dataesr/scientific-paragraphs-categorization huggingface dataset:
The relative fatty acid composition was quantified using external FAME standards (Supelco) run after every 20 samples and Turbochrom 6. 3 software (Perkin-Elmer). Concentrations of single fatty acids were calculated as mass % of total identified peaks of 13 fatty acids of chain length 14-22. Due to mistakes in the lipid extraction phase, samples from two individuals (one from each dietary group) were excluded from the results of the fatty acid composition for SR PL.
Statistical Analysis Data analyses were carried out using R 3. 1. 1 (R Core  Team, 2014). The normality of residuals from statistical models was assessed by inspecting quantile-quantile-plots and histograms. If necessary, response variables were Box-Cox transformed to achieve normality. We employed linear models to compare body mass of dormice from CO and MO groups prior to their diet treatment, and to determine effects of dietary treatment (CO vs. MO) and sex on body mass prior to hibernation and energy intake of animals during the period of the feeding treatment. We used linear mixed-effects models (R package "nlme")  (Pinheiro et al. , 2014)  with animal ID as random factor to determine effects of dietary treatment (CO vs. MO) and time (pre-hibernation vs. mid-hibernation) on WAT fatty acid composition.
Tukey-like post hoc multiple comparison tests (R package "multcomp")  (Hothorn et al. , 2008)  were applied to test for differences between groups (CO vs. MO) and periods (pre-hibernation vs. mi-hibernation). General linear models with Tukey-like post hoc multiple comparison tests were used to assess differences in SR PL fatty acid composition between the two dietary groups (MO vs. CO). All p-values from linear mixed-effects models and general linear models were adjusted for multi-comparisons between fatty acid proportions using False Discovery Rate  (Benjamini and Hochberg, 1995). We used analysis of variance to test effects of diets on variables derived from the hibernation patterns, except for the number of arousals for which we used a generalized linear model with a Poisson distribution.
This GLM showed no signs of overdispersion. Body mass was entered as a random factor in the model for hibernation onset. Initial inspection of the data gave no evidence for a sex effect on any of the response variables derived from the hibernating patterns, including hibernation onset. Since animals were sacrificed within a period of 2 weeks, individual experimental hibernation duration was also included as a random factor in the model for number of arousals as well as total arousal and torpor durations. We also used an analysis of variance to test for effect of diets on SERCA activity levels at mid-hibernation. For regression analyses of variables derived from hibernation patterns vs. SERCA activity levels, we applied ranged major axis (RMA) models (R package "lmodel2")  (Legendre and Legendre, 1998)  to account for measurement errors in both the dependent and predictor variable.
Cell cycle analysis Cells were seeded in T75-flasks (0. 25 X 10 6 cells/flask) and cultured for three and seven days in presence or absence of Tet (1 μg/ml). At each time point, cells were harvested and analyzed by flow cytometry with propidium iodide staining using BD Cycletest Plus DNA kit (BD Biosciences) according to the manufacturer's instructions. The percentage of cells in each cell cycle phase was measured by flow cytometry on BD FACS-Canto II (BD Biosciences) and analyzed using Modfit LT v4. 1. 7 software (Verity Software House).
Total RNA was extracted, labeled and hybridized according to kit manufacturer's recommendations. Raw intensity data were log2-transformed and normalized (intra-array and inter-array scaling) using GeneSpring GX software (Agilent Technologies). Gene expression data are shown in Additional file 1: Appendix A. Probes were pre-selected according to their level of expression (above background) and to their level of variation between samples (sd ≥ 50%). Two-way analysis of variance (ANOVA) using 'day' and 'Tet-induction' as factors was performed to identify Tet-induced probes. Adjusted p values were calculated by controlling for the false discovery rate using the Benjamini & Hochberg procedure. Probes were considered significantly differentially expressed (DE) if the adjusted p value was below 1.
e. , observations close in time are more correlated than observations further apart). From plotting optical density over time, a quadratic relationship between both variables was suggested. Then, we fitted polynomial (i. e. , quadratic model: y = β 0 + β 1. x + β 2. x 2 ) models to the set of points and retained the polynomial shape of the relationship if the quadratic term of the model that represents the amount of curvature in the data (i. e. , β 2 ) was significantly different from zero. For statistical modeling, the R package 'nlme'  [36]  was used. Estimation of model parameters was based on restricted maximum likelihood (REML). Residuals of fitted models were visually inspected (quantile-quantile plot and residuals vs. predicted values plot), and all were approximately normal, i. e. , unimodal and symmetric.
(n=8223) Significant up (n=979) Poly-P accumulation genes q-value (-log10) Fig. 2. Differential gene expression in M. pusilla grown under low-and replete-phosphate availability. The volcano plot showing fold-changes in transcript abundances for individual M. pusilla genes that are significantly (q < 0. 01) enriched (red) or depleted (blue) in the low-P treatment compared to the P-replete treatment for uninfected cultures. Data from T1. 5 and T3 were analyzed together for the low-P, and these time points were also combined for the replete-P since these cultures were grown under continuous light and therefore should be replicates for the uninfected cultures. This allowed us to compare quadruplicated transcriptomes of the low-P to the high-P treatments surmounting statistical issues created by sample loss in sequencing that resulted in only having duplicates from these time points.
Genome-wide MpV-SP1 transcription We also mapped the RNA-seq data to the genome of the 173-kb MpV-SP1 virus  (Finke et al. , 2017b)  which contains 242 predicted protein-encoding genes. Fragments per kilobase per million mapped reads (FPKM) values were generated for all genes at each of the three time points in the replete and low-P treatments (Supporting Information Table  S5 ). Overall, 99. 9% of the virus genome sequence recruited transcripts at T12. The 0. 1% that did not recruit reads was at the extremities of the genome, which is comprised of inverted terminal repeats. Transcripts from all 242 viral genes were detected in both the P-replete and low-P treatments. Ninety-three% of the nucleotides in the MpV-SP1 virus genome had previously been incorporated in gene models (GenBank accession PRJNA47589).
Differential expression analysis and hierarchical clustering The experiments were performed and sequenced in triplicate; however, a robotic mishap resulted in sample destruction such that only duplicates could be analyzed for each treatment. Thus, RNA-seq from biological duplicates for each condition and time point (N = 24) were analyzed. The Tuxedo suite was used by first aligning reads to the respective genomes with gene models version of July 1, 2016 (M. pusilla and MpV-SP1 genomes, accessions NZ_ACCP01000000 and PRJNA47589 respectively) using TopHat version 1. 4. 0  (Trapnell et al. , 2009)  with parameters -r 27 -mate-std-dev 100 -max-intron-length 10000 -min-intron-length 20 -library-type fr-firststrand. Gene level expression estimates in units of FPKM were calculated with Cuffnorm 2.
2. 1  (Trapnell et al. , 2012)  using quartile normalization (-library-norm-method quartile). For each sample, the number of host reads per gene was normalized among all libraries based on the total number of host-mapped reads in that sample. For each sample, the number of viral reads per gene was normalized based on the total number of viral mapped reads in that sample. Differentially expressed genes were identified using Cuffdiff 2. 2. 1  (Trapnell et al. , 2013)  to test the significant changes in relative transcript abundances between treatments from both the host and virus. All q-values provided have been False Discovery Rate (FDR)-corrected. The expression ratio between conditions for gene abundances was normalized as a log 2 fold-change value. Q-values < 0. 01 and fold-change values > 1.
Read counts and enrichment analysis To compare the number of sequence reads assigned to viral or M. pusilla genomic features, we used HTSeq using a modified gff file comprising genome annotation information  (Worden et al. , 2009; van Baren et al. , 2016)  of the host and the virus  (Anders et al. , 2014). For the reads mapping to the virus genome, we assessed the percentage of nucleotides aligned to genes or intergenic regions using the Picard command line tool RnaSeq-Metrics (http://broadinstitute. github. io/picard). Enrichment of KOG classes by differentially expressed genes was performed using gene set enrichment analysis (GSEA)  (Subramanian et al. , 2005). KOG classes with upregulated genes are displayed in supplementary tables and considered significantly enriched when FDR ≤ 0. 05.
ACKNOWLEDGEMENTS We are grateful to  Alexander J. Limardo  for help with RNA extractions,  Amy E. Zimmerman  for stimulating discussions,  Govindarajan Kunde-Ramamoorthy  for assistance, and  Pam  and  Greg Landers  for facilitating the first draft. This research was supported by  NSERC ,  Canadian Foundation for Innovation (CFI) , and  BCKDF  grants to CAS, a  JGI Technology Development Project  and funding from the  David and Lucile Packard Foundation , the  Gordon and Betty Moore Foundation  ( GBMF3788  and  GBMF3305 ),  NSF -IOS0843119  and  DOE-DE-SC0004765  to AZW.
Methods and Data Sets As mentioned above, the total FWF comprises a liquid component, made up of runoff from land ice and snowmelt on tundra (R) and a solid component (D), here defined as the flux of ice crossing the grounding line of marine-terminating glaciers around the margins of the GrIS. In sections 2. 1 and 2. 2 we describe, in detail, how these two components were determined. In summary, we estimate the solid ice fluxes for the GrIS using satellite-based observations of surface velocity alongside a new compilation of ice thickness  (Morlighem et al. , 2017). We have a sub-annual time series of velocities for  2000-2016 and annual values for 1992-2010, 1958, and 1964. For those years with no observations, we employ a correlation between timeaveraged R and D. To estimate R, we use the output of a regional climate model (RACMO2.
First, we describe the approach used to derive the seasonally varying discharge for the 195 outlet glaciers, which has not been previously presented. Monthly glacier discharge values were estimated by first deriving continuous time series of outlet glacier velocities. Most velocity estimates were obtained from featuretracking techniques applied to combinations of both repeat-pass and cross-path orthorectified imagery obtained from ASTER, LANDSAT-7 and LANDSAT-8. Existing velocity data products from RADARSAT-1 and 22, and TerraSAR-X satellites were also included when available. When SAR products are unavailable, data gaps during winter are common due to the limitations of optical imagery at high latitudes. To overcome this, data were interpolated to fill monthly gaps using a glacierspecific seasonality, constructed from available velocity data for a particular month, available throughout a representative sample of the time series.
We estimated the mean monthly discharge to obtain the amplitude of the seasonal cycle at each outlet, which we then applied to annual estimates of discharge at these outlets prior to 2000 (in other words we assumed the seasonal cycle is time invariant). This provided a monthly time series for the whole ice sheet for  1958, 1964, and 1992-2016. For those years with no observed discharge value we followed the approach in  (Bamber et al. , 2012)  by fitting a linear least squares regression between ice-sheet-wide runoff over the previous four years and the current year with ice-sheet-wide D for that year (correlation coefficient of 0. 81, p < 0. 01). The results of this linear regression are shown in Figure  2. We excluded 1958 in the regression as the discharge data for this year has a larger, but poorly defined, uncertainty  (Rignot et al.
Land Ice and Tundra Runoff To estimate runoff from the GrIS and GIC we used the output of a regional climate model (RACMO2. 3p2 for the GrIS and version p1 for GIC), which has been statistically downscaled to 1 km from the native resolution of the model of 11 km  (Noel et al. , 2015; No€ el et al. , 2017). Monthly data from 1958 to 2016 at 1 km nominal resolution were projected onto a 5 km polar stereographic grid. We applied a latitudinal scaling correction to preserve area and, hence, volume, away from the projection central meridian  (Snyder, 1987). To determine runoff fluxes along the coast, we routed runoff from its source to the downstream coastal point. First, for Greenland we split runoff into its ice and tundra source components using the land type classification employed in the regional climate model.
We then used the PyGeoProcessing library (http://pythonhosted. org/pygeoprocessing/) in conjunction with a digital elevation model  (Howat et al. , 2014)  of the land (tundra component) and ice surface (ice sheet component) to route the water to the coastal margin. We allocated each coastal pixel to an oceanographic sub-basin as defined by the International Hydrographic Organisation  (IHO, 1953). Conservation of mass (runoff) was assessed by comparing the total flux at the ocean boundary with the integral over the ice and tundra areas to ensure that none was lost during the routing procedure or between the ice edge and the closest ocean grid cell. Although RACMO2. 3p1 contains improved physics compared to the version used in the previous FWF assessment, we assume the same errors as given in  Bamber et al.
Acknowledgments JLB was supported by  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  69418. JLB and AJT were supported by  NERC  grant  NE/M021025/1. MvdB acknowledges support from the  Netherlands Earth System Science Centre (NESSC). The authors would like to thank the editor,  Fiamma Straneo  and an anonymous referee for their constructive comments. The data set described here is available from the  British Oceanographic Data Centre  (www. bodc. ac. uk) and also directly from the corresponding author. The DOI is 10. 5285/643aa9bc- bcd6 -45ad-e053-6c86abc07da0. Journal of Geophysical Research:  Oceans 10. 1002/2017JC013605.
falciparum protein PfRH4. We confirm that recombinant PvRBP1a 30 preferentially binds reticulocytes and test the ability of antibodies raised against recombinant PvRBP1a 30 to recognize native PvRBP1aantigen as well as block reticulocyte binding by PvRBP1a and reticulocyte invasion by P. vivax isolates. In addition, given the key role played by PvDBP in mediating interaction with DARC during invasion we also tested the ability of antibodies raised against PvDBPII to block reticulocyte invasion both individually and in combination with antibodies against PvRB1a 30. Data from these functional studies will be used to validate individual P. vivax antigens as well as antigen combinations for inclusion in a multi-antigen blood stage vaccine candidates for P. vivax malaria.
The purity of reticulocytes was assessed by flow cytometry (BD FACS Calibur) using antibodies against reticulocyte surface marker CD71 conjugated to phycoerythrin (PE). Briefly, 10 7 reticulocyte enriched erythrocytes were incubated with anti CD71-PE antibodies (1:10 dilution) for 15 mins at 4 °C. Following incubation, the cells were washed and resuspended in 200 µl of PBS and analyzed on FACS using BD CELLQUEST software.
The samples were washed two times with PBS, incubated with fluorescein isothiocyanate (FITC) conjugated anti-rabbit IgG (Sigma, USA) goat antibodies for 1 h at room temperature, washed two times with PBS and used for analysis by flow cytometry using a Becton Dickinson FACS Calibur. For binding inhibition assays, the recombinant proteins were incubated with different concentrations of purified IgG (10 µg/ml to 100 µg/ml) followed by incubation with erythrocytes or reticulocytes. For each experiment, data was acquired for 50,000 cells with the same FACS settings for each condition. Data was analyzed using Flow Jo software. The desired populations were gated based on size and granularity. Detection of native PvRBP1a in late schizonts and merozoites by IFA. Blood from patients infected with P.
(F) IgM, IgA, IgG1, and IgG2b isotype distribution among spleen EYFP + memory (B220 + ) and plasma cells (B220 -) and BM B220 -plasma cells, 3-4 mo after tamoxifen gavage. Intracellular staining was performed for plasma cells. *, P < 0. 05; **, P < 0. 01; ***, P < 0. 001; ****, P < 0. 0001, one-way ANO VA with Holm-Sidak correction for multiple comparisons. Nonsignificant differences are not indicated. Mean values are indicated, with data from each individual mouse represented. Each analysis originates from two or more independent tamoxifen-labeling experiments. See also Fig. S1. the food and its intermittent consumption. Conventional housed animals fed with the same diet were used as controls. Total EYFP + cell numbers were moderately, but significantly reduced in the spleen of germ-free mice, but the most drastic impact was on the IgA-positive subsets, both IgA memory and IgA plasma cells in spleen and BM (Fig.
*, P < 0. 05; **, P < 0. 01, one-way ANO VA with Holm-Sidak correction. Mean values are indicated. Cumulative data of all analyzed animals are indicated. (D) Representative FACS profiles of naive (B220 + IgM + EYFP -GL7 -CD73 -CD80 -), B220 + GL7 -CD73 + CD80 + EYFP + and EYFP -subsets (gates represented in A [right], including for naive B cells). (Fig. 5 C ), indicating that, whereas spontaneous AID activation still occurs in germ-free mice, the GC reaction that gives rise to EYFP + B cells might be more transient. This differential mutation load was confirmed by V H sequencing of hybridomas generated from EYFP + IgM + cells of conventional and germ-free mice (see below). Reduction of the total splenic pool of CD73 + CD80 + IgM + B cells was accordingly observed (3. 9 vs. 6. 6%; Fig. 5 D ).
Hybridoma supernatants were also tested for polyreactivity, and no hybridoma showed corecognition of insulin and dsDNA (data not shown). Early response of CD73 + CD80 + B cells to bacterial challenge and protective capacity of their secreted IgM products Reactivity against Klebsiella pneumoniae was observed in culture supernatants from both germ-free and conventional mice. We thus assessed whether CD73 + CD80 + B cells would show specific mobilization upon inoculation of these bacteria. To avoid differentiation into GC or plasma cells that would alter their surface phenotype and compromise their identification, we focused on the proliferation induced 3 d after bacterial injection (10 7 the B cell compartment is largely restored. FACS profiles show that residual EYFP + cells resisting the anti-CD20 treatment are mainly GC B cells in PPs (B, left) and plasma cells in the spleen, with a Blimp-1 + profile (C, left and middle), while EYFP + memory B cells reemerge during reconstitution of the B cell pool in both spleen and PPs (B and C, right).
PCR products were cloned with the Zero Blunt cloning kit (Invitrogen) and sequences were determined with an ABI Prism 3130xl Genetic Analyzer. Mutations were identified within 448 bp of the J H 4 intron and analyzed with the help of the CodonCodeAligner software. Between 25 and 96 sequences per sample were determined for mutation frequency determination. Clonal relationships were established using CodonCodeAligner, plus manual inspection, notably to discard clonal relationships that would include sequence changes at the V-D or D-J junction. For hybridoma sequences, total RNA was isolated with the RNeasy Micro kit (QIA GEN) and reverse transcribed by random priming with the Affinityscript Multiple Temperature cDNA synthesis kit (Agilent Technologies). The following primers amplifying V H 1, V H 3, V H 5, V H 11 and V H 12 families (in a 5:1.
*, P < 0. 05; **, P < 0. 01, one-way ANO VA with Holm-Sidak correction. Mean values are indicated. Cumulative data of all analyzed animals are indicated. (D) Representative FACS profiles of naive (B220 + IgM + EYFP -GL7 -CD73 -CD80 -), B220 + GL7 -CD73 + CD80 + EYFP + and EYFP -subsets (gates represented in A [right], including for naive B cells).
Acknowledgments We thank  Frédéric Delbos  for carrying out the  BAC-AID construction ,  Cherié Butts  ( Biogen Idec ) for provision of the 18B12 anti-CD20 antibody;  Isabelle Cremer  ( Centre des Cordeliers, Paris, France ),  Ivo Gomperts Boneca  ( Institut Pasteur, Paris, France ),  Sophie Ezine  ( INEM, Paris, France ) for giving us, respectively, TLR7-, MyD88xTrif-, and CD3ε-deficient mice; and  Barbara Bertocci  for advice with BrdU experiments. We thank the excellent animal care of the Necker animal facility (LEAT, SFR Necker,  INS   ERM US24/CNRS UMS 3633 ), the expertise of the Orléans CDTA team ( CNRS UPS44 ) for generation and housing of germ-free mice and the  SEAT (Villejuif, France)  for the BAC-AID transgenesis. We thank  Nicolas Goudin  ( Cell Imaging core facility, SFR Necker ) for his help with confocal microscopy.
LL= [yt(d) log(S(d)) + (Ni(d) -yi(d)) log(l -S(d))] The null distribution (under H0) of the test statistics T is approximated using a permutation method  (Good, 2005; Pesarin and Salmaso, 2010)  where a permutation sample is generated by randomly partitioning the clonogenic survival data within each dose into groups of the same size as G0 and G1. Computations for this study were carried out using MATLAB Software, version 8. 2. 0. 701 (Mathworks R2013b).
Dosimetry measurements on the SARRP irradiator Measurements of beam quality index: HVL For the measurements of beam quality index, the procedure described in the AAPM protocol  (Ma et al. , 2001)  was followed. Nevertheless, as the distance between the irradiation source and the floor of the irradiator is limited, it was not possible to achieve a distance of 1 meter between the source and the ionization chamber as recommended  (Ma et al. , 2001). Due to this limitation, we chose a detector-source distance of 70 cm. HVL value were measured for HV between 40 and 220 kV for the two additional filtrations available on the SARRP (supplementary data II). On the following of this work, where irradiations were performed at 80 kV, HVL values about 0. 056 and 0. 138 mm of copper were measured for 1 mm or 0.
The dose variation measurements performed with EBT3 films when the cell culture volume was changed were perfectly verified by clonogenic assay. Using additional copper filtration, when the reference volume 3 mL/well was replaced by 1 or 9 mL/well, EBT3 films indicated dose variations of +1% and -8%, respectively. The clonogenic assay for 1 mL/well showed a significant difference (p=0. 03) mainly for the 4 Gy data, which could be partly explained by the higher distribution of the survival fraction values obtained at 4 Gy in the reference conditions. Moreover, we detected a significant difference for the 9 mL/well condition, the survival fraction being significantly greater (p<0. 001) than in the reference condition from 0 to 4 Gy. These biological data fully corroborate the dosimetric measures of EBT3 films when considering the 9 mL/well condition.