dataesr/scientific-paragraphs-categorization huggingface dataset:
Modal analysis and frequency response of the rock glacier We build a mechanical model on Comsol software based on the finite-element method, in order to numerically compute its resonance frequencies and modal response (see Appendix D for details). The rock glacier is modeled as a 2-D rectangular vibrating structure embedded in the bedrock (or a stable bottom layer). The height H of the structure is fixed at the corresponding depth of bedrock (see Figure  8  for Laurichard and Figure  9  for Gugla). The model is vertically sub-sampled into 2 m thick sublayers, with elastic parameters interpolated from averaged values of seismic tomography results, and with a usual isotropic attenuation factor of 1%  (Bonnefoy-Claudet et al. , 2006). Depending on the direction of the model (longitudinal or transversal), the width of the structure varies in accordance with the whole rock glacier size (several tens of meters), permitting an infinity of vibration modes.
Comparison between observed and modeled resonance frequencies We show the results from the modal analysis on Comsol software for only the observed modes with a vertical component (first mode for Laurichard, the two first modes for Gugla) in Figure  13. Nine mechanical models have been tested, corresponding to different steps of thawing with elastic parameters selected as described in 4. 3. Thus, we present modeled resonance frequencies with respect to the maximal depth of thawing, from the surface to 8 m depth (Figure  13 ). As expected, resonance frequencies of these modes match the frequency band of measurements below 50 Hz and generally decrease with thawing. We compare them to the maximum (in winter) and minimum (in summer) values observed on all sites (depicted in squares in Figure  13 , which are values from Figure  3  for Laurichard and Figure  4  for Gugla).
It consists in evaluating the changes (of frequency or dV/V) after a 50% increase of seismic velocities  ? and  3 for a 0. 5 m thick sublayer along the depth of the modeled rock glacier. All the parameters are those of the summer models (for Gugla in Figure  9 , for Laurichard C00 and C05 in Figure  8 (c)), and kernels have been computed for all these three sites. These results are presented in Fig. 15 : 1) for ambient noise correlation method: we compute the dispersion curves of the Rayleigh waves using the Geopsy package  (Wathelet et al. , 2004). The theoretical relative velocity changes (  ⁄ ) are computed by measuring the difference between the modified dispersion curve with the reference one at each frequency ; 2) for modal analysis method, the resonance frequency of the fundamental mode of the vibrating structure modeling the rock glacier is obtained using Comsol software.
Results HMGA2 is required for pH2A. X deposition at transcription start sites. We have previously reported that HMGA2-mediated transcription requires phosphorylation of the histone variant H2A. X at S139, which in turn is catalyzed by the protein kinase ATM  10. To further dissect this mechanism of transcription initiation, we decided first to determine the effect of Hmga2-knockout (KO) on genome wide levels of pH2A. X. We performed next generation sequencing (NGS) after chromatin immunoprecipitation (ChIPseq; Fig. 1  and Supplementary Fig. 1a ) using pH2A. X-specific antibodies and chromatin isolated from mouse embryonic fibroblasts (MEF) from wild-type (WT = Hmga2 + /+) and Hmga2deficient (Hmga2-KO = Hmga2-/-)  26  embryos. The analysis of these ChIP-seq results using the UCSC Known Genes dataset  27  revealed that pH2A.
To monitor transcription initiation, ChIP-seq was performed using antibodies specific for transcription initiating S5 phosphorylated Pol II (further referred to as pPol II) and chromatin isolated from Hmga2+/+ and Hmga2-/-MEF (Fig. 2  and Supplementary Fig. 1a ). Analysis of the ChIP-seq results using the UCSC Known Genes dataset revealed that pPol II was enriched at TSS in Hmga2-dependent manner (Fig. 2a ). In addition, we observed that pPol II enrichment coincides with pH2A. X peaks at TSS (Fig. 2b ) also in an Hmga2-dependent manner. Visualization of Gata6, Mtor and Igf1 using the UCSC genome browser (Fig. 2c ) and ChIP analysis of their promoters (Fig. 2d , left) confirmed the reduction of pPol II at specific regions close to TSS in Hmga2-/-MEF when compared to Hmga2 + /+ MEF. Furthermore, the reduced pPol II levels after Hmga2-KO correlated with the reduced expression of the analyzed genes as shown by quantitative reversetranscription PCR (qRT-PCR, Fig.
X; asterisks, P-values after two-tailed Mann-Whitney test, ***P ≤ 0. 001. See also Supplementary Figs. 2  and 3. Source data are provided as a Source Data files 01 and 02. the UCSC Known Genes as reference dataset, we found that in fraction 4 the sequencing reads were enriched with TSS in an Hmga2-dependent manner (Fig. 4e ), since this enrichment was abolished after Hmga2-KO. In fraction 3, we did not detect TSS enrichment of the sequencing reads. Our results indicate that the native chromatin in fraction 4 contains mono-and di-nucleosomes (Supplementary Fig. 4b ), which are enriched with TSS, HMGA2, pH2A. X and pPol II in WT MEF. To link the results obtained by MNase-seq and mass spectrometry after fractionation by SGU, we performed ChIP-seq using SUPT16-and SSRP1-specific antibodies and chromatin from Hmga2 + /+ and Hmga2-/-MEF (Fig.
X and H2A. X levels specifically at the promoter of the selected HMGA2 target genes. In addition, doxycyclineinducible expression of WT HMGA2 in Hmga2-/-cells reconstituted the levels of pH2A. X and H2A. X, demonstrating the specificity of the effect observed after Hmga2-KO. However, doxycycline-inducible expression of RΔA HMGA2 in Hmga2-/-MEF did not rescue the effect induced by Hmga2-KO. Although the mutations inducing the loss of the lyase activity did not affect the HMGA2-FACT complex interaction (Supplementary Fig. 6e ), these results show that the lyase activity is required for proper pH2A. X and H2A. X levels at the promoter of the selected HMGA2 target genes. Interestingly, crossing the NONCODE database with our top 15% candidates revealed that 79% of the candidates have annotated noncoding RNAs (ncRNAs) in close proximity (n = 7535), including Gata6, Mtor, Igf1 and Rptor (Fig.
X axis reduced all fibrotic hallmarks in vitro (using primary hLF) and ex vivo (using hPCLS). Interestingly, the FACT complex is a potential marker of aggressive cancers with low survival rates  48  and FACTin is being tested in a clinical trial for cancer treatment (ClinicalTrials. gov Identifier: NCT01905228, NCT02931110). Our work provides the molecular basis for future studies developing therapies against IPF using FACTin.
X (Millipore; 1:1,000 dilution), H2A. X (Abcam; 1:1,000 dilution), H2A (Abcam; 1:1,000 dilution), H2B (Abcam; 1:1,000 dilution) and H3 (Abcam; 1:5,000 dilution) were used (see Supplementary Table  2  for antibody details). Immunoreactive proteins were visualized with the corresponding HRP-conjugated secondary antibodies (Jackson; 1:10,000 dilution) using the WesternBright ECL detection solutions (Biozym). Signals were detected and analyzed with Luminescent Image Analyzer (Las 4000, Fujifilm). Mass spectrometry: sample preparation, methods and data analysis. Proteins were methanol/chloroform precipitated from sucrose gradient fractions and dried pellets reconstituted in 8 M urea  54. Per fraction, 140 µg of protein (according to the 660 nm protein assay, Pierce), were subjected to in-solution digest using protein to enzyme ratios of 1:100 and 1:50 for Lys-C (Wako Chemicals GmbH) and trypsin (Serva), respectively  55.
9 μm, Dr. Maisch GmbH) with a buffer system comprising solvent A (5% acetonitrile, 0. 1% formic acid) and solvent B (80% acetonitrile, 0. 1% formic acid). Relevant instrumentation parameters are extracted using MARMo-SET and included in the supplementary material as Source Data File 03  58. Peptide/ protein group identification and quantitation was performed using the MaxQuant suite of algorithms (v. 1. 6. 5. 0) against the mouse uniprot database (canonical and isoforms; downloaded on 2019/01/23; 86695 entries)  59, 60. For downstream analysis, intensities of fractions were divided by their corresponding inputs and samples that were divided by zero were set to 0. 1. MNase-sequencing and data analysis. For DNA purification from sucrose ultracentrifugation fractions, 200 µl of fractions were resuspended with 200 µl 1× PBS and incubated with 0.
GC skew values were obtained from the R-loop database (http:// rloop. bii. a-star. edu. sg/gb2_database/mm10/bw/mm10. gc_skew. w200. s10. bw). Enrichment of R-loops, GC skew and nascent RNA (GRO-seq) in the Top15% candidate genes were analyzed using deeptools. Mean GC skew scores for the TSS plus 250 bp downstream region were obtained by using deeptools computeMatrix function with a bin size of 250. Genome browser snapshots from GRO-seq data were created with Homer using makeTagLibrary and makeUCSCfile. Available DRIPc plus-strand track file (http://rloop. bii. a-star. edu. sg/gb2_database/mm10/ bw/mm9. To. mm10. GSM2104456_3T3_DRIPc_RNaseA. plus. bw) and GC skew track files were directly loaded into the genome browser. standard curve and related to the employed amount of lung tissue. The hydroxyproline content in lung tissue is given as μg hydroxyproline per mg lung tissue.
Acknowledgements We thank  Roswitha Bender  for technical support. Guillermo Barreto  was funded by the  "Université Paris-Est Créteil" (UPEC, Créteil, France) , the  Max-Planck-Society  ( MPG, Munich, Germany ), the  "Deutsche Forschungsgemeinschaft" (DFG, Bonn, Germany)  ( BA 4036/4-1 ), the  "Centre National de la Recherche Scientifique" (CNRS, France) , " Délégation Centre-Est " ( CNRS-DR6 ) and the  "Lorraine Université" (LU, France) through the initiative "Lorraine Université d'Excellence" (LUE)  and the dispositif " Future Leader". Karla Rubio  was funded by the  "Consejo de Ciencia y Tecnología del Estado de Puebla" (CONCYTEP, Puebla, Mexico)  through the  initiative International Laboratory EPIGEN. Indrabahadur Singh  is funded by the  DFG (Bonn, Germany)  through  Emmy Noether program  ( SI 2620/1-1 ).
Data availability The data that support this study are available from the corresponding author upon reasonable request. In addition, sequencing data of ChIP, RNA and MNase have been deposited in NCBI's Gene Expression Omnibus  67  and is accessible through GEO Series with accession number GSE141272. The mass spectrometry-based interactome data have been deposited into the PRIDE archive and assigned to the project accession PXD016586. In addition, we retrieved and used a number of publicly available datasets to aid analysis of our data:.
Experts from the Chemical community and the OECD AOP Programme provided an overview of AOPs, what they are, how to build them and how to effectively design experiments to support their development. The OECD AOP Programme was also introduced, with examples of AOP application and a real-time démonstration of the AOP knowledgebase (AOP-KB), including the AOP-Wiki (www. aopwiki. org), the main repository and development platform of AOPs. Current subgroups within the Programme and the review process, developed for scientific and technical review of AOPs were discussed including how the radiation com munity could engage with the Programme. The workshop presenters also described initiatives that could be harnessed to develop joint case studies by identifying priority areas for AOP building in the radiation field, and tools that could facilitate the vetting and filtering of data to support decision-making.
What are AOPs? Opening remarks were provided by Dr. Donald Cool, Technical Executive in the radiation safety program at the Electric Power Research Institute (EPRI), and by the Chair of the (EAGMST), Dr. Maurice Whelan. Dr. Whelan described the origin and objectives of the OECD AOP Programme. He highlighted that the AOP approach is a bridge that can facilitate coordination across different scien tific and regulatory domains, in order to break-down silos. He was optimistic for the radiation biologists and Chemical toxicologists to collaborate and enthusiastic to understand the concepts and driving questions in the radiation field that could be supported through the AOP approach. It was explained that Chemical risk assessment is changing from what used to be an observational science heavily dépendent upon animal testing to a knowledge-led approach that inté grâtes data to support pivotai decisions, such as which assays to include, how to quantify a risk, or whether a compound should be progressed or abandoned.
Defining questions and examples of the nature of datasets associated with high, moderate and low confidence for each of the three modified Bradford-Hill modified considérations are provided in the OECD AOP Handbook  (Meek 2017; OECD 2017a OECD , 2017b;; Becker et al. 2015). The modified considérations assist developers in focusing data identification on the most important informa tion to address confidence for application, drawing upon past regulatory expérience. The weight of evidence descrip tions for AOPs increases the transparency and consistency of documentation which can then facilitate application by the regulatory community and contribute to the early identi fication of critical data gaps. Dr. Magdalini Sachana, Policy Analyst from the OECD, described the history and purpose of the OECD Chemical Safety Programme, which is to assist member countries, to protect the environment and human health, save resources, and harmonize tools and instruments through the develop ment of standards such as the OECD test guidelines and guidance documents.
S. Army Corps of Engineers, discussed how 'omics' datasets can inform AOP development. To link AOPs to detailed biological pathways represented in transcriptomics data, a small neighborhood subnetwork of molecular pathways was developed into AOP KEs. This enables one to collapse and expand different areas of an AOP to examine how different types of data support the activation of a pathway. Bayesian Network models can be parameterized using transcriptomic data to estimate the likelihood that a KE is activated. Molecular events with sufficiency to predict the State of a network is another approach to examine activation of KEs. These approaches open many exciting opportunities for the use transcriptomics in examining the presence and activa tion of AOPs.
In so doing, pathways to outcomes can be elucidated separately from the stressors involved. With such a biologically based approach, the knowledge gained in the study of radiological toxicology can be supplemented with the knowledge gained from regulatory Chemical testing, thereby providing a database of KE(s)/KER(s) that are well-supported and valid targets to support low dose risk assessment. The value of AOPs to the radiation community can be also be highlighted for their merits for identifying experimental test gaps, knowledge gaps and for developing a quantitative understanding of the KERs across the pathway to build prédictive toxicology models. Overall, the création of such a repository of know ledge works well through collaboration since it requires the intégration of both public and proprietary knowledge and data that need to be combined with a clear understanding of risk and uncertainty.
2021). The value of the tool was demonstrated using well-developed AOPs to skin sensitization and cleft palate. Dr. Catalina Anghel, Computational Research Scientist with Canadian Nuclear Laboratories, discussed how new machine learning methods and improved computational infrastructure hâve allowed us to process and extract information from complex data sets in ways not previously possible, including in the development of AOPs  (Ciallella and Zhu 2019; Hemmerich and Ecker 2020). Data-driven computational methods are being used in research on AOPs associated with Chemical toxicity. These methods fall into several categories, such as text mining/data mining methods to search and fil ter through hundreds of publications or database entries to uncover relevant associations which could link a Chemical to a toxicity pathway  (Carvaillo et al.
Immédiate next steps could be the for mation of a joint NEA/radiation protection -OECD/AOP advisory committee that would work to: • Identifying strategies for engaging journals, societies and international radiation governing bodies. To better understand the value of the AOP approach to practically support human radiation risk assessment, élé ments of the path forward may involve developing strategies for identifying research gaps across the communities, possibly using the AOP knowledgebase and HLG-LDR database. This may be facilitated by a radiation protection research results database, which could help map existing radiation research into the AOP framework as a basis to facilitate data sharing and the development of collaborative projects through joint funding initiatives.
Cohort This study was registered and approved by the Ethics Committee of Rennes Hospital (notice n°20. 05) and was registered in clinicaltrials. gov (NCT04510350). All participants provided written informed consent. Blood samples were obtained from 11 early MS patients and 8 age and sex-paired healthy controls (HC). MS patients included in this work participated to the OFSEP (Observatoire Francais de la Scleŕose en Plaques) cohort. Participating centers were Rennes, Lille, Nancy, Nimes and Bordeaux. Inclusion criteria were: i) age > 18 years old, ii) MS diagnosis according to McDonald 2017 criteria at the last visit (9), iii) sampled during their first neurological episode, and iv) with at least one visit/year during the follow-up. Progressive MS patients were excluded. At time of PBMC sampling, all MS patients included were drug-naïve and so had never been treated by a disease modifying therapy (DMT).
Data Analysis The experimental design of the study is summarized in Figure  1. After acquisition, intrafile signal drift was normalized and. fcs files were obtained using CyTOF software. To diminish batch effects, all files were normalized on EQ Beads (Fluidigm Sciences) using the premessa R package (https://github. com/ParkerICI/ premessa). Files were then uploaded to the Cytobank cloudbased platform (Cytobank, Inc. ). For all files, live single cells were selected by applying a gate on DNA1 vs. DNA2 followed by a gate on DNA1 vs. Cisplatin, then beads were removed by applying a gate on the beads channel (Ce140Di) vs. DNA1. Each file was first analyzed using viSNE, based upon the Barnes-Hut implementation of t-SNE. The analysis was performed based on the event count of the file or on the maximum total events allowed by Cytobank.
The following parameters were used: perplexity = 30; iterations = 5000; theta = 0. 45. For the lymphoid panel, all 36 channels were selected and for the myeloid panel, all 33 channels were selected. The two panels included CD45, CD3, CD19, CD16, CD36, and HLA-DR, allowing selection of B-, T-, NK-, and myeloid cells. Then, we performed a t-SNE algorithm per lineage, to compare immune profile of MS patients and HC. Equal downsampling was performed, based on the lowest event count in all files. The following parameters were used: perplexity = 30; iterations = 5000; theta = 0. 45. We then applied a clustering method using the CITRUS algorithm. CITRUS proceeds through an unsupervised hierarchical clustering to identify clusters of cellular populations within the overall dataset, and then calculate biologically relevant features of these clustered populations.
We performed the CITRUS algorithm on the previous viSNE results for each lineage, using all panel channels, and the following parameters: association model = SAM (Significance Analysis of Microarrays); mode of cluster characterization = abundance; event sampling = equal; minimum cluster size = 5%; FDR (False Discovery Rate) = 1%. Finally, phenotypic description of identified clusters was performed. Data generated during this study can be shared upon reasonable request.
Patients Analyses were performed on PBMC samples isolated from 11 MS patients and 8 HC. The demographic, clinical, and radiological data of MS patients and HC are detailed in Table  1. Gender and age did not differ between patients and controls [sex (% women): MS = 72. 7, HC = 62. 5, p > 0. 99; age (years ± SD): MS = 31. 4 ± 9. 2,   1, 2 ) and acquired on the CyTOF instrument. The data were normalized and single intact cells were manually selected. Primary analysis was performed to identify lineage cell subsets and secondary analysis was performed to describe phenotypic alterations per lineage between MS and HC (using t-SNE and CITRUS algorithms). Phenotypic description of the identified clusters was performed. HC = 33. 4 ± 9. 5, p = 0. 65]. Mean duration of follow-up was 30. 8 ± 12. 1 months.
g. gene-gene) and inter-omics (e. g. metabolite-gene) relationships. Using large-scale association data of AD -including data from AMP-AD, NIAGADS, and other large studies and consortium efforts -this framework was then transformed into an integrated multi-omics knowledgebase for markers of AD. The resulting comprehensive catalogue of multi-omics relationships, stored using the graph-based database Neo4j, provides disease-relevant information on over 20,000 protein-coding genes, 8,000 proteins and nearly 1,000 metabolites as well as associated genetic variants. Lastly, we have developed a publicly available network-and web-based user interface featuring several data analysis tools (www. adatlas. org) to enable access to these complex data independent of in-house bioinformatics capacities. The AD Atlas allows users to construct, expand and explore context-and tissue-specific molecular subnetworks surrounding either individual or multiple entities of interest.
Data collection and preprocessing Biological relationships between omics layers were downloaded from public databases, such as gene-transcript-protein mappings from Ensembl  [12]  and mappings between single nucleotide polymorphisms (SNPs) and genes from SNiPA  [13]. SNPs, genes, transcripts and proteins were stored in the database as individual nodes and relationships were added as edges between them. Furthermore, metabolites (and their corresponding meta-information) measured by metabolomics platforms were also collected and stored as nodes in the database. Large-scale quantitative data from population-based studies were then used to establish data-driven relationships within (e. g. tissue-specific gene co-expression) and across omics (e. g. expression QTLs, protein QTLs, or metabolite QTLs) layers.
Implementation The integrated multi-omics data is stored as a heterogeneous graph using the graph database management system Neo4j (community v4. 2. 1). To enable easy access to the data, we implemented a network-and web-based user interface to the AD Atlas. This frontend was built as a ShinyApp using R v3. 6. 2 and is deployed using ShinyProxy (v2. 3. 1_amd64. deb). Communication between Neo4j and R is established through the official Neo4j python driver neo4j (https://github. com/neo4j/neo4j-python-driver) and the R-to-python interface package reticulate (v1. 18). Interactive networks are visualized using VisNetwork (v2. 0. 9). Enrichment analysis of the generated subnetworks can be performed using the R package enrichR (v3. 0)  [15] , which enables the analysis of a variety of different gene sets, including drug perturbation signatures and biochemical pathways, via the Enrichr webservice (https://maayanlab.