective Analysis for Research Applications version 2 (MERRA-2). The increment is then applied as a forcing to the meteorology at every time step during a second run of the same simulation period. This simulation includes full interactive tropospheric and stratospheric chemistry from the Goddard Modeling Initiative (GMI) chemical mechanism  (Nielsen et al. , 2017)  with output for the years 1980-2018 at 0. 625 • × 0. 5 olution and 72 vertical levels  (Orbe et al. , 2017; Stauffer et al. , 2019; Wargan et al. , 2018). This simulation is referred to as "GEOS Replay".
Whether strong differences in J O 1 D are caused by clouds or overhead ozone should be clarified by inclusion of O 3 COL as an input. The neural network architecture is consistent with that of  Nicely et al. (2017)  and is shown in Fig. 1. However, the number of computational nodes was doubled from 15 to 30 given the availability of more powerful computing resources. Two hidden layers each containing 30 nodes provided strong performance of the NN in reproducing the OH mixing ratios from a given model. For training, the model output is randomly split 80 %/10 %/10 % into training, validation, and test datasets. During that process, the data from the training set are used to actively adjust weighting factors, and the validation set is evaluated to determine a training stopping point. When errors in predicting the validation data grow after adjusting weighting factors some number of iterations in a row, it is determined that the NN model prior to the growth in errors likely reached a local minimum in its cost function.
A fourth issue is the fact that NNs can exhibit some degree of random behaviour based on how they were trained and initialized. Our method involved training five NNs and selecting from those the one that performed best when compared to the independent test dataset. That single NN was used in all subsequent analysis. However, it is a useful exercise to evaluate the role of NN randomness in our results. We show, in Figs. S5  and S6 , the left and right panels of Fig. 4 , reproduced for the alternate NN trainings of the GMI and OsloCTM models, respectively. A visual comparison of tropospheric OH column differences among the five trainings of each model's NN reveals markedly similar spatial distributions and magnitudes. The values of calculated τ CH 4 changes ( τ CH 4 ) do differ somewhat between the training instances, with larger effects on some variable swaps than for others.
18 years to 9. 48. This budgeting rarely provides a perfect accounting of the τ CH 4 gap due to the same reasons that give rise to asymmetric OH responses to a given swap: chemical mechanism differences and asymmetric swaps of inputs due to extrapolation control. As a result, a remainder term, found as the difference between the other model's τ ORIG and the present model's τ ORIG + τ , is attributed to these factors. This term is listed in the last row of Table  1  with the label "Mech. " Results from analysing individual model pairs reveal a multitude of insights regarding idiosyncrasies in emissions of, global distributions of, and OH sensitivities to the various input parameters. These results, available in the archived dataset described in the data availability section, may be especially useful to the reader with an interest in a particular species or model.
However, with over 4000 plots (12 species × 10 models × 9 submodels × 4 months = 4320) and 180 τ CH 4 budget tables generated, it is beyond the scope of this paper to highlight and explain every interesting feature. Instead, we aggregate the results across all models to identify some primary conclusions. Figure  5  shows the change in τ CH 4 for a specific model and substituted input variable, averaged over all nine pairings. For example, the data point shown for CAM4-Chem J O 1 D is calculated from the nine τ CH 4 values obtained when swapping the J O 1 D fields from the other nine models into the CAM4-Chem NN. The circular point represents the mean of those nine values, while the whiskers indicate 1 standard deviation about the mean. Aggregate results shown in this manner are compiled both for individual months (available in the archived dataset noted above) as well as for annually averaged output.
As such, any efforts to improve a particular model should instead focus on the results specific to that model. For instance, HCHO plays a very small role in describing intermodel differences in OH on average, but for the OsloCTM model, HCHO is a much more important factor. Thus, we refrain from offering an across-the-board solution for remedying the large model spread in τ CH 4 and instead suggest a more individualized approach of studying plots such as those shown in Fig. 4  for more spatially and temporally resolved information. Visualizations of all model swaps, for all months and species, are available in our archived dataset described in the data availability section for this purpose. There are several other qualifications to note when considering the results of the intermodel comparison.
This may result from inaccurate representations of stratospheric ozone in the CCMI models, mischaracterization of the impacts on UV photolysis in the troposphere, or a combination of both. Overall, the results depicted in Fig. 10  show relatively robust findings regarding the responses of [OH] TROP and τ CH 4 to the factors examined through these two independent studies. Because the methane used as input for the CCMI NNs was normalized, as discussed above, the trend in τ CH 4 found in this analysis due to CH NORM 4 did not represent a methane feedback factor in the traditional sense. As such, it is not comparable to the trend in [OH] TROP due to methane found by  Nicely et al. (2018)  and so was not included in Fig. 10. However, even in the event that one were to retrain new NNs using absolute values of methane and sampling across all years to generate the training dataset, we would question the physical meaning of the resulting trends.
While these challenges are significant, they are not insurmountable, especially as machine learning and other advanced statistical analysis techniques continue to be developed and honed. Data availability. All output from most of the models that participated in CCMI is available at the Centre for Environmental Data Analysis (CEDA), the Natural Environment Research Council's Data Repository for Atmospheric Science and Earth Observation, at http://data. ceda. ac. uk/badc/wcrp-ccmi/data/CCMI-1/output (CEDA Archive, 2019). WACCM and CAM4-Chem output for CCMI is available for download at http://www. earthsystemgrid. org (Climate Data Gateway at NCAR, 2019). For instructions for access to both archives, see http://blogs. reading. ac. uk/ccmi/ badc-data-access (IGAC/SPARC, 2019). Output from the models that were not formal participants in CCMI phase 1 is available from the co-authors who performed the model simulations; please contact the corresponding author with requests.
A complete set of figures and tables generated by the model intercomparison and time series analyses is available at https://doi. org/10. 13016/vvbp-p6o8 (Nicely et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-1341-2020-supplement. Financial support. This research has been supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by the Universities Space Research Association under contract with NASA; the JSPS KAKENHI (grant no. JP19K12312); China Southern; the Bundesministerium für Bildung und Forschung (BMBF); and the New Zealand Government's Strategic Science Investment Fund (SSIF). Review statement. This paper was edited by Paul Young and reviewed by Peer Johannes Nowack, Leif Denby, and one anonymous referee.
Acknowledgements. Julie M. Nicely was supported by an appointment to the  NASA   Postdoctoral Program  at the  NASA Goddard Space Flight Center , administered by the  Universities Space Research Association  under contract with  NASA. The authors also acknowledge the joint  WCRP SPARC-IGAC Chemistry-Climate Model Initiative  ( CCMI ) for organizing and making available the suite of model simulations used here. Special thanks is extended to the modelling groups which, at times, provided extra output that enabled this intercomparison to take place with the maximum number of participants. We also thank many colleagues who engaged in helpful discussions that shaped the direction of this work and inspired additional analyses, including, but not limited to,  Vaishali Naik ,  Sarah Strode ,  Jason St.
Conversely, elevated IGF-1 is associated with improved cardiac remodeling and function post-myocardial infarction. Thus, as elevated IGFBP2 levels may be associated with IGF-1 inhibition that results in unopposed renin angiotensin effects, elevated IGFBP2 may actually modulate LV dysfunction  [9]. That IGFBP2 may be both a diagnostic and prognostic HF marker is further supported by our recent report in patients presenting with acute dyspnea, where IGFBP2 displayed greater diagnostic accuracy than "grey zone" B-type natriuretic peptides  [12]. Thus, in view of the above, our purpose was to determine the ability of IGFBP2 to predict outcomes using a large cross sectional HF data base. To assess the potential use of IGFBP2 for HF prognosis, we evaluated 3 international cohorts with varying severity of disease.
Methods We used 3 previously described international HF datasets. This included chronic hemodynamically stable HF data  [23]  from the Toulouse University Hospital (Identification of BLOod Marker of Asymptomatic left Ventricular Dysfunction (IBLOMAVED))  [23] , acute HF data from Maastricht University Medical Center (Netherlands)  [24] , and chronic hemodynamically outpatient HF data from the Atlanta Cardiomyopathy Consortium  [25]. Cardiovascular mortality was used as endpoint and determined by reviewing the patient clinical registry. Using -80°C stored plasma from each institution, IGFBP2 levels were determined by enzyme-linked immunosorbent assay (R&D Systems, Inc. , Minneapolis, MN, USA), performed per the manufacturer's specifications. The detection range for this assay is 62. 5 pg/ml to 4000 pg/ml.
Chronic Stable HF The IBLOMAVED cohort study (ClinicalTrials. gov NCT01024049) enrolled patients receiving care at the cardiology department of the University Hospital of Toulouse (France) from July 2007 to November 2013. This data set consists of 197 stable HF patients, defined as being without any HF decompensation for at least 3 months, and with complete survival data  [23]  Of these, 367 had acute HF and formed our study population. HF was diagnosed by a senior cardiologist  [24] , and patient characteristics were determined by clinical chart review. Left ventricular ejection fraction (LVEF) was obtained from echography, when available, within a range of 1 year before to 1 month after presentation. All patients were followed for 1 year. Follow-up data were obtained via chart review and the patient's general practitioner, or by inquiry of the municipal register.
Outpatient HF The Atlanta Cardiomyopathy Consortium (TACC) prospective cohort study  [25]  enrolled 335 patients from November 2007 to February 2014, at 3 university-affiliated hospitals in the greater metropolitan Atlanta area (ClinicalTrials. gov, NCT00599014). All patients underwent history surveys, physical examination, and collection of blood samples at baseline. Cardiovascular mortality data were collected through medical record review, family members, and U. S. social security death index query. Of 335 patients, 306 (91. 3%) had plasma available for IGFBP2 assessment. The primary outcome was any major clinical event, defined as a composite of death, heart transplantation, or left ventricular assist device placement. The Institutional Review Board has approved the study. Written informed consent was obtained from all participants or their legally authorized representatives.
Statistical methods and identification of biomarkers Evaluation of normally distributed continuous variables was by Student's t-test, otherwise a Mann-Whitney rank sum test was used. Evaluation of categorical variables was by chi square test. We tested the survival of HF patients, stratified by IGFBP2 level, using Kaplan Meier curves and multivariate Cox regression analyses, performed with Medcalc software (version 14. 8. 1, Medcalc software bva, Belgium). Poisson regression analysis was performed using R computation language with "Modern Applied Statistics with S" package version 7. 3-35. Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI) analyses for improvement in predictive accuracy for mortality by the addition of IGFBP2 to clinical models were performed using R software and the "survIDINRI" package version 1.
Results Demographic and clinical characteristics of the total dataset are presented in Table  1. While the Toulouse and Atlanta chronic stable HF had a number of similarities, the ED cohort had several important differences. ED patients had more than double median IGFBP2 levels compared to the chronic stable and outpatient cohorts; 393 vs 165 and 172 ng/mL, respectively. ED patients were also older (median age 78, vs 60 and 57 years) and more likely women (46%, vs 21 and 36%) than the chronic stable HF and cardiomyopathy clinic cohort patients, respectively. ED patients also had less guideline recommended care in every single category, with the exception of angiotensin receptor blocker administration, for which patients were 50% more likely to receive this than in the chronic stable HF cohort.
Treatment and follow up Patients received triptorelin acetate 3. 75 mg as an intramuscular injection (Diphereline®, Ipsen) once in the cycle of 28 days for up to 24 weeks ([?] 6 injections). The prescription and number of required treatment cycles were determined by physicians based on routine clinical practice, without external intervention. Moreover, other treatments and management of adverse events were in accordance with routine clinical practice. Patients were assessed at baseline (pre-surgery) and then at routine post-operative hospital visits every 3 months in the first year of follow up and every 6 months in the second year of follow up. At each visit, data and information relating to endometriosis symptoms and pregnancy status, as well as imaging and laboratory examinations were collected.
Efficacy outcomes In the Core Outcomes in Women's and Newborn Health (CROWN) database, a core outcome set (COS) for endometriosis is currently in development. The primary outcome in this study was the change in intensity/severity of endometriosis symptoms from baseline (pre-surgery) during the 24-month follow up. Symptoms of pelvic pain, dysmenorrhoea, pain at the time of ovulation and dyspareunia were assessed using a visual analogue scale (VAS) with a range from 0 to 10 cm, and symptoms of amenorrhea, menorrhagia, metrorrhagia and gastrointestinal and urinary discomfort were assessed using a numerical scale ranging from 0 to 10. A score of 0 indicated that the patient was free of pain or symptoms, while a score of 10 represented pain or symptoms that were severe and intolerable. Symptoms with a VAS or numerical score > 0 and [?] 3, > 3 and < 7, and [?] 7 were considered as mild, moderate and severe, respectively.
Patients In total, 402 patients were screened for the study. Of these, 399 were enrolled and 384 (96. 2%) were included in the final analysis. Fifteen patients were excluded from the final analysis because of missing data on triptorelin administration. The baseline characteristics of patients are shown in Table  1. The mean +-SD age of patients was 33. 4 +-6. 2 years; 80 (20. 8%) patients had received a previous surgical diagnosis of endometriosis, 4 (5. 0%) of them had received previous surgery for DIE. Overall, 63 (16. 4%) patients had received a previous medical treatment for endometriosis. During surgery, 149 (38. 8%) patients displayed peritoneal superficial endometriosis and 357 (93. 0%) patients had endometrioma. DIE lesions were located on the intestine, bilateral uterosacral ligament, vagina, ureter and bladder in 55.
Data Sharing Where patient data can be anonymised, Ipsen will share all individual participant data that underlie the results reported in this article with qualified researchers who provide a valid research question. Study documents, such as the study protocol and clinical study report, are not always available. Proposals should be submitted to DataSharing@Ipsen. com and will be assessed by a scientific review board. Data are available beginning 6 months and ending 5 years after publication; after this time, only raw data may be available.
Acknowledgements The authors thank all patients involved in the study, as well as their caregivers, care team, investigators and research staff in participating institutions. The authors also thank  Beijing Preintell Biomed Co. , Ltd , who provided medical writing support, which was sponsored by  Ipsen , in accordance with Good Publication Practice guidelines. Furthermore, the authors gratefully acknowledge all the investigators of research institutions for their contribution to the study:  Li Hong  ( Remin Hospital of Wuhan University ),  Juxin Zhang  ( Henan Provincial People's Hospital ),  Min Hao  ( The Second Hospital of Shanxi University ),  Jianliu Wang  ( People's Hospital of Peking University ),  Ouping Huang  ( Jiangxi Maternal and Child Health Hospital ),  Hongbo Wang  ( Wuhan Union Hospital of Tongji Medical College of Huazhong University of Science and Technology ),  Jun Zhang  ( Beijing Anzhen Hospital of Capital Medical University ),  Dong Zhao  ( The First Maternal and Infant Hospital of Shanghai ),  Weidong Zhao  ( Anhui Provincial Cancer Hospital ),  Zhiqing Liang  ( Southwest Hospital of the Third Military Medical University ),  Lifang Sun  ( The Fourth Medical College of Peking University and Jishuitan Orthopaedic College of Tsinghua University ).