dataesr/scientific-paragraphs-categorization huggingface dataset:
The final local differentiation score was normalised to ensure that the range of possible scores was between 0 and 1, local differentiation score was defined as • Study information: Details of the 49 contributing partner studies, including description, contact information and key people. • Sample provenance and sequencing metadata: sample information including partner study information, location and year of collection, ENA accession numbers, and QC information for 7,113 samples from 28 countries. • Measure of complexity of infections: characterisation of within-host diversity (FWS) for 5,970 QC pass samples. • Drug resistance marker genotypes: genotypes at known markers of drug resistance for 7,113 samples, containing amino acid and copy number genotypes at six loci: crt, dhfr, dhps, mdr1, kelch13, plasmepsin 2-3.
○ I guess that all samples were collected from symptomatic patients seen at health facilities level? Unfortunately, this makes that data presented capture only P. falciparum populations infected this population. With the rise of new technologies, I am wondering whether the MalariaGEN consortium could investigate samples collected from asymptomatic individuals and explore the genomic profiles of this hidden reservoir but representing the major parasite biomass? ○ I am aware that the authors have performed a difficult and complex exercise by providing high quality genomic data and comprehensive description of their data for a large audience. The major challenge that is not addressed in the manuscript is how these important data can be translated into concrete actions in the field by health providers.
○ Last comment regarding the database. It will be helpful to provide for each sample/genome sequence, the location (country) and the date of collection. Asymptomatic infections are indeed an incredibly significant reservoir that needs to be explicitly considered to achieve a complete and accurate picture of the transmission landscape. The development of new technologies has begun to dig deeper and deeper in this area and initial results seem to be very encouraging that good quality data can indeed be obtained from asymptomatic and/or low parasitemia subjects. MalariaGEN would certainly be supportive of this kind of effort and we have indeed active collaborations with partners exploring these questions. To the best of our knowledge, though, some of these methodologies are still of limited sensitivity and in part experimental and will require further work in order to be deployed on the large scale required by this scientific question, but that is certainly an area for future investigation.
2. 5) Last comment regarding the database. It will be helpful to provide for each sample/genome sequence, the location (country) and the date of collection. This information is included in the "Sample provenance and sequencing metadata" file available at the resource page https://www. malariagen. net/resource/26 Competing Interests: No competing interests were disclosed. The analysis of whole-genome sequences obtained from Plasmodium falciparum is particularly challenging due to the presence of hypervariable regions, highly repetitive sequences, and frequent mixture of parasites due to multiple infections of the host. The authors of this study describe a curated list of over three million high-confidence polymorphisms obtained from the genome sequence analysis of more than 7000 samples of P.
The decision of primarily utilising the VCF format comes from the recognition that these files are the standard de facto in the genomic community, which in turn has developed a large ecosystem of tools to handle them: please see the README at ftp://ngs. sanger. ac. uk/production/malaria/pfcommunityproject/Pf6/Pf_6_README_20191010. txt for some examples, e. g. to subset the data. However we agree this might still be limiting for some use cases and we are working towards a more integrated solution. As an example of our direction of travel, please see https://malariagen. github. io/vector-data/landing-page. html, which presents some simplified data access workflows for the MalariaGEN Anopheles gambiae 1000 Genomes Project.
Acknowledgements This study was conducted by the  MalariaGEN Plasmodium falciparum Community  Project, and was made possible by clinical parasite samples contributed by partner studies, whose investigators are represented in the author list and in the associated data release (https://www. malariagen. net/resource/26). This research was supported in part by the  Intramural Research Programme  of the  NIH ,  NIAID. In addition, the authors would like to thank the following individuals who contributed to partner studies, making this study possible:  Dr Eugene Laman  for work in sample collection in the Republic of Guinea;  Dr Abderahmane Tandia  and  Dr Yacine Deh  and  Dr Samuel Assefa  for work in sample collection in Mauritania;  Dr Ibrahim Sanogo  for work in sample collection in Mali;  Dr James Abugri  and  Dr Nicholas Amoako  for work coordinating sample collection in Ghana.
Grant information: The sequencing, analysis, informatics and management of the  Community  Project are supported by  Wellcome through Sanger Institute core funding  ( 098051 ), a  Strategic Award  ( 090770/Z/09/Z ) and the  Wellcome Centre for Human Genetics  core funding ( 203141/Z/16/Z ), by the  MRC Centre for Genomics and Global Health  which is jointly funded by the  Medical Research Council  and the  Department for International Development (DFID)  ( G0600718 ;  M006212 ), and by the  Bill & Melinda Gates Foundation  ( OPP1204628 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the.
By compiling data on all samples collected from 2002-2015, they present clear profiles of drug resistance by regional sub-populations for the most used antimalarial drugs. Finally, they reveal a global landscape regarding a major challenge for malaria elimination, that are deletions in hrp2 and 3 genes linked with false negative results of HRP2-based malaria RDT. Written in a very clear way, it must be point out that the authors have made huge efforts so that these data are understandable for a general audience, especially for the non-experts in genomics or for policy makers in malaria endemic countries. Their data effectively depict the main challenges currently encountered in the fight against malaria: the monitoring of the strategies deployed by the assessment of the impact on P. falciparum parasite populations, the geographical evolution of antimalarial drug resistances and the effectiveness of diagnostic tools used in malaria Author Response 06 Jul 2021 Richard Pearson, Wellcome Sanger Institute, Hinxton, UK We thank the reviewers for the extremely positive and supportive feedback.
2. 1) Sample collection -P. falciparum samples investigated are not from systematic sampling collections dedicated to this study but rather from multiple studies conducted by groups with different objectives and from heterogeneous populations (patients living in malaria endemic areas, travelers, etc. ). I think this issue should be discussed in the manuscript. Thanks for raising this point. On one hand, the heterogeneity of sampling approaches offers a unique opportunity to investigate questions in a variety of epidemiological settings in a systematic way. Specifics of each study are provided in ftp://ngs. sanger. ac. uk/production/malaria/pfcommunityproject/Pf6/Pf_6_partner_studies. pdf and users of the resource can contact individual investigators for further details. At the same time, we agree that this can also act as a confounder in some analysis, which is why we've devoted significant time to the curation of the dataset to make it "analysis ready".
2. 2) Likewise, the long time period covering the samples collection (2002-2015) is also a major bias which can alter the final results. This is an important point in particular for interpreting drug resistance results, and one we explicitly bring out in the paragraph: "Note that samples were collected over a relatively long time period (2002-15) during which there were major changes in global patterns of drug resistance, and that the sampling locations represented in a given year depended on which partner studies were operative at the time. To alleviate this problem, we have also divided the data into samples collected before and after 2011 (Supplementary Data;.
Reviewer Expertise: Molecular epidemiology, antimalarial drug resistance We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Author Response 06 Jul 2021 Richard Pearson, Wellcome Sanger Institute, Hinxton, UK We thank the reviewers for the extremely positive and supportive feedback. In their comments and suggestions both reviewers have well captured the spirit of this data resource and of the large collaborative network behind it. We are pleased to submit detailed responses and a revised version of the manuscript that addresses their comments.
1. 1) Increasing the accessibility of the table listing polymorphisms in supplementary data. The authors do provide the data in VCF and zarr files, which are not very user friendly nor allow a fast search of a specific polymorphism. We understand that developing a web interface for this purpose would be a challenge beyond this research article but possibly exporting the VCF file data into tables that could be available in online repositories. We thank the reviewer for this important feedback on how to increase the reach of this resource. Since the publication of this article, we have been working on an initial web interface that allows users to navigate some aspects of the data: please see https://www. malariagen. net/apps/pf6. The current version mainly focuses on epidemiologically relevant data and emphasises the community behind the project and at the moment doesn't provide access to the genomic variation information, which will require further work.
Of course accessibility is a relative criteria and as such it requires balancing out different priorities. In the past we have provided tabular versions of the data ( www. malariagen. net/data) but the benefits have been very limited. For example, handling multiallelic and non-SNP variations requires somewhat arbitrary encoding decisions that significantly affect the simplicity and intuitiveness of the tabular format. Increasing the sample size has made these variations more common (e. g. in this release there are about 50% non-SNP variants and 50% multiallelic variants) to the point that there was no real ambiguous and we've replaced "complex rearrangements" with an explicit description of the event. Competing Interests: No competing interests were disclosed.
Data are available under the MalariaGEN terms of use for the Pf Community Project: https://www. malariagen. net/data/termsuse/p-falciparum-community-project-terms-use. Depending on the nature, format and content of the data, appropriate mechanisms have been utilised for data access, as detailed below. This project contains the following underlying data that are available as an online resource: www. malariagen. net/resource/26. Data are also available from Figshare. Figshare: Supplementary data to: An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples. https:// doi. org/10. 6084/m9. figshare. 13388603 92. 1 - ( ) N max N. An earlier version of this article can be found on bioRxiv (DOI: https://doi. org/10. 1101/824730). Data availability Underlying data.
Extended data This project contains the following underlying supplementary data available as a single document download: www. malari- agen. net/resource/26. Extended data are also available from Figshare. Figshare: Supplementary data to: An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples. https:// doi. org/10. 6084/m9. figshare. 13388603 92. 'File9_Pf_6_supplementary' contains the Supplementary Note, Supplementary Tables and Supplementary Figure: • Supplementary Note ○ Analysis of local differentiation score.
oxysporum f. sp. albedinis has been recently sequenced and assembled  (Khayi et al. 2020). To understand the mitogenomic background of F. oxysporum f. sp. albedinis, we present here, for the first time, the complete mitochondrial (MT) genome of FOA. The Fusarium oxysporum f. sp. albedinis strain Foa44, originally isolated in 1999, from an infected date palm in Tafilalt-Rissani, Morocco, and deposited in the Moroccan Coordinated Collections of Microorganisms (CCMM) (ccmm@cnrst. ma) and the Belgian Coordinated Collections of Microorganisms (BCCM) under the accession numbers MUCL 41814 and Foa44, respectively. Total genomic DNA of Foa44 strain was extracted from freeze-dried mycelium using the cetyltrimethylammonium bromide (CTAB) method  (M€ oller et al. 1992). A paired-end library was prepared using a Nextera DNA Flex library kit from total genomic DNA (0.
5 lg), following the manufacturer's protocol. The library was sequenced (2 Â 150 bp) on a NovaSeq 6000 platform (Illumina, San Diego, CA). Adapters and low-quality reads were removed using CLC Genomics Workbench V12. De novo assembly was performed using MaSurca V3. 4 with default parameters  (Zimin et al. 2013 ). The obtained assembly was subject to BLASTN searches against the reference mitochondrial genome sequence for Fusarium oxysporum f. sp. lycopersici (GenBank accession number CM010346). The BLASTN output identified a single homologous contig of 67,535 bp in length. This contained an $14 kbp flanking inverted repeat region, as determined by dot plot analysis in CLC genomics Workbench. The mitogenome sequence was circularized by fragmenting the contig in half and then assembling the two fragments using CLC Genomics Workbench.
The raw reads were remapped on the assembled sequence to correct any conflicts created during the assembly step. The final length of FOA mitogenome is 51,601 bp with 31% in G þ C content. The mitogenome of Foa44 was deposited at GenBank under accession number MW493386. The annotation was performed using GeSeq  (Tillich et al. 2017)  and MFannot pipelines (https://github. com/BFL-lab/ Mfannot). The resulting annotations were manually inspected and curated in comparison to published F. oxysporum mitogenomes. In total, 28 protein-coding genes, one ribosomal RNA (rRNA), and 26 transfer RNA (tRNA) genes were predicted. Among 28 predicted genes, 14 genes are commonly found in MT genomes including those implicated in ATP production (atp6, atp8, and atp9), oxidative phosphorylation (nad1-6 and nad4L), apocytochrome b (cob), and cytochrome C oxidase subunits (cox1-3).
The 14 remaining genes are of unknown functions except for four genes (orf529, orf304, orf292, and orf348) that are coding for Homing Endonucleases (GIY-YIG and LAGLIDADG) that are commonly found in fungal MT genomes  (Megarioti and Kouvelis 2020). To highlight the phylogenetic position of F. oxysporum f. sp. albedinis Foa44 within Fusarium oxysporum complex, a whole MT genome alignment of 16 Fusarium mitogenome sequences retrieved from Genbank, was performed with MAFTT software  (Katoh and Standley 2013). The Maximumlikelihood phylogenetic tree was constructed using MEGA7, under a Kimura 2-parameter model  (Kumar et al. 2016). The resulting phylogenetic tree shows that our mitogenome is clearly clustered with two other strains F. oxysporum f. sp. lycopersici 4287 (CM010346) and F. oxysporum F11 (NC017930) (Figure  1 ).
graminearum (NC_009493) as an outgroup). The tree with the highest log likelihood (À64,062. 86) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with a superior log-likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. All positions containing gaps and missing data were eliminated. There were a total of 27,505 positions in the final dataset. Evolutionary analyses were conducted in MEGA7  (Kumar et al. 2016 ).
Data availability statement The genome sequence data that support the findings of this study are openly available in GenBank of NCBI at https://www. ncbi. nlm. nih. gov/ under the accession no. MW493386. The associated ÃÃ BioProject ÃÃ , ÃÃ BioSample ÃÃ , and ÃÃ SRA ÃÃ numbers are PRJNA658960, SAMN15893572, and SRP313423, respectively.
Abstract-The future Surface Water and Ocean Topography (SWOT) satellite mission will provide images of surface water topography for inland water bodies and oceans. Over land, water surface elevation will be retrieved at 10 cm accuracy for water bodies with areas > 250 m x 250 m and rivers with widths > 100 m, when averaging over 1 km 2. Studies have shown that the Ka-band used by SWOT's main payload can be affected by aquatic and emergent riparian vegetation, which in turn could influence SWOT capacity to correctly observe water extent. The current study investigates effects of aquatic and emergent riparian vegetation on SWOT water extent and water surface elevation (WSE) detection capabilities through the use of NASA/JPL's SWOT simulator (HR). Data from the AirSWOT airborne campaign over Mamawi Lake (163 km 2 ) in the Peace-Athabasca Delta (PAD; Alberta, Canada), are used to establish a land cover classification and backscattering values for simulation inputs.
Yet, the main limitations of such instruments are their coarse time sampling and spatial coverage. To overcome the latter, the Surface Water and Ocean Topography (SWOT) mission is being developed by the National and Space Administration (NASA) of the United States, the Centre National d'Etudes Spatiales (CNES, the French space agency), the Agence Spatiale Canadienne/Canadian Space Agency (ASC/CSA), and United Kingdom Space Agency (UKSA). SWOT, the launch of which is scheduled for 2022, will offer a new perspective on acquiring critical data that are needed to complete the global water balance  [4]. SWOT will have a Ka-band (35. 75 GHz/8. 6 mm) SAR interferometer sensor (KaRIn) with near-nadir incidence angle  [5]. This will allow SWOT to observe WSE over water bodies with an extent that is equal to or greater than 250 m x 250 m and rivers that are greater than 100 m wide  [6].
Validation of AirSWOT measurements was first accomplished by Altenau et al. [15]  by comparing the water surface elevation and slope to in-situ data on the Tanana River (Yukon, Alaska). An RMSE of 9 cm for water surface elevation over 1 km 2 and of 1 cm km -1 for slope along a 10-km reach were achieved, making AirSWOT a possible SWOT validation measurement. AirSWOT measurement validation was also tested on lakes and rivers by Pitcher et al. [20]  in the Yukon Flats (Alaska). An RMSE of 8 cm over 1 km 2 was attained on rivers, but a 21-cm RMSE was exhibited for lakes, which falls short of SWOT's science requirements for 10 cm over 1 km 2  [6]. The authors explain that the poor results on lakes could be improved with advances to the AirSWOT InSAR processor and calibration data. One of the notable advantages of the Ka-band, used in KaRIn, when compared to lower frequencies (especially Kuband used by most nadir altimeters), is that it allows finer spatial resolution for SAR processing  [5].
B. Data This study uses an AirSWOT dataset featuring four passes that were acquired on 13 August 2017 (Figure  2 ), which are freely available at https://daac. ornl. gov/ABOVE/guides/ ABoVE_AirSWOT_Radar_Data. html. The dataset consists of colour-infrared images (a sensor that SWOT will not carry), SAR backscattering values (magnitude), incidence angle and elevation at 1 m spatial resolution. The colour-infrared images were used in the land classification process (further description in section IIIa). Given that AirSWOT coverage did not extend to the western part of the lake, a Landsat 8 image (30 m spatial resolution; 10 August 2017) was used to complete the colour-infrared map. A RADARSAT-2 image from 24 August 2017 was also used in the land cover classification process to retrieve areas of open water and emergent riparian vegetation (section IIIa).
Magnitudes and incidence angles from the AirSWOT data were used to retrieve backscattering values that served as input to the simulator (described in section III. B). Figure  2  shows AirSWOT coverage and incidence anglesIt should be noted that the AirSWOT backscattering values did not show signs of double-bounce backscattering in emergent vegetation in contrast to other InSAR missions due to AirSWOT's low incidence angles angles (as previously discussed in section I). SWOT is expected to show similar backscattering values to AirSWOT for the same incidence angles independently of the altitude difference. Surface water extent for the AirSWOT acquisition data was 163. 79 km 2 as calculated with RADARSAT-2 (see section III. A). In situ WSE and bathymetry were obtained during summer 2017 fieldwork.
Three water loggers were deployed at different locations around the lake (shown in Figure  1 ) and their averaged WSE measurement is equal to 209. 35 m (± 0. 03 m, with reference to CGVD2013) on AirSWOT data acquisition day. Last, a Digital Elevation Model (DEM) with 2-m resolution was obtained from LiDAR missions over the PAD  [26]. Bathymetry, in situ WSE, and DEM were used as inputs to the SWOT simulator and are described in section III. B.
B. Input to simulator Prior to running the simulator, the data need to be assembled in a manner that can be read by the SWOT simulator (green boxes in Figure  3 ). First, the DEM and bathymetry are combined with the observed WSE at the time of the AirSWOT pass over Mamawi Lake. This input data are used as a three-dimensional reference model of the lake water elevation and surroundings. This was accomplished by simply assigning a constant surface water elevation value from an averaging of the three water loggers measurements (see figure  1  for their locations) to all pixels in the DEM/bathymetry data that fell below that value within the lake boundaries. Second, the SWOT simulator requires land cover with associated backscattering values. For each land cover type in the land cover classification (described in section III.
C. SWOT-HR Simulations As stated in section I, NASA/JPL developed the SWOT-HR simulator, which produces synthetic SWOT Single Look Complex (SLC) data over a given area. This study used the simulator version from September 2019. Further details on the simulator can be found in, Domeneghetti et al. [28]  and Durand et al. [29]. Input to the simulator consists of digital elevation model (DEM) with water surface elevations and the land cover classification (Figure  3 : blue and green boxes) with their associated backscattering values. The SLC simulator and its associated processing chain outputs three levels of data processing (L0, L1 and L2). The simulator directly produces two SLC from each KaRiN antenna, and the L2 interferometric processing chain is then used to compute the geolocated heights (from the interferometric phase) and the pixel classification (from the SLC intensities).
D. Pixel cloud product post-processing Two attributes of the L2 pixel cloud product were used to evaluate effects of aquatic and emergent riparian vegetation on SWOT classification: area (extent of a given pixel) and elevation (height measurement of a given pixel), as shown by the red boxes in Figure  3. First, all sub-scenarios were filtered to retain only pixels that are within the true extent of both WSE and water extent. To compute WSE error, the simulated SWOT elevation data are first filtered, retaining only pixels that were classified as OP and WNL. Note that DW and DNL are not used for WSE error calculations because of increased errors that cam be attributed to the simulated DW process. The difference between SWOT WSE and the input scene WSE (i. e. , 209. 35 m) is then computed for each pixel.