19,657 research outputs found
Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat
A crop can be viewed as a complex system with outputs (e.g. yield) that are
affected by inputs of genetic, physiology, pedo-climatic and management
information. Application of numerical methods for model exploration assist in
evaluating the major most influential inputs, providing the simulation model is
a credible description of the biological system. A sensitivity analysis was
used to assess the simulated impact on yield of a suite of traits involved in
major processes of crop growth and development, and to evaluate how the
simulated value of such traits varies across environments and in relation to
other traits (which can be interpreted as a virtual change in genetic
background). The study focused on wheat in Australia, with an emphasis on
adaptation to low rainfall conditions. A large set of traits (90) was evaluated
in a wide target population of environments (4 sites x 125 years), management
practices (3 sowing dates x 2 N fertilization) and (2 levels). The
Morris sensitivity analysis method was used to sample the parameter space and
reduce computational requirements, while maintaining a realistic representation
of the targeted trait x environment x management landscape ( 82 million
individual simulations in total). The patterns of parameter x environment x
management interactions were investigated for the most influential parameters,
considering a potential genetic range of +/- 20% compared to a reference. Main
(i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity
indices calculated for most of APSIM-Wheat parameters allowed the identifcation
of 42 parameters substantially impacting yield in most target environments.
Among these, a subset of parameters related to phenology, resource acquisition,
resource use efficiency and biomass allocation were identified as potential
candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On
Scientific opinion of Flavouring Group Evaluation 502 (FGE.502): grill flavour ‘Grillin’ 5078’
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Ezrin interacts with the SARS coronavirus spike protein and restrains infection at the entry stage
© 2012 Millet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Entry of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and its envelope fusion with host cell membrane are controlled by a series of complex molecular mechanisms, largely dependent on the viral envelope glycoprotein Spike (S). There are still many unknowns on the implication of cellular factors that regulate the entry process. Methodology/Principal Findings: We performed a yeast two-hybrid screen using as bait the carboxy-terminal endodomain of S, which faces the cytosol during and after opening of the fusion pore at early stages of the virus life cycle. Here we show that the ezrin membrane-actin linker interacts with S endodomain through the F1 lobe of its FERM domain and that both the eight carboxy-terminal amino-acids and a membrane-proximal cysteine cluster of S endodomain are important for this interaction in vitro. Interestingly, we found that ezrin is present at the site of entry of S-pseudotyped lentiviral particles in Vero E6 cells. Targeting ezrin function by small interfering RNA increased S-mediated entry of pseudotyped particles in epithelial cells. Furthermore, deletion of the eight carboxy-terminal amino acids of S enhanced S-pseudotyped particles infection. Expression of the ezrin dominant negative FERM domain enhanced cell susceptibility to infection by SARS-CoV and S pseudotyped particles and potentiated S-dependent membrane fusion. Conclusions/Significance: Ezrin interacts with SARS-CoV S endodomain and limits virus entry and fusion. Our data present a novel mechanism involving a cellular factor in the regulation of S-dependent early events of infection.This work was supported by the Research Grant Council of Hong Kong (RGC#760208)and the RESPARI project of the International Network of Pasteur Institutes
Natural variation at XND1 impacts root hydraulics and trade-off for stress responses in Arabidopsis
Soil water uptake by roots is a key component of plant performance and adaptation to adverse environments. Here, we use a genome-wide association analysis to identify the XYLEM NAC DOMAIN 1 (XND1) transcription factor as a negative regulator of Arabidopsis root hydraulic conductivity (Lp). The distinct functionalities of a series of natural XND1 variants and a single nucleotide polymorphism that determines XND1 translation efficiency demonstrate the significance of XND1 natural variation at species-wide level. Phenotyping of xnd1 mutants and natural XND1 variants show that XND1 modulates Lp through action on xylem formation and potential indirect effects on aquaporin function and that it diminishes drought stress tolerance. XND1 also mediates the inhibition of xylem formation by the bacterial elicitor flagellin and counteracts plant infection by the root pathogen Ralstonia solanacearum. Thus, genetic variation at XND1, and xylem differentiation contribute to resolving the major trade-off between abiotic and biotic stress resistance in Arabidopsis
The impact of within-herd genetic variation upon inferred transmission trees for foot-and-mouth disease virus
Full-genome sequences have been used to monitor the fine-scale dynamics of epidemics caused by RNA viruses. However, the ability of this approach to confidently reconstruct transmission trees is limited by the knowledge of the genetic diversity of viruses that exist within different epidemiological units. In order to address this question, this study investigated the variability of 45 foot-and-mouth disease virus (FMDV) genome sequences (from 33 animals) that were collected during 2007 from eight premises (10 different herds) in the United Kingdom. Bayesian and statistical parsimony analysis demonstrated that these sequences exhibited clustering which was consistent with a transmission scenario describing herd-to-herd spread of the virus. As an alternative to analysing all of the available samples in future epidemics, the impact of randomly selecting one sequence from each of these herds was used to assess cost-effective methods that might be used to infer transmission trees during FMD outbreaks. Using these approaches, 85% and 91% of the resulting topologies were either identical or differed by only one edge from a reference tree comprising all of the sequences generated within the outbreak. The sequence distances that accrued during sequential transmission events between epidemiological units was estimated to be 4.6 nucleotides, although the genetic variability between viruses recovered from chronic carrier animals was higher than between viruses from animals with acute-stage infection: an observation which poses challenges for the use of simple approaches to infer transmission trees. This study helps to develop strategies for sampling during FMD outbreaks, and provides data that will guide the development of further models to support control policies in the event of virus incursions into FMD free countries
Gene flow across geographical barriers - scaling limits of random walks with obstacles
In this paper, we study the scaling limit of a class of random walks which
behave like simple random walks outside of a bounded region around the origin
and which are subject to a partial reflection near the origin. If the
probability of crossing the barrier scales as as we rescale
space by and time by , we obtain a non trivial scaling limit
which behaves like reflected Brownian motion until its local time at the origin
reaches an independent exponential variable. It then follows reflected Brownian
motion on the other side of the origin until its local time at the origin
reaches another exponential level, and so on. We give a martingale problem
characterisation of this process as well as another construction and an
explicit formula for its transition density. This result has applications in
the field of population genetics where such a random walk is used to trace the
position of one's ancestor in the past in the presence of a barrier to gene
flow.Comment: Stochastic Processes and Their Applications, in pres
Heterologous expression screens in Nicotiana benthamiana identify a candidate effector of the wheat Yellow Rust Pathogen that associates with processing bodies
Rust fungal pathogens of wheat (Triticum spp.) affect crop yields worldwide. The molecular mechanisms underlying the virulence of these pathogens remain elusive, due to the limited availability of suitable molecular genetic research tools. Notably, the inability to perform high-throughput analyses of candidate virulence proteins (also known as effectors) impairs progress. We previously established a pipeline for the fast-forward screens of rust fungal candidate effectors in the model plant Nicotiana benthamiana. This pipeline involves selecting candidate effectors in silico and performing cell biology and protein-protein interaction assays in planta to gain insight into the putative functions of candidate effectors. In this study, we used this pipeline to identify and characterize sixteen candidate effectors from the wheat yellow rust fungal pathogen Puccinia striiformis f sp tritici. Nine candidate effectors targeted a specific plant subcellular compartment or protein complex, providing valuable information on their putative functions in plant cells. One candidate effector, PST02549, accumulated in processing bodies (P-bodies), protein complexes involved in mRNA decapping, degradation, and storage. PST02549 also associates with the P-body-resident ENHANCER OF mRNA DECAPPING PROTEIN 4 (EDC4) from N. benthamiana and wheat. We propose that P-bodies are a novel plant cell compartment targeted by pathogen effectors
Analysis of the meiotic segregation in intergeneric hybrids of tilapias
Tilapia species exhibit a large ecological diversity and an important propensity to interspecific hybridisation. This has been shown in the wild and used in aquaculture. However, despite its important evolutionary implications, few studies have focused on the analysis of hybrid genomes and their meiotic segregation. Intergeneric hybrids between Oreochromis niloticus and Sarotherodon melanotheron, two species highly differentiated genetically, ecologically, and behaviourally, were produced experimentally. The meiotic segregation of these hybrids was analysed in reciprocal second generation hybrid (F2) and backcross families and compared to the meiosis of both parental species, using a panel of 30 microsatellite markers. Hybrid meioses showed segregation in accordance to Mendelian expectations, independent from sex and the direction of crosses. In addition, we observed a conservation of linkage associations between markers, which suggests a relatively similar genome structure between the two parental species and the apparent lack of postzygotic incompatibility, despite their important divergence. These results provide genomics insights into the relative ease of hybridisation within cichlid species when prezygotic barriers are disrupted. Overall our results support the hypothesis that hybridisation may have played an important role in the evolution and diversification of cichlids
Random Forests for Big Data
Big Data is one of the major challenges of statistical science and has
numerous consequences from algorithmic and theoretical viewpoints. Big Data
always involve massive data but they also often include online data and data
heterogeneity. Recently some statistical methods have been adapted to process
Big Data, like linear regression models, clustering methods and bootstrapping
schemes. Based on decision trees combined with aggregation and bootstrap ideas,
random forests were introduced by Breiman in 2001. They are a powerful
nonparametric statistical method allowing to consider in a single and versatile
framework regression problems, as well as two-class and multi-class
classification problems. Focusing on classification problems, this paper
proposes a selective review of available proposals that deal with scaling
random forests to Big Data problems. These proposals rely on parallel
environments or on online adaptations of random forests. We also describe how
related quantities -- such as out-of-bag error and variable importance -- are
addressed in these methods. Then, we formulate various remarks for random
forests in the Big Data context. Finally, we experiment five variants on two
massive datasets (15 and 120 millions of observations), a simulated one as well
as real world data. One variant relies on subsampling while three others are
related to parallel implementations of random forests and involve either
various adaptations of bootstrap to Big Data or to "divide-and-conquer"
approaches. The fifth variant relates on online learning of random forests.
These numerical experiments lead to highlight the relative performance of the
different variants, as well as some of their limitations
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