The role of PKA and PKC epsilon pathways in prostaglandin E(2)-mediated hypernociception

Background and purpose: Protein kinase (PK) A and the epsilon isoform of PKC (PKC epsilon) are involved in the development of hypernociception (increased sensitivity to noxious or innocuous stimuli) in several animal models of acute and persistent inflammatory pain. The present study evaluated the contribution of PKA and PKC epsilon to the development of prostaglandin E(2) (PGE(2))-induced mechanical hypernociception. Experimental approach: Prostaglandin E(2)-induced mechanical hypernociception was assessed by constant pressure rat paw test. The activation of PKA or PKC epsilon was evaluated by radioactive enzymic assay in the dorsal root ganglia (DRG) of sensory neurons from the hind paws. Key results: Hypernociception induced by PGE(2) (100 ng) by intraplantar (i.pl.) injection, was reduced by i.pl. treatment with inhibitors of PKA [A-kinase-anchoring protein St-Ht31 inhibitor peptide (AKAPI)], PKC epsilon (PKC epsilon I) or adenylyl cyclase. PKA activity was essential in the early phase of the induction of hypernociception, whereas PKC activity was involved in the maintenance of the later phase of hypernociception. In the DRG (L4-L5), activity of PKA increased at 30 min after injection of PGE(2) but PKC activity increased only after 180 min. Moreover, i.pl. injection of the catalytic subunit of PKA induced hypernociception which was markedly reduced by pretreatment with an inhibitor of PKC epsilon, while the hypernociception induced by paw injection of PKC epsilon agonist was not affected by an inhibitor of PKA (AKAPI). Conclusions and implications: Taken together, these findings are consistent with the suggestion that PKA activates PKC epsilon, which is a novel mechanism of interaction between these kinases during the development of PGE(2)-induced mechanical hypernociception.

The bubbles or the boiling pot? An ecosystemic approach to culture, environment and quality of life

For the diagnosis and prognosis of the problems of quality of life, a multidisciplinary ecosystemic approach encompasses four dimensions of being-in-the-world, as donors and recipients: intimate, interactive, social and biophysical. Social, cultural and environmental vulnerabilities are understood and dealt with, in different circumstances of space and time, as the conjugated effect of all dimensions of being-in-the-world, as they induce the events (deficits and assets), cope with consequences (desired or undesired) and contribute for change. Instead of fragmented and reduced representations of reality, diagnosis and prognosis of cultural, educational, environmental and health problems considers the connections (assets) and ruptures (deficits) between the different dimensions, providing a planning model to develop and evaluate research, teaching programmes, public policies and field projects. The methodology is participatory, experiential and reflexive; heuristic-hermeneutic processes unveil cultural and epistemic paradigms that orient subject-object relationships; giving people the opportunity to reflect on their own realities, engage in new experiences and find new ways to live better in a better world. The proposal is a creative model for thought and practice, providing many opportunities for discussion, debate and development of holistic projects integrating different scientific domains (social sciences, psychology, education, philosophy, etc.).

Light modulates the melanophore response to alpha-MSH in Xenopus laevis: An analysis of the signal transduction crosstalk mechanisms involved

Melanin granule (melanosome) dispersion within Xenopus laevis melanophores is evoked either by light or alpha-MSH. We have previously demonstrated that the initial biochemical steps of light and alpha-MSH signaling are distinct, since the increase in cAMP observed in response to alpha-MSH was not seen after light exposure. cAMP concentrations in response to alpha-MSH were significantly lower in cells pre-exposed to light as compared to the levels in dark-adapted melanophores. Here we demonstrate the presence of an adenylyl cyclase (AC) in the Xenopus melanophore, similar to the mammalian type IX which is inhibited by Ca(2+)-calmodulin-activated phosphatase. This finding supports the hypothesis that the cyclase could be negatively modulated by a light-promoted Ca(2+) increase. In fact, the activity of calcineurin PP2B phosphatase was increased by light, which could result in AC IX inhibition, thus decreasing the response to alpha-MSH. St-Ht31, a disrupting agent of protein kinase A (PKA)-anchoring kinase A protein (AKAP) complex totally blocked the melanosome dispersing response to alpha-MSH, but did not impair the photo-response in Xenopus melanophores. Sequence comparison of a melanophore AKAP partial clone with GenBank sequences showed that the anchoring protein was a gravin-like adaptor previously sequenced from Xenopus non-pigmentary tissues. Co-immunoprecipitation of Xenopus AKAP and the catalytic subunit of PKA demonstrated that PKA is associated with AKAP and it is released in the presence of alpha-MSH. We conclude that in X laevis melanophores, AKAP12 (gravin-like) contains a site for binding the inactive PKA thus compartmentalizing PKA signaling and also possesses binding sites for PKC. Light diminishes alpha-MSH-induced increase of cAMP by increasing calcineurin (PP2B) activity, which in turn inhibits adenylyl cyclase type IX, and/or by activating PKC, which phosphorylates the gravin-like molecule, thus destabilizing its binding to the cell membrane. (C) 2009 Elsevier Inc. All rights reserved.

Genomic Analysis of Wild Tomato Introgressions Determining Metabolism- and Yield-Associated Traits

With the aim of determining the genetic basis of metabolic regulation in tomato fruit, we constructed a detailed physical map of genomic regions spanning previously described metabolic quantitative trait loci of a Solanum pennellii introgression line population. Two genomic libraries from S. pennellii were screened with 104 colocated markers from five selected genomic regions, and a total of 614 bacterial artificial chromosome (BAC)/cosmids were identified as seed clones. Integration of sequence data with the genetic and physical maps of Solanum lycopersicum facilitated the anchoring of 374 of these BAC/cosmid clones. The analysis of this information resulted in a genome-wide map of a nondomesticated plant species and covers 10% of the physical distance of the selected regions corresponding to approximately 1% of the wild tomato genome. Comparative analyses revealed that S. pennellii and domesticated tomato genomes can be considered as largely colinear. A total of 1,238,705 bp from both BAC/cosmid ends and nine large insert clones were sequenced, annotated, and functionally categorized. The sequence data allowed the evaluation of the level of polymorphism between the wild and cultivated tomato species. An exhaustive microsynteny analysis allowed us to estimate the divergence date of S. pennellii and S. lycopersicum at 2.7 million years ago. The combined results serve as a reference for comparative studies both at the macrosyntenic and microsyntenic levels. They also provide a valuable tool for fine-mapping of quantitative trait loci in tomato. Furthermore, they will contribute to a deeper understanding of the regulatory factors underpinning metabolism and hence defining crop chemical composition.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

An approach for solving the lot-sizing problem of a market-driven foundry

Foundries can be found all over Brazil and they are very important to its economy. In 2008, a mixed integer-programming model for small market-driven foundries was published, attempting to minimize delivery delays. We undertook a study of that model. Here, we present a new approach based on the decomposition of the problem into two sub-problems: production planning of alloys and production planning of items. Both sub-problems are solved using a Lagrangian heuristic based on transferences. An important aspect of the proposed heuristic is its ability to take into account a secondary practice objective solution: the furnace waste. Computational tests show that the approach proposed here is able to generate good quality solutions that outperform prior results. Journal of the Operational Research Society (2010) 61, 108-114. doi:10.1057/jors.2008.151

Job rotation in assembly lines employing disabled workers

In this paper we consider the programming of job rotation in the assembly line worker assignment and balancing problem. The motivation for this study comes from the designing of assembly lines in sheltered work centers for the disabled, where workers have different task execution times. In this context, the well-known training aspects associated with job rotation are particularly desired. We propose a metric along with a mixed integer linear model and a heuristic decomposition method to solve this new job rotation problem. Computational results show the efficacy of the proposed heuristics. (C) 2009 Elsevier B.V. All rights reserved.

Heuristics for the one-dimensional cutting stock problem with limited multiple stock lengths

This paper deals with the classical one-dimensional integer cutting stock problem, which consists of cutting a set of available stock lengths in order to produce smaller ordered items. This process is carried out in order to optimize a given objective function (e.g., minimizing waste). Our study deals with a case in which there are several stock lengths available in limited quantities. Moreover, we have focused on problems of low demand. Some heuristic methods are proposed in order to obtain an integer solution and compared with others. The heuristic methods are empirically analyzed by solving a set of randomly generated instances and a set of instances from the literature. Concerning the latter. most of the optimal solutions of these instances are known, therefore it was possible to compare the solutions. The proposed methods presented very small objective function value gaps. (C) 2008 Elsevier Ltd. All rights reserved.

A Lagrangian relaxation approach to a coupled lot-sizing and cutting stock problem

Industrial production processes involving both lot-sizing and cutting stock problems are common in many industrial settings. However, they are usually treated in a separate way, which could lead to costly production plans. In this paper, a coupled mathematical model is formulated and a heuristic method based on Lagrangian relaxation is proposed. Computational results prove its effectiveness. (C) 2009 Elsevier B.V. All rights reserved.

A coupling cutting stock-lot sizing problem in the paper industry

An important production programming problem arises in paper industries coupling multiple machine scheduling with cutting stocks. Concerning machine scheduling: how can the production of the quantity of large rolls of paper of different types be determined. These rolls are cut to meet demand of items. Scheduling that minimizes setups and production costs may produce rolls which may increase waste in the cutting process. On the other hand, the best number of rolls in the point of view of minimizing waste may lead to high setup costs. In this paper, coupled modeling and heuristic methods are proposed. Computational experiments are presented.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Magnetic Susceptibility Measurements at Ultra-low Temperatures

We report the design and operation of a device for ac magnetic susceptibility measurements that can operate down to 1 mK. The device, a modification of the standard mutual inductance bridge, is designed with detailed consideration of the thermalization and optimization of each element. First, in order to reduce local heating, the primary coil is made with superconducting wire. Second, a low-temperature transformer which is thermally anchored to the mixing chamber of a dilution refrigerator, is used to match the output of the secondary coil to a high-sensitivity bridge detector. The careful thermal anchoring of the secondary coil and the matching transformer is required to reduce the overall noise temperature and maximize sensitivity. The sample is immersed in liquid (3)He to minimize the Kapitza thermal resistance. The magnetic susceptibility of several magnetic compounds, such as the well-known spin gap compound NiCl(2)-4SC(NH(2))(2) and other powdered samples, have been successfully measured to temperatures well below 10 mK.

Comparative study of liponucleosides in Langmuir monolayers as cell membrane models

Liponucleosides may assist the anchoring of nucleic acid nitrogen bases into biological membranes for tailored nanobiotechnological applications. To this end precise knowledge about the biophysical and chemical details at the membrane surface is required. In this paper, we used Langmuir monolayers as simplified cell membrane models and studied the insertion of five lipidated nucleosides. These molecules varied in the type of the covalently attached lipid group, the nucleobase, and the number of hydrophobic moieties attached to the nucleoside. All five lipidated nucleosides were found to be surface-active and capable of forming stable monolayers. They could also be incorporated into dipalmitoylphosphatidylcholine (DPPC) monolayers, four of which induced expansion in the surface pressure isotherm and a decrease in the surface compression modulus of DPPC. In contrast, one nucleoside possessing three alkyl chain modifications formed very condensed monolayers and induced film condensation and an increase in the compression modulus for the DPPC monolayer, thus reflecting the importance of the ability of the nucleoside molecules to be arranged in a closely packed manner. The implications of these results lie on the possibility of tuning nucleic acid pairing by modifying structural characteristics of the liponucleosides. (C) 2010 Elsevier B.V. All rights reserved.

Continuous GRASP with a local active-set method for bound-constrained global optimization

Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic-based on the CGRASP and GENCAN methods-for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP-GENCAN on a set of benchmark multimodal test functions.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.