dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction To obtain an integrated view of how molecular machinery operates inside cells, biologists are increasingly combining structural data at different length scales, obtained using a range of techniques such as electron tomography, electron microscopy, NMR spectroscopy and X-ray crystallography. Structural data is held in public archives such as the Electron Microscopy Data Bank (EMDB; emdb-empiar. org; Tagari et al. , 2002) , the Electron Microscopy Public Image Archive (EMPIAR; empiar. org; Iudin et al. , 2016) , and the Protein Data Bank (PDB; wwpdb. org;  Bernstein et al. , 1977)  Integration between PDB and EMDB data is based on atomic models in the PDB that have been fitted to or built into EMDB volume maps. For purified biological molecules or larger defined complexes this approach is done routinely.
Sequence information from the models can be used to link to other bioinformatics resources such as the Universal Protein Resource (UniProt; uniprot. org; UniProt Consortium, 2013). However, atomic models are not always available for a variety of reasons, such as when molecular averaging fails to obtain high-resolution features or the inherently lower resolution associated with molecules being imaged in more complex or even cellular environments. In such cases, the identification of features often relies on prior knowledge or correlation of structural data obtained at different scales. Once features have been identified, segmentation (defined here as the decomposition of the 3D volume into regions that can be associated with defined objects) can be employed to facilitate and visualise the interpretation of the map.
, 2016). Another example revealed the lateral clustering of viral membrane proteins mediating membrane fusion  (Maurer et al. , 2013). The archiving of segmentation data in EMDB entries was identified as an area requiring urgent attention in previous workshops on "Data-Management Challenges in 3D Electron Microscopy" in 2011  (Patwardhan et al. , 2012)  and "A 3D Cellular Context for the Macromolecular World" in 2012  (Patwardhan et al. , 2014) , as was the improved biological annotation of structural data to make it more accessible to the wider biological audience and to enable integration with structural and other bioinformatics resources. Crucially for data integration we need "structured biological annotation" which is here defined as the association of data with identifiers (e. g. , accession codes from UniProt) and ontologies taken from well established bioinformatics resources.
(Ontologies are formal collections of statements defining concepts, relationships and constraints; for example, the mitochondrial large and small ribosomal subunits are parts of the mitochondrial ribosome which, in turn, is a part of the mitochondrion). To our knowledge, none of the segmentation formats widely used in electron microscopy and related fields currently support structured biological annotation. Furthermore, spatial transformations relating sub-tomograms to their parent tomograms are not currently captured in EMDB. Moreover, wider usage of both segmentation and transformation data by non-expert users is hindered by a plurality of formats. To discuss and address the challenges of representing and capturing segmentations and transformation data, the Protein Data Bank in Europe (PDBe) organised an expert workshop on "3D Segmentations and Transformations -Building Bridges between Cellular and Molecular Structural Biology" in December 2015.
Data models and file formats for segmentations and transformations Prior to the workshop, PDBe developed a draft data model to support segmentations and their annotations in EMDB that could accommodate segmentation descriptions from a range of existing formats and software packages as well as structured biological annotation. It supported the key features of major segmentation packages such as Amira (www. fei. com/software/ amira), IMOD  (Kremer et al. , 1996)  and Chimera  (Pettersen et al. , 2004) , and provided scope for extension and flexibility as the field developed. However, the draft data model did not cover minor features (e. g. , surface rendering parameters), especially those that are only relevant in the context of a particular software package. The data model was implemented in an XML schema with the following features: It may be useful to allow separation between the metadata (annotations) and the actual segmentations (e.
Structured biological annotation As previously explained, structured biological annotation is the association of data with identifiers and ontologies taken from well-established bioinformatics resources. The use of structured biological annotation is not common practice in the electron microscopy or structural biology communities. Therefore, ontology experts were invited to the workshop to explain why these are useful and what resources and tools are available for assigning annotations. Use-cases such as mouse imaging data helped to explain the principles and practice of structured biological annotation. By the end of the meeting there was a clearer appreciation of the importance of structured biological annotation for searching and linking imaging data across different scales, between different imaging and structural databases and with other bioinformatics resources.
Structured annotation would enable the seamless integration of structural, imaging and bioinformatics data from different resources, thus making it possible to provide problem-centric views of biology that incorporate structural and imaging data and are easily accessible by the broader biological community (and in contrast to the highly specialised structure-centric resources that are available today and mainly serve domain-specific communities). However, there were concerns that many in the electron microscopy community would find navigating the landscape of ontologies challenging and that this approach would only gain traction in the community if tools were developed to simplify the biological annotation process. It was also discussed whether annotation should be performed by the depositor or by EMDB curators.
This tool would allow a user to add structured biological annotation to segmentations obtained from a variety of different software packages and then output an annotated segmentation file in EMDB-SFF that could be deposited to EMDB or EMPIAR. Annotation could either be done during deposition, in which case the biological annotation from the segmentation file could be harvested by the deposition system to facilitate the deposition process, or it could be done post deposition. The workflow would consist of: (i) the user uploading segmentation files (there could be several if the segments have been saved as separate files) and the corresponding map (unless it is already released in EMDB or EMPIAR); (ii) conversion to an EMDB-SFF file; (iii) use of a GUI-based interface to view the segmentations overlaid on the map and to select segmentations and add annotation; (iv) output of a fully annotated EMDB-SFF file that could be uploaded to EMDB (Figure  4 ).
(The SAT mock-up accommodates annotation on only one level of hierarchy: this might be sufficient in many cases, but it could become problematic as more automated segmentation techniques are developed and their usage expands. ) A desktop version of the SAT would help users concerned about the upload of large amounts of data to a web-server. Another option would be to integrate the functionality for structured biological annotation into existing packages such as IMOD, Chimera and Amira; this would also avoid the problem of users having to annotate the segmentations twice. This alternative would require the development of libraries and widgets that facilitate the use of ontologies and the EMDB-SFF by third parties. For example, the program for segmentation in IMOD already has a 'Name Wizard' plugin that helps the user to choose standardized object names from a CSV file: however, additional development would be need to provide access to on-line ontologies.
Funding This work was supported by grants from the  European Research Council   SYNERGY Grant scheme (HELMHOLTZ, ERC Grant  Agreement#  610110 ;  European Research Council, Europe)  and  LABEX WIFI (Laboratory of Excellence   ANR-10-LABEX-24 ) as part of the  French Program 'Investments for the Future'  under reference  ANR-10-IDEX-0001-02 PSL.
ACKNOWLEDGMENTS We would like to thank  A. Guguin  and  A. Henry  of the flow cytometry platform of IMRB, Inserm  U955 , Creteil. We are grateful to  D. Mademtzoglou ,  M. Gervais-Taurel ,  A. Prola ,  D. Hardy , and  M. Borok  for critical reading of the manuscript and  A. Fu  for technical assistance. This work was supported by funding to F. R. from the  Association Francaise contre les Myopathies (AFM)  via  TRANSLAMUSCLE  ( PROJECT 19507 ),  Labex REVIVE  ( ANR-10-LABX-73 ),  Fondation pour la Recherche Medicale (FRM ; grants  FDT20130928236  and  DEQ20130326526 ),  Agence Nationale pour la Recherche (ANR)  grant  Epimuscle  ( ANR 11 BSV2 017 02 ),  Bone-muscle-repair  ( ANR-13-BSV1-0011-02 ),  BMP-myomass  ( ANR-12-BSV1-0038-04 ),  Satnet  ( ANR-15-CE13-0011-01 ),  Crestnetmetabo  ( ANR-15-CE13-0012-02 ), and  RHU CARMMA  ( ANR-15-RHUS-0003 ).
5 mg ml -1 , catalase at 0. 2 mg ml -1 (#C3155-50MG, Sigma), 10% glucose, and 50 mM of β-mercaptoethylamine (MEA, #M9768-5G, Sigma). Coverslips were sealed with duplicating silicone (Twinsil, Rotec). Oligopaint libraries. Oligopaint libraries were constructed from the Oligopaint public database (http://genetics. med. harvard. edu/oligopaints). All libraries consisted of 42-mer sequences discovered by OligoArray2. 1 run with the following settings: -n 30 -l 42 -L 42 -D 1000 -t 80 -T99 -s 70 -x 70 -p 35 -P 80 -m "GGGG;CCCC;TTTTT; AAAAA" -g 44. Oligonucleotides for libraries 1-18 and BX-C were ordered from CustomArray (Bothell, WA). The procedure used to synthesize Oligopaint probes is described below. Chr3R-69 borders oligonucleotides were purchased from MYcroarray (Ann Arbour, MI). Oligopaint probes for this library were synthesized using the same procedure as for the other libraries except for the initial emulsion PCR step.
3D nuclei segmentation from 3D-SIM data. 3D-SIM images were analyzed employing homemade software written in Matlab. In order to identify nuclear shells, nuclei are first segmented by manually selecting rectangular regions of interest (ROIs) of the DAPI signal in the XY-plane and keeping all the Z-planes, and then, a low-pass filter is applied to the DAPI intensities, so that only the largescale information (i. e. , nuclear shape) is kept. For each plane of the 3D ROIs, an intensity threshold is computed, as described by Snell et al. 51  in order to distinguish voxels inside or outside the nucleus. The average intensity threshold calculated from the threshold of the single planes is used to identify the complete nuclear shell. After nuclei segmentation, foci were identified by calculating, for each channel separately, the maximum entropy threshold of the fluorescence intensities in the 3D ROIs.
The microscope was equipped with a motorized stage (MS-2000, ASI) to translate the sample perpendicularly to the optical axis. To ensure the stability of the focus during the acquisition, a homemade autofocus system was built. A 785-nm laser beam (OBIS, LX 785-50, Coherent Inc. ) was expanded twice and directed toward the objective lens by a dichroic mirror (z1064rdc-sp, Chroma). The reflected IR beam was redirected following the same path than the incident beam and guided to a CCD detector (Pixelfly, Cooke) by a polarized beam splitter cube (PBS). Camera, lasers, and filter wheel were controlled with software written in Labview  52. For image acquisition, on average, 30,000 frames (per detection channel in twocolor acquisitions) were recorded at a rate of 50 ms/frame. Continuous excitation and activation were employed for all fluorophores employed in this work with powers as follows: 1 kW cm -2 at 641 nm (for AF647), 0.
8-1. 2 kW cm -2 at 561 nm (for mEos2), and 0-0. 1 kW cm -2 at 405 nm for activation. The intensity of activation was progressively increased throughout the acquisition to ensure a constant amount of simultaneously emitting fluorophores within the labeled structures. These excitation powers were optimized to ensure single-molecule detection, despite the large nuclear density of epigenetic compartments. More technical details and the method used to ensure single-molecule detection were previously described  32, 53. Postprocessing and analysis of two-color dSTORM data. Unless stated otherwise, all homemade software and routines were developed in Matlab. Before further processing, super-resolution image quality was quantitatively assessed by using NanoJ-SQUIRREL  54. Next, single-molecule localizations were obtained by using multiple-target tracing (MTT)  55.
Localization coordinates were further processed using SMLM_2 C, a custom software written in Matlab  32. Fluorescent beads were used to correct for drift and chromatic aberrations. Lateral drift was corrected with 5 ± 3-nm precision, with a method already described elsewhere  52. Chromatic aberration correction was performed using well-established protocols  32, 56. Samples with abnormal drift or lesser precision of drift or chromatic aberration correction were discarded. Clustering of localizations was performed using an algorithm that was previously described in Cattoni et al. 53. Colocalization of single-molecule detections was performed by using a custom implementation of the coordinatebased colocalization (CBC) analysis  57  adapted for whole-cell automated analysis  32. Three additional methods were employed as controls: pixel, Pearson, and Manders correlation.
2e, f ). The parameters of the power law depended on chromatin type (active or repressed) and on cell type (S2, early or late embryo), and are shown in the insets of Fig. 2e , f. After repeating this process for all genomic domain sizes, we obtained the distribution of domain sizes in nm for a specific chromatin type and cell type (Fig. 4f, g ). Clustering of domains of different epigenetic marks was defined as the ratio between the number of clusters of sizes larger than 150 nm obtained from Chip-seq vs. microscopy imaging. Changes in this threshold did not affect our main conclusions. In situ Hi-C data processing and normalization. Hi-C data were processed using an in-house pipeline based on TADbit  61. First, the quality of the reads was checked using the quality_plot() function in TADbit, which is similar to the tests performed by the FastQC program with adaptations for Hi-C data sets.
Next, the reads are mapped following a fragment-based strategy, as implemented in TADbit where each side of the sequenced read was mapped in full length to the reference genome (dm3). After this step, if a read was not uniquely mapped, we assumed that the read was chimeric due to ligation of several DNA fragments. We next searched for ligation sites, discarding those reads in which no ligation site was found. The remaining reads were split as often as ligation sites were found. Individual split read fragments were then mapped independently. Next, we used the TADbitfiltering module to remove noninformative contacts and to create contact matrices. From the resulting contact matrices, low-quality bins (those presenting low contact numbers) were removed, as implemented in TADbit's filter_columns() function.
Acknowledgements We thank  Brian Beliveau ,  Hien Hoang , and  Ting Wu  for their help with oligopaints design. This research was supported by funding from the  European Research Council  under the  7th Framework Program  ( FP7/2010-2015 ,  ERC  grant agreement  260787  to M. N. and FP7/2007-2013, and  ERC  grant agreement  609989  to M. A. M. -R. ). M. A. M. -R. and G. C. acknowledge support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  676556. This work has also benefited from support by the  Labex EpiGenMed , an  «Investments  for the future» program, reference  ANR-10-LABX-12-01 , the  Spanish Ministry of Economy and Competitiveness  ( BFU2013-47736-P  to M. A. M. -R. ), and from " Centro de Excelencia Severo Ochoa 2013-2017 ",  SEV-2012-0208  to the CRG.
Separated raw reads were processed to eliminate MID-tags, primers and low quality regions (Phred quality score threshold of 25) using cutadapt  (Martin, 2011). Cleaned reads have been deposited in the sequence read archive of GenBank (accession numbers: SAMN05933069-SAMN05933092). We used BlastN and BlastX to compare contigs assembled with CAP3  (Huang and Madan, 1999)  and non-assembled reads (minimum length 45 bp) to GenBank sequences  (Altschul et al. , 1990). Whenever query sequences matched a plant-associated virus with an e-value o0. 001, we used the open reading frames Finder NCBI analysis tool (http:// www. ncbi. nlm. nih. gov/gorf/gorf. html) to identify open reading frames that were 420 codons long. For each potential open reading frames, a protein sequence translation was aligned using MUSCLE 3.
Identifying potentially novel viruses It can be challenging to determine which viral reads and contigs represent novel viruses because of the uneven and crop-biased distribution of GenBank accessions available for comparison, and familyspecific differences in how plant virus species are differentiated. We therefore used an approach that combined BlastN and BlastX determination of sequence similarity with detailed expert review of each taxon identified. On the basis of pairwise sequence similarity, related groups of virus-like sequences (operational taxonomic units, OTUs) were tentatively assigned to known plant virus families. We then generated maximum likelihood phylogenetic trees from alignments of OTU protein sequences with homologous GenBank accessions using PhyML 3. 1 implemented in MEGA version 6.
06  (Tamura et al. , 2013)  with a JTT amino acid substitution model and 100 bootstrap replicates to quantify branch support. Each OTU-specific tree was individually evaluated to determine whether the OTU-sequences nested within clades containing sequences from the candidate family or appeared to be distinct. Once the most appropriate family was determined, then the OTU was evaluated to determine whether it represented a known species within that family or a potentially novel one. Traditionally, species identities within plant virus families are determined by pairwise sequence identity; viruses are classified as belonging to the same species if their sequence identity is greater than a specific threshold that is particular to each family. To ensure that our ad hoc OTU classification system aligned with the diverse classification systems of known virus families, viral OTUs were classified as known species when they shared 475% pairwise amino acid sequence identity with recognized species in GenBank-75% is the approximate consensus of such species identity thresholds in these plant virus families.
It must, however, be emphasised that the familylevel partitioning of virus populations within these analyses limited their power to resolve differences in virus diversity between cultivated and uncultivated areas. Further, it is possible that the proportion of identifiable viruses from uncultivated plant species was lower than that from cultivated species due to biases within GenBank. Our inability to identify highly divergent plant viruses may have been particularly pronounced for the large numbers of indigenous plant species sampled at the South African site since, to our knowledge, prior to our study only a single virus species infecting any of Bold text indicates a statistically significant difference with a P-value less than 0. 05. Spatial plant viral metagenomics at the ecosystem scale P Bernardo et al these plants had ever been characterised.
Consistent with this possibility, we detected no viruses at the South African site within indigenous plant species in the families Ebenaceae, Proteaceae and Rhamnaceae. Similar difficulties with the identification of virus-related sequencing reads either from the environment or from uncultivated plant species have been reported elsewhere  (Rosario and Breitbart, 2011; Brum et al. , 2016). For example, up to 70% of sequence reads generated during some environmental viral metagenomic studies have no detectable homology to sequences within public databases  (Rosario and Breitbart, 2011 ). Here we found that 30. 9% of single reads and contigs (Table  1 ) were not obviously related to any previously submitted GenBank sequences. Finally, the most obvious bias in any viral metagenomics study is that viral nucleic acids cannot all be isolated with the same efficiency from all environments or hosts.
Extreme anatomical and physiological variations between different plant species can strongly impact the ease with which viral nucleic acids can be isolated from different hosts which could in turn bias apparent viral prevalence estimates in favour of the hosts from which nucleic acids are easiest to isolate. Similarly, using isolation procedures that we have employed, the genomic sequences of viruses with more labile capsids should have been more difficult to isolate than those of viruses with stable capsids. These current limitations of viral metagenomics studies emphasise the possibility that inherent sampling biases, both during the isolation of virus genomic sequences, and within the databases that are used to identify virus-related sequence reads, are likely to result in the underestimation of viral prevalence and diversity.
Acknowledgements We thank  Mr Paul Loubscher  and colleagues from  Buffelsfontein Game  and  Nature Reserve and to La Tour du Valat. DPM, AV and GWH are supported by the  National Research Foundation of South Africa. PR is supported by  EU  grant  FP7-PEOPLE-2013-IOF  (No. PIOF-GA-2013-622571 ). CMM was supported by  MSU AgBio-Research  and  NSF   DEB-08343140. This work was supported by  Direction Générale de l'Armement ,  MEM INRA Metaprogram , the  Fondation pour la Recherche.
Acknowledgements The technical help of the STnano cleanroom facility is gratefully acknowledged. This work was financially supported by  EC  through the  ERC  project  SUPRAFUNCTION  ( GA-257305 ) and the  Marie Curie ITN  project  iSwitch  (GA No. 642196 ), the  ANR Equipex Union  ( ANR-10-EQPX-52-01 ), the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02 , and the  International Center for Frontier Research in Chemistry (icFRC) , and by the  Slovenian Research Agency , program  P1-0055.
Regulations -VHR Imagery remains highly sensitive information and legislation could continue to evolve impacting the new business models. 3. The digital Earth challenge: one Earth, one meter, one day… or more? 3. 1. High resolution and high revisit: like squaring the circle around the Earth Commercial providers of EO imagery are used to say that resolution or Ground Sampling Distance (GSD) is not the sole criterion for choosing a geospatial data source. Depending on the specific application, revisit time, speed of delivery to the customer, radiometric and spectral quality, geolocation accuracy and acquisition capacity (km 2 / day) are other key performance factors. Nevertheless, both for defence and commercial markets, there is a worldwide and continuous race for sharper images and higher revisit (Fig.