Chaotic Systems with Hyperbolic Sine Nonlinearity

In recent years, exploring and investigating chaotic systems with hyperbolic sine nonlinearity has gained the interest of many researchers. With two back-to-back diodes to approximate the hyperbolic sine nonlinearity, these chaotic systems can achieve simplicity of the electrical circuit without any multiplier or sub-circuits. In this chapter, the genesis of chaotic systems with hyperbolic sine nonlinearity is introduced, followed by the general method of generating nth-order (n > 3) chaotic systems. Then some derived chaotic systems/torus-chaotic system with hyperbolic sine nonlinearity is discussed. Finally, the applications such as random number generator algorithm, spread spectrum communication and image encryption schemes are introduced. The contribution of this chapter is that it systematically summarizes the design methods, the dynamic behavior and typical engineering applications of chaotic systems with hyperbolic sine nonlinearity, which may widen the current knowledge of chaos theory and engineering applications based on chaotic systems

Cell Cycle-Dependent Expression Dynamics of G1/S Specific Cyclin, Cellulose Synthase and Cellulase in the Dinoflagellate Prorocentrum donghaiense

Dinoflagellates undergo a typical eukaryotic cell cycle consisting of G1, S, G2, and M phases and some of the typical cell cycle related genes have been computationally identified. However, very few of these genes have been experimentally linked to the cell cycle phases. Besides, although thecate dinoflagellates are known to possess theca composed of cellulose, information on cellulose synthesis and degradation associated with the cell cycle is also limited. In this study, we isolated G1/S cyclin, cellulose synthase and cellulase encoding genes in dinoflagellate Prorocentrum donghaiense. Further, using reverse transcription quantitative PCR (RT-qPCR), we characterized the expression profiles of the three genes throughout the cell cycle. All three showed clear expression dynamics throughout the cell cycle, with fold changes of 26, 2.4 and 9.3 for G1/S cyclin, cellulose synthase and cellulase gene, respectively. The transcript abundance of G1/S cyclin increased in late G1 phase and dropped in early S phase, indicating that this protein is involved in the G1/S transition. Throughout the cell cycle, the average transcript level of cellulose synthase was 4.5-fold higher than that of cellulase. Cellulose synthase and cellulase gene expressions showed peak transcript abundances at middle G1 phase and G2M phase, respectively, indicating the respective roles of these enzymes in the growth of newly divided cells and in cytokinesis. Our results suggest that G1/S cyclin, cellulase, and cellulose synthase genes associated with G1/S transition, G2M, and G1 phases of the cell cycle and are candidates of biomarkers for assessing growth status of P. donghaiense

Combination of TRAIL and actinomycin D liposomes enhances antitumor effect in non-small cell lung cancer

The intractability of non-small cell lung cancer (NSCLC) to multimodality treatments plays a large part in its extremely poor prognosis. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising cytokine for selective induction of apoptosis in cancer cells; however, many NSCLC cell lines are resistant to TRAIL-induced apoptosis. The therapeutic effect can be restored by treatments combining TRAIL with chemotherapeutic agents. Actinomycin D (ActD) can sensitize NSCLC cells to TRAIL-induced apoptosis by upregulation of death receptor 4 (DR4) or 5 (DR5). However, the use of ActD has significant drawbacks due to the side effects that result from its nonspecific biodistribution in vivo. In addition, the short half-life of TRAIL in serum also limits the antitumor effect of treatments combining TRAIL and ActD. In this study, we designed a combination treatment of long-circulating TRAIL liposomes and ActD liposomes with the aim of resolving these problems. The combination of TRAIL liposomes and ActD liposomes had a synergistic cytotoxic effect against A-549 cells. The mechanism behind this combination treatment includes both increased expression of DR5 and caspase activation. Moreover, systemic administration of the combination of TRAIL liposomes and ActD liposomes suppressed both tumor formation and growth of established subcutaneous NSCLC xenografts in nude mice, inducing apoptosis without causing significant general toxicity. These results provide preclinical proof-of-principle for a novel therapeutic strategy in which TRAIL liposomes are safely combined with ActD liposomes

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

Odorranalectin Is a Small Peptide Lectin with Potential for Drug Delivery and Targeting

BACKGROUND: Lectins are sugar-binding proteins that specifically recognize sugar complexes. Based on the specificity of protein-sugar interactions, different lectins could be used as carrier molecules to target drugs specifically to different cells which express different glycan arrays. In spite of lectin's interesting biological potential for drug targeting and delivery, a potential disadvantage of natural lectins may be large size molecules that results in immunogenicity and toxicity. Smaller peptides which can mimic the function of lectins are promising candidates for drug targeting. PRINCIPAL FINDINGS: Small peptide with lectin-like behavior was screened from amphibian skin secretions and its structure and function were studied by NMR, NMR-titration, SPR and mutant analysis. A lectin-like peptide named odorranalectin was identified from skin secretions of Odorrana grahami. It was composed of 17 aa with a sequence of YASPKCFRYPNGVLACT. L-fucose could specifically inhibit the haemagglutination induced by odorranalectin. (125)I-odorranalectin was stable in mice plasma. In experimental mouse models, odorranalectin was proved to mainly conjugate to liver, spleen and lung after i.v. administration. Odorranalectin showed extremely low toxicity and immunogenicity in mice. The small size and single disulfide bridge of odorranalectin make it easy to manipulate for developing as a drug targeting system. The cyclic peptide of odorranalectin disclosed by solution NMR study adopts a beta-turn conformation stabilized by one intramolecular disulfide bond between Cys6-Cys16 and three hydrogen bonds between Phe7-Ala15, Tyr9-Val13, Tyr9-Gly12. Residues K5, C6, F7, C16 and T17 consist of the binding site of L-fucose on odorranalectin determined by NMR titration and mutant analysis. The structure of odorranalectin in bound form is more stable than in free form. CONCLUSION: These findings identify the smallest lectin so far, and show the application potential of odorranalectin for drug delivery and targeting. It also disclosed a new strategy of amphibian anti-infection

Study on the Blowing Scheme of a Propellant Storage Vessel

The blowing flow field of a propellant storage vessel are simulated using Fluent software. Through steady flow field calculation, some important flow field parameters, such as pressure, temperature, velocity and Streamline, are obtained under three different working conditions. Based on these parameters, the effects of three different working conditions are analysed. According to the analysis, the improved blowing scheme is given, which can effectively improve the efficiency of the blowing process

Compressibility and thermal expansion of natural clinopyroxene Di0.66Hd0.13Jd0.12Ts0.05

A natural clinopyroxene (cpx) has been experimentally studied by in-situ X-ray powder diffraction combined with an externally heated diamond anvil cell (DAC) technique in the temperature range 300–673 K and at pressures up to 15 GPa. The specimen has a composition of Di0.66Hd0.13Jd0.12Ts0.05 on the basis of four end-members, diopside (CaMgSi2O6), hedenbergite (CaFeSi2O6), jadeite (NaAlSi2O6), and Mg-Tschermak (MgAl(AlSi)O6). By combining the current data with literature results, an empirical relationship, V0 (Å3) = 392.5(6.2) + 46.7(6.5) × Di + 57.6(6.3) × Hd + 9.9(6.2) × Jd has been obtained to describe the relationship between the unit-cell volume and the composition of Di-Jd-Hd solid solution. The isothermal bulk modulus KT0 = 112(3) GPa and its pressure derivative K′T0 = 5.0(7) are obtained by fitting pressure-volume data with a 3rd-order Birch-Murnaghan equation of state (B-M EoS). The volume thermal expansion coefficients are determined to be α0 = 2.97(8) × 10−5/K and 2.82(2) × 10−5/K at ∼6.9(2) GPa and 8.5(2) GPa, respectively, from which ∂α/∂P = −9.3 × 10−7/K GPa−1 and ∂KT/∂T = −0.012 GPa/K are resulted for the current Di0.66Hd0.13Jd0.12Ts0.05. Keywords: Clinopyroxene, X-ray diffraction, Diamond anvil cell, Equation of state, Thermal expansio

Novel Three-Stage Framework for Prioritizing and Selecting Feature Variables for Short-Term Metro Passenger Flow Prediction

Short-term metro passenger flow prediction is vital for the operation and management of metro systems. Most studies focus on the higher prediction accuracy with statistical and machine learning methods, but little attention has been paid to the prioritization and selection of feature variables, especially for different metro station types. This study aims to analyze the effect of feature variables on the prediction results, and then select appropriate predictor variables accordingly. A novel three-stage framework is proposed to prioritize feature variables for short-term metro passenger flow prediction, including station clustering, feature extraction, and variable prioritization. A hierarchical clustering algorithm (AHC) is developed for station clustering, the results of which are verified by the K-means and Davies-Bouldin (DB) statistical index. We then extract the temporal, spatial, and external features. Finally, the association between the variables and the prediction results is explored using tree-based models. The proposed framework is demonstrated and validated with data collected from Shanghai Metro Automatic Fare Collection (AFC) system. The results highlight that the importance of feature variables for developing models varies between stations, whereas only a few variables are found to explain most of the variation in the testing dataset; different feature variables lead to distinct differences in prediction accuracy, and simply adding more predictor variables does not necessarily lead to higher prediction accuracy. In addition, the station type and prediction type (i.e., tap-in and tap-out) have little influence on the selection of feature variables