dataesr/scientific-paragraphs-categorization huggingface dataset:
DB. 10 Record the number of stages that PM used in its processing. The following procedure was used in PS in the final S5 experiment: PS. 1 Load the project. PS. 2 Select all cameras. PS. 3 Select Reset Camera Alignment. PS. 4 With all cameras still selected, select Align Selected Cameras. PS. 5 Save the project. PS. 6 Generate a report (File/Generate Report. ). The default accuracy values were used: 0. 1 pixels for manually measured markers, 1 pixel for automatically measured tie points. The DBAT procedure for experiment S5 followed steps DB. 1-DB. 6, except that the data was loaded from the PS project file from step PS. 5.
For the CAM data set, PM always used two-stage optimization. The results show that the DBAT and PM estimates of the EO, OP, and CP parameters were equal but the image residuals were not. Finally, the S4/S5 comparison show differences at the one meter scale for the EO positions (6. 5σpost) and at the decimeter level for OP (around 2σpost between the DBAT and PM/PS estimates of the EO, OP, and CP parameters, and the image residuals. The position, angle, and residual units are meters, degrees, and pixels, respectively. All differences below 5 × 10 -4 in their respective units are marked as zero and were furthermore below 0. 1 times their respective posterior standard deviation σpost. The non-zero values except for experiment S5 were all below 0. 5σpost. The S5 EO differences were around 6. 5σpost, the OP/CP differences 1.
DBAT reports the same quality parameters are PM. The recent development of DBAT has enabled weighted observations to be processed. In this paper, the weighted observations were restricted to control points. However, the same code allows the toolbox to do camera self-calibration with or without weighted IO observations. A conclusion to draw from the results reported in this paper is that if you want to trust your data, it is important to understand how your tool works. This applies to both closed and open tools. However, understanding implementation details is of course easier if the tool is open. The use of an external verification tool such as DBAT will enable users to get an independent verification of the computations of their software. It is our hope that the availability of an external, open-source bundle verification tool will increase the quality and transparency of critical photogrammetric computations in commercial software.
XMM-Newton data and processing We processed the XXL data using the XMMSAS package and calibration files v10. 0. 2 and the data reduction pipeline Xamin  (Pacaud et al. 2006 ) to obtain cleaned event files for each observation (see Paper II for details on the data reduction scheme). We extracted photon images in the [0. 5-2. 0] keV band for each EPIC instrument and created co-added EPIC images by summing the images obtained for each detector. We then created exposure maps using the XMMSAS task eexpmap for each EPIC detector and summed them, each weighted by its respective effective area. The non-X-ray background (NXB) was estimated for each observation by measuring the count rate in several energy bands in the unexposed corners of the three EPIC instruments. A template image of the NXB was created using a collection of closedfilter observations and scaled to match the count rates recorded D.
Validation using hydrodynamical simulations To validate the method, we used mock X-ray images created from the OverWhelmingly Large cosmological Simulation (cosmo-OWLS,  Schaye et al. 2010; McCarthy et al. 2010; Le Brun et al. 2014 ) using the AGN-8. 0 model (see Sect. 5. 4) in a field comparable to the XXL Survey  (Faccioli et al. , in prep. ). The mock images were then folded through the XMM-Newton response and a realistic background was added to obtain simulated data that were as close as possible to real XXL observations  (Valtchanov et al. 2001). We selected a sample of 61 bright clusters from these mock observations and applied the method described above to reconstruct the gas mass. The true value of r 500 was used to integrate the gas density profile. In Fig. 2  we show the reconstructed gas mass within r 500 compared to the true 3D gas mass within the same radius.
Acknowledgements. XXL is an international project based on an  XMM Very Large Program surveying two 25 deg 2 extragalactic fields  at a depth of ∼ 5 × 10 -15 ergs s -1 cm -2 in the [0. 5-2] keV band. The XXL website is http://irfu. cea. fr/xxl. Multi-band information and spectroscopic followup of the X-ray sources are obtained through a number of survey programmes summarised at http://xxlmultiwave. pbworks. com/. DE acknowledges support from the  Swiss National Research Foundation. AMCLB acknowledges support from an internally funded PhD studentship at the  Astrophysics Research Institute of Liverpool John Moores University  and from the  French Agence Nationale de la Recherche  under grant  ANR-11-BD56-015. FP acknowledges support from the  BMBF/DLR  grant  50 OR 1117 , the  DFG  grant  RE 1462-6  and the  DFG Transregio Programme TR33.
Various types of genome and gene similarity networks along with their characteristics have been increasingly used for retracing different kinds of evolutionary and ecological relationships. Here, we present a new polynomial time algorithm and the corresponding software (BRIDES) to provide characterization of different types of paths existing in evolving (or augmented) similarity networks under the constraint that such paths contain at least one node that was not present in the original network. These different paths are denoted as Breakthroughs, Roadblocks, Impasses, Detours, Equal paths, and Shortcuts. The analysis of their distribution can allow discriminating among different evolutionary hypotheses concerning genomes or genes at hand. Our approach is based on an original application of the popular shortest path Dijkstra's and Yen's algorithms.
Genome similarity networks are typically constructed at different stringency thresholds (e. g. 50, 60, 70,   2 ) are available in the GenomeNetwork directory. 90, 99%)  [6] , or by using constantly increasing datasets, thus producing a range of inclusive networks. Such a strategy allows the detection of ancient evolutionary connections  [1]  or the verification of ecological distribution of taxa  [9]. Furthermore, network analysis can be used with heterogeneous types of biological data, e. g. for linking protein structures to their functions  [10, 11]. Previous works in this field have focused on the use of conventional graph-theoretic measures describing the evolution of networks, such as the numbers of nearest neighbors or certain network motifs  [12, 13] , or the distribution of shortest paths  [9].
Do, for each node k of the ordered list L ij , starting from the first element of L ij : Step 4. 1. If the concatenation of paths [i,k] from P ik and [j,k] from P jk is a simple path, set d(i,j) = d(i,k) + d(j,k) and update the BRIDES statistics (see Table  1 ) with the results obtained for the pair of nodes (i, j), set the next pair (i,j) as the current pair of original nodes and go to Step 3; otherwise, go to Step 4. 2. Step 4. 2. Since there are repeating nodes, except k, on the paths [i,k] and [j,k], temporarily remove them from network Y and recalculate: (1) the shortest path from j to k in the reduced network Y using Dijkstra, storing the result in P jk ', and (2) the shortest path from i to k in the reduced network Y using Dijkstra, storing the result in P ik  '. Repeat  The default values of the parameters MaxPathNumber, MaxDistance and MaxNode in our program are all equal to 100.
BRIDES_EC (BRIDES algorithm based on an Exhaustive Concatenation approach) The difference with the original BRIDES algorithm is in Step 4, where we examine all the nodes k of the ordered list L ij , even though a simple path has been found in. The presented BRIDES algorithm can be applied to analyze undirected graphs with non-negative edge lengths. When negative edge lengths exist in either network X or network Y, the improved version of Bellman-Ford's algorithm  [23]  could be applied instead of Dijkstra. We created an R function implementing the BRIDES algorithm using the graph manipulation tools available in the igraph package  [24]. The R version of BRIDES can be applied for the analysis of small networks (<1,000 nodes). For larger networks (>1,000 nodes), we recommend using the.
Simulation study To test the BRIDES algorithm, we carried out a simulation study using three popular network models implemented in the igraph package (version 1. 0. 0) [24]. Precisely, the Erdős-Rényi  [26] , Barabási-Albert  [27]  and Watts-Strogatz  [28]  random graph generation models were considered. The Barabási-Albert model is a well-known approach for generating scale-free (or powerlaw) networks, while the Erdős-Rényi and Watts-Strogatz models are among the most popular generation models for random graphs that do not exhibit a scale-free degree distribution. Using each of these three models, we generated 1000 random original networks X with 100 nodes, and then added to them 5, 25, 50 or 100 additional nodes to create augmented networks Y. The simulations were performed using the C++ version of our program executed on a PC computer equipped with an Intel i7-3770 CPU (3.
Conclusion In this paper, we introduced a new fast algorithm and associated software for characterizing different types of paths existing in evolving similarity networks. In particular, our algorithm calculates the number of Breakthroughs, Roadblocks, Impasses, Detours, Equal paths and Shortcuts (BRIDES), which can be present in these networks. Our program, implemented in the C++ and R programming languages, includes four heuristic algorithms for calculating the BRIDES statistics discussed and compared in our study (see Algorithm 1). These statistics can be viewed as a new tool for the characterization and comparison of evolving genome and gene similarity networks, transcriptional networks  [30]  or interactome networks  [31]. The analysis and comparison of evolving networks can be carried out for different network stringency thresholds.
Availability Statement: The R and C++ source codes are available from the Github repository (https://github. com/etiennelord/BRIDES/) with a GPL version 3 license. The sample networks (Figs 1 and 2) are located in either the R or C++ source directories. The source code for the simulations (Fig 3) is located in the Simulation directory of the github repository. The genome similarity networks (Fig 4, Table.
R and C++ source codes are available from the Github repository (https://github. com/etiennelord/BRIDES/)withaGPL version 3 license. The sample networks (Figs 1 and 2) are located in either the R or C++ source directories. The source code for the simulations (Fig 3) is located in the Simulation directory of the github repository. The genome similarity networks (Fig 4, Table2) are available in the GenomeNetwork directory. S1 Table. Average computational time in seconds (s) obtained for the four heuristics and for different network models. The original network X contained 100 nodes and the augmented networks Y contained 5, 25, 50 or 100 added nodes. For each model, 1000 networks were created, and 100 path were randomly selected for evaluation. The reported values are average time (in seconds) for the evaluation of each path.
Data Availability Statement: The R and C++ source codes are available from the Github repository (https://github. com/etiennelord/BRIDES/) with a GPL version 3 license. The sample networks (Figs 1 and 2) are located in either the R or C++ source directories. The source code for the simulations (Fig 3) is located in the Simulation directory of the github repository. The genome similarity networks (Fig 4, Table.
Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of proteinprotein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.
Given the fast growth of biological sequence databases, it is tempting to design computational techniques for identifying protein-protein interactions  [19]. Prominent techniques to date include: the genomic co-localization of genes  [10, 18]  (with bacterial operons as a prominent example), the Rosetta-stone method  [28]  (which assumes that proteins fused in one species may interact also in others), phylogenetic profiling  [34]  (which searches for the correlated presence and absence of homologs across species), and similarities between phylogenetic trees of orthologous proteins  [24, 33, 37, 43]. Despite the success of all these methods, their sensitivity is limited due to the analysis of coarse global proxies for protein-protein interaction. An approach that exploits more efficiently the large amount of information stored in multiple sequence alignments (MSA) seems therefore promising.
Data extraction and matching for the ribosomal and trp operon proteins The input data is given by N multiple sequence alignments D p consisting of M p sequences of length L p for every protein family p. These alignments are extracted from UniProt  [8]  using standard bioinformatics tools, in particular Mafft  [25]  and HM-Mer  [16]  (cf. Section 1 in S1 Text for details on the extraction pipeline and Tables S1,S2 in S1 Text listing the values of N , M p , L p for ribosomal and trp-operon proteins). For the analysis, it is necessary to concatenate the MSAs of two putative co-evolving protein families. This means to create, for each pair of protein families (p, p ), a new alignment D p,p of sequence length L p + L p. Each line contains the concatenation of two potentially interacting proteins.
MSA1 teins. The general problem of producing a concatenated alignment out of single MSAs of two protein families is straightforward in two cases only: (i) we have prior knowledge which pairs of sequences represent interaction partners; (ii) no paralogs are present in the considered species (i. e. all species have at most a single homolog of each of the sequences to be matched). Often, as displayed schematically in Fig. 2 , MSAs contain multiple protein sequences within a given species and no prior knowledge can be used to know who is (potentially) interacting with whom. In prokaryotes, interacting proteins are frequently found to be coded in joint operons. This suggests to use genomic co-localization as a matching criterion. To do so, as explained in Section 2 in S1 Text, we approximated the genomic distance between sequences using UniProt accession numbers.
A better distance between sequences could be defined in terms of their genomic location. Unfortunately, genomic locations are available only in the context of whole genome sequencing projects. The majority of sequences in Uniprot originate from fragments or from incomplete genome sequencing projects. These difficulties lead us to content ourselves with the proxy of accession numbers. Having defined distances between each protein pair in the MSA, we calculate the matching which minimizes the average distance between matched sequences by linear programming. Additionally, we introduce a distance threshold used to discard matched distal protein sequence pairs. The numeric value for this threshold was determined using the small ribosomal subunit as a test case. The average number of paralogs per species varies from system to system: For both ribosomal subunits the proteins have between 1.
Simulated data As the basis for the simulated data we used a fictitious protein complex consisting of 5 proteins. Each protein has a length of 53 residues. The individual contact map of each one is given by the bovine pancreatic trypsin inhibitor (PDB ID 5pti  [42] ), which is a small protein performing well for the prediction of internal contacts by DCA. Each P i has 551 internal contacts. Moreover, each protein interacts with two others in a circular way. The inter-protein contact matrices between P i and P i+1 (as well as between P 1 and P 5 ) are random binary matrices with a density of 10% of the internal contacts. This models the sparsity of the inter-protein contacts as compared to the intra-protein contacts. A contact map for the artificial complex can be found in Figure  S5  in S1 Text, There are no contacts between other pairs of proteins.
In order to define as realistically as possible the coupling parameters of the Potts model used for generating the artificial sequences, we used the Pfam protein family PF00014 of the pancreatic trypsin inhibitor  [15]. Note that a member of this family was also used to define the structure. The couplings describing the co-evolution within the single proteins were directly extracted from the Pfam MSA using DCA. For the couplings corresponding to the co-evolution between the proteins, we used a random subset of the internal parameters and used them to couple sites that are in contact according the contact map as defined above. Non-contacting pairs of sites remain uncoupled between artificial proteins. Using this model, a joint MSA D 12345 of sequences of length 265 = 5 × 53 was generated using standard MC simulations.
Given that in this artificial setting we use the same model to generate the data as to analyze it, the approach is guaranteed to infer the model correctly for a large number of analyzed sequences and therefore to assign a higher interaction score to any interacting protein pair than to any non-interacting pair. To assess the coupling strength between two proteins, we average the four strongest residue coupling strengths between them. This leads to a score oriented toward the strongest signal while also reducing noise by averaging. In panel B of Fig. 3   , 000 is what we expect to be available in a few years from now, seen the explosive growth of sequence databases. The thickness of each link in Fig. 3  is proportional to the inferred inter-protein interaction score. The five strongest links are colored in green when they correspond to actual PPI according to panel A, and in red when they correspond to non-interacting pairs.
All other proteins catalyze individual reactions, but one might speculate that the efficiency of the pathway could benefit from co-localization of enzymes involved in subsequent reactions. Interestingly, the Pfam database  [15]  reports that in many species pairs of genes in the operon appear to be fused, suggesting that some of the fused pairs are actually PPI candidates. An example is the TrpCF protein, which is fused in Escherichia coli and related species (but not in the majority of species). After applying our method to all 21 protein pairs we find elevated interaction scores only for TrpA-TrpB and TrpE-TrpG, which are the only known interacting pairs (see Fig. 5  and Table  S10  in S1 Text for the interaction scores of all pairs). Those two pairs have interaction scores of 0. 375 and 0.
Limitations of the method The main limitation of the method we present in this paper is arguably the concatenation of the two MSAs. In the test cases we analyzed this step was rendered difficult due to the presence of paralogs in the majority of species. The solution based on the minimization of the genomic distances within the concatenated MSA as outlined in subsection "Data extraction and matching for the ribosomal and trp operon proteins" can be problematic when used naively. An example is the homo-dimerization of OmpR-class Response Regulators  [38]. In this class of response regulators, DCA discovers a very clear homo-dimerization signal. However, the corresponding Pfam family PF00072 also contains a large number of response regulators not belonging to the OmpR class, functioning either in the monomeric form or in different dimeric structures.
The performance of the same algorithm on different and dissimilar systems suggests that the approach could be used to detect interactions experimentally unknown so far. In fact, if we trust our results on the trp operon we can already draw some speculative biological inferences. While there are many high-resolution structures of the ribosome available, one might have expected that in the trp operon there could be more transient previously unreported interactions in the tryptophan biosynthesis pathway beyond the two interactions that have been structurally characterized. As mentioned, various enzyme fusions can be observed in the databases, suggesting that there is an evolutionary benefit to co-localizing the enzymes of the pathway in the cell. An obvious benefit of such co-localization would be that the pathway intermediates do not have to diffuse throughout the cell from one enzyme component to the next.
In the tryptophan biosynthesis pathway in particular, there are numerous phosphorylated intermediates that need to be protected from unspecific cellular phosphatase activity. Organizing the enzymes in the pathway in a multi-protein complex would seem like an efficient way to protect the intermediates from decay. However, our data indicate that the only statistically relevant co-evolutionary signals that can be observed are restricted to the known strong interactions between TrpA with TrpB and TrpE with TrpG. This could be interpreted in a number of ways: (i) The most obvious explanation is that there are no additional protein-protein interactions beyond those that are known and that no multi-enzyme complex exists for the tryptophan biosynthesis pathway. Alternatively (ii) it seems plausible that there are numerous structural solutions to form a tryptophan biosynthesis complex and that there is no dominant structure from which a co-evolutionary pattern can be observed in the sequence databases.
As long as Prokaryotes are concerned, it turns out empirically that proteins are more likely to interact if their genes are co-localized on the DNA  [6, 38]. This suggests to try to match proteins that are close on the genome when creating a concatenated MSA. As a proxy to the genomic distance we use a distance between Uniprot accession numbers (UAN). This UAN consists of a 6 digit alphanumeric sequence for every sequence and can be extracted from the sequence annotation, e. g. the "D8UHT6" part of the sequence annotation "D8UHT6_PANSA". We define the distance between UANs as follows: Different positions in the UAN can take on different values, some only numeric (0-9) and some alphanumeric values (0-9,A-Z). We define for every position i ∈ 1. 6 the number B i as the number of different values position i can take, i.
Inference technique As a simple but meaningful statistical model, we consider a pairwise generalized 21 states (to mimic the 20 amino acids + 1 insert symbol alphabet of MSAs) Potts model with the following Hamiltonian H = - 0≤i<j≤L J i,j (x i , x j ) - L i=1 h i (x i ) (7) We can now assume to have a dataset D = {x 1 ,. , x M }, where x represents one sequence, either artificially generated, or extracted using the bioinformatic pipeline discussed above. Notice that if the sequences x are concatenations of two sequences (x, x ), the sums in Equation 7 can be split into three parts: One in which appear only sites in x, one in which appear only sites in x and one interaction part with J ij for which i is in x and j in x. By labeling the first part H(x), the second H (x ) and the third H int (x, x ) one arrives at the representation referred to in the main text.