dataesr/scientific-paragraphs-categorization huggingface dataset:
These devices are ready to be used with some basic software components running and connected to a local network, e. g. , a 5G access point. As for the software components already deployed on the robots, we foresee the ROS environment correctly set up, with some basic ROS components already running. The sensors can be either pre-deployed in the area or carried by firefighter personnel. When a SAR mission is started, the Service Consumer connects to the NEPHELE HDA repository and looks for the HDA to deploy and provides the input data that are required. Then, the following operations will have to be initiated by the NEPHELE platform (see Figure  2  for reference of the building blocks): 1. The synergetic meta-orchestrator (SMO) receives the HDA graph, the set of parameters for the specific instance of the SAR application, the VO descriptors needed for the application and a descriptor of the supportive functions to be deployed in the continuum.
2. Once the VOs are deployed, a southbound interface for VO-to-IoT device interactions will be used to interoperate with the physical devices (i. e. , robots, drones and sensor gateways). The VO will have knowledge on how to communicate with the IoT devices (i. e. , robots, sensor gateways), as this will be stored and available on the VO storage. We assume the IoT devices to be up and running with their basic services and to be connected to the network; 3. Physical robots and sensor networks will communicate with each other through the corresponding VOs using a peer interface, whereas the application component that will use the data stream from the VOs will use the Northbound interface. Application components such as map merging, decision-making, health monitoring, etc. , will interact with the VOs to exchange relative information; 4.
1. Reduced delay in time-critical missions: by exploiting compute and storage resources at the edge of the networks, with the possibility of the dynamic adaptation of the application components deployment over the continuum, lower delays will be expected for computationally demanding tasks on a large amount of data. This will be of high importance as it will strongly enhance first responders' effectiveness and security in their operations; 2. Efficient data management: a large amount of data available and collected by sensors, drones and robots will be filtered, compressed and analyzed by exploiting supportive functions made available by the VOStack in NEPHELE. Only a subset of the produced data will be stored for future reuse based on data importance. This will reduce the bandwidth needed for communications from the incident area to the applications layers that introduce intelligence into the application and by this reduce the delay in communication and the risk of starvation in terms of networking resources; 3.
5. HW-agnostic deployment: the introduction of the VO concept and the multilevel meta-orchestration open for device-independent deployment and bootstrapping using generic HW. Different software components of an HDA can be deployed at every level of the IoT-to-edge-to-cloud continuum, which reduces the HW requirements (e. g. , in computation and storage) at the IoT level for enabling a given application; 6. AI for computer vision and image processing: advanced AI algorithms can be deployed as part of the supportive functions made available through the VOSstack innovation from NEPHELE. These can then be enabled on demand and deployed over the compute continuum for image and analysis and computer vision to locate and identify victims and perform risk assessments and predictions; 7. End-to-end security: IoT devices and HDA users will benefit from the security and authentication, authorization, and accounting (AAA) functionalities offered by the NEPHELE framework.
These functions will be offered as support functions for the VOs representing the IoT devices and will help in controlling access to the services, authorization, enforcing policies and identifying users and devices; 8. Optimal network resource orchestration: based on the HDA requirements, an optimized network resource allocation policy will be enforced over the IoT-to-edgeto-cloud continuum. Here, the experience in network slicing and software-defined networking (SDN) will be exploited to be able to support time-critical applications such as the SAR operations presented in this paper. To summarize, the NEPHELE framework will enable and support the integration of different technologies and solutions over the cloud-to-edge-to-IoT continuum. Indeed, combining all these elements into a single framework represents a breakthrough advance in the cloud-to-edge-to-IoT continuum-based applications.
The orchestration and smart placement of application components exploiting AI models will enable adaptation to current status and dynamics factors. The VOStack in the NEPHELE project will enable the elaboration of data effectively and efficiently with supportive functions tailored to the specific use-case requirement. Our future work will consist of the implementation of a hyper-distributed application that demonstrates the benefits described in the paper for a post-earthquake scenario in a port. In such a scenario, we can imagine that the network infrastructure is down, the map of the port is not reliable due to collapsed infrastructure and buildings and several dangerous factors (e. g. , containers with dangerous materials or at risk of collapsing) are of high risk for the SAR operations.
It will be an ROS application for multiple robots and drones that enables the dynamic mapping of an unknown area. AI and computer vision models will be used for object detection and victim identification in the area and to update the map of the post-disaster scenario. Advanced data aggregation solutions will be investigated including consensus-based solutions as proposed, e. g. , in  [73]  to extract information from a wide set of different sources in an efficient and effective manner. The extracted information will be used for mission control purposes, for priority definition in the SAR tasks and the assessment of risks and the health of victims. For integration with the NEPHELE framework, virtualization techniques and cloud-native technologies will be adopted.
Methodology To objectively explore potential differences in prescribing behavior concerning the treatment of children with XLH with burosumab, a preliminary survey was conducted through a third party involving 10 doctors from Germany, 5 from France and 5 from the UK. To ensure participants had sufficient experience to meaningfully complete the survey, they had to be personally responsible for treating a minimum number of XLH patients with burosumab. The survey comprised a 30-minute, moderated, webassisted, in-depth interview. Anonymized dosing data collected through patient record forms documenting the last patients seen in clinic treated with burosumab (3-5 per participant) was used to support discussion. Each participant was given three hypothetical patient cases and asked how they would prescribe burosumab in terms of starting dose, titration and treatment goals.
Hypothetical cases are provided in Appendix 1. The results from this preliminary survey revealed national and individual differences in prescribing behavior. We (the authors of this paper) were then convened to form an expert practice exchange group to comment on the data generated by the survey, drawing on our experience with the use of burosumab in treating patients with XLH. We are Europebased clinical experts specialized in pediatric and adult endocrinology, nephrology and metabolic bone diseases from seven countries (Table  1 ). Experts with experience managing adult patients with XLH were also included to facilitate discussion on transition of XLH children to adult services. We met at two virtual working sessions in December 2020, firstly to discuss the data in the context of our own personal experience and, secondly to discuss what actions, if any, would be appropriate to provide consistency in treatment practice across Europe (Figure  1 ).
Limitations The views reflected in this paper are based on our experience, as a select group of European experts, on the use of burosumab in children with XLH, in addition to relevant data obtained from treatment guidelines. The survey methodology used to formulate the expert opinions reported used a structured approach, with predefined clinical scenarios and iterative rounds of responses. We acknowledge that other expert survey methodologies, such as the Delphi process  (49) , can be used to develop expert consensus on clinical needs, such that our chosen method may have introduced bias. Therefore, the opinions reported herein may not necessarily reflect those of experts in other regional centers or countries. There is a variation in the indication for use, as well as regulatory and reimbursement constraints, for burosumab treatment in different European countries, which impacts treatment in all age groups.
The 'Coordinator' motif is active specifically in embryonic face and limb mesenchyme The Coordinator motif  20  is a composite of the TAATT[A/G] motif bound by many HD factors and a version of the CANNTG E-box bound by most bHLH factors  6  , separated by a fixed spacing (Figure  1A ). Therefore, we wondered whether any cell types other than CNCCs also exhibit enrichment for the Coordinator motif in their active cis-regulatory regions. We first defined a robust signature of Coordinator activity, based on the observation that in hCNCCs the Coordinator motif is enriched most highly in the top ~10,000 promoter-distal open chromatin peaks as defined by the assay for transposase-accessible chromatin with sequencing (ATAC-seq) (Figure  S1A ). We searched for enrichment of known TF motifs in the top 10,000 distal open chromatin regions for each of the 549 ATAC-seq and 1,781 DNase-seq datasets from human and mouse in ENCODE  25  , and then collapsed similar motifs into 287 motif clusters to remove redundant motifs (Materials and Methods).
However, many developing limb samples and a smaller subset of fibroblast and neuroblastoma samples also meet our definition of Coordinator activity. Notably, neuroblastoma is a cancer originating from neural crest-derived lineages  26  , whereas fibroblasts are mesenchymal cells of either mesodermal or neural crest origin  27. Importantly, across analyzed cell types and tissues, most samples showing strong enrichment of E-box and HD motifs are not enriched for Coordinator (Figure  1B , Figure  S1C ). For example, many developing brain samples are highly enriched in both E-box and HD halves of the motif but not for the combined motif (Figure  1B ,C; Figure  S1C ). To corroborate this finding, we gathered additional published human and mouse ATAC-seq datasets from cell types related to those in which we initially detected Coordinator enrichment and from relevant negative controls (Figure  1D )  28- 39.
The amino acid residues, and more broadly the loops, involved in the TWIST1-HD contact are not invariant in amino-acid sequence, even among TFs with highly similar DNA binding motifs (Figure  6D, F ). To assess whether these variable loops form distinct structures, we aligned our TWIST1-TCF4-ALX4-Coordinator structure to previously solved (individual) HD and bHLH structures. Despite amino acid differences at the contact residue position (i. e. His to Gln substitution), MSX1 (PDB: 1IG7) and DLX3 (PDB: 4XRS) both form highly similar structures as ALX4 (Figure  6D ). While the amino acid identity could impact the affinity of the contact, this result is consistent with our ChIP data suggesting that MSX1 and PRRX1 can also bind DNA at many of the same sites as ALX1/4 in hCNCCs, albeit at much reduced occupancies compared to the ALX factors (Figure  3D ).
D. Sequence alignment of selected homeodomain TF loop sequences (left) with the TWIST1 contact residue highlighted in cyan, and structural alignment of ALX4 (magenta with red loop) with MSX1 (PDB: 1IG7) in gold, and DLX3 (PDB: 4XRS) in beige (right). E. Homeodomains shift TWIST1 toward Coordinator to varying degrees. Indicated Flag-tagged HD proteins were co-expressed with TWIST1 in HEK293 cells and TWIST1 binding was measured by ChIP-seq (see Figure  S6B  for protein levels). TWIST1 ChIP-seq peaks are ranked from strongest to weakest (left to right) and grouped into bins of 1000 peaks. F. Sequence alignment of TWIST1 and NEUROD1 loops (left), and structural alignment of TWIST1 (cyan with purple loop) with NEUROD1 (PDB: 2QL2) in green (right). G. TWIST1 loop is required for Coordinator binding.
Data and code availability All sequencing datasets have been deposited in NCBI GEO at accession GSE230319. Accession numbers of analyzed publicly available datasets are listed in Table  S2. ENCODE datasets were downloaded from https://www. encodeproject. org/. CCLE data were downloaded from https://depmap. org/portal/download/all/, Release 22Q1 "CCLE_expression. csv" and "sample_info. csv". Mass spectrometry peptide spectrum match counts are provided in Table  S3. The TWIST1-TCF4-ALX4 crystal structure atomic coordinates and diffraction data have been deposited to Protein Data Bank under accession 8OSB. All original code have been deposited to Zenodo at https://doi. org/10. 5281/zenodo. 7847853.
Cell culture H9 cells (WiCell, WA09, RRID:CVCL_9773) were cultured in feeder-free conditions, in mTeSR1 medium (Stem Cell Technologies, 85850) on Matrigel Growth Factor Reduced (GFR) Basement Membrane Matrix (Corning, 356231) and passaged using ReLeSR (Stem Cell Technologies, 05872) every 4-6 days. Cells were switched to mTeSR Plus medium (Stem Cell Technologies, 100-0276) prior to and during genome editing and clonal expansion, but switched back to mTeSR1 before differentiation to CNCC. hESCs were differentiated to CNCC as previously described  20, 28. Briefly, hESC colonies were partially detached from the plate with collagenase IV (Gibco, 17104019) in Knockout DMEM medium (Gibco, 10829018) for 30-60 min and scraped to break up large colonies, and then cultured in Neural Crest Differentiation Medium (50%-50% v/v mixture of DMEM/F12 1:1 medium with L-glutamine, without HEPES (Cytiva, SH30271.
These hCNCC were then passaged every 2-3 days upon reaching confluency, with cells in the third or subsequent passages defined as 'late hCNCC' and cultured with added 50 pg/ml BMP2 (Peprotech, 120-02) and 3 uM CHIR-99021 (Selleck, S2924). dTAG V -1 (Tocris, 6914/5) was dissolved in DMSO at 5 mM and then diluted to 250 uM in 60% DMSO/40% water (v/v) before dilution to 500 nM for acute depletions (up to 1 day) or diluted directly from the 5 mM stock for long-term depletions. For acute depletion time courses, an equivalent amount of DMSO (0. 12% v/v final) was added to all samples starting 24 h before harvest, and cells for all time points were harvested simultaneously. RS4;11 cells (ATCC, CRL-1873, RRID:CVCL_0093) were cultured in RPMI-1640 medium (Gibco, 11875093) supplemented with 10% v/v FBS and 1x antibiotic/antimycotic.
HEK293 cells (ATCC, CRL-1573, RRID:CVCL_0045) and 293FT cells (Invitrogen, R70007, RRID:CVCL_6911) were cultured in DMEM high glucose medium with sodium pyruvate and L-glutamine, supplemented with 10% v/v FBS and 1x GlutaMAX, non-essential amino acids, and antibiotic/antimycotic. O9-1 cells (Millipore, SCC049, RRID:CVCL_GS42) used for spike-in controls for ChIPs of TWIST1 depletions were cultured in Complete ES Cell Medium with 15% FBS (Millipore, ES-101-B), 25 ng/ml bFGF, and mLIF (Millipore, ESG1107).
Complex was crystallized in sitting drops by vapor diffusion technique from solution containing 50 mM sodium cacodylate buffer (pH 7. 5), 100 mM magnesium acetate, 18% glycerol, 20% 2-Methyl-2,4-pentanediol and 6-7% PEG (MW 8000). The X-ray data set was collected at European Synchrotron Radiation Facility (ESRF) (Grenoble, France) from a single crystal on beam-line ID23-1 at 100 K using the reservoir solution as cryo-protectant. Prior to data collection, crystals mounted on the goniometer were located and characterized using X-ray mesh scans analyzed by Dozor-MeshBest 107,108. The experimental parameters for optimal data collection were designed using the program BEST 109. Data were integrated with the program XDS 110 and scaled with program AIMLESS as implemented in CCP4 111. Statistics of data collection are presented in Table  S5.
Structure determination and refinement The structure was solved by molecular replacement using program Phaser 112 as implemented in Phenix 113 and CCP4 111 with the structure of TCF4 (PDB: 6OD3) as a search model for TCF4 and TWIST1, and NMR structure of ALX4 (PDB: 2M0C) as a search model for ALX4. After the positioning of proteins, the density of DNA was clear and the molecule was built manually using Coot 114. However, we did not find any density for the BRG1 fragment. The rigid body refinement with REFMAC5 was followed by restrain refinement with REFMAC5 115 , as implemented in CCP4. Resulting statistics of the refinement are presented in Table  S5. Structural alignments and figures were generated with PyMOL. The resulting structure was submitted to Protein Data Bank with ID 8OSB.
Peaks from all hCNCC experiments were merged into a unified peak set by concatenating all significant summits, clustering peaks within 150 bp with bedtools 120 cluster, keeping only the most significant summit (in any sample) per cluster with a p-value < 1E-20, extending by an additional 100 bp in both directions, and then merging any overlapping peaks with bedtools merge, resulting in 213,151 total peaks. The most significant summit within each merged peak was used as the overall summit, which was used to generate heatmaps and perform motif analyses. Counts of reads in each sample overlapping the merged peak set were generated using bedtools, and differentially accessible peaks were called using DESeq2 121 using only samples pertinent to each comparison, and using CNCC differentiation replicate as a covariate, excluding peaks with fewer than an average of 10 reads per dataset in the comparison.
Genome browser tracks were generated by MACS2 v2. 2. 7. 1 119 and plotted using IGV v2. 7. 2 122. Peaks with a summit within 500 bp of a TSS (from refGene GFF files from UCSC for hg38 and mm39, and ncbiRefSeq for galGal6) were considered promoterproximal, and the remaining peaks were considered distal. For published data with multiple replicates, all summit files were concatenated and then summits within 100 bp were clustered with bedtools cluster, and the most significant summit in each cluster was retained. For ENCODE data, bed narrowPeak files and metadata were downloaded on 1-18-2023 for all GRCh38 and mm10 non-control ATAC-seq (n=549) and DNase-seq (n=1781) experiments. All replicates were processed separately. Samples were annotated into tissue/cell types as follows: facial, limb, or lung if the annotation included that corresponding term; fibroblast if it included "fibroblast" or "HFF-Myc", "BJ", "AG09319", "AG09309", "AG10803", "GM03348", "GM04504", or"NIH3T3"; muscle if it included "muscle" or "gastroc"; neuroblastoma if it included "SK-N" or "BE2C"; and brain if it included "brain", "cereb", "front", "nucleus", "hippo", "occipital", "gyrus", or "ceph".
SLAM-seq and RNA-seq analysis Newly generated sequencing data (read 1 only) were trimmed of adapters and low-quality bases then poly A strings using skewer 116 and processed using slamdunk v0. 4. 3 103 with map options -n 100 -5 0 -qss, using the hg38 analysis set reference genome. Differentially expressed genes were called using DESeq2 121 using only samples pertinent to each comparison, and using CNCC differentiation replicate as a covariate, excluding genes with fewer than 30 reads across datasets in the comparison. Publicly available RNA-seq data were trimmed of adapters and low-quality bases using skewer 116 and mapped using salmon 124 quant --seqBias -l A to hg38_cdna and mm10_cdna pre-built indices (http://refgenomes. databio. org/). Salmon abundance files were summarized to the gene level and imported into R with the tximport 125 package v1.
20. 0 with countsFromAbundance = 'lengthScaledTPM'. When multiple replicates were available, the mean TPM of all replicates was used. Human-mouse orthologs were downloaded from https://www. informatics. jax. org/downloads/reports/index. html#homology and only one-to-one orthologs were kept for analyses of RNA levels across cell types. The list of human TFs and their family definitions were downloaded from http://humantfs. ccbr. utoronto. ca/download. php (Full Database). CCLE processed TPM values were downloaded, and these values for TWIST1 were plotted against the average for all homeodomain TFs with known motifs aligned to the HD portion of Coordinator.
IP-MS analysis For data analysis, the. RAW data files were checked using Preview (Protein Metrics) to verify calibration and quality metrics. Data were processed using Byonic (Protein Metrics) to identify peptides and infer proteins. Proteins were held to a false discovery rate of 1%, using standard approaches described previously 133 (Elias and Gygi, 2007). Known contaminants and any proteins with peptides detected in a control IP with the same V5 antibody on untagged hCNCC protein extracts were excluded.
5 and 0. 5 and adjusted p-value > 0. 1 using the Matching package for R v4. 10-8 with distance. tolerance = 0. 01 and ties = F 136. ATAC-seq datasets on ENCODE. Points are jittered to avoid overplotting. Zoom-in highlights the pluripotent stem cell samples among those with OCT:SOX motif enrichment. C. Rankings of the Ebox/CAGATGG and HD/2 motif clusters in enrichment in the top 10,000 distal accessible regions, for all DNase-seq and ATAC-seq datasets on ENCODE. Points are jittered to avoid overplotting. Purple circles indicates samples with Coordinator motif enrichment (Coordinator rank < 10 and Coordinator rank < E-box and HD ranks). Zoom-in highlights samples lacking Coordinator enrichment despite enrichment of both E-box and HD motifs. D. Top motif clusters enriched in distal ATAC-seq peaks of chick and mouse forelimbs  67.
Acknowledgments We thank  Katherine Xue ,  Raquel Fueyo ,  Christina Jensen ,  Tiffany Chern , and  Liang-Fu Chen  for critical feedback,  Hannah Long  for generating the pGL3-noSV40-humanEC1. 45_min1-2_4xEboxMutant plasmid, and  Seppe Goovaerts  for assistance retrieving facial GWAS data. Mass spectrometry data were.
Materials & Methods Taxon Sampling To infer the number of colonization events in the Hajar Mountains, we assembled a dataset following the most updated reptile taxonomy  (Uetz et al. 2022) , including candidate species in the process of being described, representing a total of 284 squamate species and one outgroup (Table  S1 ). This dataset contained i) all available species from the eight genera with species endemic to the Hajar Mountains; ii) at least one representative of each Squamata family; iii) the only extant species of the order Rhynchocephalia (the tuatara, Sphenodon punctatus) as outgroup; and iv) several key species to set 13 calibration nodes in the phylogenetic tree. To delimit the current diversity of the endemic reptile fauna of the Hajar Mountains, we selected specimens spanning throughout the whole mountain range and including, when possible, individuals of all known genetic lineages of each species  (Carranza and Arnold 2006; Carranza et al.
2016; de Pous et al. 2016; Tamar et al. 2016 Tamar et al. , 2019b Tamar et al. , 2019a;; Garcia-Porta et al. 2017; Simó-Riudalbas et al. 2017 , 2018; Fattahi et al. 2020). We complemented each monospecific dataset with its closest described relative to root the phylogenetic trees. The final dataset included 661 samples of 27 described species (19 endemic species and eight outgroups). All samples were collected between 2006 and 2017, preserved in absolute ethanol, and stored at -20ºC until library preparation (see below "ddRADseq protocol").