dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifying and Reconciling Primary Data We collated all research articles reporting any estimates of patient-incurred costs due to TB in South Africa from the Unit Cost Study Repository (UCSR) of the Global Health Cost Consortium  [18]. Patient-incurred costs included any costs paid out-of-pocket by TB patients and their households, and any lost income or productivity due to TB. Eleven studies presenting patient cost data in South Africa were identified  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]. Of these, four were excluded due to outdated models of care and one was excluded as a duplicate of previously published data. Corresponding authors of seven eligible studies were invited to participate, and a protocol identifying variables to be included in the pooled dataset was provided.
Collaborators from three of the seven eligible studies agreed to participate in the analysis. Due to data availability, the scope of this analysis was restricted to costs whilst on treatment for DS-TB; we did not consider costs for drug-resistant (DR) TB, nor did we consider costs during the diagnostic process  [10]. All datasets had obtained ethical approval for their original study. Ethical approval for the pooled analysis was granted by the London School of Hygiene and Tropical Medicine (reference 14486). We reconciled timeframes for cost data by identifying the treatment start date, interview date and recall period for each participant. Direct out-of-pocket costs incurred in each treatment phase (intensive and continuation phase) were categorized as direct medical costs (consultation fees, medicines, diagnostics), direct non-medical costs (transportation, accommodation) and food costs (food supplements, special foods).
Cost estimates were distinguished by treatment phase (intensive and continuation phase) and by type of healthcare provider, including public healthcare (PHC) facility (study site), another PHC facility (non-study site), private general practitioner, pharmacy, hospital inpatient service, hospital outpatient service, and traditional healer. All data in different studies were collected using adaptations of the Tool to Estimate Patient Costs  [30] , and thus definitions for out-of-pocket cost variables were homogeneous; however, the Researching Equity in ACcess to Healthcare (REACH) dataset did not contain information on direct non-medical costs or time spent accessing providers other than the main study clinic. As this was omitted entirely from data collection, we assumed these values to be missing at random and used imputation to complete these costs (imputation methods described in Sect.
2. 2. 2). In contrast, methods for collecting data on income and estimating indirect costs varied widely across datasets and were not reconcilable. To complete the datasets, we took a statistical approach to predict income quintile for households in the dataset. Assuming income distribution to be the same as the national distribution of income amongst people with TB, we used regression coefficients from an analysis run on the most recent (2015) South African National Income Dynamics Survey (NIDS) for variables including asset holdings, housing quality indicators and basic demographics to predict income. Full methods to predict household income quintiles are described in Electronic Supplementary Material (ESM) Appendix 2. All costs are reported in 2017 US dollars. Data collected before 2017 were inflated using the US consumer price index  [31].
Generating Model Parameters We tested two approaches to estimate mean and standard error values for direct costs and hours lost due to treatment by household income quintile, HIV status and treatment phase: (1) meta-analysis of summary statistics from  Our first approach was meta-analysis to calculate pooled estimates of available (study-level) mean values for the above-described cost categories for each treatment phase, by HIV status and household income quintile  [32]. Given that patient demographics varied significantly across datasets, and assuming that patient costs vary according to demographics, we used a random effects meta-analysis approach, which does not assume that all studies investigate the same population  [32]. Data on direct costs, travel time and consultation time were log-transformed for the meta-analysis as they were highly skewed, and results were exponentiated following meta-analysis.
EsƟmaƟon of prevalence of catastrophic costs in the cohort model 1. GeneraƟon of hypotheƟcal cohort 1. 1 Parameterising the cohort model: cohort characterisƟcs Our second approach was to identify a regression model to predict the above-described cost categories for each treatment phase, by HIV status and household income quintile. Firstly, we imputed missing values in the pooled dataset. Where total consultation hours were missing, we used multivariate imputation with chained equations (MICE) to impute these values based on the number of visits by phase and provider type. Total travel hours and total direct nonmedical costs were imputed based on number of visits and transport method, as well as demographic variables included in the regression analysis. All imputations used predictive mean matching (PMM), as a non-parametric alternative for imputing skewed data.
Imputations generated 20 plausible datasets, which were then used for analysis. The number of missing observations by dataset are listed in ESM Table 3  [33]. Following imputation, we conducted a series of regression analyses to predict the cost of each cost category for each treatment phase. The regression analyses used a generalized linear model (GLM) approach assuming a gamma distribution and a log link to accommodate skewed data  [34]. The specification of each regression was held constant across analyses and included independent variables identified following theory, as well as previous published evidence  [35] [36] [37] [38] [39] [40]. Independent variables were defined as urbanicity (1 = rural), education level (1 = educated to grade 8 and above), employment status (1 = employed), HIV status (1 = HIV positive) and household income quintile (quintiles 1-5).
An interaction between employment status and income quintile was also included. Following the regression analysis, marginal estimates for each of the above-described cost variables were obtained by HIV status, household income quintile and employment status, with urbanicity and education values held constant at the mean observed among people with TB in the NIDS dataset (urbanicity = 0. 327; education above grade 8 = 0. 683). To help us interpret the results of the regression analysis using the pooled dataset, we also tested the extent to which cost drivers varied across datasets. To do this, we conducted a regression on the unimputed data for each dataset separately. As there were insufficient observations to do this for the intensive phase, we conducted this test only on observations in the continuation phase.
Demographic Characteristics, Direct Costs and Time Accessing Care in Published Studies Three datasets were obtained and merged  [27] [28] [29] , resulting in a total of 1573 observations; 1219 were from the REACH study  [27] , 148 from the MERGE trial  [29]  and 171 from the XTEND (Xpert for TB: Evaluating a New Diagnostic) trial  [28]. Table  1  shows the demographic data for each dataset, as well as the pooled dataset. Several demographic variables, including urbanicity, age, education and employment status, were significantly different across datasets. Although each study sample was randomly selected, the pooled datasets were not representative of the TB-affected population in South Africa as measured in the NIDS 2015 dataset.
Tuberculosis-Related Patient-Incurred Costs from Published Studies Tables  2  and 3  show the meta-analysis results for direct medical costs, direct non-medical costs, food costs and hours lost (also see ESM Figures  1 2 3 4 5 ). The considerable uncertainty observed was a result of several factors, including the small numbers of observations, wide variation in visit frequency across datasets, and wide variation in costs within and between datasets. Availability of data also varied by dataset and treatment phase. ESM Table  1  lists the mean number of visits per month, direct costs per visit and time spent per visit by provider type and treatment phase for each of the datasets. The variation in demographics observed across datasets provided a motivation for pursuing a regression analysis, which allowed inclusion of other explanatory variables in estimation of costs and time associated with accessing care.
The results for the regression analysis are listed in Tables  4  and 5. Several independent variables were found to have a significant effect on cost. HIV status had a consistently positive effect. Being in a rural setting and having a higher education level both had a negative effect on direct costs and a positive effect on time spent accessing providers. Our tests of the regression model on the raw unimputed data separately for each dataset (ESM Tables  6 7 8 ) found some regression model coefficients were not consistent across datasets. There were no substantial differences observed in significant coefficients across datasets; where multiple datasets had significant coefficients for a given variable, coefficients were in the same direction and similar magnitudes.
Although the regression approach was better parameterized, our analysis indicates that better information is still needed on the household-incurred costs due to TB. Our findings on significant determinants of patient-incurred costs identified in the regression approach are largely supported by the existing literature  [28, 36, 37] ; however, these determinants were not consistently identified within the individual datasets and predictive power of the regression analyses were low. This may have been a consequence of small sample sizes and varying demographics across datasets; however, it could also reflect substantial differences in models of care in different settings. The extent to which TB and HIV services are integrated varies widely across South Africa  [39] ; this could lead to variation in the number of visits prescribed for those with both TB and HIV and thus a substantial difference in the degree to which positive HIV status is a driver of costs.
Similarly, the extent to which directly observed therapy (DOT) is followed and its modality (facility-based or community-based) varies between settings. This is likely to have produced differences in cost drivers across datasets, for example increasing travel time for rural participants in settings where DOT is prevalent. To enable deterministic models such as the one presented here, further information is also needed on the healthcare pathway and drivers of care-seeking behaviour, including sub-national variations in models of care. This may also be evolving as the model of care for TB in South Africa continues to change. Given these uncertainties, our analysis indicates that access to patient-level data is vital for researchers looking to extrapolate existing cost estimates to national settings, at least until the cost function can be better defined.
Acknowledgements The authors are grateful to the study teams for the MERGE, XTEND and REACH studies for use of their data. The authors are also grateful to  Nicola Foster  (collected original data for the  XTEND  project),  Carol Levin  (provided feedback on an earlier draft) and  Carlos Jesus Pineda Antunez  ( developed a STATA ® program  to facilitate data pooling, and advised on the analysis). Fig. 2 Prevalence of catastrophic cost of tuberculosis by approach and household income quintile (baseline results from 10,000 model runs).
Data availability statement The dataset generated during the MERGE study is available in the LSHTM Data Compass repository at https :// doi. org/10. 17037 /DATA. 00001 593. The dataset generated during the XTEND study is available in the LSHTM Data Compass repository at https ://datac ompas s. lshtm. ac. uk/791/. The datasets generated during the REACH study are available from the corresponding author (NC) on reasonable request.
8a-c ). Hereby, we found increased occupancy of GATA1 at over 3000 sites in the genome overlapping with 1362 genes (p < 0. 01) in cells expressing Nsd1 in comparison to the catalytically inactive Nsd1 N1918Q mutant (Fig. 8d ). Of genes with significantly increased binding of GATA1, 731 of them had the promotor regions decorated by H3K27 ac while H3K36 me3 marks overlapped with 1179 gene bodies (Supplementary Data 6). Hence, while global levels of GATA1 remains constant, reintroduction of Nsd1 resulted in increased DNA binding to available GATA1 sites in promotor and Nsd1 -/-BM-derived erythroblasts transduced with control virus (Ctrl) or Gata1-expressing virus (Gata1) after 5 days in differentiation medium (×600, size bars = 10 μm). f HbbA and g Spi1 mRNA levels in BM-derived Nsd1 -/-BM-derived erythroblasts transduced with control virus (Ctrl, black dots) or Gata1-expressing virus (Gata1, red dots) measured 0, 5, 24, 48, and 72 h in differentiation medium.
KK7352; KapaBiosystems, Wilmington, USA) following the manufacturer's instructions. PCR reaction program was 5 min 95 °C, 40× cycles 95 °C 15 s, 60 °C 15 s, 72 °C 30 s followed by 5 min 72 °C and 1 min 4 °C. PCR amplicons were visualized on 2% agarose gels containing ethidium bromide. Used oligonucleotide primers can be found in Supplementary Table  4. Analysis of the mouse phenotype. Mice were sacrificed by CO 2 asphyxia, organs removed and fixed in buffered 4% formalin solution. Paraffin-embedded tissue sections were stained with hematoxylin and eosin (H&E). Blood was collected from the tail vein or by vena cava inferior puncture (terminal) and counts were determined using an Advia120 Hematology Analyzer using Multispecies Version 5. 9. 0-MS software (Bayer, Leverkusen, Germany). Differential blood counts were analyzed on smears stained using Wright-Giemsa staining (Hematology, University Hospital Basel).
Stained cells were analyzed on a CyAn ADP analyzer (Beckman-Coulter) or LSR Fortessa (BD, New Jersey, USA). Data were analyzed with FlowJo software (Tree Star). For CD71/Ter119 staining, the preparation still contained red blood cells, for CD71/Kit/Sca-1/FcγRII/III stem and progenitor staining the red blood cells were depleted. For stem and myeloid progenitor staining, lineage-positive cells were depleted as described before. All antibodies used in this study are indicated in Supplementary Table  5. For calculating number of stem and progenitor cells in BM, lineage-marker-depleted cells were counted and absolute numbers of cells adjusted to this number. For differentiation analysis of mouse or human cells in vitro, cells were filtered, washed twice with PBS, and stained in 100 µL FACS buffer.
Acknowledgements We thank  P. Schär ,  D. Schürmann ,  S. Kaspar ,  M. Florescu ,  D. Jankovic ,  E. Paraskevopoulou ,  B. Schlegelberger ,  P. Lundberg ,  R. Skoda ,  M. Kristiansen ,  C. Porcher ,  P. Vyas ,  P. Fillipakopoulos ,  A. Jeltsch ,  S. Kudithipudi ,  L. Steiner ,  S. Atanasoski ,  A. Schmidt ,  D. Labes ,  E. Traunecker ,  N. Meier ,  B. Fahrenkrog ,  L. Brault ,  T. Pabst ,  R. Hasserjian , and  M. Hayman  for reagents and/or other support of this study. We also appreciate the service of the flowand animal experimentation facilities of the  DBM & FMI , and we particularly thank  Tianke Wang ,  Fred Zilbermann ,  Patrick Kopp , and  Jean-Francois Spetz  for the generation of the Nsd f/f mouse strain from the  Nsd1 +/L3  allele. This work was supported by funding from the  Swiss Cancer League  (SKL):  KFS-3487-08-2014  and  KFS-4258-08-2017 ; the  Swiss National Science Foundation  (SNF,  31003_A_173224/1 ), the  Gertrude Von Meissner Foundation Basel , the  Novartis Biomedical Research Foundation , Basel, the  San Salvatore Foundation , Lugano (201525), and the  Wilhelm-Sander Foundation ,  München  ( 2017-035.
, 2005). Remote sensing (RS) through automatic image analysis of Earth-Observation (EO) has been widely recognized as the most feasible approach to derive LC information over large areas  (Manakos, Braun, 2014). It is now accepted that manual or semi-automatic generation of LC geodatabases through visual inspection of EO images is not a sustainable answer for current needs  (Grekousis et al. , 2015). Such issue is exacerbated with the increasing demand for semantic and geometric accuracy, and up-to-date knowledge. However, currently, none of the existing products meet all the required needs. Global databases (E. U. , USA, China) exhibit limited semantic accuracy and heterogeneous quality. They are updated at best every 5 years  (Yu et al. , 2014 , Ban et al. , 2015 , Skidmore et al. , 2015).
Beyond large-scale monitoring purposes, they are of low relevance. Without flexibility and customization capacities, their exploitation potential is significantly narrowed down. Conversely, local geodatabases can be semantically very detailed but will cover a limited area. They correspond to very peculiar needs. The human effort spent for such a generation can hardly be repeated without significant financial inputs and maps are often obsolete when the collection process ends. Existing solutions have been recently improved with the unprecedented amount of remote sensing images of heterogeneous physical nature resulting from new EO satellites * Corresponding author with short revisit time (Sentinel programme, 1 image/5 days) and increased spatial resolutions. Such data abundance indeed brings new opportunities for the long-term and yearly description of Earth surface, up to the scale of a continent.
Current products Initiatives are increasingly being taken for both land-cover status and change monitoring. Geodatabases and products now exist at various scales, corresponding to specific policies' needs. In general, the finer the spatial resolution, the more targeted the nomenclature. The existing LC databases can be coarsely categorized into four main groups, as detailed below. They can be evaluated through four main features (Figure  1 ): spatial coverage, spatial and semantic accuracies (linked to the temporal, spatial and spectral resolutions of the sensor of interest), and updateness. They are further described in Section 2. 2. Global maps. They aim to fully described the Earth's surface, such as Global Land Cover Characterization (USGS), Glob-Cover (ESA), or GlobLand30 (China). From > 1 km products, we recently moved to 30 m resolution.
First products were derived from time series optical satellite data at coarse spatial resolution (300 m-1 km). They did not provide sufficient thematic detail or change information for global change studies and resource management  (Bontemps et al. , 2012 , Giri et al. , 2013). Higher spatial resolution and more frequent data products were then developed (30 m products, the spatial resolution of Landsat 7-8 images). With superior details and finer-grained cat- egorization, this opens the field for change detection of most human activity and offers the increased flexibility of environmental models needed for global change studies  (Buchanan et al. , 2009 , Pereira et al. , 2013 , Skidmore et al. , 2015). Continental-scale products. Maps such as the U. S. National Land Cover Database  (Homer et al.
, 2015 , Yang et al. , 2018) , or Copernicus Corine Land Cover  (Aschbacher, Milagro-Pérez, 2012 ) are available at ∼ 25 m resolution (several epochs), and accompanied with change maps (Figure  1 ). Mostly produced with automatic classification tools, they are complemented with human intervention (visual inspection of RS images). Although global maps are rather general, targeted (class-specific) byproducts also exist at such scales: e. g. , the tree canopy and imperviousness layers (NLCD), High-Resolution urban layers (Urban Atlas) or the delineation of the riparian areas in the European Environment Agency Copernicus catalogue. National datasets. They frequently either correspond to standard land-cover maps (full partition on the space) or twodimensional topographic databases that exist in many developed countries.
They have often been produced and updated by national mapping and cadastral agencies and focus on specific objects (buildings, roads, crops). LC maps are often semantically less accurate than topographic databases. The nomenclature often gathers land-cover and land-use classes or proposes only coarse classes (e. g. , urban areas). This is sufficient for statistical but not for mapping purposes. Newly (semi) automatic solutions have been recently proposed  (Inglada et al. , 2017 , Jalal et al. , 2019)  and pave the way for products with superior spatial coverage and updateness (dashed line, Figure  1 ). Local initiatives. Two possibilities co-exist. On the one hand, such DBs can be a subset of national products with a higher spatial accuracy and often a higher completeness in terms of objects.
This is due to the fact that more frequent updating is possible at such small scales (dashed line Figure  1 ). On the other hand, they may focus on classes that are not present in topographic databases at coarser scales. They are often generated through initiatives of local authorities. Automation was not considered here. Fully automatic solutions based on EO image analysis exist, as well as those integrating human inputs or existing land-cover databases. While automation is often inversely correlated to the spatial coverage of the LC DB, the different solutions and products may be hardly comparable: these pipelines are rarely fully described. In-depth analysis of global land-cover products is available in the literature  (Yu et al. , 2014) , featuring more comparison criteria (sensor, accuracy, classification method, ability to detect changes).
Current requirements First initiatives in map generation from remote sensing data have highly stimulated various stakeholders and end-users, in recent years. This allows today a sharper definition of the current expectations of the communities in terms of land-cover products and topographic databases. Frequent updating, related to the capacity in detecting changes, requires techniques to spot specific patterns or update LC DBs established with a significant contribution of manual operators (over several decades). Volunteered Geographic Information (VGI) solutions have recently gained significant interest so as to reduce the manual workload and upscale the updating processes  (See et al. , 2015 , Brovelli et al. , 2018). Secondly, in case of missing reference data, post-classification strategies prevail  (Tewkesbury et al.
Automation. Still today, many DBs are created with manual inputs: rules for fusing basic multi-source LC DBs, landscape specific parameter tuning, supervision in the discrimination of the classes of interest, manual improvements at the end of the process, etc. For efficient updating, upscaling and diagnostic, such an intervention should be minimized as much as possible. However, human input should not be totally discarded since it offers some invaluable advantages  (Wuttke et al. , 2018). Harmonization. It first corresponds to a clear need to improve LC DB quality. Indeed, significant variations in class accuracy exist. The relative importance of different class accuracy also varies with the users. Secondly, there are significant differences in the LC DB generation approaches and, most of the time, poor agreement among datasets (i) at the same level and (ii) between spatial scales.