dataesr/scientific-paragraphs-categorization huggingface dataset:
Because our samples collection focuses on ectoderm, this dataset would be most suitable for phenotypes associated with ectodermal-derived neonatal disorders (e. g. , neurocristopathies, ciliopathies, neural tube defects), tumors of ectodermal origin (i. e. , melanoma, schwanoma, neuroblastoma), and pathologies related to epithelium-to-mesenchyme transition (EMT) such as cancer and fibrosis  [1, 65]. Melanoma is an aggressive, NC-derived tumor, initiation of which involves a NC-like state, i. e. , the reactivation of early, neurula-stage NC-specific genes such as sox10 in adults  [47]. This correlates with the reactivation of "super-enhancers" proximal to NC genes. Super-enhancers are regulatory elements highly enriched in active chromatin marks (K27-acetylated histone 3 [H3K27Ac] mark) and involved in cell differentiation and cancer  [66, 67].
The presence of super-enhancers close to the sox10 gene was associated with elevated sox10 expression both human melanoma cells and in a zebrafish melanoma model  [47]. Additionally, these authors provided a list of 843 super-enhancers present in human melanoma cell lines in close proximity to 778 other human genes. Here, we asked if we could extract information on the genes associated with those super-enhancers using the gene co-expression network derived from the neurula-stage embryo (S9 Table ). We selected X. laevis genes corresponding to human gene names in immediate proximity with the top super-enhancers in human melanoma cell lines (ranked by enrichment levels of H3K27Ac)  [47]. Sixty-five Xenopus orthologs were expressed in our neurula-stage developmental dataset. Using WGCNA, we found that transcripts highlighted in red were significantly enriched in groups associated with posterior patterning and canonical Wnt signaling (G-23, p-value = 3E-02) and NC (G-51, p-value = 2E-02) (Fig  10A ).
In contrast, no enrichment was found for the 64 genes close but not immediately adjacent to the top 200 super-enhancers (  [47] , not shown). We then asked the complementary question: which of the genes associated to the 843 super-enhancers defined in human melanoma were part of G-23 and G-51, and found 26 genes falling in these groups (Fig 10  ; S9 Table ). This analysis further supports the notion that melanoma initiation, WNT signaling, and NC development share common molecular signatures  [47, 68]  and supports investigating the roles of uncharacterized genes either in NC development or during melanoma initiation. To investigate how often these 26 genes were altered in cancer, we looked in public repositories of human tumor datasets and found that melanomas have one of the largest proportions of genomic alterations in at least 1 of these genes (Fig  10B ).
Currently, the functional characterization of frog cdon is lacking, but our expression predictions and analysis on other model organisms  [74, 75]  support potential roles of CDON in human melanoma. Our NMF expression pattern predictions localize cdon predominantly in the NB in neurula (St. 12. 5 and St. 14) (Fig  7C ). Likewise, the adjacency coexpression matrix and the network positions cdon in close relationship with other NC transcripts (e. g. , sh3pxd2a, sox9, rapgef2, and gdf5), Sonic Hedgehog receptor (disp1), and genes related to Wnt signaling pathway (e. g. , axin2 and xarp). Hence, we conclude that a combined strategy of mining human cancer databases and searching our developmental gene coexpression network may be useful to prioritize gene candidates for further functional studies.
Integration of the temporal and spatial transcriptomic datasets in EctoMap, a user-friendly online tool We integrated this large transcriptomic dataset as a comprehensive suite of interactive tools to explore the expression of 1 or more genes of interest (Fig  11 ). The application was called "EctoMap" and it was intended to allow users to explore gene expression levels over different developmental time points and tissues and retrieve expression pattern prediction obtained by NMF along with a list of co-expressed genes identified by WGCNA analysis (see example in S1 Web Archive). We also provided the interactive gene co-expression network, to which we recommend adding landmark genes for a more informative interpretation of the temporal and spatial relationships between genes. The tool was implemented in R using the Shiny-app and the dynamic network visualization using the Network-D3 library (please download the full application and run locally).
This predicted pattern is accompanied by an average pattern and an ectoderm specificity index for each region to provide maximum information in queries regarding unknown gene spatial and temporal expression. As orthologs for over 79% of genes implicated in human disease are conserved in frog  [16, 94] , the discovery of novel genes implicated either in cancers derived from ectoderm or during stem cell reprogramming into ectoderm-derived fate could prompt immediate in silico analysis of their expression during development, even in absence of in situ hybridization data in the current databases. Importantly, NMF defined 5 basic areas from the transcriptome of the 7 dissected domains, unlike PCA, which discriminated all 7 tissues at each stage. Theoretically, this could either reflect mixed cellular contributions in the dissected samples, due to slight spatial variations between replicates or due to cell heterogeneity within the tissue, or it could reflect that a given ectoderm domain transcriptome can be explained by a combination of 2 basic transcriptomes.
Future single cell transcriptome analysis should help in resolving between these options. With NMF, we also defined a specific gene signature for each area using the Gini coefficient  [42]  to evaluate enrichment in gene expression for each domain. We retrieved a characteristic gene signature defining each NMF-predicted ectoderm domain (S5 providing the first genome-scale unsupervised annotation of the different ectoderm spatial domains. These results will allow the derivation of accurate area-specific lineage reporters for these early neurula stages. A recently study in late chick neurula identified a few transcription factors controlling NC heterogeneity along the A-P axis, based on their differential expression in transcriptome datasets  [95]. Similarly, but in earlier embryos, our spatial and temporal ectoderm gene signature should stimulate functional analyses on differential fate determination during early stages of neural, NC, and placode formation.
92% of the reads mapped on X. laevis genome. Paired reads mapping multiple times on the genome (approximately 4% of them) were excluded from the downstream analysis, leaving 88% of the reads for analysis. Putative transcript models were predicted using cufflinks 2. 2. 1 with the following parameters (-library-type fr-firststrand -3-overhang-tolerance 0-intron-overhang-tolerance 0), then merged with the annotation of the X. laevis genome (annotation version 1. 8 of the 9. 1 version of the genome, Xenbase ftp://ftp. xenbase. org/pub/Genomics/JGI/Xenla9. 1/) using cuffmerge v2. 2. 1  [102]. Transcript annotation was done with a custom script BLASTing transcript nucleotide sequences against the X. tropicalis transcript nucleotide sequence database (JGI 4. 2)  [94]  retaining only the first hit for each transcript with an E-value lower than 1E-100.
Gene models and annotations were computed by merge overlapping transcript models and their annotation using BedTools  [103]  and a custom Perl script. Read counts were computed on each gene model using HTSeq  [104]. Only genes with at least 10 counts in at least 2 samples were further considered. Read counts were then normalized using library size with trimmed mean of M-value normalization  [105] , as implemented in EdgeR  [106] , then transformed to log2-CPM (counts per million), and processed by the limma/voom package  [107]  to estimate the mean-variance relationship of the log-counts for subsequent statistical analysis. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [108]  and are accessible through GEO Series accession number GSE103240 (https://www.
PCA, tSNE PCA was computed with the standard R library on the sample expression vectors. The gene expression matrix was normalized by subtracting from each gene expression vector its average across all samples considered and dividing it by its standard deviation. To identify significant eigenvalues and genes displaying significant loading in each eigenvector, we generated a null distribution of eigenvectors by scrambling sample expression vectors through random permutation and computing PCA eigenvectors on the scrambled dataset 10,000 times. We inferred from this null distribution the 1% p-value threshold for each eigenvalue and the range of significant loading values for each eigenvector. tSNE (perplexity parameter = 7 or 9) plots were computed from the gene expression matrix using, respectively, the R tSNE package Rtsne version 0.
Biological process enrichment in WGCNA gene groups To identify overrepresented GO terms in gene groups, we used "clusterProfiler v1. 9" R library  [110] , which relies on the Fisher's exact test. As background dataset, we provided a list of nonredundant genes from the frog genome, which was obtained by retrieving, for each of the X. laevis transcripts, the HUGO and corresponding Entrez IDs. All GO biological function terms used were from the ensembl88 human annotation database. We required p-value and q-value cutoffs of 5%, and the p-values were adjusted by applying the Bonferroni multiple sample correction test. S6B Table has GO biological functions associated with the WGCNA groups. Randomized datasets were produced by swapping the group assignments from each transcript while maintaining the number of transcripts per group, similar to those from the WGCNA.
Co-expression network To create a customizable co-expression network, the user needs to provide 1 or more transcript names and select a p-value cutoff associated with the Pearson's correlation (rcorr function of the Hmisc library from R). To visualize the gene co-expression network, we used the networkD3 library (https://cran. r-project. org/package=networkD3) in the R shiny app (https://cran. r-project. org/package=shiny). NetworkD3 creates a dynamic network after receiving 2 data frames, one that describes the nodes and the other one, the edges (links). Each transcript of interest (input node) is represented as a large node. The absolute value of the correlation coefficients defines the distance between the transcript of interest (input node) and its associated transcripts (associated node).
Positive correlations are represented as thin lines, while negative correlations are represented by a thick line. The node colors are assigned according to the co-expression group obtained by WGCNA. The user can modulate the number of links by changing the p-value cutoff used for the correlation. To create such a gene co-expression network, we generated an unsigned adjacency matrix (power = 22, corFnc = "bicor") with WGCNA  [44]. The unsigned adjacency matrix allows identifying positive and negative associations for each of the transcripts. For the network visualization, the program retrieves the 50 most adjacent transcripts and computes pairwise comparisons (Pearson's correlation and p-value) between the input gene and the associated transcripts using the log-transformed normalized CPM values from all 79 samples.
A heat plot of hierarchical cluster based on the Pearson or Spearman's correlation on 79 samples, i. e. , from dissected tissues and WEs at different developmental time points, is provided to gain insights into the expression profile similarity of a particular gene compared to other well-characterized genes that serve as ectodermal landmarks (e. g. , NC, NB, epidermal, ventral, anterior). (  5 ) A gene co-expression network shows the expression pattern relationships between the genes selected by the user (input nodes) and genes having strong correlations. The application tool was built in R using the shiny app (http://shiny. rstudio. com/). The code and its documentation are deposited in Github and can be downloaded from supplementary materials (EctoMap. v1. 3. zip). The link for online access of a light version of EctoMap is https://monsoro-lab-ectomap.
Acknowledgments Authors are grateful to the members of  Monsoro-Burq, Harland, and Rokhsar labs for discussion  and constant support and to  A. Session  for technical advice and sharing the last version of X. laevis genome prior to publication. Authors thank the  Animal facility  and the  Sequencing facility in Institut Curie , especially  E. Belloir ,  C. Alberti ,  T. Rio-Frio , and  A. Lhermine.
Agence Nationale pour la Recherche  (grant number  ANR-10-EQPX-03 ). Highthroughput sequencing has been performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  (Equipex). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Agence Nationale de la Recherche  (grant number  ANR-Blanc e SVSE2 -2011-CRESTNET ,  ANR-15-CE13-0012-01-CRESTNETMETABO ). Funding to AHMB and JPV. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Association pour la Recherche Contre le Cancer  (grant number  ARC # SFI20101201882 ). Funding to AHMB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Fondation pour la Recherche Medicale  (grant number  DEQ20150331733 ,  SPF20111223488 ). Funding to AHMB and JLP. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NIH  (grant number  GM42341 ). Funding to RMH. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. European Research Council  (grant number  ERC-SMAC-280032 ). Funding to JPV and EB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Agence Nationale pour la Recherche  (grant number  ANR-10-INBS-09-08 ). High-throughput sequencing has been performed by the  ICGex NGS platform of the Institut Curie  supported by  ANR-10-INBS-09-08  ( France Genomique Consortium ).
All relevant data are within the paper and its Supporting Information files. Sequencing files are available at Gene Expression Omnibus (GEO): Series accession number GSE103240 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE103240). Access to online version of EcoMap App : https://monsorolab-ectomap. shinyapps. io/EctoMAP/.
The Material Properties Open Database (MPOD, http://mpod. cimav. edu. mx) is a functional element of the web-based open databases system linked with crystallography. MPOD delivers single-crystal tensor properties in several representations, ranging from numerical matrices to 3D printing. Longitudinal moduli surfaces can be displayed in computers as well as in smart cell phones. Properties are stored as '. mpod' files. IUCr formatting standards (CIF) are followed. The original published paper containing the data is cited. Structural and experimental information is also registered and linked. 'Coupling properties', say piezo-effects and magnetoelectricity, represent interactions linking different subsystems in a material. Currently, piezoelectricity occupies a significant fraction of cases in MPOD.
The Materials Project  [2, 3] , UC-Berkeley, represents a highly interesting effort in the direction of calculating theoretically and disclosing the physical properties of materials. Kaminsky's programmes and data concerning crystalline morphology and properties are also of interest  [4]. In this universe, the contribution of the Material Properties Open Database (MPOD) is to openly offer a collection of properties values based on published experimental data. MPOD was initiated by Chateigner in 2010 and developed in collaboration with Pepponi and Grazulis  [5]. In the recent years, with contributions from the Crystallography Group at CIMAV (Centro de Investigación en Materiales Avanzados), MPOD is being expanded. Some directions in which MPOD has grown comprise the inclusion of new electromagnetic properties and the delivery of graphic representations of properties  [6].
Data treatment in MPOD The open database MPOD describes the physical properties of single crystals. The information provided is based on experimentally measured tensor magnitudes. In all cases, reference to the original source is included. Following standard treatments  [7, 8, 9]  physical properties are defined, in linear approximation, by means of constitutive equations  (1). Y = K • X (1) Tensor X (rank m) represents the action (cause) on the sample, Y (rank n) is the materials response (effect) and K (m+n) is the linking property. Principal and coupling properties scan several tensor ranks. To decrease the dimensionality of writing tensors of rank equal to or greater than 2, the so-called matrix or compact notation is frequently used. 3 × 3 symmetric matrices (say the stress tensor) are represented as 6 × 1 hypervectors.
Piezoelectric and elastic tensors are written, respectively, as 3 × 6 and 6 × 6 matrices. The constitutive equation for the piezoelectric charge coefficients tensor d is written as follows. P = d • T (2) ⎡ ⎣ ⎤ ⎦ • T 1 T 2 T 3 T 4 T 5 T 6 ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (3) P is the electric polarisation and T is the mechanical stress. The characterisation of material's symmetry is essential for properties investigation. MPOD is consistent with theoretically derived 'selection rules' for the structure of principal and coupling properties tensors  [10, 11]. In MPOD, the introduction of new data is performed through a programme that builds the properties matrices, depending on the rank of the considered tensor and according to symmetry. Besides the atomic distribution symmetry, magnetic symmetry may also be taken into account.
The polar or axial nature of the property, as well as magnitudes' behaviour under time inversion, is part of the symmetry analysis. This issue is discussed in Fuentes-Cobas et al. [12, 13]. The MPOD data files are written in the International Union of Crystallography 'CIF' format. The contents of these files include a link to the original source, the applicable symmetry point group, the measurement technique and conditions and the properties matrices. The user can search files by chemical composition, by phase name or by property. The default output consists of the properties matrices. Commands are available for plotting the properties in the so-called longitudinal surface representation. The output screen contains the commands for generating 'STL' files for 3D prints. We illustrate the conversion from tensor to surface representation through the example of piezoelectricity.
Principal interactions in BaTiO 3 BaTiO 3 is registered in the Database as file '1000304. mpod'. The link to the original source leads to the classical report by Berlincourt and Jaffe  [14]. The structure point group is 4 mm. Dielectric constant ε r Equation (  6 ) gives the ε r tensor and Figure  1  represents the ε r (h) surface 1 r = 2920 0 0 0 2920 0 0 0 168 ⎡ ⎣ ⎤ ⎦ (6) Elasticity Equation (  7 ) represents the BaTiO 3 elastic compliance tensor (at constant electric field) s E. Figure  2(a)  shows the longitudinal compliance surface s E (h). ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ 10 -12 Pa -1 (7) Piezoelectricity in BaTiO3 and quartz BaTiO 3 Equation (  8 ) represents the d tensor for BaTiO 3. Figure  3  shows the longitudinal piezoelectric surface Piezoelectric tensor e for quartz, Equation (  9 ), is taken from Ogi et al.
Conclusions Crystal thermo-electro-magneto-elastic couplings exhibit a wide spectrum of anisotropic responses, linked with structural and magnetic symmetry, polar and axial nature of magnitudes and tensor ranks. MPOD (http://mpod. cimav. edu. mx) is an open database that delivers measured materials properties in matrix, surface and 3D printing descriptions. Properties' data in MPOD are computer processed, based on tensor analysis and group theory criteria.
S. Szilner reported on sub-Coulomb studies with closed shell and superfluid systems performed with the PRISMA spectrometer, where the experimental probabilities of one and two nucleon transfer could be well reproduced by microscopic calculations. She stressed the importance to treat, on the same footing, structure and dynamics to compare experiment and theory in absolute scale and to learn about correlations. The problematic issue connected with the pair correlations is of current interest in ongoing research with radioactive beams, where for example the pairing interaction is expected to be significantly modified in nuclei with extended neutron distributions. The newly accumulated data are also attracting a renewed interest by theorists. The identification of the e↵ects of the pairing interaction partially motivated the work presented in the talk by G.
In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities.
Ultimately, these studies with complex imaging and extended data (PIx) require multi-center acquisitions to build a large target population. A regular PIx infrastructure has to cover the following three main topics: (1) data collection, (2) quality control (QC) with data processing, and (3) data indexing and publication with controlled data sharing mechanisms. Furthermore, PIx infrastructures must evolve during the life cycle of a population imaging project, and they must also be resilient to extreme evolutions of the data content and management. In the projects we manage, we experience several extreme evolutions. The first kind of evolution may affect the published dataset such as adding a new modality for all subjects, a new time point or a new subcohort. Second, the amount of data requested evolves dramatically as the project consortium gets enlarged  (Gorgolewski et al.
, 2015). Finally, internal ontologies have to evolve constantly in order to match the ongoing initiatives on interoperability  (Scheufele et al. , 2014; Gorgolewski et al. , 2016). Several existing open-source frameworks support one or several of the described topics sometimes only for one specific data type. We propose in the following a brief overview of existing systems. Some of these systems have also been reviewed by  Nichols and Pohl (2015). IDA  (Horn and Toga, 2009)  is a neuroimaging data repository and management system that supports data collection (topic (1)) and data sharing (topic (3)). With this system, the published datasets can be searched using automatically extracted metadata. The XNAT framework  (Marcus et al. , 2013)  is widely used for neuroimaging data and supports all the PIx infrastructure topics, focusing on tools to pipeline, and to audit the processing of image data (topic (2)).