dataesr/scientific-paragraphs-categorization huggingface dataset:
Trace gas measurements Pinnacles is part of the NOAA Global Greenhouse Gas Reference Network (http://www. esrl. noaa. gov/gmd/ccgg/). Measurements are made through collaboration with the NOAA Earth System Research Laboratory (ESRL). CO and CO 2 mixing ratios have been measured at 5, 10 and 17 m agl since August 2008 along a 17 m tower at the site. Both CO and CO 2 are sampled approximately every 10 min at each height, and half-hour means of these data are used in all analyses. In the present paper, we use data collected during the first four full years of measurements from 1 January 2009 to 31 December 2012. The CO and CO 2 data record over this period is 84% complete, as there are occasional gaps due to on-site power outages and miscellaneous system malfunctions. CO and CO 2 are measured using a Thermo Electron Corporation 48C CO analyser and Li-COR 7000 closed path gas analyser, respectively.
Detailed uncertainty estimates and algorithms are provided in the data files available from NOAA ESRL  (Andrews et al. , 2014). Random and non-random (i. e. systematic) measurement uncertainties are reported separately during archival and post-processing. These uncertainties come from three sources: atmospheric trace gas variability, measurement uncertainty and scale uncertainty. The total uncertainty is the sum of these three uncertainties  (Andrews et al. , 2014). The atmospheric variability represents the standard deviation (SD) of the trace gas concentration over the 30-second period during which the trace gas of interest is sampled. Measurement uncertainty is a function of multiple systematic uncertainties, for example, differences in moisture content of the samples which can affect the measured trace gas concentration and extrapolation uncertainties that arise when trace gas samples are made that are outside the calibrated range of the monitoring system.
Meteorological measurements Meteorological measurements at Pinnacles began in July 2008 and include temperature, humidity, wind, radiation, pressure and rainfall (Table  1 ). Data from a CSAT sonic anemometer and a Li-COR 7500 water vapour and CO 2 gas analyser are used to compute 30 min mean sensible heat,   1b ). Images are recorded every 15 min. The meteorological data set is mostly complete ( !90%), but there are occasional gaps in the data due to data logger malfunctions and on-site power outages. Data quality assurance and quality control procedures include filtering the cup anemometer wind data by removing all wind measurements made when the wind speed is 0 m s (1 for !30 min. Extended periods (sometimes !12 h) of 0 m s (1 wind speeds are caused by rime ice on the instruments that occasionally occurs between October and April.
Wind speeds of 0 m s (1 for !12 h also occurred when the cup anemometer's potentiometer malfunctioned for several periods in summer 2009 and summer 2011. Because temperature and humidity are measured at multiple levels along the tower, we remove measurements that are unrealistically different from the others, and we remove flux measurements made during periods of precipitation. Measurements of temperature, humidity, wind, insolation and precipitation from Big Meadows (38. 52 N, 78. 44 W, 1079 m msl), a mountaintop air quality monitoring site located 14 km south of Pinnacles (Fig. 1a ), are used to fill in occasional gaps in the Pinnacles data record. Because frozen precipitation is not measured at Pinnacles but is a routine occurrence between October and April, we use precipitation data from Big Meadows (which has a heated rain gauge) to determine precipitation patterns.
Reanalysis products We use the North American Regional Reanalysis (NARR) to assist with the interpretation of our observations. NARR uses boundary conditions from the National Center for Environmental Prediction (NCEP) global reanalysis, NCEP Eta model, and surface and rawinsonde observations to generate meteorological fields over North America at a 32 km spatial resolution and 3 h temporal resolution. There are 29 vertical levels, 17 of which are below 5 km  (Mesinger et al. , 2006). NARR data are obtained from ftp. cdc. noaa. gov/NARR. Whereas higher resolution models such as the NAM are also available, NAM compares well with NARR over the region of interest  (Lee, 2015)  and thus we are confident in our choice to use NARR.
Acknowledgements This research and maintenance of the Pinnacles research station was partly funded by an  MOU  between the  NOAA ESRL Global Monitoring Division  and the  University of Virginia  and by  NSF-CAREER  Grant  ATM-1151445. The authors thank staff from SNP, especially  Elizabeth Garcia  and  Alan Williams , and students from the  Environmental Sciences Department at the University of Virginia  for helping to maintain data collection at Pinnacles, especially  Nevio Babic´ ,  Stephanie Phelps  and  Mark Sghiatti. Many thanks to  Zeljko Vecenaj  at the  University of Zagreb  for on-site support in 2008, 2009 and 2012, and to  Jonathan Williams  at  NOAA ESRL. We also thank the anonymous reviewer whose suggestions helped us improve the manuscript.
(  3 )-(  5 )) presented for mycologists collecting fungi. Specific groups and topics covered on this day included Orbiliomycetes (Hans Otto Baral), Dothideomycetes (Ewald Groenewald), Lecanoromycetes ( Jolanta Miadlikowska), Ophiostomatales and Microascales (Wilhelm de Beer), the workings of GenBank (Conrad Schoch), Lichinomycetes (Maria Prieto) Pezizomycetes (Karen Hansen), Laboulbeniomycetes (Meredith Blackwell), the Open Tree of Life Project (David Hibbett). The day was rounded off with the two CBS awards, namely the Johanna Westerdijk (awarded to Walter Jaklitsch) and Josef von Arx Awards (awarded to Uwe Braun) (see pp. (  18 )-(  19 )). Following the reception, members of the ICTF departed for Amsterdam Science Park, where the ICTF convened its annual meeting (see p. (  14 )). Friday started with a change of venue, as mycologists moved to Artis zoo, where they also were given free access to the world's first living zoo for microbes, Micropia (see IMA Fungus 5 (2): (  30  It is envisioned that outputs of the meeting will be published in two separate volumes.
MycoBank Vincent Robert (online) announced that MycoBank now had a new version (Version 10) of software in development, and the upgrade would be done after it has been tested on smaller websites. By the end of May 2015, all websites would be migrated and then the CBS and MycoBank databases will follow. Scripts to facilitate the use of remote curators were to be prepared. Resolving discrepancies between the MycoBank and GenBank classifications are.
cbs. knaw. nl. Pedro W. Crous (p. crous@knaw. cbs. nl) under discussion, and MycoBank needed a strategy to deal with Latinized names and epithets. Following the retirement of Arthur de Cock, Nathalie van de Wiele had been appointed to this role, and orthographic checks were being managed by Shaun Pennycook and Joost Stalpers. The long-term stability of the system needed to be guaranteed, and May suggested that the MycoBank Advisory Board assess what the user community needs, what extra features the database would need, the technical aspects, and the costs involved. The Board membership was discussed and some additional members agreed, and it was charged with advising on remote curatorship and the improvement of the treatments of synonymy and taxonomic opinions.
5 m) and 154 MHz (λ = 1. 9 m), made using international LOFAR baselines, thereby setting a new record in terms of combined image resolution and point source sensitivity for science images at frequencies below 327 MHz. The contents of this paper are as follows: in Sect. 2 we describe in detail the observational setup and calibration procedures applied to the LOFAR data. This section also briefly describes the eMERLIN data used for comparison throughout this paper. In Sect. 3, we describe how images were obtained from LOFAR data and summarise the calibration and imaging procedures. In Sect. 4, we present the images obtained and briefly discuss our results in relation to previous work. Finally, we summarise our conclusions in Sect. 5. A more extensive scientific discussion regarding the compact and extended emission in M82 will be the subject of a forthcoming paper.
eMERLIN data The eMERLIN data set was observed on January 20th 2014 at 1. 6 GHz, under project code CY1203 (P. I. : Pérez-Torres), with additional observations taken in collaboration with the LeM-MINGs e-MERLIN legacy project (P. I. : Beswick & McHardy). These data were calibrated and imaged in a standard way by eMERLIN staff. Since these data are only used for comparison in this paper, we refer any details on the calibration to the paper describing these data, see  Pérez-Torres et al. (2014). This experiment was designed to observe, as targets, both the AGN in M81 (hereafter M81*) and the nucleus of the galaxy M82; these two objects are only separated by 0. 61 • on the sky (scientific results on M81* will be presented in a future paper). Three objects, J0958+6533 (hereafter J0958), M81* and M82, were observed simultaneously using three beams.
LOFAR data Data were taken in 8-bit HBA joined mode, where the data from each HBA subfield (or "ear") in a CS are combined at the station level. The available total bandwidth of 96 MHz was divided equally between the three beams on M81*, M82 and J0958. The single beam on 3C196 covered the same 32 MHz. The observed 32 MHz bandwidth per beam was divided into two contiguous blocks of 16 MHz each, one centred on 118 MHz and one centred on 154 MHz. The lower frequency block was chosen to include the lowest frequencies observable with the HBA, and the higher frequency range was placed in the region with the highest HBA sensitivity. The two blocks were calibrated separately but following the same procedure. The data were correlated producing all four linear polarisation products (XX, XY, YX, YY).
After correlation, the data were stored in the LOFAR long term archive (LTA) as measurement sets (MS) with integration time 1. 0 sec. and 64ch. /subband, corresponding to a channel resolution of 3 kHz. In addition to the raw high-resolution data, the LOFAR pipeline was used to automatically edit bad data, and save the edited data set averaged in time and frequency down to 2 sec. integration time and 4ch. /sub-band (giving a channel width of 48 kHz). These averaged data were saved in the LOFAR long term archive (LTA). Inspection of data for the bright calibrator J0958 revealed that ionospheric phase errors varied slowly enough over time and frequency to enable further averaging without significant coherence losses. To speed up the processing the data were therefore finally averaged, using the LOFAR new default pre-processing pipeline (NDPPP), to 10.
Given the final spectral and temporal resolution of the data (10. 0 sec. 1ch. /sb. ), we estimate the coherence loss due to time and frequency smearing at an angular distance of 30 from the phase centre to be less than 3% for the longest international (1158 km) baseline. This estimate includes a simple upper bound for time smearing assuming the source to be at the celestial north pole. Similarly, we estimate the coherence loss to be less than 3% for the longest Dutch remote baselines (121 km) for emission at an angular distance of 5 from the centre. Including residual rates and delays (typically 3 mHz and 300 ns) present when averaging the data we estimate a total coherence loss of less than 5%. Hence, we may image a field of 1 around the central M82 position using INT baselines and 10 using RS baselines, without significant loss due to averaging.
Since Faraday rotation does not mix R and L polarisations we may calibrate RR and LL independently. Hence, in a circular basis, corrections for phase and amplitude can be derived independently. The data were converted from linear to circular polarisation using the tool mscorpol v1. 6, developed by T. Carozzi. This tool includes corrections for dipole-projection effects as a function of the correlated sky position relative to all included LOFAR stations. After the conversion, the data are circularly polarised, with full (but approximated) parallactic angle correction. The data were then converted to UVFITS format using the task exportuvfits in CASA and read into AIPS. A&A proofs: manuscript no. LOFARM82paper Before further processing, the task FIXWT was used to set the relative weights of all visibilities to the inverse square of the standard deviation within five minutes of data.
Solutions were found separately for each IF and polarisation. Since all CS are close and share a common clock, no corrections were needed for the core stations, only for the INT and RS. Typical residual delays were found to be 100-300 ns, and typical residual rates were 1-3 mHz. To correct a few obvious outliers, the corrections were filtered using a median window filter before applying them to the data. The difference in residual delay between the lowest and highest frequencies of the three IFs were typically 10-15 ns for the international stations. Bad data were edited using UVFLG in AIPS. No fringes were detected to DE604 and this station was not used in the imaging. We note that the problems with DE604 have been fixed since these data were taken.
Using baselines > 60kλ and pixel size 0. 03 we only see a very compact source of flux density 38. 0 mJy at 118 MHz (CLEAN beam 0. 47 × 0. 33 σ = 0. 20 mJy/beam), and 42. 3 mJy at 154 MHz (CLEAN beam 0. 41 × 0. 25 σ = 0. 11 mJy/beam). The cumulative corrections derived from J0958 and M81* were then applied to the M82 data. Since M82 is at angular distance 0. 6 • from M81* residual phase errors might be present in the M82 data limiting the dynamic range, although no clear phase errors are visible in the images. We therefore tried phaseonly self-calibration of M82 deriving one solution every five minutes, but this did not result in any significant improvement of the final images. We therefore discarded these corrections, and the images presented in this paper were obtained without any self-calibration on M82.
Imaging of M82 Because of the large fractional bandwidth (10%) and large-scale emission present  (Adebahr et al. 2013, 5  at λ = 92 cm), deconvolution should be done using multi-scale multi-frequencysynthesis (MSMFS). We deconvolved the calibrated M82 data using the MSMFS algorithm v2. 6 as implemented in the task CLEAN in CASA 4. 2. 1  (Conway et al. 1990; Rau & Cornwell 2011). A possible dependence of intensity on frequency was taken into account, using the MFS algorithm with two Taylor terms, for all images presented here. Note that no corrections were made for the station beam or other wide-field effects, apart from the corrections applied by mscorpol. However, due to the small field of view when imaging, these effects are negligible for all the results presented here, and we did not need to use widefield imaging software (such as the AW-imager).
The impact of editing should be minor and is in principle possible to calculate, but in practice it is hard for this observation when combining manual and automatic editing procedures and we did not include losses due to editing in the above estimate. Furthermore, the internal calibration of the international stations was not optimal at the time of these observations, thereby increasing S INT. The station calibration has, however, been improved since these data were taken. We note that, despite these hindrances, our attained ratio of image noise to theoretical noise is better than that often attained at lower resolution with LOFAR, where dynamic range constraints can be severe.
Imaging of extended structure in M82 Before imaging the extended structure using RS-RS and RS-CS baselines, the model obtained through deconvolution of the compact structure (see Sect. At RS baselines we are limited by dynamic range rather than random noise. This is not surprising, since at RS baselines the signal from interfering distant cosmic sources is much stronger because of less decorrelation due to averaging. To properly deconvolve such interfering sources, imaging of a larger field of view (possibly with multi-directional calibration) might be needed. Further improvement of the image fidelity by, for example, using different deconvolution algorithms (such as the LO-FAR software AW-imager) is however beyond the scope of this work. We detect a weak (peak 1. 96 mJy/beam) signal in Stokes V at 154 MHz tracing the brightest part of the extended emission visible in Fig.
A resolved but not clearly shell-like object At position 09 h 55 m 54 s. 62 and 69 • 41 00. 5 (J2000) is a clearly resolved object (see zoom in Fig. 7d ) with peak flux density 0. 8 mJy/beam and integrated flux density 4. 5 mJy at 154 MHz. With chosen input parameters, PyBDSM did not detect it (parameters given in Sect. 4), but it is clearly seen also in recent eMERLIN 1. 6 GHz observations where an integrated flux density of 5. 5 mJy and a peak brightness of 140 µJy/beam (eMER-LIN beam 150 × 126 ) is recovered for this source. This object 46. 56+73. 8, α = -0. 57 ±0. 03 Fig. 5. Spectra of four compact objects detected with LOFAR, and also reported at 408 MHz by  Wills et al. (1997). These objects show no lowfrequency turn-over at LOFAR frequencies. LOFAR data points are plotted in red. Non-LOFAR data points are VLA and MERLIN, as presented in Table  2  by  Allen & Kronberg (1998).
65 ±0. 18, EM=1. 1 ±0. 3 ×10 5 cm -6 pc Fig. 6. Spectra of six objects detected with LOFAR, and also reported at 408 MHz by  Wills et al. (1997). Five of these are compact and listed in Table  2 , while "47. 37+68. 0" is resolved and discussed in Sect. 4. 6. These objects show evidence of a low-frequency turn-over at LOFAR frequencies. LOFAR data points are plotted in red. Non-LOFAR data points are VLA and MERLIN, as presented in Table  2  by  Allen & Kronberg (1998). A model of SNR spectra was fitted to each object as described in Sect. 4. 5. The best fit spectral index α and emission measure EM are given in the respective figure title. whole field must be imaged using multi-directional calibration techniques. In the intermediate regime of LOFAR Dutch remote (< 100 km) baselines we have shown that on a very bright target source such as M82 it is also possible to get useful science images by standard VLBI techniques, as demonstrated by Fig.
Other data sets on M82 have been observed, optimised to image the extended halo structure at HBA and LBA frequencies and will be presented elsewhere  (Adebahr et al, in prep. ). This paper only presents results on M82, the M81* data is yet to be fully analysed and we will attempt in future work to characterise the low-frequency structure of the M81 nucleus, detect or set limits on the flux density of SN1993J, and try to detect other supernova remnants in M81. It is clear from the observations presented here that there are many compact objects in M82 at, or just below, our sensitivity detection level. Modest improvements in sensitivity, achievable in follow-up observations, will allow us to detect more sources. It should be noted that the observational setup used for the observations presented in this paper was designed in order to minimise the data processing needed rather than to maximise sensitivity.
In particular, the bandwidth was split equally three ways between M82, M81* and the bright calibrator, and each was allocated a separate station beam. The sources M81* and M82 are close enough that they are well within the same station beam, and can in subsequent observations be observed simultaneously within one beam at full bandwidth. In addition to this, we can use the procedure suggested by  Moldón et al. (2014)  to identify a closer primary calibrator for M82, which can also be observed in the same beam. To use this "three source in one beam" -technique we need to store initially the data after correlation with high temporal and spectral resolution, phase rotate the data to the position of the two sources, and average in time and frequency. This is conceptually simple but requires processing large volumes of (temporary) data.
Efforts are underway to implement and automate such a pipeline  (Moldón et al. 2014). As a practical matter such processing should therefore be done immediately after correlation on the correlator output cluster, before shipping data to other facilities. The results in this paper show that the prospects for science exploitation of LOFAR international baseline data are excellent and the data analysis in principle straightforward. To make it more exploitable by the general community, software for nonexperts is being implemented. A significant issue that has to date limited the exploitation of LOFAR international baselines has been the reliability of the data links between the interna-tional stations and the correlator (the international stations themselves show high reliability) but recently much progress has been made in solving this issue.
Acknowledgements. We acknowledge assistance by the LOFAR science support as well as  Simon Casey  for installing LOFAR software in Onsala. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. The research leading to these results has received funding from the  European Commission Seventh Framework Programme  (FP/2007-2013) under grant agreement No  283393  ( RadioNet3 ). e-MERLIN is the UK's National Radio Interferometric facility, operated by the  University of Manchester  on behalf of the  Science and Technology Facilities Council (STFC). LOFAR, the  Low Frequency Array , designed and constructed by  ASTRON , has facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy.
Double soh715hsr8-1 mutants comprised 1/4 th of the segregating population instead of the expected 1/16 th. Preliminary genetic analysis (data not shown) showed the soh715 locus mapped to a region of chromosome 1 where HSR8 also maps, confirming that the soh715 and hsr8-1 mutations may be genetically linked. A transcriptbased cloning approach  [15]  was then used to identify deletions in the mapped region. Gene expression in hsr8-1 and soh175hsr8-1 seedlings was assessed using the ATH1 Gene Chip. Comparison of gene expression levels revealed that 6 consecutive genes on chromosome 1 showed strongly reduced RNA levels in soh715hsr8-1 compared to hsr8-1 (At1g25510, At1g25520, At1g25530, At1g25540, At1g25550, At1g25560; Fig. 2a ). This result, taken together with the preliminary genetic mapping data, indicated that a deletion encompassing 6 genes on chromosome 1 suppressed the hsr8-1 phenotype.
All steps were conducted at the Nottingham Arabidopsis Stock Centre. Gene expression data were analysed using Partek Genomics Suite 6. 6 software (Partek Incorporated, St Louis, USA). The raw CEL files were normalized using the RMA background correction with quantile normalization, log base 2 transformation and mean probe-set summarization with adjustment for GC content. Differentially expressed genes (DEG) were identified by a two-way ANOVA, and P-values were adjusted using the FDR (false-discovery rate) method to correct for multiple comparisons. DEG were considered significant if P-value was ≤ 0. 05 at a foldchange (FC) of ≥ 2 with an FDR < 0. 05. Hierarchical clustering in was performed using the default settings in Partek. The average distance between all pairs of objects in the two different clusters was used as the measure of distance between the two clusters, and was measured using Un-weighted Pair-Group Method using arithmetic Averages.