dataesr/scientific-paragraphs-categorization huggingface dataset:
Lesion mask delineation and estimation of the extent of injuries High-resolution 3D T1-weighted anatomical SPGR (spoiled gradient recalled) image datasets were obtained for each patient on a 3 T MRI scanner (General Electrics) at the Babinski clinical facility of the Pitié-Salpêtrière Hospital, with a standard head signal reception coil and the following parameters: RT (repetition time) = 7,164 ms; TE (echo time) = 3,124 ms; inversion time = 380 ms; flip angle = 15 ; acquisition matrix = (0, 288, 256, 0); voxel resolution = 0. 5 × 0. 5 × 1. 2 mm 3 ; slice thickness = 1. 2 mm; spaces between slices = 1. 2 mm.
Notice in this example panel the presence of a lesion mask (in black) encompassing parts of the cingulum bundle. The percentage of damage was estimated for each of these tracts. The "Neuropsychological evaluation tests" section shows patient's behavioral performance in the bells cancellation, line bisection, and letter cancellation tests (red circles represent patients' correct performance, while green circles represent distractors mistaken as targets) and the binarization of the score (deficit = 0, normal performance = 1). The MSA approach included the generation of the original-graded dataset, the application of the estimated MSA with 1,000 sampled permutations that we performed 1,000 times with bootstrap samples and the computation of each white matter tract contributions. MSA, multiperturbation Shapley value analysis; WM, white matter; L, left; R, right Source: Illustrations are adapted from Rojkova et al.
Letter cancellation and bells cancellation shared positive contributions from the ATP, the OR and the IFOF, and negative contributions by the CP and the CA. One may also note that the RoB, representing the ensemble of white matter bundles not considered individually in the coalition, showed negative contribution to line bisection but positive contributions to the letter and bells cancellations tasks. F I G U R E 4 MSA contributions to visuospatial attentional orienting processes of white matter tracts for each of the three neuropsychological tasks. Normalized mean MSA contribution values (± SD) for line bisection, bells cancellation, and letter cancellation, respectively (top, middle, and bottom). Values are computed using the original-graded dataset based on machine-learning predictions of performance scores, corresponding to configurations generated with 1,000 sampled permutations, and a bootstrap procedure with 1,000 samples to improve the robustness of the results (contributions and estimation of error).
M. , SFB 936/A1, Z3, and TRR 169/A2 to C. C. H. We are thankful to Dr. Bruno Dubois for scientific and logistic support, Dr. Bénédicte Batrancourt for managing the CAC database, and Dr. Paolo Bartolomeo for advice with the selection and scoring approaches of neuropsychological neglect tests. We thank two anonymous reviewers for their help in improving the manuscript. We also thank the EFRAD foundation for providing equipment funds and the Naturalia & Biologia Foundation for providing travel grants for some of the coauthors. The authors declare no competing financial interests. Preliminary results of this work were presented at the Cognitive Neuroscience Society, San Francisco, USA, 2019.
2. 4 | 4 Lesion mask delineation and estimation of the extent of injuries High-resolution 3D T1-weighted anatomical SPGR (spoiled gradient recalled) image datasets were obtained for each patient on a 3 T MRI scanner (General Electrics) at the Babinski clinical facility of the Pitié-Salpêtrière Hospital, with a standard head signal reception coil and the following parameters: RT (repetition time) = 7,164 ms; TE (echo time) = 3,124 ms; inversion time = 380 ms; flip angle = 15 ; acquisition matrix = (0, 288, 256, 0); voxel resolution = 0. 5 × 0. 5 × 1. 2 mm 3 ; slice thickness = 1. 2 mm; spaces between slices = 1. 2 mm.
Dietary Records During the period, the three different running exercise modalities were performed, and participants recorded food intake. At the first visit tothe laboratory, a standardized individual information session was performed to instruct subjects to record their daily food intake. Food quantities were estimated, specifying the number of units and a code corresponding to the size of the portion, using a reference portion guideline book. Data collected from each participant was analyzed using the Bilnut 4 software package (SCDA Nutrisoft, Cerelles, France) and the food composition tables published by the Tunisian National Institute of Statistics in 1978  [22].
Statistical Analysis All data are presented as means ± standard deviation (SD), and were analyzed using STATISTICA for Windows software (version 6. 0, StatSoft, Inc, Tulsa, OK, USA). The normality of every dependent variable and homogeneity of the variances of the distributions (equal variance) were confirmed using the Shapiro-Wilk test and the Levene test, respectively. For all physical and biochemical data, a two-way ANOVA with repeated measures [exercise (continuous, 15/15, 30/30) × time (pre-exercise vs. post-exercise)] was used. When appropriate, post hoc comparisons were made with the Bonferroni test. Statistical significance was accepted at p < 0. 05.
With this aim the detailed geometry of the PTW 30013 Farmer ionization chamber currently used at GSI was implemented in the transport code FLUKA and the beam quality correction factor k Q for different energies and ion species was calculated. Further details about the robustness of the calibration are investigated as well, e. g. , appropriate irradiation depth of biological samples. Evidence is presented that for ions above 1 GeV/u the k Q factor decreases due to the density effect, which modifies the water-to-air stopping power ratio at relativistic energies. These findings are of particular importance for future biophysics experiments with ion beams from the SIS100 in the framework of the FAIR project. For energies in the regime of several GeV/u the constant kQ value as used in common practice should be replaced with the energy-dependent correction factor provided in this work.
Monte Carlo Simulations Monte Carlo simulations using the FLUKA code (version 2011. 2x. 5) were performed to obtain D w / Dair ratios to calculate k Q correction factors according to Equation  (7). Simulations were carried out for different ion species ( 1 H, 4 He, 12 C, 40 Ar, 56 Fe) at different energies (350 MeV/u, 1 GeV/u, 4 GeV/u, 10 GeV/u) for a field size of 5 × 5 cm 2. The geometry of the PTW 30013 Farmer chamber was modeled according to blueprints from the manufacturer (geometry with delta electron tracks shown in Figure  2 ) and it was positioned with the effective point of measurement at a water depth of 7 mm, a typical measurement depth at GSI Cave A (4. 8 mm phantom depth plus about 2 mm vacuum window, monitor chamber and air gap). The mean dose in the active volume Dair was scored in the air volume inside the chamber.
g. , full Rayleigh and Coulomb scatter corrections, heavy fragment evaporation and coalescence). Recently, a Fano cavity test performed by Lourenco et al. [35]  showed that the FLUKA code passes the test within 0. 15% if the step size in the multiple Coulomb scattering algorithm is set small enough compared to the dimensions of the cavity of interest. Therefore, in order to maximize the transport precision for the simulations of the energy deposition in the small cavities, the multiple Coulomb scattering was suppressed in these regions by adding the MULSOPT card to the FLUKA input file. Using this card, the minimum step length for multiple Coulomb scattering was increased by a factor of 10000 and single scattering was activated. The material definitions of air, graphite and water were defined according to the ICRU 90 recommendations  [25]  (details reported by Baumann et al.
Energy and Ion Dependence of the Water-to-Air Stopping Power Ratio The FLUKA code allows an extraction of stopping power tables. From the tables for water and air, the water-to-air stopping power ratio S w,air as a function of energy for different ions was obtained. The results are shown in Figure  3  and are in accordance with recent data reported by other authors  [36]. It can be observed that the water-to-air stopping power ratio is independent of the ion species for energies above 100 MeV/u, which justifies the assumption of a k Q value independent of the ion species if ion type specific detector perturbations and variations of the W air value are neglected. The (W air ) Q term of Equation (  6 ) is indeed assumed to be ion independent within a 1. 5% uncertainty  [25]. Concerning the p Q term, ion specific variations in the level of permille or even percent can be expected, however, there are no reliable experimental data on heavy ion perturbation factors and their calculation would require very realistic and well benchmarked transport codes.
Energy and Ion Dependence of the PTW 30013 Farmer Chamber Beam Quality Correction Factor k Q D w / Dair ratios for 1 H, 4 He, 12 C, 40 Ar, and 56 Fe ions were obtained with FLUKA radiation transport simulations through a geometrical model of the PTW Farmer chamber and a water geometry with a small scoring voxel at the effective measurement position of the chamber. From these dose ratios, beam quality correction factors k Q were obtained for the ions listed above over a wide span of energies using Equation  (7). The results are reported in Table  2. In Figure  4  they are compared with the constant value k Q = 1. 032, which is recommended for heavy ions by the TRS-398 protocol  [15]  and currently used for the beam monitor calibration at GSI Cave A and M. Additionally, they are compared with an energy-dependent calculation based on the formalism from TRS-398 (Equation  6 ) using as input for the term (S w,air ) Q the energy-dependent water-to-air stopping power FIGURE 4 | Beam quality correction factor k Q for the PTW 30013 Farmer ionization chamber positioned with the effective point of measurement at a water depth of 7 mm for 1 H, 4 He, 12 C, 40 Ar, and 56 Fe calculated using (D w / Dair ) Q from FLUKA simulations as inputs for Equation (7) (symbols) are compared with the constant heavy ion k Q recommended in the TRS-398 dosimetry protocol  [15]  (dashed line) and an energy-dependent calculation using the water-to-air stopping power ratio for protons extracted from the FLUKA code in Equation (6) (solid line).
For comparison a 12 C k Q value measured by means of water calorimetry  [21]  is shown. The energy ranges provided by the heavy ion accelerators SIS18 and SIS100 are indicated. TABLE 2 | Beam quality correction factors k Q for the PTW 30013 Farmer ionization chamber positioned with the effective point of measurement at a water depth of 7 mm for 1 H, 4 He, 12 C, 40 Ar, and 56 Fe calculated with the FLUKA code for kinetic energies of 350 MeV/u, 1 GeV/u, 4 GeV/u, and 10 GeV/u. The σ column reports the uncertainty related to the k Q values (1σ ). Energy 1 H 4 He 12 C 40 Ar 56 Fe GeV/u k Q σ k Q σ k Q σ k Q σ k Q σ 0. ratios from FLUKA (Figure  3 ). Also the k Q value measured for 383 MeV/u 12 C by means of calorimetry  [21]  is reported. The error bars of the FLUKA data points contain the statistical uncertainty of the Monte Carlo simulation and the systematic uncertainties of the other input parameters of Equation  (7).
Uncertainty Analysis The type A uncertainty of Monte Carlo simulations due to the limited number of particle histories can be calculated as the standard deviation of the output from individual simulation runs using different random number seeds. They are considerably larger for the heavy ion simulations than for the 60 Co photons because photon simulations are more efficient in terms of calculation time. The type B uncertainty of Monte Carlo calculated (D w / Dair )60 Co ratios (due to uncertainties in the radiation physics and transport models, geometry, etc. ) were estimated to be 0. 5% based on an intercode comparison by Baumann et al. [33]  and a benchmark experiment by Renner et al. [40]. The corresponding type B uncertainty of the (D w / Dair ) heavy ion ratios from FLUKA simulations is more difficult to estimate but certainly larger than the uncertainty for 60 Co photons.
The water-to-air stopping power ratios in FLUKA (shown in Figure  3 ) can be considered realistic, however, inaccuracies in the physics models (in particular those describing nuclear reactions) or simplifications in the detector geometry model can lead to uncertainties in the predictions of detector perturbations. The type B uncertainty of the (D w / Dair ) heavy ion ratio was estimated to be about 1 -2% and probably depends on the ion species and energy since the FLUKA code is for some ions better benchmarked than for others. Especially for 12 C ions in the therapeutic energy range its transport and physics models are well-developed  [41]  which reflects in the good agreement of the Monte Carlo result and the water calorimetry measurement (see Figure  4 ). Since the W air values are required as input in Equation (  7 ) also their uncertainties have to be considered for calculated k Q values.
FE Model The finite element of the face model, developped in the ANSYS software environment, consists of three layers totalling of 6342 hexahedral linear elements (with 8720 nodes). The outer layer corresponds to the dermis layer and the two other sublayers up to bony structure represent the hypodermis tissue of the face (Fig. 1a ). On the outer surface a thin membrane layer (0. 1mm thick) has been added to account for the epidermis layer. The material properties of the dermis and hypodermis layer are modelled with a Yeoh 2nd order model. Since skin stiffens exponentially when stretch ratio exceeds a limiting value, a Gent material, which well accounts for this behaviour, has been used. The material parameters of these layers are given in Table  1. The inner surface of the deepest face layer is in contact with mandible and maxilla.
That is, that children and adolescents often sample sport, whereby they play multiple sports before specialising in fewer or even one sport only. Therefore, by using o cial participation records across sports and combining the data for analysis and reporting, some participants would be counted multiple times if they play multiple sports  (27). However, a recent study of 907,150 participant records for 11 major sports used demographic data to achieve more accurate matching across sports to identify the number of individual players within the sport cohort (26). The results showed that the effect of sampling, people playing multiple sports, was highest for ages 5-14, after which this number diminishes as specialisation increases. Furthermore, the study con rmed that after adjusting for this change in behaviour, the drop-off in communitylevel sport participation during adolescence is real and not simply an artefact of sampling/specialisation behaviour (26).
Participants We conducted a longitudinal analysis of sport participation records for 8 major sports from 2015-2017, whereby individual participation within sports was tracked from 2015 to subsequent years 2016 and 2017. The data for this analysis were collected and analysed as a part of the longitudinal Sport and Recreation Spatial (www. sportandrecreationspatial. com. au) program of research and have been published in detail previously  (27, 34). The Sport and Recreation Spatial program monitors participation in sport for the purpose of informing policy development and program planning for the sport and recreation sector. A participant, or player, was de ned as a registered member of a Victorian sporting club competition or program that was a liated with one of the State Sporting Associations.
Data analysis {Eime, 2016 #4705;Eime, 2016 #4413}The primary data analysis for this investigation involved following an individual's participation in a sport from 2015 to 2017. To do this, sport participation records from eight sports (Australian football, basketball, cricket, football (Soccer), golf, gymnastics, netball, and swimming) were amalgamated. These represent popular Australian sports and are within the top 10 Australian club based sports  (35). A participant must have been playing the sport in the rst year (2015), and live in Victoria to be part of the analysis. Those aged 4-29 in 2015 were chosen for analysis, as this age range has been shown to generally have higher participation rates (26, 27). Participation pro les for players in ve-year age cohorts (4-29) were produced. The separate four year old group was speci cally included due to a considerable number of community-level club-based sport participants being aged less than 5 years, aged 4, but not younger  (27).
Participants who had missing data for age or sex were excluded from the analysis. The characteristics of interest in this study was dichotomous participation or no participation. Participants were tracked over time using their sport's unique identi er (i. e. sport 'ID') and were further categorised into a pattern of participation with three categories. 1) Drop-out, where a participant played only in 2015, or played in 2015 and 2016 (but not 2017). 2) Continuous participation from 2015 to 2017. 3) Discontinuous, where the participant played in 2015 and 2017, but not 2016. Participation pro les for players in ve-year age cohorts (4-29) were produced. Ethics approval was granted by the University Human Research Ethics Committee, project number C13-007.
Therefore, to optimise enjoyment in sport participation at all stages of life, 'how to retain' participants naturally sharpens the focus of sport managers on the needs of the customer -the sport participants. A strength of the sport registration data is that it is a large dataset of all registered sport participants within each given sport and that it provides a comprehensive and accurate record on which to base longitudinal analysis. However, a limitation of this study and the associated sport participation dataset is that it does not cover all sports, nor does it include other non-organised sport participation, or any general physical activity. Recommendations for further research include understanding individuals' transitions into and out of sports and other physical activities across the lifespan, which would provide sport and physical activity pro les that collectively can measure and monitor individuals' health enhancing behaviours.
Radar Data Analyses Raw instantaneous velocity-time data captured with the radar was manually processed using the Stalker ATS system software (Version 5. 0, Applied Concepts, Inc. , Dallas, TX, USA) following procedures outlined by Simperingham et al. [28]  and then exported to Microsoft Excel (version 14. 6. 4, Microsoft, Redmond, DC, USA). Using processed data, the start of the deceleration phase was defined as the time point immediately following maximum velocity (V max ). The end of the deceleration phase was defined as the lowest velocity (V low ) following V max. The maximal horizontal deceleration ability was assessed using two methods: (1) average horizontal deceleration (HDEC) and (  2 ) average horizontal braking impulse (HBI). Both of these variables were calculated from the average of the instantaneous HDEC and HBI data points captured from the start to the end of the deceleration phase.
The instantaneous horizontal deceleration was calculated between each data point throughout the entire deceleration phase using the following equation: Deceleration m•s -2 = v f -v i t f -t i , ( 1 ) where v is the velocity, t is the time, f indicates the final velocity or time and i indicates the initial velocity or time. The instantaneous HBI was calculated between each data point throughout the entire deceleration phase using the following equation to calculate the change in momentum: J(t) = M f -M i ( 2 ) where J is the impulse, M f is the final momentum and M i is the initial momentum. The instantaneous momentum was calculated for each data point throughout the entire deceleration phase using the following equation: Momentum (t) = v × mass (3) The coefficient of variation (CV%) values calculated from the two best trials were 4.
Force Platform Analyses CMJ force-time data were acquired and analysed using commercially available software (ForceDecks, Vald Performance Pty Ltd. , Brisbane, Australia), which calculates a range of NMP variables that characterise performance during the jump. The definition and absolute reliability (coefficient of variation, CV%) for each CMJ NMP variable are reported in Table  1. All CMJ kinetic variables were divided by body mass to enable normalization amongst participants. Sample force-, power-and velocity-time curves during the eccentric and concentric phases of the CMJ are shown in Figure  2. Before each CMJ test being performed, the force-plates were zeroed using the manufacturer's software. The start of the CMJ (movement onset) was defined using a 20 N offset from the measured body mass.
Statistical Analysis The mean ± SD was calculated for all CMJ variables. Participants were dichotomised into highand low-horizontal-deceleration-ability groups based on a median split of the HDEC performance and separately split based on HBI performance. The differences in mean CMJ NMP variables between high-and low-deceleration-ability groups were examined using the independent samples t-test and Cohen's ds effect size with 90% confidence intervals, calculated using an online Microsoft Excel spreadsheet  [30]. The magnitude of the effect size was interpreted using thresholds as suggested by Cohen  [31] : 0. 0 to 0. 19-trivial; 0. 20 to 0. 49-small; 0. 50 to 0. 79-moderate; >0. 80-large. The common language (CL) effect size was also calculated using the online Microsoft Excel spreadsheet to provide a percentage probability of a player from the high-deceleration-ability group having a greater measurement than someone from the low-deceleration-ability group  [30].
Statistical Analysis The mean ± SD was calculated for all CMJ variables. Participants were dichotomised into highand low-horizontal-deceleration-ability groups based on a median split of the HDEC performance and separately split based on HBI performance. The differences in mean CMJ NMP variables between highand low-deceleration-ability groups were examined using the independent samples t-test and Cohen's d s effect size with 90% confidence intervals, calculated using an online Microsoft Excel spreadsheet  [30]. The magnitude of the effect size was interpreted using thresholds as suggested by Cohen  [31] : 0. 0 to 0. 19-trivial; 0. 20 to 0. 49-small; 0. 50 to 0. 79-moderate; >0. 80-large. The common language (CL) effect size was also calculated using the online Microsoft Excel spreadsheet to provide a percentage probability of a player from the high-deceleration-ability group having a greater measurement than someone from the low-deceleration-ability group  [30].
Subsequently, when measuring a player's maximal horizontal deceleration ability, we recommend that practitioners should consider both the HDEC and HBI performances. These are derived from the same test and do not require additional data collection, and both may inform decisions based on the individual's deceleration profile relative to group-based normative data. For the purposes of obtaining an indirect neuromuscular indicator of a player's horizontal deceleration capacity, concentric mean power was the only variable that differentiated both higher and lower performers characterized with both HDEC and HBI. Accordingly, based on currently available evidence, this metric would seem to be the best overall indicator. Despite the significance of the eccentric phase to horizontal deceleration ability, our findings demonstrate that concentric phase variables had the largest differences between both high and low horizontal deceleration abilities (HDEC and HBI).