dataesr/scientific-paragraphs-categorization huggingface dataset:
For ancestral host-range reconstruction (order, family and genus levels), a maximum likelihood (ML) inferred tree, for all major genetic groups of B. tabaci and two related outgroups (Bemisia afer and Dialeurodes citri), was first produced (Supporting Information Figure  S1a ). The mtCOI nucleotide sequences were downloaded from the GENBANK database, clustered with CD-HIT  (Fu, Niu, Zhu, Wu, & Li, 2012)  at 98% identity, and a cluster representative for each major group was selected (in case where more than one cluster was obtained, a single representative was selected). A codon-based alignment was performed with the REVTRANS2. 0 web server  (Wernersson & Pedersen, 2003) , and IQ-TREE  (Nguyen, Schmidt, von Haeseler, & Minh, 2014)  was used to calculate the best codon model (MGK+F3X4+R2) and ML tree (5,000 ultrafast bootstraps and 5,000 SH-aLRT).
The host plant ranges of the last common ancestors (LCAs) of the different B. tabaci species were estimated with the ace function (type = "discrete," method = "ML," CI = T, marginal = F) in the APE package  (Paradis et al. , 2004 ) from R using the information from Supporting Information Table  S1. The resulting tree, together with the corresponding presence/absence matrix of order or genus host usage, was used for the ML (marginal) and maximum parsimony (MP) ancestral states reconstruction, using the ace (equal rate) and MPR functions of the APE package, respectively (R software). The Solanaceae/Solanales seems to be one of the ancient host families/orders of B. tabaci, common to many species in the complex (Supporting Information Figure  S1b ), but this observation relates mostly to the Solanum genus.
| Performance assay Groups of 50 B. tabaci adults, from each of the six species reared on eggplant, were transferred 1-3 days after emergence, to one of the four host plants (eggplant, pepper, kale and cassava), at the 5to 8-leaf stage. The adults fed on the four host plants for 24 hr, after which the proportion of survivors was recorded. Proportional data were arcsin-square root transformed. Two-way ANOVA followed by sequential Bonferroni comparisons, using the conservative Dunn-Sidak method  (Sokal & Rohlf, 1995) , was carried out to compare the mean survival rate of the six species on the four plant hosts.
B. tabaci Raw data from several B. tabaci transcriptomes were downloaded: MEAM1: SRX022878, SRA036954 and SRR835757; MED-Q1: SRX018661, SRR316271, SRR835756 and PRJNA293094; and Asia II-3: SRR062575. Transcriptomes were assembled with TRINITY V2. 0. 6 adapted to pair-end libraries (with the exception of SRA036954, which was assembled as single end) and with the following options "trimmomatic" and "normalize_reads" activated. Re-assembled transcriptomes were deposited in an in-house Galaxy server to perform the following steps. The sequences were clustered by CD-HIT-EST at 95% identity, considering the sequences belonging to the same cluster as allelic variants. For sequence annotation, a blastx similarity search against the NCBI protein database nr (e-value threshold 10 -6 ) was performed, keeping the accession number of the top hit in the insect model species: Drosophila melanogaster, Helicoverpa armigera and Acyrthosiphon pisum (Supporting Information Table  S2 ).
| Gene expression analysis The reads obtained were then subjected to quality control using the FASTQC software (http://www. bioinformatics. babraham. ac. uk/projec ts/fastqc/). For mapping and expression analysis, a reference backbone of 46,898 genes data set, established for MED-Q1, was used (provided by Prof. Xiao-Wei Wang, Zhejiang University, China). The data set was manually curated to include the consensus detoxification gene data set described above. The reads were mapped using RSEM (reads per kilobase of transcript per million mapped reads) v1. 2. 18. The transcript reference was first prepared (rsem-preparereference), followed by rsem-calculate-expression with the parameter Bowtie2. The percentage of mapped reads ranged from 41 to 78. Genes that did not have at least 10 reads in 4% of the samples were filtered out.
| Verification of differential expression by real- time polymerase chain reaction The expression levels of nine detoxification genes that were differentially or equally expressed by the RNA-Seq approach were validated using quantitative reverse transcription-PCR (qRT-PCR). Comparisons were made between the SSA1-SG3 and MEAM1 species, which showed the highest (98. 33%) and lowest (95. 58%) gene identity to the curated data set. The technical details of the qRT-PCR analyses appear in Supporting Information Table  S3. A perfect match was observed between the RNA-Seq and qRT-PCR analyses (all qRT-PCR results are summarized in Supporting Information Table  S4 ).
| Do all the species of the B. tabaci complex share a common host range? We first conducted a literature survey in which we documented all of the major sampling efforts of B. tabaci. Clustering analysis, at the botanical family level (Figure  1a ), indicated that one species in the complex (MEAM1) can be considered as a true "generalist," four species can be considered to be species with "extended" host ranges (Asia I, Indian Ocean, MED and SSA-1), while the others can be roughly divided into two groups based on their characteristics of being with a more or less restricted host range. Mapping the data to the host plant orders (Figure  1b ) allowed the identification of nine orders (belonging to the Asterids and Rosids clades) that are commonly shared by most B. tabaci species: Asterales, Fabales, Rosales, Cucurbitales, Malvales, Malpighiales, Brassicales, Solanales and Lamiales.
For each listed gene, the Bta number or accession number in the B. tabaci MEAM1 genome database  (Chen et al. , 2016)  is provided in Supporting Information Table  S2. The circular plot was made with circos [Colour figure can be viewed at wileyonlinelibrary. com] 3. 6 | Do B. tabaci species share a common "essential detoxification machinery"? It has been predicted that more specialized species retain an essential "machinery" that allows some level of utilization of host plants that have been lost from the species repertoire  (Nylin & Janz, 2009). We asked, therefore, whether a common response in detoxification gene expression exists in B. tabaci. To avoid considering nonconsistent changes from which a clear pattern cannot be obtained, only genes differentially expressed in more than one species and showing the same expression pattern (upregulation or downregulation) in at least two species were considered.
Second, our literature survey included multiple sampling attempts from every relevant continent spanning more than 20 years. Therefore, we consider the effect of sampling bias negligible. Still, for some specific species, especially in the Asia II and China genetic groups, more data are required in order to determine the species "true" host range. In addition, it is also possible that some species in the complex, identified in the literature survey as having an "extended" host range, are in fact a mixture of individual genotypes that are adapted to certain plant types  (Loxdale & Harvey, 2016). This did not affect our study, as each species was represented by one genotype (specific mtCOI barcode), but should be taken into consideration when comparisons to other studies are made in the future.
The identity of the six species was verified using their mtCOI DNA sequences (deposited in study accession number PRJEB21948). At least 2 months (~3-4 generations) before starting the experiments, ~500 founders from each of the six colonies were transferred to eggplant, to allow them to establish a common baseline host plant. Colonies were reared under standard conditions of 28 ± 2°C, 60% humidity and a 14:10-hr light:dark cycle. The selection of the experimental host plants was based on the results of the literature survey and the host reconstruction analysis, which identified common host plants, shared by many B. tabaci species, and noncommon host plants, that are utilized by only few species. Based on this, four host plants were selected, eggplant, a common host (Solanum melongena, cv. Black Beauty, Solanaceae/ Solanales), and three noncommon host plants, also known to produce toxic phytotoxins: pepper (Capsicum annuum, cv.
ACKNOWLEDGEMENTS We thank  Drs. Pnina Moshitzky  and  Rita Mozes-koch ,  Galit Eakteiman ,  Ofer Aidlin Harari  and  Ksenia Juravel  for their excellent assistance. We also thank four anonymous reviewers for their excellent comments on earlier versions of this manuscript and the subject-editor  Tatiana Giraud  for her inspiring and punctilious editorial work. This work was supported by the  Israel Science Foundation  grant  1039/12  to SM and the  Natural Resources Institute, University of Greenwich , from a grant provided by the  Bill & Melinda Gates Foundation  (Grant Agreement Number  OPP1058938 ).
At each site, a textural description of the debris was made, and digital photographs were taken before and after emplacement of the sensors (Figure  2 ). The iButton temperature sensors at Sites 7 to 13 were placed within a 90 × 90 m area to investigate variability in T s across an area typical of the resolution of remotely sensed thermal satellite data (e. g. ASTER) often used for supraglacial debris thickness mapping. On retrieval of the iButton temperature sensors at the end of the monsoon season, comparison with the initial site photographs was used to evaluate any surface change at each site. For all 16 sites reported, the debris showed little or no disruption after sensor installation, and none of the temperature sensors were exposed at the time of collection. A further 42 iButton sensors were installed on the glacier surface but, due to topographic change during the monsoon season, they could neither be located or retrieved.
Ancillary data Clast size and lithology Clast size at each site was estimated from 18. 0 Mpix digital site photographs acquired using a Canon 550D camera and processed in ImageJ, v. 1. 49  (Rasband, 2008) , following the method outlined by  Igathinathane et al. (2009). At all sites, images covered approximately 1 m 2 and a known scale in each photograph was used to define the metre: pixel ratio. Clasts were selected using a random sampling method. For each site photo, every clast identified was assigned a number, and a random number generator was used to subsample 25 clasts for measurement within ImageJ. Assuming from the 2D imagery that the long and intermediate clast axes were visible, the intermediate axis length was retrieved and a mean representative clast size for each site calculated (Table  I ).
Where the intermediate axis of a clast was larger than the photo (Sites 9 and 13) the maximum length measurable from the scaled image was used. Clast lithology was determined in the field using clast size, colour and mineral composition. Two major lithologies were identified; granite and schist. The dominant lithology at each site (Table  I ) was determined by manually classifying the lithology of all clasts in each of the site photographs in ImageJ and then calculating the percentage of granite for each site  (Solano et al. , 2016). Local meteorological data Meteorological data were collected at four locations: on the debris-covered glacier surface of Khumbu Glacier at an elevation of 4950 m a. s. l. (Figure  1(c )); at the Pyramid Observatory (Figure  1 (b); 27°57 0 32" N, 86°48'47" E; 5050 m a.
2°C. The on-glacier thermistor probe was placed in a naturally aspirated radiation shield mounted on a tripod 1 m above the debris surface. T aG was resampled to give hourly values corresponding to the resolution of the T s data. Incoming shortwave (SW in ) and longwave (LW in ) radiation (Kipp&Zonen CNR4 sensor, 1. 0 m above debris surface, stated accuracy ±3%) and relative humidity data (RH: Vaisala HMP45C sensor, 2. 15 m above debris surface, stated accuracy ±2%) were recorded at an automatic weather station at the Changri Nup Glacier. Meteorological data from the Changri Nup station were collected at 30 min intervals and resampled to 1 h resolution using an hourly mean algorithm. Precipitation (P) was measured using a Geonor T-200 all-weather rain gauge at the Pheriche site where summer precipitation predominantly occurs as rainfall; these data were corrected for undercatch of solid precipitation using air temperature and wind speed  (Sherpa et al.
Acknowledgements-Fieldwork was funded by the  British Society for Geomorphology  and a  Royal Society  ( Research Grant   RG120393  to AVR and DJQ). Thank you to our Nepalese guides, Karma,  Karma Tindu  and  Rajesh  for their invaluable help during fieldwork. Also thanks to  Dr P. Porter ,  University of Hertfordshire  for the loan of field equipment and  Owen King  for the use of the corrected SETSM DEM. We thank the anonymous reviewers for their constructive and thorough comments that have much improved this manuscript.
Bioinformatic analyses Raw reads were de-multiplexed based on individual barcode information and subsequently end-clipped to 95 bp to homogenize read length after removing barcodes of different lengths. Read quality filtering was performed using the sliding window approach implemented in the module pro-cess_radtags from the stacks pipeline  (Catchen et al. 2013). This allowed us to exclude reads in which the average quality of 15 adjacent bases fell below a raw phred score of 10. Retained read were then aligned to a draft assembly of the S. senegalensis genome (98,590 scaffolds, total length 740 Mb, N50 contig length 10,767 bp,  Manchado et al. 2016 ) using bowtie 2v. 2. 1. 0 (Langmead and Salzberg 2012)  with the --very-sensitive option in --end-to-end mode. In order to take into account both the level of divergence among species and the possibility of introgression while accounting for hidden paralogy, we used a subset of individuals to empirically determine the optimal maximum number of mismatches allowed between aligned reads and the reference genome, following a procedure described previously  (Le Moan et al.
01. Next, we excluded loci displaying more than 20% of missing genotypes in at least one locality. Over the 116,385 remaining SNPs, we randomly selected one single SNP for each pair of RAD loci associated with the same restriction site (i. e. , within a distance less than 200 bp), in order to limit the impact of linkage disequilibrium. Finally, we only retained loci with available sequence data in the outgroup species S. solea, which had to be fixed for the polymorphic site found in S. senegalensis and S. aegyptiaca. This resulted in a final dataset containing 10,758 independent SNPs that were specifically filtered to comply with the δaδi analysis requirements. The same SNPs were used in all subsequent analyses unless specified otherwise.
Genetic structure and hybridization Genetic variation within and among S. senegalensis and S. aegyptiaca was characterized with a principal component analysis (PCA) performed with the R package Adegenet  (Jombart 2008; Jombart and Ahmed 2011). In order to document the possible existence of population structure within each species and distinguish parental and admixed genotypes, we estimated individual admixture proportions from two to four differentiated genetic clusters that were inferred from the data with no a priori information on individual membership, using fastStructure  (Raj et al. 2014)  with 10 8 iterations. In addition, we estimated the proportion of alleles with S. aegyptiaca ancestry for each individual, using the R package Introgress (Gompert and Buerkle 2010) to estimate a Hybrid Index.
3a ). The size of the JAFS was projected down to 40 sampled chromosomes per species to account for missing data. For each model, we estimated the parameter values that maximize likelihood using two successive SA procedures before quasi-Newton (BFGS) optimization  (Tine et al. 2014). Comparisons among models was made using the Akaike information criterion (AIC) to account for variation in the number of parameters among models. A total of 20 independent runs were used for the optimization of each model. Parameter uncertainties were estimated from non-parametric bootstrapped data using the Godambe information matrix as implemented in δaδi. We used 1000 bootstrapped datasets to estimate confidence intervals as the maximum likelihood parameter value ±1. 96 × SE. Estimated parameter values were converted into biologically meaningful quantities by taking into account our SNP filtering procedures following the method in  Rougeux et al.
3c ). The best-fit SC2m model estimated that only 5% of the loci can still introgress between species with effective migration rates m 1-2 and m 2-1 , whereas the remaining 95% of loci experience a highly reduced introgression rate with effective migration rates m′ 1-2 and m′ 2-1. We used estimated model parameters to perform coalescent simulations with msms (Ewing and Hermisson 2010) under the SC model (assuming theta = 747. 202; N 1 = 0. 818; N 2 = 1. 136; T S = 4. 803; T SC = 0. 081) using two different conditions for gene flow (assuming either m 1-2 = 4. 607 and m 2-1 = 0. 381, or m′ 1-2 = 0. 057 and m′ 2-1 = 0. 153) to generate a free introgression and a reduced introgression rate dataset. One thousand JAFS with 40 sampled chromosomes per species were produced under each model. We then averaged the number of derived alleles within entries across replicates to obtain a single JAFS for both the free introgression (Y) and the reduced introgression (Φ) model (Fig.
3c ). Finally, we used the average number of SNPs per entry (i,j) in each JAFS to estimate the probability P i,j that a SNP with a derived allele count i in species 1 and j in species 2 can be obtained under the free introgression model: P i;j ¼0:05Âϒ i;j = 0:05 Â ϒ i;j þ 0:95 Â Φ i;j À Á ; ð1Þ where Y i,j and Φ i,j are the average number of SNPs predicted in the JAFS entry (i,j) under the free introgression and reduced introgression model, respectively. Every SNP from the real dataset was finally associated to the introgression probability P i,j given by Eq. (1) based on its corresponding entry in the JAFS.
Geographic clines The geographic cline analysis was carried out in order to link allele frequencies at individual loci with geographic position along a transect spanning the hybrid zone. We used the R package HZAR  (Derryberry et al. 2014 ) that fits allele frequency data to classic equilibrium models of geographic clines  (Szymura and Barton 1986 ) using the MCMC algorithm. Instead of searching for the best model separately for each cline, we used the full model that fits cline center, width, and independent introgression tails using estimated values for minimum (p min ) and maximum (p max ) allele frequencies. In this way, we avoided the potentially confounding effects of fitting different models for the comparison of cline center and slope parameters across loci.
66% of genotypic variance) captured a subtle differentiation signal between the Atlantic samples from Dakar and Cadiz and the Mediterranean S. senegalensis samples (Fig. 2b ). The fastStructure analysis (Fig. 2c ) confirmed the separation of the two species into different genetic clusters and their coexistence in Bizerte lagoon, as detected from the PCA. However, it failed to detect any further genetic subdivision (Fig. S3 ), confirming that the signal of genetic structure within each species is at most very small. The finding of intermediate admixture proportions in some individuals revealed signs of introgressive hybridization between S. senegalensis and S. aegyptiaca around the contact zone. The relationship between individual hybrid index and interspecific heterozygosity represented in a triangle plot illustrated well the absence of F1 and F2 hybrids in our dataset, and the presence of a few backcrosses together with several introgressed genotypes (i.
Links between different approaches Linear regression models were used to investigate whether different analyses of individual locus introgression that use different aspects of the data tend to produce similar results. First, we evaluated the extent to which an excess of ancestry from a given species relates to a spatial shift of cline center into the other species range. The genomic cline parameter α showed a significant positive correlation with the geographic cline center C on both sides of the hybrid zone (Fig. 5 ). The correlation was, however, much stronger in the S. aegyptiaca side (R² c-α = 0. 274, P < 10 -10 ) compared to the S. senegalensis side where it was barely detectable (R² c-α = 0. 023, P < 10 -10 ). We then tested whether the loci exhibiting the most abrupt transition in ancestry between the two species (i.
Acknowledgements This work was conducted during the Ph. D. thesis of AS with the financial support of program  PHC  Maghreb No. XE27961  from the  French Ministère des Affaires Etrangères to FB and Ministère de l'Enseignement Supérieur et de la Recherche  to LB-S. The authors are indebted to  M-T Augé  for skillful technical assistance and thank  Nicolas Bierne  for insightful discussions. Compliance with ethical standards.
IKKβ expression is enhanced in breast cancer bone metastasis The canonical NFκB signaling pathway promotes breast cancer bone metastasis  [10, 22]. Given that IKKβ is a key component of this pathway, we first assessed its expression in clinical datasets of human primary breast carcinoma and bone biopsies from breast cancer patients with known clinical outcome (Figure  1 ). A retrospective analysis of a cohort of patients with metastatic breast cancer  [23]  revealed that mRNA expression of IKKβ is elevated in primary breast carcinoma (Figure  1A , p = 0. 00032, n = 185) and it is strongly associated with disease recurrence (Figure  1B , left panel, p = 0. 0001, n = 560) and bone metastasis relapse (Figure  1B , right panel, p = 0. 03, n = 560). To further evaluate the clinical importance and potential role of IKKβ in breast cancer bone metastasis, we performed detailed immunohistochemical assessment of IKKβ expression in breast tumours and lytic lesions in bone in biopsies from breast cancer patients who developed bone metastasis.
RNA extraction and microarray Ribonucleic acid (RNA) was extracted using RNeasy Mini Kit with RNAse-free DNAse treatment (74014, Qiagen Crawley, United Kingdom), according the manufacturer's instructions. 500 ng of total RNA from each sample was amplified and biotinylated using Illumina ® TotalPrep RNA Amplification Kit (Ambion, AMIL1791), according the manufacturer's instructions, and then quantified using a Bioanalyser 2100. 750 ng of high-quality labeled cRNA per sample was hybridized to Illumina HumanHT-12v3 Expression Beadarrays in triplicate (Illumina, Cambridge, United Kingdom) using Whole-Genome Expression Direct Hybridisation kit (Illumina) and scanned with a 500GX (Illumina). The raw and normalized microarray data have been deposited in the Gene Expression Omnibus (GEO) database under accession number GSE71444.
Analysis of clinical data Retrospective analysis of IKKβ expression with respect to primary tumour growth and bone relapse was conducted using published cohorts of data. The MSK82, EMC192 and EMC286 datasets were directly integrated with batch-correction using ComBat. Exhaustive survival analysis was performed by considering all possible cut-points using the survivALL package. P values were derived from Wilcoxon test and were twotailed. Immunohistochemical analysis of IKKβ was also performed in cohort of 13 primary breast carcinomas and 5 matched bone metastasis samples obtained from the Pathology Unit, San Salvatore hospital (L'Aquila, Italy), with informed consent from all subjects.