dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, visualization of metabolic pathway signatures showed that in these resting PBMC, myeloid cells display higher scores than lymphocytes for glycolysis, oxidative phosphorylation (OXPHOS) and tricarboxylic acid (TCA) cycle, but not for fatty acid synthesis (Supplementary Figure  S2b ), consistent with  (25). In cancer, the metabolic reprogramming is even more pronounced within immune cells from the intra-tumoral microenvironment  (26). Using Single-Cell Signature Explorer, this feature was explored in a lung cancer dataset comprising matched samples of malignant and adjacent nonmalignant tissues. The scRNA-seq datasets of 19k cells from 20 tumors and 4 matched normal adjacent biopsies from 6 lung cancer patients (5) were downloaded from Array Express E-MTAB-6149 and E-MTAB-6653, processed, integrated, sctransform-normalized and the resulting t-SNE was plotted.
We then reasoned that this strategy enabled the visualization of two signatures combined through simple subtraction or addition of their normalized scores, and projection of the result on a dimensionality reduction map. This was illustrated using the human testis cell atlas scRNASeq dataset of 6. 5 k cells involved in adult spermatogenesis  (6). This publicly available data set was downloaded, processed as above and its partitioning into 17 clusters was projected onto a UMAP featuring the germ cell maturation trajectory reported earlier (6). Both cluster identities and positions on the trajectory (Figure  5A ) were consistent with the molecular transitions underlying this development  (6). Of note, the 'GO embryonic organ development' signature was observed in spermatogonial stem cells (SSC) and their cell partners (testicular macrophages, Leydig, Sertoli, myoid and endothelial cells) (Supplementary Figure  S4a ), while 'GO male meiosis' and 'Hallmark spermatogenesis' signatures hit only the early primary spermatocytes and mature sperm cells, respectively.
Software benchmarks The recently published algorithms Seurat's Cell CycleScore module  (16) , AUCell  (29)  and GSVA/ssGSEA  (21)  can also compute the enrichment scores of gene set-based signatures from single cell transcriptomes. We thus compared Single-Cell Signature Explorer with these algorithms for their respective computation efficiency and displayed the results on a t-SNE map. Benchmarks were performed on a Linux Xubuntu 18. 10 workstation with two processors Xeon E5-2687w-v3 and 128Gb RAM. The KEGG database was downloaded from MSigDB v6. 2 (http://www. broad. mit. edu/gsea/msigdb/index. jsp). For this benchmark of Single-Cell Signature Scorer, the KEGG database was downloaded from the software web site (https://sites. google. com/site/ fredsoftwares/products/databases) where additional, readyto-use databases of ∼17 000 gene sets for Human, Mouse, Rat, Zebra Fish and Macaccus obtained from MSigDB v6.
2 are also available. For the ssGSEA speed test, both software issues and the excessive computation time (>12 h) for the entire KEGG database did not permit this comparative evaluation. Hence this speed test was only performed on two gene sets and its result was extrapolated. Since Seurat function CellCycle-Score can only compute gene sets with anti-correlated expression levels, Seurat computation time for all gene sets of the KEGG database could not be performed. Performance, computing time and display of the benchmark results are shown in Supplementary Table  S3  and Figure  S5. While Seurat CellCycleScoring function computes almost as quickly, and yields similar results, as Single-Cell Signature Explorer (Supplementary Figure  S6 ), it only scores few gene sets pairs with anti-correlated expression and does not display t-SNE plots or UMAP of the results.
GSVA/ssGSEA, incepted for scoring gene sets from bulk transcriptomes but not zero-inflated single cell transcrip- tomes, requires such tedious computing times for a single signature that it cannot perform massive parallel scoring of large collections of gene sets, and does not display t-SNE results. Although the gene rank-based AUCell algorithm computes scores five-times faster than GSVA/ssGSEA, it remains 30-times slower than Signature Explorer and does not display interactive t-SNE maps of the resulting scores. In addition to its computing performances, the versatility of Single-Cell Signature Explorer permits analysis of data from various scRNA-Seq and sequencing platforms (Figure  3 ). Beyond the above B-cell signature of 64K human melanoma cells from MARS-Seq, our algorithm allows likewise exploration of non-human samples using gene set databases for macaccus, mouse, rat and zebra fish (https://sites.
CONCLUSION Functionally meaningful displays of gene set-based signature enrichment are essential to understand t-SNE or UMAP maps of complex cell samples, but so far, no current method perform this rapidly for a plethora of signatures at the single cell level across large scRNAseq datasets. By quickly delineating multi-gene features such as cell lineage or metabolic pathways with Single-Cell Signature Explorer in lung tumors and normal human blood and testis, we showed that gene set-based signatures outperform sin- gle genes and provide a straightforward visualization of the sample's hallmarks. Within a few minutes from any computer, this new method provides users with thousands of gene set-based signatures for thousands of single cells, and the immediate visualization in t-SNE or UMAP of any of these single signatures or combination of signatures.
ACKNOWLEDGEMENTS Aviv Regev  and  Asaf Madi  ( Broad Institute ) are acknowledged for kindly sharing R scripts for density plots,  Eric Espinosa (CRCT)  for providing the mastocyte gene set and  Cathy Greenland  for english editing of the manuscript. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation  2019-T19001. We are also grateful to the  Genotoul bioinformatics platform  ( Bioinfo Genotoul, Toulouse Midi-Pyrenees ) for providing computing resources.
Acknowledgements. The authors would like to thank  D. Aubert  for making his SPL code available to us. We acknowledge fruitful discussions with  K. Benabed ,  S. Colombi ,  M. Kilbinger  and  S. Prunet  in early phases of the project. We also thank  G. Lavaux ,  Y. Rasera  and  M-A Breton  for stimulating interactions around this project. We are also thankful to  A. Leauthaud  for constructive comments about the comparison with her GGL lensing results. CL is supported by a  Beecroft Fellowship. This work was supported by the  Agence Nationale de la Recherche (ANR)  as part of the  SPIN(E)   ANR-13-BS05-0005  http://cosmicorigin. org) ERC grant  670193 , and  AMALGAM ANR-12-JS05-0006  projects, and by the  Centre National des Etudes Spatiales (CNES). NEC is supported by a  RAS research fellowship.
II. Introduction T present article aims at highlighting the capacity of morphing to increase lift, decrease drag and reduce the amplitude of instability modes associated to aerodynamic noise. Morphing refers to the adaptation of the real-time shape and vibrational behavior of the aerodynamic surface. In this context, a multi-disciplinary research platform involving the IMFT (Institut de Mécanique des Fluides de Toulouse) and LAPLACE (Laboratoire Plasma et Conversion d'Energie) and laboratories has been continuously working in this topic. By this synergistic platform among six Institutes in Toulouse (www. smartwing. org), coordinated by IMFT and in close collaboration with AIRBUS Emerging Technologies and Concepts Toulouse, advanced electroactive morphing designs for the wings of the future are currently being developed and studied in the context of the European research program of H2020, "Smart Morphing & Sensing for Aeronautical configurations" (www.
In addition, a shape optimization is included in order to evaluate the sensitivity of the lift variation due to any surface deformation. Time Resolved Particle Image Velocimetry (TRPIV) measurements will also be included in this paper for the validation of the numerical approach. For a detailed description of the experimental equipment and procedure the reader should refer to  [2]. The measurement of the unsteady velocity fields by means of Time-Resolved PIV (TRPIV) was realized with the contribution of the Signal and Image processing service of IMFT (S. Cazin and M. Marchal). The post-processing of the raw PIV results was carried out with the CPIV-IMFT open-source software, developed and validated with the contribution of the software services of IMFT (P. Elyakime) for parallel (MPI) post-treatment of the results in supercomputing architectures.
It is highly scalable and specifically improved for acoustic simulations with a pressure-based code. Its discrete adjoint formulation is derived using the TAPENADE  [15]  software and manually enhanced to increase the code's performance. The adjoint solver uses check-pointing to reduce the extreme storage needs of an unsteady adjoint flow solver. For the optimization simulations, the compressible unsteady RANS equations are solved with the k-Omega SST turbulence model. The computational 2D C-type grid M4 has 195. 000 cells. To ensure reasonable results, multiple constraints were enforced on the free node parametrization: The transition from the deformed part to the fixed part of the profile is realized by blending the step size of the shape deformation during the optimization towards zero.
VII. Conclusion In this paper the electroactive morphing effects created by Macro-Fiber Composite piezoelectric patches disposed along the span of an Airbus-A320 wing have been studied by means of TRPIV and High-Fidelity numerical simulations. These actuators introduce optimal vibrations and slight deformations of the trailing-edge region. Having available a detailed experimental database and newly acquired computational results, a combined examination of high Reynolds dynamics in the wake of this supercritical wing has been carried out in respect with the aerodynamic performance increase. The main flow characteristics have been underlined. Various frequencies and amplitude combinations have been studied numerically to evaluate the morphing effects in order to enable future experiments around the same prototype, focusing on the most optimal morphing actuations.
Acknowledgments The authors are grateful to the  Engineering and Technical services of IMFT and LAPLACE Laboratories , to the Signal and Image Services of IMFT enabling the TRPIV measurements, to the Software Services of IMFT enabling the MPI treatment of the TRPIV data bases on the supercomputer CALMIP, as well as to the three national supercomputing centers: CINES, IDRIS, CALMIP for having provided a significant CPU allocation for the present study. A special acknowledgment should be made to the  SMS , "Smart morphing and Sensing for aeronautical configurations" European project (www. smartwing. org/SMS/ EU , H2020  project  N°723402 ) funded by the  European Commission.
Objective Diagnostic tests, such as immunoscore, predict prognosis in patients with colon cancer. however, additional prognostic markers could be detected on pathological slides using artificial intelligence tools. Design We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on cD3 and cD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (Tc) and invasive margin (iM). Using a lassO algorithm, DgMate (Digital tuMor parameTers), we detected digital parameters within the tumour cells related to patient outcomes. results Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (rFs) was associated with high iM stromal area (hr 5. 65; 95% ci 2. 34 to 13. 67; p<0. 0001) and high DgMate (hr 2.
What are the new findings? ► Based on pathological slides obtained from a large prospective study, we generated a new artificial intelligence software for studying both tumour intrinsic prognostic variables and CD3 and CD8 immune infiltrates in stage III colorectal cancer using an automatic procedure. ► We observed that CD8 does not provide added prognostic value in comparison to CD3 analysis alone; therefore, a single CD3 slide is sufficient for determining patient prognosis. ► The density of tumour stroma and tumour cell intrinsic variables are prognostic. ► Determining the density of tumour stroma and tumour cell intrinsic variables combined with CD3 could outperform the CD3/CD8 Immunoscore-like scoring in tumour prognosis. How might it impact on clinical practice in the foreseeable future? ► Our findings show that both tumour stroma and tumour cell intrinsic variables, in association with immune cell infiltrates, should be taken into account during colorectal cancer prognosis.
► Here, we provide a novel, freely available and improved alternative to colorectal cancer prognosis using a single standard CD3 pathological slide. ► Validation by other studies involving stage II patients or patients treated with 5-fluorouracil alone are warranted to extend the clinical importance of this observation. cr conducted the majority of the experiments. QK generates the r software and perform statistical analysis. VD generates the groovy software. VD and cr perform histological slide analysis. JT and cl are clinical researchers who coordinate PeTacc08 study. JFe collect and store all tissue sample, provide unstained slide for cD8 labelling and perform cD3 labelling. P-lP makes all molecular analysis (ras, BraF, MMr determination), KlM performed statistical analysis of PeTacc08 and provided clinical database.
While Laghi et al showed that CD3 infiltrates cannot be independently used to predict patient clinical outcomes,  17  Sinicrope and Pagès, in two different clinical trials addressing FOLFOX-based adjuvant chemotherapy, demonstrated that CD3+ densities can independently predict patient outcomes in stage III CC. 18 19  Additionally, non-immune factors are associated with outcomes in localised CC. 4] [25]  Further, artificial intelligence (AI) could be used to analyse virtual microscopic images and determine, with good accuracy, prognostic and tumour molecular characteristics. 26 ere, we hypothesised that an AI software could be developed to analyse, in a single procedure on a tumour slide, both immune infiltration and tumour-related prognostic parameters. We further hypothesised that analyses of tumour-related variables generated by AI or combination of tumour-related and immune variables could outperform Immunoscore analyses.
generATIOn Of AI sOfTWAre Tissue library generation step All WSI files were automatically segmented by script with the QuPath software 30 using a super pixel strategy. This method tiled the tissue into thousands of parts. Then, 127 parameters (colour, saturation, brightness, texture, etc) were automatically calculated and extracted from each tile. The coordinates of each tile were exported to determine localisation subsequently. Next, two pathologists hand-annotated the WSIs into different classes, that is, healthy (mucosa), tumour, stroma, immune cells, necrosis and empty space. By definition, tumour stroma consists of the basement membrane, fibroblasts, extracellular matrix, immune cells and vasculature,  31  but we requested that the pathologists exclude immune cells and designated the zone as 'stromal area'.
Classification model set-up step From this training tissue library, we built a random forest  32  classification model. For histology differentiation, we selected the variables that most discriminated the different tissue classes described above using the VSURF algorithm. 33 A training model was then built for each histology differentiation group using the variables selected. For patients with unknown histological differentiation, we used meta training, regrouping all the training data available. The model used to classify WSI tiles was selected based on the data available from histology differentiation in the PETACC8 database. This model was called ColoClass.
CD3 and CD8 detection step After measuring and exporting data from all WSI tiles, a script was run to detect any cell on WSI and export the coordinates. Thus, using QuPath detection scripting, positive cells for each marker (ie, CD3 or CD8) were differentiated from negative ones. By gathering cell and tile coordinates, we were able to determine the accurate position of each cell and the class to which it belonged (ie, healthy, tumour, immune or stroma). The replicability of the method was tested by scanning several CD3 slides thrice, thus generating several. ndpi files. An independent bioinformatician then processed each file within our QuPath and R scripts, and checked DGMunes (DGMate (DiGital tuMor pArameTErs) associated with immune and stroma information) and subsequent scores. DGMuneS variance was ~5%.
Artificial intelligence figure 2 Predictive value of interest areas and digital features. (A) Forest plot representing the predictive value of stroma area (IM, TC and WSI), immune area (IM, TC and WSI), total tumour area and total healthy area on RFS. (B) Kaplan-Meier survival curve on discovery dataset (n=713) using DGMate split at median. (C) Kaplan-Meier survival curve on validation dataset (n=305) using DGMate split at median. IM, invasive margin; RFS, relapse-free survival; TC, tumour core; WSI, whole slide imaging. meeting. The annotated tiles were then exported and processed in ColoClass. All data, the R code and Groovy script for QuPath are available on GitHub (https:// github. com/ Klopfe/ PETACC8). A tutorial is supplied as online supplementary file.
DgMate score construction QuPath  30  was used to measure 127 parameters in each software segmented tile. We had computed the mean of each tumour tile parameter for each slide, yielding 127 parameters per slide. Then a LASSO 36 algorithm was performed to select the variables that were related to the RFS using the glmnet R package. 37 38 he DGMate score is the linear predictor of the Cox model built on the discovery cohort with selected variables via the LASSO procedure.
Using the open source QuPath software, we performed tissue segmentation based on megapixel strategy, which regrouped pixels (called tiles) based on their similarity (figure  1A , methodological workflow and figure  1B ), using a training set of 80 slides from different histological types. In total, 27 466 tiles were used to set up this tissue library. From this training tissue library, we built a random forest classification model (figure  1C ) called ColoClass. To validate our classification model, we randomly selected 54 additional slides, which were classified by two pathologists. Annotated tiles were then exported and processed in ColoClass. In total, 26 659 tiles were processed and ~85% concordance (22 652 tiles) was found between the pathologist and ColoClass classifications (figure  1D ).
For a comparable dataset, the concordance between both pathologists was 87%. Similar results were observed independent of the histological differentiation type (online supplementary table  S1 ). Positive T-cell (CD3 or CD8) detection was performed with QuPath and automatically attributed to the classified area. Pooling these information, the software is able to automatically determine the area corresponding to a particular tissue on each slide, as well as the CD3 and CD8 cell infiltration in each tissue (figure  1E ).
718; 95% CI 1. 853 to 3. 988; p=3. 1e-07 for continuous variable). We confirmed the prognostic role of this tumour signature in the validation dataset n=305 (HR=2. 128; 95% CI 1. 162 to 3. 898); p=0. 01 for continuous variable) (figure  2B, C ). The DGMate score increased with T and N stage, sidedness, RAS status and dMMR status (online supplementary table  S5 ).
Composite variable, including immune infiltrate, stromal area and DgMate, improved patient prognosis estimation CD3+ TC, stromal area in IM and DGMate were weakly correlated (online supplementary figure  S5 ). By combining CD3+ TC, stromal area in IM and DGMate, based on the discovery set, we generated a DGMuneS score, which was strongly associated with RFS in the discovery dataset and similar results were observed in the validation dataset (figure  4A, B ). A multivariate Cox analysis for RFS, including all available clinical parameters and the DGMuneS composite score, revealed that the composite score remained independently associated with the outcome in both training and validation cohorts (table 1, online supplementary tables S2 and S7). Similar prognostic results were observed in both FOLFOX and FOLFOX-cetuximab groups (online supplementary figure  S6 ).
17 While our Immunoscorelike variable could not predict the prognosis in high-risk stage III patients, DGMuneS remained prognostic, suggesting that adding digital variables to an assessment of immune infiltrates improves tumour prognostic prediction. We have designed a nomogram to implement the scoring system. Currently, no prognostic model is available for estimating RFS in patients with stage III CC. Using this strategy, we have identified patients with reduced 5-year RFS (50%) rates. Such patients might probably require intensive follow-up and should be included in clinical trials testing intensive adjuvant therapy, such as the IROCAS study (NCT02967289). We have also pinpointed patients with a very good prognosis (90% 5-year RFS). In this context, the relative risk/benefit of adjuvant therapy should be discussed and clinical trials addressing adjuvant therapy minimisation should be initiated for such patients.
We have provided here a nomogram and the required software, together with a tutorial. Using this open software, CD3 analysis could be performed by every pathologist. Although this study involves a large and homogeneous cohort of stage III CC, our work also has limitations. The post hoc design of the analysis and the limited number of patients in some subgroups may restrict our conclusions. Training and validation cohorts for this work were performed by sampling the global trial population. Despite internal validation, external validation in prospective trials is warranted to validate the reproducibility of our software using different datasets. Similar studies should also be performed for stage II tumours, to test if our data can be validated in the context of patients who do not receive chemotherapy frequently.
Artificial intelligence figure 4 4 figure 4 Composite variables improve prognosis prediction. (A) Kaplan-Meier relapse-free survival curve on discovery dataset (n=713) using DGMuneS split at third quartile. (B) Kaplan-Meier relapse-free survival curve on validation dataset (n=305) using DGMuneS split at third quartile. (C) Kaplan-Meier relapse-free survival curve on low-risk clinical stage (T1-3, (N1) (n=549) and high-risk clinical stage (T4 or N2) patients (n=469), both split at median, using DGMuneS. (D) Kaplan-Meier relapse-free survival curve on low-risk clinical stage (T1-3, (N1) (n=549) and high-risk clinical stage (T4 or N2) (n=469), using dichotomic ISlike score. (E) Predictive accuracy on patients' relapse depending on clinical parameters (blue shading), staining parameters (green shading) or combined parameters (red shading) based on 1018 patients from PETACC08 study using a 1000× bootstrap strategy.
Artificial intelligence figure 5 5 figure 5 Nomogram tool based on variable retained in the multivariate model and relapse-free survival according to total score. (A) Nomogram representation of the multivariate model. Each parameter gives a number of points indicated on the upper line. The sum is indicated on total line and provides a 5-year survival probability. (B) Kaplan-Meier survival curve on discovery dataset (n=713) when nomogram score is split depending on relapse risk (low, light red line; high, dark red line or intermediate, red line), grey dotted line displays survival of whole discovery dataset. (C) Kaplan-Meier survival curve on validation dataset (n=305) when nomogram score is split depending on relapse risk (low, light red line; high, dark red line or intermediate, red line), grey dotted line displays survival of whole validation dataset.