dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: National Center for Biotechnology Information (NCBI) BioProject database under accession number PRJNA802819. The raw Hora WGS Illumina sequencing data are under accession numbers SAMN25598580 to SAMN25598599. The data of the raw MinION nanopore long reads are under accession numbers SAMN25610565 to SAMN25610567.
Auger spectra showed the decrease of the P LVV, S LVV and Cl LVV transition peaks from the Arg and the increase of the Li transition peak (Fig. 3b ), indicating possible Li plating. This latter was confirmed by the peak shape and position that corresponded perfectly to the Li KVV transition of a pure Li metal reference available in the JEOL database (not shown here). Again, by comparison with JEOL references, another component transition appeared during the experiment and is attributed to the reduction of Arg into Li2S (not shown here). The Li2S area corresponds to the secondary electron white constrast area surrounding the Li metal plating in the SEM image (Fig. 3b ). Note that Li2S was also found to form first before Li plating (not shown here). The SAM image overlay of Limetal(KVV), LiLi2S(KVV) and SArg(LVV) clearly illustrates the Li2S formation and Li plating (Fig.
Combined transcriptomics and proteomics analysis supports inflammatory pathways associated with critical disease Consistent with the proteomics data, differential gene expression and gene set enrichment analysis of RNA-seq data from whole blood samples collected from the patients showed that regulation of the inflammatory response, myeloid cell activation and neutrophil degranulation were the main enriched pathways in critical patients with normalized enrichment scores of 2. 33, 2. 65 and 2. 66, respectively (Figure  4A  and B ). To identify enriched pathways that were supported by different omics layers, we performed nested GOSeq (nGOseq)  (36)  functional enrichment of the differentially expressed genes or proteins identified from the RNA-seq, plasma and PBMC proteomics data. Figure  4C  shows the nGOseq terms that were found to be statistically enriched in at least two omics datasets in critical compared with non-critical patients.
In line with the cytokine profiling results (Figure  2A ), inflammatory signaling and the response to proinflammatory cytokine release (IL-1, IL-8 and IL-12) were supported by multiple omics datasets. As suggested by the results from immune cell profiling (Figure  2C  and D ) and previous studies, the B-cell response was activated, whereas the T-cell response was impaired  (17, 37). As previously witnessed  (8, 14, 15, 38) , the activation of neutrophils and monocytes was confirmed by the enrichment of nine different nGOseq terms (Figure  4C ). The nGOseq enrichment analysis also indicated that dysfunction of blood coagulation involves a fibrinolytic response, an observation that could, however, be linked to the anticoagulant therapy administered to most critical patients. Moreover, nGOseq terms related to viral entry and even viral transcription were strongly enriched in the three omics datasets.
The receiver operating characteristic curves (ROCs) for the 100 partitions of the patient data as well as other classification performance metrics are shown in Figure  5A  and Table  S3. The classification performance on the test set provided a high degree of confidence that the signals learned by the various AI/ML algorithms are generalizable. After successfully classifying non-critical versus critical patients based on wholetranscriptome RNA-seq profiling, we assessed feature scores across the six distinct ML algorithms (see Methods section) and all partitions to determine an ensemble feature ranking, ignoring features from the partitions of patient data where the test AUROC was less than 0. 7. Aggregating the best performing features across both the algorithm and data partitions afforded a more robust and stable set of generalizable features.
Although neither isoform could be detected using our proteomics approach, ADAM9 was upregulated at the RNA level, and the secreted form was found at a higher concentration in the serum of critical versus non-critical patients (Figure  6A  and B ). The transcriptional upregulation of ADAM9 was also found to be associated with disease severity in a previously published bulk RNA-seq dataset (Figure  S6 )  (45). To assess a potentially increased metalloprotease activity in the critical group, we quantified the soluble form of the MICA protein  (46) , which is known to be cleaved by ADAM9  (47)  by ELISA. The concentration of soluble MICA was indeed significantly higher in the plasma of critical patients as compared to noncritical patients (P=0. 016) and healthy controls (P=0. 0001; Figure  6C ).
A global eQTL analysis using WGS and RNA-seq data identified eight SNPs associated with three of the top five putative driver genes with genome-wide significance (Table  S6 ). Among these SNPs, rs7840270 is localized just 0. 3 kb upstream of the ADAM9 gene and an eQTL for blood expression reported in GTEX. In the present cohort, including all 3 groups together, the C allele was associated with a higher amount of ADAM9 transcripts (Figure  6D ) as it is in the GTEX dataset. The higher expressing allele C was indeed more frequent in critical than in non-critical patients (71. 3% vs. 50%, OR=2. 48, 95% CI: [1. 14-5. 36], P=0. 017) (this was not due to any difference in ethnicities between critical and non-critical groups; Figure  S7 ;  (48) ). ADAM9 RNA expression was significantly higher in the CC compared to CA (P=0.
049) and AA (P=0. 0046) genotypes only in the critical group, suggesting that the CC genotype was contributing to higher ADAM9 RNA expression most strongly in critical patients although the other phenotype subsets are smaller datasets (Figure  S8 ). To assess the role of ADAM9 in viral infection, we set up an ex vivo assay in which ADAM9 was silenced by siRNA in Vero 76 or A549-ACE2 (49) cells and subsequently infected the cells with SARS-CoV-2. Viral replication was monitored by flow cytometry quantification of the intracellular nucleocapsid protein and by quantitative viral RT-PCR of the culture supernatant (Figure  6E ). The average silencing efficiency reached 66% in Vero 76 cells and 93% in A549-ACE2 cells (Figure  S9 ). In both cell lines, the amount of intracellular virus and the quantity of released virus were lower when ADAM9 was silenced as compared to the control condition that was treated with a control siRNA (Figure  6F  and G ).
Data analysis The raw data obtained from each sample (45 critical patients, 23 non-critical patients, and 22 healthy controls) were processed using MaxQuant (version 1. 6. 14). Peaks were assigned using the Andromeda search engine with trypsin/P specificity. A database containing all human entries was extracted from the UniProtKB-SwissProt database (May 11 2020, 20410 entries). The minimal peptide length required was seven amino acids, and a maximum of one missed cleavage was allowed. Methionine oxidation and acetylation of the proteins' N-termini were set as variable modifications, and acetylated and modified methionine-containing peptides, as well as their unmodified counterparts, were excluded from the protein quantification step. Cysteine carbamidomethylation was set as a fixed modification.
The "match between runs" option was enabled. The maximum false discovery rate was set to 1% at the peptide and protein levels with the use of a decoy strategy. The normalized label-free quantification (LFQ) intensities were extracted from the ProteinGroups. txt file after the removal of nonhuman and keratin contaminants, as well as reverse and proteins only identified by site. This resulted in 336 quantified proteins. Complete datasets have been deposited in the ProteomeXchange Consortium database with the identifier PXD025265  (73).
Data analysis The raw data obtained from each sample (34 critical patients, 21 non-critical patients and 22 healthy controls) were processed using MaxQuant (version 1. 6. 14). Peaks were assigned using the Andromeda search engine with trypsin/P specificity. A combined human and bovine database (because of potential traces of fetal calf serum in the samples) was extracted from UniProtKB-SwissProt (8 September 2020, 26413 entries). The minimal peptide length required was seven amino acids and a maximum of one missed cleavage was allowed. Methionine oxidation and acetylation of the proteins' N-termini were set as variable modifications, and acetylated and modified methionine-containing peptides, as well as their unmodified counterparts, were excluded from protein quantification. Cysteine carbamidomethylation was set as a fixed modification.
The "match between runs" option was enabled. The maximum false discovery rate was set to 1% at the peptide and protein levels with the use of a decoy strategy. Only peptides unique to human entries were retained and their LFQ intensities were summed to derive the protein intensities. This process resulted in 2196 quantified proteins. Complete datasets have been deposited in the ProteomeXchange Consortium database with the identifier PXD 025265  (73).
Spike pseudotype neutralization assay was conducted using vesicular stomatitis virus (VSV) where glycoprotein gene (G) has been deleted and substituted in trans with SARS-CoV-2 Spike protein (D614G variant) lacking terminal eighteen amino acids of the cytoplasmic domain. The pseudotype VSV-ΔG SARS-CoV-2 S D614G is cytopathic and expresses the firefly luciferase. For the neutralization assay, VSV-ΔG SARS-CoV-2 S D614G was incubated with serial dilutions of patient sera starting at 1:40 dilution for 1 h at room temperature. A monolayer of Vero cells seeded at 60000 cells/well in black-well plates was infected with the pseudotype virus (virus only) or virus and serum mixture overnight at 37C -5% CO2. The following day, cells were lysed using passive lysis buffer (Promega, Madison, WI, USA) and luciferase activity was measured upon addition of substrate (Promega ® CellTiterGlo ® , Madison, WI, USA) using Biotek plate reader and Gen5 software.
Whole-genome sequencing (WGS) WGS data was generated from DNA isolated from whole blood. Novaseq TM 6000 (Illumina Inc. , San Diego, CA, USA) machines were used for DNA sequencing to a mean 30X coverage. The raw sequencing reads from FASTQ files were aligned using Burrows-Wheeler Aligner (BWA)  (77) , and GVCF files were generated using Sentieon version 201808. 03 (78). Functional annotation of the variants was performed using Variant Effect Predictor from Ensembl (version 101). GATK version 4 (79, 80) was used for the joint genotyping process and variant quality score recalibration (VQSR). We removed one duplicate sample based on kinship (king cutoff of 0. 3) and retained 24476739 SNPs that were given a 'PASS' filter status by VQSR. The analysis of the 72 samples from the critical and non-critical groups identified 15870076 variants with MAF < 5%.
RNA extraction Whole-blood RNA was extracted from PAXgene tubes with the PAXgene Blood RNA Kit following the manufacturer's instructions (Qiagen, Hilden, Germany). A total of 69 samples, including 46 critical and 23 non-critical patients were processed. The RNA quantity and quality were assessed using the Agilent 4200 TapeStation system (for the RIN) (Agilent Technologies, Santa Clara, CA, USA) and RiboGreen TM (for the concentration) (Thermo Fisher Scientific, Waltham, MA, USA). RNA sequencing libraries were generated using the TruSeq Stranded Total RNA with Ribo-Zero Globin kit (Illumina, San Diego, CA, USA) and sequenced on the Illumina NovaSeq 6000 instrument with S4 flow cells and 151-bp paired-end reads. The raw sequencing data were aligned to a reference human genome build 38 (GRCh38) using the short reads aligner STAR  (82).
Differential gene expression (DGE) analysis DGE analysis was performed for two different purposes: 1) for the combined omics analysis of differentially expressed genes and proteins, and 2) as step to determine feature selection for classification in the in silico computational intelligence approach. For the combined omics analysis, we first removed low expressed genes for the 69 samples by removing genes with less than 1 count per million in less than 10% of the samples. We then performed DGE analysis on all 69 samples using the trimmed mean of M-values method (TMM) from the edgeR R package  (84, 85). In our computational intelligence approach, we performed DGE analysis for each partition of the train data using a frozen TMM normalization to calculate normalization factors based only on the training data, in order to avoid data leakage.
After classification, feature scores for each method were determined and combined across all 100 partitions of the data and six of the ML algorithms, not including the deep learning algorithm. In order to capture as much information as possible while still being able to finish the analysis in a reasonable amount of time, the 600 most informative features were retained for structural causal modeling (600 features is the maximum that the structural causal modeling can finish in a reasonable amount of time). More details about the classification algorithms used, the feature ranking, and the structural causal modeling are provided below.
Deep artificial neural network (DANN) We adapted common deep learning methodologies to analyze genomic datasets  (99). Typical deep neural networks use a series of nonlinear transformations (termed layers), with the final output considered a prediction of class or regression variables. Each layer consists of a set of weights () and biases () that are tuned during the training phase to learn which nonlinear combinations of input features are most important for the prediction task. These types of models "automatically" learn patterns in the data and combine them in some abstract nonlinear fashion, to gain an ability to make predictions about the dataset. The basic formulation of a fully connected DANN is given as 𝐹𝑜𝑟 𝑚 𝑙𝑎𝑦𝑒𝑟𝑠 … { 𝑓 1 = 𝜌 1 (∑(𝑊 1,𝑗 × 𝑋 𝑗 ) 𝑑 1 𝑗=1 + 𝑏 𝑑 1 +1 ) 𝑓 2 = 𝜌 2 (∑(𝑊 2,𝑗 × 𝑓 1 ) 𝑑 2 𝑗=1 + 𝑏 𝑑 2 +1 ) 𝑓 𝑚 = 𝜌 𝑚 (∑(𝑊 𝑚,𝑗 × 𝑓 𝑚-1 ) 𝑑 𝑚 𝑗=1 + 𝑏 𝑑 𝑚 +1 ) where the dimensions of  and  are determined by the number of neurons in each layer ( 1 ,  2 , … ,   ).
Each layer used rectified linear units as activation functions: ρ 𝑙 (𝑧) = 𝑚𝑎𝑥(𝑜, 𝑧). The final layer used a softmax function, with the number of neurons equal to the number of classes (), to convert the logits to probabilities: ϕ(𝑓 𝑚 ) 𝑗 = 𝑒 𝑓 𝑚,𝑗 ∑ 𝑒 𝑓 𝑚,𝑘 𝐾 𝑘=1 for 𝑗 = 1, ⋯ , 𝐾, where  , is the output of the -th neuron of the -th layer. In addition, we used the concept of "dropout," which randomly sets a portion of input values (η) to the layer to zero during the training phase  (100). This has a strong regularization effect (essentially by injecting random noise) that helps prevent models from overfitting. Layers that included dropout were formulated as 𝑓 = ρ (∑(𝑊 𝑗 × 𝑋 𝑗 ) 𝑑 𝑗=1 + 𝑏 𝑑+1 ) × 𝑚 𝑙 , where   ~ Bernoulli(). When evaluating models on test datasets, the dropout mask was not used.
We acknowledge that our data might not strictly meet all of these assumptions, however, the generated BBNs provide useful biological hypotheses that could be experimentally validated. We determined BBNs using the bnlearn R package with the score-based hill-climbing algorithm that heuristically searched the space of all possible DAGs  (Scutari, 2010). As the hill-climbing algorithm can get trapped in local optima and is quite dependent on the starting structure, we initialized 100 BBNs starting from different network seeds. During the hill-climbing process, each candidate BBN was assessed with the Bayesian information criterion (BIC) score  (103, 104) : BIC = 𝑙𝑜𝑔 𝐿 (𝑋 1 , … , 𝑋 𝑣 ) - 𝑑 2 𝑙𝑜𝑔 𝑛, where  1 , … ,   is the node set,  is the number of free parameters,  is the sample size of the dataset, and  is the likelihood.
We assessed the statistical significance of the edges within the imposed consensus network by randomly permuting the dataset 10000 times and evaluating the consensus structure on these scrambled datasets (thus providing an estimate of the null distribution). BBN edges with a FDR of at least 5% (i. e. , the edge occurred in ≥500 of the random BBNs) were removed from the final network. After deriving a final consensus network structure, we performed a series of in silico tests to determine the importance of each gene to the network. For each of the 600 genes, we removed all incident edges (both incoming and outgoing) and recalculated the BIC of the entire network. Doing so resulted in a lower BIC, and the magnitude of the change in BIC is a measure of how important a gene is to the network. We also experimented with permuting the data corresponding to a single gene; the results for the mean change in BIC using the permutation test and removing all the incident edges did not significantly differ (Pearson's correlation > 0.
Acknowledgments We thank  Drs. Benjamin Terrier , and  Philippe Georgel  for critical reading of this manuscript,  Olivier Schwartz  and his team for sharing the A549-ACE2 cell line and  Christel Thauvin-Robinet  and  Franck Lethimonnier  ( AVIESAN ) for providing access to the AURAGEN PFMG2025 platform. We would like to acknowledge the  Cytometry Core Facility Hyperion (Brest, France)  for their technical assistance as well as colleagues from the  Immunophenotyping Module of CIPHE (Centre d'Immunophénomique -CIPHE (PHENOMIN) ,  Aix Marseille Université  ( UMS3367 ),  Inserm  ( US012 ),  CNRS  ( UMS3367 ),  Marseille, France)  for acquiring the mass cytometry dataset used in this work. We thank  Sweta R. Bajaj ,  Chen-Hao Chen , and  Allison R. McLean  for the bioinformatics processing of patient data,  Emmanuelle Jouanguy  for help with anti-IFN autoantibodies assessments,  Cecilia Hellström  for expert technical and bioinformatics assistance and  Léa Resmini  for technical assistance.
Funding This work was supported by the  Strasbourg's Interdisciplinary Thematic Institute  ( ITI ) for Precision Medicine, TRANSPLANTEX NG, as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  INSERM , funded by  IdEx Unistra  ( ANR-10-IDEX-0002  to  S. Bahram ) and  SFRI-STRAT'US  ( ANR-20-SFRI-0012  to  S. Bahram );  Institut National de la Santé et de la Recherche Médicale   UMR_S 1109 , the  Institut Universitaire de France , and  MSD-Avenir  grant AUTOGEN (all to  S. Bahram ); the  University of Strasbourg  ( including Initiative d'Excellence IDEX UNISTRA ; to  S. Bahram  and  R. Carapito ); the  European Regional Development Fund (European Union )  INTERREG V program  PERSONALIS (to  R. Carapito  and  S. Bahram ); the  French Proteomic Infrastructure  ( ProFI ;  ANR-10-INBS-08-03 ; to  C.
A method to include micro and macroplastic losses in LCI of seafood products is proposed. • Fishing gear, marine coating, tire abrasion, pellets and mismanaged waste are considered. • Fourteen fish products from the French Agribalyse database are analysed. • Plastic losses range from 75 mg to 4345 mg per kg of fish at the consumer. • Main plastic losses come from lost fishing gears and tire abrasion.
, 2016). The sustainability of the seafood sector involves reduction of plastic use and losses all along the supply chain. In order to support and strengthen the sector towards a systemic reduction in environmental impacts, a life cycle thinking is required  (Ruiz-Salmón et al. , 2020). In this context, life cycle assessment (LCA) is the most established methodology to assess environmental impacts of products. LCA has been increasingly applied to assess the environmental impacts of seafood products with more than 60 case studies published in the scientific literature  (Ruiz-Salmón et al. , 2021). Also, an increase of seafood datasets is observed in life cycle inventory (LCI) databases  (ADEME, 2020). However, the quantification of plastic emissions to the environment and their related impacts are still not considered in LCA (in both LCI database and LCIA methods).
Currently, LCA is not adequately addressing the impacts due to marine debris plastics and microplastics  (Sonnemann and Valdivia, 2017). The LCI datasets do not take into account plastic leakages to the environment. With regard to a methodology to assess the environmental impacts associated with these plastic emissions  Woods et al. (2021)  have published a first framework for the assessment of marine litter impacts in life cycle impact assessment (LCIA), which is planned to be made operational as part of the MariLCA working group  (Verones et al. , 2020). Several set of effect factors have already been developed, e. g. , for macroplastic entanglement impact  (Woods et al. , 2019) , or physical impact of microplastics on ecosystem quality  (Lavoie et al. , 2021). Another challenge is to quantify the transport and the degradation of plastics within the environment in order to provide a complete fate factor  (Saling et al.
, 2020). Concerning LCI, the Plastic Leak Project (PLP) has been an important step forward as they provide several data and factors that can be used for building life cycle inventories and modelling the pre-fate  (Peano et al. , 2020). Maga et al. (2021)  state that LCA datasets containing product or rather process specific inventory flows to address the initial release of plastics into the environment have not yet been generated, and that developing a stringent methodology to address plastic emissions as part of an LCA bears multiple complex challenges. Also, GreenDelta aims to create a plastic litter extension for the ecoinvent database, as part of its PLEX project  (Ciroth and Kouame, 2019). The PLP and the GreenDelta approaches have already been applied and compared by the European Commission in the frame of the LCA of alternative feedstocks for plastics production.
The considered background processes are the plastic production activities for packaging and other plastic materials used in the life cycle. The next step is the identification of inputs and outputs related to plastics materials. Two types of input and output are considered in LCI: intermediate flows which are exchanged within the technosphere, and elementary flows which are exchanged between technosphere and ecosphere. Intermediate flows are already well defined in LCI datasets. However, there is none information on the elementary flows of plastics that reach the environment. These flows are also named plastic losses. The methodology we propose aims to identify and quantify these flows for seafood product life cycles. Based on existing framework  (Peano et al. , 2020)  we identified five types of plastic losses from the seafood product life cycle: -Abandoned, lost or discarded fishing gears during fishing activities (macroplastics), -Marine coatings applied to boats that can leak during fishing activities (microplastics), -Plastic pellets that can be lost to the environment during plastic production in the background (microplastics), -Plastics from tire abrasion during transport activities (microplastics), -Mismanaged plastics during the end-of-life (macroplastics).
The authors define average loss for nets, pots and trap, and lines. We selected the fishing gears most commonly found in Agribalyse 3. 0 LCI datasets. Fishing boats also use plastic fishing boxes, that can also be lost to the sea. However, there is no available data on this type of loss. Therefore, plastic items used on boats that are not fishing gears were considered with the same loss rates as packaging material (see Section 2. 2. 5). For active fishing activities, data for loss rates are reported as fragments of nets lost, as opposed to whole net loss  (Richardson et al. , 2019). This is because such nets are fixed to the boats and an entire net loss is rare for these gear types, while the incidence of net tear offs is more common. Nevertheless, the size of these fragments in relation to the total size of the net is not defined by the authors.