dataesr/scientific-paragraphs-categorization huggingface dataset:
5 objective lens (MVPLAPO 2x) equipped with a 6 mm working distance dipping cap. Version v144 of the Imspector Microscope controller software was used. The microscope is equipped with LED lasers (488 nm and 640 nm) with three fixed light-sheet generating lenses. Scans were made at the 0. 8X zoom magnification (1. 6X effective magnification), with a light-sheet numerical aperture of 0. 1. Emission filters used are 525/50 and 680/30. The samples were scanned with a step-size of 3 mm using the continuous light-sheet scanning method with the included contrast blending algorithm for the 640 nm channel (20 acquisitions per plane with a 50 ms exposure), and without horizontal scanning for the 480 nm channel (50 ms exposure). To speed up the acquisitions, both channels were acquired in two separate scans.
Maximum 3D projections in Figure  2A  and all panels of Figures 3, 4 and 7B were performed using Imaris (Bitplane, http://www. bitplane. com/imaris/imaris), and generated from manual 3D segmentation of the raw data using the surface tool. ClearMap  (Renier et al. , 2016)  (https://www. idisco. info) was used to quantify and register c-Fos+ cells in Figure  7A. Parameters were set as previously described, and automated isolation of the cortex was done using scripts available online.
Acknowledgements We thank  P Charnay  for providing the Krox20:Cre line, and  S Arber  for the Tau GFP mice. We also thank  N Narboux-Ne  ˆme for technical help. FMR laboratory was supported by the  Swiss National Science Foundation  ( 31003A_149573 ) and the  Novartis Research Foundation. The teams of AC and PG are part of the  Ecole des Neurosciences de Paris training network.
Additional information Funding Funder Grant reference number draft, Writing-review and editing; CFK, Data curation, Formal analysis, Investigation, Writingreview and editing; FMR, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing-review and editing; PG, Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing-original draft; AC, Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing-original draft, Project administration.
Dealing with these challenges has reinvigorated research into genome assembly methodology, resulting in several novel strategies  [24] [25] [26] [27] [28]. However, when dealing with large eukaryotic genomes, the computational demands for long-read assembly are often higher than for short reads (using De Bruijn-graphs), even though the raw data are more informative of genome structure. Especially now that sequencing very large plant and animal genomes is finally becoming both technologically feasible and affordable, the computational costs may turn out to be prohibitive. For example, using the state-of-the-art Canu assembly software  25  , assembling a human genome from long reads takes tens of thousands of CPU hours, or several days on a computer cluster (https://genomeinformatics. github.
Assembly quality. In order to assess its completeness and structural correctness, we added nanopore sequence to the selected TULIP assembly and aligned it to the Illumina-based draft genome 2. As a high-quality reference genome for the European eel is not yet available, such a comparison need take into account the possibility of error in either assembly. However, with appropriate caution, agreement between the assemblies -which are completely independent in both sequencing data and assembly algorithms -can confirm the integrity of both. Figure  5a  shows a full-genome alignment of the new (uncorrected) nanopore-based assembly to the 2012 draft  2  , based on best pairwise matches. This confirms that at this large scale, all sequence in the new assembly is also present in the older assembly.
Survey sequencing indicates that the 32 Gbp axolotl genome contains mostly unique sequence  29  , as do many tulip genomes (C. Henkel, unpublished data). The selection of sparse seeds by the user adds an unusual level of flexibility to the assembly process. In an early phase of this study, we opted for essentially randomly placed Illumina-based seed sequences. This choice was motivated by their very high sequencing identity, which aids alignment quality when working with noisy long reads. This strategy should work equally well with PacBio data or early, error-prone nanopore chemistries (i. e. R7. 3). The genome assembly generated here is a hybrid, incorporating two different sequencing technologies, three generations of nanopore sequencing, and two different animals. At the time, it was unavoidable to use a combination of multiple nanopore sequencing chemistries, as these rapidly replaced each other.
Although the later R9 and R9. 4 chemistries have better sequencing error profiles, they still retain structural biases that cannot be resolved by taking a consensus of nanopore data only (e. g. using Racon). In the final Pilon polishing stage, the nanopore data are therefore corrected using Illumina data obtained from a different eel specimen than used for nanopore sequencing. As the European eel is highly heterozygous (Table  2 ), in theory this generates a consensus between up to four different haplotypes. In practice, we expect this to have little influence on the quality of the final assembly, as the variation resulting from heterozygosity is much lower than the raw nanopore error rate. In other words, Pilon will treat SNPs and small indels not occurring in the Illumina data as sequencing errors to be corrected.
Methods Eel samples. Two different European eels were used to generate the genome assembly. For all Illumina sequencing, a female specimen caught in Lake Veere, The Netherlands, was used. These data were previously used for the Illumina-based draft assembly 2. For nanopore sequencing, a farmed female eel was obtained from Passie voor Vis, Sevenum, The Netherlands. As the European eel is a panmictic species  16  , these sequenced eels belong to the same population. The experiments were approved by the animal ethical commission of Leiden University (DEC #13060), and carried out in accordance with the relevant guidelines and regulations. Genome size estimation and k-mer analyses. We used Jellyfish 46 version 2. 2. 6 to count k-mers in sequencing reads and assemblies. In order to estimate genome size, we obtained frequency histograms for 19to 25-mers in raw Illumina sequencing data.
Reads were truncated to a uniform length of 76 nt, except for A. japonica, for which we used 100 nt (the model did not converge for short lengths). For the American eel, which has been sequenced to much higher coverage than the European and Japanese species, we used a subset of the available data (NCBI Sequence Read Archive SRR2046741 and SRR2046672). Histograms were analyzed using the GenomeScope  33  website in order to obtain estimates for genome sizes, heterozygosity and duplication levels. Illumina seed selection. We selected unique seed sequences from 11. 9 Gbp in sequence previously generated at 2 × 151 nt on an Illumina Hiseq 2000 (NCBI Sequence Read Archive SRR5235521). Pairs were merged using FLASh  47  , requiring a minimum of 15 nt terminal overlaps, resulting in 29. 16% merged fragments.
In these, 25-mers were counted using Jellyfish. We used a custom script to filter out all fragments that contained 25-mers occurring over 25 times in the remaining data. This corresponds to a maximum occurrence of approximately 6. 25× in the 860 Mbp genome. Finally, fragments were selected based on size (either 270 nt or 285 nt). MinION library preparation and sequencing. High MW chromosomal DNA was isolated from European eel blood and liver samples using a genomic tip 100 column according to the manufacturer's instructions (Qiagen). For each nanopore sequencing library, we used 2-3 μg genomic DNA, approximately twice the recommended quantity. In this way, we compensated for the decreased molar quantities of DNA ends at increased fragment lengths (see below). First the DNA was sequenced on R7.
For SQK-LSK108 the procedure was essentially the same as for SQK-NSK007 except that only adapters and no hairpins were ligated to the DNA fragments. As a consequence the final purification step was done using Ampure XP beads instead of MyOne C1 beads. Libraries for R7. 3 and R9 flow cells were directly loaded on the flow cells. To load the library on the R9. 4 flow cell the DNA fragments were first bound to beads which were then loaded on the flow cell. The MinKNOW software was used to control the sequencing process and the read files were uploaded to the cloud based Metrichor EPI2ME platform for base calling. Base called reads were downloaded for further processing and assembly.
Nanopore read alignment. From the base called read files produced by the Metrichor EPI2ME platform sequence files in FASTA format were extracted using the R-package poRe version 0. 17 (ref. 48). We used BWA-MEM 49 (version 0. 7. 15-r1140) to align nanopore reads to selected seeds, using specific settings for each nanopore chemistry. The built-in -x ont2d setting (-k 14 -W 20 -r 10 -A 1 -B 1 -O 1 -E 1 -L 0) is too tolerant for newer chemistries. We therefore optimized alignment settings (-k and -W only) on small subsets to yield the highest recall (number of aligning reads) at the highest precision (number of seeds detected/number of alignments). With all other settings as before, this yielded the following parameters: -k 14 -W 45 (R7. Genome assembly using TULIP. Currently, TULIP consists of two prototype scripts in Perl: tulipseed.
perl and tulipbulb. perl (version 0. 4 'European eel'). The tulipseed script constructs the seed graph based on input SAM files and a set seed length, and outputs a simplified graph and seed arrangements (scaffold models). tulipbulb adds seed and long read sequence to the scaffolds, and exports either a complete set of uncorrected scaffolds, or for each scaffold two separate files: the uncorrected sequence, and a FASTA 'bundle' consisting of all long reads associated with that scaffold. For each scaffold, we used the long read bundle and Illumina data to polish it according to ONT guidelines (https://github. com/nanoporetech/ont-assembly-polish). We first corrected nanopore-derived scaffolds with nanopore data using Racon  22  , based on alignments produced by Graphmap 50 version 0. 3. 0. Ultimately Racon sequence correction is performed by SPOA  51  , which is a partial order alignment algorithm that generates consensus sequences.
Subsequently, we used previously generated  2  Illumina data (NCBI Sequence Read Archive SRR5235521-SRR5235523), trimmed to Phred 30 quality values (using Sickle version 1. 33, https://github. com/najoshi/sickle) in a second correction step using Pilon (version 1. 21), an integrated software tool for assembly improvement  37, 38. Pilon uses evidence from the alignment between short-read data and Racon-corrected scaffolds to identify events that are different in the draft genome compared to the support of short-read data. All genome assembly steps and analyses were performed on a desktop computer equipped with an Intel Xeon E3-1241 3. 5 GHz processor, in a virtual machine (Oracle VirtualBox version 4. 3. 26) running Ubuntu 16. 04 LTS with 28 GB RAM and 4 processor threads available. For the final candidate assembly, the TULIP scripts required a maximum of 4.
4 GB RAM. Genome alignment. Uncorrected scaffolds were aligned against the 2012 scaffolds using nucmer 52 version 3. 23, with settings --maxmatch and --minmatch 100, filtered for optimal correspondence (delta-filter -1), and visualized using mummerplot (with the --layout option). The five largest scaffolds were likewise aligned against the 2012 scaffolds, but with settings encouraging longer alignments ( --breaklen 1000 and --minmatch 25) and not filtered. The 285 nt seeds were aligned against the 2012 draft scaffolds using BWA-MEM with default settings. BUSCO assembly assessment. The completeness of the genome assemblies was tested with BUSCO  36  (version 3. 0. 0), which tries to find orthologues of a curated dataset of near-universal genes in new assemblies. A more complete assembly will result in a higher percentage of genes retrieved.
Acknowledgements We are grateful to the  DUPAN Foundation  for making this project possible, as well as to  Rosemary Dokos  and  Oliver Hartwell  at  Oxford Nanopore  for technical support and encouragement. This project was funded by grants from the  DUPAN Foundation  for sustainable eel farming and fishing, the  Dutch Ministry of Economic Affairs  (to A. P. P. ,  KB-21-001-001 ), the  Austrian Science Foundation  (to B. P. , FWF  P26363-B25 ), the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Actions: Innovative Training Network IMPRESS , grant agreement No  642893  (to F. -A. W. ),  ZonMW/DTL  (to C. V. H. ), and local funds from  CNRS  (to S. D. ) and Generade, the  Leiden Centre of Expertise in Genomics  (to C. V. H. ).
The generation was performed using a method that allows sampling high probability chord sequences with respect to a given semiotic structure  (Conklin, 2016)  (see Section 2. 1. 4. 2). To generate the chord sequences, a statistical model was used to encode chord transition probabilities from a corpus of 100 uplifting trance chord loops compiled specifically for this study (described in Section 2. 1. 4. 1) 1. The sequences (generated by the statistical model) with the highest probability between successive chords were selected, and key modulations within the sequences were avoided as much as possible. To create the audio files used in the listening study, the selected chord sequences were then rendered within the Digital Audio Workstation Logic Pro X (LPX) by starting from an existing uplifting trance template 2  and applying as few as possible pitch modifications necessary to make its harmonic structure fit with the generated chord sequence  (Bigo and Conklin, 2015).
ACKNOWLEDGMENTS This research is an expansion of the extended abstract of Agres et al. (2016). It is supported by the project  Lrn2Cre8  which is funded by the  Future and Emerging Technologies (FET)  programme within the  Seventh Framework Programme for Research  of the  European Commission , under FET grant number  610859. This project also received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement number  658914. The authors thank  Geraint Wiggins  and  Kerstin Neubarth  for their valuable comments on the manuscript.
28-fold (AE 1. 7; P < 0. 05) upregulated by 30 mM GED and 17. 93-fold (AE 5. 1; P < 0. 05) upregulated for Pio compared to unstimulated cells. 5-ASA also upregulated LCT mRNA expression to the same extend (8. 76-fold AE 2. 06, P < 0. 05) (GEO Series accession number GSE68852; http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE68852). These results were confirmed in independent experiments by evaluating LCT gene expression by quantitative RT-PCR (qRT-PCR). Significant LCT overexpression was induced by 1 mM GED (5. 76 AE 0. 89-fold change), 1 lM Pio (14. 77 AE 1. 37-fold change), and 30 mM 5-ASA (9. 57 AE 1. 96-fold change)  (Fig 1A). In order to strengthen these results, we also evaluated the ability of two other PPARc agonists to increase LCT expression: rosiglitazone (Rosi, 1 lM), another TZD drug class ligand, and the trans-10, cis-12-conjugated linoleic acid (CLA, 1 mM) isomer, a natural PPARc modulator.
Microarrays Dual-color gene expression microarrays were used to compare the cRNA from the samples. 44,000 genes were screened. The RNAs from the samples were first reverse-transcribed into cDNA (Affinity-Script RT, Agilent), which were then used as the substrate for the synthesis and amplification of cRNA by T7 RNA polymerase in the presence of cyanine 3-CTP for the CTL sample (green fluorescence) and cyanine 5-CTP for the PPARc agonist sample (red fluorescence). The two-labeled cRNAs were mixed, hybridized on the same array (G4851A Agilent 8 × 44K), and then scanned (with an Agilent G2505B scanner). Fluorescence was visualized after laser excitation and the relative intensities of the two fluorophores were expressed as a ratio, in order to yield the over-or under-expression status of each gene (using GeneSpring software (Agilent)).
Quantitative PCR Expression of genes of interest was quantified by quantitative PCR of corresponding reverse-transcribed mRNA. One lg of total RNA was reverse-transcribed into cDNA using the High Capacity cDNA Archive kit (Applied Biosystems). Amplification was performed using an ABI PRISM 7000 sequence detection system (Applied Biosystem) using Power SYBR â Green PCR master Mix (Applied Biosystem). Primer pairs for each human transcript were chosen with qPrimer depot software (http://primerdepot. nci. nih. gov). See Appendix Table  S2  for the oligonucleotides used in this study. Quantification of qPCR signals was performed using DCt relative quantification method using GAPDH as a reference gene for human and rat samples and b-actin for mouse samples. Values were represented in terms of relative quantity of mRNA level variation or fold increase compared to control conditions.
05% Tween-20 and probed 2 h a RT with primary antibodies directed against lactase (Novus Biological, monoclonal mouse, clone 3C105. 1, 1:200), PPARc (Cell Signaling, monoclonal rabbit, clone C 6 H 12 , 1:1,000), and b-actin (Sigma, monoclonal mouse, clone AC-15, 1:5,000) diluted in 1% non-fat dry milk in 0. 05% TBS-Tween. Membranes were then incubated with secondary horseradish peroxidase-conjugated antibodies [anti-rabbit (Jackson ImmunoResearch) and anti-mouse (Sigma)], 1:10,000 in 1% non-fat dry milk in 0. 05% TBS-T for 1 h at room temperature. Immunodetection was performed with SuperSignal West Pico chemiluminescent substrate (Thermo Scientific Pierce, Erembodegem) according to manufacturer's protocol. Chemiluminescent signals were captured by a cooled charged coupled device (CCD) camera, and the optical density of target bands was determined using a computer-assisted densitometer and the ImageJ public domain software (W.
Immunofluorescence After 24 h of stimulation by 1 mM GED, 1 lM Pio, 1 lM Rosi, and/ or 1 mM CLA, Caco-2 cells were fixed in 4% paraformaldehyde for 10 min, permeabilized in 0. 1% Triton X-100 phosphate-buffered saline (PBS) for 15 min at room temperature, and then incubated with blocking buffer [3% bovine serum albumin (BSA) in PBS]. Incubation with primary lactase antibody (HPA007408 from Atlas Antibodies; dilution 1:200) was performed overnight 4°C. Normal Goat IgG (Invitrogen) was used as negative control. Incubation with secondary antibody (goat anti-rabbit Alexa 488 labeled (Invitrogen); dilution 1:100) was performed 1 h at room temperature. Nuclei were stained with Hoechst 33342 solution (0. 125 mg/ml) (Sigma-Aldrich). Cells were visualized under a fluorescence microscope (Leica, Bensheim, Germany).
Statistics The data are presented as mean with SEM or SD. All graphs were plotted and analyzed with GraphPad Prism 5 Software (San Diego, CA, USA) using a two-tailed nonparametric Mann-Whitney test. Statistical tests were validated with the support of a statistician. P-values < 0. 05 were considered statistically significant, and exact P-values were indicated (except when P < 0. 0001).
Acknowledgements We thank  Céline Villenet ,  Sabine Quief , and  Frédéric Lepretre  ( Functional and Structural Genomic Platform, Université de Lille ) for conducting microarray experiments and the management of microarray dataset. We acknowledge the support of the  DigestScience Foundation. This study was supported by  Institut National de la Santé et de la Recherche (Inserm) ,  Société Nationale Française de Gastro-Entérologie  ( FARE 2015 ) and  NograPharma Ltd , Dublin. ª 2017 The Authors EMBO Molecular Medicine Vol 9 | No 11 | 2017 Mathurin Fumery et al PPARc regulates lactose metabolism  EMBO Molecular Medicine.
However, since parameters have different physical meaning in both models, they can not be exactly equal. Thus, the choice has been made in this work not to consider uncertainties associated with model parametrizations and to focus on uncertainties associated with model structures. Model performance was assessed against observed discharge data using the four efficiency criteria : coefficient of determination r 2 , Nash-Sutcliffe Efficiency (N SE), N SE for the square root of discharges (N SE sqrt ) and relative bias (Bias r ), computed at the daily time scale. The N SE sqrt has the property of flattening flow peaks and therefore it is used to assess performance for low-flow periods  (Krause et al. , 2005). To assess performance for high-flow, the N SE criteria is also computed separately for the high flow periods, i.
Acknowledgments The authors address special thanks to  Professor Isabelle Sacareau  ( Passages Laboratory of the CNRS and Montaigne University of Bordeaux, France ), coordinator of the  PRESHINE  Project. They are also grateful to the hydrometry team and the administrative staff of the  Laboratoire Hydrosciences Montpellier, France , the hydrologists and glaciologists of the  Institut des Geosciences de l'Environnement in Grenoble, France , the meteorologists of the  Centre National de la Recherche Meteorologique in Toulouse and Grenoble, France , the  Association Ev-K2 CNR  and the  Pyramid Laboratory staff in Bergamo, Italy, Kathmandu and Lobuche, Nepal  as well as the  Vice-Chancellor of the Nepalese Academy of Science and Technology (NAST)  and its staff, especially  Devesh Koirala  and  Anjana Giri.
Funding This work was funded by the  Agence Nationale de la Recherche  (references  ANR-09-CEP-0005-04/PAPRIKA  and  ANR-13-SENV-0005-03/PRESHINE ), Paris, France. It was locally approved by the  Bilateral Technical Committee of the Ev-K2-CNR Association (Italy)  and the  NAST  within the Ev- K2-CNR/NAST Joint Research Project. It is supported by the  Department of Hydrology and Meteorology, Government of Nepal. The study was also supported in part by  ICIMOD  core funds contributed by the  Governments of Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway, Pakistan, Switzerland , and the  United Kingdom.
Variation in codon usage among functional categories results from differences in GC-content To better understand the causes of the differences in codon usage between sets of genes involved in cellular proliferation and differentiation (reported by  [Gingold et al. , 2014] ), we started by investigating the main factors that discriminate codon usage between functional categories in general. For this purpose, we grouped genes per functional category (687 biological processes, associated to more than 40 genes in the Gene Ontology database), and computed codon frequencies for each of these gene sets. We used the classification proposed by  Gingold et al. (2014)  to distinguish GO gene sets associated to 'proliferation' or 'differentiation'. Variation in relative synonymous codon usage (RSCU; see Materials and methods) among GO gene sets was analyzed by Principal Component Analysis (PCA).
The first principal component of this analysis segregates 'proliferation' (red dots) from 'differentiation' (blue dots) GO categories (Figure  1A ). Thus, in agreement with  Gingold et al. (2014) , synonymous codon usage clearly varies between functional categories in general, and between proliferation and differentiation in particular. Previous studies had shown that synonymous codon usage is correlated to GC content at third position of codons -termed GC3  (Mouchiroud et al. , 1988). And indeed, we observed that the average GC3 of each GO gene set is perfectly correlated to their coordinates on the first PCA axis (R 2 = 0. 99; Figure  1B ). Hence, variation in SCU between functional categories is fully explained by variation in GC3. On average, in our dataset, each gene is associated to nine GO biological processes.