dataesr/scientific-paragraphs-categorization huggingface dataset:
The data analysis performed for this study was done on a Windows computer with an Intel Core i5-4210 central processing unit and 8 GB of random-access memory. An installer for installation of all required modules for Windows computers is provided at https://github. com/LM-UGent/SPeDE. The SPeDE source code is freely distributed under the MIT license and is available at https://github. com/LM-UGent/SPeDE. Validation on a data set of 5,228 spectra representing 167 strains and parameter optimization. To set the optimal threshold used for the peak accuracy window and the local PPMC used to determine a USF, the benchmark data set was analyzed using local PPMC threshold values (the Ϫl flag in the command-line version of SPeDE) ranging from 1 to 100 in increments of 2. To determine the optimal value of the peak accuracy window (the Ϫd flag), the values tested ranged from 500 to 1,000 in increments of 25.
We counted true positives as sample spectra matched to a reference spectrum originating from a strain within the same OTU. OTUs were defined as groups of strains sharing at least 98% genome-wide ANI (see above for details on ANI calculations). False positives were recovered as sample spectra matched to a reference spectrum outside of the expected OTU. Precision was calculated as number of samples with true-positive results/(number of samples with true-positive results ϩ number of samples with false-positive results). The dereplication ratio was determined as the number of OTUs divided by the number of observed OIUs. Analysis of the Lactobacillus brevis data set. Spectra were analyzed with SPeDE with default parameters (local PPMC threshold ϭ 50 and PPMC window ϭ 700), including for each run 2 technical replicates per culture, randomly selected without replacement with the random.
choice function of the Numpy package  (47) , and one or more randomly selected cultures per strain. The analysis was repeated 100 times for each condition, and results were plotted using the R package ggplot  (48). Comparison to other dereplication methods. To compare the performance of the SPeDE algorithm with that of conventional clustering approaches, the spectra were imported in BioNumerics (v7. 6. 2) software (Applied Maths, Belgium). The similarity between spectra was expressed using PPMC. UPGMA was used as a hierarchical clustering algorithm to obtain OIUs. The dendrogram was further processed by grouping branches at 94. 75% similarity, as proposed by Ghyselinck and colleagues  (21). Subsequently, spectra were imported in R via the MALDIquant Foreign package and analyzed according to the method described by Strejcek and colleagues  (22).
ACKNOWLEDGMENTS This work was funded by the  Geconcerteerde Onderzoeksacties (GOA) of Ghent University  (grant  BOF15/GOA/006 ). The  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  is funded by  Wellcome Trust  grant reference  203141/Z/ 16/Z. The  BCCM/LMG Bacteria Collection  is supported by the  Federal Public Planning Service-Science Policy, Belgium. We thank the  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  for the generation and initial processing of the sequencing data. We thank the 2017-2018 computational biology class (UGent, B. S. and M. S. in computer science) and M. S. students for scrutinizing the performance and memory footprint of SPeDE software. We thank the technical staff from the  BCCM/LMG Bacteria Collection , especially  Leentje Christiaens ,  Jolien Claeys ,  Marjan De Wachter ,  Sofie Leyman ,  Cindy Snauwaert ,  Liesje Willekens , and  Chris Willems , for the cooperative work in preparing DNA for whole-genome sequencing, performing MALDI-TOF MS analysis, and providing the strains used in this study.
The core development consisted of adding a mechanistic representation of lianas as a novel plant functional type (PFT) within the model and parameterizing it according to the available data. To evaluate and demonstrate the predictive capacity of the model, we performed simulations of the new model version for two different Neotropical forest sites. The simulations were systematically confronted with multiple (existing and newly collected) datasets, including data from forest inventories and eddy flux tower observations. Our main objective was to quantify the role of lianas in the fully integrated forest carbon cycle at old-growth and secondary tropical forest sites, which we achieved by comparing model simulations with and without lianas. Due to the observed high density of lianas in young forests, we hypothesized that their impact on carbon uptake will be larger for secondary forests than for old-growth forests.
Liana-specific allometries were chosen based on an extensive literature meta-analysis (see Supporting Information 1). For structural biomass, we used the pantropical liana equation of  Schnitzer, DeWalt, and Chave (2006) , which is derived from the most comprehensive liana allometry dataset available. For leaf biomass, we used the latest published equation  (Gehring, 2004) , corrected for small individuals: we used the early successional tree allometric parameterization of the ED2 for lianas with small DBH because the aforementioned equation for lianas would lead to unrealistically low leaf biomass compared to trees (see Supporting Information 1 for details), thus preventing their establishment. In ED2, plant height is one of the most important properties of a given cohort because of its direct impact on light interception and photosynthetic rates.
| Simulation sites and additional data We evaluated the model for two forest sites, Paracou (French Guiana) and the Gigante Peninsula (Panama). We chose these two sites because they present key differences in terms of forest successional stage and liana infestation (see Table  S1  and Supporting  Information 4 for more details) and because supporting datasets exist for these sites to benchmark the model. The Paracou research station is located in the coastal part of French Guiana and is classified as a lowland moist primary forest. Records indicate a mean annual precipitation (MAP) of 3,088 mm, with a wellmarked dry season from mid-August to mid-November (Table  1 ; Figure  S4 ). The meteorological forcing data span from 2004, when the flux tower was installed, to 2016. For this study, we used the flux tower measurements using the eddy covariance technique as described in  Bonal et al.
, 2006). The inclusion of a representation of stem length in the model could lead to improved biomass estimates  (Gehring, 2004). For these new types of allometry to be considered in the model, there is a need for larger allometric datasets, especially those that include large lianas. Our model predicts that the burden of hosting lianas is borne dis- were not represented in the model and could have different impacts on tree PFTs. The transition rates from and to a liana-infested state have recently been investigated  (Visser et al. , 2018)  and could be used in the future to improve the model parameterization. We consider the direct competition for light between lianas and their hosts as one of the most speculative parts of our current model implementation; this process needs further attention in observational studies for future model improvement.
field measurements indicate a liana density of 1,332 stems/ha and 131 stems/ha for Gigante and Paracou, respectively. By comparing these numbers with those in the Global LianaDatabase (DeWalt et al. ,   2015), it appears that Gigante and Paracou stand at the opposite sides of the liana density spectrum for the Neotropics (min = 105 stems/ha at km 41, Brazil; max = 1,414 stems/ha at Fazenda Sete, Brazil). According to our simulations, lianas were most abundant in the canopy of young forest, such as that in Gigante, and progressively declined in older patches (Figure4a) to become almost absent in the top canopy of oldgrowth forests. The simulated liana basal area was 1. 53 cm 2 /m 2 for Gigante (2. 41 cm 2 /m 2 in the field data) and 0. 48 cm 2 /m 2 for Paracou (0. 42 cm 2 /m 2 in the field data).
H2020  European Research Council , Grant/ Award Number: 637643; Ghent University; FWO; Flemish government; BAEF; WBI; French national research infrastructure;  ANAEE-F, Grant/Award Number :  ANR-11-INBS-0001 ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-10-LABX-25-01  and  ANR-11-LABX-0002-01 ; STRI;  NGEE-Tropics  project.
Here we combine stratophenetic datasets on the Gephyrocapsa fossil record  17  with genome sequence data from a diverse set of 10 strains isolated across the world oceans to integrate evolutionary analysis of detailed fossil records and extant patterns of genetic variation. Based on whole-genome sequences, we reconstruct the phylogeny for five closely-related Noëlaerhabdaceae species and infer their evolutionary history and likely mode of speciation. The combined analysis of fossil record and genome sequence data reveals evidence for repeated species radiations and extinctions in this lineage through the Quaternary.
huxleyi and G. muellerae and between G. huxleyi and G. ericsonii/parvula (Supplementary Fig. 6 ; Supplementary Tables  3  and 4 ). Thus, closely-related Gephyrocapsa species are not completely reproductively isolated and they co-occur to occasionally form hybrids. This finding rules out a purely allopatric mode of speciation and indicates that new Gephyrocapsa species formed in parapatry or sympatry, which is consistent with the frequent co-occurrence of different Gephyrocapsa species in the water column and in marine sediments. Repeated species radiations in Gephyrocapsa fossil record. Using morphometric data from Matsuoka & Okada  39, 40  we analysed the evolution of Gephyrocapsa coccolith morphology over the Pleistocene (1. 8 Ma; Fig. 3a ) measuring coccolith size, bridge angle and central opening (Fig.
Methods Origin and morphological characterisation of strains. Clonal Noëlaerhabdaceaen strains (Supplementary Table  1 ) from the Roscoff Culture Collection (RCC; roscoff-culture-collection. org) were maintained in K/2(-Si,-Tris,-Cu) medium  65  at 17 °C with 50 µmol-photons. m -  s -1 illumination provided by daylight neon tubes with a 14:10 h L:D cycle. Calcified cells were harvested at early exponential growth phase on 0. 22 µm nucleopore filters, then dried in a 55 °C oven for 2 h. Following gold coating, filters were observed with a Phenom scanning electron microscope. Morphometric measurements (Supplementary Table  5 ) were carried out according to Bollmann 66 with a minimum of 60 isolated coccoliths per strain using ImageJ software (http://imagej. nih. gov/ij/). Stratophenetic data.
Our reconstruction of the fossil record of Gephyrocapsa is based on the study of Okada and Matsuoka  39. Morphometric data from this study, including measurements of coccolith size, bridge angle and central opening (Fig. 3b ; Supplementary Table  7 ) over the Pleistocene were provided by Prof Matsuoka (Kochi University, Japan) and is available from Pangaea database (https://doi. pangaea. de/10. 1594/PANGAEA. 903745). This data were reanalysed and re-interpreted in the context of modern taxonomic concepts of the species represented. The data was based on measurement of transmission electron microscope images of carbon-replicated coccoliths from sediments collected from the tropical Indian Ocean during Ocean Drilling Project Leg 115  39. DNA extraction. Cell cultures were harvested by centrifugation (4500 × g, 15 min), washed twice with TE buffer, and suspended in 10 ml of lysis buffer (Tris, 0.
1 M; EDTA, 0. 05 M; NaCl, 0. 1 M; 1% SDS; 2% N-lauroylsarcosine, proteinase K 200 mg/ mL, pH 8. 0) and incubated at 55 °C for 2 h for extraction of total genomic DNA. DNA was then purified with equal volumes of phenol and chloroform and precipitated with ethanol  67. For each sample, quantifications of nucleic acids were performed either with a Qubit 3. 0 fluorometer (Thermofisher Scientific, Inc. ) or a Nanodrop. DNA extracts were sent to the Wellcome Trust Centre for Human Genomics, Oxford (WTCHG) for sequencing. Paired-end libraries were prepared individually, barcoded, and then combined prior to sequencing. Libraries were sequenced using an Illumina HiSeq 2500 sequencing platform to produce 150 basepair (bp) paired-end reads. The amount of raw data generated for each strain is listed in Supplementary Table  1.
All sequences are available from NCBI under bioproject number PRJNA532411. Mapping of reads. After quality trimming with Trimmomatic 68 , the sequence reads from Gephyrocapsa strains were mapped to the G. huxleyi CCMP1516 reference genome  69  with BWA-MEM  70. Despite low sequence divergence (<3% total sites) between the strains analysed, the proportion of reads mapped to reference was relatively low (40-69%; Supplementary Table  1 ) likely due to the known variability of G. huxleyi pan-genome with a large proportion of the genome missing in many strains  69. Duplicated reads were removed using Picard (https:// broadinstitute. github. io/picard/). The Genome Analyses Toolkit (GATK)  71  was then used for base quality recalibration, local realignment around Indels (insertions/deletions), and SNPs (Single Nucleotide Polymorphisms) calling.
For downstream analyses, obtained SNPs in vcf file format were converted into fasta multiple alignment sequences per contig (7795 contigs) using a custom script vcf2fas (available at https://github. com/brunonevado/vcf2fas). Phylogenetic inference. For phylogenetic analysis of the Noëlaerhabdaceae, alignments of each contig with at least one missing individual were discarded reducing the dataset from 7795 to 2137 contigs, hence shrinking the total length of the alignment from 167. 7 to 154. 7 megabases, accounting for 4,105,458 positions which were phylogenetically informative. Initially, we conducted a species tree reconstruction using a concatenation-based approach, by first concatenating all 2137 contigs. We then performed a maximum likelihood (ML) tree inference on this concatenated alignment in RAxML 8  72  using the GTRGAMMA model and 100 bootstrap replicates.
The resulting alignments were filtered with alignment columns with >20% missing data excluded before concatenating all genes. We then performed a maximum likelihood tree inference on this concatenated alignment with RAxML using the PROTGAMMAAUTO model and 100 bootstrap replicates. To assess the reliability of the position of the root we also used the more complex mixture model CAT-GTR. The position of the root for the Noëlaerhabdaceae phylogeny on the branch connecting G. oceanica with other Gephyrocapsa species was consistent regardless of the outgroup and model used (Supplementary Figs. 2  and 3 ). Molecular clock analyses. In order to better interpret patterns of speciation and morphological changes, we estimated divergence times for the Gephyrocapsa using uncorrelated and autocorrelated relaxed-clock approaches implemented in the mcmctree program  42  from the PAML 4.
9b package  78. As mcmctree is able to incorporate multiple loci, we based this analysis on the 6278 genomic sequence alignments 10 kb long obtained as described above. Alignment columns with missing data, gaps and ambiguity characters were excluded from analysis (mcmctree setting cleandata = 0). A uniform prior with min = 290 Ka and max = 350 Ka was used as a calibration time for the divergence between G. huxleyi and other Gephyrocapsa (node 2 on Fig. 2a ), based on first occurrence of G. huxleyi in the fossil record 291 Ka 28 , which is the only confident calibration point available in the fossil record of the group. As the mcmctree program requires definition of a "safe maximum" for the age of the root, we set this parameter to "<10" Ma. The analysis was conducted with an autocorrelated relaxed molecular clock (clock = 3) under HKY85 + G5 model (model = 4; alpha = 0.
5) and full likelihood option (data = 1) in mcmctree and comprised two runs of 500,000 MCMC steps after a 50,000 burn-in. To test the robustness of the age estimates to the parameter values and the type of relaxed molecular clock used, we also conducted multiple shorter (200,000 MCMC steps after a 20,000 burn-in) runs with a subset of the 6278 genomic sequence alignments that included 500 randomly selected alignments. This subset was used for running the mcmctree program with both the independent rates model (clock = 2) and the correlated rates model (clock = 3) under HKY85+G5 model with full likelihood option, which yielded similar results (Supplementary Table  6 ). Posterior time estimates were shown to be sensitive to the prior for the distribution of divergence time across the loci (parameter rge-ne_gamma in mcmctree)  79.
Thus, we ran the program with fairly diffuse Dirichletgamma prior for the mean substitution rate (rgene_gamma with shape parameter, α = 1) and different values of β parameter ranging from 10 to 500, which yielded consistent time estimates (Supplementary Table  6 ). The convergence of the runs was monitored using Tracer 80. Phylogenetic discordance. A DensiTree plot was produced using Densitree 81 version 2. 2. 1 to visualise phylogenetic discordances between loci based on 6278 ML trees reconstructed for 10 kb long genomic regions. For each tree retrieved from these 10 kb fragments, we used the pruneTree function in the R phangorn package  82  to collapse nodes with bootstrap support <75%. Trees with no nodes over 75% bootstrap support were discarded. Using the root function, each of the pruned trees was then rooted by G.
oceanica, as the protein concatenated tree suggested, and each tree was made ultrametric using the chronos function with default settings in the R ape package  83. Resulting trees were then loaded into DensiTree 81. Den-siTree plots were produced using the consensus trees produced by DensiTree (in which branch lengths are averaged across all trees for a given topology) with the following settings (star tree, consensus width = 1, consensus intensity 28. 1, and default values for all other settings). To determine how much of the observed variation among the 10 kb fragment trees was due to genuine incongruence, rather than simply lack of phylogenetic signal, we used Shimodaira-Hasegawa (SH) tests  33  implemented in CONSEL version 0. 2 on all 10 kb alignments. The procedure first uses the phylogenetic inference program PhyML version 3.
0 84 over two runs for each locus. The first run uses an unconstrained topology, and the second run constrains the topology to that of the species tree. Both runs were performed using the GTR substitution model without bootstrap replicates. Site-likelihoods from these runs were then compared in CONSEL and FDR correction  85  was applied to the P values for each SH test. Tests for which the FDR corrected SH-test P value (Q value) for the constrained tree is <0. 05 were considered to significantly reject that species tree. Analysis of demographic history. The analysis of demographic history, including divergence times, effective population sizes, and gene flow, was conducted with the Generalized Phylogenetic Coalescent Sampler (G-PhoCS) program 1. 2. 3  36. Following the philosophy of Bpp 86 , G-PhoCS is based on a full coalescent isolationwith-migration model and allows asymmetric gene flow between taxa.
For this analysis we used 1000 randomly selected 1 kb long regions throughout the genome; alignments of sequences of these regions from the 10 sequenced Gephyrocapsa strains were used to run G-PhoCS with 1,000,000 MCMC steps with 100,000 burn-in. Runs were visualised using Tracer v1. 6 program 80 to ensure convergence. As the hypothesis of introgression between Gephyrocapsa species was formulated in an earlier study based on cytoplasmic markers, introgression between species was also investigated as a cause of the observed phylogenetic discordances  20. We performed a Patterson's D-statistic test  38  which compares two phylogenetically incongruent site patterns of ancestral (A) and derived (B) alleles ABBA-(((A,B),B),A) and BABA-(((B,A),B),A) on a four-taxon phylogeny with the topology: (((P1,P2),P3),Outgroup).
If the incongruence is due to ILS, the frequencies of these site patterns are expected to be equal, but in the case of introgression between P3 and either P1 or P2, they are expected to be biased toward the site pattern that clusters the introgressed taxa together. Block Jackknifing (with each locus representing a single block in the context of our dataset) was then used to determine significance. We used a custom ABBA/BABA script (available at https://github. com/brunonevado/calcD_from_fas) to test every phylogenetically congruent three-species subtree using G. oceanica as outgroup. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability All genome sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI; https://www. ncbi. nlm. nih. gov) and are accessible under bioproject number PRJNA532411. Two previously published G. huxleyi genomes (RCC4028 and RCC4030) that were used in this study are available from NCBI under accession numbers ERR695589 and ERR695590. Coccolith morphometric data analysed in this study is available from Pangaea database (https://doi. pangaea. de/ 10. 1594/PANGAEA. 903745).
Knickpoint detectability for the reference model River profiles are used in many studies to extract co-seismic knickpoints and to assess fault activity and local-to-regional Earth Surf. Dynam. , 7, 681-706, 2019 www. earth-surf-dynam. net/7/681/2019/ seismic hazard (e. g. Ewiak et al. , 2015; Wei et al. , 2015; Sun et al. , 2016). It is therefore required to investigate whether modelled knickpoints are statistically detectable. Knickpoint detection often relies on the use of digital elevation models and topographic data (e. g. Neely et al. , 2017; Gailleton et al. , 2019) , which are obtained at a certain scale or resolution. The detectability of each individual knickpoint depends not only on its distance to its adjacent knickpoints but also on the horizontal resolution and vertical precision of the topographic data and on the roughness of the riverbed.
In the following, we consider that a knickpoint is detectable if its height is greater than the vertical precision of topographic data and if its distance to adjacent knickpoints is greater than the horizontal resolution of topographic data. Resolutions of topographic data available at the global scale (e. g. SRTM or ASTER) are between 10 and 100 m, with precision not better than a few metres. Local-to-regional topographic datasets obtained from current airborne lidar or photogrammetric data or derived from aerial or satellite imagery (e. g. Pléiades) display a resolution between 0. 5 and about 1-5 m and a typical vertical precision of 10 cm above water. Moreover, in the vertical direction, knickpoint detectability depends also on the inherent bed roughness, mean alluvial deposit thickness and the local distribution of sediment grain size.
Here, we investigate how knickpoint detectability varies with slope ϕ (Fig. 12b ). We consider that river channels have a slope below 0. 2, colluvial channels between 0. 1 and 0. 5, and hillslopes above 0. 2, following classical slope-area relationships. In terms of horizontal detectability, rivers with a slope ϕ < 10 -2 have a good detectability, more than 80 % using lidar or even DEM resolution. Rivers with 10 -2 < ϕ < 2. 10 -1 have a good horizontal detectability using lidar data and a moderate one using DEMs (10 % to 80 %). Colluvial channels have a moderate horizontal detectability using lidar data and a poor one using DEMs (< 10 %). Hillslopes with ϕ > 10 0 have a poor detectability even with lidar data. However, the overall detectability of DEM data is below 20 % due to the issue of the vertical detectability; that is low for DEM data.