dataesr/scientific-paragraphs-categorization huggingface dataset:
These data indicate that 45 min of amoeba-colon cells interaction was able to induce morphological changes associated with apoptosis in agreement with previous reports in other human cells including colonic Caco-2 and T lymphocyte Jurkat cells  (Huston et al. , 2000 (Huston et al. , , 2003;; Kim et al. , 2014; Lee et al. , 2014). To confirm whether E. histolytica parasites induce apoptosis in SW-480 cells we performed flow cytometry analysis using annexin V method. Data showed that in the absence of trophozoites a low percentage of SW-480 control cells (3. 7 ± 0. 4%) were in apoptotic stage. A similar proportion of apoptotic cells (3. 9 ± 0. 6%) were found at time 0 interaction, which corresponds to initial time when trophozoites and SW-480 cells were co-incubated (Figure  1E ). The fraction of apoptotic SW-480 cells significantly increased in a time-dependent way from 19.
Modulated miRNAs Potentially Target Multiple Apoptosis-Related Genes The identification of genes that could be targeted by miRNAs is a necessary step to understand its functions in host response to parasites. Several of the regulated miRNAs identified here have been previously described in various biological contexts and human diseases, and some gene targets have been functionally validated (Table  2 ). However, most of these studies have been performed in cancer cells from diverse types of human malignancies and until we know, there are no reports related to infectious diseases. To gain insights into the biological roles of cellular miRNAs that were modulated in SW-480 cells, we performed computational predictions to identify their gene targets using DIANA-microT, TargetScan, PicTar, and miRanda software's.
A DNA fragment corresponding to the 3 ′ UTR sequence of XIAP which contains the putative miR-643 binding site was cloned downstream of the luciferase coding region into pGL3 vector (Figure  4B ). Recombinant pGL3-XIAP plasmid was transfected alone or together miR-643 inhibitor into SW-480 cells and luciferase activity was analyzed after 24 h. Data showed that SW-480 cells transfected with pGL3-XIAP construct alone showed a high luciferase activity which was taken as 100% (Figure  4D ). Interestingly, transfection of pGL3-XIAP into SW-480 cells co-incubated with trophozoites for 45 min resulted in a substantial reduction of the relative luciferase activity in comparison with SW-480 monoculture used as control (Figure  4D ). A similar reduction (81%) was observed when cells were co-transfected with scramble control.
Then, we performed Western blot assays for the detection of caspases 3 and 9 using colon cells transfected with anti-miR-643 and controls. Results showed that activation of caspases 3 and 9 was decreased up to 47 and 49%, respectively, after 45 min interaction of antagomiR-643 transfected-SW-480 cells with amoeba (Figures  5B, C ) in comparison to controls. Altogether these data suggested that E. histolytica induced apoptosis in SW-480 cells, at least in part, through induction of miR-643, which in turn downregulates XIAP.
, 2007; Delić et al. , 2011; Lemaire et al. , 2013). Thus our datasets size is in the range of regulated miRNAs observed in different pathogen-host cells studies. Importantly, one should keep on mind that a single miRNA can potentially regulate as many as 1,000 different genes  (Krek et al. , 2005). Indeed, the 6-modulated miRNAs at 45 min were individually predicted to regulate from 365 (miR-615) to 1057 (miR-525). Altogether, they can potentially affect the expression of genes participating in at least five important biochemical pathways in SW-480 cells, including mRNA surveillance pathways, biosynthesis of unsaturated fatty acids, ubiquitin mediated proteolysis, PI3K-AKT signaling pathway and apoptosis. These data indicate that trophozoites interaction represents a stress condition that induces a physiological and adaptive response in host cells.
Commensurability parameter value In the second step, we set the commensurability parameter c. This parameter takes care of the possible conflict between the historical and current datasets. When the two datasets are very different, a non-informative prior should be preferable; when they are similar, a complete borrowing is preferred. We suggest linking c to a measure of distance between the two datasets, that is, between D 0 and D. This distance should be a positive number between 0 and 1, and it should tend towards the maximum value when the two datasets are very different, and to zero when D 0 and D are close. Since in some applications, such as dose finding, the data can come from a nonhomogeneous population, the approach of Pan and Yuan 25 cannot be directly used. For instance, in dose-finding trial data, each dose of the panel will produce a different population outcome.
Simulation studies To evaluate the performance of the proposed method, we carried out an extensive simulation study, in which 1000 independent phase I trials per scenario were simulated. The aim is to evaluate the performance of the dose-finding methods in different scenarios, when the generating probabilities of the prospective trial are similar to the ones estimated by D 0 and when they are different. In any case, D 0 is considered fixed, since we are not interested in their generated probabilities: the best guesses are, of course, the estimated ones. First, to compare the dose-finding methods, we simulated subject responses, which follow Bernoulli distributions with parameters defined depending on the scenario, to all doses for each trial. Then, each simulated dataset was stored, and when running a trial, regardless of the method applied, subject responses were read from this stored dataset.
For comparison, we ran the classic CRM (denoted by P_NI) and the CRM with power prior where the power a, which is denoted by AP_EB, was chosen using the Empirical Bayes method. 30 n the latter case, a is chosen to maximise the marginal likelihood mðD; D 0 ; aÞ ¼ Z LðhjDÞLðhjD 0 Þ a p 0 ðhÞdh Z LðhjD 0 Þ a p 0 ðhÞdh As a third competitor model, we run the bridging CRM (denoted by BCRM) of Liu et al. 18  We utilised the code that was provided online by the authors. However, using D 0 , the principal skeleton estimated gave two doses at the same prior probability; therefore, we added 0. 01 to the estimation of the higher dose in the couple to distinguish between them and then ran the BCRM. Computations were carried out in R (version 3. 5. 0),  31 running under macOS High Sierra 10. 13. 6. rstan package (2.
Discussion The aim of our work was to propose a modification of the power prior by incorporating external fixed data into a sequential adaptive design. Indeed, during sequential clinical trials, several adaptations are possible, including dose or regimen modification which are usually performed in early phase dose-finding studies. As noted in the Introduction, using the data from another population, indication or schedule can empower the trial results when the data are not in conflict. Our method has been shown to be able to detect data conflict during sequential adaptive trials and to add it when there is a strong reliable belief in its usefulness. To note, our method is tailored to check if the prospective trial is similar to a fixed historical dataset, not if both datasets come from the same population, that is, if both trials have the same generating probabilities.
If c is higher than 1, it will reduce the computed distance and will lead to more borrowing, while a value less than 1 will lead to a more conservative approach with an increase in the computed distance. Trial statisticians can decide to use other possibilities for c, but an extensive sensitivity analysis is required. Third, the method is based on the power prior approach and, therefore, on some notion of exchangeability. 34 hen the two datasets can be seen as a realisation from the same process, we would assume full exchangeability; that is, the two datasets would have the same parameter, but not otherwise. Many models, such as hierarchical models and the power prior, are based on the assumption of exchangeability. As pointed out by Psioda et al. ,  35  we prefer to see historical data that give the prior as non-random.
Then, it should be noticed that the term "fixed historical dataset" does not mean necessarily a unique dataset; this can be extended either for the averaging of several datasets or for a meta-analysis of several phase I datasets in order to take into account the inter-and intra-trials' variability. 36 eing able to incorporate external data into small sample trials is an advantage when the data are not in conflict. Proposing a full borrowing when not appropriate will conduct to a wrong choice of the MTD in the majority of cases. Full borrowing should not be considered, unless there is a strong evidence of similarity. Our method was shown to be able to detect conflict and avoid it. This method will help investigators and clinical trial statisticians use historical data without the fear of adding bias to the results when the data are in conflict.
MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2. 6) through concomitant searches against Uniprot (Homo sapiens taxonomy, December 2018 version), a classical contaminant database (homemade), and the corresponding reversed databases. The Proline software (http://proline. profiproteomics. fr) was used to filter the results: a conservation of rank 1 peptides, a peptide identification false discovery rate <1% as calculated on peptide scores by employing the reverse database strategy, a minimum peptide score of 25, and a minimum of one specific peptide per identified protein group. Proline was then used to perform a compilation, grouping, and spectral counting-based comparison of the protein groups identified in the different samples.
Statistics Results were analyzed with the Mann-Whitney test using GraphPad8 software. Data are given as mean ± SEM and results with p < 0. 05 were considered significant. For proteomic analyses, the protein list was explored for statistical over-representation of GO (Gene Ontology) biological process and cellular component terms using Fisher's exact test and the Bonferroni correction for multiple testing. Only GO terms with an adjusted p-value < 0. 05 were considered as significant hits.
This prompted us to compare our list of calcification-contained proteins to those reported to be present in connective tissues. For this, we compiled proteomic datasets previously reported to produce three lists of entries for (i) tendon  [22] [23] [24] [25] [26] , (ii) bone  [27, 28] , and (iii) cartilage  [29, 30]. As the compiled and compared datasets were acquired from samples of different species, we relied on the gene names extracted from Uniprot  [31]  to perform the comparison between sample types. This analysis confirmed the presence of proteins already found in tendons, but also in bone and/or cartilage (some of them are reported in Figure  6D ). Interestingly, we identified several proteins, such as ENPP1, NT5E (CD73), SERPINF1 (PEDF), or POSTN, which are known enzymes or regulating factors implicated in the mineralization process  [32] [33] [34].
It has an effect on distance only when r = 0. 25 (when r = 0. 25, u = 191. 0, u = 191. 0, p = 0. 007, n = 56; when r = 0. 50, u = 283. 0, p = 0. 686, n = 55) and no effect on price (when r = 0. 25, u = 321. 0, p = 1. 000, n = 56; when r = 0. 50, u = 311. 0, p = 1. 000, n = 55) and profit (when r = 0. 25, u = 252. 0, p = 0. 143, n = 56; when r = 0. 50, u = 296. 5, p = 1. 000, n = 55). A table summarizing the results is available in the supplementary section (see Table  S4 ). Although the general shape of data is close to what has been observed with simulations using the PM decision rule, the dispersion of results is much more spread out and we assume this scattering of the data can be attributed to inter-individual differences. In order to study this inter-individual variability, we computed three individual scores for each subject, assessing the compatibility of their behavior for each time step of the experiment with the use of (i) a PM decision rule, (ii) a DM decision rule, (iii) a TC decision rule.
Code and data availability Simulations were implemented using Python and the Python scientific stack  (Jones et al. , 2001; van der Walt et al. , 2011; Hunter, 2007). The code is available at https://github. com/ AurelienNioche/SpatialCompetition. The software used for the experimental part of the study is based on a client/server architecture. The client part was developed using the Unity game engine, hosted on a dedicated server and ran in the subjects' web browser using WebGL API. The code and the assets are available at https://github. com/AurelienNioche/ DuopolyAssets. The experiment server was hosted on a dedicated server and developed using the Django framework. The code of the server part is available at https://github. com/AurelienNioche/ DuopolyDjango. The analysis program is available at https://github.
patens, we assign it to Eukaryotes. We confirm that the trends remain unaffected (supplementary table 2 and fig. 2 , Supplementary Material online). Finally, to determine if D. melanogaster CAPs are enriched at certain ages, we used ProteinHistorian  (Capra et al. 2012 ) (http://lighthouse. ucsf. edu/proteinhistorian/; last accessed February 6, 2019). ProteinHistorian regroups databases of D. melanogaster proteomes with protein age assigned by different methods. We calculated enrichment using five different sets of protein family prediction of the Princeton Protein Orthology Database  (Heinicke et al. 2007 ) (DROME_PPODv4 clustered with OrthoMCL, Multiparanoid, Lens, Jaccard, and Panther7) and two different methods  (Wagner and Dollo parsimony)  to account for the expected differences according to the different phylogenies and data sets (supplementary table 3, Supplementary Material online).
Intron/Exon Extraction and Analysis We extracted genome-wide exon data from Ensembl Biomart (https://www. ensembl. org, v93, and Ensembl Metazoa v40, both released July 2018) for four species (D. melanogaster, A. gambiae, H. sapiens, M. musculus). Introns were computed by subtracting exons from the coding sequence. For each gene, introns were divided into two groups: the first two 5 0 introns, named "first" and any other introns as "rest". We verified our method against the Drosophila precomputed intron data of FlyBase and detected no major differences in our results. Note that the precomputed intron data includes introns in the 5 0 -UTR. The chromatin type of genes (and their introns) in Drosophila was determined by the "color" of their TSS as done in  Filion et al. (2010). In the three other species, genes were assigned the chromatin color of the ortholog in D.
By studying protein domains, we found evidence that a subset of young GREEN proteins are part of the family of proteins with BESS domains that is expanding in the Drosophila lineage. BESS domains direct protein-protein interactions, including with itself. Among all known proteins (not just the ones in our data set) with an inferred BESS domain (InterPro identifier IPR004210), >80% are restricted to insects and >50% are restricted to diptera. A comparison among drosophilids has shown that the BESS domain family expanded through duplications in a lineage-specific way 40 Ma  (Shukla et al. 2014 ). In our data set, five of 107 proteins have a BESS domain (SU(VAR)3-7, LHR, BEAF-32, CC20, and CC25). They are all found in the arthropod cluster (VI), and with the exception of CC20, they are GREENassociated.
Acknowledgments We thank  Joke van Bemmel  for help interpreting the genomewide binding data and the network model of chromatin organization. We thank  Guillaume Filion  for critical and stimulating comments and additional insight into the data, and  Christian Ro  ¨delsperger for critical and encouraging comments. The authors thank the  Center for Interdisciplinary Research in Biology (Coll ege de France, CNRS)  for funding. AC kindly acknowledges the support by  LabEx MemoLife ,.
Although the schemes were initially hosted at two different locations (both using first the mlstdbnet;  Jolley et al. , 2004)  then the BIGSdb software  (Jolley and Maiden, 2010) , in 2013, the two schemes were united into a single database. This move facilitated curation requests (sometimes using both schemes for the same set of isolates) and harmonized the data analysis functionalities. The hosting of both schemes within a single BIGSdb database, which can incorporate genomic sequences, facilitated the joint MLST analysis of genomic sequences using both schemes. Molecular epidemiology investigations revealed the occurrence of genetically distinct clonal lineages among populations of A. baumannii  (Diancourt et al. , 2010; Zarrilli et al. , 2013). Three of these lineages, which were initially defined as European clones I to III and subsequently regarded as International Clones (IC) I to III, are distributed worldwide (IC I and IC II are also known as Global Clones, GC).
Genome Datasets Bacterial genomes included in the analysis were manually selected from the PubMLST database. In detail, we selected all complete genomes and all high-quality genomes, i. e. , in which all loci of the MLST schemes and the ribosomal MLST scheme  (Jolley et al. , 2012 ) could be detected. The resulting dataset contains the genomes of 730 strains, belonging to the Acb complex, i. e. , A. baumannii (n = 703), A. nosocomialis (n = 13), A. seifertii (n = 1), A. dijkshoorniae (n = 1), A. pittii (n = 7), A. calcoaceticus (n = 3) (see Supplementary Figures  S1A, B  for geographical and temporal distribution of the isolates, respectively). A complete list of the genomes is available here: https://pubmlst. org/bigsdb?db=pubmlst_abaumannii_isolates& page=projects. The allelic variants of all gene fragments of both schemes were extracted from all the genomes, using an in-house Python script based on Blast  (Altschul et al.
, 1990) , keeping all results above 95% of identity with known alleles and subsequently selecting only perfect matches, procedure that allowed to assign the corresponding STs. The allelic sequences obtained were then aligned with Muscle  (Edgar, 2004). The resulting alignments were concatenated using an in-house Perl script, to obtain two multigene alignments (one per MLST scheme) to be used as input for downstream analyses. A core genome alignment was obtained to be used as a reference for determining the reliable phylogenetic trees. Gene calling was performed using Prodigal software v2. 6. 1  (Hyatt et al. , 2010)  on all 730 genomes in the dataset. A Perl script, which uses the double best Blast hit algorithm, was then used to identify genes orthologs to the previously published core genome by  Higgins et al.
(2017). Groups of ortholog genes were built and aligned using  Muscle (v3. 8. 31, Edgar, 2004). The resulting core alignments were polished for poorly aligned positions and divergent regions using Gblocks software version 0. 91b  (Castresana, 2000) , and merged in a concatenate of all ortholog genes, via another Perl script. A Single-nucleotide polymorphism (SNP) alignment was built as a second reference. SNPs were detected using the procedure developed by Gaiarsa and coworkers  (Gaiarsa et al. , 2015)  based on the software Mauve  (Darling et al. , 2004). Each genome was individually aligned to a reference (the complete genome AB307-0294), and alignments were then concatenated. Core SNPs were defined as single-nucleotide mutations flanked by conserved bases present in all the genomes in analysis.
Phylogenetic Analyses Phylogenies of all four datasets (Oxford, Pasteur, core genes, core SNPs) were inferred using the same approach. The best model of evolution was determined using ModelTest-ng version 0. 1. 3 (Darriba et al. , 2011). The selected model was GTRGAMMAIX for the three gene datasets, while the analysis for the SNP alignment was performed considering the ascertainment bias and using the Lewis correction  (Lewis, 2001) , thus with model ASC_GTRGAMMAX. Maximum Likelihood phylogeny was performed using RAxML  (Stamatakis, 2014)  with 100 bootstrap replicates.
Monophyly of Clonal Complexes Clonal Complexes were defined using eBURST as described previously  (Feil et al. , 2004) ; CCs were defined as groups of Sequence Types that differ from one or more members of the group by just one allele. Monophyly of the CCs was checked on the core genome tree using the R environment and the library spider. Minimum spanning trees of sequence types (STs) were built using Phyloviz Online using the goeBURST algorithm  (Ribeiro-Gonçalves et al. , 2016). Minimum spanning trees were generated from the 7 alleles of each MLST scheme and species were assigned based on clustering with reference STs. The species field was updated in the PubMLST database for all STs with no ambiguous assignment.
Nucleotide Distance Analysis To evaluate how well each gene fragment variation correlates with the genome variation, we compared the pairwise nucleotide distances between each pair of genomes in our dataset with the corresponding distances between each gene fragment pairs. The MLST gene fragment sequences, as well as all the concatenated core genome gene sequences, were aligned with Muscle  (Edgar, 2004)  and then used to calculate pairwise genetic distances via the function "dist. dna" of the R package APE  (Paradis et al. , 2004). In order to test the discrimination resolution of each MLST scheme, we plotted the pairwise sequence distance between each MLST locus for each pair of genomes, against the corresponding core genome-wide sequence distance. The correlation was determined adopting the regression linear model in the R environment.
Due to an uneven large distribution of genomic distances, we decided to split each dataset in three blocks (as in  Bleidorn and Gerth, 2018) , based on the genomic distance (first block: 0. 0-0. 05, second block: 0. 05-0. 1, third: >0. 1). Finally, a heatmap was generated to evaluate R 2 and slopes of all regression lines.
GdhB Analysis To investigate the Oxford scheme gene gdhB putative duplication, we extracted all variants of both gene copies from the 730 genomes in our dataset, including sequences not registered in the PubMLST database, through a custom approach based on Blast (identity > 95% with the gdhB-1 or the gdhB-182 allele). To check primer alignment, the flanking region was extracted for all alleles found, and aligned with the PCR primers used to sequence the gdhB locus. Mismatches between the aligned sequences and the primers were calculated for the entire primer length and for the ten bases at the three-prime end. Finally, all gdhB and gdhB2 variants were analyzed with a phylogenetic approach. Variants were aligned with the software muscle  (Edgar, 2004)  and used as input for a Maximum Likelihood phylogeny, executed with fasttree  (Price et al.
, 2010). The genes surrounding the gdhB and gdhB2 sites were extracted using a python script and the software Prodigal  (Hyatt et al. , 2010). Functions were predicted using COGnitor  (Galperin et al. , 2015). Codon adaptation index was calculated for all coding sequences in all 730 genomes in the dataset using the CAIcalc script  (Puigbo et al. , 2008).
Status of the Database Contents of the Pasteur and Oxford MLST Schemes In order to evaluate the two MLST schemes available for A. baumannii, we start by describing them in detail. Both schemes are built on 7 genes. The concatenation of allele 1 of the 7 Oxford and Pasteur genes yields 2895 and 2976 nucleotides, respectively. Three genes are shared between the two schemes: cpn60, gltA, and recA. The subsequences used for typing, though, differ between the two schemes, as depicted in Figure  1A. We downloaded all sequence and profiles definitions on date 14 September 2018. Oxford contained 1866 profiles (STs), while the Pasteur scheme had 1234 STs defined. The number of alleles ranged from 121 to 334 for Oxford, 87-187 for Pasteur. Regarding the three common genes, the allele numbers were 128(Oxford)/187(Pasteur) for cpn60, 121(Oxford)/173(Pasteur) for gltA, 133(Oxford)/185(Pasteur) for recA.