dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, as the cells sizes increase with distance from the well, the same gas flux can occur across a cell at lower gas saturation. During the gas injection, a small water flux occurs toward and across the model external boundary: it corresponds to the variation of Sw , as the main moving fluid is gas. Therefore, less than a pore volume of water has swept across any cell. The very low values of Sw close to the well are due to the injection of a dry gas that leads to a drying of the medium through water evaporation. The saturation patterns are different in the two models and we did not find a clear explanation for this, despite numerous tests in muRT. Both models provide a correct water/gas volume balance. To simulate the geochemical reactions, the TOUGHREACT chemical database, as provided by  André et al.
(2007) , has been translated to a Phreeqc reaction database. The reactions are first linked to the equilibrium-based dissolution of CO 2 into water, as calculated based on the Peng Robinson formulation for the Henry coefficient. As the pressure and temperature remain close to 200 bar and 75 • C in the entire domain, the Henry coefficient varies barely in space. The high pressure of CO 2 in the injected gas phase leads to an acidification of the water. This acidification is compensated by the buffering effect that is provided by an assemblage of carbonates. Their dissolution and/or precipitation is assumed to be kinetically controlled, using rate laws that were originally proposed by  Lasaga and Kirkpatrick (1981) , and described in more detail in  André et al. (2007)  as well as in Data S1.
Conclusions This article presents the development and capabilities of a new code that couples the two widely used tools OpenFoam and PhreeqcRM through the creation of the gwaterFreakFoam library. Benchmarking the newly developed code against existing, well-tested codes demonstrates the accuracy and efficiency of the new code. The major motivation for the development of the new code was to generate a reactive transport code for unstructured grids with both wide-ranging geochemical capabilities and a high accuracy of the transport solution under advection-dominated conditions, which is currently not available elsewhere. Moreover, the library also allows for the simulation of the diffusion, advection and reactions of gaseous compounds, which adds further to the usefulness of the code and its applications to advanced biogeochemical simulations problems.
This is demonstrated in Figure  1C , which displays geometric indices of the macaque hippocampus next to those from the human. To investigate the hippocampal microstructure in the macaque, we reconstructed the hippocampal surface in a single macaque scan from the BigMac dataset, an open resource combining multi-contrast and ultra-high-resolution microscopy and MRI in a single macaque brain  35  (Figure  1D ). We mapped histological measures (Cresyl violet stain for Nissl bodies and Gallyas Silver stain for myelin), manual labels of hippocampal subfields and three MRI metrics (fractional anisotropy [FA], mean diffusivity [MD], multi-gradient-echo intensity [MGE]) to the hippocampal surface. The microstructural mappings demonstrated that hippocampal microstructure varies primarily along the proximal-distal axis and the characteristic spiral configuration of subfields is represented as a sequence of vertical subfields in the flatmap (Figure  1E ).
The functional embedding of the hippocampus is diverse We continued to study the functional anatomy of the hippocampus using resting-state functional MRI (rs-fMRI) data from 10 adult individuals in both species. Rs-fMRI has been widely used to investigate the intrinsic functional brain organisation in both species  37  , and the comparability of functional networks between awake and lightly anaesthetised states has been firmly established  [38] [39] [40]. Image preprocessing and analysis were equivalent in both species and the macaque fMRI data are of higher tSNR than most previously used datasets  41  , altogether suggesting that the data in both species allow for a quantitative comparison. Application of non-linear dimensionality reduction techniques  42  to hippocampus-to-cortex connectivity matrices provided spatial maps of connectivity variations, also referred to as Connectivity Gradients  22, 43, 44  , of the hippocampus (Figure  2A ).
We leveraged two unique ultra-high-resolution multiscale databases from the macaque and human for a cross-scales and cross-species comparison. This novel combination of resources allowed us to demonstrate that the hippocampal macro-and microstructure are overall conserved across species, a long-standing evolutionary hypothesis that was lacking spatially resolved quantification to date. The histological mapping further functioned as a microstructural validation of our hippocampal surface and flatmap, confirming that it represents a comparative space across primates. Our findings overall show that both species have a comparable long axis, comprising head, body and tail regions that are characterised by a canonical sequence of subfields. This demonstrates that microstructural patterns and gradients in the hippocampus are conserved across species, suggesting that the basic microcircuit and hippocampal computation remained largely unchanged over evolutionary time.
Methods to automatize hippocampal subfield segmentation  30, 56, 57  and to enhance cross-modal and inter-individual registrations  30  are already well established in humans, and current efforts to adapt them to non-human primate brains will facilitate that process. In this context, open data sharing projects such as the BigMac dataset  32  used in this study, but also initiatives such as PRIME-DE  58  may make an invaluable contribution, as they will allow for the aggregation of a diverse set of data in non-human primates, and their dissemination to a wide range of researchers. Building up on the microstructural analysis, our resting-state fMRI results demonstrated that the functional differentiation along the hippocampal long axis is also largely preserved across species. The cortical embedding of the hippocampus recovered a homologous default-mode network in both species comprising dorso-lateral frontal, inferior-parietal, anterior-temporal, as well as fronto-and posterior-medial regions.
Importantly, this homologous DMN was defined based on joint embedding of hippocampal connectivity and did not require the specification of homologous cortical regions-ofinterest. Because of the data-driven nature, anchored only by the hippocampus, our approach is readily applicable to all mammalian species, and represents an important further development to previous remapping approaches  47, 48. General limitations of cross-species comparisons using restingstate fMRI were mitigated by using a macaque dataset with high temporal signal to noise, long scanning time, light anaesthesia and by using the equivalent HCP preprocessing pipeline in both species. Despite the overall similarity of functional networks, we also observed notable species differences in functional connectivity. In particular, we showed that the DMN, known to act as a major integrated network in humans  24, 45, 59  , constitutes two subcomponents in the macaque, when studied on the cortical level only.
Macaque -Post-mortem MRI data A high-resolution (0. 15 mm 3 ) rhesus macaque (Macaca mulatta) structural gradient-echo macaque template based on 10 individuals was accessed from the CIVM Macaque Brain Atlas  34. Furthermore, we accessed 7T post-mortem whole brain data from a male adult rhesus macaque, the BigMac Dataset  35  , provided openly accessible by the Oxford Digital Brain Bank (open. win. ox. ac. uk/DigitalBrainBank)  99. Full details of the BigMac data acquisition and preprocessing are provided in the original publication  35. Specifically, we accessed the multi-gradient-echo (MGE) high-resolution structural scan (0. 3 mm 3 ) as well as pre-processed fractional anisotropy and mean diffusivity data from the b=4k diffusion acquisition (0. 6 mm 3 ). Histological data (see below) from the same individual was also accessed.
Macaque -In-vivo MRI data All macaque in-vivo data were acquired for previous studies  37, 39  and reanalysed for the purpose of the present paper. Resting-state functional and in-vivo structural MRI data were obtained from 10 rhesus macaques (Macaca mulatta, 1 female, mean age at scan 7. 2 ± 2. 5 years). Details of the scanning protocol and physiological monitoring are described in a previous publication  37. In short, the animals were scanned in a 3T scanner under light isoflurane anaesthesia whilst placed in an MRI compatible stereotactic frame or resting on a custom-made mouth mould. In short, BOLD fMRI was acquired for 1600 volumes (~1 h) with the following parameters: 1. 5 mm 3 spatial resolution, TR = 2280 ms, TE = 30 ms. Structural scans using a T1-weighted MPRAGE sequence were acquired at 0.
Hippunfold To reconstruct the hippocampal surface, we used a recently developed computational tool, hippunfold v. 0. 3. Hippunfold requires a tissue segmentation to define unfolded coordinate boundaries of the hippocampal surface. In humans, the segmentation can be derived automatically by hippunfold using a convolutional neural network, but an adaptation for the macaque brain required us to manually segment a set of hippocampus and surrounding structures. We developed the macaque segmentation in a high-resolution (0. 15 mm 3 ) gradient-echo MRI scan from the CIVM database. The following labels were manually segmented in ITK-SNAP v. 3. 8. 0 102 : The hippocampal grey matter, the dentate gyrus, stratum radiatum, lacunosum and moleculare (SLRM, the 'hippocampal dark band'), the grey matter of the temporal lobe adjacent to the hippocampus, the uncus, the hippocampalamygdalar transition area, and indusium griseum (Supplemental Information, Figure  S1A ).
The volumetric microscopy data were then mapped to the high-resolution hippocampal surface. For representation in the flatmap space, the data were resampled to a regular matrix. To account for the gap between histological slices of the same contract, we performed linear interpolation (nearest-neighbour for subfield labels) in 2D space, which is topographically more suitable than volumetric interpolation, and smoothed the data (sigma = 3 mm). Data was sampled across all hippocampal surfaces and then averaged. The same workflow was applied to MRI metrics of microstructure following linear registration to the structural MGE volume using FSL's FLIRT. To account for slice-to-volume registration error and deviations in the labelling, the surface subfields map was manually corrected in GIMP v. 2.
8. 22, guided by overlays of the microstructural surface maps as previously recommended 108. This procedure ensured spatial continuity and plausibility of the map especially in the tail and head region of the hippocampus, where non-optimal cutting posed limitations on a serial manual labelling approach 109. The surface mapping of the non-corrected raw subfield labels is shown in Figure  S1F. Note that the human BigBrain subfield labels were drawn in a dense 3D volumetric reconstruction of the histological data, which meant that spatial continuity was ensured, and no such correction was needed. Applying a 3D labelling approach, however, was not applicable for the BigMac dataset, given the sparser sampling of slices and therefore the larger slice gap.
Macaque in-vivo MRI pre-processing In-vivo structural scans of the macaques were processed with an NHP adaptation of the openly available HCP pipeline  97. We adapted the pipeline scripts to run with only T1w scans as T2w scans in the same individuals were not available. Further, we adapted the brain-extraction step with an inhouse-script from the MrCat toolbox (github. com/neuroecology/MrCat). Structural processing included, amongst others, the reconstruction of the individual's brain surface using FreeSurfer 110 and registration to the Yerkes-19 space based on FSL's FLIRT 111 and FNIRT 112. Volumetric processing of the resting-state fMRI scans was performed using a custom shell script pipeline of FSL commands (v. 6. 0, 113 ) as other automatic pipelines did not provide adequate results, particularly for brain extraction and cleaning.
Following volumetric processing, the resting-state scans were processed with the fMRI surface processing part of the NHP-HCP pipeline. As part of the pipeline, the macaque fMRI data were mapped to each individual's brain surface in fsLR_10k space and converted into cifti-format, paralleling the available human fMRI data. All following resting-state analyses were carried out in parallel for both species using the same tools and parameters unless otherwise specified. We use the conventional term 'cortex' to refer to the HCP surface data, which covers mainly the neocortex. However, we do not mean to imply that the hippocampus is part of the subcortex. To assess reliability of the fMRI-based species comparison, we derived voxel-wise temporal signal-to-noise-ratio (tSNR) images for each individual and averaged these for the group.
Functional cortical gradients Cortico-cortical gradients were derived based on cross-correlation of all cortical parcels in the left and right hemispheres, followed by Fisher R-to-Z transform, affinity kernel computation, and diffusion map embedding as described above (see Figure  3D  for a schematic). To test the relationship between cortico-cortical gradient maps and the 1 st joint cross-species gradient map, we performed feature selection via LASSO regression as implemented in scikit-learn (alpha = 0. 1). Prior to the regression, data were transformed to a Gaussian distribution using scikit-learn's QuantileTransformer. To compute a goodness of fit for each gradient map, we used Ordinary Least Squares regression as implemented in the Python statsmodels package. The significance of the correlations was determined using a spin test approach (1000 permutations) to control for spatial auto-correlations 119 as implemented in BrainSpace 118.
Dual regression of hippocampal axes To study the cortical reflection of the two hippocampal axes, we used a dual-regression approach  50, 51. For each individual, cortical and hippocampal resting-state-data were concatenated in space to form a target 4D dataset. For each of the two hippocampal axes, we generated a regressor by stratifying the hippocampal flatmap (16 bins for the anterior-posterior axis and 8 bins for the proximal-distal axis) and assigning values ranging from -1 (most posterior or most proximal) to 1 (most anterior or most distal) in each bin. Those elements in the regressor corresponding to cortical and not hippocampal elements were set to 0. The two hippocampal regressors and an additional regressor modelling an intercept formed an orthogonal design matrix. In the first stage of the dual-regression we multiplied the pseudo-inverse of the design matrix with the data matrix, to obtain a single time-series for each hippocampal axis.
Acknowledgements NE is supported by a  Sir Henry Wellcome Postdoctoral Fellowship  from the  Wellcome Trust  [ 222799/Z/21/Z ]. JdK is supported by a  Natural Sciences and Engineering Research Council of Canada   -Postdoctoral Fellowship  ( NSERC -PDF). AFDH and INH were funded by the  Wellcome Trust  [ WT215573/Z/19/Z ]. KLM and the  BigMac  dataset were funded by the  Wellcome Trust  [ WT202788/Z/16/Z ]. SZ was supported by the  Chinese Government Scholarship. SJ is supported by a  Wellcome Collaborative Award  [ 215573/Z/19/Z ] and a  Wellcome Senior Research Fellowship  [ 221933/Z/20/Z ]. BCB acknowledges research support from the  National Science and Engineering Research Council of Canada  ( NSERC Discovery -1304413 ),  CIHR  ( FDN-154298 ,  PJT-174995 ),  SickKids Foundation  ( NI17-039 ),  Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) ,  Healthy Brains and Healthy Lives   (HBHL) ,  BrainCanada , and the  Tier-2 Canada Research Chairs program.
HCP data are publicly available at https://www. humanconnectome. org/. The CIVM macaque post-mortem template is publicly available at https://civmvoxport. vm. duke. edu/. Histological and MRI data as well as the slide-to-volume registrations from BigMac are existing and publicly available via the Digital Brain Bank platform (https://open. win. ox. ac. uk/DigitalBrainBank). /fcon_1000. projects. nitrc. org/indi/indiPRIME. html). The hippocampal segmentation used for unfolding the macaque hippocampus in the template macaque scan will be shared via the Wellcome Centre of Integrative Neuroimaging's GitLab server as of the date of publication. • Hippunfold is openly available as a BIDS App at https://github. com/khanlab/hippunfold. All original code generated for this project has been deposited at the Wellcome Centre of /git.
The protocol described below outlines the detailed steps and resources required for a high throughput interactomic study of alternative proteins (AltProts). The study of this kind of protein has been disregarded because mRNA was considered monocistronic. AltProts, also known as ghost proteins  2  or short open reading frames (sORF)-encoded proteins (SEPs),  3  are translated from alternative open reading frames (AltORFs), such as 3 0 and 5 0 UTRs, reading frame shifts or long non-coding RNAs (LncRNAs). Despite their physiological presence in the cell, studying these proteins has been difficult due to the absence antibodies and databases that cover this type of proteins. The number of potential Human AltProts has been estimated to be around 450,000 sequences,  4, 5  five times larger than the actual reference proteome available in Uniprot.
Dynamic exclusion is enabled, and only MS/MS spectra from peptide ions with charge states between +2 and +8 are selected. 7. Shotgun data analysis of non-cross-linked samples Note: as previously described, the databased OpenProt 5 can be oversized for some identification nodes, we recommend the use of the limited AltProt database with at least 1 identification in other MS data or Riboseq analysis. 5 According to size limitation we recommend the use of SequestHT (Thermo ProteomeDiscoverer V2. 5) which is not size limited. CRITICAL: To eliminate false positives, use protein BLASTP  13  The basic parameters of BlastP are preserved (or the automated adaptation for small protein) and the database used for the comparison is "non-redundant protein sequences". An AltProt is considered "false positive" if it presents a sequence homology (identity+ coverage) > 80%, ideally no identification should be detected, if an alignment <80% homology is identified, it should be checked that the peptide/PSM identified in MS is specific to the AltProt.
9. Modeling and prediction of interactions between AltProts and RefProts (Figure  5A ). a. Retrieve the AltProts sequences from OpenProt database. b. Generate the 3D models at I-TASSER (Iterative Threading ASSEmbly Refinement). 21 te: I-TASSER generates five models with the lowest free energy and highest confidence, and the first model usually has the highest score and better quality. However, lower-ranked models might have better quality. For more information, visit the I-TASSER server website. c. Download the Alphafold  22  or PDB  23  3D structures of the RefProts involved in the cross-links. d. For RefProt-AltProt docking ClusPro 24 tool is used, submit the RefProt as a receptor and the AltProt as a ligand at the ClusPro protein-protein docking server. Do not use any restraints in the docking.
Cross-linking network analysis Timing: 1 week For the visualization and gene ontology (GO) enrichment of the network obtained by cross-link identification, we recommend the use of Cytoscape. 30 Additional apps have to be downloaded at Cytoscape App Store: STRING,  31  ClueGO,  32  CluePedia 33 and yFiles Layout Algorithms. CRITICAL: Before starting your network analysis, we recommend performing the Cytoscape and ClueGo tutorials found on their websites. This will provide you with a general i. Assign the source node to the Gene A column and the target node to Gene B column. e. Select the network (Figure  7A ) and STRINGify it (STRING App). Note: Verify that all RefProt nodes are now STRING nodes. If not (plain gray nodes at Figure  7B ), verify that there are no spaces in the accession numbers or genes.
For Cytoscape a space after the gene/accession creates a duplicate non-referenced node. f. Once all the RefProts have a STRING node, copy the column of accession numbers, and input them in the STRING protein query. This step will identify the already described interactions between the RefProts (Figure  7C ). Note: If there are RefProt nodes that do not have any interactors, select them one by one and add known interactors in the STRING menu (Figure  7C , bright gray STRING nodes). g. Merge the networks.
Note: The resulting merged network (Figure  7D ) will simplify the redundant cross-links. We recommend formatting this network to visualize the interactions of interest. h. Cross-validate the RefProt interactions in other databases. We recommend BioGrid  34  and IntAct. 35 i. GO term enrichment (Figure  6B ). i. Open ClueGo App. ii. Select the functional analysis as analysis mode. iii. Input the whole list of RefProt genes at the load marker list box. iv. Select the ontologies/pathways to use for the enrichment. v. Select the specificity of the network. vi. Enable the GO term fusion. vii. Run the app. j. Merge the ClueGO network (Figure  7E ) and the merged STRING network (Figure  7D ). k. At the resulting network (Figure  7F ) select the preferred network layout and edit the node's properties.
The optimal outcome should be that the signal of the antibody is present in just one fraction, but traces of specific markers may appear in other fractions (Figure  2B ). For AltProts identification, an AltProt/RefProt ratio of 5%-10% can be expected. It is always recommended to validate identifications by performing a BLAST search and using the NextProt peptide uniqueness checker. Additionally, different properties and characteristics of the AltProts identified can be retrieved from the OpenProt database. Although peptide fractionation using SEC  36  and SCX  37  columns can increase the number of cross-links identified, our fast and non-fractionated method can only be expected to identify a couple of hundred interactions at most. Even a small number of AltProt-RefProt cross-links is sufficient to infer their possible function or pathway involvement.
LIMITATIONS The methodology described above has some limitations. The first limitation (I) is related to the lack of information on AltProts. Still, the concept of alternative proteins has limited spread, and few tools and databases have been developed for the analysis of this ghost proteome. Therefore, the methodology used here provides us with a snapshot of some possible functions of a limited number of AltProts. Targeted studies need to be conducted to confirm or expand the information about these AltProt "hits". The second limitation (II) is linked to the protein fractionation technique. The kit employed in this protocol is based on pelleting the non-extracted fraction and removing the supernatant. If the removal of the supernatant is not optimal, traces of proteins that don't correspond to the next fraction could remain.
Two different databases and consensus steps are employed to analyze AltProts (in a FASTA file combined AltProt, new isoforms and RefProt) and RefProts alone. The common consensus and processing parameters are enumerated at 7a. Specific parameters for RefProts (7b) and AltProts (7c) are displayed below. a. Analyze the RAW LC-MS/MS data using Proteome Discoverer V2. 5 (Thermo Fisher Scientific)with the Sequest HT search engine. i. Select LysC-trypsin and chymotrypsin as cleaving enzymes and 2 possible missed cleavages. ii. Variable modifications: methionine oxidation and protein N-terminus acetylation. iii. Static modifications: carbamidomethylation of cysteines. iv. Minimum peptide length: six amino acids. v. Minimum precursor tolerance: 10 ppm. vi. PSM and peptide validator: between 0. 01 and 0.