dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Acromegaly is caused by chronic hypersecretion of growth hormone (GH) and insulin-like growth factor-1 (IGF-1), usually due to a GH-secreting pituitary adenoma (somatotropinoma)  (Melmed 2017). Acromegaly is a rare disorder; modern epidemiological data from various population-based  (Daly et al. 2006 , Fernandez et al. 2010)  and insurance database studies  (Burton et al. 2016 ) are available and suggest that acromegaly has a prevalence of 2. 8-13. 7 cases/100,000 and an incidence of 0. 2-1. 1 cases/100,000  (Lavrentaki et al. 2016). Chronically elevated GH and insulin-like growth factor-1 (IGF-1) levels lead to a complex spectrum of signs that include acral overgrowth, facial changes, musculoskeletal disease or gigantism if the GH hypersecretion occurs before epiphyses have fused  (Melmed 2017).
Radiotherapy techniques have undergone significant developments leading to the gamma-knife used today. Modern acromegaly therapy is guided by recommendations from consensus publications, with primary neurosurgery potentially offering cure in pituitary tumors that are smaller or uncomplicated  (Giustina et al. 2010 , Katznelson et al. 2014). In many patients, multimodal therapy is needed, particularly for those with aggressive disease or non-resectable tumors. As a rare disease, studies on acromegaly have generally focused on relatively small populations or have addressed regional or national cohorts and patients enrolled in treatment-specific safety databases  (Jenkins et al. 1995 , Sherlock et al. 2009 , Trainer 2009 , Tritos et al. 2014). Data from such studies have provided valuable information about acromegaly and have contributed to improvements in patient management.
Large international studies of the clinical characteristics and therapeutic evolution of unselected groups of acromegaly patients do not exist. We were interested in studying multiple aspects of acromegaly, including detailed assessments of large numbers of data points covering hormonal, pathological, genetic, clinical and therapeutic measures and how these factors are interrelated. We developed and deployed a relational database, the Liège Acromegaly Survey (LAS) Database, for the analysis of data collected from large populations of patients with acromegaly. Following preliminary studies to validate the data collection and analysis potential of the LAS Database  (Theodoropoulou et al. 2009 , Petrossians et al. 2012 , Franck et al. 2017) , we report the first comprehensive study of 3173 acromegaly in patients from 14 participating centers across Europe.
24:10 University, Sofia), Czech Republic (Charles University, Prague), France (Paris Sud University, Le Kremlin-Bicêtre; Hôpital de la Timone, Marseille, Centre Hospitalier Universitaire de Reims), Germany (Max Planck Institute of Psychiatry, Munich), Italy (Federico II University, Naples; University of Genoa, Genoa; University of L'Aquila; and Neuromed, Pozzilli), the Netherlands (Erasmus University Medical Center, Rotterdam), Portugal (Centro Hospitalar S. João, Porto), Spain (Hospital Universitario de la Ribera, Alzira) and Sweden (Karolinska University Hospital, Stockholm). The LAS Database is a relational database that permits the analysis of comprehensive arrays of data covering laboratory values, dose adaptation of treatment and clinical evolution. The goal of the LAS Database was to design a framework to capture available data on >2000 acromegaly patients and to permit statistically robust analysis of clinically relevant topics.
The database management system was kept separate from the data capture interface. The open source mySQL server (Oracle, USA) was used to store the data, while the data capture interface used locally at each participating center was programmed using the Delphi RAD system. The initial development and validation of the framework is described in Petrossians and coworkers  (Petrossians et al. 2012). The current study ran from 30 September, 2012, to 1 January, 2015, and data cutoff for this analysis was 1 October, 2016. All patients with a diagnosis of acromegaly at the participating centers up to 1 January 2015 were valid for inclusion. Those with valid demographic data and at least one post-diagnosis/baseline follow-up dataset were included in the statistical analysis. There was no upper or lower limit to the duration of follow-up, number of treatments or treatment adaptations, drug dose alterations or hormonal/clinical/radiological results recorded over time.
Complete data on the 147 variables that were collected over the course of the patient's clinical follow-up were to be entered; when an assessment had not been performed (e. g. cardiac ultrasound, colonoscopy, polysomnography), these individuals were not included in the statistical analysis for that particular parameter. Hormonal data have evolved over time due to refinements in assay methodologies, which can lead to inconsistencies when comparing values. The LAS Database accounted for changes in GH assay reference ranges by automatically converting values in ng/mL to µU/mL based on the date and reference used in the center at that time. For IGF-1, absolute measured values were encoded along with the upper limit of normal for age and sex based on the assay used at the center. Results were then expressed as percent of upper limit of the normal value (%ULN).
Radiological data for the maximal tumor diameter were used to calculate the proportion of patients with micro (<10 mm) and macroadenomas (≥10 mm) on MRI scans at diagnosis. Nodular thyroid disease was considered present when a solitary thyroid nodule or a multinodular goiter was confirmed on ultrasound. Diabetes was considered as being present when a diagnosis had been made in the medical history of the patient and/or a recorded glucose value of ≥200 mg/dL was found at 120 min during a standard oral glucose tolerance test (OGTT). Genetic studies were not performed specifically over the course of this study and only information on familial diseases or previously established genetic diagnoses was collected. The study was performed under a central Ethics Committee approval covering all centers from the Centre Hospitalier Universitaire de Liège, while each individual center complied with their individual local ethics requirements and procedures.
Statistics To examine the evolution of factors over time the study population was divided by study center, gender and decade of diagnosis. Data were analyzed using the R software package (R Core Team 2015; http://www. R-project. org) and graphics were plotted using the Lattice package  (Lattice, Sarkar D. New York (2008). For continuous variables, data were plotted and tested for normality. As none of the variables had a normal distribution, data were expressed as median and interquartile range (IQR) from the first to the third quartile (25th and 75th percentiles). Data distribution was represented graphically with density graphs using Gaussian kernel smoothing with individual data points plotted at the abscissa ('rug'). Data spreads were drawn using boxplots showing the medians and interquartile ranges, while the whiskers represented 1.
Discussion Acromegaly is a rare endocrine disorder that is due to chronic GH hypersecretion, usually from a pituitary adenoma. It is usually diagnosed and managed in expert referral centers, but due to its rarity even pituitary specialists might see only a couple of hundred cases over their full careers. One way to improve our understanding of rare disorders is by pooling data from many centers using patient registries. In acromegaly, this has been done extensively on a regional and national basis in Europe and Mexico  (Jenkins et al. 1995 , Mestron et al. 2004 , Reincke et al. 2006 , Bex et al. 2007 , Portocarrero-Ortiz et al. 2016 , Maione et al. 2017). Commercial entities support clinical databases to detect and report on the safety of medical therapies, such as, the pegvisomant ACROSTUDY program (van der  Lely et al.
2012 , Freda et al. 2015 , Bernabeu et al. 2016). The data obtained from these registries have stimulated ideas on aspects of morbidity, hormonal control and medical treatment patterns that have later been proven in independent clinical trials. National registries do have limitations in terms of patient numbers and the applicability of data to treatment norms in other countries. International acromegaly databases with a common underlying data capture methodology have been long called for  (Stewart 2004 ). The LAS Database was originally developed and validated as a single-center study tool  (Petrossians et al. 2012) , and thereafter, was expanded across multiple European centers in the current study; it has been used successfully to facilitate analyses of disease characteristics and treatment responses in various centers  (Theodoropoulou et al.
2009 , Franck et al. 2017). The LAS Database provides some specific advantages in that it is not limited to a national dataset nor does it deal with patients managed with a single treatment modality. The programming of the LAS Database is a relational database that permits integrated statistical analyses of independent variables, which is a challenge for other registry-based listing. The LAS Database variables (nearly 150 in total)were chosen based on extensive input from acromegaly specialists in order to permit clinically relevant questions and changes in criteria over time to be addressed with robust statistical methods. In the cohort, there was a small female predominance overall (54. 5%), which is in keeping with results from other national centers in Europe and elsewhere  (Sesmilo et al.
2012 , Portocarrero-Ortiz et al. 2016 , Lesén et al. 2017 , Maione et al. 2017). Over time, though, the sex prevalence changed, such that those patients diagnosed post-2010 were nearly evenly balanced (M:F 49. 4%: 50. 6%). Acromegaly usually has an occult onset and a long period of symptoms can occur before a diagnosis is made. In a two-center study in the United States, Reid and coworkers suggested that delayed diagnosis contributed to acromegaly patients presenting with similar disease characteristics over the period  1981 -2006 (Reid et al. 2010 ). In the LAS Database, first symptoms were seen in the mid-30s in both sexes. However, it took significantly longer (2 years) for females to achieve a diagnosis than males, which is clinically relevant and indicates improved awareness of acromegaly in women is needed.
2015 (Potorac et al. , 2016)). Further studies comparing T2 imaging signal, histologic features and tumoral secretion may shed more light on this observation. Improvements in diagnosis of acromegaly can come from greater awareness among those who first see the patient. In the LAS Database cohort, the initial diagnosis of acromegaly was made by an endocrinologist in nearly 45% of cases. As shown in Fig. 1 , the variety of nonendocrine specialists that make acromegaly diagnoses is quite marked. Given the range of potential signs/ symptoms and the specific problems caused by a pituitary adenoma, it is crucial that awareness of pituitary tumors continues to be widened across medical specialties and related groups  (Surchi et al. 2017). Delays in diagnosis in patients that attend with multiple symptoms of acromegaly still occur as illustrated clearly by De and Foucault, leading to unnecessary exposure of excessive GH/IGF-1  (De & Foucault 2014).
This raises the question as to what role acromegaly plays in the cardiovascular health of the aging patient? This is particularly of relevance as the current study has shown that more aged patients with acromegaly are being diagnosed. In this situation, it becomes difficult to attribute a causative role for GH hypersecretion to cardiac morbidities in acromegaly, and as patients age, the presence of acromegaly may simply represent one of the many contributory risk factors. In the case of colonoscopy that is recommended for surveillance of acromegaly patients, this was performed in 820 patients at diagnosis. While incomplete with respect to the total cohort size, it is still one of the largest datasets on colonic findings at diagnosis in acromegaly; 13% of patients had polyps but only four cases of colorectal cancer were already present at diagnosis.
Indeed, the rate of recorded cancer cases either overall or by specific types (e. g. breast cancer in women) does not appear as being markedly elevated in the LAS Database patients in relation to general European populations  (Lutz et al. 2003). The prevalence of thyroid nodules was high in acromegaly at diagnosis; and 11 cases of thyroid cancer were identified at that time. The prevalence of thyroid nodules was probably an underestimation as ultrasound examinations were not performed routinely at diagnosis of acromegaly. There were some interesting findings regarding emerging comorbidities. Red blood cell count and hemoglobin concentrations were also raised in acromegaly, and we confirmed that these increased with IGF-1 levels but not GH. Again, this suggests that IGF-1 levels may be a better representation of the activity of acromegaly overall.
The role of excessive GH-IGF-1 hypersecretion on erythropoiesis in acromegaly is a recognized but relatively neglected subject  (Grellier et al. 1996 , Teramoto & Ouchi 1997 , Zoppoli et al. 2011) ; however, in pediatric and adult GH deficiency, it is well established that GH replacement can lead to increased red blood cell measure and correction of anemia  (Christ et al. 1997 , Valerio et al. 1997 , Bergamaschi et al. 2006 , Esposito et al. 2016). The role of increased red cell counts and potentially other hematological measures in relation to respiratory pathology (e. g. sleep apnea syndrome), cardiovascular disease and outcomes is a potentially valuable avenue of future research. The LAS Database is the first international relational database used to study acromegaly following a standard methodological design.
This first report of >3100 enrolled patients at diagnosis shows that the clinical and hormonal characteristics of acromegaly are evolving over time. While acromegaly affects slightly more females than males, female patients have a significantly longer delay before diagnosis; this may be due in part to males having larger tumors than females and these occur at a younger age. The age at first symptoms and at diagnosis of acromegaly is rising over time, indicating that improvements in diagnostic measures are detecting a greater proportion of older patients. In keeping with this, the LAS Database cohort also shows a triangular relationship between age, tumor size and GH secretion, with older patients having smaller tumors and lower GH secretion. Cardiometabolic comorbidities of acromegaly were frequently present at diagnosis, such as diabetes mellitus (29.
Acknowledgements The authors would like to thank all of the physicians and scientists who formed part of the  'LAS Club' , and through the various planning and discussion meetings gave their time and inspiration to the final LAS Database structure. They thank  Barbara Zabl  for her help with testing of the initial version of the LAS Database software,  Dr. Maria Tichomirowa  for her work on the initial Liège cohort described in Petrossians et al. 2012 and  Dr Marily Theodoroupolou  for her input and ideas during the LAS Club sessions. The authors dedicate this study to the memory of the late  Professor Franco Minuto , who was an early and enthusiastic contributor to the  LAS Club  and who generously contributed his experience and ideas to the Liège Acromegaly Study Database.
Strong gravitational lensing by galaxy clusters is a fundamental tool to study dark matter and constrain the geometry of the Universe. Recently, the HST Frontier Fields program has allowed a significant improvement of mass and magnification measurements but lensing models still have a residual RMS between 0. 2" and few arcseconds, not yet completely understood. Systematic errors have to be better understood and treated in order to use strong lensing clusters as reliable cosmological probes. We have analysed two simulated Hubble Frontier Fields-like clusters from the Hubble Frontier Fields Comparison Challenge, Ares and Hera. We use several estimators (relative bias on magnification, density profiles, ellipticity and orientation) to quantify the goodness of our reconstructions by comparing our multiple models, optimized with the parametric software Lenstool, with the input models.
THE DATA We have analysed two mock HFF-like clusters from the FF-simulations Challenge archive  (Meneghetti et al. 2016 ): Ares and Hera. They were initially created for the lens modelling comparison project, which, for the first time, compares the reconstructions obtained using different techniques, such as parametric, non-parametric and hybrid, performed by different teams. Both clusters have been simulated to reproduce, not only the characteristics of the HST Advandced Camera for Survey (ACS) and Wide-Field Camera 3 (WFC3) observations (depth, passbands and spatial resolution), but also the complexity of the FF clusters themselves even if these two have been created using different techniques. Both clusters have then been modelled to be realistic, complex, bi-modal clusters (see Figure  1 ), generated in a flat Λ CDM cosmological model.
During the challenge, all participants first performed a blind analysis (i. e, not knowing the true mass distribution) of these clusters for which only the HFF-like images as well as catalogues of multiple images (position and spectroscopic redshifts) and cluster galaxies (positions and magnitudes in all ACS/WFC3 bands) were provided with m F814W < 24 (see Table  1  for further details). Ares is a more powerful lens than Hera, producing thus many more multiple images  (Meneghetti et al. 2016). Their respective redshift distributions are shown in Figure  A1. The source galaxies resemble the luminosity and the redshift distribution of the galaxies in the Hubble Ultra-Deep-Field  (Coe et al. 2006). For both clusters, the software Skylens  (Meneghetti et al. 2008 ) was used to ray-trace the lensed galaxies to the image plane and to create simulated HFF-like images in all bands.
Hera Hera is from a N-body simulation of cluster-sized dark matter halos for a flat ΛCDM model (see  Planelles et al. 2014 ) at z=0. 507 which was re-simulated using a TreePM-SPH GADGET-3 code with only collision-less dark matter particles. The properties of cluster galaxies are created from Semi-Analytic Methods of galaxy formation  (De Lucia & Blaizot 2007). Hera is also created in a flat ΛCDM cosmological model with a matter density parameter Ω M = 0. 24. For both clusters, the software Skylens  (Meneghetti et al. 2008 ) was used to ray-trace the lensed galaxies to the image plane and to create simulated HFF-like images in all bands.
Mass modelling We perform the strong lensing modelling in the source plane using the public software Lenstool 3   (Kneib et al. 1996; Jullo et al. 2007 ) which performed very well for the FF-SIMS Challlenge. Lenstool utilizes a Bayesian Markov chain Monte-Carlo (hereafter MCMC, see  Jullo et al. 2007, for details)  sampler to optimize the model using the positions of the multiply imaged systems. The matter distribution of clusters is decomposed into several smooth large scale components and individual contributions from cluster galaxies. In this work, the reconstructions are based on the strong lensing information only. We have modelled both clusters as up to three components: i) large-scale potentials, ii) brightest cluster galaxies (BCGs) and iii) individual galaxies identified spectroscopically, with masses scaling with luminosity.
ACKNOWLEDGMENTS We thank the referee for her/his useful comments that helped improving this work. A. A is grateful to  Gabriel Bartosch Caminha  for useful and constructive discussions. We also thank  M. Meneghetti ,  P. Natarajan  and  D. Coe  for providing the Ares and Hera mock clusters for the  Frontier Fields Lens Modeling Comparison Project. This work has been carried out thanks to the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR. This work was granted access to the  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the program " Investissements d'Avenir " supervised by the  Agence Nationale pour la Recherche.
Further details and references are given in section 2. The main difference between our model and previous models (e. g. ,  Calderone et al. , 2012; Paliya et al. , 2014 ) is that we consider several emission lines from the BLR  (Cerruti et al. , 2013b)  and we describe different states of the same source by applying minimal changes to the jet parameters of the model. In the following sections, we present the multi-component model (section 2), along with the application to two datasets of each selected source (section 3) to conclude with a brief discussion and outlook (section 4). Throughout this work, a CDM cosmology is assumed with H 0 = 70 km s -1 Mpc -1 , m = 0. 3 and = 0. 7.
APPLICATION In the following section, we present some of the main characteristics of the gamma-loud NLSy1s to which we applied our multi-component model, namely 1H0323+342 and B2 0954+25A. For the modeling, two different datasets are considered for each source: a quiescent/average dataset and a flaring state dataset w. r. t. gamma-ray emission seen by Fermi. The parameter sets that best describe the data along with a brief discussion on the model interpretation are then provided.
Data Sample and Analysis To construct the average state, we consider archival Chandra data from 2009 from  Calderone et al. (2012)  along with longterm averaged (3FGL, 48 months of observations) Fermi-LAT data  (Acero et al. , 2015). For the flaring state, we analyzed Fermi-LAT data from the flaring period of the source, i. e. , Jan 2010-Jul 2010, and considered them along with Swift XRT and UVOT data from the X-ray flaring period in June 2007. Although there was a contemporary Swift observation taken on June 15, 2010, the statistics are poor due to short exposure time and no good-quality spectrum can be extracted. Given that the count rates between both observations are compatible, we consider the observation from June 2007 for modeling purposes, All the quantities are input parameters to the model except for the last four quantities i.
In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH 3 -v2. 1R-I) which relies on ERA-Interim ECMWF meteorological input data, along with surface temperature retrieved from a dedicated network, rather than the operationally provided Eumetsat IASI Level 2 (L2) data used for the standard near-real-time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH 3 time series, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014).
In addition, we formulate a new set of recommendations on how to post-process, treat and interpret the data. In the final part we introduce a new dataset, ANNI-NH 3 -v2. 1R-I, which differs from the baseline dataset, ANNI-NH 3 -v2. 1, in that it uses different input data. While our baseline version uses operationally provided meteorological Level 2 (L2) data, this reanalysed dataset relies on input data from the ERA-Interim ECMWF reanalysis  (Dee et al. , 2011)  and a secondary neural network for surface temperature retrieval. The need for such a dataset arose after discontinuities were found in the analysis of time series which could be traced back to version changes in the IASI L2 processing chain for temperature and clouds. This new self-consistent dataset is introduced and detailed in Sect.