Attila citriniventris
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA920153 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Recently, we investigated the effect of the wheat 90K single nucleotide polymorphic (SNP) array and three gene-specific (Ppd-D1, Vrn-A1 and Rht-B1) markers on quantitative trait loci (QTL) detection in a recombinant inbred lines (RILs) population derived from a cross between two spring wheat (Triticum aestivum L.) cultivars, 'Attila' and 'CDC Go', and evaluated for eight agronomic traits at three environments under organic management. The objectives of the present study were to investigate the effect of conventional management on QTL detection in the same mapping population using the same set of markers as the organic management and compare the results with organic management. Here, we evaluated 167 RILs for number of tillers (tillering), flowering time, maturity, plant height, test weight (grain volume weight), 1000 kernel weight, grain yield, and grain protein content at seven conventionally managed environments from 2008 to 2014. Using inclusive composite interval mapping (ICIM) on phenotypic data averaged across seven environments and a subset of 1203 informative markers (1200 SNPs and 3 gene specific markers), we identified a total of 14 QTLs associated with flowering time (1), maturity (2), plant height (1), grain yield (1), test weight (2), kernel weight (4), tillering (1) and grain protein content (2). Each QTL individually explained from 6.1 to 18.4% of the phenotypic variance. Overall, the QTLs associated with each trait explained from 9.7 to 35.4% of the phenotypic and from 22.1 to 90.8% of the genetic variance. Three chromosomal regions on chromosomes 2D (61-66 cM), 4B (80-82 cM) and 5A (296-297 cM) harbored clusters of QTLs associated with two to three traits. The coincidental region on chromosome 5A harbored QTL clusters for both flowering and maturity time, and mapped about 2 cM proximal to the Vrn-A1 gene, which was in high linkage disequilibrium (0.70 ? r2 ? 0.75) with SNP markers that mapped within the QTL confidence interval. Six of the 14 QTLs (one for flowering time and plant height each, and two for maturity and kernel weight each) were common between the conventional and organic management systems, which suggests issues in directly utilizing gene discovery results based on conventional management to make in detail selection (decision) for organic management.
| S-EPMC5291526 | biostudies-literature
Project description:Model published in the paper
Chaperone availability subordinates cell cycle entry to growth and stress
by
David F. Moreno1, Eva Parisi1, Galal Yahya, Federico Vaggi, Attila Csikász-Nagy, Martí Aldea
2019-06-21 | MODEL1808310001 | BioModels
Project description:Introduction:The objective of this study was to define current assistive technology and telecare (ATT) practice for people with dementia living at home. Methods:This is a randomized controlled trial (N = 495) of ATT assessment and ATT installation intervention, compared with control (restricted ATT package). ATT assessment and installation data were collected. Qualitative work identified value networks delivering ATT, established an ATT assessment standard. Results:ATT was delivered by public and not-for-profit telecare networks. ATT assessments showed 52% fidelity to the ATT assessment standard. Areas of assessment most frequently leading to identifying ATT need were daily activities (93%), memory (89%), and problem-solving (83%). ATT needs and recommendations were weakly correlated (? = 0.242; P < .000), with ATT recommendations and installations moderately correlated (? = -0.470; P < .000). Half (53%) of recommended technology was not installed. Safety concerns motivated 38% of installations. Discussion:Assessment recommendations were routinely disregarded at the point of installation. ATT was commonly recommended for safety and seldom for supporting leisure.
| S-EPMC6728826 | biostudies-literature
Project description:This model is described within the paper: A G1 arrest due to proteostasis decline delimits replicative lifespan in yeast
David F. Moreno, Kirsten Jenkins, Sandrine Morlot, Gilles Charvin, Attila Csikász-Nagy, Martí Aldea
To be implemented in Copasi.
Missing Event due to difficulties in SBML conversion to run model upon loading model in COPASI, create an event called Budding.
Set the trigger expression to: {Whi5.ParticleNumber} lt 250*({Whi5i.ParticleNumber}+{Whi5.ParticleNumber})/({Whi5.InitialParticleNumber}+{Whi5i.InitialParticleNumber}) AND {Values[Budding]} lt 1.
Uncheck boxes for fire at initial time if true and trigger must remain true.
Leave priority box blank.
Set delay to None.
There are 3 targets:
the value of budding which should be set to one;
the time at budding which should be set to the time;
and the unsynced generation which should be set to generation.
2019-11-29 | MODEL1901210001 | BioModels