dataesr/scientific-paragraphs-categorization huggingface dataset:
tRNA Reference Sequence Analysis of tRNA reference sequences was performed in R-studio with R version 3. 5. 3 and R packages seqinr and msa. Initial tRNA sequences (mature reference sequences) were downloaded from gtRNAdb (http://gtrnadb. ucsc. edu/GtRNAdb2/index. html), introncontaining sequences were not included in the analysis. No modification of the reference (except U->T conversion to get standard DNA *. fasta file) was performed prior to downstream analysis. Conserved 3'-CCA tRNA sequence was not introduced at this stage since 3'-CCA can be already encoded in genomic sequences, such as in Escherichia coli, and thus included in the sequences from gtRNAdb. However, this is not the case for Homo sapiens, and many other eukaryotes. In most complex cases like for Bacillus subtilis, some tRNA genes encode 3'-CCA while the others do not.
We decided not to introduce International Union of Pure and Applied Chemistry (IUPAC) codes for ambiguous DNA residues since these codes are not systematically interpreted by popular alignment algorithms (such as Bowtie/Bowtie2  [33]  used in this work). Collapsing into clusters is performed in semi-automatic mode, and a heatmap showing calculated Levenshtein distances between sequences is displayed to help the user to define the appropriate number of clusters. The script suggests the optimal number of distant clusters (with default max distance of 8 substitutions), but, if required, this number can be overridden by manual entry of a more appropriate value. Such manual correction is optional for the majority of simple bacterial and lower eukaryote tRNA references, but was found to be necessary for very complex clusters found for tRNA genes in higher eukaryotes.
The script suggests the optimal number of distant clusters (with default max distance of 8 substitutions), but, if required, this number can be overridden by manual entry of a more appropriate value. Such manual correction is optional for the majority of simple bacterial and lower eukaryote tRNA references, but was found to be necessary for very complex clusters found for tRNA genes in higher eukaryotes. If the number of clusters to create is erroneously selected too low, the tRNA sequences, forced to merge in the same cluster, but still distant more than 10 substitutions are removed and stored in the "Removed sequence" file. After collapsing to clusters, a final heatmap is created for visual inspection and verification, sequences are re-annotated to use only the amino acid identity and anticodon sequence, isoacceptors/isodecoders with the same anticodon are numbered sequentially.
Calculation scripts were tested for 20 organisms listed in the Table  1 , Step 1 for merging identical sequences worked in all tested cases, Step 2 failed only when the number of fully identical sequences was > ~100, due to the excessive length of fasta file header, not accepted by R package seqinr. When the semi-automatic clustering of tRNA sequences was not sufficient to obtain <10-15% of multiply mapped reads after analysis of real tRNA datasets, tRNA reference was manually inspected and redundant isoforms creating ambiguous mapping were removed or collapsed in a single entry. Finally, if not present in the genomic tRNA sequences, 3'-CCA end was manually added to the reference, to improve alignment quality in an end-to-end mode with sequencing reads issued from mature CCA-containing tRNA species.
Alignment of the Experimental Datasets to Reference tRNA Sequences Available experimental datasets obtained for total RNA or enriched tRNA fractions were treated by the same analytical pipeline. Demultiplexing and trimming was done without option for removal of potential PCR duplicates, since this is not appropriate for large datasets generated from relatively short reference sequence (here, collection of tRNAs). Raw reads were trimmed using Trimmomatic version 0. 32  [34] , using the following parameters (minLen 8 nt, maxLen 50 nt, single-end mode, stringency 7 (with these parameters the sequence of adapter over 10 nt is detected and removed). Taking into account very short cumulated reference sequence (maximum 250 sequences of 75-90 nt only, so <15 kb) minLen of 8 nt is, in principle, sufficient for unambiguous mapping.
3. Use the distance of 8 nt for genomic datasets of <250 tRNA genes (<60 Step 1 sequences) and the distance of 10 nt for larger genomic references. There may be intermediate cases for organisms having between 250 and 300 tRNA genes. 4. Verify the number of optimized (Step2) sequences, values close to 40 (or less) are indication of a good quality non-redundant reference, while numbers > ~50 mean still complex and potentially redundant tRNA collection. 5. Validate the optimized (Step 2) reference with experimentally obtained tRNA dataset. If proportion of uniquely mapped reads is still <90%, repeat collapsing in Step 2 with increased distance threshold.
Analysis of Simple tRNA References (<100 tRNA Genes) For common bacterial species, the proposed approach was validated using tRNA references for E. coli and B. subtilis. The full genomic E. coli tRNA reference contains 89 tRNA genes (source gtRNAdb). Fully identical duplicated genes were found and collapsed, creating a non-redundant tRNA dataset of 48 tRNA sequences ("Step1", see Table  1 ). Further collapsing of closely related tRNA species with the maximal distance of 8 substitutions gave an optimized tRNA reference of 41 tRNA genes. The representativeness of tRNA anticodons was verified and compared to the initial full dataset. All anticodons were found to be correctly represented, except for tRNA Ala (GGC) and tRNA Ala (TGC) which were merged in a single entry tRNA Ala (NGC) with 7 substitutions and two tRNA Thr (CGT) isodecoders, which were maintained, since relatively distant (see Supplementary Data).
The optimized reference sequence for E. coli contains all tRNA species reported in MODOMICS and tRNAdb and is similar to the one used previously for tRNA modification analysis  [21]. Similarly, collapsing of the full tRNA gene set from B. subtilis (86 sequences) gave 43 non-redundant tRNA sequences at the first step and 35 optimized tRNA references with a maximum of 8 allowed substitutions. The resulting optimized reference is fully representative for all used codons and adds 11 additional tRNA isoacceptors compared to MODOMICS/tRNAdb reference databases. Additional tests with other bacterial species demonstrated that semi-automatic collapsing is sufficient to obtain highly optimized tRNA references in such cases (see Table  1 ). The minimal number of retained tRNA genes is 31 (which is close to anticipated minimum required for correct mRNA decoding), and the average is about 40 species in low complexity genomic reference (<100 tRNA genes).
tRNA References of Intermediate Complexity (<300 Genes) Collections of tRNA gene reference sequences of intermediate complexity are found in lower eukaryotes or representative insect species, such as Saccharomyces cerevisiae (275 tRNA genes in full genomic reference) or Drosophila melanogaster (total of 290 tRNA genes). For S. cerevisiae, an automatic merging of identical tRNAs reduced the number of tRNA sequences to 54 species and further optimization by collapsing of sequences with minimal distance of eight substitutions reduced this number to 38. Three closely related tRNA pairs showing very short distance (3-4 substitutions only, tRNA Ser (CGA/TGA), tRNA Glu (CTC/TTC) and tRNA Gln (CTG/TTG)) were merged together, the other codons are represented as in the full genomic tRNA dataset. All sequences listed in MODOMICS/tRNAdb are present in the optimized reference sequence, but this last reference contains 10 supplementary tRNA species compared to MODOMICS /tRNAdb.
thaliana. The human non-redundant tRNA reference has 177 non-identical tRNA genes, while collapsing with the default 8 nt distance gave 61 entries in Step2 Optimized sequences. Despite a larger genomic tRNA gene collection (590 sequences), A. thaliana tRNA genes were found to be less diverse, leading to only 133 entries in non-redundant Step1 collection and 48 after Step2 (see Table  1 ). The algorithm was also tested for highly complex models, like plant Z. mais (770 tRNA genes) and frog X. tropicalis, where >3000 tRNA genes were found. Collapsed reference contains, respectively, 191 and 245 non-redundant tRNA species, while "optimized dataset" uses only 70 and 68 representative tRNA sequences (Table  1 ).
Validation of the Optimized tRNA Reference Sequences For an experimental validation we selected six representative tRNA reference sequences: two from bacterial, common model organisms E. coli and B. subtilis, respectively, from Gram-and Gram+ groups, and four from eukaryotic species showing rather variable number of tRNA genes in the genome and optimized tRNA references: S. cerevisiae, D. melanogaster, H. sapiens and A. thaliana (see Table  1 , in red). For each organism, RiboMethSeq-like datasets were prepared for 6-20 samples, from total RNA, or from size-selected tRNA fraction. Alignment statistics for analyzed datasets is shown in Table  S1. Raw trimmed reads were cleaned up from rRNA contamination and aligned first to non-duplicated (non-redundant) full tRNA reference (Step 1, see Figure  1B ).
The final reference for H. sapiens contained only 45 sequences, instead of 61 for a maximal distance of eight substitutions (see Table  1 ). Similarly, the drosophila tRNA reference sequence was reduced to 35 unique entries. Analysis of uniquely and multiply mapped reads from experimental datasets showed substantial improvement compared to the previous reference sequence with n = 8 as maximum distance. The proportion of multiply mapped reads decreased to ~10-12%, which was considered acceptable (see Figure  3 ). Inspection of multiply mapped reads showed that only tRNA Leu , tRNA Lys and tRNA Arg in the D. melanogaster dataset still showed substantial multiple mapping (shown in red, Figure  3 ). For the human reference, this was the case for pairs of tRNA Leu , tRNA Arg , one tRNA Tyr and tRNA Ala /tRNA Val (Figure  3 ).
These cases were manually inspected to find the origin of such ambiguous mapping. Both in D. melanogaster and in human, two tRNA Leu isoacceptors were found well represented in the RNA-seq data and showed very similar sequences at the 5 -and 3 -extremities but differed in the anticodon loop (Supplementary Figure  S3  and information in the Supplementary Data). Human tRNA Tyr 3 reads were rather scarce compared to two other tRNA Tyr isoacceptors and thus tRNA Tyr 3 was removed from the curated reference sequence. Finally, tRNA Val 3 (encoded by the unique gene tRNA-Val-AAC-6-1) differs from tRNA Ala 1 only at 10 positions and appeared to be an anticodon (missense suppressor) mutant of tRNA Ala 1. Thus, this tRNA Val 3 was also removed from the final reference sequence list. Final manually curated reference sequences for six model organisms now include the 3 -CCA end, and all anticodon A34 residues were replaced by G since A34 is systematically converted to inosine (I)  [37] , which behaves as G in base pairing.
Alignment results of full tRNA reads to curated references are shown in Table  2. Inspection of multiply mapped reads showed that only tRNA Leu , tRNA Lys an tRNA Arg in the D. melanogaster dataset still showed substantial multiple mapping (show in red, Figure  3 ). For the human reference, this was the case for pairs of tRNA Leu , tRNA Ar one tRNA Tyr and tRNA Ala /tRNA Val (Figure  3 ). These cases were manually inspected to fin  Highly redundant RNA sequences issued from gene duplication and sequence mutations are difficult targets for extensive and unambiguous bioinformatic analysis of deep sequencing data, since short sequencing reads obtained in many popular protocols do not allow to distinguish between these very similar species. Transfer RNA (tRNA) populations in most organisms are well-studied examples.
g. , demethylase tRNA sequencing, DM-tRNA-Seq, AlkB-facilitated RNA methylation sequencing, ARM-Seq, etc. ) improve efficiency of primer extension, but do not remove all RT-blocking modified nucleotides. Thus, tRNA deep sequencing data are known to by heavily biased, with preferential amplification of only certain tRNA species  [13]. Up to now, no general approach allowing rapid analysis of tRNA deep sequencing data was established in the community, and so far, every new organism required particular attention and almost manual inspection of numerous tRNA sequences. In this work, we propose a generalized approach to reduce the complexity of tRNA reference sequences, intended to be used for reads mapping. The first, most evident, step consists in removal of all duplicated tRNA genes of exactly identical sequence.
Other protocols, such as AlkAnilineSeq for detection of m 7 G/m 3 C/D/ho 5 C  [38]  and HydraPsiSeq for pseudouridine mapping and quantification  [39] , also result in relatively short tRNA reads. To obtain unambiguous reads mapping, which may ultimately be used for both tRNA quantification and modification analysis, we propose to group (merge) closely related species into a single entry, replacing "ambiguous" mutated nucleotides by most frequent nucleotide or by N. Even if the use of IUPAC nucleotide code seems to be more appropriate, such nucleotides are considered as N by many alignment utilities (Bowtie2, STAR, BWA)  [40, 41]. The number of N nucleotides should be limited, to reduce ambiguous mapping, but not become too small, since otherwise closely related species will lead to multiple mapping events.
Representativeness of Optimized tRNA Datasets In order to check if such reduced tRNA reference datasets remain representative, anticodon compositions of full and optimized datasets were compared (see Supplementary Materials). In general, when some apparent loss of rarely used tRNA anticodons was found, manual inspection revealed that those correspond to rare tRNA isoacceptors (encoded by genes with low copy number) and represent point mutant of major tRNA isoacceptor. These conversions are generally limited to tRNAs decoding the same amino acid, with an exception of one human tRNA Val (AAC), which is in fact derived from tRNA Ala (NGC) by a limited number of mutations, including two in the anticodon. Interestingly, this tRNA Val (AAC) is most likely behaving as a missense suppressor in vivo since it retains a G3*U70 base pair and a discriminator nucleotide A73, two well-known identity determinants for aminoacylation by alanine tRNA-synthetase  [42].
Known Limitations and Troubleshooting Counting of tRNA reads and comparative studies of tRNA expression are possible with a reduced/optimized reference; however, if only one particular tRNA isoacceptor has to be quantified, the best approach would be to use a non-redundant tRNA dataset and to select only (rare) uniquely mapped reads for a given tRNA. Analysis of tRNA modification with a reduced dataset is based on the assumption that all species in the group have identical tRNA modification profiles, independently from the presence of a point mutation. This assumption is generally relevant, since mutations occur mainly in the tRNA regions, which are not known to be extensively modified; however, this may not be true for some essential tRNA anticodon positions (N34, for example). In such cases, inspection of nucleotide proportion (e.
Conclusions Applications in Analysis of tRNA Expression and Modifications Since currently used approaches for tRNA analysis by deep sequencing suffer from multiple and thus very ambiguous mapping of tRNA reads, we foresee multiple practical applications of the proposed general approach for tRNA reference sequences. Unified and straightforward analysis of tRNA deep sequencing data is still lacking in the field. Quantification of tRNA pool and modulations of its composition under stress or in disease represent one of the major applications. One other major field that will certainly benefit from these improvements is analysis of epitranscriptomic tRNA modifications (2'-O-methylation, pseudouridine, m 7 G/m 3 C, D and others), important for adaptation of cellular translational machinery in stress or disease.
Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github. com/elolaine/HOPMA and https://team. inria. fr/nano-d/ software/nolb-normal-modes.
Materials and Methods Datasets. We considered four reference protein benchmark sets, namely the iMod benchmark  (92) , the HR-PDNA187 benchmark  (93) , the Protein-Protein Docking Benchmark v5 (PPDBv5)  (94) , and the dataset reported in (author?) (90) (Table  1 ). We used the iMod benchmark as the validation set and the other benchmarks as testing sets. Opening/closing set. The iMod benchmark, available at http://chaconlab. org/multiscale-simulations/imod/imod-donwload/ item/imod-benchmark, was previously used to assess coarsegrained elastic network model-based flexible fitting methods  (95). It comprises 23 proteins, each given in "open" and "closed" conformations, and represents a wide variety of macromolecular motions, predominantly hinge motions but also shear and other complex motions.
The structures come from the molecular motions database MolMovDB  (96). All of them have less than 3% Ramachandran outliers (as computed by the MolProbity program (97)), do not have any broken chain or missing atom. Unbound-to-bound sets. The HR-PDNA187 benchmark, available at http://www. lcqb. upmc. fr/PDNAbenchmarks/, was recently compiled to assess methods predicting DNA-binding patches at the surface of proteins. It comprises 187 high-quality structures (better than 2. 5 Å resolution) of protein-DNA complexes and covers all major groups of protein-DNA interactions. The proteins have less than 25% sequence identity. 82 complexes come along with the unbound forms of the proteins. We extracted 11 monomeric unbound-bound (free-complexed) pairs with Cα RMS deviations between the two states above 2 Å.
The PPDBv5 benchmark, available at https://zlab. umassmed. edu/benchmark/, is well suited for assessing the range of applicability of flexible docking methods  (98). It contains 230 protein complexes with at least one of the partners solved in both bound (complexed) and unbound (free) states. All structures have a resolution better than 3. 25 Å, and some of them contain more than one chain. We extracted 68 singlechain proteins with Cα root-mean-square deviation (RMSD) between the two states above 2 Å. Large diverse set. The dataset reported in (author?)  (90)  was originally compiled to assess the ability of coevolutionary signals to guide the discovery of alternative functional protein conformations. It comprises 92 proteins sharing less than 70% sequence identity and corresponding to 224 protein chain structures.
4 ). This hexameric ATPase is required for bacterial type IV pilus retraction and surface motility. In the hexamer, the ATPase core of one subunit extensively interacts with the N-terminal domain of the next subunit. As a result, the interdomain distance and orientation vary from one subunit to another. The modelled transition takes place between two subunits (chains B and C) and implies some rearrangements of the contacts between the two domains (Fig. 4 , compare the two colored maps). HOPMA identifies and removes the contacts between the two domains in both structures, leading to an increased transition coverage, from 39% to 86% in the forward direction (Fig. 4 , on top), and from 62% to 78% in the backward direction (Fig. 4 , at the bottom). We also tested HOPMA on two other datasets, namely PPDBv5 and HR-PDNA187, dedicated to the study of proteinprotein and protein-DNA interactions respectively (Fig.
We investigated whether HOPMA could also be useful in this multi-iterative scheme. We focused on the 11 transitions associated with DNA binding from the HR-PDNA187 dataset. We found that accounting for HOPMA results to define the elastic networks leads to conformations that are closer to the target, after three iterations, for the three very large transitions of the set (Fig. S3 , initial RMSD>10 Å). The highest gain is obtained for the Y-family DNA polymerase Dpo4 (2rdiA-1jxoA). This enzyme plays a crucial role in translesion synthesis and undergoes a dramatic conformational change upon DNA binding (Fig. 5 ). The transition implies a ∼130 degrees rotation of the little finger domain relative to the polymerase core resulting in a RMS deviation of 11. 47 Å between the unbound and bound structures.
Computational details and availability. The HOPMA code was written in Python3 and R. All the calculations were performed on a 2,7 GHz Intel Core i7 CPU with 16 Go RAM. Given a protein structure, we used the following command to run HOPMA: "python3 $HOPMA_PATH/hopma. py prot -c chain", where prot is the name of the input PDB structure file without the extension, and chain is the name of the chain to analyze. Given two protein structures protInitial and protTarget, we used the following command to run NOLB: "NOLB protInitial protTarget -n 10 --nlin -m". To account for HOPMA results, we used the "--excl" option with the output of HOPMA. The computations are efficient, for example, predicting a transition for a protein of about 300 residues takes 12 seconds, including 9 seconds to perform HOPMA analysis and 3 seconds to run NOLB.
However, substantial evidence suggests that simply establishing the existence of a relationship between two variables in a dataset represents a significant loss of information  (Woodside, 2013). Within the same dataset, the two variables might be positively related, negatively related or not associated at all  (Schneider and Eggert, 2014; Woodside, 2013). Complexity theory and fsQCA allow us to explore the possibility of multiple realities, whereby the outcomes of animosity are explained by different causal configurations, rather than just one  (Urry, 2005)  A further assumption of earlier research is that a model of animosity developed for a specific international relation (e. g. , the animosity felt by Chinese consumers toward Japan) can also explain animosity in another context (e. g. , the animosity felt by American consumers toward China).
Data collection and sample characteristics The study examines two different animosity contexts. Data was collected from Chinese participants evaluating Japan, and from American participants evaluating China. The former animosity context has substantial historical roots, with animosity mostly being linked with the atrocities committed by the Japanese army in certain areas of China during  (Christensen, 1999). The animosity between the USA and China has different dimensions. Political and military tensions date back to the Cold War and the role played by China in the Korean War and its aftermath  (Christensen, 1999). In the last decade, the relations between countries have been also complicated by commercial disputes  (Liu, 2018). Research suggests that both contexts are appropriate to examine consumer animosity as there is evidence of significant animosity toward Japan in China and toward China in the USA  (Harmeling et al.
, 2015). During the time of data collection (approximately one week for each study during May 2016), there were no specific incidents reported in the news that might have reignited the disputes and thus affected our results. E u r o p e a n J o u r n a l o f M a r k e t i n g 15 World War II Two online surveys collected nationally representative data in China (evaluating Japan) and the USA (evaluating China) via a commercial panel firm (Survey Sampling International). An instrumental manipulation check appeared at the beginning of the survey to maximize participants' engagement and ensure their attention (see Oppenheimer, Meyvis and Davidenko, 2009 for details). An attention check question appeared toward the end of the survey  (Meade and Craig, 2012). Participants who failed this attention check were excluded from the dataset.