dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, standard phasor histograms of NADH and FAD, measured through standard fluorophores solutions (1 mM in Tris Buffer, pH = 8. 5 for NADH and 1 mM in PBS, pH = 7. 4 for FAD), were added to each global type histogram and marked in red on the grid in order facilitate the tracking of the tissues emission peaks. For NIR molecular ratios extraction, the same spectral fitting technique used for DUV spectral data processing was applied to fit the NIR spectral data. The mean fluorescence spectrum of all pixels in each spectral image was fitted to extract the integral proportion of each fluorophore emission. At 810 nm excitation wavelength, six fluorophores were chosen and fitted by Gaussian curves through a literature review  18  : NADH free, NADH protein bound, FAD, lipopigments, porphyrin I and porphyrins II.
While at 890 nm excitation wavelength, the NADH bound was removes and replaced by SHG emission. The Gaussian parameters used for these fluorophore emission fitting is recapitulated in Table  1 , NIR section. An example of two fitted NIR spectrum is shown in Fig. 9a, b. Rationing the different calculated integral proportion (Int) of these fluorophores, we were able to extract three different NIR molecular ratios: FLIM analysis. FLIM acquisition were performed using 810 nm as excitation wavelength. The acquired data were extracted through Symphotime software (PicoQuant, GmbH, Berlin, Germany), then a Matlab interface was used to treat these data. For protein-bound NADH lifetime, the channel 1 FLIM images were selected where several region of interest (ROI) were chosen in each image. In each ROI, the decay curves of its pixels were averaged and adjusted by a biexponential fit.
To get an acceptable fit, two criteria were taken into account: (1) having χ 2 -values of around 1. 0 (χ 2 range 0. 8-1. 2) and (2) the residuals had to be randomly distributed around 0 within the intervals + 4 and -4. For free and protein bound FAD, the channel 2 FLIM image were selected where the phasor approach was used to extract their lifetime values. For each sample, FLIM measurements were performed on a 3 × 3 mosaic images (9 images). Each image was extracted and treated using "FIJI" software, the size of each image was reduced from 512 × 512 pixels to 16 × 16 pixels. So each new reduced pixel presents a real size of 32 × 32 = 1024 pixels. The decay curves of each 1024 pixels were added together to obtain one decay curve I(t). Each decay curve I(t) is represented in a graphical view by a unique vector having its unique location, called phasor (or count) in the "phasor plot"  [42] [43] [44].
Acknowledgements This work is financially supported by  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health)  within the framework of the Cancer Plan for MEVO & IMOP projects, by  CNRS  with  "Défi instrumental" grant , by ligue nationale contre le cancer (LNCC) and the  Institut National de Physique Nucléaire et de Physique des Particules  ( IN2P3 ). Part of this work was done in the PIMPA Platform partly funded by the  French program  " Investissement d' Avenir " run by the  "Agence Nationale pour la Recherche"  (grant " Infrastructure d'avenir en Biologie Santé - ANR -11-INBS-0006 "). We would like to thank  Synchrotron SOLEIL  for the accorded beam-time under the project #  20191511. We would like also to thank the  Delegation for Clinical Research and Innovation (DRCI)  and the  Biological Resources Center (CRB) of Sainte-Anne hospital center  for providing the samples.
g. , HGT) is used to assess network-based predictions  (19, 30). However, the effectiveness of previous and novel co-occurrence network methods has never been quantitatively tested for NCLDV host prediction. The current lack of quantitative assessment hinders the widespread use of this approach. Therefore, dedicated methods are needed to test the accuracy of NCLDV host prediction with co-occurrence networks and to improve the performance of co-occurrence-based predictions. The Tara Oceans expedition is a global-scale survey on marine ecosystems that expands our knowledge of microbial diversity, organismal interactions, and ecological drivers of community structure  (34). The present study used Tara Oceans metagenomic and metabarcoding data sets to predict virus-host relationships between NCLDVs and eukaryotes by constructing co-occurrence networks using different methods.
To quantitatively assess the performance of network-based host prediction, we employed the positive likelihood ratio (LR1) using reference data for known NCLDV-host relationships. We developed a phylogeny-based enrichment analysis approach, Taxon Interaction Mapper (TIM), to enhance the performance in detecting positive signals in the intricate inferred networks. TIM has previously been used for viruses with high importance in predicting the carbon export efficiency  (35) , but without a quantitative assessment of its effectiveness. In this study, we assessed the performance of TIM as a filter of co-occurrence networks. We examined NCLDV-virophage networks, which further justify the use of co-occurrence and filtering approaches to identify NCLDV interaction partners.
22% of total interactions). The viral associations with Metazoa and Collodaria increased with increasing size fractions. In the largest 180-to 2,000-mm size fraction network, Metazoa contributed 39. 31% of the total polB-V9 edges. We calculated the degree of nodes (number of connected edges) for each NCLDV polB OTU (Fig. 3A  and B ). Naturally, the average degree of positive associations per polB was higher than negative edges in all size fractions and decreased along with increasing size fractions (2. 69, 2. 40, 2. 25, and 2. 10 from 0. 8 to 5 mm to 180 to 2,000 mm and 2. 76 for 0. 8-inf). Most of the polB nodes had more than one positive association (Fig. 3A ). Together with the taxonomic annotation of nodes, polB-V9 associations in the networks generated with the Tara Oceans data revealed their high dimensionality and complexity.
DISCUSSION NCLDVs can infect a wide range of eukaryotes, from unicellular to multicellular organisms  (36). However, we are still far from a comprehensive knowledge of their hosts because few have been isolated so far. Therefore, better host prediction algorithms are needed to understand the ecological functions and evolutionary significance of NCLDVs. To make these predictions, we constructed global ocean co-occurrence networks based on the marine metagenome and metabarcoding data sets from 85 stations of the Tara Oceans expedition, which cover all major oceanic provinces across an extensive latitudinal gradient from pole to pole. The edges (associations) between polB and V9 nodes (OTUs) in the networks were generated using FlashWeave. The networks were particularly dense (Fig. 2A ; see also Fig.
For eukaryotes, we used the metabarcoding data for eukaryotes, which target the 18S rRNA gene hypervariable V9 region (V9)  (64). Taxonomic annotation of the eukaryotic metabarcoding data were previously performed by the Tara Oceans consortium using an extensive V9_PR2 reference database  (64) , which was derived from the original Protist Ribosomal Reference (PR2) database  (67). The diversity index of eukaryotic communities was calculated using the package "vegan" (68). Data processing. A relative abundance matrix for the NCLDV polB OTUs was extracted from OM-RGC. v2 for the samples derived from the pico-size fractions (0. 22 to 1. 6 mm or 0. 22 to 3. 0 mm). We converted the relative abundances of polB OTUs back to absolute read counts based on gene length and read length (assumed to be 100 nucleotides).
The FlashWeave-HE settings were the same as FlashWeave-S but with a command "heterogeneous. " For Spearman, we used stats. spearmanr in package "Scipy"  (70). In FastSpar, we used 50 iterations, 20 excluded iterations, and a threshold of 0. 1 to generate associations. To reduce the high dimensionality of the data sets, upper quartile (Q3) filtered matrices were used for comparison among FlashWeave-S, Spearman, and FastSpar. Network validation. We validated the virus-host associations in inferred networks based on a confusion matrix defined by the known NCLDV-host information (Fig. 1 ). Briefly, we manually compiled 69 known virus-host relationships for NCLDVs (see Table  S2 ). In the validation process, eukaryotic taxonomic groups were annotated at the level of the "Major lineages" in the extensive PR2 database (updated after publication) (64).
The "Major lineages" were used in the present study because (i) the deficiency of known virus-host relationships limited the use of lower eukaryotic taxonomy ranks, such as genus, for assessment and (ii) these lineages adequately represented marine eukaryotes by covering the full spectrum of cataloged eukaryotic V9 diversity at a comparable phylogenetic depth  (64). We then performed BLASTp (2. 10. 1  [71] ) searches from the Tara Oceans PolB sequences against the NCLDV reference database to define groups of metagenomic PolBs with a threshold of 65% sequence identity by retaining only the best hit for each environmental PolB sequence. This threshold was determined because, by using reference PolB sequences and RefSeq protein sequence databases, we found that 60 to 70% of sequence identity could distinguish whether the NCLDVs infected hosts of the same major lineages; this was mainly tested for Phycodnaviridae because of the lack of host information for closely related viruses in other NCLDV families (see Table  S3 ).
S7A )  (35). TIM is available from https://github. com/RomainBlancMathieu/TIM. We pooled network associations using FlashWeave analysis for five size fractions. To build a concise and credible viral phylogenetic tree, we removed all of the PolB sequences that were absent in the FlashWeave network associations, and the remaining sequences were filtered by the amino acid sequence length ($500 amino acids). Protein alignment was conducted using MAFFT-linsi (version 7. 471  [75] ), and 18 sequences were manually removed because they were not well aligned with other PolB sequences. A total of 501 PolB sequences were used to make a maximum likelihood phylogenetic tree with FastTree (version 2. 1. 11 [76] ). Then, the PolB-V9 associations were mapped on the tree to calculate the significance of the enrichment of specific associations using TIM.
We inferred the networks between NCLDVs and virophages using mcp as the marker gene for virophages. First, 47 reference MCP amino acid sequences were collected from public databases and used to build an HMM profile. The HMM profile was used to search against the amino acid sequences of OM-RGC v2 using HMMER hmmsearch [version 3. 3. 1] with the threshold of E value , 1E290 (79). This threshold was determined by searching reference sequences against the GenomeNet nr-aa database. The search detected 195 Tara Oceans virophage MCP sequences in the OM-RGC database. Together with 47 reference MCPs, a phylogenetic tree of MCP amino acid sequences was built using MAFFT and FastTree. We extracted the abundance profiles for the 195 MCP sequences from the pico-size (0. 22-to 1. 6-mm or 0. 22-to 3. 0-mm) and femto-size (,0.
22-mm) fractions. We used samples from the SRF and DCM depths. PolB and MCP abundance profiles were merged into two matrices corresponding to the two virophage size fractions. Then, network inference was conducted using the FlashWeave default settings after Q1 filtration. We did not apply a quantitative assessment of NCLDV-virophage associations due to the limitation of the known NCLDV-virophage pairs and reference genomes of isolated virophages. In the MCP phylogenetic tree, three virophage clades contributed most of the NCLDV connections. Thus, an NCLDV enrichment analysis for the three clades was carried out using a Fisher exact test, and the P value was adjusted by the Benjamin-Hochberg method. This approach was the same as TIM, but we did not use the TIM software because the current version of TIM requires inputs of eukaryotic nodes with NCBI taxonomy annotations.
We used another approach, HGT, to predict the virophage-NCLDV interactions. First, we generated an NCLDV genome database, which includes 56 reference NCLDV genomes corresponding to our polB data set and 2,074 metagenome-assembled genomes from a previous study  (25). A total of 827,548 coding sequences were included in this database. Then, 195 virophage MCPs from the metagenomic data were BLASTp searched against this database using an E value cutoff of 1E210 (with a minimum query coverage of 50% and a minimum sequence identity of 50%). If a virophage MCP obtained a hit in the NCLDV genome database with a lower E value compared to hits in the MCP database (the hit to itself was removed), the hit in the NCLDV genome database was considered an HGT candidate. Data availability. Processed frequency data and associations used in assessment are available from GenomeNet (https://www.
ACKNOWLEDGMENTS This study was supported by  JSPS/KAKENHI  ( 18H02279  and  19H05667  to H. O. and  19K15895  and  19H04263  to H. E. );  Scientific Research on Innovative Areas  from the  Ministry of Education, Culture, Science, Sports and Technology (MEXT) of Japan  ( 16H06429 ,  16K21723 , and  16H06437 );  Kyoto University Research Coordination Alliance  (funding to H. E. ); and the  Collaborative Research Program of the Institute for Chemical Research, Kyoto University  ( 2019-30  and  2020-27 ). Computational time was provided by the  SuperComputer System, Institute for Chemical Research, Kyoto University. We further thank the  Tara Oceans consortium  and the people and sponsors who supported  Tara Oceans. Tara Oceans  (including both the Tara Oceans and  Tara Oceans Polar Circle  expeditions) would not exist without the leadership of the  Tara Expeditions Foundation  and the continuous support of 23 institutes (https://oceans.
Epidemic curves for SARS-CoV-2-associated deaths in various countries We used the cumulated daily mortality from the COVID-19 data repository operated by Johns Hopkins University (39) available at https://github. com/CSSEGISandData/COVID-19 (accessed on 25 June 2021), which covers 221 countries and locations from 22 April 2020 to 24 June 2021. Data mining of time series is a complex scientific field where the analysis process must be tailored to the characteristics of the series and the clustering objectives  (40, 41). Our objective was to cluster the dynamic of epidemics at country scale as reflected by COVID-19 mortality, taking in account the time location of the different series peaks but not the country mortality incidences. A threshold of 500 deaths was used for countries to analyse their SARS-CoV-2-associated death curves.
Genome sequence analyses Genome consensus sequences were generated 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 (with the following thresholds: 0. 8 for coverage and 0. 9 for similarity) (https://digitalinsights. qiagen. com/), as previously described (3), or the Minimap2 software  (44). A frequency of the majoritary nucleotide ≥70% and a nucleotide depth ≥10 (when sequence reads were generated on the NovaSeq Illumina instrument (Illumina Inc. )) or ≥5 (when sequences reads were generated on the MiSeq Illumina instrument) were used. Detection of mutations was performed using the freebayes tool (https://github. com/freebayes/freebayes) (45)  with a mapping quality score of 20. SAMtools was used for soft clipping of Artic primers (https://artic.
network/), and to remove PCR duplicates  (46). Sequences described in the present study have been deposited on the GISAID sequence database (https://www. gisaid. org/) (12) and can be retrieved online using the GISAID online search tool with "Marseille" as keyword, then selecting sequence names containing "IHU" or "MEPHI". In addition, they have been deposited on the IHU Marseille Infection website: https://www. mediterranee-infection. com/sequences-genomiques-sars-cov-2-completes-partielles-sequences-spike-protein-jusquen-mai-2021/. Numbers of nucleotide changes in the SARS-CoV-2 genomes and of amino acid changes in the SARS-CoV-2 spike protein were obtained using the Nextclade tool (https://clades. nextstrain. org/results). They were plotted using the GraphPad software v5. 01 (https://www.
SARS-CoV-2 classification and naming system Phylogeny reconstructions based on the SARS-CoV-2 genomes obtained in our laboratory were performed using the nextstrain/ncov tool (https://github. com/nextstrain/ncov) then visualised with Auspice (https://docs. nextstrain. org/projects/auspice/en/stable/) or FigTree v1. 4. 4 (http://tree. bio. ed. ac. uk/software/figtree/); the limitation of sampling per date of collection of respiratory specimens was desactivated. Genome sequences were kept for analyses when larger than 24,000 nucleotides. Within reconstructed trees, we identified and counted all mutations between nodes and between nodes and leaves. This allowed us to define the set of mutations for the most recent ancestor of the clusters corresponding to each of the variants. Nomenclature uses our city name, "Marseille", followed by a number according to the chronology of their description (e.
The Marseille-4 variant (B. 1. 160) was the most prevalent SARS-CoV-2 variant in our geographical area in 2020 and was only outdone by the Alpha (UK) variant in 2021 (4). It replaced the Marseille-1 variant in August 2020 and faded away in April 2021. It was first detected in south-west France and in Marseille. Its emergence coincided with the re-increase in France of SARS-CoV-2 incidence starting with a department in northwest France (Mayenne) close to the Eure-et-Loir department where a mink farm was found to be massively infected with SARS-CoV-2 (4, 24). The only genome from this farm, obtained from a mink sampled in mid-November 2020 but released in March 2021, is a Marseille-4 variant (GISAID Accession no. EPI_ISL_1392906). In addition, the co-occurrence of 13 hallmark mutations in the Marseille-4 variant without any available genome showing a gradual appearance of these mutations suggested that genetic evolution had been overlooked.
These data led us to conclude, based on genomic, geographical and temporal coincidences, that the source of the Marseille-4 epidemic was the mink. The Marseille-4 variant was progressively replaced from January 2021 by the epidemic linked to the Alpha (UK) variant. Meanwhile, it circulated in 76 countries and accounted for 31,299 genomes in the GISAID database as of 30 June 2021, mostly obtained from European countries (in 29,906 cases (96%)) (Fig. S3). During July and August 2020, two additional variants, Marseille-2 and Marseille 5, emerged in our geographical area, which became the second and third most prevalent variants from week 11 of 2020 after the Marseille-4 variant. The Marseille-2 variant (B. 1. 177) most likely emerged in Spain, where it accounted for two-thirds of released genomes and it spread to our geographical area and through Europe during the summer of 2020 (19).
The Marseille-5 variant (B. 1. 367) disappeared in October 2020 and its source and origin are unknown. The Marseille-8 and Marseille-9 variants were first detected in August 2020. They accounted overall for approximatively 1. 2% of the cases of SARS-CoV-2 infections in our geographical area, but nonetheless circulated until March and January 2021, respectively. The source and origin of these variants are unknown. The Marseille-501 variant emerged in Marseille in January 2021 as the first variant of lineage 19B harbouring amino acid substitution N501Y in the spike protein (25). A majority of the genomes primarily available from the GISAID database were linked to Mayotte, which is part of the Comoros archipelago located in the Indian Ocean from where originated some of the patients we diagnosed to be infected with this variant.
Acknowledgments We are thankful to the  IHU Méditerranée Infection COVID-19 Task Force that notably includes Sophie Amrane ,  Camille Aubry ,  Sofiane Bakour ,  Karim Bendamardji ,  Cyril Berenger ,  Nadim Cassir ,  Anne Darmon ,  Claire Decoster ,  Catherine Dhiver ,  Barbara Doudier ,  Sophie Edouard ,  Véronique Filosa ,  Séverine Guitton ,  Marie Hocquart ,  Morgane Mailhe ,  Florine Pelisson ,  Coralie Porcheto ,  Isabelle Ravaux ,  Piseth Seng ,  Laurence Thomas ,  Raphael Tola ,  Christelle Tomei ,  Catherine Triquet. This manuscript has been edited for English language by a native English speaker.
Motivation: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e. g. , experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task.
Results: To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5. 54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction.
Introduction Adverse drug-drug interactions (DDI) are modifications of the effect of a drug when administered with another drug, which is a common and dangerous scenario for patients with complicated conditions. Undetected adverse DDIs have become serious health threats and caused nearly 74, 000 emergency room visits and 195, 000 hospitalizations each year in the United States alone  (Percha and Altman, 2013). To mitigate these risks and costs, accurate prediction of DDIs becomes a clinically important task. Two types of data are being utilized for developing DDI detection models: Manually curated DDI networks and large biomedical knowledge graphs. Curated DDI networks: Researchers have curated DDI networks based on experimental datasets and literature such as TWOSIDES  (Tatonetti et al. , 2012) , MINER  (Zitnik et al.
, 2018b)  and  DrugBank (Wishart et al. , 2018; Ryu et al. , 2018). These curated data are of higher quality but expensive to create and usually smaller in size. Knowledge Graph: Over the years, large knowledge graph (KG) such as  (Rotmensch et al. , 2017) ,  Hetionet (Himmelstein and Baranzini, 2015)  and DRKG  (Ioannidis et al. , 2020)  have been constructed from literature mining and database integration. However, these KGs are large and noisy: out of their tens of thousands of nodes with millions of edges, only a small subgraph is relevant to a prediction target. Deep Learning: Graph neural networks (GNN) have achieved great performance by casting DDI prediction as a link prediction problem on DDI graphs  (Gysi et al. , 2020; Zitnik et al. , 2018a; Huang et al. , 2020a). However, existing deep learning models are often trained only based on the DDI dataset at hand, ignoring the large biomedical knowledge graph  (Ioannidis et al.