Neuroprotective and Anti-Inflammatory Effects of Evernic Acid in an MPTP-Induced Parkinson’s Disease Model

Oxidative stress, mitochondrial dysfunction, and neuroinflammation are strongly associated with the pathogenesis of Parkinson’s disease (PD), which suggests that anti-oxidative and anti-inflammatory compounds might provide an alternative treatment for PD. Here, we evaluated the neuroprotective effects of evernic aid (EA), which was screened from a lichen library provided by the Korean Lichen Research Institute at Sunchon National University. EA is a secondary metabolite generated by lichens, including Ramalina, Evernia, and Hypogymnia, and several studies have described its anticancer, antifungal, and antimicrobial effects. However, the neuroprotective effects of EA have not been studied. We found that EA protected primary cultured neurons against 1-methyl-4-phenylpyridium (MPP⁺)-induced cell death, mitochondrial dysfunction, and oxidative stress, and effectively reduced MPP⁺-induced astroglial activation by inhibiting the NF-κB pathway. In vivo, EA ameliorated MPTP-induced motor dysfunction, dopaminergic neuronal loss, and neuroinflammation in the nigrostriatal pathway in C57BL/6 mice. Taken together, our findings demonstrate that EA has neuroprotective and anti-inflammatory effects in PD models and suggest that EA is a potential therapeutic candidate for PD.     

Efficient Multi-user Similarity Search Over Encrypted Data in Cloud Storage

Wireless Personal Communications - In cloud-assisted data outsourcing systems, the privacy of sensitive data is a major concern. Thus, data are uploaded in encrypted form in many cloud applications...     

A Generalized Crystallographic Description of All Tellurium Nanostructures

Despite tellurium being less abundant in the Earth's crust than gold, platinum, or rare‐earth elements, the number of industrial applications of tellurium has rapidly increased in recent years. However, to date, many properties of tellurium and its associated compounds remain unknown. For example, formation mechanisms of many tellurium nanostructures synthesized so far have not yet been verified, and it is unclear why tellurium can readily transform to other compounds like silver telluride by simply mixing with solutions containing silver ions. This uncertainty appears to be due to previous misunderstandings about the tellurium structure. Here, a new approach to the tellurium structure via synthesized structures is proposed. It is found that the proposed approach applies not only to these structures but to all other tellurium nanostructures. Moreover, some unique tellurium nanostructures whose formation mechanism are, until now, unconfirmed can be explained.     

Low‐Temperature Sintering and Piezoelectric Properties of CuO‐Added KNbO3 Ceramics

Dielectric and piezoelectric properties of CuO‐added KNbO₃ (KN) ceramics were investigated. The CuO reacted with the Nb₂O₅, formed a CuO–Nb₂O₅‐related liquid phase during the sintering, and assisted the densification of the KN ceramics at low temperatures. Moreover, some of the Cu²⁺ ions replaced the Nb⁵⁺ ions in the matrix and behaved as a hardener. The dielectric and piezoelectric properties of the KN ceramics were considerably influenced by the relative density. The 1.0 mol% CuO‐added KN ceramic sintered at 960°C for 1.0 h, which showed a maximum relative density, exhibited a high phase angle of 86.9°, P_r of 14.8 μC/cm², and E_c of 1.8 kV/mm. This specimen also exhibited good dielectric and piezoelectric properties: ε^T₃₃/ε_o of 364, d₃₃ of 122 pC/N, k_p of 0.29, and Q_m of 611.     

Characterization of spectral responses of humic substances upon UV irradiation using two-dimensional correlation spectroscopy

The spectral responses of a leaf litter derived humic substance (LLHS) and Suwannee River fulvic acid (SRFA) upon ultraviolet (UV) A irradiation were characterized using two-dimensional correlation spectroscopy (2D-COS) based on the absorption and the synchronous fluorescence spectra at different irradiation times. A 12 day irradiation on the humic substances (HS) resulted in higher reduction of the absorbance relative to the dissolved organic carbon concentration, suggesting that aromatic chromophores were preferentially oxidized and/or non UV-absorbing compounds were generated by the photobleaching. Synchronous fluorescence spectra revealed the preferential removal of fulvic-like and humic-like fluorophores and delayed response of protein-like fluorescence upon the irradiation. The spectral features at long wavelengths (>430 nm) appear to be affected by intra-molecular interactions of the individual chromophores associated with shorter wavelengths. Absorption-based 2D-COS demonstrated that there are three types of absorption bands for the two HS, which changed sequentially in the order of 290-400 nm → 200-250 nm → 250-290 nm. In addition, two or three distinctive fluorescence bands in response to the irradiation were identified from 2D-COS. The sequential orders and the associated wavelength bands were possibly explained by the irradiation wavelengths and the differences between direct and indirect photochemical reactions. The interpretation of the 2D-COS results was very consistent with the kinetic rate constants individually calculated at several discrete wavelengths. Our study demonstrated that 2D-COS could be used as a powerful tool in identifying distinctive bands of HS that have dissimilar behavior and the associated sequential orders by visualizing the spectral changes at continuous wavelengths.     

Atmospheric pollution for trace elements in the remote high-altitude atmosphere in central Asia as recorded in snow from Mt. Qomolangma (Everest) of the Himalayas

A series of 42 snow samples covering over a one-year period from the fall of 2004 to the summer of 2005 were collected from a 2.1-m snow pit at a high-altitude site on the northeastern slope of Mt. Everest. These samples were analyzed for Al, V, Cr, Mn, Co, Ni, Cu, Zn, As, Rb, Sr, Cd, Sb, Pb, and Bi in order to characterize the relative contributions from anthropogenic and natural sources to the fallout of these elements in central Himalayas. Our data were also considered in the context of monsoon versus non-monsoon seasons. The mean concentrations of the majority of the elements were determined to be at the pg g(-1) level with a strong variation in concentration with snow depth. While the mean concentrations of most of the elements were significantly higher during the non-monsoon season than during the monsoon season, considerable variability in the trace element inputs to the snow was observed during both periods. Cu, Zn, As, Cd, Sb, and Bi displayed high crustal enrichment factors (EF(c)) in most samples, while Cr, Ni, Rb, and Pb show high EF(c) values in some of the samples. Our data indicate that anthropogenic inputs are potentially important for these elements in the remote high-altitude atmosphere in the central Himalayas. The relationship between the EF(c) of each element and the Al concentration indicates that a dominant input of anthropogenic trace elements occurs during both the monsoon and non-monsoon seasons, when crustal contribution is relatively minor. Finally, a comparison of the trace element fallout fluxes calculated in our samples with those recently obtained at Mont Blanc, Greenland, and Antarctica provides direct evidence for a geographical gradient of the atmospheric pollution with trace elements on a global scale.     

Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels

Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behaviour of the programme. But the challenges in creating suitable oracles limit the ability to perform automated testing in some programmes, and especially in scientific software. Metamorphic testing is a method for automating the testing process for programmes without test oracles. This technique operates by checking whether the programme behaves according to properties called metamorphic relations. A metamorphic relation describes the change in output when the input is changed in a prescribed way. Unfortunately, finding the metamorphic relations satisfied by a programme or function remains a labour‐intensive task, which is generally performed by a domain expert or a programmer. In this work, we propose a machine learning approach for predicting metamorphic relations that uses a graph‐based representation of a programme to represent control flow and data dependency information. In earlier work, we found that simple features derived from such graphs provide good performance. An analysis of the features used in this earlier work led us to explore the effectiveness of several representations of those graphs using the machine learning framework of graph kernels, which provide various ways of measuring similarity between graphs. Our results show that a graph kernel that evaluates the contribution of all paths in the graph has the best accuracy and that control flow information is more useful than data dependency information. The data used in this study are available for download at http://www.cs.colostate.edu/saxs/MRpred/functions.tar.gz to help researchers in further development of metamorphic relation prediction methods. Copyright © 2015 John Wiley & Sons, Ltd.