dataesr/scientific-paragraphs-categorization huggingface dataset:
Interpretable predictions Our proposed object detector proved effective in identifying inconsistencies within MSAs to assess their accuracy. However, the obtained predictions by the model, while valuable, are limited to the visual representation of the MSAs and cannot be directly exploited by bioinformaticians. To this end, we designed a program to parse all predicted errors for a given MSA into a more accessible format. The program takes YOLO predictions as input and outputs a text file which contains all identified inconsistencies with their corresponding position coordinates (i. e. the affected sequence, and start and end columns of the detected error). This information allows researchers to analyze an MSA with commonly used MSA visualization tools such as Jalview  [26].
Acknowledgements The authors would like to thank the  BiGEst bioinformatics platform  for technical support. This work was supported by the  French Infrastructure Institut Français de Bioinformatique (IFB)   ANR-11-INBS-0013 ,  ANR ArtIC   ANR-20-THIA-0006  and Institute funds from the  French Centre National de la Recherche Scientifique  and the  University of Strasbourg.
Introduction Methods for phylogenetic inference based on genomic rearrangements have been developed and refined over the past several decades  (Moret et al. , 2013) , but the majority of biologists continue to rely primarily on phylogenetic inference methods based on nucleotide substitutions. As a major evolutionary mechanism, genomic structural variation cannot be neglected, yet the application of gene-order methods has been limited to specific cases of well-assembled eukaryotic genomes (e. g. Feng et al. , 2017; Pevzner and Tesler, 2003) , as well as to small plastid genomes  (Moret et al. , 2002). For example, the tool CREx is currently the standard software for analyzing plastid gene order evolution  (Bernt et al. , 2007). The widespread application of these methods to larger genomes, however, has faced many challenges.
Thus, unless compromises are made that resolve duplicated segments beforehand, the first two approaches are the only types of method that are practical for datasets of even moderate size. Several algorithms exist from both categories, and the relative accuracy of these methods has been the subject of study (e. g. Biller et al. , 2016; Lin et al. , 2011). Regardless of which one is used, a more prosaic question is that of preparing the input to these methods. Although there are exceptions  (Doerr et al. , 2018) , the input to rearrangement phylogeny reconstruction consists of a set of homologous blocks of nucleotide sequence, their homology assignments within and across genomes, and their relative positions and directions along the genomes. Clearly, there are many ways to define such blocks  (Ghiurcuta and Moret, 2014) , and detection of homology is far from trivial.
Genome assemblies Genome assemblies made available by  Modlin et al. (2020)  are used. These include 85 isolates resequenced from the set collected by the Global Consortium for Drug-resistant Tuberculosis Diagnostics (GCDD)  (Hillery et al. , 2014)  (NCBI Bioproject PRJNA555636), 7 isolates from  Berney et al. (2015)  (NCBI Bioproject PRJEB8783), reference strains H37Rv (NCBI Bioproject PRJNA555636), H37Ra (NCBI accession NZ_CP016972. 1) and Mycobacterium canettii (NCBI accession NC_019951. 1). All sequencing data used for the assemblies, except the M. canettii assembly, are Pacific Biosciences SMRT sequencing reads. The assembly protocol of  Modlin et al. (2020)  was based on HGAP2  (Chin et al. , 2013)  or, if that failed to produce a single contig, Canu  (Koren et al. , 2017). The contigs were circularized using minimus2 (http://amos.
sourceforge. net/) or circlator  (Hunt et al. , 2015) , followed by iterative assembly consensus polishing with Quiver  (Chin et al. , 2013). To validate our assemblies, we used structural variant detection method PBHoney  (English et al. , 2014) , which detects irregularities such as softclippings in the alignment of reads to a reference. We applied PBHoney to the reads' alignment to the assembly that was generated from them, so any structural variant detected by PBHoney would in fact be a candidate misassembly. None of the genomes used here had any misassemblies detected. Lineages were identified using TB-profiler  (Phelan et al. , 2019 ) version 4. 1. 1 with database version 2022-01-25.
These arbitrary thresholds clearly have the potential to impact results. Thus, we also ran the annotation pipeline with relaxed thresholds for the orthology mapping: 75% minimum identity and 66% minimum alignment coverage. These results are subsequently referred to as 'annotationrelaxed'. Note that due to the low divergence of sequences in our dataset, the use of CD-HIT is justified as it is designed for finding similarity at 60% or higher  (Chen et al. , 2016).
This maximum-likelihood phylogenetic tree inferred using the GTRCAT substitutions model from concatenated variable sites with respect to the reference strain H37Rv (NC_000962. 3) shows the separation of our Mtb isolate set into 7 of the defined lineages and the level of diversity between them. This includes 3 Mycobacterium africanum isolates (lineages 5 and 6). Mycobacterium canettii is used as the outgroup distance matrix computed using Mash  (Ondov et al. , 2016)  using the maximum k-mer size (-k) of 32 due to the clonality of the dataset, and a sketch size (-s) of 1 billion, and phylogenetically corrected using the  Jukes and Cantor (1969)  (JC) model. We also used as a guide tree an adjacency-based tree (Section 2. 4) inferred using synteny blocks computed from an alternative method, SibeliaZ-LCB, described in the next section.
The guide tree used is denoted in parentheses if it is not the SNP tree. For example, Cactus(Mash) refers to the Cactus alignment based on the Mash guide tree. SibeliaZ  (Minkin and Medvedev, 2020 ) is a whole-genome alignment method for closely-related genomes based on de Bruijn graph analysis. It was run with graph order (k-mer length) set to 15, the developer recommended value for bacterial datasets, and two different vertex frequency thresholds. To reduce the de Bruijn graph complexity, SibeliaZ removes k-mers that appear more frequently than the vertex frequency threshold. One of our runs used the default value of 150, and a second run (which we refer to as SibeliaZ-LCB-highVF) was performed using a documentation-guided setting of 8924 ¼ 97 (number of genomes) Â 23 [maximum multiplicity of any one gene from the annotation (Table  1 )] Â 4 (scaling factor).
Building synteny blocks Cactus and SibeliaZ-LCB each produce a collection of sets of alignable genomic intervals that are inferred to be homologous, called alignment blocks. We used a custom script (https://gitlab. com/ LPCDRP/syntement) to formulate these alignment blocks as synteny block permutations for direct input into tree inference, as well as for formulating genes from the annotation output as such. The WGA blocks can be very short, which can lead to two problems: (i) the running time of tree inference methods increases rapidly with the number of blocks and especially the number of duplicates, and (ii) blocks that are very small may not represent true homology. A potential solution is maf2synteny  (Kolmogorov et al. , 2018) , a program that can combine multiple adjacent small blocks into larger blocks if they are consistently syntenic across genomes.
Evaluations Because we analyze a real dataset, all trees in this study are inferred from the data. To use as a reference, we inferred trees using substitution models (Section 2. 4). Clearly, there is no guarantee that these trees represent the true evolutionary history, and we invite readers to keep this point in mind when interpreting results. Nevertheless, when a method shows more similarity to the SNP tree, especially among highly supported branches, we can interpret this similarity as combined evidence for a branch in the true history. One caveat is the use of SNP tree as the guide tree for Cactus, which will be explored. For each pair of fully resolved trees, we compare the trees using the normalized  (Robinson and Foulds, 1981 ) (RF) distance and matching split distance  (Bogdanowicz and Giaro, 2012 ) (MS) metrics, computed using TreeCmp  (Bogdanowicz et al.
Phylogenetic tree inference Reference SNP tree: SNPs were called with respect to the reference H37Rv (NCBI accession NC_000962. 3) using show-snps from the MUMmer package  (Kurtz et al. , 2004) , then concatenated into a PHYLIP-formatted alignment. From this, RAxML  (Stamatakis, 2014)  was used to create a maximum-likelihood tree using the GTRCAT model with Felsenstein ascertainment bias correction using the count of sites in the reference genome where no SNP was observed, and bootstrapping with 100 replicates. Rearrangement-based trees: For each of the block assignments specified in Section 2. 2, we test both an adjacency-based and a distance-based method. Among adjacency-based methods, we used a tree inferred using MLGO  (Hu et al. , 2014)  phylogenetic tree reconstruction with bootstrapping on 100 replicates.
, 2012b) , but is not, to our knowledge, available as a software package), we compare the distance trees only to the fully resolved adjacency trees using MS and RF metrics. Overall, the distancebased and adjacency-based trees inferred from the same blocks were most similar to each other (Fig. 6 ), making the underlying synteny blocks the largest factor in the agreement of the trees. For example, the highest RF distance between any pair of methods was between Cactus(SNP)þmaf2syntenyþMLWD and annotationþMLWD methods at 56% (Supplementary Fig. S5 ). In contrast, distances between trees A B Fig. 4. (A) Adjacency tree produced from filtered Cactus synteny blocks. (B) Combination of compatible, highly supported branches from the Cactus adjacency tree and SNP tree. Since the two trees were fully compatible for branches with ! 75% bootstrap support, they are easily combined to form this tree with greater resolution.
Discussion As inferring phylogenies based on large-scale mutations becomes increasingly more feasible in terms of data availability, many questions about the best practices remain unanswered. Before these reconstruction methods are more broadly adopted, we need more empirical analyses that guide the practitioners in building robust and reliable analysis pipelines. In this article, we took a step in that direction. Using a dataset of high-quality Mtb genomes, we interrogated the robustness of methods used for preparing the syntenic blocks, which form the input to methods that infer phylogenies using rearrangements. The space of possible methods for obtaining input to rearrangement phylogenies is wide and this study is necessarily incomplete. While we have made an effort to represent the most modern WGAbased strategies for block assignment, many alternatives exist and future work should explore them.
While we considered two sets of parameters for orthology assignment of the annotation blocks, we did not consider alternative assignment methods  (Linard et al. , 2021) , and the full phylogenetic impact of the different methods for annotation, orthology detection and post-processing of orthology, remains a daunting task. Additionally, we limited our study to the use of maf2synteny for the agglomeration of basic homology statements into syntenic blocks. Other methods such as i-ADHoRe3. 0 (Proost et al. , 2012)  and Cyntenator (Ro ¨delsperger and Dieterich, 2010) exist for this purpose. Unfortunately, we were unsuccessful in running each of these on our dataset. i-ADHoRe3. 0 has an array of parameters and, using the recommended values, we received empty output. Running Cyntenator with default parameters produced only 2 synteny blocks.
g. Drillon et al. , 2020; Zhao et al. , 2021). While comparing these methods is beyond the scope of this work, it seems reasonable to expect that similarly to the two methods that we did test, they would also show sensitivity to block formation strategies. It should also be noted that our dataset consists of primarily Mtb isolates, chosen partially because the vertical evolution of Mtb is advantageous for our purposes. Thus, our conclusions may be specific to closely-related or microbial genomes without many horizontal events. Alignment difficulty is clearly a function of divergence, and as the divergence levels increase, various aligners may be impacted unevenly. In fact, it is possible that, for very divergent species, annotation works better than alignment. Moreover, the choice of best parameters for aligners and annotators will likely depend on the divergence levels.
Thus, additional future studies on more datasets are needed to test whether the patterns observed here are replicated across longer evolutionary times and more complex evolutionary scenarios. Finally, method evaluation on real data, while free of concern about realism of the data, is complicated by a lack of access to the ground truth. Realistic genome simulation remains challenging; options that do exist tend to have important shortcomings such as lack of user support, bugs, and limited features. We require a method that supports genome-scale rearrangement as well as genescale substitutions, a model for intergenic nucleotide evolution, and the specification of a real genome at the root of the simulated species tree. Most existing methods lack some of these features. For example, the program ALF  (Dalquen et al.
, 2011)  supports genomescale rearrangement, substitutions, and specification of a root genome, but does not model intergenic nucleotide evolution. As far as we know, Evolver (https://www. drive5. com/evolver) is the only option that has the complete feature set, but the software is complex to set up, was designed specifically for eukaryotic genomes, and is no longer supported by its authors. Once improved methods for genome simulation are available, repeating our analyses will allow a more direct measurement of accuracy, at every step of the pipeline.
, 2008). Beyond parameters, our results provide reasons to prefer some methods versus others. Compared to using gene annotations, the alignment-based methods showed superior compatibility with the reference SNP tree and with each other. In contrast, the annotation-based tree has numerous incompatibilities, even among branches with 100% bootstrap support, with the SNP tree. The large number of blocks in the annotation input is cause for concern, and not greatly improved by further aggregation. It appears that annotation pipelines, even run with relaxed settings, fail to make complete orthology calls in our dataset. Such failures to find orthology can easily lead to inconsistent adjacencies across genomes, and perhaps high support for wrong branches. Thus, our results support the use of WGA.
Data Acquisition Protocol Our private dataset was acquired at Broca Hospital in Paris, in the framework of the ALWRITE project  [25, 26] , a French research project on handwriting analysis for early AD assessment. This study was approved by the Ethics Committee "Ile-de-France III" of APHP (Assistance Publique Hôpitaux de Paris). All participants freely signed a consent form after receiving information on the study. Our database contains one spiral for each of the 75 participants captured in one session, namely 30 early-stage AD patients and 45 HC subjects with a mean age of 80. 2 ± 8. 8 and 73. 5 ± 6. 1, respectively. AD patients were diagnosed based on DSM-5 criteria  [27]  and considered as having early-stage AD if their MMSE was over 20. On the other hand, HC performed neuropsychological tests to ensure their cognitive profile is normal.
In each sampling, we train the SVM on 20 HC and 20 AD and test on the remaining 10 HC and 10 AD. Average performance is given in terms of accuracy, specificity (percentage of HC well classified), and sensitivity (percentage of AD patients well classified), considering the 10 samplings, along with their standard deviations. We applied a grid search optimization of the SVM parameters by performing a 5-fold cross-validation of the training data (each sampling of 20 HC and 20 AD). The best configu-ration on average on the validation dataset (one-fifth of 20 HC and 20 AD) is used to assess the SVM performance on the test dataset (the remaining 10 HC and 10 AD). In order to enhance the discrimination between HC and AD populations, we combine the decisions of different SVM classifiers by majority voting; note that each SVM is an expert of a specific type of dynamic information.
Data Availability Statement: We conducted the study on a private dataset acquired at Broca Hospital in Paris, in the framework of the ALWRITE project, which is a French research project on handwriting analysis for early AD assessment. This study was approved by the Ethics Committee "Ile-de-France III" of APHP (Assistance Publique Hôpitaux de Paris). All participants freely signed a consent form after receiving information on the study. There are legal and ethical restrictions on sharing these data. We did not have permission to share these data publicly.
Many SMIs use both the label in the beginning of the caption and add a label in form of a hashtag at the end of the caption. For our sample, SMIs labeled sponsored posts either in German (e. g. , Werbung) and/or in English (e. g. , advert). Results also showed that SMIs care about being transparent and honest with their followers about their sponsorship and label their posts properly. We collected and combined all labels from the three prestudies to build a sponsorship labels list. To identify the sponsored posts in the data set, we used the labels reported in Table  1. We used a textanalytical approach to reliably quantify whether a post was sponsored in the caption. We performed word detection to identify whether the caption contained one of the sponsorship labels in the list. To ensure that the list does correctly detect sponsored posts in our sample, we performed a manual face validity check of a subsample of 2,000 sponsored posts and NSPs each to check and control the accuracy of the identification of the type of post.
Finally, we analyzed 64,438 Instagram posts. To achieve this breadth of Instagram data, we had to accept some limitations in our data and empirical setup. Some posts included incomplete information due to data extraction, data privacy, and security. Nonetheless, we carefully prepared, cleaned, and tested the data to provide a reliable data set of highest quality. We demonstrated the reliability of the measurements and robustness of the abovementioned effects. While we are confident that the results remain unaffected, a small risk of unobserved heterogeneity persists.
If this assumption is correct, then with sufficient calculation capabilities, an artificial intelligence would be able to predict the 3D structure of a protein from sequence data. This is precisely what AlphaFold, a machine learning method, has recently announced  [6]. Indeed, AlphaFold has made available the entire human proteome, and the 3D structure of any human protein can be downloaded freely from the European Bioinformatics Institute database (https://www. ebi. ac. uk, accessed on 1 September 2022) and from the Uniprot server (https://www. uniprot. org, accessed on 1 September 2022). But can we really say that "the problem of protein structure has been solved", as was announced by many media, social networks, and even top scientific journals (https://www. science. org/content/article/game-has-changed-ai-triumphs-solving-protein-structures, accessed 1 September 2022) following the publication of AlphaFold's results on the human proteome? The success of AlphaFold is generally illustrated by the superposed images of a selected protein structure determined experimentally and predicted by AlphaFold  [6]  (https://www.
Structural and Functional Study of the Luminal Domain of h-SV2C To compare the structural reliability of the luminal domain of h-SV2C predicted by Robetta and AlphaFold2, we performed a structural alignment of each model with the luminal domain of h-SV2C resolved by Xray diffraction (PDB: 4JRA). Then, we merged the coordinates of botulinum neurotoxin A1 (BoNT/A1) with each model to obtain a complex BoNT/A1-h-SV2C for each h-SV2C structure predicted by Robetta and AlphaFold2. The complexes were submitted to energy minimization with the Polak-Ribière algorithm of HyperChem (CHARMM force field, 0. 1 kcal/mol as the gradient conditions) in order to optimize the inter and intra-molecular contacts of the proteins. The energy of inter-action of the minimized complexes were measured on Molegro Software using the tool "ligand energy inspector" (http://molexus.
Soluble Proteins Study AlphaFold2 predicted models for botulinum neurotoxin A (BoNT/A), botulinum neurotoxin B (BoNT/B) and complex factor H (CFH) were obtained from uniprot sequence data using the uniref #P0DPI0 for BoNT/A, #P10844 for BoNT/B and #P08603 for CFH. To compare the inter-molecular contacts of each neurotoxin model in initial interaction with their membrane receptors, we docked them with their protein receptor, and we inserted the receptor into a lipid bilayer that mimics a lipid-raft environment.
Docking of BoNT/B with its Membrane Receptors The synaptotagmin binding pocket surface of the crystal structure of BoNT/B (PDB: 2NP0) was manually docked to the surface of h-SYT1 in an initial orientation compatible with the membrane topology of h-SYT1 and surrounding ganglioside cofactors  [11]. These initial conditions take into acccount the experimental data that identify the BoNT/B domains that bind to h-SYT1 and to the ganglioside cofactor  [12] [13] [14]. Molecular details of these initial conditions are given in Figure  S1.
TM-Score and Root-Mean-Square Deviation The measurements of TM-score and Root-mean-square deviation (RMSD) for the comparison of the global structures were performed using the online tool "TM-score" available on Zhong Lab website (https://zhanggroup. org/TM-score/, accessed on 5 October 2022) by taking the experimental structure of the corresponding model as reference. Similar calculations were performed for the domain comparison by using the domain structure of AlphaFold2 and Robetta models as reference. Additionally, we computed the values of RMSD for the comparison of each global structure and each protein domain structure using PyMOL software (https://pymol. org/2/, accessed on 5 October 2022).
The luminal domain of h-SV2C in complex with BoNT/A1 was previously resolved by Xray diffraction (Figure  2B ). Topologically, both the AlphaFold2 and Robetta models proposed a structure similar to the experimental structure (Figure  2C ). To study which of the AlphaFold2 or Robetta models is the most accurate for interacting with BoNT/A1, we docked the neurotoxin near the interaction site of each h-SV2C model and we submitted them to energy minimization. The energy of interaction of the major amino acid residues interacting with the BoNT/A1 are presented in Figure  2D. Biomolecules 2022, 12, x FOR PEER REVIEW 5 of 18 Cryo-EM (PDB: 7SYD, resolved from the residue 25 to 638) ((C), green) with the structure obtained by Robetta ((D), red) and AlphaFold2 ((E), blue). In (E), the arrow points to an unstructured region that was predicted and inserted by the AlphaFold2 algorithm between the two extra-cellular domains.
BoNT/A1 and BoNT/B1 Since the AlphaFold2 algorithm suffers from obvious deficiencies concerning the relative spatial organization of the domains of a protein, we were interested in investigating the case of large soluble proteins that have several domains. For this purpose, we selected the microbial neurotoxins BoNT/A1 and BoNT/B1, and factor H of human complement (a regulatory cofactor for the protease factor I in the breakdown of C3b in the complement system of immune defence)  [21]. For each selected protein, we compared the models obtained by AlphaFold2 and Robetta with the experimental structure solved by X-ray diffraction. Taken together, these data suggested that Robetta is more accurate than AlphaFo for the prediction of membrane protein structure and topology.