dataesr/scientific-paragraphs-categorization huggingface dataset:
3. b. Structural data Attitudes of bedding surfaces were acquired by two different methods. A preliminary remote sensing approach (Fig. 4 ) was employed using the software Openplot  (Tavani et al. 2011). Data extraction was obtained by the best fit of polylines drawn over three-dimensional objects (e. g. Fernández et al. 2009 ). Threedimensional objects for data extraction were derived draping 2D images (i. e. aerial imageries and geological maps) over the ASTER Global DEM v2 (30 m x-y resolution, provided by the US Geological Survey at https://gdex. cr. usgs. gov/gdex/). A selection of the resultant best-fit planes was made in real time (e. g. Corradetti et al. 2017 Corradetti et al. , 2018) )  to discard those planes affected by rough error. For instance, typical sources of error were the low resolution of the model or the colinearity of the digitized polylines (e.
g. Fernández et al. 2009; Jones et al. 2016; Seers & Hodgetts, 2016). More than 700 bedding attitudes were extracted in this way. Approximately 300 further bedding attitude data were collected in the field, together with mesoscale fault and fold data. Faults, small-scale folds and bedding data from the post-nappe sedimentary succession were acquired in the frontal portion of the belt, in the area of Figure  2. The regional trend of map-scale folds involving the Hadhramaut Group in this area slightly changes from north to south, being c. 155°in the Jabal Hafit structure to the north, and c. 130°toward the south (Fig. 2 ). This gentle change mimics the arcuate shape of the Al-Hajar Mountains. Both the remotely sensed bedding attitude and the bedding collected in the field from the post-nappe show a positive overlap and display a poles distribution along a N49°-striking plane (Fig.
3. c. Low-temperature thermochronometry New AHe data were obtained in this study. Helium is produced by the decay of U in a time-dependent function  (Hourigan et al. 2005). The closure temperature for He in apatite is between 80 °C and 55 °C  (Farley, 2000). Considering a standard geothermal gradient of 25-30 °C km -1 , and an average surface temperature of c. 30 °C (at present-day latitude), AHe cooling ages record the time when a rock sample passed through the depth between c. 1 and 2 km. Eleven rock samples were collected (Fig. 8a ) in siliciclastic rocks from different structural domains of the mountain belt for AHe analysis. In detail, seven samples were collected from the upper Matbat Formation (Jurassic) of the Hamrat Duru Group of the Hawasina Basin, two from the syn-tectonic Muti Formation of the Aruma Group, one from the quartzitic member of the autochthonous Jurassic Sahtan Group and one from a quartzitic level in Precambrian autochthonous.
Conclusions Integrated field structural analysis, remote sensing and seismic interpretation were used in this study to unravel the architecture and tectonic evolution of the foreland basin postdating the Cretaceous nappe emplacement in the Oman Mountains. Development of the late-Eocene, post-nappe foredeep occurred in relation to foreland flexuring controlled by the tectonic load that, in turn, resulted from renewed shortening within the mountain belt. Unroofing, recorded by both available thermochronological data and our new AHe cooling ages (at ~42 Ma), furnished the detrital material filling the newly developed foredeep. The inner portion of the foredeep was then uplifted during the Miocene, leading to a change of the pinch-out direction observed in seismic sections between Oligocene and Miocene foreland basin units.
The Miocene sequence, deposited in a wedge-top basin setting, experienced later layer-parallel shortening and folding. This younger shortening event is also recorded by renewed unroofing of the mountain belt. This is also supported by our new thermochronometric data that shows a cluster of AHe ages at ~17 Ma. The results of this study allowed us to correlate Tertiary shortening-exhumation events that occurred in the Oman Mountains with the development and tectonic evolution of the post-nappe foreland basin. This was here achieved integrating AHe thermochronometry, furnishing exhumation timing of the culmination areas, with seismic sections and structural data of the post-nappe to decipher the deformation of the frontal areas during the main uplift events. This may have major implications for a better understanding of this classic, extensively studied mountain belt and for further oil and gas exploration in the area.
Acknowledgements. The manuscript benefited from insightful constructive revisions by  François Roure and Christoph von Hagke. The authors wish to acknowledge  Matteo Basilici ,  Jason Williams  and  Charlie Kergaravat  for their field assistance,  Sandrine Choy  for her support for sample preparation at  GET , and  Patrick Monié  and  Michael Bonno  for He analyses at Géosciences Montpellier. The  Geological Research Department of the Public Authority for Mining (PAM) of the Sultanate of Oman  is thanked for its cooperation with the  University of Camerino. Mr. Ibrahim Al Sawafi  from PAM is also warmly thanked for facilitating our work during the past two years. This work forms part of a research project funded by  Total E&P  through a contract with the  University of Camerino. The publication of this article was funded by the  Qatar National Library.
Microinjection and Live-cell Imaging Zygotes were injected with 5 ng/ml mRNAs encoding EGFP-a-tubulin and H2B-mCherry at 5 hours post fertilization  (Yamagata et al. , 2009; Yamagata and Ueda, 2013). Live-cell imaging was performed on CV-1000 (Yokogawa Electric Corp) from 7. 5 to 102 hours at 10-min intervals. Raw images were stacked and projected by CV1000 Software (Molecular Devices) to generate reconstructed movies.
WGBS Data Analysis Reads were trimmed to remove low quality bases and adaptor sequences using Trim-Galore! v0. 3. 3 (Babraham Institute) and mapped to mouse genome mm10 using Bismark v0. 14. 5  (Krueger and Andrews, 2011). Methylation data at CG (or non-CG) sites covered with 3-100 reads were extracted for the downstream analyses. Windows with less than 5 informative CG (or non-CG) sites were excluded. To study CG methylation at ERVs, reads were mapped to the Repbase annotations. For allelic-specific analysis, reads were mapped to N-masked mm10 genome using published SNP data of JF1  (Takada et al. , 2013). Allelic reads were selected using SNPsplit v0. 3. 2 (Babraham Institute). The methylation data from the ICM  (Wang et al. , 2014) , ESCs  (Habibi et al. , 2013) , liver, and brain  (Hon et al.
RNA-seq Data Analysis Reads were trimmed and mapped to mouse genome mm10 by HISAT2 v2. 0. 5  (Kim et al. , 2015). Transcripts were assembled by StringTie v1. 3. 3  (Pertea et al. , 2015). Repeat masker-annotated microRNA, rRNA, tRNA-like, and snRNA, and transcripts with a CDS < 200 bp were excluded in the downstream analyses. Genes with (1) FPKM R 1, (2) absolute FPKM difference R 5, and (3) at least 2-fold changes in all replicates were defined as DEGs. Expression of ERVs was determined using VisRseq  (Younesy et al. , 2015). ERVs with R 100 copies in the genome and at least 2-fold increase in all replicates were defined as derepressed. RefSeq transcripts containing an extra ERV sequence at their 5 0 ends were identified as additional chimeric transcripts. For allelic-specific analysis, reads were mapped to N-masked mm10 genome using published SNP data of JF1  (Takada et al.
ACKNOWLEDGMENTS We thank  T. Ishiuchi ,  M. Unoki ,  H. Toh ,  J. Oishi ,  K. Toriyama ,  T. Akinaga ,  M. Miyake ,  M. Tanaka  ( Kyushu University ),  J. Richard Albert  ( University of British Columbia ),  A. Yamada  ( RIKEN ), and  H. Miyachi  ( Kyoto University ) for their assistance. This work was supported in part by  JSPS KAKENHI  grants to H. S. ( JP25112010  and  JP18H05214 ) and K. Y. ( JP25116005 ), a  Canadian Institutes of Health Research  grant to M. C. L. ( MOP-133417 ), and funding from  Genome British Columbia  to M. C. L. ( SOF154 ).
The relative fold change was measured by 2 -ΔΔCt formula compared with the control cells. Means and differences of the means with 95% confidence intervals were obtained using GraphPad Prism (GraphPad Software Inc. ). Two-tailed Student's t test was used for unpaired analysis comparing average expression between conditions. P-values < 0. 05 were considered statistically significant. Gene expression analysis. The RNA extracts were first analyzed by a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA). RNA quality was determined by the ratios of A260/A280 (close to 2) and A260/A230 (close to 2). Qualified RNAs were further tested using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), and samples with RIN > 7 were selected for microarray analysis using the Affymetrix MTA 1.
0 (Affymetrix). The labeling, hybridization, scanning, and data extraction of microarray were performed according to the recommended Affymetrix protocols. Briefly, the fluorescence signals of the microarray were scanned and saved as DAT image files. The AGCC software (Affymetrix GeneChip Command Console) transformed DAT files into CEL files to change image signals into digital signals, which recorded the fluorescence density of probes. Next, we used Affymetrix Expression Console software to pretreat CEL files through Robust Multichip Analysis (RMA) algorithm, including background correction, probeset signal integration, and quantile normalization. After pretreatment, the obtained chp files were analyzed by Affymetrix Transcriptome Analysis Console software to detect differentially expressed genes.
Data availability The data discussed in this publication have been deposited in NCBI's Gene expression Ominbus under the GEO Series accession: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE81701(GSMP190229, GSM190230). The TCGA data referenced during the study are in part based upon the data generated by the TCGA Research Network: http:// cancergenome. nih. gov/ and are available in a public repository from the cBIoportal for Cancer Genomics website http://www. cbioportal. org/. All the other data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding author upon reasonable request. Received: 14 June 2018 Accepted: 14 February 2019.
Acknowledgements We gratefully acknowledge the generous help from  Flow Cytometry ,  Genomics Core  facilities, and  Labaratory of Animal Services. We thank  Drs. Rafi Ahmed  and  Paulo C. Rodriguez  for insightful discussions and comments,  Dr. Iskander Asm  for for helping with and training of our staff on the tail vein injections. This work was supported by start up funds to H. K. by  Georgia Cancer Center. Additional research fundings to H. K. provided by  American Cancer Society Institutional fund, Forbes Institute research fund, and Bridge Fund by Augusta University Research Inc.
Rarefaction was performed with the rich package in R  (Rossi 2011) , setting a sample size of N = 5, which corresponds to the minimal number of trees sampled per plot in both experiments. Linear models were used at plot level, because rarefaction resulted in a continuous distribution of data. Different models were used for the SDE and GDE because of the additional tree density treatment in the SDE. Miner abundance and species richness data were square root transformed to improve the homogeneity of variance and normality when needed. For the SDE, we ran models separately for each sampling year as the tree density effect (high density in unthinned plots vs low density in thinned plots) was included only in 2014 following thinning in 2013. For each year, linear mixed-effects models included tree species diversity as fixed factor in interaction with season; the tree density was included as fixed factor in interaction with others fixed factors in the 2014 model.
Area (the three different experimental sites) was included as an additional fixed factor and plot was included as a random factor. We also tested for the effects of tree species diversity on the abundance of generalist and specialist leaf miners separately. For the GDE, we performed linear mixed-effects models with genetic diversity in interaction with year and season as fixed factors and with plot as a random factor to take into account repeated measurements across seasons. We additionally ran a generalized linear mixed-effects model (GLMM) at tree level to analyse the effect of genotype identity in interaction with genetic diversity, season, and year as fixed factors. A negative binomial distribution was chosen because of the overdispersion of count data  (Zuur et al. 2009 ). To take into account the temporal autocorrelation (repeated measures across seasons) and the spatial autocorrelation (individual trees within plot), we used tree ID nested within plot as a random factor.
An abundance gradient is a pattern whereby some individuals are lost from one site to the other. All these indices were first computed at plot level within each experiment, separately within monocultures and within polycultures (and separately for each year and each season). Because, in each experiment, the number of mixtures was higher than the number of monocultures, these indices in polycultures were computed by resampling the same number as numbers of monocultures present in each experiment. The differences among mono-and polycultures were assessed by a paired t test (using the β-diversity indexes within year and season as temporal replicates). We also computed the within-plot beta-diversity indices for each year and each season using data at tree level. These indices were computed by resampling the same number of trees for each plot (i.
The expression of these proteins, quantified in plants and insect vectors in natural and experimental pathosystems, appeared to be modulated in a host-dependent manner. Two of the eight FtsH C-tails were predicted by Phobius software to be extracellular and, therefore, in direct contact with the host cellular content. As phytoplasmas cannot synthesize amino acids, our data raised questions regarding the involvement of FtsH in the adaptation to hosts via potentially enhanced recycling of phytoplasma cellular proteins and host protein degradation.
On the other side, three "Ca. P. mali" ftsH sequences (ATP_00457, ATP_00034 and ATP_00464, GenBank FR863639, FR863637 and FR863645 respectively), five "Ca. P. ziziphi" ftsH sequences (WP_121464094. 1, WP_121464242. 1, WP_121463917. 1, WP_121464225. 1 and WP_121464146. 1), and the seven other FDP ftsH paralogs constituted a common phylogenetic branch resulting from ancient duplication events. Interestingly, except for ftsH1 and ftsH2, which possibly emerged from independent divergent evolution, the other FDP ftsH paralogs were clustered according to their position in the FDP chromosome. The ftsH genes are dispersed along the chromosome and are 20 to 81 kbp apart, with six of the genes present on the positive strand (black arrows) and two copies on the negative strand (white arrows) (Figure  3 ).
FtsH Proteins Are Predicted to Be Differentially Oriented in the Membrane Many bioinformatic tools are available to predict the membrane spanning topologies of proteins. One benefit of the HMM-based Phobius server is the separate models for cleavable signal peptides and transmembrane (TM) segments  [34]. Seven ftsH genes identified in the FDP genome encode proteins with two TM segments identified using Phobius (Table  1 ). This feature is common to the unique FtsH found in bacteria other than phytoplasmas. For FtsH2, a signal peptide and a TM segment were predicted. Cleavage of the signal peptide sequence cannot be predicted in silico, as it has been formerly shown that phytoplasma membrane proteins such as IMP can possess typical signal peptides while remaining anchored at the outer surface due to lack of cleavage  [35].
Substrate-Binding Proteins Are Encoded by Genes Upstream of Several ftsH Genes Analysis of the ftsH gene environment revealed the occurrence of genes encoding homologous proteins upstream of the complete ftsH genes in the same operon, with the exception of ftsH2 and ftsH6. The shortest genes were considered to encode truncated proteins. The four longest proteins (from 419 aa to 720 aa) were encoded by genes preceding ftsH1, ftsH3, ftsH4 and ftsH5. For these four proteins (in grey in Figure  3B ), internal sequences corresponding to functional domains of the substrate-binding unit of bacterial ABC transporters for oligopeptides were identified using the NCBI Blast automatic domain detection tool. Proteins encoded by genes upstream of ftsH1, 3, and 5 also contained a predicted signal peptide for membrane translocation, while the protein encoded by the gene upstream of ftsH4 appeared to be truncated in the N-terminal region (Figure  3B ).
Molecular Phylogenetic Analysis by the Maximum Likelihood Method The eight FDP ftsH genes were deposited in the European Nucleotide Archive under the accession numbers LT999755 (FTSH1) to LT999762 (FTSH8). The evolutionary history was inferred using the maximum likelihood method based on the JTT matrix-based model  [32]. The tree with the highest log likelihood (-17,027. 0586) is shown. The percentage of trees in which the associated taxa clustered together upon 250 bootstraps is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the neighbor-joining method to a matrix of pairwise distances estimated using a JTT model. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 42 amino acid sequences.
Acknowledgments: We thank our colleagues  Thierry Mauduit ,  Thierry Lusseau ,  Martine Coindre ,  Kaëlig Guionnaud , and  Denis Lacaze  for their technical assistance in production and care of plants and insects and their help in collecting canes and growing S. titanus hatchings. We acknowledge  Sébastien Carrère  ( LIPM, INRA-Occitanie in Toulouse ) for establishing the  FD2 genome Browser Platform hosted at BBRIC Toulouse.
tritici and two closely related species, Z. pseudotritici, and Z. ardabiliae, used genome-wide estimates of nonsynonymous and synonymous divergence to quantify positive selection and identify candidate genes  (Stukenbrock et al. 2010 (Stukenbrock et al. , 2011)). Here, we specifically address the impact of meiotic recombination on the rate of adaptive evolution at the genome scale and contrast it with other genomic factors such as the occurrence of transposable elements (TEs). We analyze an extended population genomics dataset comprising 1. 4 million single nucleotides polymorphisms and infer genome-wide signals of natural selection among 13 isolates of Z. tritici collected from bread wheat in Europe and the Middle East, by contrasting patterns of polymorphism and divergence and modeling of the distribution of fitness effects.
Results and Discussion We generated a population genomic dataset of 13 Z. tritici haploid isolates obtained from different field populations in Europe and Iran (Table  S1 ). Given the vast extent of structural variation in genomes of Z. tritici isolates, we de novo assembled and aligned the 13 genomes. After filtering (see Material and Methods), the resulting multiple-genome alignment of ß27 Mb (Table  S2 ) comprised a total of 1,489,362 SNPs of which approximately 50% located in protein-coding regions. The SNP data were used to compute the overall genetic diversity of the sample showing a mean value of π = 0. 022 per site. Importantly, the multiple-genome alignment of de novo assembled genomes a priori contains only alignments of orthologous regions.
While we find higher support for the logarithmic model (Fig. 4 ), the effect is weak. When using ρ instead of r, the power law offers a better fit (Supporting Information). These results do not allow us to conclude confidently for or against the existence of an asymptotic value  (Castellano et al. 2016). As several authors (e. g. ,  Ma et al. 2010; Raffaele et al. 2010; Rouxel et al. 2011; de Jonge et al. 2013)  reported that TEs are associated with effector genes in several species and suggested that they play a role in adaptation, we tested whether the rate of adaptive substitutions was correlated with the amount of TEs in the vicinity of the genes. We stratified the dataset according to the local density of sites annotated as belonging to a TE (see Methods and Materials) and computed both α and ω a for each category.
Z. tritici ISOLATES In this study, we used a geographical collection of 13 haploid field isolates of Z. tritici isolated from infected leaves of bread wheat (Triticum aestivum; Table  S1 ). Genome data of three isolates, including the reference isolate IPO323, were published previously  (Goodwin et al. 2011; Stukenbrock et al. 2011). For the remaining 10 isolates full genomes were sequenced. DNA extraction was performed as previously described  (Stukenbrock et al. 2011). Library preparation and paired-end sequencing using an Illumina HiSeq2000 platform were conducted at Aros, Skejby, Denmark. Sequence data of the 10 isolates have been deposited under the NCBI BioProject IDs PRJNA312067. We used SOAPdenovo2  (Luo et al. 2012)  to construct de novo genome assemblies for each isolate independently.
For each genome, the k-mer value maximizing the N50 of contigs and scaffolds was selected. Before generating a multiple-genome alignment, we preprocessed the individual genomes of the 13 Z. tritici isolates. First, we masked repetitive sequences using a library of 497 repeat families de novo identified in four Zymoseptoria species  (Grandaubert et al. 2015). Repeats were soft-masked using the program Re-peatMasker (option -xsmall) to retained information of repeat sites in the alignment (A. F. A. Smit, R. Hubley and P. Green Re-peatMasker at http://repeatmasker. org). Second, we filtered the genome assemblies to contain only contigs with a length ࣙ1 kb. Multiple-genome alignments were generated by the MULTIZ program using the LASTZ pairwise aligner from the Threaded Blockset aligner (TBA) package  (Blanchette et al.
In some cases, due to indels in the genome alignment, the codon phasing of some genes was lost. This issue was overcome by refining the CDS alignment using the codon-based multiple alignment program MACSE  (Ranwez et al. 2011). The final dataset contained 9412 gene alignments, among which 7040 contained a sequence for all 13 isolates. We further created a dataset containing an outgroup sequence, taken from the sister species Z. ardabiliae, leading to 6767 alignments with all 13 isolates together with the outgroup sequence. The CDS alignment with outgroup was used to infer the synonymous and nonsynonymous divergence based on the rate of synonymous and nonsynonymous substitutions. The synonymous and nonsynonymous unfolded site frequency spectra (SFS) were computed, using the outgroup sequence to reconstruct the ancestral allele.
To do so, we first reconstructed a BioNJ tree for each gene and fitted a codon model of evolution using maximum likelihood. Then ancestral state was inferred using the marginal reconstruction procedure of  Yang et al. (1995). All calculations were performed using the BppPopStats program from the Bio++ Program Suite  (Guéguen et al. 2013). We used the Grapes program in order to estimate the distribution of fitness effects from the SFS and compute a genome-wide estimate of α and ω a , the proportion of mutations fixed by selection and the rate of adaptive substitutions, respectively  (Galtier 2016). The following models were fitted and compared using Akaike's information criterion: Neutral, Gamma, Gamma-Exponential, Displaced Gamma, Scaled Beta, and Bessel K. Analyses were conducted on the complete set of gene alignments, as well as on sub-datasets sorted according to whether the individual genes encoded a predicted effector protein or not.