dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Management • Photo, audio and video documentation of the interpretation process. • Recordings can be used in many ways, e. g. for further analysis of traces and the trace-reading method in archaeological and indigenous contexts, but also for community reports, etc. • Data storage accessible to all participants, if necessary a specially set up and indigenous group trained in dealing with databases, translation programs, etc. should therefore also be given a lot of space in the interview. Interim results can be summarized and discussed at regular intervals.
Acknowledgments Acknowledgements We dedicate this contribution to  Mr. Russell Savage , our dear friend and colleague, who contributed extensively to this work before his sudden and untimely death in late 2016. We thank  Drs. Karl Bates  and  Todd Pataky  for their contributions to core analysis of footprint depth and pressure. We thank  Dr. Craig Feibel  for his advice on the taphonomy of Laetoli  G1 , and the extent and identity of usable G1 prints, and  Prof. Ronald Clarke  for related advice from his perspective as one of the chief excavators of Laetoli G1. We thank our senior colleague  Prof. Michael Day  for his kindness in entrusting his photogrammetric records of  G1  and  G2/3 , made shortly after excavation and prior to subaerial erosion, to RHC. We thank  Prof. Matthew Bennett  for giving us access to his Namibia and Ileret data.
The participation of JMcC in both excavations at Ileret and  Walvis Bay ,  Namibia , has enabled us to work on these scans with confidence. The research was funded by the  Leverhulme Trust , the  UK Natural Environment Research Council  and the  Institute of Ageing and Chronic Disease at the University of Liverpool. Acknowledgements We are grateful to the editors for allowing us to contribute to this chapter of this book. Our work is supported by the  Projet Collectif de Recherche Cussac , and we thank all the members of its team. We are also grateful to the  French Ministry of Culture  and to the LaScArBx, a research programme supported by the  ANR  ( ANR-10-LABX-52 ). We are also grateful to the  University of Bordeaux , the  PACEA laboratory and the Pôle mixte de recherche archéologique de Campagne.
Acknowledgements I would like to thank  North Norfolk District Council  for permission to undertake the work and the people of  Happisburgh  for their continued support. I also thank  Martin Bates ,  Richard Bates ,  Sarah Duffy ,  Isabelle De Groote ,  Peter Hoare ,  Simon Lewis ,  Simon Parfitt ,  Chris Stringer  and  Craig Williams  for their contributions to the fieldwork and subsequent analyses. I am very grateful to  Julia Blackburn ,  Martijn Deen  and  Anita Malchik  for conversations and their writings, which have taken the footprints to new levels of meaning. Finally I thank the  Calleva Foundation  for support of the Pathways to Ancient Britain Project of which the research at  Happisburgh  forms a part. Acknowledgements We thank  Dr. Tsitsiloni ,  Mrs. P. Alfieri  and  Mrs. E. Malliou  (for the permission to cast their children's fee).
We are grateful to  M. Friess  and  F. Detroit  ( Musée de l'Homme, Paris, France ) for their help during the photogrammetric digitization. The field work is supported by the  French Ministry of Culture  and the  French department of La Manche , and data acquisition has been funded by the  CNRS-Institut Écologie et Environnement International Research Network IRN-GDRI0870. The first author was granted funding for a doctoral degree ( Muséum national d'Histoire naturelle, Paris, France ). Acknowledgements  A. Posmoșanu  and  G. Ponta  are thanked for providing the human footprints photographs from the  Sala Pașilor in Ciur Izbuc Cave. Chris Stringer 's research is supported by the  Calleva Foundation  and the  Human Origins Research Fund. Acknowledgements This contribution forms part of my PhD research submission to The University of Manchester, 2019 entitled: An 8,000-year record of prehistoric footprints in a dynamic coastal landscape, Formby Point, UK.
Acknowledgements We appreciate  Andreas Pastoors  and  Tilman Lenssen-Erz 's invitation to participate in the International Conference on Prehistoric Human Tracks (Colonia 2017) and to write this article. We thank  Peter Smith  for the comments and corrections on the paper, and for the revision of the translation of the text, which have significantly improved the manuscript. The research was done with the permission of the  DG Cultural Heritage of the Regional Government of Castilla y León , which also funded the  Carbon Dating of the Cultural Heritage of Ojo Guareña Cave  system (2017)(2018)(2019)(2020). The authors also wish to thank the  Team of the Grupo Espeleológico Edelweiss  for their constant scientific and technical support, especially with the documentation of photographs and maps of the Ojo Guareña caves.
and professional cooperation and the sponsors for their generous financial support. Foremost, we would like to mention the  Volkswagen Foundation. Further funds came from goldschmidt and the  Heinrich Barth Institute. The book is integrated into a research project supported by the  German Science Foundation  ( UT 41/6-1 ). Thanks are due to the universities of  Erlangen-Nuremberg and Cologne  and the  Association Louis Bégouën  for their institutional support and helpfulness. As mentioned above, the book is based on the international Prehistoric Human Tracks conference which was held in cooperation with the  German Commission for UNESCO in Cologne/Mettmann  ( May 11-13 , 2017). We would like to thank all participants and the donors:  Volkswagen Foundation, GO-AIDE Foundation ,  Neanderthal Museum Foundation, University of Cologne, Cultures and Societies in.
Variability of human rRNA 2ʹ-O-methylations (Nm) Analysis of 2ʹ-O-methylation profiles was performed for a large dataset of human total RNA samples obtained for >100 samples extracted from ~20 human cell lines. The coverage and correlation of raw reads for all available datasets were inspected by rRNA type (Supplementary Figure  S3 ) and only samples showing sufficient coverage (typically >1000 reads' extremities/rRNA position) and a good correlation coefficient were retained (see Materials and Methods for further details). The final list of retained samples included 62 RiboMethSeq datasets for multiple biological replicates of 16 cell lines: HeLa (and HeLa S3 as well), HEK293, HCT116 (± p53), Jurkat T, HMEC, HUVEC cells, as well as human fibroblasts, chondrocytes, and stem cells from bone marrow and Wharton jelly.
Using this large human rRNA RiboMethSeq dataset, we calculated a correlation value of MethScore across all available samples. As anticipated, multiple Nm residues (52/110) show a mutual positive correlation (Figure  3  and Figure  S6 , shown in blue), and thus vary in the same direction, this is likely related to a coordinated regulation of C/D-box snoRNA expression or natural variations of Fib expression, which would affect a subset-(s) of sites in a coordinated manner. Noteworthy, positively correlated Nm positions form 4-5 distinct groups (see Figure  3  and Figure  S6 ). In contrast, few modified positions showed negative correlation with the others, their methylation level varies in an opposite direction to the others (Figure  3 , shown in red, group A1). This is the case for 14 positions (4 in 18S and 10 in 28S rRNA) (18S-Um172, 18S-Cm174, 18S -Gm436, 18S-Am576 and 28S-Am1310, 28S-Cm1327, 28S -Cm1868, 28S-Am2774, 28S-Cm2791, 28S-Cm2848, 28S -Gm2863, 28S-Am3846, 28S-Um4468, 28S-Gm4607), these opposite variations may represent an adaptive mechanism(s) intended to functionally compensate the Nm loss at some other important/functional rRNA sites.
The most variable Nm in 18S rRNA are located at the surface of the SSU, this is also the case for variable and highly variable locations in 28S rRNA/LSU. Visualization of the variation rates for Nm residues in the human ribosome 3D structure. RNA traces found in PDB entry 6EK0 are displayed using a ribbon representation. 5S, 5. 8S, 18S, 28S and transfer RNAs are coloured in green, magenta, yellow, cyan and black, respectively. Four classes of modified nucleotides were defined. Stable, low variable, variable and highly variable nucleotides are represented using tiny, small, medium and large spheres, respectively. Colouring of the sphere follows the RNA ribbon colour. In panel A, all modified positions are represented with spheres on a face view of the human ribosome. The A, P and E regions of the ribosome are highlighted in grey boxes.
Cumulative variability map of human rRNA modifications Finally, in order to get better insights into the global variability of rRNA modification positions, maps for Nm residues and our recently published observations on variable ψ levels in human rRNA  [32]  have been merged together. Since samples included into datasets were different, this comparison does not allow a thorough analysis of a direct correlation between Nm and ψ level variations, but rather to have an overview of the proximity for intrinsically stable and variable Nm and ψ residues, presented at the same scale. All, except two (28S-Am3739 and 18S-ψ1004) sites known to be important for critical ribosome functions and located in a close proximity to PTC, DC and intersubunit bridge, show only very limited variability, pointing out that variations of such vitally important rRNA modifications are not tolerated.
Moreover, a preferential decrease of 28S rRNA modifications may result from a differential stability or turnover of 18S and 28S rRNAs, or, as it was also suggested, from inequal accessibility of the modification sites during ribosome subunit assembly. Globally, the list of Fib-sensitive rRNA modification sites correlates rather well with the variability of Nm modifications observed in different human cell lines. Only 28S-Am1858 (snoRD38A/B), 28S-Am3703 (snoRD36C) and 28S-Um4276 (snoRD41) which are stable in our dataset, were found to be decreased upon Fib-depletion  [6, 7]. Variable and highly variable rRNA Nm modifications were also decreased upon transient Fib depletion. On the other hand, NPM1 deficiency was reported to negatively affect the levels of 5 Nm residues (28S-Cm1327, 28S-Am3764, 28S-Cm3866, 28S-Um3904 and 28S-Gm4198)  [21].
Only 28S-Cm1327 (snoRD104) in this list was found to be highly variable in our dataset, other positions are either stable or low variable. Other relevant observations of rRNA modification alterations come from analysis of cancer cells and tissues. In many studies, deregulation of snoRNA expression was also observed, and, in some instances, these variations were directly correlated with modulated levels of Nm or ψ residues. The most prominent disease-related correlations are 28S-Cm2848/Gm2863 (catalysed by snoRD50A/B) which are both increased in colon cancer  [13, 18] , as well as 18S-Cm1703 (snoRD43) and 18S-Gm1328 (snoRD32A) shown to be affected in leukaemia  [19]. In this study, both 28S-Cm2848 and 28S-Gm2863 were found to be rather variable in different human cell lines, in contrast, both leukaemia-related 18S-Cm1703 and 18S-Gm1328 belong to the stable and low variable groups, showing a limited correlation between 'natural' and 'disease-related' variations of rRNA modifications.
Strikingly, many of Nm sites in this disease-related list show a negative correlation with the majority of human rRNA Nm positions. Recently, a RiboMethSeq analysis was applied to large clinical cohorts of breast cancer tissues and diffuse large B-cell lymphoma samples  [26, 27]. Since identical analytical technique (albeit in different flavours) was applied in both studies, the results can be compared almost directly. With some noticeable exceptions, the degree of Nm level variations in these two cancer types correlates well with variations, observed here in human cell lines. This may be also related to the fact that many cell lines used here are also either immortalized or originate from different aggressive cancer types. However, 28S-Gm4593 (snoRD78) found to be variable in cancer is almost stable in our dataset, the same observation is made for 28S-Cm4032 (snoRD75), 18S-Um1288 (snoRD110), 18S-Am27 (snoRD27), 18S-Am1678 (snoRD82) and 18S-Am668 (snoRD36A/B).
Visualization of the human ribosome and mapping of modified positions The PDB entry 6EK0 describing the 3D structure of the human ribosome in complex with the initiator tRNA Met in the E site was used to visualize modified positions in ribosomal RNAs  [45]. All representations were drawn with PyMOL 2. 3 (https://pymol. org/2/). The modified positions were represented using spheres, the geometrical centre of the nucleotide corresponding to the centre of the sphere. For the sake of clarity, only ribosomal RNA traces were displayed and ribosomal proteins were omitted from the representations.
Furthermore, dense vegetation, which degrades the correlation process, covers the central part of the rupture, east of the Sefid Rud River. Therefore, we develop a new robust processing workflow, which takes advantage of the open-source Ames-Stereo Pipeline software  (Beyer et al. , 2018) , and existing 3D correlation approaches  (Zinke et al. , 2019)  to precisely co-register, orthorectify and correlate all imagery. Detailed knowledge of the location and along-strike variability of the Rudbar surface rupture provides important new observational constraints needed to (1) reconcile seismic, geodetic, remote, and field-based observations of coseismic slip, (2) reassess seismic hazard for the region, including estimation of how co-seismic stress changes impact potential failure of neighboring fault systems, and (3) address which geological factors (e.
SPOT Satellite Data We use COSI-Corr (an IDL softeware package for the Coregistration of Optically Sensed Images and Correlation) to investigate the magnitude and distribution of coseismic ground displacement from SPOT 10 m satellite images  (Leprince et al. , 2007  regional view of the earthquake displacement field. However, this comes at the cost of reduced precision for the displacement values, and lower spatial detail (compared with correlation of higher resolution images, such as aerial photos, see Section 3. 2). We first orthorectify and co-register the SPOT images using an external digital elevation model (DEM), with referencing information coming from the satellite metadata (which was found to be well-registered to the Earth's surface). Orthoimages are then correlated using sub-pixel phase correlation, which gives east-west and north-south displacement maps  (Leprince et al.
The GCPs allow us to solve for an approximate starting camera, which is subsequently refined in the stereo processing; in this way we mitigate registration problems that may arise from using postevent images to register pre-event images. We then automatically generate match points linking all available stereo images, which are then used in a bundle adjustment procedure to refine the camera parameters (e. g. , pose, distortion parameters, etc), before computing stereo disparities, triangulating the ground position at each pixel, and finally resampling onto a regular grid to produce a high-resolution DEM. This procedure is repeated for both pre-and post-earthquake datasets. We then use the high-resolution DEMs and refined camera parameters to orthorectify the raw images. For large aerial photo networks, we also mosaic the component aerial orthoimages to make a regional-scale orthomosaic, which significantly simplifies the final correlation step.
DEMs are generated for each overlapping stereo pair (typically images with 60% stereo overlap), which are then mosaiced together using a blending technique to generate a seamless final DEM. Any errors in the camera pose which were not corrected during bundle adjustment will result in a final DEM which is misaligned to the Earth's surface (or a good quality reference DEM); this is quite common, as bundle adjustment attempts to solve a highly non-linear problem with many free parameters. Fortunately, ASP has several tools to address these issues. One powerful approach is to find a global transformation that brings the final DEM into close alignment with a reference DEM. This is important for subsequent image processing, as mis-registrations between datasets can corrupt the final displacement estimates.
, 2020). Where the fault crosses Sefid Rud, the FZW is relatively large (>1 km), and possibly reflects an enhanced distribution of slip across mechanically weak sediments within the river valley. Estimates of FZW from higher resolution aerial photos are shown in Figure  12c  (and 16a for the Zard Geli segment), alongside estimates made from KH-9 and SPOT data; FZW decreases substantially with increasing image resolution. This raises doubts about the ability of medium resolution optical satellite datasets (e. g. , SPOT1-4, Landsat-8, or Sentinel-2) to reliably capture information about FZW. Because correlation windows require enough long-wavelength information to obtain a reliable displacement, which in turn requires larger windows, any discontinuity in the displacement field becomes smoothed by the correlation process (by ∼50% of the correlation window size for COSI-Corr, due to the use of a windowing function when computing the FFT, see  Milliner et al.
Because the amplitude of geometric variability increases at longer wavelengths, the larger widths used in stacked SPOT displacement profiles will result in greater geometric smoothing bias compared with smaller stacked windows used with aerial photo correlations. Using the mapped rupture trace, we estimate the contribution of geometric smoothing bias on the SPOT FZW estimates (Figure  17c ) and find it to contribute significantly. Therefore, the increase in FZW with lower resolution data will vary for different earthquakes based on the scaling properties of the fault rupture (geometric) roughness. If this can be characterized based on the mapped fault trace, this may offer a potential way to estimate the geometric smoothing bias, and thus infer reliable FZW estimates from lower resolution optical data.
The FZW for the Kabateh and Zard Geli segments appears relatively similar (over wavelengths of several km); both segments generally rupture through bedrock, which may explain the relatively narrow damage zones. While the FZW estimates from SPOT data may be corrected in an absolute sense, does the along-strike variability in FZW also reflect real variation recorded in higher resolution data? To address this, we smooth the aerial photo FZW estimates for a section of the Zard Geli fault (Figure  18a ) segment to an equivalent spatial wavelength reflected by the SPOT data (Figure  18b ), before adjusting them to account for the amplitude bias seen with SPOT FZW estimates (Figures  16a  and 18a ). We find these "synthetic" FZW estimates agree well with the raw SPOT estimates; a two-sample Kolmogorov-Smirnov test confirms that the two curves follow the same distribution (at the 5% significance level).
Iran, for providing scans of the aerial photos  (1968 and 2001-3). Oleg Alexandrov and Scott McMichael provided invaluable processing support. Correlations were made using the COSI-Corr software  (Leprince et al. , 2007). We also thank Alex Copley for additional support with satellite imagery. Thanks to Erik Bergman for access to the calibrated earthquake locations of the Rudbar earthquake cluster. Various figures were made using GMT. This paper was co-financed by Labex, Labex International, and BQR grants to (ISTerre and Geoazur) James Hollingsworth and a visit grant by Ministry of Science, Technology and research of Iran to Najmeh Ajorlou.
Data Availability Statement KH-9 satellite imagery and 1955 USGS aerial photos were obtained from the Earth Explorer website (http:// earthexplorer. usgs. gov/). SPOT images were acquired through the DINAMIS program. Sentinel-2 imagery was obtained from ESA (https://scihub. copernicus. eu/). We processed all optical data sets using the opensource Ames Stereo Pipeline (ASP) software package, developed by NASA  (Beyer et al. , 2018).
Related Work As mentioned in the Introduction, this paper is a revised and extended version of  [16] , where causalconsistent replay has been introduced. However,  [16]  concentrates on replay and mentions reversibility only briefly, while the present paper integrates the notion of causal-consistent replay with the reversible semantics in  [12]. Our replay mechanism is strongly related (indeed dual) to causal-consistent rollback, and its instance on debugging, namely causal-consistent reversible debugging. Causal-consistent reversible debugging has been introduced in  [5]  in the context of the toy language µOz, and, beyond this, it has only been used so far in the CauDEr  [7, 28]  debugger for Erlang. Causal-consistent rollback has also been studied in the context of the process calculus HOπ  [6]  and the coordination language Klaim  [11].
Moreover, we have proved that the same misbehaviors appear in the logged derivation and in all causally equivalent replays. Therefore, the user can focus on the actions and processes of interest, and still be able to find the bug. For this purpose, we have introduced a replay/rollback semantics controlled by the user requests which is sound and minimal. Thus, it constitutes an excellent basis for the implementation of a causal-consistent replay debugger. We have undertaken the development of a proof-of-concept implementation of a replay debugging tool for Erlang, which is based on the developments presented in this work. In particular, we are working on an extension of our debugger CauDEr  [7, 28]  to accept both a source program and a log of a computation. Furthermore, we will incorporate all replay and rollback requests introduced in Section 5.
* This work has been partially supported by the  COST Action  IC1405 on Reversible Computation -extending horizons of computing. The first author has also been partially supported by  INdAM  - GNCS 2020 project Sistemi Reversibili Concorrenti: dai Modelli ai Linguaggi. The first and third authors have been also partially supported by  French   ANR  project  DCore   ANR-18-CE25-0007. The second and third authors have been also partially supported by the  EU (FEDER)  and the  Spanish MCI/AEI  under grants  TIN2016-76843-C4-1-R  and  PID2019-104735RB-C41 , and by the  Generalitat Valenciana  under grant  Prometeo/2019/098  (DeepTrust).
These 100 days were selected for our analysis because precipitation events during this period were sampled for their isotopic concentration in 5 mm increments, making this a unique dataset on which to test our hypothesis since many other sampling approaches integrate precipitation sampling over a week or longer time scales. The observed precipitation samples were used within a statistical downscaling method  31  to simulate realizations of possible precipitation inputs corresponding with observed precipitation amounts. The model represented the top 100 cm of the soil pro le, no evaporative effects were considered from the surface, and no root water uptake was simulated within the column. All pore water heterogeneities were driven by model parameterization of the soil physical properties. Refer to the Supplemental Material for further details on model con guration, parameterization, and initial conditions and the simulated input precipitation datasets.
75 (L f H ω ), and 4) a model with a high f representing 40% of the VWC sat and low ω equaling 0. 25 (H f L ω ), and 5) a model with a low f representing 20% of the VWC sat and low ω equaling 0. 25 (L f L ω ). These ve model con gurations were tested across a range of different precipitation inputs and soil hydraulic properties, which were varied in relation to the original soil column con gured from observed datasets from Watershed 10, H. J. Andrews Experimental Forest. In total, we present 650 simulations of HYDRUS-1D with varying f, ω, VWC sat , K s , total accumulated precipitation, and amount effects describing the correlation between precipitation amount and its isotopic composition. When varying soil hydraulic parameters across model con gurations, all other parameters were held constant and VWC sat or K s was altered.