dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing Depth Pteropus medius DNA was extracted from cultured primary bat cells and sequenced using both paired-end Illumina and Nanopore sequencing, generating 114. 5 Gb of Illumina DNA-Seq data (after primary analysis; see section "Materials and Methods"). Based on the analysis on distinct k-mer counts in DNA-Seq short-reads, the software kmergenie  (Chikhi and Medvedev, 2014)  predicted a genome size from 1. 92 to 1. 98 Gb, corresponding to the range of size of previously published bat genomes  (Teeling et al. , 2018). Using an alternative method based on the C-value  (Smith and Gregory, 2009) , the genome size was estimated at 1,994 Gb, with a GC content of about 38% deduced from DNA-Seq data. We then used a genome size of 2 Gb to calculate the depth of sequencing and subsequently normalized assembly metrics.
, 2018) , for a genus without available genome sequences, there will be more resources available for reference-assisted scaffolding methods and for sequencing species of interest at lower cost. Our results suggest that the combination of different sources of information for secondary scaffolding is necessary to limit the introduction of misassembles and improve quality; we thus developed the scaff2link software, which is compatible and reusable with Ragout and AGOUTI. We have shown that the Ma_sr-lr_union100, produced by scaff2link, is slightly better than other assemblies. Indeed, Scaff2links prevents the linkage of fragments within a new scaffold if contradictory information exists between gene-based and synteny-based secondary scaffolding. This is expected to limit the number of scaffolds produced with mis-assemblies.
However, the percentage of total genome coverage for interspersed repeats found in this study was lower than previous reports about closely related bat species  (Wen et al. , 2018). This difference is linked with the lower sensitivity of the tool used to search similarities between the genome and the repeat database (RMBlast, used in this study). To our current knowledge there is no published work with significant statistical support allowing the resolution of phylogenetic relationship of P. medius with P. alecto and P. vampyrus. Phylogenetic analysis shown the clustering of P. medius genome with two other Pteropus bat species, P. alecto and P. vampyrus. The overall tree topology for bats is concordant with other studies  (Teeling et al. , 2005; Hawkins et al. , 2019). Finally, the tree topology is coherent with the geographical distribution of those bats: P.
medius  (Molur et al. , 2008)  and P. vampyrus  (Bates et al. , 2008) , which clustered together, are both present on the Asian continent. On the other hand, phylogenetically more distant species P. alecto is mainly located in Australia  (Roberts et al. , 2017). We estimated in this study the divergence time between Pteropus species lower than the minimum ranges reported on Time Tree database. However, some other studies reported divergence times focused on Pteropus genus even lower than ours  (Almeida et al. , 2014) ; in that study the 95% confidence interval for the divergence time between P. vampyrus and P. medius (0. 5-1. 4) overlaps with ours (0. 9-2. 57948). We conducted our timescale estimation using SCO sequences, where the proportion of site under non-neutral selection may vary among branches.
ACKNOWLEDGMENTS The work was supported by  LABEX ECOFECT  ( ANR-11-LABX-0048 ) at " Université de Lyon , " within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR) , by  Aviesan Sino-French  agreement on Nipah virus study and ViroScan3D. JF was supported by the doctoral fellowship CIFRE-Défense operated by the  Direction Générale de l'Armement (DGA). We wish to thank  A. Weissenbacher  and  Tiergarten Schönbrunn  ( Vienna, Austria ) for the Pteropus bat samples. We are also grateful to  Drs. K. Dhondt  and  M. Roche  ( Lyon ) for the help to initialize and realize this work; to  E. Guillot-Combe  for the tutorship during the JF Ph. D. thesis work; to  A. Raout  ( Bhopal, India ) for sharing sequences from P. medius nuclear introns; and to  N.
CONCLUSIONS In the present study, the availability of control data from 7 LSCFSs presented a unique opportunity for the assessment of the BEEHAVE model. The studies represent a distinct study design that was not considered previously with BEEHAVE. This also applies to the geographical (and climatic) region (North Carolina) where all studies were conducted. We used data from 2 LSCFSs for calibration of the model, and the remaining 5 for validation. The detailed visual and quantitative performance analyses suggested that the calibrated BEEHAVE model provides good agreement with apiary-specific data across the first study year in the 5 validation study data sets. However, model outputs did not match with observed colony conditions after overwintering, assessed in the following spring. The visual and quantitative validation methods applied may be useful to assess performance of ecological models in general whereby quantitative indicators should always be applied in combination with visual methods.
We highlight the considerations necessary to address variability and uncertainty in empirical data for model validation as well as model stochasticity. We recommend the calibrated BEEHAVE model as useful tool to predict colony dynamics in LSCFSs prior to overwintering. A dependence on study conditions including weather, landscape, initial conditions, and feeding can be further explored with the model. Simulated outcomes are typically less variable than observations from colonies in the field. Accordingly, predictions of colony sizes (number of adult bees and honey stores) from BEEHAVE should not be used as precise predictions of properties of individual colonies, but as predictions of trends across colonies that experience similar conditions. In that respect, the model can be used to estimate the relative impact of the different conditions on colony outcomes at different times of the first year.
Acknowledgment-We thank the  Pollinator Research Task Force , an industry interest group of crop protection companies, for funding and for providing comments during the project, as well as  BASF, Bayer Crop Science, Mitsui Chemicals, Syngenta Crop Protection, and Valent  for providing control study data. We thank  N. Guth ,  B. Kearns , and  K. Kapo  for their help with the data compilation and initial analysis and  G. Hancock  for project support. Thanks to  M. Becher  for discussions about the calibration of BEEHAVE.
Information on the position will occupy a place around which all other information will be dynamically structured. This work is constituted of a complete dataset of six workers in a Non-Line-Of-Sight (NLOS) industrial assembly line for three hours, and the creation of an algorithm to smooth worker's data. We also proposed a classification of area depending on the environment, and an analysis of a worker's rig. Two modalities are proposed: an ultra-wide-band (UWB) system and a motion capture system called MoCap. This paper is divided in six parts. We established a survey on existing datasets in NLOS. We presented our experimental set-up. We studied the behavior of our raw synchronized data and results. We proposed a way to improve our data. We proposed our dataset used and interpretation. We presented our further work.
Detailed of these techniques can be found in  [32]. Dead reckoning is the process of estimating known current position based on last determined position and incrementing that position based on known or estimated speeds over elapsed time. Recent works, such as those in  [33, 34] , present several improvement of this method. Most indoor localization approaches adopted fingerprint matching as the basic scheme of location determination. The main theme is to collect features of the scene from the surrounding signatures at every location in the areas of interest and then build a fingerprint database. The location of an object is then determined by matching online measurement with the closed location against the database  [35]. The proximity detection technique is based on the examination of the location of a target object with respect to a known position or an area.
Survey on Existing Dataset Datasets using a motion capture system and UWB exist. Bregar et al. [38]  present a UWB position dataset in seven different locations such as offices, bedroom apartments, and a small workshop. Seven location are presented in NLOS; we proposed a dataset of person flows during an assembly phase in a realistic industrial environment. Cung Lian Sang et al. [39]  proposed an experimental dataset for multilabel classification results of a UWB ranging system. We proposed experimental data that reproduce the real localization of workers in an industrial environment. Kevin Minne et al. [40]  proposed data and results related to static measurement of the optimal hardware position on a cyclist for indoor track cycling. This hardware uses ultra-wide-band technology to track cyclists during indoor track cycling.
We proposed dynamic indoor localization of workers in an industrial environment with a MoCap as ground truth. Raza et al. [41]  proposed a dataset using UWB, BLE, and a motion capture system covering two scenarios; these being walking and trolley in a room. We proposed two modalities an UWB system in NLOS and a motion capture system in industrial conditions with six different scenarios. Jorge Pena Queralta et al. [42]  present a dataset for UWB-based localization of aerial robots including data from an autonomous flight with an Unmanned Aerial Vehicle (UAV). The ground truth is using an Optitrack motion capture system. We proposed six scenarios of workers flow instead of UAV with same modalities. Valentín Barral et al. [43]  developed a realistic software simulation tool. They have also built a UWB simulator based on real scenarios under three different situations: LOS, NLOS soft, and NLOS hard.
They employed machine learning techniques to generate the different models capable of providing simulated UWB ranging. Moreover, the authors of  [44]  provide a UWB NLOS dataset for NLOS identification and mitigation in the middle of two corridors and a contiguous room; we proposed six dynamics scenarios of workers in an assembly line in NLOS for UWB. We proposed experimental data in real NLOS conditions with a motion capture system as ground truth. Jiaxin Li et al. [45]  proposed the fusion of UWB and inertial data with an Extended Kalman Filter (EKF) to achieve high-accuracy three-dimensional localization. Their dataset, however, contains data from a single flight. We proposed six different workers in industrial environment. Bernhard Großwindhager et al. [46]  proposed a dataset that contains an extensive measurement campaign in two complex indoor environments with one UWB anchor.
It contains line-of-sight as well as non-line-of-sight situations. We proposed an indoor industrial situation with six workers and a MoCap system as ground truth. In relation to these datasets, we offer an NLOS experimentation of six workers in representative industrial conditions, on assembly stations for three hours. By comparing previous datasets, we provide six different scenarios in a realistic environment. We provide real behavior of workers during assembly phase. The environment of workers is surrounded by metals and concrete with obstacles.
Motion Capture System We used a motion capture system from Optitrack similar to that in  [48]  with millimeter accuracy  [49]  as a ground truth. This tool does not directly give the position of people but reflective points. It is by combining them in shapes determined by groups of 4 or 5 (called "solid bodies") that identification becomes unique and makes it possible to track an object or an individual. The disadvantage of this type of location is the masking of one or more points, making identification unique or impossible. In our case, the metal structures of the manual stations block many lines of sight and therefore it happens regularly that we lose the location. In the database, the result is simply a hole in the data until all reflective points are recovered to ensure the uniqueness of the object or person detected (from 4 points).
We have chosen to place reflective dots on people's heads, which limits data loss. The table, therefore, consists of a synchronous time column (every 100ms) and the coordinates of the center of each solid body. We have chosen to put the data in the form of an Euler angle for its readability and x, y, and z coordinates. We had to apply the necessary translations and rotations to move the axes and center as shown in Figure  5. Thus, the Z axis of the experiment becomes the X axis of the database, and the X axis becomes the Y axis. A translation of 5. 6 m on the X axis and 5. 5 m on the Y axis are finally applied. We do this change because the MoCap system has its proper reference in the workshop and the UWB system also has its proper reference in the workshop. We used several static positions to calibrate the MoCap system in time and positions.
Discussion on Raw Data These two modalities means (MoCap and UWB) each have their means of localization, so it is difficult to guarantee that the axes are perfectly aligned because of the size of the system (working area of more than 50 m 2 ). We know that an error in the positioning of the UWB reference badges, or the referencing of these positions directly induces an error in the positioning depending on the position of the badges. The MoCap guarantees positioning accuracy of a few millimeters, but only when it is in use. The very high number of data losses during measurements shows the limits of this system in a dense manufacturing workshop in masking elements. All comparisons are made against the MoCap and only when the location is good. There is no interpolation on the intermediate positions in order not to distort the results.
Dataset All sensor readings of a sequence are zipped into two single files named "Filtered_datas_UWB. zip" and "Raw_datas_UWB_Mocap. zip". The directory structure is shown in Figure  6. We provide raw data from the Motion capture system, which can be compared to the work in  [48]  as a motion capture system with a millimetric accuracy and UWB system with centimeters accuracy  [29, 50, 51]. Timestamps are stored in timestamps. txt and data format are stored in dataformat. txt. Each line in timestamps. txt is composed of the date, time in hours, minutes, and seconds. Each line in the data folder provide X position and Y position for UWB system and X, Y, and Z position for the motion capture system. A snapshot of the dataset can be seen in Figure  7 , with an example of the Rig1Mocap_raw. All zipped file provide six rig position corresponding to the Figure  4.
We calculate the GDOP value as described in  [55] , GDOP = RMSE loc RMSE range ( 1 ) the result is shown in Figure  9. One can see a correlation between the travel speed in Figure  10  and the calculation of the Geometric Dilution Of Precision (GDOP) in Figure  9. The anchors are placed at the corner of the black rectangle in Figure  9. We can see that the worst GDOP is in a rectangular plane at the position of the anchors, maximum GDOP is in purple (~5. 5), and cyan means that no data were available. The combination of speed and high GDOP gives all the explanations for calculation errors in NLOS. GDOP just gives us an indication of geometry, the calculation takes into account the measurement of noise because we are in NLOS and also a DOP due to the person wearing the tag. However, we can see that the biggest measurement error is in the plane on the side of the rectangle, see Figure  9.
Conclusions and Further Work In this article, we propose a new data set for indoor localization in NLOS with six dynamic scenarios in an industrial site during an assembly phase. We also suggest ways of improvement for future data set in order to compare with other research areas. We have shown that geometry and speed to have an influence on the position calculation. We are introducing a new way to filter the UWB position estimation without merging the data with other modalities. This study aims to provide a better understanding of the position estimation errors on relying on a UWB medium in real conditions of use. Both modalities can be implemented for indoor localization. The MoCap system shows a better accuracy than UWB but requires a more expensive and complex infrastructure set-up. For use cases requiring less accuracy, or a faster and less expensive set-up, UWB is an excellent choice to increase safety in industrial environments at a low cost.
Sequence variants are identified as outliers relative to this robust error model. This method, called needlestack, allows the identification of sequencing variants in a dynamic manner relative to the variable error pattern found in NGS data, and is particularly appropriate to call variants that are rare in the sequenced material. By combining this method with additional laboratory processing for further error correction  (11)  and very deep next generation sequencing, we are able to robustly identify VAFs well below 1% while maintaining acceptable false discovery rates. We conducted multiple rigorous performance estimations and comparisons with methods for both somatic and germline variant detection. We deployed our pipeline focusing on efficiency and robustness using the Domain-Specific Language (DSL) nextflow  (12) , and on reproducibility by providing Docker and Singularity images.
Needlestack overview Needlestack estimates for each candidate alteration, i. e. each pair of position and base change (the three non-reference nucleotides and each observed insertions and deletions) the systematic sequencing error rate across a series of samples, typically more than twenty to ensure a reasonable estimation of this metric. Then, for each sample, it computes the p-value for the observed reads under the null hypothesis of this estimated model of errors, and transforms this p-value into a Phred-scale Q-value reported as a variant quality score (QVAL) for the candidate mutation. As such it measures the evidence that the observed mutation is not explained by the error model, and should therefore be considered a mutation. Needlestack takes as input a series of BAM files, and is based on three main piped processes, the generation of the mpileup file containing read counts at the target positions using samtools  (13) , the reformatting of this file into readable tabulated file and finally the estimation of the error model using our R regression script (see below) coupled with the computations of Q-values (Supplementary Figure  1 ).
Comparison with other variant callers We used BAMsurgeon software  (18)  to introduce single nucleotide variations (SNVs) at varied VAF in the 125 cfDNA samples in TP53 in order to benchmark and compare the method through in-silico simulations. BAMsurgeon presents the advantage of synthetic benchmarking methods that allow the simulation of mutations for which gold standards don't exist to evaluate the performance (here low VAF, that are in addition challenging to validate), while maintaining the real data background such as the true error profiles. We introduced 1000 SNVs at random positions in the gene in random samples, and we replicated this process in ten batches. As each sample has been sequenced twice, we introduced each in-silico mutation in the two technical duplicates of a sample.
In addition, needlestack implements a power calculation to estimate if the coverage is sufficient to call a mutation (see Supplementary Methods for details). Using this power analysis, it can predict the germline or somatic status of a mutation when applied to tumour-matched normal mode. This also allows needlestack to flag mutations with an "unpredictable" status (when the coverage is to low) to accurately control the false discovery rate. Source code is available on GitHub and is versioned using a stable git branching model. Importantly, this approach is relatively computationally efficient and parallelisable. This allows error models to be built even across large target stretches of DNA, enabling applications at the exome level, genome levels or to most forms of sequencing data.
Bioinformatics processing Short reads from NGS sequencing were aligned to the hg19 human reference genome using the Torrent Suite software (v4. 4. 2) with default parameters. Somatic mutations were detected with needlestack using the version 1. 0 and a QVAL threshold at 50. As recommended by Martincorena et al. (7) , we used a threshold of 20 for the shearwaterML statistic. Only cfDNA mutations detected in a high confidence base change were considered. A high confidence base change satisfied P<0. 05 with P the probability that the number of duplicated mutations under the null hypothesis would be greater than or equal to the observed number of duplicated mutations, and is given by: P= P 𝐶 R R max p=p obs with Cp corresponding to the probability of observing p pairs when randomly picking k elements from a total of 2N paired elements calculated as: 𝐶 R = V W 𝑘 -2𝑖 2 [ R\; i=] ^V (2𝑁 -2𝑗) a\R\; j=] b 1 p!k! W 2𝑁 𝑘 [ with N the number of sequenced samples in duplicates (2N sequenced libraries in total), k the number of libraries with a mutation called by needlestack, pobs the number of duplicated called mutations, and pmax the total number of possible pairs when picking up k elements from a total of N pairs of elements.
The detailed source code for cfDNA mutation analysis including all quality filtering step description is available on GitHub at: https://github. com/IARCbioinfo/target-seq. For the germline analysis, GATK-HC variant calling was performed using version 3. 4 and the HaplotypeCaller algorithm, followed by the joint genotyping step  (8). Finally, Variant Quality Score Recalibration following the GATK best practices was applied, using dbSNP 138, HapMap 3. 3, 1000 Genomes phase 1 and OMNI 2. 5 databases. Options provided were "-tranche 100 -tranche 99. 9tranche 99. 0 -tranche 90. 0" for both INDEL and SNP modes. GATK-HC variant calls were filtered on PASS and on Phred-scaled likelihood (8) larger than 20 and on VAF>0. 1. BAM files were locally reassembled with ABRA version 1. 0 (9) before launching the variant calling by needlestack.
However, there is currently little evidence to support this hypothesis. Based on serological evidence, wild ungulates do not appear to have sustained transmission  [35, 36]. Similarly, serological testing of cattle sampled in 2014 indicated a rapid decline of seropositivity after vaccination ceased in France in 2010, consistent with a new (re-)introduction of the virus in, or just before, 2015  [35, 37, 38]. We describe the use of phylogenetic and evolutionary analyses of BTV-8 virus samples, collected during the first and second European outbreaks, to gain insights into the mechanisms that allowed BTV-8 to re-emerge in France in 2015. For this, we generated a novel dataset of full genome sequences for more than 150 viruses sampled throughout both outbreaks.
BTV-8 re-emergence associated with exceptionally slow evolution To test if the lower amount of divergence along the branch separating the two outbreaks was unusual, we estimated the evolutionary rate of BTV-8 from the set of 164 genomes. For this, we applied a lognormal relaxed clock model that allows for branch-specific heterogeneity in clock rates, implemented in the Bayesian phylogenetic software BEAST (Fig  3    The tree is scaled in years, with the final sampling date being October 2018. Clades with posterior support of 0. 9 or higher are indicated by a white circle. Samples from the first and second outbreaks are shown as purple and orange circles, respectively. The branches are coloured accordingly to their median evolutionary rate across the posterior (see heatmap within the figure).