dataesr/scientific-paragraphs-categorization huggingface dataset:
We focused on the most abundant GO terms dataset corresponding to biological processes, molecular functions, and cellular compartments with an arbitrary value of > 5000 TPM for each GO term. Taxonomy was assigned to clean reads using the TaxMapper search tool and corresponding database with default settings  48. Reads were mapped to two taxonomic levels: seven main eukaryotic lineages (supergroups, e. g. , Alveolata) and 28 groups within these lineages (e. g. , Apicomplexa, Chromerida, Ciliophora, Dinophyta and Perkinsea within the Alveolata supergroup). To validate the process of the de novo assembly, we mapped transcripts in our core dataset against metatranscriptomic data from the Tara Oceans expeditions, including Tara Oceans Polar Circle sampled in 2013. The reads mapping pipeline used is the same as described previously  49.
Briefly, reads from each Tara Oceans' metatranscriptomic read set were mapped onto transcript isoforms in our core dataset using bwa  50  and 95% identity over at least 80% of the length of the read picking the best match (or in case of several putative best matches-a random one). Annotation of the top ten abundant transcripts was done by a blastn search of the NCBI nucleotide database followed by a manual curation.
Statistical methods. The map of Adventfjorden was generated using MATLAB R2013b. All statistical analyses were performed in R v3. 5. 2  51  , and data were visualised using tidyverse v1. 2. 1  52. Diversity indices (richness, Shannon-Wiener index, and Pielou's evenness) were calculated using vegan package v2. 5-4  53. Principal component analysis (PCA) of environmental factors was calculated on centred and scaled data with prcomp function (stats package v3. 5. 3) and visualised using factoextra v1. 0. 5. To explore differences between transcript abundances a Bray-Curtis dissimilarity matrix (vegdist function in vegan package v2. 5-4  53  was constructed and clustered using a 'complete' method within hclust function (stats package v3. 5. 3). Pvclust package was used to assign support to the clustering topology  54.
To identify the strongest contribution of individual transcript isoforms to clustering patterns, we applied the simper function on the transcript matrix. To explore GO annotations, for each metatranscriptome, we summarised relative counts for each transcript that was assigned to a specific GO term. We explored each of the three categories of GO terms: molecular functions, biological processes, and cellular components. For each category, a Bray-Curtis dissimilarity matrix of GO abundance tables was used to calculate global non-metric multidimensional scaling (GNMDS  55  ). The envfit function (vegan package) was used to fit environmental parameters onto the GNMDS ordination. Analysis of similarities (ANOSIM; vegan package) was used to test if there were differences between polar day and polar night associated with light.
3 ). The polar day samples formed distinct subclusters. The core dataset containing almost 70,000 of the most abundant transcripts showed similar or identical clustering, indicating that the pattern was not altered by the high contribution of rare transcripts. Further functional descriptions were therefore conducted using the core dataset. We identified the transcripts with the strongest contribution to the differences between the main clusters (Supplementary Table  S2 ). Ten of the transcripts contributing to the difference between polar night and polar day were also the most abundant transcripts in our dataset. Only the most abundant transcript out of these was functional annotated by our pipeline and was classified as cytochrome b (Supplementary Table  S3 ). Manual annotation of the remaining nine transcript isoforms revealed that six mapped to subunit III of cytochrome oxidases from different clones of Karlodinium micrum (Supplementary Table  S3 ).
The remaining three transcripts were either less than 25% percent identical to any NCBI sequence or no significant similarity was found. Activity of expressed genes in a seasonal perspective. Through the lens of biological processes. Annotation of the core dataset gene catalogue against the GO database resulted in 24,643 transcripts with at least one annotation (36% of the core dataset). Environmental variables fitted into biological processes (GO category) dissimilarity matrix confirmed the importance of light as a structuring parameter (i. e. , day length (R 2 GNMDS = 0. 88, p = 0. 019), declination (R 2 GNMDS = 0. 85, p = 0. 025) and PAR (R 2 GNMDS = 0. 54, p = 0. 082). On the other hand, the analysis did not support water mass (R 2 GNMDS = 0. 04, p = 0. 974) and temperature (R 2 GNMDS = 0.
Fumarate reductase (NADH) activity consisted of transcripts mapping to an enzyme that catalyses the reversible anaerobic reduction of succinate to fumarate, generating NADH and protons  69. Sialic acid, phospholipase and oligosaccharide binding contained transcripts mapping mainly to e-selectin, a protein involved in an inflammatory response that changes properties of the cell surface  70. Mapping to heat shock-related 70 kDa proteins was found in glycolipid binding, whereas nucleolin in nucleosome binding. Nucleolins are also plurifunctional proteins that play important roles in viral infections  71. 17-beta-hydroxysteroid dehydrogenase (NAD +) activity and  Low level of functional annotations. Levels of functional annotation were overall low, regardless of the database used. Mapping to UniProt (with blastp), Pfam, TmHMM, GO (based on Pfam) resulted in < 10% of transcript annotation, while eggNOG and KEGG gave 10-20% successful annotation.
Only UniProt (with blastx) and GO (with blastp) annotated 38% and 36% of transcripts, respectively. However, mapping our assembled transcripts to the Tara Oceans datasets showed that most of our transcripts had hits, matching especially samples from the Arctic (Fig. 7 ). Up to 75% of our transcript isoforms mapped to the surface samples at station 196, north of Alaska, up to 78% mapped to the deep chlorophyll maximum layer at station 173, northeast of Novaya Zemlya, and up to 74% to the mesopelagic zone at station 201 in west part of Baffin Bay. The mean proportion of transcripts mapping to surface samples from Tara Oceans stations located north of 60°N was much higher than for stations in the temperate and tropical regions (μ ↑60N = 64%, σ ↑60N = 9% and μ ↓60N = 21%, σ ↓60N = 8%, respectively).
It is not clear what is their function in microbial eukaryotes  89. However, recently, it was suggested that they are involved in G protein-coupled receptor-based signalling in Dinophyta  90. In marine diatoms microbial rhodopsins have been shown to acidify plastids through proton pumping, and a role as a carbon dioxide concentration mechanism inside of the cells has been proposed  91. Gene expression is likely to be more strictly controlled in many organisms during polar night due to the overall lower availability of energy in the ecosystem  4. In our dataset we found several GO functions that might hint at expression of genes that are involved in energy conservation. An increased expression of histones or major binding nuclear proteins or similar genes could serve as a way to control gene expression by binding and thus preventing DNA from being transcribed  92.
This could be another argument for strict control of gene expression during polar night  94. Overrepresentation of different types of chaperon and heat shock protein transcripts during polar night may be connected to the high demand for energy conservation by assuring correct assembly, maintenance and stability of proteins' structures within the cell  95, 96. Moreover, heat shock proteins could influence increased cell survival by several mechanisms able to attenuate apoptosis  97. Among the most expressed transcripts in our study were a few functionally annotated sequences reaching up to 38% of the total number of transcripts which coincides with similar numbers of taxonomically annotated reads in our study. Metatranscriptomic studies often report low levels of functional annotations (down to 19%) that might be a result of various factors, such as the complexity of the studied environment  98  , available reference databases  49  , choice of algorithms, bioinformatic tools and parameters used for data analysis  99  , etc.
To date, the most extensive marine global survey examining expressed eukaryotic genes based on Tara Oceans 2009-2012 reported 51. 2% unannotated clusters of expressed genes  49. Although the overall rates of annotation in our study were low, the data mapped successfully against the Tara Oceans dataset (including Tara Oceans Polar Circle campaign in 2013) by matching to 78% of transcript isoforms, specifically in polar regions (Fig. 7 ). Therefore, we conclude that de novo assembled transcripts in our bioinformatic pipeline were robust and contained novel genes that are less likely to be found at lower latitudes (Fig. 7 ). We hypothesize that the proportion of successfully mapping transcripts in our study would have been higher if the Tara Oceans campaign in the Arctic was extended beyond June-October to collect samples during polar night.
Acknowledgements The authors are thankful to all the people that contributed to the marine part of MicroFun project, collected and processed the samples. This work was performed on the Abel Cluster, owned by the  University of Oslo  and the  Norwegian Metacenter for High Performance Computing (NOTUR) , and operated by the  Department for Research Computing at USIT , the  University of Oslo  IT-department. http:// www. hpc. uio. no/. We are thankful for help from Abel Cluster support team. We are also deeply thankful to the  Tara Oceans Consortium  who provided open and freely available data. We are thankful to  Ragnheid Skogseth  for providing a map of Adventfjorden. Finally, we would like to thank the two reviewers for their insightful comments that helped us to improve the manuscript.
Funding This research was funded by  University Centre in Svalbard , as well as  ConocoPhillips  and  Lundin Petroleum  through The  Northern Area Program  and by the  Norwegian Research Council  (project number:  230970 ). The cost of sequencing was partly covered through  Jan Christensens Legat. Open access funding was provided by  UiT-The Arctic University of Norway, Tromsø, Norway.
Results and discussions In this section, we present the results of our experiments and some comparisons with existing work. We implemented the method in C++ using OpenMP for parallel computation. We used Geometry Central  [32]  as our primary geometry processing and meshes handling library and polyscope  [33]  for visualization. Additionally, LSCM was implemented using LibIGL  [34] , and the triangulation was performed using CGAL  [31]. We share our code at: https://github. com/g-gisbert/Inpainting-Holes-In-Folded-Fabric-Meshes.
Finally, the method could be extended to work with holes containing islands or of different nature. Regarding the input data, we also want to address the challenge of working directly on point clouds when the borders of the holes are not clearly identified. funded, in whole or in part, by l'Agence Nationale de la Recherche (ANR), project StableProxies ANR-22-CE46-0006. For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version.
During the first trimester of 2022, incoming travellers were screened for infection at the border using PCR (polymerase chain reaction)/antigen tests. The third wave had largely subsided by April 2022. French Polynesia experienced a fourth wave of cases mainly caused by the Omicron BA. 5 and BA. 4 variants between June and September 2022  [8]  and a fifth wave mainly caused by the BQ. 1. 1 Omicron sub-variant in November and December 2022. During the third, fourth and fifth waves, no strong NPIs (curfews or case isolation) were implemented. To understand how immunity and control measures shaped observed dynamics in French Polynesia, we fit an age-structured multi-strain COVID-19 transmission model to reported case, hospitalisation and death data up to May 2022, as well as data from two seroprevalence surveys conducted in February 2021 and November-December 2021.
Methods Data Multiple data streams are used in the fitting of the model. Anonymised line lists of confirmed cases and hospitalisations compiled by the Ministry of Health of French Polynesia with testing date and admission date, and date of death for those that died, and 10-year age group were aggregated into age-stratified time series of daily cases, hospitalisations and hospital deaths. Only 493 out of 74986 confirmed cases (0. 66%) were missing their age group, so these cases were treated as unreported cases, since under-reporting of cases is accounted for in the model fitting (see Confirmed cases). As testing dates were missing for a large number of cases early in the first wave and cases were numbered approximately sequentially by testing date in the surveillance system, we imputed the missing dates as being between the testing dates of the nearest numbered cases with recorded testing dates.
Data from two sero-surveys, the first conducted by Cellule Epi-surveillance COVID and the Health Department of French Polynesia in February 2021, the second by Institut Louis Malardé in November-December 2021, was also used. This data is described in detail in  [8]  and summarised in Table  3. Briefly, in February 2021, 463 unvaccinated adults aged 18-88 years on the islands of Tahiti and Moorea were randomly selected and tested for anti-SARS-CoV-2 immunoglobulin type G (IgG) antibodies with the Siemens SARS-CoV-2 IgG (sCOVG) test. Overall, 88 (19. 0%, 95% CI 15. 5-22. 9%) individuals had detectable IgG antibodies. In November-December 2021, 673 randomly selected individuals aged ≥18 years on Tahiti were tested for antibodies against the SARS-CoV-2 N antigen (i. e. for evidence of past infection) with the Roche Elecsys anti-SARS-CoV-2 assay, and 388 (57.
7%, 95% CI 53. 8-61. 4%) were positive. For the purposes of the modelling, we assume that the seroprevalence in the 20-29 years age group in the model is the same as that in the 18-29 years age group in the data. We use data on the population of French Polynesia by year of age in 2020 from the UN World Population Prospects  [19]  (for which the total population was estimated to be 280,904) aggregated into 10-year age groups for the age group populations in the model. We use data on daily numbers of first, second and booster doses administered by age group (12-17, 18-29, 30-39, 40-49, 50-59, 60-69, 70+ years) collected by the Ministry of Health of French Polynesia to determine the numbers of individuals moving between the different vaccination strata in the model. Since the model is stratified into 10-year age groups, we split the doses in the 18-29 years age group in the data into the 10-19 and 20-29 age groups in the model according to population proportion (the proportions of 18-29 year-olds that are 18-19 and 20-29 years old).
Social contact data for France was used due to the absence of estimates for French Polynesia and the fact that French Polynesia is a French territory. The total force of infection on a susceptible individual in age group i and vaccination stratum k is then the sum of the variant-specific forces of infection: Λ ik (t) = 2 j=1 λ ijk (t). Cross-immunity between variants is modelled via partial immunity to infection with the other variant following infection with one variant, such that the force of infection on an individual in age group i and vaccination stratum k recovered from infection with variant j is: (1 -η j )λ i,3-j,k (t), if j ∈ {1, 2} 0, if j ∈ {3, 4} where η j is the cross-immunity of variant j with the other variant. The time-varying transmission rate, β(t), represents temporal changes in the overall contact rates in the population due to changes in restrictions and behaviour.
Code The code used in this analysis uses the odin, odin. dust, dust and mcstate R packages for simulating discrete-time stochastic processes  [34] [35] [36] [37]. The model structure is similar to that of the COVID-19 transmission model in the sircovid R package  [38] , and some of the code from this package is reused. All data and code used in the analysis is available online at https://github. com/LloydChapman/covid_multi_strain.
MS Analysis, SWATH Acquisition, and Data Interpretation Prior to mass spectrometry (MS) analysis, reverse-phase chromatography was used to separate the extracts. The reverse-phase column (15 cm in length, 75 mm in diameter, flow 300 nL/min; PepMap C18, Thermo Scientific Dionex, Waltham, MA, USA) was equilibrated with 4% (v/v) acetonitrile for 20 min, and peptide elution was carried out over an acetonitrile gradient from 4% to 35% (v/v) for 120 min. The separated peptides were then analyzed online by a TripleTOF 5600 mass spectrometer (AB Sciex, Framingham, MA, USA). Peptide spectra were acquired in data-dependent (DDA) and data-independent (DIA) acquisition modes. The MS/MS library was acquired in the DDA mode and analyzed by the ProteinPilot software (version 4. 5, AB Sciex, USA) using the algorithm Paragon (version 4.
5. 0. 0, AB Sciex, USA). Briefly, the trypsin was chosen as the cleavage specificity and alkylation (C) set to iodoacetamide, carbamidomethylation as fixed modifications, oxidation (M) and deamination (N, Q) as variable modifications were set. All biological modifications and amino acid substitutions were considered, and a thorough ID search was applied with peptide confidence set at 0. 99. The raw spectral data obtained served as the input for ProteinPilot against the Mycobacterium bovis UniProt database. For the database search, the cut-off peptide confidence limit was set at 95%. ProteinPilot provided a global false discovery rate of 1% and a local false discovery rate of 5%. Accumulation time was set to 0. 1 s for the MS1 scan and 65 ms for the MS2 scan, with the total cycle time being approximately 3.
8 s. For the SWATH analysis (DIA, AB Sciex), 32 incremental steps defined as windows of 25 m/z containing 1 m/z for the overlap of the window were passed over the full mass range (400-1250 m/z). The peak intensity method was used for the quantitation of peptides. The ion chromatogram of the top six fragmented peptides was extracted, and their area was integrated over 15 min on six transitions. The tolerance was set at 100 ppm. The SWATH data were processed with the PeakView software (version 2. 1. 0. 11041, AB Sciex, USA). The retention time was calculated manually from a group of 15 selected peptides with retention time in the range of 20-100 min. MarkerView Software (version 1. 2. 1, AB Sciex, USA) was used for the analysis of the relative abundance of the peptides. For all experiments, proteins identified with one peptide were rejected for interpretation.
Statistical Analysis The unpaired t-test was applied for comparisons between two groups (for the proteomic analysis). A p-value of <0. 05 was considered statistically significant. The graphs were prepared with OriginPro 8 (OriginLab Corporation, Northampton, MA, USA) or with GraphPad Prism version 9. 5. 1 for Windows (GraphPad Software, San Diego, CA, USA).
Telacebec Decreased the fadD26 Promoter Activity We further assessed the expression of the faD26-papA5 operon, as representative PDIM/PGL-synthesizing gene loci. The transcriptional activity of the first promoter of this operon, upstream to the fadD26 gene, was investigated using a luciferase reporter system (Figure  2A )  [15]. We constructed the pfadD26-luxAB plasmid containing the luxAB luciferase genes from Vibrio harveyi in the pSMT1 vector  [23]. A 705 bp 5 -upstream region of the fadD26 gene, predicted to harbor 2 putative promoter sites using the BPROM software (http://softberry. com, accessed on 1 July 2018) and reported to contain 2 transcriptional start sites (TSS) (Figure  2A ), was inserted in the pSMT1 vector to replace the original mycobacterial cpn60. 2 promoter to control the luxAB transcription  [23, 28].
Telacebec Decreased the fadD26 Promoter Activity We further assessed the expression of the faD26-papA5 operon, as representative PDIM/PGL-synthesizing gene loci. The transcriptional activity of the first promoter of this operon, upstream to the fadD26 gene, was investigated using a luciferase reporter system (Figure  2A )  [15]. We constructed the pfadD26-luxAB plasmid containing the luxAB luciferase genes from Vibrio harveyi in the pSMT1 vector  [23]. A 705 bp 5′-upstream region of the fadD26 gene, predicted to harbor 2 putative promoter sites using the BPROM software (http://softberry. com, accessed on 1 July 2018) and reported to contain 2 transcriptional    2B ), suggesting that the effect on PDIMs/PGLs occurs befo of bacillary growth. This effect was observed after 7 h treatment (Figure  2C  with our proteomic data showing a decreased level of proteins encoded i   [28].
Funding: The study was funded by a research startup fund granted by Nanfang College Guangzhou to S. Z. (2021BQ011) and by Guangdong Basic and Applied Basic Research Foundation to S. Z. (2023A1515012220). Z. Z. was funded by the China Scholarship Council (201908210292). The bioprofiling platform used for proteomic analysis was supported by the European Regional Development Fund and the Walloon Region, Belgium.
Analysis of 22 Full Factorial Design of the Developed Nanophytosomal Dispersions The developed formulae were optimized using 2 2 full factorial designs (Design-expert ® software version 7, Stat-Ease, Inc. , Minneapolis, MN, USA). Four experimental runs were generated by the software for the development of nanophytosomes (Table  3 ). Two-Figure  2. The 2D (A) and 3D (B) interactions of 12,13-DiHome with the active site of (C) the surface area of the interaction of the ligand atom interaction with nucleic/amino acids of the HMGB1 protein.
Analysis of 22 Full Factorial Design of the Developed Nanophytosomal Dispersions The developed formulae were optimized using 2 2 full factorial designs (Design-expert ® software version 7, Stat-Ease, Inc. , Minneapolis, MN, USA). Four experimental runs were generated by the software for the development of nanophytosomes (Table  3 ). Two-factor interaction (2FI) was the chosen model. To ensure that the model could be used to navigate the design space, an adequate precision ratio was chosen. A ratio larger than four is preferred, and this was recorded for all dependent variables (Table  3 ). The predicted and adjusted R 2 values were acceptable for all dependent variables. Considering both the PC amount (X 1 ) and CH amount (X 2 ), it was found that they influenced all investigated dependent variables (Table  4  and Figure  3 ).
Selection of the Optimum Nanophytosomal Formulation Using Design Expert ® software, numerical analysis was used to determine the desired level of variables to obtain S. platensis nanophytosomes with the highest EE% and ZP (absolute value) and the lowest PDI, PS, and Q6hrs. Accordingly, (F4), which included 100 mg PC and 50 mg CH (Figure  4 ), was selected for further investigations.
Molecular Networking and Annotation of Molecules An MSConvert program 64 bit (https://proteowizard. sourceforge. io/download. html accessed on 6 January 2020) was used to convert the data acquired on a Thermo Scientific HRMS instrument to Mzmine compatible format (i. e. , Mzml). The data were then processed and visualized with Mzmine 2. 53 (MZmine3Download) to obtain a Global Natural Product Social Molecular Networking (GNPS) compatible format. The results were then exported to a molecular networking (MN) analysis on a GNPS site (GNPS-Analyze, Connect, and Network with your Mass Spectrometry Data (ucsd. edu)). The following parameters were considered: precursor ion and fragment ion mass tolerance were set both at 0. 02 Dalton, minimum pair cosine was 0. 6, network TopK was 10, and maximum connected component size was 100.