885 resultados para dependency
Resumo:
Autophagic flux involves formation of autophagosomes and their degradation by lysosomes. Autophagy can either promote or restrict viral replication. In the case of Dengue virus (DENV) several studies report that autophagy supports the viral replication cycle, and describe an increase of autophagic vesicles (AVs) following infection. However, it is unknown how autophagic flux is altered to result in increased AVs. To address this question, and gain insight into the role of autophagy during DENV infection, we established an unbiased, image-based flow cytometry approach to quantify autophagic flux under normal growth conditions and in response to activation by nutrient deprivation or the mTOR inhibitor Torin1. We found that DENV induced an initial activation of autophagic flux, followed by inhibition of general and specific autophagy. Early after infection, basal and activated autophagic flux was enhanced. However, during established replication, basal and Torin1-activated autophagic flux was blocked, while autophagic flux activated by nutrient deprivation was reduced, indicating a block to AV formation and reduced AV degradation capacity. During late infection AV levels increased as a result of inefficient fusion of autophagosomes with lysosomes. Additionally, endo-lysosomal trafficking was suppressed, while lysosomal activities were increased. We further determined that DENV infection progressively reduced levels of the autophagy receptor SQSTM1/p62 via proteasomal degradation. Importantly, stable over-expression of p62 significantly suppressed DENV replication suggesting a novel role for p62 as viral restriction factor. Overall our findings indicate that in the course of DENV infection, autophagy shifts from a supporting to an anti-viral role, which is countered by DENV.
IMPORTANCE: Autophagic flux is a dynamic process starting with the formation of autophagosomes and ending with their degradation after fusion with lysosomes. Autophagy impacts the replication cycle of many viruses. However, thus far the dynamics of autophagy in case of Dengue virus (DENV) infections has not been systematically quantified. Therefore, we employed high-content, imaging-based flow cytometry to quantify autophagic flux and endo-lysosomal trafficking in response to DENV infection. We report that DENV induced an initial activation of autophagic flux, followed by inhibition of general and specific autophagy. Further, lysosomal activity was increased, but endo-lysosomal trafficking was suppressed confirming the block of autophagic flux. Importantly, we provide evidence that p62, an autophagy receptor, restrict DENV replication and was specifically depleted in DENV-infected cells via increased proteasomal degradation. These results suggest that during DENV infection autophagy shifts from a pro- to an antiviral cellular process, which is counteracted by the virus.
Resumo:
The AMPA-receptor subunit GluA4 is expressed transiently in CA1 pyramidal neurons at the time synaptic connectivity is forming, but its physiological significance is unknown. Here we show that GluA4 expression is sufficient to alter the signaling requirements of long-term potentiation (LTP) and can fully explain the switch in the LTP kinase dependency from PKA to Ca2(+)/calmodulin-dependent protein kinase II during synapse maturation. At immature synapses, activation of PKA leads to a robust potentiation of AMPA-receptor function via the mobilization of GluA4. Analysis of GluA4-deficient mice indicates that this mechanism is critical for neonatal PKA-dependent LTP. Furthermore, lentiviral expression of GluA4 in CA1 neurons conferred a PKA-dependent synaptic potentiation and LTP regardless of the developmental stage. Thus, GluA4 defines the signaling requirements for LTP and silent synapse activation during a critical period of synapse development.
Resumo:
This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.
Resumo:
This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.
Resumo:
Stakeholder analysis plays a critical role in business analysis. However, the majority of the stakeholder identification and analysis methods focus on the activities and processes and ignore the artefacts being processed by human beings. By focusing on the outputs of the organisation, an artefact-centric view helps create a network of artefacts, and a component-based structure of the organisation and its supply chain participants. Since the relationship is based on the components, i.e. after the stakeholders are identified, the interdependency between stakeholders and the focal organisation can be measured. Each stakeholder is associated with two types of dependency, namely the stakeholder’s dependency on the focal organisation and the focal organisation’s dependency on the stakeholder. We identify three factors for each type of dependency and propose the equations that calculate the dependency indexes. Once both types of the dependency indexes are calculated, each stakeholder can be placed and categorised into one of the four groups, namely critical stakeholder, mutual benefits stakeholder, replaceable stakeholder, and easy care stakeholder. The mutual dependency grid and the dependency gap analysis, which further investigates the priority of each stakeholder by calculating the weighted dependency gap between the focal organisation and the stakeholder, subsequently help the focal organisation to better understand its stakeholders and manage its stakeholder relationships.
Resumo:
Business process modelling can help an organisation better understand and improve its business processes. Most business process modelling methods adopt a task- or activity-based approach to identifying business processes. Within our work, we use activity theory to categorise elements within organisations as being either human beings, activities or artefacts. Due to the direct relationship between these three elements, an artefact-oriented approach to organisation analysis emerges. Organisational semiotics highlights the ontological dependency between affordances within an organisation. We analyse the ontological dependency between organisational elements, and therefore produce the ontology chart for artefact-oriented business process modelling in order to clarify the relationship between the elements of an organisation. Furthermore, we adopt the techniques from semantic analysis and norm analysis, of organisational semiotics, to develop the artefact-oriented method for business process modelling. The proposed method provides a novel perspective for identifying and analysing business processes, as well as agents and artefacts, as the artefact-oriented perspective demonstrates the fundamental flow of an organisation. The modelling results enable an organisation to understand and model its processes from an artefact perspective, viewing an organisation as a network of artefacts. The information and practice captured and stored in artefact can also be shared and reused between organisations that produce similar artefacts.
Resumo:
The South American low level jet (SALLJ) of the Eastern Andes is investigated with Regional Climate Model version 3 (RegCM3) simulations during the 2002-2003 austral summer using two convective parameterizations (Grell and Emanuel). The simulated SALLJ is compared with the special observations of SALLJEX (SALLJ Experiment). Both the Grell and Emanuel schemes adequately simulate the low level flow over South America. However, there are some intensity differences. Due to the larger (smaller) convective activity, the Emanuel (Grell) scheme simulates more intense (weaker) low level wind than analysis in the tropics and subtropics. The objectives criteria of Sugahara (SJ) and Bonner (BJ) were used for LLJ identification. When applied to the observations, both criteria suggest a larger frequency of the SALLJ in Santa Cruz, followed by Mariscal, Trinidad and Asuncin. In Mariscal and Asuncin, the diurnal cycle indicates that SJ occurs mainly at 12 UTCs (morning), while the BJ criterion presents the SALLJ as more homogenously distributed. The concentration into two of the four-times-a-day observations does not allow conclusions about the diurnal cycle in Santa Cruz and Trinidad. The simulated wind profiles result in a lower than observed frequency of SALLJ using both the SJ and BJ criteria, with fewer events obtained with the BJ. Due to the stronger simulated winds, the Emanuel scheme produces an equal or greater relative frequency of SALLJ than the Grell scheme. However, the Grell scheme using the SJ criterion simulates the SALLJ diurnal cycle closer to the observed one. Although some discrepancies between observed and simulated mean vertical profiles of the horizontal wind are noted, there is large agreement between the composites of the vertical structure of the SALLJ, especially when the SJ criterion is used with the Grell scheme. On an intraseasonal scale, a larger southward displacement of SALLJ in February and December when compared with January has been noted. The Grell and Emanuel schemes simulated this observed oscillation in the low-level flow. However, the spatial pattern and intensity of rainfall and circulation anomalies simulated by the Grell scheme are closer to the analyses than those obtained with the Emanuel scheme.
Resumo:
This paper presents a two-step pseudo likelihood estimation technique for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the intractable integrals in the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study. An application of it with a binary response variable is presented using a real data set on credit defaults from two Swedish banks. Thanks to the use of two-step estimation technique, the proposed algorithm outperforms conventional pseudo likelihood algorithms in terms of computational time.