dataesr/scientific-paragraphs-categorization huggingface dataset:
Object size thus depends on a ratio between average intensities. These two different approaches (volume versus intensity) probably do not yield similar object size. Benchmarking of the two plugins using fluorescent beads of well-known size and a common plant nuclei dataset will determine the accuracy of object size definition by the two methods and its potential impact on the computed distances/volumes. These customized image analysis software packages and plugins pave the way for analysing thoroughly the spatial positioning of nuclear domains, as demonstrated for chromocenters  [119, 121] , but also for the spatial positioning of loci and transcribed regions (e. g. in combination with 3D DNA and RNA FISH  [88, 94, 96, 123] , or of other nuclear factors of interest. In other words, these approaches offer new ways of analysing the 3D organisation of plant nuclei that deserve a more systemic application in the community to gain insight into the yet poorly described spatial and functional principles.
Intelligent segmentation Whatever the segmentation methods applied, having an accurate and generalizable solution for segmentation is urgently required for automated processing of large datasets, which are key to evidence-based discoveries. ML/DL algorithms could be the key to achieving an 'intelligent' segmentation where best parameters can be automatically adapted on an image-basis. Trainable Weka Segmentation (TWS) and Active Segmentation  [142] , now implemented in Fiji, have recently presented new opportunities for analysing complex datasets  [143]. The tool relies on learning the properties of the objects to be segmented via examples presented to it. The software is then trained, and the user allowed to improve the classification by adding new characteristics or examples. For instance, in Active Segmentation, the image regions presenting the objects of interest are convolved with a series of multiscale filters, which are then used to train the model to classify the objects.
Cell segmentation can be implemented with the help of nuclear segmentation  [144]. ILASTIK  [145]  and Cell classifier  [139]  are user-friendly tools combining ML and DL for interactive 2D and 3D image classification, segmentation and analysis based on fluorescent labelling (allowing the user to separate background, cell membrane, nucleus…). A future solution could involve using pretrained networks coming from computer vision applications and applying additional training with the microscopic images of interest. This could be developed as a community effort of experts, which could either nurture a large pre-classified database of representative objects (i. e. nuclei in different conditions) or provide a sharing mechanism for pretrained models.
They function on the same broad principle of comparing successive time-points to predict the next position and re-adjusting a motorized microscope stage accordingly. However, a tracking solution that was both compatible with a spinning disc confocal system and suitable for sub-nuclear 4D imaging in our dynamic environment was missing at the time of our study. As the microscope's proprietary control software was nonextensible, a new tracking program called TARDIS was written and is described in Supplemental data 1. In brief, the position of the stage was corrected whilst imaging with a 10 min interval by communicating vectors of readjustment calculated based on segmentation on the fly. Examples of time-lapse images acquired with TARDIS are shown in Supplemental Files 1 and 2 (7 h, 10 min time step).
Several problems were posed by the experimental setup. Firstly, growing roots implied a displacement of the nuclei in the imaging fieldan effective tracking method was thus developed for this specific experiment. Secondly, imaging of fresh tissue over several hours necessitated compromising spatial resolution to accommodate repetitive imaging and preserve both tissue integrity and fluorescence levels. As a consequence, the image contrast did not allow existing software to robustly segment closelypositioned nuclei as distinct objects. Thirdly, the diversity of nuclear architecture in growing roots and variability in signal intensities defeated batch image segmentation pipelines where non-adaptive thresholding impaired robust nucleus and chromocenter segmentation. To circumvent this, manual segmentation was opted for, together with manual processing of each dataset and time point.
However, image analysis is not a simple task. This review does not intend to provide an exhaustive assessment of existing possibilities but rather to sketch some affordable solutions. The Bioimage Informatics Search Engine (BISE. http://biii. eu/) is associated with a forum enabling evaluation of an image analysis method for individual case studies. The emergence of automated pipelines for multi-angle image reconstruction  [163]  and machine-learning-based algorithms for reconstructing images at nanoscale resolution  [140]  promise tremendous progress in the coming decades. Moving from supervised learning to 'intelligent' image processing is also highly desirable for speeding up feature recognition and segmentation from complex 3D/4D images. Plant-specific datasets are needed to train the algorithms for plant nucleus-specific feature classification.
Establishing a plant-specific nuclei image repository is therefore necessary and should be fostered by the community and consortium-based funding. Here, we made 3D images and 4D movies available through an OMERO repository (login 'Public', password 'omero') provided by the Florida State University Biological Science Dept. (omero. bio. fsu. edu)  [164]. Allowing more researchers in plant sciences to store published datasets will allow for software/method benchmarking, as has been initiated through BIAFLOWS (https://www. biaflows. neubias. org; login: guest/password: guest). In silico reconstruction of temporal sequences, although inherently only a proxy to reality, allows researchers to focus on large-scale measurements of the plant cell nucleus at high/super resolution. The idea is to probe and image hundreds of nuclei (isolated or in fixed/cleared tissues) from several plant tissue replicates at different time points of (ideally) ultra-controlled treatments.
Acknowledgments This review is the result of a collaborative efforts supported by the  EU COST action INDEPTH  ( CA16112 , www. brookes. ac. uk/indepth/). The authors acknowledge their respective funding agencies and programs:  CB, University of Zürich ,  Swiss National Science Foundation  ( SNSF IZCOZ0_182949 ,  31003A_149974 ), The Velux foundation, The Ricola foundation. CT and SD,  Université Clermont Auvergne ,  CNRS  and  INSERM ,  Noyau-HD  project ( Pack ambition Recherche Région Auvergne Rhône Alpes 2017 ),  Architec  project ( SCUSI-Région Auvergne Rhône Alpes 2017 ) and the  CAP20-25  project;  OMS ,  Vienna Science and Technology Fund  ( WWTF LS13-057 ) and  Austrian Science Fund  ( FWF W1238 ). The authors thank  Aline Probst  for critical reading of the manuscript and the anonymous reviewers for valuable suggestions.
crAssphage populations in the human gut We assessed the origin, evolution and epidemiology of crAssphage-one of the most ubiquitous human gut viruses-to understand the stability of the human gut virome. We previously recovered the crAssphage sequence from more than half of 466 faecal metagenomic datasets  12  and the first member of the expansive crAss-like family to be cultured  13  was recently reported  14. We screened the crAssphage genome for regions that were present in many different datasets-regions in which variable segments were flanked by conserved regions suitable for targeting by PCR primers-and we identified three amplicon regions of approximately 1. 3 kilobases (kb; see Methods). We tested faecal samples of 45 healthy individuals from four cities in two different continents and found that almost half of these volunteers (21 individuals) were crAss-positive, as determined by gel electrophoresis.
We followed six individuals over two months to assess the stability of crAssphage populations (Fig. 1 ). Two individuals were consistently crAss-negative, whereas others displayed more variable dynamics. Notably, DNA sequencing revealed that crAssphage strains from each individual tended to be phylogenetically clustered, although the sequences are not phylogenetically clustered by date (Fig. 1g-i ). This suggests that multiple closely related crAssphage populations may coexist within one individual in whom the abundance-and thus detection-of these populations varies in time. Such ecological dynamics may also explain the fact that Male 1 was intermittently crAss-negative, although his sequences were still clustered in the phylogenetic trees. To confirm the intraindividual evolution that we observed, we recovered 20 different crAssphage genomes from the faecal viromes of 3 adult female twin pairs and their mothers, using the same datasets that we originally used to discover crAssphage  3, 12  , and built a phylogenomic tree.
crAssphage is globally distributed and locally clustered The phylogenies shown in Fig. 1g -i and Supplementary Fig. 1  suggested that individuals have a dominant and stable crAssphage population in their gut microbiome; however, these results might be skewed by PCR amplification or metagenome assembly. Although higher order groups, including species and genera, remain controversial in viral taxonomy and depend on complete genome sequences  15, 16  , here we defined strains as unique sequences (that is, 100% identical)  17. To analyse the number of strains that could co-occur within a single sample, we downloaded 95,552 metagenomic datasets for all environments from the Sequence Read Archive  18. Using a strain-resolved bioinformatics pipeline that was developed for this analysis  19  (see Methods), we extracted the three amplicon regions from 2,216 datasets, most of which contained only a single crAssphage strain (Fig.
2 ). Although 95% of all recovered strains were only found in a single sample, one strain of amplicon C was identified 104 times in different datasets (Supplementary Table  1 ), showing the exceptional ubiquity of some strains around the world. It has been previously suggested that crAssphage is not acquired early in life  20  ; by contrast, our global analysis identified crAssphage in at least 134 infant samples (26 with locality information; Supplementary File 1), confirming recent incidental findings that crAssphage can be found in infants  20, 21. Sixteen metagenomes contain more than 100 strains. Phylogenetic trees containing these sequences showed that-as in Fig. 1  and Supplementary Fig. 1 -strains within a single individual tend to be recently diverged, although different co-occurring clusters could be observed in some cases (Supplementary Fig.
crAssphage has evolved with humans The global distribution of crAssphage led us to investigate whether this virus was present in early humans and whether it evolved with us as we spread out and colonized the planet. Alternatively, and consistent with the view of viruses as rapidly evolving entities, it is possible that crAssphage emerged recently-perhaps through recombination of other viruses -and spread around the world either due to factors that relate to the human host-for example, the global food supply chain or international travel-or through the epidemiology of our intestinal bacteria. To assess the possible ancient association of crAssphage-like phages with the human lineage, we screened the datasets from our global data survey for remote human populations. We found a few crAssphage-like sequences in faecal samples from rural Malawi and from the Amazonas of Venezuela 29 (Supplementary Table  3 ).
Phylogenomic tree of crAssphages from the twin study. To assess the evolution of the intraindividual crAssphage population and its within-family relations, we assembled 20 different near-complete crAssphage genomes from the faecal viromes of three female twin pairs and their mothers 3 using SPAdes v. 3. 11. 0 with its default metagenomics settings  48. Contigs related to crAssphage were identified by querying the contigs against the crAssphage reference genome sequence (RefSeq identifier NC_024711. 1) using BLASTn 32 v. 2. 5. 0+ (E < 0. 001). Next, the bitscore (which is independent of the database size) was summed for each SPAdes contig, and contigs with a total summed bitscore of at least 4,000 were selected. Note that shorter contigs with homology to crAssphage existed in the datasets, but we limited our analysis to the longest contigs with the strongest similarity signal to the crAssphage genome.
A phylogenomic tree (Supplementary Fig. 1 ) was created on the basis of the near-complete crAssphage genomes from the gut viromes of twins. ORFs were identified in all contigs using Prodigal v. 2. 6. 3  49  and were queried against the crAssphage genome using BLASTp 32 v. 2. 5. 0+ (E < 0. 001). Proteins that were missing from more than three genomes were excluded. This resulted in a dataset of 68 proteins that were aligned using Clustal Omega 50 v. 1. 2. 0 with default parameters (crAssphage proteins orf00003, orf00007, orf00009, orf00010, orf00011, orf00012, orf00013, orf00014, orf00015, orf00016, orf00017, orf00018, orf00020, orf00022, orf00023, orf00024, orf00025, orf00026, orf00027, orf00029, orf00031, orf00032, orf00033, orf00035, orf00037, orf00038, orf00040, orf00041, orf00042, orf00044, orf00045, orf00046, orf00047, orf00053, orf00054, orf00055, orf00056, orf00057, orf00059, orf00060, orf00062, orf00063, orf00065, orf00066, orf00067, orf00068, orf00070, orf00071, orf00072, orf00074, orf00075, orf00078, orf00079, orf00080, orf00081, orf00082, orf00084, orf00086, orf00088, orf00091, orf00092, orf00093, orf00094, orf00095, orf00096, orf00097, orf00098 and orf00099).
PCR primer design. PCR primers were designed to facilitate the identification of crAssphage in a diverse range of sampling sites around the world and amplify a variable region of the genome for phylogenetic analysis (Supplementary Tables  4 5 6 ). Several studies designed primers for selected crAssphage proteins  25, 27, 28, 55, 56  but we took a data-driven approach by identifying regions of the crAssphage genome that are suitable for phylogeographical analysis, that is, variable regions that were flanked by conserved regions that might be targeted by the primers. We identified these regions by determining the consensus sequence of the full crAssphage genome in 148 datasets in which at least 10,000 reads were aligned to crAssphage in our previous study  12. We used Bowtie 2 v. 2. 3. 4. 3  57  to map metagenomic sequencing reads against the crAssphage reference genome, and called the consensus using Samtools v.
Strain-resolved metagenomics. The Sequence Read Archive 63 (SRA) contains approximately ten petabases of DNA sequence (10 16 bp), including data from many metagenomes. We developed a pipeline to search the SRA using the Jetstream platform  [64] [65] [66]. Initially, we screened the 95,552 metagenomes identified by PARTIE  67  for the presence of crAssphage by comparing 100,000 reads from each metagenome with the crAssphage reference genome sequence using Bowtie 2  57. Metagenomes that had one or more matching reads in this initial screen were compared with the crAssphage genome to identify any sequencing reads in those metagenome libraries that match crAssphage. All metagenomic sequences were cleaned using our parallel version of Prinseq, called Prinseq++ v. 1. 2 (https://github. com/Adrian-Cantu/PRINSEQ-plusplus)  68, 69.
Sequences were trimmed to ensure that the mean quality score was at least 20, no nucleotides annotated as N were included in the sequences, all sequences were dereplicated, the ends were trimmed on the basis of the quality score cut-off and each read was required to be a minimum 30 nucleotides long. The sequences were mapped and indexed using Bowtie 2  57  to generate a BAM file. Details of the screening procedure  66, 70  are provided at https:// github. com/linsalrob/SearchSRA. A total of 10,260 metagenomes had matches to crAssphage over 1 kb (Supplementary Fig. 7a , Supplementary File 5). The variability of the three amplicon regions is shown in Supplementary Fig. 7b-d. Entire amplicon regions were recovered from 2,216 metagenomes derived from 121 SRA Bioprojects, and those were used as input to Gretel v.
0. 0. 8  19  for probabilistic haplotype recovery. Initially, single-nucleotide polymorphisms were identified from the BAM files using snpper from Gretel-Test (https://github. com/SamStudio8/gretel-test) for each of the three regions used in the PCR. Variants predicted by Gretel were combined into a single FASTA file for downstream analysis  19. As in the global collaboration, we focused only on the crAssphage-positive samples. Owing to persistent inconsistencies in the metadata of metagenomes submitted to SRA, we avoided an extensive search of, for example, all human faecal samples and/or all sewage or wastewater samples. Instead, we identified crAssphage in all metagenomic datasets-regardless of their environmental origin-and we refrained from making statements about the percentage of crAss-positive individuals on the basis of this analysis.
Global phylogenetic trees of three amplicon regions. Using the methods outlined above, we collected sequencing data for each of the three amplicon regions from several different sources (Supplementary Fig. 8 , Supplementary Table  2 , Supplementary File 1). As shown in Supplementary Table  2 , only a subset of the sequences contained locality information and could thus be included in the global phylogeographical analysis. All sequences were then processed through a pipeline that is provided as a Makefile  71  in the GitHub repository (https://github. com/linsalrob/crassphage). The trees can be built using the GNU Make program. After alignment with MUSCLE 72 v. 3. 8. 31 (using a maximum of two iterations and with the -diags option to find diagonals), alignments were trimmed to remove any columns that contained gaps in more than 10% of the sequences using a custom-written Python program, which deleted some of the sequences (Supplementary Fig.
Rural Malawi and Amazonas of Venezuela. To investigate the presence of crAssphage in the faecal microbiota of human populations that were relatively remote from western culture, we used metagenomic sequencing data from samples of people from rural Malawi and the Amazonas of Venezuela  29. The datasets were retrieved from MG-RAST  75  and compared with the crAssphage reference genome sequence using Bowtie 2  57. As shown in Supplementary Table  3 , a few reads mapped from both the Malawi and Venezuelan samples. As these hits did not cover the amplicon regions sampled in our global analysis, the sequences were not included in the global phylogeny.
Mummies. To investigate the presence of crAssphage in the mummified faecal remains of ancient humans, we used metagenomic sequencing data from three pre-Columbian Andean mummies  30  and the 5,300-year-old intestinal content of the Tyrollean glacier mummy, Ötzi  31. For the three pre-Columbian Andean mummies, sequences were downloaded from MG-RAST  75  (MG-RAST project identifier mgp13354; 12 samples, 115,174,154 reads and 11,488,857,080 bp). For Ötzi  31  , sequences were downloaded from the SRA (ENA project identifier ERP012908; 43 samples, 2,797,498,968 reads and 282,547,395,768 bp). The datasets were compared with the crAssphage reference genome sequence using Bowtie 2  57  (nucleotide search) and RAPsearch2 v. 2. 22  76  with an E-value threshold of <10 -5 (protein search). No hits were found.
These apes were also sanctuary animals rescued from Congo and Uganda and have relatively close contact with humans. Metagenomic DNA libraries were constructed using the TruSeq DNA Sample Prep kit (Illumina). All of the sequencing was performed at the Roy J. Carver Biotechnology Center's High-Throughput Sequencing and Genotyping Unit at the University of Illinois, Urbana-Champaign. Metagenomic sequences were assembled and contigs related to crAssphage were identified as described for the crAssphage genomes from the twin study (above). Shorter contigs with homology to crAssphage existed in the primate faecal metagenomic datasets, but we limited our analysis to the longest contigs with the strongest similarity signal to the crAssphage genome (total summed BLASTn bitscore of ≥4,000) to validate the existence of ancient relatives of crAssphage in primate faeces.
Acknowledgements We thank  R. Matthews ,  M. Wright ,  J. Alexander ,  S. Arredondo ,  N. Branch ,  D. Campbell ,  R. Chea ,  D. McDougle ,  J. Parks  and  V. Vipatapat  for providing access to wastewater treatment samples; the members of the  Mountain Gorilla Veterinary Project  and the staff of  Maryland Zoo  for collecting the gorilla faecal samples in Rwanda;  G. Britton  for collecting the baboon faecal samples in Ethiopia; staff of the  CSWCT , the  UWA  and the  UNCST  for collecting the chimpanzee faecal samples in Uganda;  J. Manor  at  Central Virology Laboratory, Chaim Sheba Medical Center, Tel-Hashomer Hospital  and  G. Steward ,  Department of Oceanography, University of Hawai'i at Manoa  for help with sample collection; the  COMPARE and LifeLines-DEEP  projects for sharing data; O.
14. 004 ;F. L. N. by the  NWO Veni  grant  016. Veni. 181. 092 ;S. J. J. B. by the  European Research Council   Stg grant  ( 638707 ) and the  Vidi  grant  864. 11. 005 ;O. C. and  K. Mazankova  by the  Ministry of Health of the Czech Republic  grant numbers  15-31426A  and  15-29078A ; P. C. F. by a  Rutherford Discovery Fellowship  from the  Royal Society of New Zealand. J. J. B. by the  ARC   Discovery Early Career Researcher Award  ( DE170100525 ); S. L. D. M. by an  NIH   Pathway to Independence Fellowship  ( 1K99AI119401-01A1 ); K. B. by award number  1510925  from the  United States National Science Foundation ; M. T. I. by  National Geographic Society (CRE)  and  NSERC ; and C. D. by the  Agence Nationale de la Recherche JCJC  grant  ANR-13-JSV6-0004  and  Investissements d'Avenir Méditerranée Infection   10-IAHU-03.
The LifeLines-DEEP sample collection and analysis was funded by the  Netherlands Heart Foundation  ( IN-CONTROL CVON grant   2012-03 ) to A. Z. and J. F. , by the  Top Institute Food and Nutrition, Wageningen , the  Netherlands  (TiFN  GH001 ) to C. W. , by  NWO Vidi  grants  864. 13. 013  to J. F. and  016. 178. 056  to A. Z. ,  NWO Spinoza Prize   SPI 92-266  to C. W. , and by the  ERC   FP7 /2007-2013/ ERC Advanced Grant  agreement  2012-322698  to C. W. ,  ERC Starting  Grant  715772  to A. Z. A. Z. also holds a  Rosalind Franklin Fellowship  from the  University of Groningen. The COMPARE data collection was funded by The  Novo Nordisk Foundation  ( NNF16OC0021856 ).
Data availability Sequence data that support the findings of this study have been deposited in GenBank under BioProject accession PRJNA510571 and at https://github. com/linsalrob/crAssphage. Each of the samples has a unique BioSample accession number (SAMN10656826-SAMN10658627, SAMN10658653 and SAMN10659294). The SRA runs used in this analysis are included in Supplementary File 5. The data that support the findings of this study are also available from the corresponding authors on reasonable request.
The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind referencefree computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens.