dataesr/scientific-paragraphs-categorization huggingface dataset:
The design of a method to deal with the Point Spread Function (PSF) effect will also be a task of future work. Different ensemble forecasting methods can be exploited for a better forecast skill. Different detection statistics can be derived from ensemble forecasts to detect a fire as soon as possible and be able to track it as long as it is still active. The content of the validation reference dataset will be improved. The source codes are publicly available online at https://github. com/satfiretech/fire-dataAssimilation.
This branch of shape analysis has been usually understood as the quantitative study of shape and its covariates  [94]. Landmark acquisition was carried out by one of us (TP) in Landmark Editor v. 3. 6 software (IDAV)  [95]  by collecting 31 homologous and well-defined 3D points using the nomenclature of  [96]  (Table  2 ; Fig  2 ). These raw coordinates are provided in S1 Table. Geometric morphometrics and statistical analysis were carried out in MorphoJ v. 1. 06d  [97]  and the R package 'geomorph'  [98]. One surface file was warped using Landmark Editor v. 3. 6  [95]  in order to visualize shape changes depending on the applied analysis. Measurement error (ME) has a critical importance when performing morphometric analyses, therefore to assess it, a sub-sample of 170 skulls were digitized twice and compared via a Procrustes ANOVA  [99].
Angular comparison between principal components and PLS axes The PC's and PLS axes of the complete dataset were compared using the same procedure outlined above in order to test whether the observed pattern of variation (PC's) followed a similar trend as the observed patterns of covariation between viscero-and neurocranium (PLS axes). In a similar fashion, from all the possible comparisons only a few of them were significant (Symmetric component: θ PC1 v/s PLS1 = 13. 361˚, P-value <0. 00001; Asymmetric component: θ PC1 v/s PLS1 = 34. 190˚, P-value <0. 00001; θ PC2 v/s PLS2 = 49. 545˚, P-value <0. 00001).
Matrix correlations The matrix correlation between the covariance matrices for the symmetric and asymmetric components of variation for the complete dataset was 0. 638 (P-value: < 0. 0001; 10. 000 perm. ). Below are presented the pairwise matrix correlation results for the different cranial categories for both the symmetric and asymmetric component of variation. Symmetric component. Antero-posterior v/s non-deformed: 0. 618 (P-value: < 0. 0001; 10. 000 perm. ). Antero-posterior v/s oblique: 0. 592 (P-value: < 0. 0001; 10. 000 perm. ). Non-deformed v/s oblique: 0. 750 (P-value: < 0. 0001; 10. 000 perm. ). Asymmetric component. Antero-posterior v/s non-deformed: 0. 754 (P-value: < 0. 0001; 10. 000 perm. ). Antero-posterior v/s oblique: 0. 745 (P-value: < 0. 0001; 10. 000 perm. ). Non-deformed v/s oblique: 0.
For the symmetric component of variation, the CR coefficient was significantly lower than one, thus suggesting that there was a degree of independence between the two modules. The asymmetric component also showed a lower and significant CR value, which suggest a strong degree of independence between the two modules. Thus, there is support for the hypothesis that the human cranium displays significant modularity when compared to the null hypothesis of no modular structure. It is relevant to keep in mind that modularity and integration are not two ends of the same continuum, thus it is perfectly possible to have both modularity and integration in the same dataset as in the present case. Modularity implies greater covariation of variables within modules than between them, whereas integration means that two modules are more correlated than it would be expected from chance by random arrangements of pairs of observations from each module.
Whereas the PLS1 shows, in a similar fashion as the PCA, a morphological continuum ranging from one deforming type to the other (with non-deformed individuals in between) (Fig 5 ), the PLS2 correlation plot of the complete dataset (symmetric component) shows a total overlap between the categories (S4A Fig). From morphological perspective, the crania at the top of this correlation plot display more elongated and laterally expanded neurocrania with a depressed area between the frontal and the parietals, whilst the skulls at the bottom show more rounded and vertically expanded vaults and flatter faces. PLS2 correlation plots for each one the cranial categories (symmetric component) can be found in S2B, S2C and S2D Fig. Conversely, the developmental integration analysis showed that non-deformed skulls exhibit an overall lower level of integration as compared to the deformed skulls as shown by their r-PLS values.
Acknowledgments We would like to thank  Manuel Arturo Torres  ( Museo Arqueolo  ´gico  R. P. Gustavo Le Paige ,  San Pedro de Atacama  and  Corporacion Cultural y de Turismo, Calama ) and  Philippe Mennecier  ( Musee de l'Homme, Paris ) for the access to the specimens under his care. TP is also grateful with  Hugo Benı  ´tez and  Elis Damasceno  for their useful comments about the analyses carried out in the present study. We also thank the anonymous reviewers for their contributions.
032 0. 10635 0. 8422 PLS13 0. 00000069 0. 0815 0. 022 0. 13983 0. 2208 Singular values and pairwise correlations of the PLS scores between corresponding blocks (i. e. neurocranium and viscerocranium) for the complete dataset (10. 000 perm. ). https://doi. org/10. 1371/journal. pone. 0227362. t004.
ACKNOWLEDGMENTS This work was supported by the  Agence Nationale de la Recherche  '' JCJC program '' ( ANR-19-CE15-0032-01 ) and by the  Region Centre-Val de Loire  under the  program ''ARD 2020 Biomedicaments' ' (Project  7UP ). M. S. -T. and C. P. are supported by  Inserm. F. T. and M. L. -d. -M. are supported by  CNRS. T. B. , L. G. , and C. B. are supported by the  University of Tours. Roxane Lemoine  ( BCR Facility, University of Tours ) and  Julie Cazareth  ( CNRS, IPMC, Sophia-Antipolis ) are acknowledged for their assistance with cell sorting. We acknowledge the UCAGenomiX platform, partner of the National Infrastructure France Genomique, supported by the  Commissariat Aux Grands Investissements  ( ANR-10-INBS-09-03 ,  ANR-10-INBS-09-02 ). Y. Wei  ( Institut Pasteur ),  M. Kronenberg  ( La Jolla Institute for Allergy & Immunology ), J.
Data and Code Availability The accession number for the sc RNA-seq of WT and Fhl2 -/-thymic iNKT cells reported in this paper is GSE141895. Data are also available through a dedicated web interface (https://www. genomique. eu/cellbrowser/ucagenomix/baranek2020/). All Python and R scripts used for single cell statistical analysis can be accessed on GitHub (https://github. com/ucagenomix/sc. baranek. 2020).
The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pairrule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.
Introduction The recent advances in single-cell sequencing technologies have revolutionized the biological sciences. In particular, single-cell RNA-sequencing (scRNAseq) methods allow for transcriptome profiling in a highly parallel manner, resulting in the quantification of thousands of genes across thousands of cells of the same tissue. However, with a few exceptions  (1, 2, 3, 4, 5, 6 Preprint) , current highthroughput scRNAseq methods share the drawback of losing during the cell dissociation step the information about the spatial arrangement of the cells in the tissue. One way of regaining spatial information computationally is to appropriately combine the scRNA dataset at hand with a reference database, or atlas, containing spatial expression patterns for several genes across the tissue.
In this project, in situ hybridization data were collected resulting in a quantitative high-resolution gene expression reference atlas  (13). Indeed, Karaiskos et al  (11)  showed that the combinatorial expression of these 84 BDTNP markers sufficed to uniquely classify almost every cell to a position within the embryo. In the absence of a reference database, it is also possible to regain spatial information computationally solely from the transcriptomics data by leveraging general knowledge about statistical properties of spatially mapped genes against the statistical properties of the scRNA dataset  (1, 14). Bageritz et al  (1)  were able to reconstruct the expression map of a Drosophila wing disc using scRNAseq data by correlation analysis. They exploited the coexpression of non-mapping genes to a few mapped genes with known expression patterns, to predict the spatial expression of 824 genes  (1).
Challenge setup The challenge, a first of its kind for single-cell data, consisted of predicting the position of 1,297 cells among 3,039 Drosophila melanogaster embryonic locations for one half of a stage 6 pregastrulation embryo from their scRNAseq data (Fig  1A )  (11). At this stage, cells in the embryo are positioned in a single twodimensional sheet following a bilateral symmetry (left-right), so that only positions in one half of the embryo where considered accounting for the 3,039 locations. Participants used the scRNAseq data for each of the 1,297 cells obtained from the dissociation of 100-200 stage 6 embryos and the spatial expression patterns from in situ hybridizations of 84 genes in the BDTNP database  (13). As a source of domain-specific background knowledge that can aid the development of prediction algorithms, we provided information about gene determinants of different tissues such as neuroectoderm, dorsal ectoderm, mesoderm, yolk, and pole cells.
The quality and reproducibility of each provided solution are also ensured. A distinctive feature of this single-cell transcriptomics challenge was the public availability of the entire dataset and the ground truth locations produced by DistMap, a method using the in situ hybridizations available at BDTNP  (13) , published together with the data  (11). We took three actions to mitigate the issue of not having a blinded ground truth. First, for the purpose of predictor gene selection, we allowed the use of scRNAseq data and biological information from other databases but prohibited the use of in situ data. Second, to assess the quality of predictions, we devised three scores (detailed in the Materials and Methods section) that were not disclosed to the participants during the challenge. The scores measured not only the accuracy of the predicted location of the cell but also how well the gene expression in the cell at the predicted location correlates with the expression from the reference atlas, the variance of the predicted locations for each cell, and how well the gene-wise spatial patterns were reconstructed.
As stated, given that the ground truth for this challenge was publicly available, we decided to invite the top 10 performing teams to contribute to a post-challenge collaborative analysis phase to assess the soundness and stability of their gene selection and cell location prediction. Consequently, teams were tasked with providing predictions for a 10-fold CV scenario of the Drosophila dataset used in the open phase of the challenge. Each team used the same assignment of cells to folds and was evaluated with the challenge scoring approach. To ensure the validity of the findings, we performed all further analysis and interpretation using only the results of the post-challenge phase. In brief, we found that the most frequently selected genes had a relatively high expression entropy, showed high spatial clustering and featured developmental genes such as gap and pair-rule genes in addition to tissue defining markers.
In addition, given the high diversity of approaches to gene selection, we focused our analysis on better understanding the properties of frequently selected genes to provide recommendations for future experimental designs. To confirm that the Drosophila findings were robust, we included in our analysis an additional dataset consisting of scRNAseq measurements of 851 cells from a zebra fish embryo previously considered by Satija et al  (8)  together with spatial information for 64 locations from in situ hybridizations of 47 landmark genes from the ZFIN collection  (12). As ground truth for predicting the position of the zebra fish embryo cells, we used the location predictions produced by applying DistMap, choosing a threshold for scRNAseq expression to maximize the MCC with all 47 in situ genes.
The method proposed by Satija et al  (8)  can also be used to produce ground truth locations, but for consistency with the challenge we decided to use DistMap. Note that we compared the predicted locations and the redundancy that arises from trying to place 851 cells in 64 different locations when using DistMap and Seurat. The results in Fig  S5  show notably that on average DistMap and Seurat agree for 5 of 10 positions for a cell, with only 46 cells showing no agreement between the methods when assigning 64 possible locations. This represents a high level of overall agreement and enhances the confidence in the silver standard. We tasked once again the top 10 teams to select 20 and 40 genes to place correctly the zebra fish cells in a 10-fold CV scheme. Team ranks varied on this additional dataset (Figs  S6  and S7 ), but the scores achieved were of the same order as in Drosophila (Tables  S1  and S5 ), and we found that the selected genes had similar statistical properties (Fig  S8  and Tables 2  and S6 ).
We observed for all subchallenges a significant value shift for the autocorrelation statistic as evaluated by a one sided Mann-Whitney U test (see bottom of Fig  3A ). Although we see a decrease of the statistical significance of the mean value shift for the distribution of entropy values of the selected subsets of genes, the shift is significant for all subchallenges. At the same time, we observed that the tail of the distribution shortens. To test whether the information relative to different cell types is retained with the selected subset of 60, 40, or 20 most frequently selected genes, we embedded the cells into a 2D space using t-distributed stochastic embedding (t-SNE) (17) (Figs  3B  and S11 ). Notably, we found that the nine prominent cell clusters identified in the study by Karaiskos et al  (11) , while using the whole scRNAseq expression dataset, are preserved in our t-SNE embedding and clustering experiments when considering only the most frequently in situ selected 60 or 40 genes from subchallenges 1 and 2.
Discussion In this article, we report the results of a crowdsourcing effort organized as a DREAM challenge  (15)  to predict the spatial arrangement of cells in a tissue from their scRNAseq data. Analysis of the top performing methods provided many unbiased insights such as the usage of either similarity-based approaches or machine learning models to predict cell location. The latter, in accordance with current literature  (8, 11, 14) , were shown to be preferable. We do not think this is due to bias induced by the fact that the silver standard was generated using a similarity-based approach. Indeed, we showed for the zebra fish dataset that the silver standard is robust to the usage of Seurat (Fig S5 ), a different method to generate the cells' positions. Also, the good performance and robustness of nonlinear machine learning methods (Table  S1 ) is proof that the association between the expression of mapped genes and a cell's position is not due to a simple gradient of expression in space.
Scoring We scored the submissions for the three subchallenges using three metrics s 1 , s 2 , and s 3. s 1 measured how well the expression of the cell at the predicted location correlates to the expression from the reference atlas and included the variance of the predicted locations for each cell, whereas s 2 measured the accuracy of the predicted location and s 3 measured how well the gene-wise spatial patterns were reconstructed. Let c represent the index of a cell, given in the transcriptomics data in the challenge where 1 ≤ c ≤ 1,297. Each cell c is located in a bin ε c 2 {1…3,039} at a position with coordinates r(ε c ) = (x c , y c , z c ). Each cell is associated with a binarized expression profile t c = (t c1 , t c2 ,…,t cE ), where 1 ≤ E ≤ 8,924, and a corresponding binarized in situ profile f c = (f c1 , f c2 ,…,f cK ), where the maximum possible value of K for which we have in situ information is K = 84.
Implementation details The challenge scoring was implemented and run in R version 3. 5, the post-analysis was performed with R version 3. 6 and the core tidyverse packages. We used the publicly available implementation of DistMap (https://github. com/rajewsky-lab/distmap). MCC calculated with R package mccr (0. 4. 4). t-SNE embedding and visualization produced with R package Rtsne (0. 15). DBSCAN clustering with R package dbscan (1. 1-4). We used t-SNE aiming for high accuracy (θ = 0. 01), then clustered the t-SNE embedded data using density-based spatial clustering of applications with noise (DBSCAN)  (29). DBSCAN determines the number of clusters in the data automatically based on the density of points in space. The minimum number of cells in a local neighborhood was set to 10 and the parameter ε = 3.
Acknowledgements This research was funded in part by  PROACTIVE 2017  " From Single-Cell to Multi-Cells Information Systems Analysis " ( C92F17003530005 Department of Information Engineering, University of Padova ) for BD Camillo;  National Institutes of Health  grant number  U54CA21729  for  J Ruan ;  Indian Council of Medical   Research-Junior Research Fellowship  for  S Ahmad  and  X Wang  was funded by the  National Natural Science Foundation of China  (No. 61702421  and No. 61772426 ). P Meyer thanks KV for help editing. C Hafemeister: software, formal analysis, and methodology. P Qiu: software, formal analysis, methodology, and writing-review and editing. D Tran: data curation, software, methodology, and writing-review and editing. T Nguyen: software, formal analysis, methodology, and writing-review and editing.
Study design and participants. Patients with S. aureus community-acquired pneumonia (CAP; criteria in Appendix) and admitted to a participating intensive care unit (ICU) were included between January 2011 and December 2016. Exclusion criteria were HIV-positive status, hospital-acquired pneumonia, or admission to hospital in the past 3 months. Clinical, laboratory, and therapeutic data were prospectively collected, and patients were assessed at admission to ICU, on day 1, 3, and 7. Severity was evaluated using age-adapted severity scores (Appendix).
Acknowledgments The study was funded by  French Ministry of Health  under the  Programme Hospitalier de Recherche Clinique 2011. This report presents independent research commissioned by the  Ministry of Health ; the views and opinions expressed in this publication are those of the authors and do not necessarily reflect those of the Ministry of Health. We thank the technicians of the  French Reference Centre for Staphylococci  for their contribution in characterizing S. aureus isolates. Finally, we thank  Philip Robinson  ( DRCI, Hospices Civils de Lyon ) for copy-editing the manuscript.
2018) , etc. Furthermore, Promicromonospora genus showed interesting biological properties, such as antimicrobial activity against bacteria and yeast pathogens  (Zothanpuia et al. 2018) , bioremediation by the removal of cadmium from polluted waste  (Hamedi et al. 2015) , plant growth-promoting rhizobacterium through the production of gibberellins  (Kang et al. 2012 (Kang et al. , 2014)) , etc. Despite the potential applications of Promicromonospora species, there have been a limited number of reports that investigated genomic and phenotypic aspects such as multiresistance to extreme conditions (radiation, desiccation, oxidative stress, etc. ) of this genus members. The number of related genomes available at the National Center for Biotechnology Information (NCBI) and the Genomes OnLine Database is limited to 25 as of August 08, 2020.
Polar lipids pattern was determined following the modiied protocol of  Minnikin et al. (1984)  by  Kroppenstedt and Goodfellow (2006). Cellular fatty acids were extracted according to the protocol of  Miller (1982)  with minor modiication from  Kuykendall et al. (1988). Fatty acid patterns were determined from the biomass of isolate PT9 T using the Standard Microbial Identiication (MIDI) system Version 4. 5 and the ACTIN6 database  (Sasser 1990).
Genome sequencing, assembly and annotation The genome sequencing of PT9 T was performed on the Illumina MiSeq™ platform (Illumina, USA) by Bioidal society, Vaulx-en-Velin, France (https ://www. bioi dal-lab. com) using NextGen High Throughput Sequencing. Thus, a standard Illumina shotgun library "Nextera XT DNA Library Prep" was constructed and sequenced using the Illumina MiSeq technology by synthesis. A paired-end sequencing strategy was used with an average size of 2 × 300 bp in length generated and a total number of reads of 25,425,220 bp obtained. Quality control of raw data was assessed using the FastQC tool (https ://www. bioin forma tics. babra ham. ac. uk/proje cts/ fastq c). The Unicycler software was used for de novo assembly. Genome annotation was performed with the Micro-Scope platform version 3.
10. 0 using the integrated microbial genomes non-redundant database, KEGG and COG databases  (Vallenet et al. 2017). Reads were processed and assembled as described by  Castro-Wallace et al. (2017). The inal draft assembly had an average depth coverage of 6. 9 Mbp and 59 contigs. In addition, the percentage of G + C in the complete genome sequence was calculated.
Phylogenetic analysis and genomic comparisons Analysis of the 16S rDNA gene used the complete sequence retrieved from the genome using MAGE platform  (Vallenet et al. 2009). This gene was identical to the 16S rDNA gene sequence originally deposited in GenBank (MG712873) after the initial single colony isolation and ampliication by polymerase chain reaction using the universal 27F and 1492R primers  (Sun et al. 2010). The resulting 16S rDNA gene sequence was compared with those of all validly named species using the EzTaxon database EzBioCloud (https :// www. ezbio cloud. net/ident ify/)  (Yoon et al. 2017 ) and the web tool leBIBI(QBPP) available at https ://umr55 58-bibis erv. univ-lyon1. fr/lebib i/lebib i. cgi  (Flandrois et al. 2015). The phylogenetic tree highlighting the position of PT9 T relative to the type strains of the other species within the genus Promicromonospora was constructed using W-IQ-TREE software accessed at https ://iqtre e.
cibiv. univi e. ac. at/,  (Triinopoulos et al. 2016 ) based on the Shimodaira-Hasegawa approximate likelihood ratio (SH-aLRT) (%)  (Guindon et al. 2010 ) and the ultrafast bootstrap (UFboot) (%)  (Minh et al. 2013). These 16S rDNA sequence data have been submitted to the DDBJ/EMBL/GenBank databases through https ://submi t. ncbi. nlm. nih. gov/ under accession number MG712873. Genome based phylogenetic tree was constructed using a high throughput wed server, Type Strain Genome server (TYG) available in the Genome to Genome Distance Calculator webserver (Meier-Kolthof and Göker 2019).