dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses of miRNA and mRNA expression data along with miRNA target predictions are well represented for 73 human datasets in miRGator. 20  However, miRGator lacks the focus on tissue repair and regeneration. A database resource that aims to provide a systems-level analysis of tissue regeneration genetic circuits would ideally include these existing resources and provide complementary functionality pertinent to comparative studies of regeneration. In this work, we describe a newly created Comparative Models of Regeneration Database (RegenDbase; https://regenDbase. org), which focuses on understanding the regulatory control of noncoding RNAs on evolutionarily conserved regeneration signaling pathways. Employing the unique functionality and web interface of RegenDbase, we studied newly generated zebrafish heart regeneration miRNA and mRNA expression datasets and made comparisons with similar data from neonatal mouse heart regeneration.
Comparative Models of Regeneration Database The Comparative Models of Regeneration Database (RegenDbase) is a resource designed to identify and compare signaling pathways that underlie tissue regeneration across research models (Fig. 1 ). RegenDbase is an integrated, multi-faceted platform consisting of data analysis pipelines, a relational database, database load software, and web interface software. Represented within RegenDbase are genes, transcripts, and miRNAs from Ensembl  21  and miRBase  22  for zebrafish, axolotl, and mouse. In order to integrate additional studies in the future, we also represent human and C. elegans genes, transcripts and miRNAs. GO  23  functional annotations, pathway assignment from BioSystems,  24  and homology relationships among genes from OrthoDB  25  are also integrated together in the RegenDbase platform (Fig.
1c ). Alternatively, intra-species comparisons may be performed to identify shared genes for regeneration following different injury models. For example, the zebrafish heart ventricular resection injury may be compared to the cryoinjury model. Alternatively, the expression of proliferative genes during blastema formation during regeneration of zebrafish caudal fins could be compared to early stages of zebrafish heart regeneration. Integration of homology data into RegenDbase permits searches of orthologous genes across regeneration datasets. This functionality allows for comparisons across species, such as comparing zebrafish and neonatal mouse heart regeneration (Fig. 1a ).
Integrating a new dataset for zebrafish heart regeneration To better understand the transcriptional changes in gene expression during zebrafish heart regeneration, we profiled miRNA and mRNA expression across a time course of zebrafish heart regeneration, from 0, 1, 3, 7, 14, 21 to 30 dpa. These time points encompass the various cellular processes of heart regeneration that are coordinated to ensure synthesis of new heart tissue and restoration of tissue function. 29  These stages include clotting and endocardial activation (1 dpa), epicardial activation and cardiomyocyte dedifferentiation and proliferation (3 dpa), peak cardiomyocyte proliferation (7 dpa), vascularization and muscle integration with the uninjured heart tissue (14 dpa), electrical coupling and clot dissolution (21 dpa), and functional recovery of tissue function (30 dpa).
Although we did not consider multiple miRNAs in this analysis, the RegenDbase miRNA search function can be used to identify genes co-targeted by two or three miRNAs. Thus, RegenDbase is a powerful tool to elucidate miRNA regulatory networks in regeneration. lncRNA expression during zebrafish heart regeneration lncRNAs are multi-exonic transcripts >200nt without known protein function that control gene expression at the epigenetic, transcription and post-transcriptional levels. 59, 60  Recently, lncRNAs have been shown to control key genetic circuits important for development, disease, and tissue repair. [61] [62] [63] [64] [65] [66]  The identification and extent that lncRNAs control cardiac regeneration circuits, however, remain largely unknown. Employing the RegenDbase functionality for analyses of RNA-sequencing dataset across zebrafish heart regeneration, we identified a subset of previously annotated lncRNAs that were differentially expressed during 1 and 3 dpa of regeneration and the antisense or adjacent proteincoding genes that were also differentially expressed (Supplementary Fig.
S1 ). Differentially expressed lncRNAs were first identified by annotated biotype. As some lncRNAs have been shown to regulate the expression of neighboring protein-coding genes, we used the RegenDbase proximity search features to identify the subset of lncRNAs that have overlapping gene coordinates or that were adjacent to a differentially expressed protein-coding gene. Lnc-si:ch211-169j21. 5 transcript is antisense to arcp5b, encoding a tumor suppressor, and adjacent to the extracellular matrix gene, hemicentin 1 (hmcn1). Expression of hmcn1 is inversely correlated with lnc-si:ch211-169j21. 5 at 1 and 3 dpa (Fig. 4a ). Mouse hmcn1 has been shown to have a role in myocardium remodeling in a mouse model of myocardial infarction. 67  Lnc-si:ch211-260o22. 1 also had an opposite expression pattern to the centrosomal protein 85 like (cep85l) gene.
Identification of novel lncRNAs expressed during early stages of zebrafish heart regeneration We applied an analysis workflow to identify potential novel lncRNAs expressed during heart regeneration that had not been previously annotated (Supplementary Fig. S4a ). After modeling transcripts based on aligned reads using StringTie,  71  we identified a set of 3741 genes not annotated by Ensembl and identified a subset of 2820 putative noncoding genes. A set of 743 putative novel lncRNAs had multiple exons and a length greater than 200 bp and 429 were differentially expressed at early (1 and 3 dpa) or later time points compared to uninjured tissues (Supplementary Fig. S4b , Supplementary Table  S10  and S11 ). Examination of the 119 putative novel lncRNAs unique to 1 dpa and/or 3 dpa revealed 28 that were not highly repetitive and lacked strong protein alignments.
Comparative analysis of zebrafish and neonatal mouse heart regeneration The ability to perform cross-species comparisons is necessary to identify a conserved regulatory circuit for regeneration of specific tissues. In contrast to other regeneration databases, RegenDbase represents orthologous genes, thereby enabling comparisons among organisms. Here, we compared a previously published RNA-Seq dataset from resected neonatal mouse hearts  78  with our own profiling data from zebrafish hearts at comparable time points during regeneration. Neonatal murine hearts have been shown to fully regenerate missing heart muscle in a manner analogous to the adult zebrafish heart. 79  For the neonatal mouse study, we used the expression data from the in vivo study so that we could directly compare regeneration in both models.
DISCUSSION We designed and built RegenDbase to facilitate the comparison of diverse regenerative models with a unique focus on noncoding RNAs, critical regulators of regeneration signaling pathways at multiple levels of gene expression. These comparisons may be performed among models with high regenerative capacity or between models with varying capacity. This resource represents gene expression datasets from multiple model systems relevant to regeneration. Our large RNA-sequencing study of zebrafish heart regeneration presented in this study serves as a foundation for comparative analyses to identify noncoding and protein encoding transcripts. The RegenDbase web user interface facilitates the rapid identification of genes differentially expressed within one experiment and across multiple experiments.
The utility of RegenDbase will grow as more datasets from more models are added. In contrast to miRNAs, lncRNAs are not highly conserved. It is interesting to speculate that a common set of lncRNAs may control common regeneration pathways for multiple tissues, or that different sets of noncoding RNAs are used to modulate regenerative signaling cascades for individual tissues. Further studies of noncoding RNAs in vertebrates with varying regenerative capacities will help define their roles and test the extent to which they do regulate regenerative capacity. Nonetheless, the Fig. 6  Comparative model of lncRNA-regulation of heart regeneration. Known and novel zebrafish lncRNAs adjacent or antisense to proteincoding genes (top) were connected to a set of previously characterized genes required for heart regeneration in the zebrafish (bottom).
However, RegenDbase supports analysis of genes that are not commonly differentially expressed. For example, the regenerative potential of the neonatal mouse heart is reduced after 7 days. Genes that modulate regeneration may be identified through the comparison of genes that change during postnatal development and those associated with regeneration. In a similar fashion, comparisons of wound healing across organisms with high regenerative capacities to those with low capacities may identify genes that modulate these capacities. While this current release of RegenDbase is focused on zebrafish, axolotl, and mouse data, it is noteworthy to mention that we have established pipelines to integrate future datasets from C. elegans, human tissues, and other model organisms to further refine regenerative circuits through comparative analyses.
Additionally, search functionality and more datasets will enhance the system. Studies of inhibited or enhanced regeneration provide valuable insight into genetic mechanisms. Additional data on genomic regulatory features, such as chromatin state and methylation patterns, provide new opportunities to understand these mechanisms. Improvements in single-cell technologies will make it easier to study the contributions of different cell types on regeneration. We also plan to enhance comparative analysis capabilities to facilitate meta-analysis across experiments conducted using different technologies with varying sensitivity and representation of genes. When these experiments are conducted, resources like RegenDbase are primed to integrate, search, and visualize these data to form testable hypotheses for functional studies.
Data collection, relational database, and web interface Published datasets were loaded in the relational database and checked for any errors related to gene annotation. RegenDbase integrates datasets from 15 experiments and uses 24 external data resources that provide lists of genes and annotations of those genes (see https://regendbase. org/data for list). These resources include genes and transcripts from Ensembl (version 86) and miRBase (version 21), orthologs from OrthoDB (version 9), proteins from UniProt and annotations from the GO and NCBI BioSystems for pathways. miRNAs from miRBase and 3p UTRs from Ensembl were used to make miRNA-binding sites predictions. These data were loaded into the relational database where we have implemented a robust data model (Supplementary Fig. S1 ). We have designed the system to update these data on a regular basis to take advantage of the latest annotation of genes and genomes.
Central to the RegenDbase data model are RNA segments that represent transcripts and microarray probes. These RNA segments were used to represent genes, assign expression data, and represent miRNA target predictions. Genes may have one or more RNA segments that were assigned any number of annotations. Representing homologous genes, such as orthologs, requires relating genes and their RNA segments to one another across organisms. For protein-coding genes, we represented groups of orthologous genes from OrthoDB (version 9). For axolotl, we first mapped of microarray probe set target sequences to an axolotl transcriptome assembly 14 using NCBI BLAST blastn to return the first alignment with an expectation value less than 1. 0 × 10 -5. The transcriptome contig that best represented the target sequence was then aligned to the human UniProt protein sequences using NCBI BLAST blastx.
The first UniProt alignment returned by BLAST with an expectation value less than 1. 0 × 10 -5 was used to assign to the microarray probe set to the axolotl gene record based on human UniProt mapping to the OrthoDB homolog group. For orthologous miRNAs, we used miRBase (version 21) mapping of miRNAs to RFAM miRNA families. Gene expression data were represented in the data model as experiments, with samples organized into sample groups. Normalized expression values were assigned to RNA segments for each sample. Precomputed comparisons of RNA segment expression between sample groups from the analysis pipelines were represented as fold change and adjusted p-values. All these data can then be quickly searched using the web interface. RegenDbase was implemented using Java and the PostgreSQL relational database management system (Supplementary Fig.
RNA preparation and sequencing Total RNA samples were extracted from triplicate tissue samples using the Zymo Direct-zol RNA microprep kit (Zymo Research Corp. , Irvine, CA). Each replicate consisted of 4-6 ventricles. Indexed small RNA libraries and strand-specific polyA+ selected mRNA libraries were prepared and pairedend sequenced on an Illumina HiSeq2500 at the HudsonAlpha Institute for Biotechnology following the manufacturer's protocols. Sequence data are available in the Gene Expression Omnibus (GSE106884) and Short Read Archive (SRP124960).
2. 25 (ref. 92  ) and Bowtie 1. 1. 2. 93  Read counts expressed as transcripts per million were analyzed using R/edgeR. All heatmaps were generated using the R/ComplexHeatmap version 1. 0. 0 package. GO enrichment analyses were performed using GOrilla  50  where all genes expressed at each time point were used as background. Zebrafish genes annotated to specific GO terms were obtained using AmiGO. 23  miRNA-binding sites were predicted using miRanda version 3. 3a  94  using annotated 3p UTRs from Ensembl version 79 and mature miRNA sequences from miRGeneDB version 1. 1. Novel lncRNAs were identified by aligning the RNA-Seq reads to the zebrafish genome using HISAT2 version 2. 0. 5,  95  generating gene models using StringTie version 1. 3. 1c,  71  comparing transcripts to Ensembl version 79 annotation using gffcompare.
ACKNOWLEDGEMENTS We thank  Dr. Ken Poss  from  Duke Medical Center  for generously providing us with zebrafish Tg(tcf21:Dsred), Tg(cmlc2:GFP), and Tg(fli1a:GFP) transgenic strains. We thank  Ari Dehn ,  Karlee Markovich , and  Anne Yu  for excellent zebrafish care at  MDI Biological Laboratory. We are grateful to  MaryLynn FitzSimons  in the  Yin  lab and  Dr. William Schott  at the  Jackson Laboratory Flow Cytometry Scientific Services  for their guidance and assistance with cell sorting experiments. This work was supported by  Center of Biomedical Research Excellence (COBRE)  grant  P20 GM104318  and by  Institutional Development Award   (IDeA)  grant  P20 GM103423  from the  National Institute of General Medical Sciences. Additional support was provided to V. P. Y. by a  Scott A. MacKenzie Foundation   Award  ( 17003 ) and an  American Heart Association   Scientist Development Grant  ( 11SDG7210045 ).
Numerical Experiments In this section, we are interested in the numerical performance of the asynchronous parareal method. We utilize Alinea  [41]  to carry out the mathematical operations, which is implemented in C++ for both central processing unit and graphic processing unit devices. Note that it has basic linear algebra operations  [42]  and plenty of linear system solvers  [43] [44] [45] , together with some energy consumption optimization  [46]  and spatial domain decomposition methods  [47]. The parareal algorithm code is compiled with the "-O2" option in GCC 7. 3. 0 environment, which is a well-known compiler system produced by a free-software project. Besides, the experiments are executed on the SGI ICE X clusters connected with Fourteen Data Rate (FDR) InfiniBand network at 56 Gbit/s.
Acknowledgments The first author gratefully acknowledges financial support from the  Technology Foundation STW , the technological branch of the  Netherlands Organization of Scientific Research, NWO , and the  Dutch Ministry of Economic Affairs  under contract  12538 , entitled Interfacial effects in ionized media. The second author thanks  European Research Council  for supporting this research under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  grant agreement  341225. The third author acknowledges the CRCT granted by  Grenoble INP  in 2012 which allowed this joint research. All authors gratefully acknowledge the  Van Gogh Program 2016  under  35530VM. All data used to support this work are reported in the manuscript in the respective tables and figures or as supporting information.
A Rayleigh-Mie-Raman LIDAR provides vertical profiles of tropospheric variables at Clermont-Ferrand (France) since 2008, in order to describe the boundary layer dynamics, tropospheric aerosols, cirrus and water vapor. It is included in the EARLINET network. We performed hardware/software developments in order to upgrade the quality, calibration and improve automation. We present an overview of the system and some examples of measurements and a preliminary geophysical analysis of the data.
ACKNOWLEDGEMENTS This work is dedicated to  Vincent Giraud  , who was involved in LIDAR activities at Clermont-Ferrand, at the beginning. The LIDAR system is operated by  OPGC observatory and LaMP research laboratory. It has been supported by  Clermont-Auvergne ,  CNRS ,  CNES, ROSEA-ORAURE  and now ACTRIS-FR, and regional founds ( FEDER, CPER, Region Auvergne ). We acknowledge the  ICARE  service center for furnishing the BASIC/STRAT figures. We thank all the people involved in CO-PDD instrumented site, and in the LIDAR data processing ( M. Hervo ,  G. Payen ,  D. Dionisi ).
Deep learning shows promise in addressing data processing challenges in ecological and environmental fields. In the last few years, deep learning has been applied to the task of land cover classification from both satellite images  [25] [26] [27]  and crowd-tagged ground-based photography  [28, 29]. Specifically for snow, deep learning has been used in conjunction with support vector machines to classify snow in unlabeled Flickr images with 71. 7% accuracy  [30]  and hand-labeled Flickr images with 80. 5% accuracy  [31]. One advantage of deep learning is that is can accommodate heterogeneous data, allowing for data processing of "found" data that can be analyzed for purposes other than for which it was originally gathered. However, deep learning requires a large labeled dataset on which to train the identification algorithm.
We investigated the use of automated near-surface cameras as "snow detectors", using crowdsourcing to develop a labeled dataset to which we then applied deep learning techniques. Our overarching question was, "Can deep learning be used to accurately detect snow in digital camera images?" The images we used came from the PhenoCam network, which is currently comprised of nearly 500 cameras (predominantly in North America) for vegetation monitoring  [33]. We used crowdsourcing to determine the presence or absence of snow in nearly 185,000 images from 133 camera sites that were available to us at the time. We then trained a deep learning model on the image labels to automate the detection of snow in camera images. We tested the trained model using classic cross-validation methods. Finally, we compared our results with estimates of snow cover derived from satellite remote sensing (MODIS snow fractional cover product) as an example of how these data might be put to practical use.
These sites encompass a wide range of North American ecosystem types, including deciduous broadleaf forest, evergreen coniferous forest, deciduous coniferous forest, mixed forest, savanna, shrubland, grassland, tundra, and wetland. Additionally, a wide range of human uses of the land are represented, including agricultural sites, urban and suburban sites, research sites, and protected natural sites. At each site, we used imagery from the start of collection until December 31, 2014. For each day of collection, we selected the image closest to noon local time to be representative of the snow presence or absence for that day. A total of 184,453 such midday images across all sites comprised the total imagery data set.
A crowd consensus could not be calculated for three of the images (<0. 002% of the total), and these images were manually classified by the authors. We received no information about the participants from CrowdMobile. To assess the accuracy of the crowd consensus, crowd consensus classifications were compared with a 'gold standard' set of classifications. From the total imagery data set, 2013 images (1% of the total) were randomly selected and classified by PhenoCam scientists using the same Knowxel platform employed by the participants. Each image was independently evaluated by three scientists, and images without unanimous consensus among scientists were reviewed and discussed by the authors to reach a definitive gold standard classification (S3 Table ).
Deep learning classification We used a deep convolutional neural network (CNN) to classify the presence or absence of snow in images. For CNN classification, we excluded images from the total dataset with a crowd classification of 'bad image'; a total of 172,927 images remained for CNN classification. We used the Places365-VGG CNN  [37]  to classify each image. The Places dataset and corresponding CNNs result from forefront research on automatic scene classification. Our interest was to apply this technology to a pressing environmental research need, and so we chose the best-performing network according to the analyses performed by computer vision experts  [37]. Places365-VGG is trained on the Places2 database and consists of a standard VGG architecture  [38]  of 16 convolutional layers. Its output is a prediction confidence score for each of 365 scene categories.
As a result, transfer learning is faster and less complicated than training a network from scratch and requires fewer training data. We exchanged the 365 labels built into Places365-VGG for a pair of labels: 'snow' and 'no snow'. We modified the source code for the Places2 CNN to extract the 4096 values of the nodes in the last fully-connected layer and used them as input to a support vector machine (SVM) with the output labels of 'snow' or 'no snow'. We trained SVMs using the software LIB-LINEAR (version 2. 11, http://www. csie. ntu. edu. tw/~cjlin/liblinear [40] ), with the solver set to 'L2-regularized L2-loss support vector classification (primal)' and with cost parameter = 0. 0078 for all models. The cost parameter was chosen using LIBLINEAR's cross-validation method for finding an optimal value.
There is considerable heterogeneity in the imagery from the PhenoCam network in terms of camera model, camera view, amount of vegetation, and camera configuration. Some camera sites are more similar to one another in terms of camera model, angle, orientation, and configuration, however, and it we did not know whether the CNNs would work better for prediction among a more homogenous subset of the full set of camera sites. To investigate this, we created two subsets of the CNN data set: a set that contains only images from Type I sites (82,698 images) and a set that contains images from Type I and Type II sites, but not Type III sites (126,908 images). We repeated the two sets of 10-fold cross validation on these two subsets of data to determine if camera configuration accounts for errors in CNN classification.