Ribosome modulation factor protects Escherichia coli during heat stress, but this may not be dependent on ribosome dimerisation

The role of ribosome modulation factor (RMF) in protecting heat-stressed Escherichia coli cells was identified by the observation that cultures of a mutant strain lacking functional RMF (HMY15) were highly heat sensitive in stationary phase compared to those of the parent strain (W3110). No difference in heat sensitivity was observed between these strains in exponential phase, during which RMF is not synthesised. Studies by differential scanning calorimetry demonstrated that the ribosomes of stationary-phase cultures of the mutant strain had lower thermal stability than those of the parent strain in stationary phase, or exponential-phase ribosomes. More rapid breakdown of ribosomes in the mutant strain during heating was confirmed by rRNA analysis and sucrose density gradient centrifugation. Analyses of ribosome composition showed that the 100S dimers dissociated more rapidly during heating than 70S particles. While ribosome dimerisation is a consequence of the conformational changes caused by RMF binding, it may not therefore be essential for RMF-mediated ribosome stabilisation.

The intracellular genistein metabolite 5,7,3 ',4 '-tetrahydroxyisoflavone mediates G2-M cell cycle arrest in cancer cells via modulation of the p38 signaling pathway

The cellular actions of genistein are believed to mediate the decreased risk of breast cancer associated with high soy consumption. We have investigated the intracellular metabolism of genistein in T47D tumorigenic and MCF-10A nontumorigenic cells and assessed the cellular actions of resultant metabolites. Genistein selectively induced growth arrest and G2-M phase cell cycle block in T47D but not MCF10A breast epithelial cells. These antiproliferative effects were paralleled by significant differences in the association of genistein to cells and in particular its intracellular metabolism. Genistein was selectively taken up into T47D cells and was subject to metabolism by CYP450 enzymes leading to the formation of both 5,7,3',4'-tetrahydroxyisoflavone (THIF) and two glutathionyl conjugates of THIF THIF inhibited cdc2 activation via the phosphorylation of p38 MAP kinase, suggesting that this species may mediate genistein's cellular actions. THIF exposure activated p38 and caused subsequent inhibition of cyclin B1 (Ser 147) and cdc2 (Thr 161) phosphorylation, two events critical for the correct functioning of the cdc2-cyclin B1 complex. We suggest that the formation of THIF may mediate the cellular actions of genistein in tumorigenic breast epithelial cells via the activation of signaling through p38. (c) 2006 Elsevier Inc. All rights reserved.

Modulation of detoxification enzymes by watercress: in vitro and in vivo investigations in human peripheral blood cells

Epidemiological studies indicate that consumption of cruciferous vegetables (CV) can reduce the risk of cancer. Supposed mechanisms are partly the inhibition of phase I and the induction of phase II enzymes. The aim of this study was to investigate in vitro and in vivo effects of watercress (WC), a member of the CV family, on chemopreventive parameters using human peripheral blood mononuclear cells (PBMC) as surrogate cells. We investigated the hypothesis that WC reduces cancer risk by inducing detoxification enzymes in a genotype-dependent manner. In vitro gene expression and enzyme activity experiments used PBMC incubated with a crude extract from fresh watercress (WCE, 0.1-10 mu L/mL with 8.2 g WC per 1 mL extract) or with one main key compound phenethyl isothiocyanate (PEITC, 1-10 mu M). From an in vivo perspective, gene expression and glutathione S-transferase (GST) polymorphisms were determined in PBMC obtained from a human intervention study in which subjects consumed 85 g WC per day for 8 weeks. The influence of WC consumption on gene expression was determined for detoxification enzymes such as superoxide dismutase 2 (SOD2) and glutathione peroxidase 1 (GPX1), whilst the SOD and GPX activities in red blood cells were also analysed with respect to GST genotypes. In vitro exposure of PBMC to WCE or PEITC (24 h) increased gene expression for both detoxification enzymes GPX1 (5.5-fold, 1 mu L/mL WCE, 3.7-fold 1 mu M PEITC) and SOD2 (12.1-fold, 10 mu L/mL WCE, 7.3-fold, 10 mu M PEITC), and increased SOD2 activity (1.9-fold, 10 mu L/mL WCE). The WC intervention had no significant effect on in vivo PBMC gene expression, as high individual variations were observed. However, a small but significant increase in GPX (p = 0.025) and SOD enzyme activity (p = 0.054) in red blood cells was observed in GSTM1*0, but not in GSTM1*1 individuals, whilst the GSTT1 genotype had no impact. The results indicate that WC is able to modulate the enzymes SOD and GPX in blood cells in vitro and in vivo, and suggest that the capacity of moderate intake of CV to induce detoxification is dependent in part on the GSTM1 genotype.

The activity of ribosome modulation factor during growth of Escherichia coli under acidic conditions

Expression of the gene encoding ribosome modulation factor (RMF), as measured using an rmf-lacZ gene fusion, increased with decreasing pH in exponential phase cultures of Escherichia coli. Expression was inversely proportional to the growth rate and independent of the acidifying agent used and it was concluded that expression of rmf was growth rate controlled in exponential phase under acid conditions. Increased rmf expression during exponential phase was not accompanied by the formation of ribosome dimers as occurs during stationary phase. Nor did it appear to have a significant effect on cell survival under acid stress since the vulnerability of an RMF-deficient mutant strain was similar to that of the parent strain. Ribosome degradation was increased in the mutant strain compared to the parent strain at pH 3.75. Also, the peptide elongation rate was reduced in the mutant strain but not the parent during growth under acid conditions. It is speculated that the function of RMF during stress-induced reduction in growth rate is two-fold: firstly to prevent reduced elongation efficiency by inactivating surplus ribosomes and thus limiting competition for available protein synthesis factors, and secondly to protect inactivated ribosomes from degradation.

Self-assembly of PEGylated peptide conjugates containing a modified amyloid β-peptide fragment

The self-assembly of PEGylated peptides containing a modified sequence from the amyloid beta peptide, FEK LVFF, has been studied in aqueous solution. PEG molar masses PEG1k, PEG2k, and PEG10k were used in the conjugates. It is shown that the three FFK LVFF-PEG hybrids form fibrils comprising a FFKLVFF core and a PEG corona. The beta-sheet secondary structure of the peptide is retained in the FFK LVFF fibril core. At sufficiently high concentrations, FEK LVFF-PEG1k and FEK LVFF-PEG2k form a nema tic phase, while PEG10k-FEK LVFF exhibits a hexagonal columnar phase. Simultaneous small angle neutron scattering/shear flow experiments were performed to study the shear flow alignment of the nematic and hexagonal liquid crystal phases. On drying, PEG crystallization occurs without disruption of the FFK LVFF beta-sheet structure leading to characteristic peaks in the X-ray diffraction pattern and FTIR spectra. The stability of beta-sheet structures was also studied in blends of FFKLVFF-PEG conjugates with poly(acrylic acid) (PAA). While PEG crystallization is only observed up to 25% PAA content in the blends, the FFK LVFF beta-sheet structure is retained up to 75% PAA.

Strong-segregation limit of the self-consistent field theory for diblock copolymer melts

The self-consistent field theory (SCFT) introduced by Helfand for diblock copolymer melts is expected to converge to the strong-segregation theory (SST) of Semenov in the asymptotic limit, $\chi N \rightarrow \infty$. However, past extrapolations of the lamellar/cylinder and cylinder/sphere phase boundaries, within the standard unit-cell approximation, have cast some doubts on whether or not this is actually true. Here we push the comparison further by extending the SCFT calculations to $\chi N = 512,000$, by accounting for exclusion zones in the coronae of the cylindrical and spherical unit cells, and by examining finite-segregation corrections to SST. In doing so, we provide the first compelling evidence that SCFT does indeed reduce to SST.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Blind equalisation of multilevel PAM data for nonminimum phase channels via second- and fourth-order cumulants

This paper introduces a new blind equalisation algorithm for the pulse amplitude modulation (PAM) data transmitted through nonminimum phase (NMP) channels. The algorithm itself is based on a noncausal AR model of communication channels and the second- and fourth-order cumulants of the received data series, where only the diagonal slices of cumulants are used. The AR parameters are adjusted at each sample by using a successive over-relaxation (SOR) scheme, a variety of the ordinary LMS scheme, but with a faster convergence rate and a greater robustness to the selection of the ‘step-size’ in iterations. Computer simulations are implemented for both linear time-invariant (LTI) and linear time-variant (LTV) NMP channels, and the results show that the algorithm proposed in this paper has a fast convergence rate and a potential capability to track the LTV NMP channels.

Correcting for local oscillator phase offset in a DVB-T compliant receiver under multipath conditions

In 1997, the UK implemented the worlds first commercial digital terrestrial television system. Under the ETS 300 744 standard, the chosen modulation method, COFDM, is assumed to be multipath resilient. Previous work has shown that this is not necessarily the case. It has been shown that the local oscillator required for demodulation from intermediate-frequency to baseband must be very accurate. This paper shows that under multipath conditions, standard methods for obtaining local oscillator phase lock may not be adequate. This paper demonstrates a set of algorithms designed for use with a simple local oscillator circuit which will allow correction for local oscillator phase offset to maintain a low bit error rate with multipath present.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

Kinetics of layer hopping in a diblock copolymer lamellar phase

In the ordered state, symmetric diblock copolymers self-assemble into an anisotropic lamellar morphology. The equilibrium thickness of the lamellae is the result of a delicate balance between enthalpic and entropic energies, which can be tuned by controlling the temperature. Here we devise a simple yet powerful method of detecting tiny changes in the lamellar thickness using optical microscopy. From such measurements we characterize the enthalpic interaction as well as the kinetics of molecules as they hop from one layer to the next in order to adjust the lamellar thickness in response to a temperature jump. The resolution of the measurements facilitate a direct comparison to predictions from self-consistent field theory.

The plastic self organising map

A novel extension to Kohonen's self-organising map, called the plastic self organising map (PSOM), is presented. PSOM is unlike any other network because it only has one phase of operation. The PSOM does not go through a training cycle before testing, like the SOM does and its variants. Each pattern is thus treated identically for all time. The algorithm uses a graph structure to represent data and can add or remove neurons to learn dynamic nonstationary pattern sets. The network is tested on a real world radar application and an artificial nonstationary problem.

Monte Carlo phase diagram for a polydisperse diblock copolymer melt

The phase diagram for an AB diblock copolymer melt with polydisperse A blocks and monodisperse B blocks is evaluated using lattice-based Monte Carlo simulations. Experiments on this system have shown that the A-block polydispersity shifts the order-order transitions (OOTs) towards higher A-monomer content, while the order-disorder transition (ODT) moves towards higher temperatures when the A blocks form the minority domains and lower temperatures when the A blocks form the matrix. Although self-consistent field theory (SCFT) correctly accounts for the change in the OOTs, it incorrectly predicts the ODT to shift towards higher temperatures at all diblock copolymer compositions. In contrast, our simulations predict the correct shifts for both the OOTs and the ODT. This implies that polydispersity amplifies the fluctuation-induced correction to the mean-field ODT, which we attribute to a reduction in packing frustration. Consistent with this explanation, polydispersity is found to enhance the stability of the perforated-lamellar phase.