dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analyses Analyses of each gene fragment involved the same three steps: i) Alignment: after removing the primers, the retained clusters from our samples were aligned using Muscle  (Edgar, 2004)  with the most similar sequences identified using Blastn against the 38 Trypanosoma cruzi WGS assemblies recorded until 2022 in NCBI databases except assembly ASM274941v1 (strain Bug2148), mislabeled TcV while it is actually TcI, and assembly ASM274942v1 (strain Y) mislabeled TcII while it is actually TcI as previously reported  (Majeau et al. , 2021; Probst et al. , 2021) , resulting in 36 assemblies. The T. cruzi WGS sequences were extracted and used as reference sequences (see list in Table  S1 ); ii) Model selection: to better describe the DNA substitution pattern under the probability methods, the most adequate model was chosen according to the lowest BIC (Bayesian Information Criterion) score among the list of models included in MEGA7  (Kumar et al.
The new nuclear sequences obtained from our sampling have been deposited in Genbank with the following accession ) resulting from, the concatenation of nine single-copy nuclear gene fragments (DHFR-TS, GPI, GPX, HMGAR, AP, PDK, RB, RHO1, and TR) for 31 strains: 16 presently sequenced by NGS sequencing (leaf name beginning by "sample") and from BLAST hits against 15 WGS assemblies of Trypanosoma cruzi. The numbers at the nodes are 1) the posterior probability of the Bayesian analysis, and 2) the bootstrap value for the ML tree. The terms h1, 2, 3, …n correspond to the different haplotypes for a specific sample, if any. numbers: DHFR-TS, OR513547 -OR513569; GPI, OR513570 -OR513587; GPX, OR513588 -OR513612; HMGCR, OR513613 -OR513636; AP, OR513637 -OR513662; PDK, OR513663 -OR513684; RB, OR513685 -OR513704; RHO1, OR513705 -OR513729; TR, OR513730 -OR513751; SODA, OR532281 -OR532309 and STPP, OR532310 -OR532374.
cruzi (278 bp each, see File S1) against the 36 T. cruzi WGS assemblies from NCBI, resulted in 2132 sequences in total ranging from zero for 12 WGS projects (no significant similarity for these parameters) up to 712 for the PRFA01project (strain Dm28c, TcI). For the construction of the phylogenetic tree, this set of sequences from WGS assemblies was reduced in number by clustering the sequences into centroid sequences. A total of 50 centroid sequences was obtained (the number of centroids by project ranging from one to six, see Table  S1 ). Additionally, to increase the number of sequences from TcIII and TcIV strains, which are drastically under-represented in the WGS, seven mini-exon sequences from the nucleotide NCBI database including sequences from TcIII and TcIV were added. The alignment of 327 nucleotide positions with a total of 85 sequences was processed for the construction of the ML tree (Fig.
, 2012)  and the simultaneous sequencing of at least one nuclear gene is necessary to identify potential states of introgression Fig. 3. Maximum likelihood (ML) tree involving 85 mini-exon intergenic sequences coming from our NGS sequencing (leaf names beginning by "sample"), from BLAST hits against the 36 WGS of Trypanosoma cruzi, and seven informative mini-exon sequences from the nucleotide NCBI database, including sequences from TcIII and TcIV which are drastically under-represented in the WGS. See alignment in File S2 (C). The numbers at the nodes are 1) the posterior probability of the Bayesian analysis, and 2) the bootstrap value for the ML tree. The terms q1, 2, 3,. 0. 9 in sequence names refer to the best query hits obtained with BLASTn using the corresponding nine mini-exon reference sequences (File S1).
, 2013). With T. cruzi, the relevance of using MLST, even with diploid organisms, has been demonstrated  (Yeo et al. , 2011)  and MLST has been used successfully in several studies  (Lauthier et al. , 2012; Ramirez et al. , 2013; Tomasini et al. , 2013; Diosque et al. , 2014; Tomasini et al. , 2014). In fact, a team working on T. cruzi has created a program (MLSTest) that specifically manages MLST data from diploid organisms  (Tomasini et al. , 2013). Unfortunately, unlike the mitochondrial genome, the concatenation of MLST nuclear fragments is impossible in the case of highly recombinant organisms. Nevertheless, concatenation is possible in certain organisms with predominant clonal evolution as suggested for T. cruzi  (Tibayrenc and Ayala, 2015). Moreover, as stated in the previous section, the association of nuclear and mitochondrial typing should be mandatory to detect scarce but possible events of mitochondrial introgression  (Messenger et al.
Digital Elevation Model Generation and Image Orthorectification We obtained high-resolution scans of historical stereo aerial photographs of the study area acquired in 1969 and 1972 from the United States Geological Survey's Center for Earth Resources Observation and Science (EROS; http://earthexplorer. usgs. gov). Image identifiers and acquisition dates are listed in Table  S1  in Supporting Information S1. The scope of the pre-earthquake aerial survey was to image the San Fernando Valley, therefore providing limited coverage of the hanging wall: other pre-earthquake datasets that had broader coverage suffered greatly in terms of striping artifacts and other sources of noise. We reduced errors created by scanning by first rotating and cropping the photographs such that the corner fiducials were located at the corners of the scan.
We enhanced the image contrast using contrast-limited adaptive histogram equalization, and applied a Gaussian blur (σ = 0. 5) to reduce speckle. To orthorectify the photographs, we produced pre-earthquake and post-earthquake DEMs using the open-source photogrammetry software Ames Stereo Pipeline (ASP), which has extensive documentation and applications in digital Earth observation datasets, historical (scanned film) datasets as well as Lunar and Martian images  (Beyer et al. , 2018). Orthorectification accuracy depends on the accuracy of the area of interest's topography, the position and orientation of the camera, as well as the camera's intrinsic parameters including focal length, principal point and distortion coefficients. Since historical imagery usually has very limited metadata, the ASP workflow for processing historical imagery begins by computing estimates for each camera's extrinsic parameters.
This is done using the THEIA Structure from Motion library (http://www. theia-sfm. org/index. html) invoked from ASP, given initial intrinsic parameter values which are fixed in this step (in this study, the U. S. Geological Survey [USGS] provided the initial estimates for focal lengths, and we assumed the optical center to be in the center of the cropped and rotated image). We then use ASP to perform a bundle adjustment with ground control points collected using SRTM as a reference DEM to estimate absolute camera positions  (Ajorlou et al. , 2021). We further refine the intrinsic and extrinsic parameters of each camera by collecting dense and uniformly distributed match points between overlapping images, and using these in subsequent bundle adjustments where intrinsic and extrinsic parameters for multiple cameras in the dataset are jointly optimized.
The stereo reconstruction (i. e. , DEM-creation) process is performed using images that are projected onto a reference DEM, and ASP's MGM Final stereo matching algorithm. Normally the reference DEM has a much coarser resolution than the aerial photographs, which supplies the stereo reconstruction with the long-wavelength topography of the area of interest; however, due to the highly varied topography of the study region, we use a 1-m lidar DEM (USGS 3D Elevation Program; https://www. usgs. gov/core-science-systems/ngp/3dep) since lower resolution reference DEMs lead to great inaccuracies in the stereo reconstruction. The use of a high-resolution reference DEM may add to the high-frequency noise of the DEMs generated. The resulting point clouds may be shifted in space from the reference DEM, and therefore are then aligned to the reference DEM using an iterative closest point algorithm (using the libpointmatcher library, invoked from ASP; https://github.
, 2019; Delorme et al. , 2020; Oskin et al. , 2012). In this study, we measured the vertical component by (a) downsampling the pre-earthquake and post-earthquake DEMs to the same grid as the COSI-Corr displacement maps. Using a MATLAB code, we then calculated the vertical displacement for each pixel by (b) resampling and (c) warping the pre-earthquake DEM such that the pre-earthquake topography is shifted based on the amount of lateral coseismic displacement from our COSI-Corr displacement maps, then (d) subtracting this grid from the post-earthquake DEM. We masked outliers and pixels where the optical image correlation signal-to-noise ratio is < 0. 97  (Leprince et al. , 2007). We used the detrending tool in COSI-Corr to remove a second-order polynomial trend which does not impact the near-field offset estimate, and corrected undulating artifacts by sampling the undulations away from the rupture and subtracting the average from the displacement field.
Coseismic Offset and Distributed Deformation Measurements To quantify offsets along the length of the main fault and secondary strands, we extract fault trace-parallel, -normal and vertical displacement profiles oriented perpendicular to the simplified fault traces at regular intervals along strike. For each profile, we first rotated the E-W and N-S displacements into components parallel and normal to the local fault strike. We consider secondary fault ruptures mapped in the field in the San Fernando area as separate, discrete fault strands rather than distributed deformation around the main fault strand (e. g. , USGS Quaternary Fault and Fold Database, https://doi. org/10. 5066/F7S75FJM;  Barrows et al. , 1975). The profiles are 2 km-long, are regularly-spaced 21 pixels (168 m) apart and are the average of 21 pixel-wide swaths such that each measurement is independent from its neighbors.
2 and 2. 0 m in the Tujunga segment. The north-south tear fault connecting the two segments is dominated by right-lateral slip; however, a north-south zone of diffuse deformation ∼800 m to the east is accommodating most of the vertical component of deformation at the bend connecting the Sylmar and Tujunga segments (Figure  2b ). Barrows et al. (1975)  mapped vertical deformation at this location but were unable to determine whether it occurred during the earthquake and most subsequent surface rupture maps do not include this detail (e. g. , USGS Quaternary Fault and Fold Database, https://doi. org/10. 5066/F7S75FJM). Determining that this deformation did occur during the 1971 earthquake demonstrates the effectiveness of historical image correlation in mapping complex coseismic surface deformation in greater detail.
The increased scatter in %OFD measurements compared to the offsets measured from the optical image correlation and DEM differencing displacement fields is due to scatter in the field measurements, reflecting the increasing irregularity of offsets near the fault (e. g. ,  Gold et al. , 2019 Gold et al. , , 2021;; Milliner et al. , 2015; Scott et al. , 2018)  and uncertainties related to field data collection. For instance, greater fault trace-parallel offsets may be measured where there are features cross-cutting the fault such as roads, sidewalks and fences. The surface trace of the Sylmar segment is centered in the neighborhood of Sylmar, but the surface trace of the Tujunga segment is located at the edge of the urban area. Lateral offsets were likely less perceptible and more prone to underestimation in areas where the surface trace is located in the foothills of the San Gabriel Mountains.
Data Availability Statements Scanned aerial photographs were obtained from the United States Geological Survey's Center for Earth Resources Observation and Science (EROS; http://earthexplorer. usgs. gov). Ames Stereo Pipeline  (Beyer et al. , 2018)  and COSI-Corr  (Leprince et al. , 2007)  were used for data processing. The digital elevation models and displacement maps produced are available at https://doi. org/10. 5281/zenodo. 7327824. The figures in this paper were generated using Generic Mapping Tools  (Wessell et al. , 2013). Offsets measured in the field used to calculate off-fault deformation are from  Bonilla et al. (1971) ;  Kamb et al. (1971) ;  Barrows et al. (1973) ;  Baize et al. (2020).
At the Materials layer, we use available experimental data or the material's crystal structure to predict the adsorption thermodynamics using molecular simulations. This thermodynamic data is the input for the Process layer together with process and equipment data. We compute process performance parameters at this layer, such as purity, recovery, productivity, and energy requirements. In the Techno-economic (TEA) layer, the economic and technical viability of the capture plant is assessed. Next, the Life Cycle Assessment (LCA).
We envision that the crystal structure of any newly synthesized MOF is uploaded to the PrISMa platform together with adsorption data, if available. To illustrate this functionality of the platform, we uploaded the crystal structure of a novel MOF, MIP-212 (see Supplementary Information Section 5) to determine its performance potential for carbon capture from a cement plant. Interestingly, this novel material is also promising as it outperforms the MEA benchmark and can potentially reduce the cost by as much as a factor of two. Also, for this material, Figure  11  shows that the KPIs computed with experimental thermodynamic data agree very well with those predicted from the crystal structure. Screening over a thousand materials allows us to apply data-driven methods to identify the molecular characteristics of the top-performing materials.
Acknowledgement This work is part of the  PrISMa Project  ( 299659 ), funded through the  ACT Programme (Accelerating CCS Technologies, Horizon 2020  Project  294766 ). Financial contributions from the  Department for Business, Energy & Industrial Strategy (BEIS) , together with extra funding from the  NERC  and  EPSRC Research Councils, United Kingdom , the  Research Council of Norway (RCN) , the  Swiss Federal Office of Energy (SFOE) , and the  U. S. Department of Energy  are gratefully acknowledged. Additional financial support from TOTAL and Equinor is also gratefully acknowledged. C. C. , J. W. , L. H. , and S. G. are also supported by the  UKRI   ISCF Industrial Challenge  within the  UK Industrial Decarbonisation Research and Innovation Centre (IDRIC)  award number  EP/V027050/1. We also gratefully acknowledge the contribution of  Solverlo Ltd  to the development of the TEA module.
Data availability All the results obtained by the platform for all case studies presented in this work have been deposited on Zenodo (https://doi. org/10. 5281/zenodo. 8042599). On this website, one can also find the crystal structure (cif files) of all materials studied in this work, together with the simulated isotherms, values of the heat capacity, and data that characterize the materials. The results of this work can also be accessed through our visualization tool on the Materials Cloud https://prisma. matcloud. xyz/. This tool allows users to inspect all case studies and all KPIs. In addition, the Materials Cloud provides interactive versions of the graphs presented in this work. Updates and new case studies will be made available through the Materials Cloud. This tool also allows uploading the crystal structure of novel materials to be analyzed across the various case studies.
Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA, forming either complex or co-binding to the same region, is required. Indeed, the combinatorial binding of TFs is essential to cell differentiation, development, and response to external stimuli. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to onehot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 8,293 TFBS datasets from UniBind for 404 TFs in seven species. The method uncovered already established co-binding patterns (e.
g. , between POU5F1 and SOX2 or SOX17) and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 84% of the studied TFs share a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used singlemolecule footprinting data from mouse embryonic stem cells to confirm that the co-binding events captured by COBIND were likely occurring on the same DNA molecules.
INTRODUCTION The interactions between DNA and transcription factors (TFs) are crucial to transcription regulation as they intrinsically determine cell growth, development, and response to stimuli. TFs bind DNA at TF binding sites (TFBSs) in a sequence-specific manner through direct contact between DNA nucleotides and amino acids of the TFs' DNA-binding domain (DBD)  (1). Structural similarities between DBDs classify TFs into structural classes and families, and TFs from the same class or family usually recognize similar DNA patterns (or motifs). The binding properties of individual TFs have been vastly studied  (2) (3) (4) , and several databases store DNA binding profiles for TFs across multiple taxonomic groups (e. g. , JASPAR, CIS-BP, and HOCOMOCO (5-7)). While these databases primarily provide TF binding motifs for individual TFs, there is a need to increase our understanding of how TFs cooperatively bind DNA to regulate transcription  (8).
seqArchR is the first novel application of NMF for the simultaneous identification of sequence features and corresponding clusters  (31). The seqArchR tool identifies critical DNA elements in promoter regions by applying NMF to the corresponding one-hot encoded sequences  (31). This approach inspired us to use NMF to address the discovery of TF co-binding patterns. We present COBIND, a Snakemake-based (32) pipeline for the de novo discovery of TF co-binding patterns from input sets of TFBSs (https://bitbucket. org/CBGR/cobind_tool). We applied COBIND to 8,293 sets of high-quality TFBSs from seven species (Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, and Rattus norvegicus) for 404 unique TFs stored in the UniBind database  (33).
COBIND recovered established and unreported cobinding events between TFs. Among TFs from the same structural family, the majority shared cobinding patterns. In human and mouse genomes, genomic regions harboring the predicted co-binding events are evolutionarily more conserved than genomic regions without co-binding. Moreover, increased chromatin accessibility in matching human cell lines supported the predicted co-binding events from bulk experimental data. Finally, using single-molecule footprinting data from mouse embryonic stem cells, we confirmed that the predicted co-binding events between TFs likely occur on the same DNA molecules. Overall, COBIND can de novo discover regions in the genomes that harbor patterns of co-binding events between TFs.
Number of components. COBIND runs NMF several times with different numbers of components (k). We used synthetic data to estimate a suitable range of values for k. Specifically, we implanted different proportions of instances of predefined motifs in random sequences. We considered sets of 1000, 5000, 10,000, 50,000, and 100,000 random sequences. For each set of random sequences, we injected an instance of the considered motif in 0. 5, 1, 2, 3, 4, 5, and 6% of the sequences. We ran COBIND with k  [2, 17]  for each synthetic dataset. We assessed specificity and ∊ sensitivity by counting the number of correct motifs predicted by COBIND for each value of k. Moreover, we reported the proportion of sequences correctly predicted to contain the injected motifs. We considered that COBIND predicted the correct motif if it was similar to the injected motif -as assessed by Tomtom  (41)  with a p-value < 0.
05 (Additional file 1: Table  S1 ). We used the results from the synthetic data to select k  [3, 6]  for further analyses (Additional file 1: Figure  S3  for ∊ an example). Gini coefficient threshold. To empirically determine the Gini coefficient threshold, we compared the distributions of Gini coefficients for the motifs predicted by COBIND on the TFBS datasets from UniBind (see below) with those of predictions from random sequences. For each UniBind set of TFBSs, we constructed a set of random sequences by shuffling the DNA sequences flanking the TFBSs with the kmer shuffling module of the BiasAway tool with k=1  (42). For each species considered, we selected the Gini coefficient threshold corresponding to a 1% false discovery rate (Additional file 1: Figure  S4 ). This strategy resulted in the following Gini coefficient thresholds: 0.
51 for Arabidopsis thaliana, 0. 45 for Caenorhabditis elegans, 0. 52 for Danio rerio, 0. 48 for Drosophila melanogaster, 0. 51 for Homo sapiens, 0. 53 for Mus musculus, and 0. 52 for Rattus norvegicus. Finally, we estimated the stability of the thresholds obtained with this strategy by performing the same analysis multiple times and subsampling the number of datasets from H. sapiens and M. musculus. Overall, we observed that the Gini coefficient thresholds were stable across the different runs (Additional file 1: Figure  S5 ).
Transcription factor binding site datasets We used the robust collection of TFBS sets from the UniBind database (2021 version) (https://unibind. uio. no/) derived from 7 species (Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus)  (33). UniBind TFBS datasets derive from pairs of ChIP-seq peak datasets and JASPAR TF binding profiles; some TFs are associated with several binding profiles. Datasets with less than 1000 TFBSs were filtered out (Additional file 1: Table  S2 ).
Benchmarking Synthetic datasets We generated the synthetic datasets following the methodology mentioned above. We constructed synthetic datasets with 500, 1000, 3000, 5000, 10,000, and 50,000 sequences with instances of a known motif inserted in 0. 5, 1, 3, 5, and 10% of the sequences. We considered the motif associated with the ATF4 TF (MA0833. 2) in the JASPAR database  (7). To run SpaMO, we inserted the CTCF (MA0139. 1) motif at the center of the flanking sequences since SpaMO requires a motif to anchor its search for co-binding patterns.
Shared co-binding motifs across transcription factor structural families To assess the conservation of co-binding motifs across TFs, we ran COBIND (up to Step 5) on all UniBind datasets, excluding UniBind predictions derived from dimer anchor JASPAR TF binding profiles. We independently clustered all the predictions (COBIND Step 6) for each TF structural family; note that this step considered TFs from multiple species. When the co-binding motifs associated with two (or more) TFs from the same family were similar (i. e. , belonged to the same cluster), we considered the TFs to share the same co-binding motif.