dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). To narrow down the list of relevant GO terms, and to use them as a tool to find candidates, we used a twosided Fisher's exact test (false discovery rate (FDR) <0. 05) to determine which functional GO categories were enriched in the genes over-expressed in the egg-spot in comparison to the total embryonic transcriptome. Five categories were significantly enriched in our overexpression gene dataset: 'Pigmentation' (GO:0043473), Fig. 2  Gene ontology (GO) ID representations: (Biological process, Molecular function and Cellular component) for both over-expressed and under-expressed genes in the egg-spot tissue. Asterisks (in legend) denote significant differences in proportion of genes between the two datasets, as shown by chi-squared test (p-value < 0. 05) 'Developmental pigmentation' (GO:0048066), 'G-protein coupled peptide receptor activity' (GO:0008528), 'Peptide receptor activity' (GO:0001653) and 'Cell adhesion molecule binding' (GO:0050839) (Fig.
Potential lineage specific genes are DE in the egg-spot How novel traits emerge and are modified is one of the many unresolved problems in evolutionary biology  [39] [40] [41]. It has long been advocated that new traits can emerge via the co-option of conserved regulators  [42]. More recently, however, evidence is accumulating that new, i. e. lineage specific, genes can also play an important role in the development of novel traits  [43] [44] [45]. Around 41 % of our candidate contigs did not have a BLAST hit against the D. rerio protein database. This could be due to the incompleteness of this database or to the lack of homologs in this species. To control for these factors we performed BLASTx and tBLASTx searches against the NCBI non-redundant (nr) protein and nucleotide databases  [30].
Around 15. 5 % (191/ 1229) of the DE contigs could not be assigned to a specific gene present in either nr database (Additional file 1). The contigs without positive BLAST hits could represent non-coding RNAs, partial sequences of known genes that could not be identified, or lineage specific genes (new or fast evolving genes)  [46]. These results add to previous work on comparative transcriptomics of East African cichlids reporting that only 51 % of the total transcriptomes of the species studied (A. burtoni and Ophthalmotilapia ventralis) have hits on the NCBI nr nucleotide database  [46]. In our case, the reduction in percentage of non-identified contigs is, most probably, due to the recent availability of five cichlid genomes  [29]. It has previously been shown that lineage specific genes might play a role in the emergence and development of novel traits.
Rates of evolution of the egg-spot DE genes Changes in gene function can result either from modification in a cis-regulatory element that changes gene expression pattern and timing, and/or from a modification in the protein sequence that alters its function  [47] [48] [49] [50]. To test for protein sequence evolution in the egg-spot DE genes we calculated the rates of evolution in the form of dN/dS (ratio of non-synonymous substitutions over synonymous substitutions) of this gene dataset and compared the values obtained with a previously Fig. 3  Enrichment of functional GO terms in the egg-spot over-expressed genes (yellow bar) when compared to the total transcriptome of A. burtoni (blue bar). Those were calculated with a two-tailed Fisher exact test (FDR < 0. 05) published dataset that estimated transcriptome-wide dN/dS values between cichlid species  [46].
Overall, there was no obvious trend with respect to functional GO categories associated with the top DE genes (see Additional file 2). Note that six out of the 46 candidates remained unidentified after tBLASTx searches against a non-redundant NCBI database. While the egg-spots of A. burtoni are located in the proximal region of the anal fin, C. pulpican has its eggspots in the distal region of the anal fin. By measuring the expression of these genes in this species, we effectively control for positional effects in gene expression along the proximal-distal axis. We also aimed to determine whether egg-spots and blotches share a conserved gene expression profile, which would indicate a common origin of these two traits. We thus tested if the candidate genes identified in A. burtoni had similar expression levels in the blotches of a basal haplochromine species (Pseudocrenilabrus philander) and in the blotches of a member of a distinct cichlid tribe, an ectodine species (Callochromis macrops), where this trait has likely evolved independently.
Included in the list are five of the unidentified contigs. The remaining nine genes that were over-expressed in the egg-spots of A. burtoni either showed no difference in expression (4) or were under-expressed (5) in the eggspots of C. pulpican (Fig. 5a ). These genes are most likely involved in fin rather than egg-spot patterning, as Fig. 4  Rates of evolution (dN/dS) for the over-expressed genes (yellow bar), under-expressed genes (blue bar) and for a previously published dataset that estimated transcriptome-wide dN/dS values between cichlid species. No significant difference was detected between the over-and under-expressed dataset, although both had significantly higher dN/dS than the transcriptome average (as determined by t-test, p-value < 0. 01). Error bars denote standard error of the mean suggested by the fact that three of these of genes are known to participate in fin development (retinol binding protein 7 (rbp7), retinol binding protein 4 (rbp4) and insulin-like growth factor 1 (igf1))  [62] [63] [64].
5a, b ). None of the 14 genes that were overexpressed in both A. burtoni and C. pulpican egg-spots were over-expressed in the blotch of P. philander (Fig. 5a ), and only four of the 15 genes under-expressed in the two modern haplochromines were also underexpressed in P. philander (Fig. 5b ). Although not conclusive, the poorly conserved expression pattern between the two traits suggests that the haplochromines' egg-spots and the blotches have emerged independently within the Haplochromini lineage. These results have to be taken with caution, though, as haplochromine egg-spots could have evolved from   4 ). In the first column are the RNAseq results for A. burtoni. In the second, third and fourth column are the results for C. pulpican, P. philander and C. macrops respectively. b Results for egg-spot under-expression dataset (Table  5 ).
The individual RSEM files were concatenated into one single dataset and analyzed using the Bioconductor R package EdgeR  [75]. Transcripts that had less than one count per million in one of the samples were discarded. We tested for differential expression between egg-spot and anal fin samples, using anal fin as reference. Since the samples were paired (each replicate of the egg-spot and anal fin belong to one individual fish), we included the individual information in the statistical model. For that we used a negative binomial generalized linear model (GLM) based on common dispersion using the individual as the blocking factor, i. e. we tested for consistent differences in expression between egg-spot and anal fin within individuals. Transcripts were considered as DE if, after correction for multiple testing, the false discovery rate (FDR) was lower than 0.
Functional annotation of differential transcripts Gene ontology (GO)  [77]  annotation of the differential expressed transcripts was conducted with Blast2GO version 2. 5. 0  [32]. BLASTx searches were done against the Danio rerio protein database using a threshold of e -5 and maximum number of hits of 20. These GO terms were used to estimate transcript function. A table with the list of the differential expressed transcripts, their respective values of expression, and their GO terms is provided in Additional file 1. Between dataset differences in the proportion of genes for individual level 2 GO terms were tested by means of chi-squared tests with p-values adjusted for multiple tests using Bonferroni corrections  [78]. The enrichment of functional GO terms in the eggspot over-expressed gene dataset was calculated with a two-sided Fisher's exact test with a FDR of 0.
Rates of evolution for the differential expressed transcripts Transcriptome data from the five available cichlid species (Pundamila nyererei, Neolamprologus brichardi, Oreochromis niloticus, Maylandia zebra, and Astatotilapia burtoni) were downloaded from Broad Institute  [29]. Each species' transcriptome consisted of multiple libraries that were concatenated. The 1229 DE genes from A. burtoni were compared using a BLASTn search (threshold: e -50 ) against each species' transcriptome and DE genes with a hit in all cichlid species were retained (599). The 599 DE genes were then compared using BLASTx (threshold: e -20 ) against the tilapia (Oreochromis niloticus) proteome from the ENSEMBL database and corresponding coding sequences (cds) retrieved (378). Finally, the database of 378 tilapia cds was queried against the individual cichlid transcriptomes using BLASTn (threshold: e -35 ).
BLAST outputs were parsed and filtered to retain hits with identity >90 %, length >200 bp and bit score >200. We obtained 298 tilapia cds that have at least a hit on all cichlid transcriptomes. A concatenated fasta file was built to include the ten top hits from each cichlid transcriptome and the 298 tilapia cds. Sequences were then aligned using MAFFT v7. 245  [79]  with einsi -adjustdirection options (einsi is suitable for sequences containing large unalignable regions, as expected with the presence of UTRs (untranslated regions) and splicing variants in our transcriptome data). Alignments were trimmed using the tilapia cds as a reference and visually inspected. Alignments with paralogous sequences resulting from recent duplications were discarded. Within each individual alignment a consensus was built across transcripts from each cichlid species with 'cons' from EMBOSS  [80]  (-plurality 1.
5, indicating the cut-off for the number of positive matches below which there is no consensus). Alignments were then translated to proteins and checked for all sequences being in the corresponding tilapia reading frame (no stop codons). The whole pipeline was run with customized R and Unix scripts. We obtained 196 good alignments, 74 % of which comprised of all five cichlid species sequences, while the remaining included at least three species each. Average alignment length was 1716 bp, ranging from 270 to 7794 bp. Alignments are available from the author upon request. dN/dS estimates were calculated using the script kaks. pl in Bioperl  [81]  which computes the dN/dS for all sequence pairs, using the Nei-Gojobori method  [82].
Acknowledgments We would like to thank  Anya Theis  for the fish photographs;  Adrian Indermaur ,  Anya Theis ,  Bernd Egger ,  Judith Bachman  and  Fabrizia Ronco  for the sampling of Pseudocrenilabrus philander. This study was financially supported by a doctoral research fellowship from the  Fundação para a Ciência e Tecnologia -FCT  ( SFRH/BD/43421/2008 ) to MES and the  European Research Council -ERC  ( Starting Grant 'INTERGENADAPT' and Consolidator Grant  ' CICHLID ~X ') to WS.
To identify field-level niches where soyabean management options perform best, experiments were conducted on 205 farmers' fields in 2011 and on 222 fields in 2012 in northern Ghana. Each farmer implemented a single replicate of four treatments (10m × 10m each) with the promiscuously nodulating variety TGx 1448-2E: (i) a control plot without the use of inputs, (ii) a plot with P fertilizer application only (20 kg P ha -1 applied as TSP), (iii) a plot where seeds were inoculated with rhizobia, and (iv) a plot with both P fertilizer and inoculation. Yield data were recorded by weighing the whole plot grain harvest at the end of the season, and data on agronomic management and socio-economic background of the farmers were also recorded. Restricted maximum likelihood (REML) mixed model analysis was performed to assess the impact of agronomic parameters on yield, using region, year and site as factors in the random model.
Common forcing fields To focus on the impact of resolution on the design of the HighResMIP, simulations should minimize the difference in forcings and model set-up that would hamper the interpretation of the results. Most of the forcing fields are the same as those used in the CMIP6 Historical Simulation that are described separately in this Special Issue  (Eyring et al. , 2016)  and are provided via the CMIP6 data portal. For the future time period, GHG and aerosol concentrations from a high-end emission scenario of the Shared Socioeconomic Pathways (SSPs) will be prescribed, which in the following will be denoted by SSPx. A summary of the differences in forcing between the CMIP6 AMIPII protocol and the Tier 1 and 2 simulations is given in Table  1.
Country T. By applying this kind of methodology, we believe that the sustainability of the ETRS89 realizations at national level can be extended substantially, to the benefit for both end users as well as authorities responsible of the national geodetic reference frames. X [m] T Y [m] T Z [m] D [ppb] R X [mas] R Y [mas] R Z [ Country T X [m] T Y [m] T Z [m] D [ppb] R X [mas] R Y [mas] R Z [ We have defined and introduced transformation parameters to align the velocities of the NKG RF03vel model accurately to the ETRF2000 velocities. The intraplate corrections to be applied in the transformation should be taken from the re-aligned NKG RF03vel model. However, we have not introduced nor published a corresponding new model (e. g. grid files) from the old NKG RF03vel model since a new model with amended geodetic data is in plans of the Nordic Geodetic Commission in the near future.
We decipher the resistome of Chryseobacterium indologenes MARS15, an emerging multidrug-resistant clinical strain, using the whole genome sequencing strategy. The bacterium was isolated from the sputum of a hospitalized patient with cystic fibrosis in the Timone Hospital in Marseille, France. Genome sequencing was done with Illumina MiSeq using a paired-end strategy. The in silico analysis was done by RAST, the resistome by the ARG-ANNOT database and detection of polyketide synthase (PKS) by ANTISMAH. The genome size of C. indologenes MARS15 is 4 972 580 bp with 36. 4% GC content. This multidrug-resistant bacterium was resistant to all β-lactams, including imipenem, and also to colistin. The resistome of C. indologenes MARS15 includes Ambler class A and B β-lactams encoding bla CIA and bla IND-2 genes and MBL (metallo-β-lactamase) genes, the CAT (chloramphenicol acetyltransferase) gene and the multidrug efflux pump AcrB.
Genome annotation The annotation was performed by Rapid Annotation using the Subsytem Technology (RAST) bioserver  [22]. Gene Ontology (GO) in terms of Biological Process and Molecular Functions and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were considered. Detection of plasmid was performed by PlasmidFinder  [23]. The Antibiotic Resistance Gene ANNotation (ARG-ANNOT database) was used to improve the annotation of the antibiotic resistance gene  [24]. The intergenomic distances between genome sequences were determined from fully or partially sequenced genomes using average nucleotide identity and the percentage of conserved DNA (minimum identity, 70%)  [25]. The genome of C. indologenes MARS15 was compared to the complete genome of Chryseobacterium sp. stRB126 (accession no.
NZ_AP014624) and to the related genomes C. indologenes NBRC 14944 (accession no. NZ_BAVL00000000. 1) and C. indologenes J31 (accession no. NZ_LAZY00000000. 1). The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment. RNAs were found by using RNAmmer  [26]. PHAST (PHAge search Tool) was used to identify phage sequences  [27]. The exhaustive bacteriocin database available in our laboratories (Bacteriocins from the URMITE database; http:// drissifatima. wix. com/bacteriocins) was performed by collecting all currently available sequences from the databases and from the National Center for Biotechnology Information. Protein sequences from this database allowed putative bacteriocins from human gut microbiota to be identified using the BLASTp methodology  [28].
Genome features The genome size of C. indologenes MARS15 was 4 972 580 bp with 36. 4% GC content and assembled into 381 scaffolds. No plasmid was detected. A total of 4592 open reading frames (ORFs) were predicted, including 1754 (38%) ORFs annotated as hypothetical proteins and 69 RNAs (one 23S rRNA, one 16S rRNA and four 5S rRNA). Of the 4592 ORFs, 3072 were assigned a putative function (by the Clusters of Orthologous Groups (COGs) database), and 260 genes were identified with an unknown function. Moreover, it contained a complete prophage 57 kb in size and 35. 5% GC content (Table  1 ). Comparative analysis of genomes of C. indologenes MARS15 reveals more similarity of genome size, GC content, average nucleotide identity (Table  1 ) and percentage protein sequence identity (Fig. 3 ) between the clinical strain C.
Their maximaat which chrono-amperometric polarizations are often carried out to speed up reduction are situated at about 0 and -1 V/Ag-AgCl. Secondly, we investigated how the previous parameters varied between the electrolytic pencils. To compare the data from the drop cell and the second pencil prototype, voltammetric plots were acquired from a seventeenth century gilded silver candlestick   (Fig. 12 ). Measurements were again taken in static mode. To enable comparison, the plots presented in Fig. 13  are given versus the vitreous carbon reference electrode (0. 06 V/Ag-AgCl). The open-circuit potential of the candlestick with the drop cell is situated at about 0. 19 V/vitreous carbon, while it is around 0. 08 V/vitreous carbon for the prototype 2 pencil (Fig. 13 ). It appears that without cathodic polarization, the prototype 2 pencil induces a lower corrosion potential (by about 0.
Acknowledgements This project would not have been possible without the financial support received from the  HES-SO  ( University of Applied Sciences, Western Switzerland ), which the authors wish to sincerely thank. Thanks are also due to the  Conservation Research Unit at the University of Applied Sciences Arc (UR-Arc CR)  for their administrative and technical assistance. We would also like to thank the community at  Saint-Maurice Abbey  and more particularly  Abbot Joseph Roduit. Abbot Roduit  helped us investigate and work on some of the most prestigious masterpieces of the Treasury. Throughout the project he showed a genuine interest in our work and an enthusiasm for the new technologies that we brought inside the quiet atmosphere of the  Abbey.
Genetic epidemiological studies We successfully genotyped 902 SNPs across a 1-Mb region containing ESR1 in 50 casecontrol studies from populations of European (89,050 participants) and Asian (12,893  participants) ancestry in BCAC, together with 15,252 BRCA1 mutation carriers in CIMBA. Mammographic density measures were available for 6,979 women from the BCAC studies and an additional 1,621 women from the MODE Consortium, who had also been genotyped using the iCOGS array. Subsequently, the genotypes of additional variants with minor allele frequency (MAF) >2% were imputed in all European-ancestry participants, using data from the 1000 Genomes Project as a reference. In total, data from 3,872 genotyped or imputed (imputation info score >0. 3) SNPs were analyzed. Results for all SNPs associated with overall breast cancer risk (P < 1 × 10 -4 ) are presented in Supplementary Table  1.
(iv) Expression quantitative trait locus (eQTL) analysis using the Gene-Tissue Expression (GTEx) database identified a significant association for SNPs in signal 3 with CCDC170 expression in normal breast tissues (Supplementary Table  10 ). We also performed cis-eQTL analyses on the 12 flanking genes in 135 normal breast tissue samples from the METABRIC study; however, no additional associations were detected (Supplementary Table  11 ).
Study populations and genotyping Epidemiological data were obtained from three separate consortia that had all conducted genotyping using the iCOGS array, a custom array comprising approximately 200,000 SNPs. (i) Data on overall breast cancer risk, tumor subtypes and grade came from 50 breast cancer case-control studies participating in BCAC; these comprised 41 studies from populations of European ancestry and 9 studies from populations of East Asian ancestry  3. Details of the participating studies, genotype calling and quality control are given elsewhere  3. After quality control exclusions, we analyzed data from 46,451 cases and 42,599 controls of European ancestry and 6,269 cases and 6,624 controls of Asian ancestry. A further 23 SNPs were directly genotyped in two case-control studies (CCHS and SEARCH).
The ER status of the primary tumor was available for 34,539 European and 4,972 Asian cases; of these, the tumor was ER -for 7,465 (22%) European and 1,610 (32%) Asian cases  3. (ii) Data on BRCA1 mutation carriers were obtained through CIMBA. Eligibility is restricted to females 18 years or older with pathogenic mutations in BRCA1 or BRCA2. The majority of the participants were sampled through cancer genetics clinics  37  , including some related participants. Fifty-one studies from 25 countries contributed data on BRCA1 mutation carriers who were genotyped using the iCOGS array  38. After quality control of the phenotypes and genotypes, data were available on 15,252 BRCA1 mutation carriers, of whom 7,797 had been diagnosed with breast cancer, all of European ancestry. Analyses in BRCA1 mutation carriers assessed associations with breast cancer risk.
Expression analysis eQTL analyses were conducted in 57 normal breast samples from the GTEx Project  43  and 135 adjacent normal breast samples from women of European origin in the METABRIC study  44. For the METABRIC analyses, matched gene expression (Illumina HT-12 v3 microarray) and germline SNP data from either genotyping (Affymetrix SNP 6. 0) or imputation (1000 Genomes Project, March 2012 data using IMPUTE version 2. 0) were used. Correlations between the five signal-representative SNPs and expression levels of nearby genes (500 kb upstream and downstream of the SNPs) were assessed using a linear regression model in which an additive effect on expression level was assumed for each copy of the rare allele. Calculations were carried out using the eMAP library in R.
Allele-specific expression analysis ASE analysis has been described previously  11. Three SNPs for signal 1, two SNPs for signal 3 and a proxy SNP for signal 2 (r 2 = 0. 85) were on Affymetrix SNP Array 6. 0. TCGA genotype calls and corresponding confidence scores were retrieved using level 2 TCGA SNP array Birdseed data downloaded from the TCGA portal. Genotyping data with a confidence score of 0. 1 were excluded. We selected 742 breast cancer samples with European ancestry. The corresponding RNA-seq BAM files and metadata are available from the Cancer Genomics Hub (CGHub). Marker SNPs, the exonic SNPs of the target genes, were extracted from dbSNP human Build 142 (collectively ~800 SNPs for ESR1, RMND1, ARMT1 and CCDC170), and RNA-seq read counts on SNP sites for reference and alternative alleles were computed.