dataesr/scientific-paragraphs-categorization huggingface dataset:
The use of coexpression network to provide evidence for functional annotation transfer has been previously demonstrated  [13] [14] [15]. These studies combined samples from various tissues, while we analysed two tissues (liver and heart) independently. Both approaches are complementary. While mixing tissues might result in broader co-expression network (many edges), it might also lack the fine resolution needed to improve functional annotation inference in a tissue specific context. We used microarray data in this study as it is by far the most abundant dataset. However, consortia like GTEx  [24]  have generated large amount of RNA sequencing data, and we envisage application of the method described here to RNA sequencing data in the future. The greater sensitivity of RNA sequencing over microarray  [25]  might allow the identification of more co-expressed genes.
Despite rigorous data normalisation, liver experiments from the same series tended to cluster together, cases and controls included. While this could be a sign of technical biases, gene ontology analysis of the variable genes demonstrated that they are related to liver functions. Thus it appears that the gene expression variability we observed is, at least partially, reflecting biological variations impacting the liver physiology. Importantly, individual experiments from series did not cluster together in SCHype clusters. We applied various approaches but could not identify the biological origin(s) of the observed variations. This is in part due to the lack of standardised experiment metadata fields in GEO (not all datasets even had a strain or a sex information, for example), and the lack of controlled vocabulary used to describe experiments.
Funding AJ is a Chancellor's fellow and AJ and TM labs are supported by institute strategic funding from  Biotechnology and Biological Sciences Research Council (BBSRC ,  BBSRC-BB/P013732/1-ISPG 2017/22  and  BBSRC-BB/P013740/1-ISPG 2017/22 ). GD is funded by the  People Programme (Marie Curie  Actions FP7/2007-2013) under REA grant agreement No  PCOFUND-GA-2012-600181.
Transmission electron tomography Thin slabs were cut from the deformed specimen and mechanically polished down to a thickness of 30 μm. In order to reach electron transparency, the sample foils were Ar-ion milled with a Gatan® DuoMill TM model 600. Carbon films were deposited on the thin foils to ensure electron conduction. TEM investigations were performed with a FEI® Tecnaï G 2 20Twin microscope, operating at 200 kV with a LaB 6 filament, using a double tilt sample-holder with a maximal angular range of ±60°. Olivine crystal structure is orthorhombic (a ≈ 4. 75Å, b ≈ 10. 19Å and c ≈ 5. 98Å). It is described here within a Pbnm space group  [18]. The diffraction patterns were simulated with the Electron Diffraction software, in kinematic conditions  [19]. Dislocations were characterised using the weak-beam dark-field (WBDF) technique.
Each micrograph has been numerically filtered with a kernel matrix  [23]  and/or a polynomial fit, using the ImageJ software, to homogenise the background and the dislocation contrasts, and to increase the dislocation contrast. The weighted back projection algorithm  [24]  was used to reconstruct the dislocation volumes. This algorithm is accessible on the TomoJ plugin  [25] , which is available on ImageJ. Most dislocations have been redrawn using the UCSF Chimera software  [26] [27] [28].
This mechanism has already been mentioned in a previous study on the dislocation population characterisation in deformed sample PoEM11 (see Figure  5  in  [13] ). From Table  1 , approximately half of sessile dislocation segments lie on unexpected {hkl} planes. This seems to be the signature of a high occurrence of the double cross-slip mechanism in PoEM8. The activation of [100] glide in PoEM8 may explain the difference of microstructure with PoEM9. [100] and [001] dislocations may interact and create sessile junctions which have already been predicted in olivine using Dislocation Dynamics simulations  [32]  and observed in TEM  [12]. The observed 3D configurations may result from the need for entangled dislocations to find other degrees dislocation obtained with g = 2 2 2 (projected angle of -143°, -117°, -53°, -27°, 37°, 63°, 127° and 153°), the (1 1 0) planes are edge-on for the -117° and 63° tilt angles ( 1 1 0 zone axis) and the 1 1 0 planes are edge-on for the -53° and 127° tilt angles ([1 1 2] zone axis); (b) Reconstructed volume of a [1 0 0] dislocation performed with the same diffraction vector as (a) (projected angle of -117°, -41°, -12°, 63°, 116° and 168°), the (0 0 1) planes are edge-on for the -117° and 63° tilt angles and the (0 1 0) planes are edge-on for the -12° and 168° tilt angles ([1 0 1] zone axis); (c) complex dislocation configuration composed of a [1 0 0] and a [0 0 1] dislocation, a 1 0 1 junction (yellow segment) and an attractive [0 0 1]/[1 0 0] dislocation crossed state; moreover a double cross-slip configuration of the [0 0 1] dislocation is revealed (same description as (a)); (d) simulation of the Kikuchi lines, between -165° and 180°, in kinematic conditions (electron Diffraction software  [33] ); the (0 1 0) and (0 0 1) Kikuchi lines are shown in blue colour (in reference with the [1 0 0] dislocation in blue colour) and the (1 1 0) and 1 1 0 Kikuchi lines are shown in red colour (in reference with the [0 0 1] dislocation in orange colour); (e) summary of the three dislocation configurations in (a), (b) and (c) for tilt angles of 0°, 63° and -27°, respectively (the white dislocations segments designate the [0 0 1](1 1 0) slip systems, the light grey dislocations segments refer to the [0 0 1] 1 1 0 slip systems and the black dislocation segments designate the [1 0 0](0 0 1) slip systems), the yellow arrow points out a 1 0 1 junction.
Acknowledgements A  Marie Curie  fellowship awarded to S. D. (PoEM: Plasticity of  Earth Mantle ,  FP7-PEOPLE-20074-3-IRG ,  N°230748-PoEM ) supported the experimental work used in this study. The TEM national facility in Lille is supported by the  CNRS (INSU)  and the  Conseil Régional du Nord Pas de Calais, France. We thank  D. Wallis  for a careful review of the manuscript, which has contributed to improve clarity.
Funding This work was supported by the  Marie Curie  fellowship awarded to  Sylvie Demouchy  (PoEM: Plasticity of  Earth Mantle ,  FP7-PEOPLE-20074-3-IRG ) [grant number  N°230748-PoEM ]; the  European Research Council  within the  Seventh Framework Program  ( FP7)  [grant number  N°290424 ] -RheoMan to  Patrick Cordier.
Each concentration was analyzed in three independent experiments run in triplicate. The IC 50 values were determined by the data analysis and graphing software Origin 8. 6, 64-bit. Kinetic analysis of tyrosinase inhibition activity. The carvacrol derivatives 4c, 6b and 6c were selected to determine their kinetic mechanism of enzyme inhibition. A series of experiments were performed to determine the inhibition kinetics by following the already reported method  [42] [43]. Inhibitor 6b with concentrations 0. 0, 7. 0, 28 μM, in case of Lineweaver-Burk plot and with concentrations0. 0, 1. 75, 7. 0, 14. 0, 28 μM in case of Dixon plot was used respectively. Compound 6c with concentrations0. 0, 0. 01, 0. 02, 0. 04μMand 4c with concentrations 0. 0, 1. 68, 3. 37, 6. 75, 13. 5 μM,respectively were usedin case of Lineweaver-Burk plot as well as in Dixon plot.
Molecular docking studies Retrieval of target structure. The crystal structure of mushroom tyrosinase (PDBID: 2Y9X) was retrieved from Protein Data Bank (PDB) (http://www. rcsb. org). The energy minimizations target protein was carried out by employing conjugate gradient algorithm and Amber force field in UCSF Chimera 1. 10. 1  [44]. The stereo-chemical properties, Ramachandran graph and values  [45]  of mushroom tyrosinase structure were assessed by Molprobity server  [46] , while the hydrophobicity graph was generated by Discovery Studio 4. 1 Client  [47]. The protein architecture and statistical percentage values of helices, beta-sheets, coils and turn were accessed by using online tool VADAR 1. 8  [48]. Candidate structures. The synthesized candidate molecules 4a-f and 6a-d were sketched in drawing ACD/ChemSketch tool.
Participants From the LBC1936, which comprises community-dwelling surviving members of the Scottish Mental Survey of 1947  (8) , 1091 individuals (548 men and 543 women) with an average age of 69. 5 (SD=0. 8) years completed cognitive tests, and provided personality, demographic, health, lifestyle, habitual diet information (participants completed a 165 item Food Frequency Questionnaire) and blood samples on a first wave of data collection, between 2004 and 2007. On a second wave of data collection, 866 participants (mean age 72. 7 years, SD 0. 8 years) repeated almost all assessments from wave 1 with the exception of the dietary questionnaire, and a subgroup (n=700) had an MRI brain scan. The main causes for withdrawal at wave 2, as reported elsewhere(9), were: death (n=19), lost contact (n=20), health reasons (n=64), dementia (n=7), care roles (n=13) and lack of time (n=17).
This study uses dietary information (wave 1), laboratory data obtained from the analyses of the blood samples (waves 1 and 2), and cognitive and imaging data (wave 2). The research was carried out in compliance with the Helsinki Declaration. Written informed consent was obtained from all participants under protocols approved by the Lothian (REC 07/MRE00/58) and Scottish Multicentre (MREC/01/0/56) Research Ethics Committees.
sourceforge. net/projects/bric1936), was used to extract white matter hyperintensities (WMH) and cerebrospinal fluid from the colour data fusion of co-registered T2*W and FLAIR images. Superficial subarachnoid space (SSS, the space between the inner edge of the dura and the brain cortical surface) volume was calculated as the difference between the total cerebrospinal fluid and the ventricular volumes. Finally, cerebellar white matter and cortical volumes were obtained automatically using FreeSurfer (http://freesurfer. net/).
Diet All study participants (n=1091) were asked to complete the Scottish Collaborative Group Food Frequency Questionnaire (SCG-FFQ) at home and return it by post. Of these, 98 were not returned, 26 were returned blank, and 39 had more than 10 missing items and were therefore excluded from the analyses. Individuals with extreme energy intakes (<2. 5 th or >97. 5 th centile, n=46) were also excluded to obtain the most reliable food frequency data  (17). The SCG-FFQ is a self-report instrument validated for older adults  (17) , where respondents rate the frequency of consumption of standard portions of 175 different foods and drinks over the last 2-3 months (rarely/never, 1-3 per month, 1 per week, 2-3 per week, 4-6 per week, 1 per day, 2-3 per day, 4-6 per day or 7+ per day) and responses are used to estimate typical micro and macro nutrient intakes.
Statistical analysis In all analyses, volumetric MRI data were standardised by head size (i. e. expressed in percentage with respect to the intracranial volume) and adjusted by age in days at the time of the MRI scan. To examine the associations between diet and cognition and brain health related indicators at older age, and the associations between the haematological parameters that relate to the thyroid function in waves 1 and 2 (i. e. acquired 3 years apart), we applied robust univariate regression analysis using iteratively reweighted least squares with a bisquare weighting function from MATLAB R2014a Statistical Toolbox and an adding bootstrap, and repeated the analyses without excluding outliers (ANOVA). False Discovery Rate (FDR) was applied to adjust for multiple comparisons. Kruskal-Wallis and Mood's Median tests were used to evaluate differences in the cognitive and imaging parameters between the four groups with extreme/middle levels of iodine consumption.
Acknowledgements and Funding This study was funded by The  Scottish Government  under the  Heriot-Watt University Theme Fund   2012-13  assigned to the Scottish Crucible initiative http://www. scottishcrucible. org. uk/aboutcrucible. html"Projects for Scotland". The  LBC1936 Study  was funded by  Age UK  and the  UK Medical Research Council  (http://www. disconnectedmind. ed. ac. uk/) (including the  Sidney De Haan Award for Vascular Dementia ). Funds from the  Centre of Cognitive Ageing and Cognitive Epidemiology  (http://www. ccace. ed. ac. uk/) ( MR/K026992/1 ),  Row Fogo Charitable Trust  ( R35865 ), the  Biotechnology and Biological Sciences Research Council , the  Engineering and Physical Sciences Research Council  and the  Economic and Social Research Council  are gratefully acknowledged.
Data The main dataset used in this study concerns 2484 complete genomes of Bacteria that were downloaded from NCBI Ref-Seq (http://ftp. ncbi. nih. gov/genomes/refseq/bacteria/), in November 2013. A post-hoc validation dataset was obtained from the same database in November 2016 and included a total of genomes. We used the classification of replicons in plasmids and chromosomes as provided in the GenBank files. We searched for conjugation systems in all replicons of all genomes of the two datasets. Yet, the delimitation of ICEs was restricted to species having at least one chromosomally encoded conjugative system and at least four genomes completely sequenced (37 species, 506 genomes) in the main dataset. The validation dataset was used to delimit novel ICEs of the MPF T type. These were used for post-hoc validation only.
Detection of conjugative systems Conjugative systems were found with the CONJscan module of MacSyFinder (29), using protein profiles and definitions following a previous work  (16)  (File S1). Protein profiles are probabilistic models built from the information contained in proteins alignments. They allow more sensitive identification of distant homologs than classical pairwise sequence-search approaches  (30). MacSyFinder uses the protein profiles and a set of rules (defined in models) about their presence in a given MPF type and their genetic organization. For the latter, we used definitions from previous works from our laboratory: two components of the conjugation genes must be separated by less than 31 genes, an exception being granted for relaxases that can be distant by as much as 60 genes.
An element was considered as conjugative when it contained the following components of the conjugative system: VirB4/TraU, a relaxase, a T4CP, and a minimum number of MPF typespecific genes: two for types MPF FA and MPF FATA , or three for the others. MacSyFinder was ran independently for each given MPF type with default parameters (hmmer evalue < 0. 001, protein profile coverage in the alignment higher than 50%). Conjugative elements of some taxa lack known relaxases, this is the case of some Tenericutes and some Archaea. Since T4SS can be mistaken by protein secretion systems in the absence of relaxases, such systems were excluded from the analysis. The models in CONJscan can be modified by the user. The CONJscan module for MacSyFinder (downloadable at https://github. com/ gem-pasteur/Macsyfinder models) can be used with command lines in a unix-like terminal, or in a webserver (https: //galaxy.
We identified the gene families of the spots that encode at least one ICE (spot pan-genome). For this, we searched for sequence similarity between all proteins in the spot using blastp (version 2. 2. 15, default parameters). The output was then clustered to identify protein families using Silix (version 1. 2. 8)  (32). Proteins whose alignments had more than 80% identity and at least 80% of coverage were grouped in the same family. The members of the spot pan-genome that were not part of the core genome constituted the accessory genome.
Specific functional analyses Antibiotic resistance genes were annotated with the Resfams profiles (core version, v1. 1) (33) using HMMER 3. 1b1  (34) , with the option -cut ga. The cellular localization was determined with PsortB (version 3. 0)  (35) , using the default parameters for diderms and monoderms separately. Genes encoding stable RNAs were annotated using Infernal  (36)  and Rfam covariance models (37) (hits were regarded as significant when e-value ≤ 10 -5 ). Integrons were detected using IntegronFinder v1. 5 with the -local max option  (38). DDE transposes were annotated with MacSyFinder (29) following the procedure described in  (31). Integrases were detected with the PFAM profile PF00589 for tyrosine recombinases and the pair PF00239 and PF07508 for Serine recombinases (http://pfam.
xfam. org/) (39). HMMER hits were regarded as significant when their e-value was smaller than 10 -3 and their alignment covered at least 50% of the protein profile. Specific HMM protein profiles were built with HMMER v3. 1b1 for partition systems  (40, 41) , replication proteins  (42) , and entry exclusion systems  (43). In the general case, we started from a few proteins with experimental evidence of the given function, curated by experts, or reported in published databases. Since these sets were usually small and present in a small number of species, we used a two-step procedure (described below): we started by building preliminary profiles, used them to scan the complete genome database, and then used these results to make the final profiles. First, the proteins of experimental model systems were aligned with mafft v7.
Functional annotation We used HMMER v. 3. 1b1 (e-value <0. 001 and coverage of 50%) to search ICEs for hits against the EggNOG Database of hmm profiles (Version 4. 5, bactNOG). These results were used to class genes in broad functional categories. We added a class 'Unknown' for genes lacking hits when queried with EggNOG. We tested the over-representation of given func-tional categories in ICE using binomial tests where the successes were given by the number of times that the relative frequency of a given functional category was higher in the ICE than in the rest of the host chromosome. Under the null hypothesis, any given category is as frequent in the ICE as in the rest of the chromosome (relative to the number of genes). Formally: H0 : ∀x ∈ Cat Egg NOG : N( f (x ICE ) > f (x HOST )) ∼ B (N ICE , 0.
Phylogenetic tree The phylogenetic analysis used the 134 integrases from ICEs containing exactly one tyrosine recombinase. We excluded the other elements because additional recombinases might be involved in other functions (dimmer resolution, DNA inversions) and could confound the results. We added to this dataset: 60 phage integrases randomly selected from a database of 296 non-redundant (<90% identity) phage integrases from RefSeq; 11 integrases from pathogenicity islands (Supplementary Table  S2  from (  48 )); 25 experimentally studied XerC and XerD from  (49) , XerS from (50), XerH from  (51, 52) ; seven integrases representing the diversity of integron integrases  (38). The final dataset was composed of 237 integrases. The initial alignment was made with mafft (with parameters -maxiterate 1000 -genafpair), and 100 alternative guide-trees were built.
Identification and delimitation of ICEs by comparative genomics We developed a procedure to identify and delimit ICEs in two stages (see Materials and Methods). First, we identified conjugative systems in bacterial chromosomes using the CONJscan module of MacSyFinder, a methodology that we have previously shown to be highly accurate  (17, 57). We then concentrated our attention on the conjugative systems of species for which at least four complete genomes were available. The comparative genomics data provides information on the maximal size of the ICE, given by the flanking core genes, and on the frequency of the genes in the locus. Hence, we defined for each species: the core-genome, the intervals (locations between consecutive core genes), the spots (sets of intervals flanked by the same families of core genes, see Methods), and the pan-genomes of each spot.
These latter elements are most likely the result of multiple independent integrations in the same locus, not of a single ancestral integration, since ICEs are never part of the species core genome. Hence, instead of arbitrarily selecting one ICE per spot, we opted to maintain all elements in the subsequent analyses and control for this effect, when necessary. We used information from the ICEberg database to validate the delimitation procedure. We could not use the whole database because there was no complete conjugative system in 51% of the elements of ICEberg (184 out of 358), and 40% of them even lacked the essential protein VirB4/TraU. Hence, we restricted our comparisons to the 19 ICEs in ICEberg that were derived from experimental data or predicted from literature and that were in a genome available in our dataset (Supplementary Table  S1 ).
Among these elements, 16 ICEs had their start and end positions within less than 2. 5 kb of ours (typically less than 500bp, Supplementary Figure  S3 ). We analyzed in detail the three cases showing discordance between the two datasets. Two of them corresponded to a tandem of ICEs that we split in our procedure because they had two different complete conjugative systems. They were identified as a single ICE in ICEberg. The third discordance arose because the ICEberg annotation included an MPF FATA ICE interrupted by an MPF FA ICE. Our procedure spotted the complete MPF FA ICE. These results suggest that our delimitation is more accurate. It is important to note that our procedure does not use any information on the experimental models of ICE; it is only based on the identification of conjugation proteins, and the frequency of accessory genes.
Hence, the accuracy of our method is expected to remain high even when studying poorly known ICE families. It is also expected to improve upon inclusion of more genomes within a species. By the end of this study there was an influx of novel complete genomes in the public databases. We took advantage of this data to make a post hoc validation of our results. We concentrated our attention on MPF T elements because they are the most abundant and the best studied. We identified 1181 conjugative systems in the chromosomes of the novel genomes. We delimited 124 novel MPF T ICEs in the novel genomes (out of 498 detected) and compared them with the ICEs of the same type in the main dataset, according to five measures. The results showed no significant difference between the two sets (Supplementary Tables  S7  and S8 ), suggesting that our results are robust to sampling effects, i.
General features of ICEs We grouped the 200 ICEs (delimited in the main dataset) on the basis of the MPF and relaxase (MOB) types. We identified five of the eight previously defined MPF types among these elements: type F (9), FA  (72) , FATA (29), T  (49)  and G  (40). Three types were absent from our data because they are strictly associated with clades for which not enough complete genomes were available (MPF B for Bacteroides and MPF C for cyanobacteria) or corresponded to systems that were previously shown to be extremely rare among ICE (MPF I )  (17). A preliminary analysis of the post hoc validation dataset mentioned above revealed no ICEs for MPF I and MPF C , and very few MPF B (because they are in poorly sampled species). Study of these elements will have to wait before more data becomes available.
As a result, the distribution of MPF types differed from that of the 601 conjugative systems identified in all chromosomes of the main dataset ( 2 , P-value < 0. 001, Supplementary Figure  S4 ), and included mostly Firmicutes (for MPF FA and MPF FATA ) and Proteobacteria (for the others). One ICE had an undetermined MPF type (albeit it included a VirB4, a MOB and a coupling protein), and was excluded from the remaining analyses (except from the wGRR network, see below). Expectedly, the types of relaxases identified in the ICEs corresponded to those frequently found in Proteobacteria and Firmicutes  (17)  (Supplementary Figure  S5 ). The size of ICEs described in the scientific literature varies between ∼13kb (ICESa1 in Staphylococcus aureus  (62) ) and ∼500kb (ICEMlSymR71 in Mesorhizobium loti R7A (  63 )).