dataesr/scientific-paragraphs-categorization huggingface dataset:
Models were fitted via Hamiltonian Markov chain Monte Carlo sampling implemented in Stan 2. 17. 2  [50]. Convergence was checked according to R, the potential scale reduction factor measure  [51, 52] , which approaches 1 for perfect convergence. Values below 1. 2 are typically used as a guideline for determining model convergence and 1. 1 is a stringent criterion  [51]. Importantly, we used this stringent criterion as a safeguard against arbitrary and incorrect assignment of variance to different parameters  [51]. In the current study, most of the models had an R<1:1, except for Model 4 in the sub-chronic 10 mg/kg experiment in rats ( R ¼ 1:7) and Model 1 in the 5,7-DHT experiment in rats ( R ¼ 1:5). We assumed the four models examined had the same prior probability (0. 25). Models were compared via a bridge sampling estimate of the likelihood  [53] , using the "bridgesampling" package in R  [54].
Bridge sampling directly estimates the marginal likelihood, and therefore the posterior probability of each model given the data (and prior model probabilities), under the assumption that the models represent the entire group of those to be considered. Posterior distributions were interpreted using the highest density interval (HDI) of posterior distributions, which is the Bayesian "credible interval", at different posterior probability levels including 75%, 80%, 85%, 90% and 95%. Together with the HDI, the group mean difference (MD) was also reported. Supplementary Table  1  lists the priors used for each parameter. For the human experiments, trials were sequenced across all 80 trials of the PRL task, and on each trial the computational model was supplied with the participant's identification number and condition, whether the trial resulted in positive or negative feedback, and which visual stimulus was selected.
FUNDING This research was funded by the  Wellcome Trust  (Grant  104631/Z/14/Z  to TWR) and the  Lundbeck Foundation  (Grant  R281-2018-131  to BJS and GMK). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. QL was supported by the  National Key Research and Development Program of China  (Grants  2023YFE0109700  and  2019YFA0709502 ), the  National Natural Science Foundation of China  (Grant  82272079 ), the  Natural Science Foundation of Shanghai  (Grants  23XD1423400  and  20JC1413400 ), the  Shanghai Municipal Science and Technology Major Project  (Grant  2018SHZDZX01 ). Most of the analyses of this study had been conducted when QL was a Visiting Fellow at the  Clare Hall, University of Cambridge, Cambridge, UK.
JWK was supported by a  Gates Cambridge Scholarship  and an  Angharad Dodds John Bursary in Mental Health  and Neuropsychiatry. RNC was funded by the  UK Medical Research Council  ( MC_PC_17213 ,  MR/W014386/1 ). NS was supported by an  Academic Clinical Fellowship  ( University of Cambridge/Cambridgeshire  and  Peterborough NHS Foundation Trust ). BUP was supported by an  Angharad Dodds John Bursary in Mental Health  and Neuropsychiatry and is a current employee of  AstraZeneca plc. All research at the  Department of Psychiatry in the University of Cambridge  is supported by the  NIHR Cambridge Biomedical Research Centre  ( NIHR203312 ) and the  NIHR  Applied Research Collaboration East of England; the views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care.
The experiments were performed at high pressure (HP; $ 3-5 GPa) and temperature (HT; $ 300-500 C), and conducted on the PSICHE beamline at Synchrotron SOLEIL. From a hardware point of view, we provide a temperature calibration for the cell, and report pressures and strains with increasing anvil twist in the cell/ samples. From the point of view of microstructural data, we report our analysis workflow, processing possibilities and their limitations. With these, we illustrate potential for microstructural observations and analysis in our tomographic datasets, providing representative examples of 2D and 3D quantifications with increasing deformation. We also outline possible improvements for the instruments and methodology all along the different sections.
Hydrostatic pressure is estimated from a beam-time session experiment, using the same assembly, during which we collected in situ energydispersive (white-beam mode) X-ray diffraction from the h-BN sleeve [Fig. 1   (a) ]. The h-BN 002 diffraction line and the c-axis compressibility were used to calculate the pressure (Le  Godec et al. , 2000). The thermocouple was placed in the equatorial plane of the gasket and at the centre of the cell assembly (Fig. 2 ). The extremities of the thermocouple wires were bent to make a junction [Figs. 2(a  Power (W) versus temperature ( C) data were collected during a single run, at two different loads: first at 42. 5 tons ($ 4 GPa) and then at 25. 5 tons ($ 2 GPa) (Fig. 3 ). At higher pressure, the first cycle of heating and cooling  [A1 and B1; Fig. 3(a) ] shows a different trend compared with the following ones [from B2 to B3, Fig.
Deformation experiments The deformed aggregates are made of two or more minerals, whose chemical and density differences result in a significant X-ray absorption contrast. Data were collected for: (i) Powders with controlled volume fraction of olivine  [(Mg, Fe)  2 SiO 4 ; density: $ 3. 2 g cm À3 ] and serpentine [ideal formula: Mg 3 Si 2 O 5 (OH) 4 ; density: $ 2. 7-2. 9 g cm À3 ]. (ii) Aggregates of pyroxene [(Ca,Na)(Mg,Fe)Si 2 O 6 ; density: $ 3. 3 g cm À3 ] and garnet  [(Mg, Fe, Mn, Ca)  3 (Al,Fe) 2 (SiO 4 ) 3 ; density: $ 3. 6 g cm À3 ], which were either retrieved from a rock specimen (M. P. Terry, personal communication;  Robinson et al. , 2003)  and ground to a powder with controlled volume fraction, or core-drilled out of another rock specimen  (Locatelli et al. , 2018). A list of the experiments is given in Table  2.
XRT acquisition and reconstruction Pink-beam illumination at the PSICHE beamline (e. g. King et al. , 2016 King et al. , , 2019) )  was used to collect the tomographic datasets. The beam was filtered with a mirror to cut the high energies, then aluminium and tin filters were used to define the spectrum. This gave an average X-ray beam energy of 39 keV, with a beam size of $ 2. 6 mm Â 2. 6 mm. With this setting, a voxel edge was 1. 3 mm in the later reconstructed tomographic datasets. A speed of anvil rotation of 0. 15 s À1 was used to collect $ 3000 projections from 0 to 180 rotation. A standard flat-field correction is applied to each acquired X-ray image. The image is corrected for inhomogeneous illumination using a white-field reference image (the beam without the sample) taken prior to each tomography scan and from the electronic noise by a dark-current image (image taken without beam).
In situ XRT acquisition  [Fig. 1(b) ] was performed at specific anvil twisting angles to sequentially image the deformation microstructure. In the first series of experiments (#15 to #21, excluding 15b and 18b, Table  2 ), the majority of the tomographic datasets were acquired at high temperature. This enhanced motions in the samples during in situ XRT acquisition. For instance, in #15, #19 and #21, heterogeneous motions were significant, and the tomographic datasets could not be fully corrected during pre-processing (before volume reconstruction). Therefore, in the second series of experiments (#23 to #25, Table  2 ), we opted for quenching the samples by shutting down the power after each twisting step, and letting them stabilize for $ 30-40 min before tomographic acquisition. Then, the samples were again heated up to the target temperature and deformed to higher twisting angles.
For each deformation experiment, the twist interval was either 45 or 90. The maximum twisting angle depended on the gap remaining between the opposed anvils. Because of the sideways expansion of the cell assembly, this gap shortens during the experiment (by a maximum of $ 900 mm, see Section 4). Since the anvils used here are not transparent to X-rays, it constrains the tomography view, which is therefore smaller than the actual sample height (see Section 3). XRT data were pre-processed and reconstructed at the PSICHE beamline using Python scripts (Tomodata) and PyHST2  (King et al. , 2016; Mirone et al. , 2014). PyHST2 is available at https://ftp. esrf. fr/scisoft/PYHST2/installation. html. During pre-processing of the tomographic datasets, sample motion artefacts (e. g. Kastner & Heinzl, 2018)  were present due to the sample response to the applied deformation.
In some cases, motions at the sample scale resulted in the position of the rotation axis being completely off [Fig. 4(a) ], and it was necessary to repeat the acquisition. The common artefacts due to motions consisted of ring artefacts (e. g. Koeberl et al. , 2002; Wang et al. , 2011; Gharbi & Blunt, 2012; Mirone et al. , 2014; Berg et al. , 2018; Kastner & Heinzl, 2018) , image 'blurriness', shading and 'triple-point' (or the so-called 'Mercedes') structures  [Fig. 4(b) ]. A Paganin filter (e. g. Paganin et al. , 2002)  was used to reduce the rings artefacts. For the remaining artefacts, we used the so-called 'Mercedes' correction filter, which is implemented in the scripts at the PSICHE beamline (A. King, personal communication). The filter relies on the selection of multiple 'triple-point' (or 'Mercedes') structures, and uses them as markers from which to derive an average rigid-body movement of the sample, which is taken into account in the reconstruction by PyHST2.
XRT post-processing The post-processing workflow (after reconstruction) of the tomography images consists of: selection of a representative volume (RV) in the whole sample image; application of various filters; segmentation and binarization; post-segmentation processing; quantitative analysis. A detailed description of each of these steps is given in the following sections. The first steps determine the quality of the subsequent quantitative analysis. All steps before the analysis were performed using the commercial software Avizo (https://www. thermofisher. com/ fr/fr/home/electron-microscopy/products/software-em-3d-vis/ avizo-software. html). The quantitative analyses on XRT images were performed with both Avizo and Fiji. The latter is a specific distribution of the open-source program ImageJ (https://imagej.
6(c) ]. This makes the window for XRT smaller  [Fig. 5(b) ], leading to a reduced volume size of the tomography [Fig. 8(a) ]. For the purpose of observation and statistics   2 ). For #23, #24, #25, the dashed line is a guide-to-the-eye for the confining pressure trend. consistencies, for each sample we select similar sizes of RVs for all tomographic datasets (i. e. at different twisting step). The size of the RV for each sample is chosen on the basis of the height of the last image at the highest twisting angle. The 'Extract sub-volume' and the 'Volume-edit' tools in Avizo are used to obtain the RV. 'Extract sub-volume' is used to define the bounding box containing the actual sample and remove portions of the tomography image including parts of the assembly or the anvils. 'Volume-edit' is used to apply a user-defined cropping, based on a geometric 3D mesh.
Filtering and segmentation Noise-reduction and edge-preserving filters are used to erase, or reduce, some of the remaining artefacts in the RVs. The basic '3D Median' filter is often used in this study. We run the 'Anisotropic diffusion' module to smooth rings artefacts if still present. For the grey-levels images, choices made by the user for filters parameters can be non-unique. This results in uncertainties on the quantitative analysis performed on the segmented RV. Here, the influence of these choices was estimated by manually iterating over a range of filter parameter values, from visual inspection of the filtered image. Then, an uncertainty range was obtained above and below which the filtering was considered incorrect. This uncertainty range was subsequently taken into account to estimate the total uncertainties in a 3D quantification for our RV tomographic datasets (see Section 6.
Deformation microstructures 6. 1. Analysis tools and quantifications The first part of the analysis is performed on Avizo and run on the 3D doughnut-shaped RV to (i) obtain phase volume proportions, (ii) observe the morphology of the clusters of a phase, and (iii) obtain the degree of connectivity of the clusters of a phase (see Section 6. 2) with increasing deformation. The second part of the analysis is performed on Fiji, and run on selected 2D unrolled sections extracted from the RV at a selected radius using a Matlab script developed by M. Thielmann (personal communication) from the University of Bayreuth (BGI, Germany). The extracted unrolled sections can allow 2D microstructural analysis consistent with the transport of matter in torsion  [Figs. 5(b)  and 8(b)], along the whole RV perimeter.
This limits the duration of the deformation experiment, and possibly leads to blow-out and anvil failure. As of now, a compromise has to be found between achieving large shear strains and keeping a sufficiently large height of view for the microstructural investigation in the tomographic datasets. In the series of experiments where the tomographic datasets were acquired on quenched samples, the gaskets kept a larger height of view than those where the whole experiment was under HT. This suggests that the pressure-transmitting medium (gasket) loses performance over time under elevated temperature. In other words, lateral extrusion is enhanced by longer exposure of the assembly to HT. Quenching the assembly after each step of twist is, as of now, the most efficient way to reduce the extrusion and loss of pressure in the cell/sample.
g. Dobson et al. , 2020). This would minimize the potential biases when using 2D sections outlined in Section 6. 1, and allow an easier use of the 3D information. It would also simplify fractures patterns, which follow in 3D a helicoidal distribution within our samples. In order to fully characterize the deformation behaviour of materials, stress measurement is required. Use of the RoToPEc to collect tomographic datasets and perform global or local stress measurements using X-ray diffraction, either in angular-dispersive mode with a monochromatic beam or in energy-dispersive mode with multiple Ge-detectors, could be a mid-to long-term goal. This would require modifications of anvil design and material.
, 2019; Giovenco et al. , 2021). This variation of the PEc design allows high-pressure (> 1 GPa), high-temperature experiments but does not have deformation capacity. The RoToPec is the only variation that allows deformation and tomography. However, the current type of motors for the anvil rotation imposes a duration of $ 20 min per acquisition. Implementing a faster tomographic dataset acquisition requires heavy hardware modifications that should be the scope of future developments. Such developments would be highly desirable to minimize the motion in the samples, reduce the amount of work during pre-processing or post-processing, and improve in situ imaging of deformation microstructures under HP/HT. Such technological breakthroughs would for instance allow study of the microstructural evolution of rocks relevant for the earth's lithosphere, upper mantle and subduction zones (down to $ 150-200 km depths), which are of paramount importance for understanding the upper part of earth convection and deep seismicity.
Acknowledgements We acknowledge  Synchrotron SOLEIL  for provision of synchrotron radiation facilities and we would like to thank the staff for assistance in using beamline PSICHE (proposals numbers 20170324, 20181545, 20201435). We thank  M. Thielmann  for his collaboration in this project. We also thank  M. Locatelli ,  P. Agard  and  M. P. Terry  for donating the eclogite specimens. This work benefited from discussions with and suggestions by  J. P. Perrillat  and  E. Giovenco.
Transposable Element MOnitoring with LOng-reads (TrEMOLO)  is a new software that combines assembly-and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high-or low-quality genome assemblies, TrEMOLO can detect most TE insertions and estimate their allele frequency. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Benchmarking with simulated data revealed that TrEMOLO outperforms other computational tools. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics.
5 In this paper, we present the Transposable Element MOvement detection using LOng-reads (TrEMOLO) software that combines the advantages offered by LR sequencing (i. e. , highly contiguous assembly and unambiguous mapping) to identify TE insertion (and deletion) variations at the global and population level, for TE detection and frequency estimation. TrEMOLO (available at https://github. com/DrosophilaGenomeEvolution/TrEMOLO) is a SnakeMake pipeline that can be used as it is, or with Singularity to ensure higher reproducibility, and to facilitate future updates. This pipeline is designed to be flexible, thus simplifying the streamlined addition of new samples. We demonstrated its efficiency using simulated LR sequencing data and experimental LR sequencing data from a highly polymorphic Drosophila population.
A detailed manual on how to run TrEMOLO with all its options is available at TrEMOLO github (https://github. com/DrosophilaGenomeEvolution/TrEMOLO). The INSIDER TE detection module. This module takes as input a reference genome and the genome assembly of the studied biological sample. This assembly must be generated with the input LR sequencing data to be compatible with the second module. Pairwise alignment is performed between the reference and assembled genome using minimap2  (29) , and is then parsed with Assemblytics (33) for SV identification (Fig. S1 ). Before providing the final list of detected TE insertions, the alignment data are scanned to filter out low-quality TE detections (Methods). This analysis returns a list of SVs from the pairwise alignment of the assembly against the reference genome with their positions.
This list is then compared with a TE library to keep only insertions belonging to already known TE families (Fig. 1 , left panel, and Fig. S1 ). This module detects the so-called INSIDER TEs. These are the non-reference TE insertions that are found frequently enough among the reads incorporated in the assembly (generally TEs present in the major haplotype of the sample). The OUTSIDER TE detection module. This module takes as input a genome assembly and the LR sequencing data used to build this assembly. The genome assembly and reads are aligned using minimap2  (29). Reads that contain a TE insertion not retained in the genome assembly are identified as unmapped (or partially mapped) reads of the output SAM file that contains the TE sequences identified based on the TE library (Fig. 1  right panel, Fig.
TrEMOLO benchmarking using simulated datasets To assess TrEMOLO, we compared the results obtained with TrEMOLO and with TLDR (25) and TELR  (24) , two recently developed computational tools dedicated to TE detection using LR sequencing data. The ab initio approach of these tools is similar to the one used with SR sequencing data: they use LR data (mapped to a reference sequence, through a BAM file for TLDR  (25)  or directly from the mapping tool output for TELR. Insertions are detected based on discordant mappings (i. e. clipped reads or partially mapped reads or both). Finally, like for SRbased approaches, the non-mapped parts of the reads are aligned against a library of canonical TE sequences. Then, discordant mapped reads associated with TE matches are selected, clustered, and analyzed to identify the TE insertion site in the reference genome.
We generated a simulated dataset in which 1,100 TE insertions with different allele frequencies (simulated sample S1; see Methods) were added to a reference D. melanogaster genome called G0-F100  (13). We mapped the simulated reads to this reference genome using TLDR  (25) , TELR  (24) , and the TrEMOLO OUTSIDER module with minimap2 (29) (here called "TrEMOLO_NA", see Methods). We also launched the TrEMOLO software (OUTSIDER and INSIDER modules) with the G0-F100 reference genome. For the OUTSIDER module, as a proxy of the assembly, we used this reference genome that contained 100 highly frequent TEs (segregating at 75% in the simulated reads). As TLDR parameters are fixed  (25) , we decided to use the same parameters for the four methods/tools (TrEMOLO, TrEMOLO_NA, TLDR, and TELR): 200 bp as minimum TE insertion length and 80% as minimum TE sequence identity.
To limit false-positive TE detections, we also set at 15kb the largest TE insertion length. Using these simulated datasets, TrEMOLO and TrEMOLO_NA detected more TE insertions (949 and 925 among the 1,100 insertions, respectively) than the other two tools (866 and 14 for TLDR and TELR, respectively). Moreover, sensitivity was higher for TrEMOLO and TrEMOLO_NA (84. 6% and 83. 3%, respectively): 931 and 917 true-positive results compared with 813 (73. 9%) and 12 (1%) for TLDR and TELR, respectively. TrEMOLO and TrEMOLO_NA were also the most specific: 18 and 8 false-positive results compared with 53 and 2 for TDLR and TELR, respectively (Fig. 2 ). The difference between TrEMOLO and TrEMOLO_NA was relevant, and it might more important if a more different reference sequence than the tested sample were used.
Validation of TE frequency estimated by TrEMOLO using simulated data To validate the accuracy of TrEMOLO frequency estimation, we used simulated datasets with known frequencies of INSIDER and OUTSIDER TEs (see Methods). For OUTSIDER TEs, we wanted also to determine whether taking into account clipped reads would improve the accuracy of TrEMOLO frequency calculation (Fig. 4a ). We first observed that the TrEMOLO frequency module slightly overestimated the frequency of INSIDER TEs in the simulated datasets (Fig. 4b ). Moreover, estimation of the OUTSIDER TE frequency was very accurate only when the clipped reads present in the samples were taken into account (Fig. 4c ). Therefore, we decided to keep this parameter for the next frequency computation.
Impact of sequencing depth on TE identification and frequency estimation Using the initial G73 dataset, we generated different sequence subsets using Filtlong (41) (see Methods), resulting in seven "samples" of reads for the same DNA with 24 (i. e. original dataset, 183x; G73), 19 (G73_F19), 15 (G73_F15), 12 (G73_F12), 10 (G73_F10), 5 (G73_F5), and 2 (20x; (G73_F2)) Gbases of data. We then launched TrEMOLO with these datasets, and compared the number of OUTSIDER TEs detected by mapping these reads to the G73 assembly (Table  3 ). Using the initial dataset (total read depth = 183x), TrEMOLO detected 1,038 OUTSIDER TEs. The percentage of detected OUTSIDERs is relative to this dataset. The depth of coverage started to have a major impact at around 76x, with only 87% of OUTSIDER TEs recovered (Table  3 ).
Decreasing the coverage depth from 183x to 92x reduced by only ~10% the number of OUTSIDER TEs detected. Below 76x of coverage depth, TrEMOLO detection performance decreased more rapidly (69% and 46% of detected TE insertions with 38x and 20x depth). Nevertheless, even with a quite low coverage (20x), TrEMOLO could still identify 46% of OUTSIDER TEs. Among the 1,038 original OUTSIDER TE insertions, only 465 were detected in all subsamples. We used these 465 TEs to compute the TE frequencies in each sub-dataset in order to estimate, the frequency computation bias linked to the sequencing depth. The accuracy of OUTSIDER TE frequency estimation was acceptable when using datasets with sequencing depth ≥76x (DNA extracted from ~ 100 flies, i. e. , 200 haploid genomic samples), with a decrease of 12% maximum compared with the initial frequency computed at 183x (Fig.