dataesr/scientific-paragraphs-categorization huggingface dataset:
Virus detection in the antennae of VSH and non-VSH bees. Differences in the antennal transcriptomes of VSH and non-Varroa sensitive (NVS) bees were identified using RNA-seq analysis. Age-matched samples from VSH and NVS bees were analysed (see Supplementary Table  S1  online). Four RNA libraries were generated for each behavioural group (VSH and NVS). The total number of unique aligned reads ranged from 13. 8 to 43. 8 million, of which around 74% were unambiguously and uniquely aligned to the A. mellifera genome assembly 4. 5 (see Supplementary Table  S2  online). The percentages of alignment were very similar between the two phenotypic groups suggesting an absence of bias in the sequencing data. Raw and processed genomic data are available from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database with the accession number GSE63613.
Differential gene expression in the antennae of VSH and non-VSH (NVS) bees. A total of 258 coding transcripts were found to be differentially expressed (173 up-and 85 down-regulated) in the antennae of VSH as compared to non-VSH (NVS) bees at an adjusted P-value < 0. 05 (see Supplementary Table  S3  online). The magnitude of the log-fold change ranged from 0. 43 to 3. 53 for the under-expressed genes and from 0. 42 to 8. 14 for the over-expressed genes. Genes that were found to be differentially expressed in the antennae of VSH and NVS bees were explored using ontological classification provided by the Apis mellifera reference sequence entry in the NCBI and the DAVID databases. Gene Ontology analysis revealed that the 'defence response' category (defined as reactions triggered in response to the presence of a foreign body or the occurrence of an injury, which result in restriction of damage to the organism attacked or prevention/recovery from the infection caused by the attack) is enriched in the VSH bee antennae (p = 0.
RNA-seq analyses and sequence alignments. RNA sequencing and analysis of the RNA-seq data were carried out at the Institut de Genomique Fonctionnelle (Montpellier, France). The RNA-seq libraries were prepared using the TruSeq Stranded mRNA Sample Preparation kit according to the manufacturer's instructions (Illumina). Qualitative and quantitative library validations were performed on a DNA 1000 Agilent chip as well as by quantitative PCR using SYBR Green (Applied Biosystems 7500). Clusters were generated on a flow-cell within a cBot using the Cluster Generation Kit (Illumina) and libraries were sequenced on a HiSeq2000 using a SBS (Sequence By Synthesis) technique (Illumina). Image analysis was performed with the HiSeq Control software (HCS) and base calling using the RTA software. Quality control was operated through the FastQC software.
Potential contaminants were investigated with the FastQ Screen software; several animal and bacterial genomes, as well as honey bee virus genomes were tested. TopHat2 (TopHat2. 0. 12 with Bowtie 2. 2. 3) was used to align RNA-Seq reads to the A. mellifera genome assembly 4. 5, and aligned reads were counted using the HTSeq-count software (version 6. 1p1), with GeneID (NCBI) identifiers. Mapping was also performed on sequences of honey bee virus genomes (Sacbrood virus: NC 002066. 1, Deformed wing virus: NC 004830. 2, Black queen cell virus: NC 003784. 1, Acute bee paralysis virus: NC 002548. 1, Kashmir bee virus: NC 004807. 1, Varroa destructor virus: NC 006494. 1 and Chronic bee paralysis virus: NC 010711. 1).
EdgeR  61, 62  is an exact test based on a negative binomial distribution. Due to high performance in the ability to uncover true positives, this package is recommended for differential expression analysis of RNA-Seq data  63. The P-value was adjusted for multiple testing using the Benjamini-Hochberg procedure. Comparison of virus abundance between VSH and NVS antennae was performed using a non-parametric Mann-Whitney test due to the low number of replicates in each group (n = 4, two-tailed). DAVID 6. 7 tool was used to determine the enriched functional groups, based on GO terms, within the complete list of gene expressed in antennae  64. Exact hypergeometric probability test was performed to test the statistical significance of the overlap between the VSH/NVS and NUR/FOR gene sets  65. We also determined whether the gene expression profile of VSH bees was more similar to nurses or to foragers using Chi-square tests with Yates correction.
Acknowledgments We thank  Frans Laas  for providing honey bee colonies,  Murray McKenzie  for building the observation hive and enclosure,  Sally Kim  for her great help during the observations and sampling, and  Jamie McQuillan  for assistance with the elaboration of the RNA extraction protocol. We are grateful to  Aaron Jeffs  for Qubit measurements and Bioanalysis of the samples in New Zealand. This work was funded by a  BEEDOC grant  ( FP7 ,  RTD REG/E. 4 (2009) D/561221 ) to YLC.
Acknowledgements. Parts of the research leading to these results have received funding from the  European Union 's  Seventh Framework Programme FP7  within the  MEGAPOLI  project, grant agreement no. 212520 , and the  ATMOPACS FP7 IDEAS  project. The research conducted by  MPIC  was supported by internal funds. Support from the  French ANR  project  MEGAPOLI -PARIS  ( ANR-09-BLAN-0356 ) and from the  CNRS-INSU/FEFE  via l'ADEME (no. 0962c0018 ) is acknowledged. We are grateful for the logistical support in the field by IPSL/SIRTA, by  Laboratoire d'Hygiène de la Ville de Paris (LHVP)  and by the staff of the  Golf Départemental de la Poudrerie. The  SAFIRE team  is acknowledged and thanked for performing ATR-42 flights and measurements. Edited by:  R. MacKenzie.
Acknowledgments This work was performed within the the  Laboratory of Excellence (Labex) of Integrative Biology of Emerging Infectious Diseases (IBEID). This work was supported by the  Alfort National Veterinary School (ENVA) , by the  French National Institute for Agricultural Research (INRA)  and by the  French Agency for Food, Environmental and Occupational Health and Safety (Anses). TD was funded by  Anses.
Discussion To the best of our knowledge, this is the first report of a patient with PWLS associated with a complex sSMC, which involved chromosome 6 and 10 at breakpoints of 6q16. 3q23. 3 and 10p11. 21p11. 1, respectively. Complex small supernumerary marker chromosomes (sSMC) are a subgroup of sSMC derived from more than one chromosome  [2]. The Liehr sSMC database  [12]  has collected approximately 412 cases of complex sSMC from the literature, which represent 8. 4 % of all sSMC cases  [4]. The majority of complex sSMC cases are der(22)t(11;22)(q23;q11. 2) or Emanuel syndrome cases (339/412 cases; 82 %) and present generally in banding cytogenetics as centric minutes  [3]. Among the 73 complex sSMC described by Liehr T. excluding Emanuel syndrome, 64 % were inherited from a balanced translocation in one parent (83 % maternally derived)  [3]  and 10 % were in mosaic.
All of the human chromosomes, except chromosome 10, have already been shown to be involved in complex sSMC  [3]. To investigate whether the duplicated 6q16. 3q23. 3 and 10p11. 21p11. 1 regions could play a role in PWL syndrome, all cases with comparable chromosome 6q and 10p duplications documented in PubMed, the sSMC Liehr database  [12]  and DECIPHER  [13]  were reviewed. Duplications of the 6q16. 3q23. 3 region are rare and no similar cases with sSMC comprising the 6q16. 3q23. 3 region have ever been described in the literature  [12, 14]. Two cases with 6q16. 3q23. 3 duplications are reported in the DECIPHER database  [13]  (available from http://decipher. sanger. ac. uk and via email from decipher@sanger. ac. uk. ) with the phenotype: case 4145 (7 Mb duplication) with brachycephaly, epicanthus, intellectual disability, intrauterine growth retardation, muscular hypotonia and ptosis; case 2068365 (2.
Methods Patients were selected among controls from a multicentre nested case-control study on the risk of myocardial infarction. Patients with prior cardiovascular or cerebrovascular disorders were not eligible. Potential confounders, including variables that were associated either with statin use and/or death occurrence and statin use were evaluated within the last 3 Data Availability Statement: Data are not owned by the authors. SAS dataset "statin_mortality_fhdh" is owned by 'Institut National de la Santé et de la Recherche Médicale (INSERM)' and the 'Ministère de la Santé'. Data requests may be submitted to the FHDH and must be approved by the French computer watchdog authority, la Commission Nationale de l'Informatique et des Libertés (CNIL). Data requests may be sent to Murielle Mary Krause at the FHDH ( murielle.
Study population and methods The patients selected for this study were the controls from a case-control study of myocardial infarction (MI)  [10] , nested within the French Hospital Database on HIV (FHDH-ANRS CO4), an ongoing, prospective, observational, nationwide, hospital-based cohort of HIVinfected individuals  [11]. Enrolments took place from January 2000 through December 2009. Patients with prior cardiovascular or cerebrovascular disorders were not eligible. The outcome measure was all-cause mortality, censored at 7 years of follow-up. Individuals were considered to be statin users if a statin had been prescribed within 3 months prior to the index date, defined as the date of MI in the corresponding case. Patients were followed from the index date until 7 years, or the last follow-up, or death, whichever occurred first.
An intention-to-continue approach was used to mimic what would happen in a clinical trial. To include all patients in the analyses, missing values of potential confounders were imputed as previously described  [12]. Cases with prior exposure to statin were also corresponding to the definition of statin use for primary prevention, and therefore removing them could lead to a selection bias. However, including all of them would also bias the result by over representing people with MI in our dataset. From January 2000 through December 2009, 96,091 HIV-infected individuals were followed in the FHDH ANRS CO4 cohort. During the same period, 600 HIV-infected individuals had a first validated myocardial infarction, among which 95 patients were on statin therapy, that is 95/96,091; roughly 1/1000. Therefore, to be representative, we should have add around two cases to our study population.
First, using the available in situ data derived from tower-based measurements and the remote-sensing GPP product we seek to assess model efficacy in simulating spatial and temporal variations in GPP, ER, and NEP across the region. In doing so we elucidate issues complicating evaluations of model carbon cycle estimates across Northern Eurasia and, by extension, other areas of the northern high latitudes. Second, we estimate time changes in NEP and soil organic carbon (SOC) residence time and its controls as an indicator of climate sensitivity and potential vulnerability of soil carbon stocks. We focus the analysis and discussion on assessing how well the models capture the seasonal cycle and spatial patterns in GPP and ER flux rates, evaluating uncertainties in the net CO 2 exchange given reported biases in respiration rates, and in advancing understanding of the land-atmosphere cycling of CO 2 over recent decades.
Modeled data We used outputs from retrospective simulations of nine models participating in the model integration group of the Per- mafrost Carbon Network. All simulation outputs available at the time of writing were included in the analysis (http: //www. permafrostcarbon. org). The simulation protocol allowed for the choice of a model's driving data sets for atmospheric CO 2 , N deposition, climate, disturbance, and other forcings (Tables  1  and 2 ). Simulations were run at daily or sub-daily time steps in some models and at 0. 5 • resolution over all land areas north of 45 • N latitude. The present study focuses on analysis of spatial patterns and temporal changes in land-atmosphere CO 2 fluxes over the period 1960-2009. Quantities analyzed are GPP, ER, and NEP, defined here as NEP = GPP-ER, where a positive value represents a net sink of CO 2 into the ecosystem.
Flux tower eddy covariance data Model estimates for GPP, ER, and NEP are evaluated against data from six eddy covariance flux towers in four research areas located across Russia. The data are contained in the La Thuile global FLUXNET data set  (Baldocchi, 2008). FLUXNET represents a global network of tower eddy covariance measurement sites for monitoring land-atmosphere exchanges of carbon dioxide and water vapor (http://daac. ornl. gov/FLUXNET/fluxnet. shtml). For these sites, GPP and ER data records overlap in the years 2002-2005. Observations during colder months are few. Tower sites are identified  here by their locations: Chersky (CHE), Chokurdakh (COK), Hakasija (HAK), and Zotino (ZOT). Data from three towers are available for Hakasija; HAK1 is in an area of grasslandsteppe; HAK2 is grassland; HAK3 an abandoned agricultural field.
Satellite-based estimates of GPP Satellite-data-driven estimates of annual total GPP are also obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) MOD17 operational product  (Running et al. , 2004; Zhao et al. , 2005). The MOD17 product has been derived operationally from the NASA EOS MODIS sensors since 2000 and provides a globally consistent and continuous estimation of vegetation productivity at 1-km resolution and 8-day intervals. MOD17 uses a light use efficiency algorithm driven by global land cover classification and canopy fractional photosynthetically active radiation (FPAR) inputs from MODIS. The product also uses daily surface meteorology inputs from global reanalysis data  (Zhao and Running, 2010) , and land cover class specific biophysical response functions to estimate the conversion efficiency of canopy absorbed photosynthetically active radiation to vegetation biomass (g C MJ -1 ) and GPP  (Running et al.
, 2004). The MOD17 algorithms and productivity estimates have been extensively evaluated for a range of regional and global applications, including northern, boreal and Arctic domains  (Heinsch et al. , 2006; Turner et al. , 2006; Zhang et al. , 2008; Zhao and Running, 2010). We use the MOD17 Collection 5 product, which has undergone five major reprocessing improvements since 2000. The MOD17 data are used in this study as a consistent satellite-derived baseline for evaluating GPP simulations from the detailed carbon process models.
, 2010). An empirical relationship is derived to estimate ER during that time and it is extrapolated into the daylight hours. GPP is then generally calculated as the difference between NEE and ER (accounting for appropriate signs). Since there is generally daylight for photosynthesis during the middle of the summer, ER could potentially be underestimated if primary production had occurred during the hours used for ER model calibration. Direct validation of the partitioning of measured NEE flux to GPP and ER is not possible. However, in a recent sensitivity study  Lasslop et al. (2010)  compared two independent methods for partitioning and found general agreement in the results. This agreement across methods increases our confidence in the partitioned GPP and ER estimates in the LaThuile FLUXNET data set.
Acknowledgements. This research was supported by the  US National Aeronautics and Space Administration NASA  grant  NNX11AR16G  and the  Permafrost Carbon Network  (http://www. permafrostcarbon. org/)funded by the  National Science Foundation. The MODIS Land Cover Type product data was obtained through the online Data Pool at the  NASA Land Processes Distributed Active Archive Center (LP DAAC ),  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac. usgs. gov/data_access). We thank  Hans Dolman  and a second reviewer for their insightful comments which helped improve the manuscript. We thank the researchers working at FLUXNET sites for making available their CO 2 flux data. We also thank  Eugenie Euskirchen  and  Dan Hayes  for comments on an earlier version of the manuscript, and  Yonghong Yi  for assistance with the FLUXNET data.
Charles Koven was supported by the  Director of the Office of Biological and Environmental Research, Office of Science, US Department of Energy , under Contract  DE-AC02-05CH11231  as part of the  Regional and Global Climate Modeling Program (RGCM). Eleanor J. Burke  was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) and the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 282700. Bertrand Decharme  and  Christine Delire  were supported by the  French Agence Nationale de la Recherche  under agreement  ANR-10-CEPL-012-03. Several of the authors were funded by the  European Union   7th Framework Programme  under project  Page21  (grant  282700 ). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  US Government.
Ex vivo Cell Stimulations and Cytokine Analyses For cytokine secretion analyses, cells were suspended in R10 buffer, seeded at 2. 5 x 10 6 cells/ml in TC MicroWell 96U Nunclon plates (Thermo Fisher Scientific), and incubated with 50 μg/ml E/S antigens for 48 hours at 37°C, 5% CO 2. Cell-free supernatants were collected and frozen at -20°C for subsequent analyses. Cytokine secretion was measured with BD cytometric bead array (BD Biosciences) according to manufacturer's instructions with the following modification: the amount of capture beads and detection reagents as well as sample volumes was scaled down five-fold. Samples were acquired on a BD LSR II flow cytometer (BD Biosciences) and data analysed with FCAP Array v3. 0 (SoftFlow Inc. ). For intracellular cytokine analyses, cells were suspended in R10 buffer, seeded at 5 x 10 6 cells/ml in 5 ml polystyrene round-bottom tubes (BD Falcon), and incubated with 250 ng/ml phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich), 500 ng/ml ionomycin (Sigma-Aldrich) and 10 μg/ml brefeldin A (Sigma-Aldrich), or brefeldin A alone as a negative control, for 3 hours at 37°C, 5% CO 2.
7; Bio-Legend), APC-eF780-conjugated streptavidin (eBioscience), along with Violet Live/Dead (Life Technologies) according to manufacturer's instructions to label dead cells. Cells were stained intracellularly with FoxP3/Transcription factor staining buffer set (eBioscience) according to manufacturer's instructions. Cells were analysed on a BD LSR II flow cytometer, and data analysed with FlowJo software v9. 7 (Tree Star Inc. ). Dead cells and aggregates were excluded from all analyses.
Bioinformatics Generated sequences were analysed using qiime_pipe (https://github. com/maasha/qiime_ pipe) using QIIME v1. 7. 0 with default settings, which performs quality-based sequence trimming, primer removal and assembly of paired-end sequences followed by the execution of a QIIME workflow including chimera checking  [41]. De novo OTU-picking was performed using UCLUST  [43]  with 97% sequence similarity. Representative sequences were assigned taxonomy against the Greengenes database v11_2  [44]  using the RDP-classifier  [45]  with an 80% confidence threshold. Subsequent analyses were performed in R v3. 1. 1 using the metagenome-Seq  [46] , PhyloSeq  [47] , Vegan  [48]  and GGplot2  [49]  packages. Data was filtered for low-abundance OTUs by removal of OTUs present in fewer than 3 of the 140 samples and with a relative abundance across all samples 0.
005%. A single day 35 faecal sample from one of the infected mice and a single day 0 caecal sample from one of the uninfected mice were left out due to our cut off of at least 10,000 sequences per sample after filtering. Analyses in R were performed with an average of 25,060 ± 6,480 (SD) sequences per sample before, and with 20,912 ± 5,091 (SD) sequences per sample after filtering. Alpha and gamma diversity were estimated using unfiltered data. Beta diversity was performed using filtered data and calculated for time point/treatment using Vegan. Read counts were normalised with metagenomeSeq  [46]  that uses a cumulative-sum scaling in which raw counts are divided by the cumulative sum of counts up to a particular quantile. Statistical analyses for Table  1  and S2 Table comparing unin fected with infected were performed on data filtered based on effective sample sizes.
Taxa were not included if they had fewer than X effective number of positive samples, where X is the median of estimated effective samples per feature calculated using metagenomeSeq. Phylogenetic analyses were conducted using 16S rRNA gene sequences from the given family downloaded from The Ribosomal Database Project (RDP)  [50]. The representative sequences, from the OTUs classified to be members of the given family were combined with the RDP sequences. Sequence-alignment using MUSCLE  [51]  and phylogenetic tree building using the Maximum Likelihood method based on the Tamura-Nei model  [52]  were performed using MEGA v6. 06  [50]. All sequence data is available from the European Nucleotide Archive (ENA) with study accession number: PRJEB6560. Immunological raw data is available from Dryad Digital Repository (doi:10.
By phylogenetic tree analysis, comparing the representative sequences for the OTUs classified within each family and 16S rRNA gene sequences downloaded from the Ribosomal Database Project  [50] , we identified candidate species for the Lactobacillaceae family affected by T. muris infection. A single OTU (L#1 in S8 Fig)  accounted  for more than 80% of the relative abundance in faecal samples 35 days after infection, the sequence of which was identical to that of L. apodemi, L. murinus and L. animalis (S8 Fig). The second most abundant OTU was identical to L. gasseri and L. taiwanensis. The same OTUs and similar relative abundance within families were found for caecal samples (data not shown). A Skewed Intestinal Regulatory/Inflammatory T Cell Balance is Induced upon Chronic T. muris Infection Finally, we wanted to investigate whether the profound changes observed in the microbiota composition correlated with infection-driven responses in the intestinal immune compartment, which could indicate a potential causal link between these processes.
This work was supported by grants from the  Novo Nordisk Foundation  (www. novonordiskfonden. dk), The Carlsberg Foundation  (www. carlsbergfondet. dk),and the  Swedish Medical Research Council  (www. vr. se). Yuliaxis Ramayo-Caldas  was funded by the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills' fellowship (under grant  agreement  n°267196 ; www. agreenskills. eu). The funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
GOODS-S and UDS multiwavelength catalogs The CANDELS multiwavelength group has adopted a standardized method to build catalogs in the CANDELS fields. Sources are extracted from the CANDELS F160W mosaic using SExtractor. Total fluxes of the sources in the high-resolution HST bands (WFC3 and ACS) are derived from the aperture-corrected isophotal colors from SExtractor, run in dual mode on PSF-matched images (where the PSF is the Point Spread Function). The photometry of the lower-resolution dataset (e. g. , groundbased and Spitzer) is derived using the template-fitting software TFIT  (Laidler et al. 2007; Papovich et al. 2001). In brief, TFIT uses the a-priori information on the source location and surface brightness profile on the F160W image to measure its photometry on the low-resolution image.