Calmodulin Supports TRPA1 Channel Association with Opioid Receptors and Glutamate NMDA Receptors in the Nervous Tissue

Transient receptor potential ankyrin member 1 (TRPA1) belongs to the family of thermo TRP cation channels that detect harmful temperatures, acids and numerous chemical pollutants. TRPA1 is expressed in nervous tissue, where it participates in the genesis of nociceptive signals in response to noxious stimuli and mediates mechanical hyperalgesia and allodynia associated with different neuropathies. The glutamate N-methyl-d-aspartate receptor (NMDAR), which plays a relevant role in allodynia to mechanical stimuli, is connected via histidine triad nucleotide-binding protein 1 (HINT1) and type 1 sigma receptor (σ1R) to mu-opioid receptors (MORs), which mediate the most potent pain relief. Notably, neuropathic pain causes a reduction in MOR antinociceptive efficacy, which can be reversed by blocking spinal NMDARs and TRPA1 channels. Thus, we studied whether TRPA1 channels form complexes with MORs and NMDARs that may be implicated in the aforementioned nociceptive signals. Our data suggest that TRPA1 channels functionally associate with MORs, delta opioid receptors and NMDARs in the dorsal root ganglia, the spinal cord and brain areas. These associations were altered in response to pharmacological interventions and the induction of inflammatory and also neuropathic pain. The MOR-TRPA1 and NMDAR-TRPA1 associations do not require HINT1 or σ1R but appear to be mediated by calcium-activated calmodulin. Thus, TRPA1 channels may associate with NMDARs to promote ascending acute and chronic pain signals and to control MOR antinociception.     

Characterization of poly(diethoxyphosphazene) by size exclusion chromatography and viscometry

Two samples of poly(diethoxyphosphazene) (PDEP) having very different molecular weights have been studied by viscometry and size exclusion chromatography in THF solution. The results obtained, together with light scattering data of these samples, allow the calculation of the Mark-Houwink constants a=0.65 and K=2.5 10^-4 in THF at 25°C. The method of calculation employed takes into account the great polydispersity of the samples. The characteristic ratio of the unperturbed dimensions was also calculated giving C_n = r²_o/n² 18, a value slightly higher than those previously reported for poly(dihexoxyphosphazene), C_n13 and poly (dichlorophosphazene), C_n13.5.     

Contribution of a disulfide bridge to the stability of 1,3-1,4-P-D-glucan 4-glucanohydrolase from Bacillus licheniformis

Bacillus 1,3-1,4-ß-glucanases possess a highly conserveddisulfide bridge connecting a ß-strand with a solventexposedloop lying on top of the extended binding site cleft The contributionof the disulfide bond and of both individual cysteines (Cys61and Cys90) in the Bacillus licheniformis enzyme to stabilityand activity has been evaluated by protein engineering methods.Reduction of the disulfide bond has no effect on kinetic parameters,has only a minor effect on the activity-temperature profileat high temperatures, and destabilizes the protein by less than0.7 kcal/mol as measured by equilibrium urea denatu ration at37°C. Replacing either of the Cys residues with Ala destabilizesthe protein and lowers the specific activity. C90A retains 70%of wild-type (wt) activity (in terms of Vmax), whereas C61Aand the double mutant C61A–C90A have 10% of wt V_max. Alarger change in free energy of unfolding is seen by equilibriumurea denaturation for the C61A mutation (loop residue, 3.2 kcal/molrelative to reduced wt) as compared with the C90A mutation (ß-strandresidue, 1.8 kcal/mol relative to reduced wt), while the doublemutant C61A–C90A is 0.8 kcal/mol less stable than thesingle C61A mutant. The effects on stability are interpretedas a result of the change in hydrophobic packing that occursupon removal of the sulfur atoms in the Cys to Ala mutations     

Chemical Composition Determination at the Bottom Region of a Recovery Boiler Furnace by Direct Minimization of Gibbs Free Energy

An optimization strategy has been applied to describe the chemical composition at the furnace bottom in the Kraft recovery boiler of a pulp production process. The concentrations of each involved chemical species were calculated through an optimization approach, minimizing the Gibbs free energy of the system. Various systems were proposed and tested, assuming different chemical species and phases number. Because serious initialization problems were found at this stage for some of the proposed systems, an optimization heuristic method (PSO) was used for the first approach to the problem. Once the appropriate phases number and chemical species in the system were determined, the initialization problems disappeared and the use of a deterministic optimization method (SQP) became viable. The proposed approach has shown to be satisfactory to reproduce industrial data and also data reported in the open scientific literature.     

Caving performance through the integration of microseismic activity and numerical modeling at DOZ-PT Freeport, Indonesia

This article describes an undergoing research at PT Freeport,Indonesia,in which the main goal is to use the microseismic information recorded as a result of mining to analyze cave propagation and stress performance on the actual production and fixed in-frastructure.At the moment,several numerical experiments have been conducted to correlate the mining activity with the microseis-mic events using the data collected during year 2005 and 2006.As a result of the preliminary analysis a micro-and a macrocracking envelop were proposed on the basis of computation of stress behavior at the location of the events.Stresses have been computed us-ing standard elastic continuous boundary element models.The correlation between the average source radius and the stress perform-ance has provided a method to propose a macrocracking criterion.Several techniques have been tested to nucleate the microseismic activity around different geological features.This last attempt was aimed to look at potential overstresses induced over the undercut and extraction level drifts.A method was devised to integrate the microseismicity into a 3-dimensional ride distribution model.This model has shown to be very effective to quantify the overstress induced as a result of computing volumetric microseismieity density.The volumetric microseismic model showed to induce overstress up to 10 MPa over a period of two months.The future work will concentrate on the calibration of the integrated model with actual damage observations made at the current mining infrastructure.     

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.     

Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.     

WHAD: Wikipedia historical attributes data

This paper describes the generation of temporally anchored infobox attribute data from the Wikipedia history of revisions. By mining (attribute, value) pairs from the revision history of the English Wikipedia we are able to collect a comprehensive knowledge base that contains data on how attributes change over time. When dealing with the Wikipedia edit history, vandalic and erroneous edits are a concern for data quality. We present a study of vandalism identification in Wikipedia edits that uses only features from the infoboxes, and show that we can obtain, on this dataset, an accuracy comparable to a state-of-the-art vandalism identification method that is based on the whole article. Finally, we discuss different characteristics of the extracted dataset, which we make available for further study.     

Semi-parametric nonlinear regression and transformation using functional networks

Functional networks are used to solve some nonlinear regression problems. One particular problem is how to find the optimal transformations of the response and/or the explanatory variables and obtain the best possible functional relation between the response and predictor variables. After a brief introduction to functional networks, two specific transformation models based on functional networks are proposed. Unlike in neural networks, where the selection of the network topology is arbitrary, the selection of the initial topology of a functional network is problem driven. This important feature of functional networks is illustrated for each of the two proposed models. An equivalent, but simpler network may be obtained from the initial topology using functional equations. The resultant model is then checked for uniqueness of representation. When the functions specified by the transformations are unknown in form, families of linear independent functions are used as approximations. Two different parametric criteria are used for learning these functions: the constrained least squares and the maximum canonical correlation. Model selection criteria are used to avoid the problem of overfitting. Finally, performance of the proposed method are assessed and compared to other methods using a simulation study as well as several real-life data.