dataesr/scientific-paragraphs-categorization huggingface dataset:
This is consistent with the typical timescales of the transient events of interest to the core science of LOFT, which ranges from a week to several months. Both the LAD and WFM data will be promptly analyzed in the context of a Quick Look Analysis. The LAD data will then follow the processing associated to its proprietary data rights. The WFM will instead be made publicly available to the community soon after their validation. The data policy proposed by the LOFT Consortium foresees that all LAD observing time is made available as a Guest Observer Program. The Consortium will have a minimum fraction of reserved data (order of 20%) which will be granted on the basis of observation proposals, competing with the standard GO. If the guaranteed fraction will not come out of the standard peer review process, the relevant proposal will override the bottom of the list.
This innovative approach to the data rights was conceived to guarantee that the best targets are in any case granted to the best proposers. Due to the capabilities of the WFM to autonomously detect and localize transient events, such Gamma Ray Bursts, the LOFT payload is equipped with an onboard VHF transmitter able to distribute the relevant information to the ground within 30 s from the event. To this purpose, the LOFT ground segment includes the LBAS (LOFT Burst Alert System), which is composed of 15 VHF ground stations, distributed over the equator to guarantee the realtime reception of the VHF alert data packet. Additional information on this system may be found in Schanne et al. [23]. Further details about the LOFT science ground segment may be found in Bozzo et al. [4].
ACKNOWLEDGEMENTS The Italian team is grateful for support by  ASI  (under contract  I/021/12/0-186/12 ),  INAF  (under contract  PRIN-INAF-2011  " Strong Gravity ") and  INFN  (under contracts XDXL and REDSOX). The work of the MSSL and Leicester groups is supported by the  UK Space Agency. The work of SRON is funded by the  Dutch national science foundation (NWO). The work of the group at the  University of Geneva  is supported by the  Swiss Space Office. The work of IAAT on LOFT is supported by the  Germany 's national research center for aeronautics and space DRL. The work of the IRAP group is supported by the  French Space Agency. LOFT work at ECAP is supported by  DLR  under grant number  50 00 1111. IEEC-CSIC  has been supported by the  Spanish MINECO  (under grant  AYA2011-24704 ).
ACKNOWLEDGEMENTS We thank  Vladimir Makarkin  and two anonymous referees for their valuable comments on the first version of this paper. We sincerely thank the  Société des Mines d'Orbagnoux  for the kind authorization to collect fossils in their property. Special thanks to  Torsten Wappler  for information and references on insect herbivory on Zamites. We also thank the following colleagues for their kind help during the field campaigns in 2012 and 2013:  Bernard Béreyziat ,  Michel Béreyziat ,  Guy Robert ,  Pierre Roncin ,  Jean-Marie Demaury ,  Virton ,  Bernard Janvier , and  Daniel Grand. A special thanks to Claire Griot because she rendered possible these investigations, inviting us into her wonderful country and home. We thank  Sylvain Pont  from  LMCM lab/MNHN Direction des collections MEB Service  for MEM-EDS Imaging.
If needed, a co-convenor can be appointed, but the convenor remains the single responsible person. It is preferred that the convenor is not a member of the EULAR EC. 2. The methodologist works in close collaboration with the convenor. The convenor and methodologist submit the application together as a sign of the agreement with the proposed design by the methodologist. The methodologist is a person who has expertise in performing both a systematic literature search and the development of recommendations, which can be proven by a scientific track record, and-if applicable-experience in the data analysis techniques required for a project. The methodologist, preferably not a member of the EULAR EC, is not necessarily an expert in the topic of the recommendations. The methodologist supervises the systematic literature search and will co-chair all meetings and is in charge of the methodological aspects of the various steps of the project.
In addition, the outcome measures that will be extracted need to be defined (eg, ACR20 response rates, mean change in Simple Disease Activity Index (SDAI), etc. ). The databases that will be used for the search should be defined with a minimum inclusion of MedLine/PubMed and the Cochrane Library (the latter in case of management recommendations). In addition, further evidence may need to be collected prior to the first meeting (eg, compilation of existing guidelines), to provide all participants with a background to start discussions. Special attention should be paid to collecting data on costeffectiveness when developing management recommendations and should be an integral part of the SLR. The quality of all retrieved papers needs to be assessed by validated assessments tools. Examples are the Cochrane Risk of Bias tool for all studies and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) II for diagnostic accuracy studies and the Quality In Prognosis Studies (QUIPS) for observational studies.
FORMULATION OF RECOMMENDATIONS During the second meeting of the task force, the results of the SLR and-if applicable-of the original data analysis will be presented. The next step will be the wording of the recommendations. Normally, a maximum of 10 recommendations will be defined. This might be adjusted depending on the project, but too many recommendations are likely to result in a loss of focus and might be problematic in the implementation process. Recommendations should in principle be based on the data obtained from the SLR. The evidence for each recommendation should be categorised. This has been clearly defined for the evaluation of treatment in table 1. This labelling will mostly apply to the SLR; any recommendation driven by original data analysis will most likely fall into category 3, as they typically represent secondary analysis of available datasets.
Under high-powered regulation, such as price cap, prices are unresponsive to the probability of bankruptcy; this reduces the firm's incentive to use leverage. Our model therefore predicts that firms operating under high-powered regulation will have lower leverage than others, all else equal. In this model, firms find high level of debt attractive if the share of cost reimbursed is high enough to compensate for the higher probability of bankruptcy. As the cost of debt increases, the threshold of cost reimbursement for which this holds goes up and as a result firms operating under high-powered regulation make proportionally larger reductions in leverage. To test these predictions we have created an original panel dataset of 124 transport concessions in Brazil, Chile, Colombia and Peru covering the period 1992-2011.
Regulatory information is sourced from project contracts, sector legislation and renegotiation agreements (which we have for every project). Financial data is sourced directly from regulatory agencies and from commercial databases. To capture the power of the regulation we include a dummy equal to 1 if the firm is regulated by price cap. It has previously been claimed however that the high incidence of renegotiation in Latin America leads to a convergence of regulatory regimes (e. g. Sirtaine et al 2004). For our purposes, if firms can renegotiate whenever they are in financial difficulty, then the incentive to use leverage will not vary across different types of regulation. That is, even under high-powered regulation firms can increase prices by using high leverage. By examining contract renegotiations, we show that this is not the case: being in financial difficulty is not a significant determinant of renegotiation.
Data We construct an original dataset of 124 transport concessions in Brazil, Chile, Colombia and Peru over the period 1992-2011. The complete panel -tracking all projects since the year of contract signingcovers 1,360 observations and financial data covers 1,037 observations. Details on the design of price regulation come from project contracts, sector legislation and renegotiation agreements. In addition we have information on the specifics of the projects (investment size, duration, etc. ), on the institutional and regulatory environment, the timing and content of renegotiation agreements, as well as the evolution of key economic variables. Table  1  provides summary statistics of the main variables. Table  A1  in the Appendix provides a full list of variables and sources. Our principal measure of leverage is the ratio of total liabilities to total assets.
The unit of observation is the project company, as opposed to the project sponsor(s). As argued by  Esty (2004) , project companies are "strategic research sites" and "provide a new and, potentially, very powerful laboratory to analyse structural decisions and to show why they matter". The fact that project companies are created for a specific and well-defined purpose is greatly beneficial from a research perspective. Firm-level financial data comes directly from regulatory agencies and from commercial databases. OSITRAN, the Peruvian federal regulator, provided the data for all Peruvian projects and ANI, the Agencia Nacional de Infraestructura provided data for Colombian road projects. The two commercial databases we use are the ISI Emerging Markets Database of Euromoney Institutional Investor and the Orbis Database of Bureau van Dijk.
Acknowledgments The eastern sector of SAMBA is supported by the  South African Departments of Environmental Affairs and Department of Science and Technology , in partnership with the  Institut Français de Recherche pour l'Exploitation de la Mer (Ifremer) programme "Circulation Océanique ," the  Agence Nationale de la Recherche , and the  Institut National des Sciences de l'Univers. The western side of SAMBA is supported by the  Argentine Naval Hydrographic Service , the  Brazilian São Paulo State Funding Agency , the  National Council for Research and Development , the  Inter-American Institute for Global Change Research , and  NOAA. For the September 2013 cruise, the authors would like to acknowledge the extremely professional support of  Captain Gavin Syndercombe , as well as the officers and crew onboard the SA Agulhas II.
Statistical analysis Continuous data were described with descriptive statistics, including mean6SD and/or median [range] as appropriate and categorical data with frequencies (%). Categorical data were analyzed by univariate analysis with Fisher's exact test as appropriate and continuous data by nonparametric Mann-Whitney test. Univariate analysis was used to identify factors associated with overall complete response at end of treatment (EOT). P #. 05 was considered statistically significant. Antifungal treatments which duration was shorter than six months were excluded from statistical analysis. Statistical analyses involved use of SPSS software.
Acknowledgments The members of the  French Mycosis Study Group are: Institut Pasteur, Paris  ( M. Desnos-Ollivier ,  D. Garcia-Hermoso ),  La Croix St Simon  ( N. Desplaces) ,  Mantes (M. Salvucci) ,  Nantes (F. Morio) ,  Rennes  ( J. P. Gangneux ),  Saint Germain en Laye (A. Durand) ,  Saint Louis (S. Bretagne) ,  Tenon  ( M. Develoux ).
Acknowledgments We thank  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  for financial support (project number  2012/01273-8 ). We would like to make a special acknowledgment to  Dr Adriane Siqueira ,  Dr Silvia Regina Rogatto , and his student  Juan Augusto Moyano Munõz  for help with the culture of the sarcoma-cell lineage HT1080.
Chemical and physical data Nutrients. Concentrations of nitrate, nitrite, phosphate and silicate were measured with a Skalar auto-analyser following a previously described protocol  (Treguer and Le Corre, 1975). Ammonium. Dissolved ammonium was measured by spectrophotometry at 630 nm following conversion to indophenol via a monochloroamine intermediate  (Solorzano, 1969). Chlorophyll. One litre of seawater was collected on a GF/F filter at low pressure (o0. 2 bar). Measurements were conducted according to previously published protocols  (Yentsch and Menzel, 1963; Strickland and Parsons, 1997). Extended protocol details can be found in Supplementary Information. Mixed layer depth (MLD). Raw seabird data were processed to bin temperature and salinity data at 0. 25 m intervals. MLD is based on a 0.
Nucleic acid extraction, reverse transcription-PCR and pyrosequencing The extraction of nucleic acids (DNA and RNA) was performed according to a previously published method  (West et al. , 2008). A combination of mechanic (freeze-thaw) and enzymatic cell lysis techniques were applied directly to Sterivex cartridges, followed by extraction using an AllPrep DNA/RNA kit (Qiagen, Venlo, Netherlands). The RNA samples were tested for the presence of contaminating genomic DNA by PCR and then reverse-transcribed with random primers using the SuperScript III Reverse Transcriptase kit (Invitrogen, Carlsbad, CA, USA). We are unable to account for potential biases associated with reverse transcription efficiency and RNA secondary structure within the present data set. The amplification of the V1-V3 region of the 16S rRNA gene was performed with universal bacterial primers 28F (5 0 -TTTGATCNTGG CTCAG-3 0 ) and 519R (5 0 -GTNTTACNGCGGCKG CTG-3 0 ), followed in pyrosequencing in a commercial laboratory (Research and Testing Laboratory, Lubbock, TX, USA) using a Roche 454 GS-FLX system (Roche, Basel, Switzerland) with titanium chemistry.
Sequence data analyses Sequences were analysed as previously described  (Blanquer et al. , 2013). Briefly, the reads were first filtered by removing low quality reads and subsequently trimmed to remove those with X3% of bases with Phred values o27 (0. 2% per-base error probability). This is recommended to ensure that when clustering at 97% the influence of erroneous reads is minimised  (Huse et al. , 2010; Kunin et al. , 2010). Sequences were then clustered at a 97% threshold using Uclust algorithm  (Edgar, 2010)  and each operational taxonomic unit (OTU) classified by comparison to the Greengenes database  (DeSantis et al. , 2006). Sequence analyses were conducted with Pyrotagger  (Kunin and Hugenholtz, 2010). A phylogenetic tree was constructed with the sequence representatives of the abundant SAR11 OTUs (o0.
2%). Sequences included in the tree were deposited in GenBank under accession numbers KJ094527-KJ094552. Representative sequences for each OTU together were aligned using MUSCLE  (Edgar, 2004)  and phylogenetic analyses were completed with the programme PHYLIP  (Felsenstein, 2008). DNADIST was used to calculate genetic distances using a Kimura-2 model. The distance tree was estimated with the phylogenetic algorithm FITCH in the PHYLIP programme. To compare bacterial communities for diversity analysis, all samples were randomly re-sampled to the size of the sample containing fewest sequences (n ¼ 446) using Daisy Chopper  (Gilbert et al. , 2009). The Shannon diversity index (H 0 ) was calculated using the software PAST  (Hammer et al. , 2001).
Statistical analyses and partial least squares regression (PLSR) Unpaired t-tests. Samples were grouped into high or low diversity regimes using a constant statistical threshold determined empirically across the 7-year data set. The two categories contained sample populations of 13 and 12, respectively. Average values and standard deviations for each measured environmental parameter were determined and unpaired t-tests were used to calculate statistical differences between individual parameters within the two groups. The null hypothesis (H0) was rejected if the P-value was o0. 05 (GraphPad, GraphPad Software Inc, San Diego, CA, USA). A relatively large number of independent comparisons were made and we therefore applied Benjamini-Hochberg False Discovery Rate to calculate an adjusted P-value  (Benjamini and Hochberg, 1995) , which we denote as P* in Table  1.
The model was constructed using a 4-year sub-set  (2007) (2008) (2009) (2010) (2011)  of the 7year data, for which all environmental parameters were available (Supplementary Table  1 ). All analyses were performed with UnscramblerX (Camo, Oslo, Norway). Input data were auto-scaled by dividing each x and y value by its standard deviation. We used the Non-Linear Iterative Partial Least Squares algorithm. Cross-validated R 2 values (Q 2 ) were taken as a measure of the models Goodness of Fit  (Wold et al. , 2001). The PLSR model we developed was optimised with a Q 2 value of 0. 70 at three latent factors (Supplementary Figure  1 ). Weighted regression coefficients were calculated for each x-variable and SAR11 sequence abundance data set (that is, Y 1:4 ) to ascertain the most important measured environmental variables describing SAR11 ecotype abundance.
Sequence distributions We pyrosequenced 57 16S rDNA and 50 16S rRNA Total diversity and empirical identification of diversity regimes Diversity in each individual sample (Shannon index) was estimated using the rDNA data set after random re-sampling. The Shannon diversity index ranged from 1 to 3. 5 and a distinct seasonal cycle in diversity was observed with maxima occurring in the winter and minima in the summer (Figure  1 ). Diversity within the SAR11 group was calculated in a similar way without re-sampling. The median number of SAR11 sequences in diversity calculations was 344 ( À 0. 68s ¼ 209; þ 0. 68s ¼ 388). The SAR11 Shannon index ranged from 0. 03 to 2. 3. The number of SAR11 OTUs ranged from 1 to 65. The observed seasonality in bacterial diversity was used to define an empirical framework of high and low diversity regimes.
APicT values, derived from in situ absorption profiles, are only based on H 2 O spectral data (taken from the HITRAN 2008 (HIgh-resolution TRAnsmission molecular absorption) database and from PCI/KIT reference measurements  (Hunsmann et al. , 2006) ) plus length, pressure, and gas temperature measurements. Hence, APicT performance in AquaVIT-1 did not depend on a laboratory or in-field calibration procedures (i. e. , a direct comparison to a reference H 2 O instrument or generator). As an absolute performance check, APicT water vapor mixing ratios were compared to equilibrium ice-saturation values during the AquaVIT-1 dynamic experiments. During these experiments, dense ice clouds were present in the AIDA chamber under almost constant pressure and temperature conditions. As a consequence, the water vapor mixing ratios inside the chamber can be assumed to be ice-saturation values at the respective gas temperatures.
-Time series of water vapor mixing ratios from the core and non-core instrument were divided into 31 segments of 900-3600 s duration for analysis. The intercomparison was based on reference values derived for each segment using linear fits to the core instrument data. -The core instrument values are in fair to good agreement in the 1-150 ppm H 2 O range, with instrument averages over all segments agreeing within about ±20 %. For individual segments, agreement is generally found to be close or within the combined uncertainties for any instrument pair. Below 1 ppm H 2 O, fewer instruments reported data and overall agreement is poorer. -The non-core instrument group included mature instruments and newly developed prototypes. This group demonstrated generally poorer agreement with each other and with the core reference values than the core instruments.
, 2005) : -A. : the outer part, placed within the user accessible space outside the AIDA insulated chamber, contains a fiber-coupled, temperature-stabilized, distributedfeedback (DFB) diode laser module in an N 2 -purged container and all electronics (laser/Peltier driver, signal generator, detector preamplifiers, data acquisition, and computer). -B. : the middle, opto-mechanical part is a cryogenic transfer optic (including two detectors for I and I 0 ) directly attached to the outside wall of the AIDA chamber. Using the fiber from the laser module and a lens collimator, a free-space laser beam is formed, which is then focused through wedged CaF 2 windows into the absorption cell in the inner chamber. Through a second identical window the laser light behind the measurement zone is collected and directed onto an uncooled In-GaAs (indium gallium arsenide) detector (1 mm diam) using a 2.
The instrument, parts thereof, as well as the principles behind the APicT software and absolute evaluation procedure were successfully validated in numerous cases in view of achieving absolute accuracy without a regular calibration. Tuning accuracy has been checked via retrieved Doppler line widths at AIDA (yielding 0. 15 % deviation). APicT water vapor values were compared at AIDA with the MBW-373LX and FISH instruments during cloud-particle-free conditions yielding typically less than 3 % deviation  (Hunsmann et al. , 2006). A similar open-path TDLAS spectrometer for CH 4 was compared at atmospheric background levels of CH 4 with a NOAA-calibrated extractive gas chromatograph and yielded < 1 % average deviation over 36 h  (Lauer et al. , 2007). Recently an improved, extractive version of our TDLAS hygrometer series (using the same data evaluation principles) was investigated in a smaller water vapor intercomparison at FZ Jülich  (Buchholz et al.
Liquid-ice transition The AquaVIT-1 experiment focused on low water vapor concentrations, which translated to low frost-point temperatures. However, in the atmosphere there exists a frost-point temperature range in which the phase of the mirror condensate is not uniquely defined and in which the measurement of reflectivity does not provide sufficient data to distinguish the condensate phase. The transition between liquid phase and solid phase of the condensate on the mirror was studied after the completion of one experiment day. These experiments clearly show that the liquid-to-ice transition for the mirror condensate may extend to mirror temperatures colder than -30 • C. Without special precaution, the condensate on the mirror may remain liquid down to these temperatures. Spontaneous frost formation will then create a frost layer whose morphology is undetermined.
Flow rates on the order of 10 standard liters per minute (SLM) could be achieved and are comparable to those during aircraft operations; only a few periods with flow rates below 5 SLM had to be removed from FISH-2 data when contamination could not be excluded. Calibrations were carried out on 10, 15, 20, 21, 24, and 30 October. No significant drift of the calibration factors c k and f u could be detected over the AquaVIT-1 period. The reproducibility of c k was better than 3 % (FISH-2) and 4 % (FISH-1) for individual calibrations  (Meyer, 2008). The MBW-DP30 reference hygrometer was calibrated by the manufacturer with traceability to National Physical Laboratory (NPL, Middlesex, UK) standards a few months before AquaVIT-1. During AquaVIT-1, this DP30 was also compared to the secondary PTB standard (PTB-WVPS in Sect.
A3. 3 Lessons learned during AquaVIT-1 At high mixing ratios and low pressure, an unexpected low bias of FISH data compared to the 18 other AquaVIT-1 instruments occurred. Vice versa, at low mixing ratios and high pressure, a slight high bias of FISH data was found. In response, additional laboratory calibrations carried out over an extended pressure range in the home laboratory were conducted, and correction schemes based on these calibration procedures were developed  (Meyer et al. , 2014). The AquaVIT-1 data reported here have not been corrected. The airborne data of FISH, however, are not impacted by these biases since mixing ratios of several tens to hundreds of ppm at pressures below 100 hPa and mixing ratios below 10 ppm at several hundreds of hPa do not occur in the atmosphere.