Docking and molecular dynamics simulation of quinone compounds with trypanocidal activity

In this work, two different docking programs were used, AutoDock and FlexX, which use different types of scoring functions and searching methods. The docking poses of all quinone compounds studied stayed in the same region in the trypanothione reductase. This region is a hydrophobic pocket near to Phe396, Pro398 and Leu399 amino acid residues. The compounds studied displays a higher affinity in trypanothione reductase (TR) than glutathione reductase (GR), since only two out of 28 quinone compounds presented more favorable docking energy in the site of human enzyme. The interaction of quinone compounds with the TR enzyme is in agreement with other studies, which showed different binding sites from the ones formed by cysteines 52 and 58. To verify the results obtained by docking, we carried out a molecular dynamics simulation with the compounds that presented the highest and lowest docking energies. The results showed that the root mean square deviation (RMSD) between the initial and final pose were very small. In addition, the hydrogen bond pattern was conserved along the simulation. In the parasite enzyme, the amino acid residues Leu399, Met400 and Lys402 are replaced in the human enzyme by Met406, Tyr407 and Ala409, respectively. In view of the fact that Leu399 is an amino acid of the Z site, this difference could be explored to design selective inhibitors of TR.

Creating Prestige and Global Networks through Conference Rituals

An international conference is a secular ritual which serves to create, recreate and shape global-wide translocal cultural sharings. Social anthropological theories and methods are used to show that, besides being an information flow junction, the international conference is a network crossroad and a way of socialising new members into aninternational research community. It is also capable of creating prestige and honour for the individual researcher,for the arranging research team, university and city. Rituals do not merely reflect the social relations or cosmology of a society, but are events that in themselves do important things through ritual forms and symbolic statements.

Manoeuvring between anxiety and control : Patients experience of learning to live with diabetes: a lifeworld phenomenological study

Research shows that people with diabetes want their lives to proceed as normally as possible, but some patients experience difficulty in reaching their desired goals with treatment. The learning process is a complex phenomenon interwoven into every facet of life. Patients and healthcare providers often have different perspectives in care which gives different expectations on what the patients need to learn and cope with. The aim of this study, therefore, is to describe the experience of learning to live with diabetes. Interviews were conducted with 12 patients afflicted with type 1 or type 2 diabetes. The interviews were then analysed with reference to the reflective lifeworld research approach. The analysis shows that when the afflicted realize that their bodies undergo changes and that blood sugar levels are not always balanced as earlier in life, they can adjust to their new conditions early. The afflicted must take responsibility for balancing their blood sugar levels and incorporating the illness into their lives. Achieving such goals necessitates knowledge. The search for knowledge and sensitivity to changes are constant requirements for people with diabetes. Learning is driven by the tension caused by the need for and dependence on safe blood sugar control, the fear of losing such control, and the fear of future complications. The most important responsibilities for these patients are aspiring to understand their bodies as lived bodies, ensuring safety and security, and acquiring the knowledge essential to making conscious choices.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Structural integrity identification based on smart materials and neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically>30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology.

Extensive Comparisons of Optical Fast-OFDM and Conventional Optical OFDM for Local and Access Networks

A performance comparison between a recently proposed novel technique known as fast orthogonal frequency-division multiplexing (FOFDM) and conventional orthogonal frequency-division multiplexing (OFDM) is undertaken over unamplified, intensity-modulated, and direct-detected directly modulated laser-based optical signals. Key transceiver parameters, such as the maximum achievable transmission capacity and the digital-to-analog/analog-to-digital converter (DAC/ADC) effects are explored thoroughly. It is shown that, similarly to conventional OFDM, the least complex and bandwidth efficient FOFDM can support up to similar to 20 Gb/s over 500 m worst-case multimode fiber (MMF) links having 3 dB effective bandwidths of similar to 200 MHz X km. For compensation of the DAC/ADC roll-off, a power-loading (PL) algorithm is adopted, leading to an FOFDM system improvement of similar to 4 dB. FOFDM and conventional OFDM give similar optimum DAC/ADC parameters over 500 m worst-case MMF, while over 50 km single-mode fiber a maximum deviation of only similar to 1 dB in clipping ratio is observed due to the imperfect chromatic dispersion compensation caused by one-tap equalizers.

pH at the micellar interface: Synthesis of pH probes derived from salicylic acid, acid-base dissociation in sodium dodecyl sulfate micelles, and Poisson-Boltzmann simulation

The study of the H+ concentration at the micellar interface is a convenient system for modeling the distribution of H+ at interfaces. We have synthesized salicylic acid derivatives to analyze the proton dissociation of both the carboxylic and phenol groups of' the probes, determining spectrophotometrically the apparent pK(a)'s (pK(ap)) in sodium dodecyl Sulfate, SDS, micelles with and without added salt. The synthesized probes were 2-hydroxy-5-(2-trimethylammoniumacetyl)benzoate; 2-hydroxy-5-(2-dimethylhexadecylammoniumacetyl)benzoate- 2-hydroxy-5-(2-dimethylhexadecylammoniumhexanoyl)benzoate-, 2-hydroxy-5-(2-diniethylhexadecylammoniumundecanoyl)betizoate; 2-hydroxy-5-acetylbenzoic acids and 2-hydroxy-5-dodecanoylbenzoic acid. Upon incorporation into SDS micelles the pK(ap)'s of both carboxylic and phenol groups increased by ca. 3 pH units and NaCl addition caused a decrease in the probe-incorporated pKap. The experimental results were fitted with a cell model Poisson-Boltzmann (P-B) equation taking in consideration the effect of salt on the aggregation number of SDS and using the distance of' the dissociating group as a parameter. The conformations of the probes were analyzed theoretically using two dielectric constants, e.g., 2 and 78. Both the P-B analysis and conformation calculations can be interpreted by assuming that the acid groups dissociate very close to, or at, the interface. Our results are consistent with the assumption that the intrinsic pK(a)'s of both carboxylic and phenol groups of the salicylic acid probes used here can be taken as those in water. Using this assumption the micellar and salt effects on the pKap's of the (trialkylammonium)benzoate probes were described accurately using a cell model P-B analysis. (c) 2005 Elsevier B.V. All rights reserved.

Aircraft interior failure pattern recognition utilizing text mining and neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Biodiversity, Species Interactions and Ecological Networks in a Fragmented World

Biodiversity is organised into complex ecological networks of interacting species in local ecosystems, but our knowledge about the effects of habitat fragmentation on such systems remains limited. We consider the effects of this key driver of both local and global change on both mutualistic and antagonistic systems at different levels of biological organisation and spatiotemporal scales.There is a complex interplay of patterns and processes related to the variation and influence of spatial, temporal and biotic drivers in ecological networks. Species traits (e.g. body size, dispersal ability) play an important role in determining how networks respond to fragment size and isolation, edge shape and permeability, and the quality of the surrounding landscape matrix. Furthermore, the perception of spatial scale (e.g. environmental grain) and temporal effects (time lags, extinction debts) can differ markedly among species, network modules and trophic levels, highlighting the need to develop a more integrated perspective that considers not just nodes, but the structural role and strength of species interactions (e.g. as hubs, spatial couplers and determinants of connectance, nestedness and modularity) in response to habitat fragmentation.Many challenges remain for improving our understanding: the likely importance of specialisation, functional redundancy and trait matching has been largely overlooked. The potentially critical effects of apex consumers, abundant species and supergeneralists on network changes and evolutionary dynamics also need to be addressed in future research. Ultimately, spatial and ecological networks need to be combined to explore the effects of dispersal, colonisation, extinction and habitat fragmentation on network structure and coevolutionary dynamics. Finally, we need to embed network approaches more explicitly within applied ecology in general, because they offer great potential for improving on the current species-based or habitat-centric approaches to our management and conservation of biodiversity in the face of environmental change.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Improving a process in a brazilian automotive plant applying process mapping, Design of Experiments and Discrete Events Simulation

The objective of this article is to apply the Design of Experiments technique along with the Discrete Events Simulation technique in an automotive process. The benefits of the design of experiments in simulation include the possibility to improve the performance in the simulation process, avoiding trial and error to seek solutions. The methodology of the conjoint use of Design of Experiments and Computer Simulation is presented to assess the effects of the variables and its interactions involved in the process. In this paper, the efficacy of the use of process mapping and design of experiments on the phases of conception and analysis are confirmed.

The accumulation process and agrofood networks in Latin America

Includes bibliography

Oil extraction and deforestation: a simulation exercise

Includes bibliography

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.