dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Detailed participant information has been reported previously  (Whalley et al. , 2015). Briefly, participants were recruited as part of the Scottish Family Mental Health Study, approved by the Multicentre Research Ethics Committee for Scotland (09/MRE00/81). Detailed clinical and MRI data were obtained for HC (n=41) and patients with a DSM-IV diagnosis of  SCZ (n=38)  or BD (n=20) aged between 18 and 67 years. Clinical diagnoses were established using the structural interview for the DSM-IV (SCID;  (First et al. , 2002)  conducted by one of two trained psychiatrists. For analyses purposes, SCZ and BD participants were combined into one patient group. Table  1  shows demographic information for both groups. -  ---------------------------------- Insert Table  1  Here -----------------------------------.
2, 300kb window) was performed to create a SNP-set in approximate linkage equilibrium. Marker weights (logarithm of the Odds Ratio) and p-value association statistics for SNPs were derived from the most recent PGC GWAS of schizophrenia (9. 8 million autosomal SNPs) (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Five scores were generated for each individual, using SNPs selected according to the significance of their association with the phenotype in the discovery GWAS at nominal P-value thresholds of 0. 01, 0. 05, 0. 1, 05, 1, as previously described  (International Schizophrenia Consortium, 2009). These data were not available for 11 of the patients and 8 of the controls and they were therefore excluded (Patients=46, HC=33). Multi-dimensional scaling (MDS) was conducted to identify outliers within the population stratification using previously described protocols (ENIGMA2 Genetics Support Team, 2013).
Statistical analysis Statistical analyses were conducted in R (v3. 2. 2). The effects of left and right global, frontal and temporal CT were investigated in line with our hypotheses. SCZ and BD patient data were combined into one patient group due to overlap between genetic risk, environmental risk and structural MRI findings. However, a secondary analysis examining the patients separately was performed and reported in Supplement 3. Firstly, we assessed whether an accumulative ERS and, post-hoc, individual environmental risks were related to CT. Secondly, we tested for associations between CT and PGRS-SCZ and finally, we calculated whether any environmental effects remained after controlling for potential genetic effects. As the aim of the study was to determine whether there was an effect of environmental and polygenic risk on CT, and more environmental information was available for patients than controls, analyses were conducted separately for each of these groups.
Lilly. HCW is supported by a JMAS SIM Fellowship from the Royal College of Physicians of Edinburgh and by an ESAT College Fellowship from the University of Edinburgh. AMM, SML and HCW have also previously received support from Pfizer (formerly Wyeth). These received funds do not present a conflict of interest with the present study. JD and NB were full time employees and shareholders of Pfizer at the time of these studies. None of the other authors has any biomedical financial interests or potential conflicts of interest to disclose. consisting of the Universities of Edinburgh, Aberdeen, Dundee and Glasgow, the four associated NHS Health Boards (Grampian, Tayside, Lothian and Greater Glasgow and Clyde), Scottish Enterprise and Pfizer. The Dr Mortimer and Theresa Sackler Foundation also offered financial support for imaging aspects of this study.
Acknowledgments We acknowledge  Douglas Blackwood  ( University of Edinburgh ) for his assistance with study design and data collection. The investigators also acknowledge the  Scottish Mental Health Research Network  (http://www. smhrn. org. uk) for providing further assistance with participant recruitment and cognitive assessments. We would like to thank both, the participants who took part in the study and the radiographers who acquired the MRI scans. Scans were collected at the  Clinical Research Imaging Centre  (http://cric. ed. ac. uk). This work was funded by an award from the  Translational Medicine Research Collaboration  ( NS_EU_166 )a consortium.
Applied to our data set, this approach estimated blocks of cotransferred and coduplicated genes, enabling us to test hypotheses on how cofunctioning gene clusters were formed. Then we compared the level of functional cooperation of genes within blocks of cotransferred clade-specific genes to the expectation under a neutral model of horizontal gene transfer where genes are randomly picked from the donor genome. This comparison showed that clade-specific genes were more functionally related than expected, supporting the hypothesis that domestication of at least some clade-specific genes resulted from ecological selection. Our estimated pangenome history-from single gene trees with transfer and duplication events to blocks of coevolved genes and functional annotations-was compiled in an integrative database called Agrogenom, which can be visualized and queried through an interactive web interface accessible at http://phylariane.
Genome Sequencing and Assembly Genomic DNAs of the 16 At strains (table  1 ) extracted with the phenol-chloroform method were used to prepare libraries with DNA sheared into 8-kb inserts (median size). Raw sequence data were then generated using 454 GS-FLX sequencer (Roche Applied Sciences, Basel, Switzerland) with a combination of single-read (SR) and mate-pair (MP) protocols that yielded coverage ranging from 6. 5Â to 11Â and from 5Â to 8Â, respectively (supplementary table  S1 , Supplementary Material online). Genome sequences were then assembled with Newbler version 2. 6 (Roche Applied Sciences, Basel, Switzerland), using 90% identity and 40-bp thresholds for alignment of reads into contigs and the "-scaffold" option to integrate duplicated contigs into the scaffold assembly. Virtual molecules (chromosomes and plasmids) gathering scaffolds were manually created on the basis of plasmid profiles obtained from Eckhart gels (data not shown) and minimizing rearrangements between closely related genomes by taking into account whole-genome alignments obtained with the NUCmer program from the MUMMER package version 3.
Genomic Sequence Data Set The study focused on the Agrobacterium biovar 1 species complex a. k. a. A. tumefaciens (At) with an original data set of the aforementioned 16 new genomes, plus six publicly released ones  (Goodner et al. 2001; Wood et al. 2001; Li et al. 2011; Ruffing et al. 2011; Wibberg et al. 2011; Hao, Lin, et al. 2012; Hao, Xie, et al. 2012). These 22 genomes covered ten closely related but genomically differentiated species (G1 to G9 and G13), with up to five isolates per species. The data set also included all Rhizobiaceae genome publicly available at the time of the database construction (spring 2012), and a few more distant relatives from the Phyllobacteriaceae and Rhodobiaceae families (table 1 and supplementary fig. S1 , Supplementary Material online).
Homologous Gene Family Database Based the 47 complete genome sequence data set, we built a database of homologous gene families following the model of Hogenom databases  (Penel et al. 2009). All annotated protein coding sequences (CDSs) were extracted and translated into protein sequences on which a all-versus-all pairwise BLASTP similarity search was performed to build a similarity network. Homologous gene families were derived from the connected components of the network using HiFix  (Miele et al. 2012). Gene family sequences were then aligned at the protein level using MUSCLE  (Edgar 2004 ) and reversetranslated into CDS alignments with pal2nal  (Suyama et al. 2006).
Reference Species Tree To construct the reference species tree, we used 455 unicopy core gene families (i. e. , families with exactly one copy per genome, listed supplementary table  S2 , Supplementary Material online), proceeding to 500 jackknife samples (draws without replacement) of 25 gene alignment sets, which were each concatenated and used to infer a maximum-likelihood (ML) tree using PhyML  (Guindon and Gascuel 2003)  using the same parameters as for gene trees (see supplementary text, Supplementary Material online). The reference phylogeny was obtained by making a consensus of this 500-tree sample with the CONSENSE algorithm from the Phylip package  (Felsenstein 1993) , and branch supports were derived from the frequency of the consensus tree bipartitions in the sample (supplementary fig.
S3 , step 6, Supplementary Material online). This gave us a final estimate of the collection of duplication and horizontal transfer events leading to the emergence of new gene lineages. We then defined subfamilies of orthologues (nested in homologous gene families) as the descendants of every gene gain (ODT) event (supplementary fig. S3 , step 6, Supplementary Material online). Finally, we used the Wagner parsimony algorithm implemented in the Count program  (Cs} uro ¨s 2008)  to estimate scenarios of orthologous subfamily evolution, where transfers can be inferred to explain heterogeneous profiles of gene occurrence. This led to the annotation of 19,553 additional transfer events (supplementary fig. S3 , step 7, Supplementary Material online). The illustrated description and programming details of the reconciliation pipeline used in this studies are available at: https://github.
Leaf Block Event Inference Using a greedy algorithm similar to that defined by  Williams et al. (2012) , we built leaf block events by iterative inclusion of events from contiguous gene families with compatible coordinates. For each replicon (chromosome or plasmid) in the database, we iterated over each gene following their position on the replicon; the nodes on the reconciled gene tree lineage leading to this gene were evaluated from tip to root. If a node was associated to an ODT event, we initiated a leaf block event containing this event as seed, and set the block coordinates as those of the seed ODT event. Then we looked for a similar event in the gene tree of the direct neighbor gene, using the same procedure to scan its lineage from tip to root. If the event associated to a node was of the same nature (O, D, or T) and with compatible coordinates (fig.
Functional Homogeneity of Gene Groups To measure to which extent cotransferred genes showed coherence in the functions they encoded, we used metrics of semantic similarities of the Gene Ontology (GO) terms annotated to the gene products. First, we retrieved GO annotations from UniProt-GOA (http://www. ebi. ac. uk/GOA/downloads, last accessed December 7, 2017)  (Dimmer et al. 2012)  for public genomes, and used a similar pipeline of association of GO terms to gene products to annotate the genomic sequences produced for this study. The results of several automatic annotation methods were retrieved from the PkGDB database  (Vallenet et al. 2013 ) based on similiraty searches: HMM profile searches on InterPro, HAMAP, and PRIAM databases and BLASTP searches on the SwissProt and TrEMBL databases (as of the February 5th, 2013), with a general cut-off e-value of 10e-10.
GO annotations were then mapped to gene products using mappings between those method results and GO terms as provided by Uniprot-GOA for electronic annotation methods (http://www. ebi. ac. uk/GOA/ ElectronicAnnotationMethods, last accessed December 7, 2017): InterPro2GO, HAMAP2GO, EC2GO, UniprotKeyword2GO, UniprotSubcellular_Location2GO. The annotation data set was limited to the electronically inferred data to avoid biases in the annotation of certain model strains or genes. The resulting functional annotations of proteomes were analyzed in the context of Gene Ontology term reference (full ontology file downloaded at http://www. geneontology. org/GO. downloads. ontology. shtml, last accessed December 7, 2017)  (Ashburner et al. 2000). Functional homogeneity (FH) within a group of genes is defined as the average value of the pairwise functional similarities between all gene products in the group, each of which is the average value of pairwise similarities between all terms annotated to a pair of genes.
Similarities were measured using the Rel (within a gene) metric and the funSim metric (between genes)  (Schlicker et al. 2006; Pesquita et al. 2009 ). Computations were done using a custom Python package derived from AIGO package v0. 1. 0 (https://pypi. python. org/pypi/AIGO; last accessed December 7, 2017). To assess if cotransfer of genes was associated with coherent functions, we compared the FH of cotransferred gene blocks to that of random groups of genes, obtained either FIG. 1. Continued and one subsequent gene loss. (B) Scheme of block event estimation. Origination, duplication and transfer events were first estimated separately in each gene family (1); for the sake of clarity, the example shows only transfer events, represented as arrows on gene tree branches (top) and between species tree branches (bottom).
Agrogenom Database All data about genes (functional annotations and gene families), genomes (position of genes, architecture in replicons. ), the species tree (nodes and taxonomic information), reconciliations (gene trees and ODT events), block events, inference analyses (parameters, scores. ), and all other data relative to the present work were compiled in a PostgreSQL relational database called Agrogenom. The database schema, input data and build procedure are available at https://github. com/flass/agrogenom/tree/master/pipeline/database; last accessed December 7, 2017; its content is browsable through a web interface at http://phylariane. univ-lyon1. fr/db/agrogenom/3/, last accessed December 7, 2017.
Genomic Data Set and Reference Species Tree To explore the genomic diversity of the Rhizobiaceae pangenome, we gatherred 47 genomes from the Agrobacterium, Rhizobium, Sinorhizobium/Ensifer, Mesorhizobium/ Chelativorans, and Parvibaculum genera into the Agrogenom database. These genomes contain 281,223 coding sequences (CDSs, or genes hereafter) clustered into 42,239 homologous gene families. Out of these families, 27,547 were singletons with no detectable homologues (ORFan families) and 455 were found in exactly one copy in all 47 genomes (unicopy core gene families). Following the procedure used in  Abby et al. (2012) , a species phylogeny was inferred from the concatenation of unicopy core gene family alignments, using jackknife resampling of genes to compute branch supports (supplementary fig.
Identification of Clade-Specific Genes in A. tumefaciens Key Clades We investigated the histories of gene gain and loss in the clades of At to identify the synapomorphic presence/absence of genes in these clades. We used an automated method that recognizes profiles of contrasted gene occurrence among sister clades by spotting ancestral gene gains or losses that resulted in their conserved presence or absence in the descendant clade (see Materials and Methods). Doing so, we accounted for convergent gains/losses of orthologous genes in distant clades, notably in cases of a transfer from one clade ancestor to another; this allowed us to evidence the specific sharing of genes between nonsister species of At. Listings of clade-specific genes of those key At clades can be found in Data Set S1, Supplementary Material online, or can be browsed on the Agrogenom database website http://phylariane.
We found that different pAt types were restricted to certain genomic backgrounds (based on their replication gene phylogenies) and carried clade-specific gene clusters at the species level (in G1, G8, G4, and G7 species) or higher (in [G6-G8] clade) (supplementary figs. S11, S12, S13, and S14 and supplementary text, section 8, Supplementary Material online). pAts therefore appear as core replicons of a majority of At species. In addition, while many megaplasmids of the same repABC family are known to recombine intensely within species  (Kumar et al. 2015; Epstein et al. 2012) , the occurrence of clade-specific genes on pAts and never on the other plasmids (pTis and smaller 2) Detailed annotation of the sequences at the tip of the tree, including locus tag (linking out to MaGe genome browser), chromosomal location, taxon name, database cross-references, etc.
Acknowledgments This project was supported by the  French National Research Agency (ANR , http://www. agence-nationale-recherche. fr/; last accessed December 7, 2017) grant  ECOGENOME  ( ANR-BLAN-08-0090 ) and  ANCESTROME  ( ANR-10-BINF-01-01 ) and by  AGROMICS grant  from the  ENVIROMICS challenge of the Interdisciplinary Mission of the French National Centre for Scientific Research (CNRS). This work was performed using the computing facilities of the  CC LBBE/PRABI. We thank the  LABGeM  ( CEA/IG/Genoscope & CNRS UMR8030 ) and the  France Genomique National  infrastructure (funded as part of the " Programme Investissement d'Avenir"  of the  ANR , contract  ANR-10-INBS-09 ) for support within the MicroScope annotation platform. We thank the associate editor  David Bryant  and two anonymous reviewers for their thoughtful comments on the manuscript, and  Annie Buchwalter  for her careful correction of English language.
The 16 new genome sequences used in these projects were have database under the accessions LN999991-LN999996, LT009714-LT009764, and LT009775-LT009780. Accession numbers of all genomic data used in this data set are listed table 1. All other relevant data (output of analyses) of this project have been deposited on Figshare under the project accession 20894, available at https://figshare. com/projects/Ancestral_genome_reconstruction_reveals_the_history_of_ecological_diversification_in_Agrobacterium.
Participants The THBP  (Summers et al. , 2013 ) is a prospective longitudinal study of older adults engaging in university-level education. The THBP sample was recruited progressively from 2011 to 2014 and has undertaken annual comprehensive assessments. Data analyzed in the present article were collected from 459 adults aged between 50 and 79 years who had participated in the THBP as of the 31 December, 2014. Inclusion criteria for entry into the THBP were that participants were aged 50-79 years at the time of entry and were healthy. Participants were excluded from entry into the THBP if they reported a diagnosis of a condition that is independently associated with impairments to cognitive function (dementia; multiple sclerosis; prior head injury requiring hospitalization; epilepsy; cerebrovascular complications including stroke, aneurysm, transient ischemic attacks; poorly controlled diabetes; poorly controlled hypertension or hypotension; other neurological disorders [e.
Procedure After obtaining consent, the elements of the full THBP test battery used in the present analysis were administered to each participant in the following order: WTAR, DRS-2, Medical Health Questionnaire, PAL, RAVLT, LMI, SSP, Digit Span, SWM, Letter-Number Sequencing, LMII, vocabulary, comprehension, Boston Naming Test, RVP A', STROOP C, TMT B, and HADS. An approximate 20-minute delay occurred between the administration of LMI and LMII. Lifetime Experience Questionnaire (LEQ;  [37] ), WTAR, and DNA data were only collected once, at baseline. The full THBP took approximately 4 hours to complete, and subjects were encouraged to take short breaks as needed to avoid fatigue  [24]. Participants were reassessed at 1-year intervals (61 month) for a total of 4 years (baseline-T0, T1, T2, and T3).
FLUKA2011 FLUKA was the first package used in this work, and its capabilities to model radiation transport for most common scenarios is well suited to the evaluation of conversion media, however, 110 it is subject to certain limitations. It treats transport of what it refers to as low energy neutrons, neutrons with energy < 20 MeV with a system using energy groups. This does not readily permit the production and transport of correlated secondary particles, and instead deposits all energy from a nuclear interaction and its subsequent secondaries in an averaged fashion along the particle track, by use of calculated kerma factors. This leads to non representative spectra when attempting to fully simulate detector energy deposition, as this is reliant on accuracy of each event rather than on average.
aeruginosa (PDB code 1FLG) revealed that, similar to other characterized representatives of the PQQ-dependent ethanol dehydrogenase type, the PedE protein harbors a serine residue at amino acid position 295 that is involved in the coordination of the Ca 2ϩ ion (Fig. 1A ). In contrast, in PedH, this residue is an aspartate (Fig. 1B ). As this aspartate residue has recently been associated with the coordination of trivalent lanthanide ions in the active site of PQQ-dependent methanol and ethanol dehydrogenases in methylotrophs  (15, 16) , we tested PedH for activity with ethanol in the presence of a variety of REMs (Fig. 2 ). From these experiments we found that PedH showed no activity when 1 M Er 3ϩ , Sc 3ϩ , Y 3ϩ , or Yb 3ϩ was added to the reaction mixture. However, in the presence of the lanthanide La 3ϩ , Ce 3ϩ , Pr 3ϩ , Nd 3ϩ , Sm 3ϩ , Gd 3ϩ , or Tb 3ϩ at 1 M, enzymatic activity was detected, with maximal specific activity observed with Pr 3ϩ and Nd 3ϩ and only very low activity observed with Gd 3ϩ and Tb 3ϩ.
All data are presented as the mean value of biological triplicates with error bars representing the corresponding standard deviation. Agar plate assay. For growth on solidified medium (1. 5% [wt/vol] agar) with different substrates, M9 medium plates without a carbon source or trace element solution added were freshly prepared with or without the addition of 20 M lanthanum chloride. Cell mass of the strains was obtained from LB agar plates, suspended in M9 medium without a carbon and energy source, and adjusted to an OD of 0. 5. After the plates were dried for 20 min in a laminar-flow cabinet, 10 l of each cell suspension was dropped onto the same plate and distributed with an inoculation loop over about 1/6 of each plate's surface. When all strains were distributed, a 10-l drop of a 1:1 (vol/vol) mixture of ethanol, 1-butanol, 2,3-butanediol, 1-octanol, or 2-phenylethanol in dimethyl sulfoxide (DMSO) was placed in the middle of the plate.
03 (GraphPad Software, Inc. ). Homology models. The PedE and PedH homology models were built with Swiss-Model  (69). As ExaA has the highest sequence similarity to both PedE (60%) and PedH (49%) of all available crystal structures in the Swiss-Model template library, the crystal structure of ExaA, the PQQ-ADH of P. aeruginosa (PDB code 1FLG), was used as a template for model construction  (23). Visualization of the models was carried out with PyMOL (70).
ACKNOWLEDGMENTS We thank  Lena Stehle  and  Svenja Moors  for their help in the establishment of the enzymatic assay. Josef Altenbuchner ,  Nadja Graf ,  Joanna Goldberg , and  Herbert Schweizer  are acknowledged for providing different strains and plasmids. We also thank  Thorsten Thomas  and  Brendan Colley  for critical reading of the manuscript and  Bernhard Hauer  for his continuous support. The work of  Matthias Wehrmann  and  Janosch Klebensberger  was supported by an individual research grant from the  Deutsche Forschungsgemeinschaft  ( KL 2340/2-1 ). The work of  Patrick Billard ,  Audrey Martin-Meriadec , and  Asfaw Zegeye  was supported in part by  Labex Ressources21  ( ANR-10-LABX-21-01 ). The research described here was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.