dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we sequenced 17 representative complete plastomes of the genus Mahonia, and used 13 plastomes from GenBank to conduct comparative and phylogenetic analyses. Morphological and micromorphological traits of different species of Mahonia were recorded. We combined the evidence from the molecular and morphological data to resolve the phylogenetic relationships in Mahonia. Our goals are to 1) reconstruct phylogenetic relationships within Mahonia using nuclear internal transcribed spacer (ITS) and plastid genome sequences; 2) describe and interpret the plastome structure and evolution of Mahonia; 3) explore an integrative method for better distinguishing among Mahonia species.
Comparative plastid genome analyses Using an annotated plastid genome (Mahonia bealei RC601 [MZ158266]) as reference, we plotted two graphs for the overall sequence identity of ten Mahonia species and their outgroups using the program mVISTA (Figs. 3, S1 ). The results reveal that there are only slight variations within Mahonia plastid genomes. These variations are usually observed in the intergenic spacers (IGS) instead of coding-regions, which implies that coding regions are more conserved than non-coding regions (Fig. 3 ). The whole plastid genome of M. bealei RC601 [MZ158266] is also compared with those of Berberis aristata DC. , Ranzania japonica (T. Itô ex Maxim. ) T. Itô, Gymnospermium kiangnanense (P. L. Chiu) Loconte, Leontice armeniaca Boivin, Caulophyllum robustum Maxim. and Nandina domestica Thunb.
Phylogenetic analyses In this study, nine alignment matrices were used to perform phylogenetic analyses using Bayesian inference (BI) and Maximum Likelihood (ML) method. The matrices consist of 20 ingroup accessions (Mahonia) and ten outgroup accessions. Notably, the ITS gene matrix includes only 28 samples, as the ITS sequences of Leontice armeniaca and Ranzania japonica are not available. The genus Mahonia is resolved as monophyletic and is sister to Berberis with strong support (bootstrap support (BS) ≥ 99%, posterior probabilities (PP) ≥ 0. 99) across almost all trees (Figs. 7, S4, S5, S6). In the tree built using the complete plastid genome datasets, about 75 percent of the nodes are well supported (BS/PP = 99%/0. 99). The phylogenetic trees exhibit that Mahonia species are grouped into four subclades.
Subclade IV is sister to subclade III, and together form a clade that is sister to subclade II (Fig. 7A ). To test the conflicting signals between plastomes and ITS sequences, both BI tree and ML tree based on ITS datasets were generated and together compared with the trees based on plastomes (Fig. 7B ). As shown in Fig. 7B , the genus Mahonia is also recovered as a clade with moderate support (BS/PP = 0. 96/85). Subclades I and II in the plastome tree are completely congruent with subclades A and B in the ITS tree. The tree topologies outside subclades I and II are incongruent. Indeed, the tree based on ITS sequences possessed the highest number of polytomies and could not provide any valuable information to resolve the infrageneric relationships. However, given that support values at internal nodes are much higher than the external, we can properly cluster the several subclades into subclades III and IV (in the plastome tree).
Subclade III is largely identical to subclade G. Subclade IV gathers the remaining clades (C, D, F and H). Notably, the positions of Mahonia fortunei Chung 3342 [KX549421], M. gracilipes (Oliv. ) Fedde RC606 [MZ158272] + M. nitens (subclade E) in the ITS tree are severely in conflict with the plastome tree. It is noteworthy that the tree topology based on eight concatenated hypervariable regions is mainly identical to the whole plastid genome tree. Whereas the phylogenetic relationships within subclade III and IV based on whole plastid genomes (Fig. 7A ) are not fully consistent with the topology from the hotspots (Fig. 7C ). The trees based on concatenated rbcL, matK, and trnH-psbA, have the lowest phylogenetic resolution (Fig. 7D ). Furthermore, none of the phylogenetic reconstructions based on the concatenated rbcL, matK, and trnH-psbA datasets provides any evidence for the monophyly of Mahonia (Fig.
They inferred that a large IR expansion is the main cause of the significant increase in genome size in Mahonia bealei  [20]. A similar 10 kb IR expansion has been described in Ranzania japonica  [45]  (GenBank ID: MG234280), although this result was not supported in other studies  [46]. The plastome structure of Ranzania japonica is controversial and deserves further investigation. We present here the first comprehensive plastome analysis in Mahonia and show that there is a large IR expansion in the plastid genome of all species we investigated. Further intergeneric comparative plastome analyses have attested that a large-scale IR expansion was present in Berberideae  [19, 46]. Although sequences in IR regions are commonly well conserved in comparison with single-copy (SC) regions, the IR-SC junctions are relatively variable.
Hsieh et al. [2]  used 93 plastomes representing all 19 genera of Berberidaceae to resolve the long-standing disputable taxonomic issues of Berberidaceae. They also paid attention to the phylogeny and plastome structure of the tribe Berberideae, corroborating the considerable topological discordance between nrDNA and plastomes. Our phylogenomic analysis of the genus Mahonia based on more representative taxon sampling than previous studies, provides valuable genetic resources and improves our understanding of the relationships among phylogenetically challenging groups. Determining the discordance between the topologies generated based on plastome and nuclear DNA has profound significance for clarifying the evolutionary events and evaluating the current phylogenetic frameworks generated by plastome datasets  [51, 52].
9C1, D1, S7D 1 , E 1 , S8A 1 ). The leaflets of the two species M. gracilipes and M. nitens in subclade E show highly similar shapes, which are distinctive from the leaf shapes of all species we investigated (Figs. S8E 1 , F 1  ). These results indicate that nuclear datasets may have broader implications for morphological character evolution, hybridization and/or incomplete lineage sorting. Standard DNA barcodes have been shown to often lack sufficient variable characters and thus often fail to discriminate species among many lineages  [50, [54] [55] [56]. Chen et al. [8]  added two hypervariable plastid genes (accD and ndhF) and combined them with ITS, rbcL, matK, trnH-psbA. The combined ITS and plastid DNA dataset was used to conduct phylogenetic analyses, revealing that this kind of dataset could significantly improve the intergeneric resolution but had rarely power to address the interspecific phylogenetic relationships.
Given the efficiency and objectivity of molecular data, standard DNA barcodes are used as essential elements for discriminating plants  [56]. However, standard DNA barcodes (ITS, concatenated rbcL, matK, and trnH-psbA) could not be used to distinguish the species in genus of Mahonia, due to the limited diagnostic information. In the era of NGS technology, an increasing number of research groups can afford the cost of whole plastid genome sequencing and then employ the data sets to resolve challenging phylogenetic relationships  [55, 62, 69, 70]. In this context, we generated a robust phylogenetic framework for the species of Mahonia using plastome datasets. Complete plastid genomes encompass adequate sequence variations for detailed identification, but their sequencing encounters some problems: high sequencing cost, huge-scale datasets and sophisticated computational process.
Conclusion Based on the integration of molecular data from hypervariable regions and epidermal characters of leaflets, we can distinguish all the species we investigated within the genus Mahonia. Although our sampling for next-generation sequencing is not extensive enough to delimit species boundaries clearly, our results shed a light on the taxonomic, phylogenetic, and evolutionary analysis of the genus Mahonia. For further investigations, on the basis of a more comprehensive sampling, we propose an integrative method based on special barcodes and broader macroscopical evidence (e. g. , morphological, micromorphological, anatomical and even cytological characteristics) to distinguish closely related species. Furthermore, genetically variable hotspots could be developed as clade-specific barcodes for efficient and rapid species identification especially in medicinal plants.
For this study, complete plastomes were obtained from 17 species of Mahonia. In addition, we obtained from GenBank plastomes of three Mahonia species (M. eurybracteata subsp. ganpinensis (H. Lév. ) Fedde, GenBank ID: MN417307, M. fortunei, GenBank ID: NC_042167, and M. bealei, Gen-Bank ID: MH795308) and plastomes for ten outgroup species to conduct subsequent comparative and phylogenetic analyses (Table  3 ). Total genomic DNAs were extracted from dried leaves using Cetyltrimethylammonium Bromide (CTAB) method  [72]  and sequenced using the Illumina Miseq platform (Illumina, San Diego, California, USA) at the Beijing Genomics Institute (BGI). Paired-end sequence reads have been trimmed to remove low-quality reads and adapter sequences using Trimmomatic v0. 40  [73].
Plastome assembly, annotation and visualization We obtained approximately 2 GB high-quality data for each sample. The quality-filtered reads were then subjected to de novo assembling with GetOrganelle  [74]  or NOVOPlasty v4. 3  [75] , using Mahonia oiwakensis (Gen-Bank ID: MN735221) as a reference for assembly. The 17 newly assembled plastome sequences were deposited in GenBank. We also submitted all the raw sequence data to GenBank and obtained SRA accessions (Table  3 ). We annotated the 17 assembled plastomes with default parameters using Plastid Genome Annotator  [76]  (PGA) and inspected the accuracy of annotations with the annotation results from GeSeq  [77]. On the basis of the results from PGA, we corrected the errors using Geneious v11. 0. 2  [78]. We checked the annotations of tRNA using tRNAscan-SE v2.
Comparative genomic analysis Plastomes of ten Mahonia species were selected for further comparative genomics analyses and also repeated sequence identification. The ten species were representative of the different clades of Mahonia. We aligned the ten representative plastomes of Mahonia using MAFFT v7. 450  [81]  and adjusted the boundaries in Geneious. Following the same procedure, we aligned seven plastomes from different genera of Berberidaceae for further comparison of intergeneric sequence identity (see the details from section Results). Then, the two aligned matrices were visualized using online mVISTA program  [82]  under Shuffle-LAGAN mode with default options for other parameters. In both cases, the reference sequence was M. bealei RC601 [MZ158266]. Gene rearrangement events in Mahonia were detected using Mauve v2.
Phylogenetic analyses Phylogenetic analyses were made based on 17 newly sequenced complete plastomes of Mahonia and 13 already published plastid genomes (three species from Mahonia and ten species from Berberidaceae and Ranunculaceae for outgroup species). We aligned the plastid genomes and ITS sequences using MAFFT. Phylogenies were reconstructed based on the following datasets: (1) complete plastid genomes; (2) large-single-copy (LSC) region; (3) small-single-copy (SSC) region; (4) one inverted repeat (IR); (5) coding sequences; (6) non-coding sequences; (7) ITS; (8) concatenated sequences of matK, rbcL, and trnH-psbA; and (9) concatenated sequences of eight identified hypervariable regions. We applied the Maximum Likelihood (ML) method and Bayesian inference (BI) for each of the nine datasets to reconstruct phylogenetic trees, respectively.
Acknowledgements We thank  Fu-zhen Guo ,  Min-rong Luo, Ying-cui Yu, Guo-yun Zhang, Ning-juan Fan and Xiao-hua He in Northwest A&F University  for assistance with the SEM and LM. We thank  Dr. Zhi-rong Zhang  from  Molecular Biology Experiment Center ,  Germplasm Bank  of  Wild Species in Southwest China  for help with the analysis.
Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary materials. All Illumina data have been deposited in NCBI's Sequence Read Archive (SRA). Raw sequence reads are available on NCBI in the BioProject PRJNA727409 and PRJNA727753 (SAMN19020769-SAMN19020785). These sequence data have been submitted to the GenBank databases under accession number MZ086770, MZ158265-MZ158280.
On 26 November 2020, the first SARS-CoV-2 genomic sequences from Burkina Faso were submitted to the global initiative on sharing avian influenza data (GISAID) database  [10]. In this study, we provide a genomic epidemiology study of SARS-CoV-2 from the region of western Burkina Faso by analyzing 372 viral genomes obtained from isolates collected between 5 May 2020 and 31 January 2022. We carried out phylogenetic analyses to infer the geographic sources and timing of virus introductions in Burkina Faso.
Ethical Approval This study was carried out by collecting data from patient records as part of the COVID-19 surveillance. Patients data was anonymized before analysis an only the laboratory number was mentioned to ensure confidentiality. The results were shared with the Ministry of Health, Public Hygiene, and Welfare as part of the routine surveillance of SARS-CoV-2 variants. Data collection was performed in the frame of studies conducted by ANDEMIA, which adheres to the tenets of the Declaration of Helsinki, as well as any national legislation and ethical standards. The study protocol was therefore approved by the Burkina Faso national ethics committee (Comité d'Ethique pour la Recherche en Santé (2017-5-057)).
Phylogenetic and Phylogeographic Analyses Genomic sequences were assigned to phylogenetic clades using NextClade (version 0. 13. 0) and to Pango lineages using the pangolin toolkit (version 2. 3. 2) with pangoLEARN (version v10 February 2021). To infer the geographic sources of SARS-CoV-2 introduction in western Burkina Faso, the SARS-CoV-2 genomic sequences from worldwide that were closest to those from our study were introduced in the analysis. As the number of available SARS-CoV-2 genomic sequences is exceptionally high  (8, 209, 303  complete genomic sequences on GISAID on 9 March 2022), a computational rationale approach was followed to subsample the complete global dataset available on GISAID. Specifically, curated Nextstrain continent-representative datasets were downloaded from the GISAID database (9 March 2022) for all six continents (2620 to 3937 genomes per continent).
Moreover, all the global sequences of Pango lineages identified in western Burkina Faso SARS-CoV-2 genomes were included after carrying out a lineage-based search of genomic sequences in the GISAID database. Only complete SARS-CoV-2 genomes (defined by a length > 29,500 nucleotides) and those with complete information on the collection date were downloaded. Burkina Faso sequences from our study and the downloaded global sequences were aligned against the reference Wuhan Hu-1 genome using the uvaialign function of the uvaia software v1 (Quadram Institute Bioscience, Norfolk, UK). Uvaia was also used to identify the closest sequences to the western Burkina Faso sequences in the global SARS-CoV-2 dataset; at most five global sequences for each Burkina Faso sequence were selected. When two or more global sequences from the same country were identical, only one was included in the analysis.
A Maximum Likelihood (ML) phylogenetic tree was generated for the dataset using IQTREE2 and a General Time-Reversible (GTR) nucleotide substitution model that took into account the inter-site heterogeneity through a discretized gamma distribution with four rate categories (GTR + Γ). This model was identified as the best fitting model for ML inference by jModelTest v2. 1. 10. Branch support was inferred using 1000 bootstrap replicates. The ML tree was analyzed with TempEst to identify the temporal signals of a dataset and possible outlier sequences (e. g. , sample contamination, data annotation errors, sequencing and alignment errors, or assembling issues). The GTR + Γ substitution model was then used to derive a dated SARS-CoV-2 phylogeny using the Bayesian Markov Chain Monte Carlo (MCMC) approach implemented in BEAST v1.
A total of 313 SARS-CoV-2 genomes could be classified in 27 Pango lineages whereas 59 remained unclassified. A Pango lineage is defined by the carriage of specific SNPs, the introduction and circulation in a new region, and the constitution of a well-supported monophyletic group. Many genomes clustered together, although they were classified as distinct Pango lineages (Figure  S1b ). The GISAID quality standards consider that 50% of confident calls is sufficient and is the minimum amount of data to be phylogenetically useful. However, this threshold probably allows the inclusion of many genomes without enough specific SNPs for accurate Pango classification, leading to a high proportion of misclassified genomes. According to the authors at the origin of the Pango nomenclature, sequences with more than 30% of missing data should not be used for lineage classification  [14].
Yet, many mismatches between lineage classification (Pango) and phylogeny persisted even after using this threshold (data not shown). Therefore, a conservative subsampling of our dataset was carried out by only including genomes with more than 90% of confident calls for all subsequent analyses. This curated dataset included 188 genomes belonging to 19 Pango lineages and one unclassified genome. This curation step resulted in a significant improvement of the phylogenetic support of Pango lineage classification A total of 313 SARS-CoV-2 genomes could be classified in 27 Pango lineages whereas 59 remained unclassified. A Pango lineage is defined by the carriage of specific SNPs, the introduction and circulation in a new region, and the constitution of a well-supported monophyletic group. Many genomes clustered together, although they were classified as distinct Pango lineages (Figure  S1b ).
The GISAID quality standards consider that 50% of confident calls is sufficient and is the minimum amount of data to be phylogenetically useful. However, this threshold probably allows the inclusion of many genomes without enough specific SNPs for accurate Pango classification, leading to a high proportion of misclassified genomes. According to the authors at the origin of the Pango nomenclature, sequences with more than 30% of missing data should not be used for lineage classification  [14]. Yet, many mismatches between lineage classification (Pango) and phylogeny persisted even after using this threshold (data not shown). Therefore, a conservative subsampling of our dataset was carried out by only including genomes with more than 90% of confident calls for all subsequent analyses. This curated dataset included 188 genomes belonging to 19 Pango lineages and one unclassified genome.
Geographic Sources of Virus Introduction into Western Burkina Faso The 188 high-quality genomic sequences collected in western Burkina Faso were then analyzed in the context of the global SARS-CoV-2 genetic diversity. By using our subsampling approach, 237 global sequences available in GISAID were selected according to quality and genetic closeness criteria. The regression of root-to-tip genetic distance against sampling dates revealed two outlier sequences that were excluded from the dataset. The curated dataset (n = 423) included 187 sequences obtained in western Burkina Faso (this study) and 236 global sequences and exhibited a strong temporal signal (r 2 = 0. 88) (Figure  S3 ). The ML phylogeographic analyses estimated the occurrence of at least 44 independent SARS-CoV-2 introductions into western Burkina Faso (Figure  S4 ).
Discussion Since the publication of the first SARS-CoV-2 genome, scientists around the world have quickly understood the crucial need to monitor the evolution of this rapidly spreading novel virus. In western Burkina Faso, viral genome sequencing was conducted between 12 May 2020 and 31 January 2022 in the framework of the SARS-CoV-2 surveillance efforts carried out in this region. In this retrospective study, we took advantage of these sequencing data to describe patterns of SARS-CoV-2 introduction and spread in this region, and to evaluate the efficiency of COVID-19 control policies implemented in the country. The aim of the first COVID-19 control policies decided by the Burkina Faso government was to prevent new SARS-CoV-2 introductions from abroad and to disrupt transmission chains inside the country.
Acknowledgments: The contribution of all involved partners is acknowledged: (i) ANDEMIA, COVADIS,  FASO-COVID, Partnership in Postgraduate Education  (PPE) and  Global Health Protection Program (GHPP) , (ii) the partners involved in the frequent sample collection, logistics and laboratory analysis and (iii) the different study participants and their communities for their support to the field work. We thank  Jessica Samba ,  Andreas Sachse ,  Manneh Jarra ,  Sona Jabang ,  Arnaud Zougmore ,  Maxime Damolga ,  Laetitia Some ,  Korotime Traore ,  Abdoul Aziz Belem  and  Emmanuel Zongo  for technical assistance. The authors acknowledge the ISO 9001 certified IRD itrop HPC (member of the  South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper.
Funding: This work was supported by the  Souro Sanou University Hospital Center through , the following projects: (i) " COVADIS " project ( COVID-19 Epidemic in West Africa: Infection Dynamics and Diagnostic Approaches ) of the EDCTP2 program supported by the  European Union  (grant number  RIA2020EF-3012 ), (ii) " ANDEMIA" Project (African Network for improved Diagnostics, Epidemiology and Management of Common Infectious Agents ) funded by the  German Ministry of Education and Research  (grant number  01KA1606 ), and (iii) the Solidarity Fund for Innovative Projects, FSPI:.
Here we quantify acceleration and thinning in the deeper section of the NEGIS (>100 km inland) and show that these measurements are crucial for future simulations because they can better constrain the basal conditions. Over the past decade, the NEGIS has been rapidly speeding up, but the uncertainty of the flow speeds obtained from satellite interferometry has hampered the detection of ice flow acceleration more than 100 km upstream of the terminus  10, 11, 25, 26. To detect how far the ice flow acceleration has propagated upstream, we use GNSS data  12, 27, 28  and recent improved Sentinel-1 ice velocity products  29. We use GNSS data from three stations located between 90 and 190 km inland; these stations recorded data from 2016 to 2019. The GNSS receivers were installed on the glacier in a configuration that followed the centre of the main trunk.
Observations of change from 2007 to 2022 To estimate the ice flow speed, we estimate the positions of the GNSS sites using the GIPSY-OASIS software package  30. We calculate the position of the GNSS receivers at 15-s intervals and derive mean daily velocities for each site by fitting a trend to the position estimates using east, north and up coordinates (Extended Data Fig. 2  and Methods). GNSS time series have error sources that produce temporal correlations. To take this temporally correlated (non-Gaussian) noise into account, we use the daily solutions for each station and estimate the monthly mean ice speed and associated standard deviation (Fig. 2a-c ). We correct for changes in speed related to the GNSS station moving downhill. All GNSS stations show an acceleration in the surface speed, suggesting a propagation of the flow acceleration more than 190 km inland.