dataesr/scientific-paragraphs-categorization huggingface dataset:
These data were then used to estimate total DEE, the sum of the energetic costs of all behavioural bouts within 24-h periods of activity in terms of the rate of oxygen consumption (l day -1 ). This included both activity and RMR and any costs of thermoregulation in water (see  Hicks et al. , 2018  for full details). Adult RMR and DEE have been found to vary with mass, reproductive stage and age in many seabirds  (Elliott et al. , 2014; Green et al. , 2013; Grémillet, 1997; Weimerskirch et al. , 1995). Thus, adult mass and age were recorded at endoscopy and chick age at time of sampling was later back-calculated from wing length at ringing  (Granroth-Wilding et al. , 2014).
Adult age, adult body mass, brood age (at the time of sampling), brood size and year were also included as explanatory variables (Table  1 ). We controlled for variation between birds and repeated sampling by including individual as a random factor. All models were fitted using the lme4 package in R  (Bates et al. , 2014 ; http://www. R-project. org/) and model selection for both sets of models was based on Akaike's information criterion (AIC)  (Burnham and Anderson, 2001).
Relationship between parasite load and RMR A total of 87 (48 males and 39 females; 2015: 8, 2016: 42, 2017: 37) individuals were blood sampled and endoscoped over 3 years (of which 27 were sampled in more than one year). Whilst the sample size in 2015 is clearly smaller than the other years, removal of these data had no impact on the conclusions of the analyses and were therefore included. The best-supported model for females showed a positive relationship between parasite load and T3 concentration, an effect of year, and an interaction between brood age and parasite load in that there was a more positive relationship between parasite load and T3 when broods were younger (see Fig. 2  and Table 2 ). When year and brood age are accounted for, T3 concentration increased by 150% across the range of natural parasite load.
, 2008). Most of the products of these genes were confirmed to be 'striatal-enriched' by checking against a freely available transcriptomic database (e. g. Allen Brain Atlas). In our initial screen, some of these newly identified striatal gene products displayed significantly lower than normal levels of expression in the brains of Huntington's disease mouse models  (Brochier et al. , 2008)  and patients  (Kuhn et al. , 2007). More recent studies confirmed these findings, showing that a subset of approximately 70 striatal gene products (e. g. ADORA2A, DCLK3, GPR88, RGS2, RGS9, SCN4B) are downregulated in knock-in Huntington's disease mice  (Langfelder et al. , 2016). However, the functions of these striatal gene products are frequently unknown, and their potential roles in the vulnerability of the striatum in Huntington's disease remain a matter of speculation.
Lentiviral and AAV vector construction, production and administration DNA sequences encoding green fluorescent protein (GFP) and C-terminally haemagglutinin (HA)-tagged mouse short and long forms of DCLK3 were inserted into the SIN-W-PGK lentiviral vector  (de Almeida et al. , 2002)  to generate LV-GFP, LV-S-rDCLK3-HA and LV-L-rDCLK3-HA, respectively. The following PCR primers were used to generate S-rDCLK3-HA: 5 0 CACCATGGGCAAAGAGCCGCTGAC 3 0 and 5 0 CTAGGAGGCGTAGTCAGGCACGTCGTATGGGTAGGC-ACTGTTGGGGGACTCCTC 3 0. We also cloned the C-terminal domain of DCLK3 (from amino acid 330 to the C-terminal end) for expression of the kinase domain alone, in the vector LV-Kin-rDCLK3-HA. The LV-L-rDCLK3-HA and LV-Kin-rDCLK3 vectors were cloned and produced by GeneArt based on the RNA sequence in the GENE database.
Yeast two-hybrid analysis Yeast two-hybrid screening was performed by Hybrigenics Services (http://www. hybrigenics-services. com). The coding sequence for human DCLK3 (GenBank accession number gi: 149589020) was amplified by PCR and inserted into pB29 as an N-terminal fusion to LexA (N-DCLK3-LexA-C). The construct was checked by sequencing the entire insert and used as bait for screening a random-primed human adult brain cDNA library constructed in pP6. The pB29 and pP6 constructs were derived from the original pBTM116  (Vojtek and Hollenberg, 1995; Beranger et al. , 1997)  and pGADGH  (Bartel et al. , 1993)  plasmids, respectively. We screened 110 million clones (11-fold coverage) by a mating approach, with YHGX13 (Y187 ade2-101::loxP-kanMX-loxP, mat) and L40ÁGal4 (mata) yeast strains, as previously described  (Fromont-Racine et al.
, 1997). We selected 36 His + colonies on a medium lacking tryptophan, leucine and histidine. The prey fragments of the positive clones were amplified by PCR and their 5 0 and 3 0 junctions were sequenced. The resulting sequences were used to identify the corresponding interacting proteins in the GenBank database (NCBI) by a fully automated procedure. A confidence score (predicted biological score) was attributed to each interaction, as previously described  (Formstecher et al. , 2005). The protein interactions from this publication have been submitted to the IMEx (http://www. imexconsortium. org) consortium through IntAct  (Orchard et al. , 2014)  and assigned the identifier IM-26152. Human embryonic stem cell-derived neuronal samples and transcriptomic analyses RC9 human embryonic stem cells (XY, passage 20-40; Roslin Cells) were maintained on CELLstart TM (Invitrogen) and cultured in STEMPRO medium (Invitrogen; cultures were fed daily and manually passaged every 4-5 days).
0) according to the manufacturer's protocol (Life Technologies). The cDNA libraries were amplified and barcoded with the Ion AmpliSeq Transcriptome Human Gene Expression core panel and the Ion Xpress Barcode Adapter (Life Technologies). The amplicons were quantified with the Agilent High Sensitivity DNA kit and the samples were then pooled in sets of six. Emulsion PCR and Enrichment and loading onto the Ion Chef Instrument were performed with the Ion PI IC 200 Kit (Life Technologies). Samples were loaded on an Ion PI v2 BC Chip and sequenced on the Ion Proton System by Ion PI IC 200 Kit chemistry (200bp read length; Life Technologies). The Ion Proton reads (FASTQ files) were imported into the RNA-seq pipeline of Partek Flow software (v 4. 0, Partek Inc), with hg19 as the reference genome. We identified genes differentially expressed between groups, by quantifying mapped reads with the Partek E/M algorithm (the resulting counts represent the expression levels of more than 20 800 different genes present in the AmpliSeq Human Gene Expression panel) and applying the Partek Gene Specific Analysis (GSA) algorithm.
The data of this experiment have been deposited on GEO under accession number GSE104091. For the RT-PCR study of human striatal neurons infected with LV-Htt171-82Q, the protocol for RNA extraction and analysis was similar to that described above. The first analysis involved identifying the genes differentially expressed between Kin-rDCLK3 WT and GFP for ontology analysis. Relatively stringent criteria were used [fold-change 41. 20 and adjusted P 5 0. 0005, false discovery rate (FDR) 5 0. 05]. The second analysis involved identifying the genes for which expression was specifically regulated by the activity of the kinase domain. We searched for genes displaying expression changes triggered by kin-rDCLK3 WT and not Kin-rDCLK3 K543M. We used a lower stringency to identify genes displaying differences in expression between Kin-rDCLK3 WT and GFP conditions [Fold Change (FC) 4 1.
Statistical analysis All data are expressed as means AE standard error of the mean (SEM). Normality of data distribution was tested using the Shapiro-Wilk test and homogeneity of variance was tested with Levene's test using the commercially available software (Statistica, 13. 0). When normality and homogeneity of variance were met (on raw or log transformed data), unpaired Student's t-tests were used for pairwise comparisons between groups. For comparisons of more than two groups, one-way ANOVA for multiple comparisons was carried out, with Fisher's post hoc partial least-squares difference (PLSD) test. In the cases where assumption of normality and/or homogeneity of variance are not met, non-parametric tests were applied: Mann-Whitney and Kruskall-Wallis for comparison of two and more groups, respectively.
, 2008)  and available transcriptomic datasets (http://mouse. brain-map. org/experiment/show/ 72079921). We also checked that DCLK3 is an active kinase. Recombinant mouse long-and short-forms of DCLK3 with a HA-tag (termed hereafter L-or S-rDCLK3-HA) were produced in HEK293 cells, purified by immunoprecipitation and tested in an autophosphorylation assay with [-32 P]ATP. The autophosphorylation of proteins with apparent molecular weight corresponding to those of full-length S-rDCLK3 and L-rDCLK3-HA, respectively, was easily detected (Supplementary Fig. 1C ). First, we analysed the role of each mouse DCLK3 form, by using lentiviral vectors. As DCLK3 levels are low in Huntington's disease patients and mouse models  (Kuhn et al. , 2007; Brochier et al. , 2008; Langfelder et al. , 2016) , we investigated whether the silencing of the endogenous mouse protein using a lentivirus coding a shRNA targeting Dclk3 mRNA (LV-shDCLK3) aggravated mHtt toxicity in vivo.
TADA3 appeared to be the most relevant of the interactors identified, as the level of confidence for interaction with DCLK3 was highest for this molecule. Furthermore, an analysis of the expression of the genes encoding these seven interactors in the brain based on previously reported mouse SAGE data  (Brochier et al. , 2008) , or more recent region-and cell type-specific transcriptomic databases (http://mouse. brain-map. org/; http://web. stanford. edu/group/barres_lab/brain_rnaseq. html) (Supplementary Table  1 ) revealed that TADA3 was more strongly expressed in the striatum than the other DCLK3 interactors. It was strongly expressed in neurons and other brain cell types. We obtained evidence supporting DCLK3-TADA3 interaction, by performing co-transfection followed by co-immunoprecipitation.
5B ), providing strong support for the hypothesis that DCLK3 could regulate transcription through SAGA-induced histone modifications. We asked whether the DCLK3 kinase domain could modulate transcriptomic profile in neurons. We overexpressed human Kin-rDCLK3 WT , Kin-rDCLK3 K543M or GFP (as a control) in striatal neurons derived from wildtype human embryonic stem cells (Fig. 6A ) using lentiviral vectors. After 10 days, we performed transcriptome analyses by ultrahigh-multiplex PCR (Ion AmpliSeq technology). A relatively stringent analysis of differential gene expression (fold-change 1. 2; P 5 0. 0005, FDR, 0. 05) between cells expressing Kin-rDCLK3 WT and cells expressing GFP showed that 88 genes were upregulated and 73 downregulated in the presence of Kin-rDCLK3 WT (Fig. 6B  and Supplementary Table  2 ; GEO dataset GSE104091).
Data and Methods To study the 6 September 2017 SFs and to investigate their effects on the ionosphere, GNSS, and HF communication we analyzed the following sets of data: A. X-ray 0. 1-0. 8 nm flux measured by GOES-13 (http://satdat. ngdc. noaa. gov/). B. Solar radio emission from San Vito radiospectrograph of Radio Solar Telescope Network. We used spectrums in 18 to 180-MHz band and 1,415-MHz flux. A value of 1,415-MHz frequency is close to GPS/GLONASS operating frequency. (ftp://http://ftp. ngdc. noaa. gov/STP/space-weather/solar-data/solarfeatures/solar-radio/rstn-1-second/). C. Fifteen-second SOHO/SEM EUV flux at 0. 1-50 nm and at 26-34 nm. (https://dornsifecms. usc. edu/spacesciences-center/download-sem-data/; Space Sciences Center, University of Southern California). D. Dual-frequency phase measurements by GNSS receivers.
, 2016)  and variations of the absolute vertical TEC (VTEC). Data of the GPS-based VTEC with 5-min resolution are available from the Massachusetts Institute of Technology Haystack Observatory Madrigal database  (Rideout & Coster, 2006; openmadrigal. org). To better understand the impact of the SF, we removed the quiet-time values from the disturbed ones, and we analyzed the resulting differential VTEC (dVTEC). E. The SF impact on GNSS is estimated from the above data by analyzing the "quality" of the performance of GPS, GLONASS, and Galileo systems. The quality of GNSS performance was analyzed based on the following parameters: 1. Occurrence frequency, or the "density" of LOL (or phase slips), that is estimated as the total number of LOL divided by the total number of observations: P = (N/S) * 100%.
, 2018). We performed calculations for GPS and GLONASS separately. The median values of X, Y, Z coordinates during 24 hr are considered to be a true position. The error was calculated from the difference in the true and estimated coordinates as   12 ). The Cyprus transmitter (35°N, 34°E) is equipped by the chirp sounder with the sweep rate of 100 kHz/s, the frequency range of 8-32 MHz  (Vertogradov et al. , 2017) , and intervals between soundings of 15 min. The path lengths are 3,100, 2,300, 2,200, and 5,700 km for Magadan-Tory, Khabarovsk-Tory, Norilsk-Tory, and Cyprus-Tory paths, respectively. σ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ΔX 2 þ ΔY 2 þ ΔZ 2 p (1) The OIS data represent a set of ionograms. Each ionogram is a three-column array where the frequencies, group paths, and normalized amplitudes for the received HF signals are recorded.
, 2016). In addition, coupling between the ionosphere and the thermosphere which is also largely perturbed by SF can play a role. A hemispheric asymmetry in the ionospheric response to SF is often attributed to seasonal thermospheric effects and to related changes in the neutral composition  (Tsugawa et al. , 2006). Besides the thermosphere, the asymmetry can be introduced by electrodynamical changes caused by SF  (Zhang et al. , 2017). In addition to the absolute VTEC, we also analyze TEC variation data (dTEC). This kind of data allows excluding the effects of regular ionosphere. To create the dTEC maps, we used data from ~4,200 receivers of permanent global and regional GNSS networks. Figure  5  shows 2-20 min dTEC maps for several moments of time on 6 September 2017. The full dynamics of the dTEC for the period of time from 8 to 18 UT can be found in Animation S2 (available as the supplementary materials).
For both X-class events of 6 September 2017 we compute slant TEC derivatives, apply simple geometric oblique factor, and adjust the results for the solar zenith angle at IPPs. We also account for undisturbed TEC behavior using polynomial fit to the data before and after the flare. The results are presented in Figures  8b  and 8c. Among Space Weather local TEC variations, coherent response of TEC derivative to both SFs is clearly observed. Note that the maximum values of the observed flare induced TEC derivatives vary significantly from station to station even after the correction for oblique factor and solar elevation. For MSU0 station this result can be to some extent explained by higher sampling of the data (15 s as compared to 30s for all other stations). Closer look at the Figure  8  that the most outfitted TEC derivative values are observed for the stations with the lowest elevation angles of Beidou C05 satellite (such as once again MSU0, CZTG, and KITG).
Acknowledgments We thank the  OMNIWeb Plus service  (http://omniweb. gsfc. nasa. fov)for the geophysical and interplanetary data, the  World Data Center (WDC) for Geomagnetism, Kyoto  (http://wdc. kugi. kyoto-u. ac. jp/aedir/) for the auroral electrojet data, and  Radio Solar Telescope Network  (ftp://ftp. ngdc. noaa. gov/STP/space-weather/solar-data/ solar-features/solar-radio) for the solar radio emission data. We acknowledge the  CEDAR OpenMadrigal Services  for the data of the absolute VTEC (http:// cedar. openmadrigal. org). We thank the global and regional networks of GNSS receivers: IGS (Dow et al. , 2009), CHAIN (Jayachandran et al. , 2009), TrigNet (ftp://ftp. trignet. co. za), GEOSCIENCE AUSTRALIA  (ftp://ftp. ga. gov. au/geodesy-outgoing),  NOAA  (ftp://www. ngs. noaa. gov/),LPIM (ftp://geoftp.
| In silico design of the PCR-RFLP assay In silico design of PCR-RFLP was performed using CLC Bio main workbench software (version 7. 9. 1). Cytochrome C oxidase subunit I (COI) nucleotide sequences were downloaded from the National Center for Biotechnology Information (NCBI) and aligned using multiple sequence alignment (MUSCLE) tool with default parameters  (Edgar, 2004). We included COI sequences from ecologically relevant, Drosophila-like species. We hypothesized that "contaminant" larvae (i. e. , hardly distinguishable from D. melanogaster in the field) would likely belong to the Drosophilidae family. Therefore, we included COI sequences from Drosophilidae detected in the Rhône-Alpes region according to previous field surveys, although most of these species were sporadically observed  (Withers & Allemand, 2012).
We also in-   S1 ). Sequences under 650 base pairs (bp) were filtered out, and only unique sequences (i. e. , no duplicate) were retained for each species. These unique sequences were verified manually using the Basic Local Alignment Tool (BLAST) to ensure that they encode for COI. In silico digestion of the clean COI sequences was performed using the 1,562 commercially available restriction enzymes present in CLC Bio, which correspond to 340 distinct cutting sites. This method allowed us to attribute a PCR-RFLP profile number to each species. The 20 μl-digestion mixture was composed of 1 μl of MboII (5 U/ μl), 2 μl of 10× reaction buffer, 7 μl of PCR product, and 10 μl of DNase-free water. A control sample without the restriction enzyme (replaced by water) was used as a nondigested control.
| In silico identification of D. melanogaster species using COI-MboII PCR-RFLP assay The selected COI sequences were digested in silico using the set of restriction enzymes available in CLC software (version 7. 9. 1). We retained commercially available restriction enzymes that display, after a single digestion step, a D. melanogaster profile that would be distinct from Drosophilidae of the region  (Withers & Allemand, 2012)  and especially from the co-occurring Drosophila species present in the field stations. Among the potential candidate enzymes, AciI, AceIII, and MboII discriminated D. melanogaster from the Drosophila species detected in the two field stations. We selected MboII as it also provided a specific PCR-RFLP profile for the nontarget Drosophila species (Supporting information Figures  S1-S2 ).
, 2014). When an individual was caught more than once in a given year, only the last measure was considered. When both antlers were intact, the average length was computed and retained for the analyses. When one antler was broken, the value of the remaining intact antler was retained. When both antlers were broken, the individual was removed from the dataset. Although a few captures occurred in October-November in the very first years of monitoring, we restricted our analyses to data collected from 1 December to avoid possible overlap between antlers growing during the current year and uncast antlers from the previous year. All males included in our analyses were of known age because they were caught within their first year of life, when age can FIGURE 1 A fawn with visible buttons (photograph reproduced with permission from F.
022 ± 0. 002, N = 224). Fawn antler length did not differ between populations (∆AIC of + 1. 93 for the model including "population"), and the daily growth rate did not differ between populations (∆AIC of +3. 57 for the model including the "population × date of capture" interaction term). We computed a standardized fawn antler length from the selected model by adjusting antler length to the median date of capture (i. e. 8 February) and pooling the data for both populations. The best model accounting for variation in yearling antler length in relation to capture date was the threshold model with a single slope (Table  S2 ). Antler length increased linearly with date of capture until day 59 (i. e. 28 January) (Figure  2b ; slope of 0. 024 ± 0. 002, N = 257) and remained constant thereafter. Antler length did not differ between populations (∆AIC of +1.
When analysing adult antler length, we also included adult body mass as a covariate (e. g. Table  S5b ) to measure the consequences of early allocation to antler growth on both absolute and relative adult antler length. For analysis of longevity, adult body mass and adult antler length, we fitted linear mixed-effects models ("lmer" function in the r package lme4,  Bates, Mächler, Bolker, & Walker, 2015). For survival beyond 3 and 6 years of age, we used generalized linear mixed-effects models with survival entered as a binomial variable. We tested the short-term survival costs (i. e. survival beyond 3 years of age) only for yearling antler length as our sample size was too limited to investigate short-term survival costs in fawns. All traits except survival were log-transformed. For each set of analyses, we used AIC for model selection (see above for a full description of the procedure).
All analyses were performed using r version 3. 4. 0 (R Core Team, 2015) , and all results are reported as M ± SE. In our dataset, two individuals had particularly short antlers for their age (one fawn with antlers measuring 1 mm and one yearling with antlers measuring 10 mm). All analyses were performed with and without these males, and in most cases, results were qualitatively similar (Table  S5  vs. Table  S6  for fawn antler length; Table  S7  vs. Table  S8  for yearling antler length). As we did not have any biological reason to exclude these males, we decided to present the results obtained with the complete dataset. We explicitly specified in the text when including these outliers mattered. Population-specific sample sizes used for each analysis are provided in Table  2.
The recent development of two-sex models  (Schindler et al. , 2015)  now offers a way to quantify how variation in the expression of sexual traits such as antler length influences the demography of a population, provided the required data (i. e. sex-specific relationships between demographic parameters [recruitment and survival] and antler length, father-offspring relationships and age-specific changes of antler length, pedigrees) will be available. As paternity analyses are becoming very common in wild vertebrates, we hope that our study will stimulate such studies that might reveal important insights on the role of sexual selection on population demography. Finally, one striking paradox from our study is that the honest signal of individual performance conveyed by antler length is apparent in fawns when males do not compete for females.
ACKNOWLEDGEMENTS Field operations were supported by grants from the  Agence Nationale de la Recherche  ( ANR-15-CE32-0002-01 ), and performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We thank all the  ONCFS  staff, in particular Claude Warnant and  Gilles Capron , and the field volunteers for the organization of the roe deer captures. We warmly thank  Dylan Childs  and two anonymous referees for helpful and constructive comments that markedly improved our manuscript. The authors have declared that no conflict of interest exists. The land manager of both sites,  Office National des Forêts (ONF) , permitted the study of the populations (Partnership Convention ONCFS-ONF, dated  23-12-2005 ).
Another explanation may be the coverage of Mycobacterium spp. included in different commercial database platforms  19, 22, 28, 29. The objective of the present study was to evaluate the identification performance of clinical Mycobacterium spp. using the VITEK MS Knowledge Base Version 3. 0 in combination with the VITEK ® MS Mycobacterium/ Nocardia Kit (bioMérieux SA, Marcy L'Étoile, France) to generate highly reliable results in sample processing.