dataesr/scientific-paragraphs-categorization huggingface dataset:
3 and 0. 5. (Fig. 4c ). Sprovieri et al. (2016)  found a consistent seasonal cycle of observed GEM concentration at most measurement sites in both Northern and Southern hemispheres, with higher concentrations during winter and spring and lower concentrations in summer and fall. However, it should be noted that the seasonal variation of monthly mean concentration was not significant at temperate and low latitudes, where most of the sites were located, and commonly did not exceed ±20 %. Therefore, reproduction of GEM temporal variation is a challenging task for models in the absence of data on seasonal variation in the anthropogenic emissions used in this study (AMAP/UNEP, 2013b). Switching off anthropogenic emissions (NoANT) led to a decrease in GEM levels in the atmosphere (the bias was -40 %) and some decrease in the spatial correlation with measurements.
The model run with the first Br dataset (BrCHEM1) showed practically unbiased results, whereas the GEM concentrations from the BrCHEM2 run based on the second dataset were biased high (Fig. 4b ) due to the lower Br concentrations provided by this dataset compared to the first one (Fig. S7  in the Supplement). The O3CHEM and OHCHEM simulations also showed a positive bias, indicating the smaller oxidation capacities of these mechanisms. The spatial correlation coefficient varied among the model runs with different chemical mechanisms (0. 61-0. 73). Somewhat higher spatial correlations were obtained for the oxidation reactions with Br (BrCHEM1, 0. 72±0. 01) and OH radical (OHCHEM, 0. 71± 0. 02), which better reproduced the meridional profile of GEM concentration. The reaction with O 3 (O3CHEM) pro-vided poorer spatial correlation (0.
Inclusion of effective RM removal in the marine boundary layer by sea-salt aerosols in GEOS-Chem  (Holmes et al. , 2010)  resulted in lower RM concentrations over the oceans than those simulated by other models (Fig. 5b ). The scarcity of long-term RM observations did not permit reconstruction of reliable spatial trends on a global scale. Annual mean RM observations for the year under study are available only at nine sites in North America, two sites in Europe, one site in the Arctic, and two sites in the Southern Hemisphere (Fig. 1 ). Considering the short lifetime of RM in the atmosphere with respect to its deposition  (Gustin et al. , 2015; Ariya et al. , 2015)  this limited observational dataset cannot characterize spatial RM variation around the globe. Nevertheless, the measurements can be used to evaluate modeling results at particular locations.
6a ). To reduce the effect of this uncertainty in the current study, a modified speciation of emissions data was used for the model experiments focused on comparing chemical mechanisms, with all Hg emissions treated as GEM (Sect. 2. 4). The overprediction of observed RM concentrations by a factor of 2. 5 was also found by  Weiss-Penzias et al. (2015)  for a number of high-and mid-elevation sites and was found to be connected with the collection inefficiency of the KCl denuder used for the GOM measurements  (Gustin et al. , 2013). The models differed in their ability to reproduce the temporal variation in RM concentration (the correlation coefficient varied from -0. 5 to 0. 6 in the BASE case) (Fig. 6b ). This variation was connected both with the different chemical mechanisms used in the standard model configurations (Table  1 ) and with deviations in model treatment of the removal processes responsible for dry and wet deposition.
The simulated RM concentrations showed (Fig. S6  in the Supplement) that all the  selected sites (except one) could be classified as being far from sources (0-30 pg m -3 ). This observation agrees with the characteristics of the North American sites as given by  Lan et al. (2012). The only site that was probably directly affected by anthropogenic emissions was Waldhof, Germany  (Weigelt et al. , 2013). Nevertheless, because both the mean levels and the seasonal variation of RM concentrations measured at this site did not differ significantly from other sites, it was retained in the dataset. However, it should be noted that this analysis essentially depended upon the emissions data used and could translate any data uncertainties into the classification results. (a) (b) (c) (d) GLEMOS GEOS-Chem GEM-MACH-Hg ECHMERIT -1 -1 -1 -1 -1 -1 -1 Figure  7  shows a comparison of simulated and observed annual mean RM / GEM ratios for different chemical mechanisms.
The whiskers show the standard deviation of monthly mean simulated and observed values. It should be pointed out that the observed values of RM / GEM (1-10 pg ng -1 ) correspond to the background conditions of the continental boundary layer and are considerably lower than those from mountain sites analyzed by  Weiss-Penzias et al. (2015)  (10-100 pg ng -1 ). Exceptions are the site at Alert, Canada, located in the High Arctic (86 pg ng -1 ) and the elevated site at Salt Lake City, United States (21 pg ng -1 ). It is interesting to note that the other elevated site (Longobucco, Italy) did not show similarly increased RM / GEM values (9. 5 pg ng -1 ). As shown in Fig. 7 , the best qualitative agreement between model and measurements was found in the BrCHEM1 experiment with the Br chemistry and one of the Br concentration datasets (Fig.
7a ). Three of the four models demonstrated good performance in this experiment, reproducing observations at most sites with a deviation within a factor of 3. The fourth model (ECHMERIT) significantly overestimated the observed values, which was also typical of the other model experiments (except for O3CHEM). This overestimation was caused not by the chemistry used but by underestimation of wet scavenging as shown below. Use of Br chemistry with the other Br concentration dataset (BrCHEM2) led to less consistent results (Fig. 7b ). The RM / GEM ratios simulated by the models varied from moderate underestimation to overestimation of observed values. The inter-model differences could have been caused both by discrepancies in removal process formulation and by particular implementations of the Br chemical mechanism (Table  1 ).
Hence, the collection of sites characterizes the seasonality of Hg oxidation in the continental boundary layer in northern temperate latitudes. The observed values demonstrated pronounced seasonal changes in RM / GEM, with a maximum in March and a minimum in September (Fig. 8 ). Similar seasonal variations of Hg oxidized forms at background sites were observed in previous studies  (Poissant et al. , 2005; Sigler et al. , 2009; Nair et al. , 2012; Weigelt et al. , 2013). The chemical oxidation mechanisms differed in their ability to reproduce the observed seasonal variation. Use of Br chemistry with both Br concentration datasets (BrCHEM1 and BrCHEM2) provided the best agreement with measurements. Three of the four models (GEOS-Chem, GEM-MACH-Hg, and GLEMOS) reproduced the maximum RM / GEM ratios during the spring months (Fig.
Oxidation chemistry considerably affected both the general level and the temporal variation of Hg wet deposition. The Br oxidation mechanism provided a relatively high correlation with observations, but the results for the two Br concentration datasets (BrCHEM1 and BrCHEM2) differed widely in terms of relative bias. The highest correlation was obtained for OH oxidation chemistry (OHCHEM). Note that, unlike the other models used for this study, ECHMERIT is based on the ECHAM climate model, which is expected to reproduce actual weather behavior, in particular precipitation events, over a relatively longer temporal period and a wider area than the other models, but may diverge on shorter timescales and over smaller regional areas (see, for example,  Angálil et al. , 2016). Because simulated Hg wet deposition is largely driven by model-generated precipitation, the results of the climate-based ECHMERIT model can be significantly biased with regard to other wet deposition models and observations (Fig.
Conclusions The study presented here provides a complex analysis of the processes governing Hg cycling in the atmosphere, involving both measured data from ground-based sites and application of chemical transport models. A variety of long-term GEM and RM concentration measurements as well as wet deposition flux readings have been compiled from different global and regional monitoring networks. Four contemporary global-scale Hg transport models were used, both in their state-of-the-art configurations and for a number of numerical experiments aimed at evaluation of particular processes. Results of the model simulations were evaluated against measurements. The models predicted similar global spatial GEM concentration patterns in near-surface air. The modelto-model difference in simulated GEM concentrations did not exceed ±20 % and agreed with observed values within ±30 %.
O 3 chemistry did not predict significant seasonal changes of wet deposition flux in these regions. Use of OH chemistry enabled reproduction of both the periods of max-imum and minimum values and the amplitude of observed seasonal variations. Hence, the performance of the Hg oxidation mechanisms studied differed in the quality of their reproduction of the various observed parameters, which can imply the possibility of more complex chemistry and multiple Hg oxidation pathways occurring concurrently in various parts of the atmosphere. More extensive measurements of atmospheric RM (including identification of Hg chemical species) and wet deposition are needed in various geographic regions and under different climatic conditions to achieve further improvement of Hg chemical transport models. Data availability.
Mercury modeling and measurement data discussed in this paper are reported within the GMOS central database and are available upon request at http://sdi. iia. cnr. it/geoint/ publicpage/GMOS. The Supplement related to this article is available online at doi:10. 5194/acp-17-5271-2017-supplement. Author contributions. The names after the first author in the above list are in alphabetical order and all authors have made significant contribution. In particular, H. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. The authors wish to thank  David Gay  from the  National Atmospheric Deposition Program (NADP)  and the principal investigators for the AMNet sites ( E. Edgerton ,  W. Luke ,  J. Chaffin ,  R. Callison ,  B. Call ,  M. Pendleton ,  E. Miller , and  M. Allen ) for providing the Hg measured data used in this study. The authors also gratefully acknowledge the  Canadian Air and Precipitation Monitoring Network (CAPMoN)  for the data and the  National Atmospheric Chemistry (NAtChem) Database and Analysis Facility of Environment Canada  (www. ec. gc. ca/natchem) for internet provision of the data. This study was financially supported in part by the  EU   FP7-ENV-2010 project , " Global Mercury Observation System" (GMOS , grant agreement no. 265113 ). Noelle E. Selin  and  Shaojie Song  also acknowledge the  US National Science Foundation Atmospheric Chemistry Program  (grant no.
Results X-ray and neutron diffraction. The maximum entropy method (MEM) is an analysis technique that can be applied to diffraction data that generates density maps without prior knowledge of symmetry and unit cell content, and therefore unbiased towards any specific structural model. It can provide information on subtle local distortions even when this scattering is extremely weak compared to the bulk diffraction  [15] [16] [17]. To gain a deeper insight into the structure of MAPbI we have performed both powder neutron, single crystal X-ray diffraction, and powder synchrotron X-ray studies. Details of the structure determination strategy are given in the Supplementary Note 1. The structure was solved in I4/m space group with lattice parameters of a ¼ 8. 8756(1) Å and c ¼ 12. 6517(3) Å. The I4/m symmetry is not an isomorphic subgroup of the high temperature cubic perovskite Pm 3m space group, so not a common perovskite symmetry  18.
Methods Synthesis and data treatment. Crystals of MAPbI with a volume of B2 cm 3 were grown by slow evaporation of CH 3 NH 3 I and PbI 2 in g-butyrolactone over a 14-day period. These crystals were ground to perform the powder neutron and X-ray studies, and smaller crystals were cleaved to perform the single crystal X-ray studies. Powder neutron diffraction data were collected on the BT1 diffractometer at NCNR at the National Institute of Standards and Technology, Gaithersburg, MD, USA using Cu(311) monochromator (l ¼ 1. 5401(1) Å). Single crystal X-ray diffraction data was collected on a dual-source Rigaku Oxford Diffraction Supernova diffractometer. Powder synchrotron diffraction experiments were performed at the Swiss-Norwegian beamline (SNBL) at the ESRF, France, with an incident wavelength of l ¼ 0.
6932 Å. Whole pattern fitting based on MEM was carried out using PRIMA  36  and RIETAN  37  with a 96 Â 96 Â 128-voxel density map for both the powder neutron and single crystal X-ray diffraction data. MEM density maps were analysed using the Vesta program  38. The FULLPROF  39  suite of programs were used to perform Rietveld refinement on both the neutron and synchrotron X-ray powder diffraction data. Refinement of single crystal X-ray diffraction data were performed using the SHELX program  40. Raman spectroscopy was performed with a Horiba LabRAM S3000 Raman microscope using a near infrared excitation wavelength of 784. 15 nm, and described in more detail in Supplementary Methods.
Summary Following the submission of application EFSA-GMO-RX-005 under Regulation (EC) No 1829/2003 1  from Syngenta Crop Protection NV/SA, the Panel on Genetically Modified Organisms of the European Food Safety Authority (GMO Panel) was asked to deliver a scientific opinion on the data submitted in the context of the renewal of authorisation application of the herbicide-tolerant genetically modified (GM) maize GA21. The scope of the renewal application EFSA-GMO-RX-005 is for food and feed uses, import and processing, but excludes cultivation within the European Union (EU). In delivering its scientific opinion, the GMO Panel took into account application EFSA-GMO-RX-005, additional information provided by the applicant, scientific comments submitted by the Member States and relevant scientific publications.
The data received in the context of the renewal application EFSA-GMO-RX-005 contained: post-market environmental monitoring reports, an evaluation of the literature retrieved by a systematic search, updated bioinformatics analyses, and additional documents or studies performed by or on behalf of the applicant. The GMO Panel assessed these data for possible new hazards, modified exposure or new scientific uncertainties identified during the authorisation period and not previously assessed in the context of the original application. In conclusion, under the assumption that the DNA sequence of the event in maize GA21 considered for renewal is identical to the corrected sequence of the originally assessed event, the GMO Panel concludes that there is no evidence in the renewal application EFSA-GMO-RX-005 for new hazards, modified exposure or scientific uncertainties that would change the conclusions of the original risk assessment on maize GA21.
Data The data for application EFSA-GMO-RX-005 provided by the applicant at the time of submission, or in reply to requests for additional information, are specified below. In the context of this renewal application, no new sequencing study was submitted among the additional documents or studies performed by or on behalf of the applicant. The applicant clarified that the maize GA21 sequence considered in the context of this renewal application is the sequence originally submitted (EFSA, 2007), corrected for sequencing errors consisting of one-base pair addition, a three-base pair deletion and a difference in the number of complete mepsps cassettes present within the insert which were already assessed by the GMO Panel (EFSA GMO Panel, 2015a). In accordance with the GMO Panel guidelines for renewal of applications of GM food and feed authorised under Regulation (EC) No 1829/2003 (EFSA GMO Panel, 2015b), the GMO Panel evaluated the data provided in the context of this maize GA21 renewal application under the assumption that the event sequence is identical to the corrected sequence of the event (EFSA GMO Panel, 2015a).
Systematic search and evaluation of literature 6 As part of the annual PMEM reports, nine separate literature searches were provided covering a reporting period from March 2008 till July 2016. Search terms and databases were not consistently used throughout the reports. Therefore, the applicant performed a systematic search of studies published between 1 June 2005 and 30 June 2016, relevant to the food/feed and environmental safety assessments of event maize GA21 and mutated 5-enol-pyruvyl-shikimate-3-phosphate-synthase (mEPSPS) protein, covering the authorisation period, and following the principles outlined in the EFSA guidance on the application of systematic review methodology for food and feed safety assessment (EFSA, 2010). The applicant searched several general and subject-specific databases to identify relevant studies.
Updated bioinformatic data 7 At the time of submission of the renewal dossier, the applicant provided a bioinformatics package for maize GA21 including an analysis of the insert and flanking sequences and an analysis of the potential similarity to allergens or toxins of the newly expressed proteins and of all possible open reading frames (ORFs) within the insert and spanning the junction sites. The bioinformatics package was based on the original sequence corrected for sequencing errors (EFSA GMO Panel, 2015a). The bioinformatics package also included an analysis of possible horizontal gene transfer (HGT).
Recent studies have focused on how climate change could drive changes in phytoplankton communities in the Arctic. In contrast, ciliates and dinoflagellates that can contribute substantially to the mortality of phytoplankton have received less attention. Some dinoflagellate and ciliate species can also contribute to net photosynthesis, which suggests that species composition could reflect food web complexity. To identify potential seasonal and annual species occurrence patterns and to link species with environmental conditions, we first examined the seasonal pattern of microzooplankton and then performed an in-depth analysis of interannual species variability. We used high-throughput amplicon sequencing to identify ciliates and dinoflagellates to the lowest taxonomic level using a curated Arctic 18S rRNA gene database.
To test for interannual variability vs. trends over time, we reanalyzed the 8 years of ciliate and dinoflagellate amplicon data from Amundsen Gulf reported in  Comeau et al. (2011). For both the seasonal and interannual studies, we classified the microzooplankton reads using an improved reference database  (Lovejoy et al. , 2016)  and constructed robust phylogenies. We applied multivariate statistics to link changes in community structure and composition with prevalent environmental drivers, to provide insights on how microzooplankton communities respond to seasonal changes and inter-annual variability.
Post-sequence Data Processing and Taxonomic Classification The seasonal data reads were quality filtered using the Quantitative Insights into Microbial Ecology program (QIIME;  Caporaso et al. , 2010b). Short sequences and primers were identified in mothur and removed  (Schloss et al. , 2009). Data were then de-noised following  Reeder and Knight (2010). Chimeric sequences were identified in UCHIME  (Edgar et al. , 2011)  and removed. Interannual data was processed using mothur  (Schloss et al. , 2009)  as described in  Comeau et al. (2011). The two data sets were then processed for OTU picking at 98% similarity utilizing USEARCH  (Edgar, 2010)  against the Silva Reference Database v. 102  (Pruesse et al. , 2007). All OTU representative reads were aligned in PyNast  (Caporaso et al. , 2010a) , manually curated in BioEdit v.
7. 2. 5  (Hall, 1999)  and used to construct a phylogenetic tree (FastTree;  Price et al. , 2010). Assignment of taxonomic identity was performed in mothur with a 0. 8 confidence threshold against the Northern Reference Database v. 1. 0 (Lovejoy et al. , 2016) , which follows the Silva taxonomy but includes high-quality longer environmental sequences from the Arctic and North Atlantic  (Terrado et al. , 2009 (Terrado et al. , , 2011;; Charvet et al. , 2012; Monier et al. , 2013; Dasilva et al. , 2014). However, using this database, around 30-45% of the reads were still not classified beyond "Other Dinoflagellates" or "Other Ciliates. " To further increase taxonomic resolution, dinoflagellate sequences from  Saldarriaga et al. (2001 Saldarriaga et al. ( , 2004)) ,  Logares et al. (2007) , and  Potvin et al.
(2013) , and ciliate sequences from  Dunthorn et al. (2014) , along with Evolutionary Placement Algorithm, Randomized-Axelerated Maximum Likelihood-(EPA RAxML,  Stamatakis, 2014)  abundant Arctic-derived HTS reads were added to the v. 1. 0 of Northern Reference  Database (v. 1. 1, Lovejoy et al. , 2016). Taxonomic classifications of the dinoflagellate sequences used as references were verified in the AlgaeBase. org (Guiry and Guiry, 2015)  and by literature searches, while ciliate taxonomic classification followed  Lynn (2012). Using the v. 1. 1 of the database, taxonomy assignment was re-performed to generate the final OTU matrix (OTU reads per sample) with improved results (i. e. , "Other Dinoflagellates" and "Other Ciliates" went down to 5-10%). OTUs matching metazoa, fungi, higher plants and reads that could not be classified to any taxonomic level below Eukaryota were removed and not analyzed further.
Statistical and Diversity Analyses Statistical tests were carried out in the R environment v 3. 0 (R Development Core Team, 2008). Spearman's Rank Correlation (rho) in the Vegan package  (Dixon and Dixon, 2003)  was used to test correlations between taxa and environmental parameters, and results were plotted in igraph  (Csárdi and Nepusz, 2006). Only correlations with Spearman's rho ≥ 0. 3 and significant at p < 0. 001 were retained  (Barberán et al. , 2012). Speciesvariable relationships were then visualized by network analysis as target-source plots in Cytoscape 3. 0  (Shannon et al. , 2003;  see Supplementary Methods 1). Analysis of variance (ANOVA) was applied to determine significant differences between samples and linear regression tests were used to infer relationships, both analysis were carried out using PAST v3.
0  (Hammer et al. , 2001). Alpha diversity (Chao1 index) of the ciliate and dinoflagellate communities in each seasonal sample was estimated as implemented in QIIME  (Chao and Shen, 2003; Caporaso et al. , 2010b). Since the samples used for the interannual part of the study were collected in different months (Jul to Nov) over the 8 years, we tested for variability associated with the month of collection and found no significant differences in species richness of OTUs in ciliate or dinoflagellate abundance between summer and autumn (Supplementary Figure  1 ). Phylogenetic unweighted UniFrac dissimilarities (beta diversity) among the taxa were computed separately for seasonal and interannual datasets by the jackknife method and generalized UniFrac procedure  (Lozupone and Knight, 2005; Chen et al.