dataesr/scientific-paragraphs-categorization huggingface dataset:
The latest grapevine gene annotations based the 12× V1 models were obtained from  [37]. A separate functional annotation of transcripts was performed using the Mercator pipeline  [38]  to ascribe potential gene function and MapMan BINs  [39]  prior to gene enrichment analysis. MapMan BIN categories were considered significantly enriched (adjusted P-value <0. 05) as determined by Fisher's exact test adjusted with Bonferroni correction for multiple testing correction. Based on the 12× grapevine genome assembly, all grapevine promoter sequences (1 kb upstream of the 5′ UTR) were retrieved from the CRIBI grape genome database (http://genomes. cribi. u nipd. it/grape/) [40]. Target sequences (cis-regulatory elements, CREs) of 63 plant transcription factors (TFs) representing 25 families recently characterized in Arabidopsis thaliana  [41]  and the ones from PLACE  [42]  were retrieved.
[28] , suggesting that small berries are ahead in ripening at these later stages. Differentially expressed genes were clustered into 8 clusters based on their log2 fold change differences in small with respect to large berries at each development stage using the k-means clustering (Fig. 4c ). Functional annotation of DE genes according to high-level MapMan ontology categories  [39]  showed that the largest proportion was involved in RNA regulation and protein metabolism (8-10 %), while transport, cell wall, signaling, and primary and secondary metabolism categories contributed between 5 and 8 % of the total DE genes (Table  1 , Additional file 1: Table  S3 ). A total of 382 (of ~2213) transcripts annotated as potential grapevine transcriptional regulators  [37]  were DE between small and large berries within each sampling date (Additional file 1: Table  S3 ), indicating a difference in transcript regulatory programs between small and large berries.
e. closer in size to extant urodeles with the smallest genome;  Roth et al. , 1994) , which is intuitive because most other vertebrates have smaller genomes  (Gregory, 2004). However, this hypothesis has yet to be tested with paleogenomic data. Some preliminary steps in this direction were recently taken. Organ et al. (2011)  reassessed the relationship between osteocyte lacuna volume and genome size using a dataset of 54 extant tetrapod species, including four urodele and six anuran extant terminal taxa. The most relevant paleontological data in that study were from three Paleozoic taxa, namely the lepospondyls Brachydectes, Cardiocephalus, and Diplocaulus, which have been suggested to be on the amphibian stem (Marjanovi ć and  Laurin, 2009 Laurin, , 2013)) , although others view these as stem-amniotes  (Ruta and Coates, 2007).
We also regressed, for a subset of the data, lacuna estimated volume against bulk index, to assess a possible interaction between both variables. To assess the potential impact of bone identity on our inferences, we performed a sensitivity analysis. For this, we removed all data points of the previously published dataset  (Organ et al. , 2011)  derived from non-long bones in order to minimize the error induced by bone identity. We thus retained only sections from femora, humeri, and to a lesser extent tibia for extant species (anurans were thus absent in this dataset, with a total of 36 extant tetrapod species; the full data set contained 57 taxa). The phylogenetic framework follows  Organ et al. (2011)  with the additional species positioned according to Marjanovi ć and  Laurin (2007 Laurin ( , 2014)).
We used the program BayesTraits V2. 0 (http://www. evolution. rdg. ac. uk) to perform phylogenetic comparative analyses. The Markov Chain Monte Carlo (MCMC) option was used to produce posterior distributions of regression models while estimating phylogenetic signal () of the data, given the tree. We used a binary dummy variable during the regression to separate urodeles from other tetrapods, due to their extremely large genome sizes. This resulted in a multiple linear regression model that had considerably more support than a simple linear regression model, as shown by their respective AIC (see  Organ et al. , 2011  for details on these models). Phylogeneticallyinformed predictions of genome size in Marmorerpeton were made by sampling regression models of genome size and osteocyte lacuna volume for extant taxa (both natural log transformed).
These predictions (retrodictions) are adjusted by reference to the variance-covariance matrix derived from the tree (but scaled by ) and are therefore informed by the extinct species' phylogenetic position (see  Organ et al. , 2007 Organ et al. , , 2011  for additional details). The MCMC settings were 2,001,000 iterations with a burn-in of 100,000 and a sample period of 1000. We used uninformed flat priors (-100. 00 to 100. 00). According to standard statistical protocol, outliers in our lacuna volume dataset were identified, taxon by taxon, in JMP and excluded from analysis. Standard phylogenetic regression assumes uniform rates of evolution across a phylogeny. We therefore tested our osteocyte lacuna data for variable rates of evolution in BayesTraits using the protocol of  Venditti et al.
The results reported below use all bone samples in our database. For our full data set regression model (n = 57) the posterior slope parameters for the urodele group and nonurodele group deviate from 0 (99% and 92% respectively). The mean of the posterior distribution for r 2 = 0. 6. We also find moderate levels of phylogenetic signal (mean = 0. 76) and no evidence for variable rates of evolution in osteocyte lacuna volume (Bayes factor = 1. 4). Interestingly, when variable rates are tested using extant species alone, we find more, but still weak, support for variable rates of evolution (Bayes factor = 4. 2). Phylogenetically-informed predictions of genome size for Marmorerpeton (Fig. 4 ) (by considering it an urodele through the binary dummy variable) suggest that it had a large genome (ln C-value = 3.
Acknowledgments We thank  Susan Evans  ( University College London ) for providing the Marmorerpeton specimens,  Francis Renoult  and  Daniel Robineau  for the specimens from the Comparative Anatomy collections of the  MNHN (Andrias japonicus, Cryptobranchus alleganiensis, Ambystoma mexicanum ),  Hélène Francillon-Vieillot  ( University Paris 7 ) for the Desmognathus specimen, and  Victor Hugo Reynoso Rosales  ( Universidad Nacional Autonoma de México ) for the loan of the Ambystoma andersoni specimen. We thank  Jean-Claude Rage  and two anonymous reviewers for comments that improved this draft.
Acknowledgements We are especially thankful to all the institutions that participated in the sample collection:  UP-MRI and State Vets (D. Cooper, Hluhluwe-iMfolozi Park) ,  Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD ) partners based in Botswana, CIRAD and RP-PCP partners based in Zimbabwe, and the  Fondation Internationale pour la Gestion de la Faune (IGF, France). We would also like to thank  S. Le Bel ,  C. Foggin ,  R. Bengis ,  M. Hofmeyr ,  N. Owen-Smith  and  A. Marchal  for providing supplementary specific information about buffalo demographic parameters. Furthermore, we would like to thank  Pim Van Hooft  ( University of Wageningen, The Netherlands ) for his deep knowledge and support in reconstructing the male haplogroup network. This study was supported by research grants from the  Belgian Fond National pour la Recherche Scientifique (FRS-FNRS)  provided to  J.
We then asked if the Fisher information could predict the sensitivity measured above from the recorded responses. We compared the sensitivity coefficients measured experimentally to the prediction by the Fisher information, for all perturbations (Fig. 5B ). We found that the empirical sensitivity could be well predicted by the Fisher information (Pearson correlation: 0. 82, p = 10 -8 ). Although the Fisher prediction always overestimates the observed sensitivity, a large fraction of this difference can be attributed to the limited sampling of the responses, as can be shown on simulated data (Fig. S2 ).
They are directly evaluated from the responses to repetitions of the reference stimulus, with a small pseudo-count to avoid zero values. To infer the filters F ib,t , we only include perturbations that are small enough to remain within the linear approximation. We first separated the dataset into a training (285 × 16 perturbations) and testing (20 × 16 perturbations) sets. We then defined, for each perturbation shape, a maximum perturbation amplitude above which the linear approximation was considered no longer valid. We selected this threshold by optimizing the model's ability to predict the changes in firing rates in the testing set. Model learning was performed for each cell independently by maximum likelihood with an L 2 smoothness regularization on the shape of the filters, using a pseudo-Newton algorithm.
Ackownledgements. We thank  Stéphane Deny  for his help with the experiments, and  Jean-Pierre Nadal  for stimulating discussions and crucial suggestions. This work was supported by  ANR TRAJEC-TORY ,  ANR OPTIMA , the  French State program Investissements d'Avenir  managed by the  Agence Nationale de la Recherche  [ LIFESENSES :  ANR-10-LABX-65 ], European Comission grant from the  Human Brain  Project n. FP7-604102 , and  National Institutes of Health  grant n. U01NS090501.
To address this challenge, the FAANG Consortium (www. faang. org) developed from the grass roots and set out its goals in a recent white paper  (Andersson et al. 2015). In this publication, the FAANG community provided a vision for a first phase of FAANG, including initial guidance on parallel deep sample and metadata collection from species with high-quality genome assemblies as well as definition of specific data types and infrastructure needed for this initial research. In the 18 months before the GO-FAANG Workshop, the critical organizational and communications infrastructure necessary for accomplishing the first phase of FAANG was built, with over 300 members joining the consortium worldwide. Importantly, membership in the following groups is open to all scientists interested in participating in FAANG and agreeing to established policies for data use (visit www.
faang. org for details). A steering committee was created to develop and implement strategies to advance the FAANG project, involving representatives from many countries, multiple continents, and species of research interest. Four specific activity committees were formed, including Animals, Samples and Assays (ASA); Metadata and Data Sharing (M&DS); Bioinformatics and Data Analysis (B&DA); and Communications (COM). These committees convene frequently by conference call to develop policies and standardize procedures to advance FAANG. Although excellent progress has been and continues to be made in these meetings, the GO-FAANG Workshop was organized in recognition of the need to involve the wider community in these discussions to broaden and strengthen the benefits of consortium activities.
The GO-FAANG Workshop The GO-FAANG Workshop objectives included establishing priorities for research efforts; planning the management structures required for efficient use and sharing of samples, data and computational tools; and identifying resources needed to accomplish these goals. Three plenary talks and presentations by funding agency representatives set the stage for what is possible in functional annotation of genomes. An emphasis was then placed on small group discussions; these were designed to develop policies and approaches to maximize the success of this crucial next phase in animal genomics.
Plenary talks set the stage The workshop included plenary lectures by leaders in the genomics field (John Stamatoyannopoulos, University of Washington; Christine Wells, University of Glasgow; and Paul Flicek, European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI), who described the state of the art in genome function analysis in the human and mouse species. A recurring theme was that the domesticated animal community is well-positioned to exploit the knowledge gained in human and model organism projects through adapting technologies and data analysis pipelines and in comparative analyses. In addition, several FAANG members described current status and future plans of the FAANG community. Reports on pilot FAANG projects in the US and France included the collection and initial analysis of selected tissues as well as development of plans for bioinformatic pipelines to collect, share and analyze data produced in the FAANG project.
g. DNase-seq and ATAC-seq). Furthermore, some tissues can only be collected in amounts sufficient for Since 2015, members of the ASA Committee have collaborated with the M&DS Committee to define minimum requirements and suitable ontologies for organizing sample collections in the EMBL-EBI BioSamples database  (Gostev et al. 2012 ) and refer to the B&DA Committee for defining metrics for data quality assessment (see below). Following GO-FAANG, a strategy was proposed to capitalize on efforts from independent projects or expertise on different assays (see ASA Committee's information on www. faang. org). The expected outcome is reducing redundancy and enhancing synergy through facilitated sharing of common samples for performing complementary assays, using a limited number of easily collected tissues that are the most relevant for the genotype-to-phenotype research projects of FAANG groups (ftp://ftp.
faang. ebi. ac. uk/ftp/protocols). Central to this effort is the registration of all sample/data collections in the BioSamples database. An item emerging from the discussions was that the FAANG community will benefit from developing interactions with partners actively involved in ENCODE and other consortia to efficiently transfer cutting-edge technologies. For example, new or more powerful assays continue to emerge, greatly extending the range of methods both for basic and translational research (e. g. Buenrostro et al. 2015; Jin et al. 2015). Importantly, the interaction of FAANG with other consortia will enhance the value of domesticated animals across different research fields, for example as alternative biomedical models  (Andersson et al. 2015).
Data analysis group The role of the B&DA group is to agree and define standard pipelines for the analysis of FAANG data, with the aim that datasets produced by different laboratories are comparable. The breakout session began by discussing bioinformatics pipelines and whether these should be tightly or loosely defined. The challenge of having very specific, tightly defined software standards is that some collaborators may not be able to meet those standards and some analyses may need to be repeated. However, the results from similar but not identical (loosely defined) pipelines are not comparable and introduce batch effects, which may be indistinguishable from genuine effects. The consensus was that FAANG should adopt tightly defined standards for software pipelines to include software names, versions and all parameters used.
The discussion then moved to data standards and considered standards for both data type (e. g. stranded RNA-Seq) and meta-data. Specifying such standards is essential to support the reuse of the data, but there exists a tension between rigid standards, which ensure all datasets will be comparable, and more relaxed standards that allow existing data to be included in the FAANG collection and continue to benefit from the existing investment made by the funders and investigators to generate this data. A possible compromise was discussed: data (past, present and future) could be allowed to meet different specifications within the standards, allowing older data to be validated and integrated into the collection, with any differences clearly annotated. Only data that had met the same specification of the standard would then be compared, and this would avoid the introduction of batch effects.
This compromise would allow researchers to include previous data generated before the FAANG project began. The discussion also highlighted how standard reference datasets will be critical for evaluating how FAANG pipelines change over time, through demonstration that new methods may improve previous results. Reproducibility of the FAANG pipelines is also essential to ensure that the whole community can benefit from such evaluations. The breakout session discussed the possibility of using virtual machine images or containerization solutions such as Docker (https://www. docker. com/). The advantage of either, for providing FAANG analysis pipelines, is that sharing specific pipelines is much easier, ensuring that the same analysis is done with the same program and version at every institution.
The M&DS group strongly encourages the use of ontologies to describe samples and experiments, but an appropriate balance needs to be struck so that groups working on new or unusual samples or assays can still describe their experiments. There are technological solutions to this problem, and FAANG needs to investigate and implement as appropriate, ensuring the highest quality metadata without overburdening the data collectors. A third topic was how FAANG can use incentives to stimulate appropriate collaborations. One mechanism under development is a generally available list of projects and grant applications that would enable (i) formation of appropriate collaborations with existing work and (ii) identification of gaps in existing work that could be filled. A second potential mechanism would be sharing of personnel between research groups that could advance existing projects in a host lab while simultaneously providing training for personnel to use on return to home research groups.
Implementation of community discussions The discussions at the GO-FAANG meeting clearly demonstrated that the FAANG datasets must be well described. The M&DS committee worked during 2015 to develop FAANG metadata standards; version 1 was released in early 2016 (http://www. faang. org/groups?name=metadata). These standards what metadata should be collected for samples, experiment and analyses run by FAANG and how that metadata should be structured. FAANG recommends all sample collection groups pre-register their samples in the EMBL-EBI BioSamples database  (Gostev et al. 2012 ; https://www. ebi. ac. uk/biosamples/). The BioSamples database allows FAANG to provide structure to their sample records and create sample records for donors, tissues and cell samples. It allows relationships to be recorded so that sampling across pedigrees can be clearly recorded for downstream use.
Another important aspect of well-structured metadata is the use of appropriate ontology terms, ensuring that everyone can have a specific understanding of the animal, tissue and cell type collected by a group or the experiment conducted. FAANG looks to use actively maintained ontologies and aligns its recommendations with other large efforts such as ENCODE. The current ontology recommendations include the Livestock Breed Ontology, the Vertebrate Trait Ontology, UBERON and the Cell Ontology.
In one, the 'WUR-pigENCODE' project, led by Wageningen University (NL) in collaboration with the University of Illinois (US), is adding new functional annotations to previous methylome studies  (Schachtschneider et al. 2015). In another, a complete range of FAANG annotation is being generated in parallel to eQTL and allele specific expression approaches in dairy cattle  (Chamberlain et al. 2015). Two projects recently funded by the Genome Canada Competition -'Genomics and Feeding the Future'are linked to FAANG. These projects focus on feed efficiency in dairy cattle and disease resilience in pigs. The initial activities focus on the collection of FAANG-ready samples (http://www. genome canada. ca/sites/genomecanada/themes/genomecanada/back grounders/BK-2014-competition. pdf). As well, members of the horse genome community were funded to start the development of a database of tissues to investigate gene expression and regulation in the healthy adult horse (http:// www.
Future goals As biology further transitions from a descriptive to a predictive science, collection and interpretation of genomic data across a wider range of species is contributing significantly. Although datasets obtained from easily collected tissues are a start on this long journey, datasets from additional tissues and assays are expected to come on-line as future funding becomes available. Additionally, groups are beginning to implement extensive antemortem and postmortem phenotyping of animals and tissues, including histologic assessment of tissue sections to be used for assays, e. g. to accurately account for the absence of subclinical disease while defining cell populations. This will represent an important step forward in the second phase of FAANG (i. e. studying how environmental, physiological and genetic variation affects regulatory elements;  Andersson et al.
2015). Thus FAANG's data will drive forward predictive biology, particularly in the genomic selection arena for the domesticated species. Further, the resources developed here lower the activation threshold for emerging model organism communities to initiate genome annotation. FAANG should therefore increase understanding of the basic biological principles behind functional conservation and divergence across the different species involved. Such integrative analysis of this data will also result in new analysis techniques as well as updated standards for data re-use. Taken together, the insights gained from this initiative will have positive impacts within biological/genetic research, which will help researchers address the genotype-to-phenotype challenge.
Acknowledgements The organizers of the GO-FAANG Workshop gratefully acknowledge the support of the  National Science Foundation  ( NSF-IOS-1548275 ),  United States Department of Agriculture (USDA-NIFA-AFRI ,  2015-68004-24104 ),  Illumina, Inc. ,  Iowa State University , and funds from the  USDA-NRSP-8 Bioinformatics, Poultry, Sheep and Swine Coordinators. NRSP-8 coordinators also supported travel of individual scientists. The Plant and Animal Genome 2016 FAANG Workshop was supported by  USDA-NIFA-AFRI  ( 2016-67015-24579 ),  Genome Canada ,  Affymetrix, Inc. , and  Geneseek, Inc. UK participation in GO-FAANG was enabled by funding from the  Biotechnology and Biological.
Due to the poor resolution, the size of the paraspeckles was imprecise and could not be measured under conventional fluorescence microscope (red staining). By contrast, dimensions could be precisely measured under STORM (bottom panel of the right part in Figure  2C ). In addition to the precision in the real size of paraspeckle bodies, super-resolution analysis allowed also to show that Neat1 labeling of paraspeckle bodies was not round in shape, being merely elliptical. Indeed using an imaging software (NIS-Elements, Nikon France S. A, Champigny sur Marne, France) we showed that the averaged ratio of Height to Width (n=10) was >1 (1. 53 ± 0. 1) and the mean surface area was 24,960 ± 4831 nm 2 (Figure  2C ).
Rhythmic expression of protein components To determine whether paraspeckle nuclear bodies displayed a circadian expression pattern we looked for rhythmic expression of their protein components as determined by Western blot analysis in nuclear protein extracts. We previously reported that the two proteins NONO and SFPQ followed a rhythmic expression pattern in GH4C1 cells  (Guillaumond et al. , 2011). It holds also true for two other paraspeckle-associated proteins, RBM14 and PSPC1, as reported in Figure  3A. Indeed, RBM14 and PSPC1 proteins displayed a rhythmic pattern in GH4C1 cells over the T2-T30 time period that could be fitted with a non-linear cosinor fit in which the period value (2pi/Frequency) was constrained to the circadian period value 24 hr (equation values given in Figure  3 -source data 1).