dataesr/scientific-paragraphs-categorization huggingface dataset:
The Plant DNA C-values database provides insights into plant genome size diversity Overall, analysis of the data available in the Plant DNA C-values database illustrates the considerable diversity in genome sizes between the different land plant and algal groups, both in terms of the range of genome sizes encountered and the distribution of genome sizes (Figure  3 , Table  1 ). Such different genome size profiles highlight the contrasting genome size dynamics operating between plant lineages  (39, 40)  and argue strongly for the need to continue to collate and analyze genome sizes across the plant tree of life to form a more holistic understanding of plant genomic diversity.
THE GSAD GSAD (http://www. asteraceaegenomesize. com) provides genome size data specifically for Asteraceae (Compositae), which are considered to be one of the largest plant families (24 000-30 000 species) with a worldwide distribution, except Antarctica. Overall, Asteraceae account for approximately 7-9% of angiosperm species on Earth and include many economically important representatives such as those used for food (e. g. artichoke-Cynara cardunculus, sunflower-Helianthus annuus), medicine (e. g. artemisinin, an active compound against malaria extracted from the sweet wormwood-Artemisia annua) and horticulture (e. g. Chrysanthemum and Dahlia species and hybrids), or which are invasive noxious weeds (e. g. Taraxacum). This family has been the target of numerous molecular systematic and genomic studies (e.
g. 41-43) and  the focus of evolutionary-developmental research such as floral development in Gerbera or Helianthus  (44). The sunflower is also the subject of an ongoing whole genome sequencing project  (45) , with the current release containing data for >80% of the genome  (45, 46). Development of GSAD was initiated by research groups based at the Universitat de Barcelona and Institut Bota`nic de Barcelona (IBB-CSIC-ICUB) in collaboration with a team from the Universite´de Paris Sud-CNRS (http://www. etnobiofic. cat). It arose from their long-term scientific interest in Asteraceae, particularly from a genome size perspective  (16, (47) (48) (49) (50) (51). Given the large amounts of genome size data for Asteraceae generated by these and other research groups, the decision to develop and curate an online genome size database focused specifically on Asteraceae was taken.
The aim was to complement the Plant DNA C-values database in the same the Index to Chromosome Numbers in Asteraceae (http://www. lib. kobe-u. ac. jp/infolib/meta_pub/G0000003asteraceae_e) complements the more general Index to Plant Chromosome Numbers (http://www. tropicos. org/Project/ IPCN). Additionally, GSAD provides data for hybrid taxa, varieties, forms and cultivars of Asteraceae, which are not usually included in the Plant DNA C-values database [e. g. see  (10, 17) ]. GSAD was launched in July 2010 (Release 1. 0) and a detailed description of its content and organization is given in Garnatje et al. (16). In the 3 years since the first release, data for a further 412 species have been collated from 40 publications (either already published or in press by June 2013) reflecting both the continued scientific interest in the field and the inclusion of previously overlooked articles (Figure  4 ).
These new data have been incorporated into the new release (Release 2. 0, June 2013), which contains genome sizes for 1219 species [Currently GSAD contains C-value data for approximately 400 species of Asteraceae not listed in the Plant DNA C-values database (Release 6. 0, December 2012). Many of the additional species are from unpublished data and hence were not available for inclusion in the Plant DNA C-values database. In addition, GSAD includes some data that have been published or were in press in 2013 and hence were not included in the 2012 release of the Plant DNA C-values database. ], 186 genera, 20 tribes and six subfamilies compiled from 133 original references. Currently, GSAD is the only genome size database focused on a single plant family.
Database content update Overall, the total number of species and genera listed in GSAD has grown by 51 and 72%, respectively. In addition, Release 2. 0 now includes some well-known genera such as Leontopodium and Mutisia for which no previous records were available. Table  2  provides information on the percentage of species with genome size data for the 6 subfamilies and 20 tribes comprising Asteraceae, together with their minimum, maximum, mean and range of C-values. With respect to Release 1. 0, the most studied genera from a genome size perspective are still the same (Table  3 ), although Hieracium has moved from third to second position. Given the increasing rate at which new genome size data are being generated (Figure  4 ), it is clear that interest in this key biodiversity character in Asteraceae remains high and indeed, seems likely to increase in the coming years.
Web interface features new to Release 2. 0 Release 2. 0 of GSAD includes several new features to enhance the user's experience. (i) A genome size representation tool is now included to enable the user to visually compare genome sizes for a set of species. This allows genome size differences within a given search output to be easily compared. A bar, whose size is directly proportional to genome size, is shown next to the genome size value of the species, together with a red line representing the mean value of the genus. (ii) Following the recommendations of Bateman on how to improve the usability of a database  (54) , another novel feature is the option to export data from a search to an Excel TM file, and/or email the results.
Future prospects The second release of GSAD arose from a considerable compilation effort and has led to a significant increase in the number of Asteraceae species with genome size data. Given this remarkable growth of data in recent years, annual updates are planned so that readily accessible global knowledge on Asteraceae genome sizes remains up to date. Other improvements to GSAD in the near future are likely to include the incorporation of links to published molecular phylogenetic and sequence data for species listed in any given search output, together with data for closely related genera, if available. Despite the many species already listed, there are still conspicuous and important gaps in the knowledge of genome size in this large family. Species representation only stands at approximately 5%, and C-values are missing for most tribes (approximately 60%) and for 6 The ranking of the best represented genera is in brackets.
of the 12 recognized subfamilies. Nevertheless, the construction of this database has enabled such gaps to be highlighted and will hopefully encourage the development of working strategies to fill them. In this regard, the following 5-year targets are proposed to improve representation of genome sizes in Asteraceae: to estimate a further 1200 species, 130 genera, 10 tribes and 6 subfamilies to raise taxonomic representation to approximately 10% of species, approximately 20% of genera, approximately 70% of tribes and 100% of subfamilies by 2018.
R I S K A S S E S S M E NT, M A N AG E M E NT PRACTICES, AND MODELING TOOLS. In a period when weather events and changes in climatic patterns are increasingly affecting various aspects of societal activities, particularly the energy industry, the demand for relevant weather observations and meteorological prediction, on almost all time and space scales, is becoming increasingly evident. Presentations at the ICEM exposed a range of requirements for meteorological data, from the satellitederived products, to the ground station observations, to the output from complex models such as numerical weather prediction (NWP) models. Aside from data availability, critical aspects were identified in the consistency of the data, whereby informative quality control flags and uncertainty measures need to be provided.
EXCHANGE OF INFORMATION BETWEEN ENERGY AND METEOROLOGY. The way in which information and data are shared between the meteorology community and the energy sector varies considerably. Examples range from the very protective attitude, one for which information sharing is viewed as a commercial threat, to a more collaborative approach, one for which external input is viewed as strengthening the business. While ICEM 2011 has definitely helped in the process by opening additional channels of communication between the energy industry and the meteorology community, that more protective component of the energy industry might still prefer other ways to interact with the meteorology community. Presentations from industry leaders such as Mr. Peter Cowling of General Electric encouraged opening the dialogue with the energy industry.
Over Australia, the multi-model mean is sim-ilar to MISR, and does not show the extreme lows or highs seen by MODIS. The models underpredict AOD over Arctic land areas and the Southern Ocean. We next quantify correlations and biases. All calculations use regridded 1 • × 1 • monthly mean model fields sampled only when and where the satellite instruments report observations. Correlations between the models and the two satellite datasets are generally similar, but slightly higher for MISR (Table  2 ). Differences between all-sky and clear-sky are very small for the GISS-E2-R-TOMAS model, modest for CSIRO-Mk3. 6, and quite large for GISS-E2-R (see Appendix A). We therefore use clear-sky data for GISS-E2-R and CSIRO-Mk3. 6 hereafter. The models show a substantial diversity in their ability to capture the observed distribution and magnitude of AOD.
Some models have comparable magnitude In OA-rich locations, several models appear to have too little AOD while GFDL-AM3 appears to have too much. These biases have no clear relationship to global mean OA AOD values other than GFDL-AM3 having the largest OA AOD. Dust and sea-salt are primarily naturally occurring aerosols, and thus their absolute amount tells us little about their contribution to forcing. In areas with largest dust loading, the GISS-E2-R-TOMAS model overestimates AOD (and has the highest global mean dust AOD), while MIROC-CHEM underestimates in comparison with MISR. The latter may be part of the reason that model has the lowest global mean AOD of any of the models in ACCMIP (though it is only marginally lower than some others). Note that differences between the satellite datasets are particularly large for dusty areas.
52 and 0. 77  (Textor et al. , 2006). The Aero-Com phase II models report correlations from 0. 26 to 0. 78, with 8 of the 10 models between 0. 60 and 0. 78 (M. Schulz, personal communication, 2012 ). In the ACCMIP set, 7 of the 9 models have correlations between 0. 54 and 0. 69. The slightly lower correlations in the ACCMIP models could reflect the use of meteorological reanalyses in AeroCom versus free-running models in ACCMIP, the use of less sophisticated aerosol physics in some ACCMIP/CMIP5 models, the use of daily data in AeroCom as opposed to monthly mean in ACCMIP, or a combination of these factors. Biases in the models tend to be smaller with respect to AeroNet than in comparison with satellite datasets, perhaps indicating that primarily anthropogenic aerosols over land are better simulated than aerosols over remote oceans.
Overall, evaluation against ground-based and satellite datasets is fairly consistent, however. Separating the analysis into temporal and spatial components, we find that the models all capture the monthly variations of AOD in every continental-sized region fairly well, with nearly all correlations between 0. 6 and 0. 8. There is no relationship between the quality of the seasonality in the models and the quality of the spatial structure. Hence we focus the remainder of the analysis on the spatial pattern, which differs more strongly across models, comparing the annual mean values in the model against observations. Looking regionally, we find that the spatial correlations in the models are highest for North Africa (0. 46-0. 80), with North America second (0. 48-0. 67 in all but one model). Spatial correlations vary widely across models for East Asia (0.
This is especially the case for the CICERO-OsloCTM2 and GFDL-AM3 models, consistent with the analysis against satellite AOD in sulfate-rich areas. Further towards the equator, dust becomes a large contributor to AOD with a peak around 20 • N in most models. Dust is by far the largest component of AOD in several models, but not in others, and there is no clear relationship between the models' skill in capturing observed total AOD and the magnitude of the dust AOD. The GISS-E2-R-TOMAS model's dust AOD is larger than the total observed AOD around 15 • N, indicating that there is too much dust in that model and accounting for the positive bias in North African total AOD (Fig. 4 ; and consistent with the screening by dust mass against satellite data in Table  4 , though that model actually has the highest spatial correlation with AeroNet in that region), but in other cases the zonal mean AOD does not constrain the dust loading.
OMI data indicates large Arctic AAOD values, but satellite AAOD retrievals are especially challenging over bright surfaces, so these observations may not be reliable. Correlations with OMI are substantially better excluding the Arctic (Table 5). It is difficult to measure AAOD accurately from space, and the OMI measurements may have substantial biases. In particular, the reported AAOD exceeds the total AOD in many locations. We therefore also compare with AeroNet observations (see Appendix C), which likewise shows that the models severely underestimate AAOD over South and Southeast Asia, South America, Southern Hemisphere Africa and East Asia. Since dust dominates the total BC+dust AOD over South Asia and parts of East Asia, part of the low bias there could by due to underestimates of dust loading as well as BC.
The low biases in modeled AAOD over South America and Southeast Asia occur in areas where BC's contribution to total AAOD is quite large, however. Though AAOD retrievals from ground-based lidar may themselves have biases (e. g. due to required removal of NO 2 absorption), it seems likely that the at least a portion of the modeled AAOD bias versus the two datasets analyzed is real. The model biases could stem from underestimates in the emissions inventory, the absorption efficiency of BC, or both factors.
AOD trends Long-term data on aerosol AOD is fairly limited. The most complete long-term satellite record is from the Advanced Very High Resolution Radiometer (AVHRR) and extends back to 1981. We compare the difference between the AC-CMIP 1980 and 2000 timeslices with differences between AVHRR over  1981 -1987 (excluding March 1982 -December 1984  to avoid the effects of the El Chichon volcanic eruption) and 1997-2003. We primarily analyze average monthly mean AVHRR data produced by the Global Aerosol Climatology Project (GACP)  (Geogdzhayev et al. , 2005)  and by NOAA  (Zhao et al. , 2008)  over oceans and lakes, where the retrievals are most reliable for trend analysis. Both AVHRR datasets show substantial decreases over most of the globe (Fig. 7 ). Decreasing trends in AOD are most pronounced around Europe, especially in the NOAA product, and off of eastern North America and over the Great Lakes (though GACP shows large decreases over most of the Northern Hemisphere).
AOD also decreases off West Africa, though as interannual variability in dust emissions is large, our relatively short averaging periods may not be representative for long-term trends there. The models show decreases in AOD over continental Europe and eastern North America that extend out over the nearby oceans. Quantitative comparisons were made by sampling the models where satellite data were reported for at least eight months during at least two years of both the  1981-1987 and 1997-2003 periods. Tests show that values in most locations are extremely similar using thresholds of 9 or 10 months. The multi-model mean decreases near Europe and eastern North America are in good agreement with NOAA AVHRR, though North American trends are too small in comparison with GACP AVHRR (Table  6 ). A very recent reprocessing of the AVHRR dataset by the same NOAA group has produced the NCDC AVHRR-CDR dataset  (Chan et al.
, 2013). This dataset has less coverage (i. e. no Great Lakes data), but at least nominally improves over the prior processing. This version shows more negative trends everywhere relative to the earlier NOAA analysis, and the models underpredict AOD decreases near both Europe and North America with respect to this analysis (Table  6 ). The models show large increases in AOD in east Asia, south Asia and Indonesia between 1980 and 2000 (Fig. 7 ). Observed trends in nearby oceans show strong increases in the NOAA product, but only weak increases in the AVHRR-CDR product and decreases in GACP. It seems counterintuitive that AOD would decrease near rapidly developing Asian countries during this period, and thus we favor the NOAA analyses. The multi-model mean increases are similar to the older NOAA values averaged over a broad area of near-Asian oceans, but are substantially smaller than those seen the Yellow/Eastern Sea region where observed trends maximize (Table  6 ).
The modeled increases, however, extend over a broader area of the Indian and western Pacific Oceans. Modeled trends are much too large near Asia relative to the new AVHRR-CDR product. It is difficult to reconcile modeled increases being far too large with the consistent underprediction of 2000 total AOD in south and east Asia (Fig. 1 ). Hence the older NOAA AVHRR trends may be the most plausible in this region. There are clearly substantial limitations to AVHRR data, however, as highlighted by the large differences between the three datasets, with many The multi-model mean is from 9 models, with HadGEM2 excluded. OMI retrieval is based on OMAERUVd. 003 daily products from 2005-2007 that were obtained through and averaged using GIO-VANNI  (Acker and Leptoukh, 2007). White indicates no data in the satellite record.
We also weight GISS-E2-R nitrate by 0. 5 to account for its biases against AVHRR trends in nitraterich areas (Sect. 4). Multi-model means are -0. 05 W m -2 for SOA and -0. 16 W m -2 for nitrate. The mean, standard deviation, and range for Aer+ are -0. 39, 0. 14 and -0. 12 to -0. 62 W m -2 , respectively. Since accounting for missing components improves the agreement between models and satellite AOD in nearly all cases (Sect. 3), we consider the missing component-adjusted values to be more realistic. We also test if there is a relationship between model skill and aerosol RF. The models with the highest correlation (over 0. 60) against the satellite datasets or AeroNet do have a narrower RF range, -0. 16 to -0. 49 W m -2 , than the full set of models (Fig. 9 ). The models with smallest biases (< 15 %) have the same forcing range.
AOD trends are available from a large number of CMIP5 models. Using the NRF of -7. 6 W m -2 derived from AC-CMIP, we estimate the direct aerosol RF based on CMIP5 AODs (Fig. 19 ; Appendix G). The estimated aerosol RFs in the subset of ACCMIP models with CMIP5 AOD data is -0. 31 W m -2 , a value larger than the true forcing diagnosed in those models (-0. 24 W m -2 ). This suggests that if anything, the AOD-based RF estimates may be biased high. The estimated aerosol RFs in the non-ACCMIP CMIP5 models have a mean of -0. 21 W m -2 , and most of the CMIP5 models not in ACCMIP have estimated aerosol RF less than the range encompassed by our best estimate (-0. 42 ± 0. 09 W m -2 ). Across both projects, only a few models have aerosol RF within the range of our best estimate, with all the others too small.
, 2012; Bitz et al. , 2012; Gettelman et al. , 2012)  and GISS-E2-R simulations) (Fig. 22 ). However, ECS is not particularly correlated with aerosol ERF, which is the more relevant quantity, nor with total forcing. Similar conclusions come from comparing forcing with transient climate response. We examine modeled change in comparison with both the Climate Research Unit (CRU) and GISS historical temperature datasets  (Brohan et al. , 2006; Hansen et al. , 2006). As the latter begins only in 1880, we use GISS 1880s to 1996-2005 plus CRU 1850s to 1880s in that case. Many models have difficulty in capturing the observed global mean historical warming (Fig. 22 ). We hypothesize that representations of aerosol-cloud interactions have become so complex that the emergent aerosol ERF cannot be readily predicted or adjusted, and hence models must accept limitations in their historical simulations to maintain their most realistic representation of aerosol-cloud physical processes.
However, the association of this syndrome with micropenis and/or cryptorchidism as well as the occurrence of familial or syndromic forms suggest an antenatal origin, in which the abnormal birth data are a consequence and not a cause of PSIS. The GH-N, GH releasing hormone (GHRH) receptor, and Pit-1 genes do not seem to be implicated in PSIS  [6]. Abnormal genes (HESX1  [7, 12] , LHX4  [13] , SOX3  [14] , and recently PROKR2  [15] ) have been reported in rare cases of PSIS. We analyzed data from 53 patients with PSIS who had reached pubertal age. Our objective was to identify the factors influencing each clinical and biological symptom and MRI feature. We focused on the roles of the HP deficiencies and the associated malformations or syndromes. We used our findings to identify symptoms indicative of the type of HP deficiency.
A search for neutral Higgs bosons of the Minimal Supersymmetric Standard Model (MSSM) is reported. The analysis is based on a sample of proton-proton collisions at a centre-of-mass energy of 7 TeV recorded with the ATLAS detector at the Large Hadron Collider. The data were recorded in 2011 and correspond to an integrated luminosity of 4. 7 fb -1 to 4. 8 fb -1. Higgs boson decays into oppositely-charged muon or τ lepton pairs are considered for final states requiring either the presence or absence of b-jets. No statistically significant excess over the expected background is observed and exclusion limits at the 95% confidence level are derived. The exclusion limits are for the production cross-section of a generic neutral Higgs boson, φ, as a function of the Higgs boson mass and for h/A/H production in the MSSM as a function of the parameters m A and tan β in the m max h scenario for m A in the range of 90 GeV to 500 GeV.
Data and Monte Carlo simulation samples The data used in this search were recorded by the ATLAS experiment during the 2011 LHC run with proton-proton collisions at a centre-of-mass energy of 7 TeV. They correspond to an integrated luminosity of 4. 7 fb -1 (τ + τ -channels) or 4. 8 fb -1 (µ + µ -channel) after imposing the data quality selection criteria to require that all relevant detector sub-systems used in these analyses were operational.
The selected events are split into a b-tagged sample and a b-vetoed sample to exploit the two dominant production mechanisms for neutral Higgs bosons in the MSSM. Events in which the leading jet is identified as a b-jet are included in the b-tagged sample. The transverse momentum of this jet is restricted to the range of 20 GeV to 50 GeV to reduce the t t background. Events without jets, or in which the leading jet is not identified as a b-jet, are included in the b-vetoed sample. Due to the higher background levels in this sample, the threshold on the transverse momentum of the leading τ had candidate is raised to 60 GeV. Identification efficiency and misidentification corrections for hadronic τ decays: The τ had identification efficiencies, the τ had trigger efficiencies and the corresponding misidentification probabilities are corrected for differences observed between data and simulation.