dataesr/scientific-paragraphs-categorization huggingface dataset:
The on-board electronics comprise an internal 16 bits data logger and a 4 MB internal memory with Ethernet communications for data acquisition, display, download, set-up and diagnosis of the instrument. On-board processing is possible through its microcontroller board, with 16 MIPS of CPU speed and 96 KB of RAM memory, which result in a fast user-oriented data delivery (no additional parts or components are required) and a friendly user interface. The power and communications are performed together through a PoE port. The instrument can work with fixed IP or in DHCP mode. Data can be downloaded manually or automatically and can also be sent via UDP or requested via TCP/IP protocol. It allows ZEN-R41 to form part of a ZEN-1 network for aerosol monitoring. This instrument can be also used independently or as a dependent sensor part of a weather station or other instrument which requires AOD as input.
ZEN-R41 and CE318 radiometric comparison at Izaña In order to check the goodness of the radiometric measurements of the ZEN-R41, we have compared ZSR observations from this device with those provided by CE318 instruments, derived from PPL measurements. The ZSR measurements performed in Izaña station in 2015 were cloud screened using ancillary SONA all-sky images. In this comparison we have not included ZSR data for SZA < 20 • and SZA > 65 • as we have detected higher discrepancies in ZSR for such SZA ranges. In the case of SZA < 20 • , the signal measured by ZEN-R41 device is larger than that obtained with CE318, which might be attributed to the lower stray light rejection and/or larger field of view of the ZEN-R41 radiometer. For SZA > 65 • , we attribute the observed discrepancies to lower signals measured, which reduces the signal to noise ratio, increasing the instrument's uncertainty.
ZEN-LUT method For estimating the AOD from downwelling ZSR measurements under cloud-free conditions, we used the ZEN-LUT method. This method is based on the comparison of measured ZSRs, corrected from earth-sun distance, with a set of simulated ZSR values at the four wavelengths available in the ZEN-R41 radiometer (440, 500, 675 and 870 nm). The LUT was determined using the LibRadtran radiative transfer model (RTM). This model is available in a complete software package containing a suite of tools for radiative transfer calculations in the Earth's atmosphere (freely available from http://www. libradtran. org; Mayer and Kylling, 2005; Emde et al. , 2016). The LibRadtran structures the atmosphere as multi-layers, considering the vertical profiles of temperature, pressure and atmospheric components, such as gases and aerosols.
ZEN-LUT method validation at three different AERONET sites The performance of the ZEN-LUT methodology was tested in three different sites in which mineral dust plays an important role in their respective aerosol climatologies (Izaña, Santa Cruz and Tamanrasset) using a CE318 instrument as a reference, since it has been already widely tested under quite different conditions  (Eck et al. , 1999 (Eck et al. , , 2001 (Eck et al. , , 2003a (Eck et al. , , b, 2010;; Holben et al. , 2001 Holben et al. , , 2006;; Dubovik et al. , 2000). The CE318-LUT AOD was inferred after applying the ZEN-LUT methodology on ZSR data derived from PPL data provided by AERONET. AOD data from Izaña and Santa Cruz stations were cloud screened using information from the SONA all-sky imager. Since any all-sky camera was available at Tamanrasset station, the cloud screening was performed by applying the PPL smoothness criteria described in Sect.
2. 2. 1. We have restricted this analysis to desert dust events. We have identified the desert aerosol conditions following the criteria given in  Guirado (2014), and Guirado et al. (2014) (Sect. 2. 1). We have used AERONET data for the year 2013, as it was the most recent period of available AERONET 2. 0 level AOD data in the three sites. The comparison analysis performed at the three stations at every available wavelength (440, 675 and 870 nm) is shown in Figs. 5, 6 and 7 and Table 3. We present the AOD CE318-LUT/CE318-AERONET scatter plot in Figs. 5a-c , 6a-c and 7a-c, which shows a good agreement for all channels in the three locations, with high correlations (R 2 > 0. 97). We have also found low RMSE val- ues for Izaña and Santa Cruz (up to 0. 011 and 0. 021) but higher values for Tamanrasset (up to 0.
ZEN-R41 and CE318 AERONET AOD comparison at Izaña We have performed an intercomparison of AOD from ZEN-R41 and CE318 at Izaña station during 2015. ZEN-R41 AOD, retrieved by applying the ZEN-LUT method to cloudscreened ZSR data, was compared with AERONET level 1. 5 AOD for the 20-65 • SZA range. The results of the intercomparison for the four spectral bands are presented in Fig. 8  and Table  4. In Fig. 8a-d  we present a scatter plot between ZEN-R41 and CE318-AERONET AOD. A good correlation can be observed between both AODs, with R 2 = 0. 97 and RMSEs ∼ 0. 026-0. 027 for all channels, indicating that the ZEN-R41 instrument and ZEN-LUT method together are adequate for AOD estimation. However, as we pointed out in the previous subsection, the linear dependence between the two AOD data sets is different in the four channels.
Conclusions In this study we have presented the new ZEN system for dust AOD monitoring from ZSR observations. The ZEN system comprises the development of a new and robust radiometer (ZEN-R41) and a methodology to retrieve AOD from ZSR measurements through a look-up table (LUT) method (ZEN-LUT), especially designed for desert aerosols. This methodology, inspired by previous methodologies commonly applied to on-board satellite sensors, uses the radiative transfer code LibRadtran and its packages to simulate ZSRs and the associated AODs. Then, AOD is inferred by minimizing a function which depends on the differences between simulated and measured ZSR at the corresponding solar zenith angle (SZA) in all the available wavelengths. The main conclusions of this study are as follows. 1. The comparison of ZSR from ZEN-R41 and CE318 showed a high coefficient of determination (R 2 ) for all wavelengths (0.
Acknowledgements. The authors are grateful to LibRadtran team for their assistance with the radiative transfer simulations performed in this paper. We also acknowledge to  Izaña staff  for maintaining the instrumentation thus ensuring the quality of data. This work has been developed within the framework of the activities of the  World Meteorological Organization (WMO) Commission for Instruments and Methods of Observations (CIMO) Izaña Testbed for Aerosols and Water Vapour Remote Sensing Instruments. AERONET sun photometers at Izaña have been calibrated within the  AERONET Europe TNA , supported by the  European Community-Research Infrastructure Action  under the  FP7 ACTRIS  grant agreement no. 262254. Edited by: Omar Torres Reviewed by: two anonymous referees.
Data availability The reference aerosol optical depth and zenith sky radiance data at the three sites (Izaña, Santa Cruz and Tamanrasset) were obtained from Cimel Electronique 318 photometers and they are available from the AERONET website (https://aeronet. gsfc. nasa. gov/). All the look-up tables as well as the sensitivity study data were generated by means of radiative transfer simulations carried out with LibRadtran version 1. 7 code, available at http://www. libradtran. org/. All input files for performing these radiative transfer simulations are available upon request. All ZEN-R41 data are available upon request. The SONA all sky camera images for Izaña and Santa Cruz station are available upon request. Competing interests. The authors declare that they have no conflict of interest.
1 ), and in different datasets from marine and terrestrial ecosystems (see Methods). Overall, airborne microbial assemblages could be explained by a mixture of about 25% of microbes from marine origin with about 42% of terrestrial microorganisms (median values). A relatively large fraction (median 24%) could not be explained by our reference data set suggesting either terrestrial sources or marine contributions from remote areas not represented in our reference data set. This indicates that long-range dispersal of airborne prokaryotes and substantial inputs from non-marine sources shape the composition of airborne microbial assemblages. The regions where the largest excess of prokaryotes relative to the potential marine contribution were estimated (West and Central Pacific, Supplementary Fig.
4a ) showed the lowest percentage of marine sequences (1% and 2% respectively, Fig. 3b , Supplementary Fig. 4b ), a clear dominance of terrestrial sequences (around 90%, Fig. 3b , Supplementary Fig. 4c ), and a small percentage of sequences not found in our marine reference data set (5% and 8%, respectively, Fig. 3b , Supplementary Fig. 4d ). This is consistent with a high excess of prokaryotes predicted over these regions, where most of the air samples were located relatively close to land resulting in the dominance of advective flows (Supplementary Fig. 4e ). Unexpectedly, terrestrial prokaryotic sequences and those not present in the databases comprised more than half of the sequences detectable in the South Atlantic and Indian Oceans, two regions located far from land and apparently influenced by air masses of marine origin (Supplementary Fig.
5  and 6 ). At these altitudes airborne particles can be advected from land to the ocean where they can easily mix down to lower altitudes because the thermal inversion at the top of the boundary layer is weaker in the Indian Ocean than in other locations  27  , enhancing the bacterial exchange at the top of the boundary layer. However, around 30% of the community composition found over the Indian Ocean could not be attributed to either terrestrial or marine sources, suggesting a significant contribution of remote sources not included in our reference dataset. The Central Atlantic was characterised by low prokaryotic abundances consistent with those expected from a marine origin and represented the highest percentages of marine sequences (Fig. 3a , Supplementary Fig. 4b ). Back trajectory analysis showed that the air masses sampled in the Central Atlantic were originated from land locations.
The sequences generated were processed using the QIIME 1. 8. 0 software pipeline  46  for quality control, demultiplexing according to the unique 12 bp barcodes, denoising, screening for chimeras and binning of phylotypes at a 97% similarity threshold. Only samples resulting in >800 quality controlled reads >200 bp were considered for the analysis. OTUs were assigned a phylogenetic affiliation by comparison with the SILVA database release 111  47. We did not use the phylogenetic affiliation as an indication of terrestrial or marine origin because of the limited resolution afforded by short sequences for most OTUs was unlikely to provide unequivocal evidence of their origin for many of the OTUs. Instead, we used a more robust community-wide approach by comparing the whole airborne communities to a set of reference end-member communities from different sources.
A reference database was constructed to represent a range of terrestrial and marine environments using surface seawater samples (<5 m depth) collected at 36 locations along the track of the Malaspina 2010 Circumnavigation Expedition plus additional 83 data sets using the same primer (Supplementary Table  4 ) from the SRA. This reference data set comprising a total of 119 samples was divided into two categories (terrestrial and marine) and used for attributing the relative contribution of marine and terrestrial sources to each of the airborne microbial assemblages using a community-wide approach by means of the software package 'Source Tracker'  48. Atmospheric parameters. Wind speed, air temperature, atmospheric pressure and humidity were measured in situ along the Malaspina 2010 Circumnavigation Expedition.
The spray fluxes over the study area were estimated by taking into account the minimum and maximum values of the observed abundances of prokaryotes on the sea surface water. Data availability. The measured abundances and calculated fluxes of microbes reported in this paper are included as Supplementary Information. The original sequences of airborne microbes reported in this study are available at the Sequence Read Archive (SRA, http://www. ncbi. nlm. nih. gov/sra/SRP074223) under Bioproject ID PRJNA319484 with accession numbers SAMN04903953 to SAMN04904051.
Acknowledgements This is a contribution to the Malaspina Expedition 2010, funded by the  INGENIO   2010 CONSOLIDER program  (ref. CDS2008-00077 ) of the  Spanish Ministry of Economy and Competitiveness. We thank the crew of  R/V Hespérides  for their invaluable support and for providing the chlorophyll data,  J. Baldrich Justel  for his help with processing samples and the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT model (http://www. ready. noaa. gov)used in this publication. E. M. and M. A. J. acknowledge the ' Junta para la Ampliación de Estudios' program  (JAE-predoc and JAE-doc contracts, respectively) from  CSIC , supplied by the  European Social Fund. A. M. -A. acknowledges an  FPI grant  funded by the  Spanish Ministry of Science and Innovation. B. G. -G.
acknowledges a predoctoral fellowship from the  BBVA Foundation. We thank the  Boundary-Layer Height data  and the NCEP atmospheric reanalysis provided by the  NOAA-ESRL Physical Sciences Division, Boulder, Colorado  from their Web site at http://www. esrl. noaa. gov/psd/. Bathymetry was obtained from the  GEBCO Digital Atlas  published by the  British Oceanographic Data Centre , on behalf of IOC and IHO, 2003.
Using the latest version of the Global Fire Emissions Database (GFEDv4. 1s) and the global Hg chemistry transport model, ECHMERIT, the impact of Hg speciation in BB emissions, and the factors which influence speciation, on Hg deposition have been investigated for the year 2013. The role of other uncertainties related to physical and chemical atmospheric processes involving Hg and the influence of model parametrisations were also investigated, since their interactions with Hg speciation are complex. The comparison with atmospheric Hg P concentrations observed at two remote sites, Amsterdam Island (AMD) and Manaus (MAN), in the Amazon showed a significant improvement when considering a fraction of Hg P from BB. The set of sensitivity runs also showed how the quantity and geographical distribution of Hg P emitted from BB has a limited impact on a global scale, although the inclusion of increasing fractions Hg P does limit Hg 0 (g) availability to the global atmospheric pool.
The biomass burning inventory The reference BB inventory in this study, Global Fire Emissions Database version 4 (GFED4. 1s), is based on an updated version of the inventory of van der Werf et al. (  2010 ) with burned area from  Giglio et al. (2013) , and with the addition of small fire-burned area  (Randerson et al. , 2012). The standard temporal resolution of the emissions files is monthly, but data are provided to distribute these daily, and a diurnal cycle based on  Mu et al. (2011)  is also available. Daily BB emissions from two other global inventories, GFASv1. 2 (Kaiser et al. , 2012 (Kaiser et al. , , 2015) )  and FINNv1. 5  (Wiedinmyer et al. , 2011) , were also included in the model for sensitivity runs. These three inventories are all compiled using the imagery obtained from the MODIS instruments.
Data availability Mercury 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/gmos_ historical. zul. When mapped to CO, the emissions of Hg 0 (g) were calculated from those of CO using a global averaged ER (1. 96 × 10 -7 ). These were unchanged in the run assuming Hg emissions from BB to be 100 % Hg 0 (g) and divided between Hg P and Hg 0 (g) species, with ratios 4 : 96, 15 : 85 and 30 : 70, in mass, in the runs considering the respective constant fractions of Hg P. Consequently, the geographical and temporal distributions of Hg 0 (g) and Hg P BB emissions follow those of CO. For all cases, the GFEDv4 inventory was used, except for those sensitivity runs performed to test the impact of different inventories, FINNv1.
Acknowledgements. We are grateful to  Sebastian Rast  and colleagues at the  Max Planck Institute for Meteorology in Hamburg, Germany , for the distribution of their software  ECHAM5  and for providing the access to the processed ERA-INTERIM data. We are grateful to  Xin Yang  for providing the  Br / BrO fields from p-Tomcat. The research was performed in the framework of the  EU  project  GMOS  ( FP7-265113 ). The authors would also like to thank the referees whose helpful suggestions and comments contributed much to improving the original manuscript. Edited by: R. Ebinghaus Reviewed by:  M. Cohen ,  T. Dvonch , and two anonymous referees.
Results Comparative analysis of L. bicolor repertoire of predicted TFs We curated 285 TFs derived from the L. bicolor gene catalogue v2. 0. As revealed by a DBD-based classification, putative Laccaria TFs belong to 28 different classes (Additional file 1: Table  S1 ). This in silico generated TF repertoire was compared with the corresponding repertoires of 70 saprotrophic, endophytic, mycorrhizal and pathogenic fungi of diverse clades (Ascomycota and Basidiomycota)  [26]. The overall distribution within known TF families is highly similar for the TF repertoires of these fungi and apparently not related to their different lifestyles. Amongst the examined fungi, the three most prevalent TF families are those containing the C2H2 Zinc-finger (PF00096), the Zn2/Cys6 Zinc-cluster (PF00172) and the fungal-specific TF (PF04280) domains (Fig.
glaucopus (Fig. 1  and Additional file 2: Table  S2 ). One hundred eighty three of the 285 predicted Laccaria TFs displayed similarities with characterized fungal TFs (Additional file 1: Table  S1  lines 5 to 187). Among them, 91 were identified as orthologs of known fungal transcription factors by a BLASTP search conducted against the non-redundant GenBank database, using reciprocal BLAST to infer orthologous relationships (Table  1 ). These TFs were classified into different functional categories (e. g. cell wall modification, development, cell cycle, metabolism, or response to stress and stimuli) based on the putative or known function(s) of their orthologs (Table  1 ). Eight additional orthologs of known fungal TFs were retrieved from a BLASTP search using DBD-lacking regulatory proteins as a reference (Additional file 1: Table  S1  lines 284 to 291); 94 predicted L.
In addition, the TAT-screen allowed the identification of two DBD-containing activators that were not retrieved from in silico analysis (JGI ID# 457991 and JGI ID# 700637) and five activators containing an aspecific nucleic acid binding domain (Table  2 ). The other 57 TAT-positive putative TFs, all of which lacking a recognizable DBD, are collectively designated as 'unconventional transcriptional activators' (see below). Of note, nine of the 16 functionally validated L. bicolor TFs resemble known transcription factors regulating development and invasiveness (or pathogenicity). These include two genes closely related to Prf1, the pheromone a b Fig. 5  Distribution of differentially-expressed TF gene families in several types of mycorrhizal associations. We retrieved gene expression data of TF-encoding genes from roots colonized by the following species: ECM fungi (L.
Discussion This study provides a genome-wide overview of the repertoire of transcriptional activators of the ECM basidiomycete L. bicolor and its regulation during symbiosis development. The latter is a complex multistep process involving a series of sequential morphological changes, with a substantial cell wall remodelling, but also an attenuation (and avoidance) of the host plant defense systems and an extensive metabolic reprogramming. A total of 285 TFs were in silico predicted in the genome assembly v2. 0, classified according to their DBDs, and compared with the TFs of 70 fungal species with different lifestyles and taxonomic designations. In accordance with previous data  [67] , the three most abundant TF families are those containing C2H2 zinc-finger (PF00096), Zn2/Cys6 Zn-cluster (PF00172) and fungalspecific (PF04280) DNA binding domains.
Bioinformatic predictions and annotations of TF Fungal genome assemblies and annotations used in this study are available via the JGI fungal genome portal MycoCosm (http://jgi. doe. gov/fungi) (Additional file 2: Table  S2 ). Each DNA-binding domains (DBD) of eukaryotic transcription factors is related to a pfam domain, used in combination with other DBDcharacteristiscs to classify transcription factors (TFs). Searching for proteins having a TF specific pfam domain retrieved TFs from L. bicolor and the other fungi. Functional annotations were performed using BLASTP (https://blast. ncbi. nlm. nih. gov/Blast. cgi) search against the nr database from the National Center for Biotechnology Information (NCBI) with a threshold e-value of 10 -5. Best hits associated with experimentally characterization and/or publications were reported.
Transcript profiling of TF-encoding genes in ECM To retrieve expression data on ectomycorrhizal root tips for different mutualistic interactions, we used the complete expression datasets published and available as series at the Gene Expression Omnibus at the National Center for Biotechnology Information website (http:// www. ncbi. nlm. nih. gov/geo/). L. bicolor-P. trichocarpa, L-bicolor-Douglas fir time course experiments are available under GSE62225 and GSE62226 accession numbers  [55]. Expression data for A. muscaria-Populus tremula x tremuloides, H. cylindrosporum-Pinus pinaster, P. involutus-Betula pendula, P. croceum-Quercus robur, S. luteus-Pinus sylvestris ectomycorrhizal root tips, T. calospora-Serapias vomeracea mycorrhizal protocorms, O. maius-Vaccinium myrtillus mycorrhizal roots and S.
vermifera-Arabidopsis thaliana mycorrhizal roots are published in  [26]  and available at GSE63947 accession number. Expression data of C. geophilum-P. sylvestris are available under GSE83909 accession number  [48]. Expression data of T. melanospsorum-hazelnut ECM are available under GSE17529 accession number  [35]. In all cases, only transcription factors significantly regulated (up-or down-regulation ≥ or ≤2. 5 fold, Benjamini Hochberg, modified t-test p-value <0. 05,) were considered for the comparative analysis. When microarray data were used, only transcription factors with an expression ≥200 (arbitrary units) in at least one of the condition tested were retained. For L. bicolor time course, clustering of the transcription factors with significant regulation was performed manually, according to their expression pattern throughout the time-course and explained for each cluster in the results section.
Data were normalized using log transformation with a Log base of 2 and the heat maps were generated using the pheatmap package in R  [27]. Quantitative real-time qPCR cDNA was generated from 500 ng total RNA samples using the i-Script cDNA reverse transcription kit from Biorad. Real-time PCR reactions were prepared using SYBR Green Kit (Biorad) including 10 ng cDNA and 300 nM forward and reverse primer in each reaction. PCR was performed in the RotorGene (Qiagen) with the standard cycle conditions: 95 °C for 3 min; 40 cycles at 95 °C for 15 s and 65 °C for 30 s, followed by a melting curve analysis (temperature range from 65 °C to 95 °C with 0. 5 °C increase every 10s) A no template control, containing H 2 O instead of cDNA, was included. Transcript abundance was normalized using L. bicolor histone H4 (JGI ID# 319764) and ubiquitin (JGI ID #446085) -encoding genes.
Fundings YD was supported by a Ph. D. scholarship from the  French Ministère de la Recherche et de la Technologie. JR was an  AgreenSkills Marie Sklodowska Curie  postdoctoral fellow co-funded by the  European Commission  ( FP7-267196 ). This research was sponsored by the  Laboratory of Excellence ARBRE  ( ANR-12-LABX-0002_ARBRE ), the  Oak Ridge National Laboratory Genomic Science Program, U. S. Department of Energy, Office of Science -Biological and Environmental.