242 research outputs found
An implementation of synthetic generation of wind data series
Wind power fluctuation is a major concern of large scale wind power grid integration. To test methods proposed for wind power grid integration, a large amount of wind data with time series are necessary and will be helpful to improve the methods. Meanwhile, due to the short operation history of most wind farms as well as limitations of data collections, the data obtained from wind farms could not satisfy the needs of data analysis. Consequently, synthetic generation of wind data series could be one of the effective solutions for this issue. In this paper, a method is presented for generating wind data series using Markov chain. Due to the high order Markov chain, the possibility matrix designed for a wind farm could cost a lot of memory, which is a problem with current computer technologies. Dynamic list will be introduced in this paper to reduce the memory required. Communication errors are un-avoidable on long way signal transmission between the control centre and wind farms. Missing of data always happens in the historical wind data series. Using these data to generate wind data series may result in some mistakes when searching related elements in the probability matrix. An adaptive method will be applied in this paper to solve the problem. The proposed method will be verified using a set of one-year historical data. The results show that the method could generate wind data series in an effective way. © 2013 IEEE.published_or_final_versio
Improving slurryability, rheology, and stability of slurry fuel from blending petroleum coke with lignite
Cytotoxic and antibacterial activities of endophytic fungi isolated from plants at the National Park, Pahang, Malaysia
<p>Abstract</p> <p>Background</p> <p>Endophytes, microorganisms which reside in plant tissues, have potential in producing novel metabolites for exploitation in medicine. Cytotoxic and antibacterial activities of a total of 300 endophytic fungi were investigated.</p> <p>Methods</p> <p>Endophytic fungi were isolated from various parts of 43 plants from the National Park Pahang, Malaysia. Extracts from solid state culture were tested for cytotoxicity against a number of cancer cell lines using the MTT assay. Antibacterial activity was determined using the disc diffusion method.</p> <p>Results</p> <p>A total of 300 endophytes were isolated from various parts of plants from the National Park, Pahang. 3.3% of extracts showed potent (IC<sub>50 </sub>< 0.01 μg/ml) cytotoxic activity against the murine leukemic P388 cell line and 1.7% against a human chronic myeloid leukemic cell line K562. <it>Sporothrix </it>sp. (KK29FL1) isolated from <it>Costus speciosus </it>showed strong cytotoxicity against colorectal carcinoma (HCT116) and human breast adenocarcinoma (MCF7) cell lines with IC<sub>50 </sub>values of 0.05 μg/ml and 0.02 μg/ml, respectively. Antibacterial activity was demonstrated for 8% of the extracts.</p> <p>Conclusion</p> <p>Results indicate the potential for production of bioactive agents from endophytes of the tropical rainforest flora.</p
Numerical analysis of seepage–deformation in unsaturated soils
A coupled elastic–plastic finite element analysis based on simplified consolidation theory for unsaturated soils is used to investigate the coupling processes of water infiltration and deformation. By introducing a reduced suction and an elastic–plastic constitutive equation for the soil skeleton, the simplified consolidation theory for unsaturated soils is incorporated into an in-house finite element code. Using the proposed numerical method, the generation of pore water pressure and development of deformation can be simulated under evaporation or rainfall infiltration conditions. Through a parametric study and comparison with the test results, the proposed method is found to describe well the characteristics during water evaporation/infiltration into unsaturated soils. Finally, an unsaturated soil slope with water infiltration is analyzed in detail to investigate the development of the displacement and generation of pore water pressure
Growth factors in multiple myeloma: a comprehensive analysis of their expression in tumor cells and bone marrow environment using Affymetrix microarrays
<p>Abstract</p> <p>Background</p> <p>Multiple myeloma (MM) is characterized by a strong dependence of the tumor cells on their microenvironment, which produces growth factors supporting survival and proliferation of myeloma cells (MMC). In the past few years, many myeloma growth factors (MGF) have been described in the literature. However, their relative importance and the nature of the cells producing MGF remain unidentified for many of them.</p> <p>Methods</p> <p>We have analysed the expression of 51 MGF and 36 MGF receptors (MGFR) using Affymetrix microarrays throughout normal plasma cell differentiation, in MMC and in cells from the bone marrow (BM) microenvironment (CD14, CD3, polymorphonuclear neutrophils, stromal cells and osteoclasts).</p> <p>Results</p> <p>4/51 MGF and 9/36 MGF-receptors genes were significantly overexpressed in plasmablasts (PPC) and BM plasma cell (BMPC) compared to B cells whereas 11 MGF and 11 MGFR genes were overexpressed in BMPC compared to PPC. 3 MGF genes (AREG, NRG3, Wnt5A) and none of the receptors were significantly overexpressed in MMC versus BMPC. Furthermore, 3/51 MGF genes were overexpressed in MMC compared to the the BM microenvironment whereas 22/51 MGF genes were overexpressed in one environment subpopulation compared to MMC.</p> <p>Conclusions</p> <p>Two major messages arise from this analysis 1) The majority of MGF genes is expressed by the bone marrow environment. 2) Several MGF and their receptors are overexpressed throughout normal plasma cell differentiation. This study provides an extensive and comparative analysis of MGF expression in plasma cell differentiation and in MM and gives new insights in the understanding of intercellular communication signals in MM.</p
Turnover of BRCA1 Involves in Radiation-Induced Apoptosis
Background: Germ-line mutations of the breast cancer susceptibility gene-1 (BRCA1) increase the susceptibility to tumorigenesis. The function of BRCA1 is to regulate critical cellular processes, including cell cycle progression, genomic integrity, and apoptosis. Studies on the regulation of BRCA1 have focused intensely on transcription and phosphorylation mechanisms. Proteolytic regulation of BRCA1 in response to stress signaling remains largely unknown. The manuscript identified a novel mechanism by which BRCA1 is regulated by the ubiquitin-dependent degradation in response to ionization. Methodology/Principal Findings: Here, we report that severe ionization triggers rapid degradation of BRCA1, which in turn results in the activation of apoptosis. Ionization-induced BRCA1 turnover is mediated via an ubiquitin-proteasomal pathway. The stabilization of BRCA1 significantly delays the onset of ionization-induced apoptosis. We have mapped the essential region on BRCA1, which mediates its proteolysis in response to ionization. Moreover, we have demonstrated that BRCA1 protein is most sensitive to degradation when ionization occurs during G2/M and S phase. Conclusions/Significance: Our results suggest that ubiquitin-proteasome plays an important role in regulating BRCA1 during genotoxic stress. Proteolytic regulation of BRCA1 involves in ionization-induced apoptosis. © 2010 Liu et al
Drugs and herbs given to prevent hepatotoxicity of tuberculosis therapy: systematic review of ingredients and evaluation studies
Background: Drugs to protect the liver are frequently prescribed in some countries as part of treatment for tuberculosis. The biological rationale is not clear, they are expensive and may do harm. We conducted a systematic review to a) describe the ingredients of "liver protection drugs"; and b) compare the evidence base for the policy against international standards.
Methods: We searched international medical databases (MEDLINE, EMBASE, LILACS, CINAHL, Cochrane Central Register of Controlled Trials, and the specialised register of the Cochrane Infectious Diseases Group) and Chinese language databases (CNKI, VIP and WanFang) to April 2007. Our inclusion criteria were research papers that reported evaluating any liver protection drug or drugs for preventing liver damage in people taking anti-tuberculosis treatment. Two authors independently categorised and extracted data, and appraised the stated methods of evaluating their effectiveness.
Results: Eighty five research articles met our inclusion criteria, carried out in China (77), India (2), Russia (4), Ukraine (2). These articles evaluated 30 distinct types of liver protection compounds categorised as herbal preparations, manufactured herbal products, combinations of vitamins and other non-herbal substances and manufactured pharmaceutical preparations. Critical appraisal of these articles showed that all were small, poorly conducted studies, measuring intermediate outcomes. Four trials that were described as randomised controlled trials were small, had short follow up, and did not meet international standards.
Conclusion: There is no reliable evidence to support prescription of drugs or herbs to prevent liver damage in people on tuberculosis treatment
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
[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
Epithelial cell polarity: a major gatekeeper against cancer?
The correct establishment and maintenance of cell polarity are crucial for normal cell physiology and tissue homeostasis. Conversely, loss of cell polarity, tissue disorganisation and excessive cell growth are hallmarks of cancer. In this review, we focus on identifying the stages of tumoural development that are affected by the loss or deregulation of epithelial cell polarity. Asymmetric division has recently emerged as a major regulatory mechanism that controls stem cell numbers and differentiation. Links between cell polarity and asymmetric cell division in the context of cancer will be examined. Apical–basal polarity and cell–cell adhesion are tightly interconnected. Hence, how loss of cell polarity in epithelial cells may promote epithelial mesenchymal transition and metastasis will also be discussed. Altogether, we present the argument that loss of epithelial cell polarity may have an important role in both the initiation of tumourigenesis and in later stages of tumour development, favouring the progression of tumours from benign to malignancy
- …