Effect of γ-radiation on green onion DNA integrity: Role of ascorbic acid and polyphenols against nucleic acid damage

The effect of γ-radiation on green onion DNA integrity, phenol content, oxygen radical absorbance capacity, employing pyrogallol red and fluorescein as probes, as well as ascorbic acid content has been evaluated. Measurements using thiazole orange-DNA fluorescence and agarose gel electrophoresis show that γ-radiation does not lead to an apparent DNA change in green onion. However, it was readily cleaved upon irradiation from the previously isolated nucleic acid. Furthermore, green onion exposure to γ-radiation produces slight increases in the polyphenol concentrations (163–188 μM Trolox eq.) and a decrease in the oxygen radical absorbance using fluorescein (from 245 to 200 Trolox eq.) Interestingly, a high ascorbic acid content (364 μM), which decreases by 40% after γ-ray exposure was measured by using pyrogallol-red-based oxygen radical absorbance capacity induction times from green onion aqueous extracts. Thus, our results suggest that ascorbic acid present in green onion plays a fundamental role in the plant antioxidant response toward γ-radiation exposure, while polyphenols remain largely unchanged, as revealed from oxygen radical absorbance capacity, employing pyrogallol red.     

Nonlinear Versus Linear Learning Devices: A Procedural Perspective

We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be substantively rational. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow procedurally rational: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.     

Specific interactions study in complexes of poly(mono-n-alkyl itaconates) with tertiary polyamides

This paper reports an FT-IR study of blends of poly(mono-n-alkyl itaconates) with poly(N,N-dimethylacrylamide) (PDMA) and poly(ethyloxazoline) (PEOX). Strong hydrogen bonding has been found, and both polybases have shown similar acceptor strengths. Derivative techniques show asymmetric profiles for the free carbonyl band of the polybases, resulting in shifted band locations. The extent of the interassociation has been estimated by spectral curve fitting of the polybase carbonyl band. The results show that the interaction degree in blends with PEOX does not depend on the length of the poly(monoalkyl itaconate) side group, while an inter-associating ability loss is observed in blends with PDMA as the side-group size of the polyacid increases. This different behavior is attributed to the greater interspacing between vicinal carbonyl groups in PEOX. This band shows conformational sensitivity and reflects the conformational changes that occur as the steric hindrances present in the medium (due to the bulky side groups of the polyacids) increase.     

Background synaptic activity as a switch between dynamical states in a network

A bright red light may trigger a sudden motor action in a driver crossing an intersection: stepping at once on the brakes. The same red light, however, may be entirely inconsequential if it appears, say, inside a movie theater. Clearly, context determines whether a particular stimulus will trigger a motor response, but what is the neural correlate of this? How does the nervous system enable or disable whole networks so that they are responsive or not to a given sensory signal? Using theoretical models and computer simulations, I show that networks of neurons have a built-in capacity to switch between two types of dynamic state: one in which activity is low and approximately equal for all units, and another in which different activity distributions are possible and may even change dynamically. This property allows whole circuits to be turned on or off by weak, unstructured inputs. These results are illustrated using networks of integrate-and-fire neurons with diverse architectures. In agreement with the analytic calculations, a uniform background input may determine whether a random network has one or two stable firing levels; it may give rise to randomly alternating firing episodes in a circuit with reciprocal inhibition; and it may regulate the capacity of a center-surround circuit to produce either self-sustained activity or traveling waves. Thus, the functional properties of a network may be drastically modified by a simple, weak signal. This mechanism works as long as the network is able to exhibit stable firing states, or attractors.     

Compile and Run-Time Support for the Parallelization of Sparse Matrix Updating Algorithms

This work presents a survey of the capabilities that the sparse computation offers for improving performance when parallelized, either automatically or through a data-parallel compiler. The characterization of a sparse code gets more complicated as code length increases: Access patterns change from loop to loop, thus making necessary to redefine the parallelization strategy. While dense computation solely offers the possibility of redistributing data structures, several other factors influence the performance of a code excerpt in the sparse field, like source data representation on file, compressed data storage in memory, the creation of new nonzeroes at run-time (fill-in) or the number of processors available. We analize the alternatives that arise from each issue, providing a guideline for the underlying compilation work and illustrating our techniques with examples on the Cray T3E.     

Polychlorinated dibenzo-p-dioxins and dibenzofurans in the air of Seveso,Italy, 26 years after the explosion

This study reports the current levels of polychlorinated dibenzo-p-dioxins (PCDDs) and furans (PCDFs) in air at Seveso, where an explosion in a 2,4,5,-trichlorophenol production reactor occurred 26 years ago. The aims were to assess if residues of the 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) released during the accident and still present in soil could contaminate the above air and to investigate other potential sources in the area. Long-term air collection was carried out in zones A and B in Seveso and in a reference location in Milan, and samples were analyzed for PCDD and PCDF concentrations by gas chromatography-mass spectrometry (GC-MS). Experimental results showed that no important contribution to the air concentrations is due to the soil contamination and that contemporary sources essentially control the atmospheric burden of PCDDs and PCDFs in the Seveso area. The theoretical release of 2,3,7,8-TCDD from the soils of zones A and B of Seveso was calculated using the SoilFug model. In the worst case, the model simulated an enrichment in atmospheric 2,3,7,8-TCDD concentrations of 4 and 22% for zones A and B, respectively. The investigation of the potential emission sources in the area indicated that combustion of wood residues from furniture factories may be an additional local source of PCDDs and PCDFs.     

Integrate-and-fire neurons driven by correlated stochastic input

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.     

Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit

In this paper, we review an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function. We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas we have previously (Lai et al., 2000; Fyfe and MacDonald, 2002) viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit. We illustrate this on both artificial and real data sets.     

Dynamic modeling of DC–DC converters with peak current control in double‐stage photovoltaic grid‐connected inverters

In photovoltaic (PV) double‐stage grid‐connected inverters a high‐frequency DC–DC isolation and voltage step‐up stage is commonly used between the panel and the grid‐connected inverter. This paper is focused on the modeling and control design of DC–DC converters with Peak Current mode Control (PCC) and an external control loop of the PV panel voltage, which works following a voltage reference provided by a maximum power point tracking (MPPT) algorithm. In the proposed overall control structure the output voltage of the DC–DC converter is regulated by the grid‐connected inverter. Therefore, the inverter may be considered as a constant voltage load for the development of the small‐signal model of the DC–DC converter, whereas the PV panel is considered as a negative resistance. The sensitivity of the control loops to variations of the power extracted from the PV panel and of its voltage is studied. The theoretical analysis is corroborated by frequency response measurements on a 230 W experimental inverter working from a single PV panel. The inverter is based on a Flyback DC–DC converter operating in discontinuous conduction mode (DCM) followed by a PWM full‐bridge single‐phase inverter. The time response of the whole system (DC–DC + inverter) is also shown to validate the concept. Copyright © 2011 John Wiley & Sons, Ltd.