dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledging the non-linearity in climate change effects, their potential long-lasting consequences (i. e. , carry-over effects) and their interactions with additional stressors  (Darling and Côté, 2008; Montoya et al. , 2017)  is also necessary. Maintaining and promoting long-term studies is key to fill most of these knowledge gaps. Climate change seems unlikely that it will stop or slow down in the coming decades. However, even if there is no real conservation measure to dampen the effects of climate change at the scale of a species or population, the reduction of human stressors operating locally (e. g. , bycatch, invasive species or human-induced loss of breeding habitats) has been proposed as a more achievable way of retaining ecosystems within a "safe operating space" (Sensu;  Rockström et al.
Challenges to Understanding Ecology at Sea Recent advances in miniaturization of tracking devices (e. g. , in light-level geolocators, accelerometers, GPSs, and PTT-Argos devices) have provided detailed knowledge on the atsea distribution and the foraging ecology of many petrels  (Ramos et al. , 2017; Hedd et al. , 2018)  as well as our understanding of their behavioral flexibility to deal with a changing environment  (Dias et al. , 2011). The number of datasets collected for petrels increased exponentially between 2005 and 2015 (BirdLife International, 2018b; Figure  5 ). In the same period, we saw an increase of the number of tracked species (Figure  5 ). Currently, the Seabird Tracking Database managed by BirdLife International holds data for 46 species. However, caveats remain for the spatial ecology of some of the rarest species as well as the smallest ones, i.
e. , most storm-petrels, diving petrels and some prions. From almost 500 datasets collected in the Seabird Tracking Database for petrels and shearwaters, only eight are for storm-petrels (Leach's and Band-rumped Storm-Petrels). A particular challenge is lack of information on juvenile survival and biology at sea  (Afán et al. , 2019; Weimerskirch et al. , 2019). As tracking technology use is growing in small petrel species and immatures, we foresee this data gap will be addressed, at least at some extent, over the next 5 years. Species distribution models (SDMs) can delineate habitat preferences of a given population based on spatial, count and tracking data  (Guisan et al. , 2013). SDMs enable researchers to draw species suitability maps based on the habitat preferences of certain populations.
Next Priorities The key priorities to fill the gap in our knowledge of petrel biology include: (a) Identifying breeding localities of rare and threatened species. This does not mean necessarily finding the breeding colonies but at least the general breeding locations (e. g. , island, mountain), to evaluate the extent of the colony, the approximate size and numbers of colonies, and their current threats. Several species are currently data deficient (all are stormpetrels). (b) Collecting data on at-sea distribution for less studied taxa or populations. Though improving, our current knowledge of the foraging ranges and distribution at sea is far from complete, especially for the south Pacific taxa, and for the juveniles of most species. (c) Standardized surveys to assess population trends. Censuses could be based on visual and/or acoustic detections, but they should be repeatable.
, 2012). This effort has already been enhanced by important knowledge sharing tools such as the Seabird Tracking Database, the Threatened Island Biodiversity Database, and the various IUCN databases  (BirdLife International, 2018a,b; IUCN, 2018b). In addition, the biologging revolution, i. e. , the miniaturization of tracking or logging devices and the development of new ones, which has enabled seabird researchers to answer questions that 20 years ago would have been unanswerable  (Brooke, 2018) , will continue to reveal the secret life of the most enigmatic, small-sized, shy petrels in the near future. The clear message that emerges from this review is the continued need for research and monitoring to inform and motivate effective conservation at the global level.
FUNDING AR and FR were supported by  Juan de la Cierva programme ,  Spanish Ministry of Economy, Industry and Competitiveness  ( IJCI-2015-23913  and  IJCI-2015-24531 ). ML was funded by the  Ramón y Cajal programme  ( RYC-2012-09897 ),  Spanish Ministry of Economy, Industry and Competitiveness. WM was supported by an  NSERC   Discovery grant. MG was partially supported by the  European Union  ( MINOUW Project ,  H2020-634495 ). JMA and VC were supported by  ZEPAMED  Project,  Pleamar programme, Fundación Biodiversidad ,  Spanish Ministry of Ecological Transition. We are grateful for the logistic support and funding from the  Phillip Island Nature Parks  and the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI).
Different approaches were used to verify the quality of the contigs. First, the assembly quality and annotation completeness of the transcriptome were assessed by BUSCO analysis. Out of 4584 single-copy ortholog genes common to Actinopterygii, the assembly was 67. 2% complete (2560 complete single-copy BUSCOs and 524 complete duplicated BUSCOs), while only 2. 8% of contigs were fragmented (130 BUSCOs) and 30. 0% were missing (1370 BUSCOs). Second, a comparison to a phylogenetically close species reference proteome was achieved using the Atlantic salmon database from the NCBI. Out of 97555 Salmo salar proteins, 47419 were aligned with at least 80% identity over 80% of their length on our assembly, which corresponded to 12238 Arctic charr contigs. Third, initial reads were mapped to the contigs in order to validate to what extent the contigs reflected the initial information.
The contigs had high realignment rates that ranged between 88 and 89% depending on the sample (15 libraries). Furthermore, the construction of the RNAseq data set was verified by mapping the reads from an Arctic charr gill transcriptome  [22]  on our set of contigs. On average, 79. 35% of reads from this gill transcriptome (SRA accession: SRX314607) were aligned to the 15 Arctic charr libraries. Finally, both proteins and RNAs from the S. alpinus reference genome (NCBI ID: 12179)  [23]  were aligned to our set of contigs. Out of 59926 proteins, 49212 (82%) had a hit on the contigs, 31316 of these proteins shared over 80% similarity and 80% of coverage with the built contigs. Regarding RNAs from the reference genome, 76% (51217 out of the 67196) had a hit on our contigs, with 24432 RNAs having over 80% of similarity and 80% of coverage with the contigs.
Chi-square tests of the 2 × 2 contingency tables of up-and downregulated genes indicated that there was a correlation of the proportion between uniquely and shared differentially expressed contigs and feeding regime over time (up- Fig. 2  Top 20 species contributing the most to best-hit annotations. Best hits were based on all databases involved in the annotation process. Yellow: teleosts, orange: salmonids, blue: mammals regulated: X-squared = 387. 74, p-value < 0. 0001, downregulated: X-squared = 153. 5, p-value < 0. 0001) (Fig. 4 ). Only five contigs were up-regulated in the Fed vs T 0 comparison whilst down regulated in the FDP versus T 0 comparison. No contigs were down-regulated in the Fed vs T 0 and at the same time up-regulated in the FDP vs T 0 comparison (Fig. 4 ). GO enrichment analysis for biological processes (BP) was employed to study the biological functions of the differentially expressed contigs.
Further, due to a strong effect of season and temperature seen on the brain transcriptome (Fig. 3 ), genes involved in seasonal rhythms were included in the search. Lastly, we searched the dataset for possible new actors involved in the regulation of energy homeostasis and food intake, not previously described in fish. Several candidate genes possibly involved in seasonality and food intake control in fish were found to be differentially expressed between one or several comparisons and are displayed in Table  10. Differences in expression were mainly found between the T 1 and T 0 for FDP and/or fed  fish. Deiodinase 2b (Dio2b) was also two-fold lower expressed in FDP charr than in fed charr at the end of the experiment (Fig. 5 ). The lists of top annotated genes that were found differentially expressed in the different comparisons (Additional file 11: Table  S9 , Additional file 12: Table  S10 , Additional file 13: Table  S11 , Additional file 14: Table  S12 , Additional file 15: Table  S13 , and Additional file 16: Table  S14 ) were screened for genes that, based on prior knowledge from mammals, are known to be involved in energy homeostasis and control of food intake.
Data release Our raw RNA-seq data are available in the SRA database  [24]  under accession number SRP151983. In addition, we specifically developed an interface for the present de novo transcriptome. The entire dataset can be browsed at the following URL: http://ngspipelines2. toulouse. inra. fr:9002/ the main menu enables the user to enter the contigs, SNPs and download sections. The contig section main page displays three blocks providing (1) statistics upon the assembly and the annotations, (2) information and access to statistical analysis tools (digital differential display, Venn diagram) on the library and (3) a table that displays data of the contigs that are of the user's strongest interest. Once a contig has been selected, the user can view its general annotation including Gene Ontology, check its sequence for possible open reading frames, visualize the annotation location in the "jbrowse view" and scan the alignment coverage along the contig for each library.
Indeed other fish transcriptome de novo assemblies brought results in the same order of magnitude with 70. 2% completeness for the gut tench (Tinca tinca)  [27]  and 64% for 4 combined tissues of the noble crayfish (Astacus astacus)  [28]. In addition, (1) the comparison to the phylogenetically close Atlantic salmon reference proteome, (2) the calculation of the realignment rates and (3) the mapping of an Arctic charr public raw data using a de novo assembly from short read RNA-seq data on our dataset also confirmed a high completeness of the built transcriptome.
The cleaved RNA fragments were reverse transcribed into first stranded cDNA using random primers, and second strand cDNA was then synthesized. Adaptors were ligated to the end-repaired cDNA, which contributed to fragment selection after the PCR enrichment step. Each library quality was validated measuring sample concentration and fragment size on an Agilent High Sensitivity DNA chip. Sequence hybridization to the flow cell and cluster generation was achieved using a cBot system and the cluster generation kit (Illumina, Hayward, USA). Hundred base pair fragments were sequenced in paired-end for the 15 samples. The samples were multiplexed and Sequenced By Synthesis (SBS) on an a single lane of the eight-lane flow cell of an Illumina HiSeq 2500 sequencer The sequencing lane of the flow cell was screened by a camera, driven by the HiSeq Control Software.
General statistics, data assembly and annotation Testing for differences in body weight and K Data for weight and K were not normally distributed and therefore, statistical testing was conducted on LOGtransformed data. Changes over time were tested using a repeated measures ANOVA. When differences were found, a pairwise comparison applying Bonferroni correction for multiple comparisons was used to determine main effects. Differences between treatment groups were tested using a 2-sample t-test. All statistical testing was done with SYSTAT 13 and figures were drawn using Sig-maPlot 13 (both Systat Software, CA, USA). The significance level was set to p < 0. 05.
Sequencing data RNA-Seq data assembly, annotation and quality assessment Read quality was checked within the ng6 environment  [76]  using fastQC  [77]  and Burrows-Wheeler Aligner BWA  [78]  to search for contamination. The reads were assembled with the Drap pipeline (version 1. 7)  [79]. The individual sample assemblies were performed with runDrap using Oases with kmers 25, 31, 37, 43, 49, 55, 61, 65, 69. The individual contig files filtered by FPKM (Fragments Per Kilobase per Million mapped reads) over one were then merged with run-Meta and filtered again by FPKM over one to produce the reference contig set. Contigs were annotated searching sequence homologies against the following Ensembl protein databases: blastx  [80]  Danio rerio, Gadus morhua, Oreochromis niloticus, Oryzias latipes, Takifugu rubripes, Tetraodon nigroviridis, Xiphophorus maculatus, refseq_rna blastn  [81] ; swissprot blastp  [82] ; unigene_Takifugu_rubripes.
9 (blastn); unigene_Oryzias_latipes. 30 (blastn); unigene_ Danio_rerio. 126 (blastn); NCBI Arctic char ESTs (blastn); the contigs (blastn). Repeats were identified with repeatMasker  [83]  (version open-4-0-3, with standard parameters) using Repbase database  [84]. The GO annotations were extracted from InterproScan  [85]  (May 2015 version)  [86]. The best SwissProt, RefSeq, or Salmo salar NCBI ESTs hit result was used to classify species by best hits contribution. Different approaches were used to verify the quality of the built contigs. First, the contigs were processed with BUSCO V2  [87]  to verify the number of actinopterygii_ odb9 reference genes found and their reconstruction state (partial or complete). Then the Salmo salar protein sequences made available by the NCBI (GCF_ 000233375.
Differential expression of contigs Patterns of gene expression Data exploration and gene expression analyses were performed using various packages implemented in R version  3. 3. 1 (2016-06-21). A sample correlation heatmap based on Pearson's coefficient of correlation was drawn with pheatmap. The number of reads counts per contig was retrieved and each sample normalized, accounting for compositional differences between the libraries (calcNormFactors function EdgeR). Paired comparisons of treatments groups were performed in EdgeR package version 3. 8. 6  [93]  according to the users' guide procedure. We identified differentially expressed contigs using a general linear model and a quasi-likelihood F-test, and correcting for false discovery rate (corrected Benjamini and Hochberg p < 0. 05).
e. a seasonal effect independent of the feeding regime (the shared portion of the Venn diagram). Resulting Venn diagrams where drawn in Venn Diagram Plotter  [96]  (Additional file 1: Figure  S1 ). All lists of differentially expressed genes generated from the initial EdgeR comparison (no log fold change cut-off ) were searched for candidate genes known to be involved in appetite regulation and energy metabolism. GO enrichment Gene ontology (GO) term enrichment was obtained using the TopGo package  [97]. It consisted of the identification of terms that host more differentially expressed contigs than expected by chance in a specific comparison. Enrichment of terms by differentially expressed contigs was assessed using Fisher's exact test (p < 0. 05). This analysis focused on Biological Processes.
Acknowledgements We thank the staff at  Tromsø Aquaculture Research Station  for taking care of the fish. Further, we like to thank  Olivier Bouchez  and his team at  Genome & Transcriptome GeT Genotoul  for sequencing of the samples. Last but not least, we thank  Hélène Volkoff  for reading and commenting on the manuscript prior to submission and two reviewers for constructive comments during the revision process.
Funding UiT -The Arctic University of Tromsø was the main funding body for the experiment and data collection. Joint data analysis and writing of the article by Norwegian and French partners was supported by the French Ministries of Europe and Foreign affairs (Ministère de l'Europe et des Affaires étrangères -MEAE) and of higher education, research and innovation (Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation -MESRI) and the The Aurora mobility programmeresearcher exchange between Norway and France (IS-AUR). The publication charges for this article have been funded by a grant from the publication fund of UiT -The Arctic University of Norway.
Supplementary Videos 1 and 3 show the raw tomograms, followed by the filtered data, then 2D segmentation of the data. Finally, the data are shown rendered in 3D. Atomic absorption spectroscopy. The quantities of Au NPs in the calcite crystals were determined using AAS with a Perkin Elmer Atomic Absorption Spectrometer AAnalyst 400, operating with an air-acetylene flame. All calcite nanocomposite samples were measured after being bleached in 4 w/v % sodium hypochlorite for 48 h to remove surface-bound particles. After the bleaching process, the samples were thoroughly washed with water, then ethanol, before drying in air. Samples for AAS were prepared by dissolving the nanocomposite crystals in 200 μL of aqua regia solution made by mixing HCl and HNO 3 in a 3:1 ratio, then diluting the solution to 20 mL with DI water.
The amounts of Au and Ca present within the same solution were then measured after calibration with 1, 2, 5 and 10 μg mL -1 Ca and Au standard solutions. PXRD analysis. PXRD measurements were carried out using a PANalytical X'Pert 3 diffractometer equipped with a Cu-anode (λ = 1. 54056 Å). Sample powders were gently ground, and then loaded onto a Si wafer sample holder. The samples were rotated at a rate of 60 r. p. s. and were measured using a 0. 02 step and 0. 5 s exposure time. Quantification of Au in nanocomposite crystals was estimated using Rietveld analysis using PANalytical X'Pert HighScore Plus software. Synchrotron high-resolution PXRD analysis. The high-resolution PXRD measurements were carried out on beamline I11 at Diamond Light Source Ltd, Didcot, UK. Instrument calibration and wavelength refinement (λ = 0.
824681 (10) and 0. 824677(10) Å) were performed using high-quality NIST silicon powder (SRM640c) and instrumental contribution to the peak widths does not exceed 0. 004°(ref. 77 ). Diffractograms were recorded from the specimens at room temperature. Sample powders for analysis were loaded into 0. 5 mm borosilicate glass capillaries, and to avoid intensity spikes from individual crystallites, the samples were rotated during measurements at a rate of 60 r. p. s. PXRD data were then obtained using high-resolution MAC (multi-analyser crystal) diffraction scans with scan times of 1800 s. The structural parameters were refined by Rietveld analysis using PANalytical X'Pert HighScore Plus software. Lattice parameters were determined using Rietveld analysis for whole spectrum and strain and size analysis were performed using line profile analysis for {104} [110} and {006} reflections.
The partial layer was made of six ionic rows, and the steps were separated by 31 Å. In all cases, the two surfaces were separated by approximately 50 Å, with water in-between. The simulations were performed with the LAMMPS molecular dynamics code  78. After equilibrating the pressure to 1 atm, the system was integrated in the NVT ensemble with an integration time step of 1 fs. A temperature of 300 K was maintained by a Nosé-Hoover thermostat with a time constant of 0. 1 ps. The CaCO 3 and CaCO 3 -water interactions were taken from Raiteri et al. 79. The water was modelled with the flexible SPC/FW model  80. All interactions involving the organics came from the Generalised Amber Force Field (GAFF)  81  and the cross-terms were as described in Nalbach et al. 82. It should be noted that, unlike the CaCO 3 interactions, the organic force fields have not been validated against experimental or ab initio thermodynamic data for these organic molecules.
Acknowledgements This work was supported by an  Engineering and Physical Sciences Research Council (EPSRC)   Platform Grant  (to F. C. M. , M. A. H. and  Y. -Y. K. ,  EP/H005374/1 ), an  EPSRC Programme Grant  ( EP/R018820/1 , to F. C. M. , Y. -Y. K. , R. D. , A. B. , and D. M. D. ), an  EPSRC research grant  ( EP/P005233/1 ) and the  EPSRC 'Complex Particulates' CDT  (to O. N. ). We acknowledge  Diamond Light Source  for time on beamline I11, and  Dr. Nicole Hondow  and  Dr. Stuart Micklethwaite in Leeds Electron Microscopy  and  Spectroscopy Centre (LEMAS)  for help with STEM tomography and FIB imaging, and we thank  Dr. Paul Thornton ,  Chemistry, Leeds , for advice regarding the polymer synthesis and APC analysis. We are grateful to  YiFei Xu  and  Christopher Pask ,  Chemistry, Leeds , for helping with the tomography imaging analysis and single crystal diffraction analysis, respectively.
Background: Although physical activity has many known health benefits, its association with lung function in childhood/adolescence remains unclear. We examined the association of physical-activity trajectories between 11 and 15 years with lung function at 15 years in 2266 adolescents. Methods: A population-based cohort of 14 305 singleton births alive at 1 year was recruited in the UK population-based Avon Longitudinal Study of Parents and Children cohort. Physical activity (counts/minute and moderate-to-vigorous physical activity) was assessed for 7 days using an accelerometer at 11, 13 and 15 years. We identified sexspecific physical-activity trajectories applying K-means for longitudinal data in children with at least two accelerometer measurements (n ¼ 3584). We then estimated the sexspecific associations of these trajectories with post-bronchodilation lung-function parameters using multivariable linear-regression models (n ¼ 2266, 45% boys).
Study design and population We used data from the ALSPAC study, which has been described previously. 19, 20 Briefly, ALSPAC recruited 15 247 pregnant women residents in Avon, UK, with expected dates of delivery between the 1 April 1991 and 31 December 1992. This recruitment resulted in a cohort of 14 305 singleton children alive at 1 year of age. Children were regularly followed up by postal questionnaires and health examinations. The ALSPAC Ethics and Law Committee and the Local Research Ethics Committees gave ethical approval. The ALSPAC study website contains all the data that are available through a fully searchable data dictionary (http://www. bris. ac. uk/alspac/researchers/data-access/data-dictionary/). We used data from children with at least two accelerometer-based measures of physical activity between 11 and 15 years to identify the physical-activity trajectories.
Physical activity Physical-activity measurement has been described previously. 21, 22 Children who attended the research visits at 11, 13 and 15 years were asked to wear an Actigraph AM7164 2. 2 accelerometer (Actigraph LLC, Fort Walton Beach, FL, USA) around their waist, at the right hip, for 7 days. A valid day was defined as providing data for at least 10 hours per day (excluding sequences of 10 or more minutes with consecutive zero counts) and children were only included in the analyses if they provided at least 3 valid days of recording. We derived the total volume of physical activity [counts per minute (CPM)] and time spent in MVPA (3600 CPM).
Covariates Socio-demographic, anthropometric, psychological and other lifestyle factors were collected at different time points. Questionnaires during the antenatal period provided information on: maternal education; maternal and paternal occupational social class; maternal anxiety; and maternal smoking during pregnancy. At birth, sex, birthweight, pre-term delivery, age of mother at delivery and parity were extracted from medical records. Postnatal questionnaires provided data on exposure to tobacco smoke, breastfeeding and presence of furry pets at home. At 10 years, we obtained dietary energy-intake data based on a 3-day diet report. At the 11-year clinic visit, body weight and body composition (assessed using a dual-energy x-ray absorptiometry scanner) were measured. We calculated BMI (kg/m 2 ), lean BMI (kg/m 2 ) and fat mass index (kg/m 2 ).