A State of the Art of Antioxidant Properties of Curcuminoids in Neurodegenerative Diseases

Neurodegenerative diseases represent a set of pathologies characterized by an irreversible and progressive, and a loss of neuronal cells in specific areas of the brain. Oxidative phosphorylation is a source of energy production by which many cells, such as the neuronal cells, meet their energy needs. Dysregulations of oxidative phosphorylation induce oxidative stress, which plays a key role in the onset of neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). To date, for most neurodegenerative diseases, there are no resolute treatments, but only interventions capable of alleviating the symptoms or slowing the course of the disease. Therefore, effective neuroprotection strategies are needed. In recent years, natural products, such as curcuminoids, have been intensively explored and studied for their therapeutic potentials in several neurodegenerative diseases. Curcuminoids are, nutraceutical compouns, that owen several therapeutic properties such as anti-oxidant, anti-inflammatory and neuroprotective effects. In this context, the aim of this review was to provide an overview of preclinical and clinical evidence aimed to illustrate the antioxidant effects of curcuminoids in neurodegenerative diseases. Promising results from preclinical studies encourage the use of curcuminoids for neurodegeneration prevention and treatment.     

Glass and Jamming Rheology in Soft Particles Made of PNIPAM and Polyacrylic Acid

The phase behaviour of soft colloids has attracted great attention due to the large variety of new phenomenologies emerging from their ability to pack at very high volume fractions. Here we report rheological measurements on interpenetrated polymer network microgels composed of poly(N-isopropylacrylamide) (PNIPAM) and polyacrylic acid (PAAc) at fixed PAAc content as a function of weight concentration. We found three different rheological regimes characteristic of three different states: a Newtonian shear-thinning fluid, an attractive glass characterized by a yield stress, and a jamming state. We discuss the possible molecular mechanisms driving the formation of these states.     

Plasmonic and photonic scattering and near fields of nanoparticles

We theoretically compare the scattering and near field of nanoparticles from different types of materials, each characterized by specific optical properties that determine the interaction with light: metals with their free charge carriers giving rise to plasmon resonances, dielectrics showing zero absorption in wide wavelength ranges, and semiconductors combining the two beforehand mentioned properties plus a band gap. Our simulations are based on Mie theory and on full 3D calculations of Maxwell’s equations with the finite element method. Scattering and absorption cross sections, their division into the different order electric and magnetic modes, electromagnetic near field distributions around the nanoparticles at various wavelengths as well as angular distributions of the scattered light were investigated. The combined information from these calculations will give guidelines for choosing adequate nanoparticles when aiming at certain scattering properties. With a special focus on the integration into thin film solar cells, we will evaluate our results.     

Migration cost optimization for service provider legacy network migration to software-defined IPv6 network

This paper studies a problem for seamless migration of legacy networks of Internet service providers to a software-defined networking (SDN)-based architecture along with the transition to the full adoption of the Internet protocol version 6 (IPv6) connectivity. Migration of currently running legacy IPv4 networks into such new approaches requires either upgrades or replacement of existing networking devices and technologies that are actively operating. The joint migration to SDN and IPv6 network is considered to be vital in terms of migration cost optimization, skilled human resource management, and other critical factors. In this work, we first present the approaches of SDN and IPv6 migration in service providers' networks. Then, we present the common concerns of IPv6 and SDN migration with joint transition strategies so that the cost associated with joint migration is minimized to lower than that of the individual migration. For the incremental adoption of software-defined IPv6 (SoDIP6) network with optimum migration cost, a greedy algorithm is proposed based on optimal path and the customer priority. Simulation and empirical analysis show that a unified transition planning to SoDIP6 network results in lower migration cost.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Evaluation of <Emphasis Type="Italic">flaA</Emphasis> Short Variable Region Sequencing,Multilocus Sequence Typing,and Fourier Transform Infrared Spectroscopy for Discrimination between <Emphasis Type="Italic">Campylobacter jejuni</Emphasis> Strains

Discriminatory and robust typing methods are needed to improve the understanding of the dynamics of food-borne Campylobacter infections and epidemiology in primary animal production. To evaluate the strain discriminatory potential of typing methods, flaA short variable region (SVR) sequencing and Fourier transform infrared (FTIR) spectroscopy were applied on a collection of 102 epidemiologically related and unrelated Campylobacter jejuni strains. Previous application of FTIR spectroscopy for subtyping of Campylobacter has been limited. A subset of isolates, initially discriminated by flaA SVR sequencing, were further subjected to multilocus sequence typing (MLST). It was found that flaA SVR sequencing had a slightly higher discriminatory power than FTIR spectroscopy, based on the Simpson diversity index. The clustering of strains indicated that FTIR spectroscopy is indeed a suitable method for discrimination of Campylobacter. The isolates were assigned to six clusters based on flaA SVR sequences and nine clusters based on the FTIR spectroscopy profiles. Furthermore, the cluster analysis of flaA SVR sequences, MLST, and FTIR spectroscopy profiles showed a high degree of congruence, assigning the isolates to similar cluster structures. In conclusion, FTIR spectroscopy can be applied for subtyping of Campylobacter, and the high discriminatory potential of both flaA SVR sequencing and FTIR spectroscopy render them suitable screening methods for large numbers of strains.