dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparative genomics To assess the composition of the completed Spirochaetes type-strain genomes, we extracted the COG IDs from their IMG annotations  [50]  and determined the absolute and relative numbers of genes present in each COG category  [51]. Heatmaps were generated using the opm package  [52]  for the statistical environment R  [53]  from the arcsine-square root transformed (see, e. g. , p. 386 in  [54]  for the rationale of this transformation) COG proportions (Fig. 4 ) and from the log-transformed absolute numbers (data not shown). The results indicate that the relative COG category content mainly reflects changes in life style, with the intracellular parasites (Borrelia spp. ) and the coccoid forms (Sphaerochaeta spp. ) forming clusters of their own. Expectedly, the Sphaerochaeta genomes are impoverished with regard to category N ("Cell motility").
The genomes of the flagellated forms, however, also differ regarding their proportion of genes in this category. Hence, we calculated the correlation between this proportion and the average number of flagella reported for each species in the literature  [55]  (Fig. 5 , left side). The correlation was high (0. 917) and significant (p < 10 -07 ). The number of flagella obviously has a historical component, with flagella lacking in one clade (Sphaerochaeta) and the number of flagella being particularly high in other clades (Borrelia, Brachyspira). To rule out a pseudocorrelation caused by common ancestry (see chapter in  [56]  for the background), we thus converted the data to phylogeny-independent contrasts using the CONTRASTS program available in the PHYLIP package  [57]  and the ML tree inferred from the 16S rRNAs from the genome sequences as the underlying phylogeny.
The correlation between the contrasts was almost as high (0. 818) and significant (p < 10 -05 ) (Fig. 5 , right side). Thus, Spirochaetes appear to rely on increasing their number of motility genes for increasing their number of flagella. Other relations to the life-style include the lower proportions of many COG categories in the reduced genomes of the Borrelia species, apparently as an adaptation to their lifestyle (the higher proportion of genes in category J is simply due to the absolute number of genes in this category being held constant during genome reduction; data not shown), which is considered by some as parasitic, and symbiotic by others. The coccoid forms have an increased proportion of genes in category G, related to carbohydrate transport and metabolism, but this seems not be directly linked to the loss of the typical spirochaete shape, as the Spirochaeta smaragdinae genome shows a similarly high proportion of G genes (Fig.
Phylogenomic analyses According to the results from 16S rRNA analysis (Fig. 1 ) a comparative analysis the genome sequences of Spirochaeta africana (GenBank CP003282) and Treponema primitia (GenBank CP001843) were performed. The genomes of the sequenced Spirochaeta species and T. primitia differ significantly in their size. Compared to the genome of T. primitia (4. 1 Mb, 3,579 protein-coding genes) the genomes of S. caldaria (3. 2 Mb, 2,928 protein coding genes), and S. africana (3. 3 Mb, 3,874 protein-coding genes) are smaller in size. An estimate of the overall similarity among S. caldaria, S. africana and T. primitia was computed with the Genome-to-Genome Distance Calculator (GGDC)  [59, 60]. This system calculates the distances by comparing the genomes to obtain HSPs (high-scoring segment pairs) and inferring distances from the set of formulas (1, HSP length / total length; 2, identities / HSP length; 3, identities / total length).
Acknowledgements We would like to gratefully acknowledge the help of  Maren Schröder  for growing S. caldaria cultures and  Evelyne-Marie Bramilla  for DNA extraction and quality control (both at DSMZ). This work was performed under the auspices of the  US Department of Energy Office of Science, Biological and Environmental Research Program , and by the  University of California ,  Lawrence Berkeley National Laboratory  under contract No. DE-AC02-05CH11231 ,  Lawrence Livermore National Laboratory  under Contract No. DE-AC52-07NA27344 , and  Los Alamos National Laboratory  under contract No. DE-AC02-06NA25396 ,  UT-Battelle  and  Oak Ridge National Laboratory  under contract  DE-AC05-00OR22725 , as well as  German Research Foundation  ( DFG) INST 599/1-2.
Acknowledgements We acknowledge support by the  Petromaks program  of the  Norwegian Research Council. This study was supported by a  Center of Excellence  grant from the  Norwegian Research Council  to the  Physics of Geological Processes Center (PGP). This work was supported by the  Norwegian Research Council , through the  Petromaks  project and a  Center of Excellence  grant to the  Physics of Geological Processes Center (PGP). Acknowledgements The presented model was developed in collaboration with  Dr. Stephen Wolfram  at the  Wolfram Science Summer School 2013. The first author wishes to thank  Dr. Stephen Wolfram ,  Dr. Todd Rowland  and the rest of the summer school staff from  Wolfram Research  for their assistance, advice and encouragement. He further wishes to thank  Dr. Luiza Angheluta ,  Prof.
We calculated evolutionary rate by dividing the number of NSC variants in a transcript by the number of SC variants in the same transcript, both normalized by the length of the transcript. This defined a phylogenetic vector for each transcript. Similarly phylogenetic vectors were calculated for each transcript showing frameshift due to indels. To take into account the effect of population structure on evolutionary rate, we used SNV data across the strains to estimate principal components contributing to variability in the strains  (Sato et al. , 2005). The first two principal components were found to explain $70% of the sequence variability across strains (Figure  S2A ) and clearly separate Japanese Wistarderived strains, SD-derived strains, and the remaining rat strains (Figure  S2B ). We adjusted the evolutionary rate for the The phylogenetic tree was constructed using 9.
Structural Variant Prediction To identify structural variants, we used two independent methods. Structural variants including insertions, deletions, inversions, and translocations were predicted using the software tool BreakDancer1. 2 (Chen et al. , 2009). In addition, large deletions were predicted using a custom Perl script as described previously  (Atanur et al. , 2010) , using mapping flags provided by BWA for paired-end reads  (Li and Durbin, 2009).
ACKNOWLEDGMENTS T. J. A. acknowledges funding from the  Medical Research Council ,  British Heart Foundation Center of Research Excellence  ( RE/08/002 ), and the  EU -funded  Euratrans  project ( HEALTH-F4-2010-241504 ). We thank the  National BioResource Project-Rat  (http://www. anim. med. kyoto-u. ac. jp/NBR/) for providing DNA from rat strain LE/Stm and  Jean Sassard  for provision of rats for DNA sequencing of LH, LN, and LL rats. E. C. is supported by a  TOP grant  ( 700. 58. 303 ) from the  Netherlands Research Council (NWO-CW). H. J. acknowledges funding from  NIH  grant  5R01HL069321. N. H. acknowledges funding from  Euratrans  and the  BMPF-funded German Center for Cardiovascular Research. D. G. acknowledges support from a  Wellcome Trust   Senior Fellowship in Basic Biomedical Science  ( 057733 ), a  Wellcome Trust   Core Award Grant  ( 075491/Z/04 ), and support from the  Institute of Cardiometabolism and Nutrition (ICAN ,  ANR-10-IAHU-05 ).
Equipment and measures During individual fear tests. Following an aversive event, heart rate is a possible indicator of emotional response in domestic sheep  (Palestrini et al. , 1998; Desireet al. , 2004). During the novelty and suddenness tests, we recorded cardiac activity for a 75-s period using a Polar Vantage NV system (Polar, Anglet, France). The heart rate monitor was placed directly before the test in the pre-test pen. The system consisted of two electrodes, an emitter and a watch-like receiver fixed to an elastic belt strapped around the animal's thorax. The heart rate data file created on the emitter was downloaded onto a computer for analysis. Three days before the test the lambs went through a habituation procedure in their home pen where they were caught and strapped with an elastic belt around the thorax, which was left in place for 1 h.
Animal behaviour was also recorded using cameras (Sony, Tokyo, Japan) overlooking the test pens and connected to a video recorder (Sony SVT-96LP, Sony Corporation, Tokyo Japan). The videotapes were later analysed on The Observer software (Noldus, Wageningen, The Netherlands) using several behavioural patterns by the same observer (percentage of agreements: 96. 8%; Table  2 ). In the home pen. After the treatment period, 14 animals (7 control and 7 treated) were randomly selected and their cardiac activity was recorded during two phases: (i) presence of a familiar human wearing blue clothes (0930 to 0945 h) and (ii) just after the familiar human left and with no other human activity (0945 to 1030 h). Cardiac activity was recorded as described above. Lambs were not pre-tested before the treatment period owing to technical constraints.
This may appear somewhat surprising, but a few studies in humans  (Lucini et al. , 2005)  do report that chronically stressed individuals can also show a decrease in cardiac activity. During the heart rate recordings in home pens, the phase with no human activity could correspond to a baseline cardiac activity measure. However, interpretation of heart rate in our study is very difficult because heart rate is known to be influenced by motor activity, but no behavioural measurement was made to take into account a putative difference in behavioural activity. It would have been interesting to analyse the variation in cardiac activity during fear tests from this basal activity. Owing to a lack of data (cardiac activity in home pens was recorded only on seven animals per treatment), we were not able to carry out this analysis.
Acknowledgements The authors thank  D. Chassaignes  of the experimental farm for animal care and help in data collection. They are also grateful to  H. Chande  `ze,  C. Ravel ,  E. Delval ,  C. Mallet  and  S. Andanson  for their help with data collection and analysis. This study was supported by the  French National Research Agency  (grant  ANR-09-BLAN-0339-01 ) and the  Region Auvergne.
Database and selected variables We collated forest inventory datasets from five European countries (France, the Netherlands, Spain, Sweden and Switzerland) on the basis of their quality and accurate evaluation of aboveground wood production. With the exception of France, inventories have been conducted in permanent plots surveyed from 1983 to 2009. We selected pairs of contiguous surveys ranging from 5 to 13. 5 year periods. The French forest inventory is based on temporary plots where the volume growth of each tree over the last five years is estimated retrospectively based on radial and height growth measurements. In France, only data for the Alps and the Jura Mountains (southeast France) were available for this analysis. The basic criteria of plot selection were the lack of human intervention during contiguous surveys, and that all trees in the plot had been measured above a diameter at breast height (DBH) threshold (Table  S1 ).
Detailed information on inventory data for each European country is summarized in Table S1  [28]. For each selected plot, we assigned the forest type according to the European Environmental Agency classification (EEA 2006). In total, our dataset included 55265 plots of 11 European forest types (Table  1 ). Tree species were also classified into four coarse tree functional types: evergreen conifers, deciduous conifers, evergreen broadleaved -sclerophyllous-and deciduous broadleaved trees). For each plot, tree species richness and tree type richness were calculated. The number of tree species per plot (tree species richness) ranged from one to ten. On average, 49. 39% of the plots were mixed with two and three tree species mixtures being the most common (28. 21% and 13. 56%, respectively). Less than 1% of the plots had more than six tree species.
Statistical analysis First, for each forest type, we developed Generalized Linear Models to test for differences in wood production among tree species richness using the PROC-GENMOD procedure in SAS (version 9. 2, SAS Institute Inc. , Cary, NC, USA) with a normal error distribution and identity link function  [30] , and plot area as a covariate. When differences among tree species richness were significant, pair-wise differences of Least Square means (LS means) were tested. Likewise, we tested for differences in wood production among tree type richness. To select the appropriate variables to be included in the Structural Equation Modeling (SEM)  [31] , we performed a stepwise regression analysis to test for the correlation of wood production with tree species richness, tree type richness, stand basal area, temperature and precipitation for each forest type, respectively.
Values of CFI can range between 0 and 1, with values $0. 90 confirming a good model fit. For each forest type, we calculated the standardized regression coefficients associated with each path. These values represent the amount of change in one variable given a standard deviation unit change in the other one. We also calculated the coefficient of determination (R 2 ) for each variable as an indication of the contribution of the model to the variation of that variable. The unexplained variance (u) of the model to each variable was also indicated (Table  S2 ). For models with CFI values $0. 90, differences of path coefficients among forest types were determined through Multigroup analyses  [36, 42]. SEM and Multigroup analyses were performed using the AMOS. 18. 0 software  [38].
Discussion We found a positive relationship between tree richness and wood production in most European forest types. Our analysis is the first to describe this relationship at the local scale for the largest dataset across a continent, encompassing a wide range of climatic conditions. This result is in line with other regional studies showing higher productivity in mixed compared to monospecific forests  [43, 44]. We found European mixed forests to be on average 24% more productive than monospecific forests. Although we do not have precise information on the management history of these forests, most of our study plots were not plantations but natural forests. Moreover, even if some might be plantations they had not been managed during the inventory measurement periods. This indicates that the positive relationship between species richness and productivity is found in structurally complex woody systems, encompassing a wide range of environmental conditions  [45].
The authors thank the  National Forest Inventory Services of France, The Netherlands, Spain, Sweden and Switzerland  for their long term effort in collecting forest data. The authors thank  A. Sala  and two anonymous referees for constructive comments on a previous version of this manuscript. Research has been partially funded by the  EU  project  BACCARA  (FP7/2007-2013 project  Nu 226299 ), http://www. baccara-project. eu/)and the  Spanish Ministerio de Ciencia e Innovacion  project  Consolider-Ingenio MONTES  ( CSD2008-00040 ), http://www. creaf. uab. es/montes/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.
TRMT10A localizes to the nucleolus, where tRNA modifications occur. TRMT10A silencing induces cell death in insulin-producing pancreatic b-cells, suggesting that TRMT10A deficiency may reduce b-cell mass and the pool of neurons in the brain. This is the first study describing the impact of TRMT10A deficiency in man. Our findings may have broader relevance for the understanding of the pathogenesis of type 2 diabetes and microcephaly. somal adaptor, MAPK and MTOR activator 3), DDIT4L (DNAdamage-inducible transcript 4-like), RAP1GDS1 (RAP1, GTP-GDP dissociation stimulator 1) and METAP1 (methionyl aminopeptidase 1), but no mutation was identified. Exonic sequences-enriched DNA (whole exome) sequencing was performed in one proband and results were analyzed for variants that were not found in: dbSNP135 database, the Thousand Genomes database, the Exome Variant Server, or in-house exome sequencing on 51 individuals.
TRMT10A is expressed in human embryonic and fetal brain In situ hybridization studies were performed in human embryonic brain samples at 8, 11, 17 and 19 gestational weeks (GW). TRMT10A was expressed throughout the whole thickness of the dorsal telencephalon (presumptive cerebral cortex) at 8 and 11 GW, with higher expression in the ventricular zone and marginal zone (Figure  4 ). The ventricular zone contains most neural progenitors at early stages of corticogenesis, while the marginal zone is the region where the first post-mitotic neurons migrate. At later stages TRMT10A expression was not detected in the dorsal telencephalon but was found in the cerebellar cortex and cerebellar nuclei (Figure  S3  and data not shown).
TRMT10A has predominant nucleolar localization To examine TRMT10A subcellular localization we first performed in silico TRMT10A topology prediction using PSORII and WoLF PSORT  [20]. These softwares detected monopartite and bipartite nuclear localization signals in the first 89 amino acids of the protein. This was confirmed with cNLS Mapper  [21, 22]  suggesting predominant nuclear localization. To experimentally demonstrate the TRMT10A subcellular localization we took two  cytosolic and nuclear fluorescence. The identity of the recombinant fusion protein expressed in these cells was confirmed by Western blot (Figure  5B ) using an antibody raised against purified recombinant hTRMT10A. Similar results were obtained in dispersed rat and human islet cells expressing the recombinant fusion protein (Figure  S4 ).
TRMT10A has seven transcripts in the Vega database. Two of them are non-protein coding due to a retained intron, three contain 8 exons coding for identical proteins of 339 amino acids, and differ only in their untranslated regions. InterProScan analysis indicates that these three proteins have a tRNA (guanine 9-N1) methyltransferase domain as well as tRNA (guanine-N1) methyltransferase domain, both of them present in TRM10. The last two TRMT10A transcripts contain only 6 exons and code for shorter proteins of 200 and 206 amino acids. These two variants are truncated at the C-terminus and only have the tRNA (guanine-N1) methyltransferase domain. In rat only one isoform of TRMT10A containing both domains is found. Based on these analyses, we suggest that TRMT10A functions as a tRNA-modifying enzyme, but this remains to be experimentally confirmed.
Whole exome sequencing Genomic DNA from the proband (Figure  1 , arrow) was sonicated and enriched for exonic sequences by hybridization on an Agilent SureSelect All Exon v1 capture kit. Exon-enriched DNA was paired-end sequenced over 90 bp by an Illumina HiSeq2000 sequencer (Beijing Genomics Institute). An average of 55. 6 million paired-end reads were filtered to eliminate reads with more than 6 undetermined nucleotides or 40 identical bases in tandem. The filtered reads were then aligned to the human genome GRCh36 assembly using the SOAPaligner 2. 20 software  [49]  and the genotypes were called using the SOAPsnp program  [50]. Resulting single nucleotide variants (SNVs) were filtered according to the following rules: base quality larger than 20, read depth equal to or larger than 4, and a distance between two variants larger than 4.
Insertions and deletions were identified separately, through alignment to GRCh36 using the Burrows-Wheeler alignment tool  [51] , and detection using the Genome Analysis Toolkit  [52]. SNVs and indels were annotated using the Ensembl V54 database. We considered SNVs and indels that were not found in the dbSNP135 database, nor in the Thousand Genome (www. 1000genomes. org) database, nor in the Exome Variant Server (http://evs. gs. washington. edu/EVS/), and that were not found in our other in-house exome sequencing results.
Sanger sequencing PCR primers for all exons and flanking intronic sequences were designed using the Exonprimer software (http://ihg. helmholtzmuenchen. de/ihg/ExonPrimer. html). All exons and flanking intronic regions of the candidate genes were sequenced by the Sanger method using the Big Dye Terminator cycle sequencing kit v2 (Applied Biosystems, Foster City, California, USA), and analyzed on a 3130 Genetic Analyser sequencing machine (Applied Biosystems). Sequences were analyzed in silico for mutations using the SeqScape software V. 2. 0. (Applied Biosystems).
Western blotting Rat tissues and pancreatic islets were resuspended in ice-cold PBS containing protease inhibitor cocktail and homogenized as described above. Total protein was measured in the lysates using the Protein Assay Dye Reagent (BIO-RAD). INS-1E cells and human lymphoblasts were lysed with Laemmli buffer  [59]. Cell lysates were resolved in 10 or 14% SDS-PAGE gels and transferred to nitrocellulose membranes. Immunoblotting was performed using antibodies against hTRMT10A, human cleaved caspase-3 (Cell Signaling), human a-tubulin (Sigma-Aldrich) or human b-actin (Cell Signaling). Protein detection was done using horseradish peroxidase-conjugated secondary antibodies and SuperSignal West Femto chemiluminescence revealing reagent (Thermo Scientific). Immunoreactive bands were detected with a ChemiDoc XRS+ system and with Image Lab software (BIO-RAD).
Acknowledgments We thank  A Musuaya ,  M Pangerl ,  S Mertens  and  I Millard at the Laboratory of Experimental Medicine  and  C Perazzolo  at the IRIBHM for excellent technical support,  Dr. RF Moura  for RNA extractions and PCR,  Dr. JM Vanderwinden  at the  Laboratory of Neurophysiology, ULB , for assistance with confocal microscopy,  Dr. F Fe  ´ry for providing patient DNA, patient discussions and helpful advice,  Dr. C Depondt  for performing neurological examinations,  Dr. D Konrad  at the  University Children's Hospital of Zurich  and  Dr. E Vandemeulebroucke  at the  Universitair Ziekenhuis Vrije Universiteit Brussel  for providing patient DNA, and the  CARID Consortium for Autosomal Recessive Intellectual Disability  for efficient networking.
The molar NH 3 /AC and O 2 /AC ratios were set at 1. 0 and 0. 5, respectively. For each test, 5 g of catalyst were loaded into the reactor. An aqueous solution of acrolein (7. 1 wt. %) was fed to the reactor at a flow rate of 48 g/h. These figures simulated a realistic yield of 62% in acrolein expected from the first reaction step (dehydration). The feed solution for the AC ammoxidation experiments was prepared by mixing the appropriate amount of synthetic acrolein with water. The experimental design was generated by the statistical software Design-Expert, Version 5. 0. 8, Stat-Ease Inc. The response surface methodology was used in order to investigate the influence of the varied parameters (reaction temperature, catalyst amount, molar O 2 /AC ratio, molar NH 3 /AC ratio) on the response variables.
6 catalyst. The experimental design was generated by the statistical software Design Expert, Version 5. 0. 8, Stat-Ease Inc. Fig. 2  illustrates the influence of the reaction temperature, the catalyst amount, and the O 2 /AC and NH 3 /AC ratios on the conversion of AC. Thereby, the conversion is increased with the amount of catalyst and the reaction temperature, reaching up to 82% for 6. 5 g of catalyst at 425 • C. Furthermore, the conversion of AC also increased with the O 2 /AC and NH 3 /AC ratios. The highest acrolein conversion (88%) was obtained for an O 2 /AC molar ratio of 5 and a NH 3 /AC molar ratio of 1. 5. The influence of the parameters variations on the ACN selectivity is depicted in Fig. 3. At a reaction temperature of 375 • C and a relatively low catalyst mass of 3. 5 g, the selectivity exhibited its overall minimum of 15%.