Structural analysis of human CCR2b and primate CCR2b by molecular modeling and molecular dynamics simulation

CCR2b, a chemokine receptor for MCP-1, -2, -3, -4, plays an important role in a variety of diseases involving infection, inflammation, and/or injury, as well as being a coreceptor for HIV-1 infection. Two models of human CCR2b (hCCR2b) were generated by h

Immunolocalization and expression of vascular endothelial growth factor receptors (VEGFRs) and neuropilins (NRPs) on Keratinocytes in human epidermis

Vascular endothelial growth factor (VEGF) plays an important role in normal and pathological angiogenesis. VEGF receptors (VEGFRs, including VEGFR-1, VEGFR-2, and VEGFR-3) and neuropilins (NRPs, including NRP-1 and NRF-2) are high-affinity receptors for V

Characterize dynamic conformational space of human CCR5 extracellular domain by molecular modeling and molecular dynamics simulation

The chemokine receptor CCR5 is the receptor for several chemokines and major coreceptor for R5 human immunodeficiency virus type-1 strains entry into cell. Three-dimensional models of CCR5 were built by using homology modeling approach and 1 ns molecular dynamics (MD) simulation, because studies of site-directed mutagenesis and chimeric receptors have indicated that the N-terminus (Nt) and extracellular loops (ECLs) of CCR5 are important for ligands binding and viral fusion and entry, special attention was focused on disulfide bond function, conformational flexibility, hydrogen bonding, electrostatic interactions, and solvent-accessible surface area of Nt and ECLs of this protein part. We found that the extracellular segments of CCR5 formed a well-packet globular domain with complex interactions occurred between them in a majority of time of MID simulation, but Nt region could protrude from this domain sometimes. The disulfide bond Cys20-Cys269 is essential in controlling specific orientation of Nt region and maintaining conformational integrity of extracellular domain. RMS comparison analysis between conformers revealed the ECL1 of CCR5 stays relative rigid, whereas the ECL2 and Nt are rather flexible. Solvent-accessible surface area calculations indicated that the charged residues within Nt and ECL2 are often exposed to solvent. Integrating these results with available experimental data, a two-step gp120-CCR5 binding mechanism was proposed. The dynamic interaction of CCR5 extracellular domain with gp120 was emphasized. (C) 2004 Elsevier B.V. All rights reserved.

Cooperation of Mtmr8 with PI3K Regulates Actin Filament Modeling and Muscle Development in Zebrafish

Background: It has been shown that mutations in at least four myotubularin family genes (MTM1, MTMR1, 2 and 13) are causative for human neuromuscular disorders. However, the pathway and regulative mechanism remain unknown. Methodology/Principal Findings: Here, we reported a new role for Mtmr8 in neuromuscular development of zebrafish. Firstly, we cloned and characterized zebrafish Mtmr8, and revealed the expression pattern predominantly in the eye field and somites during early somitogenesis. Using morpholino knockdown, then, we observed that loss-of-function of Mtmr8 led to defects in somitogenesis. Subsequently, the possible underlying mechanism and signal pathway were examined. We first checked the Akt phosphorylation, and observed an increase of Akt phosphorylation in the morphant embryos. Furthermore, we studied the PH/G domain function within Mtmr8. Although the PH/G domain deletion by itself did not result in embryonic defect, addition of PI3K inhibitor LY294002 did give a defective phenotype in the PH/G deletion morphants, indicating that the PH/G domain was essential for Mtmr8's function. Moreover, we investigated the cooperation of Mtmr8 with PI3K in actin filament modeling and muscle development, and found that both Mtmr8-MO1 and Mtmr8-MO2+LY294002 led to the disorganization of the actin cytoskeleton. In addition, we revealed a possible participation of Mtmr8 in the Hedgehog pathway, and cell transplantation experiments showed that Mtmr8 worked in a non-cell autonomous manner in actin modeling. Conclusion/Significance: The above data indicate that a conserved functional cooperation of Mtmr8 with PI3K regulates actin filament modeling and muscle development in zebrafish, and reveal a possible participation of Mtmr8 in the Hedgehog pathway. Therefore, this work provides a new clue to study the physiological function of MTM family members.

Mobilization potential of heavy metals: A comparison between river and lake sediments

Toxic metals introduced into aquatic environments by human activities accumulation in sediments. A common notion is that the association of metals with acid volatile sulfides (AVS) affords a mechanism for partitioning metals from water to solid phase, thereby reducing biological availability. However, variation in environmental conditions can mobilize the sediment-bound metal and result in adverse environmental impacts. The AVS levels and the effect of AVS on the fate of Cu, Cd, Zn, Ni in sediments in the the Changjiang River, a suboxic river with sandy bottom sediment and the Donghu Lake, a anoxic lake with muddy sediment in China, were compared through aeration, static adsorption and release experiments in laboratory. Sips isotherm equation, kinetic equation and grade ion exchange theory were used to describe the heavy metal adsorb and release process. The results showed that AVS level in the lake sediment are higher than that of the river. Heavy metals in the overlying water can transfer to sediments incessantly as long as the sediment remains undisturbed. The metal release process is mainly related to AVS oxidation in lake sediment while also related to Org-C and Fe-Mn oxyhydroxide oxidation in river sediment. The effect of sulfides on Zn and Ni is high, followed by Cd, and Cu is easy bound to Org-C. AVS plays a major role in controlling metals activity in lake sediment and its presence increase the adsorption capacity both of the lake and river sediments.

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

The Deposition and Burning Characteristics During Slagging Co-Firing Coal and Wood: Modeling and Numerical Simulation

Numerical analysis was used to study the deposition and burning characteristics of combining co-combustion with slagging combustion technologies in this paper. The pyrolysis and burning kinetic models of different fuels were implanted into the WBSF-PCC2 (wall burning and slag flow in pulverized co-combustion) computation code, and then the slagging and co-combustion characteristicsespecially the wall burning mechanism of different solid fuels and their effects on the whole burning behavior in the cylindrical combustor at different mixing ratios under the condition of keeping the heat input samewere simulated numerically. The results showed that adding wood powder at 25% mass fraction can increase the temperature at the initial stage of combustion, which is helpful to utilize the front space of the combustor. Adding wood powder at a 25% mass fraction can increase the reaction rate at the initial combustion stage; also, the coal ignitability is improved, and the burnout efficiency is enhanced by about 5% of suspension and deposition particles, which is helpful for coal particles to burn entirely and for combustion devices to minimize their dimensions or sizes. The results also showed that adding wood powder at a proper ratio is helpful to keep the combustion stability, not only because of the enhancement for the burning characteristics, but also because the running slag layer structure can be changed more continuously, which is very important for avoiding the abnormal slag accumulation in the slagging combustor. The theoretic analysis in this paper proves that unification of co-combustion and slagging combustion technologies is feasible, though more comprehensive and rigorous research is needed.

Modeling and optimizing of high-concentration erbium-doped fiber amplifiers with consideration of ion-clusters

Starting from the modeling of isolated ions and ion-clusters, a closed form rate and power evolution equations for high-concentration erbium-doped fiber amplifiers are constructed. Based on the equations, the effects of the fraction of ion-clusters in total ions and the number of ions per cluster on the performance of high-concentration erbium-doped fiber amplifiers are analyzed numerically. The results show that the presence of the ion-clusters deteriorates amplifier performance, such as the signal power, signal gain, the threshold pump power for zero gain, saturated signal gain, and the maximum gain efficiency, etc. The optimum fiber length or other parameters should be modified with the ion-clusters being taken into account for the amplifiers to achieve a better performance. (c) 2007 Elsevier B.V. All rights reserved.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.