Impact of delay on HIV-1 dynamics of fighting a virus with another virus

In this paper, we propose a mathematical model for HIV-1 infection with intracellular delay. The model examines a viral-therapy for controlling infections through recombining HIV-1 virus with a genetically modified virus. For this model, the basic reproduction number $\mathcal{R}_0$ are identified and its threshold properties are discussed. When $\mathcal{R}_0 < 1$, the infection-free equilibrium $E_0$ is globally asymptotically stable. When $\mathcal{R}_0 > 1$, $E_0$ becomes unstable and there occurs the single-infection equilibrium $E_s$, and $E_0$ and $E_s$ exchange their stability at the transcritical point $\mathcal{R}_0 =1$. If $1< \mathcal{R}_0 < R_1$, where $R_1$ is a positive constant explicitly depending on the model parameters, $E_s$ is globally asymptotically stable, while when $\mathcal{R}_0 > R_1$, $E_s$ loses its stability to the double-infection equilibrium $E_d$. There exist a constant $R_2$ such that $E_d$ is asymptotically stable if $R_1<\mathcal R_0 < R_2$, and $E_s$ and $E_d$ exchange their stability at the transcritical point $\mathcal{R}_0 =R_1$. We use one numerical example to determine the largest range of $\mathcal R_0$ for the local stability of $E_d$ and existence of Hopf bifurcation. Some simulations are performed to support the theoretical results. These results show that the delay plays an important role in determining the dynamic behaviour of the system. In the normal range of values, the delay may change the dynamic behaviour quantitatively, such as greatly reducing the amplitudes of oscillations, or even qualitatively changes the dynamical behaviour such as revoking oscillating solutions to equilibrium solutions. This suggests that the delay is a very important fact which should not be missed in HIV-1 modelling

Hepatitis C virus 3'UTR regulates viral translation through direct interactions with the host translation machinery.

The 3' untranslated region (3'UTR) of hepatitis C virus (HCV) messenger RNA stimulates viral translation by an undetermined mechanism. We identified a high affinity interaction, conserved among different HCV genotypes, between the HCV 3'UTR and the host ribosome. The 3'UTR interacts with 40S ribosomal subunit proteins residing primarily in a localized region on the 40S solvent-accessible surface near the messenger RNA entry and exit sites. This region partially overlaps with the site where the HCV internal ribosome entry site was found to bind, with the internal ribosome entry site-40S subunit interaction being dominant. Despite its ability to bind to 40S subunits independently, the HCV 3'UTR only stimulates translation in cis, without affecting the first round translation rate. These observations support a model in which the HCV 3'UTR retains ribosome complexes during translation termination to facilitate efficient initiation of subsequent rounds of translation

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

The Influence of Controlling Redox Potential on Plasma Membrane Fatty Acid Composition during Very High Gravity Fermentation

Fatty acid components on yeast plasma membrane were critical in maintaining proper cell activity during bioethanol fermentation. The alteration of fatty acid composition on yeast plasma membrane was recognized as an adaptive response to several environmental stress including osmotic pressure, ethanol inhibition and nutrients limit. These stresses were exacerbated under very-high-gravity condition in which excessive fermentable sugar was provided in feedstock. Controlling redox potential was proved beneficial in improving yeast performance under very-high-gravity condition. Fatty acid synthesis and desaturation pathways involved dissolved oxygen as well as balance between NAD+/NADH and NADP+/NADPH which could be influenced by the regulation of redox potential in media. In this study, fatty acid composition profiles under different glucose concentrations and different redox potential control level were examined. Its connection with yeast cell growth, ethanol productivity and other metabolites’ concentrations were studied as well to reveal any causal correlation between redox potential control, membrane fatty acid composition and yeast activity. Two glucose concentrations used in this study were 200 g/L and 300 g/L which represented normal and very high gravity respectively in bioethanol fermentation. In 300 g/L fermentation, three redox conditions were adopted while two different redox conditions were used in 200 g/L fermentation. Biomass concentration, ethanol productivity and fatty acid composition were observed to be affected by both gravity and ORP control strategy. Final biomass concentrations were 4.302 g/L in 200 g/L glucose with no ORP control condition and 7.658 in 200 g/L glucose with ORP controlled at -100 mV condition. In 300 g/L glucose fermentation, final biomass concentrations were 3.400 g/L for no ORP control, 4.953 g/L for -150 mV ORP control and 5.260 for -100 mV ORP control. Ethanol productivities were 2.574 g/Lh for 200 g/L glucose without ORP control and 3.780 g/Lh for 200 g/L glucose with -100 mV ORP control. In 300 g/L glucose fermentation, ethanol productivity decreased to 1.584 g/Lh when no ORP control was imposed. ORP control at -150 mV could improve the ethanol productivity to 1.693 g/Lh while -100 mV ORP control was able to further enhance the ethanol productivity to 1.829 g/Lh. Fatty acid composition was observed to shift to more saturated components when no ORP control was applied. Such trend of saturation was increased by higher gravity condition. ORP control was shown to change this tendency to saturation and help restore fatty acid components on plasma membrane to a more balanced distribution

Government debt and household wealth inequality: evidence from China

This study attempts to explain the relationship between government debt and household wealth inequality, and further discusses possible channels of influence to provide ideas for mitigating the increasing gap between rich and poor. This study puts forward relevant assumptions in the theoretical model, further analyses the composition of household wealth, and verifies that household housing investment is an essential factor. This study finds that the expansion of government debt raises the price of housing, leading to faster wealth growth for wealthy households with relatively more real estate and widening the gap between rich and poor. This study argues that in economic development, government debt should be tilted towards livelihood protection and infrastructure construction, providing guaranteed housing to eligible relatively poor and narrowing the gap between rich and poor to a reasonable extent

Capturing changes in gene expression dynamics by gene set differential coordination analysis

Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis - finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome