dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was financially supported by the Fondation pour la Recherche Médicale (to JvM, DEQ20160334920); the IdEx Toulouse (to PR); the Région Midi Pyrénées (to JvM, 15/06/ 12. 05); the Agence Nationale pour la Recherche (to PR, ANR-16-CE15-0015-01), and the Fondation ARC pour la Recherche sur le Cancer (to CA, DOC20180507201). Il2fl knock-in mice were generated by the Centre d'Immunophénomique (CIPHE, Marseille, France). CIPHE is supported by the Investissement d'Avenir program PHENOMIN (French National Infrastructure for Mouse Phenogenomics; ANR-10-INBS-07 to BM).
The stimulating electrode was carefully inserted through the wall from the endocardium to maintain an unobstructed optical field of view. Two electrograms were recorded from the lateral margins of the LV. For optical mapping, the heart was excited by two arrays of light emitting diodes (LEDs) (LEDEngin, San Jose, CA, USA, LZ4-00G110; 532 ± 15 nm) each directed through the side windows of the imaging bath. Fluorescence emission was acquired over an imaging field ∼12 × 12 mm 2 with a charge multiplying CCD camera (Photometrics, Tucson, AZ, USA, Cascade 128+, 64 × 64 pixels, 635 Hz) fitted with a wide aperture lens (Navitar, Ottawa, CA, NAV DO5095), a 2× close-up lens and a 565 ± 10 nm long pass filter, giving a pixel resolution of 0. 1875 mm  (Khwaounjoo et al. , 2015). Programmed stimulation was applied and electrograms recorded with a PowerLab 25T data acquisition system (ADInstruments, Dunedin, NZ).
Analysis of experimental data Instantaneous optical map signals were smoothed (Gaussian filter: 5 × 5 pixel kernel: σ = 0. 5) and noise at each pixel was reduced with a low-pass third order Savitsky-Golay filter  (Savitzky & Golay, 1964). Signals from regions adjacent to the visible boundary of the ventricles (∼2 pixels) were excluded to minimize artifact introduced by surface curvature. Activation time (AT) was found from the maximum upstroke gradient of optical APs. To estimate longitudinal and transverse conduction velocities (CV), ellipsoids were fitted to 1 ms AT isochrones between 3 and 6 ms. The CVs were determined using the average of major (longitudinal) and minor (transverse) apex-to-apex distances across the three isochrone pairs. Ellipsoid principal axes were assumed to coincide with subepicardial myofibre and cross myofibre directions  (Caldwell et al.
Fibrosis density percentage was the total patch area relative to the LV area. Patch centroids were projected into polar coordinates (Fig. 1B ) and a heat map of patch densities constructed (Fig. 1C ). Aggregated regions with patch densities ≥1 were skeletonized and the region with the longest skeletonization (aggregated fibrosis patch density length (AFL)) was used as representative for that heart (an indirect measure of functional re-entrant path length) (Fig. 1C ). The area of this region was normalized across all hearts in the study (0-1 range) to give an aggregated fibrosis patch density area (AFA) index. Additional data are available in Supporting information SI1. The AFA provides a measure of aggregated patchy fibrosis impact at scales commensurate with re-entrant paths. High-resolution 3D image volumes were acquired in optically cleared tissue from representative regions of the anterior LV mid wall in each heart with a Nikon TE2000 confocal microscope.
WGA was imaged at 488 nm and Cx43 at 568 nm excitation wavelengths using a ×60 oil immersion objective (numerical aperture 1. 25, working distance 180 μm). The Cx43 label did not penetrate the tissue consistently, providing equivocal data unsuitable for quantitative purposes. This is attributed to the larger size of anti-Cx43 antibody compared to WGA  (Sands et al. , 2022). Image stacks were typically 512 × 512 × 180 voxels with voxel dimensions 0. 41 μm × 0. 41 μm × 1 μm. Myocytes were segmented in 3D using ITK SNAP  (Yushkevich et al. , 2006). As many cells as possible were semi-manually segmented from each image. For each complete cell (no intersection with image boundaries) the long axis through the centre of mass was found with eigenanalysis of the mask covariance matrix. Cross-sectional area was computed by resampling the mask voxels at the centre of mass and orthogonal to the long axis and the cell volume was the sum of mask voxels.
Statistical analysis Between-group differences were assessed using one-and two-way ANOVA and post hoc Tukey tests. Pairwise comparisons between VT/VF risk, electrical function and structural change for each animal were made using univariate linear regression (MATLAB (The MathWorks, Natick, MA, USA) 'fitlm'; null hypothesis: slope of linear model is zero). Data are presented as mean ± SD and P-values <0. 05 are considered statistically significant.
Correlation and factor analysis Sets of 10 variables were extracted from the data gathered across 12 SHRs: age, VT/VF risk, transverse CV (for pacing >5 Hz), AR (for pacing >5 Hz), APD (mean over field of view for pacing at 2. 33 Hz), APD dispersion (for pacing >5 Hz), cell scale percentage fibrosis, number of adjacent cells, LV scale percentage fibrosis and AFA index. Data for each variable were normalized with mean 0 and standard deviation 1. Using the MATLAB 'corr' function, Pearson's linear correlation coefficients and corresponding P-values from a Student's t-distribution were found for all combinations. Approximate R 2 values were the squared correlation coefficients. Exploratory factor analysis was used to investigate if there were emergent or latent groupings of variables into a small number of factors  (Tabachnick & Fidell, 2019).
Correlation and factor analysis Ten variables collected over the three SHR age cohorts were analysed for cross-correlations and for emergent factor groupings among all variables. Two 12-month and one 6-month animal were excluded as complete individual cells could not be segmented from the 3D images. Consequently, 10 variables over 12 animals were analysed (additional data are available in Supporting information SI2). Linear regressions between a selection of the 10 variables are given in Fig. 7. Figure  7A  and B shows the strong linear correlations between (1) transverse CV and the LV scale fibrosis density (R 2 = 0. 64; P = 0. 00173, F-statistic) and (2) rate-dependent APD dispersion and the cell scale fibrosis density (R 2 = 0. 65; P = 0. 00158, F-statistic). In contrast, Fig. 7C-E  illustrates moderate and non-existent correlations.
Participants A total of 112 participants were tested in four age groups: 44 3-to 5-year-olds (23 girls, mean age 3. 98 years, range 3. 08-5. 92 years; hereafter "preschoolers"), 26 6-to 8-year-olds (13 girls, mean age 7. 57 years, range 6. 32-8. 92 years; hereafter "school children"), 22 9-to 12-year-olds (11 girls, mean age 10. 51 years, range 9. 42-12. 79 years; hereafter "preteenagers") and 20 adults (10 women, mean age 24 years, range 18-34 years). The data of six additional participants were excluded from the analyses, because they quit before the end of the experiment (3 preschoolers), because they did not speak English fluently (2 preschoolers), or because a parent reported learning difficulties in mathematics (1 preteenager). All participants resided in the greater Boston area. Children were recruited through mailing and phone by the laboratory, and received a small gift in exchange of their participation.
400002/2013-2 to P. P. and Sidarta Ribeiro), with financial help from the d'Or Foundation (to P. P). This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. We thank Konika Banerjee, Annie Douglas, Kate Ellison, Amy Heberle and Danielle Hinchey for help in data collection in the U. S. Experiments conducted in the Munducuru territory (Para, Brazil) were directed by P. P. , in accordance with the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), the Brazilian Ministry for Science, Technology and Innovation (MCTI), and the Fundacão do Indio (FUNAI, Funai Processo 2857/04). We thank A. Ramos (Nucleo de Educação, Funai), S. Ribeiro, Randy Gallistel, Johan Rooryck, Alberto Tonda and T. R. Virgil for discussion and advice; and C. Tawe and J. Saw for help with data collection and analysis of the Mundurucu vocabulary.
Acknowledgments This work was funded by the  NIH  (grant  HD 23103  to E. S. S. ), the  NSF  ( STC award   CCF-1231216  to E. S. S. ), the  ERC  ( FP7  Project  263179   MathConstruction  to V. I. ), and the  Center for Brains, Minds and Machines. The study of the Mundurucu vocabulary was partly supported by a  CNPq  grant (Processo.
, 2015). We checked by simulation that the value of τ had no impact on the separation of the 14 N dq-dq correlation C. E. Dutoit et al. :  Electron magnetic resonance of vanadyl complexes peaks, which are important for the interpretation of the spectra (Fig. S9 ). The spectra were recorded with 256 × 256 data points for the t1 and t2 time domains. The unmodulated part of the echo was removed by second-order polynomial subtraction. Final HYSCORE spectra were obtained by 2D Fourier transformation of the data set, using a Hamming apodization window function. EPR and HYSCORE spectra were simulated with the EasySpin toolbox for MATLAB (version 5. 2. 28)  (Stoll and Schweiger, 2006).
As sq-dq transitions could not be detected for VO-P3 and VO-P4, it was not possible to obtain an estimation of |Q| in these cases. The 14 N parameters of the four VO-P complexes are reported in Table  3. The smaller a iso values measured for VO-P2 and for VO-P4 do not seem to have equivalents in the literature; however, they are relatively close to those measured by  Moons et al. (2017)  in vanadyl perfluorophtalocyanine (-6. 9 MHz). All this interpretation was based on the analysis of only a small portion of each HYSCORE spectrum (rectangular boxes in Fig. 5 ). This procedure raises the question of the origin of all other correlation peaks and ridges present in the HYSCORE spectra (Fig. 5 ). To test the validity of the proposed analysis, the whole 14 N HYSCORE spectra of VO-P1 and VO-P2 complexes recorded at the m I = +3/2 ⊥ field setting were simulated with EasySpin software  (Stoll and Schweiger, 2006)  by adjusting the values of a iso , T and Q zz (the z component of the quadrupolar interaction).
Results Of 1241 participants enrolled by community support teams across Dhaka, 1172 had complete data available for analyses. The remainder were removed due to duplicated sample identification codes that prevented reliable matching of test results to symptom metadata. These duplications occur at random, due to human error, and we do not believe they could bias results. Patient summaries by age, gender, case positivity and symptoms are presented in Table 1. Case positivity in Dhaka increased from 15. 8% to 23. 8% from the first (19th-26th May) to the last week (4th-11th July) of the study, corresponding to prevalence rising from 1. 4 to 13. 8 confirmed cases per 100 000 people. 1 ). Generic model classification performance for the one and four symptom models in Classes 2 and 3 is shown by their ROC curves (Figure  2 ).
These models were developed for prediction and classification in a unique sub-population: community support team (CST)identified, symptomatic patients in low-income communities in Dhaka. Different symptoms and risk factors were retained for different model classes, despite data being collected over a short period from the same population. These differences may point to mechanisms by which CST-identified and rapid antigen test positive individuals differ from other groups. They also underline the importance of collecting a relatively broad range of symptom data as the syndromic profile of the disease shifts from population to population. Of interest is whether individuals identified by PCR but missed by rapid antigen tests are less infectious and more typical of asymptomatic cases (perhaps due to different lengths of time since symptom onset).
This could be examined using viral load measured as Threshold Cycle (Ct) values from PCR and further testing for other illnesses. [21]  Our use of PCR as a validation test should also be explored further, as it does not have 100% sensitivity so additional validation tests may be informative. It is challenging, however, to find alternative gold-standards that can still be carried out in the community. [22]  Thanks to the modelling framework chosen, it is possible to include additional covariates where they are collected reliably, such as disease prevalence, vaccination rates or the individual's socio-economic status. This boost in diagnostic performance can be achieved with data already being collected in Bangladesh and with methods being rolled out in other low and middle income countries.
[23, 24]  We ensured our method is scalable by developing it using a large community-based sample and with input from the CST program organisers. As CST data are collected via a mobile phone application the diagnostic model can be updated in real-time. The algorithm of the app could therefore be modified to reflect local epidemiological requirements, informed by local case rates and the considered cost/benefits of misdiagnosis, thereby facilitating adaptation to new variants or even new diseases. However, these models only achieve good performance if validation data are of good quality from the focal population. Similarly, honing your diagnostic to a target misdiagnosis requires an in-depth understanding of local conditions, which can be challenging, requiring socio-economic insight. These are limitations not only of our method, but of all current diagnostic approaches which do not acknowledge them, ignore potential biases and reach only a small (and privileged) proportion of the population.
Structure We developed three model classes using: 1. the rapid-antigen-test result; 2. the syndromic data, and 3. the two data sources combined (Figure  4 ). We identified cases by PCR. For Model Class 2, we used a Bayesian multivariate probit model,  [25]  with multivariate referring to multiple response variables, not multiple explanatory variables. The models were fitted to the data using Bayesian inference techniques based on Hamiltonian Monte Carlo in the Stan programming language. [26]  Further technical details and model equations are presented in Supplementary Materials: Statistical Methodology.
Supplementary Materials: Statistical Methodology Below we have extended the modelling description provided in the main text to include more technical detail. The code used to implement these tasks is available at https://github. com/fergusjchadwick/COVID19_SyndromicRAT_public. Model Class 2 uses only the syndromic data. For this model, we used a Bayesian multivariate probit model. [25]  The multivariate probit structures the outcomes of the PCR test and symptoms presence/absence as a D-dimensional vector of binary outcomes (y i = (y i1 , y i2 ,. , y id ), y ij ∈ {0, 1}). These outcomes are determined by an indicator function which takes a D-dimensional vector of continuous latent variables (z i = (z i1 , z i2 ,. , z iD ), z ij ∈ R). These latent continuous variables then covary as realisations of a D-dimensional multivariate normal, with the mean of the error structure informed by a linear predictor (in our case formed of the covariates age and gender), J j=1 x ij β jd + ǫ id , and a covariance (Σ) between dimensions.
14. The CST team members should follow up physically with the VVF and his/her household members on the 3rd and 7th day. During the visit they will check VVF's temperature, oxygen saturation and confirm if the VVF and his family are maintaining quarantine (please see SOP 1 Process Flow_Urban Areas for details) Data on body temperature, oxygen saturation, adherence to home quarantine, will be recorded through the CST mobile app during the follow-up visits. The CSTs will also follow-up through telephone on the 10th day to make sure VVF and his family maintained home quarantine properly and also to enquire if they require further food support/medical attention.
In mountain regions, forests that overlap with seasonal snow mostly reside in complex terrain. Due to persisting major observational challenges in these environments, the combined impact of forest structure and topography on seasonal snow cover dynamics is still poorly understood. Recent advances in forest snow process representation and increasing availability of detailed canopy structure datasets, however, now allow for hyper-resolution (<5 m) snow model simulations capable of resolving tree-scale processes, which can shed light on the complex process interactions that govern forest snow dynamics. We present multi-year simulations at 2 m resolution obtained with FSM2, a mass-and energy-balance based forest snow model specifically developed and validated for meter-scale applications. We simulate a ~ 3 km 2 model domain encompassing forested slopes of a sub-alpine valley in the Eastern Swiss Alps and six snow seasons.
Simulations thus span a wide range of canopy structures, terrain characteristics, and meteorological conditions. We analyse spatial and temporal variations in forest snow energy balance partitioning, aiming to quantify and understand the contribution of individual energy exchange processes at different locations and times. Our results suggest that snow cover evolution is equally affected by canopy structure, terrain characteristics and meteorological conditions. We show that the interaction of these three factors can lead to snow distribution and melt patterns that vary between years. We further identify higher snow distribution variability and complexity in slopes that receive solar radiation early in winter. Our process-level insights corroborate and complement existing empirical findings that are largely based on snow distribution datasets only.
characteristics. Recently,  Koutantou et al. (2022)  presented a time series of Uncrewed Aerial Vehicle (UAV) borne LiDAR datasets to compare the evolution of snow depth distribution patterns on opposed slopes in an Alpine valley. Despite all coming to interesting conclusions, these studies were limited to process inferences as snow distribution datasets only reflect the combined impact of forest snow processes. Instead of relying on snow data alone, understanding process interactions can be advanced through sophisticated processbased models. In absence of observational data, or for predictive purposes, models are commonly used as a best estimate of reality  (Wood et al. , 2011; Wrzesien et al. , 2022). In forest snow research, the use of hyper-resolution models that resolve tree-scale processes is gaining popularity in applications that require the small-scale variability of these processes to be adequately represented  (Harpold et al.
Modelling framework Snow cover simulations with the Flexible Snow Model FSM2  (Mazzotti et al. , 2020b, a)  are at the core of this study's methodology (see overview in Figure  2 ). FSM2 is the upgrade of the Factorial Snow Model, FSM  (Essery, 2015) , with forest canopy included. We apply the version presented by  Mazzotti et al. (2020a)  specifically developed for hyper-resolution (meter-scale) simulations, which accounts for the influence of tree-scale canopy structure on individual forest snow processes by using process-specific canopy metrics. Notably, FSM2 can leverage detailed time series of point-scale transmissivity for direct shortwave radiation obtained with an external radiation transfer model. Past research has demonstrated that FSM2 is able to capture sub-canopy snow energetics by comparing model output to spatio-temporal multisensor datasets, for incoming irradiances, air space and snow surface temperature, and wind speed  (Mazzotti et al.
, 2020a). Consequently, FSM2 has been shown to accurately replicate small-scale forest snow cover dynamics and the respective microclimatic states  (Mazzotti et al. , 2020b, a). For this study, we run FSM2 at 2 m grid spacing (i. e. , 850'000 points) for 6 winters (i. e. , water years (WY) 2016-2021). Our model application follows  Mazzotti et al. (2021) , who used equivalent hyper-resolution simulations to explore model upscaling behavior. LiDAR datasets acquired through airborne laser scanning (ALS) in 2017 in the context of the first European mission of the Airborne Snow Observatory  (Painter et al. , 2016; Mazzotti et al. , 2019a)  provided the basis for computing all canopy metrics required by FSM2, including the shortwave transmissivity time series. A canopy height model was created following the LAStools-based algorithm from  Khosravipour et al.
, 2022) , allowing direct application in this study. Additional surface datasets (e. g. , elevation) were available through SLF's Operational Snow-Hydrological Service's (OSHD) modelling framework. These data were provided at 25 m resolution and interpolated to the model resolution (2 m). Meteorological forcing was also available from the OSHD. As described by  Griessinger et al. (2019) , gridded data of all necessary meteorological input variables (incoming short-and longwave radiation, air temperature and relative humidity, wind speed, rain-and snowfall rates, and atmospheric pressure) are provided by MeteoSwiss (COSMO-1 product) at hourly interval and 1-km resolution over all of Switzerland and further downscaled to model resolution. Additional corrections (for biases or terrain effects) are applied to some of the variables, e.
Verification of model use case The validation of individual modelled fluxes within FSM2 as presented by  Mazzotti et al. (2020a)  was done in the vicinity of our study area, with the same type of input data, and for the same type of canopy data. For this reason, another model validation is beyond the scope of this study; nevertheless, an assessment of our simulations against available snow distribution datasets was performed to ensure plausibility of the model application for our use case. We use four independent data sources: (1) Snow depth data acquired by automatic weather stations (AWS) in Davos and Weissfluhjoch to assess the simulations at open sites; (2) High-and medium-resolution satellite RGB imagery available through Planet Explorer (www. planet. com) at approx. weekly intervals for the last five modelled WY's, including Landsat 8, Sentinel-2 and PlanetScope, to evaluate modelled snow cover extent during periods of partial snow cover; (3) Snow depth distribution maps over the full domain from two of the 2017 ASO LiDAR acquisitions (see Section 2.
2) at 3 m spatial resolution, parts of which were used by  Mazzotti et al. (2019a) ; and (4) Time series of snow depth maps over two 150 x 150 m domains (see Figure  1 ) on the two opposed slopes obtained with a UAV-borne LiDAR in 2020, presented by  Koutantou et al. (2022). UAV-LiDAR data (Figure  3e-f ). These assessments reveal the model captures the general characteristics of snow depth and melt out patterns well. The strong temporal lag in melt out between the south-and the north-facing slope is clearly visible in the satellite imagery and likewise reproduced by the FSM2 simulations. Snow distribution features such as preferential melt along south-exposed forest edges and higher snow amounts in canopy gaps compared to nearby under-canopy locations in the north-exposed slope are also clearly present in both the LiDAR datasets and the simulations.
The impact of inter-annual variability in meteorological conditions can lead to the same locations hosting different regimes in different WYs, where differences result from either weaker accumulation patterns or variations in strength and timing of shortwave irradiance. The categorization of snow cover dynamics into regimes provides a context to temporal snapshots of snow distribution patterns, such as those derived from singular LiDAR datasets. The temporal evolution of correlation coefficients (Figure  11d ) corroborates findings from  Koutantou et al. (2022) , who used maps of modelled sub-canopy irradiance to explain why snow distribution patterns exhibited different dependencies on canopy structure at their north-and south-exposed survey sites. A decay of the correlation between snow distribution and canopy structure between two LiDAR acquisitions in spring was also observed by  Mazzotti et al.