dataesr/scientific-paragraphs-categorization huggingface dataset:
After five intracellular punctures (named as potential drop pattern [pd]), each aphid was transferred to an 8-day-old turnip test plant for a 2-h IAP (1 aphid per plant). Twenty-two aphids were used for each treatment, and the experiment was repeated 5 times. Duration of each pd was analyzed using Styletϩ software (EPG Systems  [62] ). Database searches and sequence retrieval. For annotating RR-1 cuticular proteins in the genome of Diuraphis noxia  (63) , we used CutProtFam-Pred (http://aias. biol. uoa. gr/CutProtFam-Pred/ [29] ), with the standard settings proposed by the web interface (see Table  S3  in the supplemental material). An exhaustive search in Aphididae family was performed with the BLAST programs  (64)  in the NCBI's (NR, TSA, and SRA) databases and Aphidbase (available at http://www.
aphidbase. com/), using A. pisum ACYPI009006 (Stylin-01) and A. pisum ACYPI003649 (Stylin-02) as query sequences. Predicted translation of each full-length or partial retrieved sequence was checked for the presence of the conserved RR-1 motif (GSYSYTxPDGxxYxVxYVAD-ENGFQPxGxHLP) using CuticleDB software (http://biophysics. biol. uoa. gr/cuticleDB/ [65] ). All protein sequence analyses (predicted translation and signal peptide searches) were performed using ExPASy tools (http://www. expasy. org/tools/). Alignments and phylogenetic reconstruction of the RR-1 family. Stylin-01 and Stylin-02 homologs were aligned using T-Coffee  (66, 67). Phylogenetic relationships between A. pisum, M. persicae, and D. noxia RR-1 proteins were assessed using the Phylogeny. fr platform  (68). After removal of the signal peptide, protein sequences of RR-1 genes of A.
pisum, M. persicae, and D. noxia were aligned using MUSCLE (v. 3. 8. 31) (69) configured for highest accuracy (MUSCLE with default settings). Ambiguous regions (i. e. , containing gaps and/or poorly aligned) were removed with Gblocks (v. 0. 91b) using the following parameters: the minimum length of a block after gap cleaning was 10, no gap positions were allowed in the final alignment, all segments with contiguous nonconserved positions bigger than 8 were rejected, and the minimum number of sequences for a flank position was 85%. Then phylogenetic trees were reconstructed using the maximum likelihood method implemented in the PhyML program (v. 3. 1/3. 0 aLRT). The WAG substitution model was selected assuming an estimated proportion of invariant sites (of 0. 009) and 4 gamma-distributed rate categories to account for rate heterogeneity across sites.
The gamma shape parameter was estimated directly from the data (gamma ϭ 3. 517). Reliability for internal branch was assessed using the aLRT test (SH-Like). Graphical representation and edition of the phylogenetic tree were performed with TREEDYN (v. 198. 3) (70). OrthoMCL software (http://orthomcl. org/orthomcl/) was run on predicted RR-1 protein sets from A. pisum, M. persicae, and D. noxia in order to determine putative orthologs  (71). Statistical analysis. All statistical analyses were carried out with R software (v. 3. 3. 2). Statistical significance for transcript levels and mean duration of potential drop waveforms was assessed by one-way analysis of variance (ANOVA), and CaMV transmissions were compared by performing a chi-square test. Results of each statistical test are indicated in the figure legends.
ACKNOWLEDGMENTS We thank  S. Le Blaye ,  B. Cayrol , and  A. Morisset  for technical assistance and  J. L. Macia  for aphid rearing. We are grateful to  K. Gaget  for her help with the  RNAi experimental design. This work was supported by  Region   LR grant Chercheuse d'Avenir 2011 , a grant from the  Bill & Melinda Gates Foundation  (GCEag,  OPP1130147 ), the  French National Research Agency  ( ANR15-CE20-0011 ), and funding from the  Plant Health and Environment Division of INRA. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
A brute-force self-consistent maximization of S X over all possible assignments X of a realistic dataset would result in a combinatorial explosion, since all allowed permutations of pairs would need to be considered, and PMIs would need to be computed for each of them. In practice, since biologically meaningful pairings have to be made within a species, this means that we would need to consider all combinations of all permutations within each species, which already yields prohibitively large numbers of assignments to test. Hence, we propose an algorithm to perform an approximate maximization of S X.
The MI-IPA accurately predicts interaction partners from a training set of known partners As in  [21] , we use histidine kinases (HKs) and response regulators (RRs) from prokaryotic twocomponent signaling systems as our main benchmark. Two-component systems are important pathways that enable bacteria to sense and respond to environment signals. Typically, a transmembrane HK senses a signal, autophosphorylates, and transfers its phosphate group to its cognate RR, which in turn induces a cellular response  [33]. Importantly, most cognate HK-RR pairs are encoded in the same operon, so actual interaction partners are known, which enables us to assess performance. Unless otherwise specified, our results were obtained on a "standard dataset" comprising 5064 HK-RR pairs for which the correct pairings are known from gene adjacency.
Each species has on average m p = 11. 0 pairs, and at least two pairs (see Methods). We start by predicting interaction partners starting from a training set of known pairs. As our training set, we pick a random set of N start known HK-RR pairs from the standard dataset. The first iteration of the MI-IPA uses this concatenated alignment (CA) to compute PMIs and score possible pairs. We blind the pairings of the remaining dataset, and use it as a testing set on which we predict pairings. At each subsequent iteration n > 1, the CA used to recompute PMIs contains the initial training pairs plus the (n -1)N increment highest-scoring predicted pairs from the previous iteration (see  Methods). As in the case of the DCA-IPA  [21] , iterating, and thereby progressively adding high-scoring pairs to the CA, allows us to increase the fraction of pairs that are correctly predicted.
q) proteins A among m, to be correctly paired. In this null model, the number of possible MI-IPA assignments that share k correct pairs with the DCA-IPA is q k m-q p-k : k correct pairs are chosen among the q pairs correctly assigned by the DCA-IPA, and the other p -k ones are chosen among the m -q proteins A incorrectly paired by the DCA-IPA. Besides, the total number of possible MI-IPA assignments with p correct pairs is m p. Hence, the probability P (p, k, m, q) that k correct pairs are predicted by both algorithms is given by the hypergeometric distribution: P (p, k, m, q) = q k m-q p-k m p. ( 6 ) The expectation of k under this distribution is given by In both panels, the standard dataset of HK-RRs is used, and the CA includes N increment = 6 additional pairs at each iteration. All results are averaged over 50 replicates that differ by the random choice of HK-RR pairs in the training set.
Dashed lines represent the average TP fraction obtained for random within-species HK-RR pairings. k = p q m. (7) Hence, in this fully independent null model, the expectation of the total number of correct pairs predicted by both algorithms can be obtained by summing the expectations in Eq. 7 over all species in the dataset. Moreover, in each species, the observed number k obs of shared correct pairs can be compared to this null model, as well as to the extreme case where all correct pairs that can be shared are shared, yielding min(p, q) shared pairs. Hence, we define the relative excess E of shared predictions by E = S i=1 k obs,i -k i S i=1 min(p i , q i ) -k i , (8) where the index i corresponds to a particular species, the sums run over the S species present in the dataset, and the expectations are given by Eq.
(B) Final TP fraction obtained by the MI-IPA and the DCA-IPA versus the total number M of HK-RR pairs in the dataset, starting from random pairings. For each M , except that corresponding to the full dataset, datasets are constructed by picking species randomly from the full dataset, and results are averaged over multiple different such alignments (from 50 up to 500 for small M ). For the full dataset (largest M ), averaging is done on 50 different initial random within-species pairings. All results in (B) are obtained in the small-N increment limit. An important parameter for the performance of the MI-IPA is dataset size. Qualitatively, larger datasets imply more close neighbors, which is favorable to the success of bootstrapping, and they also allow one to estimate MI more accurately, so we expect the MI-IPA to perform best for large datasets.
Indeed, Fig. 2B  shows that the performance of the MI-IPA increases with dataset size. Moreover, the rise of performance occurs for slightly smaller datasets in the case of the MI-IPA than for the DCA-IPA. With DCA, a sufficiently large dataset is necessary to properly infer the pairwise maximum-entropy based global statistical model at the heart of the method  [4, 6, 12]. While being data-thirsty too, the MI-IPA bypasses this particular need, and thus, it is better suited for partner prediction in smaller datasets. For the complete dataset (23,424 HK-RR pairs, see Methods), both methods reach the same striking final TP fraction of 0. 93. The fact that the MI-IPA often performs better than the DCA-IPA for predicting interacting partners stands in contrast with the fact that DCA substantially outperforms MI for residue contact prediction, and suggests that relevant covariation information is contained in pairs of residues that are not in contact.
In order to test this, we ran the MI-IPA starting from random initial pairings on our standard HK-RR dataset, but suppressing all MI contributions from the contacting pairs of residues (defined with a generous 8 Å threshold on the minimum distance between amino acids). For N increment = 6, a striking TP fraction of 0. 83 was obtained. This value is only slightly smaller than the 0. 87 TP fraction obtained when all residue pairs are included, thus confirming that substantial MI relevant for partnership prediction is present in non-contacting residue pairs. As above, we investigated the extent to which the MI-IPA and the DCA-IPA tend to make the same correct predictions. Note that this time, the two algorithms do not start from shared information since there is no training set. For the standard dataset of 5064 HK-RR pairs, with N increment = 6, we found that the average excess shared fraction (see Eq.
First, we considered using both scores within the IPA. We tried a combined IPA which calculates both scores at each iteration and computes separately the two corresponding pair assignments. Our ranking of pairs puts first the pairs contained in both assignments, and these pairs are ordered by decreasing MI-based confidence scores. Next come the other pairs: the assignment predicted using MI is conserved for them, and they are ordered by decreasing MI-based confidence scores. This means that the pairs consistently predicted by both methods are going to enter the CA earlier in the iterative process than those that differ. A very minor improvement was obtained over the MI-IPA using this method (see Fig. S1B ). Since DCA typically requires large datasets to be reliable, we also tried combining MI and DCA by using MI at early iterations, when the CA is small, and switching to DCA at later iterations.
Fig. 3A  shows these three quantities as a function of dataset size M , for HK-RR datasets to which the MI-IPA is applied starting from no training set, as in Fig. 2B. We observe a global trend of all computed pairwise MIs to decrease when M is increased. This arises from a well-known finite size effect that occurs when estimating entropies from real datasets  [34] , and thus affects entropy-derived quantities such as MIs. To illustrate this point, Fig. 3A  also shows the pairwise MI of HK-RR datasets where each column of the alignment is randomly scrambled, thus destroying actual correlations while retaining finite-size noise, as well as one-body frequencies. This null model features a similar decreasing trend as the other curves, thus demonstrating that this trend comes from finite-size effects.
Interestingly, even for small datasets, where the MI-IPA yields small TP fractions (see Fig. 2B ), the pairwise MI of the assignment predicted by the MI-IPA is much closer to that of the actual assignment than to that of the initial random assignment. This suggests that the MI-IPA does a good job at maximizing pairwise MI, even though it does not reach the absolute maximum. So far, we have used naive estimates of the pairwise MI, employing empirical frequencies instead of probabilities in MI (see Eq. 2), without correcting for the finite-size effect discussed above and visible in Fig. 3A. Various approaches have been proposed in order to correct for the systematic error due to finite-size effects in entropy (and thus MI) estimates. One can use the fact that these finite-size effects have a leading-order contribution of the form MI-MI ∝ K/M , where MI is the actual value of the mutual information and MI is its naive estimate using empirical frequencies, while K is the number of independent values that can be taken by the pair of random variables considered, and M represents dataset size  [34].
Estimates MI obtained by subsampling the initial dataset can be linearly (or polynomially, to include higher-order corrections) fitted versus 1/M , yielding the actual MI as the intercept. However, if M is not larger than K, subleading corrections become too important, and this method becomes inaccurate. Here, K can go up to the square of the number of possible states per site, i. e. ∼ 400. Consistently, we found that polynomial fits become poor when considering datasets with less than ∼ 1000 sequences. Fig. S2A  presents results obtained using corrections from these fits, for datasets of at least 1000 sequences. A more sophisticated method to reliably estimating the entropies of discrete distributions was introduced in  [35]. It employs a Bayesian approach and a flat prior on the entropy, and results in a correction of finite-size bias in entropy estimates.
This NSB (Nemenman-Shafee-Bialek) correction is successful in undersampled cases  [35, 36]. In Fig. S2B , we show results obtained on our datasets using the NSB correction. Employing either of these two corrections of finite-size effects on the MI estimates (see Fig. S2 ) confirms our previous conclusion. For all dataset sizes, the pairwise MI of the assignment predicted by the MI-IPA is significantly closer to that of the actual assignment than to that of the initial random assignment, thus confirming that the MI-IPA yields results with near-maximal pairwise MI.
4 , to be compared to m p = 11. 0 for HK-RRs). (D,E) Pairs involved in enzymatic complexes; full datasets of ∼ 2000 pairs. In each case, the mean number m p of pairs per species is indicated, and dashed lines represent the average TP fraction obtained for random within-species pairings. All results are averaged over 50 replicates that differ in their initial random within-species pairings. These accurate predictions demonstrate the broad applicability of the MI-IPA. Note that HK-RRs interact transiently, while the ABC transporter and enzymatic proteins we considered form permanent complexes, which highlights the generality of the method.
Methods Here, we first explain the different steps of the MI-IPA and briefly discuss its run time. Next, we describe the datasets used and the way the statistics of these alignments are computed. Matlab implementations of both the MI-IPA and the DCA-IPA on our standard HK-RR dataset are freely available at https://doi. org/10. 5281/zenodo. 1421781 and https://doi. org/10. 5281/zenodo. 1421861 respectively. The various sequence datasets discussed here are available as S1 Data at https://doi. org/10. 1371/journal. pcbi. 1006401.
Initialization of the CA If starting from a training set of known interaction partners AB, the CA for the first iteration of the IPA is built from the pairs AB in this training set. In subsequent iterations, the training set pairs are always kept in the CA, and additional pairs with the highest confidence scores (see below) are added to the CA. In the absence of a training set, each protein A of the dataset where we wish to predict pairings is randomly paired with a protein B from its species. All these random pairs are included in the CA for the first iteration of the MI-IPA. Hence, this initial CA contains a mixture of correct and incorrect pairs, with one correct pair per species on average. At the second iteration, the CA is built using only the N increment AB pairs with the highest confidence scores obtained from this first iteration.
AB pair assignments and ranking by confidence score We make one-to-one AB pairs within each species in the dataset by maximizing the sum of the scores of all pairs in this species. Considering the matrix of the scores of all possible pairs, this amounts to choosing one element per line and per column, such that the sum of all of them is maximal. This assignment problem is solved exactly and efficiently using the Hungarian algorithm (also known as the Munkres algorithm)  [52] [53] [54]. Each pair is scored by a confidence score ∆S AB , which is the difference between the sum of the scores of the assigned pairs in the species and that obtained by using the Hungarian algorithm again while this pair is disallowed. Once pairs are made and confidence scores are calculated in each species, all the assigned AB pairs from all species are ranked in order of decreasing confidence score.
Update of the CA The ranking of the AB pairs is used to pick those pairs that are included in the CA at the next iteration. Pairs with a high confidence score are more likely to be correct because there was less ambiguity in the assignment. The number of pairs in the CA is increased by N increment at each iteration, and the MI-IPA is run until all the As and Bs in the dataset have been paired and added to the CA. In the last iteration, all pairs assigned at the second to last iteration are included in the CA. If starting from a training set of AB pairs, the N start training pairs remain in the CA throughout. The As and Bs from all the other pairs in the CA are re-paired and re-scored at each iteration, and only re-enter the CA if their confidence score is sufficiently high. In other words, at the first iteration, the CA only contains the N start training pairs.
Then, for any iteration number n > 1, it contains these exact same N start training pairs, plus the (n -1)N increment assigned AB pairs that had the highest confidence scores at iteration number n -1. In the absence of a training set, all As and Bs in the dataset are paired and scored at each iteration, and all the pairs of the CA are fully re-picked at each iteration based on the confidence score. For any iteration number n > 1, the CA contains the (n -1)N increment assigned AB pairs that had the highest confidence scores at iteration number n -1. Once the new CA is constructed, the next iteration can start.
Run time The run time of the MI-IPA strongly depends on the number of iterations made, as well as on dataset size (length of sequences, number of sequences in the dataset). Because the inversion of the correlation matrix is not necessary, the MI-IPA is substantially faster than the DCA-IPA, and this difference is stronger for longer sequences. In practice, for our standard HK-RR dataset, one round of calculation of all PMI scores from sequence data (Eq. 1) takes 7s, while for DCA, the calculation of all pairwise couplings takes 9s. The longest concatenated sequences considered here were XDHA-XDHC (see Fig. 4 ), with total length L = 911 (to be compared to 176 for HK-RRs): in this case, the calculation of all PMI scores takes 135s, while for DCA, the calculation of pairwise couplings takes 289s.
Times are given for a single processor (Intel Core i7) of a standard laptop computer. Regarding the full MI-IPA, run times with all standard datasets used here were under 10 hours. Comparison to the DCA-IPA Throughout, the DCA-IPA was used as described in  [21] , with the same parameters (threshold Hamming distance for weighting neighboring sequences θ = 0. 3, pseudocount weight Λ = 0. 5).
Dataset construction We use the HK-RR datasets described in  [21]. Briefly, the complete dataset was built using the online database P2CS  [55, 56] , yielding a total of 23,424 HK-RR pairs (known by genome proximity) from 2102 different species. We focused on the protein domains through which HKs and RRs interact, which are the Pfam HisKA domain present in most HKs (64 amino acids) and the Pfam Response reg domain present in all RRs (112 amino acids). In most of the paper, and as in  [21] , we focused on a smaller "standard dataset" extracted from this complete dataset, both because protein families that possess as many members as the HKs and RRs are atypical, and in view of computational time constraints. This standard dataset was constructed by picking species randomly, and comprises 5064 pairs from 459 species.
In our datasets, we discarded sequences from species that contain only one pair, for which pairing is unambiguous. It allows us to assess the impact of training set size (N start ) without including an implicit training set via these pairs, and it also enables us to address prediction in the absence of any known pairs (no training set). While we used HK-RRs as the main benchmark for the MI-IPA, we assessed the generality of its performance by applying it to several other pairs of protein families. For these proteins, paired alignments of homologs of known Escherichia coli interacting protein pairs were built using a method adapted from  [16] , as detailed in  [21]. Note that we kept full sequences, without restricting them to Pfam domains, for PAAH-PAAJ and XDHC-XDHA.
3  and S2 ). The MI of each inter-protein pair of amino acids was evaluated at the final iteration of the MI-IPA, for three different pairs of protein families, including two interacting ones (BASS-BASR homologs and MALG-MALK homologs) and one with no known interaction (BASR-MALK homologs). These MI scores were averaged over 500 MI-IPA replicates with N increment = 50 that differ in their initial random pairings, and then ranked by decreasing value. Datasets used are the same as in Fig. 5 : all alignments include ∼ 5000 protein pairs, with average number of pairs per species m p ≈ 5. Figure  S4 : Prediction of inter-protein residue contacts. Fraction among the top interprotein residue pairs ranked by MI or DCA scores that are in contact in the experimentallydetermined three-dimensional structure of complexes.
Inter-protein residue pairs were scored both by MI (see Eq. 2) and by the Frobenius norm of the coupling strengths from DCA  [21] , and then ranked using these scores. (A) HK-RR dataset; contacts were determined from the experimental structure with PDB identifier 3DGE  [57]. (B) Dataset of homologs of MALG-MALK; contacts were determined from the experimental structure with PDB identifier 3RLF  [58]. The alignments of ∼ 5000 cognate protein pairs employed in the rest of the paper to test the MI-IPA (see Methods) were used. A generous threshold of 8 Å on the minimum distance between two amino acids was used to define contacts. In both panels, the chance expectation for finding contacts (i. e. the overall fraction of inter-protein residue pairs that are in contact) is shown (dashed lines). Final TP fraction obtained versus N increment for the standard HK-RR dataset, starting from random within-species HK-RR pairs.