dataesr/scientific-paragraphs-categorization huggingface dataset:
2G  and S2B ). Furthermore, Pant samples expressed higher levels of MhcII, Cd74, Cxcl9 transcripts linked to antigen presentation and lymphocyte chemoattraction pathways. In contrast, control tumors overexpressed Arg1 and Nnip3, Pgam1, Ldha, Ddit4 associated with M2 polarization and tumor hypoxia  (25)  (Volcano plot in Fig. 2H  for D20 and maintained till D28 as shown in FigS2C). Within the APC metacluster (FigS3A), 7 clusters were de ned and further characterized by differential gene expression (FigS3B). Among them, the proliferating (D20) and IFNγ-responsive monocyte-derived (D28) DC subsets were enriched in Pant samples whereas control PBS samples contained more cDC2 cells (Fig. 2I-S3C ). Membrane MHCII expression by various APC was higher in Pant samples (FigS3D). This observation was con rmed by pathway enrichment analysis using Geneset Enrichment Analysis (GSEA) (Table  1 ) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG, FigS2D).
High VNN1 expression in STS correlates with higher immune response and better prognosis Since Pant therapy mimics VNN1 function  (11) , we rst queried the TCGA database (including 206 soft tissue sarcoma STS samples) searching for VNN1-coexpressed gene modules. VNN1 expression matched with MSigDB hallmarks associated with IFNγ / in ammatory responses (FigS8A). Using a Cibersort deconvolution method to link VNN1 expression with the presence of immunocyte subsets, we found a signi cant correlation between high VNN1 expression and the presence of immune cells that do not express VNN1 such as M1/M2 macrophages and T cells (including CD8 + ) transcripts (FigS8B). This suggested that VNN1 expression within the tumor mass positively correlated with the presence of immunocytes with anti-tumor potential.
The ionization source parameters for positive and negative ion mode were as follows: capillary temperature 320°C, spray voltage 3. 5 kV, sheath gas 30 (arbitrary units), auxiliary gas 8 (arbitrary units), probe heater temperature 310°C and S-Lens RF level was set at 55 v. except MS/MS experiments were performed using Higher energy Collision induced Dissociation (HCD) and the normalized collision energy (NCE) applied was ramped from 10 to 40%. To ensure a good repeatability of the analysis, a quality control sample (QC) was formed by pooling a small aliquot of each biological sample. The QC sample is analyzed intermittently (because of the small number of samples, the QC was analysed 1 out of every 3 samples) for the duration of the analytical study to assess the variance observed in the data throughout the sample preparation, data acquisition and data pre-processing steps.
NMR Samples of thawed tissue (about 15 mg) were placed into a 30 µl disposable insert in which 10 µl of D2O was added to provide eld-lock signal. The disposable insert was then inserted into a 4-mm ZrO2 HRMAS rotor for the HRMAS NMR spectral acquisition. All NMR experiments were carried out on a Bruker Avance III spectrometer operating at 400MHz for the 1H frequency equipped with a 1H/13C/31P HRMAS probe. Spectra were obtained at 278 K with a spin rate of 4 kHz. In order to attenuate NMR signals of macromolecules, the Carr-Purcell-Meiboom-Gil (CPMG) NMR sequence with an overall spin echo time of 150 ms, preceded by a water presaturation pulse during a relaxation delay of 2 s was employed. For each sample, 256 free induction decays (FID) of 32 k data points were collected using a spectral width of 6000Hz.
The FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0. 3Hz and zero-lled once prior to Fourier transformation. Subsequently, the spectra were phased and baseline corrected manually and calibrated to the alanine methyl signal (δ = 1. 47 ppm). To facilitate NMR signal assignments, 2D NMR experiments using 1H - 1H TOCSY, 1H - 13C HSQC and online databases (Human Metabolome Database, Wishart et al, 2013) were employed. The 1H 1D NMR spectra were directly exported to AMIX 3. 8 software (Bruker Biospin GmbH, Karlshure, Germany) and divided into 0. 001 ppm-width buckets. In order to remove the effects of possible variations in the water suppression e ciency, the region between 4. 70 and 5. 20 ppm was discarded. The obtained NMR dataset X-matrix (40 observations x 8001 buckets) was then normalised to the total spectrum intensity and then subjected to multivariate statistical analysis using the software SIMCA-P + v.
16 (Umetrics, Umea, Sweden). Initially, the principal component analysis (PCA) of the 1H NMR spectral data was carried out to check the homogeneity of the dataset, group clustering and detect potential outliers. Following PCA, a supervised orthogonal partial least squares discriminant analysis (OPLSDA) was then applied to the Xmatrix, in which we de ned a Y-matrix as the matrix of sample classes in order to target metabolic differences between the groups of interest. The resulting score and loading plots were used to visualize the discriminant features. A leave-one out internal cross-validation was performed in order to calculate R2Y and Q2 values representing, respectively, the explained variance of the Y matrix and the predictive ability of the model. Model validation was performed by random permutation of the Y matrix with n = 999 times and by the use of a CV-ANOVA p value from SIMCA-P + v.
Intracellular antibodies were incubated 45 minutes at 4°C. Cells were resuspended in FACS Buffer before analysis on BD LSR Symphony or Fortessa. Mitotracker Deep Red and MitroTracker Green were used following manufactured protocols to assess the membrane mitochondrial potential and mitochondrial mass, respectively. The OVA-tetramer (SIINFEKL -H2-K b ) was purchased from the NIH tetramer core facility and incubated with cell suspension prior to cell surface labeling during 1 hour at 4°C. Beads were added to cell suspension for calibration and data normalization. Flow cytometry data were analysis using FlowJo 10. 8. The gating strategies for the analysis of myeloid and lymphoid cells are indicated in FigS1E and S4C, respectively.
After individual quality control, all count matrices were loaded and processed together using R package Seurat (6. 1. 0) (76). A second Quality control (QC) was performed to evaluate potential batch-related biases, perform HTO demultiplexing, and identify poor quality cells to be excluded before downstream analyses. Cells with less than 1000 counted UMIs or more than 5000 detected genes (potential doublets), and cells with more than 10% mitochondrial gene expression (apoptotic cells) or less than 5% ribosomal genes were excluded from analyses. Then, datasets from two independent experiments were pooled and analyzed. 5 metaclusters were identi ed : Myeloid cells, Lymphoid cells, APC cells including DC cells and B cells and an unde ned metacluster call "Others" which include tumor cell, broblasts and stromal cells.
For subsequent analysis, the four immune cells metacluster were recomputed separately. After normalization (lognormalize) and centering, the 2000 most variable features were identi ed and used for dimensionality reduction by PCA (50 PCs). Further dimensionality reduction was performed using Uniform manifold approximation and projection (UMAP) embedding, and cell clustering using Louvain algorithm  (77). Marker genes for each cluster were extracted with Wilcoxon method using Seurat function 'FindMarkers'. Functional enrichment analyses were performed using the R packages clusterpro ler (78) and fgsea  (79) , and the external database KEGG (80). GSEA enrichment analyses and gures were performed with R package 'fgsea' against selected reference gmt les from MSigDB (81, 82) Heatmaps were generated using the R package complex heatmap (83), other custom gures were generated using ggplot2 (84).
RNA velocity analysis was performed with scVeloAll (85) detailed parameters for analyses are available in technical reports). For further characterization of NK cells, results of a differential expression analysis from a previous study  (32)  comparing ILC and NK cells were downloaded. The top 20 up-and downregulated genes (sorted by pvalue) from this study were used to create gene module scores in Seurat, projected on previously computed uMAP for NK cells population.
CBA Tumor cell suspensions were centrifuged and cytokine or chemokine content measured in the supernatant using the Cytometric bead array (CBA). TME cytokines were determined with mouse CCL2, CXCL9 cytometric bead array (CBA) ex set kits (BD Biosciences), in accordance with the manufacturer's instructions. Samples were analyzed on a BD LSR II ow cytometer with FCAP Array TM Software V3 (BD Bioscience) to determine the cytokine concentrations of the experimental samples.
Immunohistology Histology analysis of osteosarcoma, and MCA tumors were realized on 5µm-thick slice mounted in SuperFrost-Plus (VWR, Radnor, Pennsylvanie USA). After depara nization and rehydration, hematoxylinphlowin-safran (HPS) staining was performed to discriminate tumor and surrounding tissues. CD8, CD3, CD31 were stained by immunohistochemistry at the Lyon-Est Research Anatomopathology platform on an automated staining system Ventana Discovery XT (Ventana Medical Systems, Roche, Tucson, Arizona, USA). Slides are digitized by the Histech 3D slide scanner. The slide scans are analyzed by QuPath v0. 3. 0 software (87). After manual annotation of the tumor area, a speci c script for each IHC and HPS staining detection is created to quantify the labeled immune cells and the rate of necrotic tissue present in the tumors.
Analysis of VNN1 expression in TCGA Database and Cibersort TCGA-SARC dataset was retrieved using TCGA biolinks package. Raw HTSeq counts were normalized using DESeq2 variance stabilizing transformation. Co-expression unsigned network was produced using WGCNA with biweight midcorrelation and soft Power = 13. Each module was further 2-means clustered to separate correlated and anticorrelated patterns, and their gene sets were queried for functional enrichment against MSigDB Hallmark (78, 81).
Analysis of VNN1 expression in soft tissue sarcoma clinical samples We analyzed our database (88) including 1. 377 cases of STS clinical samples clinically annotated and with available normalized gene expression pro les. Data had been gathered from 15 public data sets (Fig. S8C ), all samples were from operative specimen of previously untreated primary tumors, and the gene expression pro les had been generated using DNA microarrays or RNA-sequencing. VNN1 mRNA expression was analyzed as discrete variable (high vs. low) by using its mean expression level of the whole series as cut-off. Because we found a link between VNN1 and immunity in our mouse model, we searched for correlations between VNN1 tumor expression and several immunity-related variables. These latter included the following multigene classi ers/scores: the 3 Gatza's immune pathway activation scores (89), the 24 Bindea's innate and adaptive immune cell subpopulations  (90) , two signatures associated with antitumor cytotoxic immune response (the Immunologic Constant of Rejection (ICR) classi er (88), and the Rooney's cytolytic activity score (91)), two metagenes associated with response to immune checkpoint inhibitors ICI (the Ayers' T-cell-in amed signature (TIS)  (92) , and the Coppola's tertiary lymphoid structures (TLS) signature  (93) , the Ballot's Tumor In ltrating Lymphocytes Signature (TILS) along with both of its inner lymphoid and myeloid signatures (94) and the Thompson's Antigen Processing and presenting Machinery Score (APMS) (95).
Acknowledgments The  ANR PLBIO19-015  grant supported the work realized in PN, JC, LS and JYB teams and RM funding. We thank the core facilities involved in this project:  CIML Flow cytometry, CIML Genomics Platform, CIML Animal facilities, Imagimm. BM and SH were supported by DCBIOL  LabEx  (grants  ANR-11-LABEX-0043.
We demonstrate the capability of the implemented network architecture by designing a metasurface absorber operating at 82 Hz for oblique wave incidences with a thickness of λ/64. To implement the deep learning methods for acoustic designs, the main challenge is to generate large and high-quality training dataset using numerical simulations. To mitigate this issue, we implement data augmentation. The presented approaches open new avenues to automate the design process of metasurfaces and enable a much more generalized and broader scope of optimal designs that go beyond acoustics applications.
The great advantage of such learningbased models lies in their ability to model physical systems that fall outside of the training dataset. Due to such generalization capability, DNN can be used as a forward design optimization engine. In the field of acoustics, the DNNs have been implemented using fully connected layers (FCLs) and convolution layers  [21] [22] [23] [24] [25] [26] [27] [28] [29]. Once properly trained with adequate data such deep learning models are very accurate and capable of generating desired acoustic responses on the millisecond timescale. In addition, various deep learning-based models are proposed to solve inverse acoustic problems  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]. Despite such development in this field, there are still challenges with the existing networks specially built using FCLs.
Such kind of networks primarily deal with simple acoustic structures with a limited number of parameters, severely limiting the accessible acoustic design DOFs and because of this, current deep learning models are insufficient as a general design scheme. Also, deep learning approaches require a large amount of training data to understand the latent patterns. For designing complex acoustics devices, the generation of the training dataset is usually dependent on numerical simulations which leads to a substantial computational burden. It is extremely challenging to generate high-quality annotated data on a large scale using numerical simulations. The tradeoff between the size of the training dataset and the model accuracy is a critical factor to be considered. To solve this issue, we have implemented a data augmentation technique that not only increases the training dataset size but, also increases the diversity in the dataset which makes the network more robust  [41, 42].
As compared to total variables for our case, the training dataset size is small, and hence to help neural networks learn the patterns more easily, PCA is implemented using the 'scikit-learn' library. For our data, we use the first 20 components which explained 96. 5% variance thus enabling us to reduce the dimensionality of the target absorption spectra from 201 to 20, i. e. 90% reduction. In prior work  [55] , good results were obtained by processing both 2D images and 1D properties (dimensions of the structure) using the separate networks and later concatenating and processing through the rest of the network. However, in our case, we find that the network shows overfitting when processing both properties separately. Therefore, we proposed a PNN that processes both in the same network by adding 1D properties (thickness and angle of incidence) into 2D binary image composed of 64 × 64 pixels as additional channels.
The output is than passed through a dense layer, D 1 having 512 neurons followed by the output layer 20 neurons corresponding to components post PCA. At the output, we applied Rectified nonlinear activation function (ReLU) to introduce the nonlinearity in the network. For the training process, the 9000 generated metasurface structures are split into training, validation, and test datasets, with 88% are used during the training process, 10% for the validation, and remaining 2% are used to evaluate the trained network. Fig. 3 (a) shows the learning curves (for the training and validation data), as a function of epochs. During the training phase, both the training and validation errors decreased and converged after 500 epochs. The network performance is quantified in term of mean squared error (MSE) which is given by, MSE = 1 N N ∑ i=1 (α p -α s ) 2 (1) where, α p and α s are the predicted and simulated absorption coefficients, respectively.
When the training was finished, the average MSE was 0. 00904 for the predicted absorption coefficients in the validation data. During the training process, the weights are continuously optimized to minimize the validation loss (the hyperparameters are included in the Supplementary Information, Section 2). We then calculate the predicted spectra and compare the results to numerical simulations to extract the prediction error. To justify the usage of the batch normalization layer, and flatten layer in the PNN, we also carry out an ablation analysis (Supplementary Information, Section 3). Results from this study indicates that including these layers improves the speed of convergence and accuracy. To check the prediction accuracy of the trained model, we have evaluated it on the test dataset. The output of the last max-pool layer, M 3 is followed by a batch normalization layer before flattening.
Such layers can be understood as the opposite of a pooling layer i. e. it increases the dimensions of the input and performs convolution after that to learn appropriate weights. Nonlinear activation function ReLU is applied after each the output of layers CT 1 , CT 2 , CT 3 , and sigmoid activation function is applied at the output of the layer CT 4. A sigmoid activation is appropriate at the output of the layer as we want the discriminator's output to be 1 or 0. The discriminator, which is a classification network, learns to distinguish between image samples that come from the training dataset and those from the generator. It accepts equally both the 2D images from the training dataset and the images generated from the generator. Our discriminator model is a convolutional neural network consisting of four convolution layers CD 1 , CD 2 , CD 3 , and CD 4 with a kernel size of 3 as shown in Fig.
4(c). Each convolution layer uses 8, 16, 32, and 64 filters respectively with the stride of 1. Here, each convolution layer is followed by a maxpool layer (M 1 , M 2 , M 3 , M 4 ) with the pooling size 2. We apply LeakyRELU activation functions in the layers of the discriminator network except for the final layer which is activated by a sigmoid function. The output of the last max-pool layer, M 4 is flattened and fed to a fully connected layer having 1 neuron to generate the output i. e. 0 -image is fake, 1 -image is real. To prevent the networks from overfitting, a dropout layer is used in both architectures. The training process of the whole network for the one epoch can be explained as follows. To begin the training process, a batch is created which is a small group of the training dataset.
It includes the latent space, the absorption spectra, and other properties from the training set. The generator creates the corresponding images based on the information in the batch. Here, as the inputs come from the randomized latent space, z, generation of the early images can be extremely noisy. Also, we have randomly initialized the parameters of the generator, G. In the next step, the same number of 2D images and corresponding absorption spectra are chosen from the training dataset. They are then mixed with the generator's predictions. Using this mixed subset, the discriminator learns to distinguish that the images are coming from the generator or training dataset. When the discriminator's output predicts either ''fake'' for a generator input (fake = 0) or ''real'' for a training dataset input (real = 1), it is considered true.
3 nM). The cells were cultured for 6 days before fixation in PBS buffered 4% paraformaldehyde and immunostained for anti  raised in rabbit, France)  overnight at 4°C and goat anti-rabbit Alexa fluor 546 for 1 hour at room temperature (Thermo Fisher Scientific, Villebon sur Yvette, France). Images were acquired with a Nikon Eclipse E800 microscope with an OPTRONICS Color digital camera (Nikon France, Champigny sur Marne, France) and opened at the proper magnification in Neurolucida V 7. 0 software (MBF Bioscience, Delft, The Netherlands). Axons from each cell body were manually traced. Measurements of drawn neurites were performed by NeuroExplorer-associated software (MBF Bioscience, Delft, The Netherlands). Three coverslips per condition were averaged.
2 nM as indicated in the figure). After 6 days in vitro, explant cultures were fixed in buffered 4% paraformaldehyde and labeled with anti-NF200 as above. Explant images were acquired with Nikon 90i microscope, opened in Fiji software (version 2. 0. 0-rc-15/1. 49k) and axons/ axon fascicles emanating from the explant manually traced by an investigator blind to the treatment condition. Total axon/fascicle length per explant was determined with the measure command and averaged for at least four explants per condition. Optic nerve crush and recombinant OTX2 injection into the eye C57 Bl6 male mice weighing 25 g were housed 4-6 per cage with a 12-hour light/dark cycle and access to food and water ad libitum. Optic nerve crush (ONC) was performed as described  (de Lima et al. , 2012). In brief, mice were anesthetized by an intraperitoneal injection of ketamine (Imalgène 500, 100 mg/kg Virbac, Carros, France) and xylazine (Rompun 2%, 10 mg/kg Bayer).
Anatomical analyses Two weeks after OTX2 injection, mice were terminally anesthetized and transcardially perfused with PBS followed by buffered 4% paraformaldehyde, eyes were removed and the dissected retina was prepared with four cuts for flat mount processing and post fixation in buffered 4% paraformaldehyde. Optic nerves were dissected at the optic nerve head, post-fixed in buffered 4% paraformaldehyde on a paper filter and prepared for cryosection. Free-floating retinas were processed for Brn3a (Santa Cruz antibodies, sc-31984) immunofluorescence and RGCs were counted in eight fields of view per retina using the Fiji software  (Danias et al. , 2002). Optic nerves were processed using anti-GAP-43, a marker for axon regrowth  (Moya et al. , 1988). Fourteen micron longitudinal optic nerve sections were mounted on Superfrost slides (Thermo Scientific).
Acknowledgments: We thank  J. Dégardin  and  M. Simonutti  of the  Institut de la Vision (Paris, France)  for visual acuity testing and  Prof. Larry Benowitz  and  Dr. Yiqing Li  of  Boston Children's Hospital (Boston, MA, USA ) for assistance in preliminary ONC experiments. Author contributions: RTI, AP and KLM designed the study, RTI, NQD and KLM performed the study. RTI and KLM analyzed the data. RTI, AP and KLM wrote the paper. All authors approved the final version of this paper.
Materials and Methods Plasmids. To generate the BRET constructs, mNeonGreen and nanoluciferase were used to improve the brightness of the assay and are referred to as mNeonG and nLuc for short in the rest of this paper. The mammalian expression vectors encoding mNeonG-TRPV1-nLuc, TRPV1-nLuc, and mNeonG-CaM were described in ref. 22. Mammalian expression vectors for the expression of the nLuc-ion_sensor-mNeonG or mNeonG-ion_sensor-nLuc fusion proteins were constructed using cDNA bricks, obtained by gene synthesis (Genescript), that encode, respectively, nLuc or mNeonG (bricks 1. 1, 1. 3, 4. 1, and 4. 3), any ion sensor described in the text (Calflux, Kbp, or I3S, brick 1. 2/4. 2), and TRPV1 ion channel (bricks 2 and 3) as indicated in SI Appendix, Fig. 7. All bricks sequences are given in Dataset S1.