dataesr/scientific-paragraphs-categorization huggingface dataset:
0. pdf). The use of these data for carrying out the presented work has been approved by the following ethical review committees: comité de protection des personnes (CPP) Ile de France III Hôpital Tarnier-Cochin under reference 3188 (ENROLL-HD) and comité de protection des personnes (CPP) Ile de France IV groupe hospitalier Pitié-Salpêtrière under reference 6407 (TRACK-HD and TRACK-ON). The research has been performed in accordance with the Declaration of Helsinki. The research was performed in accordance with relevant guidelines and regulations. Feature selection and processing. The TRACK-HD and TRACK-ON cohorts report various clinical, cognitive, and imaging measurements from which we kept the variables that meet the following conditions. Variables must show a significant difference between mutation-carriers and non-mutation carriers and their rate of change should be significantly different from 0 (Mann-Withney U test p value smaller than 0.
2B, C ); and (2) forecast of future biomarker values (Fig. 2D ). Individual parameters are estimated by maximizing the likelihood of the new participant's data using the estimated mean and variance of these parameters in the training population as priors. Such priors allow us to estimate the most probable pace of progression even from baseline measurements only. The prediction of the trajectory is entirely driven by the series of endpoints at a given age. These series of measurements contain a signature that is predictive of the progression. In particular, we do not use the number of CAG repeats to predict the progression of the endpoints. We test a posteriori that the estimated pace of progression is highly correlated with the number of CAG repeats as a validation measure. We use the publicly available software Leaspy to estimate the model parameters from a longitudinal data set and fit the trained model to new data.
The software is publicly available at: https:// gitlab. com/ icm-insti tute/ arami slab/ leaspy/. Experimental design. We calibrated HD COURSE MAP from TRACK-HD and TRACK-ON data with a minimum of two visits per participant. Confidence interval for population parameters were estimated over 100 bootstrap runs that each included only 80% of the participants. We compared the mean progression of each feature in terms of HA to predefined thresholds, thereby revealing the temporal ordering of the deviation of the biomarkers from their control values. For that purpose, we used the point where the biomarker exceeded the 95th percentile of the control distribution. We then performed multivariate regression of each individual parameter of the model against five cofactors: sex, number of CAG repeats, level of education, number of visits and handedness.
To estimate confidence intervals on the p values, we bootstrapped the model 100 times and ran 100 multivariate analyses. The reported p values correspond to the median over the runs. To use ENROLL-HD as external validation cohort, we re-estimated HD COURSE MAP from the same participants in TRACK studies but using only the biomarkers that are in common between TRACK and ENROLL-HD (see Fig. 1 ). We use then baseline data from ENROLL-HD participants and forecast their data at follow-up visits after 1, 2, 3, and 4 years. The prediction of a given biomarker is made on ENROLL-HD participants for whom the baseline value of this biomarker already deviates from control. We defined 16 target groups (4 times 4) within these participants: the participants for whom the annual percentage change of the biomarker will progress by 5%, 10%, 15% and 20% in 1, 2, 3 and 4 years.
We select these participants based on thresholds on the CAP, PIN, and the changes in biomarkers predicted by HD COURSE MAP and build the corresponding ROC curve and compute the AUC. We used PIN instead of MRS as it was conceived as an extension and improvement of the MRS given external validation cohorts  10. We adapted the procedure for the volumetric measurements of deep brain structures. We did the prediction and selection on TRACK participants as ENROLL data do not contain imaging. We used therefore a crossvalidation strategy, training HD COURSE MAP on 80% of the participants and evaluating forecasts on the remaining 20% (Fig. 1 ). We demonstrate the value of our method by applying it to identify the potential participants most likely to progress during clinical trials targeting early HD patients.
We simulate clinical trials with different durations, theoretical treatment effects, inclusion criteria, endpoints and disease stages (Table  2 ). Trial simulation designs were inspired by the past clinical trials, in particular Crest-D, Pride-HD and 2Care-HD  [37] [38] [39]. We define disease stage based on arbitrary thresholds on TMS or TFC as in real clinical trials. After choosing the disease stage, the corresponding outcome and duration for each trial, we further selected subjects by choosing thresholds predicted by each of the three methods: CAP, PIN and HD COURSE MAP. In all cases, the chosen thresholds select 50% to 70% of the participants. We ensure the number of selected participants to be the same for all three methods for a fair comparison. For these simulations, we used HD COURSE MAP calibrated on 4 biomarkers only to avoid requesting too many data from participants at screening (Fig.
Acknowledgements We are very grateful to the participants of TRACK-HD, TRACK-ON and ENROLL-HD, whose data were the basis of this analysis. The work has been funded in part by the  European Research Council (ERC)  under grant agreement No  678304 ,  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  666992  ( EuroPOND ) and No  826421  ( TVB-Cloud ), and the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( IHU ICM ) and  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). The TRACK-HD and  Track-On HD  group members are  Tanka Acharya ,  MSc, University of Iowa, Iowa City ;  Sophie Andrews, PhD ,  Monash University, Melbourne, Victoria, Australia; Natalie Arran, DClinPsy, University of Manchester, Manchester, United Kingdom ;  Eric Axelson, PhD ,  University of Iowa, Iowa City ;  Eric Bardinet, PhD ,  Institut du Cerveau et de la Moelle Epinière and Pitié-Salpêtrière University Hospital, Paris, France; Natalie Bechtel, MD, University of Münster, Münster, Germany; Claire Berna, MSc, University College London, London, United Kingdom ;  Stefan Bohlen, PhD ,  George-Huntington-Institute, Münster, Germany ;  Beth Borowsky ,  CHDI, New York, USA ;  Jenny Callaghan, BSc ,  University of Manchester, Manchester, United Kingdom; Amy Cassidy, MSc, London School of Hygiene & Tropical Medicine, London, United Kingdom; Allison Coleman, BSc, University of British Columbia, Vancouver, British Columbia, Canada ;  David Craufurd, PhD ,  University of Manchester, Manchester, United Kingdom ;  Helen Crawford, PhD ,  University College London, London, United Kingdom ;  Rachelle Dar Santos, BSc ,  University of British Columbia, Vancouver, British Columbia, Canada ;  Joji Decolongon , MSc,  University of British Columbia, Vancouver, British Columbia, Canada ;  Eve Dumas, PhD ,  Leiden University Medical Center ,.
Observations of fault geometry and cumulative slip distribution serve as critical constraints on fault behavior over temporal scales ranging from a single earthquake to a fault's complete history. The increasing availability of high-resolution topography (at least one observation per square meter) from air-and spaceborne platforms facilitates measuring geometric properties along faults over a range of spatial scales. However, manually mapping faults and measuring slip or scarp height is time-intensive, limiting the use of rich topography datasets. To substantially decrease the time required to analyze fault systems, we developed a novel approach for systematically mapping dip-slip faults and measuring scarp height. Our MATLAB algorithm detects fault scarps from topography by identifying regions of steep relief given length and slope parameters calibrated from a manually drawn fault map.
We applied our algorithm to well-preserved normal faults in the Volcanic Tablelands of eastern California using four datasets: (1) structure-from-motion topography from a small uncrewed aerial system (sUAS; 20 cm resolution), (2) airborne laser scanning (25 cm), (3) Pléiades stereosatellite imagery (50 cm), and SRTM (30 m) topography. The algorithm and manually mapped fault trace architectures are consistent for primary faults, although can differ for secondary faults. On average, the scarp height profiles are asymmetric, suggesting fault lateral propagation and along-strike variations in the fault's mechanical properties. We applied our algorithm to Arizona and Utah with a specific focus on the normal Hurricane fault where the algorithm mapped faults and other prominent topographic features well.
Hodge et al. 's  [25]  Scarp Parameter Algorithm (SPARTA) maps normal fault scarps from calibrated slope and curvature parameters. Sare et al. 's  [26]  algorithm uses a curvature template and a hillslope diffusion model to map dip-slip fault scarps and calculate height and morphologic age. Howe et al. 's  [27]  approach constrains slip by mapping paleolake shorelines from topographic inflection points. Despite these advancements, many algorithms either map faults or measure slip and are often applied to a single geomorphic setting, which limited their broad usage. Here, we present a new computational algorithm that maps normal faults and estimates scarp height from highresolution and global topography datasets. Our algorithm significantly reduces the time required to build large fault geometry and scarp height datasets.
Topographic Data We applied our algorithm to four topographic datasets in the Volcanic Tablelands with spatial resolution varying over three orders of magnitude. "High-resolution topography" includes small uncrewed aerial system (sUAS) photogrammetric data, airborne lidar, and Pléiades satellite topography (Figure  1(d) ). We use the 30 m global Shuttle Radar Topography Mission (SRTM) topography dataset in the Volcanic Tablelands (Figure  1(a) ) and for the Arizona and Utah mapping. 3. 1. Small Uncrewed Aerial System (sUAS). We collected 2334 images over a single fault system in the southern test zone (Figure  1   at ~110 m above ground level. We produced a topographic point cloud using structure-from-motion techniques  [15, 16, 40]  and Agisoft Metashape software. We used external georeferencing with differential Global Navigation Satellite System (GNSS) and ground control points.
The average 3D root-mean-square error relative to the GNSS positions is 2 cm for the six ground control points. The point cloud covers 1. 6 km 2 and has a point sampling of 278 points/m 2. The algorithm input is the point cloud resampled to 28 pts/m 2 , which reduces the computational burden for the mapping. Scott et al. [41]  published the dataset which is available from OpenTopography (https:// www. opentopography. org/).
Airborne Light Detection and Ranging (Lidar). The airborne lidar data were acquired in 2014  [42]  along predominantly ESE-oriented flight swaths with an ~375 m spacing. This dataset spans the southern half of the Tablelands and has 15. 5 pts/m 2. Like many lidar datasets, absolute errors are unreported, although surveys typically have 5-15 cm vertical errors and five times larger horizontal errors  [43] [44] [45]. The error is commonly observed as an apparent topographic offset along the flight line overlap. The algorithm input is the full resolution point cloud. The dataset is available from OpenTopography.
Stereosatellite Topography. The stereosatellite imagery was acquired on August 17, 2017, by the Pléiades satellites. To produce digital surface models (DSMs), we follow the same method as Mattéo et al. [22]. Each dataset has a one panchromatic image at a 50 cm resolution and a multispectral image with four bands at a 2 m resolution. Using the panchromatic tristereo images, we produced a 50 cm DSM with a 1 m vertical resolution (MicMac software, free open-     source solution for photogrammetry;  [46, 47] ). The algorithm input is the DSM with points stored as individual x, y, and z coordinates, like in a point cloud. Figure  2  shows an example topographic profile from the Pléiades data.
Slope and Length Mapping Parameter Calibration. Along each topographic transect, we conducted a grid search over the mapping parameters (θ length and θ slope ) based on the manual maps, as shown in Figures  4  and 5. The ideal mapping parameters depend on dataset resolution and noise, mapping scale, and the overall fault size. We evaluate the mapping parameters with two criteria that penalize for manually mapped faults missed by the algorithm and for algorithm mapped faults not mapped manually. A higher score indicates better performance. For the HRT, we used a 10 m buffer for the offset of the algorithm and manually mapped faults, which allows for some variability in fault position. The correct fault score (CorrFault) is the fraction of the algorithm's fault points with a corresponding manually mapped fault within the distance buffer: This score penalizes for false positives, in other words, for faults mapped by the algorithm but not manually.
Scarp Height Error. There are three main error sources in the scarp heights: (1) the choice of mapping parameters, (2) topographic dataset error, and (3) nonfault features identified by the algorithm. (1) The parameter calibration depends on the scale and fault map quality, which determine the landforms identified as scarps. The Fish Slough mapping results at the two scales indicate the large impact of the mapping parameters (Figure  3 ). However, this error is difficult to quantify. Due to (2), we interpret some heights with caution, for example, the EW "scarps" in the lidar-derived heights (particularly visible in the southern half of Figure  6 (e)). (  3 ) Non-fault feature like ancient fumaroles and river channels are approximately symmetric so their net height is often close to zero. Asymmetric features can be removed by a geologist (Section 5.
This process of labelling and simple mapping of faults is greatly expedited by the algorithm's results. Along composite arrays, we summed the height profiles (as in Figure  7 ). The 152 fault maps and their profiles are shown in the supplemental material (available here). We summarized this analysis with normalized cumulative scarp heights (Figure  9 ) and fault length to height scaling relationships (Figure  10 ). 5. 9. Validation: Manual Estimation of Scarp Height. To make manual scarp height measurements, we used the MATLAB code published by Scott et al. [63]  for Bello et al. [64]. We extracted 2 m wide transects of the Pléiades topography separated by 50 m and perpendicular to the average fault strike. We manually selected two points along each of the footwall and hanging wall flats and one point at the scarp middle.
Along the southern fault, the algorithm captures smaller-scale fault steps that were too small to be measured manually. 6  and 7 , the algorithm identified the major NNE striking and west dipping faults in each dataset. With SRTM data, the algorithm mapped the central portion of the two major west-dipping faults but missed many secondary faults. With the Pléiades and lidar data, the algorithm mapped the primary faults, many secondary faults, and ancient fumaroles. The lidar flight line offset produced east-west-oriented strips of "scarps" separated by 300-400 m. versus normalized length for 152 faults measured with our algorithm in the Volcanic Tablelands. Black lines: fault height profiles plotted so that their maximum height occurs along 0 to 0. 5 of the normalized length; red: 50 th percentile (i.
Southern Test Zone. As shown in Figures The optimal mapping parameters (shown in the lower right corner of each panel in Figure  6 ) have a similar θ slope for all datasets and small variations reflect resolution and noise. θ length varies more: SRTM's θ length of 30 m equals the data resolution. For the lidar and SfM datasets, θ length is 5 m. SfM's lack of extra sensitivity may reflect that the manual mapping is done on the lidar data, the algorithm ignores small-scale details, and/or the SfM data was resampled by a factor of 10. The main fault has four segments whose height decreases northward. Using all four datasets, the algorithm resolved the ~30 m maximum scarp height and the second largest scarp whose apparent height decreases with dataset resolution. The algorithm identified the two northern segments from the HRT and a single broader segment from the SRTM topography.
Comparison to the Brigham2021 Algorithm. We compared our algorithm to Brigham and Crider's  [65]  algorithm, abbreviated here as Brigham2021. Brigham2021 automatically maps fault scarps and estimates scarp height by measuring the elevation difference between the fault scarp's lower and upper extremes (the toe and crest, respectively) that bound the free face and the talus slope, as shown in Figure  11(a). The algorithm inputs are scarp profiles that are roughly perpendicular to the fault strike. To determine the local curvature minima and maxima, each topographic profile is fit with a sum of B-spline functions using the Python package NURBS-Python  [66]. To map the scarp crest position, the algorithm considers the angle a between each curvature maximum and its proximate points (situated m = 20 profile points away from the maximum); the crest is the maximum with the smallest a.
Application of the Mapping Algorithm to Arizona and Utah 7. 1. Hurricane Fault. The normal Hurricane fault  [67, 68]  crosses the western Arizona and Utah state border in the physiographic region between the Basin and Range and Colorado Plateau, as shown in Figure  12. The high-angle fault is 250 km long with west-side down synthetic scarps and eastside down antithetic scarps. We chose this as a second location for our mapping algorithm because of the prominent fault and other features such as the Grand Canyon. The Hurricane fault is larger than the Tableland faults, so this analysis demonstrates that our algorithm can be successfully applied to faults of different scales. We used SRTM topography because this dataset is ubiquitous below 60 °latitude [48]  and therefore covers the Hurricane fault as well as other locations where our algorithm could be applied in future studies.
Discussion 8. 1. Optimal Fault Mapping Parameters. The ideal fault mapping parameters depend on the dataset resolution, noise, and feature size. Without surprise, at the Fish Slough fault, the fine-scale mapping (Figure  3 ) requires a lower θ length than the coarse-scale mapping. At the southern test zone, the optimized θ slope varied from 0. 09 to 0. 13 m/m with the highest value for the Pléiades data, likely due to the high pixel to pixel noise. The SRTM data had the largest θ length equal to 30 m, the pixel size. The lidar and SfM datasets had identical mapping parameters, likely because the pixel size is much smaller than the dimension of the faults and boulders. The Hurricane fault mapping parameters exceeded those at the southern test zone in the Volcanic Tablelands with SRTM topography due to the Hurricane fault's large scale.
For the length to maximum displacement scaling law (Equation (  1 )), we solve for a best-fit n of 0. 62 for the 152 Volcanic Tableland faults. There is considerable scatter, and the n = 1 line passes through the observations, particularly for the larger faults. A recognized challenge is that there is often a low number of larger faults, and therefore, the few large faults have a disproportionate impact on the n-value. We conclude that our dataset is consistent with a large fault compilation from the literature which shows linear scaling. In detail, the small faults (length < ~300 -400 m) have a higher D max /L ratio. The faults that were active prior to the 758:9 ± 1:8 ka Long Valley eruption which deposited a 150 m thick layer of volcanic tuff will now be relatively long relative to younger faults because we can only measure height accumulated post-eruption.
Conclusions We developed a new algorithm that maps dip-slip faults and measures scarp height from well-preserved faults using the growing archive of global and high-resolution topography datasets. The algorithm detects fault scarps from the spatial extent of the scarp's high slope area based on relatively small calibration maps produced by an expert geologist. We focused the application of our algorithm on normal faults in eastern California's Volcanic Tablelands. We used topography datasets with a decimeter to tens-of-meters resolution. After applying our algorithm to the entire Volcanic Tablelands, we solved for a fault slip to length scaling relationship that is broadly consistent with literature studies and further demonstrates a fairly linear scaling between maximum slip and length. The cumulative fault height profiles are typically asymmetric, consistent with observations of other faults worldwide.
This asymmetry is likely related to the fault's mechanical properties, the surrounding damage zone, and growth by lateral propagation. We successfully applied the algorithm to the Hurricane fault in western Arizona and Utah and to the entire states of Arizona and Utah. We antic-ipate that our algorithm will serve as a powerful tool for mapping and measuring 3D fault geometry along normal faults using a variety of high-resolution and global topography datasets. 152 faults. The following figures each refer to a single fault and show the fault in map view with algorithm measurements colored by fault height and the fault height profile. (Supplementary Materials).
Acknowledgments C. Scott was funded by the  US National Science Foundation   Postdoctoral Fellowship   1625221 , the  School of Earth and Space Exploration at Arizona State University  and by the  Université Côte d'Azur, France , where  C. Scott  spent one month as a visiting scientist. The Pléiades satellite image acquisition was supported by public funds received in the framework of  GEOSUD, a project  ( ANR-10-EQPX-20 ) of the program " Investissements d'Avenir " managed by the  French National Research Agency. A portion of the field work and  Giampietro, Leclerc ,  Manighetti , and  Mattéo 's contribution were funded by the  French National Research Agency  ( ANR Grant  FAULTS_R_GEMS #  ANR-17-CE31-0008 ). D. A. Laó-Dávila  was supported by  Oklahoma State University with a sabbatical leave at  Arizona State University.
In addition, we thank  Stephane Dominquez ,  Jacques Malavieille ,  Tyler Scott ,  Simone Bello ,  Federica Ferrarini ,  Chris Milliner , and  Andrea Donnellan  for joining us in the field at the Volcanic Tablelands to collect the sUAS imagery. We thank  Dione Perkins  at the  Bishop Bureau of Land Management  office for helping us to gain access to the Volcanic Tablelands. The scarp mapping algorithm is available from GitHub (https://github. com/cpscottasu/Fault_mapping_height). This GitHub site provides the mapping algorithm, shape files for the fault maps for the southern text zone in Bishop, and the  Hurricane  fault as well as links to process and download the lidar and SRTM topography datasets for these areas from  OpenTopography  (http://www. opentopography. org/).
Data Availability The sUAS (https://doi. org/10. 5069/G97S7KXT), airborne lidar (https://portal. opentopography. org/lidarDataset?opentopoID= OTLAS. 042016. 26911. 1), and SRTM (https://doi. org/10. 5069/ G9445JDF) topography datasets are available from OpenTopography. The Pléiades satellite orthorectified image IDs used to calculate the DSM are DS_PHR1A_201709151853278_FR1_ PX_W119N37_0712_01909, DS_PHR1A_201709151853179 _FR1_PX_W119N37_0712_01933, and DS_PHR1A_201709 151852520_FR1_PX_W119N37_0712_01913. The Pléiades images can be accessed from https://www. intelligence-airbusds. com/optical-and-radar-data/.