dataesr/scientific-paragraphs-categorization huggingface dataset:
3% and 88. 9%, respectively. Each time, 100 models were generated, and the best model was selected using the DOPE score  [107]. The ModWeb server was also used to model the 6XYF sequence. This online job provided one unique model based on a single template (PDB ID: 6XYM  [257] ) that had an extremely high sequence identity percentage (98%) with the target sequence. Indeed, this V H H came from the same lab as our test V H H  [257]. These approaches can be easily compared to SwissModel. The first step with this method is to look at the best sequence identity. A V H H anti-SARS-CoV-1 (PDB ID: 6WAQ chain A) [54] was identified that shares 82. 3% sequence identity with our target. An alternative model with the best GMQE score was built with a V H H anti-Vsig4 (PDB ID: 5IML chain B) with a sequence identity of 80.
ils. seu. edu. cn, not operative at the moment of reviewing this paper, last try: 19 February 2022)  [266]. Llamanade is a dedicated open-source computational pipeline for robust V H H humanisation  [267]. More recently, an integrated nanobody database for immunoinformatics (INDI) lists V H Hs from all the major public outlets of biological sequences and patents (http://research. naturalantibody. com/nanobodies, last accessed date: 11 February 2022) from GenBank, next-generation sequencing repositories and structure databases and publications  [268]. Even the famous SAbDab has an extension for V H H with SAbDab-nano (http://opig. stats. ox. ac. uk/webapps/newsabdab/nano/, last accessed date: 12 February 2022)  [159]. All V H H structures could also be mined from the PDB. Concerning the structural features of V H Hs, specific developments are quite rare.
ils. seu. edu. cn, not operative at the moment of reviewing this pape last try: 19/02/2022)  [266]. Llamanade is a dedicated open-source computational pipel for robust VHH humanisation  [267]. More recently, an integrated nanobody database immunoinformatics (INDI) lists VHHs from all the major public outlets of biological quences and patents (http://research. naturalantibody. com/nanobodies, last acces date: 11/02/2022) from GenBank, next-generation sequencing repositories and struct databases and publications  [268]. Even the famous SAbDab has an extension for V with SAbDab-nano (http://opig. stats. ox. ac. uk/webapps/newsabdab/nano/, last acces date: 12/02/2022)  [159]. All VHH structures could also be mined from the PDB. Concerning the structural features of VHHs, specific developments are quite ra We have shed light on the first optimised VHH structural modelling approach, NanoN  [253].
Acknowledgments: We would like to thank  Anne-Elisabeth Molza  and  Madjid Zemmouche  for the fruitful discussions and  Lynda Saminadin ,  Juan-Manuel Mora-Rey  and  Damien Guimond  for the administrative support. We would also like to take this opportunity to thank all the researchers who have worked on this class of macromolecules, without whom our work would never have been possible.
Funding: This work was supported by the  POE FEDER   2014-20  of the  Conseil Régional de La Réunion  ( S3D VHH program , no. SYNERGIE RE0022962 ),  EU-H2020  and  Université de la Réunion. This work was supported by grants from the  Ministry of Research (France) ,  Université Paris Cité (formerly University Paris Diderot ,  Sorbonne, Paris Cité, France and formerly Université de Paris), Université de la Réunion, National Institute for Blood Transfusion (INTS, France ),  National Institute for Health and Medical Research (INSERM, France) ,  IdEx   ANR-18-IDEX-0001  and labex GR-Ex. The labex  GR-Ex , reference  ANR-11-LABX-0051 , was funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. A. G. d. B. acknowledges the  Indo-French Centre for the Promotion of Advanced Research/CEFIPRA  for a collaborative grant (number  5302-2 ).
Single-cell RNA sequencing Single-cell RNA-seq of tdTomato+ (hereafter referred to as tdTom+) cells isolated from Acta2-Cre-ERT2; tdTomato flox lungs was performed using the ICELL8 platform (Takara Bio) using the V2 protocol with in-chip preamplification. Samples were presorted into HBSS (Gibco) at a concentration of 20 cells/µL and stained with Hoechst dye (Invitrogen) for 5 min at RT prior to loading onto the MSND dispenser. For each sample, 3 dispenses were made on one 5,184-well chip resulting in 348 and 459 wells containing single cells after selection in CellSelect software (Takara Bio). Selected wells were processed for RT and in-chip preamplification, and cDNA was pooled for final library preparation using standard Nextera protocol (Illumina). The library was checked in Labchip Gx touch 24 (Perkin Elmer) and sequenced on Nextseq500 (Illumina) using V2 chemistry and paired-end setup (11 bp Read1 for cell barcode and 80 bp Read2 for cDNA template sequencing).
Single-cell data analysis ICELL8 platform: cDNA reads were trimmed for poly(A) tails using BBMAP (unpublished, https:// jgi. doe. gov/ dataand-tools/ bbtoo ls/). Reads of length under 50 bases were discarded. The 456 M remaining reads were then aligned using STAR (release v2. 4. 0a) against Mus musculus build mm10 following ENCODE RNA-seq mapping recommendations. STAR indices were generated using Ensembl GTF file (release 83). For counting based on read counts, we used the Dropseq Core Computational Protocol version 1. 12 (dropseq. jar) [19]. Gene-to-cell count table matrix was then used for statistical analysis performed within the Seurat package (http:// satij alab. org/ seurat/)  [20]. The pipeline goes through cell and gene filtering, data normalization, then finding the most variable genes to perform principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) dimension reduction for visualization purpose.
The t-SNE procedure returns a two-dimensional (2D) embedding of single cells utilized for dataset visualization. Cells with similar expression signatures of genes within the variable set, and therefore, similar PC loadings, are most likely to localize near each other in the embedding, and hence, distinct cell types form 2D point clouds across the t-SNE map. Identification of the clusters of cells was determined using the FindClusters Seurat function (resolution = 1). The Seurat FindAllMarkers function was used to identify differentially expressed genes across each cluster (min. pct = 0. 25, thresh. use = 0. 25). Chromium 10x platform: Single-cell suspensions were processed using the Chromium Next GEM Single-Cell 3ʹ Reagent Kits v3. 1 (10x Genomics). Each sample was run separately on a lane in Chromium controller, and gene expression libraries were prepared according to the manufacturer's protocol.
Statistical analysis and figure assembly Quantitative data were assembled and analyzed using Graph-Pad Prism software (GraphPad Software). These data are presented as average values ± standard error of mean (sem). Student's t-test was used to compare 2 groups, one-way ANOVA was used to compare 3 or more groups in the presence of 1 variable (e. g. , treatment, time or genotype), and two-way ANOVA was used to compare multiple groups in the presence of 2 variables (e. g. , treatment and genotype, time and genotype). Differences were considered significant if P < 0. 05. The number of biological replicates is indicated in the corresponding figure legends. Figures were assembled using Adobe Illustrator (Adobe).
Scanline acquisition The size and narrow shape of the exposures mandated onedimensional scanline data acquisition. Scanline data were acquired along a line oblique to the Castellas fault strike (Fig. 1b  and c ) with mean orientation N15E, as permitted by outcrop conditions. As scanline direction was oblique to the fracture set directions and fault (~60 • ), we used Terzaghi correction to obtain real spacings. We measured position along scanline, orientation, feature type (e. g. barren opening-mode fractures, cement-filled opening-mode fractures and fault) and kinematic aperture (opening displacement) for all structures that crossed the scanline trace. We use the term barren opening-mode fractures for fractures without cement fill (joints), and cement-filled opening-mode fractures for fractures filled with cement (veins).
We prefer this terminology since it gives information about the type of filling and the fracture kinematics. In rare exceptions where weathering did not allow a reliable aperture measurement the presence of the fracture was recorded without an associated size value. Position was recorded using a measuring tape and by noting the exact position where the fracture crossed the scanline. In some parts of the outcrop, extrapolation of fracture traces to the scanline trace over short distances of less than a meter was used where visibility of the feature directly on the scanline trace was poor, a standard practice (e. g. ,  Wang et al. , 2019). Aperture was measured using a logarithmic graduated comparator rule  (Ortega et al. , 2006)  and a 10 × hand-lens. Orientation was recorded using a dip azimuth/dip angle convention and the software Fieldmove Clino in an Iphone 8 mobile phone.
Structural analysis We analyzed structural data from scanlines using a combination of cross-plots, stereonet projections, rose diagrams, and histograms (Figs. 3 4 5 ). For analysis we used software including DAISY 4. 96. 5 (Structural Data Integrated System Analyser; Salvini et al. , 1999) , Python 3. 0 Jupyter notebook scripts and MSOffice Excel 2012. For visual inspection of fracture spatial distribution, we used stick plots (Fig. 4a-c ). We plotted dip angle and aperture along the scanline to observe variations in these parameters along the scanline trace (Fig. 4d  and e ). Fractures striking more than 20 • away from the mean azimuth of the identified fracture sets were not plotted, as a conservative way to analyze fracture sets individually. We used the cumulative number of features to determine damage zone width  (Choi et al.
, 2016). The curve is built by cumulatively counting the number of features along the scanline trace, starting at the fault plane position, and going towards the scanline limits (i. e. , away from the fault plane and towards both starting and ending positions). We corrected the cumulative curve in intervals where data are abruptly missing (e. g. , covered) to maintain a continuous slope, according to the method by  Choi et al. (2016)  (Fig. 4g ). We created histograms using the GeostatsPy package  (Pyrcz, 2020)  within a Python script to analyze fracture aperture distributions (Fig. 5 ).
g. Laubach et al. , 2018; Marrett et al. , 2018; Li et al. , 2018; Sanderson and Peacock, 2019; Bistacchi et al. , 2020; Wang et al. , 2019). To further assess spatial arrangement, we relied on the Normalized Correlation Count (NCC) and associated freely available open-access software CorrCount  (Marrett et al. , 2018)  (Fig. 6 ). The NCC method is based on the two-point correlation function  (Hentschel and Procaccia, 1983; Bour et al. , 2002; Du Bernard et al. , 2002; Marrett et al. , 2018). The NCC algorithm essentially compares the frequency of fracture distances in the input data with the expected value obtained from randomizations. Fractures are clustered if distances among fractures are more frequent than random, and anticlustered (or possibly regular) if distances are less frequent than random.
LA-ICP-MS U-Pb geochronology Calcite U-Pb geochronology was conducted at the UTChron laboratory at The University of Texas at Austin. The laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) system consisted of a PhotonMachines Analyte G2 ArF excimer 193 nm laser, equipped with a two-volume Helex sample cell, coupled to a Thermo Element 2 doublefocusing magnetic sector ICP-MS. Helium was used as the carrier gas and mixed with Argon before entering the ICP-MS. The analyses were conducted on polished rock billets (samples Fc3 and Sdz) and thin sections (samples Fc1 and Fc2) in static spot mode with a laser beam diameter of 110 μm, operated with an energy density of 4. 08 J/cm2 measured with an energy meter, and a pulse rate of 10 Hz. Each analysis consisted of cleaning shots, 27 s of baseline data collection, 30 s of ablation and 27 s of washout.
Elemental and isotopic fractionation were corrected by interspersed analysis of primary calcite reference material WC-1 (206 Pb/238U 254. 6 ± 6. 4 Ma;  Roberts et al. , 2017). Analyses were made in repeating blocks of two standards, followed by ten unknowns, with NIST614 measured periodically to monitor Pb isotopic fractionation. Age calculation was performed using Iolite v3. 7  (Paton et al. , 2011)  and VizualAge UcomPbine  (Chew et al. , 2014). A 207 Pb common Pb correction was applied to correct for common Pb in the primary reference material (WC-1 207 Pb/ 206 Pb = 0. 85). Between 8 and 42 spots were analyzed on each region of interest (fracture cement, host rock primary pore cement, host rock matrix grain). Time-integrated data were exported as entire 30 s ablation windows if isotopically homogenous or broken up into 2-4 shorter ablation windows if exhibiting isotopic variation.
Spatial arrangement The coefficient of variation (Cv) (ratio of std. deviation to the mean of fracture spacings; see methods section) of the fracture data in the scanline provided essential information about the spatial arrangement of the fractures. The combined Cv for both fractures sets together is lower in the host rock (i. e. regular fracture spacing) and higher inside the damage zone (i. e. clustered fractured spacing) with increasing values towards the main fault surface, especially in the northern portion of the fault zone (Fig. 4f ). Considering fractures in Set 1 and Set 2 separately, Cv = 1. 56 for Set 1 and Cv = 2. 01 for Set 2. However, large intervals within the damage zone lack Set 1 fractures. If this section is excluded from the analysis, Set 1 shows Cv = 0. 61 in the host rock outside of the damage zone, which is consistent with this set's apparent regular spacing in outcrop and upon visual inspection of stick plots (Fig.
Our samples did not yield any ages younger than Eocene, which is consistent with most Set 2 calcite-filled opening-mode fractures forming and becoming cemented prior to the Miocene Alpine orogeny. Important variables that impact the quality of the U-Pb ages are the relative amounts of common-lead to radiogenic U and Pb isotopes and the count rate, which can be seen affecting the values of 2-sigma age uncertainty in our dataset. Calcite is a common-lead-bearing mineral and usually shows very low amounts of Uranium (ca. 10 ppb to 10 ppm)  (Roberts et al. , 2020). Due to this, the Tera-Wasserburg diagrams (see Appendix) show a high common-lead composition, meaning that most data points are located on the upper left corner of the diagram, with limited amounts of higher uranium calcites used to anchor the isochron's lower intercept on concordia curve.
The presence of micritic infill in fractures could be associated with precipitation of calcite from overpressurized supersaturated fluids (e. g. ,  Salomon et al. , 2021). However, host rock clasts, quartz and asymmetric micritic inclusions in calcite crystals mixed within the micrite (Fig. 7b ) suggest that the micritic sediment is allochthonous and infiltrated the fault zone through the fracture network in a shallow burial environment. Petrographical evidence of pendant cement (U8) and sediment infiltration (in U7 and U9) strengthens interpretation of a shallow burial position during Pyrenean reactivation, possibly within the vadose zone. Our isotope dataset does not allow interpretation of fluid origin, but the shift in δ 13 C towards negative values in young cements U9 and U10 (Fig. 10 ) can be related to interaction with organic matter possibly through the soil (e.
Conceptual evolution model Based on structural, petrographic, isotopic, and geochronological data obtained in this and previous studies (e. g,  Matonti et al. , 2012; Aubert et al. , 2020) , we interpret two main stages of deformational-diagenetic development of the Castellas fault zone, which impacted fluid-flow behavior (Fig. 13 ). Because most of the diagenetic history preserved in these rocks pertains to Set 2 fractures and fault breccias, this discussion focuses on the evolutionary stages after formation of regional, early Set 1 fractures and associated cements. During the first stage at ~90 Ma the Castellas fault underwent normal slip associated with the Durancian uplift (Fig. 13a ). Faultparallel Set 2 fractures were created in the fault damage zone and BR1 breccias developed in zones of intense deformation within the fault core.
Conclusions Our study is a rare example that documents evidence for polyphase relative and absolute timing of fracturing, brecciation and subsequent porosity occlusion by cement fills in a fault zone. The evolution of the Castellas fault exemplifies how fault zones in carbonate rocks can change their permeability behavior over time, potentially impacting fluid flow in the subsurface differently by either creating new conduits or by sealing existing fluid pathways due to preferential cementation. Petrographic, isotopic, and U-Pb geochronological data show that the Castellas fault zone underwent two main deformational events during the Durancian uplift (~90 Ma) and Pyrenean orogeny (50-40 Ma). An abundance of fault-parallel, opening-mode fractures created during fault zone reactivation provided pathways for fluid flow during each deformational event.
Acknowledgments We thank  Elizabeth Catlos  for allowing access to the cold cathodoluminescence device,  Charles Kerans  for discussions during petrographic analysis,  Andras Fall  for assistance with sample preparation, and  Qilong Fu  for allowing access to the microdrill, and  Evan Sivil  for assistance with O-C isotopic analysis. We want to thank reviewers  Andrea Billi ,  Eric Salomon , and editor  Nicolas Beaudoin  for providing valuable suggestions. We thank  Stephanie Forstner ,  Mahmood Shakiba ,  Bethany Rysak  and  Eric Goldfarb  for assistance during field work.
Funding This study was funded by  Petrobras  and by grant  DE-FG02-03ER15430  from  Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U. S. Department of Energy. Our work on fractures is partly supported by the  Fracture Research and Application Consortium at The University of Texas at Austin. Field work was facilitated by a research grant to  J. E. Olson  from the  GDL Foundation.
Data and sample collection Transcriptome HTSeq count data from patients diagnosed with gastric adenocarcinoma were retrieved from Genomic Data Commons (GDC). Data from the TCGA-STAD project were downloaded using the 'TCGAbiolinks' R package  (17). Primary malignancies were chosen from fresh-frozen samples. GENCODE27 was used to translate Ensembl IDs for mRNAs to gene symbols. Patient survival data were downloaded from the PanCanAtlas project and filtered for STAD tumor type. A total of 348 TCGA patients had their expression data matched with survival data. The number of fragments per kilobase million (FPKM) was converted into transcripts per kilobase million (TPM) values, which were comparable to microarray expression values. Additionally, the microarray data of Gene Expression Omnibus (GEO) cohorts GSE15459, GSE84426 and GSE84433 and their clinicopathological data were retrieved.
Eight patients' expression data in GSE15459 were excluded due to missing survival data. As a result, there were 192, 76 and 357 patient expression profiles matched with survival data for GSE15459, GSE84426 and GSE84433, respectively. The TCGA dataset, GSE15459 and GSE84426 were combined as the training set for robustness, and GSE84433 was used as the validation set. The pathological stage status of GSE84433 and GSE84426 was derived based on the T stage and N stage information according to AJAA 7th edition, making it consistent with TCGA and GSE15459 for further analysis. The algorithm 'combat'  (18)  was chosen to eliminate the possibility of batch effects from nonbiological technological biases between various datasets, and the 'sva' R package was used  (19)  Principal component analysis was performed to examine the batch effect before and after correction.
Identification of GC subclasses First, a previously published list of 2,752 metabolismrelevant genes encoding human metabolic enzymes and transporters was obtained for downstream analysis  (20). Next, metabolism-relevant genes with a median absolute deviation (MAD) less than 0. 2 were excluded, and univariate Cox regression assessing the associations of all metabolism-relevant genes with overall survival (OS) was conducted using the R package 'survival'. Eventually, 460 candidate genes were obtained with significant prognostic value (P < 0. 05). Subsequently, unsupervised NMF clustering methods were performed using the 'NMF' R package  (21)  on the training set and the validation set using the same candidate genes. The optimal value of k was determined at the point where the magnitude of the cophenetic correlation coefficient began to fall  (22).
Enrichment analysis of DEGs The differentially expressed genes (DEGs) among GC subclasses were identified using the 'limma' package. Genes with an absolute FC > 1. 5 and adjusted P < 0. 05 were defined as DEGs. The gene set of 'h. all. v7. 5. 1. entrez', 'c5. go. bp. v7. 5. 1. entrez' and 'c2. cp. kegg. v6. 2. entrez. gmt' were downloaded from the Molecular Signatures Database (https://www. gsea-msigdb. org) and were employed for the functional and pathway enrichment analysis using the 'clusterProfiler' R package  (30) , and the significance threshold was set at an adjusted P < 0. 05.
Somatic mutation and copy number variation analysis Gene mutation data of TCGA-STAD patient samples were downloaded from cBioPortal (https://www. cbioportal. org/). The R package 'maftools' (31) was used to analyze the association between subtypes and tumor mutation. The copy number variation data of TCGA-STAD tumor samples were downloaded from Firehose (http://gdac. broadinstitute. org/). The Genomic Identification of Significant Targets in Cancer (GISTIC2. 0) algorithm was utilized to classify the copy number variant genes  (32) , and the default settings were used.
NMF identifies four subclasses in GC A flow chart was developed to systematically describe our study (Figure  1A ), and the clinical characteristics of patients from different cohorts are listed in Table  S1. NMF was performed on the 460 prognostic related metabolism genes (Table  S2 ). Cophenetic coefficients were calculated to determine the optimal number of clusters. When k = 4, the cophenetic coefficient drops (Figure  1B ), and the consensus matrix heatmap maintain distinct boundaries, suggesting stable and robust clustering for the samples (Figure  S1B ). In parallel, we performed another independent clustering analysis on 357 GC samples from the GSE84433 dataset. SubMap analysis was then conducted to determine whether the clusters identified in the two independent analyses were correlated, and the results showed that C1-C4 subclasses in the training set were highly correlated with the corresponding counterpart in GSE84433 (Figure  S1D ), suggesting that there were four distinct metabolic gene expression patterns in GC.
To visualize the subclasses' assignments, we performed t-SNE to decrease the dimension of features (Figures  1C, S1C ), and the four-subtype designation was well clustered. Hence, k = 4 was eventually chosen. Survival analysis was performed on the subclasses, and a significant difference was observed in both datasets (log-rank test P < 0. 001, Figure  1D , training set; P = 0. 013, Figure  1E , testing set). Subtypes C2 and C4 showed better overall survival than C1 and C3.