dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present work, the theoretical model is chosen as a fluid dynamics model of the cell interior comprising several quantities of interest (namely velocity, pressure, forces, and out-of-plane flow in 2D), while the realisations of this model are represented by the motion of intracellular material captured via live microscopy. As a result, the proposed method extracts motion information (i. e. intracellular velocity) from a video-microscopy sequence using optical flow, under the constraint of the fluid dynamics model, thus jointly producing estimates of the pressure, forces and out-of-plane flow (in 2D), everywhere inside the cell up to a single-pixel resolution. This computational strategy offers a number of advantages over existing methods: 1) BioFlow is non-invasive and relies exclusively on live microscopy data; 2) BioFlow produces high-resolution measurements everywhere inside the cell in two or three dimensions; 3) BioFlow is independent of the experimental context and thus easily adapts to other theoretical models, biological specimens and imaging techniques; 4) BioFlow is open-source and available as a ready-to-use module for the Icy software  29.
Firstly, the regularisation term J reg can be specifically tailored to the problem at hand, while eliminating the arbitrary smoothness constraint that may not always hold experimentally. Secondly, solving this problem readily provides estimates for the biophysical quantities that minimize the cost function, without the need for additional simulations or model-fitting steps. This data assimilation strategy is particularly appealing since it is independent of the state-control parameters or theoretical model chosen, and can therefore be applied to a wide range of analogous problems in biology. The proposed framework, able to extract biophysical parameters using optical flow and data assimilation, is coined BioFlow, and is straightforward to implement experimentally as it requires only two inputs: 1) time-lapse imaging data of intracellular dynamics (typically obtained by labelling the intracellular material with fluorescence), and 2) a theoretical model of the observable motion.
It is worth stressing that the model presented here simplifies the numerical complexity of the problem, yet the proposed methodology is highly generic, and is easily adaptable to any fluid dynamics model of the intracellular material. BioFlow in 3D: general case. Given 3D imaging data and the aforementioned fluid dynamics model, BioFlow is now able to recover the intracellular velocity, pressure and forces from the observable motion of intracellular material by solving the following problem: → → → = → ∆ + → → → → → = u p f g J u t J f g A u p f g , , , argmin( ( ) ( , )), subject to ( , , , ) 0 (7) data reg where µ → → → =            ∇ -∇ → - → Ω ∇ ⋅ → Ω → -→ Γ A u p f g p u f u u g ( , , , ) in in on , (8) 2 and the regularisation term rewrites to ∫ α γ → → = → Ω + ∇ → Γ Ω Γ Γ ∮ J f g f d g d ( , ) , (9) reg 2 2 where α and γ are non-negative empirical weights.
Biophysical measurements inside the cell. Figure  1  depicts the biophysical quantities (here velocities, pressure, forces, and out-of-plane flow in 2D) estimated from two consecutive images in 2D and 3D (cf. Supplementary Movies S6 and S7). All quantities are estimated in each node of the underlying Finite Element mesh (see Methods), which can be defined up to a single-pixel resolution (cf. Figure  S2 ). By repeating this analysis over time (cf. Figs 2 and 6(b)), BioFlow enables a rich quantitative analysis of the intracellular dynamics in both space and time. In the remainder of this work we illustrate the results in 2D for easier visualisation, noting that measures obtained on both 2D and 3D datasets were found in good agreement both qualitatively and quantitatively.
Discussion We have developed a powerful and original mechano-imaging framework called BioFlow, which brings together image analysis, physical modelling and mathematical optimisation to perform direct and non-invasive intracellular measurements of invisible mechanical quantities from 2D and 3D fluorescence video-microscopy data. BioFlow was designed as a flexible data assimilation framework, in which we adapted the standard optical flow algorithm such that the observed movement of intracellular material is extracted under the constraints of a biophysical model of the intracellular dynamics. This permits a simultaneous extraction of the intracellular velocity as well as the biophysical parameters of the chosen model, all with high spatial resolution. The flexibility and ease of use of BioFlow lies in three complementary aspects: 1) its theoretical foundation provides a method that is virtually independent of (and adaptable to) any theoretical model of the cell; 2) it relies exclusively on conventional video-microscopy data of living cells, and thus permits non-invasive single cell studies in diverse experimental contexts; 3) its open-source software implementation lets any component of the workflow be tailored to the specific problem at hand (for instance, implementing a new theoretical model solely requires deriving its weak formulation, see Supplementary Information).
Using these settings, a 3D stack of size 25 × 25 × 10 microns was acquired in 0. 663 seconds. Time-lapse sequences in 2D and 3D were acquired at full imaging rate (i. e. no delay between individual time points). Numerical and software implementation. Equations 7 and 10 were solved within a variational data assimilation framework  57  using the Finite Element Method. An iterative gradient descent strategy was chosen over the classical analytical (augmented Lagrangian) approach due to the complexity of the problem, while convergence is guaranteed given that the functional is convex and the problem well-posed. Furthermore, this strategy scales particularly well with the number of variables in the problem, thanks to the use of the adjoint method, which inexpensively computes the gradient of the functional.
Multi-scale analysis was implemented to speed up computations and handle potentially large displacements between image pairs. Finally, an automated parameter estimation procedure was implemented to optimise the empirical parameters α, γ and η (Equations 9 and 12). Full details of the numerical implementation are available in Supplementary information. The final workflow was implemented within the Icy 29 software and is available as a downloadable module (more details at http://icy. bioimageanalysis. org/plugin/BioFlow). After loading a video-microscopy sequence into the software, an initial contour is defined around the cell on the first frame (this manual step can be automated using any available segmentation technique). The main BioFlow script (written in Jython) is launched from Icy's script editor and starts by segmenting and tracking the cell boundary over time using an active contour method  58  , then creates a multi-scale version of the image and contour for each frame.
The contour-image pairs are subsequently transferred to a python script running the FEniCS  59  Finite Element software, where the remainder of the computations take place: (a) the image and contour are converted into a Finite Element mesh using the Mshr (mesh generation) module; (b) the PDE system in 3D (equation 9) or 2D (11) is then solved using the MUMPS solver 60 ; (c) finally, the functional J is minimised using the dolfin-adjoint module. This process is iterated until convergence and repeated from coarse to fine image scales using appropriate warping. To reduce computation time, each pair of frames in the video sequence is processed in parallel using all available processors on the workstation. The final results and figures were produced either within Icy or with the ParaView software (http://www.
Acknowledgements We acknowledge the contribution of  Sabrina Marion ,  Ruchi Jain ,  Christophe Zimmer  and  Beryl Cummings  at early stages of this project, and thank  Pascal Roux  and  Jean-Yves Tinevez  ( Citech-Imagopole, Institut Pasteur)  for their assistance in the image acquisition experiments. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  665807  ( Pasteur-Paris University Doctoral Programme ), the  Institut Pasteur , the  CNRS , the  AMGEN Scholars programme , and the  French National Agency for Research (ANR)  under the grant agreements  ANR-MIE-08 ,  ANR-10-INTB-1301-PARACTIN , the  Labex ParaFrap  ( ANR-11-LABX0024 ), and the  Labex IBEID  ( ANR-10-LABX-62-IBEID ).
This data set is ∼ 2 mag (in the g band) deeper than other large surveys covering this part of the sky (e. g. , Pan-STARRS or SDSS). DES is a wide-field imaging survey of the Southern hemisphere that has recently finished its third year of data taking, from an expected total of 5 yr  (Diehl et al. 2016). We also identify two previously undiscovered ultrafaint stellar systems whose inferred ages, metallicities and distances make it likely that they are associated with Sagittarius. In Section 2, we present the DES data. In Section 3, we discuss the properties of the Sagittarius stream as probed by those data. In Section 4, we present a method used to search for star clusters and other stellar systems in the DES footprint. In Section 5, we report on the identification of the two star system candidates whose properties make them likely to have been stripped from the Sagittarius dwarf.
DES DATA DES is a wide-field optical imaging survey of 5000 deg 2 in the southern equatorial hemisphere in the grizY bands. DES is scheduled for 525 nights distributed over 5 yr. It uses the Dark Energy Camera (DECam), a 3 deg 2 (2. • 2 diameter) mosaic camera with 0. 263 arcsec pixels on the prime focus of the 4-metre Blanco telescope at Cerro Tololo Inter-American Observatory  (Flaugher et al. 2015). The DES data management (DESDM) uses an image processing pipeline that consists of image detrending, astrometric calibration, nightly photometric calibration, global calibration, image coaddition, and object catalogue creation. For a more detailed description, we refer to  Sevilla et al. (2011 ), Desai et al. (2012)  and  Mohr et al. (2012). Here, we use DES Y2Q1 (year-two quick release) data derived from single-epoch imaging.
3  was calculated as the difference between the Hess diagrams of the On and Off regions weighted by their respective areas. It contains a total of 87 810 stars. We use the healpix software to determine the effective area in each region. In the decontaminated Hess diagram we can identify MS, RGB, and some younger population stars. We select stars within the decontaminated CMD region defined by 0. 4 < g-r < 0. 8 and 19. 3 < g < 20. 5. For each interval of ∼ 0. 4 mag along the CMD, we count stars as a function of colour and use python package scipy. optimize 5  to fit a Gaussian distribution to determine the mean colour value and the associated standard deviation. The peak and 1σ deviations from it are shown as the black and red dots in the right-hand panel of Fig. 3. We then choose a set of parsec isochrone  (Bressan et al.
SUBSTRUCTURE SEARCH AND OBJECT DETECTION Many more than 17 objects were selected by our compact overdensity search techniques, stellar density maps, likelihood-based search and sparsex. The sparsex code is an overdensity detection algorithm, which is based on the matched-filter (MF) method  (Balbinot et al. 2011; Luque et al. 2016). Briefly, we begin by binning stars into spatial pixels of 1. 0 arcmin×1. 0 arcmin and colour-magnitude bins of 0. 01 mag × 0. 05 mag. We then create a grid of simple stellar populations (SSPs) with the code gencmd 8. We use gencmd along with parsec isochrones  (Bressan et al. 2012 ) and an initial mass function (IMF) of  Kroupa (2001). We simulate several SSPs in a range of ages [9. 0 log(Age) 10. 2], metallicities (Z = {0. 0002, 0. 001, 0. 007}) and distance (10 D ⊙ 200 kpc).
(2) According to the statistical significance of the excess number of stars relative to background: we built a significant profile in a cumulative way, in incremental steps of 1. 0 arcmin in radius, centred on each candidate. We then applied a simple cut in significance. All candidates with significance thresholds > 5σ were visually analysed to discard artificial objects as well as contamination by faint galaxies  (Luque et al. 2016 ). Applying the method described above on DES Y2Q1 data, we successfully recovered with high significance all 19 stellar objects that have been recently reported in DES data  (Bechtol et al. 2015; Drlica-Wagner et al. 2015; Kim et al. 2015; Kim & Jerjen 2015b; Koposov et al. 2015a; Luque et al. 2016). Additionally, we detected two new candidate stellar systems potentially associated with the Sagittarius stream, DES J0111-1341 and DES J0225+0304.
1 (red circles ). (ii) The CMD parameters (age, metallicity and distance) determined for these new candidates lie within the metallicity and age range determined for the Sagittarius stream using the same DES data (Section 3. 2). In particular, they are consistent with the parameters inferred by fitting the mean CMD locus of the stream stars. (iii) The distances D orb of our candidates to the Sagittarius orbital plane, ∼ 1. 73 kpc (DES J0111-1341) and ∼ 0. 50 kpc (DES J0225+0304), are comparable to GCs previously associated with the Sagittarius dwarf, more specifically Terzan 7 and NGC 6715  (Bellazzini et al. 2003). Therefore, we speculate that these candidates are likely associated with the Sagittarius stream. However, the spectroscopic determination of the radial velocity and proper motion of these substructures will be very useful to confirm this hypothesis.
18  [Fe/H] -0. 95) as well as a distance gradient (23 kpc D ⊙ 29 kpc). This suggests that the stream is composed of more than one stellar population. Our determination of distance gradient is consistent with those determined by  Koposov et al. (2012). However, metallicity determinations in the literature suggest that the stream in the celestial equator contains more metal-rich stars than those determined in this work (see, e. g. ,  Koposov et al. 2012; De Boer et al. 2015). -1. 0 -0. 5 0. 0 0. 5 1. 0 1. 5 log(a/arcmin) Notes. a Using the relation, r h = 1. 68re  (Martin et al. 2008 ). b Adopting a distance of 26. 5 kpc. c Adopting a distance of 23. 8 kpc. d Adopting Z ⊙ = 0. 0152  (Bressan et al. 2012 ). In the future, DES will acquire additional imaging data in this region, allowing even more significant studies of the region in which the Sagittarius stream crosses the equator.
Bechtol et al. 2015;Drlica-Wagner et al. 20Martin et al. 2015;Torrealba et al. 2016)ngles; McConnachie 2012), previously reported dwarf galaxy candidates in the DES footprint (red circles;Bechtol et al. 2015;Drlica-Wagner et al. 2015), other recently reported dwarf galaxies (green diamond;Kim & Jerjen 2015a; Laevens et al. 2015a,b;Martin et al. 2015;Torrealba et al. 2016), and DES new candidates (red stars) are shown. Note that DES J0111-1341 clearly lies inside the region inhabited by ultrafaint stellar clusters, whereas DES J0225+0304 occupies the ambiguous region between stellar clusters and dwarf galaxies. The dashed lines indicate contours of constant surface brightness at µ = {25, 27. 5, 30} mag arsec -2. arepartially supported by MINECO under grantsAYA2012- 39559, ESP2013-48274, FPA2013-47986, andCentro de Excelencia Severo Ochoa SEV-2012-0234, some of which include ERDF funds from the European Union.
The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2012-39559, ESP2013-48274, FPA2013-47986, and Centro de Excelencia Severo Ochoa SEV-2012-0234. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. EB acknowledges financial support from the European Research Council (ERC-StG-335936, CLUSTERS).
ACKNOWLEDGEMENTS This paper has gone through internal review by the DES collaboration. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovação , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
Five hospitals and three not-for-profit providers managing self-care dialysis situated in three administrative regions in France are participating. The trial began in December 2015, with a scheduled 12-month inclusion period and 12 months follow-up. Outcomes include clinical and biological data (e. g. blood pressure, haemoglobin) collected from patient records, perceived health status (e. g. health related quality of life) collected from self-administered questionnaires, and health expenditure data retrieved from the French health insurance database (SNIIRAM) using a probabilistic matching procedure. Discussion: The hypothesis is that home telemonitoring enables better control of clinical and biological parameters as well as improved perceived health status. This better control should limit emergency consultations and hospitalisations leading to decreased healthcare expenditure, compensating for the financial investment due to the telemedicine system.
Randomisation is performed by balanced blocks of 20 and stratified by -participating centre and population (1, 2, or 3). Participating patients are then referred to a clinical research nurse who collects pertinent data (see data collection below) and assists patients in filling out the study questionnaires (see outcomes of interest below). Patients assigned to the intervention group are given a commercially available 10-inch tablet device with instructions for use of the telemedicine patient interface.
Each patient's telemedicine system transmits the following information to the data platform: body weight; body temperature; pulse rate; patientmeasured systolic and diastolic blood pressure for the last three days; patient-assessed presence of symptoms including vertigo, short breath, fatigue, oedema, abdominal pain, deterioration of general health status. The telemedicine system also transmits to the data platform information issuing from medical laboratories, e. g. the biological parameters recommended by the French authorities (HAS) for the follow-up of kidney transplant recipients on samples taken three days before scheduled visits.
Data collection * Data recorded at inclusion and during follow-up An electronic case report form (eCRF) is used to collect socio-demographic, clinical, biological and therapeutic data from patient records at specified time intervals in the three patient populations (Table  2 ). Study completion is noted as are patient dropouts together with the reason (lost to follow-up, medical decision, consent withdrawal, change in disease stage, death). All data are collected and checked for completeness by nine trained clinical research nurses. An independent clinical research nurse compares 10% of the completed eCRFs for consistency with the patient's medical record.
* Economics data Cost values correspond to the tariffs set by the French healthcare fund. Only direct costs are taken into account for the cost analysis. Two types of cost are considered: 1) intervention-related cost (computer equipment, maintenance, personnel training for the telemedicine system), recovered from the telemedicine system supplier; 2) patient management-related costs (hospitalisation, consultation, home visits by nurses, complementary explorations, drugs and medical devices, health transport). This part of the economics data stems from the French health insurance database SNIIRAM 2 that contains individualised, anonymous and linkable data. Prospectively recorded for all beneficiaries of healthcare in France, SNIIRAM covers the entire French population (66 million inhabitants).
Data recorded include all medical expenditure reimbursements for long-term disabling diseases, e. g. CKD. A probabilistic data linkage method  [24]  will be applied to identify patients included in the eNephro study within the anonymous SNIIRAM database. For each population, mean cost per patient and mean cost per group (intervention vs control) are estimated, allowing to calculate an Incremental Cost Effectiveness Ratio (ICER) of telemedicine as compared with usual care. The ICER relates the overall costs to the primary endpoint for each population.
* Main data analyses The statistical analysis plan includes the following procedures for each of the three study populations: (i) A description of the patients included in each group (intervention, control): number of patients, socio-demographic, clinical, biological and therapeutic parameters. To check the comparability of patient groups, the main features are compared using the χ 2 test for qualitative variables, analysis of variance or the Mann-Whitney test for quantitative variables. (ii)A description of deviations from the protocol. (iii)Inter-group comparison of clinical outcomes and cost-result ratios, with adjustment for any characteristic differing between two groups. Intention-to-treat analysis is applied. The percentage  Cost-result ratio, where result is achievement of blood pressure (systolic ≤ 130 and diastolic ≤ 80 mmHg) and proteinuria (≤0.
Templates also underscore the usefulness of multiple outcomes and the integration of patients' and professionals' points of view, important aspects included in the eNephro evaluation. Moreover, the effects of telemedicine on patient healthclinical parameters, morbidity, mortality, health-related quality-of-lifeare evaluated and all resources used are considered in the cost evaluation, according to the abovementioned recommendations. The French health insurance database (SNIIRAM) appears to offer the best way to estimate medical expenditures incurred for a given patient during a given period. SNIIRAM records data for all healthcare beneficiaries in France, and includes the reimbursed costs of all out-of-hospital healthcare consumptions, as well as all hospital stays and consultations in public or private institutions  [30].
Finally, the acceptability of the telemedicine system and satisfaction with its use are assessed, as recommended, for all stakeholders, i. e. patients randomised to intervention groups and health professionals involved in the home telemonitoring. Many types of results are expected to come forward. For patients with stage 3B/4 CKD, eNephro home telemonitoring should allow enhanced follow-up since clinical and biological information will be recorded regularly by the data platform. It is also expected that the expert systems identifies early situations where patients risk for decompensation and/or deterioration of their health status, even before the development of clinical symptoms prompt them to seek medical assistance. This should enable more rapid medical intervention and a lower rate of hospitalisation.