dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene annotation and functional analysis Associated regions were annotated considering AE1 Mb around the previously defined intervals in the rabbit genome. Gene annotations were retrieved from the Ensembl Genes 98 Database with the BIOMART software  (Smedley et al. 2015)  using the OryCun2. 0 reference assembly. Furthermore, functional predictions of the significantly associated SNPs were performed with VEP software  (McLaren et al. 2016). For functional categorization of the annotated genes, GOs were determined using ClueGO version 2. 5. 0 plug-in of Cytoscape  (Bindea et al. 2009). The functions assigned to the proposed candidate genes include metabolic, behavioural or immunological pathways. Orthologous human gene names were retrieved from the Ensembl Genes 98 Database for functional categorization when a rabbit gene name was not assigned to the gene stable id.
They implemented a multitrait Bayesian procedure similar to the single-step association methods  (Wang et al. 2012) , which relies on derivation of SNP effects from genomic predictions using a multiple regression in which the SNP effects are treated as random factors. A possible means of validation of the results from the proposed bivariate model is to analyze those traits individually recorded with the two approaches. For the case of ADG F the results obtained are partially the same, for example regions in OCC 3 are detected with both methods. However, other regions detected for this trait and all those declared for ADG R , which is a trait with lower heritability  (Piles & Sanchez 2019) , are not the same across the models. One reason for this is that the datasets used by each method are different.
Acknowledgements The work conducted by the staff of the  Institute of Agriculture and Food Research and Technology 's rabbit farm is acknowledged. This study was financed by Feed-a-Gene, which has received funding from the  European Union 's  H2020 program  under grant agreement no. 63353  and the  Spanish  projects  RTA2014-00015-C2-01 ,  RTA2011-00064-00-00  and  RTI2018-097610-R-I00. M. Ballester  is financially supported by a  Ramon y Cajal contract  ( RYC-2013-12573 ) from the  Spanish Ministry of Economy and Competitiveness. The authors declare that there is no conflict of interest regarding the publication of this article.
The use of misidentified cell lines contaminated by other cell lines and/or microorganisms has generated much confusion in the scientific literature. Detailed characterization of such contaminations is therefore crucial to avoid misinterpretation and ensure robustness and reproducibility of research. Here we use DNA-seq data produced in our lab to first confirm that the Hep2 (clone 2B) cell line (Sigma-Aldrich catalog number: 85011412-1VL) is indistinguishable from the HeLa cell line by mapping integrations of the human papillomavirus 18 (HPV18) at their expected loci on chromosome 8. We then show that the cell line is also contaminated by a xenotropic murine leukemia virus (XMLV) that is nearly identical to the mouse Bxv1 provirus and we characterize one Bxv1 provirus, located in the second intron of the pseudouridylate synthase 1 (PUS1) gene.
Using an RNA-seq dataset, we confirm the high expression of the E6 and E7 HPV18 oncogenes, show that the entire Bxv1 genome is moderately expressed, and retrieve a Bxv1 splicing event favouring expression of the env gene. Hep2 (clone 2B) is the fourth human cell line so far known to be contaminated by the Bxv1 XMLV. This contamination has to be taken into account when using the cell line in future experiments. Continuous cell lines are a cornerstone of cellular and molecular biology as well as of biomedical research. A long-known problem faced by researchers using such cell lines is contamination, whereby foreign cells or microorganisms are inadvertently introduced and remain unnoticed over multiple passages  1, 2. Characterizing cell culture contamination is of prime importance as contaminants may generate flawed experimental results and considerable confusion in the scientific literature  3, 4.
Both libraries were tagged and sequenced on an Illumina HiSeq2500 platform in a 2 × 125 bp configuration in High Output mode (V4 chemistry). Raw DNA-and RNA-seq fastq reads generated for this study have been deposited in dbGaP under accession number phs001944. v1. p1. All reads mapping onto the Bxv1 (JF908815) genome are provided in Supplementary Data 1 and 2.
Bioinformatics analyses. Demultiplexing was performed by the sequencing company (Genewiz), yielding 140,313,660 reads with a mean quality score of 35. 15 for the DNA sample and 57,593,594 reads with a mean quality score of 35. 82 for the RNA sample. Remaining adapter sequences were removed and reads were trimmed with Trimmomatic using default parameters ("-phred33, Illuminaclip:adapter_file. fasta:2:30:10, minlen:126", Bolger et al. 2014). To identify infectious agents potentially present in the cells, we mapped paired reads onto the human genome (UCSC genome data, human genome version hg19) using Bowtie2 in "sensitive-local" mode (Langmead and Salzberg 2012). Reads that did not map onto the human genome were assembled for each dataset. DNA-seq and RNA-seq reads were assembled with Masurca 30 and Trinity 31 , respectively, using default settings.
The resulting contigs were used as queries to perform blastn and blastx searches on the non-redundant (NR) database of GenBank. To assess sequencing depth of the two viruses identified in this study (Bxv1 XMLV and HPV18), the reads were mapped onto the genome of these viruses (accessions number: JF908815 for Bxv1 and GQ180792 for HPV18) with Bowtie2 in "sensitive-local" mode. To identify recombination breakpoints within each of the two genomes and between the two genomes and the Hep2 (clone 2B) genome we used all reads as queries to perform separate blastn searches (megablast option) on the human genome and on the Bxv1 and HPV18 genomes. Virus-virus and cell-virus junctions were then searched within reads using the pipeline described in Ref. 32. Briefly, only reads aligning over at least sixteen bp on a genome region only and over at least sixteen bp on another genome region only were retained.
5% agarose gel at 100 V during 25 min and bands were visualized with a Bio-Rad Transilluminator Universal Hood II under UV light. To check whether regions of the Bxv1 genome not covered by our DNA-seq dataset were in fact present in Hep2 (clone 2B) cells, we PCR-amplified and Sanger-sequenced three such regions as well as another region covered by our DNA-seq dataset using the following primer pairs: Bxv1_2-F: CCC CAG AAG AGA GAG AAG AAC; Bxv1_2-R: CAT TGG TCC TTA TCG AGT TGG; Bxv1_3-F: TGC CTT TGA GTG GAG AGA TC; Bxv1_3-R: CTA GGG TTT GTA GAA GGG CC; Bxv1_4-F: CCT TCT CAA CAA CCT GGG AC; Bxv1_4-R: ACA GGG TCA GCT TGT GTT G; Bxv1_5-F: CAG GCA AGC TAA CTA TGG GA; Bxv1_5-R: CCC AGA TTA CCT CGG TTT CA.
Confirmation of Hep2 (clone 2B) contamination by HeLa based on HeLa-HPV18 characteristics. As mentioned on the Sigma Aldrich website, the Hep2 (clone 2B) cell line (catalog number: 85011412-1VL) was originally derived "from tumours produced in irradiated-cortisonised weanling rats after injecting with epidermoid carcinoma tissue from the larynx of a 56-year-old male, but it was later found to be indistinguishable from HeLa by STR PCR DNA profiling. " It is a typical case of non-existent cell line that may cause important problems in cancer research  33. Consistent with HeLa contamination, assembling of RNA-and DNAseq reads obtained from sequencing this cell line and not mapping on the hg19 human genome yielded various contigs that were almost identical to the HPV18 (GenBank accession number: NC_001357.
1 ). We conclude that Hep2 (clone 2B) cells from Sigma-Aldrich (catalog number: 85011412-1VL) are not only undistinguishable from HeLa but they are also contaminated by Bxv1 XMLV sequences. The finding of Bxv1 in the HeLa-contaminated Hep2 (clone 2B) cell line begs the question of whether the virus is present in HeLa cells. XMLVs have been searched in a large number (> 600) of human cell lines, including HeLa and nine of its derivative subclones in which it was found to be absent  18, 19, 29. In agreement with this, our blastn search of the Bxv1 sequence onto two HeLa short read datasets available in the SRA database of NCBI (accession numbers: SRX699196 and ERX3188263) revealed no significant hit. This indicates that Bxv1 is likely absent from all or most HeLa cell lines. However, we cannot exclude that this virus was present in the very HeLa cells that contaminated Hep2 (clone 2B).
plementary Data 3). Much like Bxv1 proviruses from two prostate cancer cell lines (LAPC4 and VCaP) which all integrated into introns  17  , the Bxv1 provirus characterized here lies within the second intron of the pseudouridylate synthase 1 (PUS1) gene. Furthermore, in agreement with the known propensity of MLV to preferentially target transcription start sites (TSS) and CpG islands  40, 41  , the Hep2 (clone 2B) Bxv1 lies only 946 bp downstream of the nearest PUS1 TSS, well within a 1463-bp long CpG island. Altogether, these results indicate that the Bxv1 integration identified in Hep2 (clone 2B) cells is a bona fide proviral locus, confirming contamination by this virus of the Hep2 (clone 2B) cell line. To assess how many proviral loci may segregate in the cell line, we mapped all reads on the Bxv1 genome (accession number: JF908815).
6% of all RNA-seq reads  16. In the Lin et al. 16  study, 768 RNA-seq reads were found to contain a junction between positions 587 and 5887 of Bxv1 (accession number: JF908815), which corresponds to a splicing event leading to the expression of the env ORF. Our search for virus-virus junctions unveiled a nearly identical splicing event, with 757 reads (145 reads when potential PCR duplicates are removed), supporting a junction between positions 587 and 5884 (Fig. 2 ). The position of the splice donor site matches exactly between our study, that of Lin et al. 16  and the annotation of the N417 MLV (accession number: HQ246218) which is 99. 98% identical to Bxv1 JF908815. However, the position of the splice acceptor site differs (5884 in Lin et al. ; 5887 here; 5219 in HQ246218 corresponding to 5601 in JF908815), which may be due to context-dependent variations in splicing.
It appears from our analysis that the peculiar acceleration is comparatively easier to measure for low mass binaries, in particular for LISA alone. We also observe that accounting for multiband observations substantially improves the detection probability. Note however that, due to the low number of events detected by LISA in a single mission lifetime in the region M z 20 -40M , f min 12 mHz, more statistics (i. e. more simulated populations of BHBs) would be required in order to better characterize this region. Nevertheless, our results indicate that, when the real data is available, it will be worthwhile to check for the presence of peculiar acceleration in events having binary parameters close to the above mentioned region, as they are more likely to be affected by the acceleration effect.
Nevertheless, the number of events with acceleration measured to better than 10% in coincident LISAground based detections remains unchanged (such events are characterised by high SNR, thus the loss in sensitivity is less severe on their measurement). We also studied whether the waveform parameters would be biased if the acceleration effect is ignored in the data analysis procedure. We computed a simple estimate of the bias on each waveform parameter, and studied how many events would experience a non-negligible bias. Our results clearly show that binaries possessing rather large peculiar accelerations (scenarios 3 to 5) will be biased even for a LISA mission duration of 4 years. We also found that binaries with a time to coalescence similar to the LISA mission lifetime will have a higher probability of being biased.
The bias therefore follows the same parameter dependence of the measurement of the acceleration effect, but many events for which the peculiar acceleration would not be measurable are instead biased. This suggests caution when performing data analysis in the parameter space of stellar-mass BHB systems coalescing close to the LISA lifetime, since for these systems, ignoring the peculiar acceleration could lead to wrong estimates of the BHB physical parameters. Furthermore, we have found that the parameter most often biased is the luminosity distance of the system, which is a fundamental quantity for cosmological applications of GW observations, in particular standard siren analyses. If the peculiar acceleration is not correctly taken into account, large systematics could affect the estimates of the cosmological parameters, e.
(  19 ). Note that another possibility would be to choose t * obs as the time when LISA starts taking data t * obs = t st. For the binary systems analyzed in this work, τ st = t c -t st is at most of the order of thousands of years, so that τ st Y (z st ) 1. At first order we can therefore rewrite eq. (A. 8) as 1 + z = (1 + z st ) 1 + 2Y st τ st 1 -2Y st τ obs. (A. 9) Since τ st Y st is constant and much smaller than one, we can reabsorb it into z st , i. e. define an effective redshift 1 + zst ≡ (1 + z st ) 1 + 2Y st τ st , (A. 10) and we obtain 1 + z = (1 + zst ) 1 -2Y st τ obs. (A. 11) Comparing this with eq. (  19 ) we see that the expressions are equivalent at lowest order in Y τ obs. Indeed the constant redshift z c and zst are not observable, since they are degenerated with the mass, and therefore the difference between them does not matter.
ACKNOWLEDGMENTS This project has received funding (to E. B. ) from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. GRAMS-815673 ; project title " GRavity from Astrophysical to Microscopic Scales "). This work was also supported by the  H2020-MSCA-RISE-2015  Grant No. StronGrHEP-690904. C. B. acknowledges support by the  Swiss National Science Foundation. This work was supported by the  center National d' Études Spatiales , by the  COST Action CA15117 "Cosmology and Astrophysics Network for Theoretical Advances and Training Actions " through a  Short Term Scientific Mission  (STSM), and by the  COST Action   CA16104  "Gravitational waves, black holes and fundamental physics" (GWverse). COST actions are supported by  COST (European Cooperation in Science and Technology).
Age at MRI and symptom duration were assessed as covariates. Firstly, age was considered as a covariate to determine if patients in a particular exercise group at any given age had a different Lamminen-Mercuri score than those in another group. Secondly, symptom duration and age were combined as covariates to determine if fat replacement on MRI progressed more rapidly (from the onset of symptoms) in one exercise group than another. Post-hoc analysis of significant (p < 0. 05) ANCOVA results was completed using the "lsmeans()" function, from the package "lsmeans. " This was to identify which exercise groups were significantly different from each other, and thus causing the significant ANCOVA result. For visual representation of the exercise dependent effects, linear modeling was used. A linear model of Lamminen-Mercuri score was generated with age and exercise group as covariates.
ACKNOWLEDGMENTS This study has only been possible thanks to the international collaboration of several specialized centres promoted by the  Jain Foundation. The Jain COS consortium would like to thank the study participants and their families for their invaluable contribution and would also like to acknowledge the ongoing support the  Jain Foundation  provides in the development, management, and analysis of this Study. The  Jain Foundation , based in Seattle,  USA , is entirely focused on LGMD2B/dysferlinopathy/Miyoshi Myopathy. The foundation does not solicit funding from patients, but instead funds research and clinical studies worldwide with the goal of finding treatments for dysferlinopathy. Please visit www. jain-foundation. orgfor more information about the foundation and if you are a patient suffering from dysferlinopathy, please consider enrolling into their interactive dysferlinopathy registry that seeks to build a strong, engaged, and supportive community (patients@jainfoundation.
Measuring wind and sediment transport characteristics Data were collected at the Sand Creek field site from March 23 rd to April 16 th 2019 (24 days). For the duration of the experiment a temporary weather station was erected adjacent to the TLS measurement site (Fig. 1c ). Here wind speed and direction were measured at a height of 3. 17 m using a Gill 2D sonic anemometer with co-located measurements of saltation activity derived from a Sensit mass transport monitor  (Gillette & Stockton, 1986; Stockton & Gillette, 1990). The anemometer and Sensit were connected to a Campbell CR800 Series datalogger with data recorded at a frequency of 0. 1 Hz. For each time step (10 s), we extracted mean wind speed u i and wind direction -→ x i. Using the wind velocity and Sensit records, we defined the threshold wind velocity for sand transport, u th by computing the maximum saltation counts occurring in each 0.
TLS post-processing and surface detrending Analysis of the TLS data involved extracting a rectangular section of measured points (approximately 340 m long and 55 m wide) along the centreline of the channel to facilitate the determination of protodune morphology and migration rates and also to eliminate the effect of the channel banks on the area under investigation (Fig. 1b ). Each extracted rectangle contained approximately 3. 2 million measured data points. Point data were filtered to separate surface and active saltation points following the methods of  Nield and Wiggs (2011)  giving a mean surface point density of 775 points/m 2. Surface points were gridded with a resolution of 0. 1 m and interpolated using Matlab Natural Neighbour following the methods of  Nield et al. (2013). Finally, surfaces were de-trended for the underlying channel gradient (∼0.
Acknowledgments This work was funded by the  TOAD (The Origin of Aeolian Dunes)  project ( Natural Environment Research Council, UK  and  National Science Foundation, USA ;  NE/R010196NSFGEO-NERC ,  NSF -GEO-1829541  and  NSF-GEO-1829513 ). Research was undertaken at GSD under a Scientific Research and Collection permit  GRSA-2018-SCI-004  and we are very grateful for field, access and logistical support from  F. Bunch  and other park staff. Data processing used IRIDIS Southampton Computing Facility. We thank  R. Ewing  for the use of his Sensit instrument,  G. Kocurek ,  R. Reynolds ,  A. Gunn  and  R. Ewing  for field assistance and  C. Gadal  and  C. Narteau  for fruitful discussions about the linear stability analysis. J. M. Nield gratefully acknowledges the  Department of Geology and Geophysics, Texas A&M University , where she was supported by a Michel T Halbouty Visiting Chair during the field campaign.
88 F 2 plants were selected based on genotyping data and advanced to the F 3 generation. A total of 490 F 3 lines were used for SNP genotyping. Finally, 10 F 3 plants were selected and advanced to produce NILs (F 3:5 and F 3:6 ) that were used for phenotyping and for identifying the candidate genes. Figure  1  shows the scheme used for developing the NILs.
Development and Validation of New SNP Markers Fifty new SNP markers were identified using GBS (genotyping by sequencing) data of the CSSL population developed by Fonceka et al. [13]  and the "Axiom-Arachis" SNP array data  [42]. KASP ® markers were developed for each SNP, using 50 bp flanking sequences and validated on A. duranensis, 12CS_091, Fleur11 and a subset of 21 F 3 individuals of known genotype at selected SSR markers. SNP genotyping was performed on a LightCycler480 (LC480). The amplification program was as follows: activation HotStart at 94 • C for 15 min, touchdown at 94 • C for 20 s then at 65 • C for 1 min (10× cycles, -0. 8 • C/cycle), amplification at 94 • C for 20 s then at 57 • C for 1 min (28× cycles) and, finally, hold at 15 • C. Finally, fluorescence data of the 28th cycle were extracted from the LC480 and analysed with the snpclust package (https://github.
Near-Isogenic Lines (NILs) Development In a first step, the 2172 individuals of the F 2 population were genotyped with 3 mapped SSRs (RN13D04, Seq2E06 and TC23E04), that tagged the wild chromosome segment containing the QTL of interest. 188 F 2 that showed at least one recombination event between two adjacent markers were selected and genotyped with 23 polymorphic SSR markers developed in this study. All PCR amplifications were performed as described by Fonceka et al. [13]. The genotypic data were used for anchoring the new SSRs in the genetic map of the proximal region of chromosome A07, using Mapdisto software  [43]. In a second step, 88 F 2 out of 188 were selfed to produce 490 F 3 that were genotyped with nine SNP markers developed in this study. SNP genotyping data were generated at CERAAS, using a LightCycler96 (Roche, Basel, Switzerland).
Fine Mapping A single-marker analysis was first performed using the SSR genotyping and HSW phenotyping data measured on selected F 2:3 families. To this end, at each SSR locus, phenotypic data for lines homozygous for the cultivated Fleur11 or A. duranensis alleles were grouped, and then the phenotypic means for the two groups for the selected trait were compared using two-sample t-tests. Phenotypic differences among NILs and between the NILs and their parents were checked vis-à-vis their genotypic constitution. Three groups of NILs were defined: similar phenotypic value to 12CS_091, intermediate phenotype, and similar phenotypic value to Fleur11. The QTL position was refined based on NILs phenotypic and genotypic differences and/or similarities. The most probable location of the QTL was identified and flanking SNP markers noted.
Sequence Alignment of Selected Candidate Genes Sequence alignment (coding DNA sequences (CDS) and proteins) was performed for two putative candidate genes using ClustalW (https://www. genome. jp/tools-bin/clustalw). The protein sequences corresponding to the genes were obtained using MEGA software 10. 1. 7 (https://www. megasoftware. net/). The gene sequences of A. duranensis were aligned against the homologous genes from A. hypogaea subs. hypogaea var. Tifrunner and A. hypogaea subs. fastigiata var. Shitouqi. Homologous genes were found by performing BLASTn and/or using the keyword search option in PeanutBase for var. Tifrunner (https://peanutbase. org) and in Peanut Genome Resource for var. Shitouqi (http://peanutgr. fafu. edu. cn/).
New SNP Markers Fifty new SNPs (31 from GBS data and 19 from the "Axiom-Arachis" SNP array) were developed in the target region. Among these SNPs, 23 were polymorphic, 23 were monomorphic and four did not amplify. Validation plots of some SNPs are shown in Figure  S1  and a summary of the polymorphic SNP is presented in Table  1. GBS data provided more polymorphic markers than the Axiom-Arachis SNP array (67. 7% vs. 10. 5%). All markers developed from GBS data were co-dominant. Nine SNPs (Table  1 ) were used for the genotyping of the F 3 plants.
SFU0J 1178336 to 1181460 (Ferredoxin-dependent bilin reductase); GO:0010024 (phytochromobilin biosynthetic process), GO:0050897 (cobalt ion binding), GO:0055114 (oxidation-reduction process) Aradu. DN3DB 1190431 to 1194331 Transcriptional regulator STERILE APETALA-like [Glycine max]; IPR015943 (WD40/YVTN repeat-like-containing domain); GO:0005515 (protein binding) Glucose-1-phosphate adenylyltransferase family protein; IPR011831 (Glucose-1-phosphate Aradu. BD60N 1202798 to 1205874 adenylyltransferase); GO:0005978 (glycogen biosynthetic process), GO:0008878 (glucose-1-phosphate adenylyltransferase activity), GO:0009058 (biosynthetic process), GO:0016779 (nucleotidyltransferase activity) Phosphopyruvate hydratase; IPR000941 (Enolase); GO:0000015 (phosphopyruvate hydratase Aradu. 8G9XJ 1210713 to 1215639 complex), GO:0000287 (magnesium ion binding), GO:0004634 (phosphopyruvate hydratase activity), GO:0006096 (glycolysis) Aradu.