dataesr/scientific-paragraphs-categorization huggingface dataset:
Transdecoder v5. 5 was used to predict the open-reading frames and protein sequences for all transcripts (https://github. com/TransDecoder/TransDecoder/). Putative protein sequences were annotated using eggNOG-mapper (emapper-1. 0. 3, emapper DB: 4. 5. 1)  43. Each protein sequence was queried against the nr database using BLAST 2. 8. 0+  44  and the taxonomic provenance of the top hit was assigned with ete  45. The completeness measures assigned by TransDecder for each open reading frame was manually investigated to determine whether the sequence was full-length, 5′partial, 3′-partial or internal (5′-and 3′-partial) using BLAST again the nr database, see Data S1. We manually corrected some protein models predicted by TransDecoder that extended into the 5' untranslated region of the transcripts based on homology to other sequences in GenBank.
Interpro (IPR) domains were annotated using interproscan  46  with the iprdomain setting. Subcellular localization was predicted using TargetP Transcriptome completeness was assessed using BUSCO 52 v 4. 0. 4 (eukaryota_odb10 (eukaryota, 2019-11-20)). We recovered 61% and 47% of near-universal single-copy orthologues for each species, respectively, which is comparable to the BUSCO coverage for complete genomes and deeply sequenced transcriptomes of other metamonads (Data S1).
Phylogenomic Dataset Assembly To infer the phylogenetic placement of the two Anaeramoeba species, we updated a previously published phylogenomic dataset of 155 proteins 3 by adding orthologues from the Anaeramoebae, and three recently published Metamonada, Retortamonas dobelli  65  , Retortamonas cf. caviae  65  , and Barthelona sp. 8. For each of the 155 proteins, we used the Arabidopsis thaliana or Homo sapiens homologue as a query against the predicted proteins of each Anaeramoeba species (e-value cut-off = 10e-10) using BLASTP  44  and retained up to five hits. Those potential homologues were then used as queries for a reciprocal BLAST search against a dataset containing the initial set of identified orthologues, but also their known deep paralogues (i. e. , corresponding to duplications predating the last eukaryotic common ancestor, such as EF1-L and EF1-α).
Anaeramoeba sequences were retained for subsequent analyses if their reciprocal best BLAST hit belonged to the orthologous clade of interest. These were added to the existing orthologue dataset, which was aligned using mafftlinsi  53, 54. Ambiguously aligned positions were trimmed using BMGE v1. 12 (default settings, except from -m BLOSUM62 and -g 0. 3; 55 ). Preliminary maximum likelihood individual protein trees were computed using RAxML v8 (with the PROTGAMMALG model, branch support was estimated using 100 rapid bootstraps)  56. For the three recently published metamonads, all previously identified orthologs were used as queries against the predicted proteins for each species (e-value cut-off = 10e-5) using BLASTP  44  and retained up to five hits per query. These were added to the existing dataset, filtered for sequencing errors and non-homologous sites using PREQUAL  57  , aligned using mafftginsi  53, 54  , alignment uncertainty and errors were filtered using DIVVIER  58  , and the filtered alignments were trimmed of sites comprised of >99% gaps using trimAL (-gt 0.
01)  59. Preliminary maximum likelihood individual protein trees were estimated using IQ-TREE under the LG+C20+F+G model, and branch support was estimated using 100 RAxML rapid bootstraps inferred using the LG4X model. Each tree was then manually inspected and a single sequence corresponding to the correct orthologue was selected for each species. In case of in-paralogues specific to the newly added taxa, the sequence with the best coverage of the alignment or the shortest branching sequence was selected. Single protein trees were recomputed as previously described, and well-supported bipartitions (bootstrap support > 70) were further investigated to identify potential paralogues, contaminants, or lateral-or endosymbiotic gene transfers, which were then removed from the datasets. Final datasets were re-aligned and trimmed as described for the recently published Metamonada species and concatenated into a single large alignment (supermatrix containing 95 sequences and 53637 sites).
Phylogenetic analysis of genes related to hydrogen production and oxidative stress The initial dataset of Anaeramoeba hydrogenase proteins was built by retrieving any sequence containing the large hydrogenase domain (IPR004108) based on the InterProScan annotations. These proteins were subclassified as Group A or Group B using HydDB 62 and separated for the following analyses. For the Group A and Group B sequences, we first extracted the hydrogenase domain using hmmscan  63  (hmmer. org) and the Pfam hmm profile PF02906. 12 (Fe_hyd_lg_C) or TIGR04105 from TIGR, using default settings, respectively. These sequences were used as queries against the non-redundant database (nr) and a local database of metamonad sequences 3 using BLAST  44  (e-value < 1e-10; -max_target_seqs 1500). Sequences from these BLAST results were combined and reduced using cd-hit 67 (-c 0.
8). The retrieved Group A and Group B sequences were subclassified as above with HydDB 62 and only the regions corresponding to the large hydrogenase domain was extracted as above. For initial datasets, sequences were aligned using mafft-linsi  53  and trimmed using BMGE 55 (v1. 12; -h 0. 7, -m BLOSUM30) and the phylogeny was generated using FastTree2  68. Trees were manually inspected to identify those clades that were distantly related to the sequences of interest that could be removed to reduce the size of the dataset. Final datasets were aligned using mafft-linsi and trimmed with BMGE as above. Phylogenies were inferred using IQTREE 69 v2. 0 under the best scoring model of evolution decided by ModelFinder (supplemented with the C-series mixture models with -mset LG+C20,LG+C10,LG+C60,LG+C30,LG+C40,LG+C50) and 1000 ultrafast bootstraps (-bb 1000).
ignava and A. flamelloides, only 1 representative sequence is provided. See Sheet A_Mitochondrial_proteins for full list of sequences. For all other sequences, accession numbers of sequences were retrieved from Leger et al. 3. When more than one sequence was detected, only the longest sequences is listed. See Leger et al. 3 for a complete list. '-' or '?' indicates the gene could not be identified in a genome or transcriptome respectively. E: Phylogenetic distribution of metabolic proteins across Metamonada: For A. ignava and A. flamelloides, only 1 representative sequence is provided. See Sheet A_Mitochondrial_proteins for full list of sequences. For all other sequences, accession numbers of sequences were retrieved from Leger et al. When more than one sequence was detected, only the longest sequences is listed.
TargetP2 48 , MitoFates 49  , and DeepLoc 50  and summarized in Data S1. Net charge of putative N-terminal targeting signals was calculated at pH 5. 5, 7. 4 and 8. 0 using Isoelectric Point Calculator 51 (v. 2. 0) For each prediction software, we arbitrarily assigned a confidence point of one for any prediction with the probability of mitochondrial localization greater than 0. 5 and a point of 0. 5 if the probability of mitochondrial localization was less than 0. 5 but the software still predicted mitochondrial localization. Every protein with one confidence point were manually investigated for mitochondrial provenance. Putative MRO proteins from other free-living anaerobic protists (Pygsuia biforma 21  , Brevimastigamonas motovehiculus 64 ) were used as queries against the protein datasets of each transcriptome to identify similar homologues (-evalue 0.
We inferred ML trees using IQ-TREE under the LG+C60+F+G model, constraining the monophyly of Parabasalia+Fornicata+Barthelona, Parabasalia+Fornicata+Barthelona+Anaeramoeba, Collodictyon+Malawimonas+Discoba+Metamonada, and Discoba+Metamonada, as well as the Bayesian consensus topology (that recovered paraphyletic metamonads) using the -g option. The AU test was implemented in IQ-TREE on our ML topology, the individual constrained trees, and 100 distinct local topologies saved during ML analysis of our concatenated dataset (-wt option in IQcom/s/fa207b47f454b7129277).
FDP and OsmC analyses we added the Anaeramoeba sequences to datasets presented in Stairs et al. (2019) 73 and Meireles et al. (2017) 38 , respectively. Datasets were aligned with mafft and trimmed with BMGE, as above. Phylogenies were inferred using IQTREE 69 v2. 0 under the best scoring model of evolution decided by ModelFinder (supplemented with the C-series mixture models with -mset LG+C20,LG+C10,LG+C60,LG+C30,LG+C40,LG+C50) and 1000 ultrafast bootstraps (-bb 1000.
ACKNOWLEDGEMENTS IC, PT, and TP were supported by the  Czech Science Foundation  (project  18-18699S ). CWS is supported by the  Swedish Research Council  (Vetenskapsrådet starting grant  2020-05071 ). Work carried out in the Roger lab was supported by  Foundation  grant  FRN-142349 , awarded to A. J. R. by the  Canadian Institutes of Health Research. LE is supported by funding from the  European Research Council  ( ERC StG 803151 ). EDS was supported by the  MSCA-IF SMART  ( CZ. 02. 2. 69/0. 0/0. 0/20_079/0017809 ). MK was supported by  Czech Science Foundation  (project  18-28103S ). MK was supported by  Czech Science Foundation  (project  18-28103S ). This project was also supported by  ERD fund "Centre for Research of Pathogenicity and Virulence of Parasites " (no. CZ. 02. 1. 01/0. 0/0.
47 , 47 TargetP2 48 , MitoFates 49  , and DeepLoc 50  and summarized in Data S1. Net charge of putative N-terminal targeting signals was calculated at pH 5. 5, 7. 4 and 8. 0 using Isoelectric Point Calculator 51 (v. 2. 0) For each prediction software, we arbitrarily assigned a confidence point of one for any prediction with the probability of mitochondrial localization greater than 0. 5 and a point of 0. 5 if the probability of mitochondrial localization was less than 0. 5 but the software still predicted mitochondrial localization. Every protein with one confidence point were manually investigated for mitochondrial provenance. Putative MRO proteins from other free-living anaerobic protists (Pygsuia biforma 21  , Brevimastigamonas motovehiculus 64 ) were used as queries against the protein datasets of each transcriptome to identify similar homologues (-evalue 0.
For FDP and OsmC analyses we added the Anaeramoeba sequences to datasets presented in Stairs et al. (2019) 73 and Meireles et al. (2017) 38 , respectively. Datasets were aligned with mafft and trimmed with BMGE, as above. Phylogenies were inferred using IQTREE 69 v2. 0 under the best scoring model of evolution decided by ModelFinder (supplemented with the C-series mixture models with -mset LG+C20,LG+C10,LG+C60,LG+C30,LG+C40,LG+C50) and 1000 ultrafast bootstraps (-bb 1000.
com/articles IDENTIFIER /dataset/Anaeramoe bae_are_deeply- branching_eukaryote s_that_clarify_the_tr ansition_from_anaer obic_mitochondria_t o_hydrogenosomes/ Bacterial and virus strains Phylogenetic trees and alignments FigShare 12205517 https://figshare. com/ articles/dataset/Anae ramoebae_are_deep ly- branching_eukaryote s_that_clarify_the_tr Biological samples ansition_from_anaer obic_mitochondria_t o_hydrogenosomes/ 12205517 Experimental models: Cell lines Chemicals, peptides, and recombinant proteins Artificial Seawater Cerophyll TRIzol reagent ThermoFisher 15596-026 Scientific Experimental models: Organisms/strains (Invitrogen/Life Anaeramoeba ignava strain BMAN Technologies) Táborskýa et al. 5 Anaeramoeba flamelloides strain BUSSELTON2 Táborskýa et al. 5.
Introduction Exposome research can result in the design of cost-effective health interventions targeting environmental risk factors that affect human health. For instance, the age-adjusted incidence of chronic diseases such as cancer, is rising. The exposome risk factors behind this increment are not fully determined. An integrated research framework that efficiently provides streamlined tools for exploiting major technologies and disciplines involved in the research on exposome risk factor assessment, could dramatically contribute to identifying the environmental factors affecting human health. The HEAP is developing a global research resource that integrates an Infrastructure as a Service (IaaS: computational resources services for storage and computation) and a Platform as a Service (PaaS: software services for managing and analyzing the data) for the efficient management and processing of massive data from geographically distributed large-scale population cohorts.
The conceptual model of HEAP is illustrated in Figure  1. Abstract: The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world's fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research.
Advanced statistics analysis on HEAP We have begun to develop tools for random effect modeling using automatic differentiation and variational approximations. Code has been developed for partially separable quasi-Newton methods,  17  which is useful for optimization of models based on variational approximations; fast variational approximations for mixed models based on the probit link,  18  and variational approximations for mixed generalized survival models. 19 The next step in this process is to develop tools for joint modeling of longitudinal and survival outcomes. Synthetic datasets from observed infections may offer a solution for complex pathogen occurrence pattern and interactions estimation. We have also explored viral occurrence patterns by developing log-linear models combined with Bayesian framework for network analysis.
Based on modeling the probabilistic associations between observed data points, that is, HPV infections, we simulated HPV networks from the observed HPVs cohort datasets. Our analysis outperformed in precision all oversampling methods tested for simulating large synthetic viral prevalence dataset from observed data. 20 We have also developed tools for multistate models based on ordinary differential equations coupled with their sensitivity equations for variance calculations. An application of these models was presented in 2020 and a methods article has been submitted. 21 The source code is available on the Comprehensive R Archive Network. 22 For applications based on microsimulation, we recently modeled for the cost-effectiveness of cervical cancer screening. 23 These results have been presented to the National Board of Health and Welfare, and we plan to use this model to evaluate the upcoming Swedish cervical screening guidelines.
HEAP technical framework The technical framework implements the management and the research frameworks into the HEAP informatics platform making possible that researchers, policymakers, and other stakeholders can manage and extract knowledge from exposome data. The technical framework consists of (1) HEAP IaaS: distributed high-performance computational resources, (2) HEAP PaaS: a software platform that integrates the computing resources and enables secure management of exposome data, and (3) analysis pipelines implemented in the platform for the processing of harmonized and interoperable heterogeneous data. The software platform will apply AI and advanced statistics analyses for the continuous identification and evaluation of the roles of multiple exposures and its impact on human health.
Hopsworks Hopsworks is the software platform that enables managing and analyzing data. It is the core of the HEAP platform, which interfaces the users with the software and the computational resources. Hopsworks stores and accesses files from a distributed file system, and runs jobs over multiple workers (script running is the background) running on different nodes through a resource manager. As machine learning (ML) support is paramount, Hopsworks provides managed access to multiple graphics processing units (GPUs) located over different nodes to the running applications. Additionally, the platform provides support to common data analytics tooling, ML libraries, stream processing, and metadata search and exploration. Hopsworks is an ecosystem of open-source libraries and frameworks, which is continuously upgraded to their most recent versions to provide optimum functionality to the users.
The Hopsworks platform is deployed as a testbed on the IaaS public cloud service (cPouta) 31 at the IT Center for Science Ltd (CSC). Integration with the CSC sensitive data services 32 (IC) has started with the integration of the remote authentication service. Hopsworks now provides a possible log-in mechanism and its own native user/password mechanism, as well as a CSC test user authentication service and Elixir AAI. 33 This integrated authentication mechanism can already be used on the HEAP testbed. Ongoing work includes integration with the CSC Sensitive Data Services and Resource Entitlement Management System (REMS). 34, 35 This integration will allow Hopsworks users and applications running on behalf of the user to access sensitive data stored in IC (provided via the CSC Sensitive Data Services) if the user is supposed to have access to it, as registered in REMS.
The aim is also to make use of CSC IaaS private cloud service (ePouta),  36  which is designed for processing sensitive data. Hopsworks provides its own internal catalogue for metadata, but this was in a simple key-value format, where the value can be of string type. As part of the HEAP project, we have extended this metadata format to what we call "schematized tags. " Through the schematized tags mechanism, we have extended what types the value can take and this includes primitives, JSON objects, and arrays. Not only can the metadata value take these complex values, but the metadata is searchable through each of its component values. The schematized tags follow the JavaScript Object Notation (JSON) schemas. 37 Besides the complex type that values can take, we can also define validation rules for metadata field values such as bigger, smaller than a required number, or the fact that a subfield might be optional or mandatory, regex expression defining acceptable values.
This is already implemented and present on the HEAP testbed. As work in progress, HEAP is working on: • Allowing composition and reusability of the schemas involved in the schematized tags mechanism • In addition to the search mechanism we provide, we will provide secured access to the metadata index for more complex user defined queries through Kibana. Seeing that many existing data analysis pipelines from the project involve custom native libraries, which are not fully supported in Hopsworks, we are currently working on providing support for running custom docker images, where the researchers can set up their own custom environments and run their applications.
Progress and future development and measurements HEAP platform will deliver exposome data and an agnostic and deterministic assessment of the exposome and its importance. Bioinformatics, machine learning, and advanced statistics will be implemented into the PaaS and tested on the use case cohorts. At the moment, one single instance of HEAP PaaS is deployed on the CSC IaaS as a proof of concept of integration of the PaaS and the IaaS. Metagenomics pipelines and machine learning approaches are being implemented to analyze HEAP's cohorts.
Collaboration HEAP will enable international collaboration to make possible that scientists from different countries can use the same computational platform, following the same ethicolegal requirements, and managing and sharing data in a standardized and efficient way. All the data and knowledge produced by the pilot projects involves close collaboration of all partners from different countries. Within the European Human Exposome Network, HEAP is participating in the working groups for Information and Communication, including managing the joint web site www. humanexposome. eu; on data standardization through the Metadata working group and will be summoning the ethicolegal working group.
Spatially-explicit forward simulations In addition to drawing simulated samples from a diffusion model, we performed spatially explicit individual-based forward-in-time simulations of selection act-6 ing on a beneficial allele using a new simulation framework implemented in the R package slendr  (Petr (2021) ). This package makes it possible to define spatiotemporal population models in R and then feeds them into the forward population genetic simulator SLiM  (Haller & Messer (2019) ) for generating genotype data. We introduced a single beneficial additive mutation in a single individual and let it evolve across the European landscape. Before applying our method on the simulated data, we sampled 1,040 individuals whose ages were log-uniformly distributed, to ensure that there were more samples closer to the present, as in the real data.
Dynamics of the rs4988235(T) allele Having tested the performance of our method on simulated data, we set out to infer the allele frequency dynamics of the rs4988235(T) allele (associated with adult lactase persistence) in ancient Western Eurasia. For our analysis, we used a genotype dataset compiled by  Segurel et al. (2020) , which amounts to 1,434 genotypes from ancient Eurasian genomes individuals, and a set of 36,659 8 genotypes from present-day Western and Central Eurasian genomes  (Ségurel & Bon, 2017; Heyer et al. , 2011; Marchi et al. , 2018; Liebert et al. , 2017; Gallego Romero et al. , 2012; Itan et al. , 2010; Charati et al. , 2019). After filtering out individuals falling outside of the range of the geographic boundaries considered in this study, we retained 1,332 ancient individuals.
Dynamics of the rs1042602(A) allele Next, we investigated the spatiotemporal dynamics of the spread of an allele at a pigmentation-associated SNP in the TYR locus (rs1042602(A)), which has been reported to be under recent selection in Western Eurasian history  (Stern et al. , 2019). For this purpose, we applied our method to the Allen Ancient DNA Resource data (AADR: https://reich. hms. harvard. edu/allen-ancient-dna-resourceaadr-downloadable-genotypes-present-day-and-ancient-dna-data), which contains randomly sampled pseudohaploid genotypes from 1,513 published ancient Eurasian genomes (listed in Supplementary Text 1), from which we extracted those genomes that had genotype information at this locus in Western Eurasia. We merged this dataset with diploid genotype information from high-coverage presentday West Eurasian genomes from the Human Genome Diversity Panel (HGDP)  (Bergström et al.
, 2020) , which resulted in a total of 1,040 individuals with genotype information at rs1042602, which were as input to our analysis. Geographic locations of individuals in the final dataset are shown in Figure  6. Similarly to our analysis of the spread of the allele in rs4988235(T), we inferred the dynamics of the rs1042602(A) allele separately for the time periods before and after 5,000 years BP. The inferred parameters for both time periods are summarised in table S6 and the allele frequency surface maps generated using these parameters are shown in figure  7b. The origin of the rs1042602(A) corresponds closely to the region where the allele initially starts to segregate in the time period between 7,500 and 10,000 years BP as seen in figure  7a. Estimates of the selection coefficient for both time periods (0.