dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotyping and sample sets Direct genotyping of BRCA2 c. 68-7T > A was conducted as part of the Collaborative Oncological Gene-environment Study (COGS) detailed elsewhere  (Michailidou et al. , 2013). This study included genotype results from breast cancer cases and controls participating in the Breast Cancer Association Consortium (BCAC; http:// bcac. ccge. medschl. cam. ac. uk/), and from the carriers of assumed pathogenic variants in BRCA genes, participating in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA; http:// cimba. ccge. medschl. cam. ac. uk/). The BCAC and CIMBA datasets are described in de la  Hoya et al. , (2016). Information on breast tumor estrogen receptor and grade status were available for 189 variant carrier cases from BCAC. Via the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA; https://enigmaconsortium.
Mitomycin C (MMC) growth inhibition test and statistical analyses A total of 3 × 10 6 cells/ml were seeded in triplicate in 25 ml flasks and grown for 72 hr in the absence or in the presence of MMC (Sigma-Aldrich) at a final concentration of 170 ng/ml. Percentage of viable cells was determined using trypan blue dye exclusion assay, following the manufacture's instruction (Sigma-Aldrich). Statistical differences in cell viability after exposure to MMC compared to controls were determined by two-tailed Student t-test using GraphPad Prism software.
Such alterations require further investigation to assess pathogenicity. To address the issue, we performed a multifactorial-likelihood analysis combining the odds for causality derived from a large casecontrol study, using the datasets of BCAC, pathology likelihood based on breast tumor phenotype, and co-segregation data from ENIGMA. Overall, the posterior probability of c. 68-7T > A being pathogenic was 7. 44 × 10 -115. This value is well below the threshold established by ENIGMA for a BRCA1/2 variant to be classified as class 1, that is, not pathogenic (probability of pathogenicity < 0. 001), when considered against characteristics of the average truncating pathogenic variant. In addition, the confidence interval of the odds ratio estimate (OR 1. 09: 95%CI 0. 89-1. 33) excludes even moderate breast cancer risk  (Hollestelle, Wasielewski, Martens, & Schutte, 2010).
m. pseudomajus and A. m. striatum populations meet, creating a local barrier to gene flow  (28). We performed simulations to explore scenarios consistent with the data and modes of selection. To provide constraints on simulations, we first estimated the age of the selective sweeps. Based on the residual diversity within the sharp peaks at ROS and EL, we estimated the date of the most recent sweeps to be ∼90,000 generations ago (SI Appendix, Supplementary Text S1); this is an upper bound, since "soft sweeps" might not have eliminated all diversity. We also estimated the age of the barrier to gene flow. As detailed in SI Appendix, Supplementary Text S1, the time required for F st in the ROS/EL interval to accumulate to the observed value of 0. 125 is T ∼ 0. 54 N e ∼ 45,000 generations (where N e = effective population size).
Materials and Methods Full details of plant material, DNA extraction, genome sequence analysis, population genomics, genotyping, SNP analysis for geographic, and RNAseq analysis are given in SI Appendix, Materials and Methods. Details on inferences from pairwise diversity and divergence, geographic cline analysis, and genotypic screens are given in SI Appendix. Genomic sequence datasets are available at European Nucleotide Archive (ENA) with accession number PRJEB28287, and RNAseq datasets are deposited in National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) with accession number GSE118621. Associated scripts are provided at linked public data repositories as detailed in SI Appendix, Materials and Methods, and further information on the hybrid zone is available at www.
ACKNOWLEDGMENTS. Many thanks to  Christophe Thébaud  for sharing his finding of the herbarium specimen referenced in the text. This work was supported by  Biotechnology and Biological Sciences Research Council  Grants  BBS/E/J/000PR9773  and  BB/G009325/1  (to E. C. ),  ERC  Grant  201252  (to N. H. B. ), and a PhD scholarship (to H. T. ) from the  Portuguese Foundation for Science and Technology (FCT) , through the  Human Potential Operating Programme (POPH)  of the National Strategic Reference Framework (QREN), within the  European Social Fund  ( Scholarship SFRH/BD/60982/2009 ). This research was supported in part by the  Norwich BioScience Institutes Computing infrastructure for Science (CiS) group.
Gene transcription analysis The RNA-seq procedure yielded on average 14. 6 million pairedend mapped reads per sample, of which 98. 78 AE 0. 81% proved to be assignable to a genomic site (Table  S1 ). Only a small proportion of the reads (2. 41% in B, <1% in CS and CS + 7HL) mapped to an unexpected location. The RSEM analysis revealed that 35 301 wheat genes were transcribed by either CS or CS + 7HL at >1 count per million mapped reads, while the equivalent number of 7HL genes located in the genomic window 339-656 Mbp was 923. Of the 35 301 wheat genes, the vast majority (34 341, 97. 3%) were not transcribed differentially between CS and CS + 7HL, still leaving 960 which were differentially transcribed. Of these latter genes, 509 were down-regulated (transcript abundance lower in CS + 7HL than in CS) and 451 were up-regulated.
Transcript analysis Each of the reads for each of the 18 samples (six replicates of each of CS, B and CS + 7HL) was mapped against the 'High Confidence' coding sequences of both wheat (IWGSC RefSeq v1. 0, see IWGSC, 2018) and barley (IBSC RefSeq, see  Mascher et al. , 2017)  using Bowtie v1. 0. 0 software  (Langmead et al. , 2009)  with no mismatches allowed. The quantification of transcripts abundance was performed using RSEM v1. 2. 8 software  (Li and Dewey, 2011). The assignment of DT was based on two comparisons: one between CS and CS + 7HL, and the other between B and CS + 7HL. Transcripts recording a count of less ª 2018 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd. , 1-11 than one per million mapped reads were ignored, and the counts were then normalized by the effective library size using the trimmed mean of the logarithm of the transcript abundance ratios (TMM); DT was identified using edgeR package version 3.
Genomic distribution of the genes responsible for the transcripts The density of the transcribed genes was calculated on the basis of a sliding 10 Mbp window, with a step of 1 Mbp along each chromosome. Similarly, the ratios of down-and up-regulated to transcribed transcripts [R(Down/Trans), R(Up/Trans)] were calculated. The mean logarithm of the fold change in transcript abundance (logFC) in the comparisons CS vs CS + 7HL and B vs CS + 7HL was determined for the same genomic windows. Each of these measures was processed using the R package changepoint v2. 2. 2 program  (Killick and Eckley, 2014)  employing the 'Pruned Exact Linear Time' method  (Killick et al. , 2012).
Definition of homeologous and nonhomeologous gene sets Sets of homeologous and nonhomeologous genes between 7HL and wheat subgenomes were determined by whole genome comparison of protein sequences using BLASTp  (Altschul et al. , 1990)  leveraged by MCScanX software  (Wang et al. , 2012)  with E-value threshold of 10e-5 and a size of synteny block of 10 genes. Finally, only 1 : 1 relationships were retained for each subgenome comparison.
Acknowledgements The authors thank  Dr. Etienne Paux  for fruitful scientific discussions;  Petr Navr  atil for outstanding informatics support;  Radim Sva cina ,  Helena Tvard  ıkov a,  Marie Vyhl  ıdalov a for excellent technical support. ' National BioResource Project, Komugi ' is acknowledged for maintaining and providing the plant material. This research was financially backed by the  Czech Science Foundation  (award  P501/12/G090 ) and by the  Ministry of Education, Youth and Sports of the Czech Republic  (award  LO1204  from the  National Program of Sustainability I). The computing infrastructure required was provided by the  National Grid Infrastructure MetaCentrum  (grant No. LM2010005  under the program 'Projects of  Large Infrastructure for Research, Development, and Innovations').
Introduction Worldwide, influenza affects 10-20% of the population and causes more than 1 million deaths annually  [1]. This vaccinepreventable infectious disease belongs to the list of the World Health Organization (WHO) recommended vaccines, yet the vaccine uptake is in general low in both developed and developing countries  [2]. The finding of the WHO Flu-Net database showed that dynamic of influenza epidemics in the large majority of the Middle East and North Africa (MENA) countries was in line with the Northern Hemisphere, with the largest peak observed between January and March  [3]. Annual vaccination campaigns in the MENA region target primarily specific high-risk groups i. e. pregnant women; individuals >6 months with underlying chronic diseases, elderly, residents of long-term care facilities, children aged 6-59 months and health care providers.
Technology roadmaps have become an essential part of the European Commission's (EC) nanotechnology policy strategies. They represent socio-technical landscapes and evolving pathways, suggesting the underlying or otherwise supportive metaphorical patterns and narrative structures. For the same reason, however, roadmaps are problematic assemblages: they can simplify and distort reality, and filter things that don't fit. The presented study combines cognitive linguistics with narratology to scrutinise the European Commission's nanotechnology roadmapping as a discursive formation. It targets the systematic metaphors in approximately two-hundred news and reports on nanotechnology, compiled ad hoc from the CORDIS database (between the years 1999-2015). It is argued that the identified metaphors correspond to a discourse topology of 'locations', 'events', and their structures, especially as regards to the dilemma of 'path dependence', overcoming 'knowledge gaps', and reaching 'nanoworld'.
The results of the analysis are confronted with selected policy documents to further address their embedding in discursive and social practices. The article begins with an overview of the scholarship on technology roadmaps, followed by a discussion within the perspective of metaphor studies that can enrich understanding roadmapping concept in the political context. I place the research problem on the interface of CORDIS database and discuss its character and suitability for corpus compilation. I provide more detailed information on data and methodology of the study. Next, I present the main findings with a recovered topology and narrative dimension of the EC nanotechnology roadmapping. Discussion on metaphors and their systematicity in the context of the EC policy documents then brings the character, durability, and effects of the consolidation more into focus.
Technology Roadmapping as Situated Practice: CORDIS Database The study presupposes we can analytically extract systematic metaphors in their social contexts as long as we manage to argue that institutionalisation of such contexts occurred (cf. Cameron, 1999; Chilton & Ilyin, 1993; Chilton & Schäffner, 2002; Mussolff, 2004; Charteris-Black, 2011). The Community Research and Development Information Service (CORDIS), primary information repository of the European Commission (EC) has a specific value in targeting roadmapping discourse in the EC's institutional setting. The repository complies with the guidelines of the Publications Office of the European Union, on behalf of the European Commission's Research Directorates-General and Agencies. It contains data from different actors on the EU-funded projects in execution.
Given the current state of knowledge and the above discussion, I have placed the following research questions in the focus. The first question is exploratory, and the second one is more performance-based, aiming at the potential consequences by relating a metaphor to the real world: • Are the systematic metaphors, representing roadmapping discourse in CORDIS database, resonating with nanotechnology policy, that is, systematic in projecting 'political  (means, ends, actions, plans, actors, etc. )  is physical  (paths, locations, events, maps, travellers, etc. ) ', either as the conceptual  (Lakoff)  or narrative  (Greimas)  variation? • And if so, why are observed metaphors used, that is, are there any particular consequences of these concepts and structures that extend beyond linguistic analysis (from discourse to social practice), in terms of actor's qualifying roles, performance modes, mobilisation of resources, eligibility conditions, and so on?.
METHODOLOGY AND DATA There are different methods available in metaphor analysis, even though studies of political discourse increasingly rely on corpus-based approaches (cf. Chilton & Ilyin, 1993; Chilton & Schäffner, 2002; Chilton, 2004; Musolff, 2004; Charteris-Black, 2004 and 2011). The corpus-based approaches are ideally suited to investigate the use of conventional metaphorical expressions, and particularly, their systematicity and structures. However, a close reading of text passages is necessary for determining metaphoricity  (Wikberg, 2007) , and which sets several limitations on corpus annotation and its validity, as well as on the size of the corpus. The specialised corpus for this study was compiled ad hoc, searching for texts on 'nanotechnology' (keyword) in the 'news' section of the CORDIS database.
All data were coded in accordance with the corpus linguistics approach to conceptual metaphor (see  Semino, 2008, pp. 191-216). In the first phase of the analysis, I conducted open coding for metaphorical candidates to obtain the conceptual metaphors, that is, variations in 'political is physical' (see also  'metaphor keys' in Charteris-Black, 2004). I also used axial coding of the conceptual metaphors to obtain the appropriate dimension of these codes. In both phases, the coding process was assisted by Atlas. ti, a software for qualitative analysis. This enabled generating topology and narrative structure of the relatively large sample of discourse in question. Metaphor analysis focused on separating literal utterances (words, sentences) from conceptual domain level (concepts), traditionally marked in capital letters and searching for their systematicity.
METAPHORICAL CONVENTIONALITY AND CREATIVITY IN LAKOFF'S AND GREIMAS'S MODEL This section presents the empirical observations from the CORDIS database, which draws on the analytical models outlined above. The quotations were selected to show how the CORDIS (EC's) nanotechnology policy metaphors, and in  Lakoff (1993)  terms, 'locations' and 'events' are used systematically and creatively in order to structure and sustain rich inference patterns. In addition,  Greimas's (1983)  actantial model is adjusted to uncover 'events' as actions of different 'actors' in different narrative roles. An overview of the topology, as well as narrative dimension is presented at the end of the section.
, 2017a). A comparison of MRR and CloudSat derived surface snowfall product showed that CloudSat is able to accurately represent the snowfall climatology with biases smaller than 15%, outperforming ERA-Interim  (Souverijns et al. , 2018b). Moreover, CloudSat's blind zone (lowest measurement available at about 1200 m above the surface) leads to precipitation amounts being underestimated by about 10 % on average although differences during specific events can be much larger  (Maahn et al. , 2014). This paper focuses on the vertical structure of the precipitation. With the aim of improving CloudSat radar uncertainty estimates using ground-based observations, CloudSat tracks passing over Dumont d'Urville and Princess Elisabeth stations were compared with MRR data on a total of 4 concurrently recorded snowfall events, despite 14 overflights above DDU and 63 above PE over the total MRR recording periods.
According to these events and using the deviation of CloudSat precipitation rates from MRR observations, its uncertainties were reassessed. A systematic difference is characterized between CloudSat and the ground radars, by comparing their very low snowfall rates, but this difference could be also due to limitations in sensitivity or attenuation of the MRRs. As a first step, we characterize the general weather conditions of the four cases (section 3. 1). Then, a comparison is done between CloudSat and the vertical MRRs precipitation profiles (section 4. 1 and 4. 2). From this comparison we highlight a systematic difference (section 4. 3), then from a statistical study described in Appendix A, a nearly-perfect correlation between MRR and CloudSat datasets is derived (section 4. 4). To conclude, we assess a new range of CloudSat uncertainties at secondsshort-time and 10km-space scales based in this study (section 4.
Micro rain radars The MRR is a vertically profiling Doppler radar operating at a frequency of 24. 3 GHz (K-band). At both stations, the resolution was set to 100 m per bin ranging from 300 -first valid available measurements -to 3000 m. The MRR's Raw measurement -Doppler spectral densities -are available at 10s temporal resolution. The collected data were processed using the IMProTool developed by  (Maahn and Kollias, 2012). At DDU, the radar reflectivity derived from MRR was calibrated by comparison with a colocated X-band polarimetric radar over the period from December 2015 to January 2016 (for more details, see  Grazioli et al. (2017a) ). Through this calibration with the second radar, the reflectivity (at X-band) is converted into snowfall rates using a radar reflectivity Ze / Snowfall rate Sr relation  (Grazioli et al.
We therefore analyzed the synoptic conditions by using radiosonde data and reanalysis (ERA-Interim) from the European Centre for Medium-Range Weather Forecasts (ECMWF) in order to determine the adequate MRR time-series corresponding to CloudSat observations. We estimated a duration for which MRR observing conditions agree most with CloudSat using the following equation : ∆t avg = ∆x sat V cld (3) where ∆t avg represents the temporal range of the MRR observations wrapping CloudSat overflight date, ∆x sat is the length of the track inside the 10 km radius area over stations and V cld is the vertically averaged wind velocity. All characteristics are shown in table  1.
Precipitation profiles Focusing on the Dumont d'Urville station, figure  3a  shows a good agreement between CloudSat and MRRs snowfall rates for each vertical level. Indeed, averaged satellite precipitation rate at all levels is included within the 95 % MRR confidence interval. The MRR profile presents a maximum of the snowfall rate of 0. 75 mm/h at 750 m and an inversion of the precipitation rate likely due to low-level sublimation processes, whereas the ground clutter prevents CloudSat from seeing the inversion. This precipitation event is likely generated by the passage of the second low pressure system, as described previously using the corresponding radiosounding. According to  Durán-Alarcón et al. (2018) , this precipitation event is representative of the climatology of DDU. According to figure  3b , there is a poor concordance between the two datasets for low snowfall rate values.
Agreement between CloudSat and MRR datasets CloudSat bins above 2km height, an averaged snowfall rate of + 0. 033 ± 0. 003 mm/h is observed when MRR at PE signal is null. Concerning figure  3d , a similar value is found with a higher dispersion of + 0. 030 ± 0. 001 mm/h. This difference of measured values suggests a different calibration of the 2 radars and their sensitivities. This shift between reflectivities leads to a difference in associated snowfall rates. We selected all CloudSat and MRRs measurements smaller than 0. 1 mm/h to estimate a range of values of this difference. Indeed for every CloudSat measurement, the corresponding MRR measurement is very small or even null. A difference of + 0. 039 mm/h ± 0. 004 mm/h is estimated with the three last cases on figures 3b, 3c, 3d, which might also be a MRR uncertainty for small snowfall measurements.
Calculation of the CloudSat uncertainties By assuming that CloudSat and MRR snowfall rates datasets follow a normally-distributed deviation from the mean, a correlation coefficient is calculated in order to establish the degree of similarity between both observations. By using the covariance of both data record, we found a correlation coefficient of 0. 99, which confirms a very good agreement between both radar data (see Appendix). For each CloudSat vertical bin, we calculated the distance of satellite measurement to the corresponding interpolated MRR observation. We averaged these values by weighting them with the MRR confidence intervals and we found a range of CloudSat uncertainties from -13 % up to +22 %. By applying to CloudSat profiles the calibration difference estimated in the previous section we assessed a new range of uncertainties from -24 % up to +21 %.
Calculation of the correlation factor between CloudSat and MRRs In order to compute the correlation between both datasets, we assume that both the MRRs and CloudSat deviations from the average follow a Gaussian-shaped distribution. MRR data is a Gaussian-shaped distribution, according to its interval confidence calculation. CloudSat deviation from the mean measurements follows also a Gaussian-shaped distribution, as shown on figure  6. The figure  4  shows an evident linear fit between both dataset. We applied this calculation with both MRR and CloudSat radar datasets and calculated a correlation coefficient of 0. 99 as discussed in section 4. 2 of Discussions, and showed by a dashed line in figure 4.
, https://doi. org/10. 5194/tc-2018-236 Manuscript under review for journal The Cryosphere Discussion started: 29 November 2018 c Author(s) 2018. CC BY 4. 0 License. Because of different vertical bin altitudes, MRR snowfall rate were linearly interpolated at the CloudSat data levels. Covariance of both data populations were calculated by the following equation : cov(S CDS , S M RR ) = N i=1 (S CDSi -S CDS )(S M RRi -S M RR ) N (4) where S CDSi and S M RRi are the snowfall rate values for CloudSat and MRR and S CDS and S M RR the averaged snowfall rates of both dataset. By calculating the standard deviations σ to the mean of each instrument, a covariance matrix were obtained and used to determine the correlation factor ρ between both datasets : ρ = cov(S CDS , S M RR ) √ σ CDS σ M RR (5).
Analytical solutions for invariability were calculated in Wolfram Mathematica 10. 2. Numerical simulations were conducted in Matlab 2016. Source code for the analytical and numerical solutions can be found in the electronic supplementary material, C and D, or on the Open Science Framework (https://osf. io/hxfd2/). rsos. royalsocietypublishing. org R. Soc. open sci. 5: 180995.
Conclusions Innovative methods for creating longitudinal datasets on children will assist in examining long-term outcomes associated with early childhood risk and protective factors as well as an evidence base for interventions that promote child well-being and positive outcomes. A true understanding of the influence of the early years on long-term individual well-being requires extensive data spanning from childhood into adulthood. Most researchers have relied on longitudinal survey designs to capture functioning in early childhood, mediating or moderating factors, and outcomes later in life (e. g. , NICHD ECCD cohort  [1, 2] ). Surveys provide a wealth of information on a specific topic and can enlighten our understanding of complex phenomenon on specific groups of people or on the population at large.