dataesr/scientific-paragraphs-categorization huggingface dataset:
A 70-protein phylogenomic analysis (the same as used above for establishing the phylogenetic position of the rhizarian exLh) with the dataset including 60 orthologs from I. spirale showed this organism as a branch sister to, yet deeply diverged from, representatives of Diplomonadida (Additional file 2: Figure  S1 ). This is consistent with the previous result based on the partial 18S rRNA gene sequence and with the fact that no other non-diplomonad fornicate could be included in the analysis due to lack of genome-scale sequence data. The second analysis was based on a dataset comprising a complete 18S rRNA sequence, which we identified as one Fig. 2  In-frame UAG codons in protein-coding genes of the rhizarian exLh and I. spirale. a An example of a rhizarian exLh gene with several in-frame UAG codons: multiple sequence alignment of orthologs of the Bat1 protein (spliceosome RNA helicase).
vulgaris. Brown et al. [36]  sequenced its transcriptome using the 454 method, but did not mention any observation concerning the genetic code employed by this organism. Therefore, we analyzed the 147 transcript sequences from G. vulgaris that have been released by Brown et al. [36]  to the GenBank database (JT844885-JT845030). Only one of these sequences, JT844913, includes obvious in-frame termination codons (specifically a spot with UAG and UGA separated by a glutamine codon; see Additional file 4: Table  S3 ), but checking unpublished Illumina RNA-Seq data from G. vulgaris revealed that the occurrence of the two termination codons in JT844913 is a sequencing error (Dr. M. Brown, personal communication). Interestingly, all but one available G. vulgaris transcript sequences that cover the 3'-end of the coding sequence use UAA as the termination codon, with the sole exception putatively employing UAG (Additional file 4: Table  S3 ).
Hexamitins and the I. spirale lineage thus apparently modified their genetic codes independently of each other (Fig. 1b ). The closest I. spirale relative, from which sequences of protein-coding genes are available, is H. teleskopos. All four these sequences (GenBank accession numbers AB600290. 1 to AB600293. 1) lack in-frame UAG codons, suggesting that the stop-to-glutamine UAG reassignment occurred only after the Iotanema lineage split from the one of Hicanonectes. However, we need to be cautious, as the abundance of the UAG sense codon may be low (it corresponds to only ~8% of all glutamine codons in I. spirale genes; Fig. 3b ), and we also lack positive evidence for UAG as a termination codon in H. teleskopos due to 3'-end truncation of all four sequences. It will be interesting not only to obtain more complete data for investigating the genetic code of H.
Therefore, we predict that sequencing the genomes of the rhizarian exLh and I. spirale will reveal the presence of novel tRNAs with the CUA anticodon and with attributes characteristic for tRNAs recognized by leucinyl-tRNA synthetase and glutaminyl-tRNA synthetase, respectively. Genes for tRNA Leu (CUA) were previously found in mitochondrial genomes of the chytrid S. punctatus  [21]  and several green algae of the order Sphaeropleales  [22] , so the postulated existence of a similar gene in the rhizarian exLh is not without precedent. Similarly, the occurrence of tRNA Gln (CUA) has already been documented, namely in ciliates with the stop-to-glutamine reassignment of UAR codons, e. g. , Tetrahymena thermophila  [38]  or C. magnum  [16] , and in hexamitins, e. g. , in the Spironucleus salmonicida genome (the tRNA gene SS50377_t0098 on the scaffold scf7180000020657; GenBank accession number KI546140.
Methods Assembling the 18S rRNA sequence of the rhizarian exLh from RNA-seq data Searching the TSA from L. hesperus  [25, 52]  with blastn and the previously reported L. hesperus 18S rRNA gene sequence (U06476. 1) as a query identified five contigs as significantly similar hits. Three of them (GBHO01018850. 1, GDHC01017703. 1, and GDHC01007684. 1) were identical or nearly identical in the region aligned with the query, so they apparently come from the insect itself and were not investigated further. The remaining two contigs, GBHO01018845. 1 and GDHC01015840. 1, included a partial 18S rRNA-related region of a different length, but identical between the two contigs along a segment of ~500 bp, corresponding to the 3'-end of the 18S rRNA molecule. This partial 18S rRNA sequence did not closely match any sequence in the GenBank database and was very different from the 18S rRNA sequences of L.
hesperus (or other insects) and its symbiotic trypanosomatid Blastocrithidia sp. [18] , suggesting that it comes from a third, unknown organism. We hypothesized that it is derived from the same organism as the Rhizaria-related component detected among protein-coding transcripts. Careful examination of these two contigs by blast searches against the NCBI sequence databases and against the raw Illumina reads from L. hesperus in the NCBI Sequence Read Archive (SRA) database indicated that both are in fact chimeric and resulted from an artificial merging of separate transcripts, as described in detail in the following two paragraphs. The contig GBHO01018845. 1 comes from the first L. hesperus TSA version  [25]  and proved to consist of a segment representing the 18S rRNA 3'-end region of the unknown organism (positions 1-862) fused to a segment (the rest of the contig) corresponding to the ITS1-5.
8S-ITS2-28S rRNA region of an rRNA locus most similar the respective sequences of close relatives of L. hesperus (heteropterans of the Miridae family). The latter was thus interpreted by us as the actual rRNA locus of L. hesperus (which has not been reported before and is not represented in the NCBI nr nucleotide database). Examination of Illumina reads mapped onto this contig and detailed sequence comparisons revealed that the artificial fusion of the two segments was due to the fact that the 18S rRNA from L. hesperus and the 18S rRNA of an unknown origin share a block of 33 identical nucleotides that corresponds to a highly conserved region of the 18S rRNA molecule close to its very 3'-end. Iterative searches of the raw Illumina RNA-seq reads from L. hesperus enabled us to unambiguously extend the 5'-end of the enigmatic sequence by more than 1000 bp to cover the full length of a typical eukaryotic 18S rRNA sequence.
The best database hits of this kinase come from trypanosomatids and exhibit the same unusual genetic code as described for the L. hesperus-residing Blastocrithidia sp. , so this sequence is simply one of the many Blastocrithidia sp. -derived sequences identified in the L. hesperus TSA by Záhonová et al. [18]. The region 655-136 (again in the reverse complement orientation) of the contig GDHC01015840. 1 is identical to a region of the 18S rRNA sequence reassembled on the basis of the contig GBHO01018845. 1. The third part (positions 136-1) is identical to the 3'-end of 18S rRNA sequences from various lepidopterans, indicating another source of contamination in the more recently reported L. hesperus TSA data  [52]. We did not investigate in detail what might be the factors that misled the program used for the RNA-seq reads assembly to create the tripartite chimera (i.
025 U/μL MyTaq™ DNA polymerase (Bioline, UK), and 0. 4 μM each primer, with an annealing temperature of 57 °C (Rhiz-NF1/R1 primers) or 48 °C (Rhiz-NF2/R2 primers). PCR products were purified using GeneJET PCR purification kit (Thermo Scientific, USA) and directly sequenced with the Sanger dideoxynucleotide method using the amplification primers. Sequencing reads were edited and assembled using Sequencher v5. 0 program. The final sequence was deposited at GenBank with the accession number KY201455. Sequencing and assembly of the I. spirale transcriptome I. spirale cells (strain PHEM1, monoeukaryotic xenic culture) were cultured at room temperature for 7 days before RNA extraction (see  [30]  for details on the culturing). Cells from 50 mL of the culture were pelleted by double centrifugation (1200 g for 8 min and 2000 g for 12 min).
Total RNA was extracted using TriReagent Solution (Ambion, USA) according the manufacturer's instructions and mRNA was isolated by double polyA selection with Dynabeads Oligo(dT)25 (Invitrogen, USA). Illumina RNAseq libraries were prepared from mRNA using the NEXTflex RNA-Seq Kit (developed by BIOO Scientific) and 150 bp paired end reads were sequenced on the MiSeq platform and quality-filtered by the Genomics Core Facility, EMBL, Heidelberg. A total of 14,016,257 read pairs were further processed by removing adapters using Cutadapt v1. 3  [53]  and assembled into contigs using the Trinity package v2. 0. 6  [54] , resulting in 38,331 contigs (Trinity transcripts), 29,865 of which were longer than 250 bp. Possible contamination of the transcriptome assembly by another eukaryote was tested by identifying homologs of cytoplasmic ribosomal proteins (generally encoded by highly expressed genes, hence suitable markers for possible low-abundance contamination) and inferring their phylogenetic position with respect to orthologs from a reference set of phylogenetically diverse eukaryotes (the phylogenetic analysis is described below).
No obvious eukaryotic contamination was detected by this approach (Additional file 3: Table  S2A ). However, the transcriptome assembly was heavily contaminated by bacterial sequences (derived from bacteria coinhabiting the I. spirale culture). Hence, non-eukaryotic contigs, defined as those with the five best blastx hits of the archaeal, eubacterial, or viral origin in the trEMBL database (release 2016_10), were removed prior to deposition of the assembly to the TSA database.
Read mapping Reads from the RNA-seq projects for I. spirale (our new data) and L. hesperus  [25]  (SRR1186295, SRR1186315, SRR1186516, SRR1186658, SRR1186749, SRR1186752, SRR1186787, SRR1186788, SRR1186789) were indexed, quality trimmed using Trimmomatic v0. 32. [55] , and aligned on the assembled transcripts using Bowtie2 v2. 1. 0 in a fast local mode  [56]. The output file was then converted into a binary format (BAM), sorted and indexed using Samtools v0. 1. 19 [57]. Positions of special interest were checked by visual inspection of mapped reads using Tablet v1. 15. 09. 01  [58]  and Integrative Genomic Viewer v2. 3. 68  [59]. All output files are available upon request from the corresponding author.
As an additional test of the correct assignment of the analyzed sequences to the rhizarian exLh we more closely checked the eight transcripts (of the 71 mentioned above) that returned at least one metazoan or trypanosomatid hit among the best 10 blastx hits. We divided the respective predicted protein sequences into two halves and computed a phylogenetic tree for each segment independently. None of these segments branched specifically with metazoan or trypanosomatid sequences, indicating that the eight sequences tested are unlikely to be chimeric. The analysis of 18S rDNA sequences aimed at defining the position of the rhizarian exLh (Fig. 1a ) was based on a combination of the published datasets  [27, 62]. The multi-locus phylogenetic analysis of the Fornicata (Fig. 1b ) was based on the dataset published by Takishita et al.
[32]  expanded by the addition of the respective orthologs from I. spirale (including a complete 18S rRNA sequence identified in the transcriptome, provided in Additional file 7). For the final analysis we did not include two markersalpha-and beta-tubulin, because their I. spirale orthologs proved to be extremely divergent. All alignments were built using MAFFT 7. 245 with the G-INS-i algorithm  [63]  and manually trimmed to exclude unreliably aligned regions. The final 18S rDNA sequence alignment including the rhizarian exLh sequence comprised 1491 positions. Two multi-locus datasets were created by concatenating the relevant individual alignments using SequenceMatrix v1. 8  [64]. The final 70-protein eukaryote-wide phylogenomic matrix including 54 orthologs from the rhizarian exLh (listed in Additional file 1: Table  S1A -C) and 60 orthologs from I.
spirale (listed in Additional file 1: Table  S1A , B) comprised 21,145 aligned amino acid residues. The final five-locus matrix for the analysis of the fornicate phylogeny comprised 3589 aligned characters (1229 nucleotides of 18S rRNA and 2360 amino acid residues coming from sequences of HSP70, HSP90, EF1-α, and EF2 proteins). For the sequence analysis of the eukaryotic release factor 1 (eRF1), we combined the dataset published by Swart et al. [16]  with selected homologs from the GenBank and MMETSP databases. We also added the eRF1 sequences from both species investigated in this study (the rhizarian exLh and I. spirale) and from Trentepohlia iolithus (a representative of ulvophyte algae with a non-canonical genetic code) that we detected in our assembly of publicly available transcriptome reads from this species (SRX387803).
Phylogenetic and phylogenomic analyses Most ML phylogenetic analyses (except the analysis using IQ-TREE, see below) were done using RAxML v8. 2. 8  [65]  and run at the CIPRES Science Gateway  [66]  or the National Grid Infrastructure MetaCentrum. Single-protein ML trees were inferred with RAxML using the PROTGAMMALG4X substitution model with 100 rapid bootstrap replicates. The 18S rRNA gene tree was constructed using the GTRGAMMAI model and 500 rapid bootstraps. The phylogenomic analysis of the concatenated alignments of 70 conserved proteins was constructed with RAxML using the PROTGAMMALG4X model; branch support was estimated from 500 rapid bootstrap replicates. Because RAxML is not equipped with site-heterogeneous models, we also ran an analysis of the same dataset with IQ-TREE v. 1. 3.
8 [67]  using the LG + Γ4 + C20 model with class weights optimized from the dataset and the exchangeabilities from the LG Q-Matrix (LG + Γ4 + FMIX{empirical,C20pi1…C20pi20})  [68]. Branch support was estimated from 1000 ultrafast bootstrap replicates. The dataset consisting of concatenated alignments of 18S rDNA and four proteins was analyzed with RAxML using the GTRGAMMA (for the 18S rDNA partition) and the PROTGAMMALG4X (for the protein sequence partition) model and 500 rapid bootstraps. For this and the global eukaryotic 18S rDNA data set, we also used Bayesian analyses employing site-heterogeneous substitution models. We ran PhyloBayes MPI v1. 5a  [69]  using the CAT-Poisson + Γ4 model (five-locus data set) and the CAT-GTR + Γ4 model (18S rDNA data set). Two independent chains were run until they converged (i.
spirale sequences) that were also used in the aforementioned analysis based on hyperconserved positions. In the alignments used for the calculation of the reference trees, in-frame UAG codons were represented as an undetermined amino acid (X). Subsequent probability calculations were conditional upon, or assuming that, UAG codes for some single particular amino acids at all positions of the alignment, i. e. , we were comparing 20 possible reassignments. Consequently, we prepared 20 modifications of each concatenated alignment, each with one of the possible amino acids considered at all positions corresponding to the in-frame UAG codons. A ML tree was then inferred for each of the 20 datasets, using the LG + Γ model with the alpha parameter fixed to the value estimated for the concatenated alignment with UAG encoded as X and employing the reference species tree with fixed branch lengths as a constraint.
The resulting trees were ordered according to the log-likelihood score (Table  1 ). The dataset giving the highest score predicts the most likely amino acid encoded by the UAG codon in the respective species. To test the statistical significance of this assignment, we calculated for each of the 20 possible amino acids the conditional probability that UAG codes for the amino acid using the likelihood scores computed before. The conditional probability that the organism translates UAG codons as a given hypothesized amino acid is equal to the ratio of the likelihood score of the alignment with this amino acid at UAG positions to the sum of the likelihood scores for 20 alignments, each of which decodes UAG by a particular amino acid. The conditional probabilities for each amino acid (Table  1 ) give overwhelming support for the hypothesis that UAG encodes leucine in rhizarian exLh and glutamine in I.
spirale. Given the scarcity of UAG codons in I. spirale sequences, we carried out a more systematic analysis of the transcriptome. To exclude sequences of bacterial origin and transcripts that may contain frame-shift errors or retained introns, we used blastx to search against the trEMBL database (release 2016_10) and identified contigs that met the following two criteria: all five best hits are of eukaryotic origin, and all high-scoring segment pairs (hsps) with the best hit correspond to the same reading frame in the contig. These conditions retrieved 3064 contigs, which were conceptually translated in the reading frame corresponding to the blastx match with UAG decoded as X (i. e. , an undetermined amino acid). The resulting amino acid sequences were searched with blastp against the trEMBL database and filtered by keeping only those that returned at least 50 hits with e-value < 1 × 10 -30 to ensure that a sufficient number of close homologs is available for comparison with the I.
spirale sequence. In addition, only a single representative sequence was kept (the one that gave blastp hits with the lowest e-value) for groups of sequences corresponding to alternative transcripts of the same gene. This filtering retained 730 sequences (Additional file 3: Table  S2D ), each of which was aligned with the 50 best blastp hits using MAFFT. The alignments were inspected using a custom Python script for cases where the I. spirale sequence included at least one position corresponding to the UAG codon clearly within the coding sequence of the underlying transcript (i. e. , not in regions likely corresponding to 5'-or 3'-UTRs). For further analyses, we considered only conserved positions, defined using a more stringent (90%) or a more permissive (50%) threshold for amino acid identity across the alignment (Fig.
Acknowledgements We thank  Dr. Surendra K. Dara  ( University of California Cooperative Extension ) and  Dr. Joe Hull  ( Agricultural Research Service, United States Department of Agriculture, Arizona, USA ) for providing specimens of Lygus hesperus. Unpublished sequence data from Guttulinopsis vulgaris were generously provided by  Dr. Matthew W. Brown  ( Mississippi State University ). The access to the  Cyberinfrastructure for Phylogenetic Research Science  (CIPRES) Gateway v3. 3. and to computing and storage facilities owned by parties and projects contributing to the  National Grid Infrastructure MetaCentrum , provided under the program " Projects of Large Research, Development, and Innovations Infrastructures " ( CESNET LM2015042 ), is also appreciated. We finally thank five anonymous reviewers for constructive comments that helped us substantially improve the original version of the paper.
Geographic and socioeconomic data The administrative boundaries of New Caledonia were obtained from the Global Administrative Areas database (http://www. gadm. org/) while the administrative boundaries of the neighborhoods of Noumea were provided by the Direction of Infrastructure, Topography and Terrestrial Transportation (DITTT). Socioeconomic and geographic factors included in the analysis, as well as their median, 5and 95-percentile are presented in Table  1. They were selected according to following rationale: (i) higher vegetation coverage may provide more breeding sites for mosquitoes and increase larvae survival (shade maintains water in breeding sites); (ii) higher number of people per room (household crowding) may increase the chance of mosquitoes feeding on multiple targets, which increases the chance of transmission; household and population density were chosen for similar reasons; (iii) older and more degraded lodgings may increase opportunities for mosquito-human contacts (fewer window screens, windows and doors closing less tightly, no air conditioning); percentage of cement lodging was chosen for similar reasons; (iv) apartments were taken as a proxy for a predominantly indoors lifestyle, and an environment that offers fewer mosquito breeding sites; (v) socioeconomic indicators (e.
Spatial analysis of dengue incidence rates Choropleth maps representing the incidence rate of dengue in each neighborhood relative to the average incidence rate throughout the entire city were generated using QGIS 2. 10 (http:// www. qgis. org). The presence of spatial autocorrelation of dengue incidence rates throughout the neighborhoods was assessed using the global Moran's I statistic  [62]. Clusters of neighborhoods of high or low incidence were identified using the local Moran's I statistic, a local indicator of spatial association (LISA)  [63]. Both local and global Moran's I were computed using a weighing matrix based on a Euclidian distance of 4km using the software GeoDa 1. 6. 7  [64].
Alternatively, it could be due to the use of different databases (25% missing cases at the "block of house scale"). In 2012-2013, dengue incidence rates were positively associated with the percentage of cement lodging. The association between this variable and dengue incidence rates is characterized by a threshold effect, with a sharp increase in incidence rates from the first to the second quintile, and a smooth decrease afterwards (S3 Fig). Fitting one IRR for all quintiles captures the increase in incidence, which is likely to be caused by reporting bias. Indeed, people living in non-cement lodging (lowest quintile) may be less prone to visit health services, resulting in an artificially low reported dengue incidence rate for that quintile. It also appears that our model for the 2012-2013 epidemic may be missing a variable, causing residual confounding.
Acknowledgements We thank the students from the  universities Kiel and Hamburg  for their participation, and  H. Brendelberger  and  S. Dobler  for logistic support. Thanks are also due to  N. Maher  and the reviewers for carefully reading the manuscript, which helped us to improve our paper. This study was supported by the  Max Planck Society for the Advancement of Science; K. H. received funding through the  International Max Planck Research School for Evolutionary Biology.
, 1998) , and some previous work with ground based instrumentation  (Lindfors et al. , 2013) , we have developed a look-up table (LUT)-based methodology (ZEN-LUT) specifically designed for desert aerosols to estimate AOD. The LUTs are composed of a set of simulated ZSR and AOD values obtained with the radiative transfer code LibRadtran  (Mayer and Kylling, 2005). The AOD is then inferred by minimizing a function which depends on the difference between simulated and measured ZSR at all used wavelengths. In this case, the surface albedo does not have as much influence as in the upwelling radiance measured by satellite sensors. These characteristics make the ZEN a suitable system for aerosol monitoring in remote desert areas, filling the current observational gaps and being useful in the validation of satellite sensors and models dust products.
SONA all-sky camera In the present study we have checked cloud interference by means of independent measurements from all-sky Automatic Cloud Observation System SONA cameras developed by SIELTEC  (González et al. , 2012). The SONA cameras have been used to detect and remove CE318 and ZEN-R41 cloud-contaminated ZSR data at Izaña and Santa Cruz stations. Cloud cover detection was performed by visual inspection, by analysing each individual hemispheric image from the total sky cameras and identifying the presence of visible clouds around the zenith.