dataesr/scientific-paragraphs-categorization huggingface dataset:
The 14 M PubMed dataset We downloaded the MEDLINE/PubMed baseline files for 2015 and the up-to-date files through 8th June 2016. To transform the XML PubMed files (see  [84]  for details of the XML data elements) into a corpus of suitable textual input for Skip-gram and CBOW, two pre-processing steps are carried out. For the first step, we created a processing pipeline that uses open-source software in Python, such as Beautiful soup  [85]  and the open-source Natural Language Toolkit (NLTK)  [86]. When pre-processing the textual input for CBOW and Skip-gram, it is common practice to transform the text into lower-case and to remove systematically all numbers and punctuation marks. This is, however, unsuitable when dealing with protein/gene nomenclature and critical information will be lost if this practice is followed.
From a biological point of view, the target term "KLF7" denotes a human gene, while the candidate term in the second position in the list "Klf7" denotes the equivalent gene in mice. The genes KLF7 and Klf7 are orthologs according to the NCBI  [103]. The candidate term in the third position in the list "Klf7(-/-)" refers to mice which are homozygous for the Klf7 gene knockout. Hence, the pre-processing of the 14 M PubMed dataset that keeps uppercase, punctuation marks, and numbers, demonstrably preserves valuable biological information. The term "OLR1", which is a gene symbol, appears as such in the abstract of the PubMed article with ID = 22,738,689. Using "OLR1" as the target term in Experiment I for CBOW and Skip-gram, no candidate terms from the word embeddings were suitable as FTV or PV according to all four raters.
The last six columns display the presence (i. e. value equals 1) or absence (i. e. value equals 0) for each neural language model CBOW and Skip-gram of: full term variants (i. e. FTV) among the top twelve ranked candidate terms for the target term; FTV among the top three ranked candidate terms for the target term; and term variants (i. e. FTV and/or PTV) among the top twelve ranked candidate terms for the target term. Tables 8-11 take the data from auxiliary file Terms-Mapped_votingSystem. xls and summarise the results obtained. Table  8  shows the overall performance of CBOW and Skip-gram in Experiment I and II according to the voting system, which can be summarised as follows: In Experiment I, Skip-gram finds term variants among the top twelve ranked candidate terms (Number TV column) for 89% of the 64 unique UniProtKB entries mapped to CVDO gene and protein classes, while CBOW finds term variants for 67%.
Hence, using as target terms only terms from PubMed titles/abstracts, the word embeddings generated with the 14 M PubMed dataset can obtain a list of term variants for gene/protein names. In Experiment II (with the aid of the CVDO), Skip-gram finds term variants among the top twelve ranked candidate terms (Number TV column) for 95% of the 63 unique UniProtKB entries mapped to CVDO gene and protein classes, while CBOW finds term variants for 78%. Hence, both neural language models Skip-gram and CBOW provide more term variants (FTVs and/or PTVs) if the CVDO is used to provide more context for the target terms, and therefore increasing the chances of finding suitable term variants for a gene/protein name. Combining the results of both experiments, Skip-gram finds term variants (FTVs and/or PTVs) among the top twelve ranked candidate terms for 97% of the 79 Uni-ProtKB entries mapped to CVDO gene and protein classes, while CBOW finds term variants for 81%.
A large-scale database such as PubMed contains longer forms including phrases (e. g. "serum amyloid A-1 protein") as well as shorter forms such as abbreviations or acronyms (e. g. "SAA"). Finding all term variants in text is important to improve the results of information retrieval systems such as PubMed that traditionally rely on keyword-based approaches. Therefore, Table  11  Results for Experiment II according to the voting system and the simple categorisation introduced: Results of the voting system according to the simple categorisation introduced (see 'Setup of Experiment I and Experiment II for a gene/protein synonym detection task'), which has been applied to the terms from PubMed abstract/title from the small-annotated corpus (first column) as well as to the target terms (second column) Abbreviations: n ; number of target terms; nFTV ; number of target terms that have a FTV among the top twelve candidate terms; nFTVr3; number of target terms that have a FTV among the top three candidate terms; nTV; number of target terms that have a TV (i.
e. FTV and/or PTV) among the top twelve candidate terms the number of documents retrieved is prone to change when using acronyms instead of and/or in combination with full terms  [106, 107]. This study investigates to what extent word embeddings can contribute to keeping the CVDO up-to-date with new biomedical publications, and furthermore if the CVDO itself can aid such update. Experiment I investigates whether, in taking a gene/protein name from PubMed titles/articles as a target term, it is possible to obtain a list of term variants from the word embeddings created with a 14 M PubMed dataset. The results obtained for Experiment I confirm that it is feasible and that Skip-gram finds 22% more term variants than CBOW using 85 target terms that correspond to 64 UniProtKB entries, which are mapped to CVDO gene and protein classes.
Hence, the CVDO supplies context that is effective in inducing term variability whilst reducing ambiguity. Studies related to semantic similarity and relatedness tasks employ gold standards specific for the biomedical domain that have a relatively small number of term pairs, such as Caviedes and Cimino  [38]  with 10 term/ concept pairs, Pedersen et al. [34]  with 30 term/concept pairs, and Pakhomov et al. [36]  with 724 term pairs. This study considers a total of 3672 term-pairs from the two experiments together with human judgments from four raters. Hence, an outcome of this study is the creation of a gene/protein names dataset (larger than the MEN Test Collection  [40]  with 3 K common English word-pairs) that can be reused for the evaluation of semantic models in a gene/protein synonym detection task.
However, the overall setup of the two experiments is unbalanced as a result of capturing a realistic scenario where: a) some gene/protein names appearing in PubMed titles/abstracts do not have a vector representation; and b) a gene and its product (typically a protein) can appear together in the scientific text, and thus, the biological knowledge formally represented in the CVDO is already present. Considering only the 48 UniProtKB entries mapped to CVDO gene and protein classes that participate in both Experiment I and II, the asymmetry between the two experiments can be reduced leading to a smaller gene/protein names dataset with: a) 660 pairs of terms (target term and candidate term) taken from the word embeddings created with CBOW and Skip-gram (i. e. total of 1320 term pairs) and assessed by four raters in Experiment I; and b) 624 pairs of terms taken from the word embeddings created with CBOW and Skip-gram (i.
e. a total of 1248 term pairs) and assessed by four raters in Experiment II. Considering only these 2568 term-pairs instead of the total of 3672 term-pairs from the two experiments, the performance obtained for CBOW and Skip-gram is the same as the overall performance reported with Skip-gram outperforming CBOW in both Experiments; and both CBOW and Skip-gram find more term variants in Experiment II (with the aid of the CVDO) than in Experiment I. Besides the asymmetry between the two experiments presented, there are certain areas of improvement possible regarding the data resources. On one hand, the smallannotated corpus is very narrow in scope with only one curator performing the gene/protein name annotation for 25 PubMed articles (titles and abstracts). On the other hand, the 14 M PubMed dataset used to generate the word embeddings can be arguably larger or include more recent PubMed articles as it only contains titles and available abstracts from PubMed articles published between 2000 and 2016 (files up to 8th June 2016).
e. no postprocessing of the term vectors is performed), and this could be the foundation for building a tool similar to PubTator or BEST. As the CVDO and the PxO are formalised in OWL, it seems natural to envision a tool based on Semantic Web technologies, such as OWL and SPARQL. Therefore taking into account two annotation properties from SKOS, i. e. skos:altLabel and skos:hidden-Label, we can define the automation for the gene/protein synonym detection task as: "for each CVDO protein, find term variants for the string values within skos:altLabel and store them in skos:hiddenLabel". Levy et al. [29]  remarks that if different models "are allowed to tune a similar set of hyperparameters, their performance is largely comparable". The neural language models CBOW and Skip-gram have a similar set of hyperparameters, and thus, their performance has been already compared when accomplishing biomedical tasks  [41, 42].
This study does not exploit Skip-gram with negative sampling, which typically improves its performance  [29]. Furthermore, this study does not systematically explore alternative hyperparameter configurations that may lead to performance gains. As far as we are aware, the use of ontologies to provide more context (i. e. extra terms) for terms selected from the scientific literature has not previously been investigated. This paper demonstrates that the CVDO, and by extension the PxO, can provide better target terms for a gene/protein synonym detection task without altering the word embeddings created by Deep Learning algorithms CBOW and Skip-gram from a 14 M PubMed dataset. At the time of writing BioPortal  [112] , an open repository of biomedical ontologies, has 551 ontologies. The PxO is re-used by CVDO and is in BioPortal.
Currently, the tool used most frequently to perform a TE annotation is RepeatMasker, which provides a rough estimation of the TE content in a genome assembly  [61, 65]. However, this tool compares the genomic sequences with a databank of known TEs to realize the annotation and is therefore not suitable for realizing a de novo TE annotation  [65] [66] [67]. To perform a de novo TE annotation, pipelines employing repetitiveness-based methods of detection, such as RepeatModeler and REPET, are commonly recommended  [66] [67] [68] [69]. Here we built PiR-ATE (Fig. 1 ) to conduct a de novo TE annotation in the genome of non-model species T. lutea. PiRATE is composed of detection, classification and annotation steps.
Classification of TEs The classification step of PiRATE is performed by PAS-TEC  [72] , which partly uses databanks of known TEs to establish an automated classification of the detected sequences. To improve the classification step of PiRATE, its default databanks were upgraded, by adding 1240 TE sequences from other public databanks, non-inventoried algal TEs and by building 78 new profile HMMs (Hidden Markov Model). Adding non-inventoried data is important for improving the TE classification of species belonging to poorly studied phyla, which often have few described TEs in the databanks. This is common for numerous microalgal phyla (i. e. Haptophyta, Euglenophyta and Dynophyta). In our case, only 17 TE families belonging to the Haptophyte phylum are present in the most frequently used and complete TE databank Repbase  [73, 74].
Evaluation of the classification step To evaluate the classification step of PiRATE, we used the 292 sequences detected by PiRATE during the evaluation of the detection step, which represent the largest detected sequences of the 292 TE families of A. thaliana. These 292 sequences were classified with PASTEC using the PiRATE databanks (excluding data from Arabidopsis species). To estimate the classification efficiency, we counted the number of detected TEs with correct classification at the order level and the number of sequences that had an incorrect classification or that were uncategorized. We observed that 75% (218/292) of the detected TEs were correctly classified, 7% (21/292) were incorrectly classified and 18% (53/292) were uncategorized. The classification step of PiRATE was therefore efficient at correctly classifying autonomous TEs belonging to LTR (98%), LINE (87%), TIR (91%) and Helitron (100%) but had difficulty correctly classifying SINE (50%), nonautonomous TIR (27%) and non-autonomous Helitron (7%) (Additional file 1: Figure  S3 ).
3 ). With both types of dataset, the similarity-based approach had the weakest percentage and contributed to detecting only 2 or 3% of the T. lutea TE families. Using the draft genome assembly and the raw Illumina data, the structural-based approach contributed to detecting 1% of the TEs families of T. lutea, but 20% of the TE families of T. lutea with the new genome assembly and the raw Illumina data (Fig. 3 ). The repetitiveness-based approach contributed to detecting 7% of the TE families of T. lutea with the draft genome assembly and the raw Illumina data. However, it was the most efficient approach with the new genome and contributed to detecting 63% of the T. lutea TE families (Fig. 3 ). When a draft genome assembly is used as input, the fourth detection approach, using raw Illumina data to build repeated elements, was the most useful approach and contributed to detecting 67% of the TE families (Fig.
Class I and Class II TEs represent 15. 89 and 4. 95%, respectively (Table  1 ). We found a large quantity of Gypsy (4. 65%), LINE (3. 87%) and hAT (2. 12%) copies, suggesting ancient burst events for these elements (Table  1 ). We established that the proportion of non-autonomous TEs is 17. 05% (Table  1 ). Then, we performed the annotation Fig. 3  Comparison of the contribution of the four TE detection approaches of PiRATE on the detection of the TE families of Tisochrysis lutea, depending on the input data. For each TE detection approach, we calculated the number of TE families detected with the largest length and divide this number by the total of detected TE families of T. lutea. The input dataset was either the draft genome assembly of T. lutea and raw Illumina data of T. lutea (white bars) or the new genome assembly of T.
DNA extraction, sequencing, genome assembly and gene annotation Total DNA was extracted from the T. lutea WT-strain using a phenol/chloroform protocol. DNA quality and concentration were assessed with gel electrophoresis and Qubit Fluorometric Quantitation (ThermoFisher, Massachusetts, USA), respectively. T. lutea genome sequencing was performed with a PacBio RSII sequencer (Pacific Bioscience, California, USA) at the Plateforme GeT PlaGe (Toulouse, France); seven SMRT cells were performed. Filtered subreads were assembled using Canu1. 3 [82]. The assembly was polished with Quiver (https://github. com/PacificBiosciences/GenomicConsensus) and its accuracy was improved with Pilon  [87]  using previous Illumina Hiseq mate-pair reads of T. lutea (  [51] ; SRA: SRR3156597). The annotation of the codinggene region was performed with the pipeline MAKER2  [88] [89] [90] [91].
TE annotation in the T. lutea genome using PiRATE Step 1: TE detection The new genome assembly of T. lutea and previous raw Illumina data  ([51] ; SRA: SRR3156597) were used as input. Putative TE sequences were detected using four approaches (Fig. 1 ). The first approach was represented by two tools using similarity-based detection: RepeatMasker (setting: -s, -no_low, -lib; with the PiRATE nucleotide databank;  [61] ) and TE-HMMER (with a homemade profile HMMs databank). TE-HMMER is a homemade tool using HMMER (default setting,  [92] ) and tBLASTn (setting: -evalue 10E-300,  [93] ). The second approach consisted of five tools using structural-based detection: LTRharvest (default setting,  [75] ), Helsearch (default setting,  [94] ), MGEScan-nonLTR (default setting,  [95] ), MITE-Hunter (default setting,  [62] ) and SINE-Finder (default setting,  [96] ).
This made it possible to remove shorter sequences that aligned with 100% of identity on a part of the larger sequences. Step 2: TE classification In the second step, sequences were automatically classified with PASTEC  [72] , following the Wicker et al. classification system  [5]  This tool was improved with custom databanks (described below). Three libraries were manually constructed with a "Russian doll" strategy in order to perform separate annotations (Additional file 1: Figure  S1 ): a "potentially autonomous TEs library", a "total TEs library" containing the potentially autonomous TEs and the non-autonomous TEs and a "repeated elements library" also containing the uncategorized repeated sequences. Sequences classified as LTR, LINE and TIR were manually sorted by superfamily (according to the evidence section produced by PASTEC).
PiRATE databanks Nucleotide and protein databanks A nucleotide and a protein databank of TEs were built with sequences from Repbase (REPET version 20. 05, http://www. girinst. org/repbase), the P-MITE database (http://pmite. hzau. edu. cn) and SINE base (http://sines. eimb. ru). Because algae originally arose from the predation of a cyanobacterial organism by a eukaryotic heterotrophic organism, cyanobacterial TE sequences were also added from the IS-finder database (http://www-is. biotoul. fr) (Additional file 5). Moreover, we added noninventoried TEs of microalgae and macroalgae, retrieved from the NCBI database (Additional file 5).
Profile HMMs databank A homemade databank of profile HMMs was built with sequences of the protein databank. Multiple protein alignments were performed with Clustal Omega (http:// www. ebi. ac. uk/Tools/msa/clustalo/). When possible, TE protein sequences from algae were favoured. 78 profile HMMs were performed with the HMMbuild tool of HMMER  [92]  for 62 TE categories displayed on the Browse Repbase tool (http://www. girinst. org/repbase/update/browse. php). This databank was used with TE-HMMER at the detection step. At the classification step, we combine this databank with the default databank of PASTEC (ProfilesBankForREPET_Pfam27. 0_GypsyDB. hmm, https://urgi. versailles. inra. fr/download/repet).
Evaluation of PiRATE The efficiency of the detection and classification steps of PiRATE were evaluated with genomic data of the model plant A. thaliana. We used the genome assembly TAIR10 available on the TAIR project (https://www. arabidopsis. org/download/index-auto. jsp?dir=%2Fdown-load_files%2FGenes%2FTAIR10_genome_release%2F-TAIR10_chromosome_files) and the raw Illumina data available at the 1001 genome project http://1001genomes. org/data/SLU/SLUHenning2014/releases/current/ strains/Seattle-0). These data of A. thaliana were submitted to the step 1 of PiRATE (RepeatMasker and TE-HMMER were used without data from Arabidopsis species in the databanks). The detected sequences were submitted to PASTEC  [72]  and compared to the 359 TE families described in A. thaliana and available on Repbase (http://www.
For the evaluation of the classification step of PiRATE, we used the longest detected sequences of the 292 TE families detected by PiRATE during the evaluation of the detection step as a control. These 292 sequences were classified with PASTEC using modified versions of the three PiRATE databanks (Nucleotide, protein and profile HMMs), without data from Arabidopsis sp. We calculated the percentages of correct classification, incorrect classification or uncategorized classification. Details on the impact of the genome assembly quality on the efficiency of the TE detection step of PiRATE are available in Additional file 1: Method S3.
Transcriptomic and proteomic analyses The expression analysis was performed using eight sets of previously published transcriptomics data  [56, 58]. These data were concatenated and normalized using the tool insilico_read_normalization. pl of Trinity  [99]. Reads were then mapped on the new genome assembly of T. lutea with TopHat  [100]  and crossed with the annotation of the potentially autonomous TEs. HTseqCount  [101]  was used to count the number of mapped reads for each potentially autonomous TEs. With a homemade script we retrieved the TE families with transcripts covering at least 90% of the annotated sequences. Sequences of the TE copies of these expressed TE families were then compared with BLASTx to published proteomic data  [58]. Sequence alignments of the peptides of the Mariner (Luffy) and hAT (Ace) elements on the predicted transposases were performed with ClustalOmega and visualized with Geneious (Additional file 4).
Acknowledgements This work was supported by the  French Region of Pays de la Loire  with the  Atlantic Microalgue program  and the  French Research Institute for Exploitation of the Sea (IFREMER). We thank the platform  Genotoul GeT-PlaGe  for the genome sequencing of T. lutea. We also thank the  URGI Team  for their advice about REPET as well as  Jonathan Filée  and  Etienne Bucher  for their advice on this study. We thank  Helen McCombie  for the proofreading. The authors are grateful to the anonymous reviewers for their critical comments, which have greatly improved the manuscript.
Supporting this notion, locomotor activity during fasting was enhanced particularly during the active phase (Figures  2G-2I ). Post-transcriptional regulation plays an important role in circadian gene expression in vivo. As Gene Ontology analysis showed that RNA processing is enriched in cycling genes upon fasting (Figure  2D ), we implemented exon intron split analysis (EISA) on our RNA-seq dataset to compare intronic reads as a surrogate for precursor mRNA and exonic reads for mature mRNA  (Gaidatzis et al. , 2015). A large number of intronic and exonic transcripts displayed oscillatory expression in liver and muscle (Figures  S3A-S3D ). Approximately 77% of the cycling exonic transcripts in FED liver were oscillatory only in exonic reads, in agreement with a previous analysis (Figure  S3E )  (Koike et al.
Temporal Patterns of Fasting-Induced Differential Gene Expression Fasting results in the activation of several nutrient-sensing factors, such as GR, CREB, FOXO, TFEB, and PPARs  (Settembre and Ballabio, 2014). We examined the expression levels of these regulators along the diurnal cycle in FED or FAST mice, both in liver and muscle. By and large, fasting induces these TFs in both tissues with distinct diurnal patterns, although some TFs appear to be unaltered (Figures  S4B-S4I ). To decipher whether fastinginduced changes in the core clock impinge on temporal genome-wide expression in concert with fasting-sensitive TFs, we examined available datasets (see Experimental Model and Subject Details; Table  S5 ). We analyzed gene targets for hepatic BMAL1  (Koike et al. , 2012; Yang et al. , 2016) , REV-ERBα  (Fang et al.
Metabolic Cage Analysis-Indirect calorimetry was carried out by negative-flow system cages Oxymax/CLAMS (Columbus Instruments). Feeding and lighting conditions in metabolic cages were the same as those in the normal cages. Feeder counts less than 0 g or more than 0. 8 g per 10 min during ad libitum fed experiment were excluded from the study. Any feeder counts during the fasting experiment were regarded as noise and treated as null since mice were restricted from any access of food. Locomotor Activity Analysis-Mice were individually housed under 12hr light/ 12hr dark schedule. Locomotor activity was measured using optical beam motion detection (Philips Respironics). Data were collected using the Minimitter VitalView 5. 0 data acquisition software. Actograms and activity profiles were computed using Clocklab software (Actimetrics).