dataesr/scientific-paragraphs-categorization huggingface dataset:
Cools: Speaker fees from Boehringer-Ingelheim Pharma, Bayer AG, Pfizer and speaker fees and modest research grant from Daiichi-Sankyo Europe; Dana Johnson was supported by NIH grant T32 HL079896. AJ Camm: Personal fees from Bayer, personal fees from Boehringer Ingelheim, personal fees from Pfizer/BMS, personal fees from Daiichi Sankyo, outside the submitted work; JP Bassand: none; FWA Verheugt: Grants from Bayer Healthcare; personal fees from Bayer Healthcare, BMS/Pfizer, Daiichi-Sankyo, and Boehringer-Ingelheim; S Yang: None to disclose; A Tsiatis: None to disclose; DA Fitzmaurice: personal fees from BMS/Pfizer, Boehringer-Ingelheim, Daiichi Sankyo, and Bayer; SZ Goldhaber: Research grants from BiO2 Medical, Boehringer-Ingelheim, BMS, BTG EKOS, Daiichi, Janssen, NHLBI, Thrombosis Research Institute, Personal fee from Agile, Bayer, Boehringer-Ingelheim, BMS, Daiichi, Janssen, Portola, Zafgen.
Accepted Article This article is protected by copyright. All rights reserved AG; AGG Turpie: Personal fees from  Bayer Healthcare ,  Janssen Pharmaceutical Research & Development LLC, Astellas , Portola, Takeda; KAA Fox: grants from  Bayer ,  Johnson and Johnson , and  Astra Zeneca ; personal fees from  Bayer ,  Johnson and Johnson, Lilly ,  Astra Zeneca , and  Sanofi/Regeneron ;  K Pieper : Consultant for  Thrombosis Research Institute, AstraZeneca , and Bayer ; AK Kakkar: received grants from  Bayer AG and Sanofi ; personal fees from  Bayer AG ,  Janssen ,  Pfizer ,  Sanofi ,  Verseon  and  Anthos Therapeutics  Funding This work was supported by an unrestricted research grant from  Bayer AG (Berlin, Germany)  to the  Thrombosis Research Institute (London, UK) , which sponsors the GARFIELD-AF registry.
F Cools: Speaker fees from Boehringer-Ingelheim Pharma, Bayer AG, Pfizer and speaker fees and modest research grant from Daiichi-Sankyo Europe; Dana Johnson was supported by NIH grant T32 HL079896. AJ Camm: Personal fees from Bayer, personal fees from Boehringer Ingelheim, personal fees from Pfizer/BMS, personal fees from Daiichi Sankyo, outside the submitted work; JP Bassand: none; FWA Verheugt: Grants from Bayer Healthcare; personal fees from Bayer Healthcare, BMS/Pfizer, Daiichi-Sankyo, and Boehringer-Ingelheim; S Yang: None to disclose; A Tsiatis: None to disclose; DA Fitzmaurice: personal fees from BMS/Pfizer, Boehringer-Ingelheim, Daiichi Sankyo, and Bayer; SZ Goldhaber: Research grants from BiO2 Medical, Boehringer-Ingelheim, BMS, BTG EKOS, Daiichi, Janssen, NHLBI, Thrombosis Research Institute, Personal fee from Agile, Bayer, Boehringer-Ingelheim, BMS, Daiichi, Janssen, Portola, Zafgen.
Data and Study Design We analysed data from the Demographic and Health Surveys (DHS) conducted between 2010 and 2018 to understand the role of different mediating factors in the pathway between SEP and recent (< 12 months) HIV testing uptake. The DHS are publicly available nationally representative population-based surveys, conducted regularly in low-and middle-income countries (LMIC), collecting data on a wide range of objective and self-reported health indicators including data on HIV/AIDS, using a two-stage sampling design  [15]. All women aged 15-49 years are all eligible in all households and, in some surveys, men aged 15-54/59 from a sub-sample are also eligible to participate (https://dhsprogram. com/). The national implementing agencies or research institutes that conducted the surveys were responsible for ethical clearance which assured informed consent from the participants prior to their involvement and guaranteed confidentiality of information  [16].
Characteristics of Study Population Data were collected from 392,044 participants, 261,935 female participants and 130,109 male participants. Table  1  and Table  S2  illustrate the survey and participants characteristics while accounting for survey design and sampling weights. Overall, 93-100% of eligible women were successfully interviewed, and 86-100% of men (Table  S2 ). In many of the countries, female and male participants lived in rural areas (except in Côte d'Ivoire, Cameroon and Liberia among both genders, and in Senegal among males). They were either married or cohabitating except in Cameroon, Senegal and Lesotho where most males were single (Table  S2 ). Table  1  shows that around 18%-45% of the female participants and 17%-40% of the male participants had comprehensive HIV-related knowledge in ESA countries compared to 1%-32% and 13%-26% among female and male participants, respectively, in WCA countries.
, 2013; Sioutas et al. , 2005). Although some positive associations between short-term exposure to UFP and mortality have been reported  (Breitner et al. , 2011; Stölzel et al. , 2007) , many of the studies reported weak or no evidence  (Atkinson et al. , 2010; Braniš et al. , 2010; Lanzinger et al. , 2016; Stafoggia et al. , 2017). These inconsistencies may be due to (1) the difficulty in accurately estimating the exposure due to the large change over short distances of UFP number concentrations  (Zhu et al. , 2002)  and to (2) a different relative and absolute contribution of the sources of UFP on the different days  (Tobías et al. , 2018). However, there is a lack of studies evaluating the associations between source-specific UFP and mortality. Firstly, because UFP data collection is relatively new compared with other metrics but also because determining the sources is not an easy task for PNC.
The quantification of the different sources affecting UFP and disentangling the impact on public health of each of these sources would allow targeted policies to reduce emissions. To account for all this, we have collected data from four European cities characterized by different climatic conditions, emission sources, and urban morphology, on ultrafine particle number concentrations and their source contributions (exposure data) and daily counts of all-cause non-accidental, cardiovascular (CVD), and respiratory deaths (health outcome data). To our knowledge, no epidemiological study on the association of specific sources of UFP and mortality rates has been conducted before. To evaluate the short-term effects of each source and its total PNC on mortality we have used a time-series design, investigating the associations between the different sources of ultrafine particles and daily death counts in four European Cities (Barcelona, Helsinki, London, and Zurich) at different lag times.
Mortality data Data were collected for Barcelona, Helsinki, London, and Zurich. The selection of the cities and the period of analysis was limited to the availability of long-term measurements of particle number size distributions (PNSD). For each city, data were collected on daily counts of all-cause nonaccidental (ICD-10 Chapters A-R), cardiovascular (CVD; ICD-10 Chapter I), and respiratory (ICD-10 Chapter J) deaths with regards to the resident population of the city. Data were obtained from the official services for health statistics. Barcelona Barcelona (Spain) is a coastal location with 1. 6 million inhabitants (3. 6 million in the metropolitan area; 2016; Eurostat, 2018). Mortality data was collected for the period 2013-2016 (four years) for the city of Barcelona and surrounding cities from the metropolitan area that are close (within a 6 km radius) to the air quality monitoring station located in the south-west of Barcelona city (Hospitalet Llobregat, Esplugues de Llobregat, Sant Just Desvern, Sant Feliu de Llobregat, Cornellà de Llobregat, and Sant Joan Despí).
Helsinki Helsinki is also a coastal city located in the South of Finland. Helsinki has 0. 6 million inhabitants (1. 1 million in the metropolitan area; 2016; Eurostat, 2018) which makes it the largest city in Finland. We obtained mortality data for the Helsinki metropolitan area (Espoo, Helsinki, Vantaa, Kauniainen) for the period 2007-2016 (ten years) from Statistics Finland (Tilastokeskus).
London Greater London is the largest city in the United Kingdom and in Europe with 8. 7 million inhabitants  (Eurostat, 2018). We collected mortality data from the UK Office for National Statistics (ONS) for the period 20th January 2010-30th September 2016 (almost seven years). Zurich Zurich has 0. 4 million inhabitants and is the largest city in  Switzerland (2014; BFS, 2018). Mortality data were obtained for the municipality of Zurich and another 89 communes (listed in Table  S1 ) that are part of the Zurich metropolitan area. The total population for the area included in the study is 1. 1 million. The data for the 90 communes were obtained from the Swiss Federal Statistical Office (Bundesamt für Statistik, BFS) for the period December 2010-October 2014 (four years).
Ultrafine particle measurements, sources, and meteorological data A detailed description of the instruments (Scanning Mobility Particle Sizer Spectrometers, SMPS, or Differential Mobility Particle Sizers, DMPS), measurements, and source apportionment with Positive Matrix Factorization (PMF) method is available in  Rivas et al. (2020). Briefly, we obtained long-term time series of PNSD and gaseous pollutants (NO, NO 2 , SO 2 , O 3 and CO) from an urban background station in each city: Palau Reial in Barcelona (41 PMF is a least-squares method developed by  Paatero (1997)  that is widely used in environmental sciences to identify and apportion the sources of particulate matter or PNSD observed at the receptor site. Sources of UFP were quantified by PMF applied to each size bin of hourly averaged PNSD as well as to gaseous data (NO 2 , NO, SO 2 , CO, O 3 ) since adding additional species (other than PNSD) can help to separate and identify the sources.
Secondary: These particles are aged particles that have grown due to the condensation of volatile gaseous compounds on their surface (approximately >80 nm, with a mode diameter around 200-300 nm). In contrast to primary particles, secondary particles become more relevant as they travel away from the source or in periods of the day, week or year with lower emissions (for example due to condensation of nitrate during night time;  Poulain et al. , 2011). These particles show the larger diameters within the PNSD. In Helsinki, an extra source was identified related to biogenic emissions from the vegetation of the relatively close forests. We also evaluated the associations between mortality and the total UFP number concentrations (Total PNC). Temperature and relative humidity were collected at the air quality monitoring stations (or in a very close location) except for London, where meteorological data was collected at Heathrow Airport.
The long time series available are also a major strength that provides the study with the appropriate statistical power. Yet, this study presents the common limitations of epidemiological time-series studies. We estimated associations for multiple sources and lag times with three different mortality outcomes, therefore, some significant associations may have occurred purely by chance. Additionally, although we had long time series for ultrafine particles for most of the cities compared with other studies, missing data results in less statistical power and makes the interpretation more difficult. Another limitation is the difficulty of controlling for confounding of NO 2 and O 3 in the models because these pollutants were used in the source apportionment modelling. However, the adjusted models for total PNC seem to indicate that the patterns of association may remain stable.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  747882. While writing the manuscript,  Dr. Rivas  was funded by the  postdoctoral fellowship programme Beatriu de Pinós  ( 2018 BP 00114 ), funded by the  Secretary of Universities and Research (Government of Catalonia ) and by the  Horizon 2020 programme of research  and innovation of the  European Union  under the  Marie Sklodowska-Curie  grant agreement No  801370. Currently,  Dr. Rivas  is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  886121. This work was supported by  FEDER  funds; projects  HOUSE  ( CGL2016-78594-R ) and  CAIAC  ( PID2019-108990RB-I00 ), the  Government of Catalonia  ( AGAUR 2017 SGR41 ).
Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an "atlas" of labeled neuronal positions and other identifying characteristics. Previous analyses of such C. elegans datasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process. We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typical C. elegans fluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene.
elegans neuron positions, was produced by further processing the original version so as to translate the 1986 illustration into semi-quantifiable measurements. This version was produced by Choe et al. (Choe et al. , 2004)  and popularized by  Kaiser et al. (Kaiser and Hilgetag, 2006). It tabulates neural positions that were measured by directly scanning and tracing the physical 1986 paper. The 2004 atlas consists of only 277 of the 302 neurons found in adult hermaphrodites. Crucially, due to the 2D geometry of the scanned and processed original paper, the 2004 atlas is also 2D. The third dimension in modern 3D datasets is therefore typically discarded before matching against this 2D atlas and may further increase the misidentification rate. In total, these limitations motivate the creation of a representative 3D atlas of C.
, 2021). This is caused partly by C. elegans size and shape differences, and partly because of inherent positional variance that may be so extreme as to have nearby neurons switch relative positions. Adult C. elegans were imaged in accordance with the methods as described in  (Yemini et al. , 2021) , and the images were processed with a semiautomatic method using the software developed by  (Yemini et al. , 2021). Neuron positions were detected by the software, and any required corrections were made manually. The software predicted neuron identities for the head and tail of each worm, and these were manually checked and relabeled as needed. Since there were no prior statistical atlases for neurons in the worm midbody, the identities of these neurons were manually annotated for each worm. The original 1986 nervous system reconstruction  (White et al.
Atlas construction After applying our canonicalization procedure to each individual worm and obtaining corresponding 3D neuron point clouds for each one, we combine these point clouds into a single NeuroPAL atlas by using the median position coordinates for each neuron. We use the median rather than the mean since it is more robust to outliers and resistant to overall shrinking. The resulting atlas is illustrated in Fig. 4 , and Appendix 2 lists the 300 canonicalized neuron position coordinates. We do not provide data for the two CAN cells in our dataset, as previous broad investigations of panneuronal markers found none that solely express in neurons and also express in CAN  (Stefanakis et al. , 2015). All panneuronal markers that expressed in CAN also expressed in non-neuronal tissues such as epithelium, intestine, glands, or muscle.
If colors are provided, each unlabeled neuron can only be assigned to a neuron in the atlas with the same color, so we choose the smallest element with unlabeled colors at each iteration. We quantify the accuracy of all algorithms by testing them on simulated data where the ground truth is known. For these simulations, we use the OpenWorm dataset as the ground truth point cloud, distort it with simulated noise and biological deformations as described below, and finally measure which algorithms provide the most accurately reconstructed neuron identifications.
Alignment performance on simulated data To simulate point clouds data similar in structure to those from real C. elegans images, we start with the OpenWorm atlas  (Gleeson et al. , 2018)  and generate a point cloud of neuron positions with the ground-truth neuron IDs hidden from the alignment methods. A good alignment and identification pipeline should be robust to the above scenarios; therefore we sought to test the robustness of our alignment algorithm when deforming the Openworm point cloud dataset in these ways. In the following plots, results for optimally tuned parameters are shown.
Robustness to Cropping Commonly, C. elegans activity research focuses on the head of the worm, due to microscope FOV or resolution reasons, or hypotheses aimed at the nerve ring. During alignment, the head neurons are usually matched to a larger atlas. Therefore, a good identification method must perform well when aligning a dataset of unlabeled neurons that may reference a cropped subset of the comparison atlas. For the first test, we start with a full Openworm point cloud that has been isotropically rescaled so as to occupy ∈  [-600 , 200 ]. We choose all head neurons (located at > 0 ) as the unlabeled neuron set, and also create copies of the Openworm point cloud with various cropping thresholds to act as the comparison atlas. We perturb the larger atlas as a rigid group away from the head neurons by a single vector drawn from a uniform distibution between ±5 microns in each of the , , and directions.
Discussion The main contributions of this paper are 1. a pipeline for constructing a 3D C. elegans atlas based on optically imaged neuron data, 2. an alignment method for identification of unlabeled C. elegans neurons using such an atlas, and 3. to the best of our knowledge, the most complete C. elegans 3D positional neuron atlas, encapsulating positional variability derived from the whole bodies of 7 animals. We have presented tests suggesting that both our alignment algorithm and our pipeline-produced 3D atlas achieve higher identification accuracy than existing alternatives. Many groups around the world are in the process of producing better imaging datasets so as to enable more promising investigation of myriad aspects of C. elegans , from neuronal activity to gene expression and cell-fate.
Acknowledgments The authors wish to thank  Liam Paninski  for helpful comments. E. Y. was funded in part by the  NIH  ( 5T32DK7328-37 ,  5T32DK007328-35 ,  5T32MH015174-38 , and  5T32MH015174-37 ). E. S. B. was supported by  HHMI ,  Lisa Yang ,  John Doerr , and  NSF  Grant  1734870. M. T. was supported by the  Foundational Questions Institute , the  Rothberg Family Fund for Cognitive Science, and IAIFI  through  NSF  grant  PHY-2019786.
Furthermore, former studies using a realistic Greenland meltwater amount only included the contribution from GrIS and not the surrounding regions like Canadian Arctic Archipelago, Svalbard and Iceland. Including these regions and increasing the spatial resolution of their regional model led the authors from  Bamber et al. (2012)  to update their estimate of freshwater fluxes. The new estimations are presented in the study of  Bamber et al. (2018) , with a significantly larger freshwater release in the North Atlantic. The total cumulative anomaly for the period considered  is about twice as large as the value reported previously in  Bamber et al. (2012). Dukhovskoy et al. (2019)  included the updated dataset from  Bamber et al. (2018)  and their analysis revealed a strong freshening signal from 2010 onward.
Construction of the dataset We are using the monthly values of FWF from the recent observation-based estimate of  Bamber et al. (2018) , in the vicinity of Greenland, which covers the period 1958-2016. The dataset has several components which differ in their nature: it combines high spatial and temporal resolution satellite observations of solid ice discharge and regional climate model output of surface tundra and ice runoff. The components are further described in the next subsection (section 1. 1. 1). In  Bamber et al. (2018)  dataset, the melting of the icebergs, denoted as the "solid ice discharge" component, is located along the coasts. As we wish to include the possible drift of the icebergs, we proceed to a spatial redistribution of this component far from the coast. This step of the reconstruction is described in section 1.
1. 2. Finally, we extend all the components of the FWF back to the year 1840 using the total ice sheet mass budget closure over the 1840-2010 period from  Box and Colgan (2013)  and a regression approach described in section 1. 1. 3. Bamber et al. (2018)  Four out of the five components of the dataset from  Bamber et al. (2018)  are liquid runoff (monthly values). They are described as runoff from land ice ("ice runoff") on the one hand and snow melt on tundra on the other hand ("tundra runoff"). Both are given for two different regions: along the coast of Greenland and the coast of surrounding regions (Canadian Arctic Archipelago, Svalbard and Iceland). These runoff fluxes values were estimated from the outputs of a regional climate model (RACMO2. 3p2) forced by atmospheric re-analysis (ERA-40 and ERA-Interim) for the period 1958-2016  (Noël et al.
Description of the dataset of The fifth component of the dataset is the solid ice discharge for GrIS only: it corresponds to the flux of ice crossing the grounding line of marine-terminating glaciers around the ice sheet. It is based on satellite-observations of ice surface velocity and a compilation of ice thickness  (Morlighem et al. 2017)    The solid ice discharge related to icebergs from  Bamber et al. (2018)  is located at the coast of GrIS in the dataset. Solid ice fluxes (icebergs) are not generally included in a physically realistic manner in climate and ocean General Circulation Models (GCMs) (cf. Radić and Hock (2014)  for a review): they are incorporated as liquid inputs at their source, along the edge of the ice sheet. This approach may lead to physical inaccuracy in surface property fields and mixed layer depths as in reality icebergs can drift thousands of kilometers from their original source  (Marsh et al.
2015). In this study, we modify the spatial distribution of the solid ice discharge of  Bamber et al. (2018) , using satellite-based location of the icebergs from the Altiberg project  (Tournadre et al. 2015) , to account for the iceberg drift. The Altiberg icebergs database project  (Tournadre et al. 2015)  provides a monthly spatial evolution of the icebergs around the North and the South Poles from 1993 to 2012. The data are available on the NEMO grid with eORCA025 configuration and were interpolated for our model configuration. Let Iceb A (i, j, t) denote the Altiberg spatial and temporal evolution of the solid ice fluxes on the 2D grid ((i, j) being the indices of the grid cells), and t the time variable. Let < Iceb A (i, j, t) > t denote the 1993-2012 time averaged 2D map of the flux that depends only on (i, j).
In  Bamber et al. (2018) , a smoothing of five years was applied to the solid ice discharge to account for the years with no observed discharge value: the authors fit a linear least squares regression over the ice runoff component of the previous four years. We choose to apply the same 5-year filter to every component of the dataset of  Bamber et al. (2018)  in order to use the same methodology for all the fluxes. We first perform a linear regression between GIR and the 5-year smoothed spatially integrated Greenland Ice Runoff from  Bamber et al. (2018) , denoted (GIR b ) (blue line in Figure  2 ), over the period both data sets have in common: 1958-2012. We obtain the relation (in km 3 per year): GIR b = 2. 02 GIR -178 (2) with a quite high correlation coefficient (r 2 = 0. The same method is applied to the other components of  Bamber et al.