dataesr/scientific-paragraphs-categorization huggingface dataset:
ATLAS detector, data and simulation samples The ATLAS detector 1  consists of three main components: an inner detector (ID) tracking system immersed in a 2 T axial magnetic field, surrounded by electromagnetic and hadronic calorimeters and by the muon spectrometer (MS). A full description can be found in Ref. [17]. This analysis is based on the Run 1 data sample recorded in 2011 and 2012 by the ATLAS detector from pp collisions at the LHC at √ s = 7 and 8 TeV, respectively. Data used in the analysis were recorded during stable LHC beam periods. Data quality requirements were imposed, notably on the performance of the MS and ID systems. The total integrated luminosity of good quality data used in this analysis is 4. 9 fb -1 for the 2011 sample and 20 fb -1 for 2012. The average number of reconstructed primary vertices (PV) per event, related to multiple proton-proton interactions, is 6.
2 and 11. 4 in the two years respectively. Samples of simulated Monte-Carlo (MC) events are used for training and validation of the multivariate analyses, for the determination of the efficiency ratios, and for guiding the signal extraction fits. Exclusive MC samples were produced for the signal channels B 0 s → μ + μ -and B 0 → μ + μ -, the normalisation channel B + → J/ψ K + (J/ψ → μ + μ -), the B + → J/ψ π + channel, and the control channel B 0 s → J/ψ φ (φ → K + K -). In addition, background studies employ MC samples of inclusive semileptonic decays B → μX , samples of B 0 s → K -μ + ν, B 0 → π -μ + ν, b → pμ -ν, B 0 (s) → hh decays with h ( ) being a charged pion or kaon, and inclusive decays B → J/ψ X. Most of dimuon candidates in the data sample originate from the uncorrelated decays of hadrons produced in the hadronisation of a b and a b quarks.
Data selection For data collected during the LHC Run 1, the ATLAS detector used a three-level trigger system, consisting of a hardwarebased Level-1 trigger, software-based Level-2 and Event Filter triggers. A dimuon trigger  [23, 24]  is used to select events. The 2011 data sample contains events seeded by a Level-1 dimuon trigger that required a transverse momentum p T > 4 GeV for both muon candidates. Due to the increased pile-up in 2012 data, this dimuon trigger was prescaled at the beginning of every fill. The effect of prescaling is mitigated by including in the analysis events selected by two additional Level-1 triggers scarcely affected by prescaling, where tighter selections were applied: p T > 6 GeV or |η| < 1. 05 for one of the muons. A full track reconstruction of the muon candidates was performed at the software trigger levels, where an additional loose selection was applied to the dimuon invariant mass m μμ and the events were assigned to the J/ψ stream (2.
5 < m μμ < 4. 3 GeV) or to the B stream (4. 0 < m μμ < 8. 5 GeV). Events from the 2012 dataset are divided into three mutually exclusive trigger categories: T 1 "Higher threshold" trigger with p T > 6 GeV for one muon and > 4 GeV for the other one; T 2 "Barrel" trigger with p T > 4 GeV for both muon candidates and at least one of them with |η| < 1. 05 (and T 1 requirement not satisfied); T 3 Basic dimuon trigger with p T > 4 GeV for both muon candidates (and T 1 , T 2 requirements not satisfied). Events belonging to a given category are all associated with the same pattern of Level-1 prescaling. The event sample in the T 2 (T 3 ) category has an equivalent integrated luminosity equal to 97. 7 % (81. 3 %) of the luminosity of the T 1 category. The impact of the trigger Level-1 prescale on the total sample of collected events is minor, since the majority of the events belong to the T 1 category.
The events in the reference channels B + → J/ψ K + and B 0 s → J/ψφ collected in 2012 belong to a prescaled sample of events, which was processed together with the signal events. The effective prescaling factor is equal to 7. 3, and does not affect the sensitivity of this analysis, given the large number of available events in the normalisation channel. This factor is included in the α k parameters in Eq. (1). A fourth category is defined for events from the 2011 dataset. They were collected with a trigger requirement p T > 4 GeV for both muon candidates, and prescaling was not applied to this sample. After off-line reconstruction, a preliminary selection is performed on candidates for B 0 (s) → μ + μ -, B + → J/ψ K + → μ + μ -K + and B 0 s → J/ψφ → μ + μ -K + K -. In the ID system, muons are required to have at least one hit in the pixel detector, five hits in the semiconductor tracker (two hits per each double-sided layer), and six hits in the transition-radiation tracker, if 0.
5. The results of the fits in all data categories are shown in Table  3. Some of the systematic effects are included automatically in the fit: the effect of limited MC sample size, for example, is included in the uncertainties through a simultaneous fit to data and MC samples. Scaling factors determined in the fit to data account for the differences in mass scale and resolution between data and simulation. Additional systematic uncertainties are evaluated by varying the default fit model described above: they take into account the kinematic differences between data and the MC samples used in the fit, differences in efficiency between B + and B -decays, uncertainties in the relative fractions and shapes of PRD, and in the shape of the continuum background. In each case, the difference with respect to the default fit is recorded, symmetrised and used as an estimate of the systematic uncertainty.
Hence the slope of the mass dependence is described by independent parameters in the three intervals, subject to loose Gaussian constraints of uniformity within ±40 % between the first and second interval, and ±80 % between the first and the third. Such variations of slope are larger than those observed in simulation, and consistent with those determined from data. Deviations from these assumptions are discussed below in Sect. 11. 2. The normalisation of the continuum background is also extracted independently in each BDT interval. The SS+SV background has a dimuon invariant mass distribution peaking below the low-mass sideband region. The mass dependence is derived from data in the low-mass sideband region, and described with an exponential function with equal shape in the three continuum-BDT intervals.
In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the  Wellcome Trust UK  ( WT082825 ),  German 'Bundesministerium fu¨r Bildung und Forschung' (BMBF) program  ' Medical Infection Genomics ' ( 0315834C-D ), and the  Wellcome Trust  and the  Royal Society Sir Henry Dale Fellowship  ( 099953/Z/12/Z ).
To build the revised age-depth model (Figure  2 ), we chose to keep six radiocarbon dates  (Voelker et al. , 2006; Figures   2  and 3 ) younger than 20 ka BP (until around 600-700 cm; mean errors of 60 years) that we calibrated to calendar years with the CALIB 7. 1 program using the Marine13 calibration data  (Stuiver and Reimer, 1993; Reimer et al. , 2013). Below 700 cm, we tuned the planktonic monospecific  18 O record (G. bulloides) of core MD99-2339 (thirteen pointers; Figures  2  and 3 ) to the NGRIP ice-core GICC05 chronology, considering synchronous sea-surface warmings in the Gulf of Cadiz with the onsets of GI 3 to 12, respectively  (Wolff et al. , 2010). As a result, Heinrich Stadial (HS,  Barker et al. , 2009; Sánchez-Goñi and Harrison, 2010)  5 (HS 5) is dated around 48 ka BP in our revised age model rather than 45-46 ka BP  (Voelker et al.
Quantitative estimates of past sea-surface parameters We used the Modern Analogue Technique (MAT) based on the statistical distance between fossil (paleoceanographic record) and current (modern database) assemblages  (de Vernal et al. , 2001; 2005; Guiot and de Vernal; 2007). The dinocyst transfer function used  (Radi and de Vernal, 2008 ) is derived from a modern database comprising 67 dinocyst species and 1,492 stations from the North Atlantic, Arctic and North Pacific oceans and their adjacent seas, and is run under the "R version 2. 7. 0" software (R Development Core Team, 2008; http://www. r-project. org/). The calculation of past hydrological parameters relies on a weighted average of the values obtained for a maximum of five best modern analogues for fossil assemblages; the maximum weight being given for the closest analogue (i.
tepikiense as a species index for thermal seasonal contrasts. Stars on each graph indicate present-day values for dinocyst percentages and hydrological parameters recorded in modern sediments and overlying surface waters, respectively, at the coordinates of analogue "A184" in the modern dinocyst database (34. 32°N, 7. 02°W; http://www. geotop. ca/fr/bases-de-donnees/dinokystes. html) : SST Winter of 16. 48°C and SST Summer of 22. 47°C, SSS Winter of 36. 35 and SSS Summer of 36. 33, Mean Annual Productivity of 85. 7 gC. m -2 , percentages of L. machaerophorum of 65. 27% and B. tepikiense of 0%.
Castera, M. Georget and O. Ther for invaluable technical assistance at the laboratory. This study was supported by the French CNRS and contributes to the 2013 INSU project : « ICE-BIO-RAM : Impact des Changements Environnementaux sur la BIOdiversité marine lors des 9. Réchauffements Abrupts du cliMat » (http://www. insu. cnrs. fr/files/ao_2013_- _eynaud_validee. pdf). This work was supported by the «Laboratoire d'Excellence» LabexMER (ANR-10-LABX-19) and co-funded by a grant from the French government under the program «Investissements d'Avenir». AV acknowledges her Investigador FCT (IF) Development Grant.
tepikiense as a species index for thermal seasonal contrasts. Stars on each graph indicate present-day values for dinocyst percentages and hydrological parameters recorded in modern sediments and overlying surface waters, respectively, at the coordinates of analogue "A184" in the modern dinocyst database (34. 32°N, 7. 02°W; http://www. geotop. ca/fr/bases-de-donnees/dinokystes. html) : SST Winter of 16. 48°C and SST Summer of 22. 47°C, SSS Winter of 36. 35 and SSS Summer of 36. 33, Mean Annual Productivity of 85. 7 gC. m -2 , percentages of L. machaerophorum of 65. 27% and B. tepikiense of 0%. 10. FIGURE CAPTION Figure 1:.
Laboratory methods Genomic DNA was isolated using the DNeasy blood and tissue kit (Qiagen, CA) and quantified with a Qubit fluorometer (Invitrogen, CA). Double-digest restrictionsite associated DNA markers (ddRADtags) were generated following Peterson et al. ,  [28]. We also obtained 95 sequences of the cytochrome b mitochondrial gene, 892 base pairs long (GenBank accession numbers: KU588290-KU588384). Primers used were specifically designed for this study: F_cytb_St: 5′-TGATAACT CAAATCCTAACTGGTC-3′ and R_cytb_St: 5′-TT GTTTTCTAGGGCTCCGAT-3′. Further details on the ddRADtags and cytochrome b amplification laboratory methods are provided in the additional files section (Additional file 2).
RAD-sequence pre-processing and single nucleotide polymorphism (SNP) calling Filtering and quality control The samples were sequenced on two Illumina HiSeq 2500 lanes that produced a total of ~304 million, 151 bp single-end reads. After assessing quality using FASTQC (http://www. bioinformatics. babraham. ac. uk/ projects/fastqc/), we trimmed the final 29 bp to discard lower-quality base calls using FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/). We filtered reads with at least a single base call with a Phred quality score below 10 (90 % call accuracy), and/or more than 5 % below a Phred score of 20 (99 % call accuracy). We demultiplexed reads with the process_radtags program from the STACKS v1. 20  [29]  bioinformatics pipeline. We discarded reads that did not contain an SbfI cut site or a unique P1 barcode in the 5′ end, as well as those with adapter contamination in the 3′ end.
Reference-based RAD loci assembly and variant calling We obtained 1. 03 ± 0. 46 million, 115 bp quality-filtered sequences per individual, which were aligned to the Columba livia genome (assembly 1. 0) with BOWTIE2 v2. 2. 2  [30]. The average alignment rate was 85. 7 %. We conducted a referenced-based assembly using the pstacks/cstacks/sstacks pipeline controlled by the ref_map script in STACKS, followed by the rxstacks error correction module and a final iteration of cstacks/sstacks. This pipeline will identify a locus when a minimum coverage is reached: set to 20 with the m parameter. A catalogue of all loci is generated, and we allowed up to five differences among aligned loci of different individuals (parameter n). The error correction module examines SNP and haplotype calls across all individuals and removes loci that match more than one locus in the catalogue (presumably from repetitive areas of the genome), eliminates excess haplotypes that are uncommon and thus probably erroneous, and eliminates loci below a user established likelihood threshold (set to -200 based on the distribution across all loci).
Analysis of population genetic structure mtDNA analyses Summary statistics of levels of polymorphism (i. e. haplotype and nucleotide diversity) were obtained with DNASP v5. 10. 1  [31]. We obtained a median joining haplotype network, using the software POPART  [32]. Hierarchical analyses of molecular variance (AMOVAs) were performed in ARLEQUIN v3. 5. 2. 2  [33] , with significance values obtained after 10,000 permutations. We tested if grouping individuals by migration flyway explained significant levels of molecular variance, a signal of genetic differentiation. We tested for the possibility of three genetically differentiated groups (i. e. flyways), a Western group including samples from United Kingdom, France, Germany and Spain; a Central group containing the samples from Italy and Malta, and an Eastern group with the samples from Bulgaria and Greece.
During both runs all other settings were left as default. Mean admixture proportions (Q-values) for the value of K that better explained our data were graphed with DISTRUCT v1. 1  [35]. For non-model based methods we performed a Principal Component Analysis using the function dudi. pca, from the ADE4 R package  [36]. An AMOVA was conducted with ARLEQUIN v3. 5. 2. 2  [33]  following the grouping scheme described above (see mtDNA analyses). We also searched for loci under selection among the groups described above using BAYESCANR  [37]  included in the R package PopGenome  [38].
Demographic history We searched for signatures of past population expansion or contraction in the turtle dove effective population size (Ne). The Ne is defined as the population size that is evolutionary relevant (i. e. , the number of breeding individuals)  [39]. We used DNASP v5  [31]  to investigate our mtDNA data set, calculating Fu's F s and Tajima's D; the significance for each test was estimated after 1,000 permutations. We also explored the demographic history of the turtle dove by performing an Extended Bayesian Skyline plot (EBSP), as implemented in BEAST version 1. 8. 2  [40]. We did not find evidence of population genetic structure (Fig. 1b  and c ), and thus were able to pool all our samples into one population. Because of the computationally intensive nature of this analysis, we conducted runs including only 30 loci per individual.
We used our cytochrome b data and sequences from 29 randomly selected nuclear loci. All partitions were unlinked and we implemented a strict clock model, with a substitutions rate of 2. 1 % per million years as estimated by Weir and Schluter  [41]  for the cytochrome b gene in birds. We ran the analysis using the "Coalescent: Extended Bayesian Skyline" tree prior for 100 million generations, sampling every 4,000. We inspected the results in Tracer version 1. 6  [42] , checking for convergence, sufficiently large effective sample sizes for parameter estimates and discarding the initial 10 % of samples as burn-in. This analysis was run three times using independent sets of 29 randomly selected nuclear loci in combination with the cytochrome b sequences; all three runs produced similar results.
An Approximate Bayesian Computation (ABC) analysis was performed using DIYABC v2  [43]. ABC methods bypass exact likelihood calculation by using summary statistics and massive simulations, allowing them to handle large data sets  [44]. Thus, we included the complete SNP dataset (2954 loci) for all genotyped individuals (104) and pooled our samples into a single population (as previously explained for the EBSP). We compared five alternative demographic scenarios, with different combinations of Ne variation in time (t), see Fig. 2  for a schematic representation of the scenarios. All priors were set as normally distributed, time (t) expressed in million years before present and effective population size (N) in million individuals (Fig. 2 ). Priors N1, N2, t1 and t2 were informed by the EBSP (see results).
Niche modelling The realized niche of the present distribution of the turtle dove was modelled and extrapolated using MAXENT 3. 3. 3 k (maximum entropy;  [46] ). We combined data obtained from GBIF (Global Biodiversity Information Facility, http://www. gbif. org/), with the Bioclim data sets (http://worldclim. org/) for the present, the Mid-Holocene (MH, 6000 ybp), the Last Glacial Maximum (LGM, 22,000 ybp), and the last interglacial (LIG; ~120,000-140,000 ybp). MAXENT t is a program for modelling ecological niches from presence-only species records (further details in Additional file 2).
Demographic history and niche modelling The mtDNA dataset showed signatures of a recent demographic expansion in the turtle dove population: F s = -20. 95 (p = 0. 001) and Tajima's D = -1. 70 (p = 0. 02). The EBSP analysis that combined mtDNA sequences with 29 randomly selected nuDNA loci also supported a population expansion. Based on our assumption of molecular clock calibration for cytochrome b (2. 1 per million years), this expansion started ~14,000 years before present (Fig. 3 ). Our DIYABC analysis supported a compatible, yet more complex demographic scenario than that suggested by the EBSP. From the five simulated scenarios, scenario four (Fig. 2 ) showed the best goodness-of-fit to the observed data (Additional file 5) and had the highest posterior probability, 0. 99 (CI 95 % = 0. 97-1) when using the direct estimation method and 1 (CI 95 %-1-1) with the logistic regression method (Additional file 6).
Regardless of the method used, the confidence intervals estimated from the competing scenarios did not overlap. Scenario four assumes that the turtle dove population suffered two variations in its effective population size. The Ne during the mid-late Pleistocene (~78,300 ybp) was estimated to be ~84,400 individuals. Following a demographic expansion during the early-mid Holocene (~7,600 ybp), the population reached an estimated Ne of ~3,160,000 doves. Finally, we found evidence of a recent contraction that started between the mid-Holocene and the present, resulting in a current Ne estimate of ~172,000 individuals (see Table  1  for more details on the estimated parameters). It is possible that the recent contraction in the Ne was not detected by the EBSP analysis because of lack of statistical power in the smaller dataset  (30 loci).
Acknowledgements Would like to thank  Tanja Romeike ,  Steffen Koschkar  and  Fernando Spina  for help in the field. We are indebted to the members of the  Fuller Evolutionary Biology program , especially  Dr. Irby Lovette  and  Dr. Bronwyn Butcher  for access to the research facilities at the  Cornell Lab of Ornithology  and for providing valuable feedback for this project.
Data accessibility Data used in MAXENT was downloaded from open access sources, namely: http://www. worldclim. org (climate data) and http://www. gbif. org (occurrence data). mtDNA sequences are available in GenBank (accession numbers for mtDNA: KU588290-KU588384) and Rad-seq data is available in DRYAD (doi:10. 5061/dryad. p5cf4).
Ciprofloxacin, a broad-spectrum antimicrobial agent belonging to the fluoroquinolone family, is prescribed off-label in infants less than one year of age. Ciprofloxacin is included in the European Medicines Agency priority list of off-patent medicinal products requiring evaluation in neonates. This evaluation is undergoing within the TINN (Treat Infections in Neonates) FP7 EU project. As part of the TINN project, the present preclinical study was designed to assess the potential adverse effects of Ciprofloxacin on neurodevelopment, liver and joints in mice. Newborn mice received subcutaneous Ciprofloxacin at 10, 30 and 100 mg/kg/day from 2 to 12 postnatal days. Peak plasma levels of Ciprofloxacin were in the range of levels measured in human neonates. We examined vital functions in vivo, including cardiorespiratory parameters and temperature, psychomotor development, exploratory behavior, arthro-, nephro-and hepato-toxic effects.
Exploratory behavior We performed open-field tests at PND4, PND9 and PND14 with a custom, thermoregulated open-field system at 26 C, as previously described  (Dehorter et al. , 2011). Each mouse was placed on a transparent acrylic plate (50 cm Â 60 cm) covered with a silicone gel and its activity was tracked for 1 min. A camera recorded the trajectory of the pup. We determined the explored zones and the total distance traveled using custom software.
Statistical analysis All study variables were analyzed using repeated measure analysis of variance (ANOVA) with treatment (0, 10, 30, 100 mg/kg/ day Ciprofloxacin), litter and gender as between-subjects factors and age as a repeated factor. Weights from PND2 to PND14 and from PND16 to PND60 were analyzed separately. Unless specified, litter and gender had no significant effects, either as a main effect or in interaction with treatment. When appropriate, pairwise comparisons were done using the Fischer's PLSD with Bonferroni correction. Statistical analyses were conducted using R software (www. r-project. org).
Section 5 describes the maps, the solar dipole measurement, and the derivation of far sidelobes and zodiacal maps. Section 6 presents the noise characteristics and the null tests obtained by splitting the Planck HFI dataset into different groups based on ring period, time period, or detector sets. Consistency checks are performed in order to assess the fidelity of the maps. Finally, Sect. 7 is dedicated to the description of systematic effects, in particular in polarization. The major systematic residuals in Planck HFI data are due to the leakage from temperature to polarization induced by flux mismatch between associated bolometers. This is the result of either bandpass mismatch, or zero level uncertainty, or calibration uncertainty. We present a first attempt to correct the maps for the intensity-to-polarization leakage.
(  6 ) for each bolometer independently, rewriting the system as P = A T + G t orb + n , (7) where the unknowns are the sky-signal in absorbed power units T and the gain G. The calibration problem is thus linear and can be solved directly. The maximum-likelihood estimate of the gain G is obtained by combining all available samples using the noise covariance matrix N = n † n and marginalizing over T, solving the equation t T orb N -1 Zt orb × G = t T orb N -1 Zd, ( 8 ) where d is the vector of input data P t and we define Z = I -A A T N -1 A -1 A T N -1. (9) In practice, the noise is treated by assuming that the destriping reduces the matrix N to a diagonal one once the data d have been corrected for a constant offset (see Sect. 4. 2). For the HFI, the mapmaking problem is degenerate for the reconstruction of polarization if we solve for a single detector at a time.