dataesr/scientific-paragraphs-categorization huggingface dataset:
In those cases where information on the "probable starting" year was absent, we conservatively considered the year of publication of the primary data source as a benchmark, and derived the required information based on other temporal details provided in the source, if available. Using this information, we calculated the duration of each cost as the number of years between the probable starting and ending years. In doing so, some cost estimates with unspecified durations were either deemed to occur over a single year only, if they were provided on an annual basis, or not annualised if they have occurred over (unknown) longer periods. We acknowledge that this may cause some bias in temporal trends towards certain years, however, it does not inflate costs overall, but rather likely underestimates them.
Our focus on broader geographic scales of analysis meant that we relied on potentially variable country-level trends in economic impacts, but this was a stronger test of broadscale directionality in cost patterns, since increased noise would be expected to dampen any largescale effects. We further focused on the case of Europe and differences among European countries as the InvaCost database contains abundant cost data from various European countries  (Haubrock et al. , 2021a (Haubrock et al. , , 2021b)). Further, species invasions are not stopped by political boundaries  (Liu et al. , 2021) , making it important to assess whether the countries within one continent could similarly react to surging invasions in order to jointly reduce invasion impacts. We used the S-statistic and its variance as a comparable effect size  (Kendall, 1948)  of the trend, as recently utilised in similar meta-analytical approaches using ecological time series  (Daufresne et al.
e. , the origin of individual costs;  Viechtbauer, 2010; Viechtbauer and Viechtbauer, 2015). The analysis was performed without intercepts, forcing the linear regression to pass through the origin, thus making the assumption that costs initiate at zero. We explored heterogeneity of trends (I 2 ) following the approach by  Higgins and Thompson (2002)  as implemented in the metafor R package and investigated how trends (specifically their slope and associated increase) differed across geographic regions (i. e. , locations in which costs were incurred), environments (aquatic, terrestrial, semi-aquatic), and taxonomic classes. These explanatory variables showed little collinearity (|r| < 0. 5) and were thus all retained as predictors.
, 2013). These tend to be limited in the developing part of the world or published in languages different than the ones employed in searches used in constructing the InvaCost database  (Angulo et al. , 2021; Diagne et al. , 2021a Diagne et al. , , 2021b ). These regional increases also fail to account for trends at more granular national scales, where particular countries may exhibit disproportionate contributions to total costs, such as South Africa in the African continent, which is a pioneer with many experts in IAS science  (van Wilgen et al. , 2020). Data disparities also contributed to our inability to model countries other than Europe at the sub-continental scale, and thus we are unable to discern the potentially highly variable pattern in trends across countries. A similar difference between cost reporting and magnitude of monetary costs was also apparent in Europe, where directionality and strength of trends in cost reporting and actual costs did not coincide across countries.
This trend could also be explained by nationalscale differences, as we found only two countries (Spain and Italy) to express a significant increase in cost reporting literature, while several countries indicated a significant increase in costs (e. g. , Finland, UK, Spain, Portugal and The Netherlands). However, the faster accrual for Spain may be a database-driven artefact because: (i) while compiling InvaCost, non-English literature (especially in Spanish) constituted a considerable subset of the currently available data  (Angulo et al. , 2021) , and (ii) out of the 75 references included in the InvaCost database for Spain, 80% (60) were obtained through targeted efforts (e. g. , consultations with environmental managers from all regions in the country;  Angulo et al. , 2021) ; including several grey literature sources.
This may also partly reflect the differences in targeted searches when building the database, which was also influenced by the linguistic capacity of the database creators  (Angulo et al. , 2021) , as opposed to other countries (e. g. , Eastern European and Balkan countries) for which targeted searches were absent. Nevertheless, these gaps also highlight that the actual availability of cost information may be even higher among seemingly understudied regions due to a lack of publicly available literature or literature in English, possibly implying even higher costs and likely steeper increases in costs compared to those reported here. Beyond high levels of awareness and research capacity, European countries may also have a shorter lag time in the reporting of costs, with countries or regions in the Global South and/or the developing world lagging behind in their efforts to record costs of IAS, leading to less steep or stagnating trends.
On the other hand, the trend for Polypodiopsida may be driven by the fact that, although the invasive potential of terrestrial ferns was until recently overlooked, recent studies reveal that ferns have a high invasive potential  (Jones et al. , 2019; Yanez et al. , 2020). Therefore, the increased attention towards these groups in recent years most likely influenced the taxonomic heterogeneity at the class level. Our results highlight significantly increasing research interest as well as monetary impacts of biological invasions globally over time and also uncovers key regional differences driven by variability in cost reporting across countries and taxa. Since InvaCost is a living database for which new costs are frequently added as they become available, the observed trends only reflect the latest version of the database and are likely to change in the future.
Acknowledgement and funding The authors thank three anonymous reviewers for their valuable input and improvements to our manuscript. Further, the authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP Paribas Climate Initiative  for funding the  Invacost  project that allowed the BNP construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. RNC is funded by a  Research Fellowship  from the  Alexander von Humboldt Foundation.
Then the cDNA fragments were purified with a QiaQuick PCR extraction kit, end-repaired, poly (A) added, and ligated to Illumina sequencing adapters. The ligation products were size selected by agarose gel electrophoresis, PCR-amplified, and sequenced using Illumina HiSeq TM by Genedenovo Biotechnology Co. , Ltd. (Guangzhou, China). The obtained raw data from constructed cDNA libraries were deposited in NCBI Sequence Read Archive (SRA, http://www. ncbi. nlm. nih. gov/Traces/sra/ (accessed on 1 June 2022)) under the accession number: BioProject PRJNA876066.
AS Event Statistics The rMATS software (http://rnaseq-mats. sourceforge. net/index. html (accessed on 1 June 2022)) was used to analyze AS events. AS events were divided into five categories: SE, A5SS, A3SS, MXE, and IR. As a unit, each comparison group was used for differential AS analysis. We counted the types and numbers of AS events that occur, calculated the expression amount of each type of AS event, and calculated each type of AS event. In the quantitative process, the quantitative method adopted by rMATs is read on target and junction counts.
Functional Enrichment Analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were performed for AS genes (ASGs) and differential AS genes (DASGs). A GO term was considered significantly enriched if the false discovery rate (FDR) cut-off was <0. 05. The KEGG pathways were assigned using the KEGG software package (http://www. kegg. jp/ (accessed on 1 June 2022)), and considered significant if p < 0. 05.
DASGs between hybrid and parents were screened out. Functional enrichment analysis showed the metabolic pathways enriched by DASGs were different in different combinations. There were six pathways in which DASGs were significantly enriched in eight or more hybrids, and those metabolic pathways were believed to be significantly enriched in most hybrid combinations. Those significantly enriched pathways included ubiquitinmediated protein (ko04120), circadian rhythms-plant (ko04712), spliceosome (ko03040), microbial metabolism in diverse environments (ko01120), 2-oxocarboxylic acid metabolism (ko01210) and flavor and flavor biosynthesis (ko00944). The reasons for the formation of heterosis are complex and diverse, and these results provide a basis and reference for subsequent research. By comparing transcriptomic data collected from hybrid individuals and their parents, 76 DASGs in strong heterosis combinations were identified.
Our mining of publicly available transcriptomic expression datasets indicates that both the male and female reproductive sphere organs are sources for detecting a significant number of missing proteins  7. However, it can be anticipated that most of these will be particularly difficult to access due to their physicochemical properties (membrane proteins, defensins, etc. ) or to narrow windows of expression. In the present work, we pursue our search for missing proteins in the reproductive sphere with a focus on the early human embryonic development.
We thus hypothesized that missing proteins might be accessible in human embryos. To overcome the above-mentioned challenges, stem cell models are commonly used as a proxy for human embryos. In order to study human peri-implantation, 3 main stem cell types are used. The first is naive pluripotent stem cells (hNPSCs) with the characteristics of the preimplantation epiblast  9. The second is trophoblastic stem cells (hTSCs) with the characteristics of post-implantation cytotrophoblasts  10. Those stem cells models are often compared to the primed pluripotent stem cells (hPSCs), not studied here, which represent post-implantation epiblast, a later stage of development than the hNPSCs  9  (Figure  1 ). Here we describe a strategy combining the analysis of RNASeq datasets and MSbased experiments to identify and validate missing proteins in hNPSCs and hTSCs.
The acquisition of the MS mass spectra with the TIMS TOF Pro is done with an average resolution of 60 000 FWHM (mass range 100-1700 m/z). To enable the PASEF method, precursor m/z and mobility information was first derived from full scan TIMS-MS experiments (with a mass range of m/z 100-1700). Resulting quadrupole mass, collision energy and switching times were automatically transferred to the instrument controller as a function of the total cycle time. The quadrupole isolation width was set to 2 and 3 Th and, for fragmentation, the collision energies varied between 31 and 52 eV depending on precursor mass and charge. TIMS, MS operation and PASEF were controlled and synchronized using the control instrument software OtofControl 6. 2 (Bruker Daltonik). LC-MS/MS data were acquired using the PASEF method with a total cycle time of 1.
Protein identification Ion mobility resolved mass spectra, nested ion mobility vs m/Z distributions, as well as summed fragment ion intensities were extracted from the raw data file with DataAnalysis 6. 0 (Bruker Daltonik). Signal-to-noise (S/N) ratio were increased by summations of individual TIMS scans. Mobility peak positions and peak half-widths were determined based on extracted ion mobilograms (±0. 05 Da) using the peak detection algorithm implemented in the DataAnalysis software. Features detection were also performed using DataAnalysis 6. 0 software and exported in. mgf format. Peptides identification were performed with the Mascot search engine (version 2. 6. 2, Matrix Sciences), applying the previously described search parameters and using its automatic decoy database search to calculate the false discovery rate (FDR)  4, 7.
The search database was the complete human proteome homo sapiens UP000005640 from UniProtKB 2022_02 restricted to one protein sequence per gene. Briefly, 1 miscleavage for trypsin was allowed and mass tolerance of peptides and fragments was established at 15 ppm and 0,05 Da. Moreover, mass modifications of peptides ACCEPTED MANUSCRIPT / CLEAN COPY are taken into account. For fixed modifications, carbamidomethylation of cysteines and for variable modifications, oxidations and acetylation of lysines and N-term proteins were considered. After interrogations on Mascot, data processing was performed using the Proline software (version 2. 1. 0). All the results of the queries performed on Mascot were imported into Proline with a subset threshold of 1. After importation, the results were validated with a peptide pretty rank of 1, an FDR for PSM of 1% on adjusted e-value and an FDR for protein set of 1% with a standard scoring.
06% for PSM, 0. 09% for peptide set, 0. 52% for protein set. The merge of the two hTSCs conditions provided an expected false positive rate of 0. 13% for PSM and peptide set and 0. 85% for protein set. On top of the standard Mascot search, all datasets were searched using the Graphics Processing Units (GPU)-based PaSER 2022 V3 solution (Bruker). PaSER was configured to use the ProLucid search engine  12  with the complete human proteome homo sapiens UP000005640 from UniProtKB 2022_02 restricted to one protein sequence per gene and with modification definitions as the ones used with Mascot. With PaSER, the acetylation of lysines and N-term proteins was parameterized at the peptide level and not at the protein level like Mascot. The mass tolerance of peptides for PaSER was fixed at 30 ppm. Both protein and peptide FDR thresholds were set to 1%.
TIMScore was enabled to allow the use of the peptide Collisional Cross Section (CCS) during the scoring process. When using the PaSER search engine, the total number of expected false positives for hNPSCs classically dissociated from cultures dishes with trypsin, was 0. 22% for PSM, 0. 25% for peptide set, 0. 99% for protein set, whereas for hNPSCs directly ACCEPTED MANUSCRIPT / CLEAN COPY dissociated from culture plates with the lysis buffer, the total number of expected false positives was 0. 30% for PSM, 0. 36% for peptide set, 0. 98% for protein set. For hTSCs classically dissociated from cultures dishes with trypsin, the total number of expected false positives was 0. 31% for PSM, 0. 36% for peptide set, 0. 99% for protein set, whereas for hTSCs directly dissociated from culture plates with the lysis buffer, the total number of expected false positives was 0.
25% for PSM, 0. 33% for peptide set, 0. 98% for protein set. An essential component to TIMScore is defining the deviation between experimental and predicted CCS values. Machine learning was used in order to accurately predict CCS values from a peptide's primary amino acid sequence. A training dataset of hun-dreds of thousands of tryptic and phosphorylated peptides was used, where the dataset included peptides of doubly, triply and quadruply charge states. A transformer model of peptide CCS was developed from this training set. The model was tested for accu-racy against an independent dataset it had previously not seen. For doubly, triply and quadruply charged peptides the accuracy in predicting a peptide CCS from the primary amino acid sequence was 95% for tryptic pep-tides. Upon setting up the parameters file, in silico peptide candidates are sent to the CCS prediction model to generate a pre-dicted CCS value.
The PaSER search algorithm (in our case, ProLucid) is run as normal and the search algorithm compares the predicted and measured CCS values and calculates a correlation score, namely TIMScore for the top 5 peptide candidates for each spectra. Peptide and protein identifications summaries were generated for each sample with both the Mascot and the PaSER search engines. Datasets were further analyzed in accordance with version 3. 0 the HPP data interpretation guidelines  11. Finally peptideto-protein mappings were checked using the neXtProt uniqueness checker  13.
As a consequence, we started by analyzing 3'SRP datasets  10, 18  corresponding to the two stem cell lines. 3'SRP uses unique molecular identifiers (UMI) to correct errors in quantification of mRNA, hence reflecting the likeliness of a gene to be expressed at a level that corresponds to protein expression, and not background. This was for example the case for DPPA5 which is often detected by qPCR in both hNPSCs and hPSCs, but that is only detected by western blot when the expression levels are around 5000 mRNA molecules per million of mRNA molecules (in hNPSCs) and not detected when expression levels are around 10 molecules per million of mRNA molecules  18. 811 genes corresponding to 1343 missing proteins were unambiguously annotated in transcriptomic datasets. Out of these 811 genes, only 14 genes expressed in hNPSCs (Figure  2A ) and 9 genes expressed in hTSCs (Figure  2B ) had abundance over the threshold for credible expression, which is 20 mRNA molecules per million of RNAs.
ACCEPTED MANUSCRIPT / CLEAN COPY Total protein digests from cells dissociated with two different protocols were analyzed by MS/MS using Mascot and Proline search engines. This analysis allowed the identification of 44247 peptides for hNPSCs and 44618 peptides for hTSCs, further mapping to 5150 and 5253 proteins respectively. We analyzed the gene expression levels of genes corresponding to proteins identified by MS/MS. This showed that in both hNPSCs and hTSCs, genes that were expressed over 20 mRNA molecules per million of mRNA molecules had 70% MS identification for hNPSCs and 66% MS identification for hTSCs (Supplementary Figure  1 ). This further confirmed the likelihood of identifying missing proteins in hNPSCs and hTSCs (Figure  2A, B ). In a second time the same datasets were processed using PaSER with the objective to catch new peptides that would not have been seen by Mascot.
When using the PaSER search engine, 58446 peptides from 5906 proteins were identified in hNPSCs classically dissociated from cultures dishes with trypsin, and 51273 peptides from 5297 proteins were identified in hNPSCs directly dissociated from culture plates with the lysis buffer. As many as 61339 peptides from 6073 proteins were identified in ACCEPTED MANUSCRIPT / CLEAN COPY hTSCs dissociated from culture dishes with trypsin, and 51408 peptides from 5513 proteins in hTSCs directly dissociated from culture plates with the lysis buffer. Detailed information on the proteins identified from our MS datasets is reported in Supplementary Table  2. The list of all identified proteins in our study was searched for missing proteins against neXtProt data (release 2022-02-25). The overall workflow for the detection and validation of missing proteins was used as previously described  6.
Applying this workflow, we produced a list of 16 and 18 "candidate missing proteins" entries in hNPSCs and hTSCs respectively. In hNPSCs, UQCRHL, CTAGE15, MAP1LC3B2, ZNF98, ZNF732, ZNF728, ZNF208, ZNF804B, ZNF117, ZNF676, ZNF492, RGPD1, CPSF4L, TRIM61, NANOGP8 and WASH2P were considered. In hTSCs, the missing proteins examined correspond to 18 genes: PPIAL4E, PPIAL4C, PPIAL4D, PPIAL4F, TRBV18, RGPD1, CTAGE6, CTAGE15, MAP1LC3B2, DDTL, CGB1, CGB2, CGB7, ZNF732, FBXO47, WASH2P, OR1M1 and OR5M8. These two subsets were further analyzed in line with version 3. 0 of the HPP mass spectrometry data interpretation guidelines  11. Peptides smaller than 9 amino acids in length were removed and peptide-to-protein mappings were checked using the neXtProt uniqueness checker  13  , considering alternative mappings by taking into account the 9.
ACCEPTED MANUSCRIPT / CLEAN COPY All peptides but three could only be identified thanks to a search via the PaSER engine (Table  1 ). The capacity of the PaSER search pipeline to detect more proteins can be attributed to the use of the peptides's CCS value to add an extra scoring dimension (TIMScore). The true benefit of TIMScore can be realized during the peptide-validation and FDR estimation steps of the proteomics pipeline. In a non-CCS enabled algorithm, such as Mascot, only two dimen-sions can be utilized to estimate the FDR rate, and ACCEPTED MANUSCRIPT / CLEAN COPY so a discriminate line is fit to a 1% error to distinguish forward and reverse pep-tide candidates. With TIMScore, and the extra CCS dimension, the peptide-candidates can be vectorized in 3-dimensions, allowing a discriminate contoured plane to be applied to achieve the same 1% error.
Fluorescence immunocytochemical studies on hNPSCs in suspension were undertaken using HPA antibodies to provide orthogonal evidence of the expression of TRIM61 and ZNF728 selected based on our data mining process. However, results were not convincing enough using both antibodies and the approach requires further optimization before drawing any conclusion. We then carried out a rapid search of the literature and knowledge bases to highlight the potential relevance of the identified missing proteins to the reproduction field. Putative tripartite motif-containing protein 61 (TRIM61) is a RING finger domain protein that is predicted to bind four zinc cations. Many proteins containing a RING finger play a key role in the ubiquitination pathway and TRIM61 is predicted to possess a ubiquitin protein ligase activity  19.
Additionally, they both contain a Krüppel associated box (KRAB) domain, typically found in transcription repressors. ZNF676, and possibly ZNF728, have been recently shown to repress the transcriptional activity of a subset of ERV-embedded regulatory sequences active during gametogenesis and early development of the egg  23. Using our 3'SRP datasets analysis  10, 18  corresponding to hNPSCs cells, we observed a concordance between our proteomics and transcriptomics analyses (Figure  3 ). Indeed, three MPs identified (TRIM61, ZNF676, ZNF728) at protein level are present in the top 11 MPs most expressed at transcriptomic level. We also observed a difference in expression between hNPSCs and hTSCs/hPSCs with greater transcriptomic expression in hNPSCs than in hTSCs and hPSCs for these three MPs.
ACCEPTED MANUSCRIPT / CLEAN COPY That is consistent with our proteomic analysis by mass spectrometry. Of note, for our last identified MPs, i. e. , ZNF804B, the transcriptomic expression is low in hNPSCs, hTSCs and hPSCs. Additional unique peptides for this protein need to be identified for reinforcing its identification (Table  1 ). Six other proteins (PE2) (RGPD1, UQCRHL, ZNF208, CPSF4L, TRBV18 and OR5M8) were detected with only one distinct uniquely mapping peptide of length ≥ 9 amino acids (Table  2 ). For these 6 MPs, no additional peptide considered unique but smaller than 9 amino acids could be identified in our datasets. As a consequence, we must be particularly careful with these peptide identifications. Thus, the unambiguous ACCEPTED MANUSCRIPT / CLEAN COPY 20 validation of the corresponding MPs will rely on the identification of other unique peptides.
CP and LD co-coordinated the study. CP conceived and designed the HPP mass spectrometry experiments and analyses. SC processed transcriptomics data. CO produced stem cell lines. OG performed the proteomics sample fractionation and preparation. RL performed MS/MS analyses. RL, EC processed and analyzed the MS/MS datasets. MC and POS performed PaSER analyses. OG and EC performed the bioinformatics analyses. OG, CP, LD and LL performed data/ literature mining of the identified proteins and selected candidates. OG, SC and LD prepared the figures, tables, and supporting Information. OG, CP and LD drafted the manuscript. Corresponding Author * Charles Pineau: charles. pineau@inserm. fr, Tel: +33 (0)2 2323 5072 ORCID number: 0000-0002-7461-5433.