dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA Preparation and RT-qPCR Total RNA was extracted using TRIZOL R Reagent (Thermo Fisher Scientific; Massachusetts, USA), following the manufacturer's protocol. The quality was evaluated with a Bioanalyzer nano-RNA Kit (Thermo Fisher Scientific; Massachusetts, USA) for the RNA-seq protocols (RNA integrity value over 8. 0) and with standard agarose gels for qPCR analysis. cDNA was synthesized by RNA reversetranscription using the RevertAid Reverse Transcriptase cDNA Synthesis kit (EP0441, Thermo Fisher Scientific; Massachusetts, USA). The primers were designed using NCBI mRNA reference sequence and Primer-Blast (NCBI), taking care to introduce an exon-exon junction. Real-time qPCR was performed in StepOne thermocycler (Thermo Fischer Scientific; Massachusetts, USA) using Power SYBR Green PCR Master Mix (K0221, Thermo Fisher Scientific; Massachusetts, USA).
To obtain the primers for PCR amplification of promoter regions, we selected regions 1,000 upstream and 200 downstream after TSS that corresponded to CpG Islands, selected from the Eukaryotic Promoter Database EPD (http://epd. vital-it. ch/) and the Methyl Primer Express R Version 1. 0 software. Primers were then designed with Primer-Blast, NCBI. All primer sequences are included in Supplementary Table  1. For all PCR amplification products, we used the standard curve method.
RNA-seq Data Processing, Differential Expression, and Clustering After sequencing, raw data were obtained in the fastq format. The quality of the data was evaluated with FastQC (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc). Adapters and low-quality sequences were trimmed or discarded using Trimmomatic  (16). Quality check using FastQC was performed again on the trimmed sequences. Then reads were aligned to the human reference genome GRCh38/hg38 to generate the Binary Alignment Map (BAM) files with STAR (v2. 4. 2a)  (17). Briefly, transcripts discovery was performed using Cufflinks (v2. 2. 1) with the "library-type" argument set to fr-firstrand, and a GTF file obtained from GENCODE ("Comprehensive gene annotation, " vM1) provided as the genomic annotation. GTF files were produced by Cufflinks and combined using Cuffmerge.
Only de novo transcripts with counts >0 in at least one RNA-seq sample were kept for subsequent analyses. These de novo transcripts were combined with the GENCODE GTF file to produce the final genomic annotation used with FeatureCounts (v1. 4. 6-p4) for quantification  (18). The R package, DESeq2 (v1. 6. 3) was used to screen differentially expressed genes and normalization of the count data  (19). Differences were considered statically significant if adjusted p-value was < 0. 05 and log 2 fold change ≥ 2 was further considered, unless otherwise specified. The differentially expressed genes on basal conditions were subjected to k-means clustering using the Euclidean distance method. The expression of genes for each condition within the same cluster similarly responded to stimulation. K-means = 12 was selected because this number represented fairly the different expression profiles at the base level and in response to stimulation.
Functional Enrichment Analysis and Pathways The differentially expressed genes for each condition were loaded in the WEB-based Gene Set Analysis Toolkit (WebGestalt)  (20)  to identify enriched pathways. The top 20 Reactome pathways having adjusted p < 0. 05 were selected. Reactome pathways, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology terms (GO) biological process were obtained from the Database for Annotation, Visualization and Integrated Discovery (DAVID 6. 8, https://david. ncifcrf. gov/) software  (21).
ACKNOWLEDGMENTS We thank the  Transcriptomics and Genomics Marseille-Luminy (TGML)  platform for sequencing the RNA samples. We also thank the mothers and staff of  Hospital José G. Parres  and  Servicios de Salud Morelos  for granting the access to neonatal blood. We thank  Centro Estatal de la Transfusión Sanguínea  for donation of adult leukocyte concentrates.
FUNDING Work in the MS laboratory was supported by  CONACYT  grants  257188  and  1690. Work in SS laboratory was supported by recurrent funding from  INSERM  and  Aix-Marseille University , as well as by grants from the  Ligue Contre le Cancer (Equipe Labellisée   LIGUE 2018 ), the  Agence Nationale pour la Recherche (ANR)  and from the  Excellence Initiative of Aix-Marseille University -A * Midex , a  French Investissements d'Avenir program. This work was also supported by  ECOS/ANUIES/SEP/CONACYT  grant  M17S02. High throughput sequencing was performed at the  TGML Platform , supported by grants from  Inserm ,  GIS IBiSA, Aix-Marseille Université , and  ANR-10-INBS-0009-10.
Quantification of elemental composition, including for SiO 2 , was automatically calculated by the instrument software. Particle size analysis. Particle diameter was analyzed using the ImageJ software. Manual analysis of the particle diameter was found to be more reliable due to the significant overlap between particles. The correct length scale was set for each image. For particles larger than ~200 nm, the line tool was used to measure the diameter of each particle across two orthogonal axes, and then the final diameter was calculated as the average length of the two axes. For particles smaller than ~200 nm, single axes measurements were taken. For each sample, 100 -350 particles were measured, where more particles were measured as the heterogeneity in particle size increased. The measured diameters were binned (bin size 50 nm), normalized by dividing by the total number of particles measured, and then scaled to a 0 -100 scale.
X-ray diffraction (XRD): Atomic structure was characterized by X-ray powder diffraction (X'Pert Pro, PANalytical, Almelo, Netherlands) using a conventional 1. 8 kW sealed X-ray tube source and a Cu target (Kα = 1. 5418 Å). The tool was configured in a symmetric set up. Data was obtained from 5˚ to 70˚ with a 40 mA, 45 kV generator with 2˚ of anti-scatter slit, 10 mm beam mask, and 10 mm of irradiated length of automatic mode, and 0. 04 rad of Soller slit. The sample was prepared on a zero background plate. XRD pattern analysis was performed via HighScore Plus software. First, the background was fit using the "Determine Background" function set to square root and the bending factor and granularity were adjusted until the background line was a good fit for the data (Figure  S5c ). Then a peak search was performed using the default parameters (Figure  S5c ).
A manual inspection of the identified peaks showed wide peaks indicative of broad scattering from an amorphous structure. XRD pattern analysis was performed using the "Search and Match" function using the default parameters and the identified peaks previously defined. The possible matches were manually evaluated for their fit to the experimental data -the best fitting and highest similarity score being for silicon carbon (SiC, score 35) and amorphous silica (SiO 2 , score 22). Krypton and argon adsorption analyses: Krypton adsorption measurements were carried out using a Micromeritics 3Flex surface characterization analyzer (Micromeritics, Atlanta, GA, USA) to assess the specific surface area (m 2 g -1 ) of the samples. Large-scale silica synthesis reactions were carried out to produce 5 -60 mg of dried material (Methods "Silica synthesis").
5 -30 mg of silica samples were degassed under secondary vacuum at 120 °C for 12 hours. The outgassing temperature was chosen based on the thermograms recorded (Figure  S5b ). Kr adsorption was performed at 77K over the range of 0. 06 -0. 20 relative pressure (P/P 0 ) and a Kr cross-sectional area of 0. 210 nm 2. Under these conditions, a minimum total surface area of 0. 04 -0. 05 m 2 is required for accurate measurements, equating to 2. 38 -0. 08 mg of our silica material (measured surface areas of 21 m 2 g -1 to 644 m 2 g - 1 ). [125]  To ensure the reliability of the results obtained, a minimum of 5 mg of each sample was used. Multi-point Brunauer-Emmett-Teller (BET) analysis was used to calculate the specific surface area from the collected Kr adsorption isotherm data (Figure  4d  and 4e; Figure  S5d ).
[126] Argon adsorption was used to determine the pore size distribution (PSD) of the unmodified R5 silica sample. For Ar adsorption, 30 mg of the sample was used and outgassed at 120°C and measured at 87K. The PSD was determined using the SAIEUS 3. 0 software (http://www. nldft. com) with a 2D-NLDFT adsorption model for porous oxides with corrugated cylindrical pores and Ar at 87K. [127]  Enzyme library collation: The available diatom genomes for four diatom species (Thalassiosira pseudonana (taxid: 35128), Phaeodactylum tricornutum (taxid: 2850), Cylindrotheca fusiformis 832073)). Protein coding sequences that had a percent identity greater than 40% over at least 150 residues were selected. A small additional subset of genes were selected that had 30 -40% percent identity over at least 150 residues and had a "positives" score greater than 50%.
The collected data was analyzed using Agilent MassHunter Qualitative Analysis software and chromatograms corresponding to R5 or specific modified R5 variants were extracted (EIC) by merging specified spectral ions into one chromatogram. EIC were manually validated to ensure only peaks containing at least three spectral ions were selected for each variant. The abundance of each R5 population was calculated by integrating for the area under the EIC. The total abundance for each sample was calculated as the summation of all of the detected variants and the percentage of each variant within a sample was calculated from the total abundance. We acknowledge that this approach does not account for differences in ionizability for the different R5 variants (particularly for modifications that affect charge).
However, we felt it was unfeasible to obtain standards for each possible variant given the number and complexity of different modifications explored. MS/MS analysis: Targeted LC-MS/MS fragmentation was carried out on the Agilent 6400 QQQ by using four consecutive charge states specific to R5 or its modified variants as the precursor ion masses, fragmenting the precursor ions, and product m/z were scanned from 100 to 800 Da for in vitro modified R5 samples and 200 to 1100 Da the in vivo samples. The collision energy was adjusted depending on precursor ion masses and tuned to obtain adequate fragmentation while not over fragmenting the ions. The scan time was set to 125, the fragmentor voltage to 135, and the cell accelerator voltage to 7V. Data was manually analyzed (Methods "Probabilistic analysis of modified residues").
Probabilistic analysis of modified residues: Targeted MS/MS fragmentation data (Methods "MS/MS analysis") was manually analyzed using the Agilent MassHunter Qualitative Analysis software. The masses for fragments with phosphorylation on serine, mono-, di-, or trimethylation on lysine, monoor dimethylation on arginine, or acetylation on lysine were manually calculated using the b, y, and internal ion masses for unmodified R5 generated by ProteinPospector (http://prospector. ucsf. edu/prospector/) using the default settings. The abundance of detected fragment masses were recorded. [M + 1H] + , [M + 2H] +2 , and [M + 3H] +3 states were also evaluated for low molecular weight (< 400 Da) fragments, with priority of mass assignment given to the lowest charge state. If a particular mass (± 0. 5 Da) could be assigned to multiple fragments (including fragments that could be mapped to multiple regions within R5 due to its repetitive sequence), the detected abundance was split equally across all possible fragments (Figure  S8 ).
Acknowledgements AKW and CAV were supported by the  Vannevar Bush Faculty Fellowship program  sponsored by the  Basic Research Office of the Assistant Secretary of Defense for Research and Engineering  and funded by the  Institute of Collaborative Biotechnologies (ICB)  through contract number  W911NF-09-0001  with the  U. S. Army Research Office. AKW was supported by a  Ford Foundation  Fellowship, a  Siebel Scholarship , and the  Alfred P. Sloan Foundation's Minority Ph. D. (MPHD) Program. Received: Revised: Published online:.
Similar results were reported by Yao et al. 30  who concluded that read-depth based programs are still immature for WES-based CNV detection with a low sensitivity and an uncertain specificity. Comparable experiences were revealed by participants of the ELIXIR hCNV workshop, and it was concluded that, even if micro-array technologies provide overall better CNV detection parameters, the wide adoption of NGS technologies represents a true challenge for the accurate detection of CNV. Based on these observations, the need for an extensive assessment and benchmarking of existing tools was established as one of the working areas of the hCNV Community. The objective will be to release a set of sensitive and reliable pipelines, optimized and validated to detect CNV from various high throughput datasets.
These pipelines will be available either through ELIXIR compute nodes and/or as stand-alone solutions. Considering the actual performances of available systems, it is anticipated that we will provide a portfolio of tools, each being useful for a specific situation: CNV type; detection technology; disease context. To reach this objective, three tasks are proposed: Task #1. 1 will establish the list of available pipelines/software as well as partners' local solutions to detect CNV from gene panels, WES, low and deep coverage WGS, array CGH, and SNP arrays. Task #1. 2 will benchmark these systems using datasets from Objective #2 to select the most sensitive, specific, reliable, and rapid systems for each dataset for germline and somatic CNVs. Note that if no system is efficient enough for some conditions, the hCNV partners will develop new system(s) to address community needs.
Objective #2: definition of reference datasets The ambition is to produce reference datasets of fully validated somatic and germline CNVs representing a wide range of sample types and experimental technologies. The aim is to provide reference materials available to the community for the comparison and evaluation of pipelines and/or NGS technologies and/or for quality assurance. This will include both digital raw data and, potentially, biomaterials. To do so, the hCNV Community will establish reference datasets including various CNV (deletions and duplications) of various sizes ranging from a single exon CNV to large genomic rearrangements. Two subsets will be defined for germline and somatic CNVs. These datasets will contain samples with fully validated CNVs by other approaches such as multiplex ligation-dependent probe amplification and quantitative or semi-quantitative PCR.
Four tasks are proposed: Task #2. 1 will be dedicated to WES reference datasets. Task #2. 2 will be dedicated to WGS reference datasets. Task #2. 3 will be dedicated to gene panels reference datasets. Task #2. 4 will be dedicated to CGH/SNP microarrays reference datasets. During the strategic workshop in Hinxton, participants discussed the GDPR and its impact on reference human datasets. The participation of lawyers and ethics specialists is therefore needed, and this was proposed to be addressed at a more global level by the ELIXIR Human Data Communities as a whole. In case no human reference dataset could be exchanged at a community level, alternatives using other model organisms have been proposed. As previously mentioned, the NGS technologies are rapidly evolving and therefore the reference datasets will need to be regularly updated.
In the context of tools metrology, i. e. efficiency evaluation and improvement of detection, the Genome in a Bottle initiative has released a set of reference materials which has already been largely used for Short Nucleotide Variation(s) (SNV)  28, 29. In a recent paper, Zook et al. 31  reported the use of a new reference dataset for germline structural variant detection including CNV  32, 33. We will therefore adopt these reference samples and use them with all technologies to produce reference datasets.
Objective #3: data exchange formats International collaborative projects require harmonization and standardization of results in order to ensure efficient data aggregation and comparison. Although various international initiatives, such as the GA4GH Genomic Knowledge Standards and Large Scale Genomics Work Streams, are currently addressing aspects of this issue, no robust and exhaustive standard CNV annotation format has emerged so far. To address this issue, the hCNV community identified two tasks: Task #3. 1 will establish the list of existing formats to describe CNVs and list common and specific features. Task #3. 2 will develop recommendations to use a standardized format to report CNVs. If a few alternative formats are frequently used, it will provide bioinformatics resources to convert data into the common data exchange format.
Objective #4: identification of patients with similar genotypes and phenotypes Finding similar cases at the clinical level is a key component of clinical diagnosis and research to identify disease-causing genes and to explain genotype/phenotype correlations and intermediate clinical phenotypes. Although perhaps straightforward in the case of common diseases, this is much more of a challenge for the millions of patients affected by a rare disease, as routinely each case is similar only to a handful of patients across Europe. Within the Discovery Work Stream of GA4GH there are standards, such as Beacon  34  , to establish a federated data discovery network that is able to connect databases of genomic variations and phenotypic data using a common application programming interface (API). The GA4GH Driver Project "ELIXIR Beacons" provides a schema definition and API, together with a reference implementation to allow data owners to add their resources to the Beacon Network, as simply as possible (https://beacon-project.
io). With the recent addition of CNV representation to the Beacon API and planned ontology-based phenotype queries the Beacon ecosystem represents a prime target for the implementation of (CNV related) genotype-phenotype representation and querying. The hCNV group will work towards enabling its use for the envisioned patient discovery, through the support of extended clinical descriptions including enabling and testing of relevant annotation standards (e. g. HPO, NCIt, additional ontologies). To do so: Task #4. 1 will select ontologies required to efficiently capture phenotypic description useful for data interpretation for any genetic disease. Task #4. 2 will provide lists of common data elements that should be provided in various situations such as rare disease, oncology, or common diseases.
Various ELIXIR hCNV partners are already strongly involved in the development of ontologies, such as HPO  35  and ORDO  36  and in the mapping of various ontologies, medical terminologies, vocabularies and nomenclatures. In addition, national databases described in Objective #6, will not only be FAIRified (see below) but also adopt the recommendations from Objective #4 to ensure cross queries and the identification of similar patients.
Objective #5: creation of innovative tools CNVs often involve large genomic regions encompassing multiple genes. In addition, in recessive diseases, CNVs can alter one allele of a specific locus, whilst the second could be altered by SNV. In many situations, it is therefore difficult to identify the single, or multiple, genes harboring variation that is directly associated with a particular phenotype. The hCNV Community will develop innovative tools to: annotate CNV; facilitate their interpretation through a combinatorial approach; and help to pinpoint key genes in regions of interest. The following tasks have been identified and will most likely evolve as more data become available and as technologies evolve: Task #5. 1 will define CNV annotations including: type; genotype; genes and transcripts; expression level; exons; regulatory elements; breakpoints/ fusion fragments for WGS only (allowing detection of tandem duplications vs.
Objective #6: FAIRification of hCNV services and datasets Various CNV national databases, ELIXIR Core Data Resources, and ELIXIR Deposition Databases are currently being developed by ELIXIR hCNV partners. In order to allow interoperability (including discovery), the FAIR principles (Findable, Accessible, Interoperable, Reusable) 37,38 will be applied to those systems to demonstrate the feasibility and utility of distributed CNV databases. This will respect databases' ownerships and national regulations' compliance while allowing searching for similar patients across the network. To respect and follow these principles, we will ensure that data are: (i) Findable: • The data should contain globally unique, resolvable and persistent identifiers. • Include machine-readable descriptions to support structured search and filtering.
(ii) Accessible: • Metadata has to be accessible beyond the lifetime of the digital resource (e. g. using BioSchemas). • Clearly defined regarding the condition for access and security protocols for sharing and accessing data. (iii) Interoperable: • Usage of a specific file format • Extensible machine interpretable formats for data and metadata (e. g. YAML files, JSON-LD) • Use vocabularies (ontologies) and link with other robust resources • Integration with FAIR resources (iv) Reusable: • Provide licensing, provenance and description on community-standards. Task #6. 1 will use the French BANCCO database (http:// bancco. fr) developed at Aix Marseille University and the CIB-ERER (Spanish network for research in rare diseases) database developed at the Fundación Progreso y Salud of Sevillas prototypes to demonstrate the benefits of using the FAIR data principles for CNV in diagnostic and research contexts.
Data Platform The aims of this Platform have been described as to drive the use, re-use, and value of life science data. It aims to do this by providing users with robust, long-term sustainable data resources within a coordinated, scalable and connected data ecosystem. Thus, the ELIXIR hCNV Community that will collect high quality genetic and phenotypic data will establish strong links with the ELIXIR Data Platform by providing Deposition Databases for CNV; and by benefiting from literature data integration and scalable curation provided by the Platform.
Additionally, Objective #4 will work to extend and harmonize the use of ontologies across the ELIXIR hCNV Community. Both of these objectives align with the "Interoperability with a Purpose at Source" task of the ELIXIR Interoperability Platform Programme for 2019-23. More generally, in Objective #6, interoperability will be a major component of the hCNV Community. CNV FAIR databases will therefore strongly interact with the ELIXIR Interoperability Platform at multiple levels. Training Platform Finally, the global recognition of the ELIXIR hCNV Community's achievements will only be made possible through training, capacity building, and dissemination. A strong link has already been established with this ELIXIR Platform for training coordination and additional collaborations including capacity building will be established as systems, databases and tools are released.
Alignment with other ELIXIR Communities CNVs are genetic mutations found in all organisms. The ELIXIR hCNV Community will naturally strongly interact with the "Federated Human Data" and "Rare Diseases" ELIXIR Communities (Figure  2 ). Moreover, the hCNV community will benefit from the "Federated Human Data" Community thanks to its experience in human data secure access (GDPR) and ethical aspects (ELSI). Going beyond these, interactions could be established with the ELIXIR Marine Metagenomics and Plant Sciences Communities in the longer term (Figure  2 ). In fact, the hCNV  detection tools might allow the handling of CNV in plants and marine organisms. Its feasibility will be discussed with these Communities to allow optimal interactions. In addition, the creation of pipelines and tools will link to the ELIXIR's Galaxy Community through the implementation of tools and training materials in their platform.