dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement.
One approach to quantify the association between environmental variables and viral lineage movements combines phylogeographic reconstructions in continuous space  [25, 26]  with a novel analytical and statistical framework  [27]. However, this approach is unable to deal with environmental data that do not come in a raster-based format, such as information on the prevailing wind direction. A second phylogeographic method incorporates a generalized linear model (GLM) directly into a diffusion model in discrete space to explicitly test for several potential predictors of viral spread  [28, 29]. However, few studies have examined the robustness of this approach to the chosen level of discretization or assessed its consistency with the continuous approach outlined above. Here, we apply phylogeographic models to an extensive dataset of BTV genome sequences to determine which factors most affected BTV spread within Europe.
Material and methods (a) Genomic data We included all the 113 available geo-referenced and time-stamped BTV genomes of isolates that had been collected in European countries, and in countries flanking the Mediterranean Sea, which represent likely source populations of BTV incursions into Europe (figure  1a ; electronic supplementary material, table  S1 ). Open reading frames for each of the 10 segments within the BTV genome were aligned according to the protein sequence, then converted to codon alignment using 'PAL2NAL'  [30]. Approximate geo-references (latitude and longitude) were obtained using Google Maps for those cases where only rough meta-data on spatial origin were available (e. g. country, region or city).
(ii) Climate data We extracted monthly means of precipitation, temperature, wind speed, and the U and V components of wind (components of the horizontal wind towards the east and north) from the European Centre for Medium-range Weather Forecasts (http://www. ecmwf. Wind U and V components were used to compute prevailing meteorological wind direction (in degrees) over the 15 years. Based on this, we used the difference in degree angle between the direction of virus diffusion along a branch (see phylogeographic analyses below) and the prevailing wind direction at both starting and end nodes as predictors.
(iii) Elevation Mean and standard deviation of elevation (as a measure for the local variation in elevation) were acquired from the Global Multi-resolution Terrain Elevation Data of the United States Geological Survey (GMTED2010,  [32] ). GMTED2010 combines the available global elevation data from different public datasets and they are provided as tiles with 308 of longitude Â 208 of latitude at a 30-arc-s resolution. The mean elevation raster was used to generate three additional binary rasters. Because the airborne spread of Culicoides species and the viral velocity is thought to decrease significantly above 300 m or so  [33, 34] , cells below this elevation were assigned high values of 1000 when corresponding to cells of terrestrial habitat with a mean elevation under 300 m, whereas all other cells were given a value of 0.
(c) Phylogeography and predictor testing We reconstructed the spread of BTV in Europe and investigated the potential predictors of virus diffusion by combining information gathered from two phylogeographic analyses. First, BTV phylogeographic history was reconstructed using a continuous space diffusion model and variables were tested a posteriori with two statistical approaches. Second, we used a discrete phylogeography approach that simultaneously estimates phylogeographic diffusion parameters and the effect of different variables on diffusion, within a GLM framework. Both phylogeographic analyses were done using the BEAST v. 1. 8. 2 software package (available at http://code. google. com/p/beast-mcmc/), which uses a Bayesian Markov chain Monte Carlo (MCMC) method  [39, 40]. Further details regarding these analyses are provided below.
Addressing these limitations is timely, given the ongoing threat of disease emergence, creating an urgent need to better integrate molecular, spatial and epidemiological information to guide strategies for early warning, surveillance and control. Data accessibility. Samples information and GenBank accession numbers of the 113 genomes used in this study are summarized in electronic supplementary material, table  S1. Sources of environmental and meteorological data can be found in table 1 and rasters can be downloaded from https://figshare. com/s/61c7ae4b264d519933f8.
We have here re-analyzed our M6a-AEL molecular data according to 2016 WHO classification and compared them to a previously published cohort of MDS. 4  After written consents obtained according to our ethical committee regulations and biobank procedures 11 new M6a-AEL patients were added to the cohort and 106 genes were sequenced by tNGS as previously described. 3 The 51 ex-M6a-AELs were reclassified as either MDSs-thereafter named AEL-MDSs (N = 24)-or AMLs (N = 27). To be homogenous in terms of blasts, the comparative cohort of MDS patients-thereafter named typical-MDSs-was made of 19 MDSs with excess of blasts type 2 (MDS-EB-2). Statistical analyzes were done using the survival package (version 2. 30) in the R software (version 2. 8. 0) and the correlations were calculated with the Fisher's exact test at 5% level of significance.
Background As with many other disciplines, the advent of next-generation sequencing (NGS) platforms has revolutionized ancient DNA (aDNA) research. During the era of Sanger sequencing, the datasets within most studies were restricted to short lengths of mitochondrial DNA (mtDNA) or nuclear DNA (nuDNA), and at most, if one used multiplexing techniques, one could aim for mitochondrial genomes (mitogenomes)  [1]. However, thanks to NGS techniques, with the right sample and sufficient funds, today practitioners are able to aim for relatively complete ancient nuclear genomes (hereafter referred to as palaeogenomes), even at the population level. While there are now a range of NGS technologies available to choose from, those favoured by the aDNA field are suited to the characteristically short DNA molecules that dominate aDNA extracts  [2, 3] ; thus long-read technologies such as the PacBio (Pacific Biosciences, CA, USA) and Minion (Oxford Nanopore Technologies, Oxford, UK) are not widely used.
Today, therefore, Illumina-based sequencing has formed the basis of the overwhelming majority of palaeogenomic studies, including (but not limited to) draft genomes of humans  [20]  and related hominids  [21] [22] [23] [24] , animals  [9, [25] [26] [27] [28] , plants  [29] [30] [31]  and even pathogens  [32] [33] [34] [35] [36] [37] [38] [39] [40] , population genomic datasets  [34, [41] [42] [43] [44] [45] [46] , metagenomic studies  [47] [48] [49] [50] , and even insights into ancient transcriptomes  [38, 51, 52]  and epigenomes  [53] [54] [55] [56] [57]. For recent reviews, see  [58, 59]. Despite this remarkable progress, palaeogenomics still faces one significant limitation-the overall data generation cost. The per-base cost of Illumina-based NGS sequencing is falling thanks to improvements relating to flow-cell cluster density and the generation of longer reads (although for most aDNA this latter point is rarely beneficial).
Data Description DNA was extracted from 8 historic and ancient canid samples, chosen so as to represent a range of materials that are currently interesting to the palaeogenomics community (Table  1 )-in particular with regards to the fragment sizes of the surviving DNA and the range of endogenous DNA content within them. Two of the samples are preserved hides of wolves (Canis lupus), between 91 and 148 years old, that are believed to contain relatively pure (free of enzymatic inhibitors), although heavily fragmented, DNA (a presumed side effect of the tanning process). The remaining samples are naturally preserved wolf, dog (Canis familiaris), or undetermined large canid remains dated between roughly 600 and 14 000 years old. Sample CN 214 was acquired by and registered in the collections of the Natural History Museum of Denmark (NHMD) in 1869.
Analyses and Discussion We initially generated between 1. 35 × 10 7 and 5. 94 × 10 7 reads per Illumina library, and 2. 32 × 10 7 -3. 39 × 10 8 reads per BGISEQ-500 library (Table  2 ; Supplemental Table  S1 ). The dataset supporting the results of this article is available in the ERDA and GigaDB repositories (see Availability of supporting data). Following normalization of the data for read length and depth (Table  2 ), we found no statistically significant difference between the 2 datasets with regards to the % endogenous nuclear DNA and average length of endogenous DNA, several of the most important parameters for palaeogenomicists, given their fundamental role in affecting the overall financial cost of a study (Table  3 ). In contrast, there was a statistically significant platform-dependent difference in the % reads mapping to the mitochondrial genome, with fewer reads mapping in the BGISEQ-500 libraries.
We do caution, however, that due to the small size of the dataset (both sample numbers and sequencing depth), at this point we are not able to offer any comment as to how this overall evidence of consistency may translate into downstream analyses involving whole genome summary statistics. Thus we strongly advocate that those who may be interested in using the BGISEQ-500 platform in genomic population data explore this point further. Furthermore, as additional datasets are generated, we look forward to the results of analyses that might wish to compare the relative performance of different sequence alignment and variant calling software on such data. Ultimately, however, we anticipate that our findings will stimulate considerable interest in the BGISEQ-500 platform by palaeogenomic research teams attempting to reconstruct ancient genomes and transcriptomes, and we look forward to future exploration of its potential across a wider range of ancient substrates.
Data analyses The raw reads obtained from the HiSeq 2500 and BGISEQ-500 were analysed using FastQC (FastQC, RRID:SCR 014583)  [87]  to compute the quality metrics of the reads, such as base sequence qualities, base sequence content, %GC, and sequence composition. With the exception of the analysis on the standard vs extrapurified BGISEQ-500 libraries (Supplemental Table  S5 ), both BGISEQ-500 libraries from each extract were treated as a single dataset. We also compared the quality metrics of the reads from the same samples across the 2 platforms to ensure that the sequencing platform did not have a large impact on the quality metrics of the reads. Once the read qualities were verified using FastQC (FastQC, RRID:SCR 014583), we used the PALEOMIX pipeline (PALEOMIX, RRID:SCR 015057)  [18]  to trim the adapter sequences, trim Ns and low-quality bases from the ends of reads, estimate ancient DNA damage, and finally map the trimmed reads to the reference genome.
Mapping, indel realignment, and duplicate removal The trimmed reads were mapped to the wolf reference genome  [89]  using the mem algorithm in bwa (BWA, RRID:SCR 010910; v. 0. 7. 10), using the default settings for the mapping algorithm. The mapped reads were subsequently processed using the GATK (v. 3. 3. 0) indel realigner (GATK, RRID:SCR 001876)  [90, 91]  to fix the alignment issues arising from the presence of short indels at the beginnings and ends of reads. Since there are no catalogs of indel variations in the species included in this study, the realignment step was done using a set of indels within each sample. After the indel realignment step, the PCR duplicates were removed from the alignments using the MarkDuplicates program from Picard tools (Picard, RRID:SCR 006525; v. 1. 128)  [92].
DNA damage patterns The DNA damage patterns and parameters were estimated using mapDamage (v. 2. 0. 6; mapDamage, RRID:SCR 001240)  [17]  using a subsample of 100 000 reads from the set of mapped reads. The 3 main parameters estimated using mapDamage were θ , δD, and δS. δD and δS estimate the probability of cytosine deamination (driven by hydrolytic DNA damage) in a double-and singlestranded context, while θ estimates the background rate of difference between the reference and sample after accounting for DNA damage. Using these estimated parameters, the base qualities of putatively damaged bases were recalibrated to a lower score. The program was also used to compute the relative abundance of C→T changes at the 3 ends and A→G changes at the 5 ends of the reads and compare them across the 2 platforms.
Clonality, endogenous DNA content, and library complexity estimation The clonality of each library was computed from the reads that were identified by the MarkDuplicates program during the duplicate identification and removal step. The clonality was computed as the ratio of the number of reads retained after duplicate removal and the number of reads retained after the adapter removal and trimming step. The endogenous content of the library was computed as the ratio of the number of reads mapping uniquely to the reference genome and the number of reads retained after adapter removal. Note that this is 1 possible definition of the endogenous content, here defined as the proportion of usable reads obtained from a library, and the numbers given in Table  2  and Supplemental Table  S1  will allow you to compute the values for other definitions of endogenous content.
Mapping to the wolf mitochondrial genome Since the draft de novo wolf genome does not contain information on scaffolds that are annotated as belonging to the mitochondria, we could not identify reads that mapped to the mitochondrial genome using the initial set of mapped reads. To overcome this problem, we downloaded a complete mitochondrial genome from NCBI (GenBank Accession: AM711902)  [93]  and mapped the adapter trimmed reads to this complete mitochondrial genome. The same steps, including indel realignment and DNA damage-related recalibration of quality scores, were performed for the reads aligned to the mitochondria.
Relative abundance vs GC content The relationship between read abundance in a given genomic region and its GC content is well known and characterized for the Illumina platform  [98]. For methods that depend upon depth of coverage or fragment count, such as measuring absolute copy number or expression levels, this bias needs to be taken into consideration and corrected for; otherwise, its magnitude might confound the signal in question. We therefore compared the GC content of the mapped endogenous DNA for the 2 platforms in several ways. First, the basic GC percentage was calculated from all endogenous reads. Second, we partitioned the reference genome into bins of 100 Kbps, with an offset of 10 Kbps, and calculated the GC percentage of each bin. We then mapped all datasets onto the reference and counted the number of mapped fragments in each bin.
CNV on low-coverage data Fluctuations in depth of sequencing coverage can be used to generate personal genome-wide copy number maps of an individual as read depth is known to strongly correlate with copy number for several platforms  [99]. We sought to assess whether the same techniques might be applied to data generated on the BGISEQ-500. To this end, we generated individual genome-wide CN maps of all extracts and both platforms in varying window sizes, from 1 Kbp to 1 Mbp, to account for fluctuation in coverage, and checked concordance between them. It is worth noting that using ancient DNA libraries poses a particular challenge to this assessment as some inherent characteristics of this type of data (such as unequal degradation, fragmentation, or clonality during library preparation) make it difficult to pinpoint the source of variability between 2 call sets for a given extract, given a lack of concordance.
Acknowledgements The authors would like to acknowledge the assistance of the  Danish National High-Throughput Sequencing Centre  for assistance in Illumina data generation. The authors would like to acknowledge the  ERC Consolidator Grant  ( 681396 -Extinction Genomics ), the  Marie Sk lodowska-Curie Actions  ( H2020-MSCA-ETN-643063  "Microwine"),  Danish Council for Independent Research  ( 4005-00107  Wine-ometrics), the  Qimmeq  project, funded by  The Velux Foundations and Aage og Johanne Louis-Hansens Fond, China National GeneBank , and  BGI-Shenzhen China  for funding. We also gratefully acknowledge the  Danish National Supercomputer for Life Sciences-Computerome  (computerome. dtu. dk) for the computational resources to perform the sequence analyses. L. F. K. K is supported by an  FPI  fellowship associated with  BFU2014-55090-P (FEDER ), T.
Methods and Data This work is an outcome of the HELiospheric Cataloguing, Analyses, and Techniques Service (HELCATS) project, in which 8 academic research institutions from around Europe were involved from May 2014 -April 2017. During the time of this project, we have established, among many other catalogs from heliospheric datasets, several catalogs that are relevant for this study: the catalog of observed CMEs with HI is called HIGeoCAT, the catalog of predicted arrivals ARRCAT, the catalog of the in situ interplanetary data DATACAT, and information on the in situ CME (ICME) observations is gathered in the ICMECAT. It contains data from the solar wind observatories Wind, STEREO-A, STEREO-B, Venus Express (VEX), MESSENGER, and Ulysses. We use all spacecraft for a comparison to HI except Ulysses because there are too few CME events observed by this spacecraft.
We have also added shock times to the list by Good and Forsyth  [2016]  for those events from VEX and MESSENGER where a clear shock preceded the magnetic obstacle. For all ICMEs, we have derived 30 parameters (e. g. mean and maximum magnetic field, Bz field parameters, minimum variance analysis of the magnetic obstacle) directly from the data in a homogeneous way, thus eliminating the need to compile different parameters from the different catalogs which might differ in their definitions from one catalog to another. In summary, in ICMECAT 483 ICMEs were observed close to 1 AU (Wind, STEREO-A, STEREO-B), and 180 events in the inner heliosphere by VEX and MESSENGER, and 5 events by Ulysses. However, for our analysis only the icme_start_time, the mean magnetic field in the magnetic obstacle and the sheath speed at Wind is used for a comparison to the HI predictions.
Summary of data and methods Figure  2  is a screenshot of an animation that provides a convenient overview of the spacecraft positions, the CMEs in HIGeoCAT and the magnetic field components and total field for the full duration of the data we study. The movie has a 6 hour time resolution, and covers 2007-2015 (see links to the online animation at the end of the paper). In the left panel, the SSEF30 circles propagate away from the Sun, for the inner heliosphere up to Mars orbit, for all CMEs observed by HI. For each CME, kinematics have been established by calculating R(t) = V_sse x t, where R(t) is the distance to Sun center, V_sse the speed from SSEF30 and t the time, starting with the SSE launch time. These kinematics are then interpolated to the movie frame times. All apex positions for each CME are then sorted by time.
These results are very similar to  Möstl et al. [2014]  who quote 252 ± 302 km s -1. Because we do not have plasma speeds from VEX and MESSENGER we cannot do this comparison for < 1 AU. It is also of great interest to find constraints on the magnitude of in situ ICME magnetic fields from the HI data. Figure  7  visualizes two relationships between parameters derived from HI as part of the arrival catalog and the in situ mean magnetic field in the magnetic obstacle. Figure  7a  shows the predicted speed at the target and the mean magnetic field in the magnetic obstacle, between mo_start_time and mo_end_time. This is plotted for the hits (true positives) in the dataset. Most events cluster around slow to intermediate speeds (< 1000 km s -1 ) and low magnetic fields (< 20 nT). Only at MESSENGER, because of its closer radial distance to the Sun, high average magnetic fields of up to about 90 nT are seen, at all other spacecraft they have been quite low during this solar cycle.
Conclusions We have compared the predictions of the impact and the arrival time from the SSEF30 technique using STEREO/HI observations for more than 1300 CME events with more than 600 ICMEs that were reported in the existing literature and online data sources and merged by us into a singular catalog. Thereby we have derived some fundamental results concerning heliospheric imager-based geometrical modeling with the SSEF30 technique. This is of major importance for a space weather mission carrying a heliospheric imager at the L5 or L1 point  [Lavraud et al. , 2016 , DeForest et al. 2016] , and all other missions that carry heliospheric imagers (STEREO, Solar Orbiter, Solar Probe Plus). In summary, our results derived from science data and those by  Tucker-Hood et al. [2015]  for HI real time data show that HI modeling is currently at the same level for predicting CME arrivals as are other analytical and numerical techniques.
Contingency table results: We find that 1 out of 4 predicted impacts with the SSEF30 technique results in a clear ICME that includes a magnetic obstacle, for a time window of ± 1. 5 days around the predicted arrival time. Including a constraint on the propagation latitude of the CME in HI, so that only CMEs which centrally propagate near the solar equatorial plane are selected, improves the hit percentages to about 33 %, so 2 false alarms for 1 correct prediction. We find for time-windows from 1. 0 to 2. 0 days a stable true skill statistic (TSS) of 0. 19 to 0. 22, similar for HIA and HIB. What does this mean in comparison to other studies? We need to caution that we have used science data to discuss hindsight predictions of CMEs with heliospheric imagers. Tucker-Hood et al. [2015] , analysing the real time performance of HI with beacon data, find a slightly higher TSS of 0.
g. ,  Richardson and Cane, 2010]  and sometimes "driverless shocks" [e. g. ,  Gopalswamy et al. , 2010; Janvier et al. , 2014] , i. e. , the whole bulk of the CME is missed. Even such cases are interesting for space weather as CME sheaths alone can drive major storms  [Huttunen et al. , 2002; 2004]. In future work, the forecasted CME arrivals might be checked against the in situ data directly, not just the ICME lists, which would eliminate the identification of ICMEs which is always a subjective matter. Another clear improvement is to take into account event specific aspects. A major assumption that needs to be eliminated is the constant 60° full width of the CME [based on  Yashiro et al. , 2004] , and determine the width for each event separately. This emphasises also the importance of multi-spacecraft missions to allow CME reconstructions from multiple vantage points to obtain realistic parameter that are not subject to projection effects [e.