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Journal articles on the topic 'Yeast – Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Lachance, Marc-André. "Paraphyly and (yeast) classification." International Journal of Systematic and Evolutionary Microbiology 66, no. 12 (2016): 4924–29. http://dx.doi.org/10.1099/ijsem.0.001474.

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2

Popova, S., S. Kostova, N. Chaker, and M. Wagenknecht. "Yeast Cells Classification by Kohonen Neural Network." IFAC Proceedings Volumes 37, no. 19 (2004): 213–16. http://dx.doi.org/10.1016/s1474-6670(17)30685-7.

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3

Catty, P., and A. Goffeau. "Identification and phylogenetic classification of eleven putative P-type calcium transport ATPase genes in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe." Bioscience Reports 16, no. 2 (1996): 75–85. http://dx.doi.org/10.1007/bf01206198.

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Calcium is an essential second messenger in yeast metabolism and physiology. So far, only four genes coding for calcium translocating ATPases had been discovered in yeast. The recent completion of the yeast Saccharomyces cerevisiae genome allowed us to identify six new putative Ca++-ATPases encoding genes. Protein sequence homology analysis and phylogenetic classification of all putative Ca++-ATPase gene products from the yeasts Saccharomyces cerevisiae and Schizosacchraomyces pombe reveal three clusters of homologous proteins. Two of them comprises seven proteins which might belong to a new c

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Perera, Amal, Anne Denton, Pratap Kotala, William Jockheck, Willy Valdivia Granda, and William Perrizo. "P-tree classification of yeast gene deletion data." ACM SIGKDD Explorations Newsletter 4, no. 2 (2002): 108–9. http://dx.doi.org/10.1145/772862.772882.

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5

Holmstrup, Palle, and Tony Axéll. "Classification and clinical manifestations of oral yeast infections." Acta Odontologica Scandinavica 48, no. 1 (1990): 57–59. http://dx.doi.org/10.3109/00016359009012734.

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6

Kurtzman, Cletus P. "Use of gene sequence analyses and genome comparisons for yeast systematics." International Journal of Systematic and Evolutionary Microbiology 64, Pt_2 (2014): 325–32. http://dx.doi.org/10.1099/ijs.0.054197-0.

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Detection, identification and classification of yeasts have undergone a major transformation in the past decade and a half following application of gene sequence analyses and genome comparisons. Development of a database (barcode) of easily determined gene sequences from domains 1 and 2 (D1/D2) of large subunit rRNA and from the internal transcribed spacer (ITS) now permits many laboratories to identify species accurately and this has led to a doubling in the number of known species of yeasts over the past decade. Phylogenetic analysis of gene sequences has resulted in major revision of yeast

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7

Popova, S., and V. Mitev. "Application of artificial neural networks for yeast cells classification." Bioprocess Engineering 17, no. 2 (1997): 111. http://dx.doi.org/10.1007/s004490050362.

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8

Wang, Meng-Tian, Qiao-Juan He, Jing-Ke Guo, et al. "The Meridian Tropism and Classification of Red Yeast Rice Investigated by Monitoring Dermal Electrical Potential." Evidence-Based Complementary and Alternative Medicine 2021 (August 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/1696575.

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Red yeast rice is a traditional Chinese medicine and food that has been purported to color food, ferment, and lower cholesterol. In order to study the antioxidative capacity of red yeast rice and the effects on electrical potential difference (EPD) of 12 acupuncture meridians, the pH value, oxidation reduction potential (ORP), ABTS, FRAP, T-SOD, and particle size distribution of red yeast rice were analyzed. 20 volunteers were recruited and randomly divided into two groups, the red yeast rice group (10 g red yeast rice and 40 g water) and control CK group (50 g water). The left 12 acupuncture

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9

García, Esteve-Zarzoso, Crespo, Cabellos, and Arroyo. "Influence of Native Saccharomyces cerevisiae Strains from D.O. “Vinos de Madrid” in the Volatile Profile of White Wines." Fermentation 5, no. 4 (2019): 94. http://dx.doi.org/10.3390/fermentation5040094.

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Yeasts during alcoholic fermentation form a vast number of volatile compounds that significantly influence wine character and quality. It is well known that the capacity to form aromatic compounds is dependent on the yeast strain. Thus, the use of native yeast strains, besides promoting biodiversity, encourages the conservation of regional sensory properties. In this work, we studied the volatile profile of Malvar wines fermented with 102 Saccharomyces cerevisiae yeast strains, isolated from vineyards and cellars belonging to the D.O. “Vinos de Madrid”. The wines elaborated with different S. c

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10

Jafari-Khouzani, Kourosh, Hamid Soltanian-Zadeh, Farshad Fotouhi, Jodi R. Parrish, and Russell L. Finley. "Automated Segmentation and Classification of High Throughput Yeast Assay Spots." IEEE Transactions on Medical Imaging 26, no. 10 (2007): 1401–11. http://dx.doi.org/10.1109/tmi.2007.900694.

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11

Sayin, Ismail, Mehmet Kahraman, Fikrettin Sahin, Dilsad Yurdakul, and Mustafa Culha. "Characterization of Yeast Species Using Surface-Enhanced Raman Scattering." Applied Spectroscopy 63, no. 11 (2009): 1276–82. http://dx.doi.org/10.1366/000370209789806849.

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Surface-enhanced Raman scattering (SERS) is used for the characterization of six yeast species and six isolates. The sample for SERS analysis is prepared by mixing the yeast cells with a four times concentrated silver colloidal suspension. The scanning electron microscopy (SEM) images show that the strength of the interaction between silver nanoparticles and the yeast cells depends on the biochemical structure of the cell wall. The SERS spectra are used to identify the biochemical structures on the yeast cell wall. It is found that the density of –SH and –NH2 groups might be higher on certain

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12

Rodriguez, Susan B., Mark A. Thornton, and Roy J. Thornton. "Raman Spectroscopy and Chemometrics for Identification and Strain Discrimination of the Wine Spoilage Yeasts Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Brettanomyces bruxellensis." Applied and Environmental Microbiology 79, no. 20 (2013): 6264–70. http://dx.doi.org/10.1128/aem.01886-13.

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ABSTRACTThe yeastsZygosaccharomyces bailii,Dekkera bruxellensis(anamorph,Brettanomyces bruxellensis), andSaccharomyces cerevisiaeare the major spoilage agents of finished wine. A novel method using Raman spectroscopy in combination with a chemometric classification tool has been developed for the identification of these yeast species and for strain discrimination of these yeasts. Raman spectra were collected for six strains of each of the yeastsZ. bailii,B. bruxellensis, andS. cerevisiae. The yeasts were classified with high sensitivity at the species level: 93.8% forZ. bailii, 92.3% forB. bru

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13

Huh, Seungil, Donghun Lee, and Robert F. Murphy. "Efficient framework for automated classification of subcellular patterns in budding yeast." Cytometry Part A 75A, no. 11 (2009): 934–40. http://dx.doi.org/10.1002/cyto.a.20793.

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14

Wang, Yan, Liping Qiu, and Mengfei Hu. "Application of yeast in the wastewater treatment." E3S Web of Conferences 53 (2018): 04025. http://dx.doi.org/10.1051/e3sconf/20185304025.

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Yeast, as a very valuable microbial resource, has a good enzyme system in the body and can adapt to a variety of special environments. Therefore, it plays an important role in the biological treatment of wastewater. The classification and basic characteristics of yeast were introduced, and the application of yeast in the field of wastewater treatment such as high concentration organic wastewater, heavy metal ion wastewater and domestic sewage were summarized. With the mature of yeast technology and the development of science and technology, more techniques such as gene engineering and immobili

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15

Bohm, S., D. Frishman, and H. W. Mewes. "Variations of the C2H2 zinc finger motif in the yeast genome and classification of yeast zinc finger proteins." Nucleic Acids Research 25, no. 12 (1997): 2464–69. http://dx.doi.org/10.1093/nar/25.12.2464.

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16

Schmalreck, A. F., M. Lackner, K. Becker, et al. "Phylogenetic Relationships Matter: Antifungal Susceptibility among Clinically Relevant Yeasts." Antimicrobial Agents and Chemotherapy 58, no. 3 (2013): 1575–85. http://dx.doi.org/10.1128/aac.01799-13.

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ABSTRACTThe objective of this study was 2-fold: to evaluate whether phylogenetically closely related yeasts share common antifungal susceptibility profiles (ASPs) and whether these ASPs can be predicted from phylogeny. To address this question, 9,627 yeast strains were collected and tested for their antifungal susceptibility. Isolates were reidentified by considering recent changes in taxonomy and nomenclature. A phylogenetic (PHYLO) code based on the results of multilocus sequence analyses (large-subunit rRNA, small-subunit rRNA, translation elongation factor 1α, RNA polymerase II subunits 1

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17

Tleis, Mohamed, and Fons J. Verbeek. "Machine Learning approach to discriminate Saccharomyces cerevisiae yeast cells using sophisticated image features." Journal of Integrative Bioinformatics 12, no. 3 (2015): 44–64. http://dx.doi.org/10.1515/jib-2015-276.

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Summary In biological research, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, Image analysis platforms are developed to address these problems and to offer analysis per cell as well. The robust segmentation algorithms implemented in such platforms enables us to apply a machine learning approach on the measured cells. Such approach is based on a set of relevant individual cell features extracted from the microscope images of the yeast cells. In this paper, we composed a set of features to r

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18

Waltermann, Christian, and Edda Klipp. "Signal integration in budding yeast." Biochemical Society Transactions 38, no. 5 (2010): 1257–64. http://dx.doi.org/10.1042/bst0381257.

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A complex signalling network governs the response of Saccharomyces cerevisiae to an array of environmental stimuli and stresses. In the present article, we provide an overview of the main signalling system and discuss the mechanisms by which yeast integrates and separates signals from these sources. We apply our classification scheme to a simple semi-quantitative model of the HOG (high-osmolarity glycerol)/FG (filamentous growth)/PH (pheromone) MAPK (mitogen-activated protein kinase) signalling network by perturbing its signal integration mechanisms under combinatorial stimuli of osmotic stres

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19

Allen, Jess, Hazel M. Davey, David Broadhurst, et al. "High-throughput classification of yeast mutants for functional genomics using metabolic footprinting." Nature Biotechnology 21, no. 6 (2003): 692–96. http://dx.doi.org/10.1038/nbt823.

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20

HARA, Naruo, Haruo KUSUNOKI, Hideaki HASHIMOTO, Kyoko SAITO, and Tsuneji SUTO. "Yeast classification based on antigenic properties of lipase from Geotrichum candidum Link." Journal of the agricultural chemical society of Japan 61, no. 4 (1987): 443–50. http://dx.doi.org/10.1271/nogeikagaku1924.61.443.

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21

Paiva, Andre Cunha, Daniel Simões Oliveira, and Leandro Wang Hantao. "A Bottom-Up Approach for Data Mining in Bioaromatization of Beers Using Flow-Modulated Comprehensive Two-Dimensional Gas Chromatography/Mass Spectrometry." Separations 6, no. 4 (2019): 46. http://dx.doi.org/10.3390/separations6040046.

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In this study, we report the combination of comprehensive two-dimensional gas chromatography (GC×GC) with multivariate pattern recognition through template matching for the assignment of the contribution of Brazilian Ale 02 yeast strain to the aroma profile of beer compared with the traditional Nottingham yeast. Volatile organic compounds (VOC) from two beer samples, which were fermented with these yeast strains were sampled using headspace solid-phase microextraction (HS-SPME). The aroma profiles from both beer samples were obtained using GC×GC coupled to a fast scanning quadrupole mass spect

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22

Overy, D. P., J. G. Valdez, and J. C. Frisvad. "Revisions to Penicillium ser. Corymbifera: agents responsible for blue mould storage rot of various flower and vegetable bulbs." Canadian Journal of Botany 83, no. 11 (2005): 1422–33. http://dx.doi.org/10.1139/b05-110.

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Fifteen strains representing each Penicillium ser. Corymbifera taxa were compared using phenotypic and chemotaxonomic characters by cluster analysis and discriminant partial least squares regression. Variability in phenotypic expression of species strains resulted in a more fragmented classification compared with secondary metabolite expression. Although the observed phenotypic expression varied for strains cultured upon the same media, it was possible to classify strains into species groupings based only upon a few distinctive phenotypic traits. Data analysis of secondary metabolite profiles

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23

Gontar, Amelia, Murk J. Bottema, Benjamin J. Binder, and Hayden Tronnolone. "Characterizing the shape patterns of dimorphic yeast pseudohyphae." Royal Society Open Science 5, no. 10 (2018): 180820. http://dx.doi.org/10.1098/rsos.180820.

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Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstratin

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24

Himmelreich, Uwe, Ray L. Somorjai, Brion Dolenko, et al. "Rapid Identification of Candida Species by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy." Applied and Environmental Microbiology 69, no. 8 (2003): 4566–74. http://dx.doi.org/10.1128/aem.69.8.4566-4574.2003.

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ABSTRACT Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared

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25

Yang Yu, Bo, Caglar Elbuken, Carolyn L. Ren, and Jan P. Huissoon. "Image processing and classification algorithm for yeast cell morphology in a microfluidic chip." Journal of Biomedical Optics 16, no. 6 (2011): 066008. http://dx.doi.org/10.1117/1.3589100.

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26

Ferreira, Nelson, Carmela Belloch, Amparo Querol, Paloma Manzanares, Salvador Vallez, and Alberdan Santos. "Yeast Microflora Isolated From Brazilian Cassava Roots: Taxonomical Classification Based on Molecular Identification." Current Microbiology 60, no. 4 (2009): 287–93. http://dx.doi.org/10.1007/s00284-009-9539-z.

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27

Ghafari, Mehran, Justin Clark, Hao-Bo Guo, et al. "Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells." PLOS ONE 16, no. 3 (2021): e0246988. http://dx.doi.org/10.1371/journal.pone.0246988.

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Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had th

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28

Galpert, Deborah, Sara del Río, Francisco Herrera, Evys Ancede-Gallardo, Agostinho Antunes, and Guillermin Agüero-Chapin. "An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species." BioMed Research International 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/748681.

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Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from tw

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Hoeben, P., and G. D. Clark Walker. "An approach to yeast classification by mapping mitochondrial DNA from Dekkera/Brettanomyces and Eeniella genera." Current Genetics 10, no. 5 (1986): 371–79. http://dx.doi.org/10.1007/bf00418409.

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30

Pais, Pedro, Vanda Almeida, Melike Yılmaz, and Miguel C. Teixeira. "Saccharomyces boulardii: What Makes It Tick as Successful Probiotic?" Journal of Fungi 6, no. 2 (2020): 78. http://dx.doi.org/10.3390/jof6020078.

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Saccharomyces boulardii is a probiotic yeast often used for the treatment of GI tract disorders such as diarrhea symptoms. It is genetically close to the model yeast Saccharomyces cerevisiae and its classification as a distinct species or a S. cerevisiae variant has long been discussed. Here, we review the main genetic divergencies between S. boulardii and S. cerevisiae as a strategy to uncover the ability to adapt to the host physiological conditions by the probiotic. S. boulardii does possess discernible phenotypic traits and physiological properties that underlie its success as probiotic, s

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31

Oda, Yasuo, Mitsuhiro Kinoshita, and Kazuaki Kakehi. "Fluorometric Assay of Binding Specificity of Plant Lectins to Yeast Cells by Biotin–Avidin System and Its Application to the Classification of Yeast Cells." Analytical Biochemistry 254, no. 1 (1997): 41–48. http://dx.doi.org/10.1006/abio.1997.2396.

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32

Kolesov, Anton, Dmitry Kamyshenkov, Maria Litovchenko, Elena Smekalova, Alexey Golovizin, and Alex Zhavoronkov. "On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data." Computational and Mathematical Methods in Medicine 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/781807.

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Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of the

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33

Dimopoulou, Maria, Vasiliki Kefalloniti, Panagiotis Tsakanikas, Seraphim Papanikolaou, and George-John E. Nychas. "Assessing the Biofilm Formation Capacity of the Wine Spoilage Yeast Brettanomyces bruxellensis through FTIR Spectroscopy." Microorganisms 9, no. 3 (2021): 587. http://dx.doi.org/10.3390/microorganisms9030587.

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Brettanomyces bruxellensis is a wine spoilage yeast known to colonize and persist in production cellars. However, knowledge on the biofilm formation capacity of B. bruxellensis remains limited. The present study investigated the biofilm formation of 11 B. bruxellensis strains on stainless steel coupons after 3 h of incubation in an aqueous solution. FTIR analysis was performed for both planktonic and attached cells, while comparison of the obtained spectra revealed chemical groups implicated in the biofilm formation process. The increased region corresponding to polysaccharides and lipids clea

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34

Carl, Sarah H., Lea Duempelmann, Yukiko Shimada, and Marc Bühler. "A fully automated deep learning pipeline for high-throughput colony segmentation and classification." Biology Open 9, no. 6 (2020): bio052936. http://dx.doi.org/10.1242/bio.052936.

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ABSTRACTAdenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large numbers of colonies is extremely time-consuming, difficult to reproduce and possibly inaccurate. Using cutting-edge neural networks, we have developed a fully automated pipeline for colony segmentation and classification, which speeds up white/red colony quantification 100-fold over manual counting by an experienced researcher. Our approac

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35

Zheng, Zhiming, and Ya Wang. "DNA binding proteins: outline of functional classification." BioMolecular Concepts 2, no. 4 (2011): 293–303. http://dx.doi.org/10.1515/bmc.2011.023.

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AbstractDNA-binding proteins composed of DNA-binding domains directly affect genomic functions, mainly by performing transcription, DNA replication or DNA repair. Here, we briefly describe the DNA-binding proteins according to these three major functions. Transcription factors that usually bind to specific sequences of DNA could be classified based on their sequence similarity and the structure of the DNA-binding domains, such as basic, zinc-coordinating, helix-turn-helix domains, etc. Most DNA replication factors do not need a specific sequence of DNA, but instead mainly depend on a DNA struc

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KURAMOCHI, MICHIHIRO, and GEORGE KARYPIS. "GENE CLASSIFICATION USING EXPRESSION PROFILES: A FEASIBILITY STUDY." International Journal on Artificial Intelligence Tools 14, no. 04 (2005): 641–60. http://dx.doi.org/10.1142/s0218213005002302.

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As various genome sequencing projects have already been completed or are near completion, genome researchers are shifting their focus to functional genomics. Functional genomics represents the next phase, that expands the biological investigation to studying the functionality of genes of a single organism as well as studying and correlating the functionality of genes across many different organisms. Recently developed methods for monitoring genome-wide mRNA expression changes hold the promise of allowing us to inexpensively gain insights into the function of unknown genes. In this paper we foc

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Buvelot Frei, Stéphanie, Peter B. Rahl, Maria Nussbaum, et al. "Bioinformatic and Comparative Localization of Rab Proteins Reveals Functional Insights into the Uncharacterized GTPases Ypt10p and Ypt11p." Molecular and Cellular Biology 26, no. 19 (2006): 7299–317. http://dx.doi.org/10.1128/mcb.02405-05.

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ABSTRACT A striking characteristic of a Rab protein is its steady-state localization to the cytosolic surface of a particular subcellular membrane. In this study, we have undertaken a combined bioinformatic and experimental approach to examine the evolutionary conservation of Rab protein localization. A comprehensive primary sequence classification shows that 10 out of the 11 Rab proteins identified in the yeast (Saccharomyces cerevisiae) genome can be grouped within a major subclass, each comprising multiple Rab orthologs from diverse species. We compared the locations of individual yeast Rab

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Dröse, S., and K. Altendorf. "Bafilomycins and concanamycins as inhibitors of V-ATPases and P-ATPases." Journal of Experimental Biology 200, no. 1 (1997): 1–8. http://dx.doi.org/10.1242/jeb.200.1.1.

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Bafilomycins and concanamycins, two groups of the plecomacrolide-defined class of macrolide antibiotics, have recently been recognized as important tools for studying the physiological role of vacuolar-type, proton-translocating ATPases (V-ATPases) and ATPases with phosphorylated states (P-ATPases) in animal and plant cells as well as in yeast, fungi and bacteria. The following review will give an account of the classification and function of these antibiotics.

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Nelissen, Bart, Philippe Mordant, Jean-Luc Jonniaux, Rupert De Wachter, and André Goffeau. "Phylogenetic classification of the major superfamily of membrane transport facilitators, as deduced from yeast genome sequencing." FEBS Letters 377, no. 2 (1995): 232–36. http://dx.doi.org/10.1016/0014-5793(95)01380-6.

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40

Vancanneyt, Marc, Eddy Van Lerberge, Jean-Francois Berny, Gregoire L. Hennebert, and Karel Kersters. "The application of whole-cell protein electrophoresis for the classification and identification of basidiomycetous yeast species." Antonie van Leeuwenhoek 61, no. 1 (1992): 69–78. http://dx.doi.org/10.1007/bf00572125.

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41

Alfenore, Sandrine, Marie-Line Délia, and Pierre Strehaiano. "Quantitative analysis of the killer activity of some oenological yeasts. Effect of some additives." OENO One 34, no. 3 (2000): 137. http://dx.doi.org/10.20870/oeno-one.2000.34.3.1004.

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<p style="text-align: justify;">The Killer factor was discovered in 1963. Since this time it has been widely studied and nowadays a lot is known about genetics of the factor, the biochemistry of the toxin and also about the way of action of the toxin on sensitive yeasts. The yeast strains are classified in three groups : killer strains, sensitive strains and neutral strains. The killer strain is able to kill sensitive strains while neutral strains are unable to kill any strain and remain unaffected by the toxin. Certainly this classification depends on the couple of strains (killer and s

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42

Jacques, Noémie, Christine Sacerdot, Meriem Derkaoui, Bernard Dujon, Odile Ozier-Kalogeropoulos, and Serge Casaregola. "Population Polymorphism of Nuclear Mitochondrial DNA Insertions Reveals Widespread Diploidy Associated with Loss of Heterozygosity in Debaryomyces hansenii." Eukaryotic Cell 9, no. 3 (2010): 449–59. http://dx.doi.org/10.1128/ec.00263-09.

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ABSTRACT Debaryomyces hansenii, a yeast that participates in the elaboration of foodstuff, displays important genetic diversity. Our recent phylogenetic classification of this species led to the subdivision of the species into three distinct clades. D. hansenii harbors the highest number of nuclear mitochondrial DNA (NUMT) insertions known so far for hemiascomycetous yeasts. Here we assessed the intraspecific variability of the NUMTs in this species by testing their presence/absence first in 28 strains, with 21 loci previously detected in the completely sequenced strain CBS 767T, and second in

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43

Zhang, Jie, Qiusha Zhu, Haijuan Yu, et al. "Comprehensive Analysis of the Cadmium Tolerance of Abscisic Acid-, Stress- and Ripening-Induced Proteins (ASRs) in Maize." International Journal of Molecular Sciences 20, no. 1 (2019): 133. http://dx.doi.org/10.3390/ijms20010133.

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In plants, abscisic acid-, stress-, and ripening-induced (ASR) proteins have been shown to impart tolerance to multiple abiotic stresses such as drought and salinity. However, their roles in metal stress tolerance are poorly understood. To screen plant Cd-tolerance genes, the yeast-based gene hunting method which aimed to screen Cd-tolerance colonies from maize leaf cDNA library hosted in yeast was carried out. Here, maize ZmASR1 was identified to be putative Cd-tolerant through this survival screening strategy. In silico analysis of the functional domain organization, phylogenetic classificat

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Wu, Min, Xuejuan Li, Fan Zhang, Xiaoli Li, Chee-Keong Kwoh, and Jie Zheng. "In Silico Prediction of Synthetic Lethality by Meta-Analysis of Genetic Interactions, Functions, and Pathways in Yeast and Human Cancer." Cancer Informatics 13s3 (January 2014): CIN.S14026. http://dx.doi.org/10.4137/cin.s14026.

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A major goal in cancer medicine is to find selective drugs with reduced side effect. A pair of genes is called synthetic lethality (SL) if mutations of both genes will kill a cell while mutation of either gene alone will not. Hence, a gene in SL interactions with a cancer-specific mutated gene will be a promising drug target with anti-cancer selectivity. Wet-lab screening approach is still so costly that even for yeast only a small fraction of gene pairs has been covered. Computational methods are therefore important for large-scale discovery of SL interactions. Most existing approaches focus

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Wang, Sui Lou, Wei Liu та Hai Xiang Wang. "Dynamic Models of Cell Growth and its β-Carotene Synthesis from the Red Yeast Mutant". Advanced Materials Research 941-944 (червень 2014): 998–1002. http://dx.doi.org/10.4028/www.scientific.net/amr.941-944.998.

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The fermentative kinetic properties of different batches from the red yeast mutant strain GL-5 with high productive β-carotene were investigated by using a 5-L fermenter. Based on the Logistic equation and the Ganden classification, the kinetic models for the cell growth, base material consumption and yield of product were obtained. These mathematic models were in good consistent with the experimental values, and could provide theoretic basis for controlling and further pilot fermentative production of β-carotene from this mutant strain GL-5.

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Bhandari, Nikita, Satyajeet Khare, Rahee Walambe, and Ketan Kotecha. "Comparison of machine learning and deep learning techniques in promoter prediction across diverse species." PeerJ Computer Science 7 (February 9, 2021): e365. http://dx.doi.org/10.7717/peerj-cs.365.

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Gene promoters are the key DNA regulatory elements positioned around the transcription start sites and are responsible for regulating gene transcription process. Various alignment-based, signal-based and content-based approaches are reported for the prediction of promoters. However, since all promoter sequences do not show explicit features, the prediction performance of these techniques is poor. Therefore, many machine learning and deep learning models have been proposed for promoter prediction. In this work, we studied methods for vector encoding and promoter classification using genome sequ

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YAMADA, YUZO, and AND YASUYOSHI NAKAGAWA. "Significance of the coenzyme Q system in the classification of yeasts and yeast-like organisms. XLVII. The phylogenetic relationships of some heterobasidiomycetous yeast species based on the partial sequences of 18S and 26S ribosomal RNAs." Journal of General and Applied Microbiology 38, no. 6 (1992): 559–65. http://dx.doi.org/10.2323/jgam.38.559.

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Liew, Alan Wee Chung, Yonghui Wu, Hong Yan, and Mengsu Yang. "Effective statistical features for coding and non-coding DNA sequence classification for yeast, C. elegans and human." International Journal of Bioinformatics Research and Applications 1, no. 2 (2005): 181. http://dx.doi.org/10.1504/ijbra.2005.007577.

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Konig, J., C. Julius, S. Baumann, M. Homann, H. U. Goringer, and M. Feldbrugge. "Combining SELEX and the yeast three-hybrid system for in vivo selection and classification of RNA aptamers." RNA 13, no. 4 (2007): 614–22. http://dx.doi.org/10.1261/rna.334307.

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Gromozova, O. M., T. L. Kachur, V. V. Vishnevsky, and O. S. Sychev. "Information Technology of Color Imaging Assessment of Saccharomyces cerevisiae UCM Y-517 Yeast Volutin Granules." Mikrobiolohichnyi Zhurnal 82, no. 5 (2020): 30–35. http://dx.doi.org/10.15407/microbiolj82.05.030.

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The research is devoted to the development of color imaging information technology, which is relevant for evaluating the results of cytochemical research in both biology and medicine. The aim of this work was to study the validity of software algorithms and integrated information technology for the qualitative and quantitative analysis of metachromasia reaction of volutin granules of Saccharomyces cerevisiae UCM Y-517 yeast. Methods. The object of this study was Saccharomyces cerevisiae UCM Y-517 yeast from the Ukrainian collection of microorganisms. The yeast was cultivated for 24 hours at 28

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