dataesr/scientific-paragraphs-categorization huggingface dataset:
Model performance and including LUCAS photos in the app First, before including the LUCAS crop photos into the application, the accuracy of the Pl@ntNet algorithm to identify crops is evaluated on the photos contributed by citizen scientists. When training Pl@ntNet's recognition algorithm, a fraction of the data (randomly selected) that has been evaluated by experts is removed from the training set in order to evaluate the performance of the crop identification. Second, to decide which LUCAS cover photos of crops can be included in the application, considerations are made regarding the probability of the prediction and the accuracy of the crop identification by comparing the Pl@ntNet classification with the LUCAS label. This requires matching the LUCAS legend labels with the Pl@ntNet species reference list.
Model performance A total of 218 cultivated crop species are included in the Pl@ntNet Crops application (see supplementary table  1  and the Crops application). Along with scientific names, common names translated into 36 languages are also provided. Species covered include major crops (maize, wheat, rice, yam), but also vegetables (asparagus, eggplant), tree crops (olives, coconut), fruits (kiwi, apple), nuts (hazelnuts, pistachio), spices (cinnamon, black pepper), and cover or N-fixing crops (clover). Pl@ntNet users have already contributed 605 242 crop photos (as of 16 November 2021) from around the world to the Crops application. Out of these 605 242, the classification of 260 610 of those have also been evaluated by experts. The dataset that was used to train the model was selected from these.
Now that the legends are matched, LUCAS surveyors could (voluntarily) use the app for the crop classification task, especially if they are not agronomists. While generally only a single photo is taken of a crop at a LUCAS point (i. e. similar to the one-shot problem, very few images per species and type of view, see  [9] ), multiple photos of the same species, and of different organs, can be used for the identification by Pl@ntNet. By combining photos from volunteers and surveys, as done here, increasingly rich training datasets are created. While noise in the imagery is a drawback, a positive aspect is the diversity of such datasets. Over time, this should lead to the development of more robust identification models. Such models are needed for real-world applications. The LUCAS protocol was not built for use in computer vision and machine learning.
The cover photos of the LUCAS 2022 survey will also be included in the app once available. Besides photos of crops, the LUCAS cover photo-dataset also includes photos of trees (leaves) and grasses. These may be included within other Pl@ntNet floras. Other LUCAS legacy photos, such as those taken in the four cardinal directions, may prove useful in computer vision challenges related to land cover and use and landscape analysis  [27]. A proliferation of apps for plant recognition has appeared in recent years (e. g. LeafSnap, PictureThis, PlantStory, Seek, etc). Jones  [28]  compared ten automated image recognition apps to identify British flora. Here, overall Pl@ntNet was a mid-level performer. Otter et al  [29]  concluded that such apps cannot yet be trusted to identify toxic plants, with two out of three apps tested (including Pl@ntNet) correctly predicting about half of plants.
The particular difficulty of very visually distinct crop varieties has not been solved yet. Different phenological stages provide another challenge  [22]. In fact, plant identification can be difficult, even for a trained botanist, and benchmarked datasets are needed to improve algorithms (see for example the public dataset created by Pl@ntNet on anemones, https://plantnet. org/en/2021/03/30/aplntnet-dataset-for-machine-learning-researchers/).
Simulation design A ramp metering scenario is devised as the experimental setup, utilizing a real-life freeway stretch as the test bed, as depicted in Fig. 2. To simulate the traffic dynamics within the freeway stretch, we employ the open-source microscopic simulation software, SUMO. For our simulation experiment, we utilize the default car-following model in SUMO, known as the Krauss model. The parameter values adopted in this experiment are based on the work of  Han et al. (2022b) , in which the model was calibrated with real data. Specifically, we set the driver's desired (minimum) time headway to 1. 1 s, while the driver imperfection (where 0 denotes perfect driving) is assigned a value of 0. 4. The remaining parameters are set to their default values. The traffic demand profile of the peak hour is depicted in Fig.
Introduction: Peptides carry out diverse biological functions and the knowledge of the conformational ensemble of polypeptides in various experimental conditions is important for biological applications. All fast dedicated softwares perform well in aqueous solution at neutral pH. Methods: In this study, we go one step beyond by combining the Debye-Hückel formalism for charged-charged amino acid interactions and a coarse-grained potential of the amino acids to treat pH and salt variations. Results: Using the PEP-FOLD framework, we show that our approach performs as well as the machine-leaning AlphaFold2 and TrRosetta methods for 15 wellstructured sequences, but shows significant improvement in structure prediction of six poly-charged amino acids and two sequences that have no homologous in the Protein Data Bank, expanding the range of possibilities for the understanding of peptide biological roles and the design of candidate therapeutic peptides.
We have considered 15 peptides for which a PDB structure is available. These correspond to peptides previously studied during PEP-FOLD development and new peptides with their structures released after 1 September 2019, and determined in pure aqueous environment. The predicted models of the 15 peptides were evaluated by computing the CAD-score  (Olechnovič et al. , 2013). The reported CAD-score corresponds to the largest value of the cross CAD-scores between the five predicted models and all NMR structures. Following our previous work, if the CAD-score calculated on the backbone atoms is > 0. 60, the model is associated with largely correct secondary structure prediction, otherwise if it is > 0. 65 the model is correct in terms of secondary and tertiary structures. For the poly-charged peptides, we also computed their secondary structure contents using STRIDE program  (Frishman and Argos, 1995).
Conclusion Integrating pH variation effects to a coarse-grained model, where the side chains are represented by one single bead, is an important step toward accurate polypeptide structure prediction in aqueous solution, as coarse- graining with various granularities  (de Vries and Zacharias, 2013; Sieradzan et al. , 2022) , enhance sampling. This task has been performed by combining a Debye-Hückel formalism for chargedcharged side chain interactions and the sOPEP2 potential. By using a total of 25 peptides of amino acid lengths varying between 7 and 38 amino acids, this study provides evidence that PF-noDH, PF-DH, AlphaFold2 and TrRosetta perform similarly on peptides deposited in the Protein data Bank, but PF-DH outperforms the two recent machine-learning methods for poly-charged peptides, and peptides for which homologous sequences are not deposited in the PDB.
RESULTS scRNAseq identifies distinct muscle developmental states in RMS To characterize intratumoral heterogeneity of RMS, we profiled 14 RMS PDX-derived primary cultures and 3 conventional aRMS cell lines by droplet-based scRNAseq (Fig. 1A ). To maximize interpatient variability, we chose RMS models that originate from both aRMS and eRMS subtypes, primary and metastatic sites, and diagnostic and recurrent patients that had or had not been pretreated (table  S1 ). After filtering out low-quality cells, we retained 48,859 cells for downstream analysis. Cells clustered mainly by patient of origin (Fig. 1B ), indicating substantial intertumor heterogeneity, a characteristic previously described for other tumor types  (28). To identify shared cell subpopulations and gene expression signatures, we integrated the RMS datasets using the "anchor" approach  (29).
g. MYH3, MYH8, and MYL1) was exclusive of the differentiated subpopulation (Fig. 1, D  and E ). MuSC-like cells were the only aRMS subpopulation clearly characterized by the absence of MYOG and expression of CD44, PAX7, and AXL (fig. S1E ). To distinguish high-cycling from low-cycling cells, we scored each cell for validated gene signatures  (34). Notably, most MuSC-like and differentiated cells were low cycling, whereas cells in S-phase and cycling progenitors were high cycling (fig. S2A ). We noted that eRMS mapped mostly to the MuSC-like cluster, whereas aRMS cell lines contained a higher proportion of cells in the cycling S-phase cluster compared to eRMS or primary cultures (Fig. 1 , F and G). We confirmed these differences in a large RMS patient mRNA expression dataset  (35). Compared to eRMS, aRMS patients showed significantly lower PAX7, known to mark MuSCs, higher MYOD1 and MYOG, known to mark activated and committed cells, and no difference in MYH8 mRNA expression, known to mark differentiated myogenic cells (fig.
CyTOF reveals cell surface markers associated with the immature MuSC-like aRMS subpopulation To confirm the presence of the identified subpopulations at the protein level, we stained primary RMS cultures with an isotope-conjugated antibody panel, which included known surface markers used to isolate MuSCs as well as identified by scRNAseq, and myogenic transcription factors known to define distinct stages of myogenesis  (37) , and profiled the cells by CyTOF. We analyzed the pooled aRMS and eRMS datasets with the clustering algorithm Xshift  (38)  and visualized the spatial relationship between the resulting clusters by single-cell force-directed layout (Fig. 2A ). While cells from aRMS and eRMS shared some commonalities, they also occupied unique regions that were specific to each subtype (Fig.
RMS mirror cell-fate decisions of developing/regenerating skeletal MuSCs We investigated the relationship between different RMS subpopulations using a trajectory inference model. For each dataset, we inferred trajectories and pseudotime values using Slingshot  (40) , and embedded the single cells using PHATE  (41). We excluded two clusters ("unknown 1" and "unknown 2") from aRMS primary cultures, as these subpopulations were present only in two specific samples, which might be the result of incomplete batch effect removal (fig. S3A ). In both aRMS and eRMS, the predicted trajectory revealed a branched progression from MuSC-like cells to either cycling progenitors or differentiated cells (Fig. 3A ). To identify developmental programs that are aberrantly persistent in RMS, we reanalyzed an scRNAseq atlas of developing human skeletal muscles  (42).
Projection of our RMS single-cell transcriptomes onto this dataset (fig. S3B ) showed that tumor cells mostly map onto muscle cells transitioning from embryonic to fetal stages (weeks 6 to 14; Fig. 3B ). Similarly, we projected our primary aRMS cultures onto an scRNAseq dataset of regenerating skeletal muscles  (32)  and observed overlap of the branched cell-fate trajectories (Fig. 3C ). Notably, the cycling progenitor subpopulation, which is only transiently detected in skeletal muscle cells upon injury (fig. S3C ), represents a prominent and stable subpopulation in aRMS tumors. To validate the predicted cellular trajectory of aRMS tumors, we took advantage of the cell surface marker CD44, which we found to be predominantly expressed on MuSC-like cells (Figs. 2, B  and C , 3D and fig. S1E ).
RAF inhibitors enhance trametinib-induced aRMS differentiation and inhibit in vivo PDX growth Because clinical trials with single-targeted agents have been largely unsuccessful, we next sought to identify trametinib-potentiating drugs by performing a combinatorial screening in aRMS-1 (IC-pPDX-104) cells. We used a library of 243 compounds either alone or in combination with 50 nM trametinib and measured differentiation using MYOscopy (table  S6 ). We found 97 drugs that potentiated trametinib-induced differentiation; of the top 20 hits, we identified several RAF inhibitors (Fig. 7A ). These included regorafenib, a kinase inhibitor currently in clinical trial in RMS patients (NCT01900743), which acts as multikinase inhibitor and, besides mutant and wild-type B-RAF and RAF-1, also targets KIT, PDGFRB, RET, and VEGFR1-3  (44) , its derivatives sorafenib, a multikinase inhibitor of RAF-1 and B-RAF  (45) , and dabrafenib, a selective inhibitor of mutant B-RAF and to a minor extent of wildtype B-RAF  (46).
A meta-analysis integrating the different RMS scRNAseq datasets will be needed to confirm this hypothesis. In our study, we identify a myogenin + cycling progenitor cell state that is only transiently found in myogenesis during muscle development and regeneration  (32)  and that characterizes the more aggressive fusion-positive aRMS subtype. Myogenin is typically a hallmark transcription factor of a nonproliferative myogenic commitment state that drives expression of differentiation markers, such as MyHCs. In aRMS, the cycling progenitor state continues to proliferate, although it has initiated myogenic differentiation; hence, it could serve as a cellular target for aRMS differentiation therapy. In addition, we identify an RMS subpopulation resembling early MuSCs (MuSC-like) and one resembling differentiated muscle cells.
Clinical studies with the RAF inhibitor regorafenib are currently ongoing in RMS (NCT01900743), while studies with the MEK inhibitor cobimetinib are underway in eRMS patients (NCT02639546). On the basis of our findings, we expect that RAF inhibitors, such as regorafenib, would benefit from a combination with MEK inhibitors, an approach that could lead to increased aRMS cellular differentiation. Notably, the use in the clinic of differentiation therapy for aRMS and its combination with chemotherapy have to be carefully evaluated, as our work suggests that they induce opposite lineage shifts. To be successful, one would first need to eliminate the rapidly dividing cycling progenitors with chemotherapy and only then "mobilize" the remaining MuSC-like cells to cause their exhaustion using differentiating agents.
Cell lines The cell lines Rh4 (RRID: CVCL_5916) and Rh41 (RRID: CVCL_2176, both provided by P. Houghton, Greehey Children's Cancer Research Institute, San Antonio, TX), KFR (RRID: CVCL_S637, provided from J. Cinatl, Abteilung für paediatrische Tumor und Virusforschung, Frankfurter Stiftung für krebskranke Kinder, Frankfurt), and RMS (60) (provided from J. Shipley, Sarcoma Molecular Pathology, The Institute of Cancer Research) were cultured on uncoated plates in DMEM (Sigma-Aldrich, D5671) supplemented with 10% fetal bovine serum (Life Technologies), penicillin/streptomycin (100 U/ml; Thermo Fisher Scientific, 15140122), and 2 mM GlutaMAX (Life Technologies, 335050-061). For further passaging, cells were washed with PBS and detached with trypsin (BioConcept, 5-51F00-I). All cell lines were authenticated by short tandem repeat (STR) analysis profiling and regularly tested to ensure no mycoplasma contamination with the LookOut Mycoplasma PCR-Detection Kit (Sigma-Aldrich, MP0035-1KT).
scRNAseq hashtag sample demultiplexing Illumina sequencing reads were processed (demultiplexing features and cellular barcodes, read alignment to the reference human genome GRCh38, and gene expression matrix generation) using the 10X Genomics Cell Ranger software Suite (version 3. 0. 1). Each gene expression matrix was then analyzed independently using Seurat R package (version 3. 2. 2, 29) in R (version 3. 6. 1), with some modifications to the standard pipeline. We first demultiplexed gene expression matrices based on hashtag oligonucleotide (HTO) enrichment  (61). To do so, we added HTO data as a new independent assay to the RNA data and used centered log-ratio transformation to normalize HTO raw counts. Cells were demultiplexed using Seurat's HTODemux function with default parameters. We assigned sample identities on the basis of the maximal HTO signal and on the antibody sequence used for staining (table  S9 ).
scRNAseq data analysis Demultiplexed samples were analyzed independently. Low-quality cells, identified as cells with <200 or >8000 genes, total number of transcripts [unique molecular identifiers (UMIs) <1000 or >50,000, and/or percentage UMIs mapping to mitochondrial genes >15%, were removed. After log-normalizing the data, we assigned cell cycle scores per single cell, using Seurat's CellCycleScoring function, and scaled the expression of each gene regressing out the number of UMIs and the percentage UMIs mapping to mitochondrial genes. We then performed principal components analysis (PCA) to denoise the data and, based on elbow plots and the percentage of explained variance, selected the number of principal components (PCs) to consider for downstream analysis. We then built a shared nearest neighbor graph and used the Louvain algorithm for clustering (resolution of 0.
2 to 0. 3) the cell subpopulation. Single cells were visualized as uniform manifold approximation and projection (UMAP) plots. Differential expression analysis was performed using Seurat's FindAllMarkers function only considering genes with >log 2 (0. 25) fold change and expressed in at least 5% of cells in the cluster. We annotated each cluster in the different datasets independently. Last, to distinguish high-cycling from lowcycling cells, we used S and G 2 -M phase scores calculated by the CellCycleScoring function. High-cycling cells were defined as cells with high S or G 2 -M scores (>0), and low-cycling cells as the ones with low S and G 2 -M scores (<0).
scRNAseq dataset integration The scRNAseq datasets derived from n = 17 primary RMS cultures and n = 3 RMS cell lines were first reduced with reciprocal PCA (RPCA) and integrated with the FindIntegrationAnchors function in Seurat. scRNAseq datasets from individual RMS subtypes or models (n = 5 aRMS primary cultures, n = 9 eRMS primary culture, and n = 3 aRMS cell lines) were integrated individually using SCTransform function followed by the FindIntegrationAnchors function in Seurat. To compare heterogeneity across different preclinical RMS models, scRNAseq data generated in our study from primary cultures and cell lines were integrated with scRNAseq and singlenuclei RNA-seq (snRNAseq) data generated by Patel et al. (36)  (table  S10 ) from O-PDXs and patient tumors [Gene Expression Omnibus (GEO): GSE174376].
For this purpose, only O-PDX cells of human origin and patient tumor cells that were classified as malignant based on copy number alterations in the original publication were integrated with our scRNAseq data using RPCA and the FindIntegrationAnchors function in Seurat. To infer the developmental origins of RMS, we reanalyzed a publicly available scRNAseq atlas of human skeletal muscles during embryonic, fetal, and adult muscle development (GEO: GSE147457)  (42). We integrated cells of muscle origin, as inferred in the original publication, with our n = 5 aRMS and n = 9 eRMS primary culture scRNAseq data using RPCA and the FindIntegrationAnchors function in Seurat. To integrate aRMS primary cultures with regenerating muscle cells, we only selected MuSC-like, cycling progenitors, S-phase, and differentiated clusters from our integrated n = 5 aRMS primary culture scRNAseq dataset.
We then reanalyzed a publicly available mouse single-cell dataset of regenerating muscle (GEO: GSE143437) (32), converted mouse genes into human orthologs using the biomaRt R package  (62) , and integrated the data with our aRMS primary cultures using RPCA and the FindIntegratio-nAnchors function in Seurat. For visualization, the integrated dataset was visualized with PHATE (t = 30, k-nearest neighbors (knn) = 20)  (41).
scRNAseq pseudotime trajectory analysis Pseudotime ordering of aRMS or eRMS was performed on a subset of cells labeled as MuSC-like, cycling progenitors, S-phase, and differentiated from the integrated aRMS or eRMS primary culture datasets. We reduced and visualized the combined datasets using PHATE (41) (t = 50, knn = 20) instead of UMAP, and used the slingshot package  (40)  to organize cells in pseudotime and infer a trajectory. We set MuSC-like cells as the starting cluster for the trajectory calculation. shPAX3::FOXO1 scRNAseq analysis After hashtag demultiplexing as described above, samples derived from Rh4 or KFR cells were merged and analyzed independently. We first log-normalized the data and then assigned myogenic program scores using the gene signatures previously identified for MuSC-like, cycling progenitors, S-phase, and differentiated clusters using Seurat's FindAllMarkers function on the integrated RMS dataset (table  S2 ).
We then scaled the expression of each gene regressing out the number of UMI and the percentage of mitochondrial genes. We then performed PCA to denoise the data and, based on elbow plots, selected the number of PCs to retain for downstream analysis. We then visualized single cells as UMAP plots. To identify genes differentially expressed upon PAX3::FOXO1 down-regulation, we used Seurat's FindMarkers function across shPAX3::FOXO1 cells cultured in the presence or absence of DOX (table  S4 ).
X-shift analysis and graphic display of single-cell mass cytometry data Pooled aRMS and pooled eRMS cells were clustered on the basis of a combination of surface markers, myogenic transcription factors, and cell cycle markers (CD44, Axl, Pax-7, myogenin, IdU) using the X-shift algorithm. To visualize the spatial relationships between cells within these X-shift clusters, 2000 randomly sampled cells from each cluster were subjected to a force-directed layout  (38). All conditions were processed simultaneously so that the resulting map would capture all populations present in the entire dataset.
To test the drug sensitivity of CD44 + and CD44 -FACS-sorted subpopulations, cells were sorted as described above. After sorting, CD44 + , CD44 -, and an additional unsorted reference population were plated in 384-well plates coated with Matrigel at a cell density of 800 cells per well. The day after, the medium was replaced, and cells were incubated for further 72 hours with the indicated drugs. Data of each CD44 + , CD44 -, and unsorted populations were normalized to DMSO (vehicle)-treated conditions, defined as 100% viability. IC 50 values were determined from the dose-response curves generated using GraphPad Prism. Drugs included vincristine sulfate (ApexBio, A1765-5. 1), 4-HC (Niomech, CAS 39800-16-3), etoposide (Selleckchem, S1225), trametinib (Selleckchem, S2673), dabrafenib (Selleckchem, S2807), and regorafenib (Selleckchem, S1178).
Cell cycle analysis After treatment with trametinib as described above, cells were detached from the plates, washed in PBS, fixed in ice-cold 70% ethanol, and incubated at -20°C for >2 hours. Cells were then washed with PBS and resuspended in 500 μl of propidium iodide (PI) solution [PI (0. 4 mg/ml) and RNase A (0. 4 μg/ml) in PBS with 0. 0001% Triton X-100] before analysis on the LSRFortessa (BD) Cell Analyzer. All FACS analyses were done on FlowJo v10. 8 software.
Quantification and statistical analysis Data throughout the article are presented as individual values with SEM. Statistical analyses were performed using GraphPad Prism v9. 0 (GraphPad Software) or within the Seurat R package (for scRNAseq data). Significance of mean differences was performed using statistical details listed in the figure legends. All FACS analyses were analyzed on FlowJo v10. 8 software. To assess the clinical value of the aRMS subpopulations identified by scRNAseq, we first generated cluster-associated gene signatures by pairwise comparisons between all the clusters identified by scRNAseq in the combined aRMS primary culture dataset. Subpopulation-specific markers were identified using the likelihood ratio test  (65)  implemented in the FindAllMarkers function in Seurat and defined as genes overexpressed with a log fold change of >0.