A system for studying epithelial-stromal interactions reveals distinct inductive abilities of stromal cells from benign prostatic hyperplasia and prostate cancer

The development of normal and abnormal glandular structures in the prostate is controlled at the endocrine and paracrine levels by reciprocal interactions between epithelium and stroma. To study these processes it is useful to have an efficient method of tissue acquisition for reproducible isolation of cells from defined histologies. Here we assessed the utility of a standardized system for acquisition and growth of prostatic cells from different regions of the prostate with different pathologies, and we compared the abilities of stromal cells from normal peripheral zone (PZ-S), benign prostatic hyperplasia (BPH-S), and cancer (CA-S) to induce the growth of a human prostatic epithelial cell line (BPH-1) in vivo. Using the tissue recombination method, we showed that grafting stromal cells (from any histology) alone, or BPH-1 epithelial cells alone produced no visible grafts. Recombining PZ-S with BPH-1 cells also produced no visible grafts (n = 15). Recombining BPH-S with BPH-1 cells generated small, well-organized and sharply demarcated grafts approximately 3-4 mm in diameter (n = 9), demonstrating a moderate inductive ability of BPH-S. Recombining CA-S with BPH-1 cells generated highly disorganized grafts that completely surrounded the host kidney and invaded into adjacent renal tissue, demonstrating induction of an aggressive phenotype. We conclude that acquisition of tissue from toluidine blue dye stained specimens is an efficient method to generate high quality epithelial and/or stromal cultures. Stromal cells derived by this method from areas of BPH and cancer induce epithelial cell growth in vivo which mimics the natural history of these diseases.

A tungsten complex containing a highly delocalized metal-ligand system

Nucleophilic attack of (triphenylphosphonio) cyclopentadienide on the dichlorodiazomethane-tungsten complex trans[ BrW(dppe)(2)(N2CCl2)]PF6 [dppe is 1,2-bis(diphenylphosphino) ethane] results in C-C bond formation and affords the title compound, trans-[W(C24H18ClN2P)Br(C26H24P2)(2)]PF6 center dot 0.6CH(2)Cl(2). This complex, bis[1,2- bis(diphenylphosphino)ethane] bromido{chloro[3-(triphenylphosphonio) cyclopentadienylidene] diazomethanediido} tungsten hexafluorophosphate dichloromethane 0.6-solvate, contains the previously unknown ligand chloro[3-(triphenylphosphonio) cyclopentadienylidene] diazomethane. Evidence from bond lengths and torsion angles indicates significant through-ligand delocalization of electron density from tungsten to the nominally cationic phosphorus(V) centre. This structural analysis clearly demonstrates that the tungsten-dinitrogen unit is a powerful pi-electron donor with the ability to transfer electron density from the metal to a distant acceptor centre through an extended conjugated ligand system. As a consequence, complexes of this type could have potential applications as nonlinear optical materials and molecular semiconductors.

An HMV mapping study of a pulsating jet system

A new system for the generation of hydrodynamic modulated voltammetry (HMV) is presented. This system consists of an oscillating jet produced through the mechanical vibration of a large diaphragm. The structure of the cell is such that a relatively small vibration is transferred to a large fluid flow at the jet outlet. Positioning of an electrode (Pt, 0.5 mm or 25 mu m diameter) over the exit of this jet enables the detection of the modulated flow of liquid. While this flow creates modest mass transfer rates (time averaged similar to 0.015 cm s(-1)) it can also be used to create a HMV system where a 'lock-in' approach is adopted to investigate the redox chemistry in question. This is demonstrated for the Fe(CN)(6)(3-/4-) redox system. Here 'lock-in' to the modulated hydrodynamic signal is achieved through the deployment of bespoke software. The apparatus and procedure is shown to produce a simple and efficient way to obtain the desired signal. In addition the spatial variation of the HMV signal, phase correction and time averaged current with respect to the jet orifice is presented. (C) 2008 Elsevier B.V. All rights reserved.

Failure of a control strategy for a hybrid air-conditioning and wind catchers/towers system at Bluewater shopping malls in Kent

Purpose – To evaluate the control strategy for a hybrid natural ventilation wind catchers and air-conditioning system and to assess the contribution of wind catchers to indoor air environments and energy savings if any. Design/methodology/approach – Most of the modeling techniques for assessing wind catchers performance are theoretical. Post-occupancy evaluation studies of buildings will provide an insight into the operation of these building components and help to inform facilities managers. A case study for POE was presented in this paper. Findings – The monitoring of the summer and winter month operations showed that the indoor air quality parameters were kept within the design target range. The design control strategy failed to record data regarding the operation, opening time and position of wind catchers system. Though the implemented control strategy was working effectively in monitoring the operation of mechanical ventilation systems, i.e. AHU, did not integrate the wind catchers with the mechanical ventilation system. Research limitations/implications – Owing to short-falls in the control strategy implemented in this project, it was found difficult to quantify and verify the contribution of the wind catchers to the internal conditions and, hence, energy savings. Practical implications – Controlling the operation of the wind catchers via the AHU will lead to isolation of the wind catchers in the event of malfunctioning of the AHU. Wind catchers will contribute to the ventilation of space, particularly in the summer months. Originality/value – This paper demonstrates the value of POE as indispensable tool for FM professionals. It further provides insight into the application of natural ventilation systems in building for healthier indoor environments at lower energy cost. The design of the control strategy for natural ventilation and air-conditioning should be considered at the design stage involving the FM personnel.

Scanning LDV for vibration measurement of filiform hairs in crickets in response to induced airflow - art. no. 63450E

Cercal hairs represent in cricket a wind sensitive escape system, able to detect the airflow generated from predating species. These sensors have been studied as a biomimetic concept to allow the development of MEMS for biomedical use. In particular, the behaviour of the hairs, including airflow response, resonant frequency and damping, has been investigated up to a frequency of 20 kHz. The microscopic nature of the hairs, the complex vibrations of excited hairs and the high damping of the system suggested that the use of Laser Doppler vibrometry could possibly improve the test performance. Two types of tests were performed: in the first case the hairs were indirectly excited using the signal obtained from a vibrating aluminium plate, whilst in the second case the hairs were directly excited using a white noise chirp. The results from the first experiment indicated that the hairs move in-phase with the exciting signal up to frequencies in the order of 10 kHz, responding to the vibration modes of the plate with a signal attenuation of 12 to 20 dB. The chirp experiment revealed the presence of rotational resonant modes at 6850 and 11300 Hz. No clear effect of hair length was perceivable on the vibration response of the filiform sensors. The obtained results proved promising to support the mechanical and vibration characterisation of the hairs and suggest that scanning Laser vibrometry can be used extensively on highly dampened biological materials.

Isolation of Corynebacterium falsenii and description of Corynebacterium aquilae sp. nov., from eagles

Biochemical, molecular chemical and molecular genetic studies were performed on seven unidentified Gram-positive, rod-shaped organisms recovered from eagles. The strains were provisionally identified as Corynebacterium jeikeium with the commercial API Coryne system, but they were able to grow under anaerobic conditions and were non-lipophilic. Comparative 16S rRNA gene sequencing studies demonstrated that the isolates belonged phylogenetically to the genus Corynebacterium. Three strains were identified genotypically as Corynebacterium falsenii; the remaining four strains corresponded to a hitherto unknown lineage within the genus Corynebacterium, associated with a small subcluster of species that included Corynebacterium diphtheriae and its close relatives. The unknown bacterial strains were readily distinguished from these and other species of the genus by biochemical tests. Based on both phenotypic and phylogenetic evidence, it is proposed that the unknown bacterial strains from eagles should be classified as Corynebacterium aquilae sp. nov. (type strain is S-613(T)=CECT 5993(T)=CCUG 46511(T)).

Towards dynamical system models of language-related brain potentials

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexical-semantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Wiener-System subspace identification for mobile wireless mm-wave networks

We discuss the feasibility of wireless terahertz communications links deployed in a metropolitan area and model the large-scale fading of such channels. The model takes into account reception through direct line of sight, ground and wall reflection, as well as diffraction around a corner. The movement of the receiver is modeled by an autonomous dynamic linear system in state space, whereas the geometric relations involved in the attenuation and multipath propagation of the electric field are described by a static nonlinear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a single-output Wiener model from time-domain measurements of the field intensity when the receiver motion is simulated using a constant angular speed and an exponentially decaying radius. The identification procedure is validated by using the model to perform q-step ahead predictions. The sensitivity of the algorithm to small-scale fading, detector noise, and atmospheric changes are discussed. The performance of the algorithm is tested in the diffraction zone assuming a range of emitter frequencies (2, 38, 60, 100, 140, and 400 GHz). Extensions of the simulation results to situations where a more complicated trajectory describes the motion of the receiver are also implemented, providing information on the performance of the algorithm under a worst case scenario. Finally, a sensitivity analysis to model parameters for the identified Wiener system is proposed.

A hyperstable neural network for the modelling and control of nonlinear systems

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Dual-orthogonal radial basis function networks for nonlinear time series prediction

A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.

Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations

This paper describes a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation models using the extended Kalman filter. The method involves the use of a time-varying linearisation of a semi-explicit index one differential-algebraic equation. The estimation technique consists of a simplified extended Kalman filter that is integrated with the differential-algebraic equation model. The paper describes a simulation study using a model of a batch chemical reactor. It also reports a study based on experimental data obtained from a mixing process, where the model of the system is solved using the sequential modular method and the estimation involves a bank of extended Kalman filters.