dataesr/scientific-paragraphs-categorization huggingface dataset:
TE-presence detection module We have changed the mapper used in the TE-presence detection module: while T-lex2 used MAQ  (Li et al. , 2008) , T-lex3 uses BWA-MEM  (Li and Durbin, 2009)  (Table  2 ). MAQ has two main limitations: (i) it cannot process reads > 127 bp, while the newest Illumina technology produces longer reads (150 and 300 bp) and (ii) it is no longer updated by the developers. We chose BWA-MEM as the new mapper because it accepts reads > 127 bp and it performs mapping with high accuracy  (Hatem et al. , 2013). In addition, the bam files generated by BWA-MEM can be visualized using tools such as IGV that are extensively used in the community  (Robinson et al. , 2017). Because T-lex2 used many of the output files of MAQ to call a TE as present or absent, we have implemented in T-lex3 pipeline several Perl and AWK scripts, and included other tools such as SAMtools  (Li et al.
, 2009)  to generate these files. Furthermore, we have added an extra filtering step: T-lex3 now filters using the CIGAR string information all the reads with an alignment match < 95% compared with the reference genome. Because this percentage is calculated based on the read length specified by the user, this filter also allows to remove shorter reads in those cases in which reads of different lengths are available for a given strain or pool. This allows fixing a bug in T-lex2 code, which previously considered all reads to be of the size specified by the user leading to incorrect TE calls. After the mapping is completed, the TE-presence detection module analyzes the junction regions to call the TE as present or absent. The size of the junction region is based on the -limp and -buffer parameters (by default 15 and 60, respectively).
To consider a TE as present, three conditions had to be met in at least one of the two junction regions: (i) at least one read mapped in the junction, (ii) > 15 bp mapped inside the TE (-limp parameter) and (iii) 95% identity of the sequence inside the TE. There was a bug in T-lex2: if two conditions were fulfilled in one of the junction regions and the other condition was fulfilled in the other junction region, the TE was considered as present. T-lex3 now requires that all three conditions are met in at least one of the junction regions (Fig. 2A, Table 2 ). For the TE-presence detection module to call a TE insertion as absent, T-lex2 required < 65 Ns (missing data) in the sequence mapped to the junction, otherwise the TE call was no data. In T-lex3, we have modified this step and introduced a This study new one before calling a TE insertion as either absent or no data (Fig.
The TE-combine module takes into account whether the data comes from pooled or from individual strains to estimate the TE frequencies. When the TE frequency is estimated from pool-seq data, we have added two new parameters and we have fixed two bugs (Table  2 ). The new parameters -minR and -maxR allow the user to define the minimum and the maximum number of reads needed to estimate the TE frequency. Requiring a minimum and maximum number of reads allows false positives to be discarded (very low number of reads to make an accurate TE call), and to discard regions with an excess of coverage due to non-unique mapping or spurious reads (very high number of reads to make an accurate TE call). There was a bug in the formula for the TE frequency estimation in T-lex2: TE frequency ¼ (NP)/(NPþNA), where NP is the total number of reads supporting the presence and NA is the total number For subsequent runs, we recommend running the pipeline with the filtered list of selected TEs of reads supporting the absence.
This formula did not take into account whether there were reads providing evidence for the presence for only one or for the two flanking regions. In T-lex3 this formula is only used if there is information for only one of the flanking regions. If there are reads mapping on each of the two flanking regions, the formula is: TE frequency ¼ [(NPRþNPL)/2]/(NPRþNPLþNA), where NPR and NPL are the total number of reads supporting the presence at the right and left flanking regions, respectively. The other bug was in the estimation of the frequency of pooled samples: T-lex2 considered the reads that the presence module maps in the flanking region before the calling steps. As such, there could be reads that mapped to the flanking regions even if the TE was finally called as absent. In T-lex3, these reads are not considered to estimate the frequency.
Besides the changes in the three modules described above, T-lex3 now provides more detailed standard output that informs the user about the running process. For example, in the TE-presence detection module the program now informs the user when each step of the mapping process starts, and whether any of the mapping steps has failed. If a mapping step fails, the program stops. We have uploaded the pipeline to Github along with a test dataset that allows the user to check whether the program is properly installed. An updated user manual that provides all the information required to run and interpret T-lex3 results can also be found at Github (https://github. com/GonzalezLab/T-lex3).
Datasets To compare the performance between T-lex2 and T-lex3 we ran several datasets with both versions. For D. melanogaster, we selected 10 pool-seq datasets from the DrosEU consortium  (Kapun et al. , 2018) , and 30 individual strains: 19 from the DGRP dataset  (Huang et al. , 2014)  and 11 from the DPGP2 dataset  (Pool et al. , 2012; Supplementary Additional File S1). We also ran T-lex3 for several datasets that failed to give results with T-lex2: three pooled datasets from Italy, Austria  (Bastide et al. , 2013) , and Portugal  (Kofler et al. , 2012) , 86 individual strains from Lyon, France  (Lack et al. , 2016) , and 32 strains from Winters, CA  (Campo et al. , 2013; Supplementary Additional File S4). For all runs, we used a dataset of 1630 reference TE insertions as described in  Rech et al.
(2019). For humans, we used the same dataset as in T-lex2: the child genome of the trio human dataset NA12878 (SRR622457) with the reference genome NCBI36/hg18 (http://hgdownload. cse. ucsc. edu/ goldenPath/hg18/bigZips/chromFa. zip) (Stewart et al. , 2011). We analyzed 1600 insertions out of the 2010 that (i) have genomic coordinates with confidence intervals smaller or equal to 1, and (ii) have been analyzed in the CEU trio dataset. In this work, we also run for the first time T-lex3 with a rice genome (MH63)  (Zhang et al. , 2016). We first annotated in the Oryza sativa Nipponbare assembly (Sasaki and International Rice Genome Sequencing, 2005) 1600 MITE and 1553 LTR insertions. To detect MITE families, we run MITE-hunter  (Han and Wessler, 2010)  and combined the results with the high-quality predictions available in the PMITE database  (Chen et al.
, 2014). Only families with TSDs were considered. Clustering at 90% was performed to remove redundancy using cd-hit  (Fu et al. , 2012)  to produce a final library. RepeatMasker (http://www. repeatmasker. org/) was run to annotate all regions having significant homology with any of the MITE families. Only the annotations corresponding to full-length elements (length equal to consensus length 6 20%) were considered. LTR insertions were identified with LTRharvest  (Ellinghaus et al. , 2008)  on the Nipponbare assembly  (Sasaki and International Rice Genome Sequencing, 2005 ) using default parameters. The potential to encode for proteins was investigated using hmmscan  (Eddy, 2011) , and only elements potentially coding for proteins containing typical retrotransposon conserved domains (e. g. reverse transcriptase and integrase) were retained for further analyses.
Sensitivity, specificity and accuracy estimations T-lex3 sensitivity, specificity and accuracy in the three datasets analyzed were estimated as: (i) sensitivity ¼ number of correct presence calls/total number of presence calls in the validation dataset; (ii) specificity ¼ number of correct absence calls/total number of absence calls in the validation dataset; and (iii) accuracy ¼ (number of correct presence calls þ number of correct absence calls)/(total number of presence calls þ total number of absence calls in the validation dataset). These calculations were performed using a Python script. For D. melanogaster, we used the same individual PCR frequencies used in T-lex2  (Fiston-Lavier et al. , 2015) , except those for one strain (RAL-730) for which the raw reads were no longer available.
T-lex3 processing times For an individual D. melanogaster genome of 134 Mb, using a 18. 6x sequencing dataset of 73 bp reads, T-lex3 processing time was $ 10. 5 hours, while for T-lex2 processing time was 14 hours. Both runs were performed on a standard computer with a 2. 8 GHz Intel Core i5 with 16 GB RAM memory, and both versions were executed with a single thread. In humans (genome size 3 Gb), T-lex3 processing time was 72. 5 hours for a 3. 1Â sequencing dataset of 101 bp reads, for a dataset of 1600 TEs. In rice (genome size 362 Mb), T-lex3 processing time was 38 hours for a 23. 7Â sequencing dataset of 100 bp read length, for a dataset of 1584 TEs. Both runs with human and rice genomes were performed with a single thread on a Linux Cluster using two different nodes: one with 16 cores (126 Gb of RAM) Intel V R Xeon V R CPU ES-2630 v3 with 2.
T-lex3 genotypes and estimates TE population frequencies in datasets that could not be analyzed with T-lex2 in D. melanogaster We have run T-lex3 in three datasets that gave good results with T-lex2 (>60% of present, polymorphic or absent calls): 19 individual strains from the DGRP dataset  (Huang et al. , 2014) , 11 individual strains from the DPGP dataset  (Pool et al. , 2012) , and 10 pooled datasets  (Kapun et al. , 2018)  (Supplementary Additional File S1). The 19 individual strains from the DGRP dataset had a variable number of no data calls: from 146 in RAL-757 to 585 in RAL-399 (Supplementary Additional File S1A and B). T-lex3 substantially reduced the number of no data calls. The maximum number of no data calls is now 374 in RAL-399 with some strains having as few as 83  Fig. 3A and Supplementary Additional File S1A and B).
The number of no data calls according to T-lex2 in Zambia strains was smaller, ranging from 156 to 344, and this number was also substantially reduced in T-lex3: 128 to 181 (Fig. 3B , Supplementary Additional File S1C and D). For pool-seq datasets, we found that the correlation between the frequency estimates obtained using the two versions of T-lex was very high (Pearson correlation r 2 ¼ 0. 92-0. 97, Fig. 3C  and Supplementary Additional File S1E and F), as expected since the number of no data calls was similar between the two versions. To estimate the sensitivity, specificity and accuracy of T-lex3, we used the same DGRP strains previously used to estimate these parameters for T-lex2 (see Section 2). We found that both sensitivity, specificity and accuracy are high: 100, 93. 33 and 97. 14%, respectively (Table  4 , Supplementary Additional File S3A), with similar results among different TE classes (Supplementary Additional File S3B).
Thus, T-lex3 increases the number of present, polymorphic and absent calls with a very similar sensitivity and slightly higher specificity compared with T-lex2. As this DGRP dataset contains strains with different read lengths, we also estimated sensitivity and specificity using different T-lex3 parameters. We found that specificity decreased and the number of no data calls increased when sub-optimal parameters are used (Supplementary Additional File S3C). We then estimated the specificity for this same dataset using TIDAL, a method that identifies the absence of reference TE insertions and also discovers non-reference TE insertions  (Rahman et al. , 2015). The specificity of TIDAL was 64. 36%. Finally, we ran T-lex3 with five datasets that previously failed to give results: two individual strains datasets and three pooled datasets.
For all five datasets, T-lex3 is able to estimate population frequencies for the majority of TE insertions tested (Supplementary Additional File S4). As an example, for 78 of the 86 strains collected in Lyon, the number of no data calls with T-lex2 was >40%. For this same dataset, T-lex3 only returns >40% of no data calls for 1 of the 86 strains (Fig. 4 , Supplementary Additional File S4A).
T-lex3 provides accurate TE genotyping and frequency estimations in humans We also tested the performance of T-lex3 in a human dataset: the child genome of the trio human dataset in  Stewart et al. (2011)  (Supplementary Additional File S5A). Out of the 1600 insertions tested, T-lex3 returns present, absent or polymorphic calls for the majority of them (82. 5%: 1320/1600). Sensitivity and specificity were high: 99. 35% and 87. 78%, respectively, and accuracy was 91. 81% (Table  4 , Supplementary Additional File S5B), with similar results for the different families tested (Supplementary Additional File S5C). We estimated the specificity in this same dataset using the two algorithms described in  Stewart et al. (2011) : the read-pair (RP) algorithm and the split-read (SR) algorithm. The specificity using RP was 99.
T-lex3 accurately genotypes and estimates TE population frequencies in the rice genome We tested T-lex3 in a rice genome using our own annotated insertions (see Materials and Methods). Out of the 3067 insertions in our dataset, T-lex3 returns present, absent or polymorphic calls for 2878 (93. 84%) (Supplementary Additional File S6A). According to our validation dataset, sensitivity and specificity were 99. 96% and 92. 09% respectively while accuracy was 99. 2% (Table  4 , Supplementary Additional File S6B), with similar results for the different TE classes tested (Supplementary Additional File S6C). These results demonstrated that the T-lex3 pipeline performance is also robust with rice genomes.
Discussion Genome-wide genotyping of TE insertions is crucial in developing a complete catalog of genetic variants that can then be investigated for their potential role in adaptive evolution and/or disease. The repetitive nature of TEs complicates the correct genotyping of these variants and several computational pipelines have been designed to tackle this problem (Table  1 ). Ideally, these computational pipelines should be able to handle (i) different types of datasets, such as individual and pooled whole genome sequencing datasets; (ii) different lengths of short-reads; and (iii) a variety of TE insertions in diverse genomes (Table  1 ). In this work, we have improved and updated T-lex, a flexible and broadly applicable tool that provides accurate TE genotyping and frequency estimations for the different TE families present in a reference genome using both individual and pooled datasets.
Although there are several available tools that identify non-reference TE insertions, they have a high rate of false positives  (Rahman et al. , 2015; Rishishwar et al. , 2017). Thus, we are still limited to analyzing the TE insertions that have been annotated in the available reference genomes. Long-read sequencing technologies should allow the generation of new reference genomes and better de novo annotations of TE insertions  (van Dijk et al. , 2018). These new long-read sequencing technologies are already being applied to model and non-model species  (Miller et al. , 2018; Solares et al. , 2018). However, the number of new genome assemblies is still small as the cost of these technologies remains higher compared with short-read ones. Thus, once the coordinates of new TE insertions are defined based on the new genome-assemblies, T-lex3 would be useful to estimate the frequencies of these new insertions in all the shortread datasets already available  (Auton et al.
Funding This work was supported by the  European Commission  ( H2020-ERC-2014-CoG-647900 ) and by the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya  ( GRC 2017 SGR 880 ). Work done at CRAG was partially funded by a grant from the  Ministerio de Economia y Competitividad  ( AGL2016-78992-R ).
First, the CT dataset and DICOM structures (GTV and PTV) were imported into the treatment planning system. Instead of arcs, two perpendicular static 20 mm diameter circular horizontal fields were positioned with the isocenter at the center of the PTV as shown in Figure  1. This setup was chosen to allow a controlled irradiation geometry. The treatment plan was prescribed to deliver one dose fraction with a MCVD of 7. 7 Gy (70% of 11 Gy) in the PTV. Because the voxels in the CT dataset are much larger than the peak and valley dose distribution, the MRT treatment planning system is unable to visualise the spatial distribution of the dose. More over, in order to decrease the calculation time the dose calculation grid consists of macrovoxels of 2×2×2 voxels. The output of the treatment planning system consists of two 3D dose distributions for each beam; one corresponding to the maximum dose in the microbeams and the other the minimum dose between two microbeams.
The equation of the fitting curve is: Dose = c -a × 10 OD b × 10 OD -1 (2) With a, b, c being constants determined using the least square method and OD the optical density. Only the red channel was used in the calibration and dose determination. A Matlab (Natick, MA, USA) macro using the image processing toolbox was written to perform the calibration, curve fitting and dose conversion. The goodness of fit for the calibration was measured with the reduced chi squared method. HD-V2 (MRT) films were read out using a Zeiss (Oberkochen, Germany) AxioVision microscope using the protocol developed by Bartzsch et al. [19]. The microscope was connected to a 12 bits black and white CCD camera of 1040 × 1388 pixels (AxioCam MRm Rev. 3 FireWire, Zeiss). The field of view was illuminated by a light emitting diode (LED) with an operating voltage of 7 V.
Microbeam Peak and Valley Dose Distributions In order to compare the measured MRT dose maps with the treatment planning system dose maps, separated peak and valley dose maps must first be extracted from the film dose maps (see section 2. 2). The method used to do this is illustrated in Figure  3. First, the image of the microbeam field was rotated using Matlab to correct for the vertical misalignment of the microbeams in the microscope reading. To locate the peaks and valleys, pixels were averaged vertically to produce a line profile as shown in the top left of Figure  3. The full-width at half-maximum (FWHM) of each peak was used to define the edges of the peaks. From the microbeam edges, the center of each microbeam, represented by red vertical lines, were found. These positions were in turn used to find the center of each valley, represented by purple vertical lines.
Using a PinPoint ionization chamber and EBT3 film, the isocenter dose was measured to be 10. 82 and 10. 62 Gy respectively, compared to 10. 93 Gy calculated by the iPlan treatment planning system. The small discrepancies observed in the normalized γ-index map and between the measured isocenter doses and the treatment plan isocenter dose could be explained by positioning uncertainty. The Brainlab software used for the image-guided positioning relies on anatomical structures to register the treatment room kV images and the CT derived Digitally Reconstructed Radiography (DRR). Apart from two nylon screws that hold the phantom together, the Lucy phantom is a homogenous sphere of PMMA. The lack of anatomical structures in the Lucy phantom makes the image registration difficult. The tilt is especially difficult to be handled.
This has been checked using radiochromic film data as detailed in section 3. 1. Observations in a single microbeam profiles presented by Livingstone et al. [30]  also exhibit a flat profile across the 20 µm. However, those profiles were acquired with a detector of better spatial resolution than HD-V2 or EBT3 film but with a density significantly larger that could smooth the profile by modifying the secondary electron range in one direction. If the dose is not constant across the central 20 µm, but decreases from the center, the average dose may be lower than the dose predicted by the treatment planning system. Additionally, for the Monte Carlo part of the treatment planning, the Hounsfield Units from the CT dataset must be converted to material parameters. This conversion has been performed using an interpolation algorithm described by Schneider et al.
[49]  assuming that the material composition of Lucy is similar to that of soft tissue. From the NIST database of x-ray Mass Attenuation Coefficients  [50] , the ratio of the mass energy absorption coefficients for 105 keV x-rays in PMMA and soft tissue is 0. 93, which would lead to a discrepancy in the calculated dose. Since the dose was prescribed in the valley, the peak dose calculation will be heavily influenced on errors in the PVDR calculation. A CT scan of a calibration phantom, including PMMA material, should be performed to ensure that the calibration between electron density and Hounsfield units is accurate for the material of interest. Also, it can be noted that PMMA is not ideal in TH200 beam quality fields  [51]. Implementation of beam polarization is planned for a future version of the treatment planning system.
Acknowledgements This research was undertaken on the Biomedical beamline ID17 at the  European Synchrotron, Grenoble, France. The Grenoble-Alpes University scientists acknowledge the work and support of the ID17 staff. The authors acknowledge the  European Cooperation in Science and Technology (COST) Biomedicine  and  Molecular Biosciences  action  TD1205  members for fruitful discussions and support for initiating this large animal project. The authors thank the " Conseil Régional Rhône-Alpes", la "Ligue contre le Cancer", "l'Association pour la Recherche contre le Cancer " and the  INCA PRTK17048  for financial support. The authors also acknowledge financial support from  LabEx PRIMES  ( ANR-11-LABX-0063/ANR-11-IDEX-0007 ). Jayde Livingstone  was invited scientist in the UGA-RSRM team, funded by the  LabEx PRIMES  and the  French Embassy in Canberra mobility program.
Statistical Analyses All extractions and experiments were performed in triplicates. Significant differences between the means of the shoot and root biomasses, root colonization rate, as well as EO composition were assessed according to Fisher's least significant difference (LSD) test using Statgraphics Centurion 15. 2. 11. 0 (Statgraphics Technologies, Warrenton, VA, USA). Besides, the effect of inoculation and sterilization on root dry weights was tested by Student's t-test using the SPSS 16. 0 software (SPSS Inc. , Chicago, IL, USA). Results were considered significantly different at the 0. 05 level (p < 0. 05). Moreover, the effect of soil types on EO composition was tested by the principal component analysis (PCA) using XLSTAT 2014. 5. 03 (Addinsoft, Barcelona, Spain).
Acknowledgements The authors thank  Julie Viala  for support in the lab and  Yasmine Hassoun  for the construction of the GppA expression plasmid. The work was funded by  Agence Nationale de la Recherche  ( ANR-17-CE13-0005 ) and the IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Image processing and quantification were conducted with the open-source software ImageJ version 1. 51  (Schneider et al. , 2012). After the images were preprocessed, soil macrofauna macropores inside each core were selected by removing pores <20 mm 3 in order to reduce noise and exclude roots. Characteristics of total macropores were then described based on their number, volume (largest volume, volume of the pores connected to the surface, volume of the pores connected to the bottom and the percolating volume), diameter (the mean diameter and the critical diameter of the percolating macropores) and global connectivity (Ŵ), which reflects the probability of two randomly chosen pore voxels to belong to the same macropore cluster  (Renard and Allard, 2013). Macropores were then reconstructed and visualized using AvizoFire 8.