dataesr/scientific-paragraphs-categorization huggingface dataset:
In it, all nodes, elements, and element sets of the mesh are defined. There, the mesh is represented by ten-node tetrahedral elements (C3D10). The. vtk file, labelled XtalMesh. vtk, is a binary Visualization Toolkit (VTK) datafile provided for ease of visualization and analysis of the mesh using ParaView by Kitware. There, the mesh is represented by four-node tetrahedral elements (C3D4) to reduce system load during visualization. The mesh generated with the software suite from Simmetrix (SimModeler Voxel), is provided in an Ansys 33 input deck. The input deck contains the following APDL (Ansys Parametric Design Language) files: run_Model. mac, contains all the calls to load the mesh, assign material properties, define node components, orient each grain according to the average Euler angles from the EBSD measurement and, for example, assign boundary conditions.
17  for a reduced region of interest. BSE imaging is a common measurement technique for capturing the location and existence of slip events at sample surfaces. Every single slip event is captured by the HR-DIC measurement and their locations are identical to those identified in the BSE images. The intensity of the strain concentration and in-plane slip obtained by the HR-DIC measurement and using the Heaviside-DIC method was previously validated  10. The intensity of slip is obtained for each single slip trace with high spatial (less than 33 nm) and amplitude resolution (less than 10 nm). 3D Microstructure. To validate the structure of the grains in the 3D dataset, the grain structure at the free surface of the reconstructed 3D dataset is extracted and compared to the conventional EBSD measurement performed at the free surface of the specimen, described in the Section Surface Crystallographic Orientation Measurements.
The inverse pole figure map in Fig. 18 (a) of a reduced region of interest obtained from the surface EBSD measurement describes the grain structure at the surface of the specimen. The associated grain ID map obtained from the reconstructed 2D dataset is presented in Fig. 18(b ). An overlap of the two maps are presented  in Fig. 18(c)  showing that both measurements display identical grain structure. However a slight difference in the location of the grain boundaries is observed. As a consequence, 7% of the total surface area is observed to not match (within 1 pixel tolerance) between the free surface obtained from the reconstructed 3D dataset and the free surface obtained from conventional surface measurement. For validation of the crystallographic orientation, a direct comparison of the orientation obtained between conventional surface measurement and TriBeam serial sectioning measurements are performed for the grain labeled "FeatureID 820" in Fig.
18(b ). The average orientations obtained at the surface of the grain are displayed in Fig. 19  using pole figure. An insignificant misorientation of 1. 6° between the two measurements is observed. Meshed dataset. The generated meshes are evaluated using the tetrahedral quality metrics, that include the scaled Jacobian, shape, and minimum dihedral angle as defined by Stimpson et al. in "The Verdict Library Reference Manual"  34. Each quality metric represents a unique mathematical measure of an element's level of distortion from a perfect equilateral tetrahedron, and as no one metric is universally applied, multiple have been provided. Mesh quality statistics are provided in Fig. 20  for both the generated mesh using XtalMesh and Simmetrix' MeshSim, which each displayed high mesh quality metrics.
Usage Notes We hope this article will encourage other research groups to share their 3D datasets as a growing collection of multi-modal datasets would serve as a unique benchmark for the design of new microstructures or prediction of mechanical properties for structural materials. This set of high-resolution images provides opportunities to evaluate DIC methods and compare with the DIC method presented here. This set also was contributed to the effort of the Society of Experimental Mechanics DIC challenge (www. sem. org/dic-challenge/) by providing recent high resolution scanning electron microscope images for DIC. The 3D dataset and the various imaging modalities are also shared on the BisQue platform  35  at: https:// bisque. ece. ucsb. edu/client_service/view?resource=https://bisque. ece.
ucsb. edu/data_service/00-Go3oRQie-haBUzET2cBAeug. In BisQue, the data provenance can be explored and DREAM. 3D pipelines can be modified and executed to perform other types of analysis on the dataset. The TriBeam microscope used to collect the 3D dataset is a prototype system developed at UC Santa Barbara. However, an updated and commercially available instrument is available from Thermo Fisher Scientific  36. More broadly, data of this modality is available via serial sectioning methods  37  or via X-ray methods  38  , which are especially well suited for in-situ measurements. In addition, the multi-modal dataset can be used for direct comparison to crystal plasticity simulations including Fast-Fourier transform based calculations (FFT -operating on the voxelized data directly) or by finite element calculations (FE -operating on a meshed version of the data).
Two full 3D volume meshes suitable for FE calculations have been generated from the 3D microstructural dataset collected by TriBeam and are provided. We encourage other researchers to use it for calculation of inter-and intragranular mechanical fields, and for the study of various deformation mechanisms. publicly available on GitHub (https://github. com/BlueQuartzSoftware/DREAM3D) or in the following link for direct download (http://dream3d. bluequartz. net/Download/. One version of a mesh structure was created with XtalMesh  21  , a publicly available code on GitHub (https://github. com/jonathanhestroffer/XtalMesh). The second version was created with the software suite from Simmetrix (SimModeler Voxel), a commercially available software (http://www. simmetrix. com/). Digital image correlation were performed using the software Xcorrel HDIC.
Acknowledgements Sebastien Stinville  is acknowledged for his contribution and discussion of figure designs. Patrick Villechaise ,  Jonathan Cormier  and  Damien Texier  are acknowledged for providing the Inconel 718 material. Toby Francis  is acknowledged for his support during 3D dataset collection. This work is funded by the  U. S. Dept. of Energy, Office of Basic Energy Sciences Program DE-SC0018901. Use was made of computational facilities purchased with funds from the  National Science Foundation  ( CNS-1725797 ) and administered by the  Center for Scientific Computing (CSC). The CSC is supported by the  California NanoSystems Institute  and the  Materials Research Science and Engineering Center (MRSEC ;  NSF DMR 1720256 ) at  UC Santa Barbara. The  NSF OAC  Grant  1925717  is also acknowledged for GPU compute resources for data reindexing.
Code availability Multi-modal datasets are of great interest for evaluating the dataset-merging procedures for distortion correction and for correlative measurements analysis tools. The distortion correction algorithms and data analysis tools used in this study can be found at https://github. com/charpagne. Reindexing of the EBSD data with the dictionary indexing approach was performed with EMsoft version 4. 2, the latest version of the code is available at https:// github. com/EMsoft-org/EMsoft. The 3D voxelized dataset has been reconstructed using DREAM. 3D, a software.
Database analysis Most bioinformatics analyses were performed by the MBB platform in Montpellier (http:// mbb. univ-montp2. fr/). For each condition (2 months of starvation, 15 h, 40 h, or 216 h after feeding), cDNA libraries (with a mean insertion size of 1081 bp) were generated in duplicate (individuals A and B in Fig. 2e  and Supplementary Information SI 1). To overcome the lack of reference transcriptome for L. hypogea, cleaned reads from the eight individuals were de novo assembled into contigs using Trinity transcriptome assembly software. The cleaning step consisted in removing sequences of Artemia franciscana (used to feed sponge) and sequences of Ciona intestinalis (another biological model currently used in our lab). For this step, we mapped reads using bowtie2 on a reference file made by concatenating Artemia transcriptome coming from a parallel project of our institute (PopPhyl ANR project) and Ciona sequences (from Ensembl).
Only pairedend reads that failed to align (using -very-sensitive-local option) were retained. Bacterial sequences had no impact because the cDNA library was made with oligo-dTs targeting only eukaryotic mRNAs. Sequences from L. hypogea were extracted from these databases by TBLASTN alignments with protein sequences of stem cell markers, cell cycle regulators, phagocytosis, autophagy, and apoptosis mechanisms from other model organisms (human, Drosophila, and nematode for all mechanisms). Yeast sequences were also used for autophagy since yeast is the most studied model for this cellular mechanism. Protein sequences from reference species were found on Uniprot (http:// www. unipr ot. org/) or Ensembl (http:// www. ensem bl. org/) websites. The Lycopodina hypogea isolated sequences were scanned with InterProScan 5 (https:// www.
ebi. ac. uk/ inter pro/ search/ seque nce-search) to verify the presence of functional or structural domains specific of each protein. In parallel, each selected sequence was used to conduct TBLASTN searches on NCBI (all databases) to name the sequence. When several incomplete sequences had the same best hit, we aligned them on this best hit sequence to see if some of them overlap. In this case, concerned sequences were considered as fragments of the same sequence, and we were used to reconstitute the entire sequence. Sequences too short and without specific domains were automatically discarded to avoid false positives. Then, the best assembly contigs were used as a reference for aligning reads from the 8 different individuals to estimate the expression levels (Bowtie, RSEM). From these normalized expression levels, appropriate statistical methods (Edger, Imb) were applied to identify genes whose expression levels vary significantly between conditions (X4), to identify molecular networks mainly involved in the morphological changes caused by nutrition cycling.
Participants We considered uniform structural MRI data from 34 ET patients (both prethalamotomy and 1 year later) scanned on the same 3T MR machine, and 29 HCs. All patients were right-handed and presented with drug-resistant right-dominant tremor. All underwent left Vim thalamotomy by GK. The Timone University Hospital Ethical Committee (ID-RCB: 2017-A01249-44) granted formal approval for this study (including by the Ethics Committee at national level, CNIL-MR-03), and individual consent was also obtained from all subjects. Patients were neurologically evaluated and referred by T. W. , a neurologist specialized in movement disorders. All patients had a clear diagnosis of ET and showed no other structural abnormalities on pretherapeutic 3T MRI. Demographic characteristics of the ET patients and HC subjects can be found in Table  1 , where it can be seen that the groups were matched for both age and gender.
Imaging The imaging data was acquired on a head-only 3T machine (SIEMENS SKYRA, Munich, Germany, 32-channel receive-only phase-array head coil). Native T1-weighted images were acquired before and 1 year after thalamotomy with the following parameters: TR/TE = 2 300/2. 98 ms, isotropic voxels of 1 mm 3 , 160 slices. As medication was frequently ineffective, most patients no longer received treatment at the time of thalamotomy. Scanning was performed in a drug-naïve state (drugs having been stopped at least 3 days beforehand). Tremor Score on Treated Hand (TSTH): A clinical score that quantifies the extent of tremor in a patient, with higher values denoting greater motor impairment. Note. For healthy controls (HCs), patients before (ET pre ) and after thalamotomy (ET post ), values are reported as mean ± standard deviation, with minimum, median, and maximum into squared brackets.
Network Neuroscience Stereotactic Radiosurgical Procedure GK thalamotomy was performed using Leksell Gamma Knife (Elekta Instruments, AB, Sweden) between September 2014 and April 2016, always at the Centre Hospitalier Universitaire de la Timone in Marseille, by the same neurosurgeon ( J. R. ). To avoid artifacts, diffusion tensor imaging data was first acquired without the frame, and then co-registered with the therapeutic stereotactic images. The Leksell coordinate G frame (Elekta Instruments, AB, Sweden) was always applied under local anesthesia on the day of the GK thalamotomy. After positioning the frame, patients underwent both stereotactic CT and MRI. Landmarks of interest, including the anterior and posterior commissures, were identified on an MR scan (particularly on T2 CISS/FIESTA sequence, Siemens).
Computation of Morphometric Properties The FreeSurfer software  (Fischl, 2012)  was used to extract three morphometric measures of interest from structural MR images for a set of P cort = 68 cortical regions: CT, SA, and MC. We selected these because they have been widely used and acknowledged as relevant indicators of brain geometry; see, for example,  Harnett et al. (2020) ,  Raznahan et al. (2011) , and  Yang et al. (2013). We hoped to find possibly distinct relationships with ET and, in addition, considered to also explicitly quantify the interplay across properties (see Cross-Measure Analysis section). Briefly, after linear registration to MNI space and bias field removal, the image at hand is skull-stripped  (Ségonne et al. , 2004) , and voxels are classified as belonging to white matter or to another tissue category based on their intensity and direct neighborhood.
However, because we only assess cross-regional correlations, the distinct ranges of values do not impact our analyses. Thus, similarly for CT, SA, and MC, the P noncort noncortical volume values were appended to the P cort cortical estimates, resulting in a P-dimensional vector for each subject, with P = 87. Note that there is no redundancy in our analyses, since the correlational relationships between noncortical volume and a given morphometric measure may differ from those with another. These measurements were eventually linearly regressed out for age, gender, and total gray matter volume, separately within each group. The obtained residuals were used for all subsequent analyses. FreeSurfer: An open source brain imaging package enabling the extraction of morphometric estimates from structural neuroimaging data.
Availability of the Data and Scripts All the analytical steps described above were performed with custom scripts and MATLAB2014b (MathWorks, Natick, USA). Color maps for plotting were generated with the cbrewer toolbox (https://www. mathworks. com/matlabcentral/fileexchange/34087-cbrewer -colorbrewer-schemes-for-matlab). All the scripts used in this work are freely available at https://github. com/TiBiUan/SCA_EdgeWise. git. Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Limitations and Future Perspectives It is important to acknowledge the limitations of our study: in particular, the number of subjects at our disposal remains quite low, likely precluding the unraveling of finer ET-related structural covariance patterns. However, it should be remembered that the patients analyzed therein belong to a subgroup of drug-resistant individuals only; in addition, morphometric data could be collected at two time points before and 1 year after thalamotomy. Of course, it would be interesting to monitor how recovery continues to evolve over the course of time, with an even more longitudinal design. One might hypothesize that additional structural covariance adjustments would then start developing, although empirically, clinical and radiological changes remain minimal.
ACKNOWLEDGMENTS Dr. Tuleasca  gratefully acknowledges the receipt of a  Young Researcher in Clinical Research Grant ("Jeune Chercheur en Recherche Clinique")  from the  University of Lausanne (UNIL) ,  Faculty of Biology and Medicine (FBM) , and the  Lausanne University Hospital (CHUV). The authors also wish to thank  Andrea Sullca  for her help in compiling the presented results.
The surveillance of chemical substances in the scope of Environmental Risk Assessment (ERA) is classically performed through bio-assays from which data are collected and then analysed and/or modelled. Some analysis are based on the fitting of toxicokinetic (TK) models to assess the bioaccumulation capacity of chemical substances via the estimation of bioaccumulation metrics as required by regulatory documents. Given that bio-assays are particularly expensive and time consuming, it is of crucial importance to deeply benefit from all information contained in the data. By revisiting the calculation of bioaccumulation metrics under a Bayesian framework, this paper suggests changes in the way of characterising the bioaccumulation capacity of chemical substances. For this purpose, a meta-analysis of a data-rich TK database was performed, considering uncertainties around bioaccumulation metrics.
The TK model is expected to account for all uptake routes and elimination processes (including excretion, dilution by growth and/or metabolization) to properly describe the overall kinetics in terms of internal concentrations. In addition to the experimental procedure of bioaccumulation tests, the OECD guidelines also detail how to obtain bioaccumulation metrics depending on the exposure routes that have been considered within the experiments. Although several scientific and commercial software environments exist for the calculation of bioaccumulation metrics (e. g. , R, Matlab, Python, GraphPad Prism, OpenModel, etc. ), few are easy to handle, free and open access with ready-to-use TK modelling tools; namely, with mathematical equations automatically computed from the data entered by the user, and all relevant outcomes provided with a support for their interpretation.
To name but a few, there are the Excel macro by  Gobas et al. (2020) , the "bcmfR" package (OECD 2012) and the MOSAIC bioacc web service (http://umr5558shiny. univ-lyon1. fr/mosaic-bioacc/) that has been recently updated  (Ratier et al. 2022b; Charles et al. 2022). Furthermore, if a simple one-compartment TK model (e. g. , considering only bioaccumulation from one exposure source and excretion as elimination process) is often sufficient to obtain the bioaccumulation metrics for ERA, the goodness-of-fit can be poor, indicating that such a simple TK model is not fully suitable thus requiring the use of a more complex model. For example, one of the most common complexities to account for is growth of organisms during the bioaccumulation test (OECD 2012). Surprisingly, in such a case, guidelines only recommend to seek advice from biostatistician and/or pharmaco-kineticist experts.
Instead of the classical standard deviation, MOSAIC bioacc provides a summary of several useful quantiles. While available on-line since May 2020, the number of users of MOSAIC bioacc is continuously growing all over the world, whether they are from academia, regulatory bodies or industry (380 recordings these last 6 months). A publicly available database accompanies the MOSAIC bioacc services (http://umr5558-shiny. univ-lyon1. fr/mosaicbioacc/data/database/index_readme. html), with more than 200 accumulation-depuration data sets collected within published scientific papers  (Ratier and Charles 2022). All these data sets were automatically analyzed with MOSAIC bioacc , full analysis reports being made available via the database directly. Additionally to transparency and reproducibility, all this data can be retrieved for further analyses; this is in line with the FAIR principles, which have become almost mandatory today  (Wilkinson et al.
Calculations of bioaccumulation metrics Recent updates have been brought to the MOSAIC bioacc web application to increase the speed of calculations and improve its user-friendliness. Above all, a new R-package is today available on the official CRAN web site (https://CRAN. R-project. org/package=rbioacc) that allows to similarly perform all MOSAIC bioacc calculations and graphs directly in the R software with ready-to-use dedicated functions  (Ratier et al. 2022a). The new version of MOSAIC bioacc has entirely been rewritten to reduce the length of the source code and take advantage of this new package. So, MOSAIC bioacc is now based on a tabbed presentation that clarifies and facilitates browsing from one step to the next. A special tab gives all bioaccumulation metrics, appropriately calculated according to the input observed data that the user has uploaded.
Illustrations with case studies We provide a collection of three case studies as supplementary information (SI, see the. pdf file) to illustrate various situations where the prediction tool can be helpful: Case study 1 Plan an experiment for an already studied species exposed to a different but chemically similar compound (i. e. , with close physico-chemical properties), without accounting for the parameter uncertainty; Case study 2 Compare several species exposed to a same chemical substance accounting for the uncertainty around parameter estimates coming from a previous TK analysis conducted on a species phylogenetically (or taxonomically) close to the new set of interest. In such a case, the user will need to enter the required input information but also a tabular file with the joint posterior distribution of the parameters, either coming from a previous MOSAIC bioacc TK analysis or from its own TK software.
Matter of uncertainty in estimating bioaccumulation metrics From accumulation-depuration data, one output of great interest is the bioaccumulation capacity of a chemical substance which is assessed through the appropriate bioaccumulation metrics according to the exposure route(s) (either the BCF, the BSAF and/or the BMF). So, benefiting from the probability distributions of these metrics (coming from the propagation of the parameter uncertainty itself) is crucial to catch their precision. This latter may indeed influence the classification of the substance as bioaccumulative or not, and if bioaccumulative, influence the choice between " B", "B" or "vB" categories, leading to a potential bias in the next steps of ERA if misclassification. In order to illustrate this critical issue for ERA, we present below a meta-analysis of the TK database associated to MOSAIC bioacc  (Ratier and Charles 2022).
The accumulation-depuration TK database The TK database currently contains 211 accumulation-depuration data sets collected from a literature review and corresponding to a total of 56 studies. The 211 data sets encompass 52 genus, 124 chemical substances, three different exposure routes (water being the main one, sediment and food), 34 data sets with also biotransformation data (that is metabolization data). Figure  1   The 211 data sets of the TK database were fully analysed with MOSAIC bioacc. In brief, MOSAIC bioacc fits a one-compartment TK model to accumulation-depuration data collected under exposure conditions of interest  (Ratier and Charles 2022). MOSAIC bioacc automatically builds the appropriate TK model, based on information directly retrieved from the uploaded user data under a predefined format.