dataesr/scientific-paragraphs-categorization huggingface dataset:
Data 1). These data are in line with the results of RNA seq performed on astrocytes derived from NPCs of healthy children, brain of healthy rodents as well as optic nerve of healthy and EAE mice. Indeed, the survey of the Geo DataSets repository revealed the expression in Human astrocytes of IgL gene and in rodents' astrocytes of Ighm, Igg2, Igg1, IgA and IgK genes (Supp. Data 1 bis). We then validated our results by RT-PCR performed on cDNA from primary culture of cortex or spinal astrocytes as well as DI TNC1 cell line (Figures 2H). To mimic an in ammation as observed during the time course of SCI  26, 32  , the cells were stimulated with LPS and for 24h or 48h with a cocktail of cytokines (Figures 2H). cDNA from the spleen served as a positive control. The primers used were deduced from peptides identi ed by proteome analysis after puri cation processes using Ig enrichment procedure 8.
We then conducted 5' RACE-PCR on a cDNA library from DI TNC1 cells (Figure  3Cb ). This revealed the presence of a 5' RSS sequence followed by a joining sequence associated directly with the Igk sequence (Figure  3Cc ). Thus, the complete schematic representation of astrocytic IgG2B is presented in Figure  3C '. IgK were identi ed in rat primary spinal and cortex astrocytes and proteomic analyses con rmed their translation (Supp. Figures 4A). On the contrary, the Lambda chain encoding sequence was only ampli ed in rat primary cortex and DI TNC1 astrocytes (Figure  3Da  and 3Db ). Furthermore, from our RNAseq database and RT-PCR analyses, we also identi ed IgM encoding sequence in rat primary spinal and cortex astrocytes stimulated by LPS (Figure  3E , Supp. Figure  4B ). In astrocytes from adult rats, we also identi ed the sequences coding IgG2B and a variable IgVH chain corresponding to IGVH7-16*01, found in astrocytes isolated from pups (Supp.
These experiments were performed in HEK293 and certainly present cell-type dependent limitations. However, LAT1 is expressed in astrocytes, and we believe this dataset uncovers a putative CD98hc-like function which represents a great interest for pharmaceutical research (because of the ability of CD98 to ensure BBB large molecule crossing). Further experiments are needed to characterize IgG2B-LAT1 interaction in astrocytes and could represent an interesting path for astrocytic neoplasms understanding  48. Furthermore, it will be of peculiar interest to investigate whether the IgG2B-LAT1 proximal interaction occurs concomitantly with TMEM57 and C2CD2L, at the core of a potentially novel cell signaling platform.
Each recognized spot was then analyzed by shotgun proteomics. Their identi cation revealed, proteins involved in microtubule cytoskeleton organization i. e. Tubulin alpha chain and beta chains, Vimentin as well as Eif4a2 and Pdia6 (Supp. Data 8). These two factors were known to be involved in stem cell pluripotency  49  and metastasis  50  Taken together these results are in line with the CRISPR-Cas9 data. Intermediate laments (IFs) are key players in the control of cell morphology and structure as well as in active processes such as cell polarization and migration. Previous studies have shown that IFs are major players in fetal brain development. Vimentin was observed in astrocytes and glial progenitor cells/glioblasts while tubulin beta II was only found in some glial progenitor cells/glioblasts and not at all in mature astrocytes  51.
We observed that it was synthesized as a free transmembrane constant chain through its Kozak sequence located upstream of its rst exon and was addressed to vesicular structures such as endosomes or extracellular vesicles. Through proximal BioID studies we also found that IgG2B could be: (i) N-glycosylated by the OST complex; (ii) addressed at the membrane of ATPase complex-containing vesicles; and (iii) partially behaves as CD98hc through its association with LAT1 and its ability to recruit ILKAP, which potentially regulates downstream signaling pathways (Figure  8 ). These experiments were performed in HEK293 and certainly present cell-type dependent limitations. However, LAT1 is expressed in astrocytes, and we believe this dataset uncovers a putative CD98hc-like function which represents a great interest for pharmaceutical research since CD98 ensures BBB crossing of large molecules.
Acknowledgments This research was supported by funding from  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI) ,  Institut National de la Santé et de la Recherche Médicale (Inserm) ,  I-SITE ULNE (Nobody Project ),  Regional council Hauts de France , and  Université de Lille. The authors thank the  LIGAN-PM Genomics platform (Lille, France ) which was supported by the  ANR EquipEx  ( ANR-10-EQPX-07-01 ; ' LIGAN-PM') and "France Génomique" consortium  ( ANR-10-INBS-009 ),  APVV-19-0193 ,  APVV-20-0585.
1a ). It is important to mention that GFP is a very stable protein. Therefore, once the maximum percentage of competent cells was reached, this number stayed constant for hours. This feature does not mean that competence stays 'open' for hours but rather that once the maximum is reached, no new competent cells appear. Microscopy to determine the percentage of GFP-expressing cells. Following growth in CS2, cells were harvested and treated as explained above (see flow cytometry). Fluorescence images of cells were taken using a confocal laser-scanning microscope (ImagerieGif platform). GFP was excited at 488 nm using the blue laser, and fluorescence images were collected using the green channel. Images were reconstituted using the ImageJ software. Genetic transformation of competent S. aureus cells.
Read mapping and analysis of differential expression. Differential expression of all annotated features was assessed using the R statistical programming environment. Differential expression was determined between the wild-type strain samples (St29, n = 3) and each of the 9 samples (each n = 3) in which sigH, comK1, or comK2 were absent. The output from the differential expression analysis is presented in Supplementary Tables  2 3 4 5. Genes with false discovery rate (FDR)corrected P-values < 0. 01 and a ratio of differential expression superior to 2, 3, or 5 were considered significantly differentially expressed and are presented. Data accessibility. The whole set of RNA-seq data is compiled and accessible under the GEO submission GSE224932. S. aureus exoproteome analysis. Exoprotein isolation.
1% formic acid. MS and MS/MS spectra were recorded from m/z 100 to 1700 with a mobility scan range from 0. 6 to 1. 4 V s/cm 2. MS/MS spectra were acquired with the PASEF (Parallel Accumulation Serial Fragmentation) ion mobility-based acquisition mode using a number of PASEF MS/MS scans set as 10. Data analysis. MS and MSMS raw data were processed and converted into mgf files with DataAnalysis software (Bruker). Protein identifications were performed using the MASCOT search engine (Matrix Science, London, UK) against S. aureus database. Database searches were performed using trypsin cleavage specificity with two possible missed cleavages. Carbamidomethylation of cysteines was set as fixed modification and oxidation of methionines as variable modification. Peptide and fragment tolerances were set at 10 ppm and 0.
05 Da, respectively. Proteins were validated when identified with at least two unique peptides. Only ions with a score higher than the identity threshold and a false-positive discovery rate of less than 1% (Mascot decoy option) were considered. Mass spectrometry based-quantification was performed by label-free quantification using spectral count method. Total MS/MS spectral count values were extracted from Scaffold software (version Scaffold 4. 11. 1, Proteome software Inc, Portland, OR) filtered with 95% probability and 0. 1% FDR for protein and peptide thresholds, respectively. For statistical analysis, missing values occurring in spectral count datasets at protein-level were imputed by a constant value fixed at 0. 1. In order to take into account within-sample variation in spectral count datasets, a beta-binomial test was performed based on triplicates MS/MS analyses with P-values calculated using R package 'ibb' (version 13.
06, 61). Proteins were filtered on a P-value < 0. 05 and a fold change larger than two. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD040550 and 10. 6019/PXD040550. Oxygen concentration measurements. Oxygen concentrations were measured using the SP-PSt3-SA23-D3-OIW oxygen sensor spots (PreSens GmbH, Regensburg, Germany). These sensor spots were attached to the inner wall of 50 mL Falcon tubes with silicone glue so that the spots would always be immerged during the experiments (i. e. , below the 5 mL mark). The Falcon tubes were closed at T0 and remained closed for the entire experiment. The sensor spots are covered with an oxygen-sensitive coating where molecular oxygen quenches the luminescence of an inert metal porphyrine complex immobilized in an oxygen-permeable matrix.
Acknowledgements We thank  Kazuya Morikawa  and  Tarek Msadek  for providing S. aureus strains and constructs and for the scientific discussions. We also want to thank the  Imagerie-Gif flow cytometry and proteomics (SICaPS) facilities (Institute for the Integrative Biology of the Cell, I2BC, Gif sur Yvette, FRANCE)  for their help and support. We also acknowledge the sequencing and bioinformatics expertize of the I2BC High-throughput sequencing facility, supported by  France Génomique  (funded by the  French National Program "Investissement d'Avenir "  ANR-10-INBS-09 ). This work was supported by a  "Young Researcher grant " from the  French National Research Agency  to  Nicolas Mirouze  ( ANR-18-CE35-0004 GenTranSa ) and a  Chinese Scholarship Council (CSC)  PhD grant awarded to  Shi Yuan Feng.
Data availability The whole set of RNA-seq data is compiled and accessible under the GEO submission GSE224932. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD040550 and 10. 6019/PXD040550. All other data are available from the corresponding author on reasonable request. Source data is available as Supplementary Data table.
The architectures in the search space struggled to model the cifar10 and cifar100 datasets, although they worked well with other datasets. Convolutional network architectures were considered in another work  [26] ; a grammar-based approach to solution representation was used to encode hyperparameters such as the number of filters. The search space, at 800 architectures, was small. As a result, the extracted LONs were also small: between two and five nodes. Another study constructed LONs for the topology search space of the NATS-Bench benchmark and the cifar10, cifar100, and ImageNet16-120 datasets  [23]. They found that the landscapes contain quite a low number of local optima and demonstrated that iterated local search was, consequently, a competitive algorithm for traversing the space. Our own study considers the largest NAS space which has been subject to LON analysis, at 32 768 possible architectures; the other works had search space sizes of 800  [26] , 1 110  [24] , and 15 625  [23]  respectively.
EXPERIMENTAL SETUP In the tabular benchmark, each neural architecture is trained, validated, and tested on three image classification datasets: cifar10 and cifar100  [14] , alongside ImageNet16-120  [6]. For training, two epoch settings are used: 12 and 90. For each of those scenarios, and for each architecture candidate, training is executed three separate times, differentiated by the random seed used to initialise the neural network weights. All hyperparameters are fixed as the same for each neural network architecture with the exception of number of epochs (already described) and the channel size configurations (these constitute the search space). The hyperparameters are: a Nesterov momentum stochastic gradient descent as optimiser; batch size of 256; weight decay 0. 0005; the learning rate decays according to an annealing schedule from 0.
The training time budgets for cifar10, cifar100 and Ima-geNet datasets are 20 000, 40 000, and 60 000 seconds respectively. Each algorithm is executed 30 times per dataset. It can be observed that light colours (high number of channels) are markedly more prevalent than the darker colours (fewer channels). This is particularly the case for the cifar10 dataset in the 2 leftmost graphs. Architectures for the other 2 datasets more commonly involve lower-channel blocks. An interesting trend across all 3 datasets is the frequency with which the highest number of channels -64 -is involved in these elite-ranking architectures. This seems to be in opposition to the commonly-used "pyramid" approach to channel configuration  [10] ; this was also noted in the paper which proposed the benchmark we use here  [8].
Low-Dimensional Euclidean Embedding Combinatorial search spaces can be visualised using Low-Dimensional Euclidean Embedding (LDEE)  [18]. LDEE employs t-Distributed Stochastic Neighbor Embedding (t-SNE) to map genotypes into Euclidean space while maintaining spatial relationships between them, and then uses a vacuum embedding method to decide the layout for the solutions in a 2-dimensional grid. We noticed while visualising the channel configuration spaces using LDEE that some genes in the genotypes are more correlated to accuracy than others. For cifar10, the third gene is the most correlated; for the other two datasets, the final (fifth) gene is most correlated. The decision was made to weight the Euclidean distance calculations in light of these important genes. Specifically, we added a weight vector to the Euclidean distance calculations: each gene is assigned a weight of 1 except the most-correlated one with respect to validation accuracy -this gene is given a weight of 2.
This should have the effect of clustering together solutions which have the same value of the most-correlated gene. Figure  4  shows LDEE plots for the search space of each combination of dataset (cifar10, cifar100, ImageNet16-120) and data split (validation, test) pair. The  and  axes form the grid on which the Euclidean-transformed solutions are visualised, and the heat (colour) of points is indication of the validation or test accuracy. Notice that the validation search spaces and the test spaces appear, by this method, to be very similar; this can be observed by comparing the first row of graphs with their counterparts on the lower row. This holds true for each of the three datasets: cifar10, cifar100, and ImageNet16-120. In order to make the differences more pronounced, we plotted (test -validation) accuracy using the same coordinate mapping (4g-4i).
Local Optima Networks Table  1  shows metrics for the extracted monotonic local optima networks (MLONs). Each column contains values which relate to a dataset and a data split type (validation or test). The provided metrics are: nodes: number of nodes (local optima); global strength: normalised weighted in-degree of the global optimum (or optima); path to optimum: average path length, in LON edges, from source nodes to the global optimum (or optima); and difference to optimum: average accuracy difference between nodes and the global optimum (or optima). Notice from the first row of Table  1  that the number of local optima is low. This indicates relatively straightforward landscapes which should be well-suited to local search. For the cifar datasets, paths to a global optimum (path to optimum) are longer in the test set than for validation.
This can be observed in the third row by comparing columns 2 and 4 with 1 and 3, respectively. In the case of cifar100 and ImageNet16, difference to optimum is larger for the test set than for validation. We note also that for all datasets and data splits, the global strength is between 0. 26 and 0. 56. This means that the global optima have a relatively large share of the incoming edges in the LONs. Figure  5  shows visualisations for the constructed local optima networks. Only neutral or improving transitions between local optima are shown; it follows that these are monotonic LONs, or MLONs. Each column in the plot matrix is for a dataset (e. g. cifar10); the top row of graphs is based on validation accuracy, while the bottom row are for test accuracy. That distinction refers to the way the fitness landscape was searched and the LON consequently extracted: for example, validation accuracy was used as the fitness function for the networks shown in the validation graphs.
Algorithm Performance For algorithm performance, we consider test set accuracy only; this is in the interest of space, and also because the test accuracy (and not validation accuracy) is the "real" performance of the NASoptimised architecture. Figure  6  shows, for the three datasets, the test set accuracy of the final obtained architecture from runs. Each box contains 30 runs of the algorithm labelled on the -axis. In terms of the medians -indicated with thick horizontal line through the boxes -ILS-ordered (the left-most box) has the highest for all three datasets. The conventional ILS (ILS-shuffle) has lower medians than the directed version. For cifar10, the median is approximately equal with REA; REINFORCE is above them, with RANDOM the lowest. In the case of cifar100, shown in the middle plot, REA has a higher median than ILS-shuffle; RANDOM and REINFORCE have lower lines.
For the ImageNet dataset, ILS-shuffle has a median approximately equal to that of REINFORCE; REA is higher than them, with RANDOM the lowest. However ILS-ordered produces consistent results, albeit with a few outliers. Figure  7  shows convergence plots for the five NAS algorithms. Wall clock time is on the -axes, with the neural network accuracy  Surveying the cifar10 plot in Figure  7 , it can be observed that the non-ILS NAS algorithms initially have a steeper climb in accuracy over the ILS variants. This can be seen by comparing the three dotted lines with the two solid ones. By around 5 × 10 3 seconds, however, ILS-ordered has outpaced all other algorithms. The traditional ILS (ILS-shuffle) has the slowest climb of all the algorithms, although it does end up with higher accuracy than REINFORCE and RANDOM.
Nevertheless, it has value. We argue that this value lies both in demonstrating the benefit of incorporating search space information into search algorithms, and also in emphasising the result that -at least for this search space and these three datasets -large numbers of channels throughout neural networks are advantageous. That goes against some observations in the literature that compression with autoencoders can help with performance in CNNs  [20]  which has also been noted for the cifar datasets specifically  [40] ; it could be that high channel numbers perform better within the specific environment of the other components comprising the architecture used -which are kept constant while channels are varied. We also note that the studied image datasets are diverse  [15]  and have many possible classes (between 10 and 120), which could possibly benefit from high channel dimension to capture the feature nuances needed to differentiate between classes.
Methods: The mRNA expression and protein analysis were performed by RT-qPCR and immunohistochemistry. The log-rank (Mantel-Cox) test was used to estimate Kaplan-Meier analysis for overall survival. Using large-scale microarray data (METABRIC), digital cytometry was conducted to estimate cell abundance. Analysis was performed using RStudio software (7. 8 + 2023. 03. 0) with EPIC, CIBERSORT, and ImmuneCellAI algorithms. Tumor purity, stromal and immune scores were calculated using the ESTIMATE computational method. Finally, analysis of gene set enrichment (GSEA) and the TISCH2 scRNA-seq database were carried out. Results: Gene and protein analysis showed that A2AR was overexpressed in breast tumors and was significantly associated with high grade, elevated Ki-67, aggressive molecular and histological subtypes, as well as poor survival.
METABRIC dataset acquisition and preprocessing Transcriptomic and clinicopathological data of 1904 primary invasive breast carcinoma tumors were collected from the largescale METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) cohort. For this purpose, we exported (METABRIC, Nature 2012 & Nat communication 2016) dataset using the cBioPortal for Cancer Genomics interface (https:// www. cbioportal. org/). Clinicopathological parameters included in data_clinical_patient. txt and data_clinical_sample. txt files were merged and mapped to the corresponding gene expression data. The transcriptome file comprises mRNA expression levels of 24,368 genes measured by the Illumina Human v3 microarray, log2 transformed and normalized. To predict the 10-year survival rate, Nottingham Prognostic Index (NPI) scores were converted and categorized into 4 prognostic groups: Excellent, Good, Moderate and Poor.
Real-time PCR was performed using SYBR Green PCR Master Mix (Thermo Fischer) on the Bio-Rad CFX96 Real-Time PCR System. Specific primer pairs targeting each gene were used at 10 mM concentration. All experiments were carried out according to the following schedule: holding stage at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s, then annealing and extension at 60°C for 1 min. The specificity control of PCR reaction was applied after each experiment by analyzing the amplicon melting curves. A second specificity-checking was implemented by submitting the PCR product (the amplified cDNA) to agarose gel electrophoresis. Data were assessed as a relative mRNA expression using the housekeeping gene ß-actin and matched control tissue as internal controls. The relative quantification was computed using the 2 -DDCt approach.
Computational analysis of tumorinfiltrating immune cells To assess the abundance of tumor infiltrating immune cells and to estimate tumor purity, stromal and immune scores, the computational deconvolution approach was performed using RStudio software version (7. 8 + 2023. 03. 0) and four algorithms based on different immunological signatures: EPIC, CIBERSORT, ImmuneCellAI, and ESTIMATE. Prior to processing, the METABRIC transcriptomic dataset was standardized and converted into a non-log linear matrix. Then, according to A2AR gene expression and using the median as the cutoff, we stratified our cohort into two patient groups (A2AR low and A2AR high ).
Gene Set Enrichment Analysis (GSEA) To investigate the key signaling pathways and biological processes linked to A2AR, we performed Gene Set Enrichment Analysis using RStudio software version (2023. 03. 0) and exploiting the three molecular signature databases: Hallmark, Curated and Ontology gene sets. Enriched terms with a false discovery rate (FDR) and a (p-nominal) < 0. 05 are considered statistically significant.
A2AR exploration at single-cell resolution The scRNA-seq Tumor Immune Single-cell Hub 2 (TISCH2) database is used to investigate the distribution of A2AR expression in different cell populations. The cell type annotation of three breast cancer datasets: BRCA_EMTAB8107, BRCA_GSE114727_10X and BRCA_Alex was arranged in two levels: Malignancy and Major Lineage. The manifold learning algorithm (UMAP) is adopted for dimension reduction. A2AR expression is explored in malignant, stromal and immune cells.
Statistical analysis Statistical analysis, graphical representations and Heat map visualization were performedusing GraphPad Prism 8. 0. 1, RStudio software version 7. 8, Morpheus (Broad Institute) and BioRender. For Overall survival, Kaplan-Meier analysis was estimated using the Logrank (Mantel-Cox) test. To determine A2AR gene expression status, the median is used as a cutoff to stratify our METABRIC and experimental cohorts into A2AR low and A2AR high clusters. The non-parametric two-sided Wilcoxon signed rank test was applied for matched-pairs analysis. The Mann-Whitney rank test was conducted for unpaired analysis. Correlation coefficients were estimated with Pearson's r statistic. Analysis with a 2-sided P value less than 0. 05 (p < 0. 05) was considered statistically significant.