dataesr/scientific-paragraphs-categorization huggingface dataset:
Application to French fish products of the Agribalyse database Aiming to validate the approach presented in Section 2. 2, we have applied the factors to existing LCI datasets in Agribalyse 3. 0 database. For this case study, the functional unit (FU) is "the consumption of 1 kg of fish". System boundaries are the ones presented in Fig. 1. The quantities of plastics in the technosphere were extracted from the Agribalyse database. Agribalyse contains more than 200 specific LCI datasets of raw products, including agricultural but also fishery products. LCI datasets of fishery products were collected through the specific project ICV Pêche  (Cloâtre, 2018). It provides specific data for the fishing activities (including quantity of fishing nets and fishing boats). Data are available for 12 different species (Table  2 ), through the definition of triplets related to: target species, fishing area and fishing gear.
It should be noted that two species originally referred as "seine" fishing (skipjack and yellowfin tuna) were considered as "purse seine" fishing because they also rely on fishing aggregating devices that are usually used with purse seine. Agribalyse also provides reference data on 2500 food products consumed in France. These data rely on many assumptions and approximations and correspond to "medium/standard" products that are detailed in the Agribalyse methodological report  (ADEME, 2020). It includes data related to: transport, processing, packaging (only primary), household consumption and end-of-life of food products. Product losses are also considered with generic values (from the Agribalyse methodological report) at the processing stage, retail stage and consumer stage. Thus, the LCI provides elements related to flows of plastics in the technosphere during these stages of the seafood supply chain.
The collection of these processes as well as the computation of plastic losses has been automated in an Excel tool (available in SI), with the different steps explained hereafter (Fig. 2 ): -All process requirements for 1 kg of Fish are obtained through openLCA software and imported in the Excel tool. -The relevant processes are automatically identified in the Excel tool. The quantities of each process are multiplied with the correct LR and FRR from Table  1. -Resulting quantities of plastic loss and final release are assessed through Sankey diagrams, contribution analysis (to identify the stages that generate most plastics into the environment) and comparison analysis between different types of fish products. We selected 14 different fish product life cycles to be analysed (Table  2 ): 12 of them for the different species with a common packaging (polystyrene-PS); 2 additional datasets were selected to study the effect of packaging and processing.
Typical plastic requirements within the life cycle of a specific fish product This section focuses on the results for one specific fish product life cycle, i. e. , fresh saithe packaged in polystyrene (PS), the functional unit being 1 kg of fish at the consumer. Fig. 3  shows the flows of plastics within the technosphere, which are directly available from Agribalyse datasets. The FU requires 63. 7 g of plastics/kg of fish at the consumer. The highest plastic requirement is the polystyrene packaging (52 g). This value is higher than the packaging mass involved during the packaging phase (50 g of packaging/kg of fish) due to products losses in the distribution chain. The second highest plastic requirement is the fishing gear as it represents 9 g of plastics/kg of fish at the consumer. Fresh saithe is fished with bottompair trawl (63 m of headline) which is composed of 350 kg ethylene vinyl acetate copolymer, 5000 kg polyethylene high density, 3500 kg synthetic rubber (as well as 5934 kg of steel).
Such bottompair trawl can fish up to 2185 t of saithe over its life time, resulting in 4 g of plastics/kg of fish at fishery. In this case, 2. 23 kg of fish at the fishery is required for 1 kg of fish at the consumer (considering the losses and non-edible parts of fish). Remaining flows of plastics in the technosphere are below 1. 5 g/kg of fish at the consumer and include plastics for marine coatings, other plastics materials in the fishing boats, and truck tires. As the Agribalyse datasets is connected to ecoinvent 3 system process, it is not possible to identify all plastic requirements in the background (for example within the infrastructure). We assume these plastics flows are minor. Quantity of plastics at the end-of-life should be the sum of all plastic requirements (i. e. , 63. 7 g/kg of fish) since Agribalyse and ecoinvent do not consider emissions of plastics to the environment.
Initial plastic losses from the life cycle of a specific fish product Fig. 3  also shows plastic losses to the environment calculated based on factors from Table  1. The average value of plastic losses is 168. 8 mg/kg of fish at the consumer, which represents 0. 27% of the total plastic requirements (63 g). Major plastic losses are macroplastics from lost fishing gears (81. 3 mg) and microplastics from tire abrasion during transport (62. 6 mg). The value for tire abrasion can also be compared with ecoinvent dataset that considers "tyre wear emission" for transportation. The dataset "transport, freight, lorry 16-32 metric ton, EURO6 {RER}" considers 2. 2E-04 kg/tkm of tyre wear emissions  (Spielmann et al. , 2007). Considering 60% of this emission as plastic, this is 5 times higher than the average loss rate considered by PLP guidelines (which is 2.
Comparison between different life cycles of fish products Table  4  shows the plastic requirements, and the plastic losses for the 12 different fish species studied from the Agribalyse datasets, computed with average loss rates. The downstream life cycle stages (processing, packaging and use) are considered equivalent in all life cycles: filleting, polystyrene (PS) packaging, no preparation at the user. Total plastic losses vary from 74. 1 mg/kg of fish (mackerel/pelagic trawling) to 4344. 9 mg/kg of fish (sole/gillnetting). The life cycles of two fish species product systems (sole/gillnet and swordfish/longline) generate plastic losses with one order of magnitude higher than the other ones. This is because they rely on high quantity of fishing gear per kg of fish (72. 1 g and 14. 9 g, respectively) and their fishing gears have high loss rates (5.
Perspectives LCI data generated in this paper could be implemented into the Agribalyse datasets concerning the seafood sector. The proposed methodology and procedure presented in Fig. 2  could also be applied for all types of agri-food products studied in Agribalyse. In this case, the plastic loss rates defined in this study for plastic production, processing/packaging and end-of-life processes could be used for the agri-food products. New loss rates should be developed for specific activities that use plastic such as aquaculture (e. g. , loss of plastic buoys at sea for oyster production) and agriculture (e. g. , loss of plastic mulching in the soil). However, we acknowledge that the implementation of plastic losses would be partial because it only includes losses in the foreground system, and on some processes in the background (plastic production).
Wider database such as ecoinvent should also be updated to consider plastic emissions in all sectors and in all background processes. This is an important step towards the consideration of plastic related impacts in LCA. A more general challenge is to develop mass balanced unit processes regarding intermediate and elementary inputs and outputs. This is also the case for other types of materials such as metals for which their dissipation is not well accounted yet  (Beylot et al. , 2021). Past initiatives have shown that existing database can be fully updated to consider mass balance of a specific material or resource, e. g. , water  (Pfister et al. , 2015). In order to expand data on plastic flows, several data sources can be used. Maga et al. (2021)  classify 4 types of data sources: survey, statistical data, empirical measurement and probabilistic models.
Experimental Results The proposed QBF method is tested on a Linux machine with Intel Xeon E5-2630 CPU (2. 3 GHz) and 200 GB RAM. Several state-of-the-art QBF solvers are tested and compared, including DepQBF  [12] , RAReQS  [6] , QELL  [17] , and the 2QBF solver in Berkeley ABC  [3, 2]. We randomly generated 25 test cases by the tool FSMTest-1. 0 [15]  for performance evaluation. 3 Binary encoding is applied, and for input encoding, all the codes are used as discussed in Section 4. 1. Circuit minimization is also applied to minimize the transition relation of each case. Then Tseitin transformation is applied to convert the formulas into PCNF for DepQBF, RAReQS, and QELL. For each case, its potential homing sequences of length k, for k = 1,. , 1023, are tested under a timeout limit of 7200 seconds.
Each of "m", "o", "m+c", "o+c" uses all codes for input and output encodings. On the other hand, Columns "o+b" do not use all codes for encoding, and clauses are added to constrain inputs to legal code assignments. Both circuit minimization and formula negation are not applied in Columns "o+b". It can be seen that for DepQBF, transition relation minimization is beneficial in most cases. Also, formula negation substantially improves the performance, with the verified lengths doubled within timeout, and even case 1 reached the pre-specified upper bound 1023 before timeout (about 1542 seconds for solving case 1). On the other hand, for RAReQS, using the onset of transition relation without circuit minimization is better in most of the cases. Moreover, solving the negated formula is also much faster than solving the original formula, with the verified lengths increased to at least 2.
This set of features will be crucial knowledge when setting up functional validation experiments for data reported in patient samples. In addition, to broaden the clinical perspectives of this panel of IBC preclinical models, their sensitivity profile to a wide range of therapeutic agents was estimated using the CMap data set of 1. 3 million L1000 signatures that reflect transcriptional responses of human cells to chemical and genetic perturbations. It stands to reason that these efforts will also contribute to a more detailed comprehension of biological themes intrinsic to IBC cells.
Cluster analysis and molecular subtyping To investigate differences between IBC (n = 10) and nIBC (n = 22) preclinical models, we merged expression profile analysis performed in our Institution with external gene expression data from various public resources (Gene Expression Omnibus: GSE12777, GSE16795, and GSE40464; and ArrayExpress: E-MTAB-7). To assess the efficiency of the normalization strategy, unsupervised hierarchical clustering analysis (UHCA) was performed for all 124 profiles and for the 500 most variable genes selected by standard deviation. Results are shown in Fig. 1a. The NbClust algorithm identified four clusters in the data set that were significantly associated with the ER status (P < 0. 001), the PR status (P < 0. 001), the HER2 status (P = 0. 043), the ER/ HER2 combined subtypes (P < 0.
In a second strategy to characterize the IBC preclinical models, weighted gene co-expression network analysis (WGCNA) was applied onto the averaged GEPs. Using data for all available genes, 22 distinct gene co-expression modules were identified with sizes ranging from 104 to 871 genes. Details regarding the network construction and module detection are shown in Supplementary Fig. 3  and different co-expression module statistics are summarized in Table  2. The correlation structure of the 22 co-expression modules was investigated and revealed the existence of three coexpression clusters (Fig. 2b ). Gene set enrichment analysis (GSEA) of the gene-module memberships (GMM) scores (Supplementary Data 2) revealed distinct hallmark enrichment patterns for each of these co-expression clusters (Fig.
Identification of co-expression cluster regulators and antagonizing chemical compounds Based on the co-expression modules analysis, we then aimed to identify potential modulators of IBC biology as well as potential drug/target combinations for therapy using the CMAP data set. Connectivity Scores (CSs) are provided in Supplementary Data 3 for all comparisons. As a proof-of-concept, we focused on the CSs of RELA and MYC and demonstrate that knockdown of these transcription factors induced a gene expression profile that was opposite to the characteristic expression profile of the gene coexpression modules enriched for genes involved in NFκB and MYC signaling respectively (Table  2  and Supplementary Fig. 4 ). In addition, we also evaluated all E2F transcription factors (i. e. , E2F1 to E2F9) and revealed that particularly the CSs associated with E2F3 knockdown were reduced in those expression modules enriched for E2F target genes.
Genes color-coded yellow and blue are overexpressed in IBC and nIBC respectively at a false discovery adjusted p value of 10%. The top 10 overexpressed genes in IBC and nIBC cell lines are labeled. b Heatmap representing the correlation structure of 22 co-expression modules identified using WGCNA. Pearson correlation coefficients resulting from pairwise comparisons of the eigengenes of the different coexpression modules are coded according to a blue-red color scheme reflecting correlation coefficients ranging from -1 to 1. Row and columns are labeled with the names of the co-expression modules and are ordered according to an unsupervised hierarchical cluster analysis. The three co-expression cluster groups are indicated in orange squares. c Dot plot representing the result of a gene set enrichment analysis (GSEA) obtained by comparing the gene-module membership scores for each co-expression cluster to the hallmark gene sets.
The heatmap for example shows that MYC expression (i. e. , top row) in nIBC samples with high ER levels (i. e. , 2nd column) decreases significantly (i. e. , blue color) as compared to nIBC samples with low ER expression, whereas relative to the same category the MYC expression in IBC samples with high ER levels increases significantly. P values evaluating the significance of the changes are indicated in each cell. corresponding primary tissue sample. However, since gene expression changes are not in line with the expected results, this observation requires further investigation. To further delineate the defining principles of IBC biology, two distinct analysis strategies were undertaken. First, DEGs between IBC and nIBC cell lines were identified. Second, WGCNA was applied to detect modules of co-expressed genes in the series of IBC cell lines only.
Furthermore, the WGCNA approach revealed that several of these processes are jointly regulated. For example, MYC and NFκB target gene expression were the dominant themes in the second co-expression cluster, which suggests an intricate relationship between both transcription factors in shaping IBC biology. NFκB activation in IBC has been reported previously  29, [40] [41] [42]  , and MYC and NFκB have been shown to cooperate in breast cancer development and progression  [43] [44] [45] [46]  , amongst others by modulating stem cell behavior. By comparing gene co-expression clusters to the L1000 profiles of the CMAP database, we also revealed sets of potential regulators and target/drug combinations. Unfortunately, no single drug with predicted efficacy across all IBC cell lines was identified.
This is corroborated by earlier work of Zhang et al. who demonstrated that MYC is a central hub in the signal transduction networks of SUM149 and SUM190  47. In addition, we recently showed that MYC mediates the specific response of IBC cells to TGFβ1 treatment  10. To confirm these observations, MYC expression and transcriptional activation were explored using GEPs from IBC and nIBC tissue biopsies. Our data demonstrate that the levels of MYC expression and transcriptional activation in relation to ESR1 expression exhibit opposite patterns in IBC and nIBC with an ER-dependent decrease and increase in mRNA levels of both MYC and MYC target genes in nIBC and IBC respectively. Particularly the positive association between ER and MYC expression or transcriptional activation in IBC is notable, as it remains significant even when accounting for tumor stage and the PAM50 subtypes and thus cannot be attributed solely by the enrichment of the Luminal B phenotype amongst ER+ IBC tissue biopsies.
Gene expression data from cell lines We profiled a series of nine IBC (i. e. , SUM149, SUM190, KPL4, Mary-X, MDAIBC03, FCIBC01, FCIBC02, TJIBC04, and TJIBC09) and three nIBC (i. e. , MCF7, MDAMB231, and SUM159) preclinical models at least in triplicate using Affymetrix HGU133plus2 GeneChips. Using the same platform, an additional gene expression profile (GEP) of our in-house generated PDX model for IBC (i. e. , UAIBC01) was generated and included in the study, yielding a total of 57 GEPs. The SUM149, SUM190, KPL4 and MDAIBC03 cells were a kindly gift from MD Anderson Cancer Center, TX, USA. The FCIBC01, FCIBC02, TJIBC04, and TJIBC09 cells were a kindly from M. C. and the Mary-X model from Dr. Barsky. The nIBC cell lines were purchased from ATCC (Manassas, USA). To expand the group of nIBC preclinical models, four additional gene expression data sets generated using the Affymetrix HGU133 series were retrieved from public resources (Gene Expression Omnibus: GSE12777, GSE16795, and GSE40464; and ArrayExpress: E-MTAB -7).
Expression data for 19 extra nIBC models was avalaible, i. e. , BT-20, BT-474, BT-483, BT-549, CAMA1, HCC1937, HS578T, MDAMB134VI, MDAMB175VII, MDAMB361, MDAMB415, MDAMB436, MDAMB453, MDAMB468, SKBR3, T47D, UACC812, ZR751, ZR7530. To reduce technical bias due to interlaboratory variability in these data sets, only cell lines that were profiled at least in triplicate were included. In total, 124 expression profiles of 32 different breast cancer models (i. e. , 10 IBC models and 22 nIBC models) from five different data sets were included. For each individual data set (N = 5), expression data were normalized using the Robust Multi-array Averaging algorithm with correction for GC probe content (BioConductor package gcrma-v. 2. 60. 0) and probe sets with a fluorescence intensity above log 2 (100) in at least two samples were included.
The individual data sets were then merged based on the common probe sets (N = 10,961) and batch effects were removed using empirical Bayesian methods (i. e. , the combat function implemented in the BioConductor-package sva-3. 36. 0), with protection of cell line-specific variation in gene expression. The resulting data set was then subjected to quantile normalization and probe set redundancy was removed by retaining the probe set with the highest standard deviation per gene for a total of 7182 unique genes. As a final step, replicate GEPs were averaged.
Gene expression data from patient samples Gene expression data from 146 IBC and 252 nIBC tissue samples have been described earlier  9. However, in this study, raw GEPs were reprocessed using a similar normalization strategy as described for the preclinical models in order to ensure data comparability. Batch effects due to the inclusion of samples from three distinct sites (i. e. , MD Anderson, Institut Paoli-Calmettes and GZA Hospitals Sint-Augustinus) were removed using empirical Bayesian methods. The final processed data set contained 12,769 probes sets for 8086 unique genes. Finally, breast cancer gene expression data from the TCGA (Firehose legacy) and METABRIC series were downloaded from the cBioPortal for cancer genomics (https://www. cbioportal. org) using the R package cgdsr (v. 1.
Unsupervised analysis UHCA was performed using Manhattan distance as dissimilarity metric and Ward clustering as the dendrogram drawing method. Prior to cluster analysis, data were centered and scaled to unit variance. The optimal number of clusters, ranging from 2 to 10, was determined by evaluating cluster separability based on 30 distinct indices (BioConductor package NbClust-v. 3. 0). The clustering scheme that was supported by the majority of these indices was selected.
Classification Preclinical models and patient samples were classified according to the PAM50 molecular subtypes 63 using the BioConductor package genefu (v. 2. 20. 0). Furthermore, the preclinical models were classified according to the ER status, the PR status, the HER2 status  64  , the luminal/basal/ mesenchymal classification (LBM) system for breast cancer cell lines  65  , the DPM  66  , and the IBC-specific classification model composed of 79 probe sets  9. For the PR status, nIBC cell lines reported to be PR negative by Dai et al. were considered as PR negative whereas nIBC cell lines with weak or strong PR expression were classified as PR positive  64. In addition, IBC and nIBC patient samples were stratified into ER low, moderate and high expression groups based on the 33rd and 66th quantiles of the ESR1 mRNA levels.
To perform the classification according to the signature of 79 IBCspecific probe sets, a model based on elastic net generalized linear regression was optimized on GEPs of the tumor samples using the R package caret (v. 6. 0-86). This data set was split into a training and validation set according to a 3/1 split ratio. Prior to model construction, the data were centered and scaled to unit variance. Model construction was performed using repeated tenfold cross-validation against a tuning grid of alpha values ranging from 0 to 1 with 0. 1 increments and lambda values ranging from 0. 001 to 0. 1 with 0. 001 increments. The optimal model was selected using ROC statistics and was then applied onto the validation set of tumor samples to define the model accuracy and onto the data set of preclinical models to record posterior probabilities for each breast cancer model.
Differential expression analysis Identification of DEGs was performed using the BioConductor package limma (v. 3. 44. 3). Resulting p values were corrected for false discovery using the Benjamini and Hochberg procedure and false discovery rate (FDR)corrected p values inferior to 10% were considered significant. To identify genes presumably differentially expressed between the UA-IBC-01 preclinical models and to primary tumor sample it was derived from, gene-wise differences in expression between both samples were calculated by subtracting the expression levels measured in the cell line from those measure in the tumor sample. Genes overexpressed in the tumor sample and the cell line were then defined based on respectively the 97. 5th and the 2. 5th percentile of the global distribution of the genewise expression differences.
Weighted gene co-expression network analysis (WGCNA) To identify gene co-expression modules in the series of 10 IBC preclinical models, WGCNA was performed using the R package WGCNA (v. 1. 69) and following online instructions (https://horvath. genetics. ucla. edu/). To construct a signed co-expression matrix, pairwise biweight midcorrelation coefficients were calculated between all 7182 genes. The resulting adjacency matrix was transformed into a weighted network by raising the biweight midcorrelation coefficients to a power that was chosen for the resulting network to adhere to scale-free topology. Detection of co-expression modules was performed using UHCA by subjecting the topological overlap dissimilarity matrix of the network to Ward clustering. The resulting dendrogram was analyzed using an adaptive branch pruning algorithm combined with partitioning around medoids to assign genes to co-expression clusters enforcing a minimum size of 100 genes and co-expression clusters with a similar profile were merged.