dataesr/scientific-paragraphs-categorization huggingface dataset:
More importantly, our TCR profiling workflow can be easily adapted to other non-model vertebrates that lack genetic resources such as well-annotated reference genomes or V/J segment listings in bioinformatics databases. It does not require dedicated molecular reagents and kits -it uses universal 5′RACE kits and requires only one species-specific set of primers in the constant region of the TCR molecule. The primers can be easily designed based on e. g. , the de novo draft genome or transcriptome assemblies of this region, which are relatively easy and straightforward as compared to assemblies of highly duplicated and diversified V and J segments. Such obtained TCR sequences can be readily analysed with our new, publicly available, on-line AmpliTCR and AmpliCDR3 programs. They extract V and J segments and the CDR3 region based on conserved amino acid residues, and, unlike other published tools  68, 69  , they do not rely on availability of genomic references of V/D/J segments.
Materials and Methods Primer design. The bank vole is a non-model organism, with limited genetic information available. To design nested primers for 5′ RACE, we used de novo assembled transcriptomes from spleens of seven animals. For a detailed description of the dataset and the assembly strategy, see Migalska et al. 30  Briefly, transcriptomes were de novo assembled with Trinity 71 , and contigs highly similar to the first exon of the constant regions of TCRβ Mus musculus sequences (as extracted from Genbank records: AH002088. 2, AH002089. 2, and X03574. 1) were retrieved. Based on these contigs, three nested primers in the 3′ end of the constant region were designed: MyglTCRb_1: TGATCTCTGCTTCTGATG, MyglTCRb_2: GATGGCTCAAACAGGGTGACC, and MyglTCRb_3: GGACTCACCTTGCTCAGATCCT. The full list of primers and adaptors used in the study is given in the Supplementary Table  S4 , and a schematic illustration of the primers and adaptor location is presented in the Supplementary Fig.
Read sub-sampling, TCRβ V and J segments extraction, and retrieval of consensus V and J variants by AmpliTCR. To analyse a comparable number of TCRβ sequences for each individual, 1 mln random reads per sample were selected for further processing; 95% of these reads had lengths between 325 and 536 bps and covered the full variable region of the TCRβ, including the rearranged and spliced V(D)J segments. Identification of V and J segments was performed automatically by AmpliTCR. Although partial sequence of D segments was extracted by the software, it was neither included in further analysis nor described, as too much uncertainty about its germline sequences is introduced by N-diversity regions. The AmpliTCR uses protein patterns in the PROSITE format  75  to identify highly conserved residue positions in the translated sequence.
To extract TCRβ V segment (TRBV) the following pattern was used: x(5)-Q-x-P-x(  14 )-C-x(10,11)-W-Y-x(39,42)-[LM]-x(  14 )-C-x; and for TCRβ J segment (TRBJ): x(4)-G-x-G-x(2)-L-x-[VI]-x. Patterns were composed of conserved amino acids previously described in the literature  76, 77  as well as the multiple alignment of human and mouse TRBV and TRBJ sequences found in the IMGT database  78. On average, 91% of the analysed reads (range: 87-94%) contained both protein patterns. AmpliTCR discards sequences containing unspliced introns, frameshift indels, and premature termination codons (PTCs). Approximately 41% of TCRβ sequences with recognized protein patterns were later discarded for those reasons. Extracted TRBV and TRBJ sequences were de-replicated by AmpliTCR into unique, non-redundant variants and ordered by sequencing depths.
All alleles are available in the Supplementary Files S2 and S3. Phylogenetic analysis and naming of the V and J segments. No genomic information about segments composing bank vole TCRβ was available prior to the analysis. Provisional V and J loci identification was conducted based on formation of distinct clades in phylogeny by variants (alleles) from 7 individuals, and further supported by both orthology (co-occurrence of bank vole alleles and murine reference within the same phylogenetic clade) and sequence similarity to the mouse references from the IMGT database  3  (the full list of the murine reference sequences with accession numbers is available in a Supplementary Table  S1 , all retrieved bank vole alleles are in the Supplementary Files S2 and S3). The orthology was assessed from phylogenetic trees constructed separately for all V and J alleles in MEGA6  81.
Supplementary phylogenetic analysis was conducted for V segments, with the addition of genes downloaded from an online database Vgenerepertoire. org  49 (accessed: May 20, 2017), that stores V genes automatically extracted from whole-genome assemblies. V genes of the bank vole had been extracted from a draft genome with GenBank assembly ID: LIPI00000000. 1. V-J segment usage and CDR3 length distribution. After the V and J segment loci were identified and named, we assessed V-J segment usage and CDR3 length distribution in the seven bank vole individuals. Pre-processed Illumina reads (see above) were re-analysed with AmpliCDR3 tool, with the reference BV_TRBV and BV_TRBJ segments obtained in the previous steps. For each individual V and J segment, sequences are locally aligned with BOWTIE2  82  (-sensitive-local -k 2) to the BV_TRBV/J references.
After filtering, on average, 85% of the reads were retained (75-89%). Although the remaining sequences likely still contain substitution errors introduced during PCR or sequencing, they should not significantly affect the descriptive characterization of the TCRβ repertoire (i. e. V-J segment usage and CDR3 length distribution). Qualitative statistics were calculated and visualized with R  83. Quantitative analysis of the bank vole TCRβ repertoire. For three additional individuals (s08-s10), a quantitative analysis of the TCRβ CDR3 repertoire was conducted with a UMI-based error-correction strategy. Data were analysed with the tools AmpliMERGE, AmpliCLEAN, and AmpliCDR3 in the following steps: (i) pre-processing of paired-end Illumina reads; (ii) sub-sampling, identification of CDR3 sequences and filtering; (iii) UMI-based CDR3 error correction; (iv) repertoire size estimation; and (v) private versus public repertoire comparison.
Nonetheless, AmpliCDR3 retrieves highly comparable TCR repertoires, both in terms of total numbers of observed CDR3s and calculated diversity indices. This is shown by application of AmpliCDR3 to a murine dataset (generated with library preparation protocol incorporating UMIs, as in our study) and analysed in detail using MIGEC  19  and MiXCR  69  by Izraelson and colleagues  84. Results of both approaches where very similar (see Supplementary Table  S7 ) and highly correlated (correlation coefficient >0. 99). To infer the correct sequence of a CDR3, AmpliCDR3 groups reads with identical UMIs into molecular identifier groups (MIGs)  19. As the majority of errors arise during late PCR cycles or sequencing, they are expected to represent the minority of reads within a MIG, and a consensus sequence is inferred to be the true CDR3 sequence.
The resulting repertoire was compared among individuals-both at the nucleotide and amino acid levels. We further defined a CDR3 sequence as "private" if it was present in only one bank vole in our analysis, and as "public" if it was shared by all three individuals. Calculations were conducted with AmpliCDR3. Data accessibility. Illumina MiSeq and HiSeq raw reads (see the Materials and Methods section) are deposited in FASTQ format in the European Nucleotide Archive (ENA), with the study accession PRJEB22487. V and J segment consensus alleles are available in the Supplementary Information (Supplementary Files S2-S3) associated with this article. Bioinformatics tools AmpliTCR, AmpiCDR3, AmpliMERGE, and AmpliCLEAN are available for online use or download at our laboratory server: http://evobiolab.
Acknowledgements The authors sincerely thank  P. Koteja  for the donation of bank vole spleens. The authors also thank  K. Dudek  for running the  Illumina MiSeq sequencing ,  O. Britanova  for providing murine TCR dataset for software performance comparison and  M. Konczal  for comments on an earlier version of the manuscript. This work was funded by the  National Science Centre (NCN)  grant  UMO-2013/08/A/NZ8/00153  awarded to  J. Radwan , and supported by  NCN  PhD scholarship  UMO-2017/24/T/NZ8/00088  awarded to  M. Migalska.
Health impact: Various studies which have been recently published showcase the severe health impact that air pollution can have on public health. In 2017, the European Environment Agency (EEA) had released a detailed analysis based on official data from more than 2500 monitoring stations across Europe  [5]. Although the data shows that air quality in Europe is slowly improving due to past/current policies and advanced technological developments, the PM2. 5 concentrations were responsible for an estimated 428000 premature deaths annually. The report indicates that in 2015, about 19 % of the EU-28 urban population was exposed to PM10 above the EU daily limit value, 53% was exposed to concentrations exceeding the stricter WHO AQG value for PM10, while 30% was exposed to higher O3 concentrations above the EU target value threshold.
4 details the steps for building the 3D mesoscopic traffic simulation model using real data sets receved from the Grand Nancy traffic management centre. Although various research studies consider other factors, such as speed and acceleration to model and predict the air pollution concentrations, these data sets are not always available in many urban areas with limited traffic detectors or monitoring capabilities, this being the case for Nancy Grand Coeur as well. The results of the simulation are used to implement the simplified estimation model for NO2 (detailed in Section 3), by considering as well meteorological data received from the local air monitoring association Air Lorraine. In Section 3. 2 we further conduct a seasonality study over the traffic volume and validate the estimation results over real data.
This is equivalent to the MSA (Method of Successive Averages) procedure of the Dynamic User Equilibrium in Aimsun  [23] , which redistributes the flows among the available paths in an iterative procedure. However, this requires the number of iterations to be defined by the O-D pairs and the time interval of the simulation. Traffic flow inside the NGC neighbourhood is highly impacted by the central train station of the city and its particular suspended structure as previously shown in Figure  1c ). The study zone we are considering for the traffic simulation contains the junction made by Viaduct Kennedy with Rue Saint Léon, Avenue Foch and Rue de la Commanderie. The data used to create vehicles was received from the Grand Nancy traffic management centre and represents a time-sliced profile demand for each 15-min time interval during morning peak hours; from this data we have further computed dynamic turning rates between all road sections during the simulation time interval.
PM10 Nrcars T W H PM10 and the predictor analysis presented in Table  6. The VIF indicators remain below 5 which indicate that the chosen predictors are not correlated one to each other. On the other hand, the p-values for the number of cars and the wind are bigger than 0. 05, which indicate that these predictors will not influence the initial regression equation, therefore their role will not be significant in the prediction step. If we remove these 2 predictors, one would only have to use the temperature and the humidity to predict the evolution of fine particles in the neighbourhood. The regression test also returned a value of 44. 0 2  R for the coefficient of determination, which indicates that the data does not fit in a good proportion to the regression model. These findings put the prediction process for PM10 on hold until further data sets will be available for use.
The last step included a seasonality study in the prediction model based on a daily variation of number of cars. All results show a good forecasting for NO2 concentrations which is subject to improve if more data would be available. The same procedure was applied on SO2 and PM10 but the data sets have shown different behaviour, especially for SO2 whose evolution is very hard to analyse and predict by using only traffic and meteorological data. One would need further information about the manufacturing industry in the area, the operating hours and the products being manufactured, which might be very difficult to obtain. The PM10 data analysis and prediction indicated that more data sets would be required to build the prediction model and that one month of observations is not enough. This would imply further calibration and validation of the simulation model on extended time periods, with further manual counts and verifications which are the subject of approval from the Grand Nancy council.
Acknowledgements This work has been developed in the ERPI laboratory, from  Nancy France , under the Chaire REVES project funding. The final writing and submission of the paper has been done in the  DATA61|CSIRO research laboratory from Sydney, Australia , with further work on the analysis of SO2 and PM10 pollutants. The authors of this work are grateful for the data and support provided by  Grand Nancy ,  Air Lorraine  and  FlexSim Conseil.
Where possible, data were mined using the Reference Sequence (RefSeq) database. A custom approach incorporating the annotation pipeline MAKER was designed to optimize data mining from poorly annotated genomes (see Materials and Methods). This enabled the inclusion of additional genome data from five bat species and three eutherian species (table S5 and Supplementary Text). Two data sets were prepared for downstream selection analyses, one containing only RefSeq data and one combining RefSeq and data mined using our custom MAKER pipeline. Tests of positive and divergent selection were carried out independently along five branches of the tree (fig. S6 ): the branches leading to (i) all bats, (ii) the genus Myotis, (iii) R. ferrumequinum, (iv) Miniopterus natalensis (M. schreibersii genome data were not available), and (v) the naked mole rat, an additional exceptionally long-lived mammal given its small size (Fig.
1 ) (4). CodeML selective pressure variation tests were automated using a custom-built workflow, OH-SNAP (Optimised High-throughput SNakemake Automation of PAML; available at https://github. com/ batlabucd/OHSNAP) (see Materials and Methods and fig. S7 ). To ensure that our results were robust to the addition or removal of data, only results recovered as significant in both data sets after false discovery rate (FDR) correction are further considered (tables S6 and S7). Our analyses of selective pressure heterogeneity revealed a paucity of positive selection in all branches tested (table  S6 ). This is unsurprising given the role that telomere maintenance genes play in controlling the cell cycle and maintaining genomic integrity. For example, deletion of all shelterin components except TERF2IP are lethal in mice  (14) , suggesting that conservative evolutionary forces have shaped the evolution of these genes in mammals.
Data analysis and rTL Raw data were exported from the Stratagene Mx3000TM software. Baseline correction and per-well amplification efficiencies were calculated using LinRegPCR  (35). Data were normalized across plates for both telomere and SCG reactions using the golden sample as a reference. The coefficient of variation (CoV) was calculated for each sample. Where the CoV exceeded arbitrary cutoff values chosen to maximize the data (5% for telomeres and 2. 5% for SCG), results from individual reaction replicates were either removed or the sample was excluded from the analysis. The average Ct (cycle threshold) value was calculated across nine replicates for each sample. The Pfaffl (36) method, which takes into account the difference in amplification efficiencies between primer sets, was used to calculate rTL.
The number of individuals subsampled without replacement varied with each data set (M. myotis, n = 160; R. ferrumequinum, n = 100; M. bechsteinii, n = 30; and M. schreibersii, n = 30). The P values from 100 resulting models were compared, as were slopes where underlying models were significant, for example, R. ferrumequinum and M. schreibersii (figs. S1 and S2). To determine whether differences in sample size or sampled age ranges affected our results, we directly compared the two largest data sets, M. myotis and R. ferrumequinum, by randomly subsampling data sets to have equal age cohorts and equal numbers of samples per age cohort (1 to 6+ years; n = 91). To explore selective disappearance as a confounding factor in our analyses, we performed an upper quartile regression using the M. myotis and R.
ferrumequinum data sets. The upper quartile range of rTL for each age cohort was subsampled from the original data set, and the best-fitting model (Table  1 ) was refitted to the upper quartile data set. Significance was assessed as described above. The rate of telomere loss can be compared between species if the control sample used to calculate telomere length is consistent, as it is in this study  (16). Previous analyses have calculated the rate of telomere loss after the removal of the youngest age cohorts to control for developmental differences, which may accelerate the rate of shortening  (16). Hence, the data sets described in table  S2  were reanalyzed with the 0-age cohort removed to facilitate a comparison of slopes (effect sizes). LMMs were used to analyze all data sets to ease comparison.
Telomerase expression Blood transcriptome analysis of telomerase expression Owing to the greater availability of publicly available transcriptome data for blood tissues in species of interest, telomerase expression was principally investigated using blood; however, given that we did not detect telomere shortening in Myotis wing tissue, we also investigated telomerase expression in a smaller fibroblast transcriptome data set. Libraries used for M. myotis, pig, mouse, and human blood transcriptome analyses are listed in table  S3. Skin fibroblasts from M. myotis and Mus musculus were grown as described previously  (41) , with some modifications. Two 3-mm wing skin biopsies (M. myotis, one individual) or 3-mm ear biopsies (M. musculus, one individual) were minced into ~0. 5-mm fragments and resuspended in 3-cm cell culture-treated petri dishes in growth medium (high glucose, glutamate Dulbecco's modified Eagle's medium, supplemented with 20% fetal bovine serum, and 1% penicillinstreptomycin-Fungizone) supplemented with 0.
Transcriptomic gene expression is typically quantified using TPM (transcripts per million reads) or FPKM. Where genes of interest are expressed at low levels (such as telomerase and shelterin in these data sets), TPM returns extremely small values, which hinder useful comparisons. Hence, we calculated gene expression using FPKM for both the blood and fibroblast data sets. Raw reads from each RNA sequencing (RNA-seq) transcriptome were mapped to their respective reference genomes using TopHat (v2. 0. 11)  (42). Transcripts were further assembled and quantified using Cufflinks (v1. 3. 0) (43). FPKM values for telomerase and shelterin genes were calculated on the basis of their respective genome annotations. Laplace smoothing and a log transformation were applied to FPKM values before plotting.
Phylogenomic analyses of telomere maintenance genes M. myotis genome A draft assembly of the M. myotis genome was generated by Dovetail genomics from approximately 30× coverage of an Illumina paired-end library and 23× coverage of a Dovetail Chicago library (44), using high-molecular weight DNA extracted from tissues collected from a juvenile female from the French rTL study population. The reads were first assembled with the Meraculous assembler  (45)  and then scaffolded using custom software developed by Dovetail. The MAKER annotation pipeline  (46)  was used to annotate the draft genome assembly from (i) the coding domain sequence (CDS) of M. brandtii and M. lucifugus; (ii) the protein sequences of M. brandtii, M. lucifugus, and UniProt Swiss-Prot; and (iii) the gene predictors AUGUSTUS  (47)  and SNAP  (48).
These ab initio gene predictors were trained using the CDS of the M. brandtii genome and a publicly available reference-based blood transcriptome assembly (49) using Cufflinks  (43). The resulting transcripts were searched against the UniProt Swiss-Prot database using Basic Local Alignment Search Tool (BLAST)  (50, 51) , and gene names were assigned on the basis of a cutoff of 70% alignment length and 80% identity. This assembly was then mined for downstream selective pressure variation analyses, as detailed below. This assembly is available from the authors upon request. Data selection and genome mining Telomere maintenance genes were identified using associated GO terms through the Bioconductor package (52) in R. Gene targets used in previous analyses  (19)  were incorporated into our data set, resulting in a total of 225 genes for downstream analysis (table  S4 ).
In total, genomes from 52 eutherian mammals were mined to include a representative from each mammalian order where possible (table  S5 ). This included 12 bat genomes, of which 5 are representatives from Yinpterochiroptera, which includes R. ferrumequinum; the remaining 7 are representatives from Yangochiroptera, which includes 4 from the genus Myotis and 1 from M. natalensis, a species closely related to M. schreibersii. Where genome data existed for multiple taxa in a given mammalian order, taxa with data covering the highest number of telomere maintenance genes were chosen for inclusion in the final data set. Gene sequences from annotated mammalian genomes were downloaded using their gene identification numbers in the Entrez RefSeq database. For poorly annotated genomes, such as the 17× unannotated R.
Output transcripts from MAKER were aligned to their respective reference genes using a pairwise aligner, matcher in the European Molecular Biology Open Software Suite (EMBOSS) software  (54) , to determine sequence coverage. A minimum threshold of 70% coverage was required before a gene could be considered "useable. " Any sequences containing an internal stop codon were removed because these likely represented poor-quality data, pseudogenes, or alternative isoforms. Where multiple transcript variants were available for a target gene, the longest variant was chosen to maximize coverage. Given that significantly less gene information could be mined from the unannotated genomes, often because of poor quality, two data sets were prepared for downstream analyses. One contained RefSeq data only, whereas the other included RefSeq data and the species mined using MAKER (table  S4 ).
This approach ensured that our results would be robust to the trade-off between the quality (RefSeq) and quantity (RefSeq and MAKER) of data. Both data sets were initially aligned as nucleotides with default settings using the Aqua (55) alignment optimization workflow, which compares MUSCLE (multiple sequence comparison by log-expectation)  (56)  and MAFFT (multiple alignment using fast fourier transform)  (57, 58)  alignments, applies a refinement step using RASCAL  (59) , and provides a norMD (60) score as a measure of alignment quality. Alignments were further optimized using the phylogeny-aware aligner Prank (61) by aligning as codons, using six iterations of the alignment and the -F option to specify that insertions should be trusted. Gblocks  (62)  was used to remove any large indels from the alignment by specifying the data as codons, a minimum block length of 2 and allowing positions that were half gaps.