The deleterious effects of oxidative and nitrosative stress on palmitoylation, membrane lipid rafts and lipid-based cellular signalling : new drug targets in neuroimmune disorders

Oxidative and nitrosative stress (O&NS) is causatively implicated in the pathogenesis of Alzheimer’s and Parkinson’s disease, multiple sclerosis, chronic fatigue syndrome, schizophrenia and depression. Many of the consequences stemming from O&NS, including damage to proteins, lipids and DNA, are well known, whereas the effects of O&NS on lipoprotein-based cellular signalling involving palmitoylation and plasma membrane lipid rafts are less well documented. The aim of this narrative review is to discuss the mechanisms involved in lipid-based signalling, including palmitoylation, membrane/lipid raft (MLR) and n-3 polyunsaturated fatty acid (PUFA) functions, the effects of O&NS processes on these processes and their role in the abovementioned diseases. S-palmitoylation is a post-translational modification, which regulates protein trafficking and association with the plasma membrane, protein subcellular location and functions. Palmitoylation and MRLs play a key role in neuronal functions, including glutamatergic neurotransmission, and immune-inflammatory responses. Palmitoylation, MLRs and n-3 PUFAs are vulnerable to the corruptive effects of O&NS. Chronic O&NS inhibits palmitoylation and causes profound changes in lipid membrane composition, e.g. n-3 PUFA depletion, increased membrane permeability and reduced fluidity, which together lead to disorders in intracellular signal transduction, receptor dysfunction and increased neurotoxicity. Disruption of lipid-based signalling is a source of the neuroimmune disorders involved in the pathophysiology of the abovementioned diseases. n-3 PUFA supplementation is a rational therapeutic approach targeting disruptions in lipid-based signalling.

Can cancer therapy be achieved by bridging apoptosis and autophagy: A method based on microRNA-dependent gene therapy and phytochemical targets

A failure of a cell to self destruct has long been associated with cancer progression and development. The fact that tumour cells may not instigate cell arrest or activate cell death mechanisms upon cancer drug delivery is a major concern. Autophagy is a mechanism whereby cell material can be engulfed and digested while apoptosis is a self-killing mechanism, both capable of hindering multiplication after cell injury. In particular situations, autophagy and apoptosis seem to co-exist simultaneously or interdependently with the aid of mutual proteins. This review covers roles of microRNAs and chemopreventive agents and makes an attempt at outlining possible partnerships in maximizing cancer cell death with minimal normal cell damage.

Strand Analysis, a free online program for the computational identification of the best RNA interference (RNAi) targets based on Gibbs free energy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Food Handling Practices, Knowledge and Beliefs of Families with Young Children Based on the Health Belief Model

Objective: To determine current food handling practices, knowledge and beliefs of primary food handlers with children 10 years old and the relationship between these components. Design: Surveys were developed based on FightBac!™ concepts and the Health Belief Model (HBM) construct. Participants: The majority of participants (n= 503) were females (67%), Caucasians (80%), aged between 30 to 49 years old (83%), had one or two children (83%), prepared meals all or most of the time (76%) and consumed meals away from home three times or less per week (66%). Analysis: Descriptive statistics and inferential statistics using Spearman’s rank correlation coefficient (rho) (p<0.05 and one-tail) and Chi-square were used to examine frequency and correlations. Results: Few participants reached the food safety objectives of Healthy People 2010 for safe food handling practices (79%). Mixed results were reported for perceived susceptibility. Only half of the participants (53-54%) reported high perceived severity for their children if they contracted food borne illness. Most participants were confident of their food handling practices for their children (91%) and would change their food handling practices if they or their family members previously experienced food poisoning (79%). Participants’ reasons for high self-efficacy were learning from their family and independently acquiring knowledge and skills from the media, internet or job. The three main barriers to safe food handling were insufficient time, lots of distractions and lack of control of the food handling practices of other people in the household. Participants preferred to use food safety information that is easy to understand, has scientific facts, causes feelings of health-threat and has lots of pictures or visuals. Participants demonstrate high levels of knowledge in certain areas of the FightBac!TM concepts but lacked knowledge in other areas. Knowledge and cues to action were most supportive of the HBM construct, while perceived susceptibility was least supportive of the HBM construct. Conclusion: Most participants demonstrate many areas to improve in their food handling practices, knowledge and beliefs. Adviser: Julie A. Albrecht

Optimized edge appearance probability for cooperative localization based on tree-reweighted nonparametric belief propagation

Nonparametric belief propagation (NBP) is a well-known particle-based method for distributed inference in wireless networks. NBP has a large number of applications, including cooperative localization. However, in loopy networks NBP suffers from similar problems as standard BP, such as over-confident beliefs and possible nonconvergence. Tree-reweighted NBP (TRW-NBP) can mitigate these problems, but does not easily lead to a distributed implementation due to the non-local nature of the required so-called edge appearance probabilities. In this paper, we propose a variation of TRWNBP, suitable for cooperative localization in wireless networks. Our algorithm uses a fixed edge appearance probability for every edge, and can outperform standard NBP in dense wireless networks.

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting problem in the networks with loops, NBP’s convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks.

A Belief System's Organization Based on a Computational Model of the Dynamic Context: First Approximation

In this article we present a model of organization of a belief system based on a set of binary recursive functions that characterize the dynamic context that modifies the beliefs. The initial beliefs are modeled by a set of two-bit words that grow, update, and generate other beliefs as the different experiences of the dynamic context appear. Reason is presented as an emergent effect of the experience on the beliefs. The system presents a layered structure that allows a functional organization of the belief system. Our approach seems suitable to model different ways of thinking and to apply to different realistic scenarios such as ideologies.

Targets of opportunity: community based alcohol programs. Technical report.

Mode of access: Internet.

A simple, sensitive and selective quantum-dot-based western blot method for the simultaneous detection of multiple targets from cell lysates

Quantum dots (Qdots) are fluorescent nanoparticles that have great potential as detection agents in biological applications. Their optical properties, including photostability and narrow, symmetrical emission bands with large Stokes shifts, and the potential for multiplexing of many different colours, give them significant advantages over traditionally used fluorescent dyes. Here, we report the straightforward generation of stable, covalent quantum dot-protein A/G bioconjugates that will be able to bind to almost any IgG antibody, and therefore can be used in many applications. An additional advantage is that the requirement for a secondary antibody is removed, simplifying experimental design. To demonstrate their use, we show their application in multiplexed western blotting. The sensitivity of Qdot conjugates is found to be superior to fluorescent dyes, and comparable to, or potentially better than, enhanced chemiluminescence. We show a true biological validation using a four-colour multiplexed western blot against a complex cell lysate background, and have significantly improved previously reported non-specific binding of the Qdots to cellular proteins.

Decentralised supply chain formation:a belief propagation-based approach

Decentralised supply chain formation involves determining the set of producers within a network able to supply goods to one or more consumers at the lowest cost. This problem is frequently tackled using auctions and negotiations. In this paper we show how it can be cast as an optimisation of a pairwise cost function. Optimising this class of functions is NP-hard but good approximations to the global minimum can be obtained using Loopy Belief Propagation (LBP). Here we detail a LBP-based approach to the supply chain formation problem, involving decentralised message-passing between potential participants. Our approach is evaluated against a well-known double-auction method and an optimal centralised technique, showing several improvements: it obtains better solutions for most networks that admit a competitive equilibrium Competitive equilibrium as defined in [3] is used as a means of classifying results on certain networks to allow for minor inefficiencies in their auction protocol and agent bidding strategies. while also solving problems where no competitive equilibrium exists, for which the double-auction method frequently produces inefficient solutions.