44 research outputs found

    The relation between bar formation, galaxy luminosity, and environment

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    We derive the bar fraction in three different environments ranging from the field to Virgo and Coma clusters, covering an unprecedentedly large range of galaxy luminosities (or, equivalently, stellar masses). We confirm that the fraction of barred galaxies strongly depends on galaxy luminosity. We also show that the difference between the bar fraction distributions as a function of galaxy luminosity (and mass) in the field and Coma cluster are statistically significant, with Virgo being an intermediate case. We interpret this result as a variation of the effect of environment on bar formation depending on galaxy luminosity. We speculate that brighter disk galaxies are stable enough against interactions to keep their cold structure, thus, the interactions are able to trigger bar formation. For fainter galaxies the interactions become strong enough to heat up the disks inhibiting bar formation and even destroying the disks. Finally, we point out that the controversy regarding whether the bar fraction depends on environment could be resolved by taking into account the different luminosity ranges of the galaxy samples studied so far.Comment: 4 pages, 2 figures. To appear in the proceedings of EWASS 2012 Special Session 4, Structure of galaxy disks shaped by secular evolution and environmental processes, ed. P. Di Matteo and C. Jog, Memorie della Societ\`a Astronomica Italiana Supplement Serie

    Fossil group origins - VI. Global X-ray scaling relations of fossil galaxy clusters

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    We present the first pointed X-ray observations of 10 candidate fossil galaxy groups and clusters. With these Suzaku observations, we determine global temperatures and bolometric X-ray luminosities of the intracluster medium (ICM) out to r500r_{500} for six systems in our sample. The remaining four systems show signs of significant contamination from non-ICM sources. For the six objects with successfully determined r500r_{500} properties, we measure global temperatures in the range 2.8≀TX≀5.3 keV2.8 \leq T_{\mathrm{X}} \leq 5.3 \ \mathrm{keV}, bolometric X-ray luminosities of 0.8×1044 ≀LX,bol≀7.7×1044 erg s−10.8 \times 10^{44} \ \leq L_{\mathrm{X,bol}} \leq 7.7\times 10^{44} \ \mathrm{erg} \ \mathrm{s}^{-1}, and estimate masses, as derived from TXT_{\mathrm{X}}, of M500>1014 M⊙M_{500} > 10^{14} \ \mathrm{M}_{\odot}. Fossil cluster scaling relations are constructed for a sample that combines our Suzaku observed fossils with fossils in the literature. Using measurements of global X-ray luminosity, temperature, optical luminosity, and velocity dispersion, scaling relations for the fossil sample are then compared with a control sample of non-fossil systems. We find the fits of our fossil cluster scaling relations are consistent with the relations for normal groups and clusters, indicating fossil clusters have global ICM X-ray properties similar to those of comparable mass non-fossil systems.Comment: 17 pages, 7 figures, 8 tables. Accepted for publication in MNRA

    The combined cartilage growth – calcification patterns in the wing-fins of Rajidae (Chondrichthyes): A divergent model from endochondral ossification of tetrapods

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    The relationship between cartilage growth – mineralization patterns were studied in adult Rajidae with X-ray morphology/morphometry, undecalcified resin-embedded, heat-deproteinated histology and scanning electron microscopy. Morphometry of the wing-fins, nine central rays of the youngest and oldest specimens documented a significant decrement of radials mean length between inner, middle and outer zones, but without a regular progression along the ray. This suggests that single radial length growth is regulated in such a way to align inter-radial joints parallel to the wing metapterygia curvature. Trans-illumination and heat-deproteination techniques showed polygonal and cylindrical morphotypes of tesserae, whose aligned pattern ranged from mono-columnar, bi-columnar, and multi-columnar up to the crustal-like layout. Histology of tessellated cartilage allowed to identify of zones of the incoming mineral deposition characterized by enhanced duplication rate of chondrocytes with the formation of isogenic groups, whose morphology and topography suggested a relationship with the impending formation of the radials calcified column. The morphotype and layout of radial tesserae were related to mechanical demands (stiffening) and the size/mass of the radial cartilage body. The cartilage calcification pattern of the batoids model shares several morphological features with tetrapods' endochondral ossification, that is, (chondrocytes' high duplication rate, alignment in rows, increased volume of chondrocyte lacunae), but without the typical geometry of the metaphyseal growth plates

    Fossil group origins V. The dependence of the luminosity function on the magnitude gap

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    In nature we observe galaxy aggregations that span a wide range of magnitude gaps between the two first-ranked galaxies of a system (Δm12\Delta m_{12}). There are systems with gaps close to zero (e.g., the Coma cluster), and at the other extreme of the distribution, the largest gaps are found among the so-called fossil systems. Fossil and non-fossil systems could have different galaxy populations that should be reflected in their luminosity functions. In this work we study, for the first time, the dependence of the luminosity function parameters on Δm12\Delta m_{12} using data obtained by the fossil group origins (FOGO) project. We constructed a hybrid luminosity function for 102 groups and clusters at z≀0.25z \le 0.25. We stacked all the individual luminosity functions, dividing them into bins of Δm12\Delta m_{12}, and studied their best-fit Schechter parameters. We additionally computed a relative luminosity function, expressed as a function of the central galaxy luminosity, which boosts our capacity to detect differences, especially at the bright end. We find trends as a function of Δm12\Delta m_{12} at both the bright and faint ends of the luminosity function. In particular, at the bright end, the larger the magnitude gap, the fainter the characteristic magnitude M∗M^\ast. We also find differences at the faint end. In this region, the larger the gap, the flatter the faint-end slope α\alpha. The differences found at the bright end support a dissipationless, dynamical friction-driven merging model for the growth of the central galaxy in group- and cluster-sized halos. The differences in the faint end cannot be explained by this mechanism. Other processes, such as enhanced tidal disruption due to early infall and/or prevalence of eccentric orbits, may play a role. However, a larger sample of systems with Δm12>1.5\Delta m_{12} > 1.5 is needed to establish the differences at the faint end.Comment: 11 pages, 10 figures, accepted for publication in A&

    Fossil Groups Origins III. The relation between optical and X-ray luminosities

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    This study is part of the FOssil Groups Origin (FOGO) project which aims at carrying out a systematic and multiwavelength study of a large sample of fossil systems. Here we focus on the relation between the optical luminosity (Lopt) and X-ray luminosity (Lx). Out of a sample of 28 candidate fossil systems, we consider a sample of 12 systems whose fossil classification has been confirmed by a companion study. They are compared with the complementary sample of 16 systems whose fossil nature is not confirmed and with a subsample of 102 galaxy systems from the RASS-SDSS galaxy cluster survey. Fossil and normal systems span the same redshift range 0<z<0.5 and have the same Lx distribution. For each fossil system, the Lx in the 0.1-2.4 keV band is computed using data from the ROSAT All Sky Survey. For each fossil and normal system we homogeneously compute Lopt in the r-band within the characteristic cluster radius, using data from the SDSS DR7. We sample the Lx-Lopt relation over two orders of magnitude in Lx. Our analysis shows that fossil systems are not statistically distinguishable from the normal systems both through the 2D KS test and the fit of the Lx-Lopt relation. The optical luminosity of the galaxy system does strongly correlate with the X-ray luminosity of the hot gas component, independently of whether the system is fossil or not. We conclude that our results are consistent with the classical "merging scenario" of the brightest galaxy formed via merger/cannibalism of other group galaxies, with conservation of the optical light. We find no evidence for a peculiar state of the hot intracluster medium.Comment: A&A, 12 pages, 4 figures, 3 tables, typos corr. and paper re-numbe

    Fossil Groups Origins III. Characterization of the sample and observational properties of fossil systems

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    (Abridged) Fossil systems are group- or cluster-sized objects whose luminosity is dominated by a very massive central galaxy. In the current cold dark matter scenario, these objects formed hierarchically at an early epoch of the Universe and then slowly evolved until present day. That is the reason why they are called {\it fossils}. We started an extensive observational program to characterize a sample of 34 fossil group candidates spanning a broad range of physical properties. Deep r−r-band images were taken for each candidate and optical spectroscopic observations were obtained for ∌\sim 1200 galaxies. This new dataset was completed with SDSS DR7 archival data to obtain robust cluster membership and global properties of each fossil group candidate. For each system, we recomputed the magnitude gaps between the two brightest galaxies (Δm12\Delta m_{12}) and the first and fourth ranked galaxies (Δm14\Delta m_{14}) within 0.5 R200R_{{\rm 200}}. We consider fossil systems those with Δm12≄2\Delta m_{12} \ge 2 mag or Δm14≄2.5\Delta m_{14} \ge 2.5 mag within the errors. We find that 15 candidates turned out to be fossil systems. Their observational properties agree with those of non-fossil systems. Both follow the same correlations, but fossils are always extreme cases. In particular, they host the brightest central galaxies and the fraction of total galaxy light enclosed in the central galaxy is larger in fossil than in non-fossil systems. Finally, we confirm the existence of genuine fossil clusters. Combining our results with others in the literature, we favor the merging scenario in which fossil systems formed due to mergers of L∗L^\ast galaxies. The large magnitude gap is a consequence of the extreme merger ratio within fossil systems and therefore it is an evolutionary effect. Moreover, we suggest that at least one candidate in our sample could represent a transitional fossil stage.Comment: 14 pages, 11 figures, accepted for publication in A&

    Fossil group origins : VII. Galaxy substructures in fossil systems

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    This work has been partially funded by the MINECO (grant AIA2013-43188-P). M.G. acknowledges financial support from MIUR PRIN2010-2011 (J91J12000450001). Funding for the Sloan Digital Sky Survey (SDSS) and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, and the Max Planck Society, and the Higher Education Funding Council for England.Context. Fossil groups (FG) are expected to be the final product of galaxy merging within galaxy groups. In simulations, they are predicted to assemble their mass at high redshift. This early formation allows for the innermost M galaxies to merge into a massive central galaxy. Then, they are expected to maintain their fossil status because of the few interactions with the large-scale structure. In this context, the magnitude gap between the two brightest galaxies of the system is considered a good indicator of its dynamical status. As a consequence, the systems with the largest gaps should be dynamically relaxed. Aims. In order to examine the dynamical status of these systems, we systematically analyze, for the first time, the presence of galaxy substructures in a sample of 12 spectroscopically-confirmed fossil systems with redshift z 0:25. Methods. We apply a number of tests to investigate the substructure in fossil systems in the two-dimensional space of projected positions out to R200. Moreover, for a subsample of five systems with at least 30 spectroscopically-confirmed members we also analyze the substructure in the velocity and in the three-dimensional velocity-position spaces. Additionally, we look for signs of recent mergers in the regions around the central galaxies. Results. We find that an important fraction of fossil systems show substructure. The fraction depends critically on the adopted test, since each test is more sensitive to a particular type of substructure. Conclusions. Our interpretation of the results is that fossil systems are not, in general, as relaxed as expected from simulations. Our sample of 12 spectroscopically-confirmed fossil systems need to be extended to compute an accurate fraction, but our conclusion is that this fraction is similar to the fraction of substructure detected in nonfossil clusters. This result points out that the magnitude gap alone is not a good indicator of the dynamical status of a system. However, the subsample of five FGs for which we were able to use velocities as a probe for substructures is dominated by high-mass FGs. These massive systems could have a different evolution compared to low-mass FGs, since they are expected to form via the merging of a fossil group with another group of galaxies. This merger would lengthen the relaxation time and it could be responsible for the substructure detected in present-time massive FGs. If this is the case, only low-mass FGs are expected to be dynamically old and relaxed.Publisher PDFPeer reviewe

    Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon

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    [EN] Background: MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited. Results: Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stressresponsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development. Conclusion: Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon.The authors thank Dr. A. Monforte for providing melon seeds and Dra. B. Pico (Cucurbits Group - COMAV) for providing melon seeds and Monosporascus isolate respectively. This work was supported by grants AGL2016-79825-R, BIO2014-61826-EXP (GG), and BFU2015-66894-P (GR) from the Spanish Ministry of Economy and Competitiveness (co-supported by FEDER). The funders had no role in the experiment design, data analysis, decision to publish, or preparation of the manuscript.Sanz-Carbonell, A.; Marques Romero, MC.; Bustamante-GonzĂĄlez, AJ.; Fares Riaño, MA.; Rodrigo Tarrega, G.; Gomez, GG. (2019). Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon. BMC Plant Biology. 1-17. https://doi.org/10.1186/s12870-019-1679-0S117Zhang B. MicroRNAs: a new target for improving plant tolerance to abiotic stress. J Exp Bot. 2015;66:1749–61.Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24.Bielach A, Hrtyan M, Tognetti VB. Plants under stress: involvement of auxin and Cytokinin. Int J Mol Sci. 2017;4(18):7.Zarattini M, Forlani G. Toward unveiling the mechanisms for transcriptional regulation of proline biosynthesis in the plant cell response to biotic and abiotic stress conditions. Front Plant Sci. 2017;2(8):927.Nolan T, Chen J, Yin Y. Cross-talk of Brassinosteroid signaling in controlling growth and stress responses. Biochem J. 2017;474:2641–61.Mittler R. Abiotic stress, the field environment and stress combinations. Trends Plant Sci. 2006;11:15–9.Djami-Tchatchou AT, Sanan-Mishra N, Ntushelo K, Dubery IA. Functional roles of microRNAs in Agronomically important plants—potential as targets for crop improvement and protection. Front Plant Sci. 2017;8:378.Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signaling. J Exp Bot. 2014;65:1229–40.Golldack D, Li C, Mohan H, Probst N. Tolerance to drought and salt stress in plants: unraveling the signaling networks. Front Plant Sci. 2014;5:151.Lee SH, Li HW, Koh KW, Chuang HY, Chen YR, Lin CS, Chan MT. MSRB7 reverses oxidation of GSTF2/3 to confer tolerance of Arabidopsis thaliana to oxidative stress. J Exp Bot. 2014;65:5049–62.Carrera J, Rodrigo G, Jaramillo A, Elena SF. Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biol. 2009;10(9):R96.Shriram V, Kumar V, Devarumath RM, Khare TS, Wani SH. MicroRNAs as potential targets for abiotic stress tolerance in plants. Front Plant Sci. 2016;7:817.Sunkar R, Chinnusamy V, Zhu J, Zhu JH. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007;12:301–9.Kumar R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotechnology. 2014;174:93–115.Reis RS, Eamens AL, Waterhouse PM. Missing pieces in the puzzle of plant MicroRNAs. Trends Plant Sci. 2015;20:721–8.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.Borges F, Martienssen RA. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol. 2015;16:727–41.Axtell MJ, Bartel DP. Antiquity of microRNAs and their targets in land-plants. Plant Cell. 2005;17:1658–73.Cuperus JT, Fahlgren N, Carrington JC. Evolution and functional diversification of MIRNA genes. Plant Cell. 2011;23:431–42.Cui J, You C, Chen X. The evolution of microRNAs in plants. Current Opinions in Plant Biology. 2016;35:61–7.Sunkar R, Li YF, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203.Zhang T, Zhao YL, Zhao JH, Wang S, Jin Y, Chen ZQ, Fang YY, Hua CL, Ding SW, Guo HS. Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen. Nature Plants. 2016;2(10):16153.Chaloner T, vanKan JA, Grant-Downton R. RNA ‘Information Warfare’ in pathogenic and mutualistic interactions. Trends Plant Sci. 2016;9:738–48.Niu D, Wang Z, Wang S, Qiao L Zhao H. Profiling of small RNAs involved in plant-pathogen interactions. Methods Molecular Biology. 2015;1287:61–79.Wei S, Wang L, Zhang Y, Huang D. Identification of early response genes to salt stress in roots of melon (Cucumis melo L.) seedlings. Molecular Biology Report. 2013;40:2915–26.Clepet C, Joobeur T, Zheng Y, Jublot D, Huang M, Truniger V, et al. Analysis of expressed sequence tags generated from full-length enriched cDNA libraries of melon. BMC Genomics. 2011;12:252.GonzĂĄlez M, Xu M, Esteras C, Roig C, Monforte AJ, Troadec C, et al. Towards a TILLING platform for functional genomics in Piel de Sapo melons. BMC Research Notes. 2011;4:289.GarcĂ­a MJ. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109:11872–7.Pollack FG, Uecker FA. Monosporascus cannonballus: an unusual ascomycete in cantaloupe roots. Mycologia. 1974;66:346–9.Kofalvi S, Marcos J, Cañizares MC, Pallas V, Candresse T. Hop stunt viroid (HSVd) sequence variants from Prunus species: evidence for recombination between HSVd isolates. J Gen Virol. 1997;78:3177–86.Sattar S, Song Y, Anstead J, Sunkar R, Thompson G. Cucumis melo expression profile during aphid herbivory in a resistant and susceptible interaction. Mol Plant-Microbe Interact. 2012;25:839–48.Herranz MC, Navarro JA, Sommen E, Pallas V. Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems. BMC Genomics. 2015;16:117.Jagadeeswaran G, Nimmakayala P, Zheng Y, Gowdu K, Reddy UK, Sunkar R. Characterization of the small RNA component of leaves and fruits from four different cucurbit species. BMC Genomics. 2012;13:329.Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.Barciszewska-Pacak M, Milanowska K, Knop K, Bielewicz D, Nuc P, Plewka P, et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front Plant Sci. 2015;6:410.Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010;61:4157–68.Samad A, Sajad M, Nazaruddin N, Fauzi I, Murad A, Zainal Z, Ismanizan Ismail I. MicroRNA and transcription factor: key players in plant regulatory network. Front Plant Sci. 2017;8:565.Danisman S. TCP transcription factors at the Interface between environmental challenges and the Plant’s growth responses. Front Plant Sci. 2016;7:1930.Llave C, Xie Z, Kasschau KD, Carrington JC. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002;297:2053–6.Gupta OP, Meena NL, Sharma I, et al. Differential regulation of microRNAs in response to osmotic, salt and cold stresses in wheat. Mol Biol Rep. 2014;41:4623.Wang M, Wang Q, Zhang B. 2013. Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum ). Gene 30: 26–32.Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149:1665–76.NegrĂŁo S, Schmöckel SM, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017;119:1–11.Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101–13.Megraw M, Cumbie J, Ivanchenko M, Filichkin S. Small genetic circuits and MicroRNAs: big players in polymerase II transcriptional control in plants. Plant Cell. 2016;28:286–303.Wang St, Sun Xl, Hoshino Y, Yu Y, Jia B, et al. 2014. MicroRNA319 Positively Regulates Cold Tolerance by Targeting OsPCF6 and OsTCP21 in Rice (Oryza sativa). PLoS ONE 9(3): e91357.Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes regulate drought resistence in rice. J Exp Bot. 2014;65:2119–35.Goossens A, de la Fuente N, Forment J, Serrano R, Portillo F. Regulation of yeast H+-ATPase by protein kinases belonging to a family dedicated to activation of plasma membrane transporters. Mol Cell Biol. 2000;20:7654–61.Roig C, Fita A, RĂ­os G, Hammond JP, Nuez F, PicĂł B. Root transcriptional responses of two melon genotypes with contrasting resistance to Monosporascus cannonballus (Pollack et Uecker) infection. BMC Genomics. 2012;13:601.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011;17:10–2.R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07-0, URL http://www.R-project.org /.Tarazona S, FuriĂł-TarĂ­ P, TurrĂ  D, Di Pietro A, Nueda MJ, Ferrer A, Conesa A. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res. 2015;43:e140.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.Czimmerer Z, Hulvely J, Simandi Z, Varallyay E, Havelda Z, Szabo E, Balint BL. A versatile method to design stem-loop primer-based quantitative PCR assays for detecting small regulatory RNA molecules. PLoS One. 2013;8(1):e55168.Zhai J, Arikit S, Simon S, Kingham B, Meyers B. Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods. 2014;67:84–90.Pink S, Vogel S. 2014. D3NETWORK: Stata module to create network visualizations using D3.js http://EconPapers.repec.org/RePEc:boc:bocode:s457844 .Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Systems. 2006;1695:1–9

    Applications of yeast flocculation in biotechnological processes

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    A review on the main aspects associated with yeast flocculation and its application in biotechnological processes is presented. This subject is addressed following three main aspects – the basics of yeast flocculation, the development of “new” flocculating yeast strains and bioreactor development. In what concerns the basics of yeast flocculation, the state of the art on the most relevant aspects of mechanism, physiology and genetics of yeast flocculation is reported. The construction of flocculating yeast strains includes not only the recombinant constitutive flocculent brewer’s yeast, but also recombinant flocculent yeast for lactose metabolisation and ethanol production. Furthermore, recent work on the heterologous ÎČ-galactosidase production using a recombinant flocculent Saccharomyces cerevisiae is considered. As bioreactors using flocculating yeast cells have particular properties, mainly associated with a high solid phase hold-up, a section dedicated to its operation is presented. Aspects such as bioreactor productivity and culture stability as well as bioreactor hydrodynamics and mass transfer properties of flocculating cell cultures are considered. Finally, the paper concludes describing some of the applications of high cell density flocculation bioreactors and discussing potential new uses of these systems.Fundação para a CiĂȘncia e a Tecnologia (FCT) – PRAXIS XXI - BD11306/97
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