dataesr/scientific-paragraphs-categorization huggingface dataset:
Again, LOVE outperformed nine of the 13 other methods, in terms of the F1 score, and had a rank of 5 across the 14 methods (Table  3 ). Overall, LOVE consistently had one of the highest ranks across the two datasets (Table  3 ). The only other method that had comparable performance across these two datasets was hierarchical clustering. However, hierarchical clustering does not support fuzzy clustering, whereas LOVE can generate both overlapping and non-overlapping clusters. This is an inherent strength of LOVE. Moreover, hierarchical clustering is not applicable to some of the other datasets, such as the high-dimensional dataset of vaccineinduced humoral immune responses, used in this study. Hierarchical clustering typically uses L 2 or allied L p norms and clusters based on a distance metric.
For high-dimensional datasets with highly correlated features, these L p norms pose as inherent bias as the distance metric will be skewed toward the trends of the ''larger'' correlation blocks. Thus among all the methods evaluated in his study, LOVE is the only method that works consistently well across datasets of varying sizes (both number of variables and number of features) and correlation structures. This strength is rooted in LOVE's theoretical principles, as our method makes no distributional assumptions regarding the data-generating mechanisms, beyond the latent factor model formulation.
We successfully applied LOVE to three very different systems-scale datasets. Although the nature of the data varies both within each kind of dataset (e. g. , the structure and quality of gene expression measurements vary depending on the platform or technology used to generate it), and across datasets (different datasets had different correlation structures as summarized in Table  1 ), the theoretical guarantees provided by LOVE remain unchanged. Further benchmarking against 13 state-of-the-art methods demonstrated that LOVE is the only approach that has consistently high performance across datasets with varying properties (Table  3 ). This is primarily due to the latent model formulation of LOVE, which does not make any assumptions regarding data-generating mechanisms. Furthermore, the latent factors are not only mathematical constructs but also biologically meaningful and context dependent.
, p}; (ii) For each k = {1,. , K}, there exists at least two indices j = ∈ {1,. , p} such that |A jk | = |A k | = 1; (iii) ∆(C) := min k =k (C kk ∧ C k k -|C kk |) > 0, with a ∧ b := min{a, b}. Our specification (i) rules out the scaling ambiguity between A and Z. It also allows the existence of some variable which is not associated with any group, that is A jk = 0 for all k ∈ {1,. , K}. This is practically meaningful. For example, while analyzing a gene expression dataset, it allows genes with poor/noisy expression measurements to not be associated with any latent factor, where each latent factor could correspond to a unique biological function (as demonstrated in the main manuscript). Specification (ii) requires that, for each group, we have at least two variables that are solely associated with this group.
Gene expression dataset The data has expression values corresponding to p = 16134 genes as columns and n = 114 timepoints as rows. • For LOVE, we select δ from the grid c log p/n with c ∈ {0. 3, 0. 31,. , 0. 49, 0. 50} by crossvalidation. The selected δ cv is 0. 43 log p/n. We then use λ = δ cv as recommended in  Bing et al. (2017). For selecting µ, recall that it controls the sparsity of A which determines the size of each group via (4). We tune µ to have a large number of clusters with size between [50, 1000] and we end up using µ = 1. 91 Ω ∞,1 δ cv. • For FuzzyCmeans, we use the function cmeans in the R-package e1071  (Bezdek, 1981). Recall that X ∈ R 114×16134 where the rows correspond to independent observations. Therefore, FuzzyCmeans is designed to cluster rows instead of columns. This suggests that it is meaningless to naively apply FuzzyCmeans to X T to cluster 16134 genes.
Moreover, the number • For F uzzyCmeans, we use the true group number K = 3 as input and assign each primate based on the largest weight of the membership matrix to obtain non-overlapping groups. Since the fuzziness parameter controls the level of overlap in F uzzyCmeans, we manually tune it from the grid of {1. 1, 1. 2,. , 2. 9, 3} based on the best performance in terms of the confusion matrix. We end up with a fuzziness parameter of 1. 5. • For ClusterOne, since the pairwise correlation is rather weak in this dataset, ClusterOne tends not to assign many subjects into any group. To obtain the upper bound of performance for ClusterOne, we used effect size and significance thresholds that allowed most primates to be assigned to at least one group. We used an FDR cutoff of 0. 2 as in the earlier section.
For a given set of genes, expression heatmaps using log fold-changes, log transcripts per million (TPM), or Z-scores of these measures are produced using ComplexHeatmap  (Gu et al. 2016) , where transcript clusters are identified for a given number of clusters using the K-means algorithm, and rows are ordered within each cluster according to hierarchical clustering (Euclidean distance, complete linkage). Samples may also be hierarchically clustered. A high-resolution heatmap may be resized and downloaded by the user.
Regeneration Rosetta yields insight into cholesterol and lipid biosynthesis regulation during regeneration Cholesterol biosynthesis pathways were found to be enriched during regeneration in Dhara et al. (2019) and have been previously shown to be important in axon regeneration in mouse  (Wang et al. 2018). To more deeply investigate their behavior during optic nerve regeneration in zebrafish and demonstrate some of the capabilities of the Regeneration Rosetta, we input a list of 125 genes with cholesterol-related GO terms obtained from the Mouse Genome Informatics (MGI) database  (Smith et al. 2018 ) (Table  S1 ), corresponding to 232 expressed zebrafish transcripts, into the Regeneration Rosetta app (Fig. 1 ). Of these 232 transcripts, 62 were differentially expressed (DE) in  Dhara et al.
To illustrate, we compared CNS regeneration in lamprey and zebrafish. Following  (Kaprielian et al. 2000; Herman et al. 2018) , we used the regenerating lamprey transcriptional profiles from cell bodies located in the brain and spinal cord following spinal cord injury over a course of 12 weeks. This study identified 3,664 and 3,998 differentially expressed regeneration-associated genes at one or more post-injury time points in lamprey brain and spinal cord, respectively. After removing duplicates, we filtered these lists to 2,325 (brain) and 2,519 (spinal cord) differentially expressed genes. We looked for an overlap of genes that were differentially regulated after injury to the zebrafish optic nerve and the lamprey brain and spinal cord. We found 3,712 transcripts (corresponding to 2,150 genes) and 4,076 transcripts (corresponding to 2,404 genes) in the zebrafish optic regeneration data corresponding to the lamprey brain and spinal cord, respectively.
The Regeneration Rosetta revealed 32 (brain) and 53 (spinal cord) DE transcription factor encoding genes that were differential in the zebrafish optic nerve regeneration data after subjecting to FDR 5%, Wald tests vs 0dpi. Assessing the combined list of DE transcription factors, we found that 25 transcripts corresponding to 21 transcription factor encoding genes are shared among CNS neuron responses to axonal injury in the lamprey spinal cord and brain, and zebrafish optic nerve (Fig. 3 ). Thus, the Regeneration Rosetta highlights potential regulatory factors driving regeneration-associated gene expression among regeneration capable organisms. Approximately 10-20% of regeneration-associated genes are shared between neurons regenerating axons in brain, spinal cord and optic nerve, including a core set of 21 regeneration-associated transcription factor encoding genes that are homologous to human genes (listed in the middle with HGNC gene symbol).
analyzed, and queryable data from a unique study of regeneration-associated gene expression and chromatin accessibility during optic nerve regeneration in Dhara et al. (2019). The app was a crucial component for generating and interpreting results in Dhara et al. (2019) , as it facilitated a deep interrogation of the data that would have otherwise only been possible with extensive bioinformatic expertise. In addition, we have illustrated the broad utility of the Regeneration Rosetta app through examples focusing on time-dependent regulation during regeneration for specific biological processes of interest and regenerative mechanisms that are evolutionarily conserved across species and tissue types. All of the source code for running our analyses and implementing the app is posted on GitHub; in this way researchers can modify the code for their own applications or run a local version of the app if desired.
Acknowledgments We thank the  UWM High Performance Computing (HPC) Service  for computing resources used in this work. We are grateful to  Maria Replogle  for comments on the manuscript. We wish to thank the  University of Wisconsin Biotechnology Center DNA Sequencing Facility, Madison  for providing RNA and ATAC sample sequencing facilities. We gratefully acknowledge support from the  Research Growth Initiative Grant -RGI  (to P. L. A. and A. J. U. ),  University of Wisconsin-Milwaukee. A. R. was supported by the  AgreenSkills+ fellowship program , which received funding from the  EU 's  Seventh Framework Program  under grant agreement number  FP7-609398  (AgreenSkills+ contract). S. P. D. was supported by the  Clifford Mortimer Distinguished Scholar Award ,  University of Wisconsin-Milwaukee.
Shinyapp is available at http://ls-shiny-prod. uwm. edu/rosetta/. A FAQ page is available directly on the app website. Source code for the Regeneration Rosetta is available from GitHub: https://github. com/andreamrau/rosetta. The processed data used within the app are directly located in https://github. com/andreamrau/rosetta/tree/master/data; scripts used to process the raw data from Dhara et al. (2019) may be found at https://github. com/andreamrau/OpticRegen_2019. Archived source code at the time of publication can be found at https://doi. org/10. 5281/zenodo. 2658771. The Regeneration Rosetta app was developed under a GPL-3 software license. data. table and RSQLite R packages for fast data manipulation, DT for rendering HTML tables using JavaScript, readxl for parsing data from Excel spreadsheets, dplyr for data manipulation, and tokenizers to convert user-provided gene IDs into tokens.
The carbon budget and related fluxes are computed for each vegetated tile within a grid cell. In ORCHIDEE, the carbon in the vegetation and soil is split in 13 classes based on the concept of plant functional type (PFT). Different species, which share similar characteristics regarding their architecture, phenology or location, are gathered in a single PFT. The 13 PFTs used in ORCHIDEE are listed in Table  1. Compared to the model version that was used as the basis for OCN, a large portion of the code has been revised and new developments added  (Peylin et al. , 2019). The main changes which are not directly related to the computation of the GPP consist of the following. 1. A multi-layer soil hydrological scheme which accounts for water diffusion and deep drainage based on the initial work of de Rosnay (  2002 ) that was shown to better simulate soil water dynamics compared to the initial double-bucket scheme as described in  Ducoudré et al.
FLUXNET GPP product The free fair-use FLUXNET La Thuile collection (http:// fluxnet. fluxdata. org/data/la-thuile-dataset/, last access: August 2013) was used to evaluate the model performance at individual sites. We selected the observation-derived GPP flux based on the net ecosystem exchange (NEE) partitioning method of  Reichstein et al. (2005). From the 153 sites contained in the collection, we selected sites with vegetation belonging to a single PFT and thus excluded sites covered by a mixture of vegetation (such as savannahs or opened forests). This is to avoid having to set fractions of PFT that are usually uncertain, which would introduce substantial uncertainty in the evaluation process. Furthermore, sites were excluded if their vegetation cover was not explicitly represented in ORCHIDEE, such as shrublands or wetlands.
In addition, because the nitrogen fertiliser inputs are not reported in the FLUXNET database for managed agroecosystems such as grasslands and croplands, which are fertilised with manure or synthetic forms, they were also excluded from the analyses. These two filtering criteria reduced the validation set to 86 sites. Last, we removed eight sites for which the annual mean precipitation derived from in situ measurements was highly different of the climatological mean provided by the site principal investigator and of the value derived from the ERA-Interim reanalysis (see  Vuichard and Papale, 2015). The selected 78 sites (Table  A1 ) were distributed across the following vegetation classes: 2 tropical evergreen broadleaved forest sites (TrEBF), 29 temperate evergreen needleleaf forests (TeENF), 7 temperate evergreen broadleaved forests (TeEBF), 21 temperate deciduous broadleaved forests (TeDBF), 8 boreal evergreen needleleaf forests (BoENF) and 11 C 3 natural grasslands (GraC3).
Meteorological data In situ meteorology, which is typically observed at individual FLUXNET stations, was used to drive the ORCHIDEE simulations at the FLUXNET stations. In situ data were gapfilled as described in  Vuichard and Papale (2015)  in order to provide continuous half-hourly records of temperature, precipitation, shortwave and longwave incoming radiation, wind speed, and specific humidity to the ORCHIDEE model. The mean length of the meteorological data was 5 years and ranged between 1 and 16 years. Global-scale simulations were driven by CRU-NCEP meteorological data, available for the 1901-2016 period. CRU-NCEP consists of 6-hourly meteorological fields from the NCEP/NCAR reanalysis at 0. 5 × 0. 5 degree resolution that was bias-corrected with monthly CRU data.
Vegetation-related information For the site simulations at FLUXNET stations, the selection of the plant functional type used to characterise vegetation at each site was based on in situ information gathered within the FLUXNET dataset using the International Geosphere-Biosphere Programme (IGBP) classification. For the globalscale simulations, the PFT distribution within each grid cell over the time period 1860-2016 was derived from the History Database of the Global Environment (HYDE v3. 2;  Goldewijk et al. , 2017)  for the crop and pasture extent and from  Olson et al. (1985)  for the specification of the forest types.
Soil data The soil-related data used to drive ORCHIDEE are texture class, pH and bulk density at 0. 5 × 0. 5 degree resolution. Texture class is from  Zobler (1986) , bulk density from the Harmonized World Soil Database (HWSD; FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) and soil pH from the International Geosphere-Biosphere Programme Data Information System  (Global Soil Data Task Group, 2000). These different global datasets were used for both site-level and global simulations.
Reference simulations From the end of the transient phase at FLUXNET stations, a set of simulations (one at each FLUXNET station) was performed over the observation period with [CO 2 ] and nitrogen deposition level (from the CCMI monthly dataset) corresponding to this period. These simulations for present-day conditions (pd), in which carbon and nitrogen cycles are fully coupled (CNdyn configuration), are named pd-CNdyn (Table 2). Likewise, from the global steady state corresponding to the end of the spin-up simulation, a transient simulation from 1861 to 2016 is run, accounting for climate change, [CO 2 ] rise, land-use change (LUC), and the evolution of nitrogen atmospheric deposition (Ndep), nitrogen synthetic fertiliser (Nfert) and manure (Nman) on a yearly basis. CRU-NCEP data for the period 1901-1920 were used to simulate the period from 1861 to 1901, while from 1901 onwards the climatic forcing from the matching year was used.
Discussion We presented revision 4999 of the ORCHIDEE model that accounts for carbon-nitrogen interactions based on the initial work of  Zaehle and Friend (2010). In this model version, improvements were implemented with respect to the water, energy and carbon budgets, as well as the nitrogen cycle and its coupling to the carbon cycle, compared to the original version of ORCHIDEE used in  Zaehle and Friend (2010). A relative time axis was used because the simulations started the year following the last observational year in the FLUXNET database, which may differ across sites. Among these changes, a new carbon assimilation scheme was introduced (Yin and Struik, 2009) in which the maximum Rubisco-activity-limited carboxylation rate is a direct function of the leaf nitrogen content  (Kattge et al.
Acknowledgements. NV acknowledges  Sophie Szopa  for fruitful discussion and support. This work benefited from the  HPC  resources made available by  GENCI  ( Grand Equipement National de Calcul Intensif ),  CEA. This work used eddy covariance data acquired by the  FLUXNET community. Financial support. This research has been supported by the  EU   FP7 ERACLIM-2 project  (grant no. 607029 ) and the  EU   H20202   CRESCENDO  project (grant no. 641816 ). Review statement. This paper was edited by  Gerd A. Folberth  and reviewed by three anonymous referees.
EnteroBase is an integrated software environment that supports the identification of global population structures within several bacterial genera that include pathogens. Here, we provide an overview of how EnteroBase works, what it can do, and its future prospects. EnteroBase has currently assembled more than 300,000 genomes from Illumina short reads from Salmonella, Escherichia, Yersinia, Clostridioides, Helicobacter, Vibrio, and Moraxella and genotyped those assemblies by core genome multilocus sequence typing (cgMLST). Hierarchical clustering of cgMLST sequence types allows mapping a new bacterial strain to predefined population structures at multiple levels of resolution within a few hours after uploading its short reads. Case Study 1 illustrates this process for local transmissions of Salmonella enterica serovar Agama between neighboring social groups of badgers and humans.
However, until very recently  (Sanaa et al. 2019) , relatively few draft genomic assemblies were publicly available, and even the current comparative genomic analyses in NCBI Pathogen Detection (https://www. ncbi. nlm. nih. gov/pathogens/) are restricted to relatively closely related genetic clusters. Since 2014, EnteroBase (https://enterobase. warwick. ac. uk) has attempted to address this gap for selected genera that include bacterial pathogens (Table  1 ). EnteroBase provides an integrated software platform (Fig. 1 ) that can be used by microbiologists with limited bioinformatic skills to upload short reads, assemble and genotype genomes, and immediately investigate their genomic relation-ships to all natural populations within those genera. These aspects have been illustrated by recent publications providing overviews of the population structures of Salmonella  (Alikhan et al.
Overview of EnteroBase The EnteroBase back end consists of multiple, cascading automated pipelines (Supplemental Fig. S1 ) that implement the multiple functions that it provides (Supplemental Fig. S2A ). Many of these EnteroBase pipelines are also available within EToKi (EnteroBase ToolKit) (Supplemental Code), a publicly available repository (https://github. com/zheminzhou/EToKi) of useful modules (Supplemental Fig. S2B-E ) that facilitate genomic assemblies (EToKi modules prepare and assemble), MLST (MLSType), calling nonrepetitive SNPs against a reference genome (EToKi modules align and phylo), or predicting serotypes of Escherichia coli from genome assemblies (EBEis). EnteroBase performs daily scans of SRA via its Entrez APIs  (Clark et al. 2016)  for novel Illumina short-read sequences for each of the bacterial genera that it supports.
It uploads the new reads and assembles them (EBAssembly [Supplemental Fig. S2B] ) into annotated draft genomes, which are published if they pass quality control (Supplemental Table  S1 ). EnteroBase fetches the metadata associated with the records and attempts to transcribe it automatically into EnteroBase metadata format (Supplemental Table  S2 ; Supplemental Fig. S3 ). During the conversion, geographic metadata are translated into structured format using the Nominatim engine offered by OpenStreetMap (OpenStreetMap contributors 2017; Planet dump retrieved from https://planet. osm. org) and the host/source metadata are assigned to predefined categories (Supplemental Table  S4 ). Until recently, metadata was parsed using a pretrained Native Bayesian classifier implemented in the Natural Language Toolkit (NLTK) for Python  (Bird et al.
2019 )-which evaluate genomic sequences. Escherichia serotypes are predicted from the genome assemblies by the EnteroBase module EBEis (Supplemental Fig. S2E ). Clermont haplogroups are predicted for Escherichia by two external programs-ClermonTyping  (Beghain et al. 2018)  and EZClermont  (Waters et al. 2018 )-and fimH type by a third (FimTyper)  (Roer et al. 2017). By default, all registered users have full access to strain metadata and the genome assemblies, predicted genotypes, and predicted phenotypes, but a delay in the release date of up to 12 mo can be imposed by users when uploading short-read sequences. In September 2019, EnteroBase provided access to 364,690 genomes and their associated metadata and predictions (Table  1 ). To allow comparisons with historical data, EnteroBase also maintains additional legacy seven-gene MLST assignments (and metadata) that were obtained by classical Sanger sequencing from 18,478 strains.
Ownership, permanence, access, and privacy EnteroBase users can upload new entries, consisting of paired-end Illumina short reads plus their metadata. Short reads are deleted after genome assembly, or after automated, brokered uploading of the reads and metadata to the European Nucleotide Archive (ENA) upon user request. The search and graphical tools within EnteroBase can access all assembled genomes and their metadata, even if they are pre-release. However, ownership of uploaded data remains with the user and extends to all calculations performed by EnteroBase. Only owners and their buddies, administrators, or curators can edit the metadata; and only those individuals can download any data or calculations before their release date. To facilitate downloading of post-release data by the general community, downloads containing metadata and genotypes or genomic assemblies are automatically stripped of pre-release data for users who lack ownership privileges.
Uberstrains and substrains Most bacterial isolates/strains in EnteroBase are linked to one set of metadata and one set of genotyping data. However, EnteroBase includes strains for which legacy MLST data from classical Sanger sequencing exists in addition to MLST genotypes from genomic assemblies. Similarly, some users have uploaded the same reads to both EnteroBase and SRA, and both sets of data are present in EnteroBase. In other cases, genomes of the same strain have been sequenced by independent laboratories, or multiple laboratory variants have been sequenced that are essentially indistinguishable (e. g. , S. enterica LT2 or E. coli K-12). EnteroBase deals with such duplicates by implementing the concept of an Uberstrain, which can be a parent to one or more identical substrains. Substrains remain invisible unless they are specified in the search dialog (Supplemental Fig.