dataesr/scientific-paragraphs-categorization huggingface dataset:
To explore the amount of long-read coverage required by the hybrid solutions, we compared the performance of the latter to the results obtained by Canu  [4] , a state-of-the-art long-read assembler. In the first step, we used the genome of Arabidopsis thaliana and then in the second step we used a complete human genome (NA12878, Table  1 ). NA12878 was selected because it was sequenced on a variety of platforms  [17, 20, 22, 37]  and assembled by a variety of algorithms  [4, 20, 22, 34]. It thus allows comparison of the complete landscape of currently available long-range technologies and assembly pipelines. To assess the performance of Fast-SG for constructing the scaffolding graph from short reads, we used all the short-read datasets and Illumina assemblies defined in Hunt et al. [3]. These short-read datasets include the genomes of Staphylococcus aureus, Rhodobacter sphaeroides, and Plasmodium falciparum and the human chromosome 14 (Table  1 ) and are commonly used as the gold standard for validation of the scaffolding tools  [11] [12] [13] [14].
We coupled Fast-SG with two well-established scaffolders, Opera-LG  [11]  and Besst2  [12] , and two more recently published scaffolders, ScaffMatch  [13]  and Boss  [14] , to produce scaffold sequences from short-or long-read data. All the chosen scaffolders have different algorithms to select optimal paths from the scaffolding graph and use the Sam/Bam format as input. Besst2 was excluded from the hybrid scaffolding experiments due to an exception produced while Besst2 computes the average contig coverage from synthetic mate-pair libraries. All the software and reference genomes used are described in Supplementary Material 1.
Short-and long-read benchmarks All scaffold sequences generated from alignments produced by Fast-SG, by the short-read aligners, and by Links were evaluated following the standard defined by Hunt et al. [3]. For each dataset, the true contig layout is known and the scaffold se-quences were compared against it in order to determine the following scaffolding errors (represented as a bit-wise flag): 0 = Correct pair of contigs. 1 = Contigs originated from same reference sequence, but their orientation in the scaffolds is incorrect. 2 = Contigs originated from different reference sequences. 4 = Contigs originated from the same reference sequence but are the wrong distance apart. 5 = 4+1, Contigs originated from same reference sequence, but their orientation and distance in the scaffold are incorrect.
At the synthetic matepair read level, we observed a recall of 49. 42% and 31. 65% for the raw ONT and raw PacBio reads, respectively. The hybrid error correction increases the synthetic mate-pair read recall for ONT to 75. 12% and for PacBio to 65. 02%. We observed that Fast-SG is more effective aligning synthetic mate-pair reads from raw ONT than from raw PacBio reads. We expect that this is due to the nature of the ONT errors (major deletions) as Fast-SG is designed to deal with short indels. Despite the low k-mer recall, Fast-SG achieved a decent synthetic mate-pair read recall on this dataset from both long-read technologies and extracted near-perfect synthetic mate-pair libraries. The synthetic matepair libraries can be used as input to a short-read scaffolder to generate scaffold sequences through a combination of shortand long-read technologies.
The percentages of linked pairs extracted by both methods is comparable (with Fast-SG being slightly superior). As expected, the percentage of linked pairs increases as a function of the insert size length for both long-read technologies (Supplementary Table  S10 ). A more informative comparison involved assessing the quality of the scaffolds  [3]  produced by Links on one hand, and on the other, by the short-read scaffolders coupled with Fast-SG. To evaluate the scaffolding results, the number of correct and erroneous joins were computed in each test case using the scripts provided in Hunt et al. [3]. Moreover, the F-score metric (Shortand long-reads benchmarks subsection) was used to summarize in a single statistic the performance of each scaffolder. Based on the F-score values, the short-read scaffolders using Fast-SG reached better or comparable results than Links (Fig.
3 ). Moreover, Links produced more scaffolding errors in two out of the three datasets tested (Supplementary Table  S11 ). With respect to the E. coli dataset, the scaffolding errors made by the shortread scaffolders using Fast-SG (Fig. 3 ) were related to the gap size estimation (type error 4), orientation (type errors 1 and 5), and relocation (type errors 8 and 12). The major source of errors in the scaffolds produced by Links was of type 5. This mea- the Fast-SG alignments a minimum of 5 000 insert sizes from the mate-pair reads mapped within contigs for each combination of synthetic library and long-read technology. The percentage of outliers detected in the raw ONT reads ranged from a minimum of 0. 37% (0. 5 kb) to a maximum of 4. 24% (8 kb), while for raw PacBio it ranged from a minimum of 0.
25% (0. 5 kb) to a maximum of 9. 85% (8 kb). The number of outliers increased with the error correction for both long-read technologies, reaching an average of 9. 32% (std 1. 73%) and 8. 32% (std 3. 74%) for the ONT and PacBio reads, respectively. The box plots were drawn excluding outliers. sures the correct orientation and distance between pairs of contigs (Fig. 3 ). On the S. cerevisiae W303 dataset, the major source of scaffolding errors was translocation (type error 2) for both methods. However, Links has almost double the number of scaffolding errors compared to Fast-SG coupled with Opera-LG or Boss on this dataset (Fig. 3 , Supplementary Table  S11 ). Concerning the short-read scaffolders, Boss and ScaffMatch reached higher F-score values than Opera-LG (Fig. 3 ). However, they tended to produce more scaffolding errors (Supplementary Table  S11 ).
S1 ). Fast-SG took 2. 15 hours on 20 CPUs to process the whole dataset. Finally, Opera-LG, Boss, and ScaffMatch were fed with the Fast-SG alignments to produce the scaffold se-quences (Table  2 ). All short-read scaffolders generated scaffold sequences in at most half an hour (Opera-LG 22 min, Boss 24 min,and ScaffMatch 30 min) using a single CPU. The hybrid and the Canu assemblies available were structurally validated by a whole genome alignment against the reference A. thaliana TAIR10 genome (Table  2 , Supplementary Material 2). As can be seen in Table  2 , all hybrid assembly pipelines were able to produce long-range scaffolds (N50 >1 Mb) with a high coverage of the reference genome, low number of errors (<2. 2%), low amount of sequence gaps (1. 46 Mb as maximum), and with an identity higher than any Canu assembly.
All hybrid assemblies at 5X coverage reached a N50 scaffold size comparable to the contig N50 obtained by a polished Canu assembly requiring 20X of coverage and 100X of Illumina reads (Table  2 ). Additionally, all hybrid assembly pipelines seemed to plateau after 30X of long-read coverage as was previously observed for this dataset  [4]. However, ScaffMatch, the most aggressive scaffolder tested, at 10X-30X of coverage produced accurate scaffolds having an N50 comparable to the Canu assemblies requiring 50X or 150X of coverage (Table  2 ). All assemblies of A. thaliana (Ler-0) were comparable in terms of the number and amount of sequences involved in structural errors (Table  2 ). Moreover, the major source of structural errors observed in both assembly strategies were mainly relocations, which explains more than 50% of the amount of sequences involved in miss-assemblies (Supplementary Fig.
An optimal assembly has high continuity, low breakpoint errors, high identity, and high coverage of the reference genome. Canu-p and Canu-q are Canu assemblies polished with Pilon  [48]  and Quiver, respectively. Pilon and Quiver are tools used after a long-read assembly to improve the quality of the consensus sequence. All datasets and commands used for the hybrid assembly of A. thaliana (Ler-0) are detailed in Table  1  and Supplementary Materials 2 and 3. reconstructions of the A. thaliana (Ler-0) genome and was remarkably efficient at shallow long-read coverage (5X-10X).
In terms of continuity (N50, Table  3 ), the DFS assembly is more than 4X larger than a MaSuRCA hybrid assembly built with the same long-read dataset and 100X of Illumina reads  [49]. Moreover, it is comparable to a polished Canu assembly built with 35X of long-read coverage  [17]. DOVETAIL genomics and 10X genomics reached larger N50 scaffolds (Table  3 ), which are 2. 5X and 3. 7X larger than the DFS assembly, respectively. All assemblies are comparable in terms of size, 1-to-1 alignment length, and coverage of the reference genome (Table  3 ). In terms of identity (Table  3 ), DOVETAIL genomics and DFS are the leading pipelines. DOVETAIL genomics and DFS both use the DiscovarDeNovo assembly as input for scaffolding. Both software programs maintain the high identity of the Discovar-DeNovo assembly because contig bases are not changed in the scaffolding process.
Compatibility of Fast-SG with Illumina mate-pair libraries In this section, we explore the usefulness of Fast-SG as an alternative to commonly used short-read alignment software for scaffolding graph construction from short-read data. Indeed, Hunt et al. [3]  demonstrated that the quality of the scaffolding results is highly dependent on the short-read aligner being used and that precision is more important than maximizing the number of reads aligned to the contigs. We assessed the performance of Fast-SG for aligning short reads on simulated Illumina data from the complete human reference genome (GRCh38. p10, Supplementary Material 4) together with Bowtie  [8] , Bowtie2  [10] , Bwa-Mem  [36] , and Bwa  [9] , which are commonly used short-read aligners for constructing a scaffolding graph  [3].
S5 ). The average contig read-coverage statistic that is used to tag the repeated contigs before scaffolding  [2]  was extracted from the results of Opera-LG. Such statistics were used to compute a pairwise Pearson correlation to determine the linear relationship between the short-read aligners and Fast-SG (Supplementary Fig. S6 ). We observed that the average contig read-coverage computed from the Fast-SG alignments correlated more on average with Bowtie (x=0. 933), Bwa (x=0. 905), and Bowtie2global (x=0. 814) than with Bwa-Mem (x=0. 772) or Bowtie2local (x=0. 725) on the datasets of S. aureus, R. sphaeroides, and P. falciparum (Supplementary Fig. S6 ). The results of the four test cases in terms of F-score and error rate are illustrated in Fig. 4  and detailed in Supplementary Tables  S17 to S20.
For almost all the test cases and scaffolding tools, Fast-SG reached the largest F-score (Fig. 4 ) for some k-mer values. Moreover, Fast-SG had a superior average performance in terms of F-score in relation to the four scaffolders tested in two of the five datasets (Fig. 4 , vertical lines) and allowed the scaffolding tools to obtain more accurate scaffolding results in four of the five datasets (Fig. 4 , vertical lines). The low GC content genome of P. falciparum proved to be particularly challenging to the scaffolders using local alignment methods (namely, Bwa-Mem or Bowtie2-local). These indeed tended to produce several wrong joins (Fig. 4 ), indicating that the local alignment methods are not an appropriate choice for scaffolding this genome. A possible explanation for the poor performance observed in this particular case is that the local alignment methods mapped 10% more reads than the global ones and more than Fast-SG (Supplementary Fig.
Short-read aligners were run with the wrapper or instructions provided by the scaffolding tools when possible or using the default parameters. Single data points provide the F-score and error rate for each combination of scaffolding tool and aligner in each dataset. The vertical lines show for each dataset the average F-score or error rate values obtained by each of the short-read aligners or Fast-SG together with the four scaffolding tools. Vertical lines for Bowtie were not plotted since it cannot be used with Besst2. For the P. falciparum (short) dataset, the average F-scores (vertical lines) were omitted for Bwa, Bwa-mem, and Bowtie2-Local due to poor performance (high error rate). The commands used for the aligners and scaffolding tools are detailed in Supplementary Material 5. an efficient library that could be used to perform this task.
Conclusions Here, we introduced a new method, Fast-SG, enables the construction of a scaffolding graph from either short or long reads, allowing for accurate construction of the scaffold sequences well as for software reuse. We showed that near-perfect synthetic libraries are obtained with Fast-SG from either corrected or uncorrected PacBio and Nanopore long reads. The insert size is restricted to the actual long-read size. However, using ultralong Nanopore reads, Fast-SG is able to extract synthetic libraries of even bacterial artificial chromosome clone sizes with insert sizes of 150-180 kb. Those kinds of libraries were crucial to reach the high continuity of the current human reference genome  [40]. An estimation of the gap size with the existing long-range mate-pair technologies (10X genomics and DOVETAIL genomics) is more challenging than with the synthetic libraries due to the fact that in such tech-nologies, the linking information comes from a range of insert sizes and the relative orientation of the read pairs may not be known (DOVETAIL genomics).
Clearly, the synthetic libraries eliminate the bottleneck of sequencing a combination of mate-pair libraries, which were typically required to obtain long-range assemblies  [2, 23, 24]. We further showed that short-read scaffolders are able to produce accurate scaffolds when they are fed with the synthetic libraries extracted by Fast-SG, thus leading to results that are superior to or match those obtained by Links, a scaffolder specifically designed for hybrid long-read scaffolding. Futhermore, we showed that Fast-SG is faster than the current state-of-the-art shortread aligners and that better results are achieved by the scaffolding tools when they are coupled with Fast-SG on illumina mate-pair data. Finally, we demonstrated that Fast-SG in conjunction with efficient algorithms designed for Illumina data can be used to perform a full hybrid assembly of large genomes.
Funding This work was supported by the  Conicyt-PIA  Concurso  AFB 170001 , Fondap 150900007, MAIA  STIC-AmSud  and  CONICYT PFCHA/BECA DOCTORADO NACIONAL 2014/FOLIO 21140124  granted to A. D. G. This research was partially supported by the supercomputing infrastructure of the NLHPC ( ECM-02 ) and  ANR-16-CE23-0001  ( ASTER)  project.
Availability and requirements r Project name: Fast-SG r RRID (Research Resource Identification Initiative ID) : SCR 015934 r Project home page: https://github. com/adigenova/fast-sg r Operating system(s): Unix, Linux and Mac OSX r Programming language: C++ and PERL r Other requirements: Compilation was tested with g++ ver- sion 5. 3 (Linux) and clang version 4. 2 (Mac OSX) r License: MIT r Any restrictions to use by non-academics: none.
STARucn The simulation package STARucn was originally developed at the Institut Laue Langevin for the purpose of simulating the GRANIT  [16]  experiment at the ILL. The original version is available and documented online  [9] , licensed under the open source GPLv3 license. It is heavily based on the ROOT package  [17] , a modular scientific software framework developed for the experiments at CERN. Complex arbitrary geometries are supported. The software implements the propagation of UCN in gravity, interactions at material walls and volume interaction effects. STARucn can be programmed entirely with human-readable script files, and features an extensible plugin-based architecture, allowing additional features to be added with relative ease. The package has since been forked and extended by a group at the University of Sussex, with the intention to apply it to simulations of UCN transport and systematic effects in the neutron electric dipole moment (nEDM) experiment at the Paul Scherrer Institut  [5]  and its successor, n2EDM.
MCUCN The MCUCN code was developed at PSI, supporting the projects of this group. It was extensively used in the optimization of the UCN source, beamlines and experiments at PSI to maximize UCN density and transmission  [19]. MCUCN has already been a useful tool for estimating systematic effects in the neutron electric dipole moment (nEDM) experiment  [3, 20, 21] , for checking data analysis software by toy data, or as part of the data analysis (providing e. g. collision rates  [22] ). Future MC simulations will also be deployed for helping the planning of time-efficient experiments e. g. by improving the online analysis with MC data. MCUCN is a pure C++ code and thus platform independent. Visualization of the MCUCN output data and geometry is left to the user who can choose her or his favorite tool.
Motion in a strong magnetic field deflecting the UCN trajectories has not yet been generally implemented in MCUCN, but a local module can be called to calculate the UCN path in a cylindrical volume representing a strong local field like a magnetic valve. This module introduces small trajectory steps and thus strongly decreases the computing speed. One important application of MCUCN was supporting nEDM-systematics calculations and related effects. Here we show an example for which the physics part related to gravitational resonancefrequency shift and depolarization was treated in detail in  [3, 21]. We considered a cylindrical chamber (H =120 mm, r =235 mm) with its axis aligned with gravity, a geometry used in the nEDM experiment  [3]. As first described in  [25] , in the nEDM experiment the signal is interpolated to zero magneticfield gradient.
The intersection of a particle trajectory with such a surface is detected using a fast AABB-tree algorithm. Each part of the geometry can be deactivated in user-defined time intervals, making it possible to simulate variable properties of valves and other moving parts. All simulation parameters-material properties, geometric model files, field maps, particle sources, and particle spectra-are stored in simple text files, allowing quick changes and optimization. PEN-Track is written in C++ and its object-oriented structure simplifies the implementation of new electromagnetic fields, particles, and physics processes. It is based on open-source libraries and freely available at github. com/wschreyer/PENTrack. git All aspects of PENTrack have been validated by comparing results with other simulation tools (see Sec.
Code intercomparison examples As mentioned in the introduction, it is very important to benchmark the simulation codes against each other. Benchmarks with analytic models and code intercomparisons increase confidence in the reliability of the codes that algorithms were implemented correctly or when intricate calculations have no other means for verification. In the following, three simulation examples involving several codes are presented. For details and further discussion on the physics we refer the reader to the references given at each example. As a check of the implementation of the equations of motion under influence of gravity in STARucn and MCUCN the average vertical coordinate of UCN i. e the center of mass offset in a cylindrical chamber (see also Sec. 4) was plotted in Fig. 3  for the case of only specular reflections.
For more details of the analytic model see  [10]. Both codes reproduce very well the analytic calculations. In Fig. 4  another intercomparison is shown in which the UCN transmission of a U-shaped guide  was calculated as described in detail in  [11]. Using both Lambert and micro-roughness reflection models, PENTrack, STARucn, and Geant4UCN are in very good agreement. In the last example, the simulated data points in Fig. 5  reproduce very well the analytic calculation according to eq. (  78 ) in  [18]. UCN orbiting with only specular reflections with a section alpha in a cylindrical precession chamber produce a false nEDM signal. This false nEDM signal originates from the interplay of transverse magnetic field gradients and a relativistic effect, the motional magnetic field B v = (E × v)/c 2 seen by the UCN in the presence of an electric field (E), where v is the velocity vector of the UCN.
Conclusions Since UCN experiments involve complicated particle trajectories (due to gravity and large solid angles), multiple surface interactions, and spin-precession in inhomogeneous magnetic fields, Monte Carlo simulations are an indispensable tool for UCN optics and data analysis optimizations and also directly provide input for data analysis. Parallel development of several UCN codes allows for important checks via code intercomparisons, but also motivates friendly competition supporting different approaches, and complementary ideas in treating various problems in UCN physics.
Acknowledgements We gratefully acknowledge support from the  UK Science and Technology Facilities Council , under Grants  ST/ K001329/1 ,  ST/M003426/1 , and  ST/L006472/1. University of Sussex  students  Shane Black  and  Oliver Winston  contributed to early development of the spin-tracking facility of STARucn. We also acknowledge granting access to the computing grid infrastructure PL-Grid [23] used for MCUCN simulations. The development of PENTrack was supported by priority program SPP1491 "Precision experiments in particle-and astrophysics with cold and ultracold neutrons" of  Deutsche Forschungsgemeinschaft  and the  Cluster of Excellence Exc153  " Origin and Structure of the Universe ".
In late 2013, CHIKV was introduced into the Caribbean and soon spread throughout North and South America, infecting millions of people  [13, 34]. The first confirmed cases in Colombia were reported in June 2014, and almost 500,000 cases were reported by the end of 2015. Suspected chikungunya cases were reported at the second administrative level (municipality) in Colombia throughout the epidemic, enabling examination of its spatiotemporal dynamics. By simulating the chikungunya epidemic in Colombia at different spatial scales, we examine how model assumptions about the scale of human-mosquito interactions affect the accuracy of model predictions. Specifically, we simulate disease dynamics at a finer spatial scale than the observed time series used to fit the model and compare these model results to simulations conducted at the coarser spatial scale at which surveillance data were aggregated.
Model description We modeled CHIKV transmission dynamics using a new extension of the Institute for Disease Modeling's (IDM) Epidemiological Modeling Disease Transmission Kernel (EMOD-DTK) software  [35]. EMOD is an individualbased disease modeling platform that supports multiple disease transmission routes, including vector-based transmission initially designed to simulate malaria transmission dynamics  [35]. We modified the generic vector-transmission model to represent the transmission dynamics of arboviruses transmitted by Aedes aegypti mosquitoes. Modifications to the generic vector model included incorporating life-history parameters specific to Ae. aegypti, including parameters that capture the sensitivity of its life cycle to rainfall and temperature  [36]. The modified model also includes the ability to simulate the transmission of multiple serotypes of the same pathogen; however, for CHIKV we assume that there is a single strain.