dataesr/scientific-paragraphs-categorization huggingface dataset:
This example shows the possibilities of interconnection between a simulated system and its real environment. Finally, we demonstrated the tool's ability to communicate or direct other simulators through a distributed-simulation standard (HLA). All these results are linked with 4. 0 industry. Getting risk information from a global risk database is related to data-driven processes and big data. Opening the Papyrus tool to several distributed-simulation standards is a link with the concept of the internet of things. Orchestrating several distributed simulations can be associated to smart manufacturing and digital twins. There are still prospects in the use of HLA and FMI distributed-simulation standards within Papyrus. The implementation of more complex time-management mechanisms between HLA and Papyrus would increase the exchange and communication capacities between the two tools.
Then, we demonstrate its capacity to infer biologically relevant tissue substructures from the implemented gexel pattern recognition module and its enhanced performance relative to dimensionality reduction and clustering strategies on various SrT data, including the anatomical tissue stratification of a whole mouse embryo (DBiT-seq)  (Liu et al. , 2020). Finally, we present the performance of MULTILAYER for the analysis of high-resolution SrT data (Slide-seq;  Rodriques et al. , 2019)  as well as for processing multiple datasets through an automated comparative batch module, anticipating the future needs of this rapidly evolving field. MULTILAYER is freely available as a stand-alone computational tool (https://github. com/SysFate/MULTILAYER).
H G F as upregulated or downregulated when the normalized read counts per gene are above or below the stated average behavior, respectively (Figure  1 ). Although this analysis is performed per gexel, MULTILAYER ranks differentially expressed genes based on the number of related gexels, hence providing a rapid view of genes that are overrepresented on the digital map on the basis of their relative expression (Figure  S1B ). Similar to contextual classification strategies used for image analysis from pixel information  (Toussaint, 1978) , MULTILAYER detects gene expression patterns using an agglomerative strategy applied to contiguous gexels. This module generates a cleaner view of overexpressed genes within the tissue (Figure  1 ). Furthermore, it allows the identification of multiple patterns for the same overexpressed gene within the tissue, which per se is lost in all other strategies that rely on aggregating independent gexels by applying, for example, the t-distributed stochastic neighbor embedding (t-SNE) method (Figures  S3, S4,  and S7 ).
MULTILAYER detects differentially expressed genes with performance similar to existing methods We validated the performance of the normalization and the detection of differential gene expression relative to that of existing methods using two public SrT datasets, one for the mouse olfactory bulb (MOB) and the other a human breast cancer tissue section  (Sta ˚hl et al. , 2016). SrT landscaping over the breast cancer section gave rise to a digital map showing total counts per gexel varying between 2 and 13,874 reads, with an uneven distribution of high-count levels on one side of the tissue (Figure  2A ). Such uneven distribution is also observed for the read counts associated to housekeeping gene actin beta (ACTB) (Figure  2B ). Quantile normalization uniformized the total counts per gexel to 3,135 reads and rendered the ACTB read counts evenly distributed over the tissue.
Quantile normalization did not affect the spatial read-count distribution associated with the gene fibronectin 1 (FN1), known to be overexpressed in breast cancer  (Wang et al. , 2018) , which led to its detection as a spatially overexpressed gene with a pattern composed of several contiguous gexels (Figures  2A  and 2B ). Similarly, quantile normalization applied to the MOB dataset corrected for high (and unevenly spatially distributed) read-count levels associated with the housekeeping genes GAPDH and ACTB (Figure  S1E ). In the absence of such read-count correction, these genes would be considered to be spatially overexpressed, as illustrated by the artifactual pattern revealed by MULTILAYER. On the contrary, the spatial overexpression signature associated with the gene neurogranin (NRGN) was unchanged by the quantile treatment, as supported by the pattern detection performed by MULTI-LAYER, in agreement with publicly available in situ hybridization data (Figures  2E  and S1A ).
Despite such behavior, these genes are enriched for cancer-related terms (DisGeNET association analysis; Figure  S2D ) and among them, factors such as cytochrome c oxidase subunit 6C (COX6C) or mucin-like 1 (MUCL1), which have been reported to be overexpressed in breast cancer, were retrieved (Figure  S2C )  (Chang et al. , 2017; Conley et al. , 2016). In the case of the MOB data, MULTILAYER detected 98 of the top-100 SPDE-ranked genes (q value ranking, of which the top 67 had an FDR < 0. 05;  Sun et al. , 2020) , among which 44 were in common between all three methods (Figure  2F ). The conservative statistical behavior of SPDE is highlighted when comparing the number of contiguous gexels per pattern detected by (G) Same as (D) for the MOB dataset. Example of three genes (CCK, KCTD12, and NRGN) reported to be significantly expressed by SPARK and SPDE and two others (SEZ6 and SH3PXD2A) for which their associated confidence is beyond the defined FDR threshold by the SPARK statistical analysis only.
(H) In situ hybridization (ISH) and gene-expression data (Allen mouse brain atlas) for the neurogranin (NRGN) gene, revealing its spatial signature, which is coherent with the digitized view generated by MULTILAYER (displayed in E). The hematoxylin and eosin staining images reproduced in (A) and (E) were obtained from https://www. spatialresearch. org/. (legend continued on next page) MULTILAYER with their corresponding q value (Figure  2G ), notably by the low confidence assigned by SPDE to the genes SEZ6 and SH3PXD2A, but significantly detected by MULTI-LAYER and SPARK (Figure  S2E ). Overall, this comparative analysis using SPARK and SPDE shows that MULTILAYER allows retrieval of most of the significant genes revealed by these methods.
, 1993). This spatial region has been characterized as ''normal pancreas tissue'' as part of the histological annotation described by  Moncada et al. (2020) , (C) Spatial gene co-expression analysis for ACTA2 and TTN. Gexels colored in red correspond to the location of the target genes (ACTA2 or TTN) and the other colored gexels show their co-expression pattern (Tanimoto similarity index). (D) Spatial community tissue stratification from gene co-expression analysis and agglomerative clustering performed over the entire tissue and all overexpressed genes. The developing human heart tissue map was stratified into five spatial communities (from ''0'' to ''4''), of which two show a highly redundant spatial localization pattern (''2'' and ''3''). (E) Gene ontology analysis (ARCSH4 tissue database;  Lachmann et al.
, 2020). The corresponding transcriptome map (50-mm resolution) is composed of gexels with total counts varying between 600 and 46,000 reads unevenly distributed throughout the tissue (Figure  3K ). MULTI-LAYER uniformized the total counts per gexel to 12,000 reads and allowed to reveal up to eight distinct spatial communities (Figures  3K  and S7I ). The spatial localization of the inferred communities showed a strong correlation with the anatomical annotation presented within the DBiT-seq study  (Figures 3L and S7J; Liu et al. , 2020) , better than the unsupervised UMAP clustering (Figure  3M ) or a similar dimensionality reduction that can be obtained using the t-SNE algorithm (Figure  S7J ). This enhanced MULTILAYER performance relative to other strategies that rely on aggregating independent gexels by applying the t-SNE algorithm was also verified for the developing human heart and the pancreatic ductal adenocarcinoma datasets (Figures  S7A-S7F ).
, 2019). Aiming to use MULTILAYER to analyze high-resolution Slideseq maps but concerned by the technical constraints related to (1) the low number of read counts per gene retrieved within the gexels (Figure  S8 ) and (  2 ) the high number of gexels within the SrT map impacting the computational performance (including the display functionalities); we have implemented a complementary script allowing to reduce the SrT map complexity. This ad hoc module, called ''MULTILAYER compressor,'' generates super-gexels by agglomerating contiguous gexels defined by a user-provided compression factor. This approach, previously described for image-segmentation strategies  (Stutz et al. , 2018) , allowed enhancement of the number of counts per gene within super-gexels to levels even comparable to those retrieved in regular SrT maps (Figure  S8 ).
This was further supported by global tissue stratification from a comparison of gene co-expression patterns retrieved over the entire tissue (43 spatial communities; Fig-  ure 4C ). The use of higher compression factors (1003 and 1753) did not affect the observed overexpression signature for PPP3CA, PCP4, or SNAP25 and increased their related read counts and differential overexpression levels, in agreement with the agglomerative strategy used for generating super-gexels (Figures  4B  and S9C ). Furthermore, it reduced the number of detected spatial gexel communities (13 and 8 spatial communities for 1003 and 1753, respectively) but retained the global digital tissue substructures (Figures  4C, 4E,  and 4G ). Finally, the relevance of the various spatial gexel communities were confirmed by gene ontology enrichment analysis (ARCHS4 tissue database;  Lachmann et al.
, 2018 ) assessed using the three described compression factors (Figures  4D, 4F,  and 4H ). A similar analysis performed on the cortex map (reduction factor of 603) allowed us to identify spatially distinct overexpression signatures for factors such as the gene transthyretin (TTR) and calcium/calmodulin-dependent protein kinase II inhibitor 1 (CAMK2N1) (Figure  S10 ). We confirmed their gene expression relevance within the mouse cortex by gene ontology term analysis performed over the spatially co-expressed genes at various digital compression factors (603, 1003, and 1753) (Figures  S10E-S10I ). The extension of the co-expression pattern over all genes within the tissue map allowed us to infer >40 supergexel communities on the cortex map using the 603 compression factor (Figure  S11A ). Gene ontology analysis performed by MULTILAYER (ARCHS4 tissue database) showed the enrichment of terms such as cerebral cortex, superior frontal gyrus, dentate granule cell, motor neuron, neuronal epithelium, dorsal striatum, and spinal cord (Figure  S11D ).
1, P2. 1, P3. 2, P4. 1, P4. 2, and P4. 3) showed gene co-expression patterns associated with factors such as the membrane cell-junction protein claudin-4 (CLDN4), known to be overexpressed in primary and metastatic prostate cancer  (Landers et al. , 2008) ; growth/differentiation (F and H) Gene ontology enrichment analysis performed on the spatial communities displayed in (E) and (G), respectively. ARCHS4 tissues: GO terms database from massive mining of publicly available RNA-seq data from human and mouse  (Lachmann et al. , 2018). factor-15 (GDF-15), the overexpression of which has been associated with prostate cancer progression  (Va nhara et al. , 2012) ; the gene ACPP, encoding prostatic acid phosphatase, associated with prostatic hyperplasia and also observed in prostate carcinoma (as shown in the human protein atlas database); kallikrein-related peptidase 2 (KLK2), encoding a trypsinlike serine protease primarily expressed in the prostate, the overexpression of which is considered to be a prognostic marker for prostate cancer risk  (Shang et al.
Here, we considered SrT data as a digital image composed of an ensemble of gexels arranged in a two-dimensional grid. Hence, we demonstrated that the principles applied to digital image processing, which relies on contiguous pixel aggregation, can help to reveal biologically relevant tissue substructures. Specifically, the proposed stand-alone package MULTILAYER allows the ''rationalization'' of spatial information by analyzing the presence of contiguous gexels showing the same gene expression behavior (herein defined as a gene pattern), leading to the stratification of the digital map into molecular tissue substructures. Indeed, in contrary to other methodologies, in which gexels are considered to be independent units that converge into given clusters after dimensionality reduction processing, MULTI-LAYER captures gene expression patterns, which are then converged toward regions due to their spatial co-localization.
(J) Gene ontology analysis (DisGeNET database; Pin ˜ero et al. , 2020) for each of the spatial communities displayed in (I), as performed by MULTILAYER. (K) MULTILAYER processing of DBiT-seq data (deterministic bar coding in tissue for spatial omics sequencing; Liu et al. , 2020) from a whole mouse embryo (50-m gexel resolution). The right panel shows eight spatial communities revealed by agglomerative clustering of co-expressed gexels (patterns with >5 contiguous gexels; Tanimoto similarity index >10%). (L) Anatomical annotation elaborated by Liu et al. (2020). (M) UMAP clustering (right) and its corresponding spatial projection (left) performed by Liu et al. (2020) (images reproduced with authorization).
Data availability All spatial transcriptomics data used within this article were obtained from public repositories and converted into a uniform format compatible with MULTILAYER requirements. Human breast-cancer data (Breast Cancer Layer 2), mouse olfactory bulb data (MOB Replicate 11), published by  Stahl et al. (Sta ˚hl et al. , 2016) , human heart development data, generated by Asp. M. et al. (Asp et al. , 2019) , and prostate cancer data, generated by Berglund et al. (Berglund et al. , 2018) , were obtained from the Spatial Research portal. In addition to the raw SrT matrices, their associated hematoxylin and eosin-stained images were also obtained from the same public repository. Hippocampus and cortex high-resolution Slide-seq maps  (Rodriques et al. , 2019)  were obtained from the Spa-tialDB database  (Fan et al.
Materials and methods Following the identification by exome sequencing of a de novo missense variant in CHD5 in a patient with ID, autism spectrum disorder (ASD), and epilepsy, we collected data from additional patients with CHD5 variants through GeneMatcher  (Sobreira et al. 2015). We systematically included all patients with de novo variants as well as patients with either truncating or predicted damaging missense variants inherited from affected parents. Only patients without a detailed clinical history and/or inheritance information were excluded from the study. Exome sequencing was performed at the respective institutions. Referring physicians provided detailed developmental, neurological, and behavioral history of the patients. Patient information was anonymized before data sharing. Variants were described on the CHD5 NM_015557.
3 Ref-Seq transcript using HGVS recommendations  (den Dunnen et al. 2016 ) and classified according to ACMG guidelines  (Richards et al. 2015). All variants have been submitted to the ClinVar Database and have been assigned the following accession numbers: SCV001477999-SCV001478015. Multiple algorithms were used to assess the pathogenicity of CHD5 variants, including Mutation Taster, Polyphen-2, and SIFT  (Ng 2003; Adzhubei et al. 2010; Schwarz et al. 2014). Combined annotation-dependent depletion (CADD) scores  (Rentzsch et al. 2019)  were calculated for each variant using the GRCh37-v1. 6 version (Online Resource Table  1 ). Prediction of the consequences of the two splicing variants were carried out with Alamut ® Visual, a mutation analysis software which includes a splicing module integrating a number of prediction algorithms and splicing prediction data.
Nucleotide conservation across 100 vertebrate species was calculated for each variant using the PhastCons score obtained with the phastCons100way. UCSC. hg19 R package  (Siepel 2005)  and represents the probability that a given nucleotide is conserved (range 0-1). Codon conservation scores were calculated as the mean nucleotide conservation of each triplet. Known CHD5 NM_015557. 3 variants were retrieved from gnomAD v2. 1. 1  (Karczewski et al. 2020) , restricting to loss-of-function, missense, and synonymous single nucleotide variants. ). All variants were either absent from gnomAD or present with an allele frequency below 0. 0001% (Online Resource Table  1 ). All missense variants affect highly conserved amino acids of CHD5 (up to zebrafish, Online Resource Fig. 1 ), had CADD scores above 22, and were predicted to be damaging by at least two algorithms among Polyphen-2, SIFT, and Mutation Taster (Online Resource Table  1 ).
93  (Collins et al. 2021). CHD5 is also catalogued among haploinsufficient genes by the Genome Aggregation Database (gnomAD), with a probability to be LOF intolerant (pLI) of 1 and an LOF observed/expected upper bound fraction (LOEUF) of 0. 16. Furthermore, its missense Z-score (referring to the number of observed and expected missense variants within the same database) indicates that this gene is highly missense-constrained (Z-score = 5. 32). These metrics indicate that variants disrupting the coding sequence of CHD5 are counter-selected in human populations, and also suggest that they likely are disease-causing  (Karczewski et al. 2020 ). Nevertheless, 25 out of the 141,456 individuals present in gnomAD harbor CHD5 variants predicted to be associated with a LOF of the corresponding allele.
Acknowledgements We thank the patients and their family for their participation in this study. The research generating these results was financially supported by  Fondation Maladies Rares, Bio-Psy Labex , " Investissements d'avenir" program   ANR-10-IAIHU-06  ( IHU-A-ICM ), the  Japan Agency for Medical Research and Development (AMED)  ( JP20ek0109486 ,  JP20dm0107090 ,  JP20ek0109301 ,  JP20ek0109348 , and  JP20kk0205012  to N. M. );  JSPS KAKENHI  ( JP17H01539  to N. M. and  JP20K08164  to T. M. );  Intramural research grants for Neurological and Psychiatric Disorders of NCNP  from the  Ministry of Health, Labour and Welfare  ( 30-6  and  30-7  to N. M. ), the  Takeda Science Foundation  (to T. M. and N. M. ) and the  NIH Common Fund , through the  Office of Strategic Coordination/Office  of the  NIH Director  under Award Number  U01HG007672  ( Duke University ).
Here we introduce a novel method and software: the Functionnectome, that unlocks the function of the white matter. Functional white matter analysis is derived from the combination of task-related functional MRI and new anatomical priors for brain circuits. In doing so, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome is available as a companion open-source software (http://www. bbcblab. com and https://github. com/NotaCS/Functionnectome).
Priors: anatomical connectivity probability maps To project functional signals onto the white matter, the Functionnectome requires prior knowledge of white matter anatomy, and more specifically, the structural connectivity between a given voxel and the rest of the brain, later referred to as priors. Accordingly, each prior is a voxel and its probability of structural connectivity with all other brain voxels. These 3D maps were derived from deterministic tractography of 100 Human Connectome Project (HCP) participants using 7T diffusion MRI scans  29  already processed for tractography by our team  21. The tractography used is openly available at https://osf. io/5zqwg/. In total, 228,453 maps -one per brain voxel-were generated and are part of the Functionnectome software's priors. The Functionnectome uses the probability indicated in these priors to project the signal from a given voxel to the brain.
Validation of the priors To verify the anatomical priors' validity, we used the method developed by O'Muircheartaigh and Jbabdi  32. Briefly, it compares statistically independent components  33  of resting-state fMRI and structural connectivity. In our analysis, the independent component analysis applied to resting-state fMRI data of the HCP test-retest dataset produced 17 resting-state networks (out of 20 components). Similarly, the independent component analysis applied to the 438 "region-wise" priors produced 50 components. Cross-correlations indicated that resting-state networks were significantly associated with pairs of structural connectivity components (Figure  1a ). For instance, structural connectivity components 2 and 4 corresponded to the left and right visual resting-state network (RSN2, Figure  1b ) and connected the two hemispheres through the posterior part of the corpus callosum.
Here, we illustrated this process by applying the Functionnectome to motor (n = 46), working memory (n = 45), and language (n = 44) functional volumes of the test-retest Human Connectome Project (HCP) dataset. Results from the functionnectome maps are presented side by side with classical task activation analysis. The white matter "activations" displayed on the functionnectome zmaps correspond to the associated pathways' significant involvement during a task. Importantly, these white matter activations are arising from the projected grey matter signal, and are not direct white matter BOLD signal analysis  22, 23. The right finger taping Functionnectome analysis (Figure  3a ) revealed the wellestablished motor system circuit with significant involvement of the posterior arm of the left internal capsule, connections to subcortical areas, and the cerebellum through the brain stem (i.
Language (semantics) As expected, the classic fMRI maps displayed activation in the posterior inferior frontal gyrus, the superior temporal gyrus, and the inferior parietal lobe, forming the semantic network  18, 44. The anterior part of the temporal lobe was activated (Figure  6b ), which is in line with the literature highlighting it as a central hub for semantic processing  45. The orbitofrontal cortex has previously been linked to language and semantic cognition  45. Lastly, activation of the amygdala, mammillary bodies, and hippocampus reflect a limbic network's involvement, integrating emotions with semantic memory  46. For each analysis reported above, we repeated the analysis in a replication dataset of the same participants (same session, opposite phase of acquisition).
Discussion We propose a new method, the Functionnectome, to investigate brain circuits' functional involvement during task-related cerebral processes based on new, openly available anatomical connectivity priors. Applying the Functionnectome to a high-quality functional neuroimaging dataset revealed -for the first time -the white matter circuits supporting motor, working memory, and language activations. Results also indicated a higher reproducibility of the Functionnectome maps compared to classical task-related activation methods. To support this method's broad uptake and facilitate its application to a wide range of datasets, including in the clinic, we provide a GUI and a terminal-based companion software and simplified priors for faster processing for more modest configurations. This toolbox allows the application of the Functionnectome to any previously acquired fMRI dataset.