dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolutionary conservation We downloaded the following conservation tracks in bigwig format from the UCSC genome browser: phastCons100way (human; hg38), phastCons60way (mouse; mm10), phastCons135way (C. elegans; WBcel235), and phastCons27way (D. melanogaster; dm6)  (48). We obtained the 63 flowering plants PhastCons conservation track from PlantRegMap (49) for analyzing datasets from A. thaliana. We computed the conservation scores of a genomic region using the aggregate and extract functions of bwtool (v1. 0)  (50). We compared the distributions of conservation scores between two regions with a one-sided Wilcoxon signed-rank test.
DNase I hypersensitive footprinting analysis We downloaded DNase I hypersensitivity (DHS) data generated by the ENCODE consortium for multiple human cell types in bigwig format at https://resources. altius. org/~jvierstra/projects/footprinting. 2020/  (51). We considered the 53 cell lines or tissues for which both DHS and COBIND predictions were available; we analyzed 66 DHS datasets associated with 87 co-binding configurations for 56 TF binding profiles as anchors. We followed the methodology described above to compare evolutionary conservation scores when comparing the DHS signal between two genomic regions (with or without a co-binding event predicted by COBIND).
Single-molecule footprinting analysis We downloaded 12 single-molecule footprinting (SMF) datasets derived from triplemethyltransferases knockout experiments in mouse embryonic stem cells (mESCs) from ArrayExpress with accession numbers E-MTAB-9033 and E-MTAB-9123  (52). Whenever applicable, we performed the analyses of SMF data with the SingleMoleculeFootprinting R package following the instructions previously described  (53). To compare the SMF data in the genomic regions predicted by COBIND to contain or not a co-binding event in mESC datasets, we modified the SortReadsByTFCluster_MultiSiteWrapper functions from the development version of the SingleMoleculeFootprinting package (54) (https://bitbucket. org/CBGR/cobind_manuscript/src/master/bin/single_molecule/analyse_sm/ sort_tf_reads_by_cobind_clusters.
Proof-of-concept: POU5F1 co-binding with SOX2 or SOX17 As a proof-of-concept, we applied COBIND to UniBind TFBS datasets for the SOX2 and SOX17 TFs. Previous studies established that both TFs partner with POU5F1 in pluripotent cells, where POU5F1 co-binds with SOX2 at regulatory elements to promote pluripotency. At the same time, it cobinds with SOX17 to control endoderm differentiation at other regulatory elements  (11, 16). While POU5F1 and SOX2 cooperate through binding at instances of their respective canonical motifs, POU5F1 can associate with SOX17 to bind a compressed motif (Figure  2A ). COBIND successfully discovered the two co-binding patterns -canonical and compressed -recognized by POU5F1 when applied to the SOX2 and SOX17 TFBS datasets from H. sapiens and M. musculus (Figure  2B ).
COBIND retrieved the correct pattern in 12 human SOX2 datasets (58%), with ~7% of the sequences predicted to harbor the pattern, and in 24 mouse datasets (32%), with ~6% of the sequences harboring the pattern (Additional file 1: Figure  S8A-B ). In agreement with previous studies, the datasets where COBIND predicted the pattern derived from human and mouse ESC and mouse embryonic fibroblast  (16, 18, 55). When considering the SOX17 datasets, COBIND successfully discovered the canonical co-binding pattern and the compressed motif. It predicted the compressed co-binding pattern in 2 datasets (50%; derived from mouse ESC), with 5% of the sequences harboring the co-binding motif (Additional file 1: Figure  S8C ). In this proof-of-concept example, we knew a priori that POU5F1 was the binding partner of SOX2 and SOX17.
Nevertheless, COBIND discovered de novo the co-binding motif. In other settings, one would not know the TF binding to the co-binding motif discovered. To address this challenge, we combined motif similarity to already known motifs from JASPAR and CIS-BP with protein-protein interaction (PPI) data from the STRING database to infer the TFs potentially binding the motifs revealed by COBIND (Materials and Methods). This strategy confirmed that the framework inferred POU5F1 as the binding partner of SOX2 and SOX17 (Figure  2C-D;  Additional file 1: Figure  S8 ).
COBIND discovers known and novel co-binding patterns in a large-scale analysis across several species We ran COBIND on 8,293 UniBind TFBS datasets associated with 404 unique TFs from 7 species (Materials and Methods; Additional file 1: Table  S2 ). Beyond predicting co-binding patterns with COBIND, we inferred the TFs likely binding to the discovered patterns following the strategy described above (also see Materials and Methods). Altogether, COBIND revealed 898 co-binding patterns for 218 TFs (22 in A. thaliana datasets, 7 in C. elegans, 4 in D. rerio, 22 in D. melanogaster, 420 in H. sapiens, 407 in M. musculus, and 16 in R. norvegicus). For half of the reported co-binding motifs (450 out of 898), we found motif similarity and PPI data to support the inferred pair of cobinding TFs (Figure  3 ).
Specifically, the data supported all co-binding patterns for D. rerio and C. elegans, more than half of the patterns for R. norvegicus, H. sapiens, and M. musculus, 18. 2% for A. thaliana, and 31. 8% for D. melanogaster. Furthermore, the analysis of motif similarity between COBIND's predictions revealed that 84% (163 out of 193) of the TFs shared a co-binding motif with another member of the same TF structural family (Materials and Methods), which confirmed the conservation of co-binding patterns within families of TFs across species. We provide all predictions for the community to explore through a dedicated website at https://cbgr. bitbucket. io/COBIND_2023/. As a case example, COBIND predicted two co-binding patterns around TEAD1 TFBSs. Specifically, COBIND revealed the same motif with different spacings from the provided anchor TFBSs (2nt upstream or 3nt downstream of TEAD1 TFBSs; (Additional file 1: Figure  S9A )).
thaliana (Additional file 1: Figure  S10A ). The analyses of TFBS datasets associated with the TFs ABF1, ABF3, ABF4, GBF2, and GBF3 displayed the same co-binding pattern. We found that the canonical motifs recognized by human NF-Y TFs (NF-YC and NF-YA) were similar to the co-binding motif discovered by COBIND (Additional file 1: Figure  S10B-C ). However, no PPI data currently support the physical interactions between the anchor TFs and NF-Y orthologs in plants. Nevertheless, the bZIP67 TF, which belongs to the same basic leucine zipper (bZIP) family as HY5, ABF1, ABF3, ABF4, GBF2, and GBF3, is known to form a transcriptional complex together with NF-YC2 and bind ER stress response elements (ERSEs) to regulate omega-3 fatty acid content in A. thaliana  (59). In agreement with this observation, we noted that the anchor motif combined with the co-binding motif discovered by COBIND forms the motif associated with ERSEs  (60).
Furthermore, HY5 competes with bZIP28, another member of the bZIP family, to bind ERSEs on promoters of unfolded protein response genes, and bZIP28 interacts with NF-Y protein complexes  (60, 61). Consistent with this knowledge, we found that most of the genomic regions predicted by COBIND to harbor the co-binding events in the HY5 dataset were in promoter regions (Additional file 1: Figure  S10D ). Altogether, these multiple lines of evidence strongly support the co-binding patterns predicted by COBIND between bZIP and NF-Y TFs in A. Thaliana.
COBIND discovers the extended motif bound by CTCF We investigated another example where COBIND predicted a co-binding pattern unsupported by motif similarity and PPI data. Specifically, COBIND identified a co-binding motif in the proximity of CTCF TFBSs for several datasets (Figure  4A ). When analyzing human and mouse datasets, we observed two spacing configurations between the anchor CTCF TFBSs and the predicted co-binding events (Additional file 1: Figure  S11A-B ). When considering datasets from zebrafish, COBIND revealed only one spacing configuration (Additional file 1: Figure  S11C ). Across the datasets, COBIND predicted from 1. 4 to 4% of the sequences to harbor the co-binding patterns. All datasets from the human and 23% of the mouse datasets (87 out of 382) contained the identified co-binding patterns.
001; Figure  5A ; Additional file 1: Figure  S12A ). Notably, we observed increased conservation at the co-binding and the anchor motifs. When considering the cobinding pattern associated with HY5 in A. thaliana, we did not observe a significant difference in conservation between the regions with and without the co-binding pattern. Nevertheless, both the anchor and the co-binding motifs exhibited peaks of evolutionary conservation, confirming the likely functional relevance of the predicted co-binding pattern across the flowering plant kingdom (Additional file 1: Figure  S13A ). We systematically compared the evolutionary conservation at instances of the anchor and co-binding motifs discovered by COBIND across all datasets and species. Altogether, we observed across species that 14% (for A.
thaliana), 42% (for D. melanogaster), 50% (for C. elegans), 67% (for H. Sapiens), and 69% (for M. musculus) of the co-binding patterns exhibited significantly more conservation of the co-binding motifs in co-bound regions than in the other regions (Figure  5B ; Additional file 1: Figure  S12B-S14 ). When considering the anchor motifs solely, we found that they were more conserved in the co-bound regions for 32% (A. thaliana), 38% (for C. elegans), 50% (for D. melanogaster), 52% (for M. musculus), and 53% (H. sapiens) of the datasets (Figure  5B ; Additional file 1: Figure  S12B-S14 ). The consistent increased evolutionary conservation of the co-binding patterns supports the functional importance of COBIND's predictions across species. Furthermore, our results suggest that the underlying genomic regions harboring a fixed binding motif syntax are evolutionarily important.
DNase I hypersensitive footprints support the predicted co-binding patterns We further assessed the co-binding patterns discovered by COBIND by analyzing orthogonal experimental data probing chromatin openness. The DNase-seq assay captures open chromatin regions by revealing DNase I hypersensitive sites (DHS)  (64). Importantly, TFs interacting with the DNA in open chromatin regions protect their TFBSs from the DNase cleavage, which leaves a footprint on the corresponding TFBSs  (51, 65). We retrieved 66 DHS footprint datasets from 53 human cell types (51) that matched some of the UniBind TFBS datasets used in this study. This data allowed us to investigate DHS footprints at the discovered co-binding motifs for 87 co-binding patterns associated with 56 TF binding profiles as anchors. For each co-binding pattern, we compared the depth of the DHS footprint at the locations of the co-binding motifs between the co-bound regions and the other regions (Materials and Methods).
As we ran COBIND on regions surrounding TFBSs predicted as high-quality direct TF-DNA interactions with ChIP-seq and computational evidence from UniBind, we expected DHS footprints at the anchor TFBSs. Indeed, we observed footprints of TF-DNA interactions; for instance, the DHS footprints observed for the CTCF datasets (Figure  6A ). Furthermore, the analyses exhibited deep DHS footprints at the location of the co-binding motifs. Overall, we found significantly deeper DHS footprints at the locations of the co-binding motifs in the co-bound regions than in the other regions for 83% of the pairs of co-binding patterns -DHS dataset (Figure  6B ). Notably, the anchor TFBSs at co-bound regions exhibited deeper DHS footprints than the TFBSs in other regions for 81% of the pairs (Figure  6B ). Co-bound and anchor TFBS locations showed deeper DHS footprints in the genomic regions predicted with a co-binding pattern for 76% of the pairs (associated with 27 TFs -61%).
As CTCF datasets represented 43% of the total datasets analyzed here, we performed the same analysis excluding the CTCF datasets. We found that 45% of the co-binding pattern -DHS dataset pairs showed significant DHS footprints at the anchor TFBSs, and 57% exhibited significantly deeper DHS footprints at the co-binding motif locations in co-bound regions than in the other regions. Altogether, 38% of the pairs exhibited deeper DHS footprints at both the co-bound and anchor TFBS locations. Overall, the DHS footprint analyses supported the co-occupancy of the anchor and the cobinding motifs at the co-bound regions. We provide logos and locations of the anchor motifs (red) and the predicted co-binding motifs (green). Stacks of sorted single molecules are visualized in the lower panels (light gray indicates methylated Cs, accessible positions; blackunmethylated Cs, protected).
Based on motif similarity, we inferred that ZBTB3 could also bind the additional motif identified by COBIND. Instead, the cobinding pattern identified by COBIND corresponds to an extended binding motif for CTCF, involving zinc fingers  9-11 (62). This orthogonal evidence allowed the two variants of extended CTCF motifs to complement the JASPAR database (MA1930. 1 and MA1929. 1). Furthermore, this binding pattern is critical for optimal CTCF protein residence time on DNA  (62). Importantly, we found that the genomic regions harboring the extended motif were conserved in both mouse and human genomes, supporting the functional relevance of this binding pattern. Studies frequently reported that zinc-finger proteins bind DNA through a subset of their zinc fingers, but it remains unclear whether the other fingers have additional binding preferences  (71).
We found most cobinding patterns with significant conservation from the human and mouse datasets, which may reflect the more significant number of datasets analyzed for these species. Furthermore, there was a limited number of conservation tracks for other species, such as A. thaliana, where conservation was limited to flowering plants  (49). COBIND is a tool for the de novo discovery of co-occurring DNA patterns. It does not require a priori knowledge of DNA motifs, such as a known DNA motif binding library, to perform the discovery. The requirement of known motif collections as an input is a standard limitation for other tools dealing with TF binding analysis, such as SpaMo or TACO  (20, 21). This reliance on existing collections of DNA binding motifs can be problematic as many TF binding motifs still need to be discovered or included.
DATA AVAILABILITY COBIND is implemented in Python, R, and C++. The COBIND source code and documentation are available at https://bitbucket. org/CBGR/cobind_tool/src/master/. The source code and data to reproduce the results outlined in this report is available at https://bitbucket. org/CBGR/cobind_manuscript/src/master/ and pre-processed data deposited at https://doi. org/10. 5281/zenodo. 7681483. All results presented here are available to the community through a webpage at https://cbgr. bitbucket. io/COBIND_2023/.
The powder catalyst bed (length ~ 8-9 mm) was maintained at the center of the capillary between two pieces of quartz wool and heated using a gas blower. Raman spectra were measured at room temperature on the catalyst bed using a commercial RXN1 Raman spectrometer (Kaiser Optical Systems, Inc. ). The in situ temperature programmed carburization of the calcined precursors was performed by heating the capillary from room temperature to 650 • C (2. 5 • C min -1 ) under a 20 % (v/v) CH 4 /H 2 flow (5 mL min -1 STP). Spectra were then recorded back at room temperature. A passivated β-Mo 2 C used as reference was reactivated at 400 • C (5 • C min -1 ) under pure H 2 (5 mL min -1 STP). Energy calibration concerning the reference metal foil (Mo, Ni, Cu) and a XAS data-normalization procedure was first carried out using the Python normal graphical interface developed at SOLEIL for the fast handling of Quick-XAS data  [47].
The EXAFS signal extraction and Fourier transform of the EXAFS spectra was done using the Athena graphical interface software  [48]. EXAFS fitting of coordination numbers (N), Debye-Waller factors (σ 2 ), and interatomic distances (R) were simultaneously performed on k-, k 2 -, and k 3 -weighted χ(k) functions with the Artemis interface to IFeFFIT using least-squares refinements  [49]. Fits were first performed on the metallic foil references for the determination of the S 0 2 factor. Fourier-transformed EXAFS signals are presented as k 3 -χ(k) functions and Fourier transforms are shown without phase correction. The proportions of different Mo, Ni, and Cu species during the different stages of carburization were determined by a chemometric procedure, the Multivariate Curve Resolution by Alternative-Least Squares (MCR-ALS) methodology, using the free MCR-ALS GUI 2.
0 toolbox developed by the Tauler group on the Matlab platform  [50]. XAS spectra are considered to be linear combinations of individual spectral components (matrix S) weighted by their concentration that varies with temperature or time (matrix C). The determination of matrices S and C takes place without initial hypotheses on the chemical nature of the different species appearing along the thermal treatment, by a least-square minimization of a residue matrix. The determination of the most likely number of spectral components takes place via a preliminary Principal Component Analysis by Singular-Value Decomposition (PCA-SVD). MCR-ALS spectral components (XANES spectra, EXAFS oscillations, and Fourier transform) are then identified to successive species after checking their chemical meaningfulness, by comparison with spectra of compounds already known in the mixture or as standards, or with plausible models in accordance with the chemistry of the system.
The N 2 adsorption-desorption isotherms are displayed in Fig. S1. All supported catalysts showed a profile similar to that of bare SiO 2 , which, according to IUPAC classification, corresponds to a type II isotherm, with an H1 hysteresis loop commonly obtained for materials consisting of agglomerates or compacts of approximately uniform spheres  [53, 54]. Since catalytic testing took place directly after carburization, conventional characterization, for instance by ex situ X-ray diffraction (XRD), was not possible. The formation of the supported phases was thus followed by in situ XAS, carried out at Mo, Ni, and Cu K-edges, with further input from Raman spectra recorded on the initial calcined samples. The XAS data at the Mo K-edge related to the calcined precursors of Mo 2 C/SiO 2 , Ni-Mo x C y /SiO 2 , and Cu-Mo x C y /SiO 2 are presented in Figs.
Acknowledgments Leticia F. Sosa  thanks  Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior -Brazil  ( CAPES -Finance  code 001) and  CAPES -COFECUB program  ( 88881. 142911/2017-01 ) for the scholarship. This study was supported by the  French government  through the  Programme Investissement d ′ Avenir  ( I-SITE ULNE / ANR-16-IDEX-0004 ULNE ) managed by the  Agence Nationale de la Recherche ,  CNRS, Métropole Européen de Lille (MEL)  and  Region Hauts-de-France  for " CatBioInnov " project are also acknowledged. Fabio B. Noronha  thanks  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro  ( FAPERJ -E-26/ 202. 783/2017 ;  200. 966/2021 ) and  Conselho Nacional de Desenvolvimento Científico e Tecnológico -Brazil  ( CNPq -303667/2018-4 ;  305046/2015-2 ;  302469/2020-6 ;  310116/2019-82 ) for financial support.
We call this approach Edgelet computing. Edgelet computing paves the way for disruptive applications by  (1)  encouraging data sharing with strong privacy guarantees, (2) distributing liability for the processing among multiple devices/individuals rather than centralizing it on a single data processor (in the GDPR sense), and (3) leveraging the mobility of individuals and opportunistic communications among them to perform ad-hoc spatio-temporal queries. As a concrete example, we are today developing a real medical use-case, the DomYcile project  [6]. DomYcile is a data management solution for elderly people receiving home care in the French Yvelines district. 8,000 patients are each being equipped with a secure home box (TEE-enabled device acting as an edgelet) where their medical and social records are stored.
In  [2] , we have proposed technical solutions to tackle this challenge and have discussed their impacts in terms of result consistency, performance and privacy. The objective of this demonstration is to present their practical interest through the DomYcile project. A video is available online (https://project. inria. fr/edgelet/). II. EDGELET COMPUTING FRAMEWORK Basic assumptions. On the technical side, Edgelet computing capitalizes on one hand on the security properties provided by TEEs  [7]  (integrity of the code executed on the devices and confidentiality of the manipulated data) and on the presence of TEEs in a fast-growing population of devices (from smartphones to lightweight devices up to sensors). On the other hand, Edgelet computing exploits the asynchronous short-range communications (e.
g. , Wi-Fi or Bluetooth) and store-carry-andforward message passing among devices provided by OppNets  [8] , eliminating the need to rely on a reliable infrastructure. Note that for simplicity, we consider epidemic message transmission and let optimized routing protocols exploiting user movement patterns for future work. On the functional side, Edgelet computing aims at performing complex processing tasks (e. g. , machine learning, data mining, statistical queries) over rich data (e. g. , healthcare folders, spending habits) hosted by edgelets seen as a horizontal partitioning of a globally shared database (i. e. , each edgelet stores a partition of a database conforming to a common schema). Threat model. Edgelet computing requires the introduction of an unusual threat model to capture the shift of responsibility from the Querier to the crowd (i.
e. , the participating edgelets). The Querier (e. g. , the Yvelines District) is said "Ingenuous" in the sense that it lacks the ability or willingness to set up a secure infrastructure. The edgelets owners (e. g. , the DomYcile patients) run on their side under the so-called "wolf in sheepfold" model: while each edgelet is equipped with a TEE, side-channel attacks compromising data confidentiality cannot be totally precluded, placing it in a "sealed glass" mode  [9]  (the integrity guarantee is preserved but not the confidentiality); these attacks being highly difficult to conduct -they require instrumenting the edgelet hardware -, we assume a large majority of honest participants (the lambs) and a few corrupted ones (the wolfs). Finally, a Trusted Regulatory Agent (e. g. , the CNIL) certifies the query by reviewing and approving its code.
The rest of the infrastructure is assumed to be untrusted, including the devices' operating system and the network. Under this threat model, the goal is to achieve a form of crowd liability where the liability of data processing is equally distributed among the participating edgelets. For each edgelet owner, the counterpart of its contribution to the crowd liability is to personally benefit from a processing that would not be rolled out otherwise (e. g. , share common practices/knowledges among DomYcile patients). Moreover, for a lamb user, the TEE and its embedded code actually endorse this liability on her behalf. Execution plans. We represent computations by a Query Execution Plan (QEP), that is a directed graph where vertices materialize the operators to be computed and edges represent the dataflow among them, with messages sent through the OppNet.