dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This research was funded from several sources. The  Axiom  array design and the development and genotyping the F6 generation of the JI0281xCaméor population was within the  EU FP7  project  LEGATO  ( 613551 ) with additional support from the  Welsh Government  and the  University of Aberystwyth. The  JI2822xJI2202  RIL population and the JI0281xCaméor F7 mapping was funded by the  UK Biotechnology and Biological Sciences Research Council  (grant number  BB/W510695 ). We acknowledge support in plant and seed management from the  UK Department for Environment, Food, and Rural Affairs  (grant number  CH0111 ;  Pulse Crop Genetic Improvement Network ). NE, JH, JC, RW, NC and CD gratefully acknowledge funding from  UKRI  for the  Institute Strategic Programme grant  ( BBS/E/J/000PR799 ).
1e ,f)  26. With this information, we extracted from each sequenced bead (1) spatial coordinates, (2) associated gene expression profile and (3) cell state assignment. Overall, we observed good alignment of cell states and spatial restriction of marker genes, such as Ttn (heart), T (tail bud), Meox1 (somites) and Sox2 (neural tube, brain), among others (Extended Data Fig. 2a-e ). Additionally, we observed high reproducibility in recovering a comparable embryo composition and gene expression patterns among replicates (Extended Data Fig. 2c-f ). To translate our two-dimensional data into a 3D embryo, we developed sc3D, a computational method that enables the alignment of individual spatial transcriptomic arrays for 3D reconstruction. Specifically, we used sc3D to align serial Slide-seq 'z' samples from E8.
5 and E9. 0 embryos, which allowed us to capture the spatial distri bution and morphologies of the emerging tissues at the onset of organogenesis (Extended Data Fig. 3a, b  and Supplementary Figs. 1  and 2 ). This in turn enabled quantitative measurements of their volumes (250-39,264 × 10 3 µm 3 ), which were reproducible across individual replicate embryos (Extended Data Fig. 3c-e , Supplementary Fig. 1 , Supplementary Movie 1 and Supplementary Table  2 ). We further showed that the reconstruction remained consistent as the interval between slices increased, with very minimal distortion in the rotation axes (Extended Data Fig. 3f ). Importantly, sc3D also allows 3D reconstruction of other spatial transcriptomic datasets with high precision, speed and robustness to reduced spatial resolution  40  (Extended Data Fig.
Discussion We performed embryo-wide spatial transcriptomic profiling using Slide-seq to decipher the tightly regulated gene expression patterns of approximately 27,000 genes in developing embryos at the onset of organogenesis. The reconstruction of digital 3D embryos using sc3D enabled the quantitative exploration of gene expression patterns and gradients on a virtual in situ basis. Combined with the development of sc3D-viewer (a napari plugin)  76  , an accessible, interactive and user-friendly visualization platform to register and explore 3D spatial genomic data, we facilitate the rapid and seamless exploration of cell type distribution and gene expression patterns along any given developmental axis, including the possibility to reconstruct tissues from other spatial transcriptomic datasets.
Spatially resolved single-cell sequencing methods continue to evolve rapidly  37, 38, 40, [77] [78] [79]. The increase of available datasets will require faster and more precise computational approaches to take full advantage of the added spatial information. sc3D contributes toward the in-depth analysis of the topology and geometry of gene expression and co-expression patterns, providing the infrastructure to start modeling gene expression diffusion, and their interaction in embryonic tissues. The sc3D data structure has been purposely designed to be close to the one of imaging-based cell tracking algorithm outputs. This similarity will ease the porting of cell tracking-based inter-sample alignment algorithms, such as Tardis, to the spatial transcriptomic field  31. The dynamics at which transcriptomes evolve as progenitors differentiate in their spatial distribution has been challenging to explore.
Slide-seq data processing and cell state annotation The sequenced reads were processed using the Slide-seq tools pipeline (https://github. com/MacoskoLab/slideseq-tools) to generate the gene count matrix and match the bead barcode between array and sequenced reads. Most of the downstream analysis was performed in R (v. 4. 1. 0), except the RNA velocity analysis performed in Python (v. 3. 8. 3, v. 3. 9. 0, v. 3. 10). The gene count matrix and bead spatial coordinates were processed using Seurat (v. 3. 0. 0, v. 4. 0. 2)  81. Beads with more than 200 counts and less than 20% mitochondrial gene counts were retained for further analysis. The data from each stage were merged due to minimal batch effect and analyzed together. For the E8. 5 replicate and E9. 5 data, the top 3,000 highly variable genes were used in FindVariableFeatures and the top 40 principal components from the principal component analysis (PCA) (RunPCA from Seurat).
Seurat label transfer was used to obtain cell state annotation, with the functions FindTransferAnchors and TransferData. For the second E8. 5 replicate and E9. 0 data, we used robust cell type decomposition (RCTD)  82  to annotate the cell state because it is more robust to lower UMI counts. Data from the E8. 5 stage in our previous study were used as reference data for the Seurat label transfer function and the RCTD function  26. Plots were generated with ggplot2 (v. 3. 1. 0).
RNA velocity analysis on Slide-seq data We adapted the scVelo (v. 0. 2. 4) package  84  to analyze the RNA velocity at the spatial axis. Using the tutorial at https://scvelo. readthedocs. io/, exonic and intronic counts from each bead were extracted from the data and used as input. The stochastic model with default parameters was used to compute the velocity of each bead. Next, the velocity vectors were projected to the physical space. For visualization, the velocity vectors were computed according to a 50 × 50 µm grid; the 50 nearest neighbors were selected in each grid to calculate the average velocity. The length of velocity with regard to the speed of the transcriptomic changes was calculated by taking the average length of the velocity vector from the neighbors (n = 50) of a bead. The velocity confidence represents the coherence of the velocity direction.
Trajectory analysis in the trunk region Beads annotated as NMPs, somites and neural tube from E8. 5 and E9. 5 were selected. Next, beads with a prediction score lower than 0. 6 were discarded to remove cell mixtures. The remaining beads from E8. 5 and E9. 5 were integrated using Harmony (v. 0. 1. 0)  85  with default parameters; then, UMAP dimensionality reduction (runUMAP) was performed based on an integrated matrix. Next, we used Monocle3 (v. 1. 0. 0) to calculate the pseudotime from the UMAP output, according to the tutorial and using default parameters (https://cole-trapnell-lab. github. io/monocle3/docs/trajectories/). A generalized linear model with quasi-likelihood dispersion estimators from edgeR (v. 3. 34. 1)  83  was used to find the genes that correlated with the pseudotime trajectory.
Identification of spatial differentially expressed genes in the neural tube The beads assigned to have a neural tube identity in array E9. 5_10 were used for this analysis. Using Slingshot::slingshot (v. 2. 0. 0)  87  , we calculated the principal curves from the beads' spatial location and ordered the cells along the principal curves as their anterior to posterior distance. The distance of each bead to the convex hull of the neural tube was computed as the dorsal to ventral distance and split into eight equally spaced bins. We used SPARK (v. 1. 1. 1)  88  with default parameters to find spatially variable genes. We took the intersection of spatially variable genes from SPARK and the variable genes from Seurat as the input for the spatial module analysis. Dynamic time warping was applied to find genes with coherent spatial patterns that we defined as modules (in the Hox genes analysis).
The Dtwclust (v. 5. 5. 6) (https://CRAN. R-project. org/package=dtwclust) package was used for the analysis with the tsclust function (k = 8). The centroid of each module was calculated as their spatial pattern. Next, we used the same edgeR pipeline as the trajectory analysis to find more genes correlated with each spatial pattern. We selected genes with the same threshold and visualized them into a heatmap. The Hox genes were grouped as described in the previous section and the average expression of each module was smoothed using the gam::gam (v. 1. 2. 0) function for visualization. To determine the protein classes that are enriched in the differentially and spatially variable genes, we used PANTHER (v. 17. 0)  89.
Clusters 4 and 5 corresponded to the neural crest and neural plate, respectively, ruling out further analysis. We computed the raster spatial density of each cluster using MASS::kde2d (v. 7. 3-54) and combined them by taking the highest density for each position, which is plotted in Fig. 5b. We mapped the WT and Tbx6 mutant data to the E9. 5 trunk dataset as a reference by calculating their mutual nearest neighbor using the fast mutual nearest neighbors correction  90  method. For each bead in the Tbx6 mutant data, we selected its ten nearest neighbors in the integrated PCA space as anchors. We computed the variance of the dimensionality reduced matrix from neighbors as a metric of projection uncertainty. The averaged UMAP coordinates of ten nearest neighbors for each bead were projected on the reference UMAP, with size representing the projection uncertainty.
1a. The Slide-seq experiment involving WT and Tbx6 KO embryos was performed on one (WT) and two (Tbx6 KO) transversal sections. The Tbx6 KO experiment was performed independently five times (in total) to verify the phenotype, which was reproduced in every embryo across all experiments. Sections were obtained from the posterior part of the trunk (the representative image of the section collected before Slide-seq is shown in Fig. 5a ). Whole-mount in situ hybridization was performed once in WT E9. 5 stage embryos for the indicated number (n = 3) and showed reproducible results (Extended Data Fig. 2b ). The RNA-FISH experiments were performed from two of the independent experiments with the indicated number (n = 3 embryos) and showed reproducible results, with one representative image shown in Extended Comparison of accuracy and processing time (sc3D vs PASTE) across different datasets.
Acknowledgements We thank members of the  Meissner, Chen  and  Macosko laboratories  for critical discussion and feedback. We thank  M. Scholz-Wittler  and  B. Hermann  for sharing plasmids from the Molecular Anatomy of the Mouse Embryo Project for the in situ hybridization experiments. We thank  J. Fiedler ,  M. Peetz ,  A. Landsberger  and  C. Franke  from the transgenic animal unit of the  Max Planck Institute for Molecular Genetics  for their support with animal work. This work was supported by the  Max Planck Society  (A. M. ). L. G. received funding from the  Investissements d'Avenir program  of the  French Government  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University -A*MIDEX. F. C. acknowledges support from the  National Institutes of Health Early Independence Award  ( DP5 ,  1DP5OD024583 ), the  National Human Genome Research Institute  ( R01 ,  R01HG010647 ), the  Burroughs Wellcome Fund CASI  award, the  Searle Scholars Foundation , the  Harvard Stem Cell Institute  and the  Merkin Institute.
Data availability Raw and processed data can be downloaded from the Gene Expression Omnibus under accession no. GSE197353. The input object for 3D visualization for the following embryos can be downloaded: E8. 5_Embryo1 (https://figshare. com/s/1c29d867bc8b90d754d2); E8. 5_Embryo2 (https://figshare. com/articles/dataset/E8_5_Embryo2_ h5ad/21695849/1); E9. 0 (https://figshare. com/articles/dataset/E9_0_ Embryo_h5ad/21695879/1). Individual Slide-seq arrays can be visualized at https://cellxgene. cziscience. com/collections/d74b6979-efba-47cd-990a-9d80ccf29055. Whole-mount in situ hybridization probe sequences and plasmids are available at http://mamep. molgen. mpg. de, with accession numbers and sequences shown in Supplementary Table  10. The FISH probe accession codes can be found in Supplementary Table  10.
Code availability The code used to reproduce the analyses is indexed at https://github. com/GuignardLab/sc3D for the sc3D 3D reconstruction and https:// github. com/LuyiTian/Embryo_Slideseq_analysis for the E9. 5 analysis. The 3D embryo can be visualized with sc3D-viewer by following the detailed instructions provided (https://github. com/GuignardLab/ napari-sc3D-viewer). PuckCaller can be accessed at https://github. com/MacoskoLab/PuckCaller.
2021). Some tools need biological information such as genetic and physical mapping data, recombination rates and admixture generations, or statistical parameters such as hidden states and misfitting probabilities  (Geza et al. 2018). According to previous studies comparing the performance of several commonly-used software programs  (Cottin et al. 2019; Schubert et al. 2020; Molinaro et al. 2021 ), ELAI (Guan 2014)  has proven to be one of the most efficient ancestry inference tools for dealing with unphased data. This two-layer HMM and LD-based method uses source populations to predict the classification in two hidden state layers-haplotypes in the lower-layer and ancestries in the upper-layer. Therefore it does not require haplotype phasing, but only prior assumptions regarding the number of haplotype clusters and of admixture generations, which are needed for hidden state modeling.
2019 , Nab & Maslin 2020). Therefore, given 100 years of cultivation, the number of generations is not expected to be higher than 20. Even though the current Vietnamese Robusta varieties are mostly supposed to have originated from Java, their ancestral genetic groups are still largely unclear. Since materials of the Javanese breeding program mainly came from DRC, Uganda and Gabon  (Montagnon et al. 1998b) , the accessions historically introduced in Vietnam may have Congolese origins and putatively some extent of intergroup admixture. In this study, we implemented the ELAI approach on cultivated C. canephora with unbalanced and admixed native reference populations. We inferred ancestral genotypic frequencies for these native populations to build perfect source populations for ELAI. We assessed and validated our new approaches with simulated hybrids.
Optimized framework and application for inference of the local ancestry of the tested Vietnamese Robusta cultivated accessions Based on our validation and optimization results using simulated hybrids, we developed an LAI framework that encompassed ELAI to efficiently study the admixture origin in Robusta coffee (Figure  3 ). For each chromosome, we performed ELAI using two SNP datasets (a set of evenly distributed SNPs, and another of whole-chromosome SNPs), with simulated ancestral groups serving as source populations. The lower cluster number was set at five as this factor did not influence the detection but did reduce the run time and memory usage (Supplementary Table  S2 ). We set the number of admixture generations at 20, which reflected the maximum possible number of generations of the cultivated accessions.
Common results between the two datasets (evenly distributed SNPs and whole-chromosome SNPs) were then considered as the final local ancestry inference. The results of the two SNP sets were pooled in 3 steps. Firstly, as the theoretical dosage of a given ancestry at an SNP locus is either 0, 0. 5 or 1 but the dosage inferred by ELAI can be any value between 0 and 1, we approximated the ELAI-inferred dosage at each SNP with respect to the theoretical values, i. e. the dosage was set at 0 if the inferred dosage was in the [0, 0. 1) range, at 0. 5 if it was in the (0. 4, 0. 6) range, at 1 if it was in the (0. 9, 1] range, or classified as 'undetermined' otherwise. Secondly, the approximated dosages in the two SNP sets were compared locus-wise and also classified as 'undetermined' if they were not equal.
Lastly, ancestry blocks were determined if contiguous positions had the same dosage and the distance between adjacent positions was not > 1 Mb; and < 1 Mb segments were also classified as 'undetermined'. This framework was then applied for local ancestry inference of the Vietnamese accessions. A total of 94% to > 100% of the genome could be assigned (Figure  S7 ) for all of the 10 tested Vietnamese accessions. Undetermined regions were due to disagreement between the results of the two datasets, or the uncertainty in the ELAI inferred dosage (ancestry dosages within 0. 1 -0. 4 or 0. 6 -0. 9 ranges, or ancestry tracts < 1 Mb were treated as uncertainties). Some accessions represented the same undetermined region on chromosome 10 (Supplementary Figure  S7 ) because this region was assigned with a dosage of 1 to the ER group by the even SNPs set but with a dosage of 0.
2021). A test of this method on real data showed that WINC was comparable to or could outperform ELAI in certain admixture scenarios when only two individuals per source population were used. However, WINC requires phased data and recombination maps, which are not always available for other datasets, especially our dataset. ELAI was later adapted to use a large number of admixed samples (a cohort set) to compensate for the lack of a pure source population  (Zhou et al. 2016). The cohort set was down-weighted to not outweigh other training source sets. This method could be applicable if a large cohort sample size is available. We acknowledge that our simulation method did not preserve LD in the native populations, which might explain the misassignment or uncertainty in the inference of small ancestry tracts in admixed individuals.
However, the uncertainty only accounted for a minor portion of the genome (< 10%) and the global ancestry findings of our approach were close to the global ancestry obtained by sNMF. We also assessed the number of haplotype clusters and admixture generations required by ELAI to predict the ancestry model. The Robusta coffee breeding program was launched about 100 years ago, which could be considered as relatively recent compared to most other crops, and therefore the maximum number of Robusta generations out of Africa and especially in Vietnam was estimated at most 20. We tested the methods with 5, 10, 20 and 30 generations. Guan (2014) found that a higher number of admixture generations improved the inference smoothness. In many studies using ELAI, the number of haplotype clusters is often set at 5fold the number of source populations.
A C C E P T E D M A N U S C R I P T using cross-entropy criteria over 10 iterations with K ranging from 1 to 10. Individuals were assigned to a given cluster at an 80% ancestry threshold. Pairwise FST between the genetic groups (restricted to individuals with > 80% ancestry) were computed using the R package StAMPP  (Pembleton et al. 2013). 100 bootstraps across loci were performed to assess the significance. We also performed a principal component analysis (PCA) in the R package LEA  (Frichot & François 2015).
Inference of local ancestry We inferred the local ancestry of cultivated Robusta using ELAI (Guan 2014), which was suitable for our unphased data and did not require any additional biological information. The source populations must be specified when using ELAI. To overcome limitations due to the unbalanced structure of wild populations that include admixed individuals, we tested an alternative method using population structure analysis. This new method was validated by simulation of known hybrids, while the ELAI accuracy was assessed and optimal parameters determined. Based on these results, we built a framework to detect the local ancestry in Vietnamese cultivated Robusta accessions.
Source population Genotypes from ancestral groups of wild accessions must be defined for the purpose of ELAI analysis (Guan 2014). Given the small and unbalanced sample size of the wild populations and some evidence of admixed ancestry between groups in the wild accessions, we generated new synthetic populations exhibiting genotypic frequencies similar to those estimated in the ancestral pools. To this end, we used ancestral genotypic frequencies inferred using the sNMF analysis  (Frichot et al. 2014). For each chromosome, we ran sNMF on whole-chromosome A C C E P T E D M A N U S C R I P T SNPs of all the African and Vietnamese individuals, with 10 iterations and the optimized K value. The best run over the 10 iterations was chosen on the basis of cross-entropy criteria. We applied the G function (R package LEA) on the result of the best run to retrieve a G matrix containing genotype frequencies inferred in each different ancestral group for all diploid SNPs.
Sets of ELAI parameters We determined the optimal ELAI parameters by running the software with varying parameter values, including: the number of haplotype clusters (c), i. e. lower clusters, the number of admixture generations (mg), and the set of SNPs used for the analysis. We tested 3 values  (5, 15, and 25)  for the number of haplotype clusters, and 4 values  (5, 10, 20, and 30)  for the number of admixture generations. We also used 4 different sets of SNPs: (1) randomly selected 10K SNPs, (2) randomly selected 100K SNPs, (3) about 11K SNPs resulting from randomly selecting one SNP in every non-overlapping 5 kb window (referred to as the 'even SNP set'), and (4) whole-chromosome SNPs, i. e. the 'all SNP set' comprising 1,133,736 SNPs. The average SNP densities in the 4 datasets were 1.
Acknowledgments T. V. was funded by PhD grants from the  French Embassy in Vietnam  and the  ARTS program  (IRD). This research was funded by the  Ministry of Science and Technology of Vietnam (MOST)  under grant number  NVQG-2020/ĐT. 15. The authors acknowledge the ISO 9001 certified  IRD i-Trop HPC  (member of the  South Green Platform) at IRD Montpellier  for providing HPC resources that contributed to the research results reported in this paper. URL: https://bioinfo. ird. fr/-http://www. southgreen. fr.
Data Availability The raw sequencing data of the African accessions were taken from NCBI SRA database under project accession number PRJNA803612 (Tournebize et al. 2022). The raw sequencing data of the Vietnamese accessions are available in NCBI SRA database under project accession number PRJNA950219 (present study). The software used for this study was downloaded from: https://github. com/haplotype/elai, https://github. com/bcm-uga/LEA, and other packages were available via IRD i-Trop HPC (South Green Platform).
In this paper, a new C++ and Python library, called the Papillon Nuclear Data Library, is presented. This library gives users a simple interface to read and interact with continuous-energy neutron data in the form of ACE files. Using the library, one can easily evaluate or plot cross-sections for different nuclides and reactions, or sample scattering distributions. Currently, all continuous-energy neutron physics is implemented, and the library can read free-gas neutron and thermal scattering law ACE files. The Python interface makes using nuclear data easy and accessible for students, while the C++ interface gives experienced researchers easy access to continuous-energy physics when writing Monte Carlo codes to test new computational methods.