Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy

A simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.

New familial assignments for three species of Neotropical harvestmen based on cladistic analysis (Arachnida: Opiliones: Laniatores)

We herein propose transfer of the two monotypic genera Globibunus (type-species G. rubrofemoratus Roewer, 1912) and Rivetinus (type-species R. minutus Roewer, 1919) to the subfamily Zamorinae (Agoristenidae) and Ramonus (type-species R. conifrons Roewer, 1956), previous placed in Agoristenidae, to the Prostygninae (Cranaidae) based on several characteristics of male genitalia. These transfers are corroborated by a cladistic analysis, which also recovered the three currently recognized agoristenid subfamilies.

Chemical composition and sensory analysis of cheese whey-based beverages using kefir grains as starter culture

P>The aim of the present work was to evaluate the use of the kefir grains as a starter culture for tradicional milk kefir beverage and for cheese whey-based beverages production. Fermentation was performed by inoculating kefir grains in milk (ML), cheese whey (CW) and deproteinised cheese whey (DCW). Erlenmeyers containing kefir grains and different substrates were statically incubated for 72 h at 25 degrees C. Lactose, ethanol, lactic acid, acetic acid, acetaldehyde, ethyl acetate, isoamyl alcohol, isobutanol, 1-propanol, isopentyl alcohol and 1-hexanol were identified and quantified by high-performance liquid chromatography and GC-FID. The results showed that kefir grains were able to utilise lactose in 60 h from ML and 72 h from CW and DCW and produce similar amounts of ethanol (similar to 12 g L-1), lactic acid (similar to 6 g L-1) and acetic acid (similar to 1.5 g L-1) to those obtained during milk fermentation. Based on the chemical characteristics and acceptance in the sensory analysis, the kefir grains showed potential to be used for developing cheese whey-based beverages.

Modeling and analysis of laminate composite plates with embedded active-passive piezoelectric networks

The objective of this work is to present the finite element modeling of laminate composite plates with embedded piezoelectric patches or layers that are then connected to active-passive resonant shunt circuits, composed of resistance, inductance and voltage source. Applications to passive vibration control and active control authority enhancement are also presented and discussed. The finite element model is based on an equivalent single layer theory combined with a third-order shear deformation theory. A stress-voltage electromechanical model is considered for the piezoelectric materials fully coupled to the electrical circuits. To this end, the electrical circuit equations are also included in the variational formulation. Hence, conservation of charge and full electromechanical coupling are guaranteed. The formulation results in a coupled finite element model with mechanical (displacements) and electrical (charges at electrodes) degrees of freedom. For a Graphite-Epoxy (Carbon-Fibre Reinforced) laminate composite plate, a parametric analysis is performed to evaluate optimal locations along the plate plane (xy) and thickness (z) that maximize the effective modal electromechanical coupling coefficient. Then, the passive vibration control performance is evaluated for a network of optimally located shunted piezoelectric patches embedded in the plate, through the design of resistance and inductance values of each circuit, to reduce the vibration amplitude of the first four vibration modes. A vibration amplitude reduction of at least 10 dB for all vibration modes was observed. Then, an analysis of the control authority enhancement due to the resonant shunt circuit, when the piezoelectric patches are used as actuators, is performed. It is shown that the control authority can indeed be improved near a selected resonance even with multiple pairs of piezoelectric patches and active-passive circuits acting simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

A vaccination game based on public health actions and personal decisions

Susceptible-infective-removed (SIR) models are commonly used for representing the spread of contagious diseases. A SIR model can be described in terms of a probabilistic cellular automaton (PCA), where each individual (corresponding to a cell of the PCA lattice) is connected to others by a random network favoring local contacts. Here, this framework is employed for investigating the consequences of applying vaccine against the propagation of a contagious infection, by considering vaccination as a game, in the sense of game theory. In this game, the players are the government and the susceptible newborns. In order to maximize their own payoffs, the government attempts to reduce the costs for combating the epidemic, and the newborns may be vaccinated only when infective individuals are found in their neighborhoods and/or the government promotes an immunization program. As a consequence of these strategies supported by cost-benefit analysis and perceived risk, numerical simulations show that the disease is not fully eliminated and the government implements quasi-periodic vaccination campaigns. (C) 2011 Elsevier B.V. All rights reserved.

Anodic porous alumina structural characteristics study based on SEM image processing and analysis

The present work reports the porous alumina structures fabrication and their quantitative structural characteristics study based on mathematical morphology analysis by using the SEM images. The algorithm used in this work was implemented in 6.2 MATLAB software. Using the algorithm it was possible to obtain the distribution of maximum, minimum and average radius of the pores in porous alumina structures. Additionally, with the calculus of the area occupied by the pores, it was possible to obtain the porosity of the structures. The quantitative results could be obtained and related to the process fabrication characteristics, showing to be reliable and promising to be used to control the pores formation process. Then, this technique could provide a more accurate determination of pore sizes and pores distribution. (C) 2008 Elsevier Ltd. All rights reserved.

Negative ion `chip-based` nanospray tandem mass spectrometry for the analysis of flavonoids in glandular trichomes of Lychnophora ericoides Mart. (Asteraceae)

This paper reports a method for the analysis of secondary metabolites stored in glandular trichomes, employing negative ion `chip-based` nanospray tandem mass spectrometry. The analyses of glandular trichomes from Lychnophora ericoides, a plant endemic to the Brazilian `cerrado` and used in traditional medicine as an anti-inflammatory and analgesic agent, led to the identification of five flavonoids (chrysin, pinocembrin, pinostrobin, pinobanksin and 3-O-acetylpinobanksin) by direct infusion of the extracts of glandular trichomes into the nanospray ionisation source. All the flavonoids have no oxidation at ring B, which resulted in a modification of the fragmentation pathways compared with that of the oxidised 3,4-dihydroflavonoids already described in the literature. The absence of the anti-inflammatory and antioxidant di-C-glucosylflavone vicenin-2, or any other flavonoid glycosides, in the glandular trichomes was also demonstrated. The use of the,`chip-based` nanospray QqTOF apparatus is a new fast and useful tool for the identification of secondary metabolites stored in the glandular trichomes, which can be useful for chemotaxonomic studies based on metabolites from glandular trichomes. Copyright (C) 2008 John Wiley & Sons, Ltd.

Hollow fiber-based liquid-phase microextraction (HF-LPME) of isradipine and its main metabolite followed by chiral HPLC analysis: application to an in vitro biotransformation study

An enantioselective liquid chromatographic method using two-phase hollow fiber liquid-phase microextraction (HF-LPME-HPLC) was developed for the determination of isradipine (ISR) enantiomers and its main metabolite (pyridine derivative of isradipine, PDI) in microsomal fractions isolated from rat liver. The analytes were extracted from 1 mL of microsomal medium using a two-phase HF-LPME procedure with hexyl acetate as the acceptor phase, 30 min of extraction, and sample agitation at 1,500 rpm. For the first time, ISR enantiomers and PDI were resolved. For this separation, a ChiralpakA (R) AD column with hexane/2-propanol/ethanol (94:04:02, v/v/v) as the mobile phase at a flow rate of 1.5 mL min(-1) was used. The column was kept at 23 A +/- 2 A degrees C. The drug and metabolite detection was performed at 325 nm and the internal standard oxybutynin was detected at 225 nm. The recoveries were 23% for PDI and 19% for each ISR enantiomer. The method presented quantification limits (LOQ) of 50 ng mL(-1) and was linear over the concentration range of 50-5,000 and 50-2,500 ng mL(-1) for PDI and each ISR enantiomer, respectively. The validated method was employed to an in vitro biotransformation study of ISR using rat liver microsomal fraction showing that (+)-(S)-ISR is preferentially biotransformed.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.