dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequences of the selected taxa were concatenated using Alvert. py from the package Barrel-o-Monkeys (http://rogerlab. biochemistryandmolecularbiology. dal. ca/Software/Software. htm#Monkeybarrel). This resulted in alignments containing the following number of amino acids as a function of dataset and taxon sampling: 22,976 (SCPD49); 15,744 (BMC49); 84,810 (GBE49); 24,413 (SCPD36); 16,840 (BMC36); 96,640 (GBE36). Bayesian phylogenetic trees were inferred using PhyloBayes-MPI v1. 5  68  under the CAT-Poisson evolutionary model, applying the Dirichlet process and removing constant sites. Two independent MCMC chains for each dataset were run for >15,000 generations, saving one every 10 trees. Phylogenetic analyses were stopped once convergence thresholds were reached after a burn-in of 25% (i. e.
, maximum discrepancy < 0. 1 and minimum effective size > 100 using bpcomp). We also applied ML phylogenetic reconstruction using IQ-TREE  69  with the profile mixture model C60  23. Statistical support was obtained with 1000 ultrafast bootstraps  70  and 1000 replicates of the SH-like approximate likelihood ratio test  71. To alleviate the local computational burden, many of these analyses were carried out using CIPRES Science Gateway  72. Trees were visualized with FigTree (http://tree. bio. ed. ac. uk/ software/figtree/). To test if the best topology obtained was significantly better than other alternatives (Supplementary Data 2), we tested whether constrained alternative topologies could be rejected for different datasets. We used Mesquite  73   rozellids + microsporidians) and the monophyly of aphelids with either blastocladiomycetes, chytridiomycetes or chytridiomycetes + blastocladiomycetes.
The constrained topologies without any branch-length were reanalyzed with the -g option of IQ-TREE and the best-fitting model for each dataset. The resulting trees were then concatenated and AU tests were performed for each dataset with the -z and -au options as described in the advanced documentation of IQ-TREE. To minimize systematic error due to the inclusion of fast-evolving sites in our protein datasets, we progressively removed the fastest evolving sites at steps of 5% sites removed at a time. Among-site evolutionary rates were inferred using IQ-TREE -wsr option and its best-fitting model for each dataset (Supplementary Data 2). A total of 19 subsets were created for each dataset. We then reconstructed phylogenetic trees using IQ-TREE with the same best fitting model as for the whole dataset.
To know how supported were alternative topologies in bootstrapped trees, we used CONSENSE from the PHYLIP package  74  and interrogated the. UFBOOT file using a Python script (M Kolisko, pers. comm). Homology searches and phylogenetic analyses of specific proteins. Protein sets used in this study were obtained in May 2017 from the NCBI protein database (https://www. ncbi. nlm. nih. gov/protein/), except the following: Chromosphaera perkinsii, Corallochytrium limacisporum and Ichthyophonus hoferi genome data, obtained from the ref. 24 ; Spizellomyces punctatus, Gonapodya prolifera, Batrachochytrium dendrobatidis, Allomyces macrogynus, Catenaria anguillulae and Blastocladiella britannica, retrieved from MycoCosm portal, Joint Genome Institute* (http://www. jgi. doe. gov/); Parvularia atlantis (previously Nuclearia sp.
ATCC50694, from https://doi. org/10. 6084/m9. figshare. 3898485. v4); and Paramicrosporidium saccamoebae, from NCBI in January 2018. [* These sequence data were produced by the US Department of Energy Joint Genome Institute http://www. jgi. doe. gov/ in collaboration with the user community]. Query sequences to retrieve chitin and related cell-wall proteins (chitin synthases (CHS), chitin deacetylases (CDA), and chitinases (CTS)) were obtained from the study of Rozella allomycis  16  except for 1,3beta glucan synthase (FKS1), which was obtained from Saccharomyces cerevisiae S288C. We used sequences from Aspergillus fumigatus as initial queries for cellulases  34  and also opisthokont homologs retrieved by BLASTp against the Paraphelidium transcriptome. Homology was confirmed by searching for the identified sequences in the eggnog  38  annotation file (Supplementary Data 1), performing reverse BLAST against the non-redundant NCBI database and HMMER analyses  75.
Cell-wall proteins, notably chitinases and cellulases, were also annotated using CAZyme identification tools (http://csbl. bmb. uga. edu/dbCAN/annotate. php). We produced comprehensive alignments including sequences retrieved from previous studies and sequences identified by BLAST in GenBank or in the mycoCLAP database  35  with the MAFFT web server  64  applying the progressive L-INS-i algorithm (except for the multidomain CHS sequences for which E-INS-i was used). Alignments were trimmed from gaps and ambiguously aligned sites using Trimal  65  with the automated1 algorithm. ML trees were inferred using IQ-TREE web server  69  with the LG + G4 evolutionary model and visualized with FigTree. In the case of myosins, protein sequences were retrieved by querying the HMM profiles of the myosin head domain (Pfam accession: PF00063) against our database of Paraphelidium tribonemae proteins, using the hmmersearch utility of HMMER (http:// hmmer.
99 minimum correlation as convergence criterion, and 1000 replicates of the SH-like approximate likelihood ratio test. We classified the myosin homologs of Paraphelidium tribonemae according to the phylogenetic framework of Sebé-Pedrós et al  43. To characterize the classified homologs (Supplementary Data 5), we recorded the protein domain architectures of the full-length proteins using Pfamscan and the 29th release of the Pfam database  76. Comparative analyses of proteins involved in primary metabolism, cytoskeleton, membrane-trafficking, and phagotrophy. To get a broad view of the metabolic capabilities of Paraphelidium tribonemae in comparison with parasitic and free-living organisms, we performed a statistical multivariate analysis similar to that previously used for a similar purpose in Rozella allomycis  16.
We initially searched in P. tribonemae for the presence of 6469 unique orthologous groups as per eggnog  38  , corresponding to 8 primary metabolism categories (Gene Ontology, GO). The correspondence between GO terms and primary metabolism COGs was obtained from http://geneontology. org/external2go/cog2go, and was as follows: From these categories, we identified 1172 non-ambiguous orthologs in the P. tribonemae transcriptome that were shared with a set of other 41 protist species (Supplementary Data 3). These 41 protist species represent a broader taxonomic sampling as compared to the R. allomyces study  16. We included 11 opisthosporidians: Paramicrosporidium saccamoebae (Par_sa), Amphiamblys sp. (Amp_sp), Antonospora locustae (Ant_lo), Encephalitozoon cuniculi (Enc_cu), Encephalitozoon intestinalis (Enc_in), Enterocytozoon bieneusi (Ent_bi), Mitosporidium daphniae (Mit_da), Nematocida parisii (Nem_pa), Nosema ceranae (Nos_ce), Rozella allomycis (Roz_al), Paraphelidium tribonemae (Par_tr); 10 fungi covering most fungal phyla: Spizellomyces punctatus (Spi_pu), Gonapodya prolifera (Gon_pr), Batrachochytrium dendrobatidis (Bat_de), Allomyces macrogynus (All_ma), Catenaria anguillulae (Cat_an), Blastocladiella britannica (Bla_br), Mortierella verticillata (Mor_ve), Coprinopsis cinerea (Cop_ci), Ustilago maydis (Ust_ma), Schizosaccharomyces pombe (Sch_po); 2 nucleariids Fonticula alba (Fon_al), Parvularia atlantis (Par_at); 7 holozoans: Salpingoeca rosetta (Sal_ro), Monosiga brevicollis (Mon_br), Capsaspora owczarzaki (Cap_ow), Chromosphaera perkinsii (Chr_pe), Corallochytrium limacisporum (Cor_li), Ichthyophonus hoferi (Ich_ho), Sphaeroforma arctica (Sph_ar); 3 amoebozoans: Acanthamoeba castellanii (Aca_ca), Dictyostelium purpureum (Dic_pu), Polysphondylium pallidum (Pol_pa), Entamoeba histolytica (Ent_hi); the free-living apusomonad Thecamonas trahens (The_tr), Naegleria gruberi (Nae_gr) and 6 other eucaryotic parasites: Toxoplasma gondii (Tox_go), Plasmodium falciparum (Pla_fa), Trypanosoma brucei (Try_br), Leishmania major (Lei_ma), Phytophora infestans (Phy_in).
Finally, each sub-set of primary metabolism COGs was also analyzed separately (categories C, E, F, G, H, I, P, and Q) using the same hierarchical clustering as above to cluster species according to their metabolism gene content (based on Pearson + Ward). We carried out similar comparisons and statistical analyses for proteins involved in phagotrophy, membrane trafficking and cytoskeleton. Briefly, we looked for proteins broadly related to those systems (actin cytoskeleton, exocytosis, fungal vacuole, exosome, endosome, mitochondrial biogenesis, ER-Golgi, lysosome, autophagy, peroxisome) in the KEGG BRITE reference database (http://www. genome. jp/kegg/brite. html). We identified 849 proteins related to membrane trafficking (ko04131), 279 cytoskeletal proteins (ko04812) and 361 proteins related to phagotrophy (KEGG map04144_endocytosis, map04145_phagosome, map04146_peroxisome, map04142_lysosome, map04810_actin_regulation, map04141_autophagy and map04071_-sphingolipid_signal), which were used to carry out PCoA and clustering analyses as above (Supplementary Fig.
All proteins from the UniProt-SwissProt database were searched against the entire set of 14,095 HMMs. Each HMM was assigned a best-hit protein annotation from the UniProt-SwissProt database. KEGG ortholog identifiers (IDs) associated with each HMM were obtained from UniProtKB IDs and KEGG ortholog IDs using the mappings available from UniProtKB. Additional KEGG ortholog IDs were mapped to the HMMs by searching the HMMs against all proteins annotated to a given KEGG ortholog ID. The best hit bit score among those proteins to the HMM was compared to the best-hit bit score of the UniProtKB annotation for that HMM. If the best hit KEGG ortholog ID bit score was at least 80% of the best hit UniProtKB protein bit score, and there was no KEGG ortholog ID already associated with the UniProtKB annotation, the KEGG ortholog ID was transferred to that HMM.
To build a presence-absence matrix associated with KEGG ortholog IDs, the HMMs were searched against all proteins in a genome. Best hit HMMs below threshold (total protein e-value ≤ 1 × 10 -5 and a best single domain evalue ≤ 1e-4) were assigned for each protein in a genome. The best hit HMMs and their associated KEGG ortholog IDs were used for presence/absence calls. If at least one protein in a genome had a best hit to a given HMM, the associated KEGG ortholog was considered present for that genome. From a total of 1568 orthologs associated with the KEGG maps of interest, 633 KEGG ortholog IDs were identified in the UniProtKB annotations of the HMMs. An additional 62 KEGG ortholog IDs were mapped to HMMs by considering similarity of bit scores between the best UniProt/SwissProt protein hit to the HMM and the best KEGG ortholog ID protein hit to the HMM (Supplementary Data 3).
Those 695 KEGG ortholog IDs were used to perform the same multivariate statistical analyses as per the metabolism section for the 41 taxa. The phagolysosome KEGG maps in Paraphelidium tribonemae and Rozella allomycis were compared using KEGG tools. To do so, we first annotated the two protein sets using BlastKOALA  81  with eukaryotes as taxonomy group and genus_eukaryotes KEGG GENES database. We then uploaded our annotations in the KEGG Mapper Reconstruct Pathway and BRITE servers.
Acknowledgements We are grateful to  Wayne Pfeiffer  and  Mark Miller  from the  CIPRES Science Gateway  (http://www. phylo. org/)for computational resources and personal assistance. We thank  Victoria S. Tcvetkova  for micromanipulating infected Tribonema sp. filaments in view of establishing clean aphelid cultures,  Alex de Mendoza  for providing CHS alignments,  Philippe Deschamps ,  Rafael Ponce ,  Guillaume Reboul ,  Ana Gutiérrez-Preciado ,  Ares Rocañín-Arjó  and  Ricardo Rodríguez de la Vega  for discussion on bioinformatics and statistics aspects, and  Peter Kubicek  and  Bernard Hernissat  for helpful discussion about cellulases. Research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Program  ( ERC Grant Agreement 322669 ProtistWorld ).
We then fitted a MAR(1) model to this dataset, and stored the estimates and confidence intervals for elements of B and of the vector of environmental effects C. The MAR(1) models were fitted using the MARSS package  (Holmes et al. , 2012)  in R, with the default EM algorithm. Confidence intervals were computed using the estimated Hessian matrix, or were obtained through parametric bootstrap (as implemented in the MARSS package) if that estimation failed. Parametric bootstrap was not used systematically for computing time reasons, but showed similar results for a tested subset (not shown). For each model structure and process error level for species 1, 1000 simulation experiments were performed with the parameters drawn at random and independently. (5) J = 1 -α 11 e n * 1 -α 12 e n * 2 -α 21 e n * 1 1 -α 22 e n * 2.
| Do MAR(1) models accurately capture the environmental forcing and provide reliable short-and long-term predictions? To check for recovery of the environmental effect by the MAR(1) model, we compared the fitted environmental effects c 11 and c 12 as well as their 95% confidence intervals to the environmental forcing parameters (q and 0, as only species 1 is affected by the environment). We used the Absolute Scaled Error (ASE,  Hyndman & Koehler, 2006)  on the next point of the simulated data series as a quantitative measure of short-term forecast accuracy. ASE is a scale invariant measure of forecast accuracy that allows comparison across datasets with different scales of variation, is less sensitive to outliers and has already been used to evaluate the forecast accuracy of MAR(1) models, among others, on real time series  (Ward et al.
, 2017) , where the number of potential parameters almost always outgrow the size of the dataset (this would have occurred even with 12 species for T = 100). Regularization  (Basu & Michailidis, 2015)  or dimension reduction  (Ovaskainen et al. , 2017)  techniques may allow to fit large MAR(1) models to shorter time series than we do here, though we believe that a proper evaluation of interaction (sign) recovery may require stricter evaluation criteria than those currently employed (see Supporting Information S2). A sometimes neglected issue that becomes even more important as assumptions on web topology are introduced for model fitting (e. g. sparsity, latent variables structuring B), is that the underlying topology of the interaction web is never fully known. Thus, close attention should be paid to the consequences of mis-specifying the topology of net interaction strength prior to MAR(1) fitting, and how robust MAR(1) model estimations are to increases in the proportion of hidden players (e.
DATA ACCE SS I B I LIT Y The R code for running the main simulation experiment is available on FigShare: https://doi. org/10. 6084/m9. figshare. 6127877. v1 (Certain, 2018). The R code for running the food web experiment is available on GitHub: https://github. com/fbarraquand/MAR_food-Web_MEE. The associated DOI for the food web experiment is as follows: DOI:10. 5281/zenodo. 1218024.
Participants In this study, we first sought to leverage large neuroimaging datasets to yield greater statistical power for identifying subtle effects. To achieve this, we utilized the ABIDE datasets (ABIDE I and II) (see Supplementary Table  S1  and S2 for full list of sites used in the current analyses). Groups were subsequently matched on age using the non-parametric nearest neighbour matching procedure implemented in the Matchit package in R (https://cran. rproject. org/web/packages/MatchIt/index. html)  24. After matching case and control groups we were left with a sample size N=942 per group (Table  1 ). Because of power limitations in past work with small samples, we conducted an a priori statistical power analysis indicating that a minimum case-control effect size of d = 0. 168 could be detected at this sample size with 80% power at a conservative alpha set to 0.
005  25. For analyses looking at brain-behaviour associations, we examined a subset of patients with the data from the SRS (N=418) and ADOS total scores (N=521). Resulting surfaces were constrained to a spherical topology and corrected for geometrical and topological abnormalities. Cortical thickness of each vertex was defined as the shortest distance between vertices of the GM/WM boundary and the pial surface  28. Because surface reconstruction failed for 13 subjects (out of 1114), we chose to not conduct manual segmentations and excluded these subjects from any subsequent analysis. Across both ABIDE I and ABIDE II cortical thickness was extracted for each subject using two different parcellations schemes: an approximately equally-sized parcellation of 308 regions (~500mm 2 each parcel)  29, 30  and a parcellation of 360 regions derived from multimodal features extracted from the Human Connectome Project (HCP) dataset  31.
Statistical analyses There are likely many variables that contribute to variability in CT between individuals and across the brain. In order to visually assess the contribution of some prominent sources of variance we adopted a visualization framework derived from gene expression analysis (http://bioconductor. org/packages/variancePartition)  34  and included the most commonly available covariates in the ABIDE dataset: Age, Gender, Diagnosis, Scanner Site, Full-scale IQ, Verbal IQ, Handedness, SRS and Set (ABIDE I or ABIDE II). Given that ABIDE was not designed as an integrated dataset from the outset, it seems plausible that scanner site might be related to autism or autism-related variables (e. g. , some sites might have different casecontrol ratios or only recruited specific subgroups).
Results Case-control differences versus age-related normative modelling Our first analysis examined conventional case-control differences. As expected from prior papers utilizing large-scale datasets for case-control analysis (e. g. ,  5, 19  ), a small subset of regions (15%, 49/308 regions) pass FDR correction. Of these regions, most are of very small effect size, with 13 of the detected 49 regions showing an effect less than 0. 1 standard deviations of difference (Figure  3A  and 3B ). We suspected that such small effects could be largely driven by a few ASD patients  39  with highly age-deviant CT. Because we also had computed w-scores from our normative age-modelling approach, we identified specific 'statistical outlier' patients with w-scores > 2 standard deviations from typical norms and excluded them from the case-control analysis.
Associations between individualized metrics of age-related CT deviance and behavioural measures of symptomatology An additional advantage of the use of normative modelling over the traditional case-control modelling is that we can use the individualized deviation as a novel metric for finding associations with phenotypic features. Here we use w-scores to compute Spearman correlations for the most commonly shared phenotypic features in the ABIDE dataset: ADOS, SRS, SCQ, AQ, FIQ and Age. After correcting for multiple comparisons across phenotype and region (6 phenotypic measures * 308 regions = 1848 tests) we identified a number of brain regions that survive multiple comparison corrections for the SRS and ADOS scores (Figure  5  and supplementary figure  S4 ). SRS is associated with w-scores primarily in areas of lateral frontal and parietal cortex, while ADOS is associated with w-scores primarily in lateral and inferior temporal cortex.
Discussion In the present study, we find that with a highly-powered dataset, conventional analyses reveal small case-control differences in cortical thickness in autism restricted to a small subset of regions. In general, this idea about subtle effect sizes for case-control comparisons is compatible with other recent papers utilizing partially overlapping data -Haar and colleagues utilized only ABIDE I data  5  , while van Rooji and colleagues  19  utilized both ABIDE I and II dataset combined with further data from the ENIGMA consortium. While these statements about small effect sizes are not novel, we contribute a novel idea here in our findings that suggest that even these small effect sizes may be misleading and overoptimistic. Utilizing normative modelling as a way of identifying and removing CT-deviant outlier patients, we find here that most small case-control differences are driven by a small subtype of patients with highly CT-deviance for their age.
However, these analyses largely reinforced the notion that at least within this large dataset, autism related variation of cortical thickness is in fact largely nested within the normal variation, and that a good explanatory predictor of such variation is age. Finally, there are a number of caveats to consider in the present study. First and foremost, the present data are cross-sectional and the normative age modelling approach cannot make claims about trajectories at an individual level. With longitudinal data, this normative modelling approach could be extended. However, at the moment the classifications of highly age-deviant CT individuals are limited to static normative statistics within discrete age-bins rather than based on statistics from robust normative trajectories. The dataset also represents ASD within an age range that misses very early developmental and also very late adulthood periods.
The dataset also presents a post-hoc collection of sites accumulated through the ABIDE initiative, whereby scanners, imaging acquisition sequences and parameters, sample ascertainment, etc, are highly heterogeneous. As a result, we observed that site had a large effect on explaining variance in CT and this is compatible with observations made by other studies  5. Furthermore, it is likely that there may systematic interaction between scanner site and some variables of interest such as age (e. g. different scanning sites will likely have recruited specific age cohorts). Future work with more homogenous imaging sequences, scanner hardware, etc, that bolster multi-site combination of data is warranted in order to reduce this pronounced site related issue. In conclusion, the present study showcases a novel normative age modelling approach in ASD and one that can significantly impact the interpretation of conventional case-control modelling, but which can also shed significant new insight into heterogeneity in ASD.
We show that results from case-control analyses, even within large datasets, can be highly susceptible to the influence of 'outlier' subjects. Removing these outlier subjects from analyses can considerably clean up the inferences being made about on-average differences that apply to a majority of the ASD population. Rather than only being nuisances for standard group-level analyses, these outlier patients are significant in their own light, and can be identified with our normative age modelling approach. Normative models may provide an alternative to case-control models that test hypotheses at a group-level, by allowing additional insight to be made at more individualized levels, and thus help further progress towards precision medicine for ASD.
After gathering all the data, we discarded the irrelevant publications. We put the collected data in order based on country, year, and the phlebovirus species complex. We obtained all the accessible virus sequences from Balkan countries from GenBank. We aligned 589 nt partial nucleoprotein sequences and analyzed them with MEGA software version 6 (https://www. megasoftware. net/). We constructed a phylogenetic tree using the neighbor-joining method (Figure  1 ) and tested the robustness of each node by 1,000 bootstrap replicates.