dataesr/scientific-paragraphs-categorization huggingface dataset:
The shrinking cost of sequence data now means that it is possible to combine multiple datasets to address host-parasite interactions within the same study. For example, a recent study by  Foth et al. (2014)  focussed on a vertebrate parasite, the whipworm genus Trichuris. By sequencing the genomes of two members of the genus, the authors were able to find both species and genusspecific features, including novel gene structures and expansion of gene families such as the Chymotrypsin A-like serine proteases. Transcriptomic analysis of the parasite showed that many of these genes were abundant in the anterior body of the parasite, which is embedded in the host intestine, suggestive of important roles in parasitism for these genes and providing candidates for focussed functional studies. NGS data can tell us a range of broad patterns about host-parasite interactions.
(See Fig. 1B ) In contrast, the molecular mechanisms of HCV replication remain largely unknown. In particular, although the overall interaction network between HCV proteins has been studied by various approaches  53, 54  (indexed in HCVpro database  55  ), little is known about the detailed physical interactions between these proteins. We used coevolution analysis of protein residues to address this question on the full HCV polyprotein, that is, on the 10 HCV proteins considered at once. An analogous computational experiment was never realised before for two reasons. First, we look at genotype sequences, that is sequences that are evolutionarily very close to each other, and therefore very conserved, while existing coevolution analysis methodologies treat sets of divergent sequences and their application to sets of conserved sequences is impossible.
For simplicity, even though clusters are computed as sets of alignment positions, we refer to them as clusters of residues, referring to the residues associated to the sequence alignment. It is important to notice that HCV genotype sequences are very conserved and that BIS 2 identifies perfect patterns only among the few positions in the alignment that are not fully conserved. In practice, this means that for HCV sets of sequences, coevolution signals are only detectable on a quite restricted set of positions. In Fig. 2D , we report the ensemble of positions where BIS 2 analysis of the NS5A protein can be applied to, for three different HCV genotype datasets. For each genotype, one observes a relatively small set of non fully conserved residues that, in many cases, are not in contact with other non fully conserved residues.
) (C) The third step in BIS clusters the coevolution matrix in B and identifies groups of positions displaying the same coevolution scores with all other positions in the alignment. The schema illustrates two fictitious clusters, one made of 4 positions and the second made of 3 (involving 3 and 2 proteins, respectively), where coevolution scores correspond for each position in the cluster. Positions in a cluster might belong to different proteins P i of the polyprotein, as for cluster A where residues 68 and 183 belong to P 1 and residue 397 to P 2. (D) Structure of the NS5A protein (PDB entry 1ZH1) where only residues that are not 100% conserved in the sequence alignment associated to genotypes 1b-MD (top), 2b (middle) and 4 (bottom) are represented with yellow balls. They are 50, 24 and 71 for 1b-MD, 2b and 4 respectively (for a total of 163 residues in 1ZH1).
As an example, Fig. 9  shows the contact map of the NS5A structure (PDB entry 1ZH1) and the pairs of BIS 2 coevolving residues. In the three-dimensional structure of NS5A, most residues analysed by BIS 2 are in direct contact with conserved residues, that is residues that are ignored by coevolution analysis (see Fig. 2D  and Supplementary Figure  12 ). Hence, NS5A coevolution analysis is not expected to provide any physically connected network of residues. Instead, one expects to identify pairs of positions that need to be mutated in concert to guarantee the functioning of the virus. The display illustrates the complete set of predictions realised on the non conserved residues of the three NS5A genotype datasets (Fig. 2D ). It highlights a number of important general points for our HCV analysis: 1.
Discussion Many mechanisms of the functioning of HCV replication remain unknown and any insight on the HCV protein interactions could allow to make testable hypothesis on HCV activity. Here, we employed the coevolution analysis approach BIS 2 , to identify the HCV protein co-evolution network. BIS 2 was able to uncover an important number of coevolving residues between HCV proteins which likely orchestrate the structural rearrangements and functions of various HCV multi-protein complexes involved in the replication of the virus. This large-scale network reconstruction for all HCV proteins is expected to unravel complex functional dialogs between multiple proteins coevolving together. However, these coevolution links remain predictions which need to be experimentally verified. There are three different angles to look at this work: the data analysis with a novel way to exploit coevolution signals of viral sequences, the biology of the reconstructed protein-protein network, and the database of potentially interacting proteins described at a residue resolution.
Carefully designed experiments based on rational predictions are thus mandatory to target residues that are potentially the most interesting on the mechanistic level. In this context, our database of predicted HCV protein-protein interactions obtained from BIS 2 coevolution analyses offers a set of highly relevant predictions, which not only indicate the coevolving residues of a given protein but also indicate which amino acid substitution should be done, as well as which compensatory mutation(s) is(are) expected. Unexpected coevolution links reveal the complexity of the HCV lifecycle. Comparatively to the network of reported HCV protein-protein interactions determined by using various experimental methods (reviewed in  54  ; Fig. 5 , right), several new links are observed (colored blue in Fig.
A database of predicted interactions to guide experimentalists. Evaluation of the statistical significance of BIS  2  predictions. The coevolution analysis method BIS 2 allowed the identification of many clusters of coevolving residues within and between HCV proteins, which likely correspond to essential structural and functional motifs involved in protein-protein interactions driving the assembly of protein complexes required for the replication of the virus. Several in silico statistical tests have been realized to evaluate the significance of the predictions: -Independent analyses of different genotypes produced highly correlated domain interaction matrices (see section "A network of coevolution links", Fig. 6  and Supplementary Figures  4 5 6 ), attesting the existence of similar coevolution patterns between protein domains in different genotypes.
-All clusters of coevolving residues predicted by BIS 2 are provided with a p-value. This allows a user to screen the results and reason about the outcomes. (See section "Cluster filtering" in Methods. ) -An estimation of the statistical significance of pairs of correlated residues that are found in physical contact within HCV protein structures is given. Simulations were realised to compute this estimation. (See section "Predicted intra-protein coevolution links"). These statistical tests help to gain confidence in the predictions. A database of interactions. The list of clusters reported in Supplementary Figures  1 2 3 constitutes a helpful database of predicted HCV protein-protein interactions for researchers who wish to experimentally test the veracity of these interactions towards the identification of HCV protein complexes.
Two main observations can be highlighted for the database. First, this database collects relations between "groups of residues", where mutations are present at the same time in face of the number of viral sequences and their variable homology. This ability to identify "groups of residues" determines the advantage of BIS 2 computational platform over other technologies and makes the database unique. In fact, our protein-protein interaction network (Fig. 5A ), even though it expresses pairwise links through graph edges, has been constructed as a projection of information coming from "groups of residues" (that is, the network is a superposition of smaller graphs associated to clusters of residues, as in Fig. 4 , right). This is a crucial difference between our computational network (Fig. 5A ) and the experimental one (Fig.
Second, because of the relatively high divergence of HCV sequences used in this work, this database is certainly incomplete. One can expect that the following up of HCV polyprotein sequences of a patient over time or a limited set of patients initially infected by the same virus source to limit the number of coevolution cycles should yield more workable data, especially concerning direct/physical intra-and inter-protein-protein interactions. It would be thus of highest interest to set up specific projects to collect such sets of HCV polyprotein sequences that do not exist to date. Third, BIS 2 provides predictions that need to be experimentally tested and it is intended to drive the biologist to formulate novel hypothesis. Inferences are realised on relatively few genotype sequences and this limited data availability should be kept in mind when predictions are examined.
Methods Genotype sequences. HCV sequences are classified in 7 genotypes (numbered 1-7), most of which have multiple subtypes (denoted a, b and so on); these genotypes and subtypes differ in their nucleotide sequences on approximately 33% and 25% of their positions, respectively  74, 75. It should also be mentioned that inter-genotype and inter-subtype recombinants have been identified in the HCV-infected population (reviewed in  76  ). From the pool of full-length HCV polyprotein sequences available in euHCVdb (https://euhcvdb. ibcp. fr)  77 and shown in the associated distance tree in Supplementary Figure  13 , we extracted three groups of non-redundant sequences of genotypes 1b (denoted 1b-MD), 2b and 4 on which we realised coevolution analysis. • 1b-MD: 40 full-length HCV polyprotein sequences from a limited set of japanese patients of genotype 1b  78, 79  corresponding to accession numbers AF165045 to AF165064 and AF207752 to AF207774, except sequences AF165055, AF165056, and AF207759 which include either additional residues or gaps comparatively to the genotype 1b consensus sequence.
• 2b: 24 full-length HCV polyprotein sequences from different patients with genotype 2b corresponding to accession numbers AB030907, AB559564, AF238486, D10988, and AY232730 to AY232749. • 4: 27 full-length HCV polyprotein sequences of various subtypes of genotype 4 corresponding to accession numbers DQ418782 to DQ418789 (except DQ418783 and DQ418785), EU392169 to EU392175, FJ462431 to FJ462441, FJ839869, FJ839870, GU814265, Y11604, EF589161. To avoid the use of inter-genotypic recombinants, only "confirmed" genotypes were selected  74, 75. Genotypes 2b and 4 have been selected because they are represented by a sufficiently large number of sequences (24 and 27, respectively) and genotype 1b-MD because it has the unique characteristic of being associated to a very small number of japanese patients studied by Nagayama and co-workers  78, 79.
Genotypes 3, 5 and 7 were excluded from coevolution analysis because they contained too few non redundant sequences (less than 15) and this would have made the statistical analysis too weak. Genotype 6 was also excluded because it is composed of sub-genotypes (6a, 6b, 6c… ) with a relatively high degree of divergence and containing very few sequences. This implies a too weak detection of the coevolution signal. The available sets of full-length polyprotein HCV sequences for genotypes 1a and 1b are very large and quite divergent. Coevolution analysis realised on such sets produced no workable signal. BIS 2 analysis was performed separately on the three selected datasets and a total of 62 statistically significant clusters was identified. The union of the clusters from the three datasets was also analysed, and we refer to it as the "three genotypes" analysis.
Structures and modeling of the HCV proteins. HCV structures used in the analysis are illustrated in Fig. 1B  from left to right: (i) core protein includes the N-terminal natively unfolded domain (D1) containing a helix-loop-helix motif (PDB entry 1CWX  80  ) and two amphipathic α-helices connected by a hydrophobic loop (D2 domain  81  ) as well as the core-E1 signal peptide (PDB entry 2KQI  82  ) cleaved by SPP. (ii) E1 glycoprotein ectodomain containing the crystal structure of the N-terminal domain (residues 1-79, PDB entry 4UOI  83  ) and associated to its C-terminal transmembrane domain (residues 351-383  84  ). (iii) E2 glycoprotein ectodomain containing the crystal structure of its core region (PDB entry 4MWF  85  ) and associated to its stem region (residues 705-715, PDB entry 2KZQ  86  ) and C-terminal transmembrane domain (residues 714-746  87  ).
(iv) Monomer model of p7 solved by nuclear magnetic resonance (PDB entry 2K8J 88 and 2MTS  65  ). (v) Monomer of NS2 catalytic domain (PDB entry 2HD0  89  ) connected to its N-terminal membrane domain constituted of three putative transmembrane segments (PDB entries 2JY0, 2KWT and 2KWZ  70, 90  ). (vi) NS3 serine protease domain associated with the NS4A central protease activation domain and the N-terminal transmembrane domain and the NS3 helicase domain. This representation of NS3 (derived from PDB entry 1CU1  91  ) indicates that the helicase domain can no longer interact with the protease domain when the latter is associated with the membrane through the transmembrane domain of NS4A (BMRB entry 15580  92  ). (vii) NS4B with the N-terminal part, including two amphipathic α-helices (PDB entries 2LVG  93  and 2JXF  94  ), the central part harboring four predicted transmembrane segments, and the C-terminal cytosolic part, including a predicted highly conserved α-helix and an amphipathic α-helix interacting in-plane with the membrane (PDB entry 2KDR  95  ).
(viii) NS5A N-terminal amphipathic α-helix in-plane membrane anchor (PDB entry 1R7E  96  ) connected to globular domain 1 (D1; PDB entry 1ZH1  97  ) and intrinsically unfolded domains 2 and 3 (D2 and D3  [98] [99] [100]  ). (ix) NS5B RNA-dependent RNA polymerase (RdRp) catalytic domain (PDB entry 1GX6  101  ) associated with the membrane via its C-terminal transmembrane segment  102. Mapping to the Con1 sequence. Since each analysis is run on a separate sequence alignment (sharing no common HCV genome), we need to map the alignment positions of the clusters to a reference genome sequence. For this purpose, we used Con1, a reference sequence of genotype 1b. The positions provided in the Excel file significant_clusters. xls are relative to the Con1 reference sequence (accession number: AJ238799).
To achieve this mapping, two steps are performed. First, each sequence in a dataset is aligned separately to the Con1 genome with MUSCLE v3. 8. 31 (see below), in order to find the sequence S in this dataset with the highest similarity to Con1, as measured by the fraction of Con1 residues that are aligned to an identical residue in S. Then, the mapping between alignment positions and Con1 is performed by using the mapping from the alignment positions of all sequences in the dataset to S (which is straightforward because S is in the alignment) and from S to Con1. It may happen that some detected residues do not map to residues in Con1 (because of gaps or the hyper variable sequence domains in E2 glycoprotein). In this case, they are discarded. If this elimination of residues leaves a cluster with less than two elements, the cluster itself is discarded.
Sequence alignments. Alignments of sequences belonging to a single genotype were realised to prepare BIS 2 input and were done with MAFFT v6. 861b 103 downloaded at mafft. cbrc. jp/alignment/software/. To translate BIS 2 analysis positions for genotypes 2 and 4 into positions of the Con1 sequence, we aligned Con1 with sequences of genotypes 2 and 4 with MUSCLE v3. 8. 31  104  downloaded at www. drive5. com/muscle/. This was done to gain in precision since sequences belonging to different genotypes are more divergent. Mapping to structures. When mapping alignment positions to protein 3D structures, a similar procedure as the mapping to Con1 is used, except that the Con1 reference genome is replaced by the sequence associated to the structure. The other difference is the use of a local alignment (Smith-Waterman) instead of a global alignment since, unlike for Con1, the structure will cover a single protein in the complete genome.
Contrary to this, statistical methods measure how distant two amino acids distributions are from noise. BIS 2 implementation and parameters. In BIS 2 , the optimization of the code was oriented principally to improve the speed of the tool. BIS 2 runs 100 times faster that the original one. This speed improvement was necessary to be able to run BIS on the complete HCV polyprotein including about 3000 residues. The main reasons explaining this speed increase are the following: • The implementation of the algorithm was globally optimized. • The program was re-written using the MATLAB programming language instead of Perl. • The score calculation step, which is a critical part of the algorithm, is written in C+ + , which is faster than a MATLAB or Perl implementation. • The clustering step (i. e.
Acknowledgements Financial supports from: the  MAPPING  project ( ANR-11-BINF-0003 ) of the  Excellence Programme "Investissement d' Avenir" , the  Ministère de l'Education Nationale et de la Recherche  (PhD fellowship to RC), the  Institut Universitaire de France (AC) , and the  French National Agency for Research on AIDS and Viral Hepatitis (ANRS, FP). Thanks are due to  Dr Christophe Combet  for bioinformatics support and euHCVdb facilities, and to  François-Loïc Cosset  for carefully reading of the manuscript and for providing insightful comments that helped to improve it.
Availability of data and tools. Genotype sequences and data produced by the HCV polyprotein analysis (alignments, list of clusters, annotated pdb structures) are found at www. lcqb. upmc. fr/HCV/. BIS is available at www. lcqb. upmc. fr/BIS/. BIS 2 is made available upon request to AC. Note that clusters refer to residue positions in the Con1 sequence.
This work aims to improve the PEA calibration technique through defining a well-conditioned transfer matrix. To this end, a numerical electroacoustic model that allows determining the output voltage of the piezoelectric sensor and the acoustic pressure is developed with the software COMSOL®. The proposed method recovers the charge distribution within the sample using an iterative deconvolution method that uses the transfer matrix obtained with the new calibration technique. The obtained results on theoretical and experimental signals show an improvement in the spatial resolution compared with the standard method usually used.
We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical representations despite missing values. Package methods include principal component analysis for continuous variables, multiple correspondence analysis for categorical variables, factorial analysis on mixed data for both continuous and categorical variables, and multiple factor analysis for multi-table data. Furthermore, missMDA can be used to perform single imputation to complete data involving continuous, categorical and mixed variables. A multiple imputation method is also available. In the principal component analysis framework, variability across different imputations is represented by confidence areas around the row and column positions on the graphical outputs.
From a technical point of view, the core of all these methods is the singular value decomposition (SVD) of certain matrices with specific metrics. Unfortunately, data sets often have missing values, and most principal component methods available in software packages can only be applied to complete data sets. To this end, the missMDA package  (Husson and Josse 2016 ) for the R system (R Core Team 2016) performs principal component methods on incomplete data, aiming at estimating parameters (left and right singular vectors and singular values) and obtaining graphical representations despite missing values. The package is based on the methodology presented in  Josse and Husson (2012). As we will see throughout the paper, missing value imputation is automatically associated with parameter estimation.
Consequently, the missMDA package has a broad range of applications since it can be used to impute incomplete data sets with continuous, categorical or mixed variables (i. e. , data with both types of variables). Then, it is possible to apply statistical methods. As it is based on a principal component method, imputation takes into account both similarities between individuals and relationships between variables. However, one has to be careful when applying statistical methods to imputed data sets since a unique value is predicted for a missing entry and therefore cannot reflect uncertainty in the prediction. Indeed, a model (explicit or not) is used to predict the value for the missing entry based on the observed data, so there is uncertainty associated with the prediction. This implies that when applying statistical methods to an imputed data set, the variance of the estimators is underestimated since variability due the imputation of missing values is not taken into account.
We briefly present each method as well as the algorithm to deal with missing values, and show how to perform analyses with missMDA on real data sets. In Section 7, we highlight the ability of the package to be used for single and multiple imputation. Note that the proposed methods were designed under the missing at random (MAR) assumption  (Rubin 1976) , meaning that we do not address the case missing non at random  (MNAR)  where the probability that a value is missing is related to the value itself. Such informative missing values require to model the mechanism that generates the missing entries.
Possible competitors One of the pioneering works on missing values in PCA is that of  Wold and Lyttkens (1969)  in the field of chemometrics. They present the "NIPALS" algorithm which obtains the first principal component (also known as the scores) and first principal axis (also known as the loadings) from an incomplete data set using an alternating weighted least squares algorithm, where two weighted simple linear regressions are alternated. Then, the following dimensions are obtained by applying the same method to the residuals matrix. This method is implemented in (commercial) software products dedicated to chemometrics such as SIMCA  (Umetrics 2013)  and Unscrambler (CAMO 2013), and a MATLAB toolbox (Eigenvector Research 2011). It is also implemented in R with the function nipals in the ade4 package  (Dray 2007 ) and the function nipalsPca in the pcaMethods package  (Stacklies, Redestig, Scholz, Walther, and Selbig 2007).
However,  Josse et al. (2009)  and  Ilin and Raiko (2010)  highlighted this algorithm's overfitting problems, and suggested regularized versions to tackle these issues. A regularized version of the iterative PCA algorithm is available in MATLAB toolboxes  (Porta, Verbeek, and Kröse 2005; Ilin 2010 ). In R an implementation was provided by  Stacklies et al. (2007)  in the pcaMethods package. In the missMDA package we also implemented the iterative PCA algorithm and a regularized iterative PCA algorithm where the properties are given in the associated theoretical papers  (Josse et al. 2009; Josse and Husson 2012). The main difference between the two R packages missMDA and pcaMethods is that the primary aim of missMDA is to estimate PCA parameters and obtain the associated graphical representations in spite of missing values, whereas pcaMethods focuses more on imputation aspects.