dataesr/scientific-paragraphs-categorization huggingface dataset:
A third presentation about the transcriptional response to radiation was given by Rodolfo Negri (Sapienza Università di Roma, Italy). He first provided an overview of recent literature, showing that the transcriptional response to ionising radiation is strongly cell type-dependent. Going further on previous work  [37] , he showed results from a meta-analysis of 208 different radiation conditions (doses, time points, cell types, tissues, etc), from which a signature of 34 genes was identified which were modulated above 1. 5-fold in at least 30% of the conditions. Of these, CDKN1A, involved in cell cycle arrest and apoptosis, was found to be the most general radiation-induced gene, whereas negatively modulated genes were enriched in mitotic cell cycle regulation. Some of these data can be found on a public database of radiation-responsive genes, containing data from 180 different experiments in human, mouse and rat  [38].
These data can be useful for further estimation of RT induced health risks. However, one must be aware that estimating the health risks from neutrons is complicated and depends on the neutron dose and energy. Therefore, at this moment, it is very difficult to obtain these risk data where the neutron energy is confined to a narrow spectrum. Moreover, the occurrence of risk events in the low-dose range is extremely low resulting in poor statistics. The EU FP7 ANDANTE project which evaluates the risk of secondary cancer development from neutrons, was presented by Andrea Ottolenghi (University of Pavia, Italy). This project involves a multidisciplinary approach including physics, stem cell radiobiology and epidemiology in order to further clarify secondary neutrons risks in RT. Progress on characterising the exposure beams, initial radiobiological experiments with stem cells, and the data collection for the epidemiological studies were reviewed.
Acknowledgments The authors would like to thank the  German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety , IRSN, SCK•CEN, EURADOS,  ALLIANCE, NERIS , and the  Euratom Seventh Framework Programme  ( FP7) projects DoReMi  (Grant Agreement No. 249689 ),  STAR  (Grant Agreement No. 269672 ),  CEREBRAD  (Grant Agreement No. 295552 ),  PROCARDIO  (Grant Agreement No. 295823 ),  COMET  (Grant Agreement No. 604974 ) and  OPERRA  (Grant Agreement No. 604984 ) for sponsoring the Workshop. All the speakers, poster presenters, and session moderators are warmly thanked for their valuable contribution to this workshop.
Cross-section determination Integrated cross section In the W D ( * ) analysis the ratios of the fiducial cross sections σ OS-SS fid (W D ( * ) )/σ fid (W ) are measured and the absolute cross sections σ OS-SS fid (W D ( * ) ) are obtained by multiplying these cross-section ratios by the inclusive W cross sections σ fid (W ), while in the W c-jet analysis the cross sections are obtained directly. The production cross sections in the fiducial region, σ fid , are calculated using the equation: σ fid = N -B C • Ldt , (7. 1) where N is the number of candidate events observed in data, B is the number of background events and Ldt is the integrated luminosity of the dataset. The correction factor C is determined from MC simulation and accounts for detector efficiency, acceptance and resolution effects.
Fitting procedure The systematic uncertainties from the fitting procedure are dominated by the modelling of the shape of the signal templates. The main sources are the mass resolution and the amount of background from the reflections included in the signal templates. Systematic studies are performed on simulated events, and differences in the mass resolution between the simulation and the data are observed in the control region used to derive the signal templates. For the baseline analysis, the simulated mass distribution is smeared to reproduce the measured resolution in the data. Systematic uncertainties are assessed by varying the smearing parameters within their uncertainties. The amount of reflection background in the signal templates is altered by changing the normalisation of the combinatorial background component to provide the best agreement with the data in the control region.
Signal modelling The impact on the signal acceptance terms stemming from uncertainties on the simulated jet multiplicity is estimated by varying the amount of initial-and final-state radiation in the Pythia parton-shower parameterisation. Additionally, the ratio of one-jet events to twojet events in simulation is reweighted to the ratio measured in data and the acceptance is recomputed. The difference between the derived cross sections is less than 1% and is taken as a systematic uncertainty in the jet multiplicity modelling. Additional uncertainties on the non-perturbative physics modelling (e. g. underlying event, parton shower, color flow) are evaluated by recomputing the acceptance based on a simulation of the W c-jet signal by Alpgen + Herwig in which the Herwig charm fragmentation and decay are corrected using the procedure described previously for correcting Pythia.
Tabulated values of all observed results are available in the Durham HEP database  6. The correlation between the total uncertainties of the integrated W c-jet and W D * measurements is found to be approximately 10%, while it is about 5% for W c-jet and W D due to the larger statistical uncertainty of the W D sample. The correlation between W D * and W D is approximately 20%. Furthermore, the correlations between the uncertainties in the W + and the W -channels are 76%, 58% and 17% for W c-jet, W D * and W D, respectively. Different channels use complementary c-hadron decay modes and the statistical overlap between the different selected data samples is of the order of 1%. Therefore the correlations between the statistical uncertainties are neglected. In addition to the cross-section measurements, the averaging procedure is also applied to the measurements of the cross-section ratios σ OS-SS fid (W + c-jet)/σ OS-SS fid (W -c-jet) and σ OS-SS fid (W + D ( * )-)/σ OS-SS fid (W -D ( * )+ ).
4% for the D * meson and 3. 4% for the D meson and are included in the evaluation of the systematic uncertainty on the prediction. Events are generated in aMC@NLO using the CT10 NLO PDF set. The dependence of the results on the choice of PDF set is checked by reweighting the aMC@NLO predictions using various NLO and NNLO PDF sets: the CT10, MSTW2008, HERAPDF1. 5, NNPDF2. 3 and NNPDF2. 3coll [71]  NLO PDF sets are used in addition to the ATLAS-epWZ12 NNLO PDF set. Asymmetric uncertainties are calculated following the prescriptions from the PDF sets. For MSTW2008 and NNPDF2. 3 the s-quark sea is suppressed relative to the d-quark sea for all values of x. The ATLAS-epWZ12 PDF set, which is based on the analysis of ATLAS W and Z cross-section measurements  [62]  together with HERA data  [72] , has an squark PDF that is not suppressed with respect to the d-quark sea at x ∼ 0.
01. The s-quark sea in CT10 is less suppressed than in MSTW2008 or NNPDF2. 3. The NNPDF2. 3coll PDF set uses only data from HERA, the Tevatron and the LHC, so that the data from charm production in neutrino-nucleon scattering are excluded. The s-quark sea of this PDF is larger than the d-quark sea at most values of x. Processes with charm quarks in the initial state such as dc → W -uc and dc → W -uc can contribute to the OS-SS W + c signal if there is an asymmetry in the charm and anti-charm PDFs. The PDF sets studied here do not include a non-perturbative (intrinsic) charm component  [73] , where significant asymmetries are possible. PDF fits that include phenomenological models of intrinsic charm  [5, 74, 75]  indicate that for the values of x relevant for this analysis, these contributions are expected to be small.
The dependence of the NLO prediction on the choice of renormalisation and factorisation scales is evaluated by independently halving and doubling their nominal value which is chosen as the sum of the transverse mass of all final-state particles. The largest variation where the scales are varied in opposite directions is taken as the uncertainty and treated as fully correlated. This uncertainty is +8/ -9% for the W D ( * ) analysis and +8/ -4% for the W c-jet analysis. To study the modelling of the charm fragmentation function in Herwig++, e + e -annihilation events are generated at centre-of-mass energy √ s = 10. 6 GeV and the distribution of x p ≡ p/p max for D and D * is compared to the data from ref. [76]. The evolution of the charm fragmentation function with Q 2 in Herwig++ is validated by generating e + e -annihilation events at √ s = 91.
Discussion The measured integrated fiducial cross sections σ OS-SS fid (W D ( * ) ) and σ OS-SS fid (W c-jet) are compared to the theoretical predictions based on various PDF sets in figure  10. The inner error bars on the theoretical predictions are the 68% confidence level (CL) uncertainties obtained from the error sets provided with each PDF set, while the outer error bars show the sum in quadrature of these PDF uncertainties and theoretical uncertainties due to variations in renormalisation and factorisation scale, parton shower and charm-quark fragmentation as discussed previously. The predicted cross sections differ by as much as 25%. The six different measurements give a consistent picture; the predictions obtained with the ATLAS-epWZ12 and NNPDF2. 3coll sets are seen to overlap more with the data but simulations using CT10, HERAPDF1.
First, because the proton contains valence d-quarks, the Cabbibosuppressed diagrams involving d-quarks enhance W -+ c production over W + + c, and thereby decrease R ± c by about 5%. Second, a difference between s and s PDFs, as suggested by neutrino data  [4] , would also influence the value of the ratio: a lower population of s-quarks relative to s-quarks in the sensitive range of the measurement would push the ratio to a lower value. This effect is implemented in NNPDF2. 3 and MSTW2008. The contributions of the strange asymmetry in NNPDF2. 3 to R ± c are small. For MSTW2008, the strange asymmetry is larger and thus lowers R ± c by about 3%. This pattern of predictions is consistent with those obtained from the NLO calculation as implemented in aMC@NLO and shown in figure  12. The ratio measurement is consistent (within 1 σ) with all studied PDFs, and the measured uncertainty is comparable to the one obtained with.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and.
The whole genome of Rhodococcus ruber IEGM 231 was sequenced using Illumina technology. A mate-paired library with an insert size of 6 kb was produced and sequenced with the MiSeq system (2 ϫ 250 nt). An approximately 130-fold coverage was generated, and the data were assembled with the Velvet assembler (https://www. ebi. ac. uk/~zerbino/velvet). Gap filling was performed using GapCloser (http://soap. genomics. org. cn /soapdenovo. html) on scaffolds more than 2 kb in size. The annotation of coding sequences (CDS) and prediction of gene functions were performed using the MicroScope platform (https: //www. genoscope. cns. fr/agc/microscope/home/index. php) (7) , which integrated 115 contigs, resulting in 46 scaffolds from the assembly. The genome size of R. ruber IEGM 231 is estimated to be 6.
They could account for steroid compound transformations, as already shown for other R. ruber strains  (10, 11). The diversity of CDSs coding for glycolipid biosurfactant synthesis was presented by 1 malonyl CoA-ACP transacylase, 14 acyl-CoA synthetases, 1 fatty acid synthase I, 9 3-oxoacyl-ACP reductases, 1 cyclopropane mycolic acid synthase, 1 polyketide synthase, 1 meromycolate extension ACP, 3 mycolyltransferases, 1 maltooligosyl trehalose synthase, 1 maltooligosyl trehalose trehalohydrolase, and 1 trehalose synthase. Among heavy metal-associated CDSs, there were CDSs coding for heavy metal resistance proteins (5 CDSs), heavy metal transporters (8 CDSs), cation efflux enzymes (3 CDSs), metal binding proteins (3 CDSs), a mercuric reductase, and an alkylmercuryl lyase. Nucleotide sequence accession numbers.
ACKNOWLEDGMENTS We are grateful to the  LABGeM team of UMR8030 (Évry, France)  and the national infrastructure " France Génomique " for curation and annotation of the genome and for technical and bioinformatics support. The work was funded by a grant from the  Russian Academy of Sciences   Presidium Program  " Leaving Nature: Current State and Development Problems " (no. 01201256869 ) and by a grant from the  Russian Federation Ministry of Education and Science (State Assignment   6. 1194. 2014/K ). The exchange of researchers was made possible by a grant from the  University of Évry Val d'Essonne. crossmark Genome Announcements November/December 2014 Volume 2 Issue 6 e01297-14 genomea. asm. org 1.
Measurement equipment and procedure Experiments were conducted at free-stream velocities of U 1 ¼ 20-35 m=s corresponding to Reynolds numbers based on airfoil chord of Re c ¼ 9:2 Â 10 4 -1:6 Â 10 5. Acoustic measurements were recorded with each of the airfoils mounted (one at a time) to the side plate. Noise data were measured at a single observer location using a B&K 4190 1/2 in. microphone. The microphone was located 500 mm above the (spanwise and chordwise) centre of the airfoil with L=T ¼ 16:7 (at x ¼124. 5 mm, y¼69 mm). To provide isolation from wind noise caused by very low level recirculation in the anechoic chamber, a wind sock was placed on the microphone before data collection. Hot-wire anemometry was used to measure the airfoil wake mean flow velocity and turbulence characteristics.
Cognition cycle Here we first describe the two main features of CR: cognitive capability and reconfigurability. Then, we briefly discuss the concept of cognition cycle of CR as well as some specifics related to CR-VANETs. A CR-enabled device adapts its operational parameters as a function of its environment  [18]. CR components are mainly radio, sensor, knowledge database, learning engine, optimization tools, and a reasoning engine. CR has cognitive as well as reconfigurability capabilities  [25, 26, 58]. Cognitive capability allows CR to sense and gather information (e. g. , different signals and their modulation types, noise, transmission power, etc. ) from its environment and, for example, secondary users can identify the best available spectrum. The reconfigurability features of CR allow it to optimally adapt the operational parameters as a function of the sensed information.
The reconfiguration and optimal adaptation is finally done in the Act stage. The CR-VANETs cognition cycle has a lot of similarities with that of CR, but some differences are due to the nature of high-mobility vehicular environment. Most of the vehicles will be equipped with navigational systems, and hence location as well as mobility prediction based on the current direction of movement can be used for cognition. The use of knowledge database becomes more relevant as a vehicle may pass through the same location at approximately the same time of the day. Thus, past experiences can be combined with the current location for optimizing the operational parameters. Finally, it should be noted that the requirements for CR-VANETs are more strict in terms of faster adaptability and faster running time of the cognition cycle due to the dynamic nature of the vehicular environment.
Architecture The network architecture of CR-VANETs consists of vehicles with onboard units (OBUs) and infrastructure units such as RSUs and base stations of network providers. Note that setting up of roadside units everywhere may be costly; thus, V2V communication, including multihop, should be able to work without any infrastructure support. Several functionalities are needed for CR-VANETs paradigm, and an initial architecture related to different functionality blocks and protocols is proposed in  [65]. The architecture proposed by its authors consists of several blocks related to policies such as those imposed by FCC and local authorities and onboard sensors, including radio sensors and GPS. It also includes a knowledge database that consists of previously learned cases, rules, spectrum information, and radio maps.
Further, it consists of a cognitive engine that is the core of all functionalities. Additionally, there are software-defined radio or multiple radios, performance measurement block for feedback, and finally a user interface to allow users to configure the system offline. Figure  7  illustrates the architecture showing these different functional blocks for CR-VANETs. As illustrated in the figure, the cognitive engine (CE) uses real-time data from onboard sensors and history and map information from knowledge database to learn from the present as well as the past. It then controls the operation of software-defined radio through optimization of transmission parameters and decision making related to spectrum use and management. A taxonomy of machine learning approaches for CE is provided in Figure  8.
Spectrum sensing Spectrum sensing is required by cognitive radio to detect the presence of spectrum holes and more importantly to detect the presence of PUs. The priority is to avoid interference with PUs and thus, spectrum sensing should be highly reliable. High mobility in VANETs can deteriorate the performance of spectrum sensing due to signal fading and fast changing location. In the case of fast varying channels over time, some works focus on improving the performance of sensing  [66]  and signal identification  [67] [68] [69]. Similarly improved algorithms have been proposed for channel estimation and equalization  [70]  in high-speed environments. Additionally, cooperative spectrum sensing  [71]  can be used to tackle the problems due to high mobility. Several spectrum sensing vehicles combined with a regularly updated central spectrum database can improve the performance of spectrum sensing in VANETs scenarios.
They also propose a framework for cooperative spectrum sensing and sharing called Cognitive V2V (Cog-V2V). In Cog-V2V, each vehicle keeps a spectrum availability database that is regularly updated. Note that using such knowledge database, shared by different vehicles or kept distributed among them, is an interesting way of collaborating for spectrum sensing. The data from different cooperating vehicles is merged using weights that depend on distance between the locations at which sensing samples were collected. Intuitively, they http://jwcn. eurasipjournals. com/content/2014/1/49 calculate weights such that weights from nearby vehicles are weighed lower as they are likely to have similar radio conditions due to their similar location. After the spectrum opportunities are detected, Cog-V2V decides the channel to be used between the two vehicles.
Another work  [76]  uses spectrum database as well as support from infrastructure. They also provide guidelines for the uniform placement of RSUs that will provide information about the availability of TV channels and thereby support the mobile CR-enabled vehicles. Systems using centralized approaches and RSUs can be effective, specifically for safety applications as it improves their reliability by improving data delivery in vehicular networks. However, the centralized approach is not apt to support multihop communication, especially in the absence of infrastructure support. Nonetheless, in the absence of central infrastructure, clustering techniques can be used to create cluster leaders among several vehicles. Cluster leaders can then assume the role of central entities responsible for coordinating spectrum sensing and data fusion as used in  [35].
SUs sense the channel in the first phase and then using CCC contend for reservations of time slots belonging to data transmission phase. Data transmission phase is then used for contention-free transmission of data based on the reservations on different available channels. QoS provisioning is done by first letting only the prioritized users contend for the reservations and if some time slots are left, then other users can also make some reservations. This can be useful for prioritizing safety messages in CR-VANETs. However, the impact of vehicular mobility and short-lived links needs to be studied for such schemes. The common control channel may also get congested when there are many vehicles. The discovery process of available channels, during the sensing phase, can be improved by using a common spectrum database, as explained in Section 4.
This has motivated the railway industry to use SDRs equipped with a reconfigurable platform for packet-data transmission. Though SDR provides interoperability and enables multiple configurations, it lacks the capability of integrating additional adaptation to alleviate crowded spectrum, intentional jamming, or learning from past experiences. In this context, the project developed a railroad-specific cognitive radio namely, Rail-CR, capable of fulfilling the requirements of future wireless communication systems for trains. The Rail-CR combines AI-based decisionmaking and learning algorithms with SDR to address the railway transportations' specific need for adaptable communications. The Rail-CR cognitive radio will enhance the performance of railway communications in terms of interoperability, robustness, reliability, and spectral efficiency, and will make it cost-effective to deploy and maintain.
Virginia Tech CR network testbed (VT-CORNET) VT-CORNET  [95]  is an open-source cognitive radio network testbed, by Virginia Tech (Blacksburg, VA, USA) for the evaluation of CR wireless communication protocols and applications. The testbed consists of 48 software-defined radio nodes (USRP2-based nodes) deployed over the four floors of a building and equipped with a custom-made daughterboard covering the frequency range of 100 MHz to 4 GHz. The CORNET nodes are remotely accessible to the registered users. A unique feature of the testbed is that it uses Software Communications Architecture (SCA) framework that assumes USRP nodes at the physical layer and provides support for generating and visualizing a range of radio configurations. The testbed focuses on CE design, self-organizing networks, and topology research.
The testbed has been recently upgraded to include programmable software-defined radios (GNU USRP/USRP2) for flexible MAC/PHY experiments. The testbed users can access the radio grid through a web portal to specify their experiments and measurements. As mentioned previously, the testbed can be accessed worldwide for conducting experiments and evaluate protocols and applications related to ad hoc routing, P2P, spectrum sensing, and dynamic spectrum access. Recently, the testbed has started providing support for vehicular mobility as well. The ORBIT management software has been extended to enable mobile vehicular (vehicle-to-vehicle networks) experiments, and the radio grid is now supplemented by a number of outdoor and vehicular nodes.