dataesr/scientific-paragraphs-categorization huggingface dataset:
Example on the TCGA SARC dataset For users who simply wish to test the possibilities offered by webMCP-counter, we integrated an example dataset from the TCGA SARC cohort  8. This dataset is composed of n=213 tumors of soft-tissue sarcoma patients analyzed by RNA-seq. By selecting the example dataset on the webMCP-counter (Figure  2 , marker 1), all other parameters are internally set to the correct values, and the user-provided parameters are ignored. On the step 1 tab, users obtain a table with the MCP-counter scores for each sample in the TCGA SARC cohort (Figure  2 , marker 2). This table is interactive and users can sort samples on increasing or decreasing order for each population. It is also possible to search for one particular sample by entering its sample name in the search bar. It is important to note that scores should not be interpreted as absolute proportions of cell populations in the samples, but are instead expressed in arbitrary units, similar to gene expression values.
However, they can robustly be used to compare the abundances of the cell populations between different samples, such as what has been done in several studies  [9] [10] [11]. This table can be downloaded as comma-separated values table (. csv) for use in other software, such as Microsoft Excel (Figure  2 , marker 3). On the Step 2 tab (Figure  3 ), users can perform a classical downstream analysis from the MCP-counter scores table, sample clustering based on their immune and stromal composition. To use this tab, the first step must be completed beforehand. In this tab, the user is prompted to select the populations of interest on which to perform hierarchical clustering (marker 1) and the number of clusters (marker 2). In the example of Figure  3 , the clustering was performed on T cells, CD8 + T cells, B lineage, NK cells, monocytic lineage and endothelial cells, and 4 clusters have been required.
Comparison with other methods Several other methods to deconvolute the immune and stromal composition from transcriptomic data of human tissues have been previously reported. In a benchmark study published last year comparing human-based methods  12  , MCP-counter was found to be among the top-scoring methods for both accuracy (measured through correlation between the scores and randomized mixtures compositions) and specificity (measured through "spillover effect"). Murine counterparts for these methods are scarcer. In the article reporting the development of mMCP-counter, we demonstrated on an independent dataset that it outperformed pre-existing methods  7. Importantly, our set of methods is the only one to allow a similar analysis of human and murine samples, which can prove extremely valuable for comprehensive studies that include murine models and human samples.
Software implementation and availability webMCP-counter was developed as a Shiny application in R  13. The heatmap generation is carried using the ComplexHeatmap package  14. Other used packages include dendextend  15  , ggplot2  16  and dunn. test. All source code for webMCP-counter, placed under copyleft GNU license, is available from GitHub: https://github. com/FPetitprez/webMCP-counter. Users wishing to run webMCP-counter locally can download it from GitHub, install dependencies and run it as a regular shiny application.
Funding This work was supported by the  Programme Cartes d'Identité des Tumeurs of the Ligue Nationale contre le cancer , the  Institut National de la Santé et de la Recherche Médicale ,  Université de Paris ,  Sorbonne Université  and the  Cancer Research for Personalized Medecine programme (CARPEM). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  754923. The material presented and views expressed here are the responsibility of the authors only. The  EU Commission  takes no responsibility for any use made of the information set out.
Genomic Comparison The genome of the strain isolated from dolphin (IT-OTH-CP-36) was compared with the genomes of 376 CC121 strains, 371 of which were isolated from 25 European countries and five from the USA bovine strains. A total of 318 strains were isolated from ready to eat (RTE) food (n = 318) (fish and fishery products (n = 174), meat and meat products (n = 81), dairy products (n = 6), vegetables/fruits (n = 22), composite dishes (n = 35)). The panel also included isolates from food processing environment (FPE) (n = 32), sporadic clinical cases (n = 3), farm animals (poultry, cattle and pigs) (n = 12), wild animals (wild boar and a hedgehog) (n = 3) and the natural or farm environment (n = 8). Of the 376 genomes, 232 were available in public databases, 107 were sequenced during the LISTADAPT project  [27]  and 37 genomes were sequenced specifically in the framework of this study.
DNA Extraction and Whole Genome Sequencing DNA was extracted from the IT-OTH-CP-36 strain using the Maxwell®16 Cell DNA Purification Kit, according to the manufacturer's instructions (Promega, Madison, WI, USA) then used for library preparation using the Nextera XT library preparation kit. Sequencing was performed on the NextSeq 500 platform. NGS based MLST prediction determined the ST and the CC. The IT-OTH-CP-36 genome was compared to all the CC121 genomes available from the ANSES Laboratory for Food Safety database, including 107 genomes described by Félix et al. [27] , 54 genomes described by Henri et al. [18] , 6 by Fritsch et al. [61] , 29 by Palma et al. [25] , 143 genomes from the Liseq collection  [17]  and 21 new genomes specifically sequenced in the framework of this study. These genomes included 11 genomes of strains from Ireland, four from Republic of North Macedonia, four from France and two from Czech Republic.
This tool has been described in details by Villa nova et al. [64]  and is available at: https://github. com/afelten-Anses/ARtWORK. Normalized reads (i. e. 100x), scaffold assembly and annotated genomes were stored in an in house genomic database in the ANSES Laboratory for Food Safety. Sequences with a mean coverage bellow 30× were excluded (n = 2), as well as sequences with an abnormal assembly length (n = 2) or poor assembly quality (i. e. >200 contigs). In addition, inter-species and intra-species cross contaminations were evaluated using the confindR software (V. 0. 5. 1) [65]. The genomes that were predicted to be contaminated (n = 18) were excluded and the final dataset was of 377 genomes, including the strain isolated from the dolphin. The metadata associated with the strain collection used in this study are detailed in Supplementary Table  S1.
Phylogenomic Reconstruction of the CC121 Strain Population Structure A phylogenetic tree was produced using the iVARcall2 pipeline  [66]  to align the reads on a reference (LM6179 accession number: HG813249. 1) and to determine pairwise single nucleotide substitution (SNP) distances. The phylogenetic tree was calculated on the pseudogenome produced by the iVARcall2 workflow selecting the best model using IQ-TREE V. 1. 6. 9  [67]. The best-fitted model for this dataset was determined as a three-substitution type model with equal base parameter, empirical base frequencies  [68]  and an allowed proportion of invariant sites (K3P+F+I). The tree was corrected for homologous recombination events using ClonalframeML  [69]  and visualized using iTOL  [70]. Nested.
Acknowledgments: The authors would like to thank  Renata Karpiskova  and  Tereza Gelbíčová  from the  Veterinary Research Institute (VRI) ,  Czech Republic , for her helpful advice on writing this manuscript, and for providing strains for this study. The authors would also like to thank  Bernadette Hickey  and  Brian Byrne  from  Dairy Science Laboratory, Food Microbiology Division, Department of Agriculture, Food and Marine Laboratory, Republic of Ireland ,  Carole Feurer  from The  French pig and pork Institute (IFIP)  and  Dean Jankuloski  from the  Faculty of Veterinary Medicine-Skopje , in Republic of North Macedonia, for providing strains for this study and  Cristiano Cocumelli ,  Istituto Zooprofilattico Sperimentale del Lazio e della Toscana M. Aleandri , in charge for the histological analysis.
The discriminant of (3. 1) is then ∆ (f,g) = 4f 3 + 27g 2 and vanishes at 24 points which are the locations of 7-branes. Once one has the Weierstrass form of the elliptically fibered K3, one finds the ADE groups associated to the various singularities using Kodaira and Neron classification  [21, 22]. Moreover, the moduli can be expressed via the parameters c i as shown in section 2. 2. Sending those parameters to zero, we can therefore find possible enhancements of the group associated to a generic point in the moduli space. The third SageMath program then gives all possible enhancements obtained by sending all possible combinations of parameters c i , when the hypersurface still defines a elliptically fibered K3.
The first program uses the extended Dynkin diagram structure of reflexive polyhedras with fibers in order to construct tables of gauge groups for each fibration of every reflexive polytope. The second program gives the corresponding Weierstrass model for every fiber of reflexive polytopes. The third one uses this Weierstrass model and finds the enhancements one can obtain by simply sending the coefficient which parametrize the hypersurface equation of the K3 in some toric varieties to zero. This can be particularly useful to construct graphs of polytopes and we show how one can link polytopes up to three moduli. In the appendix we present typical outputs of the programs and explain how to use them. We are making the computer programs available on GitHub at https://github. com/lilianChabrol/ReflexivePolyhedras.
Acknowledgments I would like to thank my PhD supervisor  Mariana Graña  for her guidance on this project. I am grateful to  Anamaria Font ,  Christoph Mayrhofer  and  Hector Parra  for sharing their notes on their current work, and for helpful discussions concerning the constructions of Weierstrass models using Sagemath. I would also like to thank  Bernado Fraiman  and  Carmen Nuñez  for their clarification on heterotic compactifications. This work was supported in part by the  Ecole Doctorale Physique en Île-de-France  grant and the  ERC  Consolidator Grant  772408-Stringlandscape.
• Program 3 ( 3 The first program uses the extended Dynkin diagram structure of reflexive polyhedras with fibers in order to construct tables of gauge groups for each fibration of every reflexive polytope. The second program gives the corresponding Weierstrass model for every fiber of reflexive polytopes. The third one uses this Weierstrass model and finds the enhancements one can obtain by simply sending the coefficient which parametrize the hypersurface equation of the K3 in some toric varieties to zero. This can be particularly useful to construct graphs of polytopes and we show how one can link polytopes up to three moduli. In the appendix we present typical outputs of the programs and explain how to use them. We are making the computer programs available on GitHub at https://github. com/lilianChabrol/ReflexivePolyhedras.
• that is an invariant of the state space. A semi-flow only has positive integers as ∈ N coefficients while generalized flows may have both positive and negative integer coefficients ∈ Z. It is possible to compute all generalized flows of a net in polynomial time and space, and the number of flows is itself polynomial in the size of the net. We use for this purpose a variant of the algorithm described in  [7] , initially adopted from the code base of  [3]  and then optimized for our scenario. This provides a polynomial number of simple constraints each only having as variables a subset of places (the are fixed). The constant value of the flow in any marking can be deduced from the initial marking of the net. We do not attempt to compute semi-flows as there can be an exponential number of them, but we still first assert semi-flow constraints (if any were found) before asserting generalized flows as these have far fewer solutions due to markings being positive.
Implementation The implementation of the algorithms described in this paper was done in Java and relies on Z3  [15]  as SMT solver. The code is freely available under the terms of Gnu GPL, and distributed from http://ddd. lip6. fr as part of the ITS-tools. Using sparse representations everywhere is critical; we work with transition based column sparse matrix (so preset and postset are sparse), and transpose them when working with places. The notations we used when defining the rules in this paper deliberately present immediate parallels with an efficient sparse implementation of markings and flow matrices. For instance, because we assume that ∀ ∈ • is a sparse iteration we always prefer it to ∀ ∈ P in rule definitions. Our random explorer is also sparse, can restart (in particular if it reaches a deadlock, but not only), is more likely to fire a transition again if it is still enabled after one firing (encouraging to fully empty places), can be configured to prefer newly enabled transitions (pseudo DFS) or a contrario transitions that have been enabled a long time (pseudo BFS).
github. io/ITSTools-web/structural. html as well as graphically rendered examples. The choice of using an SMT based solver rather than a more classical ILP engine gives us flexibility and versatility, so that extending the refinement with new constraints is relatively easy. Our current directions include some new partial agglomeration rules where only some transitions stutter, investigating other more complete ways of replaying a Parikh candidate such as the approach proposed in  [17] , and extending our set of reduction rules to cover more fully the new advanced rules presented in  [2].
In particular convolutional neural networks (CNN) based on deep learning have shown success, cf. the performance of methods like RaptorX  [12] , DeepMetaPSICOV  [13]  and AlphaFold  [14]  in the last editions of the CASP structure prediction competition  [15]. Despite their impressive predictive performance, CNN and other deep neural architectures have some important disadvantages. First, they need to be trained on large datasets. While these are available for tertiary protein structures, the applicability to inter-protein contact prediction and protein-complex assembly has remained limited, even if promising results have been found when training was performed on intra-protein contacts, but testing on inter-protein contacts, resulting in a RaptorX variant called RaptorX-ComplexContact  [16].
Protein contact maps are not random graphs, they are locally structured, and this structure can be exploited for contact prediction. As was pointed out in  [17] , the density of contacts is higher in the neighbourhood of a contact than in the neighbourhood of a noncontact. Based on this observation, our questions may be made more precise: can we use local contact patterns to construct simple and interpretable supervised approaches to contact prediction? Can we use such approaches in the case of inter-protein and inter-domain contacts, which are less represented in the protein data bank PDB  [18]  than intra-domain contacts and thus provide less training data? To this end, we introduce FilterDCA: we first analyse contact patterns related to different secondary structure elements, and show that these are faithfully reflected by the scores derived using plmDCA (i.
Results and discussion To improve DCA-based contact prediction between interacting domains, we have collected an extensive dataset of more than 2500 intra-protein domain-domain interactions of experimentally known protein structures, containing more than 6 � 10 5 inter-domain contacts, cf. Materials and Methods. For each of these domain pairs, a multiple-sequence alignment (MSA) is created, with each line containing the aligned sequences of both interacting domains. According to the effective number M eff of sequences (as calculated by plmDCA in standard settings), we distinguish three cases: large (M eff > 200), medium (50 < M eff < 200), and small (M eff < 50) MSAs. These data will be used throughout the paper, if not explicitly stated differently.
Here we present results exclusively for the classes HH of two helical residues in contact, and EE for two contacting residues which are both located in β-strands, which show very characteristic contact patterns. All other classes are included in the contact prediction, too, but due to the absence of clear contact patterns, they will not particularly benefit from our new approach, as compared to standard DCA. For these two groups, HH and EE contacts, we calculate average contact maps over windows centered in all contacts (i, j), which are realised in the above-mentioned inter-domain interaction dataset for large families, cf. Materials and Methods. For a window size of 15×15, we show the results in Fig 1A ; the entries give the estimated probabilities to find another contact in the corresponding position relative to the given contact (i, j) in the center of the window.
Integrated contact patterns and DCA improve inter-domain contact prediction To this aim, we have developed a simple and transparent strategy to integrate plmDCA with the structural filters to improve inter-domain contact prediction, cf. Fig 3. The idea is to integrate the standard plmDCA score, which is known to be a good contact predictor when assuming large enough values, with a coherence measure between our contact filters and the corresponding window of plmDCA scores. To this end, we define two informative features x = (x 1 , x 2 ) for each pair of residues (i, j), as a function of the DCA scores F ij and the structural filters x 1 ¼ F ij x 2 ¼ max filters f rðf ; D ij Þ ð1Þ with D ij being a window of DCA scores around residue pair (i, j), and f denoting the 6 filters (which, to exclude overlaps between training and test data, are now determined using the dataset of small MSAs, cf.
Training on intra-protein / inter-domain contacts improves inter-protein contact prediction In the introduction, we have motivated our work on domain-domain interactions and predicting inter-domain residue contacts by the idea that these interactions can actually serve as proxies for protein-protein interactions (PPI) and inter-protein contact prediction, but have more representatives in the structural databases, thus allowing for better structure-based supervision. Is this actually true? To answer this question, we have used the PPI dataset of  [21]  as a test set, applying Filter DCA as learned on the intra-protein / inter-domain dataset used in the last section (case of large MSAs), for technical details cf. Materials and Methods. The results are depicted in Fig  9. We find that FilterDCA can be robustly transfered from inter-domain to inter-protein contacts, with comparable gains in PPV in the two cases (the general performance seems a bit higher, but we guess it is a direct effect of the smaller and curated PPI dataset in  [21] ).
The resulting dataset consequenctly differs from the MSA used for FilterDCA. • PconsC4  [20]  and DeepMetaPSICOV  [13]  can be run on user-provided MSA. However, a subtlety has to be taken into account. The convolutional filters of the CNN underlying both approaches may overlap with both proteins, when applied to residues in the direct vicinity of the concatenation point of the MSA. This situation is not present in the CNN single-protein training set. As a consequence, we observe that this type of incoherent input inflates the numbers of false positives in both PconsC4 and DeepMetePSICOV. We therefore removed residues within 5 positions from the concatenation to enable a fair comparision. Methods requiring to provide a single target sequence are not directly applicable to the PPI problem and therefore remain unconsidered in our work.
Conclusion In this work, we have presented FilterDCA, a simple coevolution-based but structurally supervised contact prediction method. It is based on two different ingredients: • Standard direct-coupling analysis DCA is used to detect coevolutionary couplings between pairs of residues from the sequence variability in sufficiently large MSA of homologous proteins. While strong couplings are known to accurately indicate residue-residue contacts, weaker couplings may not only result from coevolution, but also from the finiteness of the MSA used to learn the DCA model, and from biases in these data (e. g. phylogenetic correlations between homologous sequences). The sparsity of the strong couplings, as compared to the abundance of weaker couplings, limits the applicability of standard DCA for tertiary and quaternary protein structure prediction.
Domain-domain interaction dataset The basis of our dataset is given by the 3did database  [22] , which was constructed by selecting high-resolution PDB structures  [18]  containing multiple contacting Pfam domains  [23]. We use the Aug 5, 2017 version which is based on Pfam v. 30. 0 and contains a total of 11. 200 structurally-resolved domain-domain interactions. To get the joint MSAs we exclude homodimeric cases, and match sequences of domains co-localized on the same protein chain, i. e. we consider exclusively intra-protein inter-domain interactions. Finally, we map each residue in the MSAs to the corresponding positions in the PDB files. The mapping is done by aligning the PDB sequences to the profile HMMs of the PFAM domains through hmmalign  [24] , which allows to associate residue-residue distances to any pair of alignment columns.
Often only a part of the MSA can be mapped to the corresponding PDBs. We keep only MSA s with domain mapping coverage greater than or equal to 40% of the Pfam-domain length. The distance between two residues is defined as the minimal distance between all heavy-atoms of the two residues; in case the same residue-residue pair is associated with multiple PDBs, we assign the minimum distance between all possible copies. This assumes that any predicted contact (distance below 8Å), which is present in at least one PDB structure, is a true positive prediction. We further clean our dataset by requiring at least 10 and at most 2000 residue-residue interactions and removing few cases of coiled-coil structures which, due to repeated motifs, can lead to spurious coevolutionary signal. At the end, we keep a total 2598 joint MSAs of pairs of contacting domains.
Direct coupling analysis We have run plmDCA  [19]  in A. Pagnani's Julia implementation (available at https://github. com/pagnani/PlmDCA) with standard settings. Two outputs are of relevance: for each pair (i, j) of residues a DCA score F ij is provided (via the statistically corrected Frobenius norm of the DCA coupling matrices), and the effective sequence number M eff. With the scope of predicting inter-domain contacts, we only consider residue position pairs with i in the first, and j in the second domain. It is well known that the accuracy of DCA predictions is strongly dependent on the number of sequences in the MSA or, more precisely, on the effective number of sequences M eff -the higher the better. DCA predictions are comparable only for MSAs having similar M eff. Thereby, we decide to split the 2598 MSAs of contacting domains in 3 datasets according to M eff , see Table  1 , and to analyse them independently.
Filter score With the aim of going beyond simple DCA predictions, we define a new score by applying structural filters on DCA predictions, cf. Fig 3. Let D ij ¼ F i 0 j 0 j i 0 2 i À k À 1 2 ; i þ k À 1 2 � � ; j 0 2 j À k À 1 2 ; j þ k À 1 2 � � � � ð3Þ be the matrices of size k × k of inter-domain DCA scores, centered in residue pair (i, j) (i. e. windows of the full plmDCA output). We always choose k to be an odd number in order to get a square matrix centered around the central pair, in practice we use window sizes k between 5 and 69. The construction of the structural filters f 2 S is as described in the main text: HH and EE inter-domain contacts are clustered into three clusters each, by using 3-means clustering. The filters are the centroids of the corresponding clusters. For all learning tasks (large and medium  MSAs), we have used the same filters, determined using the contacts in the dataset of small MSAs, cf.