dataesr/scientific-paragraphs-categorization huggingface dataset:
A basic tenet of network ecology is that functions and processes arise from the underlying structure of interaction networks. Ecosystem functions can be delivered either by an individual species or by many, and these contributions are shaped by their interactions, both directly and indirectly. Consequently, management directed at network structure, supporting not only the presence and abundance of key species nodes but also their interactions, should give the best chance of maintaining ecosystem functioning  [1] [2] [3] [4]. Ecosystem services (ES) are ecological functions that have a social and/or economic value that we wish to maintain and manage  5. Network structural governance of ES and the interplay between different ESs has rarely been demonstrated empirically and little is known about how best to manage network structure to support services, largely because very few datasets that quantify ES in a network context have been assembled (but see Pocock et al.
We analyse the interlayer dynamics between weed and pest ES using data from the Farm Scale Evaluations (FSE) of genetically modified, herbicide-tolerant crops in which ecosystem functions were measured at multiple sites at the Great Britain, national scale  23  ("Methods"). The FSEs evaluated the effects of herbicide management of conventional and Genetically Modified (GM) crops on farmland biodiversity in 187 fields in a split-field design (374 half-fields) of three spring-sown crops. Different herbicide management was applied across the conventional and GM treatments, and between fields and crops, and this perturbation produced distinct community combinations of species of weed seeds, gastropods and carabids in each split field. The nodes of the 374 two-layer networks we analyse are formed from the species of weeds, gastropods and carabids in the dataset, and trophic interactions collated from the literature (Methods).
Species turnover in carabid composition across all the networks, measured as mean dissimilarity, was 0. 44 ± 0. 17 (Supplementary Fig. S3 ), and many of the same carabid species were present in the majority of networks. The structural changes in the networks were more related to the turnover of links between the carabids and their resources (mean dissimilarity 0. 65 ± 0. 25, Fig. 3c ) than to changes in carabid species composition per se. This supports our third hypothesis that link turnover of the network layers governs the delivery of weed seed regulation. Such link plasticity can be visualised as occupancy of the total potential link space, by each carabid species (Fig. 1b ). For instance, the common carabid species in the dataset, Pterostichus melanarius, previously associated with in-field mollusc and weed seedbank regulation  14, 17  , operates here as a specialist carnivore or a herbivore or an omnivore in different networks depending on the conditions of weed and gastropod resources (Fig.
The considerable variation in structure observed across all network examples, in this composite network from the same ecosystem, was consistent with network rewiring  25, 26. The site-specific multilayer networks analysed here are subsets of nodes and links, drawn from a larger, composite network of interactions across all half-fields in the dataset (Fig. 1a, b ). As such, the networks are dependent upon the quality of the trophic interaction information currently in the literature. There were many prey species present in the dataset for which no trophic information could be found and which were consequently excluded from the networks and network analysis. The species excluded were predominantly weed species (109 species removed), but also some gastropod species (3 species removed). Links were also assigned to carabids at the genus level.
34  and Bohan et al. 35  , which we briefly detail here. S1 ). The dotted line at 0 indicates the threshold where weed seedbanks decline between t 0 and t 1. The count data for the carabids, weed seedbank and seed rain, and gastropod molluscs came from 66 spring-sown beet, 55 spring maize and 66 spring oilseed rape fields. The fields were distributed across the UK (Supplementary Fig. S5 ) and each field was sampled for one cropping year  23  between 2000 and 2004. The great majority of fields were below 15 Ha in size, and typically varied between 2 and 10 Ha depending upon the crop  34, 35. The trial used a half-field design, and each field was divided in two with one half sown with a conventional crop and the other a GMHT variety of the same crop. Data from both treatments are used for the analyses presented in this study, giving a total of 374 half-fields.
Network construction. The species sample data were supplemented with carabid dietary information recovered from the literature. We assumed that where a carabid species, A, was noted to consume a resource species, B, in the literature and both these species were present in a half-field, then this trophic interaction was realised 6,39-41. To standardise the (trophic interaction) sampling effort across all carabid species and reflect the generalist nature of carabid consumers it was assumed, following Honek et al. 42  , that each carabid species would consume the same resources as other carabid species within the same genus  39, 41. A similar Fig. 3  Trade-offs between herbivory and carnivory links. a The relationship between the sum of specialist herbivory frequency interaction frequency and sum of specialist carnivory interaction frequency, transformed to the log(x + 0.
Negative values of this metric indicate a decline in the size of the weed seedbank from t 0 to t 1. Following Bohan et al. 14  , negative relationships between the metric of regulation and carabid counts are indicative of seedbank regulation by these beetles. Statistics and reproducibility. All analysis was done in R (R Core Team 2019) using the cheddar  48  , bipartite  49  and vegan 50 packages. Network plots were created with the HiveR package  51. Interaction frequencies were calculated separately for herbivores, carnivores and omnivores in each network. Species and link turnover across the collection of multilayer networks were measured using Bray-Curtis dissimilarity in the vegan package  50. Each network layer is a realisation of the interactions drawn from the composite network  52  , with the interactions only being contingent on local species composition and abundances.
Data availability The raw FSE data are free from intellectual property rights. The data can be requested by enquiry to the Environmental Information Data Centre of the Centre for Ecology and Hydrology (http://eidc. ceh. ac. uk/contact). Archived information about the FSEs are available from the National Archives of The Government of the United Kingdom (http:// webarchive. nationalarchives. gov. uk/20080306073937/http://www. defra. gov. uk/ environment/gm/fse/). Network data for the analyses that support the findings of this study are available from the public repository Zenodo 54 : https://doi. org/10. 5281/zenodo. 4268722. Requests for any further information can be addressed to the corresponding author.
Candidatus R. pediculicola housekeeping gene analysis 2. 3. 1. Primer design In order to investigate the genotypic pro le of the endosymbiotic bacteria, four standard PCR systems were designed targeting three Candidatus R. pediculicola housekeeping genes: ftsZ, groEL, and two regions of the rpoB. Four genomes of Candidatus R. pediculicola belonging to clade A and B-lice deposited in the GenBank database (accession numbers CP012841, CP012843, CP012845, CP001085)  [14, 15]  were aligned using Muscle in MEGA7 software  [20]  and screened for conserved and discriminative genes. Based on the variability in the available Riesia genomes, three housekeeping genes were selected as candidates for primer design. In order to nd a suitable and conserved region for primers, two sequence fragments (~ 100bp) separated by a minimum of 500bp for each gene were submitted to Primer3 software v.
0. 4. 0 (available online: http: //primer3. ut. ee/). The melting temperature of each primer was tested using the free online software programme Oligo Analyzer 3. 1 (available online: https://eu. idtdna. com/calc/analyzer) [21]. Designed primers (Table  1 ) were then tested in silico, using the NCBI BLAST nucleotide sequence similarity tool (https://blast. ncbi. nlm. nih. gov/Blast. cgi).
Data analysis 2. 4. 1. Phylogenetic analysis To assess clade diversity in human lice, the obtained cytb sequences were combined and compared with worldwide cytb data previously reported and deposited in the GenBank database  [8, 22]. Alignments were performed using MEGA7. 0. 26 software and a Maximum-Likelihood (ML) tree was constructed using the Kimura2-parameter model under 1,000 bootstrap replicates  [23]. Candidatus R. pediculicola sequences extracted from 21 lice specimens were combined with data reported previously by Boyd et al. [21]  and were analysed as described for the cytb gene.
Bioinformatic analysis To de ne the genomic pro le of the Pediculus humanus endosymbiont within different lice haplogroups, we screened the Candidatus R. pediculicola sequences obtained for the presence of Single Nucleotide Polymorphisms (SNPs) using the free online tool SNP-Dists (https://github. com/tseemann/snp-dists). The free online Morpheus website (https://software. broadinstitute. org/morpheus) was used to visualise and analyse the resulting heatmap. In addition, to investigate our hypothesis more thoroughly, the degree of genomic similarity of deposited Candidatus R. pediculicola genomes (listed above)  [15] , was estimated using the Orthologous ANI Tool (OAT)  [24].
Data Availability Statement: Sequences of the Candidatus R. pediculicola housekeeping genes referred to here with the attributed GenBank accession numbers: ftsZ gene: clade A (Orlando-MW588459, Senegal-MW588463), clade D (Congo-MW588466, Senegal-MW588464, clade B (Pakistan-MW588462), clade F (France-MW588460), clade C (France-MW58846) and clade E (Congo-MW588465, Guinea-MW588458). Second region of endosymbiont rpoB gene: clade A (Orlando-MW588485, India-MW588486, Algeria-MW588487), clade D (Senegal-MW588488, Congo-MW588489), clade B (Pakistan-MW588491), clade F (France-MW588490), clade C (Ethiopia-MW588492) and clade E (Congo-MW588494, Guinea-MW588493).
RPPA therefore provides a valuable tool for the targeted analysis of disease-relevant signaling mechanisms, including in cancer  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]. To date, RPPA technology has been, and is currently being, applied in several cancer clinical trials  11, 36  , mostly retrospectively to detect markers of response and resistance  [37] [38] [39] [40] [41] [42] [43] [44] [45]  , but in some cases also prospectively to select the most appropriate therapy for a given patient  46, 47. As RPPA technology is readying itself for further implementation in drug development and clinical laboratory settings, there was a demand to evaluate the robustness and reproducibility of RPPA datasets derived from different RPPA platforms.
1a-c ). The dataset consists of original RPPA slide scan images and image quantification of 86,832 sample spots derived from 972 arrayed lysates, incorporating independent biological replicate samples, serial lysate dilution series, and technical replicate sample spots, from three independent RPPA platforms. Integrative analysis of a subset of these data enabled the identification of platform-independent protein markers of breast cancer cell response to drug treatment  48. We report here the raw and processed data from our assessment of multiple RPPA workflows at different research centers. Our data will serve as a starting point for the appraisal of the reproducibility of RPPA technology and its capacity to identify robust protein markers of response to cancer therapies. The data can be reused by multiple researchers over the world to implement and validate their RPPA quality control, data normalization, and data integration methods.
Experimental design For this multicenter RPPA study, six breast cancer cell lines were cultured in the presence or absence of two kinase inhibitors for two time periods. For each experimental condition, three biological replicate cell lysates were generated, frozen, and sent to three different research centers for RPPA analysis (Fig. 1a, b ). Cell lysates were processed for RPPA analysis using the platform-specific setups at the respective research centers, including incorporation of serial lysate dilution series and technical replicate sample spots (detailed below). Raw RPPA data were processed and normalized according to the respective in-house data analysis pipelines. Each antibody used was assigned a unique antibody identifier linked to a Research Resource Identifier (RRID) (Online-only Table  1 ).
Normalized data derived from validated antibodies targeting the same antigen acquired at all three research centers were integrated to support data reuse (Fig. 1b-d ). Some of the following methods are expanded versions of descriptions in our related work  48. Cell culture and drug treatment HCC1954 (RRID CVCL_1259), MCF7 (RRID CVCL_0031), MDA-MB-231 (RRID CVCL_0062), MDA-MB-453 (RRID CVCL_0418), MDA-MB-468 (RRID CVCL_0419), and SKBR3 (RRID CVCL_0033) breast cancer cells (Table  1 ) were purchased from American Type Culture Collection (LGC Standards, Molsheim, France) and were cultured according to the supplied instructions. Cells were routinely tested for mycoplasma and used within three months of recovery from frozen. Lapatinib [Selleck Chemicals, Houston, TX, USA, catalog number (cat. no.
RPPA data analysis Platform 1. Slides were visualized using a GenePix 4000B microarray scanner (Molecular Devices, San Jose, CA, USA) at an excitation wavelength of 635 nm and a resolution of 10 µm, and images were acquired as tagged image file format (TIFF) files (stored in Images1_IC. zip)  49. Scanner settings were chosen to obtain around 10-15% of saturated spots, which improves subsequent data normalization. Signals were quantified using MicroVigene microarray image analysis software (VigeneTech, Carlisle, MA, USA). Nonspecific signals were determined by omitting the primary antibody incubation step. Means of technical replicate sample spots were calculated, and data were written to a comma-separated values (CSV) file (SampleQuantification1_IC. csv)  49. Data were normalized using NormaCurve  50  , which normalizes for fluorescence background per spot and total protein stain and defines a log2-transformed single expression value for each biological sample, based on the entire serial dilution curve and all technical replicates.
0) (Molecular Devices). Nonspecific signals were determined by omitting the primary antibody incubation step. The linear fit of the relative fluorescence intensities for the dilution series of each vehicle controltreated sample was determined for each primary antibody, and >99% of signals derived from neat samples (1. 5 mg/ml) were within the linear range of detection  48. Medians of technical replicate sample spots were calculated, background was corrected, and quality control was performed using RPPanalyzer  51  , and data were written to a CSV file (SampleQuantification2_DKFZ. csv)  49. Data were normalized for protein loading using RPPanalyzer, log2 transformed, and written to a CSV file (NormalizedData2_DKFZ. csv)  49. Platform 3. Slides were visualized using an InnoScan 710-IR infrared microarray scanner (Innopsys, Carbonne, France) at an excitation wavelength of 785 nm and a resolution of 10 μm, and images were acquired as TIFF files (stored in Images3_IGC.
zip)  49. Following a preview scan to assess signal saturation, scanner settings were chosen to enable the highest laser power and gain with no saturation of the signal. Signals were quantified using Mapix microarray image analysis software (version 6. 5. 0) (Innopsys). Nonspecific signals were determined by omitting the primary antibody incubation step. The linear fit of the relative fluorescence intensities for the dilution series of each sample was determined for each primary antibody, to assess whether signals were within the linear range of detection with R 2 > 0. 9. Mean pixel values of sample spots were corrected by subtracting the median pixel value of the local background surrounding each spot (excluding a 2-pixel region at the spot-background border) using Mapix. Means of technical replicate sample spots were calculated, and data were written to a CSV file (SampleQuantification3_IGC.
Multicenter dataset integration The log2 transforms of data normalized using the methods employed by the respective research center were median-subtracted antibody wise and concatenated into a single matrix. To enable comparison across all three research centers, data were filtered for antibodies against the same target (i. e. protein or phosphoprotein) probed at all centers. To consolidate antibody crossreactivity, antibodies recognizing related or multiple protein isoforms (e. g. MEK1 and MEK1/2) were considered to recognize the same target family (e. g. MEK1/2) and were thus included in the integrated dataset if the respective target family was probed at all centers. The final concatenated matrix was written to a CSV file (IntegratedData. csv)  49.
Data Records RPPA data records generated at each research center and the integrated RPPA dataset are summarized in Table  3. RPPA slide images generated at each research center are available as TIFF files via figshare  49. Associated slide descriptor metadata, including antibody position details, and sample descriptor metadata, including spot position details, are available as tab-delimited text files  49. Sample quantification data files (CSV files) contain background- corrected RPPA intensity data for all biological samples  49. Normalized data files (CSV files) contain log2transformed data normalized using the methods employed by the respective research center  49. Centered data files (CSV files) contain antibody-wise median-centered normalized data  49. The integrated dataset, compiled from the centered data files, is available as a CSV file  49.
Sample variability The high-throughput nature of RPPA permitted the multiplexed quantification of 108 biological samples at each research center in this study, which yielded quantitative data from a total of 86,832 arrayed sample spots. This large-scale dataset enables the assessment and comparison of variability within and between independent RPPA platforms. Normalized RPPA data for all drug treatments and cell types were similarly distributed for all biological replicate samples (Supplementary Fig. 1 ). To measure relative variability among biological replicates, robust coefficient of variation based on the median absolute deviation (RCVM) was calculated using unnormalized RPPA data for all samples across drug treatments (Fig. 2a ) and cell types (Fig. 2b ). Data from all three RPPA platforms exhibited positively skewed distributions of RCVM values, with median RCVM ≤ 0.
Cell type-specific RPPA profiles To explore the protein-level relationships between the different breast cancer cell types examined in this study, we analyzed all centered RPPA data from each research center using t-SNE (Fig. 3a ). This dimensionality reduction approach enabled the projection of the high-dimensional RPPA data to two-dimensional space, facilitating the assessment of the structure of each dataset. Visualization using t-SNE projection identified clusters of samples that were partitioned by cell type for each research center (Fig. 3a ), supporting the notion that the RPPA data from each platform captured cell type-specific molecular profiles. We confirmed the clustering of cell types in the RPPA data from each platform using an alternative dimensionality reduction approach, UMAP (Supplementary Fig.
3a ). In addition to analyses of centered RPPA data, which were used for multicenter dataset integration, we performed t-SNE and UMAP analyses of uncentered normalized RPPA data, which resulted in very similar projections to the centered data (Supplementary Fig. 3b, c ), verifying that antibody-wise centering did not distort the data structure. Samples from some cell types, such as HCC1954 and SKBR3, tended to colocate in t-SNE and UMAP space (Fig. 3a , Supplementary Fig. 3a-c ), suggesting that their RPPA profiles were more similar compared to the other cell types. Indeed, the six breast cancer cell lines were chosen to reflect different molecular subtypes of breast cancer (Table  1 ). Overexpression of the cell-surface receptor HER2 was correctly detected in HCC1954, MDA-MB-453, and SKBR3 cell lines (Fig.
Building a repository of publicly available RPPA data associated with pharmacological perturbation, such as that exemplified here, will provide valuable datasets for reuse in emerging machine learning approaches which aim to predict mechanism-of-action or differentiate novel compounds based on RPPA signature similarity with well characterized compounds. This, for example, could extend the Connectivity Map conceptthe cataloging and connecting of transcriptional responses to genetic, chemical, and disease perturbations  66  -by providing additional, orthogonal data at the dynamic posttranslational pathway level. Moreover, our multicenter analysis provides an important reference for the assessment of RPPA platform robustness and technology reproducibility. Individual RPPA platforms may use the dataset to quality control their workflows, by reproducing the experiments outlined here and comparing their results with our data (Fig.
1d ). For interplatform comparison, only antigens analyzed by RPPA at all research centers were compiled in the integrated dataset in this study (Fig. 1c ), so future data reuse may include the reintegration of data for antibodies targeting excluded antigens, which are available in the raw data files  49  , to enable analysis of data acquired on different combinations of RPPA platforms. In addition, as no standard tools exist to normalize RPPA data, the raw data reported here may be used to test and validate new data normalization pipelines (Fig. 1d ). The data also complement ongoing efforts to assess the effects of sample handling on RPPA quantification in the setting of multisite clinical trials  54. Finally, the integrated dataset can act as a framework for future interplatform comparisons, and this study opens the door for crossplatform validation of RPPA data to identify robust markers of disease and response to therapy.
Expansion of the current dataset to include additional samples, antibodies, or RPPA platforms will improve the applicability of such projects and expand the scope of analysis to, for example, other drug treatments or disease models (Fig. 1d ). The data reported here thus provide a valuable springboard for the assessment of reproducibility or benchmarking of platform-specific RPPA datasets, which will support the refinement of technology best practice and may aid development toward standardization of data processing procedures and improvements in RPPA platform interoperability.
Acknowledgments We thank  Stéphane Liva  and  Patrick Poullet  for development and maintenance of data management tools at the  Institut Curie research center and Henry Beetham ,  Philippe Hupé , and  Andy Sims  for helpful discussions. RPPA platform 1 ( Institut Curie research center ) is supported by  Cancéropôle Ile-de-France. K. G. M. is supported by the  Cancer Research UK   Edinburgh Centre award  ( C157/A25140 ). N. O. C. is supported by  Cancer Research UK  ( C42454/A28596 ) and the  Brain Tumour Charity  ( GN-000676 ). A. B. was supported by  Cancer Research UK.
Code Availability The code to implement RPPA data analysis using NormaCurve  50  is freely available at http://microarrays. curie. fr/publications/U900-RPPA_PLT/Normacurve. The code to implement RPPA data analysis using RPPanalyzer  51  is freely available at https://cran. r-project. org/package=RPPanalyzer. The code to visualize data using PlotsOfData  67  is freely available at https://github. com/JoachimGoedhart/PlotsOfData. The code to visualize data using UMAP and t-SNE is freely available at https://github. com/atamaianalytics/DimensionReduction.