Hypolipidemic and antioxidative effects of dika nut (Irvingia gabonensis) seeds and nkui (Trimphetta cordifolia) stem bark mucilages in triton WR-1339 induced hyperlipidemic rats

Two different mucilages were extracted from dika nut (Irvingia gabonensis) kernels and nkui (Trimphetta cordifolia) stem barks and the hypolipidemic and antioxidant effects were studied on Triton WR-1339 induced hyperlipidemic rats. The GC analysis revealed that, dika nut mucilage is an arabinogalactan type while nkui is a rhamnogalacturonane type. The mucilage of nkui (200 mg/kg) showed the greatest hypolipidemic effect (p<0.05). An oxidative stress marker analysis suggested that the level of malonyl dialdehyde was significantly decreased in serum by the 2 mucilages (p<0.05). There was also a significant decrease in hydroperoxide level for the tested fibers. Moreover, nkui mucilage gave the highest levels in liver homogenate and in the hemolysate and tissues (p<0.05). The results suggest that the hypolidemic effect of mucilages varies with their chemical nature. They would be effective in the prevention of increase of serum lipid levels, in the reduction of lipid peroxidation and in the improvement of antioxidant status.     

Production of Bioactive Volatiles by Different Burkholderia ambifaria Strains

Increasing evidence indicates that volatile compounds emitted by bacteria can influence the growth of other organisms. In this study, the volatiles produced by three different strains of Burkholderia ambifaria were analysed and their effects on the growth of plants and fungi, as well as on the antibiotic resistance of target bacteria, were assessed. Burkholderia ambifaria emitted highly bioactive volatiles independently of the strain origin (clinical environment, rhizosphere of pea, roots of maize). These volatile blends induced significant biomass increase in the model plant Arabidopsis thaliana as well as growth inhibition of two phytopathogenic fungi (Rhizoctonia solani and Alternaria alternata). In Escherichia coli exposed to the volatiles of B. ambifaria, resistance to the aminoglycoside antibiotics gentamicin and kanamycin was found to be increased. The volatile blends of the three strains were similar, and dimethyl disulfide was the most abundant compound. Sulfur compounds, ketones, and aromatic compounds were major groups in all three volatile profiles. When applied as pure substance, dimethyl disulfide led to increased plant biomass, as did acetophenone and 3-hexanone. Significant fungal growth reduction was observed with high concentrations of dimethyl di- and trisulfide, 4-octanone, S-methyl methanethiosulphonate, 1-phenylpropan-1-one, and 2-undecanone, while dimethyl trisulfide, 1-methylthio-3-pentanone, and o-aminoacetophenone increased resistance of E. coli to aminoglycosides. Comparison of the volatile profile produced by an engineered mutant impaired in quorum-sensing (QS) signalling with the corresponding wild-type led to the conclusion that QS is not involved in the regulation of volatile production in B. ambifaria LMG strain 19182.     

Surface Charge of Supramolecular Nanosystems for In Vivo Biodistribution: A MicroSPECT/CT Imaging Study

Bioimaging has revolutionized medicine by providing accurate information for disease diagnosis and treatment. Nanotechnology‐based bioimaging is expected to further improve imaging sensitivity and specificity. In this context, supramolecular nanosystems based on self‐assembly of amphiphilic dendrimers for single photon emission computed tomography (SPECT) bioimaging are developed. These dendrimers bear multiple In³⁺ radionuclides at their terminals as SPECT reporters. By replacing the macrocyclic 1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid cage with the smaller 1,4,7‐triazacyclononane‐1,4,7‐triacetic acid scaffold as the In³⁺ chelator, the corresponding dendrimer exhibits neutral In³⁺‐complex terminals in place of negatively charged In³⁺‐complex terminals. This negative‐to‐neutral surface charge alteration completely reverses the zeta‐potential of the nanosystems from negative to positive. As a consequence, the resulting SPECT nanoprobe generates a highly sought‐after biodistribution profile accompanied by a drastically reduced uptake in liver, leading to significantly improved tumor imaging. This finding contrasts with current literature reporting that positively charged nanoparticles have preferential accumulation in the liver. As such, this study provides new perspectives for improving the biodistribution of positively charged nanosystems for biomedical applications.     

From Discrete Duration Calculus to Symbolic Automata

The goal of this paper is to translate (fragments of) the quantified discrete duration calculus QDDC, proposed by P. Pandya, into symbolic acceptors with counters. Acceptors are written in the synchronous programming language Lustre, in order to allow available symbolic verification tools (model-checkers, abstract interpreters) to be applied to properties expressed in QDDC. We show that important constructs of QDDC need non-deterministic acceptors, in order to be translated with a bounded number of counters, and an expressive fragment of the logic is identified and translated. Then, we consider a more restricted fragment, which only needs deterministic acceptors.     

Approximating the max-edge-coloring problem

The max-edge-coloring problem is a natural weighted generalization of the classical edge-coloring problem arising in the domain of communication systems. In this problem each color class is assigned the weight of the heaviest edge in this class and the objective is to find a proper edge-coloring of the input graph minimizing the sum of all color classes’ weights. We present new approximation results, that improve substantially the known ones, for several variants of the problem with respect to the class of the underlying graph. In particular, we deal with variants which are known to be NP-hard (general and bipartite graphs) or are proven to be NP-hard in this paper (complete graphs with bi-valued edge weights) or whose complexity question still remains open (trees).     

Scalability analysis of Dalton,a molecular structure program

Dalton is a molecular electronic structure program featuring common methods of computational chemistry that are based on pure quantum mechanics (QM) as well as hybrid quantum mechanics/molecular mechanics (QM/MM). It is specialized and has a leading position in calculation of molecular properties with a large world-wide user community (over 2000 licenses issued). In this paper, we present a performance characterization and optimization of Dalton. We also propose a solution to avoid the master/worker design of Dalton to become a performance bottleneck for larger process numbers. With these improvements we obtain speedups of 4x, increasing the parallel efficiency of the code and being able to run in it in a much bigger number of cores.     

A genetic algorithm-based method for feature subset selection

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm.