dataesr/scientific-paragraphs-categorization huggingface dataset:
7 µl/min using a spray emitter (75 µm ID, 8 µm opening, and 300 mm length; new objective) and air pressure pump (Proxeon). For liquid chromatography, a mobile phase A (MQ-H 2 O and 0. 1% formic acid) and mobile phase B (80% acetonitrile and 0. 1% formic acid) were used. A 60-min gradient was used for the sample set, with a total run time of 100 min per sample. Raw data files were converted to MGF file formats with Convert (Microsoft). Data analysis performed using MAS COT using the following parameters: MAS COT version: 2. 4. 1; enzyme: trypsin; maximum missed cleavages: 2; peptide tolerance: ±6 ppm; tandem mass spectrometry tolerance: ±0. 6 Da; for fixed modifications: carbamidomethylation on cysteine; for variable modifications: oxidation on methionine. The database used was the combined UniProt Drosophila melanogaster database  (September, 2014).
Acknowledgments We are grateful to  C. MacKintosh  for her advice, to all the members of the  Ohkura laboratory  and  D. Finnegan  for their help and useful feedback, to  E. Peat ,  C. Barnard , and  D. Staneva  for their contributions, to  Y. Matsubayashi  for his ImageJ plugin, to  D. Kelly  for his help, and to the  Bloomington Drosophila Stock Center and Resource Center (National Institutes of Health  grants  P40OD018537  and  2P40OD010949-10A1 ) and the  Transgenic RNAi Project at Harvard Medical School  ( National Institutes of Health/National Institute of General Medical Sciences   R01-GM084947 ) for fly stocks. Wellcome Trust Senior Research Fellowships  ( 103139  and  098030 ) were granted to  J. Rappsilber  and  H. Ohkura. The  Wellcome Trust Centre for Cell Biology  is supported by a core grant.
Sources of data The first step in non-fatal estimation was the compilation of data sources from systematic data and literature searches conducted by cause. This process resulted in 4043 published studies newly included in GBD 2016, leading to a total of 14 521. Our network of collaborators for GBD 2016 provided 2598 data sources and studies. These were systematically screened, together with sources -6•7 (-11•2 to -2•3) † -20•0 (-23•6 to -16•3) † Impairments •• •• •• •• •• Anaemia 2 042 728 (1 979 003 to 2 110 037) •• 61 649 (41 483 to 86 424) 9•2 (6•4 to 11•2) † -2•2 (-4•8 to -0•3) † Developmental intellectual (19•1 to 22•0) † -1•3 (-2•4 to 0•8) We did not calculate incidence for the nine impairments, certain neglected tropical diseases, and causes with only birth prevalence. Blank cells mean that no estimate is available.
Table: Global prevalence, incidence, and YLDs for 2016, percentage change of YLD counts, and percentage change of age-standardised YLD rates between 2006 and 2016 for all causes and nine impairments suggested by country-level experts, surveys located in multinational survey data catalogues, and Ministry of Health and Central Statistical Office websites. We analysed 18 792 sources of epidemiological surveillance data (country-years of disease reporting), up from 14 081 in 2015. All counts reflect our updated counting criteria for GBD 2016. The supplementary methods provides details of data adjust ments, correction factors, and standardisations employed in incorporating these different data types (appendix 1, p 18). The number of location-years of hospital inpatient data by cause increased from 1176 in GBD 2015 to 3557 in GBD 2016.
This increase can be attributed to the addition of new years of data for some locations, as well as newly incorporated data for 16 countries where we had previously lacked clear information about the population covered. To allow their use in GBD, we first collated information from surveys and hospital administrative records to estimate hospital admission rates per capita for all GBD locations by age and sex, from 1990 to 2016, using DisMod-MR 2. 1 (appendix 1, p 7). We then used inpatient data by cause from locations with unclear denominators as cause fractions of the all-cause inpatient admission rates. Three adjustment factors were derived from USA health insurance claims data on more than 80 million person-years of coverage. The first factor corrected for multiple inpatient episodes for the same cause in an individual.
The methods for estimating YLDs from a number of diseases changed substantially for GBD 2016. We improved our estimation of mortality-to-incidence ratios for cancers to better reflect lower survival probabilities in low-income and middle-income locations based on each location's Socio-demographic Index (SDI) value. As a consequence, our prevalence and YLD estimates were lower in those locations but did not change much for higher-SDI locations. We made major changes to our modelling of tuberculosis. First, we made explicit estimates of latent tuberculosis infection from tuberculin skin testing data and the risk of developing active tuberculosis by induration size. Second, we predicted mortality-to-incidence ratios in locations with high dataquality ratings (4-star or 5-star using a system developed for the GBD 2016 causes of death estimation)  18  and SDI as a covariate.
We anchored the lower end of the SDI scale with a datapoint from an untreated cohort of pulmonary tuberculosis cases in the 1960s, half of whom had died after five years. 18, 19 Third, we estimated incidence from these mortality-to-incidence ratios in all locations except those with higher reported notifications. Fourth, we modelled these incidence estimates as well as the prevalence data from surveys in low-income and middleincome countries and cause-specific mortality rates among the proportion of the population with latent infection in DisMod-MR 2. 1. Fifth, we estimated the proportions of tuberculosis cases with multidrugresistant tuberculosis or extensively drug-resistant tuberculosis from notification and survey data and included an increased risk of multidrug-resistant tuberculosis in HIV/AIDS-infected patients with tuber culosis from a meta-analysis.
A systematic review was done to collect data on modified Rankin scores, a measure of neurological disability. 21 Levels of Rankin score were analysed in DisMod-MR 2. 1 and mapped to the existing GBD health state lay descriptions for mild, moderate, and severe motor impairment from stroke, and, separately, the proportion of stroke patients with moderate-to-severe motor impairment who also experienced cognitive impairment. For GBD 2016 we used the same disability weights as in GBD 2013 and GBD 2015; the supplementary methods provides a complete listing of lay descriptions of all 235 health states used in GBD 2016 (appendix 1, p 799).
YLD computation All computations in GBD were done 1000 times, every time drawing from the distribution of the sampling error of data inputs, the uncertainty of data corrections for measurement errors, the uncertainty in coefficients from model fit (eg, in DisMod-MR 2. 1), and the uncertainty of severity distributions and disability weights. Uncertainty bounds for a quantity of interest were defined by the 25th and 975th value of the ordered 1000 estimate values. If there was a change in disease estimates between locations or over time that was in the same direction in more than 950 of the 1000 samples we report it as significant. Age-standardised prevalence YLD rates were calculated based on the GBD reference population. 22 he GBD cause hierarchy is comprehensive and includes 35 residual disease categories to capture YLDs from conditions for which we do not currently make separate estimates.
For 22 of these residual categories, we made explicit epidemiological estimates of prevalence and incidence, and define sequelae based on the most common diseases in the Level 2 or 3 cause group and severity distributions from the Medical Expenditure Panel Survey (MEPS). 23 For 13 residual categories, we had no epidemiological data and estimated YLDs from the ratio of YLDs to YLLs from explicitly modelled diseases in the cause category, assuming that relative to each death, the number of YLDs was similar to that of other diseases at the same level of the GBD hierarchy (appendix 1, p 29).
SDI and epidemiological transition SDI is a summary measure that places all GBD locations on a spectrum of socioeconomic development. 24 The SDI was developed for GBD 2015 to provide a comparable metric of overall development. This was achieved by using an equal weighting of lag-distributed income per capita, average years of education in the population over age 15 years, and total fertility rate. 22 For GBD 2016, we modified the estimation of SDI by taking into consideration that SDI scales were subject to change based on increasing geographic units and an extended time period of analysis, affecting the interpretability across GBD iterations. We redefined the values of zero and one for each component of the index: zero now represents the level below which we have not observed GDP per capita or educational attainment or above which we have not observed the total fertility rate in known datasets.
Maximum scores for educational attainment and Lagged Distributed Income represent a plateau in the relationship between each of the two components and life expectancy or under-5 mortality rates, suggesting no additional benefit. Analogously, the maximum score for total fertility rate represents the minimum level at which the relationship with the selected health outcomes plateaued. An SDI value was generated for each location and year as the geometric mean of each component score. Five SDI quintiles, high, high-middle, middle, low-middle, and low, were selected based on 2016 values of SDI; additional details are available in the supplementary methods (appendix 1). 22  Gaussian process regression was used to evaluate the average relationship for each age-sex-cause group, for cause-specific YLD rates on SDI at Levels 1, 2, and 3 of the GBD cause hierarchy using data from 1990 to 2016.
Cross-cutting themes The availability and quality of epidemiological data the GBD Study draws upon to make non-fatal estimates vary enormously among diseases and by location. A multitude of study methods and preferred case definitions pose challenges to making comparable estimates. A large part of the effort in the GBD study in making non-fatal estimates is to identify and correct for known sources of measurement error. The main classes of data sources each have their own limitations. Surveys can be biased by low response rates or by exclusion of individuals who do not reside in a traditional household, and may be limited in the capacity to properly identify cases within the limited diagnostic means available during a survey. Administrative data on health service encounters include only those seeking and receiving health care and are likely biased toward more severe disease, but may offer more accurate diagnostic variables than available in surveys.
Important changes in GBD 2016 compared with GBD 2015 The intensive collaborations with countries for which we present subnational estimates and the expanding network of GBD collaborators have contributed to a 36•2% increase in data sources for non-fatal outcome estimates compared to GBD 2015, as well as greater scrutiny of our methods and results. In particular, the close collaboration with the Indian Council for Medical Research and the Public Health Foundation of India, and 14 disease expert groups convened by these partners, has greatly enhanced the quality of the estimates for India. There was a 49•9% increase in non-fatal data sources in India, and disease experts provided access to state-level data for causes, while in GBD 2015 we had only access to national aggregates. Increasingly, the GBD Study is making use of administrative data based on health care encounters.
Public access to these data sources at the level of detail required for GBD analyses remains limited, and enormous scope remains to expand access to these valuable sources of health information in many more countries. By estimating total admission rates from both administrative and survey sources, we were able to increase the number of countries with available inpatient data from 26 in 2015 to 41 in 2016. To predict the level of disease under any type of care for other countries with inpatient data available, we applied the ratio of people with a diagnosis during inpatient and outpatient episodes to that of inpatient diagnoses only in US medical claims data. In our analyses for most conditions, the administrative data sources were adjusted to the level of a reference case set for the disease of interest.
Thus, we make use of the detailed information on age and sex in these data sources while adjusting for a potential systematic bias compared to data sources with measurements according to the reference case. For instance, our reference case for diabetes prevalence was the proportion of a population with fasting plasma glucose greater than 7 mmol/L or on diabetes treatment in a representative survey. The adjustment of administrative data reflects the proportion of people with diabetes who may be unaware of their condition or who for other reasons have not contacted health services in the year of interest. To estimate indicators of sexual violence for the SDGs, we added sexual violence as a subcause of interpersonal violence in the GBD hierarchy. The focus of the effort in GBD 2016 has been to estimate the prevalence of people who report sexual violence by intimate partners and other perpetrators in the last 12 months.
For non-fatal outcomes, we quantified the immediate concurrent physical injuries and the more immediate mental health consequences. We realise this is a limited scope for estimating all relevant health loss from sexual violence. In future iterations of GBD we intend to include sexual violence as a risk factor for longer-term outcomes such as major depressive disorder, anxiety disorder, and substance use disorders. Going forward, we aim to become more comprehensive in our estimation of violence to include the wider health consequences of physical violence to children and adults as well as bullying. Although efforts were undertaken to update data for all causes of YLDs in GBD 2016, a number of sources of new data were especially influential in improving estimates for selected causes. A large number of rapid-assessment vision loss surveys  37  contributed to an increase in the number of sources to measure vision loss and its underlying causes from 397 in 2015 to 768 in 2016, with data available in 75 low-SDI and middle-SDI countries.
39 It allowed us to model active disease among those with latent infection to improve the search for a consistent fit between cause of death rates, prevalence surveys, and notification of incident disease, the three main sources of epidemiological data on tuberculosis. Next, we estimated the association between SDI and the ratio of mortality to incidence, assuming this measure of case fatality is dependent on development as a proxy for quality of case finding and management. This provided greater alignment of mortality and non-fatal estimates of tuberculosis than we have been able to achieve in past GBD studies. A substantial new effort on the severity of stroke was the analysis of 18 studies providing Rankin score data on the severity of disability in stroke survivors. Thus, we were able to make estimates by age, sex, year, and location to capture greater nuances in outcomes -for example, reflecting differences in access to rehabilitative services.
Mental and substance use disorders GBD 2016 confirms that mental and substance use disorders, led by depressive disorders, are a major cause of non-fatal burden. With age-standardised prevalence and YLD rates for mental and substance use disorders showing less than 10% change between 1990 and 2016, apart from bulimia YLD rates which increased by almost 20%, our findings show no sizeable improvement in population mental health over time at the global level. Interventions can decrease the severity or increase remission for certain disorders and can be effectively administered in low-income, middle-income, and highincome settings. 40 However, these have yet to be brought up to scale in most countries. Because there are no data measuring the severity of mental disorders in a consistent manner, we have been unable to capture treatment effects that alter severity of disease.
Limitations The annual updates of GBD allow incremental improvements to methods. However, several measurement challenges in non-fatal estimation remain. The foremost concern is how best to disentangle measurement error from true variation in disease occurrence. We correct for known bias from nonreference methods or case definitions, but often have to rely on sparse information to make those adjustments. Lack of consensus among researchers on how to measure a disease can make it difficult to define a manageable set of alternative methods for which we can make adjustments. An extreme example is dementia: among 234 data sources we identified 228 different methods of screening, diagnostic tools, and diagnostic criteria. When possible, we use predictive covariates in our non-fatal models. For diseases with a lot of information on what drives differences in disease, covariates help to estimate true variation and minimise residual measurement error.
However, many of the leading causes of YLDs lack strong predictors as indicated by our risk assessment, which assigned less than 20% of YLDs from mental, musculoskeletal, sense organ, and neurological disorders to the 79 risks evaluated in the GBD study. 51 econd, many non-fatal models continue to rely on sparse data in some regions or super-regions despite the steady increase in data sources. With the addition of some new data sources, this can lead to large variation with previous GBD estimates. Alcohol dependence is a good example. Our estimate of the global prevalence of alcohol dependence increased by 70% after adding nine new data sources for Brazil, 12 for India, seven for sub-Saharan Africa, and four in eastern and central Europe. Until there is better coverage of data, non-fatal disease models will continue to be prone to estimates varying between GBD iterations.
Third, the adjustments to our hospital admission data for multiple admissions and secondary diagnosis fields reflect all health-care episodes rather than inpatient episodes only. Adjustments derived from a large, though non-representative, source of medical claims data in the USA are assumed to be generalisable. 3] [54] [55] [56]  Also, there may be considerable intercountry variation in how diseases are treated between inpatient and outpatient settings. Our models attempt to adjust for such potential biases by using a covariate on claims and hospital admission data to correct for systematic error, although in a relatively crude manner, this assumes no geographical variation in the error. Gaining access to claims data from other countries, particularly in lowincome and middle-income countries, is a crucial endeavour for future GBD iterations.
Fifth, apart from COPD, stroke, and epilepsy, where we have epidemiological data to estimate the geographical variation in the distribution of sequelae, for most diseases we are unable to capture differences in severity that occur due to treatment. As almost all our data sources on severity are from high-income countries, this would underestimate YLDs in low-income and middle-income countries with poorer access to and quality of treatment. If greater geographical information on severity of disease becomes available, we can incorporate a more explicit gradient based on our health-system access and quality index. 58 ixth, we have not yet been able to incorporate dependent comorbidity into our simulation methods. We aim for this to take place in GBD 2017 and will require substantial developmental work to make the computational code more efficient.
Seventh, most of the uncertainty in our YLD estimates comes from the disability weights. We have not con ducted any new surveys since GBD 2013. With further data collection, we may be able to reduce some of the uncertainty by increasing the volume of data and by removing some of the ambiguities in lay descriptions, but large uncertainty is inherent to health state valuations that rely on perceptions by respondents of brief descriptions of complex health problems. Eighth, the separate estimation of non-fatal models in DisMod-MR 2. 1 for 1990, 1995, 2000, 2006, 2010, and 2016 implies uncertainty of estimates over time is independent. Also, compositional bias can lead to spurious time trends being estimated. DisMod-AT will allow for the more appealing simultaneous estimation over age and time and thus resolve the current limitations.
However, as it is a more complex modelling tool, extensive testing and understanding what sensible settings are will need to take place before we can replace the current DisMod-MR approach. Ninth, scrutiny of the multitude of results generated by GBD is a challenge. Increasingly, we generate visualisation tools to query results and intermediate steps in our analyses to better vet our results. We share our data and modelling tools with our expanding network of over 2500 collaborators who frequently query data sources and our measurements for particular diseases or locations. Also, by publishing all code and describing the methods used disease-by-disease we encourage scrutiny of every aspect of the GBD study. Tenth, as our GBD collaborators have raised, the argument of including Guinea worm disease to document pending eradication can be used to justify the inclusion of poliomyelitis in the next round of GBD.
thelancet. comVol 390 September 16, 2017 from the  Disease Control Priorities Network (DCPN)  funded by the  Bill & Melinda Gates Foundation. Ronny Westerman  acknowledges efforts on behalf of the German National Cohort funded by the  German Ministry of Education and Research. Lijing Yan is partially supported by the  National Natural Sciences Foundation of China grants  ( 71233001  and  71490732 ). No ICMJE  Conflicts of Interest. Marcel Yotebieng  is partially supported by the  NIAID   U01AI096299  and the  NICHD   R01HD087993.
Procedure Girls who had registered for the life skills program were invited to complete interviews at established safe spaces in each of the 14 villages. Consent was obtained from caregivers, and assent from girls, prior to completing the survey. A confidential survey, taking approximately 1 h, was administered by data collectors matched to participants by gender and language. Less sensitive questions were administered using Computer-Assisted Personal Interview. Adolescents age 13-14 (N = 377) answered more sensitive questions on violence, relationship status, and sexual health using Audio Computer-Assisted Self-Interview programming. Respondents were provided information about available referral services after the interview. All components of the research were reviewed and approved by Columbia University's Human Subjects Protection committee, and by the Ministry of Gender in the DRC.
Measures Demographic data were collected on age, educational status and relationship status. Age was measured as a continuous variable. Years of education completed was measured as a continuous variable, with girls who reported never attending school categorized as completing zero years of school. Only girls ages 13-14 were asked about their relationship status, based on piloting feedback that these questions were deemed sensitive for younger girls as they infer sexual activity. Respondents age 13-14 who reported living with their husband or a 'man as if married' were categorized as living with an intimate partner. Binary questions on violence were adapted from ICAST and VACS questionnaires  (Reza et al. 2009; Zolotor et al. 2009). Physical violence was defined as being hit or beaten in the previous 12 months.