dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: We describe the epidemiology of the first cases diagnosed in our institute of infections with the SARS-CoV-2 Beta variant and how this variant was imported to Marseille. Methods: The Beta variant was identified based on analyses of sequences of viral genomes or of a spike gene fragment obtained by next-generation sequencing using Illumina technology, or by a real-time reverse-transcription-PCR (qPCR) specific of the Beta variant. Results: The first patient diagnosed as infected with the SARS-CoV-2 Beta variant was sampled on January 15, 2021. Twenty-nine patients were diagnosed in January 2021 (two weeks). Fifteen (52%) patients were of Comorian nationality. Eight (28%) had travelled abroad, including six who had returned from Comoros. Phylogeny based on SARS-CoV-2 genomes from 11 of these patients and their best BLAST hits from the GISAID database showed that seven patients, including the four returning from Comoros, were clustered with 27 other genomes from GISAID that included the six first Beta variant genomes described in Comoros in January 2021.
Patients and methods Identification of the Beta variant was performed using different techniques, on viral RNA extracted from 200 μL of nasopharyngeal swab fluid collected from patients to diagnose SARS-CoV-2 infection by realtime reverse-transcription-PCR (qPCR)  [2]. Extracted viral RNA was either reverse-transcribed using SuperScript IV (ThermoFisher Scientific) prior to cDNA second strand synthesis with Klenow Fragment DNA polymerase (New England Biolabs, Beverly, MA, USA), or processed for obtention of cDNA according to the COVIDSeq protocol (Illumina Inc. ). In most cases, SARS-CoV-2 genotyping consisted of next-generation sequencing of viral genomes, or of a spike gene fragment (2129 nucleotides corresponding to positions 21,296-23,424 of the genome of the Wuhan-Hu-1 isolate GenBank Accession no.
NC_045512. 2) as previously described  [12]. Next-generation sequencing was performed using the Illumina technology, with the Nextera XT paired-end procedure on a MiSeq instrument (Illumina Inc. , San Diego, CA, USA) or with the Illumina COVIDSeq protocol on a NovaSeq 6000 instrument (Illumina Inc), as previously described  [2, 10]. Sequencing reads generated by next-generation sequencing were assembled by mapping on the SARS-CoV-2 genome GenBank Accession no. NC_045512. 2 (Wuhan-Hu-1 isolate) with the CLC Genomics workbench v. 7 software (https://digitalinsights. qiagen. com/) or with the Minimap2 software  [13]. Assembled sequences were analysed for their classification using the Nextclade online tool (https://clades. nextstrain. org/) [14] , the Pangolin online tool (https://cov-lineages.
org/pangolin. html) [15] , and an in-house script written in Python as previously described  [2]. SARS-CoV-2 genome sequences obtained in the present study have been deposited on the GISAID sequence database (https://www. gisaid. org/) [16]  (Supplementary Table  S1 ). Alternative SARS-CoV-2 genotype identification used an in house-designed qPCR system specific of the Beta variant, as previously described  [2]. This qPCR system targets the envelope gene and uses forward primer C_SA_3_MBF: TGAATTGCAGACACCTTTTGA, reverse primer C_SA_3_MBR: CAACCCTTGGTTGAATAGTCTTG, and probe C_SA_3_MBP: TGA-CATCTTCAATGGGGAATGT. Phylogenetic analyses were performed based either on SARS-CoV-2 genome sequences when available, or on spike gene sequences. Phylogeny reconstructions were performed using the MEGAX software v.
10. 2. 6 (http://www. megasoftware. net/) [17]  with the neighbour-joining method and the Kimura 2-parameter method. The five best BLAST hits from the GISAID sequence database (https://www. gisaid. org/) [16]  for each SARS-CoV-2 genome analysed here, as well as the six first Beta variant genomes detected in Comoros in January 2021  [18]  were incorporated into phylogenies. SARS-CoV-2 culture was performed by inoculating nasopharyngeal samples on Vero E6 cells, as previously described  [19].
Results The first SARS-CoV-2-infected patient to be diagnosed with the Beta variant was sampled on January 15, 2021. As of August 31, 2021, 611 patients had been diagnosed as being infected with this variant through routine SARS-CoV-2 genotyping (Fig. 1 ). Twenty-nine patients were diagnosed in January 2021 (over a two-week period), from whom the Beta variant was identified by genome or spike gene next-generation sequencing in 11 and 16 cases, respectively, and by qPCR specific of this variant, in two cases. Seventeen patients were male and 12 were female (sex ratio = 1. 4). The median age was 37 years (interquartile = [27-50], range = 10-62 years). Additional epidemiological or clinical data were available for 26 of the 29 patients. They were tested because they had respiratory symptoms or were contact cases of patients diagnosed with SARS-CoV-2-diagnosed patients.
Recent soil survey from the Joint Research Center (JRC) was performed, and maps of total metal soil content at the European Union scale were produced. These maps underlined large diffuse soil Cu contamination with some hot spots of high total soil Cu concentrations  (Ballabio et al. 2018). Applied to Cu, the application of the previous equation could fill the gap of the absence of knowledge of the large-scale distribution of Cu availability, using Cu in solution as a proxy, and the absence of knowledge of the large-scale distribution of Cu bio-availability, using free Cu 2+ as a proxy. Indeed, availability or bio-availability are data still not documented despite their importance for risk estimation and land management. In this context, the aim of this work was threefold: (1) provide a literature review of statistical empirical relationships established for estimation of available Cu (Cu in solution) or bio-available Cu (free Cu); (2) estimate, at the European scale, areas of potential risks e.
g. of Cu environmental availability or of Cu biological toxicity; and (3) link, at the European scale, the risk due to the presence of a soil Cu contamination to the risk of Cu availability and of Cu bio-availability. We decided not to explore the use of mechanistic models but rather to focus on empirical equations easier to use at larger spatial scales. Based on the literature review, we choose the more appropriate relationships in the objective of application to a European database of total Cu measurements. We highlighted areas of risks of (i) available Cu (Cu in solution) and (ii) bioavailable Cu (free Cu) with the comparison of the two values at each grid point with their respective median. This allowed us to define areas of risks without using debated threshold values  (Carlon 2007). Furthermore, the use of relative variations through comparisons with median limited misinterpretation due to intercept effects in the chosen empirical equation and allowed underlining the effects of pedological factors.
Estimation of (bio)-available Cu maps In this study, we used the European soil Cu survey from the LUCAS database provided by the JRC from which total Cu is based on the aqua regia protocol  (Ballabio et al. 2018; Tóth et al. 2016). Hence, we selected from our provided generic guide the transfer functions issued from studies using aqua regia protocols to measure total Cu. With these collected empirical equations, the estimations of Cu content in solution and of free Cu content in solution allowed building respectively maps of so-called available and bio-available Cu based on pedological mapping provided by the JRC at a 0. 5-km scale. The total Cu map was downloaded from https:// esdac. jrc. ec. europa. eu/ conte nt/ copper-distr ibuti on-topso ils  (Ballabio et al. 2018) , pH was downloaded from https:// esdac.
jrc. ec. europa. eu/ conte nt/ chemi cal-prope rties-europ ean-scale-based-lucas-topso ildata  (Ballabio et al. 2019) , clay values were obtained from https:// esdac. jrc. ec. europa. eu/ conte nt/ topso il-physi cal-prope rties-europe-based-lucas-topso il-data  (Ballabio et al. 2016) , and total organic carbon (Corga) data were obtained from https:// esdac. jrc. ec. europa. eu/ conte nt/ topso il-soil-organ iccarbon-lucas-eu25  (de Brogniez et al. 2015)  and are represented in suppl. Figure  1  and 2, respectively, for soil Corga and pH. Soil OM values were converted to soil Corga content using Corga = OM/2  (Pribyl 2010). For computational time purpose, we used the climate data operator software cdo  (Schulzweida 2017 ) to remap at 0. 01° the data originally at the 0. 5-km scale.
(2000a)  was fitted on more than 350 data collected in the literature, and seem to be the most robust empirical equations in estimation of dissolved Cu. It is also the most cited equation preferentially used to convert a large range of soil Cu total content into dissolved Cu values. The willing to include as much data point originating from various databases is however coupled with the lack of information about the measurement techniques involved. In fact, "total" Cu is mentioned without specifying the soil digestion method. In complement, we also noted the empirical equation of  Mondaca et al. (2015)  (Table  2 , Eq. 9) which was fitted with data from Chili and can thus be more appropriate for semi-arid region and their typical pedological characteristics and climate compare to Europe  (Garcia et al.
2017; Steven 2017). Finally, among the collected equations, those from  McBride et al. (1997)  (Table  2 , Eq. 3 a-c) are among the most commonly used with more than 36 citations/year. The authors provided two main regressions with exclusion or inclusion of data points (Table  2 , Eq. 3a. ) with highest (> 100 g. kg -1 ) OM content (Table  2 , Eq. 3b. ). Equations 3 a, b were built on the basis of a 70-point dataset including a long-term contamination due to sludge inputs or industrial activities deposition. For Eq. 3a (Table  2 ), a maximal total soil Cu concentration around 3000 mg. kg -1 of Cu was achieved, whereas Eq. 3c was based on a dataset with Cu contamination from 7 to 1000 mg. kg -1 of Cu. Total soil Cu concentrations were measured with acid micro-wave digestion providing values close to aqua regia extraction, whereas available Cu values came from 0.
(1997)  with inclusion of pH, total Cu and OM. This last parameter was excluded by  Sauvé et al. (1997)  in the equation they proposed (Eq. 5b) because it was shown to be strongly related to soil Cu content. Tipping et al. (2003)  proposed an equation using an extension of the Sauvé's dataset adding 98 points from moorland soils (Table  3 , Eq. 14b). They also provided an empirical equation restricted on the moorland soils (Table  3 , Eq. 14a) which can be particularly useful for soils with high OM content; this parameter has been found to significantly impact Cu availability and equation's parameter values. Finally, we chose to use Eq. 14b from  Tipping et al. (2003)  since it was based on the largest dataset and the pCu data were measured and not estimated using a mechanistic model.
Selected regression and computed maps The total Cu concentration in the LUCAS database provided by the JRC varied from 1 to 130 mg. kg soil -1 of Cu with most (75%) of the values below 20 mg. kg soil -1 of Cu and 99. 9% below 52 mg. kg -1 of Cu (Table  4 ; Fig. 2 ). With the definition of geochemical baselines as values in the range of median ± two times the average deviation  (Reimann et al. 2005) , we considered that 1. 5% of the soils are over-concentrated with a total soil Cu content > 28 mg. kg -1 of Cu, and that none are depleted. The range of risk index for total Cu was RI total [-94 to 883%]. Roughly, 25% of the grid points had RI total > 50% meaning a total soil Cu content higher than two times the median European value. Furthermore, less   1997 ) estimation (eq. n° 3b) to derive available Cu in solution (Fig.
Are contaminated soils at equilibrium? The equations we reviewed here were mostly constructed on data from long-term contaminated soils where Cu species were supposed to be in field at equilibrium. However, in this study, we used total Cu data acquired during field campaigns without precision on the temporality of the Cu inputs nor on the delay after Cu applications. However, several studies show that extractability of Cu decreases with time after addition of Cu due to a so-called "ageing process"  (Oorts et al. 2006a; Tom-Petersen et al. 2004 ). To take into account the time after contamination in Cu solubilization, kinetic descriptions of Cu availability have emerged  (Ma et al. 2006a, b; Zeng et al. 2017). These studies showed that not only the final distribution of Cu but also the kinetics of availability also depend on soil factors.
Delphi rounds and data analysis This Delphi consensus survey was conducted in three consecutive rounds, between July and December 2020. The termination criterion defined prior to data analysis was either consensus on all items -i. e. agreement by at least 75% of the expert panel  (Denman et al. , 2019; Diamond et al. , 2014)  -, or stability of responses. Round 1 was available for two months and a half; rounds 2 and 3 were available for one month. The first questionnaire was open access, with built-in duplicate checking and security procedures. The subsequent questionnaires were restricted to previous participants and required an individual single-use ID provided by the moderator. All questionnaires were piloted by five researchers to get an estimate of the response time and to detect and correct potential execution problems (glitches and logical structure issues).
In each round, participants who did not agree with a statement were encouraged to explain the reason of their disagreement, but comments were not mandatory so as not to bias towards positive answers. The qualitative and quantitative data were analysed using descriptive statistics (frequencies, means, standard deviations, medians, interquartile ranges) on Stata/MP software (version 14, StataCorp, College Station, TX). Round 1. The first round was constructed in two parts. In the first part, participants listed the measures or ratings they wished to be able to use on a standardised reading passage in open-ended responses (at least 3). Two raters, blinded to the identity of the participants, carried out conventional content analysis  (Denman et al. , 2019; Hsieh & Shannon, 2005 ) of these open-ended responses to identify semantic groupings.
Genes for ptEnolase and ptPGAM are believed to be absent from primary red or green algae, the closest relatives of land plants  (21, 22). Diatoms are a eukaryotic algal group, positioned within the stramenopile lineage that is distantly related to plants, with plastids originating from the secondary endosymbiosis of a red algal ancestor or endosymbiotic derivative thereof, separate from the primary endosymbiotic origin of plant plastids  (23, 24). Diatoms are extraordinarily successful in the modern ocean, comprising nearly half of total algal abundance in the Tara Oceans dataset, and performing about one-fifth of total planetary photosynthesis  (25, 26). The contributions of diatoms to primary production is even greater in specific oceanic regions, including the nutrient-rich and highly productive waters at high latitudes (i.
g. , given the absence of reported plastidial glycolysis pathways in primary red algae). Moreover, while the plant plastidial glycolysis pathway is principally associated with non-photosynthetic tissue, diatoms (as obligately photosynthetic and unicellular species) presumably perform photosynthesis and plastidial glycolysis in the same organelle  (18). Diatom ptEnolase and ptPGAM genes have further been shown to be transcriptionally co-regulated with photosynthetic carbon assimilation, suggesting metabolically important functions linked to photosynthetic activity  (41). Finally, ptPGAM proteins have recently been shown to overaccumulate under copper starvation, in the open-water diatom Thalassiosira oceanica, suggesting a possible connection between its function and photo-stress  (40). Here, we use combined bioinformatic profiling of large-scale sequence datasets from P.
tricornutum, the broader algal tree of life, and the Tara Oceans dataset, alongside integrative functional characterization of P. tricornutum CRISPR-CAS9 knockout mutants, to reveal heretofore unknown functions of diatom ptEnolase and ptPGAM enzymes in adaptations to high latitudes. Using phylogenetic techniques, we demonstrate that the genes encoding these enzymes arise from a recent duplication and retargeting of the mitochondria-targeted and respiratory isoform in diatoms and their closest relatives within the stramenopiles. Using meta-genomic data from Tara Oceans, we further show these genes are most highly expressed in high latitudes. Considering the growth and physiological phenotypes of knockout lines lacking plastidial glycolysis-gluconeogenesis proteins, we show that this pathway has augmented importance in cells grown under continuous illumination as opposed to light-dark cycling, and present evidence that this may be due to diminished capacities to convert glyceraldehyde-3-phosphate assimilated by the Calvin cycle into plastidial amino acids and lipid pools, which may be compensated by a redirection of flux through mitochondrial glycolysis and mitochondrial: plastid amino acid shuttling.
Phylogenetic analysis The peptide sequences of all genes with PFAM annotations coherent with either Enolase (PF00113) or Phospho-glycerate mutase (PGAM; taking histidine phosphatase, PF00300, as a proxy) encoded in the version 3 annotation of the P. tricornutum genome were extracted, and searched against a library of 289 complete cryptomonad, haptophyte and stramenopile genomes and transcriptomes by reciprocal BLASTp/ BLASTp best-hit search with threshold e-value 10 -05 , as previously described  (44) (45) (46). Sequences that retrieved a reciprocal best hit against a Phaeodactylum enolase or PGAM gene were trimmed to the first encoded methionine, and then screened through three targeting predictors: ASAFind v 2. 0, used with SignalP v 3. 0  (47, 48) , HECTAR, integrated into the Roscoff Galaxy platform, under the default settings  (49, 50) , and MitoFates, run with threshold value 0.
Co-regulation analysis Transcriptional co-regulation of P. tricornutum genes to ptEnolase and ptPGAM1A were assessed using a composite dataset consisting of two meta-studies of Phaeodactylum gene expression: DiatomPortal, based on microarray data generated in particular from cultures exposed to different light regimes and organic and inorganic stresses (57); and PhaeoNet, constructed from RNAseq data generated from N, P and Fe-starved cultures  (41, (58) (59) (60). To reconcile both datasets, which are based on the versions 1 and 3 annotations of the P. tricornutum genome, respectively, each DiatomPortal version 1 gene number was remapped to a PhaeoNet version 3 gene number by reciprocal BLASTp/ BLASTp best hit search  (45). Version 3 genes that were retrieved as reciprocal BLAST best hits by >1 version 1 gene (typically resulting from merged gene annotations between the two genome versions) were assigned the median of all matching DiatomPortal relative fold expression changes.
e. expressed per million reconciled diatom reads, or to calculated meta-genome abundances, using the formula and log 10 (1+ Σ metaT ) -log 10 (1+ Σ metaG ), to allow inclusion of null values. Pearson correlations were calculated between relative abundances and all quantitative measured environmental variables associated with Tara Oceans samples as stored within the PANGAEA repository  (62). To assess the possibility of non-linear correlations, ranked differences (i. e. , Spearman correlation) were also tested for each variable with broadly consistent results. All calculations were repeated independently for each depth (surface, or deep chlorophyll maximum/ DCM) and size fraction (0. 8-2000 μm, 0. 8-5 μm, 3/5-20 μm, 20-180 μm, and 180-2000 μm), with 3 and 5 μm filters viewed as equivalent to allow reconciliation of Arctic and non-Arctic data, respectively.
Gene expression analysis RNA sequencing libraries were prepared from 200 ng DNAse-treated RNA for each mutant line and treatment condition, with a minimum of three replicates per sample. RNA sequencing was performed by Fasteris (Plan-les-Ouates, Switzerland). After initial quality control checks, stranded Illumina mRNA libraries were prepared with a Novaseq V1. 5 kit and sequenced with an SP-flow cell with 2x 100 bp over 200 cycles, yielding approximately 130-160 Gb sequence data with ≥85% of bases higher than Q30 per sample. Output fastQ files were mapped using Nextflow's RNA sequencing assembly pipeline https://nfco. re/rnaseq/usage, and gff3 annotations of the P. tricornutum version 3 genome  (72). Total mapped read counts were then compared between all biological and technical replicates for (i) each mutant line sequenced, (ii) each genotype (ptEnolase knockout, ptPGAM1A knockout, control), and (iii) each treatment condition performed (LD, CL, 8C) by principal component analysis (PCA) using the R package factoextra, with highly variant libraries removed  (73).
The final dataset included 63 RNAseq libraries, including five ptEnolase and five ptPGAM1A knockout lines and four empty vector controls, and a minimum of four RNA libraries prepared from at least two genetically distinct mutant constructs for each genotype (ptEnolase, ptPGAM1A and control) considered (Table  S5 , sheets 1-2). , Differentially expressed genes (DEGs) were then calculated between each genotype for each treatment condition using DESeq2 with cutoff fold-change 2 and P-value 0. 05 (74) (Table  S5 , sheets 2-3). The mean transcript abundances of DEGs in knockout compared to control lines were first assessed in normalised  (41)  RNAseq data of N (59) and P-limited (58) Phaeodactylum cell lines under two and nine time-points respectively (Table  S5 , sheet 4). No significant differences were found between DEGs and other genes in the P.
tricornutum genome (one-way ANOVA, P > 0. 05;   S5 , sheets 6-10)  (41, 75). Tested traits included: PFAM and KEGG from the P. tricornutum version 3 genome annotation  (45) ; GO, annotated using combined BLASTkoala and GHOSTkoala annotations  (76) ; WGCNA coregulated gene module, as defined within the PhaeoNet meta-analysis of P. tricornutum RNAseq co-expression  (41) ; and localization, predicted in silico using ASAFind v 1. 0 with SignalP v 3. 0  (47, 48) , HECTAR under default conditions  (49) , and MitoFates with threshold value 0. 35  (51). Functional enrichments were tested by two-tailed chi-square (P < 0. 05) of a differentially expressed gene occurring in either one (ptEnolase v control; ptPGAM1A v control) knockout-versus-control line tests, or in both tests realised under each physiological condition.
61; LECO) as described previously (79). The chromatograms and mass spectra were evaluated using TagFinder software (80). Metabolite identification was manually checked by the mass spectral and retention index collection of the Golm Metabolome Database (81). Peak heights of the mass fragments were normalized successively on the basis of the fresh weight of the sample, the added amount of an internal standard (ribitol) and values obtained for loading column controls obtained from the same experiment. Normalised metabolite abundances were subsequently screened by PCA, as per the RNAseq analysis above, and outliers and biologically nonrepresentative samples were removed. The final datasets consist of 139 libraries (metabolite GC-MS), 55 libraries (lipid GC-MS) and 49 libraries (lipid LC-MS), with a minimum of three RNA libraries prepared from at least two genetically distinct mutant constructs for each genotype considered (Table  S6 , sheet 1).
Data deposition RNAseq data associated with this project is deposited with NCBI BioProject with project number PRJNA788211. All remaining supporting data not provided directly in paper supporting tables are provided in the linked Open Science Foundation Supporting database https://osf. io/89vm3/ (88). Project contents are ordered hierarchically by theme, with an overview of all contents provided on the site "wiki" page. A dedicated README file in each project folder explains the data presented and provides detailed methodology for each analysis.