dataesr/scientific-paragraphs-categorization huggingface dataset:
Thanks to collected data, the registry will facilitate and accelerate clinical and translational research on the disease. Furthermore, specific guidelines on FSHD medical care will be regularly updated and disseminated, contributing to the national standardization of medical care for FSHD patients. Moreover, thanks to the registry, the assessment of feasibility and recruitment in clinical trials and studies will be facilitated. Despite strong efforts from physicians to ensure the collection of longitudinal data, this aspect is always a weakness in patients' registries. In order to make it more efficient, we are currently modifying the system to allow the direct data submission from patients. The curators will then validate these new information to ensure their quality. The regular update of the website will contribute to the improvement of the knowledge of both patients and professionals on FSHD, as well as facilitate interaction with academic and industrial partners.
Proof-of-concept Implementation of the Reversible Semantics We have developed a proof-of-concept implementation of the uncontrolled reversible semantics for Erlang that we presented in Section 3. This implementation is conveniently bundled together with a graphical user interface (we refer to this as "the application") in order to facilitate the interaction of users with the reversible semantics. However, the application has been developed in a modular way, so that it is possible to include the implementation of the reversible semantics in other projects (e. g. , it has been included in the reversible debugger CauDEr  [22, 21] ). Let us recall that our semantics is defined for a language that is equivalent to Core Erlang  [7] , a much simpler language than Erlang. Not surprisingly, the implementation of our reversible semantics is defined for Core Erlang as well.
Alternatively, the user can move the state forward up to a normalised system. To normalise a system, one must ignore the Sched rule and apply only the other rules. A normalised system is reached when no rule other than Sched can be fired. Hence, in a normalised system, either all processes are blocked (waiting for some message to arrive) or the system state is final. Normalising a system allows the user to perform all the reductions that do not depend on the network. Reductions depending on the network can then be performed one by one to understand their impact on the derivation. The release version (v1. 0) of the application is fully written in Erlang, and it is publicly available from https://github. com/mistupv/rev-erlang under the MIT license. Hence, the only requirement to build the application is to have Erlang/OTP installed.
$ This work has been partially supported by  MINECO/AEI/FEDER (EU)  under grants  TIN2013-44742-C4-1-R  and  TIN2016-76843-C4-1-R , by the  Generalitat Valenciana  under grant  PROMETEO-II/2015/013  (SmartLogic), by the  COST Action  IC1405 on Reversible Computation -extending horizons of computing, and by  JSPS KAKENHI  Grant Number  JP17H01722. Adrián Palacios  was partially supported by the  EU (FEDER)  and the Spanish Ayudas para contratos predoctorales para la formación de doctores and Ayudas a la movilidad predoctoral para la realización de estancias breves en centros de I+D, MINECO (SEIDI), under  FPI  grants  BES-2014-069749  and  EEBB-I-16-11469. Ivan Lanese  was partially supported by  INdAM  as a member of  GNCS (Gruppo Nazionale per il Calcolo Scientifico ). Part of this research was done while the third and fourth authors were visiting Nagoya and  Bologna Universities ; they gratefully acknowledge their hospitality.
Analyses of MS/MS data The MS data were analyzed using MaxQuant software using the Uniprot-Swissprot reviewed database (Uniprot-Swissprot, release 2017-05). 11, 12 Carbamidomethylation of cysteines was set as a constant modification and variable modifications were restricted to acetylation of protein N termini and oxidation of methionines. Labelfree protein quantification (LFQ) was done using both unique and razor peptides with at least 2 ratio counts. 11 Data from all experiments were analyzed simultaneously using the match between runs (MBR) option. Functional analyses of data from MaxQuant were done with Perseus software.
To this end, we took advantage of the high dynamic range of detection that is possible with proteomic analyses (Figure  2E ). Thus, our present data provide the first true quantification of proteins expressed in erythrocytes with no detectable contribution from reticulocyte proteins. The simultaneous determination of the reticulocyte proteome allowed for identification of the modifications that occur during the last stages of red cell maturation. For each identified protein, we calculated the percentage that is lost during the maturation of reticulocytes to erythrocytes (supplemental Excel file 1). These data extend the proteomic analysis of human erythropoiesis that we recently published. In that previous study, we quantified the repartition of proteins between the pyrenocytes and the reticulocytes during the enucleation process.
Acknowledgments The authors thank  C édric Broussard ,  Evangeline Bennana , and  David Rombaut  (all from  3P5 ) for their contribution to the proteomic analyses. This work was supported by grants from  Laboratory of Excellence GR-Ex  (reference  ANR-11-LABX-0051 ). The labex GR-Ex is funded by the  Investissements d'avenir program  of the  French National Research Agency  (reference  ANR-11-IDEX-0005-02 ). The Orbitrap Fusion mass spectrometer was acquired with funds from  Fonds Europ éen de D éveloppement R égional (FEDER)  through the  Operational Program  for Competitiveness Factors and Employment 2007-2013 and from the  Canceropole Ile de France.
All subnational estimates for these countries were incorporated into model development and evaluation as part of GBD 2017. To meet data use requirements, in this publication we present all subnational estimates excluding those pending See Online for appendix 1 For the statistical code see https://github. com/ihmeuw/ ihme-modeling publication (Brazil, India, Japan, Kenya, Mexico, Sweden, the UK, and the USA); given space constraints these results are presented in appendix tables and figures instead of in the main text (appendix 2). Subnational estimates for countries with populations larger than 200 million people (measured using our most recent year of published estimates) that have not yet been published elsewhere are presented wherever estimates are illustrated with maps but are not included in data tables.
Uncertainty analysis We apply the same technique for propagating uncertainty as used elsewhere in the GBD study design. 19, 24, 25 The distribution of every step in the computation process is stored in 1000 draws that are used for every other step in the process. The distributions are determined from the sampling error of data inputs, the uncertainty of the model coefficients, and the uncertainty of severity distributions and disability weights. Final estimates are computed using the mean estimate across 1000 draws, and the 95% uncertainty intervals (UIs) are determined on the basis of the 25th and 975th ranked values across all 1000 draws.
These two studies are part of the Maternal and Child Epidemiology Estimation (MCEE). The MCEE methodology for diarrhoea and pneumonia incidence differs from GBD in that MCEE uses only communitybased studies. By contrast, the GBD uses a greater volume of data including Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and clinical data records. We also used diarrhoea-specific and LRIspecific summary exposure values as covariates in our models, which include location-specific and year-specific estimates of risk factors associated with diarrhoea or LRI. We observed that these covariates had significant relationships with diarrhoea and LRI incidence rates and therefore strengthened our estimates, particularly in data-sparse areas. Further modelling details are described in the supplementary methods (appendix 1 section 4).
For HIV/AIDS, we used new methods to adjust for representativeness bias in ANC surveillance data,  83  which led to our models predicting a lower peak and flatter trend of the epidemic in southern sub-Saharan Africa compared with GBD 2016. We updated our inputs of antiretroviral therapy coverage distribution informed by data from the International Epidemiology Databases to Evaluate AIDS and CD4+ progression parameters for children. 84, 85 We also improved our sex-specific modelling strategy in Spectrum by sex-splitting incidence based on a model fit to the sex ratio of prevalence observed in countries with representative surveys. Our estimated global prevalence results are similar to UNAIDS 2017 values, with some variation at the national level. Estimates in recent years are expected to be less stable due to improvements in HIV treatment coverage and prevention.
Further modelling details are described in the supplementary methods (appendix 1 section 4). The hepatitis estimation process has changed to improve internal consistency between mortality and nonfatal estimates for GBD 2017. First, we included casefatality rates from clinical data in our mortality models to improve the distribution of hepatitis deaths by virus. Second, we developed a time series of hepatitis B virus vaccine coverage in infants to use as a covariate. Third, the aggregate cause-specific mortality rate of acute hepatitis, cirrhosis, and liver cancer due to hepatitis B and hepatitis C was combined with virus-specific seroprevalence data to ensure internal consistency among incidence, prevalence, remission, and excess mortality rates. Fourth, the prevalence of chronic hepatitis B and C are now captured in the cirrhosis and other chronic liver diseases cause group rather than in the prevalence estimates for acute hepatitis B and acute hepatitis C.
Maternal, neonatal, and child health Several changes were adopted for GBD 2017. Adding clinical and claims records from outside the USA to our models affected congenital birth defects, haemoglobinopathies, and maternal disorders. Updating our inpatient admission per-capita estimates to include infacility deliveries as a newborn admission affected neonatal disorders and congenital birth defects. Adopting in-facility delivery estimates for processing clinical data for maternal disorders led to lower estimates of pregnancy complications, especially in high-utilisation settings. We refined our method for estimating age-specific preterm birth and its complications to be consistent with GBD risk factor analysis, which resulted in preterm birth prevalence being higher than in previous GBD estimates.
Cardiovascular diseases We split haemorrhagic stroke into subarachnoid haemor rhage and intracerebral haemorrhage as subtypes of stroke and added non-rheumatic valve disease as an additional cause and aetiology of heart failure. The overarching limitation in cardiovascular estimation is that low-income and middle-income locations are less likely to have diagnostic tests such as treadmill tests, ankle-brachial index measurement, and other modern diagnostics. This also limits the identification of preclinical atherosclerotic disease for individuals who have not had clinical events but who might have ischaemic changes with provocative cardiac testing. For stroke, there were few sources of incidence data in many low-income and middle-income countries, and many clinical records used the ICD code for "acute, unspecified stroke", which does not differentiate between ischaemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage.
Mental and substance use disorders We estimated burden for a combined group of autism spectrum disorders consistent with the Diagnostic and Statistical Manual of Mental Disorders 5 designations in GBD 2017 as opposed to separately estimating autism and Asperger's syndrome and other autism disorders. 87 odelling improvements were made for opioid use disorders by incorporating new country-level covariates, including a measure of opioid analgesic consumption by country and the prevalence of injection drug use. Enhancements in the fatal modelling of opioid use disorders also contributed to these improved estimates. Improvements were also made in terms of data additions, particularly for cocaine and amphetamine dependence, where new data showed greater subnational variation in Mexico and Brazil.
As such, YLDs estimated might not reflect improvements in disability over time as SDI increases. Even for the limited diseases such as COPD, epilepsy, and stroke for which data from epidemiological studies allow us to differentiate severity by age, time, and location, the underlying data sources are still extremely limited in coverage by age, time, and location. Relying on published data on severity is unlikely to provide improved estimates in the near future. We also plan to access linked data between administrative records with diagnostic information and health surveys using health status measures to quantify severity of disease and how this changes over time and by age and sex. Comorbidity adjustment in GBD 2017 assumes independent distributions of comorbid conditions. This is a limitation because comorbidity distributions are known to vary by cause, age, sex, location, and time.
For example, diabetes and cardiovascular disease are more likely to be comorbid than asthma and gynaecological disorders but currently our comorbidity adjustment does not capture these correlations. Clinical data records have known selection bias for subsets of the population that have access to health care. Some GBD causes rely heavily on clinical data given the lack of other sources, with efforts made to correct for this bias by using representative studies as a reference group. In recent years, the GBD study has used USA claims records to inform hospital data adjustments and various other elements of GBD estimation. Relying on data from a privately insured cohort in a high-income country poses limitations to the generalisability of these adjustments. This year, we also used tabulated claims records from Taiwan (province of China) for the first time in the GBD study, and in future publications we plan to continue incorporating claims data from additional countries to address this limitation.
Acquiring further health insurance claims data will not only inform estimates in terms of incidence and prevalence measures but will also help to develop more accurate parameters that affect GBD estimates in other modelling steps. With regard to age-time patterns, in DisMod-MR 2. 1, we start by estimating a global model that uses all the available data and covariates with hierarchical random effects for geographies to estimate a prior distribution that is passed down the geographical cascade. Time trends in our estimates result from where the covariates are changing over time, when year is included as a covariate or when data in different time periods lead to variation in the posterior estimates. This framework, however, does not capture true cohort effects. For example, we do not directly take into consideration prevalence in 30-year-olds in 1990 when estimating prevalence in 50-year-olds in 2010.
Future directions The methods and data that inform YLD estimation in the GBD study will continue to evolve. Several specific improvements have been suggested by the GBD collaborator network and the global research community, and the following future directions are planned to be incorporated in the coming cycles. Because the GBD cycle continues to be published on an annual basis, it will be important to incorporate updated data sources for older estimation years but also to add sources for the most recent estimation year. This necessitates data inclusion immediately after the end of the calendar year in order to be incorporated for the following year's estimation. This is challenging because some surveys, surveillance systems, and literature studies have a lag period between data collection and publication.
One solution to this challenge would be adding, updating, and incorporating clinical data records from inpatient and outpatient visits from hospitals and clinics from more locations. Clinical data provide a level of detail, demographic information, and diagnostic accuracy that enhances the GBD study's ability to produce accurate estimates in the more recent estimation years. Relying more heavily on clinical data will require improved methods for adjusting for known representativeness biases. In the future, we plan to link medical records to representative survey results, which is possible with certain claims databases such as the Medicare Current Beneficiary Survey in the USA. 93 n the next iteration of the GBD study, we plan to incorporate improved age-time estimation using a new tool, DisMod-AT, that simultaneously quantifies the relationships over time, by age, and by age cohorts.
Acknowledgments Research reported in this publication was supported by the  Bill & Melinda Gates Foundation , the  University of Melbourne, Public Health England , the  Norwegian Institute of Public Health, St Jude Children's Research Hospital , the  National Institute on Ageing of the National Institutes of Health  (award  P30AG047845 ), and the  National Institute of Mental Health of the National Institutes of Health  (award  R01MH110163 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. We thank the  Russia Longitudinal Monitoring Survey , done by the  National Research University Higher School of Economics  and ZAO Demoscope together with the  Carolina Population Center, University of North Carolina at Chapel Hill  and the  Institute of Sociology RAS , for.
Discussion In this study, we report abnormal NIR-AF features in achromatopsia, using semiquantitative autofluorescence plots segmented over semi-circles around the fovea. In addition, we employed this method to correlate NIR-AF with SW-AF features and structural alterations on SD-OCT. Patients with a central hypoautofluorescence on NIR-AF were older and presented more advanced structural lesions at the fovea on SD-OCT than those with normal NIR-AF, while SW-AF was not correlated to other multimodal findings. These observations highlight the interest of NIR-AF as imaging biomarker in achromatopsia, and as potential clinical endpoint in upcoming gene therapy trials. To the best of our knowledge, there is no previous report of NIR-AF findings in cone dysfunction syndromes, and we could find no reference to it in a computerized search on the PubMed database.
Based on previous reports by Sundaram et al. and Aboshiha et al. ascertaining the symmetrical presentation of achromatopsia, and allowing the investigation of imaging features in one single eye  14, 16  , the right eye of each subject was retained for analysis, except when complete multimodal imaging was only available for the left eye. NIR-AF and SW-AF images were analyzed with a custom processing based on a modification of the method by Lois et al  25, 26. Semiquantitative autofluorescence plots were obtained using the publicly available Image J software (ImageJ 64 version 1. 48q, Wayne Rasband, National Institutes of Health, Bethesda, MD, http://imagej. nih. gov/ij) and the "Radial profile angle" plugin (Philippe Carl, http://rsb. info. nih. gov/ij/plugins/radial-profile-ext. html), as illustrated in the Supplementary Figure  2.
The extension of ISe defects on horizontal SD-OCT scans was measured independently on anonymized images by the two observers using the built-in caliper function of the Heidelberg Eye Explorer software (version 1. 9. 10. 0). Each imaging parameter was measured twice by each observer, to assess intra-observer agreement, and the mean between both measures was used to assess inter-observer agreement. For statistical analyses, the mean between measures by the two observers was employed.
Statistical analyses. Mann-Whitney test, Fisher's exact test and Spearman coefficients were used for comparative, contingency and correlations statistics, respectively, on GraphPad Prism (version 5. 0 f, GraphPad Software, La Jolla, CA, USA). The Wilcoxon paired test was used to assess inter-eye symmetry. Intra-class correlation coefficients were employed to assess intra-and inter-observer agreement for subjective quantitative measurements. Based on the lower end of the 95%-confidence interval (95CI) of the ICC, agreement was considered as poor (<0. 50), moderate (0. 50-0. 75), good (0. 75-0. 90), or excellent (>0. 90). The "irr" package was used for ICC calculations on R software (Version 3. 3. 0, R Foundation for Statistical Computing, R Core Team, 2016, Vienna, Austria. http://www. R-project.
Acknowledgements The authors thank  Michel Paques, MD, PhD , for fruitful discussions on retinal structure and imaging. This work was supported by the  Foundation Fighting Blindness  [ CD-CL-0808-0466-CHNO] (IA ), the  Foundation Fighting Blindness center  ( FFB grantC-GE-0912-0601-INSERM02 ), the  Prix de la Fondation de l'Oeil (IA) ,  Ville de Paris  and  Region Ile de France , and by the  French State program  " Investissements d' Avenir " managed by the  Agence Nationale de la Recherche  [ LIFESENSES :  ANR-10-LABX-65 ].
Data availability. The clinical data generated and analyzed during the current study are provided in Tables  1  and 2. Retinal imaging data and corresponding semiquantitative plots are provided in Figs 1, 2 and in the Supplementary Figure  1. The datasets employed for statistical analyses are available from the corresponding author on reasonable request. Comprehensive genetic data is provided in Table  1  and available in the public repository ClinVar of the National Center for Biotechnology Information (https://www. ncbi. nlm. nih. gov/clinvar/).
Trial design This was a multicenter, randomized, double-blind, placebocontrolled, incomplete block, three-way crossover study. Following a prescreening visit, patients' eligibility to participate was evaluated at a screening visit. After a 14-day run-in period, eligible patients were randomly assigned to one of 10 sequences, each comprising three treatment periods. The three treatment periods were 28 days in duration, separated by a 21-day washout period. On Days 1 and 28 of each treatment period, serial forced vital capacity (FVC) maneuvers were performed at pre-dose and at 15, 30 and 45 min, and 1, 2, 4, 6, 8, 10, 11. 5 and 12 h post-dose, with slow vital capacity maneuvers (for the collection of inspiratory capacity [IC] data) performed at pre-dose, prior to the FVC maneuvers. Adverse events (AEs) and serious adverse events (SAEs) were monitored throughout the study, and vital signs (systolic and diastolic blood pressure) and electrocardiogram (ECG) data were collected pre-dose on Day 1 and post-dose on Day 28.
Outcomes The objective of this study was to identify the optimal dose of GB DPI to be further developed for the treatment of patients with COPD. The primary efficacy variable was FEV 1 area under the curve from 0 to 12 h (AUC 0-12 h ) normalized by time on Day 28. Secondary efficacy variables included the following: FEV 1 AUC 0-12 h on Day 1; trough and peak FEV 1 and FVC on Days 1 and 28 (trough values were assessed from the mean of the measurements at 11. 5 and 12 h post morning dose; peak values used data collected up to 4 h post-dose); morning pre-dose FEV 1 on Day 28 (calculated from the mean of measurements at 45 and 10 min pre-dose); and change from baseline in pre-dose (morning) IC on Day 28. An additional post hoc analysis was performed on the percentage of patients with an increase from baseline in morning pre-dose FEV 1 of at least 100 mL (ie, responders) on Day 28.