dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 9 SSE3 (64), distantly related sequences were removed, and clades of closely related organisms (e. g. , same class) were trimmed to contain only representative sequences. With the final datasets, TCOFFEE v11 using MCoffee mode  (65, 66)  was used to generate a consensus alignment of the eight default alignment softwares. Alignments were manually refined when necessary. Ambiguously aligned residues were on September 15, 2020 http://advances. sciencemag. org/ Downloaded from removed using BMGE version 1. 12 (63) with the indicated thresh olds (data S1). ModelFinder  (67)  implemented in IQTREE v 1. 6. 10 (68) was used to select the most appropriate evolutionary model [including the Cseries mixture models  (69) ] for each protein (data S1). A total of 1000 ultrafast  (70)  and SHlike approximate likelihood ratio test (SHaLRT) (71) bootstraps were calculated using the bestscoring evo lutionary model for each protein.
The resulting ML tree was used as the guide tree for rapid approximation of posterior mean site frequency (PMSF) of the Cseries of mixture models  (72)  and 100 non parametric bootstraps. For HYDE, HYDF, HYDG, and PFO phylog enies, in addition to the nonparametric bootstraps discussed above, we calculated the transfer bootstrap expectation (73) implemented in IQTREE v 2. 0  (74). Rogue taxa were selected with RogueNaRok (75) using the nonparametric bootstrap trees and ML tree from these analyses under the default settings. Taxa identified as rogue were re moved from the original dataset, and the alignments and phylogenies were recomputed as described above. In initial phylogenetic trees for HYDF (see "HYDF family" in data S2), a clade composed of eukaryotes, Anoxychlamydiales, and additional mixed prokaryotic taxa (HYDF clade I) resolved on a long branch apart from other prokaryotic sequences (HYDF clade II), indi cating two paralogs.
2 ; figs. S3, S5, and S6; and data S2. For each gene, the sequence dataset (*. fasta), sequence alignment (*. tcoffee. fasta), BMGE masked alignment (*. bmge. *. fasta), PMSF phylogeny (*. pmsf. tre), and ultrafast boot strap phylogeny (*. uf. tre) are provided in the following data repository (Figshare DOI: 10. 6084/m9. figshare. 12387980). Additional data sets with the TBE values (*. tbe. tree) and rogue taxa removed (*. RR_ removed. *) for HYDE, HYDF, HYDG, and PFO are also pro vided. Accession numbers including "CAMPEP" or "CAMNT" de rive from the MMETSP database  (55).
Data visualization In R v. 3. 2. 2 (R Development Core Team, 2008), the package ggplot2 (77) was used for plots in Fig. 1  and fig. S1 , while the package genoPlotR  (78)  was used to generate a synteny plot for visualization of the genome organization of HYDA, NUOE, and NUOF (fig. S6 ). iTOL  (79)  was used to visualize protein domains and to map them to the corresponding sequences in phylogenetic trees (figs. S5 and S6). Figtree v1. 4. 2  (80) , iTOL  (79) , and Adobe Illustrator were used to visualize and edit phylogenetic trees.
Acknowledgments Funding: This work was supported by grants from the  Swedish Research Council  (VR grant  2015-04959 ), the  European Research Council  (ERC starting and consolidator grants  310039  and  817834 , respectively), and the  Swedish Foundation for Strategic Research  ( SSF-FFL5 ) to T. J. G. E. C. W. S. was supported by the  European Molecular Biology Organization  long-term fellowship ( ALTF-997-2015 ) and the  Natural Sciences and Engineering Research Council of Canada  ( PDF 487174-2016 ). L. E. was supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 704263  and by funding from the  European Research Council  ( ERC starting  grant no. 803151. D. T. was supported by the  Swedish Research Council   International Postdoc grant  ( 2018-06609 ).
Competing interests: The authors declare that they have no competing interests. Data and materials availability: In addition to data available in the Supplementary Materials, files containing sequence datasets, alignments, and phylogenetic trees in Newick format are archived at the digital repository Figshare DOI:  10. 6084/m9. figshare. 12387980. Whole Genome Shotgun  projects for metagenome assemblies  GS08_GC12_126 ,  GS10_PC15_940 ,  GS10_ PC15_1000 , and GS10_PC15_1060 have been deposited at DDBJ/ENA/GenBank under the accessions  SDBU00000000 ,  SDBV00000000 ,  SDBS00000000 , and  SDBT00000000 , respectively. The versions described in this paper are versions  SDBU01000000 ,  SDBV01000000 ,  SDBS01000000 , and  SDBT01000000 , with MAGs generated from each linked to  BioProject PRJNA504765.
EEG Acquisition and Preprocessing During the recall test, participant brain activity was recorded with a 64-channel cap connected to a BioSemi EEG amplifier (5 kHz) and with a sampling frequency of 1024 Hz. A finite impulse response Blackman bandpass filter was applied to the data with a lower cutoff frequency of 0. 1 Hz to avoid filtering artifacts  (Acunzo, Mackenzie, & van Rossum, 2012) , a higher cutoff frequency of 100 Hz and a transition bandwidth = 1. Data were down-sampled to 256 Hz. Trials were then epoched from -1 to 2 sec relative to picture onset, their baseline removed (-0. 2 to 0 sec) and rereferenced based on the average reference. Independent component analyses were performed on the whole data, and the components were visually inspected to remove the ones that did not correspond to EEG signal (eye and jaw movements, heart activity).
For all the participants, trials with extreme values for a channel (>100 or < -100 μV), an abnormal trend (slope max: 75 μV, R 2 >. 3), or an abnormal distribution (kurtosis outside 5 SDs of the kurtosis for each single electrode) were excluded from the analysis after visual inspection. All the preprocessing steps were done with EEGlab  (Delorme & Makeig, 2004). Importantly and following the recommendations in VanRullen (  2011 ), the analysis of the EEG data was restricted to the conditions that were manipulated by the experimenter. Indeed, introducing subjective responses in the comparisons such as participant memory judgment or selecting only the correct responses would likely produce biases in the analysis. Therefore, in the following ERP and MVPAs, trials were split according to the four retention intervals: HOURS, 1D, 3W, and NEW for snapshots respectively seen a few hours, 1 day, and 3 weeks beforehand, or never seen in the learning sessions.
Decoding Decoding analyses were performed by using an open source script that was used in the studies of  Crouzet, Busch, and Ohla (2015)  and Cauchoix, Crouzet, Fize,  and Serre (2016). 2  MVPAs were conducted on the same data used for the ERP analyses. The classification was performed on two classes: one class corresponding to snapshots seen during a specific learning session (HOURS, 1D, or 3W) and the other class corresponding to NEW snapshots. Trials were randomly split 120 times according to the Monte Carlo cross-validation (CV) procedure: 120 CVs. For each CV, the number of trials was equalized between the classes: 90% of the trials were used by the algorithm as a training set, and the remaining 10% were used as a test set. To reduce the effect of outlier values in the signal, the activity of each electrode was normalized across the trials: For each CV, the signal was scaled between 0 and 1 by using parameters that were estimated from the training set.
The time course of the representative channels FC1, C1, P3, and P10 are presented in Figure  6A. Late effects were mainly observed for the HOURS/NEW and 1D/NEW contrasts. We therefore tested whether significant differences could be detected for these two contrasts when ERPs were aligned to participant response onset (paired t test, p <. 05, cluster corrected). Again, no significant differences were observed. The topographical representation of the response-locked ERPs for the HOURS and NEW conditions is shown in Figure  6B. The analysis of the response-locked ERPs did not reveal any significant motor component that would be responsible for the ERP effects observed earlier. Overall, the ERP data showed that the brain processes involved in the recall of the audiovisual clips differed according to the retention interval.
Multivariate Pattern Analysis We performed MVPA on our EEG data by comparing the activity for snapshots seen in one of the three learning sessions (HOURS, 1D, or 3W) and NEW snapshots. For each participant, classifier performance was measured for the trial tests at the same time point as for the training sets (diagonal matrix) or for any other time points (off-diagonal matrix). Performance was averaged across the 11 participants and calculated using the area under the curve where chance level corresponded to the same classification performed using random labels (Figure  7A ). Classification performance was found to be significantly different (paired t test, p <. 05, cluster corrected) from chance level from around t = 500 msec poststimulus onset and until the end of the epoch for the HOURS/NEW comparison.
This provides further evidence toward the MTT. Further investigations should be carried out to compare these data with patients suffering from memory impairment. Notes 1. All the audiovisual clips and snapshots used in this experiment are freely available for research purposes and can be downloaded using the following link: https://data. mendeley. com/datasets/nh8xsmr289/draft?a=1813589b-7737-442d-9fcb-89fa0f3bee5d. 2. The open source script can be downloaded at https:// github. com/scrouzet/classifyEEG. no. 323711 to S. J. T. The authors also acknowledge the support of the French Agence Nationale de la Recherche under grant ANR-12-BSH2-0010 (project ELMA). We thank Emmanuel Barbeau for helpful discussions and Estelle Bonin for her assistance in the stimuli preparation. We are also very grateful for the feedback that the two anonymous reviewers gave us during the revision process.
In total, antibody-secreting cells from 14 LGI1-specific wells were isolated and genes were cloned and expressed as recombinant human IgG4 antibodies  (Clargo et al. , 2014; Tickle et al. , 2015). Immunoglobulin heavy (IGH) chain gene segments [variable (V), diversity (D) and joining (J)] and immunoglobulin light chain gene (V and J) sequences were analysed using IMGT/V-QUEST (http://www. imgt. org/IMGTindex/V-QUEST. php). Relative mAb binding characteristics were assessed using live HEK293T cells expressing full-length LGI1 with fluorescence intensity measured on a FLUOstar V R Omega Microplate Reader (BMG Labtech). The K d values were calculated by fitting specific binding data to a one-site hyperbola non-linear regression analysis using Equation 1: y ¼ B max Â ½x x ½ þ K d (1) For cross-competition assays, cells were sequentially exposed to an excess amount of the first mAb (10 lg/ml), followed by a fluorescently-labelled second mAb at 2.
Then, the sum of intensity above background was calculated using: S int ¼ X n x¼1 x Â x4k ½ (2) where k was defined as the 99th percentile of the first four frames (i. e. the first hour) and brackets are Iverson brackets returning 1 if x 4 k and 0 otherwise. For static quantification of internalization, three fields (each 709 Â 709 lm) per well were captured at random using an inverted confocal microscope (Zeiss LSM710, 20Â objective). After 96 h, pHrodo-positive neurons were identified by characteristic morphology and cell counting (means of three blinded observers). Fluorescence intensity was quantified using Equation  2 , where k was defined as the 99th percentile of the control mAb. The R package pixSum, freely available from https://github. com/jtheorell/pixSum, was used for the calculations of Equation 2 results and associated image processing.
WinWCP (University of Strathclyde) software was used for recording and Clampfit (Molecular Devices) for analysis. Long-term potentiation (LTP) was induced using a 20 Â theta-burst protocol comprising a block of four stimuli at 100 Hz repeated 20 times over 20 s. The magnitude of fEPSPs was determined as the gradient of the rising slope to avoid population spike contamination. Paired-pulse ratios were obtained by delivering two stimuli at an interval of 50 ms and expressed as fEPSP2/fEPSP1.
Acknowledgements We are grateful to the flow cytometry suites and the  Wolfson Imaging Centre (Christoffer Lagerholm) in the Weatherall Institute of Molecular Medicine and Biomedical Research Centre ,  John Radcliffe Hospital. NHS  Blood & Transplant have provided material in support of the research: the views expressed in this publication are those of the author(s) and not necessarily those of  NHS Blood & Transplant.
Funding S. R. I. is supported by the  Wellcome Trust  ( 104079/Z/14/Z ), The  UCB-Oxford University Alliance , BMA  Research Grants -2013 Vera Down grant ,  Epilepsy Research UK  ( P1201 ) and by the  Fulbright UK-US commission (MS-Research Society Award ). M. R. is supported by the  Austrian Science Fund  ( FWF J4157-B30 ). J. M. M. T. is supported by the  Ministry of Health, National Medical Research Council and National Neuroscience Institute, Singapore. T. Y. is supported by the  Ministry of Health, Singapore  through the  National Medical Research Council   Research Training Fellowship  ( NMRC/Fellowship/0038/2016 ). J. R. F. is supported by the  ANR  ( LoGIK ,  ANR-17-CE16-0022 ). P. W. is supported by the  UK NMO commissioning group  and receives funding from  Euroimmun AG. The work is supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC).
Species of Psychrobacter have been recovered through culture-based and sequenced-based methods from a range of animal microbiomes, including marine mammal skin  [19] , respiratory blow  [20] , and guts  [21] [22] [23] ; the gastrointestinal tracts of birds  [24, 25]  and fish  [26] ; and many nonhost environments such as sea water  [27] , sea ice  [28] , marine sediment  [29] , glacial ice  [30] , and permafrost soil  [31]. Some Psychrobacter species are capable of causing disease in mammalian hosts  [32, 33]. However, Psychrobacter infections are very rare, and the virulence factors involved are relatively uncharacterized. Intriguingly, a previous comparative genomics analysis of 26 Psychrobacter spp. and metadata gleaned from public sources revealed differences in cold-adaptation of protein coding sequences between warm-host-associated strains versus derived marine and terrestrial strains  [34].
Furthermore, warm-adapted strains were basal in the rpoB gene phylogeny, suggesting that Psychrobacter evolved from a mesophilic ancestor. These observations make Psychrobacter an interesting candidate to assess how ecotype maps onto phylogeny and source of isolation, and to probe into the evolutionary history of a genus with a wide habitat range. We tripled the collection of Psychrobacter genomes, which we use for phylogenomic analysis, and combine these data with extensive phenotyping applied consistently for all strains. We use a large collection of wild polar bear feces collected on ice and land to assess the presence of Psychrobacter ecotypes as a function of diet determined by cytochrome b barcode sequencing. Finally, we conducted tests with select strains for colonization of the mammal gut using germ-free mice.
Materials and Methods All software versions, parameters, and the relevant citations for our analyses are detailed in Table  S1. Moraxellaceae family genomics and phenotypic data. The Moraxallaceae family includes three well characterized genera: Moraxella, Acinetobacter and Psychrobacter. To build a family-level phylogeny, we downloaded genomes of 18 species of Acinetobacter and 18 species of Moraxella from NCBI (June 2020) (Table  S2 ). We also included 15 Psychrobacter genomes generated in this study (described below). We determined the phylogenetic relationship between the genomes using whole-genome marker gene analysis software PhyloPhlAn, and determined genome quality and summary characteristics using checkM and Prokka. The Moraxellaceae phylogenetic tree was visualized and annotated using the interactive Tree of Life (iTOL) web interface.
We analyzed the Moraxellaceae pan-genome using the PanX pipeline. The input genomes used by PanX were initially annotated using Prokka. After their assignment into orthologous clusters using MCL, we re-annotated gene clusters using eggNOG mapper. We explored genome content by calculating a distance matrix using the Jaccard metric through the R package ecodist from a binary gene presence-absence table, followed by dimensional reduction of that distance matrix through principle coordinate decomposition (PCoA) with the cmdscale function from the R package stats. We investigated variables contributing to the separation of the PCoA using the envfit function from the R package vegan; we tested whether genes significantly contributing to separation were "core" (genes present in 90% to 100% of strains), "shell" (genes present in greater than two strains, but in fewer than 90%), or "cloud" (genes present in only one strain), and if general gene function -summarized by Cluster of Orthologous Groups (COG) category -contributed to the separation.
We collected   [23, 24, 27, 28,. We used the R package phytools  [77]  to map the temperature ranges onto the phylogeny. Psychrobacter strains. We obtained 92 isolates of Psychrobacter from strain catalogues for phenotypic and genotypic characterization. These represent 38 validly published species of Psychrobacter as well as unclassified strains, all isolated from a wide variety of geographical locations and diverse environmental and host samples. All strains were purchased and maintained in compliance with the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity. For a full list of accessions and their catalogue, isolation, and cultivation information, see Table  S3. Unless mentioned, we grew accessions as recommended by the strain catalogue (medium and temperature) from which they were purchased.
Note that of the initial 92 accessions, 85 had genomes that passed all QC measures (Table  S4 ). All further analyses use these 85 accessions. We annotated genomes with Prokka and eggNOG mapper. A phylogeny of the accessions was generated using PhyloPhlAn, with Moraxella lincolnii as an outgroup. Again the phylogeny was visualized and annotated using iTOL. We used PanX to analyze the Psychrobacter pan-genome and R to explore gene presence-absence data as described above with the Moraxellaceae family. Pseudogenes were predicted using the DFAST core workflow. We used treeWAS to perform a pan-genome wide association study relating data collected in the phenotypic screen to genomic data. Potential cold-adaptive trait analysis. For every gene cluster of the Psychrobacter pan-genome obtained, we used DIAMOND to map the consensus sequence against the UniRef90 database, excluding results from the family Moraxellaceae.
Statistical analysis. We performed all data processing and statistical analysis using R or Python. We compared means between groups using a Kruskal-Wallis test, followed by a pairwise Wilcoxon rank sum test to identify which groups differed when more than two groups were compared. We tested differences in frequencies between groups with more than 10 observations with a χ 2 -test, repeated a 100 times with down-sampling in order to correct for sampling sizes between groups. We measured phylogenetic signal using a loglikelihood ratio test on Pagel's λ (Pagel's λ fitted using the phylosig function from the phytools R package, and the null hypothesis being λ = 0). When applicable, we tested for the confounding of phylogeny with our groups of interest using the aov. phylo function from the R package geiger.
Results Psychrobacter forms a clade whose basal members are of the Moraxella genus. To explore the evolutionary history of Psychrobacter, we built a phylogeny based on 400 conserved marker genes using publically available whole genomes derived from cultured isolates of 51 species from the Moraxellaceae family. We included 18 species each of Acinetobacter and Moraxella obtained from NCBI, and 15 Psychrobacter genomes that we generated in this study (Table  S2 ). For all genomes, we incorporated phenotypic data collected from previously published type-strain research. Our analysis shows the 51 species To assess what genes and gene functions may be contributing to the separation between the A-, M-and P-clades' gene presence/absence, we performed an envfit function analysis using the R package vegan.
FE and RE Psychrobacter spp. have differences in genomic content. We looked for genes differentiating the FE and RE ecotypes by performing a microbial pan-genome wide association analysis (pan-GWAS) with the R package treeWAS  [83]  using gene presence/absence data. While this analysis returned no significant results, strong phylogenetic signals in the gene presence/absence data may have resulted in a loss of power for the pan-GWAS. When exploring gene presence/absence via PCoA, accessions from the basal FE-only subclade cluster closely together, indicating similar gene content. In agreement with the phylogeny, these basal FE accessions are separated from the other accessions on PC1, while the derived FE and RE accessions are more scattered, indicating more diverse gene content (Fig  3B ).
As with the separation in the Moraxellaceae PCoA of gene content, the separation between the basal FE-clade and the rest of the accessions is due largely to presence/absence patterns in shell genes (present in greater than one strain but fewer than 90%), not core (present in between 90 to 100% of strains) or cloud genes (present only in one strain), and all contributing genes were not unique to either clade. The separation is most strongly driven by genes from the COG categories T, signal transduction (r 2 = 0. 65, p-value = 0. 001); U, trafficking and secretion (r 2 = 0. 63, p-value = 0. 001); P, inorganic ion transport and metabolism (r 2 = 0. 56, p-value = 0. 001), and X, unassigned or no homologs in the COG database (r 2 = 0. 51, p-value = 0. 001)(Fig 5B, Table  S5 ). We examined if there were qualitative differences in gene content between the FE and RE ecotypes, especially in functions related to host colonization versus cold adaptation.
Our data show that the largest phenotypic divide within the genus is the ability to grow at 37 o C, which we used to sort strains into FE and RE. FE strains make up the basal clade of the Psychrobacter phylogeny. Their smaller genomes show less cold-adaptation in their protein-coding genes than RE strains with proportionally fewer cold-adaptive proteins and more transposons. The three of four FE strains tested were able to colonize germ-free mice, whereas none of the RE strains could, indicating that growth at 37 o C may be necessary (although not sufficient) to colonize mammals. Opportunistic infections in mammals caused by Psychrobacter strains are limited to P. sanguinis, P. phenylpyruvicus, P. faecalis, and P. pulmonis  [32] , which while are all FE strains. Our results suggest that the FE strains are maintaining an ancestral ability to grow at mammalian body temperatures and colonize mammalian host bodies, while RE strains have adopted a psychrophilic lifestyle.
Subsequently derived Psychrobacter strains could have then acquired other genes providing the same FE phenotype as the basal strains, making pinpointing the genes responsible for the FE phenotype difficult. Genes that are unannotated and do not have homologs in databases such as UniRef or COG may also play a role in phenotypic and genomic differentiation of FE and RE strains, so future in vitro phenotypic screens may be helpful in assigning function to the many hypothetical genes in the Psychrobacter pan-genome. The difference in ecotypes could also be due to gene regulation rather than gene presence and absence. There has been some transcriptional work done in P. arcticus  [98] , but it focused on comparing gene expression at ambient temperatures (around 20 o C) to ultra-low temperatures (-10 o C) to study P.