dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this work, we have introduced the 5AI inverse scheme: it implements Bayesian optimal estimation and uses the 4A/OP radiative transfer model with the GEISA spectroscopic database and an empirically corrected absorption continuum in the O 2 A-band. We have applied the 5AI inverse scheme to retrieve  !!! from a sample of ~77k OCO-2 clear-sky soundings with low ACOS retrieved total AOD in target and nadir mode. Its global averaged uncorrected bias with regard to TCCON is 1. 33 455 ppm with a standard deviation of 1. 29 ppm for airmasses below 3. 0. These results are comparable in standard deviation with those obtained by ACOS and FOCAL v08 for corresponding sets of OCO-2 soundings. Moreover, we showed that, similarly to ACOS, 5AI  !!! retrievals satisfactorily capture the global increasing trend of atmospheric CO 2, its seasonal cycle as well as its latitudinal variations, and that 5AI results are consistent between OCO-2 nadir and target modes.
Data availability For this work we use the B8r and B9r releases of OCO-2 data that were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the OCO-2 data archive maintained at the NASA Goddard 475 Earth Science Data and Information Services Center (NASA GES-DISC). TCCON data are available on the TCCON Data Archive (https://tccondata. org/) and FOCAL v08 data can be downloaded on the FOCAL-OCO2 website hosted by the University of Bremen (http://www. iup. uni-bremen. de/~mreuter/focal. php). 5AI retrieval results presented in this work are available upon request from Matthieu Dogniaux by email (matthieu. dogniaux@lmd. ipsl. fr). https://doi. org/10. 5194/amt-2020-403 Preprint. Discussion started: 10 November 2020 c Author(s) 2020.
Targeted capture of nuclear genes increasingly contributes to unravelling phylogenetic relationships that hitherto remained unresolved because of limitations of traditional Sanger sequencing. In particular, the study of tropical plant families has been compromised because they often rely on highly degraded DNA obtained from herbarium specimens. One such example is the pantropical Ochnaceae, which comprises 33 genera and approximately 550 species, occurring mostly in savannas and moist tropical forests. Here, we developed a set of baits covering about 660,000 bp from 275 nuclear genes used for the targeted enrichment based on all but one genus and more than 250 species. Using this novel dataset, we resolved the phylogenetic backbone of Ochnaceae, including that of Ochninae, and we established new relationships.
The only exception is  Bissiengou (2014) , analysing the genus Campylospermum. To settle the circumscription of Sauvagesia and to clarify the position of the other SE Asian taxa, a broader taxon sampling compared to previous studies is needed. This is also required for the study of the infrageneric groups in Ochna and Ouratea. Further benefit from comprehensive taxon sampling comes from minimizing phylogenetic error  (Heath & al. , 2008). Therefore, we used a hybrid enrichment strategy to increase both the amount of DNA sequence data (from ca. 4 kbp in  Schneider & al. , 2014  to up to about 143 kbp in the final dataset in the present study) and the number of taxa (from 77 in  Schneider & al. , 2014  to 254 in the present study) to generate a data-and taxon-rich nuclear phylogenetic tree of Ochnaceae.
Final taxon sampling was 254 species, including one outgroup from the clusioid sister clade, that is, Bonnetia stricta (Nees) Nees & Mart. (Appendix 1). Target loci selection and bait design. -Target loci were selected based on transcriptome data obtained from the 1KP project (https://sites. google. com/a/ualberta. ca/onekp; Matasci & al. , 2014). This included one ingroup transcriptome (1KP 4letter code: CKDK, Ochna serrulata Walp. ) and four from its clusioid sister clade (OSIP, Garcinia livingstonei T. Anderson; FWCQ, G. oblongifolia Champ. ex Benth. ; BNDE, Hypericum perforatum L. ; NFXV, Mammea africana Sabine). For the identification of intron-rich regions, we used the Ricinus communis L. genome, version 0. 1  (Chan & al. , 2010) , from JGI Phytozome (2016). For plastome and mitogenome filtering, we used Ricinus communis (GenBank accession numbers JF937588 and NC_016736).
Our pipeline for the selection of target loci consisted of the following steps, which were executed using a series of custom python scripts as described in detail at Github (https://github. com/topel-research-group/sequence_capture; https://github. com/ topel-research-group/misc; see also suppl. Appendix S1). First, we used CD-HIT v. 4. 6. 6  (Li & Godzik, 2006)  to reduce redundancy in the transcriptome data (e. g. , resulting from splice variants), with percentage identity cut-off values of 0. 99, 0. 98 and 0. 95. Length filtering was applied to retain only targets with a minimum length of 300 bp. Low-copy loci in the ingroup dataset were identified by an all-against-all comparison of the Ochna serrulata transcriptome sequences using BLASTn analysis. Sequences with no or poor BLASTn matches to any other sequences in the dataset were assumed to be low-or single-copy.
Post-capture libraries were enriched during 10-14 PCR cycles using the "reamp" primers (see  Meyer & Kircher, 2010 ; annealing temperature: 65 C) and a HiFi polymerase (KAPA HiFi HotStart Ready Mix, KAPA Biosystems, Wilmington, Delaware, U. S. A. ). The enriched post-capture libraries were cleaned with 1. 1× Ampure XP beads, eluted in 10 mM Tris-HCl, 0. 05% Tween-20, pH 8. 0, and quantified with Qubit v. 1. 0. The 325 (including 42 replicate libraries of taxa that have been sequenced twice) pooled post-capture libraries were further pooled (up to 96 dual indexed samples) at equimolar amounts for sequencing on three Illumina HiSeq 2500 lanes and one Miseq lane with 150 bp paired-end reads at Macrogen (Seoul, South Korea). The sequences in fastq format from all individuals are available in Gen-Bank SRA under the Bioproject number PRJNA602196 (http:// www.
We, therefore, generated three separate datasets: one with the goal to maximize the number of leaves in the tree that included all species of Ochnaceae (hereafter named FAM; 264 accessions) for the reconstruction of the family's backbone and as baseline data for future analysis of the diversification dynamics; a second one that only included species from tribe Ochneae with its three subtribes Lophirinae, Elvasiinae and Ochninae (LEO; 200 accessions); and a third one that only included species of Sauvagesieae, Luxemburgieae and Testuleeae (SLT; 59 accessions). The extraction of contigs that matched the target loci was achieved with LASTZ v. 1. 04. 03  (Harris, 2007)  based on the provided target sequences. The default settings were used in order to reduce the number of loci with low read coverage.
The target loci had a mean length of 2402 bp, and 56% of the extracted target contigs had a length of ≤1000 bp (for Ochnaceae). If loci had a fragmentary coverage of contigs, i. e. , multiple contigs were mapped to the same locus, these fragments were identified as putative paralogs. To avoid the automatic exclusion of these putative paralogs from further analyses, we selected the option "keepparalogs". The potential paralogs were subsequently analysed using the "paralogs_to_ref" function, which allows the identification where they map on the reference. If identified as fragmentary contigs resulting from non-optimal contig assembly, these fragments were kept (see documentation of the SecapR pipeline: https://github. com/AntonelliLab/seqcap_processor/ blob/master/docs/notebook/subdocs/align_paralogs.
For the FAM and SLT datasets, the reference-based assembly was run with the function "sample-specific", which extracts the contig sequences for all loci for each sample separately and which are then used as new sample-specific reference libraries  (Andermann & al. , 2018). For the LEO set, the reference sequences for remapping were produced with the setting "consensus-alignment", thus creating a consensus sequence from the contig alignment of all samples per locus. For this set of comparatively closely related taxa, the "consensusalignment" approach provided a better read-coverage across samples. Poorly recovered loci were removed with the locus_selection. py command. Multiple sequence alignments were produced for each of the selected loci. For the LEO set, for which the number of good-coverage loci was significantly higher, we chose a conservative approach, keeping only loci that were present in at least 95% of the taxa, thus accepting only low amounts of missing data at the level of entire loci.
For a locus to be kept in the other datasets, we aimed at an amount of missing data not surpassing a threshold of 50% (above which a locus was discarded; see  Comer & al. , 2016). Final alignments were done with MAFFT v. 7. 409  (Katoh & al. , 2002; Katoh & Standley, 2013)  in Geneious R11. 0. 4 and are deposited at Zenodo (http://doi. org/10. 5281/zenodo. 4278991). For the SLT dataset, we added DNA sequences from two representative species of Ochneae based on the LEO dataset (only a reduced set of shared loci was available) and included Medusagyne as outgroup. For the LEO dataset, gap-rich regions were subsequently masked in Geneious using a 5% threshold (i. e. , sites with more than 5% gaps were removed), and poorly aligned regions were stripped manually. For the FAM and SLT datasets, a similar approach was chosen but with the gap threshold set to 50%.
e. , species trees are reconstructed based on the gene trees obtained for each locus, which are subsequently used to infer the species tree) for the LEO dataset. Because it has been shown that MSC analysis is prone to an increased level of topological error with a low number of loci and a high amount of missing data  (Vachaspati & Warnow, 2015) , such as in the FAM and SLT datasets, we only ran an MSC analysis with the FAM data for comparison. For the concatenation approach, tree searches based on the supermatrices were performed using maximum likelihood analyses with RAxML v. 8. 2. 12  (Stamatakis, 2014)  and run on the Cyberinfrastructure for Phylogenetic Research (Cipres Science Gateway, http://www. phylo. org/index. php/, Miller & al. , 2010)  using the RAxML-HPC BlackBox (v. 8. 2. 12) with a GTR-GAMMA model and 1000 bootstrap replicates.
For the multispecies coalescent approach, gene tree searches were performed for each locus separately using IQ tree with default values on the W-IQ-TREE Web Service (http://iqtree. cibiv. univie. ac. at/; Trifinopoulos & al. , 2016). Substitution models were selected automatically. Branch support analyses were carried out with an ultrafast bootstrap analysis, setting the minimum correlation coefficient to 0. 99 and the SH-aLRT branch test to 1000 replicates. Species trees were reconstructed following the genome-scale multispecies coalescent approach using ASTRAL v. 5. 6. 3  (Mirarab & al. , 2014)  and the gene trees that corresponded to our selected loci and default parameters. This program assembles quartet trees by taking a collection of unrooted gene trees and collecting all their quartets into a set.
Subsequently, it evaluates each species tree by how well its quartet trees match those in the set. Local posterior probabilities (LPP) were calculated as branch support. In a second run that was conducted with the option "-t 1", we also obtained quartet support (QS;  Sayyari & Mirarab, 2016) , a measure to evaluate gene tree discordance by providing the proportion of quartets in gene trees agreeing with a branch. Trees were visualized in FigTree v. 1. 4. 1 (http://tree. bio. ed. ac. uk/soft ware/figtree/) and processed with the packages "ape"  (Paradis & Schliep, 2018)  and "ggtree"  (Yu & al. , 2017)   Core Team, 2019). Pearson correlation analysis for the relation between specimen age and the number of loci recovered in the targeted enrichment experiment was conducted using the R package "ggpubr".
With the "sample-specific" strategy, the number of recovered loci was much higher and more evenly distributed: 227 in Ochneae (n = 199), 222 in Sauvagesieae (n = 47), 227 in Luxemburgieae plus Testulea (n = 14) and 204 in Quiinoideae plus Medusagyne (n = 4) (Table  1 ). Although the number of loci retrieved with the samplespecific approach was comparatively high and evenly distributed across all clades, the number of loci available for the phylogenetic analysis with the FAM dataset after the filtering steps was low, mainly due to excessive missing data (incl. fragmentary coverage) and poor alignment. The characteristics of each dataset used for phylogenetic inference are summarized in Table  2. Phylogeny of Ochnaceae. -The phylogenetic relationships reported below for each of the major clades refer to their corresponding dataset (e.
g. , FAM for Quiinoideae; SLT for Sauvagesieae). Only in the case of conflicting topologies with reference to the phylogenetic backbone of Ochnaceae, we additionally report from the FAM dataset. The concatenation (CC) and multispecies coalescent (MSC) trees retrieved a clade of Medusagyne and Quiinoideae with bootstrap support (BS) of 87% and LPP of 0. 99, respectively, which is sister to Ochnoideae (BS 100%, LPP 0. 6; Figs. 1, 2A, suppl. Figs. S1, S2). Within Ochnoideae, Testulea came out as sister to the rest of Ochnoideae (BS 100%). In both analyses, Luxemburgieae is sister to Sauvagesieae and Ochneae (BS 100%, LPP 1; Fig. 2B , suppl. Figs. S1, S2), the last two forming a clade that received strong support in the FAM analysis but weak support in the SLT analysis (Fig. 2B , suppl. Figs. S1, S2 ).
Nonetheless, the number of loci remaining for phylogenetic analysis in the FAM dataset comprised only 55. 4% of those available for the analysis of Ochneae, mainly because of filtering based on excessive missing and fragmentary data. Further improvements were achieved by generating taxonomically more homogeneous smaller datasets (e. g. , LEO, SLT) that showed some advantages over the large FAM set in our study. Besides the ability to individually apply different remapping strategies (e. g. , consensus-alignment versus sample-specific), the sequence data were also more homogeneous in terms of the presence of loci in the multiple sequence alignments, which in part is certainly due to the lower bias arising from phylogenetic distance. Hence, with less filtering, more sequence data became available, which is especially relevant for taxa with a comparatively low proportion of recovered loci.
This increase in data may help to produce more robust phylogenies and improve the phylogenetic resolution of some recalcitrant clades, as was evident in Sauvagesieae (see below). Another strategy to increase the success of gene capture in the more divergent taxa is to use a relaxed stringency by reducing the hybridisation temperature during hybrid enrichment  (Li & al. , 2013) , an approach that we followed for some Sauvagesieae, Luxemburgieae and Quiinoideae in the present study. Other factors that play a significant role in reducing the enrichment efficiency and that lead to an increase in missing data include issues arising from DNA library preparation such    as the amount of sample pooling, which may imply a bias in hybridisation and library amplification, the starting DNA quantity and quality, and the library complexity  (Hosner & al.
Therefore, the inclusion of one or two additional genomic resources from among the other major clades of Ochnaceae (e. g. , Quiinoideae, Sauvagesieae) most likely would have improved the performance of the targeted enrichment for the non-Ochneae clades. Multi-species coalescent versus concatenation. -A low number of genes, including ones with partially missing data as in our FAM and SLT data, may result in wrong phylogenetic inferences  (Hosner & al. , 2015; Vachaspati & Warnow, 2015; Sayyari & Mirarab, 2016; Sayyari & al. , 2017). This is why we focussed our comparison of MSC and CC topologies on the more data-rich LEO set, for which we had chosen a strategy of employing a nearly complete dataset (i. e. , almost 100% taxa and 100% loci) for both approaches. The CC approach may also lead to erroneous phylogenetic inferences as in cases of high incomplete lineage sorting, when incorrect topologies may receive moderate to strong support  (Mirarab & al.
, 2014; Molloy & Warnow, 2018). However, with a low number of genes, the CC approach might perform better  (Bayzid & Warnow, 2013). Issues in CC analysis arising from low taxon sampling  (Roure & al. , 2012; Streicher & al. , 2016)  are expected to be less relevant in our study due to covering about 50% of all Ochnaceae species. Although both the MSC and the CC methods have their disadvantages that may lead to contrasting phylogenies, there was a remarkable congruence between the trees obtained by both methods for the LEO data (for the FAM data, both methods also recovered similar topologies but generally with lower support or resolution, showing a high level of gene tree conflict that most likely arose from the much lower number of loci and the higher amount of missing data; see suppl. Figs.
However, the poor phylogenetic resolution in Luxemburgia in the present study precludes the evaluation of this phylogenetic hypothesis. The poor resolution, even with the increased molecular data of this study, is most likely the result of a recent and rapid diversification, a scenario that is supported by divergence time estimation  (Schneider & al. , 2017). Sauvagesieae. -Sauvagesieae is a blend of neotropical and palaeotropical taxa and was recovered as sister to Ochneae. This sister-group relationship received maximum support in the FAM analysis but much weaker support in the SLT analysis, which may be due to the relatively high amount of missing data of the two representative species of Ochneae in the latter (see Methods). In  Schneider & al. (2014) , the neotropical Blastemanthus came out as sister to the remaining Sauvagesieae.
■ CONCLUSION Our targeted enrichment approach provides by far the most comprehensive molecular dataset to date for the reconstruction of the phylogenetic relationships of the pantropical Ochnaceae, particularly of the most species-rich tribe Ochneae. In line with similar target-enrichment studies, the increase in data led to an overall increase in phylogenetic resolution, although a few relationships along the backbone still lack strong support, which in part may be due to the reduced number of loci available for the phylogenetic analysis from taxa not pertaining to Ochneae. As a way to mitigate biases in enrichment efficiency that arise from sequence divergence with reference to the genome used for bait design, we encourage exploring the power of different remapping strategies for increasing the recovery of loci across taxonomically more homogeneous data subsets that in this study proved helpful.
Willaertia magna C2c maky is a thermophilic free-living amoeba strain that showed ability to eliminate Legionella pneumophila, a pathogenic bacterium living in the aquatic environment. The amoeba industry has proposed the use of Willaertia magna as a natural biocide to control L. pneumophila proliferation in cooling towers. Here, transcriptomic and proteomic studies were carried out in order to expand knowledge on W. magna produced in a bioreactor. Illumina RNA-seq generated 217 million raw reads. A total of 8790 transcripts were identified, of which 6179 and 5341 were assigned a function through comparisons with National Center of Biotechnology Information (NCBI) reference sequence and the Clusters of Orthologous Groups of proteins (COG) databases, respectively. To corroborate these transcriptomic data, we analyzed the W.
Transcriptomic Analysis of W. Magna C2c Maky The quality assessment of raw reads was checked using FastQC software (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) for reads with low quality (Q < 28), and the adaptors were removed using Trimmomatic  [39]. The identification of transcripts was performed as in the study conducted by Aherfi et al. [40]. Briefly, reads were mapped on the W. magna C2c maky assembled genome using Hisat2 with default parameters  [41]. The expression of genes was quantified using Htseq  [42] , a software that processes data from high-throughput sequencing assays. This software identifies and counts the number of reads mapped to each gene. Predicted open reading frames (ORF) with a coverage of six reads or more were considered as transcribed  [40]. This threshold was determined in order to obtain transcripts with high coverages (61 reads/ORFs).
Functional Annotation To assign the biological functions of W. magna transcripts, we proceeded to homology searches for the transcripts in public protein databases. Predicted protein sequences were used as queries against the nr database using an e-value threshold of less than 1e-03. Protein sequences were aligned to the Cluster Orthologous Groups of proteins (COG) database using EggNOG  [43, 44]  with diamond as mapping mode. To improve understanding of the biological functions and metabolic pathways of the genes, the sequences were mapped on Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG)  [45]  using BLASTp with an e-value cutoff of 1e-3. Conserved protein domains were identified by mapping of proteins against the conserved domain database  [46]  and InterProScan database  [47]. The nonribosomal peptide synthase and polyketide synthase domains were predicted using 2metDB  [48]  and NaPDoS  [49]  software.
To predict the localization of proteins in the cell, we used TargetP 2. 0 and MitoP2  [50, 51]. To perform an analysis of genes related to anaerobic metabolism, we performed a BLASTp search against a non-redundant protein sequences database (nr), with an e-value cutoff of 1e-03. Alignment of protein sequences was carried out using MUSCLE  [52]. Phylogenetic construction was obtained using the maximum likelihood method with the Jones-Taylor-Thornton (JTT) model on MEGA 7. 0. 25 software  [53]. Phylogenetic trees were analyzed using iTOL v3 online  [54].
The oven temperature was kept constant at 40 • C. The sample was analysed with the TOP20 HCD method: MS data were acquired in a data dependent strategy selecting the fragmentation events based on the 20 most abundant precursor ions in the survey scan (350-1600 Th). The resolution of the survey scan was 60,000 at m/z 200 Th. The Ion Target Value for the survey scans in the Orbitrap and the MS 2 mode were set to 3E6 and 1E5 respectively and the maximum injection time was set to 60 ms for both scan modes. Parameters for acquiring HCD MS/MS spectra were as follows: collision energy = 27 and an isolation width of 2 m/z. The precursors with unknown charge state or a charge state of 1 were excluded. Peptides selected for MS/MS acquisition were then placed on an exclusion list for 20 s using the dynamic exclusion mode to limit duplicate spectra.
Data Analysis The data were converted to mgf format using RawConverter  [55]. For each sample, the 8 fractions were merged into a single dataset and the resulting peak lists were searched against the protein sequences of W. magna C2c maky using Peaks Studio software (Bioinformatics Solutions Inc, Waterloo, Canada)  [56]. The search was performed using the following settings, based on the W. magna genome database (18,519 sequences): peptide mass error tolerance, 25 ppm; fragment mass error tolerance, 0. 1 Da; monoisotopic mass values, one missed cleavage, no non-specific cleavage, fixed modifications of carbamidomethylation; variable modifications of oxidation (M), deamidation (NQ), carbamylation and oxidation or hydroxylation; 4 maximum variable post-translational modifications (PTMs) per peptide.