dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq library preparation and Illumina sequencing. PolyA mRNA samples were sent to SeqWright Genomic Services (GE Healthcare, Houston), where the libraries were constructed and linearly amplified using ClonTech SMART Sample Prep (ClonTech). Illumina HiSeq 2500 sequencing was performed and a dataset of approximately PE100 70M RNA-seq reads were obtained for each stage and for each genotype (biological triplicates). The overall read quality was checked using FastQC ("Andrews, S. , FastQC: A quality control tool for high throughput sequence data, http://www. bioinformatics. bbsrc. ac. uk/projects/fastqc/, 15/09/2014"). Transcriptome assembly and alignment strategies. Due to its high variance, the sample AX was excluded from further analyses (Supplementary Fig. S1 ) and the overall workflow is summarized schematically in Fig.
Mapping to Genome (Methodology 1). A total of approximately 277 million paired-end reads were first mapped to the reference genome (580 Mbp) of B. schlosseri [European Nucleotide Archive accession number HF548551 ("Voskoboynik, A. , Botryllus schlosseri complete mitochondrial genome, isolate sc6a-b, http://www. ncbi. nlm. nih. gov/nuccore/HF548551, 22/09/2014) using TopHat v2. 0. 11-Cufflinks v2. 1. 1 pipeline. Bowtie2 (v2. 1. 0) was used for indexing the reference genome and subsequently aligned reads were assembled into transcripts using the Cufflinks package. After assembling all samples separately, assemblies were merged using Cuffmerge. Cufflinks measures the abundance of transcripts as FPKM. Finally, differential gene expression was tested using Cuffdiff package based on following criterions: FDR (q-value) < 0.
Reference transcriptome assembly and mapping (Methodology 2). A reference transcriptome of B. schlosseri, has already been assembled, using the Trinity 44 pipeline, by combining published ESTs databases  45  and a transcriptomic dataset  46  (Table  1 ). This transcriptome assembly is available at Octopus Bioinformatics Server ("Tiozzo, S. , http://octopus. obs-vlfr. fr/public/botryllus/blast_botryllus. php, 07/01/2015") for public use. Reads were mapped to this reference transcriptome by using Bowtie2 (v2. 1. 0) with the number of mismatches allowed (-N) set to 1 and -a option to report all alignments. The read count table was prepared using an in-house Perl script. Changes in gene expression were identified using DEGseq package  23. For significant differences in expression, P-value and FDR (adjusted P-value or Q-value) thresholds were set to 0.
Functional annotation and gene enrichment analysis. In order to find similarity with known genes and to predict their functions, multiple functional analyses on the differentially expressed contigs were performed. Sequence comparisons were performed using BLASTx and BLASTp against the NCBI non-redundant (nr) protein database ("ftp://ftp. ncbi. nlm. nih. gov/blast/db/FASTA/nr. gz, 18/04/2015). All homology searches against NCBI non-redundant (nr) database were carried out on an HTC cluster facility provided by ABiMS, Station Biologique de Roscoff (http://abims. sb-roscoff. fr/resources/cluster, 18/04/2015).
The ability of YKL-05-093 to mimic the effects of PTH with respect to SOST and RANKL gene regulation supports the hypothesis that the actions of YKL-05-093 might mimic the effects of PTH on many genes. We therefore performed RNA-seq on Ocy454 cells treated for four hours with vehicle, PTH (1 nM) or YKL-05-093 (0. 5 mM) to determine the overlap in global gene regulation by these two agents. Significantly 446 genes were (42 fold, FDRo0. 05) regulated by PTH, and 257 genes were significantly regulated by YKL-05-093. Of the 446 PTH-regulated genes, 142 (32%) were co-regulated in the same direction by YKL-05-093 (Fig. 6a ,b, Supplementary Table  3  for differentially expressed genes and Supplementary Dataset 1 for all RNA-seq data). This significant overlap was not due to random chance (Supplementary Fig.
* indicates Po0. 001 comparing WT and SIK2 cKO mice. (j,k) Mice as in (i) were treated with a single dose of PTH (1 mg kg À 1 ) and killed 2 h later. Expression of CITED1, SOST and RANKL in femur RNA was determined by RT-qPCR. * indicates Po0. 05 comparing vehicle and PTH. (l) Ocy454 cells were infected with shRNA-expressing lentiviruses targeting CRTC1, CRTC2, or CRTC3. Cells were then treated with PTH (1 nM, 4 h) and RANKL transcript abundance was measured. (m) Ocy454 cells were treated with vehicle or PTH (20 nM, 60 min) followed by ChIP for CRTC2. DNA was quantified by qPCR using primers detecting the indicated RANKL regions, and data are expressed as fold enrichment versus control IgG. *indicates Po0. 05 comparing vehicle and PTH. The À 23 kB and À 75 kB enhancers correspond to 'D2' and 'D5' enhancers  42.
Quantification of sclerostin positive osteocytes was performed on a blinded basis. All photomicrographs were taken 3 mm below the growth plate on the lateral side of the tibia. All osteocytes were counted and then scored as either sclerostinpositive or negative. Sections from at least four mice per experimental group were analysed. Quantification of immunostaining was done based on coded sample numbers in a completely blinded manner. Representative photomicrographs are displayed next to quantification in data figures. Chromatin immunoprecipitations. ChIP assay was performed using a kit (EZ-Chip, Miilipore, 17-371, Billerica, MA) according to the manufacturer's instructions. Briefly, cells were grown at 37 °C for 7 days, followed by PTH treatment (25-50 nM) for the indicated times. Cells were then cross-linked with 1% formaldehyde for 10 min and then quenched with 0.
oxfordjournals. org/content/early/2012/10/25/bioinformatics. bts635). Gene expression counts were calculated using HTSeq v. 0. 6. 0 (http://www. huber. embl. de/ users/anders/HTSeq/doc/overview. html) based on a current Ensembl annotation file for mm10/GRCm38 (release 75). Differential expression analysis was performed using EgdeR package based on the criteria of more than two-fold change in expression value versus control and false discovery rates (FDR) o0. 05. Venn diagrams from gene set analysis were generated using genes with 41. 5 fold change in expression values and FDR o0. 05. Significance testing for gene set overlap was performed according to a standard hypergeometric distribution, P-values o2. 2 Â 10 À 16. The RNA-seq data are deposited in GEO under accession code GSE76932. Small molecule synthesis.
The 10 mm sections were left unstained for measurement of dynamic parameters, and only double-labels were measured, avoiding nonspecific fluorochrome labelling. A standard dynamic bone histomorphometric analysis of the tibial metaphysis was done using the Osteomeasure analysing system (Osteometrics Inc. , Decatur, GA, USA). Measurements were performed in the area of secondary spongiosa, 200 mm below the proximal growth plate. The observer was blinded to the experimental genotype at the time of measurement. The structural, dynamic and cellular parameters were calculated and expressed according to the standardized nomenclature  70. Statistics. All experiments were performed at least twice. Data are expressed as means of triplicate biological repeats within a representative experiment plus/minus standard error.
Cells were then treated with PTH (1 nM, 4 h) and RANKL transcript abundance was measured. (m) Ocy454 cells were treated with vehicle or PTH (20 nM, 60 min) followed by ChIP for CRTC2. DNA was quantified by qPCR using primers detecting the indicated RANKL regions, and data are expressed as fold enrichment versus control IgG. *indicates Po0. 05 comparing vehicle and PTH. The À 23 kB and À 75 kB enhancers correspond to 'D2' and 'D5' enhancers 42.
Acknowledgements We thank  Drs David Fisher ,  Michael Ominsky ,  Kelly Lauter ,  Chris Cowan ,  Eric Olson ,  Jerry Feng ,  Alykhan Shamji  and  John Doench  for providing reagents and suggestions,  Dr Sean Wu  for performing blastocyst injections, and  Drs Michael Mannstadt ,  Joseph Avruch  and  Tatsuya Kobayashi  for comments on this manuscript. Sclerostin antibody was generously provided by  Amgen Inc. and  UCB Pharma. We thank for  MGH Endocrine Unit Center for Skeletal Research P30  core ( P30AR066261 ) for assistance. This work is supported by grants from the  Ellison Foundation (HMK)  and the  NIH :  K08AR067285  (MNW),  R03AR059942  (JYW),  P01DK011794 (HMK)  and  P30AR066261 (HMK).
gps-tracking data of montagu's harriers Between 2009 and 2015, we collected GPS-tracking data using UvA-BiTS GPS trackers  (Bouten et al. 2013; www. uva-bits. nl) from 36 Montagu's Harriers (25 males and 11 females). Birds were captured during the breeding season in the Netherlands (53Á2°N 7Á2°E, N = 17), France (46Á2°N 0Á4°W, N = 8) and Denmark (55Á1°N 8Á7°E, N = 6), plus five at their wintering site in Senegal (Khelcom). One male captured in the Netherlands spent the three following breeding seasons in Germany (52Á6°N 8Á37°E). One Danish male stayed in Africa during one summer (I. H. Sørensen, A. E. Schlaich, R. H. G. Klaassen, H. Heldbjerg & B. J. Koks, in prep) and was removed from the data set for further analyses. Since several individuals were tracked in more than 1 year, the total number of year*individual combinations in the data set was 53.
Of those, only 43 had complete data (in five cases, the end of the winter was missing; in two cases, the start was missing; and in three cases, both the start and the end were missing). In addition, we excluded two individuals as insufficient data were collected per day to calculate daily measures, thus keeping, in total, 41 complete year*individuals. To analyse whether foraging behaviour changed during the individuals' stay at their last wintering site where they might experience deteriorating conditions within the same area, we considered only year*individual combinations where the bird stayed longer than 60 days at its last wintering site. Those summed up to 31 year*individual combinations. Arrival and departure at different wintering sites were determined visually (see Fig. S2  for an example), with the first wintering site being defined as the first site south of 18°N in which the bird stayed for at least 3 days.
calculation of daily foraging parameters Foraging parameters such as time spent flying, distance covered and home range size were calculated for each day. Days with fewer than 75% of expected positions (54 for 10-min interval, 36 for 15-min interval and 18 for 30-min interval) were removed from the data set. Using instantaneous speed, we determined for each position if the bird was sitting or flying by means of a threshold of 1Á2 m s À1 (local minimum of a two-peaked frequency distribution of speed values, see Fig. S3  for an example of a frequency distribution of instantaneous speeds). Time spent flying per day (in hours) was calculated as the percentage of positions spent flying in order to correct for length of day. Daily cumulative distance was calculated by summing up the distances between consecutive positions during each day.
Distance between positions was calculated using function distMeeus from R package geosphere version 1. 5-1  (Hijmans 2015). Daily home range size was calculated as 95% kernel density estimation using R package rhr version 1. 2  (Signer & Balkenhol 2015). The bandwidth parameter h was determined by reference bandwidth estimation using function rhrHref. As grasshopper numbers were negatively correlated with NDVI (see Results), we used vegetation greenness as a proxy for grasshopper abundance in areas where no field data on grasshoppers were collected. Indeed, the abundance of grasshoppers will not strictly be determined by the greenness of the vegetation at the exact moment of the transect counts, but will also have been influenced, for example by the amount of rainfall during the preceding rainy season, the greenness in the previous dry season or the number of grasshoppers in the previous year.
Nevertheless, it has been shown that NDVI is a valuable tool to gain insight into trophic interactions on a global scale  (Pettorelli et al. 2005)  For each wintering site of all GPS-tracked Montagu's Harriers, 25 9 25 = 625 pixels of 250 9 250 m (~39 km², mean home range size) around the mean latitude/longitude of that site were downloaded for the years the bird was present at that site. The average of the 625 pixels was calculated for each 16-day period, and to each day of the harrier data set, the value of the corresponding period was added.
Acknowledgements We are deeply grateful for help with conducting fieldwork in Senegal to  Jean-Franc ßois Blanc ,  Vincent Blanc ,  Albert Dely Faye  and  Steve Augiron. We would like to thank  Henning Heldbjerg  for the collaboration in the Montagu's  Harrier project of Dansk Ornitologisk Forening (DOF -Birdlife Denmark ). Fieldwork in Denmark was conducted by  Michael Clausen  and  Iben Hove Sørensen , A. E. S. and  R. H. G. K. under kind permission of the  Natural History Museum of Denmark. Fieldwork in France was conducted by A. V. ,  Steve Augiron ,  Vincent Rocheteau ,  Samuel Peirera-Dias ,  Olivier Lamy ,  Beatriz Arroyo  and  Franc ßois Mougeot. Fieldwork  in the Netherlands was conducted by  BK ,  Christiane Trierweiler , A. E. S. , R. H. G. K. ,  Madeleine Postma  and many volunteers.
MATERIALS AND METHODS Study design. RAPID 1 (NCT00152386) and RAPID 2 (NCT00160602) study designs have been described previously  (19, 20). Briefly, RAPID 1 and 2 were phase III, randomized, double-blind placebo-controlled trials, with durations of 52 weeks (RAPID 1) and 24 weeks (RAPID 2), respectively. Patients were eligible for inclusion in the trials if they met the following criteria: age $18 years with a diagnosis of RA, defined by American College of Rheumatology (ACR) 1987 criteria (  21 ), of $6 months but ,15 years' duration; had received MTX ($10 mg/week) for $6 months, with a stable dose for $2 months prior to baseline and had active RA at screening and baseline, defined as $9 tender joints and $9 swollen joints; and had an erythrocyte sedimentation rate (ESR; Westergren) $30 mm/hour or a C-reactive protein concentration.
A pre-specified subgroup analysis of the primary end point (i. e. , ACR20 response at week 24) was performed in both RAPID 1 and 2 to assess the efficacy of CZP according to MTX dose category at enrollment (,15 mg/week or $15 mg/week). Subsequently, a further subgroup analysis pooled data from RAPID 1 and 2 studies by treatment group (CZP 200 mg, CZP 400 mg, or placebo) and excluded all patients with unknown MTX dose at baseline. Efficacy and safety/ tolerability were assessed in each pooled treatment group according to baseline MTX dose category: ,15 mg/week or $15 mg/week. A post hoc sensitivity analysis was also.
RESULTS Patients. Of the 1,601 patients originally randomized into RAPID 1 and RAPID 2, the MTX dose was available for 1,598 (638, 635, and 325 patients in the 200 mg CZP plus MTX, 400 mg CZP plus MTX, and placebo plus MTX groups, respectively) and were included in these analyses. Baseline demographics and disease activity were similar across the 3 treatment groups (CZP 200 mg, CZP 400 mg, or placebo) regardless of baseline MTX dose category (data not shown). Overall, more than 90% of patients were of Caucasian descent. Three patients were receiving MTX under 10 mg/week at study baseline (study protocol deviators), and 792 patients received MTX at a dosage of 10 mg/week (see Table  1  and   In the sensitivity analysis (MTX 3-dose categorization), baseline demographics and disease activity were also found to be similar across the treatment groups regardless of baseline MTX dose category (data not shown).
ACKNOWLEDGMENTS The authors acknowledge ' Matladi N. Ndlovu, PhD ,  UCB Pharma, Brussels, Belgium  for publication coordination, and  Owen Davies ,  UCB Pharma, Brussels, Belgium , for critically reviewing the manuscript. The authors would also like to thank all the investigators who participated in this study. Editorial assistance for the development of this manuscript was provided by  Shelley Lindley  and  Andrew Richardson  from PAREXEL, and by  Costello Medical Consulting.
Global mercury simulations The current study is based on multi-model simulations performed as part of the Mercury Modeling Task Force (MMTF) under the GMOS project  (Travnikov et al. , 2016). Four global models (ECHMERIT, GEM-MACH-Hg, the mercury version of the Global Environmental Multi-scale, Modelling air quality and Chemistry model, GEOS-Chem, and GLE-MOS, the Global EMEP Multi-media Modelling System) were applied for evaluating monthly-averaged atmospheric mercury concentrations and deposition at various Arctic and Antarctic ground-based sites for the year 2013. Additionally, GEM-MACH-Hg and GEOS-Chem provided hourlyaveraged data from 2011 to 2014 to allow investigations of interannual variability. A brief description of the parameterization of the four models is given below. The models differ significantly in their description of mercury atmospheric chemistry and their parameterization of processes specific to polar regions (i.
Observations Figure  2a  shows monthly box plots of all data collected at the four Arctic sites. The average Hg(0) value in the Arctic over the 2011-2014 period is 1. 46 ± 0. 33 ng m -3. This concentration falls within the range of what is observed in the Northern Hemisphere  (Sprovieri et al. , 2016b). The highest mean is at AND (1. 55 ± 0. 15 ng m -3 over the 2011-2015 period), which is closer to European industrialized areas than other sites and experiences less frequent and pronounced AMDEs in spring (see below). There is a clear Hg(0) concentration gradient (except from June to August): AND > NYA > SND > ALT. The Hg(0) concentration data from the four Arctic sites for the period 2011-2015 are presented as monthly box and whisker plots in Fig. 3. Information regarding annually and monthly based statistics at the three sites can be found in Tables  2  and 3 , respectively.
Monthly based statistics (number of hourly-averaged Hg(0) data (n), mean, median, standard deviation (SD)) of Hg(0) concentrations (in ng m -3 ) at ground-based polar sites over the 2011-2015 period. Note that 2013 data at DC refer to concentrations recorded at 210 cm above the snowpack. NA: not available due to QA/QC invalidation, instrument failure, or because the QA/QC validation is currently in progress (2015 data). cases), with fewer in March and June (14 and 10 % of the time, respectively). This result is in good agreement with the distribution reported by  Berg et al. (2013)    teorology  (Cole and Steffen, 2010). The authors found that the length, magnitude, and frequency of AMDEs decreased with increasing local temperature. These results are consistent with earlier studies on the temperature dependence of the halogen chemistry initiating AMDEs and ozone depletion events  (Koop et al.
Reactive mercury and deposition Year 2013 modeled monthly-averaged RM concentrations and wet/dry deposition are displayed in Fig. 8a. GEOS-Chem, GEM-MACH-Hg, and GLEMOS predict increased RM concentrations in spring, during the AMDEs season, consistent with the observed pattern at ALT (Fig. 5 ) and NYA  (Wang, 2015). The fact that ECHMERIT does not capture the spring enhancement is not surprising since the model does not implement any chemistry specific to polar regions. GLEMOS also predicts a RM spring maximum at AND, in line with the modeled Hg(0) spring minimum at this site (Fig. 6a ). As discussed in a previous section, this can likely be attributed to uncertainties in Br fields extracted from p-TOMCAT. Long-term measurements of RM in the Arctic are scarce and limited to ALT and NYA (data not presented here).
Comparison with models Table  4  displays goodness-of-fit statistics between monthlyaveraged modeled and observed data in 2013. ECHMERIT slightly underestimates Hg(0) concentrations at the three ground-based Antarctic sites. Contrarily, the three other global models overestimate Hg(0) levels, suggesting an underestimation of sinks. The ability of the four models to reproduce the observed seasonality of Hg(0) concentrations at ground-based Antarctic sites in 2013 is shown in Fig. 6b  and discussed in the following sections. The interannual variability of the monthly Hg(0) concentration distribution at Antarctic ground-based sites as simulated by GEM-MACH-Hg and GEOS-Chem is displayed in Fig. 7b  while Table  5  shows the percent bias between hourly-averaged modeled and observed data on a seasonal basis from 2011 to 2014.
(d) Summer The daily variation of Hg(0) concentrations was investigated based on hourly-averaged data provided by GEOS-Chem and GEM-MACH-Hg. The two models are not able to reproduce the noon maximum observed at DC and DDU in summer (Sect. 3. 2. 1), suggesting that the dynamic daily cycle of deposition and re-emission at the air/snow interface is not captured by the models. The bidirectional exchange of Hg(  0 ) is complex and influenced by multiple environmental variables (e. g. , UV intensity, temperature, atmospheric turbulence, presence of reactants) limiting the accuracy of flux modeling  (Zhu et al. , 2016). The work carried out by  Durnford et al. (2012)  in the Arctic and by  Zatko et al. (2016)  in Antarctica could be good starting points for future research. The former developed a new dynamic physically based snowpack model to determine the fate of mercury deposited onto snowpacks; the latter incorporated an idealized snowpack along with a snow radiative transfer model  (Zatko et al.
Annually based statistics (number of hourly-averaged data (n), mean, median, standard deviation (SD)) of Hg(0) concentrations (in ng m -3 ) at ground-based polar sites over the 2011-2015 period. Note that 2013 data at DC refer to concentrations recorded at 210 cm above the snowpack. The 2015 data coverage is May to June at SND and January to May at DDU (see Table3). NA: not available due to QA/QC invalidation, instrument failure, or because the QA/QC validation is currently in progress (2015 data).
Acknowledgements. Hélène Angot ,  Olivier Magand , and  Aurélien Dommergue  thank the overwintering crew:  S. Aguado ,  D. Buiron ,  N. Coillard ,  G. Dufresnes ,  J. Guilhermet ,  B. Jourdain ,  B. Laulier ,  S. Oros ,  A. Thollot , and  N. Vogel  at  DDU ,  S. Aubin ,  A. Barbero ,  N. Hueber ,  C. Lenormant , and  R. Jacob  at  DC. This work contributed to the  EU-FP7  project  Global Mercury Observation System  (GMOS, www. gmos. eu)and has been supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ) and the  Institut Universitaire de France. Logistical  and financial support was provided by the  French Polar Institute IPEV  ( Program 1028 , GMOstral). Katrine A. Pfaffhuber  thanks the  Norwegian Environmental Agency  and the  Norwegian Antarctic Research Expeditions  for long-term financial support of Norwegian mercury measurements and in particular the technicians  J.
H. Wasseng  and  A. Bäcklund  at  NILU  for their excellent care taking of the Tekran monitors. Noelle E. Selin  and  Shaojie Song  acknowledge support from the  US National Science Foundation Atmospheric Chemistry Program  under grant no. 1053648. Aurélien Dommergue ,  Hélène Angot ,  Noelle E. Selin , and  Shaojie Song  acknowledge the  MIT-France Seed Fund. Edited by: N. Pirrone Reviewed by:  L. -E. Heimburger  and one anonymous referee.
Quality assurance and quality control procedures An automatic calibration step of the Tekran 2537A analyzer was carried out every 25 h with an internal mercury permeation source. External calibrations were performed twice a year by manually injecting saturated mercury vapor taken from a temperature-controlled vessel (Tekran 2505 mercury vapor calibration unit, Hamilton digital syringe). As described by  Angot et al. (2014) , bi-monthly to monthly routine maintenance operations were performed. A software program was developed at the LGGE (Laboratoire de Glaciologie et Géophysique de l'Environnement) in accordance with quality control practice in well-established North American networks  (Steffen et al. , 2012). Based on various flagging criteria  (Munthe et al. , 2011; D'Amore et al. , 2015) , it enabled rapid data processing in order to produce clean time series of Hg(0).