dataesr/scientific-paragraphs-categorization huggingface dataset:
g. vehicles, industry and agricultural practices. Several studies have concluded that the most significant effects on aerosol Fe solubility result from the source/composition of the aerosols, rather than changes in physico-chemical parameters, such as temperature, pH and oxygen concentration of the leach medium, or the choice of batch versus flow-through techniques (e. g. Aguilar-Islas et al. , 2010; Fishwick et al. , 2014). There have been a number of studies that have focused on the role of aerosol TEs on biogeochemical cycles in the North Atlantic (e. g. Sarthou et al. , 2003; Baker et al. , 2013; Buck et al. , 2010; Ussher et al. , 2013; Powell et al. , 2015). More recently, the GEOTRACES programme has produced a number of aerosol datasets, which has stimulated further discussion on the use of this data to look for trends that link TE solubility and aerosol provenance (e.
Aerosol concentrations for a suite of other elements (Li, Na, Mg, P, Sc, V, As, Se, Rb, Sr, Sn, Sb, Cs, Ba, La, Ce, Nd, Th, U) were also determined, but will not be discussed further here. However, these data are available at BCO-DMO (GA03; www. bco-dmo. org/) and LEFE-CYBER (GA01; (www. obsvlfr. fr/proof/php/GEOVIDE/GEOVIDE. php), and on request from the lead author.
Jickells et al. (2016)  compiled solubility data from the North Atlantic and also found that the inverse relationship between Fe and Al solubility and atmospheric loading was robust over the range of atmospheric loadings found in the North Atlantic, regardless of the leach protocol employed. In this study, both the UHP soluble, and 25 % acetic acid soluble fractions of Fe and Al (Figs  4a  and d ) were related to atmospheric loading, i. e. the highest loaded North African samples had the lowest solubility. This trend is consistent with the observations of  Jickells et al. (2016). The possible exception to this trend is the fraction of Al that dissolved from North African aerosols following the 25 % acetic acid leach (Fig. 4d ). However, it could simply be that we are observing scatter in our data, which is smoothed out in the larger dataset (n > 2000) examined by  Jickells et al.
Soluble TEs: UHP water compared to seawater instantaneous leaches Seawater leaches were conducted on a subset of samples (GA03-2011), to investigate the suitability of seawater as the leach medium in the instantaneous leach. During this study, Fe solubility in seawater was lower than in UHP water (Fig. 6c ). This phenomenon has previously been observed in atmospheric aerosols from the North Atlantic Ocean  (Buck et al. , 2010). For Fe, only a few samples of North American and Marine provenance conformed to the relationship described by the equation proposed by  Buck et al. (2010) , with most of our data plotting above the regression line of the Buck et al. (  2010 ) study (Fig. 6c ), indicating that our data was relatively more soluble in UHP water compared to seawater than in this earlier study.
A challenge of using seawater as the leach medium is that it is difficult to control for natural variability in its organic composition. Consequently, it is not possible to determine conclusively why contrasting trends in the fractional solubility of TEs were observed. For this reason, we advocate for the use of UHP water as a common leach medium to facilitate comparisons of solubility resulting from differences in aerosol composition. An additional benefit is the ease of analysis of UHP water compared to seawater. That is not to say that seawater should not be used, but rather that it is difficult to draw direct comparisons between datasets due to potential differences in the composition of the seawater used, which could affect the fractional solubility.
Acknowledgements Many thanks to the captains and crews of the RV Knorr ( GA03-2010  and  2011 ) and NO Pourquoi Pas? ( GA01 ), the chief scientists (GA03  = Bob Anderson ,  Ed Boyle ,  Greg Cutter ;  GA01  =  Geraldine Sarthou  and  Pascale Lherminier ),  Alex Baker  for the loan of the aerosol sampler used on GA01, and  Alina Ebling Petroc Shelley ,  Alex Landing  and  Sarah Huff  for their help running samples. This work was supported by grants to WML ( NSF-OCE 0752832 ,  0929919  and  1132766 ), and  GS  ( ANR-13-B506-0014  and  ANR-12-PDOC-0025-01 ). RUS was supported by a  LabexMER International Postdoctoral Fellowship  and  CG29 Postdoctoral Fellowship. A portion of this work was performed at the  National High Magnetic Field Laboratory , which is supported by  National Science Foundation  Cooperative Agreement No.
74; Hdnad5 = 0. 23; HdITS1-5. 8S-ITS2 = 1) than the individuals from the clade A (Hdcox1 = 0. 70; Hdnad5 = 0. 20; HdITS1-5. 8S-ITS2 = 1). Two cox1 sequences annotated as Ae. albopictus but close the the clade C were found in the public databases (Additional file 1: Supplementary informations). In order to improve detection methods for Aedes sp. , observation of cibarial armarture through scanning electron microscopy was used to highlight any morphological difference between members of clades A and C. Three morphological differences were characterized for both species: (i) a structural difference in the ventral papillae; (ii) a difference in the angle of the lateral flange; and (iii) the cibarial teeth. While all ventral papillae in Ae. albopictus become continuously slimmer from the base to tip, the ones in Aedes sp.
Phylogenetic analysis Sequences of the cox1 and nad5 genes and ITS2 region were used to study the evolutionary relationships between the individuals sampled in the forest of the National Park at Bù Gia Mập. Sequences from different species of the family Culicidae were selected in the GenBank database and used to build the phylogenetic trees. The nad5 phylogenetic tree was built using sequences of Ae. albopictus (gi|374252068), Ae. notoscriptus (gi|508599694, gi|508599680, gi|508599936, gi|508599982, gi|508599786, gi|508599774, gi|508599736, gi|508599844, gi|508599696), Ae. vexans (gi|291193228, gi|854892238, gi|291193230, gi|291193094, gi|291192962), Ae. aegypti (gi|66351510, gi|66351520, gi|66351512, gi|66351506, gi|164370713), Culex pipiens (gi|336462537, gi|336462529, gi|336462535, gi|336462539), Anopheles gambiae (gi|1469 9845, gi|459680999, gi|90656784), An.
Sizing tables were obtained with Genemapper 4. 0 (Life technologies, Saint-Aubin, France) and imported in R software (R development core team). Only signals within the range of 100-1000 bp were considered for this study. Pick areas were transformed to Relative Fluorescence Intensity (Pick Area/Σ Pick Area ) with the interactive binner and an optimal window size of 5 bp with a shift of 1 bp was selected according to the method previously described  [51]  (Additional file 2: Table  S3 ). α and β-diversity analysis were performed with vegan package. DNA extraction was also performed from the remaining carcasses of each individual and ITS1-5. 8S-ITS2 region was amplified as described above. Samples were then assigned to a clade according to the size of amplified fragments after migration of PCR products on a 1% agarose gel electrophoresis.
MiSeq 16S rDNA gene sequencing To determine the composition of midgut associated bacterial microbiota and their diversity, we also used the Illumina MiSeq sequencing technique of the 16S V5-V6 rDNA gene region as previously described  [22]. The three Ae. albopictus individuals are part of a previous study performed with the same set of samples and their raw data reads are accessible from the European Nucleotide Archive (www. ebi. ac. uk/ena) under the accession number PRJEB6896  [22]. Two individuals corresponding to the cryptic species were included in this analysis (Table  1 ). Briefly, midgut DNA was extracted as previously described  [22]. Libraries were built with one step of PCR conducted with 784 F (5′-AGG ATT AGA TAC CCT GGT A-3′) and 1061R (5′-CRR CAC GAG CTG ACG AC-3′) modified with an 8-bp multiplex barcode and Illumina adapters.
The complementary reads were aligned with PandaSeq  [52]. Aligned sequences were then processed with the MOTHUR pipeline and were kept only if: (i) their size was comprised between 200 and 350 bp; (ii) they did not contain any ambiguous sequence; (iii) they were not chimeric according to Perseus chimera detection; (iv) they aligned on the SILVA database (released 115). OTUs were created by clustering sequences at a level of 97% similarity according to the median neighbour method  [53]. The sequences were classified with a naïve Bayesian classifier at an 80% bootstrap  [54]. A total of 51843, 81088, 32279, 69453 and 34077 sequences were obtained for the samples BGM1, BGM3, BGM4, BGM5 and BGM6, respectively. A negative control (blank extraction and PCR) was used as a reference to remove the contaminants as previously described  [22].
The sequence proportions were obtained after subsampling of 8002 sequences per sample. Because of the small samples size, no comparative analysis of the α and β diversity was performed. Only the proportions of OTUs assignment were presented. The raw reads related to the two individuals from the clade C have been deposited in the European Nucleotide Archive (www. ebi. ac. uk/ena) under the accession number PRJEB14610. DNA extraction, cox1 and nad5 barcoding were performed on the carcasses of those individuals according to the protocol previously described.
Additional files Additional file 1: Figure  S1. Correlation of pairwise nucleotidic distances for cox1, nad5 and ITS1-5. 8S-ITS2 markers. Figure  S2. Molecular features reveal differences between Ae. albopictus and a cryptic Aedes species living in sympatry. Table  S1. Analysis of haplotype and nucleotide diversity within the two Aedes clades. Table  S2. Proportions of operational taxonomic units (OTU) of bacteria identified in midgut samples by 16S rDNA Miseq sequencing. (PDF 1193 kb) Additional file 2: Table  S3. b-ARISA dataset. (XLSX 25 kb).
Acknowledgments This paper is dedicated to the memory of  Florence-Hélène Tran  who tragically left us in August 2016. The authors thank the Vietnamese authorities, director and staff of  Bù Gia M p National Park  for giving them the permission to sample Culicidae. We thank  Dr Clement Goubert  from the  University of Lyon  for his help to identify the cryptic species. We also sincerely thank  Dr Abilash Nair  from the  University of Helsinki  and  Dr Pablo Tortosa  from the  Reunion University  for their valuable advice.
Funding Funding for this project was provided by grants from  EC2CO CNRS  and  CMIRA Région Rhône-Alpes. This research was also partially funded by  ERA-NET BiodivERsA  with the national funders  ANR-13-EBID-0007-01 ,  FWF I-1437 , and  DFG KL 2087/6-1  as part of the 2012-2013 BiodivERsA call for research proposals, and was carried out within the framework of  GDRI "Biodiversity and Infectious Diseases in Southeast Asia ". We also gratefully acknowledge the contribution of the DTAMB platform of the  FR41 Bio-Environment and Health (University Lyon 1 ) and the  Academy of Finland  (Decision numbers 273098 and  265641 ).
Background: Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders. Methods: Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data to explore the molecular mechanism at play. Results: Integrating genomics, transcriptomics, and epigenomics data, we describe two novel DLG2 promoters and coding first exons expressed in human fetal brain. Their murine conservation and protein-level evidence allowed us to produce new DLG2 gene models for human and mouse.
These new genic elements are deleted in 90% of 29 patients (public and in-house) showing partial deletion of the DLG2 gene. The patients' clinical characteristics expand the neurodevelopmental phenotypic spectrum linked to DLG2 gene disruption to cognitive and behavioral categories. Conclusions: While protein-coding genes are regarded as well known, our work shows that integration of multiple omics datasets can unveil novel coding elements. From a clinical perspective, our work demonstrates that two new DLG2 promoters and exons are crucial for the neurodevelopmental phenotypes associated with this gene. In addition, our work brings evidence for the lack of cross-annotation in human versus mouse reference genomes and nucleotide versus protein databases.
As scaffolding proteins binding to both cytoskeleton proteins and signaling complexes, they play an important role in the development, plasticity, and stability of synapses  [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43]. Mice and humans share conserved functional roles of DLG2 in complex cognitive and learning tasks  [44]. A multi-omics integration approach can discover the link between genotypes and phenotypes, especially in the presence of complex pathologies  [45]. In this study, our in silico multi-omics integration analysis of several independent functional datasets contributed to the identification of two novel promoters and coding first exons in the DLG2 gene. These novel isoforms are expressed in the fetal brain and have protein coding murine equivalents. Deletions of these new elements were found statistically associated with NDDs by comparing multiple independent case and control cohorts.
Patient and control CNV datasets We first considered two public CNV datasets: i) the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER)  [15] , a collection of thousands of patients with rare copy number variations and phenotypes; and ii) the Database of Genomic Variants (DGV)  [50] , a collection of structural variations identified in healthy control samples. We used release version dated January 1st, 2015 for DECIPHER and October 16th, 2014 for DGV. As validation, we used two more datasets: the Coe et al. [11]  and Cooper et al. [51]  "Developmental Delay" case and control cohorts recently available through the UCSC genome server and public repositories, and the database of structural variants discovered in the 1000 Genomes Project  [52]. These are hereafter named GDD/ID (cases or controls, as specified) and 1KG datasets, respectively.
DLG2 has been known to be linked to psychiatric disorders  [44, [46] [47] [48] [49] ; therefore, we collected from the literature those patients whose clinical description and CNV were publicly available (Table  1 ). Genomic locations in hg18 were converted to hg19 using the UCSC liftOver tool. Since those datasets come from distinct studies, different protocols might have been used to report CNVs. Hence, we decided to apply a common set of filtering rules on all datasets as a preprocessing step. We considered CNVs having a length between 50 bp and 3 Mbp (as imposed by DGV) with an absolute log2ratio value.
Roadmap Epigenomics Project From the Roadmap Epigenomics Project, we used genome-wide profiling of six histone modification markers (H3K4me3, H3K4me1, H3K27ac, H3K9ac, H3K27me3, H3K9me3) in 13 cell lines and tissues: H1 cell line, H1 derived neuronal progenitor cultured cells (H1 NPC), H1 derived mesenchymal stem cells (H1 mesenchymal), H1 BMP4 derived mesendoderm cultured cells (H1 mesendoderm), fetal brain, and eight adult brain tissues (Additional file 1: Figures  S19  and S20 ). We compared ChIP-Seq signal to a corresponding whole-cell extract sequenced control to identify narrow regions of enrichment (peaks) using MACS v2. 1. 0 peak caller with default parameters. Since the Roadmap Epigenomics dataset provides at least one ChIP-Seq control, we identified peaks for every possible profile-control combination in the tissue.
The assemblies were then merged into a master transcriptome via the cuffmerge tool. We then computed the detection and visualization of exons and splice junctions using QoRTs v1. 0. 1  [57]  (java -Xmx4G -jar < QORTS_JARFILE > QC -stranded command) and Junc-tionSeq v0. 6. 9  [58]  software (runJunctionSeqAnalyses function with analysis. type = exonsOnly and method. GLM = advanced parameters). We ran the same pipeline on fetal and adult non-brain tissues such as liver (ENCODE ENCSR000AFB), muscle (ENCODE ENCSR000AFF and ENCSR000CUI), skin (ENCODE ENCSR000AFG), thyroid (ENCODE ENCSR000AFK), and fibroblast of dermis (ENCODE ENCSR000CUH). Because adult brain paired-end RNA-Seq data were not available through ENCODE, we used paired-end RNA-Seq data from frontal, temporal, occipital, and cerebellum adult brain tissues from Yao et al.
[59]. We also retrieved mouse newborn brain RNA-Seq data from ENCODE: hindbrain (ENCODE ENCSR749BAG), midbrain (ENCODE ENCSR255SDF), and forebrain (EN-CODE ENCSR723SZV). From these three tissues, we collect six BAM files (see Additional file 1: Table  S20  for details). In our genome-wide analysis, we estimated the presence of a splicing site by means of differential coverage between adjacent nucleotides. We used bedtools  [60]  v2. 25. 0 (subcommand genomecov with parameters -bg -split) to measure coverage per each nucleotide, resulting in a bedgraph file for each fetal brain BAM file.
Genomic coordinates, exons, and isoforms DLG2/Dlg2 genome references From UCSC we gathered and preprocessed data related to DLG2 with the following objectives: i) gather all exons belonging to all isoforms and number them uniquely; ii) understand which exons are shared across isoforms and which serve as alternative promoters. The result of this preprocessing step is reported in Fig. 1  (details in Additional file 1: Table  S1  and Figure  S1 ). In this paper we use UCSC numerical reference to identify an exon; we also provide the Ensembl DLG2 coordinates and isoforms (Additional file 1: Table  S2  and Figure  S2 ) and comparison of both annotations (Additional file 1: Table  S3 ). For the whole genome analysis, we refer to the coordinates available in the UCSC browser knownGene table (hg19) regarding genes and exons.
DLG2/Dlg2 exons and amino acid mapping We used NCBI BLAST (access date: November 2016) and UniProtKB/Swiss-Prot (access date November 2016) databases to align DNA to amino acid sequences and to map orthologous exons between human and mouse DLG2-Dlg2 genes. The mapping between DLG2/Dlg2 exons is reported in Additional file 1: Supplementary notes 3-6. We describe here the information regarding the unmapped DLG2/Dlg2 exons. In Parker  [32] , the expression at the RNA level of the six mouse DLG2 (also known as PSD-93) protein isoforms described by the authors were detected by reverse transcription RT-PCR using isoform-specific primers. We used the reported forward primers of PSD-93 zeta and PSD-93 gamma in NCBI BLAST to locate the start of their coding regions in mm10 genomic coordinates. For the former, the primer aligns to chr7:90504814-90504835, around 600-kbp upstream of the first UCSC mouse exon, possibly corresponding to the coding region of human exon 3; for the latter, the primer aligns between mouse exons 3 and 4, chr7:91711767-91711790, orthologously mapping to human exon 10 (Additional file 1: Supplementary note 6).
Likewise, RT-PCR primers for PSD-93 beta and epsilon map to the two new exons described in this work (see "Results"; Additional file 1: Supplementary Note 6). We then compared the murine protein sequences reported in UniProtKB/Swiss-Prot to the human genome (see Additional file 1: Supplementary notes 3 and 4 for details). The beginning of the mouse Q91XM9-7 (also known as PSD-93 zeta) isoform, from position 38 to position 156, aligns with the start of the human Q15700-2 isoform (encoded by human exons 3 to 6) with E-value of 1. 1 × 10 -58 (NCBI BLAST score). The first seven amino acids (MQHAFIP) of the mouse Q91XM9-3 (also known as PSD-93 gamma) isoform match with the end of human exon 10. No known mm9 or mm10 UCSC or Ensembl Dlg2 exons code for these murine zeta and gamma protein isoforms.
Statistical analyses All statistical analyses were performed using R software, version 3. 2. Regarding the whole genome analysis, while it was possible to merge DECIPHER NDD patients with GDD/ID cases, we had to deal with missing patient information in 90. 9% of the total GDD/ID CNV controls  [11, 51]. Merging DGV and GDD/ID control cohorts would filter out most of the data in the latter (Additional file 1, Supplementary notes 1 and 2). For this reason, we classify as statistically enriched for NDD cases those regions having a p value <0. 05 after Bonferroni correction in both the following settings: DECIPHER + GDD/ID cases versus DGV and DECIPHER + GDD/ID cases versus GDD/ID controls.
Describing new promoters and coding first exons inside HPin7 and HPin8 in fetal brain We investigated the functional role of HPin7 and HPin8 by gathering further independent bioinformatics datasets (see "Methods"). Evidence of multiple robust CAGE peaks, human expressed sequence tags (ESTs), transcription factors (TFs), and high expression levels located in the HPs characterized them as having promoter activity (Fig. 3 ; Additional file 1: Figures S30-S34, S39, and S40). Likewise, ENCODE and Roadmap Epigenomics Project categorize HPin7 and HPin8 as active promoters in brain tissues and as weak/repressed/poised elements in other tissues (see the "Roadmap Epigenomics" panel in Fig. 3  and Additional file 1: Figures S16, S17, S21, and S22)  [24, 64]. We studied in detail the HPin7 and HPin8 levels of expression in RNA-Seq data available from ENCODE (see "Methods").
HPin7 and HPin8 nucleotide conservation To characterize further these new isoforms, we checked for their existence in other species. Across vertebrates, elements corresponding to HPin7 and HPin8 are listed in the Conserved Elements from Genomics Alignments (CEGA) database  [63] , reporting moderate and high conservation scores, respectively (the "Conservation distribution" panel in Fig. 3 ; Fig. 3  HPin7 and HPin8 data integration. The integration of genotypic, epigenomic, RNA-Seq, and complementary functional datasets for DLG2 with a focus on HPin7 and HPin8, along with the 5′ splice site locations. Conservation is represented by the CEGA score  [63]  Table  3 ; Additional file 1: Figures  S14  and S15 ). We then studied the murine conservation of HPin7 and HPin8 nucleotides by comparing their sequences with the mouse genome using NCBI BLAST.
Human and mouse HPs promote protein coding isoforms We used NCBI and UniProt to investigate the coding potential of the exons featured in HPs. UniProt includes the manually annotated and reviewed protein isoform dataset For each source, a reference is reported. For rows 2-6, values in parentheses stand for the number of deletions overlapping the DLG2 7-9 region. Row 3: the number of total GDD/ID cases corresponds to the number of intragenic deletions, i. e. , those that are not affecting other genes; hence, nssv_3460188 and nssv_3461505 are not considered (Additional file 1: Figure  S6 ). The "5′ splice site" entry reports the two nucleotides before and after the exon-intron border (marked with the dot character) UniProtKB/Swiss-Prot. Using BLASTX  [70, 71] , we found that human HP DNA sequences match with the beginning of some human predicted proteins found in NCBI BLAST databases (Additional file 1: Supplementary note 5).
In addition to being more penetrant in males, the transmission of DLG2 7-9 deletions from clinically asymptomatic parents suggests incomplete penetrance. Such incompleteness is expected for inherited CNVs in NDDs  [82, 83]. Two patients (GC33254, GC43330) having DLG2 deletions and another relevant CNV cited in Sahoo et al. [84]  also corroborate the additive burden "two-hit" hypothesis for risk factor CNVs in NDDs  [82, 85]. Collectively, these data suggest that DLG2 7-9 deletions overlapping HPin7 and HPin8 are NDD risk factor CNVs. Larger case and control populations will help to determine their exact level of phenotypic penetrance. Note that very mild penetrance of any NDD risk factor CNV could be the norm  [7, 13]. In the recent version of DECIPHER (December 2016) and the Signature Genomic Laboratories dataset  [84] , we found ten additional patients (Additional file 1: Supplementary note 9).