dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome coverage of sequenced-indexed inserts International implementation of high-throughput PCRbased methods has allowed for the amplification and sequencing of genomic regions flanking insertion sites of T-DNA, Tos17, Ds and dSpm inserts. In total, 447,919 sequence flanking tags have been released in public databases over the past decade. From these, 336,262 sequenceindexed inserts are precisely positioned on the japonica rice chromosomes (cv. Nipponbare, MSU v7. 0 release) (Table  2 ). Many more PCR products were sequenced notably from T-DNA inserts but proven to be T-DNA or backbone sequences. Examination of the anchored FSTs has provided the community with a better understanding of the insertion behaviour of the different types of mutagens in the rice genome. The insertion preference deduced from these analyses may be slightly biased.
The following are recommendations for solving some of these issues. 1) We should complete the sequencing of all varieties that have been used to generate the mutant populations (i. e. , Tainung 67, Dongjin, Zhonghua 11, Kitaano, Zhonghua 15, Hwayoung, and Kitaake). As well, these data should be available in a public domain. They should be used as the reference sequence for detecting changes in the genome rather than Nipponbare. 2) We should pay special attention to spontaneous mutations accumulating in seeds. Although most resource groups received the seeds from rice breeders for transformations, sequence differences could still appear among seeds and among batches. To eliminate this problem, several lines may be sequenced to reveal possible identical SNPs or indels that preexist before transformation.
The candidate nuclear genes were chosen from the Littorina Sequence Database  (Canbäck et al. 2012). Three exon-primed intron-crossing (EPIC) markers were successfully designed, targeting a complete intron of the calreticulin (Cal), elongation factor 1 α (ElFac), and thioredoxin peroxidase 2 (ThioPer) genes (Table  S1 ). Each gene was amplified in 16 individuals per ecotype from each locality, polymerase chain reaction (PCR) products were cloned and inserts sequenced (see Table  S1  for details).
SEQUENCE ANALYSIS For mtDNA, contigs were assembled using Seqman (DNASTAR, Madison, WI), aligned with ClustalX  (Thompson 1997 ) and inspected in BioEdit 7. 0. 9. 0. (Hall 1999). Haplotypes were obtained with Collapse 1. 2 (available from http://darwin. uvigo. es). Polymorphism estimates, neutrality tests, and other population genetic analyses were conducted in DnaSP 4. 0  (Rozas et al. 2003)  or Arlequin version 3. 5. 2 (Excoffier and Lischer 2010). To obtain summary statistics for the ABC analysis, we used the Kimura 2parameter model for consistency with our simulated data. A haplotype network was built with Network 4. 6. 1. 0 (Bandelt et al. 1999). Sequence data for nuclear loci were processed using the software Geneious Pro version 5. 1. 7 (Biomatters Ltd. , Auckland, New Zealand). Primers and vector were trimmed and sequences were aligned with the clustalw2 algorithm  (Larkin et al.
The neutrality of the nuclear loci was verified with the Tajima's D and Fu's Fs tests, implemented in Arlequin, and the significance was assessed with 10,000 simulations. A sequential Bonferroni correction for multiple tests was applied to the neutrality tests. In addition, a recombination test was performed for each gene with IMgc Online  (Woerner et al. 2007 ). Recombination was detected only for ElFac and the first 44bp at the 5' extremity of the intron were removed from the alignment to exclude possible recombining sites in further analyses. Haplotype networks were built with TCS version 1. 21  (Clement et al. 2000)  and edited with Inkscape 0. 48. 1 (www. inkscape. org). Nuclear sequence data have been submitted to GenBank with accession numbers: Cal HG792757-HG792783, ElFac HG792716-HG792756, and ThioPer HG792699-HG792715.
Six different selective PCRs were performed and 12 primer combinations were obtained (Table  S1 ). Preselective and selective PCR conditions, electrophoresis, and scoring are described in  Galindo et al. (2009)  (and see Supporting Information). AFLPscore version 1. 4  (Whitlock et al. 2008)  was used to perform error rate analysis using replicates (15% of the samples, chosen randomly and replicated from the digestion step), remove loci with low repeatability, and create the binary matrix (0, 1) containing the AFLP phenotypes. The mismatch error rate obtained with AFLPscore was 4. 63% and the number of AFLP loci scored was 614. The AFLP dataset has been submitted to Dryad: doi:10. 5061/dryad. m186r. Outlier analysis was performed using the program Dfdist  (Beaumont and Nichols 1996 ; http://www.
maths. bris. ac. uk/∼mamab/stuff/). Dfdist input files were created using the AFLP convert program (http://webs. uvigo. es/acraaj/ tools. htm) and analyses were carried out following  Galindo et al. (2009). Between-ecotype pair-wise comparisons were performed independently for each locality and those loci above the 95th percentile were considered outliers. Outliers within locality were combined for each region to determine the degree of sharing of outliers, because sharing is one possible indication of the repeated involvement of the same loci in the response to divergent selection. All the outliers detected in any of the six localities were removed from the dataset in further analyses because demographic parameters are best estimated using exclusively loci that are not influenced by selection  (Beaumont and Nichols 1996).
After removing outliers AFLP-SURV version 1. 0  (Vekemans et al. 2002)  was used to calculate F ST values and individual pair-wise relatedness, which was used to create multidimensional scaling plots, following the methodology of  Jackson et al. (2012). Summary statistics for ABC analysis were calculated using the same custom scripts as we used for simulated data. The analysis of molecular variance (AMOVA) was conducted in Arlequin.
91, based on third position cytochrome b data from  Reid et al. (1996). For the nDNA sequences, we used an unbiased transition/transversion ratio of 0. 33. Because we conducted 10 6 simulations per model, and used the same simulation set to test multiple individual observed datasets in some cases, we set the simulated sample sizes for the coalescent simulations as the geometric mean of the real sample sizes across the ecotypes/localities used as observed datasets.
ABC sampling For each model, we performed 10 6 standard ABC simulations using the software package ABCtoolbox  (Wegmann et al. 2010)  for all markers combined. For the within-regions models, we also performed 10 6 simulations separately for the sequence data and for the AFLP data. We used fastsimcoal  (Excoffier and Foll 2011)  to simulate sequence data for four loci with lengths equal to the observed sequences (after pruning of ElFac to remove putative recombinants), and arlsumstat to calculate summary statistics. For the AFLP loci, we used fastsimcoal to simulate 462 separate loci, each one with a 20 base sequence, and an in-house program that converted the resulting sequence data into a binary matrix of AFLP alleles and calculated summary statistics from this binary matrix. For each simulated AFLP locus, one 20bp haplotype was chosen at random and designated the "1" allele.
Summary statistics and estimation step Summary statistics used for the sequence data (for each sample or sample pair) were Tajima's D, π, and ST based on Kimura 2-parameter distances between sequences, implemented in Arlequin/arlsumstat 3. 5. For the AFLPs, we used heterozygosity, mean F ST , standard deviation of F ST across loci, and Jaccard distance. Because our summary statistics were numerous (56 for the sequence data; 22 for the AFLP data; 78 when combined), we used the partial least squares (PLS) method described in  Wegmann et al. (2009)  to reduce their dimensionality in the rejection step of the ABC procedure (see Supporting Information). Separate sets of PLS components were defined for simulations under each different model because of the variation in parameters. These PLS components were used to transform the summary statistics of the entire dataset of simulations, as well as the observed summary statistics, prior to the estimation stage.
After retaining the closest 0. 5-2% of simulations to the observed data based on PLS components, we took two approaches for the "regression adjustment" step, depending on whether we were interested in model comparison or parameter estimation. For model comparison, we used all summary statistics to perform postsampling adjustment, using the GLM method of  Leuenberger and Wegmann (2010) , to produce marginal densities, which were comparable between models. For parameter estimation, we used PLS components for both the distance step and the postsampling adjustment step. For combined datasets, we retained the closest 10,000 simulations (1%) to the observed data based on the Euclidean distance between PLS-transformed observed summary statistics and PLStransformed simulated summary statistics, and used these retained summary statistics to estimate the parameter values that best reproduce the real-world data using the ABC-GLM procedure of  Leuenberger and Wegmann (2010) , implemented in ABCtoolbox.
Model comparison and validation Models were compared using Bayes Factors, which are equal to the ratio of the marginal densities between models, and posterior probabilities, which are approximately equal to the marginal density of the model of interest divided by the sum of the marginal densities of all models. The P-value was used as a measure of goodness-of-fit. In addition to these tests, we also compared the distribution of summary statistics of retained simulated datasets to the summary statistics of the observed dataset, to check that the observed dataset lay well within the distribution of simulations. We did this for distributions of both PLS (all pairs of variables) and raw summary statistics (one variable at a time). The condition was satisfied for all reported models. To validate our model choice, we simulated 1000 (new) datasets from the original priors for each competing model and used these pseudo-observed datasets to test the robustness of discrimination between models (Fig.
Trait After quality checking and removal of loci with poor repeatability, the AFLP dataset included 614 loci. We excluded 152 of these loci that showed evidence for an influence of divergent selection between ecotypes within any locality. Fewer of these outliers were observed in Britain, where morphological differentiation was also less marked than in the other regions. There was slightly greater sharing of outliers between localities and between regions than expected by chance (Table  4 ). As in previous analyses of British  (Wilding et al. 2001)  and Spanish  (Galindo et al. 2009 ) populations, differentiation between ecotypes within localities was low (F ST = 0-0. 027) relative to differentiation among localities (F ST = 0. 021-0. 134), and the highest genetic distances were between Spanish and northern European locali-ties (F ST = 0.
17; Table  S3 ) and fell within the range of postrejection simulated values (both untransformed and PLS), and the distributions of posterior quantiles did not show strong departures from uniformity, indicating a lack of bias in parameter estimation  (Wegmann et al. 2009 ). The alternative models both allow the possibility of gene flow connecting all populations for most of their history and, therefore, they are likely to be difficult to distinguish. Nevertheless, pseudosamples generated under the alternative models, using prior distributions of parameters, showed  that discrimination between models was possible using the combined dataset since for 54% of pseudosamples the correct model was supported with confidence (for 21% the wrong model was supported and in the remainder neither model had posterior probability >0.
ACKNOWLEDGMENTS The authors thank  D. Wegmann  and  L. Duvaux  for advice on ABC modeling procedures,  M. Hinsch  and  R. Whitlock  for assistance with preparation of scripts, and  NERC ,  The Swedish Research Councils VR and Formas  (through the  Centre for Marine Evolutionary Biology ),  Xunta de Galicia  ( 10PXIB 310044PR ,  Grupos de Referencia Competitiva ,  2010/80 ;  Isidro Parga Pondal fellowship  to JG),  MICINN  ( "Juan de la Cierva ,"  JCI-2010-06167 , to MMF), and  Fondos Feder  ( Unha maneira de facer Europa ) for funding.
We produced deep-coverage, strand-specific RNA-seq data for the polyadenylated RNA fraction of each cell type, resulting in >60 million mapped reads of 76 bp per sample. For comparison, we collected similar deep-coverage data for representative mouse organs spanning various degrees of transcriptome complexity: testis (high complexity), brain (intermediate to high), and liver (low complexity). Our analyses of these deep-coverage mouse data confirm the cross-mammalian observation in that a significantly larger number of autosomal protein-coding genes are transcribed in testis  ($18,700)  than in brain ($18,000) and liver ($15,500; Figure  2B ; corrected p < 10 À10 , chi-square test). We detected transcripts for a large number of genes ($18,000) in spermatogonia, spermatocytes, and spermatids, whereas fewer genes ($15,500) are transcribed in Sertoli cells.
Mouse retrocopy coordinates were derived from a previous study  (Potrzebowski et al. , 2008). Mouse pseudogene coordinates were extracted from Ensembl release 57 (http://www. ensembl. org/). Coordinates of transposable elements (SINEs, LINEs, and LTRs) were retrieved from the UCSC database (http://genome. ucsc. edu/). We defined a set of ''nonannotated'' intergenic islands by selecting intergenic islands that did not overlap with any of the annotated intergenic elements described above. For all types of genomic elements, expression levels were calculated based on the log 2 mean coverage of RNA-seq reads per base. Expression values were normalized across samples using a median scaling procedure  (Brawand et al. , 2011).
Principal-Component Analysis We performed principal-component analyses (PCA) using the dudi. pca function from the ade4 package in R, using as input the matrices of log-transformed normalized expression levels. To exploit all the information present in the data sets, we did not apply any further scaling procedures to the variables. Segmental Duplicate Regions C57BL/6J mice segmental duplicon region coordinates were retrieved from a previous study  (She et al. , 2004 ; http://mouseparalogy. gs. washington. edu/),.
We next selected the multi-exonic transcribed loci that are found in regions annotated as intergenic in the Ensembl database (release 57); these intergenic multi-exonic transcribed loci were used as a basis for further analyses. We first applied the procedure described above for the multiple species analyses based on a previously established RNA-seq data set  (Brawand et al. , 2011). In the multiple species analyses, to increase our sensitivity and to minimize prediction inequalities among species (which can be due to differences in RNA-seq read coverage and in existing genome annotations), we projected the coordinates of multi-exonic transcribed loci of each species on the genomes of the other species (A. N. , M. S. , A. Liechti, T. Daish, J. C. Baker, F. Gru ¨tzner, and H. K. , unpublished data).
ACKNOWLEDGMENTS We thank  K. Harshman  and the staff of the  Lausanne Genomics Technologies Facility  for high-throughput sequencing support;  I. Xenarios  and the staff of the  Vital-IT computational facility  for computational support;  E. Ait Yahya Graison  for helpful discussion and advice on the soft-clustering analysis;  D. Cortez ,  K. Guschanski ,  J. Meunier , and  M. Warnefors  for comments on the manuscript; and the Kaessmann group in general for helpful discussions. This research was supported by grants from the  European Research Council  ( Starting Independent Grant   242597 , SexGenTransEvolution) and the  Swiss National Science Foundation  (grant  130287 ) to H. K. ; grants from the  Centre National de la Recherche Scientifique (CNRS)  and the  Association pour la Recherche sur le Cancer  and the  Agence Nationale de la Recherche  ( ANR-09-BLAN-0269-01 ) to B.
The holotype of Cymaprimadon kenni was recovered from the top of the Crazy Johnson Member of the Chadron Formation, from a locality located on the northeastern face of the "Finney Breaks" in Pennington County, South Dakota (NW. ¼, NE. ¼, Sec. 25, T. 3S, R. 10E;  Clark 1968). The sediment at this locality has been described as: "a slightly-cemented bentonitic mudstone with abundant, very fine sand grains"  (Clark 1968 ). The quality of the locality data preserved with specimen FMNH UC 349 is exceedingly poor. Collected in 1894, and the only associated field data state that the specimen came from the "Oligocene of South Dakota, White River Badlands"  (Clark 1968 ). It was embedded in a matrix of fine sand that was slightly coarser and better cemented than that preserved with the holotype of C.
Acknowledgements We thank  Rachel Benton  ( BADL ) for providing valuable discussion on this manuscript and for access to specimens and field localities at BADL;  Mike Greenwald  (formerly of the  South Dakota School of Mines and Technology, Rapid City, USA ) for assistance in collecting material from  BADL-LOC-0236 ;  Adam Behlke  ( Yale University, New Heaven, USA ),  Darrin Strosnider  ( Museum of the Rockies, Bozeman, USA ), and  Wayne Thompson  ( South Dakota School of Mines and Technology, Rapid City, USA ) for discussions regarding the taxonomic affinities of BADL 16917;  Carl Mehling  ( AMNH ) for providing a cast of AMNH HC 750;  Joseph Hartman  ( University of North Dakota, Grand Forks, USA ) for loan assistance;  William Simpson  ( FMNH ) for access to pantolestid specimens at the FMNH;  Megan Cherry (BADL)  for access to the collections at BADL;  Timothy Cleland  ( North Carolina State University, Raleigh, USA ) and  Michelle Stocker  ( The University of Texas at Austin, USA ) for helpful comments on previous versions of this manuscript; and two anonymous reviewers for their constructive suggestions.
Computing details Data collection: CrysAlis PRO  (Agilent, 2012) ; cell refinement: CrysAlis PRO  (Agilent, 2012) ; data reduction: CrysAlis PRO (Agilent, 2012); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg, 1999) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
B) Data acquisition and background study Although the physics and the detection technique is well established and daily used in ground detectors, the observation of an Extended Air Shower (EAS) from space through the Ultraviolet (UV) light emission has never been performed previously. Observation of EAS from space for the first time will confirm the feasibility of the technique and provide valuable data for all future space-borne UHECR experiments. Since JEM-EUSO uses the Earth's atmosphere to observe UV (300-400 nm) fluorescence tracks and Cherenkov reflections from EAS, the instrument is sensitive to the variation of the background sources in the UV range. Measuring the background light / airglow in the near UV region is therefore an important goal to successful operation and optimization of the working mode of the JEM-EUSO mission.
The Infectious Diseases Data Observatory (IDDO, https://www. iddo. org) has launched a clinical data platform for the collation, curation, standardisation and reuse of individual participant data (IPD) on treatments for two of the most globally important neglected tropical diseases (NTDs), schistosomiasis (SCH) and soiltransmitted helminthiases (STHs). This initiative aims to harness the power of data-sharing by facilitating collaborative joint analyses of pooled datasets to generate robust evidence on the efficacy and safety of anthelminthic treatment regimens. A crucial component of this endeavour has been the development of a Research Agenda to.
Moreover, although the spectre of emerging anthelmintic resistance is ever-present-having already arisen to all major classes of anthelmintics in the veterinary field  [8] [9] [10] [11]  -there remains little systematic monitoring of anthelmintic efficacy in human populations. The Infectious Diseases Data Observatory (IDDO) is working with the SCH and STH research and disease control communities to develop a clinical data platform for the collation, curation, standardisation and reuse of individual participant data (IPD) on treatment responses to anthelmintics. It is becoming increasingly well-recognised that data sharing and curation to a standardised format maximises the utility of data by enabling joint analysis of pooled datasets to increase the power of analysis, uncover new information and generate new evidence  [12] [13] [14].
The goal of the IDDO initiative is to facilitate collaborative research to improve the efficacy and sustainability of treatments for SCH and STHs through ethical and equitable sharing and reuse of data for scientific outputs that produce better evidence  13, 15, 16. As part of the platform development phase, we have developed a Research Agenda highlighting research questions that could be tackled using data shared through the IDDO platform. This is intended to spur the SCH and STHs research communities to engage with the platform which will ultimately enhance research aimed at improving treatments for these diseases. Here, we first describe the consultative process used to develop the Research Agenda, an approach shared across the IDDO portfolio of infectious diseases. We then provide a contextual overview of the priority research areas identified by this process, highlighting areas where sufficient data exist and could be tackled within the short-to mid-term (2-3 years) as well as questions for which there are currently insufficient data, or which are out of the current scope of the platform, but which may in the long-term be important or trigger new research.
Box 1. Development of a Research Agenda for visceral leishmaniasis The Research Agenda for visceral leishmaniasis (VL) was completed in 2019 following the same consultative development process as used for the schistosomiasis and soil-transmitted helminthiases Research Agenda as outlined in Figure  1. The VL Research Agenda provided a clear indication of which knowledge gaps could be addressed by the research community if the individual participant data (IPD) were made available. This inspired research proposals for two collaborative Study Groups which were drafted with the VL Scientific Advisory Committee, as representatives of the global VL research community, and key partners, including non-governmental organisations, regional health agencies and funders dedicated to serving VL patients.
These proposals were in turn shared with relevant investigators identified from the scoping review inviting them to contribute data and participate in the analysis for each Study Group. By presenting clear research proposals to investigators outlining the outcomes to be achieved through their data contribution, these Study Groups-which were originally inspired by the Research Agenda-have greatly enhanced data contribution, participation and engagement with the VL platform and have led to collaborative analyses of data received from across all endemic regions. The SCH and STH Research Agenda has been developed following the same process as used for VL (and across IDDO platforms, Figure  1 ), reflecting topics identified by systematic scoping reviews  17, 18  , engagement with the communities, and written and revised through a three-stage consultation process: Version 1.