dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Study settings. Data was collected within three Egyptian hospitals located in Cairo (Hosp1 and Hosp3), and Fayoum (middle Egypt, Hosp2), that were temporarily transformed into quarantine hospitals during the first COVID-19 wave. During the quarantine-organization period (Hosp1: March 14th to August 1st, 2020; Hosp2: April 1st to July 31th; Hosp3: June 6th to July 11th, 2020, a fourfold shorter period), only externallyreferred COVID-19 confirmed patients were admitted to these hospitals for medical care. Multidisciplinary medical teams fully dedicated to COVID-19 patient management worked in total isolation, organized by shifts (Hosp1: 2-week shifts; Hosp2: 1 to 2-week shifts; Hosp3: 1-week shifts). During their shift, HCWs were assigned either to ICU or non-ICU units. HCWs were screened for SARS-CoV-2 infection before starting a working shift using rapid serological IgM/IgG antibody tests (Artron laboratories Burnaby, Canada; sensitivity: 83.
The numeric implementation of the model was coded in R 4. 0. 3  32 , using the FME package  33  for parameter estimation. The code is available at https:// github. com/ sjijon/ SARS-CoV-2_ Quara ntine Hospi tals. X A k,i ∼ Bin n = S A k,i , p = S A k,i+1 = S A k,i -X A k,i I A k,i = X A k,i θ j k -i L � |data � = � A    � k,i N S,A k,i � k,i N I,A k,i    � � k,i I A k,i � k,i S A k,i � � k,i N I,A k,i � 1 - � k,i I A k,i � k,i S A k,i � � k,i N S,A k,i - � k,i N I,A k,i , P(Infected at shift end) = 1 -(1 -) d ,.
The purpose of this study was to compare swing time and golf club angle parameters during golf swings using three, two dimensional (2D) low cost, Augmented-Video-based-Portable-Systems (AVPS) (Kinovea, SiliconCoach Pro, SiliconCoach Live). Twelve right-handed golfers performed three golf swings whilst being recorded by a high-speed 2D video camera. Footage was then analysed using AVPS-software and the results compared using both descriptive and inferential statistics. There were no signi cant differences for swing time and the golf phase measurements between the 2D and 3D software comparisons. In general, the results showed a high Intra class Correlation Coe cient (ICC > 0. 929) and Cronbach's Coe cient Alpha (CCA > 0. 924) reliability for both the kinematic and temporal parameters. The inter-rater reliability test for the swing time and kinematic golf phase measurements on average were strong.
Introduction Historically, kinesiological analyses of performances have been evaluated in both clinical  [1] [2] [3]  and sport  4  environments using low cost two-dimensional (2D) and expensive three-dimensional (3D) photogrammetry kits. It is well known that 3D motion capture is regarded as the gold standard for human movement analyses  5, 6. However, it is expensive and imposes nancial costs affordable by very few researchers and coaches. With the advent of digital and mobile technology, clinical-based, laboratorybased and eld-based kinesiological data can be captured, processed and analysed using bespoke 2D Augmented-Video-based-Portable-Systems (AVPS). Ugbolue and colleagues have captured and evaluated clinical datasets using both 2D and 3D motion systems  1, 7. This has been supported by previous studies on 3D sport related projects in the area of golf swing biomechanics  8, 9  and 2D golf swing related projects  10.
The versatility of 2D video technology in sport using high-speed video has previously been investigated  [12] [13] [14]  and still remains an inexpensive solution for motion analysis that is relevant in everyday elite coaching and training sessions. Several software products are available to analyse the 2D output from an AVPS. More products that are popular include SiliconCoach Pro, SiliconCoach Live and Kinovea. In many cases these 2D software systems are used not only for simple gait analysis but also for analysis of many sports related movements especially golf. 2D software such as SiliconCoach has been used to assess the dynamic range of motion of the knee joint  15. This study concluded that SiliconCoach was bene cial in the determination of the end range of static and dynamic motion, and valuable from a functional and costeffective perspective especially within the clinical environment.
Therefore, the shaft angle or golf club inclination angle (°) will be calculated at each of the ve golf phases (address, top of backswing, acceleration, impact and follow through) together with the total golf swing phase time (swing time). In golf coaching there are speci c times in the swing when the shaft angle alone is of interest. In the frontal plane that is mainly at impact and the top of the swing. In the sagittal plane, it would be a mid-backswing (shaft vertical from face on), or mid-downswing. Timing in the golf swing is of interest when comparing the time of the backswing versus the time of the downswing. This provides some sense of tempo. While some discrepancies in measurement techniques may have consequential effects on temporal and spatial output measures, from a software user perspective, clarity and reassurances are needed to show the viability and robustness of the 2D AVPS in the context of temporal measurements and shaft angle with respect to the Global Coordinate System.
Participants Twelve right handed subjects (Male: 6, Age: 23. 3 ± 4. 3 years, Mass: 88. 3 ± 16. 6 kg, Height: 180. 5 ± 4. 4 cm; Female: 6, Age: 21. 8 ± 1. 7 years, Mass: 65. 2 ± 5. 7 kg, Height: 165. 9 ± 8. 1 cm) participated in this laboratory based study. Informed consent was obtained from all participants. All participants completed a Physical Activity Readiness Questionnaire and consent form and were required to be healthy and injury free. In addition, all methods were carried out in accordance with relevant guidelines and regulations. No previous experience of playing golf was required (experience ranged from novice through to elite players). All experimental data collection was performed at the same time of day to minimize diurnal variation effects. There were ve novice players (3 males and 2 females) without a handicap and seven experienced golfers (6 males and 1 female) with a Mean ± SD handicap of 18.
Experimental design and protocol A golf mat and driving net were placed in the centre of the biomechanics laboratory. One 2D camera was placed facing the frontal plane of the golfer to ensure that the full motion of the golf swing was captured. To ensure consistency throughout the recording of all trials, a visible mark was placed on the oor behind the golf mat to indicate the stance/starting position of each participant prior to data collection. Each participant performed their own warm up procedures prior to the test and when comfortable, performed three recorded golf swings. The 2D camera (EXILIM, Casio, USA) was mounted on a tripod at a height of 1 m, positioned at 4 m relative to the golfer and set to record at 1/2000 shutter speed and 240 Hz to allow for successful video capture during the high-speed movement.
The golf shots were performed using a Taylormade (Basingstoke, UK) Speed Blade Stiff Shaft 7-iron, with a shaft length of 37 in. The same Titleist Pro-V1 (Titleist, Cambridgeshire, UK) golf balls were also used for all golf shots. Four evenly spaced retrore ective markers were placed on the shaft of the 7-Iron Golf Club to identify the phases of the golf swing. The golf ball was covered with retrore ective material. Its position was standardised and not placed on a tee. Other retrore ective markers were placed on the segments and joints of the lower limb of the golfers following the Plug-in-Gait model (https://docs. vicon. com/). All 2D video data was transferred from the camera to the laptop before being analysed with SiliconCoach Live (www. siliconcoach. com/SiliconcoachLIVE), SiliconCoach Pro (www.
siliconcoach. com/siliconcoachPro) and Kinovea (version 0. 8. 15, www. kinovea. org) software for all participant's golf swings. These three systems all have video playback features, drawing tools, which can interpret joint and club angles, zoom in and out features as well as markers to show where the data was analysed on the video timeline. The golf club inclination angle (°) was calculated at each of the ve golf phases (address, top of backswing, acceleration, impact and follow through) (Figure  1 ). The phases of the golf swing were de ned as follows: 1. Address: The address phase is measured as the last frame before the clubhead starts moving away from the ball. The address stance position involved taking the golf club with the arms extended, back straight and knees exed, so that the centre of the club face lines up with the ball.
Data were sampled at 240 Hz from an eight camera Vicon Nexus Motion Analysis System (Oxford Metrics Ltd, UK) mounted on scaffolding at 1. 5 m and 2. 3 m heights. The camera setup and con guration provided an optimal position for the cameras to capture and record the movement of the retrore ective markers during the golf swings. The Vicon Nexus Motion Analysis System (Oxford Metrics Ltd, UK) underwent a dynamic and static calibration prior to embarking on data collection. The software used for the analyses was the Vicon Nexus 2. 8. 1 Software. The golf club inclination angle (°) was calculated as the club angle with respect to the vertical. Speci cally, the golf club inclination angle (°) was obtained using the angle tool in the 2D commercial software. This involved selecting the rst and fourth retrore ective markers on the shaft of the golf club with respect to the vertical as the third point.
Statistical Analysis Normality of data distribution was veri ed using the Shapiro-Wilk test. The average of three trials was determined for all participants and expressed as a function of the golf phases of measurement using both descriptive and inferential statistics. Standard Error of Mean (SEM) was used to quantify the absolute consistency of the measurement 28 , while the Standard Error of Measurement (SM) estimated the amount of error in the test. Both the SEM and SM were calculated with respect to the swing time and each golf phase of measurement. Coe cient of determination (r 2 ) and Pearson product moment correlation coe cients were used to assess linear relationships between the 2D and 3D displacement golf club inclination angle (°) measures in the frontal plane with respect to the phases of golf swing measurements.
Results All temporal and kinematic datasets from the 2D commercial and 3D Vicon Motion System were normally distributed. The descriptive statistics for the temporal and phase measurement parameters are displayed in Table  1. The swing times were very close as indicated by the sizes of the mean and standard deviation for each of the commercial software. The SEM for all three AVPS showed the same output of 0. 11. However, the SM showed a small deviation across the three AVPS with Kinovea showing a 0. 01s reduction when compared to the SiliconCoach Pro and SiliconCoach Live packages. The swing time outputs were very similar to the gold standard (Vicon Nexus 2. 8. 1 Software). The repeated measures ANOVA results revealed no signi cant differences (P = 0. 763, F = 0. 099) for the within-subjects effects and no signi cant differences (P > 0.
The results showed a high intra-rater reliability for both the kinematic and temporal parameters (ICC range: 0. 929 -0. 999). With respect to swing time and golf phases, the ICC value for the intra-rater reliability test was 0. 929 for the swing time and ranged from 0. 963 to 0. 999 for the kinematic golf phase variables. The CCA reliability statistics produced a value of 0. 924 for the swing time and a range from 0. 961 to 0. 999 for the kinematic golf phase variables. The inter-rater reliability test for the swing time ranged from poor (0. 471) to strong (0. 995). All other kinematic golf phase variables were strong and ranged from 0. 779 to 0. 999. The swing time for the backswing and swing time for the downswing were similar across the 2D commercial software and 3D Vicon motion system.
Biomechanical analysis of a golf swing is widely known as being di cult to interpret due to the complexity of the swing, as it has a 3D motion, multi-planar sequence, which is performed at very high speeds  25. Many software systems available allow for the evaluation of movement. These software systems vary in cost and can be delivered across multiple platforms (PC, Phone, Tablet), or via the server, web or client base. This study has taken a subset of these systems, Kinovea (free PC based application), SiliconCoach Pro (purchased PC based application) and SiliconCoach Live (purchased web based application) and compared their outputs. The environment in which these applications may be used can differ, but the importance of the validity of the results remain the same. As a limitation, our sample size was small; however, this study has proven that the AVPS can produce a reliable and valid output with little nancial and time overheads.
Conclusion Overall, the results from the three software packages were compatible as re ected by the descriptive statistics including the SEM and SM results. Further analyses revealed no signi cant differences for the ANOVA results with respect to the measured phases of the golf swing. High intra-rater reliability, CCA kinematic and temporal parameter measurements were obtained. The inter-rater reliability test for the swing time and kinematic golf phase measurements on average were strong. Aside from the address position, in general all temporal and kinematic outputs for the golf phase measurements with respect to the 2D commercial software and 3D Vicon motion software showed good agreement and no proportional bias in the results. These results challenge the debate surrounding the purported errors and inaccuracies associated with the 2D AVPS.
The r 2 values for the pro vs live at address indeed were lower than the other comparisons, however the output interpretation was considered moderate. The variations between the trials for the pro and live were not as close as the other commercial software and Vicon Nexus software package comparisons. This is acknowledged by the size of the standard deviation. These differences we feel may be attributed to the resolution of the software or display resolution during the manual data processing using the software tools. The ratio of the swing time for the backswing and swing time for the downswing was 2:1. The swing time duration marginally varied across the different software because the frames representative of the phases varied by a frame or two across the various software packages. This means that identical frame numbers from one software were not replicated across the four software packages.
Available Data The most detailed -even though large-scale -existing geological map for the Pinchal area is the Quillagua map (1:250,000 scale,  Skarmenta and Marinovic, 1981). For the Quebrada Blanca area, the recent Guatacondo map (1:100,000 scale,  Blanco & Tomlinson, 2013)  provides detailed and updated information on the stratigraphy and structure. There, the structure of the folded Mesozoic rocks has been preliminarily mapped and qualitatively described by  Blanco and Tomlinson (2013), Armijo et al. (2015)  and  Fuentes et al (2018). Enhanced cartographic details can be deduced from high-resolution satellite imagery. We use Google Earth imagery (Landsat 7, DigitalGlobe) whose resolution varies from a few meters to a few tens of meters depending on the zones. In addition, this work benefits from very high-resolution imagery from the European Pléiades satellites.
Using the MicMac software suite  (Rosu et al. , 2014; Rupnik et al. , 2016) , we calculate high-resolution DEMs from tri-stereo Pléiades imagery, with a 0. 5 m resolution. These DEMs are down-sampled to a resolution of 2m to enhance data treatment and calculations (e. g. stratigraphic projection and image processing). Relative vertical accuracy may reach ~1m, depending on local slope. Field observations were acquired during two field surveys in March 2018 and January 2019. Difficult accessibility and field logistics in the remote and desert Pinchal area only allow detailed complementary field observations on a relatively limited area. Observation points and the off-road track followed to reach our field site in the Pinchal area are provided as supplementary material.
CC BY 4. 0 License. Allmendinger, 1998; Erslev, 1991). We use the code FaultFold Forward (version 6)  (Allmendinger, 1998)  in order to jointly model thrust displacement and anticlinal folding. Trishear models the deformation distributed within a triangular zone located at the tip of a propagating fault  (Erslev, 1991). This forward modeling relies on a set of parameters that are here adjusted by trial and error to fit observed structural geometries. By adding sedimentary layers at various steps during ongoing deformation, we model syntectonic deposition and subsequent deformation, in order to reproduce deformation of Cenozoic layers. Additional information on trishear modeling, together with the range of tested parameters, are provided later and in supplementary material. We recognize that our best-fit model parameters may not be unique.
Data availability Pleiades satellite imagery was obtained through the ISIS program of the CNES under an academic license, and is not available for open distribution. On request, the DEMs calculated from this imagery can be provided to any academic researcher, however after approval from the CNES (contact: isis-pleiades@cnes. fr, with copy to lacassin@ipgp. fr and simoes@ipgp. fr, and referring to this manuscript). Numerical computations for the DEMs were performed using the free and open source MicMac software suite  (Rosu et al. , 2014; Rupnik et al. , 2016)  freely available at https://micmac. ensg. eu/index. php. For cartographic mapping, we also used Google Earth imagery (Landsat 7, DigitalGlobe) freely accessible at https://earth. google. com. All geological maps used in this work are cited in the main text and in the reference section.
MATERIALS AND METHODS Sequence retrieval from databases Sequences were obtained from four online resources: NCBI (www. ncbi. nlm. nih. gov), UCSC (www. genome. ucsc. edu), Ensembl (www. ensembl. org) and UNIPROT (www. uniprot. org). Coding sequences were obtained as nucleotide mRNA predictions either directly or as GenBank files. BLAST searches using Human HTT (Hg18) full-length exon1 sequence were performed in unannotated genomic assemblies. Specific SRA (Sequence Read Archive) experiments were also searched for HTT orthologous genes using sequences from relatively close species as queries.
Amplification of HTT exon1 orthologs A general PCR protocol was developed to assess the nucleotide sequence of exon1 in the HTT orthologous genes. Primers were designed in the most conserved outer regions of HTT exon1 to span the entire repeat region and to produce amplicons between 100-300 bp, suitable for direct sequencing or cloning (see Supplementary dataset S22 for the primer list). A topquality replication enzyme, the Platinum Taq DNA Polymerase High Fidelity (kit Life Technologies, CAT n°11304-011) was used for PCR amplification. PCR reactions were assembled under a sterile fume hood and working environment was cleaned with a nucleic acids decontamination solution (NucleoClean TM Decontamination Solution, Merck Millipore n°3097S) prior to mix preparation. A general PCR protocol with slight modifications was applied on all vertebrate gDNA (see Supplementary dataset S22).
Cloning and sequencing of PCR products TOPO ® TA Cloning ® Kit (Life Technologies, CAT n°K4575) was used to clone the PCR amplification product for direct sequencing. A commercial kit (Qiagen, CAT n°27106) was used to perform the DNA mini-preparation. PCR product purification and sequencing reactions were carried out on ABI machines according to manufacturer specification either by us or MWG Operon (Eurofins Genomics, Ebersberg, Germany) and GATC Biotech AG (Costance, Germany). PCR products were Sanger sequenced either directly or after cloning. Technical replicates of sequencing were performed. More than one sample per species, when available, has been sequenced (as listed in Supplementary dataset S1).
Alignment of exon1 orthologs Multiple sequence alignments (MSA) were conducted with Clustal Omega  [29]. Alignment refinements were performed manually in SeaView 4 (http://doua. prabi. fr/software/seaview), CLC Sequence Viewer (www. qiagenbioinformatics. com) and Geneious version 9 (www. geneious. com) (the overall alignment used is available as Supplementary dataset S2, as well as taxa subgroups alignments are available as supplementary nexus files).
Test for purifying selection The tests for negative/positive selection were performed on MSA organized as Nexus files with custom evolutionary trees. For all analyses, MSAs including gap (e. g. CCN region in mammals) and unknown regions (i. e. , primer bind and LQ base calling) were employed. Phylogenetic tree topologies for sampled organisms were obtained from the TimeTree. org resource (http://www. timetree. org), exported in the Newick format and embedded in the Nexus files containing the MSAs. To estimate the extent of pervasive purifying selection at individual sites we used three different methods: the Single-Likelihood Ancestor Counting (SLAC) method  [30] , the Fixed Effects Likelihood (FEL) method  [30] , and the Fast Unconstrained Bayesian AppRoximation (FUBAR) method  [31]. SLAC is the simplest inference legacy method: it is based on counting how many synonymous and non-synonymous changes have occurred at each site over evolutionary time and uses the binomial distribution as a statistical test  [30].
FEL instead estimates a value for dN and dS at each considered site testing it using a maximum likelihood approach and a chi squared distribution  [30]. FUBAR is particularly efficient on larger datasets (high number of sequences) and takes a Bayesian approach to selection inference developed in the context of document classification (latent Dirichlet allocation)  [31]. Finally, FUBAR also calculates an a priori probability over a specified grid of dN and dS values thus defining a space of probability over a wide range of selection scenarios. Calculations were performed using implementations on the Datamonkey Servers (version 2, www. datamonkey. org/) and/or a local instance of the HyPhy software (version 2. 2. 4), and all methods were used with built-in defaults parameters. The taxa list of the species initially considered and the Nexus input files used for the purifying selection analyses after pruning (HyPhy discards identical sequences) are available as supplementary data files.
Retrieval and statistical testing of phylogenetically conserved polyCAG/CAA genes All human (genome assembly hg19) coding sequence (CDS) transcripts with at least four consecutive glutamines were downloaded from Ensembl and for each of these genes the longest CDS was considered. Sequence orthologs were retrieved from Ensembl for all available mammalian genomes and aligned at the protein level using Clustal Omega  [29]. Codon alignments were reconstructed using custom Python scripts. For each nonhuman sequence, the region of the alignment corresponding to the human polyQ tract was examined and the longest pure glutamine stretch overlapping the human stretch or conjoined alignment gaps was considered. Subsequently, the longest Q stretch (LQ) and the longest non-interrupted CAG interval (LNI) was calculated for each of these stretches.