dataesr/scientific-paragraphs-categorization huggingface dataset:
Curation of Position Weight Matrices (PWMs) All available position matrices were downloaded from the ATtRACT database (https: //attract. cnic. es/) (accessed on 2 February 2021) and from the RBPDB database (http: //rbpdb. ccbr. utoronto. ca/, version 1. 3. 1) accessed on 2 February 2021. The matrices were filtered to retain only matrices derived from competitive binding experiments using wildtype human RBPs. RBPDB matrices which were redundant with ATtRACT were removed. PWMs from ENCODE RNA Bind-n-Seq assays  [8]  were constructed using the ENCODE computational pipeline  [8]  and added to this list. For RBPDB, position frequency matrices (PFMs) were converted to Position Probability Matrices (PPMs) using the 'smeagol. matrices. pfm_to_ppm' function with a pseudocount of 0. 01. For ATtRACT, position probability matrices (PPMs) were downloaded directly.
Variant Effect Prediction We downloaded information on 36,688 SARS-CoV-2 mutations from the GESS database (https://wan-bioinfo. shinyapps. io/GESS/) accessed on 14 September 2021. We used the 'smeagol. variant. variant_effect_on_sites' function in SMEAGOL to predict the impact of each of these variants on the PWM match score in the surrounding region, for PWMs representing ten selected RBPs. This function selects mutations that intersect with the predicted binding sites of the selected RBPs and calculates the PWM match score of each predicted binding site using first the reference sequence and then the alternate sequence. We selected variants that reduce the PWM match score of a binding site to less than 0. 5 as potential site-disrupting variants.
Identification of Sequence Motif Enrichment/Depletion Using SMEAGOL SMEAGOL (https://github. com/gruber-sciencelab/SMEAGOL) is a Python library designed for comprehensive motif occurrence analysis in nucleic acid sequences using PWMs, which can represent the binding specificity of a variety of nucleic acid-interacting regulators. SMEAGOL can directly load PWMs that represent RBP binding specificities from the ATtRACT and RBPDB databases  [11, 12]. As curated databases of RBP binding motifs typically contain PWMs of different quality, SMEAGOL also includes modules to analyse, filter, compare, cluster, and visualize PWMs. Moreover, SMEAGOL enables scanning of sequences with the curated PWMs and filtering of these results. Post-processing modules enable the calculation and visualization of statistical enrichment or depletion of sequence motifs, as well as predicted effects of sequence variants on PWM sites.
The Genomes of ssRNA Viruses Show Evidence of Selection of RBP Binding Sites To find out which RBPs are enriched/depleted in binding sites across ssRNA virus genomes, we have used PWMs representing the experimentally determined sequence binding preferences of human RBPs from the ATtRACT  [12] , RBPDB  [11] , and ENCODE  [8]  databases. We have used SMEAGOL to filter and curate these motifs (Methods, Supplementary Figure  S1 ) and obtained a curated set of 362 PWMs representing the binding specificities of 146 human RBPs. We then used this set of PWMs and SMEAGOL to scan the complete genome sequences of 197 ssRNA viruses belonging to 19 families (Table  1 , Supplementary Data S1) to identify putative RBP binding sites (Supplementary Data S2). Total 102 94 * 1 : These two segmented genomes belong to the jingmenviruses tentatively classified in the Flaviviridae family  [13].
To find evidence of evolutionary selection for or against RBP binding to viral RNA, we determined the enrichment or depletion of RBP binding motifs in each viral genome compared to dinucleotide-randomized versions of the genome (Supplementary Data S3). It should be noted that, although the viruses in our study have single-stranded genomes, the complementary strand of the genome is also synthesized during the viral life cycle. We therefore repeated this procedure for the complementary strand of each genome. Within the (+)ssRNA (Group IV) virus genomes, we found that the number of motifs enriched or depleted varied significantly between families (Figure  2a ). While some RBP binding motifs are generally depleted or enriched in (+)ssRNA virus genomes, other motifs are much more specific to a subset of viruses.
For instance, G-rich motifs recognized by several splicing factors (GRSF1, HNRNPH1, HNRNPH2, HNRNPH3, HNRNPF, HN-RNPA2B1) are frequently depleted in the plus-strand of (+)ssRNA virus genomes across multiple families (Figure  2b  and Supplementary Data S3). The results for Group V viruses can be found in Supplementary Figure  S5  and a comprehensive figure that contains all representative PWMs is provided as Supplementary Figure  S6. On the other hand, the motif for RBMX is enriched on the negative sense molecule in 24 genomes belonging to three Group IV families. For example, all four Dengue virus (DENV) genomes in our dataset showed enrichment in the negative sense molecule for RBMX (Supplementary Data S2). Consistently, RBMX was reported to be required for efficient amplification of DENV and its knockdown significantly decreases the titre of DENV  [14].
The second most commonly enriched motif was related to the translation factor EIF4B, specifically in the positive strand of (+)ssRNA viruses. EIF4B was reported to bind to DENV RNA [15] and its depletion reduced the efficiency of translation initiation in Zika virus (ZIKV)  [16]. All DENV and ZIKV genomes in our dataset had significant enrichment for this host factor as did most flaviviruses (Figure  3 ). For readability, shown are only representative PWMs that had more than three significant enrichment/depletion events. The PWM sequence bias (see Methods) is presented on the bottom of the plot. While some PWMs have a more complex sequence (light red), others are rich in single nucleotides (A-rich in navy blue, AU-rich in purple, U-rich in yellow, and G-rich in orange). The results for Group V viruses can be found in Supplementary Figure  S5  and a comprehensive figure that contains all representative PWMs is provided as Supplementary Figure  S6.
On the other hand, the motif for RBMX is enriched on the negative sense molecule in 24 genomes belonging to three Group IV families. For example, all four Dengue virus (DENV) genomes in our dataset showed enrichment in the negative sense molecule for RBMX (Supplementary Data S2). Consistently, RBMX was reported to be required for efficient amplification of DENV and its knockdown significantly decreases the titre of DENV  [14]. The second most commonly enriched motif was related to the translation factor EIF4B, specifically in the positive strand of (+)ssRNA viruses. EIF4B was reported to bind to DENV RNA [15] and its depletion reduced the efficiency of translation initiation in Zika virus (ZIKV)  [16]. All DENV and ZIKV genomes in our dataset had significant enrichment for this host factor as did most flaviviruses (Figure  3 ).
S7 ). For a full list of motifs enriched and depleted in all viral genomes, see Supplementary Data S3. Having observed significant differences between viral families, we next examined prominent families individually. Coronaviruses, which have the longest genomes of all viruses in our dataset (Supplementary Figure  S2 , one-sided Wilcoxon rank-sum test U statistic = 1536, p = 8. 5 × 10 -7 ), also show more enrichment and depletion of binding motifs than any other family (Figure  2a ). Strikingly, the number of RBP motifs depleted on the plus strand is much higher than the number of enriched motifs. It is conceivable that given their long genomes, coronaviruses must actively prevent being bound by non-beneficial host RBPs. On examination, we found that the striking number of depleted motifs in these genomes reflects depletion of U-rich elements (UREs) bound by RBPs such as HNRNPC, RALY, CELF2, TIA1, ELAVL1 and PPIE (Figure  3 ).
This is despite coronaviruses being the most U-rich of all viruses in our dataset (Figure  3 , Supplementary Figure  S3 , onesided Wilcoxon rank-sum test U statistic = 1530, p = 5. 4 × 10 -13 ). By contrast, a subset of flaviviruses, which are relatively poor in uridines, are enriched for these UREs (Figure  3 , Supplementary Figure  S3 ). In addition to the depletion of UREs and G-rich elements, all eight coronaviruses in our dataset showed enrichment of motifs for the SRSF1 splicing factor and slight depletion for a PUM1-associated motif. Interestingly, the PUM1 RBP was reported to reduce mRNA stability  [17]. Regarding the Group V viruses, it is important to note that these viral genomes do not exist as naked RNA, as they encode nucleoproteins which encapsidate the entirety of the (+) and (-) strand molecules to form stable ribonucleoprotein complexes  [18].
Motif Enrichment Expands upon Functional Studies in the SARS-CoV-2 Genome The enrichment and depletion of RBP motifs in the SARS-CoV-2 genome is largely like that of the other coronaviruses in our dataset. However, it is unique in having strong enrichment for binding motifs of YBX1 (motif 's54', Supplementary Data S3). Like other coronaviruses, more RBPs were enriched and depleted (18 and 32 respectively) in the positive sense (d) A U-rich element (URE) is located within the 3 UTR of the HCV genome  [32]. (e) RBPs predicted by SMEAGOL to be significantly enriched/depleted in binding sites within the SARS-CoV-2 genome. URE-binding RBPs are shown to be most strongly depleted. (f) Using the local window enrichment function of SMEAGOL, we observed that binding motifs of RALY are absent in most of the SARS-CoV-2 genome but enriched in a specific region (Two-sided Fisher's exact test, odds = 36.
Motif Enrichment Expands upon Functional Studies in the SARS-CoV-2 Genome The enrichment and depletion of RBP motifs in the SARS-CoV-2 genome is largely like that of the other coronaviruses in our dataset. However, it is unique in having strong enrichment for binding motifs of YBX1 (motif 's54', Supplementary Data S3). Like other coronaviruses, more RBPs were enriched and depleted (18 and 32 respectively) in the positive sense genome sequence compared to the negative sense intermediates (14 and 13 respectively), suggesting that more functional interactions happen with the positive sense molecule. The observation that overall depletion is more common than enrichment suggests that SARS-CoV-2 has more antiviral interactions with human RBPs than pro-viral interactions. This prediction is consistent with experimental observations from CRISPR screens  [2].
PWMs for 17 known antiviral and four known pro-viral RBPs were included in our dataset. While we did not observe motif enrichment or depletion for the pro-viral proteins, motifs for four of the 17 antiviral proteins (RALY, ELAVL1, FUBP3, PCBP2) were depleted in the SARS-CoV-2 genome, whereas only two were enriched, suggesting that the viral genome may have evolved to avoid interaction with defensive host proteins, where possible. Motifs for an additional four antiviral RBPs (HNRNPA2B1, DAZAP1, TARDBP, PPIE) were also depleted at a more permissive FDR-adjusted p-value threshold of 0. 1. Out of these, RALY, ELAVL1, FUBP3 and PPIE bind to UREs. Interestingly, although the predicted binding sites for numerous URE-binding RBPs are strongly depleted overall in the SARS-CoV-2 genome (Figure  4e ), the few binding sites that are predicted are significantly concentrated within a region in the NSP6 gene.
SMEAGOL Offers Functionality to Predict Sequence Mutation Effects on RBP Binding Because computational analysis allows us to predict the probable locations of protein binding on the viral genome, it also offers the possibility of predicting how mutations may affect these binding sites. To demonstrate this functionality, we selected ten RBPs that (1) have PWMs in our dataset,  (2)  are experimentally determined to be antiviral in SARS-CoV-2 infection and/or are shown by our analysis to be significantly depleted in the SARS-CoV-2 genome, and (3) have fewer than ten predicted binding sites on the SARS-CoV-2 genome. These are CPEB4, RALY, ELAVL1, HNRNPA1, RBFOX1, HNRNPK, HNRNPA2B1, DAZAP1, SRSF7 and PCBP2. We hypothesized that mutations that disrupt the binding sites of these RBPs may enable SARS-CoV-2 to escape host antiviral defences.
Using SMEAGOL to analyse a database of SARS-CoV-2 mutations  [36] , we identified 170 mutations that are predicted to disrupt motifs for the selected RBPs (Supplementary Data S7). Interestingly, this list includes 22 non-exonic and 60 synonymous mutations. As an example, the T11078C (nsp6:p. F36L) mutation, one of the lineage-determining mutations in the N. 9 Variant of Interest found in Brazil  [37] , is predicted to disrupt binding of RALY, ELAVL1, and CPEB4 (Supplementary Figure  S8 ) to the URE at position 11074 (Figure  4f ). As discussed above, this URE is one of very few regions predicted to bind to the known antiviral RBPs RALY and ELAVL1, as well as CPEB4 which is validated to bind to the SARS-CoV-2 genome. Interestingly, the much less common T > G mutation at the same position is predicted to have a lesser effect on RBP binding (Supplementary Figure  S8 ).
Discussion There exist several web servers  [38]  and libraries  [10, [39] [40] [41]  to scan nucleic acid sequences with PWMs and identify putative binding sites. Several tools  [38, 41]  also calculate a p-value for motif enrichment that takes into account the nucleotide composition of the sequence. With SMEAGOL, we aim to provide a unified python-based framework for visualization, analysis, and clustering of PWMs, binding site discovery, variant effect prediction on RBP binding, and binding site enrichment/depletion calculations using a background model that incorporates k-mer shuffling as specified by the user. For instance, dinucleotide shuffling is considered to be more conservative compared to mononucleotide shuffling, as it better accounts for RNA structural features and genomic biases in the occurrence of dinucleotides.
In contrast, CRISPR knockout of ELAVL1 sensitized VeroE6 cells to SARS-CoV-2 infection  [35] , indicating an antiviral effect of the RBP against SARS-CoV-2, though the mechanism is unclear. We previously published an analysis of motif enrichment in the SARS-CoV-2 genome using a similar procedure  [43]. Here, we improve upon the previous findings with a more rigorous statistical procedure including dinucleotide shuffling, using an expanded dataset of PWMs, and by placing the results in the context of other Coronavirus genomes and more recent functional studies. Our analysis supports the observation that SARS-CoV-2 is more likely to form antiviral interactions with RBPs than pro-viral ones. Further, we find depletion of motifs for several known antiviral RBPs on the SARS-CoV-2 genome. We extend functional studies by providing binding site predictions for known pro-viral and antiviral RBPs as well as predicting putative interactions.
For example, it is interesting that SARS-CoV-2, unlike other coronaviruses, has strong enrichment for binding motifs of YBX1, which has been experimentally validated to bind to SARS-CoV-2 RNA  [2]  and supports infection by other viruses, including Dengue Virus  [44] , Influenza  [45] , and HIV  [46]. Finally, we predict which mutations may disrupt the binding sites of known antiviral proteins. While mutations affecting SARS-CoV-2 protein sequences have been extensively studied, the effects of other classes of mutations are less clear. SMEAGOL supports the creation of testable hypotheses on RBP-virus interactions and helps to prioritize non-coding and synonymous mutations for further investigations. Our dataset of predicted RBP-virus interactions is available (Supplementary Datas S3 and S4) along with our software.
Acknowledgments: The authors thank  Mihaela Zavolan  for many constructive discussions and ideas that greatly improved this work. The authors acknowledge the use of de. NBI cloud and the support by the  High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen  and the  Federal Ministry of Education and Research (BMBF)  through grant no  031 A535A , whereas special thanks go to  Fabian Paz ,  Maximilian Hanussek  and  Jens Krüger  for their help and great support with the computing environment.
Data Availability Statement: All results generated in this study are available in the supplementary materials. Accession numbers for external datasets used in this study are given in Supplementary Data S1. Our curated lists of PWMs (both full and representative sets) and our scripts are available online https://github. com/gruber-sciencelab/VirusHostInteractionAtlas (accessed on 18 June 2022). Figures 2a,b, 3 and 4b,e are based on raw data in Supplementary Data S3. Figure 4a is based on raw data in Supplementary Data S4. Figure 4c,f are based on raw data in Supplementary Data S2. SMEAGOL is available online https://github. com/gruber-sciencelab/SMEAGOL (accessed on 18 June 2022) under an MIT open-source license. Its documentation is available online https://grubersciencelab. github. io/SMEAGOL/ (accessed on 18 June 2022) and an introductory tutorial is available online https://github.
Statistical analysis GraphPad Prism 8. 0 (GraphPad Software) was used to perform all the statistical analyses. Distribution of the data samples were tested with d'Agostino and Pearson tests. Normally and non-normally distributed data were compared using a two-tailed t-test and Mann-Whitney rank test, respectively. Two-way analysis of variance was used for analysis with two different factors (number of APP accumulations and patient groups). Differences between groups were considered statistically significant when P <. 05.
μm outside using the ROI manager function and the gray value distribution histogram. DAB stainings were used to count APP accumulations with an automated macro that was segmenting, counting, and measuring the size of each accumulation in different regions of the brain section (https://github. com/fabricecordelieres/IJ-Macro_APP-Plaques-NanoZoomer). Sholl analysis 65 was used to measure the area and number of APP accumulations around amyloid plaques labeled for Aβ42 (https://github. com/fabricecordelieres/IJ-Macro_APP-Aggregates-Per-Plaques-NanoZoomer). 3. 1. 8 Electron microscopy-ultrastructure studies Electron microscopy was performed on samples from the cortex of two wild-type (WT) and three APP/PS1 mice fixed in 2. 5% paraformaldehyde/0. 1% glutaraldehyde. Brain sections (250 μm) were permeabilized using sodium borohydride 1% (Sigma Aldrich #452882) at RT for 30 minutes before immunostaining.
ACKNOWLEDGMENTS We thank  Takashi Saito ,  Takaomi C. Saido  from  RIKEN Brain Institute (Saitama, Japan ), and  Per Nilsson  ( Karolinska Institute, Stockholm ) for the gift of APP-NL/G/F brain slices. The post mortem human brain tissue for this study was supplied by the  Netherlands Brain Bank  ( Amsterdam, the Netherlands ) and the  Karolinska Institute Brain Bank (Stockholm, Sweden) ; we thank all the donors for the tissue used in this study. We thank  Caroline Graff  from the  Karolinska Institute Brain Bank. We thank  Juan Pita-Almenar  from  Janssen Pharmaceutica  for the gift of JRD32 antibody. Imaging was performed at the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University, member of the national infrastructure France BioImaging  ( ANR-10-INBS-04 ).
tion and 15 15 μm outside using the ROI manager function and the gray value distribution histogram. DAB stainings were used to count APP accumulations with an automated macro that was segmenting, counting, and measuring the size of each accumulation in different regions of the brain section (https://github. com/fabricecordelieres/IJ-Macro_APP-Plaques-NanoZoomer). Sholl analysis 65 was used to measure the area and number of APP accumulations around amyloid plaques labeled for Aβ42 (https://github. com/fabricecordelieres/IJ-Macro_APP-Aggregates-Per-Plaques-NanoZoomer). 3. 1. 8 Electron microscopy-ultrastructure studies Electron microscopy was performed on samples from the cortex of two wild-type (WT) and three APP/PS1 mice fixed in 2. 5% paraformaldehyde/0. 1% glutaraldehyde. Brain sections (250 μm) were permeabilized using sodium borohydride 1% (Sigma Aldrich #452882) at RT for 30 minutes before immunostaining.
SEGOMOE fully described in  [8]  has been successful applied for different applications: aerodynamic shape optimization  [10] , nacelle optimization  [9] , overall aircraft configurations  [11] , as some industrial test case in collaboration with Bombardier  [10]. Some recent developments have been made to consider highly non-linear constraints  [11]  or mixed integer variables  [12]. The newest capability concerns multi-objective described in  [12] From few initial points, an adaptive process is used to add some Pareto-optimal points chosen from an acquisition function that assigns a maximum value to the points having a high probability of improving our knowledge of the objectives and respecting the constraints in the probably optimal areas. Then, after some iterations, final surrogate models are trained on the enriched database and an evolutionary algorithm such as NSGA-II  [13]  is applied to obtain the Pareto front.
HTP components N° Components Materials & Processes First for the 4-objectives, the Pareto front can be highlighted and Figure  8  presents both the database and the Pareto front composed of 8 points. Each subfigure of Figure  8  illustrates a couple of two objectives chosen among the four. As the Quality has to be maximized, its opposite is minimized and represented in the figure. Once assessed the true Pareto front, the optimization process can be performed. Details on both MDO problems are reported in Table  6  and Table 7. Particularly, in the tables, the quantity indicates the number of variables characterizing the problem. Each quantity is estimated considering the values that can assume the design variable (reported in Table  2 ) and the number of levels, which represents the number of production and assembly sites (see  Table 5).
Total design variables 26 Defined the MDO features, for each problem, an initial DOE of 40 points is created with a Latin Hypercube Sampling (LHS) approach and 160 iterations are run. Figure  10  presents the comparison between the predicted Pareto front computed by the optimization algorithm (20 points) and the true Pareto front from the database (8 points) as described above. As it is obtained through the predictions of the surrogate models, the points belonging to the predicted Pareto front are not exactly located on the True Pareto front. Therefore, a last step to be performed should be to evaluate the true values of the predicted Pareto front and then to filter it by extracting the Pareto optimal points among this evaluated front. From an initial set of 20 points, only 8 points are kept after the filtering process.
Definition of subcellular F-actin signal regions in border cells We established a three-step semi-automatic method to discriminate subcellular actin network regions in border cells, including the inner cell-to-cell contacts, the peripheral cables and protrusions of border cells. This detailed information of this method based on Matlab software was as follows: 1. Defining the border cell edge feature by CellGeo analysis: CellGeo method has been established for the identification of cell edge feature  65. The detailed processing by CellGeo method has been described previously  65. Here, we used the 3D reconstructed images of border cell group expressing LifeAct-FP for the processing by CellGeo method, which thus allowed us to precisely define the protrusions and main body region of border cell group, as shown in Extended Data Fig.