dataesr/scientific-paragraphs-categorization huggingface dataset:
approval was obtained from the National Research Ethics Service committee, East of England-Cambridge South (ref. : 16/ EE/0032). Clinical trial authorisation was given by MHRA. The trial Sponsor is University College London. The trial has been registered: EudraCT Number 2015- 004210-26; Protocol Number 14/0647 (version 6. 0); Controlled Trials ISRCTN Registry, ISRCTN15969819.
10 B treatment is free of charge for patients and the MMA provides an incentive of 6000 Myanmar kyats (approximately US$4) to the private GPs for diagnosis and treatment. The data from the TB register have been entered into an electronic database at the MMA-PPM-TB project central office since 2010. From these data, central office prepares a quarterly report and submits it to the NTP officials at the township, regional and central levels. Population Services International, Myanmar TB programme PSI provides TB diagnostic and treatment services in 196 townships under Scheme III and PPM DOTS clinics through the Sun Quality Health (SQH) franchise network. The network comprises of more than 1300 private medical doctors trained by PSI/Myanmar in reproductive health, tuberculosis, pneumonia, diarrhoea, malaria and sexually transmitted infections, including HIV, which monitors their activities.
Data variables and sources of data For the first objective, we collected the year-wise number of childhood TB patients reported under the NTP, MMA and PSI. For the second objective, we collected the individual patient-wise data that include TB code, age (year), sex, type of TB patient (new, retreated), type of regimen (initial regimen, childhood regimen, retreatment regimen), HIV status (positive, negative, unknown/not tested), TB site (pulmonary, extra-pulmonary), treatment outcomes (cured, completed, failure, lost to follow-up, death, not evaluated) and body weight in km (at the time of diagnosis).
Analysis and statistics For the first objective of our study, we analysed the year-wise disaggregated number and proportion of childhood TB patients notified by the private sector. We performed a simple linear time trend analysis to assess the average annual increase/decrease in 1) the number of all childhood TB cases notified in the country; and 2) the number and proportion of childhood TB cases notified by the private sector over the study period 2014-2017. For the second objective, we imported the routinely collected patient-wise data in an Excel database (Microsoft, Redmond, WA, USA) into EpiData analysis (v 2. 2. 2. 183, EpiData Association, Odense, Denmark) and Stata v14. 2 (StataCorp, College Station, TX, USA) and analysed. We summarised the demographic and clinical characteristics using frequencies and proportions for categorical variables and means (and standard deviations [SD]) for continuous variables.
DISCUSSION This study revealed three important findings. First, the number of childhood TB cases notified in Myanmar is greater than the WHO's estimated number of childhood TB cases published in the Global TB reports. 1, 12, 13 Second, the trend in the overall childhood TB cases notified in Myanmar decreased. The relative contribution of the private sector to childhood TB notification was constant at approximately 17-19% over the past 4 years. Third, the profile of the childhood TB patients diagnosed and treated in the private sector indicated that 99% of the childhood TB cases were diagnosed clinically and the treatment success rate was very high. The major strength of this study is that we used routinely collected data from NTP and the PPM project partners without any exclusion criteria; therefore the study represents all recorded/reported data that were available within the country at the time of the study.
There are two major limitations to this study. First, we did not validate the patient data with any other external sources and we are not aware of the magnitude of errors in the routine recording and reporting systems. Given the strong supervision and monitoring systems under the PPM projects, we believe that these errors are likely to be unintentional, minimal and random and therefore unlikely to bias the interpretation of the results. Second, our study variables were restricted to only those variables that were routinely collected by the programme. We did not have information on clinical/diagnostic tests that the children underwent prior to clinical diagnosis with TB. We are unable, therefore, to comment on this aspect in our study. Despite these limitations, we feel the study findings have the following public health implications.
2015). Finally, available in situ data is usually fragmented and held by different stakeholders in the regional countries and not easily accessible  (Dahri et al. 2016). Point glacier mass balance measurements may be used to assess seasonal snow accumulations at high elevation locations, though this is complicated by wind erosion/drift, sublimation, and access difficulties. Ice cores and shallow firn cores provide an important method for evaluating past snow accumulation rates and can provide century-scale series of climatic data at high elevations where observations are sparse or non-existent  (Kaspari et al. 2008; Duan et al. 2015a ). However, possible locations for ice cores are limited, and the data are subject to the same complications as point mass balance measurements. Climate reconstructions have been made from ice cores taken from the Everest region  (Kaspari et al.
Hindu Kush-Himalaya-Karakoram The reported changes in snowline elevation, snow cover, and snow accumulation rates in the extended HKH are inconsistent. High interannual variability in snow cover and relatively short (<15 years) datasets also prevent trend detection. Annual snow-covered area trends over the entire extended HKH since 2000 are negative, but not significant  (Gurung et al. 2011) ; slight increases (but still insignificant) have been observed in autumn in the central and eastern Himalayas, and in spring and summer in the western Himalayas  (Gurung et al. 2011; Maskey et al. 2011; Wang et al. 2013a). At the river basin scale, snow-covered area increased in the Indus basin, but decreased in the Ganges and Brahmaputra basins between 2000 and 2011  (Singh et al. 2014). Late winter and spring snow cover, in particular, has decreased in the UIB since 2000  (Immerzeel et al.
Glacier mass changes can be measured in the field and by remote sensing. In situ measurements consist of ablation measurements with ablation stakes and accumulation measurements using snow pits  (Kaser et al. 2003). The results of these measurements are then extrapolated to the entire glacier  (Hock and Jensen 1999) , with glacier-wide errors of typically 0. 30 m w. e. /yr if the measurement locations are well distributed over the entire glacier  (Wagnon et al. 2013). In situ measurements provide detailed information but are laborious and can therefore only be obtained for relatively few glaciers. As reported by  Vincent et al. (2013)  and  Gardner et al. (2013)  glaciological mass balance data may be negatively biased due to poor spatial sampling. For this reason, we exclude data sets that have known errors from our analysis.
25%/yr are observed in north, west, and south Pamir  (Khromova et al. 2006; Aizen and Aizen 2014; Holzer et al. 2016; Zhang et al. 2016). The area change rates in eastern Pamir appear to be consistent (-0. 13%/yr) for the past 30 years. In contrast, the glacier area at Muztag Ata, the highest mountain massif in the Pamir, has not changed significantly since the 1970s  (Holzer et al. 2015). However, as yet there has been no detailed peer-reviewed assessment of glacier area changes in the whole of the Pamir based on consistent datasets and methodologies, and the above-mentioned values should be interpreted with care. w. e. /yr in eastern Pamir  (Brun et al. 2017). Similar results were published by  Lin et al. (2017)  for 2000-2014 based on TerraSAR-X/TanDEM-X and SRTM data (-0. 11 ± 0. 07 m w.
e. /yr for western and +0. 12 ± 0. 07 m w. e. /yr for eastern Pamir). As the above studies investigate different time periods, the temporal variability in mass changes will lead to varying estimates of rates of mass change. The most detailed investigations are available for the Fedchenko glacier, by far the largest glacier in Pamir  (Lambrecht et al. 2014 ). The glacier experienced only a slight mass loss of 0. 07 km 3 w. e. / yr (specific mass loss <0. 001 m w. e. /yr) between 1928 and 2000, with about 75% of the loss occurring during the second half of the 20th century. Differences in mass loss estimates based on the same dataset can be attributed to the regional aggregation, the assumptions for SRTM radar beam penetration into snow and ice, and the time period analysed.
2015a, b; Table 7. 4). A study conducted by ICIMOD in 2011 identified 1466 'glacial lakes' in Nepal, it included supraglacial and moraine-dammed lakes only (ICIMOD 2011). In contrast, for the same region  Ives et al. (2010)  found 2323 glacial lakes in 2001, which suggests a decrease in the number of glacial lakes. A closer reading suggests that the 2001 inventory number is based in part on survey data from the 1950s and 1960s. Differences in datasets and methodology for glacial lake identification are problematic when evaluating changes in glacial lake area and number. Several studies with consistent datasets and methods have subsequently found increasing glacial lake areas and numbers in most regions of the extended HKH  (Gardelle et al. 2011; Mergili et al. 2013). More critically, a subset of glacial lakes have been identified as critical or potentially dangerous based on criteria that include rapid lake expansion or height change, moraine dam condition, glacier condition, and potential for mass movements into the lake (ICIMOD 2011;  Rounce et al.
2016; Ives et al. 2010; Fujita et al. 2013 ). In Nepal, 21 glacial lakes present a potentially dangerous risk (ICIMOD 2011). The upper Indus basin, which includes parts of the Hindu Kush, Karakoram, and western Himalaya sub-regions, contains 2420 glacier lakes, of which 52 are potentially dangerous  (Ashraf et al. 2012). These inventories present an important database of glacial lakes and provide a basis for long-term monitoring and evaluation of potential outburst flood disasters caused by glacial lakes  (Fujita et al. 2008 (Fujita et al. , 2009)). The number of glacial lakes identified as 'potentially dangerous' has increased  (Richardson and Reynolds 2000; Bolch et al. 2008; Ives et al. 2010; Liu et al. 2013 ), but it is unclear if the frequency of GLOF events is on the rise. Historical records indicate that 33 GLOF events occurred in the Himalaya before 2000  (Richardson and Reynolds 2000).
Policy Relevance We identify three key areas of policy relevance with regards to the status, trends, and projections for the extended HKH cryosphere. 1. Improved resource allocation for region-and sectorspecific studies of the impacts of cryospheric change (e. g. , impacts of glacier and snowpack changes on hydroelectricity, and GLOF hazards in the Himalaya) In most regions of the extended HKH, glaciers are thinning, losing mass, and retreating, except for western Kunlun and parts of the Karakoram and Pamir where glaciers have been relatively stable over the past decades. In addition, the limited data suggests seasonal snow cover is reducing and permafrost is thawing. Cryospheric change will ultimately influence the seasonal availability of water in the extended HKH river systems. However, our understanding on these issues is limited, and we need to improve our data collection, analyses, and modelling strategies to provide sector-and region-specific details of projected changes.
Acknowledgements The authors acknowledge the contributions of all researchers working in this field, agencies that provide access to field-based and remote sensing datasets, and organizations that work to standardize cryospheric measurements and reporting. We also thank the researchers and colleagues who have graciously provided us with their data and model outputs ( Rianne Giesen ,  Valentina Radic ,  Ben Marzeion ,  Daniel Farinotti ) and information ( Phuntsho Tshering ,  Inka Koch ,  Miriam Jackson ). We appreciate the specific and general comments of our reviewers and colleagues, and the support of the review editor  Koji Fujita. Any remaining errors or omissions are ours.
New autonomous robotic platforms for observing the ocean, i. e. Biogeochemical-Argo (BGC-Argo) floats, have drastically increased the number of vertical profiles of irradiance, photosynthetically available radiation (PAR), and algal chlorophyll concentrations around the globe independent of the season. Such data may therefore be a fruitful resource to improve performances of numerical models for marine biogeochemistry. Here we present a work that integrates 1314 vertical profiles of PAR acquired by 31 BGC-Argo floats operated in the Mediterranean Sea between 2012 and 2016 into a one-dimensional model to simulate the vertical and temporal variability of algal chlorophyll concentrations. The model was initially forced with PAR measurements to assess its skill when using quality-controlled light profiles, and subsequently with a number of alternative biooptical models to analyse the model capability when light observations are not available.
Introduction In most biogeochemical models the description of optics is generally (over)simplified. The integration of more complex optical models, where inherent and apparent optical properties (IOPs and AOPs respectively) are already included as model state variables  (Fujii et al. , 2007) , therefore constitutes one of the necessary improvements. The research community is emphasizing the importance of merging different methods in order to improve the skill of numerical models, such as the assimilation of remote-sensing data or the use of in situ data for both initialization and validation purposes. Until recently, the use of the latter was especially critical due the scarcity of observations; however the emergence of autonomous robotic platforms such Biogeochemical-Argo floats (hereafter BGC-Argo) helped reduce the gap in bio-optical measurements acquired around the globe, regardless of the season.
The introduction of BGC-Argo floats has led to a drastic increase in the number of radiometric measurements in the Mediterranean Sea, such as downward planar irradiance and photosynthetically available radiation (PAR), for which specifically developed quality control procedures and refined sensor calibration  (Organelli et al. , 2016a (Organelli et al. , , 2017a) )  have made their use widespread  (Organelli et al. , 2017b; Wojtasiewicz et al. , 2018; Gerbi et al. , 2016; Leymarie et al. , 2018). BGC-Argo can therefore be an important source of high vertical spatial and temporal resolution data that can be integrated in the calibration and tuning of bio-optical numerical models for understanding marine biogeochemistry processes. Published by Copernicus Publications on behalf of the European Geosciences Union.
No studies have so far tried to assimilate radiometric quantities into numerical models to improve the simulation of chlorophyll dynamics in the Mediterranean Sea and to investigate the causes of the vertical, spatial, and temporal variability of zonal gradients. Assimilating radiometric data could prove to be more robust than chlorophyll assimilation as a result of a more accurate uncertainty characterization of optical measurements  (Dowd et al. , 2014; Organelli et al. , 2016a)  compared to other biogeochemical variables, such as fluorescence-derived chlorophyll. Specific studies are required to demonstrate to what extent the assimilation of radiometric data can improve the model skill in simulating key biogeochemical variables (e. g. chlorophyll, nutrients, primary productivity). In this paper we develop a one-dimensional (1-D) model that assimilates PAR profiles acquired by BGC-Argo floats in order to replicate the vertical and temporal dynamics of phytoplankton chlorophyll concentrations.
As a first modelling attempt, the exploration is carried out with a "voxel" approach, where light and mixing conditions were replicated from data available from floats. We analyse and validate model performances through a comparison of model outputs with the high number of co-located vertical profiles of chlorophyll concentrations (derived from fluorescence) measured by BGC-Argo floats. In particular, such analysis allows us to study some of the drivers modulating the deep chlorophyll maximum (DCM) depth in stratified conditions. Subsequently, we test different mixing and bio-optical models that simulate downward irradiance and evaluate their skills in order to estimate how well they perform compared to in situ measurements of PAR. The paper is organized as follows: in the Methods section, the Mediterranean Sea BGC-Argo float network and the model configurations are presented.
BGC-Argo float data The Mediterranean Sea BGC-Argo array operating in the period 2012-2016 (Fig. 1 ) was composed of 31 floats that acquired 1314 vertical profiles of temperature (T ) and salinity (S), chlorophyll a concentration (Chl, mg m -3 ), derived from fluorescence measurements between 0 and 1000 m (see  Organelli et al. , 2017b; Roesler et al. , 2017) , and radiometric quantities, such as downward planar irradiance (E d , µW cm -2 nm -1 ), at three different wavelengths (λ = 380, 412, and 490 nm) and photosynthetically available radiation (PAR, µmol photons m -2 s -1 ) integrated between 400 and 700 nm  (Kirk, 1994). Radiometric measurements were obtained in the upper 250 m, with vertical resolution of 1 m between 10 and 250 m and 0. 20 m between 0 and 10 m. All profiles were acquired around local noon.
, 2016; Organelli et al. , 2017b). Due to a factory calibration bias for WET Labs ECO series Chl fluorometers, Chl concentrations were corrected by a factor of 0. 5  (Roesler et al. , 2017; Organelli et al. , 2017a, b; Barbieux et al. , 2018). All the data used in this study are freely available and compiled into the database published by  Organelli et al. (2017b). Seven variables (T , S, Chl, E d (380), E d (412), E d (490), PAR) were vertically interpolated to a resolution of 1 m in the upper 400 m. Finally, we partitioned the profiles geographically into 13 (out of 16) subbasins (Fig. 1 ), with the majority of profiles located in the northwestern Mediterranean (NWM, 332 profiles), followed by northern Ionian (ION3, 172 profiles) and southern Tyrrhenian (TYR2, 162 profiles) seas. No data were available for the southwestern Ionian (ION1) and the eastern Levantine (LEV4) seas and only one profile was present in the northern Adriatic Sea (ADR1), as well as in the western Levantine Sea (LEV1).
2. 2. 1, v sink is the sinking velocity, and BFM i is the reaction term corresponding to the tracer C i. T , S, and PAR are the data measured by BGC-Argo floats. Since the surfacing of BGC-Argo floats is programmed at around local noon, the variability related to diurnal variation in solar irradiance is taken into consideration according to  Kirk (1994). The biogeochemical model BFM  (Vichi et al. , 2013 ) is a biomass-based numerical model that simulates the biogeochemical fluxes of carbon, phosphorus, nitrogen, silicon, and oxygen, characterizing the lower trophic level (producers, consumers, and recyclers) of the marine ecosystem. Its application is based on the coupled transport-biogeochemical model OGSTM-BFM  (Lazzari et al. , 2012; Lazzari et al. , 2016). It includes four phytoplankton functional types (diatoms, nanoflagellates, picophytoplankton, and dinoflagellates), carnivorous and omnivorous mesozooplankton, bacteria, heterotrophic nanoflagellates, and microzooplankton.
, 2014)  produced by the MedBFM model system. The initialization profiles for our 1-D configuration are extracted from www. biogeosciences. net/16/2527/2019/ Biogeosciences, 16, 2527-2542, 2019  The simulations' timescale corresponds to a typical BGC-Argo time-series length during the period 2012-2016, i. e. 11 months on average, with a vertical resolution of 1 m. After being initialized, the model evolves without further assimilation of biogeochemical data from the 3-D configuration. D background v = 10 -6 m 2 s -1 MLD1 as REF with D background v = 5 10 -5 m 2 s -1 MLD2 as REF with D background v = 10 -5 m 2 s -1 MLD3 as REF with D background v = 5 10 -6 m 2 s -1 MLD4 as REF with D background v = 10 -6 m 2 s -1 OPT1 Riley: K d (PAR) = 0. 04 + 0. 054Chl 2 3 + 0. 0088Chl OPT2a K d (PAR) = aChl b + c OPT2b OPT2c OPT2d OPT3 K d (PAR) Vertical eddy diffusivity coefficient profiles D v (z) are represented as Gaussian-shaped functions, thus allowing a gradual increase in vertical mixing through the pycnocline.
Vertical mixing models Vertical mixing is estimated from potential density (obtained from temperature and salinity data from floats) along the water column. Vertical eddy diffusivity coefficients (D v ) are defined as Gaussian-shaped functions in the form of D v = D MLD v e -0. 5 z (σ * MLD) 2 + D background v. (2) σ was identified after an initial tuning procedure and equals 0. 3 in all simulations. Values in the REF model are equal to D MLD v = 1. 0 m 2 s -1 and D background v = 10 -4 m 2 s -1. The mixed-layer depth (MLD) was defined with the density criterion at the threshold value  (de Boyer Montégut et al. , 2004; D'Ortenzio and Prieur, 2012) : ρ θ = |ρ θ (10 m) -ρ θ (z)| = 0. 03 kg m -3. (3) In simulations MLD1, MLD2, MLD3, and MLD4, D background v values were perturbed by 2 orders of magnitude (from 10 -6 to 10 -4 m 2 s -1 ) in order to estimate the impact such variations have on modelled Chl profile shapes compared to measured ones (see Table  1 ).
An increase in background diffusion over a critical value will produce a dispersal of patchy structures (i. e. a relative maximum of Chl concentration), whereas an increase in growth rate µ can drive the formation of finer-scale structures. The dynamics presented in this study are, however, more complex than KiSS, in terms of both BGC-Argo data and the 1-D BFM model. Vertical eddy diffusivity can simultaneously affect nutrients, phytoplankton, and mesozooplankton with intricate interactions, which in turn make it difficult to derive analytical solutions. Moreover, unlike KiSS, both the model and environment are hardly ever in a steady-state condition, as a result of daily and seasonal oscillations in phys-ical forcings, which are essentially due to variability in diel irradiance and vertical mixing.
Results also highlight the strategic relevance of BGC-Argo data: temperature, salinity, and radiometric parameters encapsulate fundamental information for the reconstruction of primary producer dynamics and are paramount to investigate hypotheses concerning DCM formation. CDOM fluorescence data measured by BGC-Argo floats could be integrated in simulations to further infer and reconstruct the observed biogeochemical processes. Considering a general 3-D biogeochemical model, it is not possible to have a full data coverage of the in-water PAR field without a fully coupled radiative transfer model. The proposed approach could thus be exported and generalized at a global scale.
Acknowledgements. This study has been conducted using EU Copernicus Marine Service information. The simulations were performed in the framework of the  ISCRA C  project  NOVBIOGE  ( HP10C8C9O6 ), created by  CINECA, Italy. Financial support. This work is part of the  PhD  project of  Elena Terzić  (funded under the  CMEMS contract for the Biogeochemistry Production Unit for the Mediterranean Sea)  and of the  BIOP-TIMOD CMEMS Service Evolution  project. CMEMS is implemented by Mercator Ocean International in the framework of a delegation agreement with the  European Union. This work was supported by the  French "Equipement d'avenir "  NAOS project (Novel Argo Ocean Observing System ) funded by  Agence Nationale de la Recherche  (grant no. ANR J11R107-F ); the "Remotely-sensed biogeochemical cycles of the oceans -remOcean" project funded by the  European Research Council  (grant no.