Multi-component Toda lattice in centro-affine Rn{\mathbb R}^n

In this paper we use the group-based discrete moving frame method to study invariant evolutions in n-dimensional centro-affine space. We derive the induced integrable equations for invariants, which can be transformed to local and nonlocal multi-component Toda lattices under a Miura transformation, and hence establish their geometric realisations in centro-affine space

Two Novel Dermaseptin-Like Antimicrobial Peptides with Anticancer Activities from the Skin Secretion of Pachymedusa dacnicolor

The dermaseptin antimicrobial peptide family contains members of 27–34 amino acids in length that have been predominantly isolated from the skins/skin secretions of phyllomedusine leaf frogs. By use of a degenerate primer in Rapid amplification of cDNA ends (RACE) PCR designed to a common conserved domain within the 5′-untranslated regions of previously-characterized dermaseptin encoding cDNAs, two novel members of this peptide family, named dermaseptin-PD-1 and dermaseptin-PD-2, were identified in the skin secretion of the phyllomedusine frog, Pachymedusa dacnicolor. The primary structures of both peptides were predicted from cloned cDNAs, as well as being confirmed by mass spectral analysis of crude skin secretion fractions resulted from reversed-phase high-performance liquid chromatography. Chemically-synthesized replicates of dermaseptin-PD-1 and dermaseptin-PD-2 were investigated for antimicrobial activity using standard model microorganisms (Gram-positive bacteria, Gram-negative bacteria and a yeast) and for cytotoxicity using mammalian red blood cells. The possibility of synergistic effects between the two peptides and their anti-cancer cell proliferation activities were assessed. The peptides exhibited moderate to high inhibition against the growth of the tested microorganisms and cancer cell lines with low haemolytic activity. Synergistic interaction between the two peptides in inhibiting the proliferation of Escherichia coli and human neuronal glioblastoma cell line, U251MG was also manifested

Inhibition of A/Human/Hubei/3/2005 (H3N2) influenza virus infection by silver nanoparticles in vitro and in vivo

AbstractSilver nanoparticles (AgNPs) have attracted much attention as antimicrobial agents and have demonstrated efficient inhibitory activity against various viruses, including human immunodeficiency virus, hepatitis B virus, and Tacaribe virus. In this study, we investigated if AgNPs could have antiviral and preventive effects in A/Human/Hubei/3/2005 (H3N2) influenza virus infection. Madin-Darby canine kidney cells infected with AgNP-treated H3N2 influenza virus showed better viability (P,0.05 versus influenza virus control) and no obvious cytopathic effects compared with an influenza virus control group and a group treated with the solvent used for preparation of the AgNPs. Hemagglutination assay indicated that AgNPs could significantly inhibit growth of the influenza virus in Madin-Darby canine kidney cells (P,0.01 versus the influenza virus control). AgNPs significantly reduced cell apoptosis induced by H3N2 influenza virus at three different treatment pathways (P,0.05 versus influenza virus control). H3N2 influenza viruses treated with AgNPs were analyzed by transmission electron microscopy and found to interact with each other, resulting in destruction of morphologic viral structures in a time-dependent manner in a time range of 30 minutes to 2 hours. In addition, intranasal AgNP administration in mice significantly enhanced survival after infection with the H3N2 influenza virus. Mice treated with AgNPs showed lower lung viral titer levels and minor pathologic lesions in lung tissue, and had a marked survival benefit during secondary intranasal passage in vivo. These results provide evidence that AgNPs have beneficial effects in preventing H3N2 influenza virus infection both in vitro and in vivo, and demonstrate that AgNPs can be used as potential therapeutics for inhibiting outbreaks of influenza.<br /

A potential relationship between MMP-9 rs2250889 and ischemic stroke susceptibility

PurposeIschemic stroke (IS), a serious cerebrovascular disease, greatly affects people's health and life. Genetic factors are indispensable for the occurrence of IS. As a biomarker for IS, the MMP-9 gene is widely involved in the pathophysiological process of IS. This study attempts to find out the relationship between MMP-9 polymorphisms and IS susceptibility.MethodsA total of 700 IS patients and 700 healthy controls were recruited. The single nucleotide polymorphism (SNP) markers of the MMP-9 gene were genotyped by the MassARRAY analyzer. Multifactor dimensionality reduction (MDR) was applied to generate SNP–SNP interaction. Furthermore, the relationship between genetic variations (allele and genotype) of the MMP-9 gene and IS susceptibility was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CIs).ResultsOur results demonstrated that rs2250889 could significantly increase the susceptibility to IS in the codominant, dominant, overdominant, and log-additive models (p &lt; 0.05). Further stratification analysis showed that compared with the control group, rs2250889 was associated with IS risk in different case groups (age, female, smoking, and non-drinking) (p &lt; 0.05). Based on MDR analysis, rs2250889 was the best model for predicting IS risk (cross-validation consistency: 10/10, OR = 1.56 (1.26–1.94), p &lt; 0.001).ConclusionOur study preliminarily confirmed that SNP rs2250889 was significantly associated with susceptibility to IS

Investigation of kinetic compensation effect in lignocellulosic biomass torrefaction: Kinetic and thermodynamic analyses

The kinetic compensation effect between the activation energy and the pre-exponential factor has extensively existed in the thermochemical conversion processes of lignocellulosic biomass. The research on the kinetic compensation effect in lignocellulosic biomass torrefaction has been insufficient yet. The torrefaction of the pinewood sample was experimentally investigated by thermogravimetric analysis (TGA) at five isothermal temperatures of 220, 250, 265, 280 and 295 °C. The reaction order model was used to analyze the isothermal torrefaction kinetics of lignocellulosic biomass, and the results showed that many sets of activation energy and pre-exponential factor could describe the experimental data at each temperature equally well and they excellently satisfied the kinetic compensation effect relationship. The linear regression lines of the kinetic compensation effect points at different temperatures intersected at one point, whose values corresponded to the obtained optimal kinetic parameters. A kinetic-compensation-effect-based method was developed and verified to determine the kinetic parameters of isothermal biomass torrefaction. Based on the optimal kinetic parameters, the thermodynamic parameters (including Gibbs free energy, enthalpy, and entropy) of biomass torrefaction processes at various temperatures were calculated and analyzed

A Survey of Chinese Pig Farms and Human Healthcare Isolates Reveals Separate Human and Animal Methicillin-Resistant Staphylococcus aureus Populations.

There has been increasing concern that the overuse of antibiotics in livestock farming is contributing to the burden of antimicrobial resistance in people. Farmed animals in Europe and North America, particularly pigs, provide a reservoir for livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA ST398 lineage) found in people. This study is designed to investigate the contribution of MRSA from Chinese pig farms to human infection. A collection of 483 MRSA are isolated from 55 farms and 4 hospitals in central China, a high pig farming density area. CC9 MRSA accounts for 97.2% of all farm isolates, but is not present in hospital isolates. ST398 isolates are found on farms and hospitals, but none of them formed part of the "LA-MRSA ST398 lineage" present in Europe and North America. The hospital ST398 MRSA isolate form a clade that is clearly separate from the farm ST398 isolates. Despite the presence of high levels of MRSA found on Chinese pig farms, the authors find no evidence of them spilling over to the human population. Nevertheless, the ST398 MRSA obtained from hospitals appear to be part of a widely distributed lineage in China. The new animal-adapted ST398 lineage that has emerged in China is of concern

Asymptotic Behavior of Solutions to Reaction-Diffusion Equations with Dynamic Boundary Conditions and Irregular Data

This paper is concerned with the asymptotic behavior of solutions to reaction-diffusion equations with dynamic boundary conditions as well as L1-initial data and forcing terms. We first prove the existence and uniqueness of an entropy solution by smoothing approximations. Then we consider the large-time behavior of the solution. The existence of a global attractor for the solution semigroup is obtained in L1(Ω¯,dν). This extends the corresponding results in the literatures

Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T(h&ndash;1) at the prediction moment