dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2004; Bjorland et al. , 2007; Shkoporov et al. , 2008). However, according to our analysis using CONJscan-T4SSscan, all these proteins belong to the TcpA family. Interestingly, none of these plasmids encode a canonical relaxase. Rather, they carry a "RCR initiator" gene (located next to the tcpA) encoding either one of the domains found in IME relaxases or a PF01446 domain (i. e. , the domain found in RCR initiator/relaxases from the small plasmids mobilized by ICEBs1). Therefore, it seems highly probable that these small plasmids are mobilizable. As previously proposed by  Lee et al. (2012) , it is probable that at least some of the small plasmids from firmicutes encoding a "RCR initiator" and lacking any canonical relaxase gene, CP, or T4SS protein, could be also mobilizable. Finally, it should also be noted that many plasmids devoid of relaxase could also carry an oriT related to those of conjugative element and therefore could be mobilizable in trans, as recently found for most plasmids from staphylococci  (O'Brien et al.
Modular and Intramodular Evolution The comparison of phylogenetic analyses of integrases with those of relaxases and CPs reveals many inconsistencies, probably due to multiple replacements of integration/excision or mobilization modules between IMEs. Such replacements were previously observed in streptococcal ICEs  (Ambroset et al. , 2016). The phylogenetic analysis of integrase families (Figure  1  and Supplementary Figure  S1 ) clearly shows that replacement of integration/excision modules can occur not only between IMEs or between ICEs but also between ICEs and IMEs. Within the mobilization modules encoding non-canonical relaxases, the comparison of phylogenetic analyses and/or cooccurrence of relaxases and TcpA CPs reveals inconsistencies. Thus, 10 out of 25 IMEs encoding a MobT relaxase that belong the PF02486_6 family do not carry a tcpA gene or pseudogene, whereas the 15 others have one.
The distribution of these latter suggests that an ancestral IME devoid of TcpA has recently acquired a tcpA gene. On the contrary, among IMEs encoding relaxases belonging to the PF01719, PF1719-PF00910, and PHA00330 superfamilies, only 13 do not encode a TcpA and probably lost their tcpA by deletion. In some cases, almost identical IME relaxases (see for example the Rel_PF02486_6 cluster in Supplementary Figure  S2 ) are associated with TcpA from different families suggesting that intramodular gene replacements occurred. In most cases, the data does not allow us to determine precisely what happened. However, the phylogenetic tree of the PF02486_6 relaxases suggests a replacement of a TcpA_5 CP by a TcpA_6 CP. Interestingly, although two superfamilies of relaxase (Rel_PF02486/MobT and Rel_PF03432/MobP) and the two superfamilies of CPs (VirD4 and TcpA) are shared by ICEs and IMEs from streptococci, there is no evidence of exchange of these genes between ICEs and IMEs.
Replication of Excised IMEs Integrative and mobilizable elements and ICEs are integrated in the chromosome and are transmitted to the daughter cell as part of the chromosome. However, an excised IME would be lost in one of the daughter cell during cell division in the absence of replication. Several recent studies indicated that extrachromosomal replication is involved in the maintenance of various ICEs transferring as singlestrand DNA  (Ramsay et al. , 2006; Lee et al. , 2012; Guerillot et al. , 2013; Carraro et al. , 2015) , but data suggesting replication of excised IMEs has not been reported so far. All streptococcal IMEs encoding a Rel_PF03432/MobP relaxase and most of those encoding a Rel_PF03389/MobQ carry one or two genes encoding proteins downstream from the relaxase gene that are distantly related to primosome proteins.
Such proteins are known to be responsible for the initiation of the theta replication of prophages. Altogether, these data suggest that these IMEs are able to replicate with a theta mechanism after excision. Ten other IMEs (IME_Class_10 family) sharing a similar organization encode two putative relaxases. One belongs to Rel_PF02486/MobT superfamily, i. e. , a family including both RCR initiators and relaxases. The other belongs to Rel_PF01076/MobV family. The structure of the region carrying these genes is reminiscent of the one of numerous mobilizable plasmids from firmicutes where the Rel_PF01076/MobV protein was found to be involved in mobilization whereas the Rel_PF02486/MobT protein is involved in RCR  (Lorenzo-Diaz et al. , 2014). Accordingly, the Rel_PF01076/MobV protein of an IME of the IME_Class_10 family was very recently shown to be a mobilization relaxase  (Lorenzo-Diaz et al.
, 2016). Therefore, it is likely that the Rel_PF01076/MobV protein is the IME relaxase, whereas the Rel_PF02486/MobT protein would be involved in RCR replication of the excised IME. Alternatively, as previously found for the MobT relaxase of ICEBs1  (Lee et al. , 2012) , the Rel_PF02486/MobT protein could ensure both functions. Finally, all IMEs encoding a single Rel_PF01076/MobV relaxase encode a protein related to the ParB proteins that could be involved in the partition of the excised IME. Taken together, these data suggest that almost all streptococcal IMEs encoding canonical relaxases (except the two elements encoding a MobC relaxase) encode proteins involved in the maintenance of their excised forms. Such proteins were not found for any IME encoding single non-canonical relaxases such as MobT.
Tyrosine integrases were found in all IMEs encoding non-canonical relaxases related to RCR initiators, i. e. , (i) a Rel_PF02486/MobT relaxase or (ii) relaxases from the four new superfamilies identified in this study. On the other hand, serine integrases were found in all IMEs encoding relaxase from canonical Rel_PF03389/MobQ, Rel_PF13814/MobC, Rel_PF01076/MobV (in the absence of Rel_PF02486/MobT relaxase) and Rel_PF03432/MobP superfamilies. Furthermore, IMEs encoding a single Rel_PF02486/MobT or a relaxase belonging to one of the four new superfamilies also encode either TcpA or no CP. TcpA CPs were never found in IMEs containing one of the canonical relaxases (Rel_PF03389/MobQ, Rel_PF13814/MobC, Rel_PF01076/MobV (alone or with Rel_PF02486/MobT) and Rel_PF03432/MobP). These IMEs contain either no CPs (MobQ, MobV, and MobP) or CPs from the VirD4 superfamily (MobC).
After amplification, plates were read and individual sample droplets analyzed on a Bio-Rad QX100 droplet reader. The amount of target miRNA or gene mRNA copies per ng of analyzed total RNA was determined using the QuantaSoft v1. 2. 10 software, applying a correction based on the Poisson distribution to the counted number of droplets positive, and then taking into account the amount of reverse transcribed total RNA input in each reaction. An additional analysis of selected ddPCR results was also conducted by normalizing measured copies of target genes relative to those of the housekeeping gene GAPDH in the same samples. Assessment of c-peptide content in culture supernatant of EndoC-βH1 upon miR-204 up-and down-regulation. Basal c-peptide levels in culture supernatants of EndoC-βH1 cells upon miR-204 up-and down-regulation were measured using the Mercodia c-peptide ELISA kit (Mercodia AB, Uppsala, Sweden) following the manufacturer's instructions.
Statistical analysis. Logistic regression was applied to the assessment of the association between miRNA levels and the endocrine phenotype. Differences in miRNAs levels between PET groups were assessed by the Mann-Whitney test. Correlation of miR-204 and miR-211 in PET samples was evaluated by linear regression. The statistical significance of changes in gene expression upon miR-204 up-regulation was assessed using the Wilcoxon matched pair signed rank test. Changes in c-peptide levels across treatments were tested using one way anova and multiple comparisons. For all analyses, a 2-tailed P value of <0. 05 was considered significant. Statistical analyses were performed using the Statistical Package for Social Science, Version 13. 0 (SPSS) or Prism 6 (Graphpad software).
High Resolution Wheat Consensus Genetic Map In order to integrate public quantitative genetic studies of traits (Figure  1 ), we constructed a high resolution consensus genetic map using Biomercator v3. 0 software  (Sosnowski et al. , 2012) , removing markers showing inconsistency in their positions between the considered genetic maps, due to errors in mapping or genotype-specific inversions and translocations, using mapinspect v2. 0 software. Following this strategy, four public genetic maps have been integrated consisting in 2,293 markers from  Xu et al. (2008) , 1,239 markers from  Somers et al. (2004) , 40,267 markers from  Wang et al. (2014)  and 104,804 markers from  Saintenac et al. (2013). The derived consensus high-resolution genetic map is made of 140,315 molecular markers (with an average of 6,682 markers per chromosome) and a recombination distance of  4,853.
Circle 6-Illustration of the MQTL intervals with a color code for yield (blue), protein content (pink), and baking quality (yellow). Center 7-Illustration of the retained homoeologous triplets (A, B, and D copies) on the 21 chromosomes. B-Wheat web viewer. Screen capture of the PlantSyntenyViewer web tool [http://urgi. versailles. inra. fr/synteny-wheat] visualizing the synteny between wheat, Brachypodium, rice, sorghum and delivering the access to the wheat syntenome made of 72,900 genes ordered on the 21 chromosomes as well as the information (populations, traits, flanking markers, trial details) related to the 376 QTLs, 32 MQTLs, and 37 candidate genes described in the current study. The table delivers the detailed information (i. e. , chromosomes in rows and marker types in columns) for the 140,315 makers of the wheat consensus genetic map.
As a consequence, we used the first function of Biomercator v3. 0 to compile four genetic maps to create a dense consensus genetic map with all the markers available from the investigated maps. A prerequisite for producing a comprehensive consensus genetic map is to eliminate inconsistent markers, i. e. , markers located on non-identical positions between two maps, so that they could not create discrepancies in the final consensus map. As a consequence, we used the MapInspect software (http://mapinspect. software. informer. com/), to verify chromosome by chromosome the marker order between the four considered maps. All the inconsistent loci (mainly noncollinear markers corresponding to large inversions) were thus discarded. Following this strategy we deliver a wheat consensus genetic map consisting of 140,315 markers (Table  1  and Supplementary Table  1 ).
For each of these five models, Biomercator predicts the most likely QTL distribution using means of the maximum likelihood method (ratio likelihood method). Then, an Akaike-type statistical criterion indicates the best model among the five available. Models with least Akaike values predicted on each chromosome was used to identify MQTLs on the chromosomes. Currently, the method used in the software does not allow to distinguish between models with more than four MQTLs on the same linkage group. If the estimated number of MQTLs is more than four, Biomercator declares that the most probable model is a number of MQTLs equal to the number of initially projected QTLs (n MQTL model). In this case, the deletion function of the software was used to select segments of a linkage group separated by regions with no QTL and the meta-analysis was applied to these segments.
Simulation Results 9 read sets were generated using GemSIM  [43] , ranging from 50 to 25,000X coverage. Each read set was analysed using one of the three callers LoFreq, VarScan or FreeBayes, producing 27 variant call sets, which were given a score between 0 and 1 defined as (Power*Accuracy)*(1-False Positive Rate) (described in full in Material and Methods, Variant Simulation). LoFreq was the best scoring caller, as shown in Supp. Figure  1 , and used to call heteroplasmies in all empirical datasets. FreeBayes consistently had the highest power, detecting more than 85% of true variants even at coverages as low 100X, however, FreeBayes also exhibited a false positive rate >5% at all coverages. LoFreq and VarScan had extremely low error rates, with each detecting only one false positive in the 27 variant call sets.
Correlation between heteroplasmy and age To test if there was an association with age we fitted a generalised linear model with age as the only explanatory variable, and the count of heteroplasmic sites as the negative binomial distributed response variable. Two samples within the 6+ cohort were found to be unduly influential points using Cook's Statistic (Supp. Figure  3A  and B ). As such they were removed and the model fitted again. With these two samples removed no significant association with age was found (ANOVA, p=0. 1), as shown in Figure  2. Further, these two samples contained primarily oxidative mutations, one of which contains the highest number of oxidative mutations in our data. To investigate any bias that may be introduced by choosing the most recent sample from recaptured individuals, we produced 1000 permutations in which the sample from recaptured individuals of usable age was chosen at random, to produce a similar dataset of 165 unique individuals.
Heteroplasmy Detection Pipeline The first and last 500 bp of the mitogenome were copied to the opposite ends, to extend the reference and account for circularity of the mitogenome. Reads were mapped against the extended M. myotis mitogenome using the BWA-MEM  [68]  algorithm. Prior to variant calling BAM files were processed using Picard Tools (v1. 90)  [69]  and the Genome Analysis Tool Kit (GATK)  [70]. Briefly, SAM files were sorted and read groups added using Picard Tools. SAM files were then converted to BAM files and indexed. Prior to base quality recalibration a set of raw variants was produced using the chosen low frequency variant caller, either LoFreq Star  [71]  or VarScan 2  [72]. These raw variants were used as the "known sites" necessary for the BQSR walker in the GATK. As no databases of variants exist for most non-model organisms, these raw variants provide an ad hoc solution.
Variant Simulation In order to gauge the sensitivity, accuracy and false positive rate of our bioinformatic pipeline, known variants and Illumina sequence data were generated in-silico using the GemSIM package (v 1. 5)  [43]. Sequence reads in FASTQ format from a reference individual (MMY104, used to generate the reference mitogenome for this species) were mapped to the previously published M. myotis mitogenome  [64]  using the BWA-MEM algorithm, was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted November 24, 2017. ; https://doi. org/10. 1101/224592 doi: bioRxiv preprint producing a SAM file which was used to produce a general error model using the GemErr. py module.
The reference mitogenome was used to generate haplotype information for variant simulation with the GemHap. py module. In total 500 known variants were simulated, in sets of 100 at frequencies of 1%, 4%, 7%, 10% and 15%. The reference mitogenome, error profile and haplotype information were used to generate 250bp paired end Illumina reads. Datasets were simulated at 50X, 100X, 250X, 500X, 1000X, 2500X, 5000X, 10,000X and 25,000X coverage. Predicted variant positions were checked against the known set. Power (proportion of true sites called), false positive rate (proportion of erroneous sites called) and accuracy (proportion of true sites with predicted frequency within ±0. 01 of the true frequency) were calculated on each dataset for each caller, FreeBayes, LoFreq and VarScan2 (see Heteroplasmy Detection Pipeline).
Correlation between heteroplasmy and age We constructed a primary dataset of n=167 unique individuals of known age and an oldest cohort, divided into cohorts from 0 to 6+, described in Table  1  and 2 , taking the most recent sample of an individual had the bat been sampled multiple times. This dataset was used to test for any significant correlation between number of heteroplasmic sites and age by fitting a generalised liner model (GLM) in R (v3. 3. 3) using the "MASS" package  [77]  with age as the sole explanatory variable and number of heteroplasmic sites as the response variable modelled under a negative binomial distribution. A negative binomial distribution was used due the presence of overdispersion in the data making a Poisson distribution unsuitable. The "boot" package  [78, 79]  in R (v3.
Availability of data and material Annotated variants and sample information used for statistical analyses are available within Additional File 1. The scripts and binaries for all dependencies needed to run the 3ML pipeline are available at https://github. com/jebbd/3ML. Sequence data generated during was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted November 24, 2017. ; https://doi. org/10. 1101/224592 doi: bioRxiv preprint   Nine read sets were generated in silico using GemSIM and the M. myotis mitochondrial genome, containing 500 known variants. The heteroplasmy detection pipeline was run on.
Acknowledgements The authors would like to thank the  European Research Council  for funding. For assistance in the field we would like to thank: members of  Bretagne Vivante ,  Dr. Serena Dool , students from  UCD  and all owners/local authorities for allowing access to the sites. We would like to thank the members of the  Teeling lab  and  Dr. John A. Finarelli  for helpful discussion of the analyses. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Using in silico miRNA target database analyses combined with proteome arrays and luciferase reporter assays of miR-193a-3p mimic treated cord blood ECFC-derived cells, we identified 2 novel miR-193a-3p targets, the high mobility group box-1 (HMGB1) and the hypoxia upregulated-1 (HYOU1) gene products. HMGB1 silencing in cord blood ECFC-derived cells confirmed its role in regulating vascular function. Thus, we show, for the first time, that miR-193a-3p negatively regulates human ECFC vasculo/angiogenesis and propose that antagonising miR-193a-3p in less proliferative and less angiogenic ECFC-derived cells will enhance their vasculo/angiogenic function. Endothelial colony forming cells (ECFCs) or late-outgrowth endothelial cells (OECs) are a subpopulation of endothelial precursors found circulating in human cord blood (CB) and adult peripheral blood (PB), although they are also resident in tissues including the bone marrow, umbilical cord and the placental vasculature 1-4.
Cell cycle analysis. After 48 hr transfection with miR-193a-3p, miR-34a or control mimics, CB ECFC-derived cells were stained with propidium iodide. The cells were fixed in ice-cold 70% ethanol for at least 16 hr at 4 °C. After fixation, the cells were incubated with 2 mg/ml RNase A (Sigma-Aldrich Ltd. Gillingham, Dorset, England) at 37 °C for 40 minutes. Then, 50 μ g/ml propidium iodide (Sigma-Aldrich Ltd. ) was added, and the cells were analyzed by flow cytometry. Single cells were selected using pulse area versus pulse height and obvious debris excluded by forward and side scatter gating of single cells, prior to propidium iodide based cell cycle analyses. The data are presented as the mean ± SEM of at least three independent experiments. CyQUANT NF proliferation assay. To assess the proliferation of CB and PB ECFC-derived cells, the CyQUANT NF assay (Invitrogen Ltd.
) fitted with a cooled CCD camera and IPLab v3. 61 imaging software (Scanalytics; BD Biosciences). Images were processed in Adobe Photoshop 7 and tubule numbers, lengths, and number of junctions were quantified using Angiosys software (TCS Cellworks, Abingdon, England). Transwell migration assay. CB and PB ECFC-derived cell migration was evaluated using a modified Boyden chamber assay  15. Briefly, CB and PB ECFC-derived cells were detached using trypsin/EDTA, harvested by centrifugation, resuspended in EBM-2 media containing 0. 5% FBS and counted. Then, 1 × 10 4 ECFC-derived cells in EBM-2 media containing 0. 5% FBS were placed in the upper chamber of a modified Boyden chamber (Corning Life Sciences, Amsterdam, The Netherlands). Complete EGM-2 media were placed in the lower compartment of the chamber.
Acknowledgements The authors are grateful for the support offered by the  Cancer Research UK ,  Breast Cancer Research Foundation and Oxford NHS Biomedical Research Centre  to A. L. H. The authors would like to thank  Mrs Sandy Britt  and  Jan Walton  for the cord blood collection. These studies have received grant support from the  National Institute of Health Research  under its  Programme Grants Scheme  ( RP-PG-0310-1003  and  RP-PG-0310-1001 ), Restore: burns and wound healing trust,  BBSRC  ( BRIC grant ), the  NHSBT Trust Fund and NHS Blood and Transplant. The views expressed in this publication are those of the authors and not necessarily those of  NHS ,  NHS Blood and Transplant ,  NIHR  or the Department of Health.
channel hypothesis. Funding was provided by STFC grants ST/N50421X/1 (F. E. G. B. ) and ST/L000776/1 (M. R. B. /A. H. / S. R. L. ). S. J. C. is supported by the French space agency CNES. We are grateful to the Mars instrument teams and particularly to the HiRISE team for their fulfillment of our imaging requests. A list of data products used in this study and the full extent of the geomorphic map, crater count plots, and animated gifs of the data in Figures  8a-8d  are available as supporting information to this article (Table  S1 , Figures  S1  and S2 , and Movies S1-S4, respectively). MOLA and THEMIS data products can be downloaded from the U. S. Geological Survey Planetary GIS Web Server (https://webgis. wr. usgs. gov/). CTX images can be downloaded from the Arizona State University Image Explorer (http://viewer.
To ensure consistency across studies, an MMSE score with the cutoff of ≤ 25 was defined as an indicator of significant global cognitive impairment as recommended by the International Parkinson and Movement Disorders Society (MDS) Task Force  17. Cohort-specific definitions of Parkinson's disease dementia were used (appendix table  1 ). The MDS-Unified PD Rating Scale (UPDRS) part II and III scores were obtained from four cohorts, and estimated for the five remaining cohorts using the UPDRS or SPES/SCOPA-motor scales  18  based on published conversion formulas  19, 20  (table  1 ). Hoehn and Yahr scales (HY)  14  were longitudinally collected in all cohorts. Ethnicity was self-reported. GBA mutations were retrospectively genotyped and defined as in Ref. 4. For several cohorts this analysis evaluated previously collected longitudinal phenotypic data; for the active HBS, PPMI, PDBP, DIGPD cohorts both retro-and prospectively collected longitudinal data elements were included.
Building the Cognitive Risk Score The discovery population was used to evaluate candidate clinical and GBA risk factors and to build the predictive score using a statistic similar to the Framingham Cardiovascular Risk score  21. A step by step calculation of the score is included in the appendix table 4, figure  6. Nine clinical and genetic risk factors previously associated with dementia in PD were considered for evaluation based on prior evidence  2, 3  and applicability to our datasets: GBA mutation status, age at onset of PD, gender, years of education at baseline, baseline MMSE, MDS-UPDRS II, MDS-UPDRS III scores, Hoehn & Yahr stage, and baseline depression status. GBA was included because of unequivocal evidence from multiple, large longitudinal studies (e. g. 4, 5 ). Other candidate progression loci are controversial and need further longitudinal replication  6.