dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods and findings We examined changes in timely ART initiation (within 6 months) at the original site of HIV care enrollment after ART eligibility expansions among 284,740 adult ART-naïve patients at 171 International Epidemiology Databases to Evaluate AIDS (IeDEA) network sites in 22 countries where national policies expanding ART eligibility were introduced between 2007 and 2015. Half of the sites included in this analysis were from Southern Africa, one-third were from East Africa, and the remainder were from the Asia-Pacific, Central Africa, North America, and South and Central America regions. The median age of patients enrolling in care at contributing sites was 33. 5 years, and the median percentage of female patients at these.
datasets provided by IeDEA are deidentified according to HIPAA Safe Harbor guidelines, with the exception of dates in some of the regions. Disclosure of a person's HIV status can be highly stigmatizing, and since reidentification of deidentified datasets may be possible when they are combined with publicly available datasets (see work of Dr. Latanya Sweeney), IeDEA promotes the signing of a Data Use Agreement before HIV clinical data can be released. To request data, readers may contact IeDEA for consideration and instructions by filling out the online form available at www. iedea. org/home/who-we-are and completing the application at www. iedea. org/wp-.
ART guidelines We conducted a systematic search for current and historical ART eligibility guidelines of countries participating in IeDEA, including sources such as the Joint United Nations Programme on HIV/AIDS (UNAIDS) Database of National HIV Guidelines  [24] , the International Association of Providers of AIDS Care (IAPAC) Global HIV Watch  [25] , the HIV Treatment Guideline Database, health ministry websites, search engines, and published literature, including ART guideline reviews  [26] [27] [28]. For countries with no publicly available ART guidelines, we obtained information from in-country HIV clinicians, researchers, and ministry of health officials. We identified the time points when each country expanded ART eligibility to include asymptomatic people living with HIV (PLWH) with CD4 counts 350 cells/μL and 500 cells/μL.
Eligibility criteria Sites eligible for inclusion had to have HIV care data available on patients for the period after care enrollment but preceding ART initiation (pre-ART data), as well as data on both ART initiators and noninitiators. Patients had to be at least 16 years old at enrollment (18 in North America) and have at least 12 months of possible follow-up between enrollment and database closure. Patients were excluded if they transferred to an IeDEA site from another clinic, lacked a clinic visit and lab record, or were known not to be ART naïve at enrollment (with ART defined as any regimen of at least 3 antiretroviral drugs, excluding treatment taken for prevention of maternal-to-child transmission). To ensure that there was no differential selection into the sample by ART eligibility, distributions of CD4 counts at enrollment were assessed with respect to possible discontinuities at the point of ART eligibility (CD4 = 350 and CD4 = 500 for the 2 analyses), which would indicate potential exclusion of non-ART-eligible patients from contributing sites' cohorts (S1 Text).
Statistical and analytic methods The CI-ART was estimated via competing risk methods, with the Aalen-Johansen estimator, and treating death and loss to clinic as competing risks, as outlined in a July 2016 concept proposal to the IeDEA Executive Committee (S2 Text), which also prespecified inclusion and exclusion criteria, outcome definitions, and key stratification variables. As discussed in the appendix (S2 Text), other analytic approaches, including the use of site-level metaregression and the pre-post within-site comparisons to derive the outcome, were selected after data became available. To assess variability of change in 6-month CI-ART across sites, forest plots were used to visualize site-level outcomes, and between-site variances (tau-squared) and p-values from chi-squared tests of between-site heterogeneity were calculated.
Major strengths of this study include the analysis of data from a large number of patients and sites across several world regions. Patient-and site-level characteristics were used to provide setting-specific estimates and to adjust for some differences in patient mix across sites. Where known, concurrent expansions of ART eligibility criteria regarding pregnancy and tuberculosis status were also controlled for, to account for varied scopes and foci of guideline changes. Findings from this large and heterogeneous sample of real-world service delivery settings may be generalizable to many contexts, especially where substantial room remains to expand ART eligibility guidelines. Additionally, the use of a 12-month buffer period around the national guideline change date helped both to reduce exposure misclassification and to control for differences in patient mix and seasonal patterns of ART initiation in within-site pre-post comparisons.
Acknowledgments The authors thank patients, providers, and administrative staff at participating IeDEA facilities. We also are grateful for information on ART eligibility expansions contributed by  Marion Achieng ,  Dr. Pedro Cahn ,  Dr. Morna Cornell ,  Dr. Andrew Edmonds ,  Aimee Freeman ,  Dr. Tsigereda Gadisa ,  Dr. Anthony Harries ,  Laura Jefferys ,  Dr. Marco Luque ,  Dr. Dominique Mahambou Nsonde ,  Dr. Paula Mendes Luz ,  Fernanda Maruri ,  Dr. Tsitsi Mutasa-Apollo ,  Dr. Sabin Nsanzimana ,  Dr. Ng Oon Tek ,  Dr. Denis Padgett ,  Boondarika Petersen ,  Dr. Erik Schouten ,  Dr. Annette Sohn ,  Dr. Christelle Twizere ,  Marco Antonio De Avila Vitoria ,  Dr. Wong Wing-Wai , and  Dr. Marcel Yotebieng. We also acknowledge the clinics and investigators within each IeDEA region who contributed data to this analysis (S3 Text).
drugabuse. gov], the  National Cancer Institute , and the  National Institute of Mental Health  under Award Number  U01AI069911  ( CY, KWK; IeDEA East Africa ); the  Eunice Kennedy Shriver National Institute of Child Health and Human Development , the  National Cancer Institute , the  National Institute of Allergy and Infectious Diseases, National Institute of Mental Health , and the Office of the Director, the  National Institutes of Health  [www. nih. gov/institutes-nih/nih-officedirector],under Award Number  U01AI069923  ( Caribbean, Central and South America  network for HIV epidemiology [CCASAnet]; BCR); and the  National Institute of Allergy and Infectious Diseases , the  Eunice Kennedy Shriver National Institute of Child Health and Human Development , the  National Cancer Institute , the  National Institute of Mental Health , and the  National Institute on Drug Abuse , under Award Number  U01AI069907  (KVN;  TREAT Asia HIV Observational Database, an initiative of TREAT Asia , a program of amfAR, The Foundation for AIDS Research [www.
amfar. org]. The Kirby Institute is funded by the  Australian Government Department of Health  and Ageing supported by  National Institutes of Health  grants  U01AI069918 ,  F31DA037788 ,  G12MD007583 ,  K01AI093197 ,  K23EY013707 ,  K24AI065298 ,  K24AI118591 , contract 90047713 from the  Agency for Healthcare Research and Quality  [www. ahrq. gov], USA; contract 90051652 from the  Health Resources and Services Administration  [www. hrsa. gov],USA; grants  CBR-86906 ,  CBR-94036 ,  HCP-97105  and  TGF-96118  from the  Canadian Institutes of Health Research ,  Canada  [www. cihr-irsc. gc. ca]; Ontario Ministry of Health and Long Term Care  [www. health. gov. on. ca]; and the  Government of Alberta ,  Canada  [www. alberta. ca]. Additional support was provided by the  National Cancer Institute, National Institute for Mental Health and National Institute on Drug Abuse (KA).
Fit to Planck observations We take the latest publicly available Planck maps of the Stokes parameters Q and U at 353 GHz (Planck Legacy Archive 1  , Planck Collaboration et al. 2016a). For the total intensity I we use the model map derived by Planck  Collaboration et al. (2014) , obtained by fitting observations that have been corrected for the cosmic microwave background anisotropies and for zodiacal light. These data are smoothed to 1 • angular resolution. Our model concerns the magnetic field in the shell of the Local Bubble only, that is, it does not include any component from the large-scale Galactic magnetic field. Therefore, to limit the contribution from the Galactic disk to the observed dust polarized emission, we restrict our analysis to the Galactic polar caps, |b| > 60 •. Given this restriction, we do not attempt to fit all the parameters in the model.
In particular, we compute normalized Stokes parameters, Q/I and U/I, which depend on the magnetic field orientation only, and then multiply them by the intensity map I. We use the python mpfit routine to derive the set of parameter values that best fit the observations. We take into account the noise in the Planck Q and U data, which we add in quadrature with a contribution from the turbulent magnetic field component, which is not accounted for in our model. The latter is estimated from the power spectra of the Planck dust polarization maps at 353 GHz using data simulations from  Vansyngel et al. (2017). In the region of the sky under study, the mean contribution from the statistical noise is σ noise Q,U = 1. 4 × 10 -3 MJy sr -1 and that from the turbulent magnetic field is σ turb Q,U /I = 0.
These val- ues are obtained by averaging the magnetic field vector within 30 • from the poles. We perform the same averaging for each of the magnetic field solutions obtained from the 12 different Planck data sub-sets, and take their standard deviations as the uncertainties in and b. The direction of the magnetic field in the southern polar cap turns out to be different from that derived by Planck  Collaboration et al. (2016c) , who found ( , b) = (70 • , 24 • ), although they obtained the same maximum dust polarization fraction, p 0 = 12 %. The discrepancy probably finds its roots in the fundamental difference between both approaches: while Planck Collaboration et al. (2016c) model the polarization data solely based on variations over the polar cap in the projection of a presumably uniform magnetic field, our model includes both projection effects and intrinsic variations in the magnetic field orientation.
Acknowledgements. We thank the referee for the useful comments. We also thank  W. Liu  for kindly providing the all-sky map of the structure of the Local Bubble. The results in this paper were derived using the HEALPix package (Górski et al. 2005) and the python mpfit routine written by  M. Cappellari. This research is supported by the  Agence Nationale de la Recherche  (project  BxB :  ANR-17-CE31-0022 ).
Data collection At enrollment, before the sigh test we will anonymously collect patients' demographic information (e. g. , age, sex, height, weight), past (e. g. , hypertension, chronic medications) and recent (e. g. , etiology of the acute respiratory failure, days since intubation) medical history, severity of lung injury (e. g. , ventilation setting, arterial blood gases, respiratory system compliance, diagnosis of acute respiratory distress syndrome (ARDS)) and of systemic involvement (e. g. , presence of shock, number of organ failures) and finally, ventilation data (e. g. , RR, Vt, PEEP, FiO 2 , PSV level). In both groups, for the first 24 h, we will assess the SpO 2 /FiO 2 ratio, RR and PSV tidal volume every 4 h to further characterize physiologic response to sigh over time. From day 1 (i.
e. , hemodynamic instability with hypotension with SBP < 90 mmHg despite vasoactive drugs; arrhythmias; radiographic evidence of barotrauma with pneumothorax, pneumomediastinum, pneumatocoele, or subcutaneous emphysema), SBT performance and reason for SBT failure, extubation or separation from mechanical ventilation, reintubation and reason for reintubation with time. We will collect data on day 28 for all enrolled patients on mortality, extubation that occurred outside the ICU and readmission to ICU.
Statistical considerations Statistical support for the PROTECTION trial will be granted by collaboration with Dr Carla Fornari and Dr Sara Conti from the Research Center on Public Health, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy. Drs Fornari and Conti are PhD statisticians with experience in public health and physiologic studies and they participated to PROTEC-TION study conception, draft of the study protocol, performed the power analysis to decide sample size and prepared the data analysis plan.
digitalcrf. eu), with protected individual access for each participating center. Patient data will be anonymous and coded according to a number. The eCRF includes tools to promote data quality, such as range checks for data values. Data monitoring will be performed by means of queries on the database done by statisticians and analyzed to identify abnormalities and inconsistencies. Based on clinical experience and results of physiologic studies, the additional risks for patients enrolled in the PSV + sigh arm is expected to be minimal in comparison to standard of care. Still, specific patient insurance will be granted to cover all unexpected adverse events caused by the study interventions and all adverse events will be monitored by and reported to the coordinating center.
Of note, the improvement in gas exchange and lung mechanics disappears after sigh discontinuation  [12]. In conceiving this study, we reasoned that long-term application of sigh during PSV might decrease the risk of VILI through various synergic mechanisms, possibly decreasing the duration of mechanical ventilation and mortality in patients with AHRF. To date, no RCT has tested the long-term safety and feasibility of the application of sigh. Therefore, before testing the effects of sigh on clinical outcome in a RCT, the feasibility of long-term use of sigh had to be investigated: this is the primary aim of the PROTECTION trial. Moreover, data on the safety of sigh will be collected, evaluating potential adverse effects including development of pneumothorax or other signs of barotrauma and hemodynamic instability.
Acknowledgements PROTECTION Study Group collaborating author names:  Giacomo Grasselli ,  Elena Spinelli ,  Chiara Abbruzzese ,  Roberto Rona ,  Alfio Bronco ,  Silvia Villa ,  Stefano Gianni ,  Alessandra Papoff ,  Riccardo Pinciroli ,  Riccardo Colombo ,  Chiara Sproccati ,  Pietro Mandelli ,  Federico Villa ,  Nicolo' Patroniti ,  Iole Brunetti ,  Lorenzo Ball ,  Carlo Alberto Volta ,  Marta Lazzeri ,  Elisabetta Maragoni ,  Davide Eleuteri ,  Giuseppe Bello ,  Antonio dell'Anna ,  Eugenio Garofalo ,  Andrea Bruni ,  Eugenio Biamonte ,  Rocco D'Andrea ,  Lorenzo Querci ,  Elisabetta Pierucci ,  Rosario Spina ,  Irene Mori ,  Francesco Tomeo ,  Alain Mercat ,  François Beloncle ,  Sebastien Jochmans ,  Sandie Mazerand ,  Loredana Baboi ,  Hodane Yonis ,  Matthieu Jabaudon ,  Thomas Godet ,  Tomas Jovaisa ,  Tom Barnes ,  Usman Tariq ,  Norbert Weiler ,  Dirk Schädler ,  Inéz Frerichs ,  Marina García-de-Acilu ,  Anxela Vidal ,  Emilia Rosas ,  César Pérez Calvo ,  Jian-Xin Zhou ,  Spiridon Karagiannis ,  Vasiliki Zisopoulou ,  Ioannis Staikos ,  Marko Noc ,  Misa Fister ,  Peter Radsel ,  Cesare Gregoretti ,  Ignazio Sabella ,  Santi Maurizio Raineri.
Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community.
The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.
Introduction Calibration of optical remote-sensing satellites is seen as a necessary first step to ensure the quality of all data derived from these sensor systems. Significant amounts of time, energy, and money have been spent to minimize the uncertainty in the data products by characterizing the sensors and 2 of 29 calibrating the data both before and after launch. The traditional goal is to provide an accurate measure of the upwelling energy from the Earth at a known location on the Earth. If the calibration effort is done well, the at-sensor imagery (or Level 1 imagery) is an accurate measure of the energy received at the sensor. The current state of the art for science grade systems, such as Landsat and Sentinel 2, is in the order of 3% absolute radiometric uncertainty and less than one half pixel for geometric uncertainty  [1, 2].
All of these improvements, obtained steadily over the last three decades, have now been made available to users in an unprecedented fashion through free and open data policies for many science-grade sensors. While many such systems are in orbit, two that are quite representative are the Landsat and Sentinel 2 sensors. Both of these systems cover the optical, near infrared (NIR) and shortwave infrared (SWIR) portions of the electromagnetic spectrum, and have been shown to be quite accurate with respect to geometric and radiometric calibration. Thus, users are able to develop time series from these data that are unprecedented with respect to calibration accuracy and temporal resolution. However, despite all of those improvements in calibration, and the presence of multiple similar sensors in space, the ability to blend these data together nearly seamlessly remains elusive.
One reason for this is that the effect of calibration on the derivation of the Level 2 surface reflectance products that are necessary to produce time series of Level 3 products, such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FaPAR), is unknown. Calibration of sensors to obtain accurate Level 1 products is generally done without consideration of downstream products, such as surface reflectance, and their derivatives. This rather large void in our understanding can limit the information obtainable from these data sets significantly. Furthermore, the advent of sensor systems such as those on Landsat and Sentinel 2 that are quite similar in their basic designs begs the question of how we can calibrate these systems so that the accuracy, or at least the consistency, of downstream Level 2 and Level 3 products can be enhanced.
In an attempt to address the effect of calibration on the interoperability of data from similar sensor systems, a 1 1 /2 day "expert-panel" workshop was conducted. The workshop was held in conjunction with the PECORA 20 conference in Sioux Falls, SD, on November 13-14, 2017. Key to the success of the workshop was an appropriate blend of expertise so that all aspects of the problem could be considered and solid recommendations could be developed. Since both Landsat and Sentinel 2 fly science-grade sensor systems and produce free and openly available data, they were selected for the focus, but with the goal being that the workshop results would not only be applicable to Landsat and Sentinel 2, but also useful to the broader remote-sensing satellite community. Key to the problem analysis was a discussion involving both the engineers calibrating the data and the scientists using the data.
Thus, a 10-member panel was selected that included Landsat calibration experts, Sentinel 2 calibration experts, and applications experts spanning a variety of disciplines. As seen in Table  1 , this group of individuals forms a well-rounded body of expertise to address both calibration and its effect on data interoperability from several perspectives. The workshop was designed to maximize the contribution from each panel member and to emphasize discussions among the panel members. To that end, each panel member was allowed 30 min to present his perspective on the subject, and the rest of the workshop focused on discussions among panel members. This paper reports on the results generated from the workshop and is organized as follows. An outline of calibration procedures for Landsat and Sentinel 2 is provided with extensive references to detailed information for the interested reader.
Prelaunch Radiometric Characterization and Calibration The radiometric calibration and characterization of the OLI instrument, both in the pre-launch and on-orbit realms, has been well documented  [5, 6]. The OLI, as indicated, has extensive on-board radiometric calibration capabilities that allow tracking its performance on-orbit. These capabilities by themselves do not ensure low uncertainty in the radiometric calibration of the instrument; key pre- Spectral consistency in long-term data records is one of the bigger challenges in providing harmonized data. Even though technologies and sensors continue to evolve, perfectly consistent data are not obtainable. Although spectral adjustment factors can be derived to better match two (or more) sensors' data, these factors are target-and atmospheric-dependent, meaning that to accurately calculate them one needs to know the signature of the target and atmosphere in advance, which is clearly not the norm in remote sensing.
Prelaunch Radiometric Characterization and Calibration The radiometric calibration and characterization of the OLI instrument, both in the pre-launch and on-orbit realms, has been well documented  [5, 6]. The OLI, as indicated, has extensive on-board radiometric calibration capabilities that allow tracking its performance on-orbit. These capabilities by themselves do not ensure low uncertainty in the radiometric calibration of the instrument; key pre-launch characterizations also required include instrument stability, linearity and non-uniformity, and the calibrators' radiometric and geometric properties. The OLI was radiometrically calibrated prior to launch relative to radiance standards traceable to the National Institute of Standards and Technology (NIST)  [5]. This calibration is available within the data products.
On-Orbit Radiometric Calibration of OLI Radiometric calibration and characterization of the OLI instrument is accomplished by processing all data through the Landsat Product Generation System (LPGS) or the Image Assessment System (IAS) at the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. To show the radiometric gain trend through time, the average response from each of the on-board calibrators (working lamp, pristine lamp, backup lamp, working solar diffuser and pristine diffuser) were normalized to a point in time shortly after launch. Figure  2  shows the radiometric gain trend over time. Only the coastal aerosol (~1. 5%) and blue (~0. 3%) bands show any significant change in gain over almost five years on-orbit. The on-board calibrators provide a very precise trend of the changes in the instrument response, but any connection to the absolute radiometric accuracy is tied back to a NIST reference through pre-launch calibration.