dataesr/scientific-paragraphs-categorization huggingface dataset:
discoideum. However, similarly to E. histolytica Mastigamoeba balamuthi Genome. doi:10. 1093/molbev/msab020 MBE FIG. 2. Losses and gains of orthologous groups in dictyostelids and Archamoebae. Predicted proteins of amoebozoans and selected representatives of opisthokonts were assigned to the orthologous groups (OGs) of the EggNOG database  (Powell et al. 2014 ) using profile-HMM homology searches (HMMER). Gains and losses of OGs were reconstructed for each node of the organismal phylogeny with the assumption of vertical inheritance of these OGs. For each branch these parameters are shown: 1) number of gains (in blue) and losses (in red) of OGs that were predicted to occur at that branch, and the resulting predicted number of OGs (in black). 2) Specific terms of the Gene Ontology enriched in the gained (blue) and lost (red) groups with high statistical significance using the Ontologizer tool  (Bauer et al.
2008). Statistical significance is shown as the P value next to the term. 3) Net graphs showing the negative logarithms of the P values describing the statistical significance of the associations between the COG database functional categories  (Tatusov et al. 2003) , and gains (blue line) and losses (red line). The negative values represent an inverse proportion, that is, a negative association between the categories. At the terminal branches color-coded presence/absence plots of OGs with an assigned COG functional category are shown that demonstrate the overall OG reduction in Entamoeba. The description of the COG functional categories is shown in the bottom left corner. Z arsk y et al. doi:10. 1093/molbev/msab020 MBE enzymes for malonyl-CoA synthesis. However, it was previously demonstrated that E.
The Role of LGT The comparison of OGs within the Conosa group revealed that Archamoebae and M. balamuthi in particular have gained a number of genes that provide functions specific to these organisms. Some of these genes have previously been studied and identified as putative acquisitions from prokaryotic lineages via LGT  (Gill et al. 2007; Nyvltova et al. 2013; N yvltov a et al. 2015). To systematically assess the overall contribution of LGT to the M. balamuthi gene repertoire, we conducted a large-scale analysis of gene phylogenies. Based on homology searches against the UniProt database, we selected conserved domains of M. balamuthi proteins and built Mastigamoeba balamuthi Genome. doi:10. 1093/molbev/msab020 MBE phylogenies for each of them. For the LGT evaluation, we introduced an LGT score (for details see supplementary Materials and Methods and fig.
Genome Assembly and Annotation The genome sequence was assembled using Newbler (Software Release: 2. 6 20110517_1502)  (Margulies et   S18 , Supplementary Material online. Gene prediction was performed using Augustus 3. 0. 2 (Stanke and Morgenstern 2005)  with a set of 298 manually curated training gene models and hints from the transcriptome mapped onto the genome assembly, which yielded 14,840 protein-coding genes. Manual curation was employed for genes of interest using the interface provided by the Online Resource for Community Annotation of Eukaryotes (ORCAE) webserver  (Sterck et al. 2012). Gene annotation was performed using InterProScan  (Jones et al. 2014 ) and Blast2GO  (Conesa et al. 2005). Structural RNA genes were predicted and annotated using the Rfam database (Griffiths-  Jones et al.
Analysis of Orthologous Groups We gathered predicted proteomes of selected amoebozoans (A. castellanii, Protostelium aurantium, Acytostelium subglobosum, D. discoideum, Mastigamoeba balamuthi, E. invadens, E. moshkovskii, and E. histolytica) and opisthokonts (Homo sapiens, Amphimedon queenslandica, S. cerevisiae, Batrachochytrium dendrobatidis) (supplementary table  S4 , Supplementary Material online). The protein sequences were searched against the profile HMMs of the EggNOG database version 4. 5 using HMMER3  (Eddy 2011). Only EggNOG OGs with the best hit e-values 1e-10 were considered. For each protein sequence, nonoverlapping hits with e-values 1e-5 were selected. The gain of the orthologous group was assigned to the last common ancestor of all the species in which the orthologous group was detected.
The losses were assigned to the nodes descending from the last common ancestor in which none of the descendants had the orthologous group considered. At each node of the tree of selected amoebozoan and opisthokont species, for each event type (gain or loss) and each COG category, we created a contingency table of orthologous groups categorizing them according to the presence of the given COG category in the EggNOG annotation, and according to whether the orthologous group was gained or lost, matching the given event type. The statistical significance of the association between those categorical variables was tested using the v 2 test. Protein sequences of E. histolytica, To. gondii, Chromera velia, Tr. brucei, and B. saltans were downloaded from the UniProt database (https://www. uniprot. org/) and annotated using KofamKOALA  (Aramaki et al.
Phylogenetic Analyses Phylogenies were estimated for each predicted M. balamuthi protein. The respective protein sequences were used as queries to search the UniProt protein database using the DIAMOND protein aligner (version diamond/0. 9. 10)  (Buchfink et al. 2015)  in sensitive mode. Up to 10,000 best hits were used for further analysis, discarding those with evalues higher than 1e-5. NCBI taxonomy was assigned to homologous sequences (hits), and hit regions homologous to the query sequence were clustered at the 80% identity level using CD-HIT  (Fu et al. 2012). The presence of hits from both eukaryotes and prokaryotes in a given 80% identity group was suspected to result from contamination. The source of contamination was determined based on the number of unique taxa in the 80% identity group and the contamination was removed.
The sequences were assigned to one of the predefined major eukaryotic taxonomic groups (generally corresponding to an intermediate hierarchy level in the NCBI taxonomic database, see supplementary table  S13 , Supplementary Material online), and from each 80% identity group up to one sequence per each taxonomic group was chosen for further analysis to minimize redundancy. A multiple sequence alignment was constructed for each sequence group using MAFFT with the default settings  (Katoh and Standley 2013)  and trimmed with BMGE (Criscuolo and Gribaldo 2010) using the BLOSUM30 matrix with a block size of 1. Phylogenetic trees were inferred with IQ-TREE  (Nguyen et al. 2015)  with automatic model selection and 1,000 ultrafast bootstraps or 500 bootstraps. The selected models are summarized in supplementary table S12, Supplementary Material online.
Domain Boundary Frequency To assess possible sources of prokaryote-to-eukaryote LGTs based on the phylogenetic tree, we introduced a measure that we call domain boundary frequency (DBF). It is defined as the prokaryotic taxon frequencies at the root of the eukaryotic subtree, that is, at the boundary between eukaryotic and prokaryotic sequences (see supplementary fig. S16A , Supplementary Material online). The most significant prokaryotic taxon was that with the highest DBF value. We classified the DBFs into three classes: 1) 0 < DBF 0. 25: unclear origin, 2) 0. 25 < DBF 0. 75: possible sister relationship with the given taxon, and 3) 0. 75 < DBF 1: the given taxon is a likely source of the LGT. Automated tools for the calculation of taxon frequency, LGTPC, LGTEC, and DBF are available at https://github.
Acknowledgments We thank  John Cawley  for proofreading the manuscript. This work was supported by the  Czech Science Foundation  ( P305/ 11/1061 ,  16-06123S ),  Centre  for research of pathogenicity and virulence of parasites ( CZ. 02. 1. 01/0. 0/0. 0/16_019/0000759 )provided by  European Regional Development Fund, Microbial Communities in Biomedical and Environmental Areas , and  Systems Biology  funded from  EU H2020  (No  810224 ),  University Research Centre (UNCE ,  SCI/12 ) from  Charles University , and the  ELIXIR CZ research infrastructure project (Ministry of Education ,  Youh , and Sport Grant No:  LM2018131 ) including access to computing and storage facilities. The genomic sequences have been deposited in the online resource for community annotation of eukaryotes (ORCAE, https://bioinformatics.
Protein Structure Analysis The 3D structure of the proteins encoded by APGs in each species was modeled to locate the position of residues under positive selection and to gain insight into their possible functional significance. The structure of the protein encoded by each gene in each species was predicted using its protein sequence, the most similar protein cryo-EM structure (hereafter template) from the Protein DataBank (PDB, www. rcsb. org, last accessed June 05, 2019), and the homology modeling software Modeller  (Webb and Sali 2016). For each protein sequence, we generated ten models and selected the one with the lowest Discrete Optimized Protein Energy score for further analysis. The quality of these modeled structures was checked in ModFOLD  (Maghrabi and McGuffin 2017). PyMOL (Schro ¨dinger 2019) was used to align these predicted 3D structures to the template, and its plug-in APBS to predict the electrostatic surface charge of each molecule.
Because amino acid similarity between human and urodele TAPBP is <35%, we decided to be conservative and did not try to infer models for this gene based on the human template (PDB accession number 6ENY). To shed light on the potential functional relevance of the positively selected sites, we estimated the surface receptiveness of each residue. For this, we calculated the relative solvent accessibility (RSA) from the residue's solvent accessibility surface area (ASA) computed from the PDB models of five distantly related species: Hyd. italicus (Plethodontidae), Hyn. chinensis (Hynobiidae), Proteus anguinus (Proteidae), Siren intermedia (Sirenidae), and Triturus dobrogicus (Salamandridae), using the webserver xssp (http://www. cmbi. ru. nl/xssp/, last accessed August 29, 2019; based on  Kabsch and Sander 1983).
PSMB8 and PSMB9 Population-Level Resequencing To clarify whether the divergent PSMB8 and PSMB9 lineages represented divergent alleles of the same gene or duplicated genes, we designed lineage-specific MIPs (supplementary table S1, Supplementary Material online) to resequence PSMB8 and PSMB9 in two populations (10-21 individuals per population) of each of seven species of the genus Triturus (supplementary table  S2 , Supplementary Material online). This genus was selected because numerous samples from natural populations were available for several species (e. g. ,  Wielstra, Burke, Butlin, and Arntzen 2017; Wielstra, Burke, Bultin, Avci, et al. 2017). The number of reads mapped to each of the two references of each PSMB gene (i. e. , the consensus sequence of a lineage obtained from the transcriptome data of all Triturus species) was counted to determine the lineage/s of each individual.
However, only seven codons were identified by more than one method: three in PSMB8 (codons 26, 104, and 190), three in TAP2 (codons 67, 144, and 470), and one in BRD2 (codon 8). The majority of substitutions in codons identified as positively selected were physicochemically nonconservative (supplementary table  S4 , Supplementary Material online), and the residues were surface accessible (supplementary table  S5 , Supplementary Material online), which allows them to interact with other molecules. TAP1 and TAP2 protein structures were modeled based on the Cryo-EM structure of the human TAP ATP-Binding Cassette Transporter (PDB accession number 5U1D). This model covered residues 144-714 of the TAP1 protein alignment and 148-702 of the TAP2 protein alignment (supplementary video S1, Supplementary Material online).
PSMB8 and PSMB9 protein structures were modeled based on chain K and chain N, respectively, of the Cryo-EM structure of the mouse 20S immunoproteasome (PDB accession number 3UNF). The chain K model covered residues 74-274 of the PSMB8 protein alignment and chain N model covered positions 19-217 of the PSMB9 protein alignment (supplementary video S2, Supplementary Material online). Considerable differences between urodele species in surface charge (e. g. , residue 295 and 470 TAP2) and volume/shape (e. g. , 470 TAP2 and 104 PSMB8) were observed at the residues under positive selection (supplementary figs. S1-S3, Supplementary Material online).
Acknowledgments The study was funded by the  Polish National Science  (Grant No. UMO-2016/23/B/NZ8/00738 ). We thank  Magda Migalska  for her insightful comments,  Mateusz Buczek  for his help in script modifications,  Samantha Garza ,  Jong Yoon Jeon ,  Eunsun Lee ,  Mi-Sook Min ,  Iñigo Martinez Solano ,  Rodrigo Meg ıa Palma , and  Kinya Nishimura  for their assistance in field work, and  Dirk-Jan de Koning ,  Marian Novotn  y, and  Christos Feidakis  for their guidance with some data analysis. Taejoon Kwon ,  Miguel Vences , and  David Weisrock  kindly provided unpublished transcriptome sequences and  Cera Fisher  generated the Batrachoseps transcriptome data. We also thank  University of Connecticut Research Foundation  for support. M. M. was supported by a  KAKENHI Grant-in-Aid for Young Scientists (B)  (No.
JP16K18613 ) from the  Japan Society for the Promotion of Science. M. T. was supported by the  Institutional Research Support  (Grant No. 260571/2020 ). B. Wa. was funded by the  Ministry of Education  (Grant No. 2015R1D1A01057282 ) and the  Ministry of Science, ICT, and Future Planning  (Grant No. 2018R1A2B6006833 ) of the Republic of Korea. M. V. was supported by a grant from  Charles University  (Grant No. PRIMUS/17/SCI/12 ). We are indebted with  Ministero dell'Ambiente e della Tutela del Territorio e del Mare  (prot  56097/T-A31 2017 ) and  Comunidad de Madrid  (Ref:  10/ 150875. 9/20and Parques Regionales Ref: 054. 20 ) for the authorizations for collecting the samples.
Funding This work was supported by the  French Government  under the  « Investissements d'avenir » (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research)  (reference: Méditerranée Infection  10-IAHU-03 ) and by  Région Provence Alpes Côte d'Azur and European funding ERDF PRIMI.
Practicability study outcomes The study was divided into three substudies. It was conducted by the trained health care workers so-called "observers", based on previously acquired experience from WHO recommendations for evaluating the practicability of HIV self-tests  [16, 24, 26]. A face-to-face, paper-based, and semi-structured questionnaires were used to obtain the data on the socio-demographic characteristics, medical history of study participants, participants' opinions or levels of satisfaction about the practicability of the Triplex HIV/HCV/HBsAg self-test, and participants' acceptability of and preferences for the Triplex self-test. Substudy 1. Observation of manipulation. After signing the consent form of participation, participants were asked to read the printed instruction for use for comprehension and, if they needed to, to follow the video version instruction via the tablets managed by the research team.
Statistical analysis All data were entered into an Excel file and analyzed on SPSS 20. 0 (Chicago, IL, USA). Descriptive statistics were computed using mean (standard deviation) or median (interquartile range; IQR) for normal or skewed distribution, respectively; proportions of all categorical variables were calculated for qualitative data. The usability index was defined as the mean of the correct answers for each question related to the performing of the Triplex self-test. The Wilson score bounds were used to estimate the 95% confidence intervals (CI)  [27]. The Cohen's κ coefficient estimated the concordance between the results read by participants in connection with the expected results read by the trained observers  [27]. The concordance was interpreted according the Landis and Koch scale  [28]  as follows: < 0 as indicating no agreement, 0-0.
Acknowledgments The authors are grateful to the participants for their willingness to participate to the study. The authors are also grateful to the  laboratory Biosynex, Strasbourg, France , and particularly  Mr. Raphael Dupont ,  DR. Thuierry Paper  and  Mr. Thomas Lamy , for providing the Triplex HIV/ HCV/HBsAg cassette to create the prototype Triplex HIV/HCV/HBsAg self-test. We also thank the  National AIDS & STI Control Program, Kinshasa, Democratic Republic of the Congo. Finally , we would like to thank  Dr. Alliance Tagoto ,  Mr. Otsatre Okuti ,  Mr. Benjamin Mungufeni ,  Mr. Faustin Ukurfwa , and  Mr. Jean-Baptiste Ufoymungu  for excellent technical assistance. Dr. Serge Tonen-Wolyec  was recipient of  ERASMUS+ program  between the  University of Kisangani, Democratic Republic of the Congo , and the  University of Liège, Belgium.
SOLUBLE CD146: A POTENTIAL CANDIDATE BIOMARKER TO PREDICT PATHOLOGICAL PREGNANCIES OR IMPLANTATION DEFECTS? Under physiological conditions, the serum concentration of sCD146 progressively decreases throughout normal pregnancy, a result confirmed in two independent cohorts of patients (18, Bouvier et al. , submitted manuscript). However, sCD146 concentration was found to be elevated in patients with at least one unexplained fetal loss as compared to women with at least one viable child  (32). Bouvier et al. have quantified sCD146 plasma concentration in a cohort of women with placentalmediated pregnancy complications. They found sCD146 to be upregulated (21%) in women with preeclampsia as compared to women with normal pregnancy (submitted manuscript; clinicaltrials. gov identifier: NCT 01736826).
ACKNOWLEDGMENTS We thank the research team at the  "Direction de la Recherche Clinique et de l'Innovation" at Nımes University hospital :  S. Cleḿent ,  C. Meyzonnier ,  N. Best ,  A. Megzari ,  E. Dupeyron ,  S. Granier ,  B. Lafont ,  C. Masseguin ,  O. Albert ,  S. Doulay ,  M. Hnida ,  S. de Bouard ,  P. Rataboul , and  M. P. Francheschi. We are also grateful to  Teresa Sawyers , Medical Writer at the  B. E. S. P. I. M, Nımes University Hospital , for revising and editing this article. Our thanks also go to the  Department of Gynecology and Obstetrics at Nımes University Hospital , particularly  Prof. Vincent Letouzey  and  Dr Eve Mousty. We are also most grateful to  Nımes University Hospital  for its structural, human, and financial support.
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of highquality sequence data and machine learning methods to study such interactions (i. e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n � 55, 000), using all observed mutations as binary representation features.
In these previous studies the goal was to make the most accurate predictions possible, using complex models such as SVMs and ANNs, therefore sacrificing interpretability. Here, we have a different approach, using simpler models that might be less accurate but whose predictions we can understand and interpret. We train these models to discriminate RTI-naive from RTI-experienced sequences. Without the need for phenotypic data, we are able to use much larger HIV-1 RT sequence datasets from the UK (n � 55, 000) (http:// www. hivrdb. org. uk/) and Africa (n � 4, 000)  [10]. By using interpretable models, we can extract mutations that are important for determining if a sequence is treated or not and potentially find new mutations potentially associated to resistance. Furthermore, we aim to detect associations between mutations and their effect on antiretroviral resistance in order to study potential underlying epistasis.
The African and UK datasets are very different both from genetic and treatment history standpoints, therefore training classifiers on the UK dataset and testing them on the African one, should guarantee the robustness of our findings and greatly alleviate phylogenetic correlation effects. In the following sections, we first describe the data then the methods used. Our results include the assessment of the performance of our classifiers even when trained on data devoid of any known resistance-associated signal; as well as a description of the main features (prevalence and correlation to known mutations, genetic barrier and structural analysis) of six potentially resistance associated mutations, newly discovered thanks to our approach. These results and perspectives are discussed in the concluding section.
Data In this study, we used all the drug resistance mutations that appeared in the Stanford HIV Drug resistance database, both for NRTI (Nucleoside Reverse Transcriptase Inhibitors; https:// hivdb. stanford. edu/dr-summary/comments/NRTI/) and NNRTI (Non Nucleoside RTI; https://hivdb. stanford. edu/dr-summary/comments/NNRTI/) as known RAMs. To discover new RAMs, assess their statistical significance and study potential epistatic effects, we used two datasets of HIV-1 RT sequences. A large one (n = 55, 539) from the UK HIV Drug Resistance Database (http://www. hivrdb. org. uk/) and a smaller (n = 3, 990) one from 10 different western, eastern and central African countries  [10]. In the UK dataset, sequences from RTI-naive individuals formed the majority class with 41,921 sequences (75%). In the African dataset, both classes were more balanced with 2,316 RTI-naive sequences (58%).
In the UK dataset, RTInaive sequences had at least one known RAM in 25% of cases, most likely due to transmissions to naive patients or undisclosed treatment history, against 48% in RTI-experienced sequences, thus making the discrimination between the RTI-experienced and RTI-naive sequences particularly difficult. In the African dataset this distribution was more contrasted, with only 14% of RTI-naive sequences having at least one known RAM, versus 83% of RTI-experienced sequences. The African dataset was also much more genetically diverse with 24 different subtypes and CRFs compared to the 2 subtypes (B and C) that we retained for this study from the UK cohort. The majority of the sequences from the African dataset were samples from Cameroon (27%), Democratic Republic of Congo (17%), Burundi (15%), Burkina Faso (13%) and Togo (11%).
It is important to note that RTI-experienced sequences in both of these datasets can be considered as resistant to treatment. Since the viral load was sufficiently high to allow for sequencing of the virus, we can consider that the ART has failed. However, in some cases this resistance might be caused by non adherence to ART, rather than by the presence of RAMs, therefore adding some noise to the relationship between treatment status and resistance. In addition to differences in size, balance between RTI-naive and experienced classes, and the genetic difference between the UK and African datasets, there are also significant differences resulting from differing treatment strategies. In the UK and other higher income countries, the treatment is often tailored to the individual with genotype testing, which result in specific treatment as well as thorough follow-ups and high treatment adherence.