dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , for very high MaxN values ~ >300) or because the shes were located too far from the rigs for accurate stereo measurements (nominally >7 m). Individual sh not measured at the time of MaxN were allocated an average fork length value based on availability of measurement, using the species average either from the BRUVS in question (or string, in the case of midwater BRUVs), from the expedition or, from across all expeditions. All individual fork lengths (cm) were converted to body mass (kg) using published Bayesian species-speci c parameters (log a vs b) for allometric length-weight conversion from FishBase 70 (www. shbase. org, Extended Data Fig. 1 ). For individuals not identi ed to species, lengths were converted to mass by allocating average a and b conversion parameters for the genus, family or assemblage.
Assessing distribution generalities across pelagic and benthic habitats Analysis of body size frequency distributions and size-spectra are highly sensitive to the spatio-temporal and taxonomic scale at which sizes are aggregated  70. Our analysis was therefore hierarchical, at two levels, to capture different ecological processes that may be occurring at either biogeographical or local scales of resolution. At biogeographical scales, we assessed high-level generalities as well as differences between pelagic and benthic habitats, by grouping body sizes into six absolute latitude brackets (0-10, 10-15, 15-20,  20-23, 23-33, 33-65), each containing an equal number of observations, (n = 146,706, Extended Data Fig  2a). For each of these body size frequency distributions, we estimated and visualised the normalised slope of the pelagic and benthic size-spectra.
Socio-environmental variables Our analysis relies on an extensive body of literature documenting mechanisms by which pelagic and benthic ecosystems are shaped through bio-geophysical processes and human activities. These processes were captured by a suite of 25 environmental, geomorphic, and socioeconomic variables, which we considered as potential explanatory variables in our explanatory models of size-based indicators (Extended Data Table  2 ). All explanatory variables were extracted at the latitude and longitude coordinates of the individual midwater or seabed BRUVS deployment, and then averaged at the level of the sampling date, to yield a value with a corresponding resolution to the size-based indicators. Environmental variables included the following variables: primary productivity, chlorophyll-a concentration (http://marine.
copernicus. eu/services-portfolio/access-to-products/? option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_082), sea surface temperature, weekly median and standard deviation (https://oceancolor. gsfc. nasa. gov/atbd/sst/) Geomorphic variables included the following variables: seabed depth (30 arcsecond, https://www. gebco. net/data_and_products/gridded_bathymetry_data/), seabed slope (computed using QGIS tools and GEBCO layer), distance to seamount (m, computed using QGIS and predictions of seamount locations  79  ), distance to coast (computed using QGIS and coast lines from GADM 3. 6), distance to coral reef (computed using QGIS tools, https://data. unep-wcmc. org/datasets/1). Socioeconomic variables related to human activities and governance, included the following national level variables: government effectiveness (World Governance Indicators, WGI, https://papers.
ssrn. com/sol3/papers. cfm?abstract_id=1682130)  74  , number of year of con ict (UCDP/PRIO Armed Con ict Dataset version 19. 1), rule of law (WGI), absence of violence (WGI), voice and accountability (WGI), control of corruption (WGI), Human development index (  73 ), number of active NGO, marine ecosystem dependency  75. Fisheries pressure speci cally was considered by including an estimate of total reconstructed catch 76 (https://www. seaaroundus. org/), total shing effort from automated identi cation systems 18 (https://global shingwatch. org/). In addition, we developed human pressure metrics which included travelling time to market and linear distance to nearest population  40  , linear distance to cities, and distance to nearest port (m, https://global shingwatch. org/). In addition to the above numerical variables, we also considered two categorical variables, namely protection status and habitat (pelagic or benthic).
Protection status was classi ed as a three-level categorical term, determined using the World Database of Protected Areas 77. Sites were classed as 'strictly protected' if geographically situated inside a no-take marine protected area (MPA) or inside an MPA with a no-take section (IUCN category I-II), 'partly protected' if inside an MPA with some shing allowed or of unknown status (IUCN category III-VI), 'not protected' if open to shing. For each site, we used the protection status two years prior to the date of sampling, to ensure su cient time had passed for the protection status to take effect. As a result of this classi cation scheme, each protection status category contained a balanced number of samples (between one quarter or one third of samples), and each habitat and protection status combination contained a range of variate values with substantial overlap (Extended Data Fig.
Explanatory modelling To disentangle the interactions between habitat (pelagic vs benthic), protection status, and human pressures on the three size-based indicators, we build explanatory generalised least-square models (GLS) using the R nlme package 78. These models controlled for background variability in socio-environmental and geomorphic conditions which varied between midwater and seabed sampling across our survey, whilst accounting for spatial and temporal autocorrelation in the model residuals. Prior to model tting, the full suite of candidate explanatory variables was tested for correlation: whenever a pair of variables were found to be correlated one of the variables was discarded (Pearson's correlation values >0. 7, Extended Data Table  2 ). The variates were further simpli ed using a variance in ation factor approach to identify and remove residual collinear variables, in which variables yielding VIF > 10 were removed.
The fully saturated models, where habitat and protection status were interaction terms of all socioeconomic variables, and habitat was an interaction term of all remaining variables (either untransformed or using the log 10 value, Extended Data Table  2 ), took the following form, for each size- Different correlation structures were tested on these saturated models and compared using their Akaike Information Criteria (AIC) values. The rational quadratic correlation structure ('corRatio') yielded the lowest AIC value, for all size-based indicators. The saturated models and correlation structure were further. Next, we applied a stepwise model selection, using the AIC, and the stepAIC function in the 'MASS' package  79. Simpli ed models were further evaluated by deriving the adjusted R 2 value, using the model_performance function in the 'performance' package 80 , and through visual inspections of model prediction vs residuals plots, and semivariograms.
We tested for spatial autocorrelation in the model residuals using a Moran's index I. Standardized effect sizes of each explanatory variables and their interactions were extracted and visualised with the function plot_model using the 'sjPlot', 'sjlabelled', and 'sjmisc' packages (Extended Data Fig. 5 6 7 , Extended Data Table  3 4 5 ). Marginal relationships between travel time to market and size-based indicator in the six alternative interaction term categories combination (habitat: pelagic or benthic; protection status: not protected, partly protected, or strictly protected) were evaluated using the ggpredict function from the 'ggeffects' package  81. Usually, marginal relationships between a response variable and an explanatory variable of interest are produced by generating model predictions across the range of the explanatory variables, whilst holding the remaining variables xed at the mean values.
Acknowledgements This work and associated analyses were only made possible through the dedicated video-processing efforts from numerous colleagues, technicians, and students. In particular we wish to express our gratitude to  Phil Bouchet ,  Jean-Baptiste Juhel ,  Tim Langlois  for Global Archive,  Sam Weber ,  Andrea Lopez ,  Gary Kendrick ,  Jock Clough ,  Nicolas Casajus ,  Jacquomo Monk ,  David Tickler ,  Germain Boussarie. We are also very grateful for the assistance of the master and crew of the numerous vessels from which the eld work was conducted. TBL acknowledges  OKEANOS ,  Department of Oceanography and Fisheries, University of the Azores , for hosting him and thus facilitating collegial discussions around the analysis. Different aspects of the size-spectra modelling also bene ted from discussions with experts at the  Institute of Marine and Antarctic Studies, University of Tasmania , and at the  Australian Antarctic Division.
The ChiA1 Ex allele is widespread in nature To analyse the frequencies of the two alleles of ChiA1, we genotyped ChiA1 across the range of the two species (for details, see Supplementary Tables  9  and 10 ). P. exserta was sampled from 18 sites representing its highly restricted distribution in the Guaritas Region of Brazil. Of the 75 P. exserta accessions genotyped, 58 were homozygous for the nonsense mutation (ChiA1 Ex ), 13 were homozygous for the P. axillaris genotype (ChiA1 Ax ) and 4 were heterozygous (Fig. 6b , right, Extended Data Fig. 9  and Supplementary Table  9 ). The overall ChiA1 Ex allele frequency among these accessions was 80% (Fig. 6b, right). P. axillaris accessions were sampled from multiple locations in Argentina, Uruguay and Brazil, including the regions of sympatry with P.
RNA extraction and quantitative PCR Total RNA was extracted from leaf tissues using an innuPREP DNA/ RNA Mini Kit (Analytik Jena; code 845-KS-20800250). Complementary DNA was synthesized using qScriber cDNA Synthesis Kit (HighQu; code RTK0104). Quantitative PCR reactions were set up with ORA SEE qPCR Green ROX L Mix (HighQu; code QPD0505). The amplification was performed using a QuantStudio 5 Real-Time PCR Instrument (Applied Biosystems). Leaves were sampled from three individual plants for each genotype. We took the seventh-oldest leaves (counting from bottom to up; approximately sixth-newest counting from up to bottom) from the 10-week-old plants as this leaf in IL5-Ax plants starts to show necrotic symptoms at this stage. The data were analysed by the ∆∆Ct method  66  , and normalized by the housekeeping RAN1 gene  21.
axillaris version 4. 03 using STAR  70. Read counts were generated with Subread  75. Differential expression analysis was performed with DESeq2 in R 76 between IL5-Ax and IL5-Ex (q < 0. 001). The detailed software versions, parameters and scripts are deposited on Github: https://github. com/Kuhlemeier-lab/Petunia_hybrid_necrosis#rnaseq.
Phylogenetic analysis ChiA1 and its homologous sequences from species listed in Extended Data Fig. 4c  were identified in the NCBI nr (nonredundant protein sequences) database by performing a BLASTp search with ChiA1 protein sequences (Supplementary Table  12 ). Amino acid sequences were aligned with MUSCLE in the MEGA-X software package using the default settings for protein multiple alignments. Evolutionary distances were computed using Poisson correction analysis. The bootstrap method with 1,000 replicates for phylogeny testing was used.
Acknowledgements We thank  T. Nuernberger  for critical reading of the manuscript,  M. Chopy  and  D. Bonnet  for helpful discussion and proofreading,  T. Mandel  and  L. Lebeigle  for technical support and  C. Ball ,  J. Sekulovski  and  S. Dolder  for expert care of the plants. This work was supported by grants from the  Swiss National Science Foundation  ( 31003A_182340  to C. K. ) and the  European Union  ( ERC AdG 741354-RESPEC  to C. K. ).
Data availability BSR-seq reads have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject PRJNA708139. Shallow whole-genome sequencing reads data have been deposited under BioProject PRJNA705072. RNA-seq reads have been deposited under BioProject PRJNA705649. The P. axillaris N 4. 03 genome assembly has been deposited at NCBI GenBank under the accession JANRMM000000000 (https://www. ncbi. nlm. nih. gov/bioproject/?term=JANRMM000000000).
For the MS 2 a data-dependent mode was chosen with a scan in ddMS 2 orbitrap high collision energy. The isolation mode was performed on a quadrupole with an isolation window m/z at 1. 2. The collision energy was fixed at 27 eV and the data type was acquired in centroid. Data analysis and processing. We optimized the previous laboratory chromatographic separation method from HPLC to ultra performance liquid chromatography, and therefore the total run time for HILIC  (31)  and RPC  (32)  was reduced from 45 to 10 and 17 min, respectively. QCs were analyzed in triplicate, initially in MS 2 scan in negative and positive ionization mode, to allow for lipid species annotation, and then in MS full scan, while samples were only analyzed in MS full scan in positive and negative ionization mode, separately.
Raw files were converted to a readable format, mzXML, and then processed in R, including peak extraction and normalization, resulting in the generation of specific files containing data on m/z, ppm, and peak intensities. Raw files were visualized with Xcalibur 4. 2 (https://www. thermofisher. com/order/catalog/product/OPTON-30965) and Freestyle 1. 8 (https://assets. thermofisher. com/TFS-Assets/CMD/ manuals/man-xcali-97962-freestyle-14-user-manxcali97962-en. pdf), whereas LipidSearch 4. 1 (https://www. thermofisher. com/fr/ fr/home/industrial/mass-spectrometry/liquid-chromatographymass-spectrometry-lc-ms/lc-ms-software/multi-omics-dataanalysis/lipid-search-software. html) was used for lipid annotation. Ions were identified based on respective adducts (H -, OAc -, H + , NH 4 + ). Results were compared to the manual annotation of all parent ions from MS 2 spectrum elucidation.
Finally, R Studio was used to acquire and process all data. Raw MS files were converted to mzXML format using MSConvertGUI 64 bit (https://proteowizard. sourceforge. io/ download. html) and then processed using a preestablished database of parent molecular ions based on their exact m/z mass for each lipid species. Prior annotation via LipidSearch and MS 2 spectrum elucidation of all lipid species in the QC samples was used to establish the database of lipid classes and species. Minimum and maximum retention time windows were set manually for each lipid class, while the m/z tolerance was chosen at 10 ppm.
General workflow The overall design of the study was depicted in Fig. 1. Erythrocytes, plasma, retina, and RPE/Choroid lipid samples were extracted and the lipid extract was separated through two alternating chromatographic techniques (HILIC and RPC) in positive and negative ionization mode. After separation, lipid extracts were introduced to the ESI ionization source connected to Orbitrap Fusion HRMS for MS and MS 2 scan. After data collection, lipid annotation was accomplished using LipidSearch and manual spectral elucidation of MS 2 scan files, whereas relative lipid quantification was attained using MS full scan files. R 4. 0. 2 was used to process data, resulting in the generation of files containing information on ppm, peak intensity, and m/z. The annotation of each lipid species within each sample, as well as the relative distribution, exact mass, and ppm features of each lipid species were reported.
METHOD VALIDATION Lipid annotation was achieved from MS 2 scans in positive and negative ionization mode in HILIC and RPC. Manual annotation on precursor ions confirmed the lipid composition of species after using LipidSearch as a database for lipid identification. Such approach resulted in the annotation of 15 lipid classes and approximately 500 lipid species. Each precursor ion's relative intensity was extracted automatically via R and the results were computed in a centroided mode. Each mzXML file was compared to the database of precursor ions, which was prior established, and then three separate files (. csv) were generated with data on m/z, ppm, and peak intensities. The lipid database comprising of m/z and all the lipid species annotated through HILIC (supplemental Table  S1 ) and RPC (supplemental Table  S2 ), is available in the supplementary section.
These characteristics make it difficult to design a global analytical procedure through a single analytical run and such studies have not yet been reported to our knowledge. We attempt to present the benefits of a dual analytical method of using RPC and HILIC, alternatively, to identify and quantify  the relative amounts of about 15 lipid classes and approximately 500 lipid species, in positive and negative ionization modes, from four different biological tissues/biofluids (erythrocytes, retina, RPE/Choroid, plasma). Lipid classes/subclasses were annotated through the combination of the LipidSearch software and additional manual spectra elucidation, which warrants a robust and reliable annotation process. We highlight the benefits and drawbacks of the RPC and HILIC method, for analyzing lipid classes, as well as pointing out their sensitivity based on lipid separation and response in both ionization modes.
Furthermore, the annotation process has not yet been fully automated, even though several software, such as LipidSearch, MZmine (http://mzmine. github. io/), MS-Dial (http://prime. psc. riken. jp/compms/msdial/main. html) and others provide a good approach. In comparison to metabolomics software, lipidomics, and data processing software, particularly the annotation process, is still in development. Based on those developments, we performed lipid annotation using a combination of LipidSearch and manual annotation. We were able to investigate the spectra of all ions obtained in HILIC and RPC in positive and negative ionization modes in MS 2. Chol, CE, DAG, and TAG, for example, were not detected in HILIC. As a result, in order to annotate these species, we investigated the parent ion of this species as ammonium adducts [M+NH 4 ] + using the RPC method.
Acknowledgments The authors would like to acknowledge and thank  St éphanie Cabaret , St éphane Gr égoire,  Lucy Martine ,  Elisabeth Dubus ,  Marine Cr épin , and  Jean-Baptiste Bizeau , for their assistance in the experimental analysis. This study was supported by : Minist ère de l'Enseignement Sup érieur, de la Recherche et de l'Innovation (MESRI) ,  R égion Bourgogne-Franche-Comt é (BFC) ,  Fonds europ éen de d éveloppement r égional (FEDER) ,  LabEX LipSTIC  ( ANR-11-LABX-0021-01 ),  Fondation VISIO , and Association DMLA.
The availability of an increasingly large amount of public proteomics datasets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, the domestic pig, is a model organism relevant for food production and for human biomedical research. Here we reanalyzed 14 public proteomics datasets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs.
The number of canonical proteins per dataset ranged from 789 (dataset PXD002918, biceps femoris) to 6,062 (dataset PXD012636, heart). Then, we studied how protein abundances compared across different datasets. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues (from a previous study performed following the same methodology), revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. Additionally, we analysed the differences in quantitative expression of proteins across organs between human and pig organs. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualisation of the protein expression data individually or alongside gene expression data.
Introduction In recent years, high-throughput mass spectrometry (MS)-based proteomics methods have made significant advances and have become essential tools in biological research  [1]. These improvements are the result of significant developments in MS instrumentation, chromatographic methods, sample preparation automation and computational analysis  [2]. The dominant experimental technique for MS-based proteomics has historically been datadependent acquisition (DDA) bottom-up proteomics  [3]. Among the quantitative techniques, label-free DDA approaches are well accepted. However, Data Independent Acquisition (DIA) approaches are currently becoming increasingly popular. In parallel to the technical developments, in recent years, the proteomics community has embraced open data practices, leading to a substantial increase in the availability of shared datasets in the public domain.
The field has mirrored the progress witnessed in genomics and transcriptomics. The PRIDE database  [4] , as part of the ProteomeXchange consortium  [5] , is the most used proteomics data repository worldwide. The availability of extensive public proteomics datasets has paved the way for various applications, including metaanalysis studies involving the reanalysis and integration of quantitative proteomics datasets  [6] [7] [8]. By systematically reanalysing these datasets, original findings can be updated, confirmed and/or strengthened. Moreover, novel insights beyond the scope of the original studies can be obtained through alternative reanalysis strategies to those used in the original studies. To enable the access to proteomics data by the wider scientific community, PRIDE is developing data dissemination and integration pipelines with existing popular resources at the European Bioinformatics Institute (EBI).
Expression Atlas  [9]  (https://www. ebi. ac. uk/gxa/home) is a well-established database for gene expression data, and has more recently incorporated protein expression information derived from reanalysed datasets into its 'bulk' section. As a result, the integration of proteomics expression/protein abundance data with transcriptomics information, primarily from RNA-Seq experiments, enhances the comprehensive understanding of molecular expression across various biological contexts. This approach ensures the long-term accessibility and integration of proteomics data, benefiting researchers, including those without expertise in proteomics, in their exploration of multi-omics information. We have already performed combined analyses of baseline (without any perturbation) protein expression for human  [7] , mouse and rat tissues  [6].
Here we are reporting an analogous study of baseline protein expression in the model organism Sus scrofa, the domestic pig. The study of pig proteomics datasets is crucial for advancing food production, animal welfare, and human biomedical research, as it offers insights into genetic and environmental factors affecting farm animal production and leverages the close genetic and proteomic similarities between pigs and humans  [10, 11]. The resource PeptideAtlas provided a few years ago a build for pig, including extensive peptide and protein identification data, but is not providing protein abundance information  [8]. Additionally, PaxDB recently released a new version 5. 0  [12] , providing expression data coming from different vertebrates including Sus scrofa, but quantitative data is based on spectral counting, a semi-quantitative technique.