dataesr/scientific-paragraphs-categorization huggingface dataset:
Runtimes for the analyses of 10 3 and 10 6 query reads are reported (h, hours; m, minutes), and for each read set the fastest combination of preparation plus placement is highlighted in red. B. Boxes describe the placement runtime in a scenario where a long-lifespan pkDB is available (meaning that its construction needs not be repeated before the placement of a new query dataset). The horizontal axis reports the number of placed reads. The blue line shows the time used to align those reads to the reference dataset, the preliminary step required by alignment-based methods. For those methods, runtimes include the alignment phase (red and orange lines). while the placement runtime of other PP methods is linear in the number of reference taxa (i. e. the size of refT)  (Matsen et al. , 2010) , for RAPPAS it depends only on the number of edges associated to the queried phylo-kmers, which may be significantly smaller than the number of taxa.
Phase Our experiments on two reference datasets with very different combinations of the parameters governing runtimes (number of taxa and alignment length) show that, on real data, the initial implementation of RAPPAS is already faster than alternative PP pipelines (Figure  5 ). This includes EPA-ng that, despite being based on low level optimizations (e. g. CPU instructions, optimized libraries, etc. )  (Barbera et al. , 2018) , is only faster than RAPPAS when few query sequences are analysed. As expected, the speed gain provided by the alignment-free approach appears to be more pronounced for long reference alignments (Figure  5B , left box). Finally, while the present implementation is based on a single thread, most steps of RAPPAS should be fully parallelizable, offering even faster placements in the future.
Accuracy limitations and future work The presence of dense gappy regions in refA appears to have a negative impact on the accuracy of RAPPAS, especially in combination with larger k-mer sizes: D218 and D628 are the most gappy of the tested datasets and cause increasingly worse accuracy (compared to EPA/PPlacer) when k increases from 8 to 12 (Figure  3 ). The likely reason behind this limitation is the following. As is standard practice in phylogenetics, substitution models used for ancestral state reconstruction do not treat gaps in refA as sequence states. As a result, our phylo-kmers are sequences composed of nucleotides (or amino acids) only and cannot be used to model the presence of indels in the query sequences. The bydefault construction of phylo-kmers described in the Methods produces phylo-kmers that will not match the query sequences having indels among the corresponding positions.
We note that low values of k and long reference alignments make it possible for the same phylo-kmer to be generated at more than one position in refA. Frequent occurrence of this event may have an undesired impact on the placement score of reads, and ultimately affect placement accuracy. This is the likely reason for the lower accuracy observed in the D155 dataset (especially for k = 6), which corresponds to a complete viral genome rather than a single short genetic marker  (Figure 3, lowest row). The choice of the value of k should be made on the basis of the available computational resources, in particular memory. Larger values of k lead to larger pkDBs that require more memory. Currently, the default value for k is set to 8, as this appears to ensure a good combination of placement accuracy (Fig.
3 ), and memory requirements (Fig. 6  and Table  1 ). The choice of k is an issue that applies to all k-mer based methods, and very few approaches to automatically adapt k to the data at hand have been proposed, for example for genome assembly  (Shariat et al. 2014 ). In the case of phylogenetic placement, this may entail runtimeexpensive analyses such as pruning experiments based on the reference dataset at hand (producing accuracy estimates for several values of k, as in Fig. 3 ). Future releases of RAPPAS may include a module allowing advanced users to test different values of k prior to pkDB construction. When placing real-world reads, for most of the tested datasets, RAPPAS produced placements relatively similar to the other methods (Table  1 ). The differences observed in the tara and the neotrop dataset are probably due to the radically different approaches of RAPPAS and alignment-based methods.
Conclusion The design principle behind RAPPAS (Figure  1 ) is particularly adapted to metagenomic analyses based on standardized references (trees and alignments) with a long lifespan. This applies to the context of medical diagnostics where, for instance, virus "typing" aims to classify viral reads within group-specific standardized references  (Sharma et al. , 2018). The references are built and manually curated by specialized teams, and may become standard for months to years in these medical communities  (Kroneman et al. , 2011). Similarly, databases of standard prokaryotic and eukaryotic markers, such as EukRef  (Del Campo et al. , 2018) , SILVA  (Quast et al. , 2013) , RDP  (Cole et al. , 2014) , PhytoRef  (Decelle et al. , 2015), and Greengenes (De Santis et al. , 2006) , are widely used for taxonomic classification.
pkDBs for such references can be built once and shared broadly, allowing many users to efficiently identify a sample's composition with RAPPAS, without the need for rebuilding the database or aligning the sampled reads. While sec. 4. 2 discusses potential improvements to placement accuracy, the computational efficiency of RAPPAS will also be a central focus of our future work. For example, tailored k-mer indexing techniques can be developed similarly to other recent work  (Müller et al. , 2017; Liu et al. , 2018) , and the memory footprint of the pkDB can be reduced by limiting storage to the most discriminant phylo-kmers  (Ounit et al. , 2015). Despite the potential improvements to RAPPAS, it is already faster than other PP implementations on real datasets (Figure  5 ). As the RAPPAS algorithm evolves to place query sequences with increased efficiency and accuracy (especially for longer queries), it will open the door to phylogenetic placement as a means to standardized diagnostic species identification.
Acknowledgements We would like to thank  Olivier Gascuel  for helpful advice, as well as  Anne-Mieke Vandamme ,  Krystof Theys ,  Pieter Libin ,  Frédéric Mahé  and  Jakub Truszkowski  for sharing data and fruitful discussions. BL is also grateful to  Vincent Lefort  for his everyday advice. Finally, we thank  A. Stamatakis ,  P. Barbera  and an anonymous reviewer for their constructive comments which helped improve the manuscript. BL, KS and FP conceived of the study; BL, KS and FP designed the algorithm; BL coded the software; BL, KS and FP wrote the manuscript.
Funding This work has been supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  634650  (Virogenesis. eu). BL was also supported by three  Labex :  Labex Agro  ( ANR-10-LABX-0001-01 ),  Labex CeMEB  ( ANR-10-LABX-0004 ),  Labex NUMEV  ( ANR-10-LABX-20 ).
1 m and the lowest altitude is 143. 39 m in the Etang des roussiè re pond. The data of drainage system was obtained from La pré fecture de Saint-Quentin-en-Yveline. In the catchment, the drainage was designed with a capacity of 10-year return period, and it consists of 4,474 nodes and 4,534 conduits, with a total length of 76 km. Soil data for the catchment was obtained from the InfroTerre Database (http://infoterre. brgm. fr). As shown in Fig. 3 , the soil data of three points are selected, which indicates that sand clay is located at the first layer of the soil profile. For point 1, there exists a layer of limestone soil, which is less permeable. Silver sand which has the best infiltration ability, was in the next layer. The last layer is silver sand and clay, which has better infiltration ability than limestone.
However, it is necessary to follow the priority rule for gully so as to connect surface module and drainage module. Finally, the impervious land uses with priority rule occupied 54 % of the whole catchment, which is 14 % higher than that of majority rule. The second step is to define the soil parameters. In Multi-Hydro, there are four soil layers, at most, that can be defined. In this study, three layers were adopted as the aforementioned. The last step is to input the data of the drainage system into MH-A, which contains the details of all the system components (inlet and outlet nodes, geometry, length, as well as diameter). Based on the fully distributed characters of Multi-Hydro model, users can choose a specific spatial resolution. In this study, after all data were pre-treated by MH-A, Multi-Hydro was implemented to simulate the hydrological response of each scenario with a 10 m spatial resolution (the grid system will create square grids with a cell size of 10 m), and 3 min temporal resolution.
practices. It is a new development in hydrological research approach in LID/NBS techniques. At present, Multi-Hydro model has been developing. In the future, more LID/NBS practice modules will be added, and among these, different combinations and configurations can be investigated, such as green roofs with different substrate porosities or thicknesses, or different ratio of LID/NBS practices. Data availability. The simulation input data and the simulated discharges of the five scenarios investigated in this paper can be accessed through: doi: 10. 5281/zenodo. 3270816. https://doi. org/10. 5194/hess-2019-347 Preprint. Discussion started: 17 July 2019 c Author(s) 2019. CC BY 4. 0 License. Zahmatkesh, Z. , Burian, S. J. , Karamouz, M. , Tavakol-Davani, H. and Goharian, E. : Low-Impact Development Practices to Mitigate Climate Change Effects on Urban Stormwater Runoff: Case Study of New York City, J.
Despite these efforts, the genetic patterns of variation within the Bantus remained largely unexplored on a genomic scale until fairly recently  [14, 15, 22]. The analysis of a large and geographically diverse dataset of 35 Bantu groups has recently confirmed the existence of this RHG ancestry as well as the acquisition of adaptive alleles from these local populations, especially at the human-leukocyte antigen (HLA) loci. By measuring the length of the introgressed genetic fragments, the admixture between western Bantu-speaking populations and RHG was estimated to occur about 800 years ago, mostly after Bantu-speaking populations began moving throughout Sub-Saharan Africa  [13]. According to this study, while the most likely parental source population of Bantu ancestry in both eastern and southern Bantus was located in northern Angola, Bakoya of Gabon and Congo were the best parental source for RHG ancestry.
Results We sequenced 13 Bubi genomes obtaining a depth of coverage up to 21x-32x (Additional file 1: Table  S5 ). All mtDNA haplogroups present in the Bubi are subclades of the L haplogroup (L1b, L2b, L3e, L3f, and L3e) and are common in other populations from the Gulf of Guinea. All male individuals of this dataset belong to subclades of the E1b1a1 haplogroup, the predominant Y-chromosome lineage in Western, Central and Southern Africa (Additional file 1: Table  S6 ). On a genome-wide scale, the first two components of the principal component analysis (PCA) separate the RHG from the Bantu speakers and the Western Africa non-Bantu populations (Fig. 1c ). When PC3 and PC4 are plotted, Bantu-speaking and Western African populations cluster separately (Additional file 2: Figure  S1 ). Three Bubi individuals that we named Bubi-subset1 (BBS014, BBS018, BBS023) fall within the Western Africa cluster showing that some individuals share a larger proportion of Western-Africa ancestry than others, while the rest (named Bubi-subset2) cluster within the Bantu diversity (Additional file 2: Figure  S1 ).
To examine if this clustering reveals the presence of population substructure, we have used the USCS liftover  [28]  to convert the chimpanzee reference sequence (Pan troglodytes 3. 0 assembly, GCA_000001515. 5) to human coordinates in 546,558 single nucleotide polymorphisms (SNPs) of our dataset. We have subsequently computed f 4 statistics in the form (Bubi-subset1, Bubi-subset2; X, chimpanzee), to examine the homogeneity of the Bubi population (Additional file 2: Figure  S2 , Additional file 1: Table  S7 ). None of the tested populations showed elevated (| > 3|) values of Z-score; therefore we have treated the Bubi as a single population in subsequent analyses. Moreover, ADMIXTURE analysis (K = 4) (Fig. 2  and Additional file 2: Figure  S3 ) shows that the Bubi contain the same components as the other Bantu-speaking populations of the dataset but lower levels of the RHG component (in grey) than most of the mainland Bantu-speaking populations (with those from Angola being the exception).
This selection represents the four major genomic components in the Gulf of Guinea. We have found that the Bubi show the highest levels of shared ancestry with the Western African populations, which do not overlap with other populations when the comparison is established with RHG components. Angolan Bantu-speaking populations such as Ovimbundu, Kongo and Kimbundu also show elevated levels of shared genetic ancestry (Additional file 2: Figure  S5 ). Bubi people seem to have very low levels of genetic admixture from the RHG populations; however, this small signal is absent in Western Africans. Furthermore, owing to the colonial history of Bioko we have explored the possibility of some Iberian contribution to the Bubi ancestry by calculating the f 3 statistic in the form (Iberia, X; Mbuti). The Bubi are placed within the range of Western African and other Bantu-speaking populations (Additional file 2: Figure  S6 ); therefore, no Iberian genetic affinities can be discerned within the current dataset.
Pairwise F st is a statistic used to measure population differentiation based on allelic frequencies. We used this test to quantify the level of genetic differentiation between all combinations of populations in our dataset. Low levels of F st statistic indicate that the tested populations share a large proportion of the genotypes, while high levels are indicators of genetic differentiation. Among all African samples, RHG tribes appear to have the highest levels of genetic differentiation, both among themselves and with the agriculturalist groups (Fig. 4 ). The lowest values of genome-wide F st for the Bubi are again with Bantu-speaking Angolan groups (Kimbundu, F st = 0. 0045; Ovimbundu, F st = 0. 0045), and also with a Bantu-speaking group from Cameroon (Yaounde, F st = 0. 0045). On the other hand, we found that the Bubi displayed the highest F st values when compared with RHG populations (Additional file 1: Table  S9 ).
FineSTRUCTURE uses the coancestry matrix obtained from ChromoPainter to classify samples based on haplotype diversity. Using this approach, the matrix and the resulting dendrogram confirm that the closest populations to the Bubi are the Ovimbundu (Fig. 5  and Additional file 2: Figure  S7 ), but also some Gabonese and Cameroonian Bantu-speaking populations. Interestingly, the Bubi are divided into two different clusters (Additional file 1: Table  S7 ), one including only individuals from Bioko Island and the other shared with other Bantu-speaking groups. This, again, suggests some level of substructure in the population. Interestingly, the RHG Fig. 4  Matrix of pairwise F st calculations. The F st values have been calculated using plink and with a dataset of 592,395 SNPs show a higher level of haplotype differentiation compared to other populations.
To additionally test for the presence of Iberian ancestry we have repeated the fineS-TRUCTURE analysis including 12 Iberian individuals (Additionlal file 2: Fig. S8 ), following the same methodology previously described and 244,897 phased SNPs. To explore possible admixture events that could have led to the origins of the Bubi, we also performed GLOBETROTTER analysis (based on ChromoPainter); however, no admixing events could be identified. We also used fineSTRUCTURE to plot a PCA based on haplotype differentiation. This method clusters the populations into the same groups as those used when considering genotypes. In this analysis, the Bubi present a clear intermediate position between Western African and Bantu-speaking populations (Additional file 2: Figure  S9 ). For computational convenience, we performed these analyses with a restricted dataset.
Genotyping and quality control tests Unique aligned reads were processed with Base Quality Score Recalibration (BQSR) implemented in the GATK version 3. 7 software  [49]. Even if the plots did not show signals of systematic errors, we applied recalibration to all filtered reads. We used GATK HaplotypeCaller in GVCF mode for scalable variant calling (using the GRCh37 as a reference sequence). Individual variant calls were merged in a single VCF file using GATK geno-typeGVCFs tool, and the variants were filtered using Variant Quality Score Recalibration (VQSR) with a filter level of 99%. We used QD, MQ ReadPosRankSum, FS, and SOR annotations in this step. We excluded any variant with less than 70% of the main depth coverage or more than 200%. We also removed those variants exhibiting qualities below 30.
Population genetics dataset We merged our filtered variants with 690,739 SNPs from 1235 genotyped individuals belonging to 35 Western Africa populations. This dataset includes: Bantu-speaking populations, hunter-gatherers and Western African groups  [13] ,using Plink 1. 9  [50]  (Additional file 1: Table  S2 ). We excluded triallelic sites, A/T and C/G mutations and all sites with a minor allele frequency (MAF) below 0. 05. We subsequently removed positions with > 10% missing data and those individuals with > 5% missing values. To ensure that genotypes were properly called after merging the dataset, the Yoruba SNP genotypes were compared against the 1000 Genomes Yoruba population. However, subsequent analyses were performed only with the Yoruba genotypes from Western Africa dataset  [51]. Positions that exhibited > 0.
2 values of pairwise F st between both samples were also removed. Based on the colonial history of Bioko, we have assessed the presence of potential genetic admixture of the Bubi with Spanish individuals, adding Iberian samples from 1000 Genomes  [52]  to the SNP dataset. After this procedure, we again removed positions with MAF below 0. 05, missing data above 0. 1 and Hardy-Weinberg disequilibrium p-values below 1e-10. For most of the analyses, we have extracted a sub-dataset with representative populations from Western and Central Africa. This reduced dataset includes 14 populations and 169 individuals (Additional file 1: Table  S3 ). Some of the population genomics analysis require an unrelated outgroup to the tested populations. We have merged our genotypes with data of eleven San individuals  [53]  from the Human Origins array  [54] , followed with the same merging procedure previously detailed.
Mitochondrial (mt) DNA and Y-chromosome analysis Reads were mapped against the Revised Cambridge Reference Sequence (rCRS) of the human mtDNA  [55]. After calling variants with GATK version 3. 7  [49]  as has been previously described, the mtDNA haplogroups were predicted using Haplogrep version 2  [56]. Y chromosome haplogroups were predicted by classifying the observed mutations according to the PhyloTree database  [57].
Population genomic analyses To situate the Bubi within the present diversity of the Gulf of Guinea and Western Africa, a principal components analysis (PCA) with the reduced dataset was generated using EIGENSOFT software  [58] , Results were plotted using R package ggplot2  [59, 60]. ADMIXTURE plots were generated to estimate the proportions of K ancestral components on each individual genome  [61]  of the reduced dataset. As the analysis assumes linkage disequilibrium (LD), we pruned the dataset. We used Plink 1. 9 to remove SNPs with an LD > r 2 = 0. 5 in windows of 50 SNPs. ADMIXTURE analyses were performed with K from 2 to 15 and were repeated five times. The ADMIXTURE iterations were consolidated using CLUMPP with the large K greedy algorithm  [62]  and the results were plotted using R package pophelper  [63].
Outgroup f 3 statistic is a useful test to determine the closest population to a target one using one outgroup population and measuring the amount of shared genetic drift with a test population. San were selected as outgroup, as they represent the most distant African population with genome-wide data, Bubi population was compared to all other populations in the dataset. The f 3 (San; Bubi, Test) statistic was calculated with popstats  [64]  and the results were again plotted using R. f statistics can also be implemented in order to determine which populations exhibited the highest genetic drift with the Bubi people, to do so, we used the popstats software to compute the f 4 statistic (Test, San; Bubi, Mbuti), (Test, San; Bubi, Baka), (Test, San; Bubi, Yoruba), (Test, San; Bubi, Fang). These combinations allow us to dissect the genetic admixture of the tested populations with the Bubi in relation to all the representative source of genetic ancestry in Western Africa: Eastern RHG, Western RHG, Western-African populations and Bantu-speaking populations.
The fixation index (F st ) is a measure of population differentiation. We calculated the mean pairwise F st values between all the populations present in the global dataset. All autosomal SNPs were included in this analysis using the approach of Cockerham and Weir integrated in Plink 1. 9  [65]  The reduced dataset was phased with SHAPEIT2 [66], using 500 states, 50 MCMC main steps, 10 burn-in and 10 pruning steps; recombination maps were interpolated from the HapMap phase 2 genetic maps. After excluding all positions with at least one missing site, we ended up with a dataset of 491,203 variable positions with no missing data. We used CHROMOPAINTER to build a coancestry matrix based on haplotype data from the phased-reduced dataset. This software estimates the admixture proportions in recipient chromosomes by painting the proportion of each genetic component from the donor populations.
We ran CHROMOPAINTER with linked data, estimating n and M parameters through an observation run with no prefixed parameters and including 30 randomly selected samples and three randomly selected chromosomes; fineSTRUCTURE analysis was performed with the counts obtained in CHROMOPAIN-TER and ran with 1000,000 Markov chain Monte Carlo (MCMC) iterations and output printed every 10,000 iterations. The best tree was calculated with 10,000 state attempts. We also generated a haplotype-based PCA with fineSTRUCTURE. To identify any admixture events between Bubi ancestors and other populations during the last 4500 years, we used the GLOBETROTTER  [41]  software on the basis of the defined clusters from fineSTRUCTURE (Additional file 1: Table  S4 ).
Identity by descent (IBD) analysis Identity by descent (IBD) blocks are defined as identical chromosome fragments present in multiple individuals that have been inherited from the same ancestral chromosome  [67]. We have used RefinedIBD software  [68]  setting "ibdcm" = 0. 5, "ibdtrim" = 62, "ibdwindow" = 2478, and "overlap" = 413; the rest of the parameters were assigned by default. All IBD blocks longer than five centimorgans (cM) were kept and the statistical threshold marked by LOD (the base 10 log of the likelihood ratio of the IBD segments, which is a figure that will depend of the size of the database and the genetic diversity within it) was assigned by default (> 3). The number of SNPs used here was 685,382. We then filtered the IBD segments to keep only those that were shared by any Bubi and another individual of the dataset (including the IBD fragments shared by two Bubi individuals).