132 research outputs found

    A new intra-specific and high-resolution genetic map of eggplant based on a ril population, and location of QTLS related to plant anthocyanin pigmentation and seed vigour

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    Eggplant is the second most important solanaceous berry-producing crop after tomato. Despite mapping studies based on bi-parental progenies and GWAS approaches having been performed, an eggplant intraspecific high-resolution map is still lacking. We developed a RIL population from the intraspecific cross ‘305E40’, (androgenetic introgressed line carrying the locus Rfo-Sa1 conferring Fusarium resistance) x ‘67/3’ (breeding line whose genome sequence was recently released). One hundred and sixty-three RILs were genotyped by a genotype-by-sequencing (GBS) approach, which allowed us to identify 10,361 polymorphic sites. Overall, 267 Gb of sequencing data were generated and ~773 M Illumina paired end (PE) reads were mapped against the reference sequence. A new linkage map was developed, including 7249 SNPs assigned to the 12 chromosomes and spanning 2169.23 cM, with iaci@liberoan average distance of 0.4 cM between adjacent markers. This was used to elucidate the genetic bases of seven traits related to anthocyanin content in different organs recorded in three locations as well as seed vigor. Overall, from 7 to 17 QTLs (at least one major QTL) were identified for each trait. These results demonstrate that our newly developed map supplies valuable information for QTL fine mapping, candidate gene identification, and the development of molecular markers for marker assisted selection (MAS) of favorable alleles

    Photometric and spectroscopic analysis of the Type II SN 2020jfo with a short plateau

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    We present high-cadence photometric and spectroscopic observations of SN~2020jfo in ultraviolet and optical/near-infrared bands starting from 3\sim 3 to 434\sim 434 days after the explosion, including the earliest data with the 10.4\,m GTC. SN~2020jfo is a hydrogen-rich Type II SN with a relatively short plateau duration (67.0±0.667.0 \pm 0.6 days). When compared to other Type II supernovae (SNe) of similar or shorter plateau lengths, SN~2020jfo exhibits a fainter peak absolute VV-band magnitude (MV=16.90±0.34M_V = -16.90 \pm 0.34 mag). SN~2020jfo shows significant Hα\alpha absorption in the plateau phase similar to that of typical SNe~II. The emission line of stable [Ni~II] λ\lambda7378, mostly seen in low-luminosity SNe~II, is very prominent in the nebular-phase spectra of SN~2020jfo. Using the relative strengths of [Ni~II] λ\lambda7378 and [Fe~II] λ\lambda7155, we derive the Ni/Fe production (abundance) ratio of 0.08--0.10, which is 1.5\sim 1.5 times the solar value. The progenitor mass of SN~2020jfo from nebular-phase spectral modelling and semi-analytical modelling falls in the range of 12--15\,MM_\odot. Furthermore, semi-analytical modelling suggests a massive H envelope in the progenitor of SN~2020jfo, which is unlikely for SNe~II having short plateaus.Comment: 20 pages (plus 5 pages appendix), 19 figures, Accepted for publication in MNRA

    On the coloniality of “new” mega‐infrastructure projects in east Africa

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    This article responds to a preference for short‐term history in research on the infrastructure turn by engaging with the longue durée of East Africa’s latest infrastructure scramble. It traces the history of LAPSSET in Kenya and the Central Corridor in Tanzania, revealing the coloniality of new and improved transport infrastructure along both corridors. This exercise demonstrates how the spatial visions and territorial plans of colonial administrators get built in to new infrastructure and materialise in ways that serve the interests of global capital rather than peasant and indigenous peoples being promised more modern, prosperous futures. The article concludes by suggesting that a focus on the longue durée also reveals uneven patterns of mobility and immobility set in motion during the colonial scramble for Africa and reinforced after independence. These “colonial moorings” are significant as they shape political reactions to new mega‐infrastructure projects today and constrain the emancipatory potential of infrastructure‐led development

    Securing the Downside Up: Client and Care Factors Associated with Outcomes of Secure Residential Youth Care

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    Although secure residential care has the potential of reducing young people's behavioral problems, it is often difficult to achieve positive outcomes. Research suggests that there are several common success factors of treatment, of which the client's motivation for treatment and the quality of the therapeutic relationship between clients and therapists might be especially relevant and important in the context of secure residential care. The objective of the present study was to explore the association of these potential success factors with secure residential care outcomes. A repeated measures research design was applied in the study, including a group of adolescents in a secure residential care center that was followed up on three measurements in time. Interviews and questionnaires concerning care outcomes in terms of adolescents' behavior change during care were administered to 22 adolescents and 27 group care workers. Outcomes in terms of adolescents' treatment satisfaction were assessed by the use of questionnaires, which were completed by 51 adolescents. Adolescents reported some positive changes in their treatment motivation, but those who were more likely to be motivated at admission were also more likely to deteriorate in treatment motivation from admission to departure. Treatment satisfaction was associated with better treatment motivation at admission and with a positive adolescent-group care worker relationship. The results suggest that outcomes can be improved by a more explicit treatment focus on improving the adolescent's treatment motivation and the quality of the adolescent-care worker relationship during secure residential care

    Deletion of Fibroblast Growth Factor Receptor 2 from the Peri-Wolffian Duct Stroma Leads to Ureteric Induction Abnormalities and Vesicoureteral Reflux

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    Purpose: Pax3cre-mediated deletion of fibroblast growth factor receptor 2 (Fgfr2) broadly in renal and urinary tract mesenchyme led to ureteric bud (UB) induction defects and vesicoureteral reflux (VUR), although the mechanisms were unclear. Here, we investigated whether Fgfr2 acts specifically in peri-Wolffian duct stroma (ST) to regulate UB induction and development of VUR and the mechanisms of Fgfr2 activity. Methods: We conditionally deleted Fgfr2 in ST (Fgfr2 ST-/- ) using Tbx18cre mice. To look for ureteric bud induction defects in young embryos, we assessed length and apoptosis of common nephric ducts (CNDs). We performed 3D reconstructions and histological analyses of urinary tracts of embryos and postnatal mice and cystograms in postnatal mice to test for VUR. We performed in situ hybridization and real-time PCR in young embryos to determine mechanisms underlying UB induction defects. Results: We confirmed that Fgfr2 is expressed in ST and that Fgfr2 was efficiently deleted in this tissue in Fgfr2 ST-/- mice at embryonic day (E) 10.5. E11.5 Fgfr2 ST-/- mice had randomized UB induction sites with approximately 1/3 arising too high and 1/3 too low from the Wolffian duct; however, apoptosis was unaltered in E12.5 mutant CNDs. While ureters were histologically normal, E15.5 Fgfr2 ST-/- mice exhibit improper ureteral insertion sites into the bladder, consistent with the ureteric induction defects. While ureter and bladder histology appeared normal, postnatal day (P) 1 mutants had high rates of VUR versus controls (75% versus 3%, p = 0.001) and occasionally other defects including renal hypoplasia and duplex systems. P1 mutant mice also had improper ureteral bladder insertion sites and shortened intravesicular tunnel lengths that correlated with VUR. E10.5 Fgfr2 ST-/- mice had decreases in Bmp4 mRNA in stromal tissues, suggesting a mechanism underlying the ureteric induction and VUR phenotypes. Conclusion: Mutations in FGFR2 could possibly cause VUR in humans. © 2013 Walker et al

    Coding SNPs analysis highlights genetic relationships and evolution pattern in eggplant complexes

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    [EN] Brinjal (Solanum melongena), scarlet (S. aethiopicum) and gboma (S. macrocarpon) eggplants are three Old World domesticates. The genomic DNA of a collection of accessions belonging to the three cultivated species, along with a representation of various wild relatives, was characterized for the presence of single nucleotide polymorphisms (SNPs) using a genotype-by-sequencing approach. A total of 210 million useful reads were produced and were successfully aligned to the reference eggplant genome sequence. Out of the 75,399 polymorphic sites identified among the 76 entries in study, 12,859 were associated with coding sequence. A genetic relationships analysis, supported by the output of the FastSTRUCTURE software, identified four major sub-groups as present in the germplasm panel. The first of these clustered S. aethiopicum with its wild ancestor S. anguivi; the second, S. melongena, its wild progenitor S. insanum, and its relatives S. incanum, S. lichtensteinii and S. linneanum; the third, S. macrocarpon and its wild ancestor S. dasyphyllum; and the fourth, the New World species S. sisymbriifolium, S. torvum and S. elaeagnifolium. By applying a hierarchical FastSTRUCTURE analysis on partitioned data, it was also possible to resolve the ambiguous membership of the accessions of S. campylacanthum, S. violaceum, S. lidii, S. vespertilio and S. tomentsum, as well as to genetically differentiate the three species of New World Origin. A principal coordinates analysis performed both on the entire germplasm panel and also separately on the entries belonging to sub-groups revealed a clear separation among species, although not between each of the domesticates and their respective wild ancestors. There was no clear differentiation between either distinct cultivar groups or different geographical provenance. Adopting various approaches to analyze SNP variation provided support for interpretation of results. The genotyping-by-sequencing approach showed to be highly efficient for both quantifying genetic diversity and establishing genetic relationships among and within cultivated eggplants and their wild relatives. The relevance of these results to the evolution of eggplants, as well as to their genetic improvement, is discussed.This work has been funded in part by European Unions Horizon 2020 Research and Innovation Programme under grant agreement No 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops) and by Spanish Ministerio de Economia, Industria y Competitividad and Fondo Europeo de Desarrollo Regional (grant AGL2015-64755-R from MINECO/FEDER). Funding has also been received from the initiative "Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives", which is supported by the Government of Norway. This last project is managed by the Global Crop Diversity Trust with the Millennium Seed Bank of the Royal Botanic Gardens, Kew and implemented in partnership with national and international gene banks and plant breeding institutes around the world. For further information see the project website:http://www.cwrdiversity.org/. Pietro Gramazio is grateful to Universitat Politecnica de Valencia for a pre-doctoral (Programa FPI de la UPV-Subprograma 1/2013 call) contract. Mariola Plazas is grateful to Spanish Ministerio de Economia, Industria y Competitividad for a post-doctoral grant within the Santiago Grisolia Programme (FCJI-2015-24835). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Acquadro, A.; Barchi, L.; Gramazio, P.; Portis, E.; Vilanova Navarro, S.; Comino, C.; Plazas Ávila, MDLO.... (2017). Coding SNPs analysis highlights genetic relationships and evolution pattern in eggplant complexes. PLoS ONE. 12(7). https://doi.org/10.1371/journal.pone.0180774Se018077412

    Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants

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    [EN] Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers are amongst the most common markers of choice for studies of diversity and relationships in horticultural species. We have used 11 SSR and 35 SNP markers derived from transcriptome sequencing projects to fingerprint 48 accessions of a collection of brinjal (Solanum melongena), gboma (S. macrocarpon) and scarlet (S. aethiopicum) eggplant complexes, which also include their respective wild relatives S. incanum, S. dasyphyllum and S. anguivi. All SSR and SNP markers were polymorphic and 34 and 36 different genetic fingerprints were obtained with SSRs and SNPs, respectively. When combining both markers all accessions but two had different genetic profiles. Although on average SSRs were more informative than SNPs, with a higher number of alleles, genotypes and polymorphic information content (PIC), and expected heterozygosity (He) values, SNPs have proved highly informative in our materials. Low observed heterozygosity (Ho) and high fixation index (f) values confirm the high degree of homozygosity of eggplants. Genetic identities within groups of each complex were higher than with groups of other complexes, although differences in the ranks of genetic identity values among groups were observed between SSR and SNP markers. For low and intermediate values of pair-wise SNP genetic distances, a moderate correlation between SSR and SNP genetic distances was observed (r(2) = 0.592), but for high SNP genetic distances the correlation was low (r(2) = 0.080). The differences among markers resulted in different phenogram topologies, with a different eggplant complex being basal (gboma eggplant for SSRs and brinjal eggplant for SNPs) to the two others. Overall the results reveal that both types of markers are complementary for eggplant fingerprinting and that interpretation of relationships among groups may be greatly affected by the type of marker used.This work has been funded by European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops) and by Spanish Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (Grant AGL2015-64755-R from MINECO/FEDER). Pietro Gramazio is grateful to Universitat Politecnica de Valencia for a pre-doctoral contract (Programa FPI de la UPV-Subprograma 1/2013 call). Mariola Plazas is grateful to Spanish Ministerio de Economia, Industria y Competitividad for a post-doctoral grant within the Juan de la Cierva-Formacion programme (FJCI-2015-24835).Gramazio, P.; Prohens Tomás, J.; Borras, D.; Plazas Ávila, MDLO.; Herraiz García, FJ.; Vilanova Navarro, S. (2017). Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants. Euphytica. 213(264):1-18. https://doi.org/10.1007/s10681-017-2057-3S118213264Acquadro A, Barchi L, Gramazio P et al (2017) Coding SNPs analysis highlights genetic relationships and evolution pattern in eggplant complexes. PLoS ONE 12:e0180774. https://doi.org/10.1371/journal.pone.0180774Adeniji O, Kusolwa P, Reuben S (2013) Morphological descriptors and micro satellite diversity among scarlet eggplant groups. Afr Crop Sci J 21(1):37–49Aguoru C, Omoigui L, Olasan J (2015) Molecular characterization of Solanum species (Solanum aethiopicum complex; Solanum macrocarpon and Solanum anguivi) using multiplex RAPD primers. J Plant Stud 4:27–34. https://doi.org/10.5539/jps.v4n1p27Arumuganathan K, Earle E (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9(3):208–218Ashrafi H, Hill T, Stoffel K et al (2012) De novo assembly of the pepper transcriptome (Capsicum annuum): a benchmark for in silico discovery of SNPs, SSRs and candidate genes. BMC Genom 13:1–15. https://doi.org/10.1186/1471-2164-13-571Augustinos AA, Petropoulos C, Karasoulou V et al (2016) Assessing diversity among traditional Greek and foreign eggplant cultivars using molecular markers and morphometrical descriptors. Span J Agric Res 14:e0710. https://doi.org/10.5424/sjar/2016144-9020Avise JC (2012) Molecular markers, natural history and evolution. Springer Science & Business Media, Berlin. https://doi.org/10.1007/978-1-4615-2381-9Blanca J, Cañizares J, Roig C et al (2011) Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genom 12:104. https://doi.org/10.1186/1471-2164-12-104Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32(3):314–331Bukenya Z, Carasco J (1994) Biosystematic study of Solanum macrocarpon—S. dasyphyllum complex in Uganda and relations with Solanum linnaeanum. East Afr Agric For J 59(3):187–204Castillo A, Budak H, Varshney RK et al (2008) Transferability and polymorphism of barley EST-SSR markers used for phylogenetic analysis in Hordeum chilense. BMC Plant Biol 8:97. https://doi.org/10.1186/1471-2229-8-97Choudhary S, Sethy NK, Shokeen B, Bhatia S (2009) Development of chickpea EST-SSR markers and analysis of allelic variation across related species. Theor Appl Genet 118:591–608. https://doi.org/10.1007/s00122-008-0923-zCoates BS, Sumerford DV, Miller NJ et al (2009) Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. J Hered 100:556–564. https://doi.org/10.1093/jhered/esp028D’Agostino N, Golas T, van de Geest H et al (2013) Genomic analysis of the native European Solanum species, S. dulcamara. BMC Genom 14:356. https://doi.org/10.1186/1471-2164-14-356Daunay MC, Hazra P (2012) Eggplant. In: Peter KV, Hazra P (eds) Handbook of Vegetables. Studium Press, Houston, pp 257–322Davey J, Hohenlohe P, Etter P et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510. https://doi.org/10.1038/nrg3012De Barba M, Miquel C, Lobréaux S et al (2016) High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17(3):492–507. https://doi.org/10.1111/1755-0998.12594Doyle J, Doyle J (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull 19:11–15Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5:435–445. https://doi.org/10.1038/nrg1348Felsenstein, J (2007). PHYLIP (Phylogeny Inference Package) Version 3.67. Department of Genome Sciences, University of Washington, Seattle, WA, USAFernandez-Silva I, Whitney J, Wainwright B (2013) Microsatellites for next-generation ecologists: a post-sequencing bioinformatics pipeline. PLoS ONE 8(2):e55990Filippi CV, Aguirre N, Rivas JG et al (2015) Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biol 15:52. https://doi.org/10.1186/s12870-014-0360-xFischer MC, Rellstab C, Leuzinger M et al (2017) Estimating genomic diversity and population differentiation—an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri. BMC Genom 18:69. https://doi.org/10.1186/s12864-016-3459-7Furini A, Wunder J (2004) Analysis of eggplant (Solanum melongena)-related germplasm: morphological and AFLP data contribute to phylogenetic interpretations and germplasm utilization. Theor Appl Genet 108:197–208. https://doi.org/10.1007/s00122-003-1439-1Gadaleta A, Giancaspro A, Zacheo S et al (2011) Comparison of genomic and EST-derived SSR markers in phylogenetic analysis of wheat. Plant Genet Resour 9:243–246. https://doi.org/10.1017/S147926211100030XGe H, Liu Y, Jiang M et al (2013) Analysis of genetic diversity and structure of eggplant populations (Solanum melongena L.) in China using simple sequence repeat markers. Sci Hortic 162:71–75. https://doi.org/10.1016/j.scienta.2013.08.004Gonzaga ZJ (2015) Evaluation of SSR and SNP Markers for Molecular Breeding in Rice. Plant Breed Biotechnol 3:139–152. https://doi.org/10.9787/PBB.2015.3.2.139Goodwin S, McPherson J, McCombie W (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):333–351Gramazio P, Blanca J, Ziarsolo P et al (2016) Transcriptome analysis and molecular marker discovery in Solanum incanum and S. aethiopicum, two close relatives of the common eggplant (Solanum melongena) with interest for breeding. BMC Genom 17:300. https://doi.org/10.1186/s12864-016-2631-4Grover A, Sharma PC (2014) Development and use of molecular markers: past and present. Crit Rev Biotechnol 8551:1–13. https://doi.org/10.3109/07388551.2014.959891Hamblin MT, Warburton ML, Buckler ES (2007) Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS ONE 2:e1367. https://doi.org/10.1371/journal.pone.0001367Hess JE, Matala AP (2011) Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin. Mol Ecol Resour. https://doi.org/10.1111/j.1755-0998.2010.02958.xHighton R (1993) The relationship between the number of loci and the statistical support for the topology of UPGMA trees obtained from genetic distance data. Mol Phylogenet Evol 2:337–343Hirakawa H, Shirasawa K, Miyatake K, Nunome, T et al (2014) Draft genome sequence of eggplant (Solanum melongena L.): the representative solanum species indigenous to the old world. DNA Res 21:649–660. https://doi.org/10.1093/dnares/dsu027Hong CP, Piao ZY, Kang TW et al (2007) Genomic distribution of simple sequence repeats in Brassica rapa. Mol Cells 23:349–356.Hu J, Wang L, Li J (2011) Comparison of genomic SSR and EST-SSR markers for estimating genetic diversity in cucumber. Biol Plant 55:577–580. https://doi.org/10.1007/s10535-011-0129-0Isshiki S, Iwata N, Khan MMR (2008) ISSR variations in eggplant (Solanum melongena L.) and related Solanum species. Sci Hortic 117:186–190. https://doi.org/10.1016/j.scienta.2008.04.003Jones ES, Sullivan H, Bhattramakki D, Smith JSC (2007) A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor Appl Genet 115:361–371. https://doi.org/10.1007/s00122-007-0570-9Kalia RK, Rai MK, Kalia S et al (2011) Microsatellite markers: an overview of the recent progress in plants. Euphytica 177:309–334Kashi Y, King DG (2006) Simple sequence repeats as advantageous mutators in evolution. Trends Genet 22:253–259. https://doi.org/10.1016/j.tig.2006.03.005Kaushik P, Prohens J, Vilanova S et al (2016) Phenotyping of eggplant wild relatives and interspecific hybrids with conventional and phenomics descriptors provides insight for their potential utilization in breeding. Front Plant Sci 7:677Kim C, Guo H, Kong W et al (2016) Application of genotyping by sequencing technology to a variety of crop breeding programs. Plant Sci 242:14–22Knapp S, Vorontsova MS, Prohens J (2013) Wild relatives of the eggplant (Solanum melongena L.: Solanaceae): new understanding of species names in a complex group. PLoS ONE 8:e57039Kruglyak S, Durrett RT, Schug MD, Aquadro CF (1998) Equilibrium distributions of microsatellite repeat length resulting from a balance between slippage events and point mutations. Proc Natl Acad Sci USA 95:10774–10778. https://doi.org/10.1073/pnas.95.18.10774Lester RN, Daunay MC (2003) Diversity of African vegetable Solanum species and its implications for a better understanding of plant domestication. Schriften zu Genetischen Ressourcen 22:137–152Lester RN, Niakan L (1986) Origin and domestication of the scarlet eggplant, Solanum aethiopicum, from S. anguivi in Africa. In: D’Arcy WG (ed) Solanaceae: biology and systematics. Columbia University Press, New York, pp 433–456Lester RN, Jaeger PML, Bleijendaal-Spierings BHM et al (1990) African eggplants-a review of collecting in West Africa. Plant Genet Resour Newsl 81:17–26Levin R, Myers N, Bohs L (2006) Phylogenetic relationships among the ‘spiny solanums’ (Solanum subgenus Leptostemonum, Solanaceae). Am J Bot 93(1):157–169Li WH, Gojobori T, Nei M (1981) Pseudogenes as a paradigm of neutral evolution. Nature 292:237–239Li YC, Korol AB, Fahima T et al (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol 11:2453–2465Liu K, Muse S (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220. https://doi.org/10.1038/214637b0Martínez-Arias R, Calafell F, Mateu E et al (2001) Sequence variability of a human pseudogene. Genome Res 11:1071–1085. https://doi.org/10.1101/gr.167701Meyer RS, Karol KG, Little DP et al (2012) Phylogeographic relationships among Asian eggplants and new perspectives on eggplant domestication. Mol Phylogenet Evol 63:685–701. https://doi.org/10.1016/j.ympev.2012.02.006Muñoz-Falcón J, Prohens J, Vilanova S, Nuez F (2009) Diversity in commercial varieties and landraces of black eggplants and implications for broadening the breeders’ gene pool. Ann Appl Biol 154(3):453–465Nandha PS, Singh J (2014) Comparative assessment of genetic diversity between wild and cultivated barley using gSSR and EST-SSR markers. Plant Breed 133:28–35. https://doi.org/10.1111/pbr.12118Nei M (1972) Genetic distance between populations. Am Nat 106:283–292. https://doi.org/10.1086/282771Nunome T, Negoro S, Kono I et al (2009) Development of SSR markers derived from SSR-enriched genomic library of eggplant (Solanum melongena L.). Theor Appl Genet 119:1143–1153. https://doi.org/10.1007/s00122-009-1116-0Page R (2001) TreeView. Glasgow University, GlasgowPeakall P, Smouse R (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research an update. Bioinformatics 28:2537–2539Pessarakli M, Dris R (2004) Pollination and breeding of eggplants. J Food Agric Environ 2:218–219Plazas M, Andújar I, Vilanova S et al (2014) Conventional and phenomics characterization provides insight into the diversity and relationships of hypervariable scarlet (Solanum aethiopicum L.) and gboma (S. macrocarpon L.) eggplant complexes. Front. Plant Sci 5:318Ranil R, Niran H, Plazas M et al (2015) Improving seed germination of the eggplant rootstock Solanum torvum by testing multiple factors using an orthogonal array design. Sci Hortic 193:174–181. https://doi.org/10.1016/j.scienta.2015.07.030Sakata Y, Lester RN (1997) Chloroplast DNA diversity in brinjal eggplant (Solanum melongena L.) and related species. Euphytica 97:295–301. https://doi.org/10.1023/A:1003000612441Sakata Y, Nishio T, Matthews PJ (1991) Chloroplast DNA analysis of eggplant (Solanum melongena) and related species for their taxonomic affinity. Euphytica 55:21–26Särkinen T, Bohs L, Olmstead RG, Knapp S (2013) A phylogenetic framework for evolutionary study of the nightshades (Solanaceae): a dated 1000-tip tree. BMC Evol Biol 13:214. https://doi.org/10.1186/1471-2148-13-214Scheben A, Batley J, Edwards D (2017) Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J 15:149–161Sneath P, Sokal R (1973) Numerical taxonomy. The principles and practice of numerical classification. W H Freeman Limited, San FranciscoStàgel A, Portis E, Toppino L et al (2008) Gene-based microsatellite development for mapping and phylogeny studies in eggplant. BMC Genom 9:357. https://doi.org/10.1186/1471-2164-9-357Sunseri F, Polignano GB, Alba V et al (2010) Genetic diversity and characterization of African eggplant germplasm collection. Afr J Plant Sci 4:231–241Syfert MM, Castañeda-Álvarez NP, Khoury CK et al (2016) Crop wild relatives of the brinjal eggplant (Solanum melongena): poorly represented in genebanks and many species at risk of extinction. Am J Bot 103:635–651. https://doi.org/10.3732/ajb.1500539Thiel T, Michalek W, Varshney R, Graner A (2003) Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet 106:411–422. https://doi.org/10.1007/s00122-002-1031-0Thomson MJ, Alfred J, Dangl J et al (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2:195–212. https://doi.org/10.9787/PBB.2014.2.3.195Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. https://doi.org/10.1093/bib/bbs017Tumbilen Y, Frary A, Daunay MC, Doganlar S (2011) Application of EST-SSRs to examine genetic diversity in eggplant and its close relatives. Turk J Biol 35:125–136. https://doi.org/10.3906/biy-0906-57van Inghelandt D, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120:1289–1299. https://doi.org/10.1007/s00122-009-1256-2Van Tassell CP, Smith TPL, Matukumalli LK et al (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5:247–252. https://doi.org/10.1038/nmeth.1185Varshney R, Graner A, Sorrells M (2005) Genic microsatellite markers in plants: features and applications. Trends Biotechnol 23(1):48–55Varshney RK, Chabane K, Hendre PS et al (2007) Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci 173:638–649. https://doi.org/10.1016/j.plantsci.2007.08.010Vilanova S, Manzur JP, Prohens J (2012) Development and characterization of genomic simple sequence repeat markers in eggplant and their application to the study of diversity and relationships in a collection of different cultivar types and origins. Mol Breed 30:647–660. https://doi.org/10.1007/s11032-011-9650-2Vilanova S, Hurtado M, Cardona A (2014) Genetic diversity and relationships in local varieties of eggplant from different cultivar groups as assessed by genomic SSR markers. Not Bot Horti Agrobo Cluj-Napoca 42:59–65Vogel JP, Gu YQ, Twigg P et al (2006) EST sequencing and phylogenetic analysis of the model grass Brachypodium distachyon. Theor Appl Genet 113:186–195. https://doi.org/10.1007/s00122-006-0285-3Vorontsova MS, Stern S, Bohs L, Knapp S (2013) African spiny solanum (subgenus Leptostemonum, Solanaceae): a thorny phylogenetic tangle. Bot J Linn Soc 173:176–193. https://doi.org/10.1111/boj.12053Weese TL, Bohs L (2010) Eggplant origins: out of Africa, into the Orient. Taxon 59:49–56. https://doi.org/10.2307/27757050Wright S (1965) The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19:395–420. https://doi.org/10.2307/2406450Xiao M, Zhang Y, Chen X et al (2013) Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest. J Biotechnol 166:122–134. https://doi.org/10.1016/j.jbiotec.2013.04.004Yan J, Yang X, Shah T et al (2010) High-throughput SNP genotyping with the Goldengate assay in maize. Mol Breed 25:441–451. https://doi.org/10.1007/s11032-009-9343-2Yang X, Xu Y, Shah T et al (2011) Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetica 139:1045–1054. https://doi.org/10.1007/s10709-011-9606-9Yu J, Zhang Z, Zhu C et al (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2:63. https://doi.org/10.3835/plantgenome2008.09.0009Zhan L, Paterson I, Fraser B (2016) MEGASAT: automated inference of microsatellite genotypes from sequence data. Ecol Resour, Mol. https://doi.org/10.1111/1755-0998.1256

    Physicochemical Characterization of Passive Films and Corrosion Layers by Differential Admittance and Photocurrent Spectroscopy

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    Two different electrochemical techniques, differential admittance and photocurrent spectroscopy, for the characterization of electronic and solid state properties of passive films and corrosion layers are described and critically evaluated. In order to get information on the electronic properties of passive film and corrosion layers as well as the necessary information to locate the characteristic energy levels of the passive film/electrolyte junction like: flat band potential (Ufb), conduction band edge (EC) or valence band edge (EV), a wide use of Mott-Schottky plots is usually reported in corrosion science and passivity studies. It has been shown, in several papers, that the use of simple M-S theory to get information on the electronic properties and energy levels location at the film/electrolyte interface can be seriously misleading and/or conflicting with the physical basis underlying the M-S theory. A critical appraisal of this approach to the study of very thin and thick anodic passive film grown on base-metals (Cr, Ni, Fe, SS etc..) or on valve metals (Ta, Nb, W etc..) is reported in this work, together with possible alternative approach to overcome some of the mentioned inconsistencies. At this aim the theory of amorphous semiconductor Schottky barrier, introduced several years ago in the study of passive film/electrolyte junction, is reviewed by taking into account some of the more recent results obtained by the present authors. Future developments of the theory appears necessary to get more exact quantitative information on the electronic properties of passive films, specially in the case of very thin film like those formed on base metals and their alloys. The second technique described in this chapter, devoted to the physico-chemical characterization of passive film and corrosion layers, is a more recent technique based on the analysis of the photo-electrochemical answer of passive film/electrolyte junction under illumination with photons having suitable energy. Such a technique usually referred to as Photocurrent Spectroscopy (PCS) has been developed on the basis of the large research effort carried out by several groups in the 1970’s and aimed to investigate the possible conversion of solar energy by means of electrochemical cells. In this work the fundamentals of semiconductor/electrolyte junctions under illumination will be highlighted both for crystalline and amorphous materials. The role of amorphous nature and film thickness on the photo-electrochemical answer of passive film/solution interface is reviewed as well the use of PCS for quantitative analysis of the film composition based on a semi-empirical correlation between optical band gap and difference of electronegativity of film constituents previously suggested by the present authors. In this frame the results of PCS studies on valve metal oxides and valve metal mixed oxides will be discussed in order to show the validity of the proposed method. The results of PCS studies aimed to get information on passive film composition and carried out by different authors on base metals (Fe, Cr, Ni) and their alloys, including stainless steel, will be also compared with compositional analysis carried out by well-established surface analysis techniques

    High-throughput 18K SNP array to assess genetic variability of the main grapevine cultivars from Sicily

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    The viticulture of Sicily, for its vocation, is one of the most important and ancient forms in Italy. Autochthonous grapevine cultivars, many of which known throughout the world, have always been cultivated in the island from many centuries. With the aim to preserve this large grapevine diversity, previous studies have already started to assess the genetic variability among the Sicilian cultivars by using morphological and microsatellite markers. In this study, simple sequence repeat (SSR) were utilized to verify the true-to-typeness of a large clone collection (101) belonging to 21 biotypes of the most 10 cultivated Sicilian cultivars. Afterwards, 42 Organization Internationale de la Vigne et du Vin (OIV) descriptors and a high-throughput single nucleotide polymorphism (SNP) genotyping array (Vitis18kSNP) were applied to assess genetic variability among cultivars and biotypes of the same cultivar. Ampelographic traits and high-throughput SNP genotyping platforms provided an accuracy estimation of genetic diversity in the Sicilian germplasm, showing the relationships among cultivars by cluster and multivariate analyses. The large SNP panel defined sub-clusters unable to discern among biotypes, previously classified by ampelographic analysis, belonging to each cultivar. These results suggested that a very large number of SNP did not cover the genome regions harboring few morphological traits. Genetic structure of the collection revealed a clear optimum number of groups for K = 3, clustering in the same group a significant portion of family-related genotypes. Parentage analysis highlighted significant relationships among Sicilian grape cultivars and Sangiovese, as already reported, but also the first evidences of the relationships between Nero d’Avola and both Inzolia and Catarratto. Finally, a small panel of highly informative markers (12 SNPs) allowed us to isolate a private profile for each Sicilian cultivar, providing a new tool for cultivar identification
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