dataesr/scientific-paragraphs-categorization huggingface dataset:
93). This cluster was highly enriched for genes involved in EIF2 signaling and translation (Figure  1A ). It is likely that type I IFN production led to activation of protein kinase R (PKR), which can phosphorylate EIF2A and thereby block translation. Only a few atypical Toxoplasma strains were capable of inducing high levels of Ifnb and Rsad2 (an IFNb induced gene also called viperin) expression, the most potent inducers being RUB and COUGAR (Figure  6B ). To validate our RNA-seq data, we infected BMDMs with several Toxoplasma strains and measured interferon beta in the supernatants 24 hours post infection. Both IFNb protein and IFNb transcripts were produced by cells infected with COUGAR and RUB, but not other canonical or atypical strains tested (Figures  6C-D ). Time course experiments showed that IFNb transcripts can be detected as early as 8 h post infection, peaking at 12 h (Figure  6E  and data not shown).
The genetic relationship among these strains was previously investigated using genome-wide SNP data  [18]. Strains originating from France and French Guiana were provided by the BCR Toxoplasma (http://www. toxocrb. com). To generate PRU (DHXGPRT) parasites expressing the type II allele of ROP38, the ROP38 coding region and putative promoter region (1,437 bp upstream of the start codon) were amplified from PRU genomic DNA by PCR (forward, 59-CGAGAGGGAAGCAACGTTTA-39, reverse, 59-TTACGCGTAGTCCGGGACGTCGTACGGG-TAAAATTGATGCGTTCTTATCCGACG-39). Sequence coding for a C terminal HA tag was included in the reverse primer (denoted with italics). ROP38 II HA was then cloned into pTKO-att  [32]  through LR recombination. The pTKO-att-ROP38 II HA vector was then linearized with NotI (New England Biolabs, Inc.
Immunofluorescence assays HFFs seeded on coverslips were infected with T. gondii and incubated for different time points. Cells were then fixed with 4% formaldehyde in PBS for 20 min at room temperature, permeabilized and blocked with phosphate-buffered saline (PBS) plus 5% goat serum, 3% serum bovine albumin and 0. 2% Triton X-100. Subsequently coverslips were incubated with primary antibody overnight at 4uC, washed twice with PBS, and incubated with fluorescent secondary antibodies and Hoechst to allow visualization of antigen and DNA respectively. Coverslips were mounted on a glass slide with Vectashield (VectorLaboratories). Photographs were taken using NIS-Elements software (Nikon) and a digital camera (CoolSNAP EZ; Roper Industries) connected to an inverted fluorescence microscope (model eclipse Ti-S; Nikon).
At the Large Hadron Collider, the identification of jets originating from b quarks is important for searches for new physics and for measurements of standard model processes. A variety of algorithms has been developed by CMS to select b-quark jets based on variables such as the impact parameters of charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton, or combinations thereof. The performance of these algorithms has been measured using data from proton-proton collisions at the LHC and compared with expectations based on simulation. The data used in this study were recorded in 2011 at √ s = 7 TeV for a total integrated luminosity of 5. 0 fb -1. The efficiency for tagging b-quark jets has been measured in events from multijet and t-quark pair production.
Data samples and simulation Samples of inclusive multijet events for the measurement of efficiencies and misidentification probabilities were collected using jet triggers with p T thresholds of 30 to 300 GeV/c. For efficiency measurements, dedicated triggers were used to enrich the data sample with jets from semimuonic b-hadron decays. These triggers required the presence of at least two jets with p T thresholds ranging from 20 to 110 GeV/c. One of these jets was required to include a muon with p T > 5 GeV/c within a cone of ∆R = 0. 4 around the jet axis, where ∆R is defined as (∆φ) 2 + (∆η) 2. Triggers with low-p T thresholds were prescaled in order to limit the overall trigger rates. Depending on the prescale applied to the trigger, the multijet analyses used datasets with integrated luminosities of up to 5.
0 fb -1. Data for the analysis of tt events were collected with single-(e or µ) and double-lepton (ee or eµ or µµ) triggers. The samples were collected in the first part of the 2011 data taking with an integrated luminosity of 2. 3 fb -1. The precision on the b-jet tagging efficiency from tt events is limited by systematic uncertainties. Using the full dataset collected in 2011 would not significantly reduce the overall uncertainty. Monte Carlo (MC) simulated samples of multijet events were generated with PYTHIA 6. 424  [5]  using the Z2 tune  [6]. For b-jet tagging efficiency studies, dedicated multijet samples have been produced with the explicit requirement of a muon in the final state. In the simulation, a reconstructed jet is matched with a generated parton if the direction of the parton is within a cone of radius ∆R = 0.
Properties of the tracks and the average multiplicity after the selection (except for the variable plotted) are shown in Fig. 1. The uncertainties shown in this and all following figures are statistical unless otherwise stated. The data were recorded with a prescaled jet trigger in the second part of 2011 when the number of pileup events was highest. The jet p T threshold was 60 GeV/c. The distributions show satisfactory agreement with the expectations from simulation. The track multiplicity and the lower part of the momentum spectrum are particularly sensitive to the modelling of the particle multiplicity and kinematics by the Monte Carlo generator, as are other distributions such as the number of hits in the innermost pixel layers. Detector effects that are not modelled by the simulation, such as the dynamic readout inefficiency in the pixel system, can also contribute to the remaining discrepancies.
The estimate for the likelihood, P jet , is defined as P jet = Π • N-1 ∑ i=0 (-ln Π) i i! with Π = N ∏ i=1 max(P i , 0. 005) , (1) where N is the number of tracks under consideration and P i is the estimated probability for track i to come from the primary vertex  [33, 34]. The P i are based on the probability density functions for the IP significance of prompt tracks. These functions are extracted from data for different track quality classes, using the shape of the negative part of the.
The PtRel and IP3D methods rely on fits to the p rel T  [37]  and muon IP distributions in the data with respect to simulated spectra for the b signal and charm+light-parton background. In the two methods, the p rel T and IP spectra for muon jets are modelled using simulated distributions that represent the spectra expected for different jet flavours to obtain the b-jet content of the sample. The efficiency for a particular tagger is obtained by measuring the fraction of muon jets that satisfy the requirements of the tagger. To make the treatment of the statistical uncertainty more straightforward, the muon jet sample is separated into those jets that satisfy and those that fail the requirements of the tagger. These jets are referred to as "tagged" and "untagged. " A dijet sample with high b-jet purity is obtained by requiring that events have exactly two reconstructed jets: the muon jet as defined above and another jet fulfilling the TCHPM b-jet tagging criterion (the "medium" operating point for the TCHP algorithm).
Efficiency measurement with the System8 method The "System8" method  [38, 39]  is applied to events with a muon jet and at least one other, "away-tag", jet. The muon jet is used as a probe. The reference lifetime tagger and a supplementary p rel T -based selection are tested on this jet. The away-tag jet is tested with a separate lifetime tagger. There are eight quantities that can be counted from the full data sample. The quantities depend on the number of passing or failing tags. A set of equations correlates these eight quantities with the tagging efficiencies. A muon jet can be tagged as a b jet using either a lifetime tagger, or by requiring that the muon has large p rel T. In this analysis, the requirement is p rel T > 0. 8 GeV/c. These two tagging criteria have efficiencies ε tag b and ε PtRel b , respectively, for b jets.
Dilepton pairs from heavy-flavour resonances and low-mass Drell-Yan production are also removed by requiring a minimum dilepton invariant mass of 12 GeV/c 2. The numbers of observed and predicted events in the lepton+jets channel and the dilepton channel are given in Tables  5  and 6 , respectively. The uncertainties include the uncertainties on the luminosity measurement and the cross sections. For all MC predictions, events are reweighted to take into account differences in trigger and lepton selection efficiencies between data and simulation  [22, 41]. The lepton selection efficiency scale factors are estimated from data using Z events. For dilepton events, the trigger efficiencies are estimated on a data sample using a trigger that is weakly correlated to the dilepton triggers. The dilepton trigger selection efficiency is estimated on events which contain two leptons that fulfil the complete dilepton event selection.
The Drell-Yan background is measured using data. Two different methods are used, and the two estimates are compatible. In the PLR method, for the ee and µµ channels, the ratio of Drell-Yan events outside and inside the dilepton invariant mass window, R out/in , is estimated from the simulation. This is used to estimate the Drell-Yan background using the number of data events inside the dilepton invariant mass window  [41]. A contamination from other backgrounds can still be present in the Z-mass window, and this contribution is subtracted using the eµ channel scaled according to the event yields in the ee and µµ channels. For the eµ channel, the DY background yield is estimated after performing a binned maximum-likelihood fit to the dilepton invariant mass distribution. In the FTM method, the number of Drell-Yan events ee and µµ channels is estimated from the shape of the distribution of the angle between the momentum of the two leptons.
These uncertainties are the combination of the statistical uncertainty and the systematic uncertainties considered as nuisance parameters. All the nuisance parameters are common to the three channels except the estimation of the backgrounds from data for W+jets and Z+jets. The Z+jets background is estimated from data as described in Section 6. 1. The small W+jets background is estimated from data using the matrix method  [41]. The expected number of b-tagged jets in events with n jets of a given dilepton final state, µ i,j in Eq. (  5 ), is derived from pre-tagged simulated events with n jets. This is carried out by applying per-jet b-jet tagging efficiencies, considering all jet tagging combinations. These efficiencies are derived as a function of p T and η, using simulated tt events for b jets and using data samples dominated by light-parton jets.
The distribution of the number of b-tagged jets observed in data and predicted in the simulation before and after the fit for tt and background events is shown in Fig. 16. In the current implementation the likelihood only uses the b-tagged jet multiplicity in tt lep-ton+jets events with between four to seven reconstructed jets, as it emphasizes the measurement of the heavy-flavour b-jet tagging efficiency. The b-jet tagging efficiencies and tt cross section are treated as free parameters in the fit. The tt cross section determined in the fits are consistent with the published values. The c-and light-parton-jet tagging efficiencies are taken from the simulation corrected for the data/MC scale factors. The systematic uncertainties are determined from ensembles of pseudo-experiments. In each of these pseudo-experiments, the number of signal and background events are generated using Poisson statistics, using as mean values the number of expected events in each channel.
Efficiency measurement from a b-enriched jet sample In this method, the b-jet tagging efficiency is measured from a sample enriched with b jets (bSample) in lepton+jets events. The contamination of this sample due to light-parton jets is estimated from data and subtracted. In order to select the correct jets originating from the decay of the top quarks, a χ 2 is calculated for each jet-parton combination based on the masses of the reconstructed W boson m qq and the hadronically decaying top quark m bqq : χ 2 = m bqq -m t σ t 2 + m qq -m W σ W 2. ( 18 ) The mean masses and widths are obtained from the tt simulation using a Gaussian fit to the mass distributions of the combination with the correct jet-to-quark assignment. The mean and width of the reconstructed top-quark mass distribution are 172.
Results from tt events The statistical properties of the four methods presented in Sections 6. 9  using data with an integrated luminosity of 2. 3 fb -1. The scale factors were stable over the whole data-taking period and can be applied to the full dataset. The measured b-jet tagging efficiencies and scale factors for the CSV algorithm are shown in Fig. 19. The PLR and FTC methods are used to calculate a combined scale factor for use in analyses, by taking the weighted mean of the scale factors from each method. The two methods are chosen because each has the smallest uncertainty among the analyses in its respective decay channel. By choosing one analysis in the dilepton channel and one in the lepton+jets channel, A continuous function for the scale factors is required in physics analyses that use b-jet tagging discriminators with multivariate methods.
Misidentification probability measurement The measurement of the misidentification probability for light-parton jets relies on the definition of inverted tagging algorithms, selecting non-b jets using the same variables and techniques as the standard versions. These "negative taggers" can be used in the same way as the regular b-jet tagging algorithms both in data and in the simulation. As the negative-tagged jets are enriched in light flavours, the misidentification probability can be measured from data, with the simulation used to extract a correction factor. The misidentification probability is evaluated from tracks with a negative impact parameter or from secondary vertices with a negative decay length (see Section 4). When a negative tagger is applied to jets of any flavour, the corresponding tagging efficiency is denoted "negative tag rate".
Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC machine. We thank the technical and administrative staff at  CERN  and other  CMS  institutes. This work was supported by the  Austrian Federal Ministry of Science and Research ; the  Belgium Fonds de la Recherche Scientifique , and  Fonds voor Wetenschappelijk Onderzoek ; the  Brazilian Funding Agencies (CNPq, CAPES ,  FAPERJ , and  FAPESP) ; the  Bulgarian Ministry of Education and Science ; CERN; the  Chinese Academy of Sciences, Ministry of Science and Technology , and  National Natural Science Foundation of China ; the  Colombian Funding Agency (COLCIENCIAS) ; the  Croatian Ministry of Science, Education and Sport ; the  Research Promotion Foundation , Cyprus; the  Ministry of Education and Research, Recurrent  financing contract  SF0690030s09  and  European Regional Development Fund , Estonia; the  Academy of Finland ,  Finnish Ministry of Education and Culture , and  Helsinki Institute of Physics ; the  Institut National de Physique Nucléaire et de Physique des Particules / CNRS , and  Commissariat à l' Énergie Atomique et aux Énergies Alternatives / CEA , France; the  Bundesministerium f ür Bildung und Forschung, Deutsche Forschungsgemeinschaft , and  Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany ; the  General Secretariat for Research and Technology, Greece ; the  National Scientific Research Foundation , and  National Office for Research and Technology, Hungary ; the  Department of Atomic Energy  and the  Department of Science and Technology, India ; the  Institute for Studies in Theoretical Physics and Mathematics, Iran ; the  Science Foundation, Ireland ; the  Istituto Nazionale di Fisica Nucleare, Italy ; the  Korean Ministry of Education, Science and Technology  and the  World Class University program of NRF , Korea; the  Lithuanian Academy of Sciences ; the  Mexican Funding Agencies (CINVESTAV, CONACYT ,  SEP , and  UASLP-FAI) ; the  Ministry of Science and Innovation, New Zealand ; the  Pakistan Atomic Energy Commission ; the  Ministry of Science and Higher Education  and the  National Science Centre, Poland ; the Fundacão para a Ciência e a Tecnologia, Portugal;  JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan) ; the  Ministry of Education and Science of the Russian Federation , the  Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences , and the  Russian Foundation for Basic Research ; the  Ministry of Science and Technological Development of Serbia ; the  Secretaría de Estado de Investigaci ón, Desarrollo e Innovaci ón and Programa  Consolider-Ingenio 2010, Spain; the  Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH ,  Canton Zurich , and SER); the  National Science Council, Taipei ; the  Scientific and Technical Research Council of Turkey , and  Turkish Atomic Energy Authority ; the  Science and Technology Facilities Council , UK; the  US Department of Energy , and the  US National Science Foundation.
In addition, circadian rhythms may be disrupted by the administration of a specific treatment. In this case, the exact period length is often unknown and may vary under different experimental conditions or treatments. Most circadian clock related studies currently estimate period by approximating the oscillatory gene expression profiles by a sum of sine and cosine functions within a Fourier approximation context  (Levine and others, 2002). Software available for gene reporter data analysis, such as Lumicycle  (Actimetrics, 2012) , attempts to find the period by looking for the largest sinusoidal component in such a representation, but provides no measure of its accuracy. The Fast Fourier Transform Non-linear Least Squares (FFT-NLLS) method by  Plautz and others (1997)  is widely used and will serve here as a benchmark for comparison.
This method is, however, sensitive to the number of AR terms  (Marple, 1980). Beyond Fourier methods and spectral analysis, software such as WAVECLOCK  (Price and others, 2008)  uses wavelet analysis to estimate the period of oscillatory circadian data as a smooth function over time but provides no routine for confidence intervals. Circadian data sets often have replicate time series of the same experiment. Figure  1 (c) shows three groups of four time series replicates, each containing measurements on skin temperature for a patient suffering from metastatic colorectal cancer. The groups represent three chemotherapy stages: before, during, and after treatment with chronoIFLO4, a combination of four anticancer drugs (see  Scully and others, 2011 , for a description of an equivalent study), and the question is whether the period of the clock is affected by the treatment.
Human gene luminescence data Time series data on the expression level of the human circadian clock gene PER2 was made available to us. Different treatments for inflammation in the lung are applied to cells from mouse embryos, and expression of the PER2:LUC construct is measured over several days at 1. 5 h intervals. Figure  1 (b) shows the average profile for two treatment groups, G1 and G2 (details of which are subject to a confidentiality agreement), each with 16 replicates, and a control group with no treatment with 36 replicates (see Section 10 of supplementary material available at Biostatistics online). We apply the SR method to the log-transformed averaged data to obtain the fitted oscillations from (4. 2) and displayed in Figure  4 (c). They suggest that cells to which G1 is applied have a longer period than cells in the G2 and control groups.
Plant luminescence data The plant data in Figure  1 (a) is part of a comprehensive study on the effect of temperature on the circadian clock of the model plant Arabidopsis thaliana. High-throughput data on the expression levels of circadian clock genes are recorded for different temperatures under constant red light using luminescence constructs. Here, we focus on data for TOC1:LUC and CCA1:LUC at 17  C, respectively, and 24. 99 h and 25. 34 h for CCA1:LUC, respectively. Note that from these plots it is not possible to say whether the change in temperature leads to a change in period. Our test T 2 gave p v = 0. 02 for TOC1:LUC and p v = 0. 002 for CCA1:LUC, strongly suggesting that this is the case.
FUNDING This research was funded by  BBSRC  and  EPSRC  under the SABR initiative. M. J. C. and B. F. are funded by  ROBuST  grant  BB/F005261/1. J. F. and K. H. are funded by  ROBuST  grant  BB/F005237/1. P. D. G. and A. H. are funded by  ROBuST  grant  BB/F005318/1. D. A. R. holds an  EPSRC Senior Research Fellowship  ( EP/C544587/1 ) and his work was also funded by the  European Union BIOSIM Network  Contract  005137.
Overall, the number of clinical trials in Europe has decreased by 20% in the last 3 years 1  2 3 4 5. The complexity of clinical trials and the regulatory requirements have increased significantly in the last few years, requiring an increasing level of scientific, methodological, regulatory and organisational know-how to be able to perform clinical trials efficiently. Despite pharmaceutical industry's increase of the percentage of multi-national trials and the number of sites involved, the number of enrolled patients per year has not increased (EUdraCT database), making the planning and execution of clinical trials difficult and costly. In Europe, clinical research (clinical trials science and methodology) is often not highly regarded academically and collaboration with pharmaceutical industry in performing the trials is still subject to suspicion of commercial bias and perceived lack of academic credibility, as presented in a European Science Foundation (ESF) report on investigator-driven clinical trials 6.
PharmaTrain AND ECRIN PharmaTrain is one of the IMI Education and Training projects and focusses on the development of a comprehensive training infrastructure in the area of pharmaceutical development which includes the areas of pharmaceutical medicine, regulatory affairs and clinical trial performance. The project is based on the creation of a network of Diploma and Master Courses in pharmaceutical development and in regulatory affairs, located in many different countries. The PharmaTrain consortium partners, consisting of universities, not-for-profit organisations and pharmaceutical companies, have developed and implemented an advanced standard for content and teaching methodology based on an agreed syllabus and curriculum and including e-learning opportunities. PharmaTrain has established a European quality management infrastructure to assess quality, content and performance of the associated courses, an infrastructure to manage the examination process in all associated courses and an IT platform within EMTRAIN's "on-course" database to enable easy identification of the most suitable training opportunities.
COURSE ACCREDITATION As part of PharmaTrain's network of course providers in pharmaceutical medicine/medicines development sciences, a European network of CLIC course providers will be established. The course providers will be accredited by the PharmaTrain Central Office according to the agreed and implemented PharmaTrain quality standards. PharmaTrain will establish a "CLIC Course Recognition" award for those courses fulfilling the PharmaTrain quality criteria and syllabus/learning outcome requirements. It is proposed to develop a database in the PharmaTrain Central Office containing the accredited courses as well as the contact details of those certified course participants who gave permission to keep their personal data in order to remind them of their CPD needs.