dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure determination of Drc Drc crystals were grown in sitting drops containing 1 l of protein solution (7 mg/ml, 10 mM Tris-HCl pH 7. 5) and 1 l of precipitant solution equilibrated against 100 l of precipitant in the reservoir. For the crystal used for the non-anomalous dataset, this precipitant consisted of 1. 8 M monobasic ammonium phosphate and 0. 1 M sodium acetate pH 4. 6, while it consisted of 2. 0 M ammonium citrate pH 7. 0 and 0. 1 M sodium acetate for the crystal used for anomalous data acquisition. The crystals grew for approximately two and a half years and were soaked in 40% PEG 400 before flash freezing in liquid nitrogen. X-ray diffraction data was collected under a 100 K nitrogen stream at the X06DA beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland).
After data acquisition using an X-ray wavelength of 1. 00 Å (12. 4 keV), seven datasets at different angles (0 • to 30 • , 5 • increment) with a wavelength of 2. 07 Å (5. 98 keV) were collected to ensure a high multiplicity necessary for S-SAD phasing. The first dataset was indexed and integrated using XDS v. 3 November 2014  (23)  and scaled and merged using Aimless  (24). Data collection and refinement statistics can be found in Table  1. The phase problem was solved using native S-SAD using the seven other datasets, as described in  (25). These datasets were also processed using XDS v. 3 November 2014, but scaled using XSCALE v. 3 November 2014  (23) , which resulted in an anomalous multiplicity of 83. 8. Sulphur positions were found using SHELXC/D  (26)  and subsequent density modification and auto-tracing of the protein backbone was done using SHELXE  (26).
Phylogenetic analysis Phylogenetic analysis was performed by the maximum likelihood method and a Jones-Thornton-Taylor matrix-based model  (30). The phylogenetic tree was generated based on the dataset and methods described in  (31) , using MUSCLE  (32)  for sequence alignment and MEGAX  (33)  for phylogram generation. A bootstrap test (1000) was performed for the percentages of replicate trees. Homologs of Drc and N4 gp2 were assigned based on a PSI-BLAST search (34) with up to three iterations using the default parameters and retaining all significant hits for the next iteration.
Only at very high concentrations unbound Drc is observed in ssDNA-bindng conditions. At this point, the ssDNA is fully coated by Drc, blocking it from being bound by DNA dyes. DsDNA will still be stained as Drc only binds the ssDNA region and the dye can easily reach the unbound dsDNA regions. five-stranded ␤-barrel that binds the nucleic acid strand  (36). Although Drc is a ssDNA-binding protein, it does not share any significant sequence similarity with the typical OB-fold or other SSB proteins. Therefore, the crystal structure of Drc was determined. The protein crystallized in space group I4 1 22 and the structure was solved by single-wavelength anomalous dispersion of Sulphur atoms (S-SAD) using a dataset of 2. 80 Å resolution. After density modification and auto-tracing of the protein backbone, the model was subsequently refined using another 2.
20 Å resolution dataset, from a crystal of which no anomalous data was recorded, to R work and R free values of 22. 72% and 28. 59%, respectively. The asymmetric unit contains furthermore 30 ordered solvent molecules and one phosphate group. Additional data collection and refinement statistics are provided in Table  1. Wild-type Drc has 99 amino acids, of which the model spans residues 18-99. The N-terminal residues 1-17 of Drc and five amino acids that remained from the tag could not be resolved from the electron density maps. This region is predicted by ESpritz to be disordered (Supplementary Figure  S3 ), which could explain the weak electron density  (37). Although this part could not be resolved in the crystal structure, it cannot be excluded that the flexibility of this terminus is required for binding ssDNA and that it might adopt a more fixed conformation once the target is bound.
The electrostatic surface potential shows two positively charged patches on the helical rim, formed by ␣2 and ␣2 , and one on and near each concave surface. As was seen at the sequence level, Drc does not share structural similarity with the canonical OB-fold. Moreover, when comparing the model to known structures in the PDB database using the DALI and PDBeFold servers  (39, 40) , no significant matches with other structures can be found. Since Drc forms a dimer, which has larger ␤-sheets than the monomer, the search was broadened by using the dimeric form. This resulted in the retrieval of 19 hits with DALI (Supplementary Table  S1 ) and three hits with PDBeFOLD (Supplementary Table  S2 ), of which the top scoring include viral matrix proteins (seven occurrences in DALI), DNA topoisomerases of type IA (four occurrences in DALI) and cyclophilines (three occurrences in DALI, three in PBDe-FOLD).
The Drc ␤-sheets might perform similarly as they have multiple lysine and arginine residues alongside the surface with two aromatic residues on the central ␤-strand (Figure  5A ). The overlay of the Drc sheet with that PC4-fold protein of bacteriophage T5 (PDB ID: 4BG7) reveals several similarly placed aromatic and positively charged residues (Figure  5B ; e. g. R29/K85, W46/H27). The most prominent of these is Drc's Y23, which localizes approximate to Y40 of the PC4-fold. This tyrosine is one of the residues reported to be critical in binding ssDNA by PC4-folds  (46). This implies that Y23 of Drc could be involved in binding DNA, potentially through base stacking interactions. In addition, Y23 is flanked by positively charged residues that could bind the phosphate backbone. Two of these residues are arginines (R90 and R95) that are bound to a phosphate group in the crystal structure, which could reflect this backbone binding (Figure  5A ).
Drc reveals an unexpected SSB diversity in an otherwise conserved transcription scheme It is remarkable that a crucial process, which is conserved among most N4-like phages, is different in LUZ7. To see whether this is a distinct case or the usage of this SSB type has a broader occurrence, a phylogenetic tree was built based on a previously described dataset  (31)  which contains vRNAP sequences of various N4-like phages with representative phages for each currently classified genus (Figure  9 ). Members encoding a homolog of either Drc or N4 gp2 were then assigned based on a psiBLAST search (Supplementary Table  S3 )  (34). From this analysis, it becomes apparent that the Drc mediated transcription scheme is spread across all phages within the Luzseptimavirus and Litunavirus gen-era, but also the recently described and more distant group of Pectobacterium-infecting CB1-like phages  (31).
DATA AVAILABILITY ProtParam is a tool for the computation of physical and chemical parameters based on protein sequence and is available at https://web. expasy. org/protparam/. PDBePISA is an interactive tool for the exploration of macromolecular interfaces and is available at http://www. ebi. ac. uk/pdbe/pisa/. PDBeFOLD is the Protein structure comparison service at European Bioinformatics Institute and is available at http://www. ebi. ac. uk/msd-srv/ssm. ESpritz is a protein disorder prediction server available at http://protein. bio. unipd. it/espritz/. The Dali server is a network service for comparing protein structures in 3D available at http://ekhidna2. biocenter. helsinki. fi/dali/. PyMOL is a molecular visualization system available at https://pymol. org/2/. The PSI-BLAST suite allows for a position-specific iterated search in the protein databases using a protein query and is available at https://blast.
Statistical analysis For data from Fiji, where serial samples were collected from the same individual, changes in seroprevalence between studies were tested using McNemar's test. In French Polynesia, chi-squared tests were performed to test for evidence of a change in seroprevalence between two cross-sectional surveys. Changes in mean titre level between groups were analyzed using a t-test. All data and code used in the analysis are available at: https://github. com/a-henderson91/zika-sero-pacific. git.
ACKNOWLEDGMENTS We are grateful to the minister for education of French Polynesia and to the directors, teachers, nurses and schoolchildren from the elementary and junior high schools selected for the serosurvey in 2018. We greatly thank all the participants and community leaders in Fiji who generously contributed to the study over the three visits. We would like to acknowledge the work of the field teams:  Jessica Paka ,  Amele Ratevono ,  Warren Fong ,  Manisha Prakash ,  Jonetani Bola ,  Mosese Ligani , and  Taina Naivalu  (2017);  Meredani Taufa ,  Adi Kuini Kadi ,  Jokaveti Vubaya ,  Colin Michel ,  Mereani Koroi ,  Atu Vesikula , and  Josateki Raibevu  (2015);  Dr. Kitione Rawalai ,  Jeremaia Coriakula ,  Ilai Koro ,  Sala Ratulevu ,  Ala Salesi ,  Meredani Taufa , and  Leone Vunileba  (2013) , and supported by  Eric J Nilles  of the  World Health Organization Western Pacific Region.
This work was part of  R-ZERO Pacific  programs funded by the  French ministry for Europe and Foreign Affairs [Pacific Funds   N°04917-19/07/17 ]. The study also received support from the  French Government 's " Investissement d'Avenir" Program [Labex IBEID N°ANR-10-LABX-62-IBEID] , and the  Wellcome Trust  [Grant number  107778/Z/15/Z ]. Fiji 2013 fieldwork was funded by the  World Health Organization Western Pacific Region  and by the  Chadwick Trust. CHW was supported by the  UK Medical Research Council  ( MR/J003999/1 ). CLL was supported by an  Australian National Health and Medical Research Council (NHMRC)  Fellowship (1109035). ADH was supported by an  MRC   LID studentship  (grant Number  MR/N013638/1 ). AJK was supported by a  Wellcome Trust/Royal Society Sir Henry Dale Fellowship  (grant Number  206250/Z/17/Z ).
The data set was built through crowdsourcing, and the final scores are the average of 30 judgments per compound. This data set will be referred to as Reddy in our experiments. Farahmand, Smith, and Nivre (2015)  collected judgments for 1,042 English noun-noun compounds. Each compound has binary judgments regarding non-compositionality and conventionalization given by four expert annotators (both native and non-native speakers). A hard threshold is applied so that compounds are considered as noncompositional if at least two annotators say so  (Yazdani, Farahmand, and Henderson 2015) , and the total compositionality score is given by the sum of the four binary judgments. This data set will be referred to as Farahmand in our experiments.
Creation of a Multilingual Compositionality Data set In Section 3. 1, we describe the construction of data sets of 180 compounds for French (FR-comp) and Portuguese (PT-comp). For English, the complete data set contains 280 compounds, of which 190 are new and 90 come from the Reddy data set. We use 180 of these (EN-comp) for cross-lingual comparisons (90 from the original Reddy data set combined with 90 new ones from EN-comp 90 ), and 100 new compounds as held-out data (EN-comp Ext ), to evaluate the robustness of the results obtained (Section 6. 3). These data sets containing compositionality scores for 2-word nominal compounds are used to evaluate our framework (Section 4), and we discuss their characteristics in Section 3. 2. 8.
Data Collection For each of the target languages, we collected, via crowdsourcing, a set of numerical scores corresponding to the level of compositionality of the target nominal compounds. We asked non-expert participants to judge each compound considering three sentences where the compound occurred. After reading the sentences, participants assess the degree to which the meaning of the compound is related to the meanings of its parts. This follows from the assumption that a fully compositional compound will have an interpretation whose meaning stems from both words (e. g. , lime tree as a tree of limes), while a fully idiomatic compound will have a meaning that is unrelated to its components (e. g. , nut case as an eccentric person). Our work follows the protocol proposed by  Reddy, McCarthy, and Manandhar (2011) , where compositionality is explained in terms of the literality of the individual parts.
To qualify for the task, participants had to submit demographic information confirming that they are native speakers, and to undergo training in the form of four example questions with annotated answers in an external form (see Appendix C for details). Data Aggregation. For English and French, we collected answers using Amazon Mechanical Turk (AMT), manually removing answers that were not from native speakers or where the synonyms provided were unrelated to the target compound sense. Because AMT has few Brazilian Portuguese native speakers, we developed an in-house web interface for the questionnaire, which was sent out to Portuguese-speaking NLP mailing lists. For a given compound and question we calculate aggregated scores as the arithmetic averages of all answers across participants. We will refer to these averaged scores as the human compositionality score (hc)s.
Measuring Data set Quality. To assess the quality of the collected human compositionality scores, we use standard deviation and inter-annotator agreement scores. Standard Deviation ( σ and P σ>1. 5 ). The standard deviation (σ) of the participants' answers can be used as an indication of their agreement: for each compound and for each of the three questions, small σ values suggest greater agreement. In addition, if the instructions are clear, σ can also be seen as an indication of the level of difficulty of the task. In other words, all other things being equal, compounds with larger σ can be considered intrinsically harder to analyze by the participants. For each data set, we consider two aggregated metrics based on σ: • σ -The average of σ in the data set. • P σ>1. 5 -The proportion of compounds whose σ is higher than 1.
Evaluation Metrics To evaluate a compositionality prediction configuration, we calculate Spearman's ρ rank correlation between the predicted compositionality scores (pc)s and the human compositionality scores (hc)s for the compounds that appear in the evaluation data set. We mostly use the rank correlation instead of linear correlation (Pearson) because we are interested in the framework's ability to order compounds from least to most compositional, regardless of the actual predicted values. For English, besides the evaluation data sets presented in Section 3, we also use Reddy and Farahmand (see Section 2. 4) to enable comparison with related work. For Farahmand, since it contains binary judgments 33  instead of graded compositionality scores, results are reported using the best F 1 (BF 1 ) score, which is the highest F 1 score found using the top n compounds classified as noncompositional, when n is varied  (Yazdani, Farahmand, and Henderson 2015).
Overall Results In this section, we present the overall results obtained on the Reddy, Farahmand, ENcomp, FR-comp, and PT-comp data sets, comparing all possible configurations (Section 6. 1). To determine their robustness we also report evaluation for all languages using cross-validation (Section 6. 2) and for English using the held-out data set EN-comp Ext (Section 6. 3). All results reported in this section use the pc uniform function.
Distributional Semantic Models Table  4  shows the highest overall values obtained for each DSM (columns) on each data set (rows). For English  (Reddy, EN-comp, and Farahmand) , the highest results for the compounds found in the corpus were obtained with w2v and PPMI-thresh, shown as the first value in each pair in Table  4. Not all compounds in the English data sets are present in our corpus. Therefore, we also report results adopting a fallback strategy (the second value). Because its impact depends on the data set, and the relative performance of the models is similar with or without it, for the remainder of the article we discuss only the results without fallback. 34 he best w2v-cbow and w2v-sg configurations are not significantly different from each other, but both are different from PPMI-thresh (p < 0.
Evaluation on Held-Out Data As an additional test of the robustness of the results obtained, we calculated the performance of the best models obtained for one of the data sets (EN-comp), on a separate held-out data set (EN-comp Ext ). The latter contains 100 compounds balanced for compositionality, not included in EN-comp (that is, not used in any of the preceding experiments). The results obtained on EN-comp Ext are shown in Table  5. They are comparable and mostly better than those for the oracle and for cross-validation. As the items are different in the two data sets, a direct comparison of the results is not possible, but the equivalent performances confirm the robustness of the models and configurations for compositionality prediction. Moreover, these results are obtained in an unsupervised manner, as the compositionality scores are not used to train any of the models.
36 For PPMI-SVD with WINDOWSIZE=8+8, a context word at distance D from its target word is weighted 8-D 8. For glove, the decay happens much faster, with a weight of 8 D , which allows the model to look farther away without being affected by potential noise introduced by distant contexts. 37 Henceforth, we omit results for EN-comp 90 and Reddy, as they are included in EN-comp. To assess which of these differences are statistically significant, we have performed Wilcoxon's sign-rank test on the two highest Spearman values for each DSM in each language. All differences are statistically significant (p < 0. 05), with the exception of PPMI-SVD. The appropriate choice of window size has been shown to be task-specific  (Lapesa and Evert 2017) , and the results above suggest that, for compositionality prediction, it depends also on the DSM used.
Dimension When creating corpus-derived vectors with a DSM, the question is whether additional dimensions can be informative in compositionality prediction. Our hypothesis is that the larger the number of dimensions, the more precise the representations, and the more accurate the compositionality prediction. The results shown in Figure  6  for each of the comparable data sets confirm this trend in the case of the best DSMs: w2v and PPMI-thresh. Moreover, the effect of changing the vector dimensions for the best models seems to be consistent across these languages. The results for PPMI-SVD, lexvec, and glove are more varied, but they are never among.
Rank Improvement Analysis To better evaluate the effect of adjusting β for the individual compounds with respect to the pc uniform score, we define the rank improvement as: improv f (w 1 w 2 ) = |rk uniform (w 1 w 2 ) -rk human (w 1 w 2 )| -|rk f (w 1 w 2 ) -rk human (w 1 w 2 )|, where rk indicates the rank of the compound w 1 w 2 in the data set when ordered according to pc uniform , human annotations hc HM , or the compositionality prediction function f. For instance, when f = maxsim, positive improv maxsim values indicate that pc maxsim yields a better approximation of the ranks assigned by hc HM than pc uniform , whereas negative values indicate that pc uniform provides a better ranking. We perform a cross-lingual analysis, grouping the hc HM scores of the EN-comp, FR-comp, and PT-comp into a unique data set (henceforth ALL-comp), containing 540 compounds.
Characterization of the Predicted Compositionality In the previous sections, we examined the performance of the compositionality prediction framework in terms of the correlation between automatic predictions and human judgments across languages. We now investigate the relation between predicted scores and other variables that may have an impact on results, such as familiarity (Section 9. 1) and conventionalization (Section 9. 2). We also compare the predicted compositionality scores with trends previously found in human scores (Section 9. 3). The experiments focus on the ALL-comp data set, which groups the predicted scores from the best configurations on.
Acknowledgments This work has been partly funded by projects  PARSEME  (Cost Action  IC1207 ),  PARSEME-FR  ( ANR-14-CERA-0001 ),  AIM-WEST  ( FAPERGS-INRIA 1706-2551/ 13-7 ),  CNPq  ( 312114/2015-0, 423843/2016-8 ) " Simplificacão Textual de Express ões Complexas ," sponsored by  Samsung Eletr ônica da Amaz ônia Ltda. under the terms of Brazilian federal law No. 8. 248/91. We would like to thank the anonymous reviewers who provided numerous helpful suggestions,  Alexis Nasr  for reviewing earlier versions of this article,  Rodrigo Wilkens  and  Leonardo Zilio  for contributing to the data set creation, and all anonymous annotators who judged the compositionality of compounds.
The VIDAA platform initially proposes LAAO setting parameters estimated after a morphological analysis of the LAA under study. The clinician later can interactively vary device parameters in VIDAA and export outside the platform the most promising configurations. Computational Fluid Dynamics (CFD) simulations were then run with a commercial solver for several LAAO configurations extracted from VIDAA to estimate their corresponding risk of thrombus formation after implantation. As a first proof of concept, the VIDAA platform was tested on retrospective data of four non-valvular AF patients that underwent a LAA closure.
The clinician could manually modify several LAAO parameters to better understand the case under study and plan the intervention. Once a given LAAO configuration was considered appropriate by the clinician, the VIDAA platform exported all data (e. g. , meshes and their spatial relationship) that will define the geometrical domain where CFD simulations were run outside VIDAA with a commercial software. Simulation results were post-processed to derive in silico hemodynamics indices estimating the risk of thrombus formation for a given LAA and device setting. The VIDAA platform in combination with CFD simulations then allowed the clinician to decide, prior to the intervention, which device configuration was safer, in terms of thrombus formation.
23 or 0. 45 mm pixel size for 512 or 256 isotropic voxels, respectively. Before and during the injection, the right ventricle was burst paced at a cycle length of 200 ms during end-expiratory apnea to interrupt the blood flow through the LA. All datasets were segmented by an experienced operator on the Advantage Workstation (GE Healthcare) using the Innova 3D software to reconstruct the LA, which is based on threshold and region-growing semi-automatic segmentation techniques. The CT image at Hospital Clínic de Barcelona was acquired using a Siemens TM machine, synchronized with ECG and supplying 80 ml of contrast, having a volumetric pixel size of 0. 33 × 0. 33 × 0. 40 mm for an in-plane matrix of 512 pixels and 430 slices. The ITK-Snap 1  software  (Yushkevich et al. , 2006)  was used to semi-automatically segment the LA in the CT image (also with region growing and threshold-based techniques, plus manual corrections).
Mesh Processing Pipeline To ensure convergence of hemodynamics simulations, LA meshes from medical image segmentations required several preprocessing phases (see Figure  4 ). First, a Taubin smoothing filter (with scale factors λ = 0. 6, µ = -0. 53 and 10 iterations) was applied. Subsequently, cylinders representing the pulmonary veins (PV) and the mitral valve (MV) were manually inserted to the LA mesh using the Autodesk Meshmixer software 2  so that simulated flow was developed normal to the surface of the vessels. Volumetric meshes were built with a 3D Delaunay refinement algorithm available in the Gmsh software (default parameters)  (Geuzaine and Remacle, 2009)   The studied LAAO devices (see Figure  1 ) were built using a Computational-Aided Design (CAD) software 6  and following the on-line product manual and instructions from the companies  (Abbott, 2018; Boston Scientific, 2018).