dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional outcome measures Functional outcome measure results are summarized in tables 1 and 2, figure  2 , and table e-1 (available from Dryad, doi. org/ 10. 5061/dryad. tp08m60). Some of the assessments, by their   nature, are suitable only for ambulant patients. In addition, some patients did not complete all assessments because of equipment or individual patient factors such as recent injury and were excluded from analysis of that assessment. The number of patients who completed each assessment is listed. The a-NSAA, MFM-20 (total and subdomain D1), the Timed Up and Go, and timed 10-meter walk showed significant change in median score over both 6-month periods and 1 year (table  1  and figure 2) ; no MMT or HHD values changed consistently over both 6-month windows (table  2 ). The MFM D2, some MMT and HHD, velocity to rise from floor, climb 4 stairs, descend 4 stairs, and the 6MWT showed significant change over only one 6-month window and over 1 year (tables 1 and 2 and figure  2 ).
Acknowledgment This study has been possible only thanks to the international collaboration of several specialized centers promoted by the  Jain Foundation. The  Jain COS  Consortium thanks the study participants and their families for their invaluable contributions and acknowledges the ongoing support that the  Jain Foundation  provides in the development, management, and analysis of this study. The  Jain Foundation , based in Seattle, WA, is focused entirely on LGMD2B/dysferlinopathy/ Miyoshi myopathy. The foundation does not solicit funding from patients but instead funds research and clinical studies worldwide with the goal of finding treatments for dysferlinopathy. Please visit jain-foundation. orgfor more information.
, 2018). Observations of Galactic polarization are a highlight and a lasting legacy of the Planck space mission. Spectacular images combining the intensity of dust emission with the texture derived from polarization data have received world-wide attention and have become part of the general scientific culture (Planck 2015 res. I, 2016). Beyond their popular impact, the Planck polarization maps represented an immense step forward for Galactic astrophysics  (Planck 2018 res. XII, 2019). Planck has paved the way for statistical studies of the structure of the Galactic magnetic field and its coupling with interstellar matter and turbulence, in the diffuse ISM and star-forming molecular clouds. SPICA, the Space Infrared Telescope for Cosmology and Astrophysics proposed to ESA as an M5 mission concept  (Roelfsema et al.
, 2013; Marsh et al. , 2016). The superimposed blue dashed curves trace the magnetic field orientation projected on the plane of the sky, as inferred from P lanck dust polarization data at 850 µm (Planck int. res. XXXV, 2016). Note the presence of faint striations oriented roughly perpendicular to the main filament and parallel to the plane-of-sky magnetic field. in terms of scale-free processes. Another major result from Herschel studies of nearby clouds is that most (> 75%) prestellar cores and protostars are found to lie in dense, "supercritical" filaments above a critical threshold ∼ 16 M /pc in mass per unit length, equivalent to a critical threshold ∼ 160 M /pc 2 (A V ∼ 8) in column density or n H2 ∼ 2 × 10 4 cm -3 in volume density  (André et al. , 2010; Könyves et al. , 2015; Marsh et al.
, 2013; Panopoulou et al. , 2016; Soler et al. , 2016). There is also a hint from Planck polarization observations of the nearest clouds that the direction of the magnetic field may change within dense filaments from nearly perpendicular in the ambient cloud to more parallel in the filament interior (cf. Planck int. res. XXXIII, 2016). These findings suggest that magnetic fields are dynamically important and play a key role in the formation and evolution of filamentary structures in interstellar clouds, supporting the view that dense molecular filaments form by accumulation of interstellar matter along field lines. The low resolution of Planck polarization data (10 at best or 0. 4 pc in nearby clouds) is however insufficient to probe the organization of field lines in the ∼ 0. 1 pc interior of filaments, corresponding both to the characteristic transverse scale of filaments  (Arzoumanian et al.
The promise of B-BOP B-BOP will image dust polarization with an unprecedented combination of sensitivity and angular resolution, providing a unique data set (Sect. 2. 4) to characterize the magnetic facet of interstellar turbulence. This leap forward will open an immense discovery space, which will revolutionize our understanding of interstellar magnetic fields, and their correlation with matter and gas kinematics. At 200 µm, for the SED of the diffuse ISM (Planck int. res. XVII, 2014), the surface brightness sensitivity of B-BOP is three orders of magnitude greater than that of the Planck 353 GHz all-sky map for deep imaging (10 hr per square degree) and a few hundred times better for faster mapping (2 hr per square degree). The analysis of dust polarization at high Galactic latitude with the Planck data is limited by sensitivity to an effective angular resolution of ∼ 1 • in the diffuse ISM and 10 in molecular clouds where the column density is larger than 10 22 H cm -2 (Planck 2018 res.
Observations have so far taught us that magnetic fields are correlated with the structure of matter in both the diffuse ISM and in molecular clouds  (Clark et al. , 2014 ; Planck int. res. XXXII, 2016; Planck int. res. XXXV, 2016) but this correlation does not fully describe interstellar magnetism. Data must also be used to characterize the intermittent nature of interstellar magnetic fields. The magnetic structures identified in MHD simulations (Fig. 6 ) may be described as filaments, ribbons or sheets with at least one dimension commensurate with dissipation scales of turbulent and magnetic energy in shocks, current sheets or through ambipolar diffusion  (Momferratos et al. , 2014; Falgarone et al. , 2015). While the viscous and Ohmic dissipation scales of turbulence are too small to be resolved by B-BOP, turbulence dissipation due to ion-neutral friction is expected to occur on typical scales between ∼ 0.
Planck 2018 res. XII (2019) did not find any correlation either between dust temperature and polarization fraction in the diffuse ISM. In conclusion, the analysis of the Planck all-sky data have so far not allowed to strongly confirm or rule out any specific grain alignment theory, but have provided an upper limit to the drop of alignement in the diffuse ISM. Owing to its much higher angular resolution and sensitivity, B-BOP will allow us to systematically map the polarization of dust emission around thousands of individual stars heating the nearby ISM locally. The good coverage of the polarized SED will allow us to measure the temperature of aligned dust grains responsible for the polarized emission. Analysis of the polarization fraction as a function of the angle between the known radiation field and the magnetic field direction derived from polarization will allow us to test, for the first time, the RATs alignment theory with statistical significance, in a way that is not affected by local variations and the complexity of individual objects.
The formation of dust aggregates by grain-grain coagulation must have its counterpart in polarization, and B-BOP will detect signatures which will have to be analyzed through detailed modeling of the coagulation process. Here again, the combination of B-BOP data with the increasing amount of high-resolution polarization observations in the optical and the near-IR will provide strong constraints on dust evolution (Planck int. res. XXI, 2015; Planck 2018 res. XII, 2019). Observations of total dust emission intensity at farinfrared and submillimeter wavelengths with Planck and Herschel have also brought surprises. One of them is evidence that the logarithmic slope of the dust emission SED at long wavelengths, often referred to as the dust emissivity index, β, exhibits significant variations at large scales across the Galaxy.
ACKNOWLEDGEMENTS This paper is dedicated to the memory of  Bruce Swinyard , who initiated the  SPICA  project in Europe, but unfortunately died on 2015 May 22 at the age of 52. He was ISO-LWS calibration scientist, Herschel-SPIRE instrument scientist, first European PI of SPICA and first design lead of SAFARI. This work has received support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( ERC Advanced Grant  Agreement no. 291294 -'ORISTARS' , and Starting Grant Agreement no. 679937 -' MagneticYSOs '). D. J. is supported by the  National Research Council of Canada  and by an  NSERC   Discovery Grant. H. B. acknowledges support from the  ERC Consolidator  Grant  CSF-648505  and financial help from the  DFG  via the SFB881 "The  Milky Way System " (subproject  B1 ).
End-to-end forecasting has been around for some time and many meteorological or hydrological centres have some form of operational end-to-end flood forecast (e. g. ,  [5, 6] ). The more sophisticated end-to-end forecasts go further down the chain, i. e. , they include hydraulic components either through a look-up library of static flood maps (e. g. ,  [7, 8] ) or by running hydraulic models (e. g. ,  [4, 9] ). Thus the FFIR programme develops the end-to-end forecasts by improving stages in the forecasting chain and the overall integration of such systems, with a specific focus on small-scale intense rainfall events that lead to flooding in high-resolution models. The FFIR programme consists of three components. It first considers the meteorological (Forecasting Rainfall exploiting new data Assimilation and Novel observations of Convection: FRANC) and hydrological aspects (Susceptibility of catchments to INTense RAinfall and flooding: SINATRA) separately before combining the results (Towards END-to End flood forecasting and a tool for ReaL-time catchment susceptibilitY: TENDERLY) and considering how the improvements in FRANC and SINATRA feed into the end-to-end forecasting chain.
• Improved infiltration rates for land surface modelling, leading to better links to the runoff for hydrological models, • data assimilation of soil moisture in the model, • more observations and data being fed into the national-scale flood models and catchment models, • use of urban models for modelled inundation maps. Many aspects of the FFIR proposed system have become operational in their own right (e. g. , radar improvements, Section 5. 1) and these are indicated throughout the following sections. Other aspects such as the data assimilation of soil moisture and links between land surface modelling and hydrology, although researched within the programme (e. g. ,  [30, 31] )*, are not covered within this article due to the results not directly considering flooding from intense rainfall.
Improvements and Lessons for Weather Forecasting Capabilities in an End-to-End System The initial stage of the end-to-end forecast focuses on the meteorological component of flood forecasting. First the observations of the rainfall are considered through the use of weather radars and improvements to the UK radar network (Section 5. 1). Secondly the process by which the observations are optimally combined with a previous forecast (i. e. , data assimilation) is considered (Section 5. 2). This data assimilation process will produce the best initial starting point for the weather forecast. Finally, the uncertainty of the weather that leads to flooding from intense rainfall is considered; this is achieved via predictability experiments to show how different the forecasts of rainfall could be (Section 5.
6 cm) with a 1 • beamwidth. This provides coverage of rainfall estimation for 99% of the UK population, most of this area with 1 km spatial resolution (Although the raw data is available at a resolution of 75 m for much of the UK) and 5 min temporal resolution. The upgraded radars have the addition of dual-polarisation, i. e. , they can transmit and receive vertically and horizontally polarised microwaves. This ability gives a number of new radar parameters over just reflectivity, and provides new data to improve the quality and accuracy of the surface-rainfall estimates (e. g. ,  [38] ). The FFIR programme has included a number of developments for the radar network, aimed to improve the rainfall estimates from heavy rain. These developments include.
Improving the Initial Conditions for Weather Forecasts To initialise a forecast, observations are combined with previous model predictions, taking account of their relative errors, using sophisticated mathematical algorithms known as data assimilation. To provide accurate forecasts of the meteorological conditions that can lead to flooding from intense rainfall it is necessary to assimilate observations that provide us with information about the factors that lead to the formation of rainfall events, where and how fast rainfall events are moving, and also their depth and spatial coverage. Work in the FFIR programme has considered how to include novel observations in the data assimilation schemes. For example, the Met Office are developing and testing an improved technique for the assimilation of radar reflectivity observations directly into the NWP model  [41, 42] * rather than through the latent heat nudging method  [43]  used currently.
Summary Flooding from intense rainfall is a short-lived, localised natural hazard that is hard to predict accurately and is the main focus of the joint NERC and Met Office FFIR programme. The technical contributions of the FFIR programme to improving end-to-end forecasting for intense rainfall as outline in this paper can be summarised under four themes: improved knowledge of vulnerability to flash flooding, improved observations during events, improved forecasting of convection (which includes the data assimilation improvements), and real time flood inundation modelling (Figure  5 ). Through consideration of how these components link together in an end-to-end framework, the programme has demonstrated the benefits of interdisciplinary collaboration and laid strong foundations for future integrated research.
Data source Data from participants with visual impairment were obtained from a larger dataset, that included more than 500 participants recruited from 4 hospitals, originally collected to study the prevalence and costs of visual impairment in Portugal (PCVIP-study)  [18] [19] [20]. In the original study, participants were recruited if their medical records indicated at least one of the inclusion criteria: 1) visual acuity in the better seeing eye less than 0. 5 decimal (i. e. 6/12; 20/40; 0. 3 logMAR); 2) visual field less than 20 degrees. In the present work, we included data collected in only two sites because reading was always tested in the same room. Luminance in each room was respectively 682±7 lux and 455±10 lux, measured at 1m above the floor and away from a direct effect of a ceiling light.
Reading curves were obtained binocularly with participants' "presenting reading glasses". Reading data was excluded from analysis when the number of data-points corresponded to less than five sentences on the MNREAD test. We ended with a dataset of 101 reading curves. The study protocol was reviewed by the ethics committee for Life Sciences and Health of the University of Minho (REF: SECVS-084/2013) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants. The study was registered with the Portuguese data protection authority with the reference 9936/2013 and received approval number 5982/2014.
MNREAD data Reading performance was measured for each participant using the Portuguese version of the MNREAD acuity chart  [21]. Reading distance was adjusted for each participant, to either 20 or 40 cm, according to his/her near visual acuity. Participants were asked to read the chart aloud as fast and accurately as possible, one sentence at a time, starting from the largest print size. For each sentence, reading time and number of misread words were recorded and reported on a score sheet by the experimenter. Data were then transferred into a digital file and further processed in R  [22]. For each individual test, a corresponding MNREAD curve was plotted using the mnreadR package  [23]  to display log reading speed as a function of print size (see S1 Appendix for all 101 curves). Because the shape of the curve can influence visual estimation of the reading parameters, reading speed was plotted using a logarithmic scale so that reading speed variability (which is proportional to the overall measure of reading speed) was constant at all speeds  [14].
Algorithms' automated scoring MRS and CPS were also calculated automatically for each 101 datasets using two algorithmbased estimations: the 'standard deviation' method and NLME modeling. The standard deviation method (SDev) uses the original algorithm described in the literature to estimate the MNREAD parameters  [14, 15]. This algorithm iterates over the data searching for an optimal reading speed plateau, from which MRS and CPS will be derived. To be considered optimal, a plateau must encompass a range of print sizes that supports reading speed at a significantly faster rate (1. 96 × standard deviation) than the print sizes smaller or larger than the plateau range (Fig  2 ). MRS is estimated as the mean reading speed for print sizes included in the plateau and CPS is defined as the smallest print size on the plateau.
In most cases, several printsize ranges can qualify as an optimal plateau and the algorithm chooses the one with the fastest average reading speed. In the present work, the standard deviation method estimation was performed using the curveParam_RT () function from the mnreadR R package. The NLME modeling method is particularly suited for incomplete datasets from individuals with reading or visual impairment as it uses parameter estimates from a larger group (101 datasets here) to allow suitable curve fits for individual datasets that contain few data points  [17]. In the present work, we used an NLME model with a negative exponential decay function, as described in detail by  Cheung et al. (2008)   [17]. In this approach, a single estimate of MRS can yield several measures of CPS depending on the definition chosen (e.
Statistical analysis Intra-class correlation coefficient (ICC) was used in all the 3 analysis to assess absolute agreement between raters and/or algorithms  [24]. The ICC reliability index (ranging from 0 to 1; 1 meaning perfect agreement) is widely used in the literature in test-retest, intra-rater, and interrater reliability analyses  [25]. In this study ICC values estimate the variation between two or more methods (whether raters or algorithms) in scoring the same data by computing the absolute agreement between them. For each analysis, the appropriate ICC form (dependent on research design and assumptions) was chosen by selecting the correct combination of "model", "type" and "definition", as detailed in Table 1  [26]. ICC values were calculated using SPSS statistical package (IBM-SPSS, v24, Chicago, Illinois) and limits of agreement were visualized with Bland-Altman plots.
Discussion Repeatability of the MNREAD chart measures has been assessed before in low-vision populations. Overall, studies have reported good intra and inter-session reliability  [11, [27] [28] [29] , as well as good repeatability across multiple testing sites and experimenters  [30]. However, to our knowledge, the variability of the MNREAD estimates scored by different raters and/or methods has never been evaluated before. This question of inter-rater variability is especially relevant (1) in the context of multisite investigations, in which data may be scored by different raters with different levels of expertise, (2) when comparing results from different studies performed by different groups, or (3) when looking at follow-up data involving different raters. With the aim to increase knowledge on inter-rater reliability of reading measures, we have chosen to study this question using the MNREAD acuity chart in order to remain consistent with the existing literature, allowing reliable comparisons with previous results of intra and inter-session reliability.
Our third result is that raters and automated methods show 'excellent' agreement for MRS values (ICC of 0. 96 and 0. 97 for the SDev and NLME methods, respectively). The agreement for CPS was more variable. It was 'poor' to 'moderate' for the SDev (ICC of 0. 66) and 'good' for the NLME (ICC of 0. 83 with a CPS criterion set to 90% MRS). It is worth noting that ICC values were almost identical when measuring agreement between raters and agreement between algorithms for both MRS and CPS. This comparison is important because it indicates somehow the robustness and efficacy of human visual inspection of MNREAD curves when compared to automated methods. This observation is especially relevant considering that algorithms present two major drawbacks: (1) they may not be easily accessible in clinical environments, (2) they may fail to provide satisfactory measures with noisy data points or small and incomplete datasets, which require human inspection of the curves for validation.
Finally, our results are also relevant for clinicians currently using the hardcopy MNREAD chart and willing to use the MNREAD iPad app, by showing that the automated scoring provided by the app is consistent with manual scoring, given a routine visual inspection of the automatically generated curves. This work presents some limitations. First, despite the relatively large sample of MNREAD data considered in the present work, it is hard to predict to what extent the different shaped curves are representative of all possible curves. Second, it is likely that the new instructions helped to reduce inter-rater variability, but there is no data to support this conclusion. Despite all raters used the new set of extended instructions the ICC value for CPS was still suboptimal, suggesting that additional fixes should be considered to help increase reliability.
Conclusions In summary, our study shows that extraction of the maximum reading speed from MNREAD data is consistent across methods and researchers. It also reveals that for curves obtained from readers with low vision it is difficult to obtain excellent inter-rater reliability for CPS estimates. Future studies, such as rehabilitation interventions aiming at improving reading ability in people with low vision, can now follow the advice and instructions resulting from our investigation. Using a standard set of instructions and criteria to analyze reading curves may help increase the reliability of the results. Additional ways to improve inter-rater reliability should also be considered, e. g. use the curve fits, collect multiple runs per participant or combine the estimates of multiple raters.
Acknowledgments Authors acknowledge  Dr. Eric Castet  for the initial discussion regarding the study and report on behalf of the  Portuguese visual impairment study group  ( PORVIS-group ): Antonio  Filipe Macedo, PhD , Research Project Principal Coordinator;  Department of Medicine and Optometry Linnaeus University Kalmar, Sweden and Vision Rehabilitation Lab Centre/Department of Physics and Optometry University of Minho Braga, Portugal. Amandio Rocha-Sousa, MD, PhD , FEBO;  Marta Silva, MD , ophthalmology resident;  Sara Perestrelo, MD , ophthalmology resident;  João Tavares-Ferreira, MD , Ophthalmologist;  Ana Marta Oliveira , research coordinator;  Department of Surgery and Physiology, Faculty of Medicine University of Porto  and/or Ophthalmology Department:  Centro Hospitalar de São João.