117 resultados para RNA localization
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Drosophila melanogaster, along with all insects and the vertebrates, lacks an RdRp gene. We created transgenic strains of Drosophila melanogaster in which the rrf-1 or ego-1 RdRp genes from C. elegans were placed under the control of the yeast GAL4 upstream activation sequence. Activation of the gene was performed by crossing these lines to flies carrying the GAL4 transgene under the control of various Drosophila enhancers. RT-PCR confirmed the successful expression of each RdRp gene. The resulting phenotypes indicated that introduction of the RdRp genes had no effect on D. melanogaster morphological development. © 2010 Springer Science+Business Media B.V.
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RNA-dependent RNA polymerase (RDR) activities were readily detected in extracts from cauliflower and broccoli florets, Arabidopsis thaliana (L.) Heynh callus tissue and broccoli nuclei. The synthesis of complementary RNA (cRNA) was independent of a RNA primer, whether or not the primer contained a 3′ terminal 2′-O-methyl group or was phosphorylated at the 5′ terminus. cRNA synthesis in plant extracts was not affected by loss-of-function mutations in the DICER-LIKE (DCL) proteins DCL2, DCL3, and DCL4, indicating that RDRs function independently of these DCL proteins. A loss-of-function mutation in RDR1, RDR2 or RDR6 did not significantly reduce the amount of cRNA synthesis. This indicates that these RDRs did not account for the bulk RDR activities in plant extracts, and suggest that either the individual RDRs each contribute a fraction of polymerase activity or another RDR(s) is predominant in the plant extract. © CSIRO 2008.
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Since the discovery of RNAi, its mechanism in plants and animals has been intensively studied, widely exploited as a research tool, and used for a number of potential commercial applications. In this article, we discuss the platforms for delivering RNAi in plants. We provide a brief background to these platforms and concentrate on discussing the more recent advances, comparing the RNAi technologies used in plants with those used in animals, and trying to predict the ways in which RNAi technologies may further develop. © 2005 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
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RNA silencing-related mechanisms have been documented in almost all living organisms and RNA silencing is now used as board term to describe the vast array of related processes involving RNA–RNA, RNA–DNA, RNA–protein or protein–protein interactions that ultimately result in the repression of gene expression. In plants, the parallel RNA silencing pathways have evolved to extraordinary levels of complexity and diversity, playing crucial roles in providing protection against invading nucleic acids derived from viruses or replicating transposons, controlling chromatin modifications as well as regulating endogenous gene expression to ensure normal plant growth and development. The aims of this chapter are (1) to provide an overview of the initial curious observations of RNA silencing-related phenomena in plants, (2) to outline the parallel gene silencing pathways of plants, and (3) to discuss current applications of RNA silencing technologies to not only study but also modify plant development
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Barley yellow dwarf virus-PAV (BYDV-PAV) is the most serious and widespread virus of cereals worldwide. Natural resistance genes against this luteovirus give inadequate control, and previous attempts to introduce synthetic resistance into cereals have produced variable results. In an attempt to generate barley with protection against BYDV-PAV, plants were transformed with a transgene designed to produce hairpin (hp)RNA containing BYDV-PAV sequences. From 25 independent barley lines transformed with the BYDV-PAV hpRNA construct, nine lines showed extreme resistance to the virus and the majority of these contained a single transgene. In the progeny of two independent transgenic lines, inheritance of a single transgene consistently correlated with protection against BYDV-PAV. This protection was rated as immunity because the virus could not be detected in the challenged plants by ELISA nor recovered by aphid feeding experiments. In the field, BYDV-PAV is sometimes associated with the related luteovirus Cereal yellow dwarf virus-RPV (CYDV-RPV). When the transgenic plants were challenged with BYDV-PAV and CYDV-RPV together, the plants were susceptible to CYDV-RPV but immune to BYDV-PAV. This shows that the immunity is virus-specific and not broken down by the presence of CYDV. It suggests that CYDV-RPV does not encode a silencing-suppressor gene or that its product does not protect BYDV-PAV against the plant's RNAi-like defence mechanism. Either way, our results indicate that the BYDV-PAV immunity will be robust in the field and is potentially useful in minimizing losses in cereal production worldwide.
Resumo:
Barley yellow dwarf luteovirus-GPV (BYDV-GPV) is a common problem in Chinese wheat crops but is unrecorded elsewhere. A defining characteristic of GPV is its capacity to be transmitted efficiently by both Schizaphis graminum and Rhopaloshiphum padi. This dual aphid species transmission contrasts with those of BYDV-RPV and BYDV-SGV, globally distributed viruses, which are efficiently transmitted only by Rhopaloshiphum padi and Schizaphis graminum respectively. The viral RNA sequences encoding the coat protein (22K) gene, the movement protein (17K) gene, the region surrounding the conserved GDD motif of the polymerase gene and the intergenic sequences between these genes were determined for GPV and an Australian isolate of BYDV-RPV (RPVa). In all three genes, the sequences of GPV and RPVa were more similar to those of an American isolate of BYDV-RPV (RPVu) than to any other luteovirus for which there is data available. RPVa and RPVu were very similar, especially their coat proteins which had 97% identity at the amino acid level. The coat protein of GPV had 76% and 78% amino acid identity with RPVa and RPVu respectively. The data suggest that RPVu and RPVa are correctly named as strains of the same serotype and that GPV is sufficiently different from either RPV strain to be considered a distinct BYDV type. The coat protein and movement protein genes of GPV are very dissimilar to SGV. The polymerase sequences of RPVu, RPVa and GPV show close affinities with those of the sobemo-like luteoviruses and little similarity with those of the carmo-like luteoviruses. The sequences of the coat proteins, movement proteins and the polymerase segments of BYDV serotypes, other than RPV and GPV, form a cluster that is separate from their counterpart sequences from dicot-infecting luteoviruses. The RPV and GPV isolates consistently fall within a dicot-infecting cluster. This suggests that RPV and GPV evolved from within this group of viruses. Since these other viruses all infect dicots it seems likely that their common ancestor infected a dicot and that RPV and GPV evolved from a virus that switched hosts from a dicot to a monocot.
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The complete nucleotide sequence of genome segment S4 of rice ragged stunt oryzavirus (RRSV, Thai-isolate) was determined. The 3823 bp sequence contains two large open reading frames (ORFs). ORF1, spanning nucleotides 12 to 3776, is capable of encoding a protein of M(r) 141,380 (P4a). The P4a amino acid sequence predicted from the nucleotide sequence contains sequence motifs conserved in RNA-dependent RNA polymerases (RDRPs). When compared for evolutionary relationships with RDRPs of other reoviruses using the amino acid sequences around the conserved GDD motif, P4a was shown to be more related to Nilaparvata lugens reovirus and reovirus serotype 3 than to rice dwarf phytoreovirus, bovine rotavirus or bluetongue virus. The ORF2, spanning nucleotides 491 to 1468, is out of frame with ORF1 and is capable of encoding a protein of 36, 920 (P4b). Coupled in vitro transcription-translation from cloned ORF2 in wheat germ extract confirmed the existence of ORF2 but in vivo production and possible function of P4b is yet to be determined.
Resumo:
An RNA molecule with properties of a satellite RNA was found in an isolate of barley yellow dwarf virus (BYDV), RPV serotype. It is 322 nucleotides long, single-stranded, and does not hybridize to the viral genome. Dimers of the RNA, which presumably represent replicative intermediates, were able to self-cleave into monomers. In vitro transcripts from cDNA clones were capable of self-cleavage in both the plus (encapsidated) and minus orientations. The sequence flanking the minus strand cleavage site contained a consensus " hammerhead" structure, similar to those found in other self-cleaving satellite RNAs. Although related to the hammerhead structure, sequences flanking the plus strand termini showed differences from the consensus and may be folded into a different structure containing a pseudoknot. © 1991.
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At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.
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Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.
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Glucocorticoids, released in high concentrations from the adrenal cortex during stressful experiences, bind to glucocorticoid receptors in nuclear and peri-nuclear sites in neuronal somata. Their classically known mode of action is to induce gene promoter receptors to alter gene transcription. Nuclear glucocorticoid receptors are particularly dense in brain regions crucial for memory, including memory of stressful experiences, such as the hippocampus and amygdala. While it has been proposed that glucocorticoids may also act via membrane bound receptors, the existence of the latter remains controversial. Using electron microscopy, we found glucocorticoid receptors localized to non-genomic sites in rat lateral amygdala, glia processes, presynaptic terminals, neuronal dendrites, and dendritic spines including spine organelles and postsynaptic membrane densities. The lateral nucleus of the amygdala is a region specifically implicated in the formation of memories for stressful experiences. These newly observed glucocorticoid receptor immunoreactive sites were in addition to glucocorticoid receptor immunoreactive signals observed using electron and confocal microscopy in lateral amygdala principal neuron and GABA neuron soma and nuclei, cellular domains traditionally associated with glucocorticoid immunoreactivity. In lateral amygdala, glucocorticoid receptors are thus also localized to non-nuclear-membrane translocation sites, particularly dendritic spines, where they show an affinity for postsynaptic membrane densities, and may have a specialized role in modulating synaptic transmission plasticity related to fear and emotional memory.
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The molecular mechanisms involved in non‑small cell lung cancer tumourigenesis are largely unknown; however, recent studies have suggested that long non-coding RNAs (lncRNAs) are likely to play a role. In this study, we used public databases to identify an mRNA-like, candidate long non-coding RNA, GHSROS (GHSR opposite strand), transcribed from the antisense strand of the ghrelin receptor gene, growth hormone secretagogue receptor (GHSR). Quantitative real-time RT-PCR revealed higher expression of GHSROS in lung cancer tissue compared to adjacent, non-tumour lung tissue. In common with many long non-coding RNAs, GHSROS is 5' capped and 3' polyadenylated (mRNA-like), lacks an extensive open reading frame and harbours a transposable element. Engineered overexpression of GHSROS stimulated cell migration in the A549 and NCI-H1299 non-small cell lung cancer cell lines, but suppressed cell migration in the Beas-2B normal lung-derived bronchoepithelial cell line. This suggests that GHSROS function may be dependent on the oncogenic context. The identification of GHSROS, which is expressed in lung cancer and stimulates cell migration in lung cancer cell lines, contributes to the growing number of non-coding RNAs that play a role in the regulation of tumourigenesis and metastatic cancer progression.
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Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.
Resumo:
Disjoint top-view networked cameras are among the most commonly utilized networks in many applications. One of the open questions for these cameras' study is the computation of extrinsic parameters (positions and orientations), named extrinsic calibration or localization of cameras. Current approaches either rely on strict assumptions of the object motion for accurate results or fail to provide results of high accuracy without the requirement of the object motion. To address these shortcomings, we present a location-constrained maximum a posteriori (LMAP) approach by applying known locations in the surveillance area, some of which would be passed by the object opportunistically. The LMAP approach formulates the problem as a joint inference of the extrinsic parameters and object trajectory based on the cameras' observations and the known locations. In addition, a new task-oriented evaluation metric, named MABR (the Maximum value of All image points' Back-projected localization errors' L2 norms Relative to the area of field of view), is presented to assess the quality of the calibration results in an indoor object tracking context. Finally, results herein demonstrate the superior performance of the proposed method over the state-of-the-art algorithm based on the presented MABR and classical evaluation metric in simulations and real experiments.