dataesr/scientific-paragraphs-categorization huggingface dataset:
tance. Within them, in the context of streptomycin resistance, the mmpS1 annotation was reported by the three methods, but not by the RWAS approach, as this gene was not included in the targeted approach prior. More generally, any reference-based approaches such as SNP-or gene-based GWAS or RWAS are limited in the context of new marker discovery, especially for species with a large accessory genome, since any causal variant absent from the chosen reference would remain non-tested. Besides being time-consuming, preparing such a list of genetic variants can even be problematic for bacterial species without extensive annotation nor reference availability. Agnostic approaches avoid the difficulty of designing an exhaustive variant database for the GWAS. However, HAWK and SEER reported several thousands kmers for M.
Where DBGWAS reported in this case only one subgraph, SEER, for instance, reported 470 kmers with the rpoB annotation. Finally, DBGWAS took less than 2 hours in all our experiments, while SEER took more than one week in some experiments, and HAWK usually ran in less than one day but failed on the most complex dataset composed of genomes of different species. Moreover, SEER required much more memory (up to 100Gb) than DBGWAS and HAWK (Supplementary Table  S2 ).
, 2014). Contrary to our approach, these studies require extensive resistance marker databases. DBGWAS will surely contribute to the extension of such databases or to the development of agnostic genomic antibiograms. In conclusion, we demonstrate for three medically important bacterial species that resistance markers can be detected rapidly with relative ease, using simple computer equipment. New links between genomic variations and phenotypes can be inferred, providing our method with a clear advantage in comparison to existing procedures. Using our graphical interface will provide future users in all domains of microbiology with an enhanced insight into genotype to phenotype correlation, also beyond antibiotic resistance. This will include complex traits such as biofilm formation, epidemicity and virulence.
We perform GWAS on strains encoded by their unitig (rather than kmer) content, and use the cDBG neighbourhood of significantly associated unitigs as a proxy for their genomic environment. Figure  4  summarises the main steps of the process. The code implementing this process is available at https://gitlab. com/leoisl/dbgwas/ under the GNU Affero General Public License. 1 2 3 4 …. p input output Step 1 Step 2 Step 3.
= X • Linear mixed model to account for the population structure; Y = X i b +W T a +e i i in 1… p >str1_contig1 catgtgctagtgtcg cagtgtcgtgtagct … • DBG construction; • DBG compaction; • Strain mapping. • Local neighborhood retrieval around significant unitigs; • Graph decoration with phenotype and statistical data, and annotations databases; • Visualization on web browsers; Tool: GATB  (Drezen, 2014)  Tool: bugwas  (Earle, 2016)  Tools: Boost (boost. org) _____-Blast+  (Camacho, 2009)  _____-Cytoscape. js (Franz, 2015)  Figure  4 : DBGWAS pipeline. DBGWAS takes as input draft assemblies and phenotype data for a panel of bacterial strains. Variant matrix X is built in step 1 using cDBG nodes. Variants are tested in step 2 using a linear mixed model. Significant variants are post-processed in step 3 to provide an interactive interface assisting with their interpretation.
Interpretation of significant unitigs (step 3) The LMM can be used to identify deduplicated minor allele presence patterns significantly associated with the phenotype at a chosen level. Because of the deduplication procedure used to build the matrix X, each of these patterns can correspond to several unitigs. We now show how the cDBG can be used in the interpretation step. Significance threshold. We select the most significantly associated patterns by defining a Significant Features Filter (SF F ). In our experi-ments, we choose not to apply a fixed FDR threshold -even though DBG-WAS offers this option, by using a SF F value between 0 and 1. Different datasets lead to different q-values, even by several orders of magnitude, and a single FDR threshold would lead to selecting a large number of unitigs generating > 1000 subgraphs on some of them (e.
g. S. aureus ciprofloxacin) as shown in Supplementary Table  S8. Instead, we use SF F = 100, i. e. , retaining the 100 patterns with lowest q-values. However arbitrary, this choice is tractable for all datasets and provides satisfactory results in our experiments. It does not guarantee control of the FDR: only the q-value provides an estimation of the proportion of false discoveries incurred when considering patterns below this value. Checking the q-values of the selected unitigs is therefore essential to assess its significance. Graph neighbourhoods. We define the neighbourhood of each significant unitig u (defined by the SF F ) as the set of unitigs whose shortest path to u has at most 5 edges. The objects returned by DBGWAS are the connected components of the graph induced by the neighbourhoods of all significant unitigs in the cDBG.
, 2012). Colours are continuously interpolated between red for unitigs with a strong positive effect and blue for those with a strong negative effect. Annotating the subgraphs. DBGWAS offers an optional annotation step using the Blast suite  (Camacho et al. , 2009)   Figure  5 : Subgraphs induced by the neighbourhood of significantly associated unitigs. In this example, a neighbourhood of size 2 was used: any unitig distant up to 2 edges from a significant unitig is retrieved to define its neighbourhood. Neighbourhoods are merged if they share at least one node, e. g. the neighbourhoods of U 1 and U 2 are merged because they share N 6 , and will be represented in a single subgraph. databases. Users can then easily retrieve the annotations which are the most supported by the nodes in the subgraph, or with the lowest E-value.
We provide on the DBGWAS website a resistance determinant database built by merging the ResFinder, MEGARes, and ARG-ANNOT databases  (Zankari et al. , 2012; Lakin et al. , 2017; Gupta et al. , 2014) , and a subset of UniProt restricted to bacterial proteins (UniProt consortium, 2017). Subgraphs discussed in the Results section were annotated using these databases.
Interactive visualization. DBGWAS produces an interactive view of the enriched and annotated subgraphs, allowing the user to explore the graph topology together with information on each node: allele and phenotype frequencies, q-value, estimated effect, and annotation. The view is built using HTML, CSS, and several Javascript libraries, the main one being Cytoscape. js (Franz et al. , 2015). Results can be shared and visualized in a web browser. A large number of components can be produced in one run of DBGWAS. We thus provide a summary page allowing the user to preview and filter the subgraphs. Filtering can be based upon the minimum q-value of all unitigs in the component (min q ), or based on the annotations. A complete description of the DBGWAS interactive interface is available in Supplementary Section 6.
Datasets We used in our experiments genome sequences from three bacterial species with various degrees of genome plasticity, from more clonal to more plastic: Mycobacterium tuberculosis, Staphylococcus aureus, and Pseudomonas aeruginosa. We build a fourth panel (see below WHO list panel), used only for time and memory performance assessment and defined according to the top-3 WHO priority pathogens list 1. These panels are summarised in Table  2. This species is known for its apparent absence of horizontal gene transfer (HGT) and accordingly, most of the reported resistance determinants are chromosomal mutations  (Gygli et al. , 2017)  in core genes or gene promoters. Intergenic regions are also described to be instrumental in multidrug-resistance (MDR) and extensively drug-resistant (XDR) phenotypes  (Zhang et al.
The species accessory genome is particularly important both in terms of size and diversity and carries more than half of the genetic determinants already described to confer resistance to antimicrobial drugs  (Kung et al. , 2010; van Belkum et al. , 2015; Jaillard et al. , 2017b). We use a panel of 282 strains, gathered from two collections which mostly include clinical strains: the bioMérieux collection  (van Belkum et al. , 2015)  (n=219) and the Pirnay collection  (Pirnay et al. , 2009) (n=63). Genome assemblies and categorical phenotypes for 9 antibiotics are available  (Jaillard et al. , 2017b). Binarised phenotypes of amikacin resistance are available on the DBGWAS project page to provide this dataset as an example for users.
Acknowledgments The authors thank  Jean-Baptiste Veyrieras ,  Sarah Earle ,  Chieh-Hsi Wu  and  Daniel Wilson , as well as  Jean-Pierre Flandrois ,  Manolo Gouy ,  Stéphane Schicklin  and  Ghislaine Guigon  for their insightful comments. LL acknowledges  CNPq/Brazil  for the financial support. This work was performed using the computing facilities of the  CC LBBE/PRABI. LL is funded by the  Brazilian Ministry of Science, Technology and Innovation (in portuguese, Ministério da Ciência, Tecnologia e Inovação -MCTI)  through the  National Counsel of Technological and Scientific Development (in portuguese ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq) , under the  Science Without Borders (in portuguese, Ciências Sem Fronteiras)  scholarship grant process number  203362/2014-4.
VL is funded by the  Agence Nationale de la Recherche   ANR-12-BS02-0008  (Colib'read) and  ANR-16-CE23-0001  (ASTER). LJ is funded by the  Agence Nationale de la Recherche   ANR-14-CE23-0003-01  ( MACARON ) and  ANR-17-CE23-0011-01  ( FAST-BIG ).
We present Fig. 1:  The KATRIN beamline with a total length of about 70 m: (a) rear section with calibration devices, (b) windowless gaseous tritium source, (c) differential pumping section, (d) cryogenic pumping section, (e) pre-spectrometer, (f) main spectrometer with air coil system, (g) focal-plane detector. The entire beamline transmits β-decay electrons in a 191 T cm 2 flux tube to the detector. measurement results from two commissioning phases of the KATRIN spectrometer section, where we investigated the removal efficiency with artificially enhanced background (using 83m Kr and 220 Rn sources) and the background reduction under nominal conditions (section 3). To further investigate the effects of the magnetic pulse on stored electrons, we discuss simulations with the particle-tracking software KASSIOPEIA, which has been developed by the KATRIN collaboration  [27]  (section 4).
Implementation The magnetic pulse was implemented in KASSIOPEIA as a new field computation module, which applies a timedependent scaling factor f (t) to a constant magnetic source field B 0 (r). The total magnetic field is given by the sum of the static contribution by the spectrometer solenoids and un-pulsed air coils (LFCS coil L14), and of the dynamic contribution by the pulsed air coils (LFCS coils L1-L13). The simulations presented here use a double-exponential time-dependency that matches field measurements at the spectrometer (section 3. 1): B(r,t) = f (t) • B 0 (r) + B static (r) , (12) f (t) = exp -t τ 1 + exp -t τ 2 -1 , (13) where τ 1 = 30 ms and τ 2 = 420 ms are the time constants used in the simulation, B 0 (r) is the source field, and B static (r) the static (unmodified) field at the electron's position r.
In addition to measurements, we examined the removal processes in more detail by particle-tracking simulations with the KASSIOPEIA software. We found that with the implementation of the magnetic pulse method described in this article, the removal efficiency is intrinsically limited due to the complex electromagnetic conditions in the main spectrometer volume. The inversion of the magnetic guiding field, which is accompanied by a considerable deformation of the magnetic flux tube, creates new electron storage conditions in the central spectrometer region. This prevents a complete removal of stored electrons from the flux tube, and a fraction of stored electrons remains after a magnetic pulse cycle. A possible improvement could be to adapt the design and change how the magnetic field reduction is applied through the air-coil system.
Acknowledgements We acknowledge the support of  Helmholtz Association (HGF), Ministry for Education and Research BMBF  ( 05A17PM3 ,  05A17PX3 ,  05A17VK2 , and  05A17WO3 ),  Helmholtz Alliance for Astroparticle Physics (HAP) , and  Helmholtz Young Investigator Group  ( VH-NG-1055 ) in Germany;  Ministry of Education, Youth and Sport  ( CANAM-LM2011019 ), cooperation with the JINR Dubna (3+3 grants) 2017-2019 in the Czech Republic; and the  Department of Energy  through grants  DE-FG02-97ER41020 ,  DE-FG02-94ER40818 ,  DE-SC0004036 ,  DE-FG02-97ER41033 ,  DE-FG02-97ER41041 ,  DE-AC02-05CH11231 , and  DE-SC0011091  in the United States.
, 2013; Braun, 2016). Besides collecting in situ data, one can also collect and date a large number of mineral grains from a sand sample collected at a given location in a river draining an actively eroding area. Such detrital thermochronology datasets provide a proxy for the distribution of surface rock ages in a given catchment  (Bernet et al. , 2004; Brandon, 1992). By repeating this operation at different sites along a trunk stream, one obtains redundant information that can be used to document more precisely the spatial variability in in situ thermochronological ages in a river catchment  (Bernet et al. , 2004; Brewer et al. , 2006). Methods have been devised to extract quantitative information from such detrital datasets concerning the erosion history of a tectonically active area as well as estimates of its spatial variability.
Ruhl and Hodges (2005)  convolved their detrital age datasets with the hypsometry of the catchment to test the assumption of topographic steady state in a rapidly eroding catchment of the Nepalese Himalaya. Similarly, focusing on the eastern Sierra Nevada and White Mountains of California, respectively,  Stock et al. (2006)  and  Vermeesch (2007)  combined detrital apatite (U-Th)/He age datasets with an age-elevation relationship established from in situ samples to predict the distribution of presentday erosion rates. Whipp et al. (2009)  used simulations from a thermo-kinematic model to define the limits of the applicability of such a technique, while  Enkelmann and Ehlers (2015)  used it in a glaciated landscape. Wobus et al. (2003 Wobus et al. ( , 2006) )  collected samples from tributaries of the Burhi Gandaki and Trisuli rivers to document the strong transition in erosion rate across a major topographic transition.
In this paper, we present a method that relies on the raw age data only. This avoids any complication or bias that may arise from trying to compare the data to theoretical probability density distributions that rely on a thermal model prediction. We recognize the value of doing so, but thermal models require making assumptions about past geothermal gradients (heat flux) or rock thermal conductivity and heat production, which introduces additional uncertainty in interpreting the data. The first part of this paper describes the method. To demonstrate its accuracy and explore the limits of its applicability, we have applied the method to synthetic age datasets for which we know the erosion rate and its spatial variability. This is done in the second part of the paper. To illustrate the use of our method, we have applied it to a dataset collected in the Himalaya (along the Tsangpo-Siang-Brahmaputra river system).
Basic assumptions We assume that we have collected a series of age datasets measured at M specific points (or sites) along a river that drains a tectonically active region where the erosion rate is likely to vary spatially. We also assume that the datasets have been used to construct age distributions decomposed into N age bins (see Fig. 1 ) that may, for example, correspond to given, known geological events or, alternatively, have been selected after constructing a kernel density estimate of the data  (Vermeesch, 2012)  and applying a mixture model to infer potential discrete age peaks in the age distribution  (Sambridge and Compston, 1994). Although each bin corresponds to an age range, it might be easier to refer to it as representative of a given "age", which can be taken as the mean age of the range, for example.
We will call H k i for k = 1,. , N and i = 1,. , M the relative height of bin k in distribution i. Because these are relative contributions, we have N k=1 H k i = 1, for all i = 1,. , M. (1) The landscape is divided into exclusive contributing areas for each of the points along the main river where we have measured a dataset and compiled a distribution from it. We take the convention that Area 1 (of surface area A 1 ) is the area contributing to site 1, whereas Area 2 (of surface area A 2 ) is the area contributing to site 2 but not to site 1. Area i (of surface area A i ) therefore contributes to site i but not to the previous i -1 sites (see Fig. 2 ). In each Area i, we will assume that α i is the relative abundance of the mineral used to estimate the age distribution in rocks being eroded from the surface.
We then constructed five grain distributions corresponding to each of the sites by adding the original distributions in proportions given by the relative contributions (product of area, mineral concentration factor and erosion rate) of each area. We then sampled these distributions by randomly selecting n = 100 grains. This number was chosen because, in many detrital age studies, it represents the average number of grains collected at any given site. We applied our method to these synthetic datasets to obtain distributions of minimum erosion rate by bootstrapping and took the median value as a reliable estimate of the minimum erosion rate in each area necessary to satisfy the synthetic age distributions. We repeated this operation 1000 times (i. e. , by generating 1000 synthetic datasets) to obtain the distributions of estimates of minimum erosion rates and compared them to the imposed erosion rates used to construct the synthetic age distributions.
Consequently, the accuracy of the estimates of erosion rate obtained from our method rely on whether the two successive age distributions used to estimate the δ m i are markedly different and whether the size of the two successive catchments increases or, at least, does not decrease substantially. These Computed rate distributions for four synthetic datasets. For each distribution, the box extends from the lower to upper quartile values, the line corresponds to the median and whiskers extend from the box to show the range of the erosion rate estimates, excluding outliers. Outliers are indicated by small circles past the end of the whiskers. For each site, the gray stars correspond to the imposed erosion rates and the dashed gray line gives the product of the imposed area and fertility factors, F i = A i α i.
Applications to a detrital age dataset To illustrate the method, we now apply it to a detrital age dataset from the eastern Himalaya that contains ages obtained using the muscovite 40 Ar/ 39 Ar thermochronometer. The data were combined from published age datasets collected along the main trunk of the Tsangpo-Siang-Brahmaputra river system, as well as along some of its tributaries (Fig. 6 ), using age bins given in Table  1. Samples A, B, C (composite sample), X and Y are from  Lang et al. (2016)  and samples Z, T-40a and T-41a are from  Bracciali et al. (2016). The complete age datasets are given in the data repository, Table  S1  in the Supplement. In Table  2 , we give the relative position of the successive samples along the main  trunk of the river, i, the respective exclusive contributing areas, A i , and the mineral concentration factors, α i.
Site The most salient result predicted by the method is that the erosion rate in the eastern Himalayan syntaxis should be at least 5-7× higher than the mean erosion rate along the Tsangpo-Siang-Brahmaputra basin. This estimate is in good agreement with the conclusions of  Stewart et al. (2008) , who used U-Pb ages of detrital zircon grains from the Brahmaputra River to demonstrate that approximately half of the sediment flux carried by the Brahmaputra River originates from an area around the eastern syntaxis that represents only 2 % of the total area of the river drainage basin. This implies that erosion rate in the vicinity of the syntaxis should be approximately 25× higher than the mean erosion rate. Similar estimates were obtained by  Enkelmann et al. (2011)   larger detrital age dataset from the area, while a relatively smaller estimate (erosion rate should only be 5× higher than the mean in the syntaxis area) was obtained by  Singh and France-Lanord (2002)  using the isotopic composition of sediments collected along the Brahmaputra River.
We have found, however, that the solution we obtain in this way is strongly dependent on the choice made for the kernel and further investigation of this issue is required. Alternatively, estimates of cumulative density functions could be built directly from the data and, in turn, used to impose condition (Eq. 16) and estimate the minimum contribution factors, δ m i. However the limited number of grains at each site makes the comparison between two successive CDFs (cumulative density functions) rather inaccurate. The accuracy of this approach can be tested by increasing the number of bins in our model such that N becomes similar to the average number of grains in any dataset. This leads to an overestimation of the minimum contribution factors. Clearly more work is required to investigate better or alternative ways to compare two successive age distributions.
Conclusions We have developed a simple method to extract spatially variable erosion rates and surface age distributions from detrital cooling age datasets from modern river sands. The method is based on what we believe are the simplest assumptions necessary to interpret such data and does not rely on a priori knowledge of the distribution of ages in surrounding catchments. In describing the method we have demonstrated that it is suited to extract two apparent sources of information pertaining to the spatial distribution of erosion rate along the river. The first comes from using the age distributions as fingerprints characterizing the areas between two successive sites where detrital samples were collected. This allows us to predict first-order estimates of the relative erosion rate between these areas and the distribution of ages in surficial rocks in each area.
These estimates of age distributions can be used as a second independent information on the past and present erosion rate in each area. By applying the method to an existing dataset from the eastern Himalaya, we show that the method provides estimates of present-day erosion rate patterns in the area that is consistent with previous, independent estimates, potentially evidencing that the fast present-day erosion rates in some parts of the study area are relatively young. We stress, however, that our method can only provide reliable estimates of erosion rate when the age distributions observed at two successive sites are different. Importantly, the method is limited to providing the spatial distribution of erosion rate; independent information is necessary to transform those into absolute estimates of erosion rate.
Figure3. Results of the method applied to the first set of synthetic datasets. Computed rate distributions for four synthetic datasets. For each distribution, the box extends from the lower to upper quartile values, the line corresponds to the median and whiskers extend from the box to show the range of the erosion rate estimates, excluding outliers. Outliers are indicated by small circles past the end of the whiskers. For each site, the gray stars correspond to the imposed erosion rates and the dashed gray line gives the product of the imposed area and fertility factors, F i = A i α i.