Wnt ligands influence tumour initiation by controlling the number of intestinal stem cells.

Many epithelial stem cell populations follow a pattern of stochastic stem cell divisions called 'neutral drift'. It is hypothesised that neutral competition between stem cells protects against the acquisition of deleterious mutations. Here we use a Porcupine inhibitor to reduce Wnt secretion at a dose where intestinal homoeostasis is maintained despite a reduction of Lgr5+ stem cells. Functionally, there is a marked acceleration in monoclonal conversion, so that crypts become rapidly derived from a single stem cell. Stem cells located further from the base are lost and the pool of competing stem cells is reduced. We tested whether this loss of stem cell competition would modify tumorigenesis. Reduction of Wnt ligand secretion accelerates fixation of Apc-deficient cells within the crypt leading to accelerated tumorigenesis. Therefore, ligand-based Wnt signalling influences the number of stem cells, fixation speed of Apc mutations and the speed and likelihood of adenoma formation.

Functional and prognostic relevance of the homeobox protein MSX2 in malignant melanoma.

BACKGROUND: The homeobox containing transcription factor MSX2 is a key regulator of embryonic development and has been implicated to have a role in breast and pancreatic cancer. METHODS: Using a selection of two- and three-dimensional in vitro assays and tissue microarrays (TMAs), the clinical and functional relevance of MSX2 in malignant melanoma was explored. A doxycyline-inducible over-expression system was applied to study the relevance of MSX2 in vitro. For TMA construction, tumour material from 218 melanoma patients was used. RESULTS: Ectopic expression of MSX2 resulted in the induction of apoptosis and reduced the invasive capacity of melanoma cells in three-dimensional culture. MSX2 over-expression was shown to affect several signalling pathways associated with cell invasion and survival. Downregulation of N-Cadherin, induction of p21 and inhibition of both BCL2 and Survivin were observed. Cytoplasmic MSX2 expression was found to correlate significantly with increased recurrence-free survival (P=0.008). Nuclear expression of MSX2 did not result in significant survival correlations, suggesting that the beneficial effect of MSX2 may be independent of its DNA binding activity. CONCLUSIONS: MSX2 may be an important regulator of melanoma cell invasion and survival. Cytoplasmic expression of the protein was identified as biomarker for good prognosis in malignant melanoma patients.

Method for determining individual neuron size in simultaneous single-unit recordings.

A technique for estimating the size of neurons is based on extracellular recordings with paired-electrode sets. Simultaneous single-unit recordings are obtained from the dragonfly mesothoracic ganglion. It is assumed that the ganglion is a passive electrical environment, where spike amplitudes decrease with the inverse of distance squared, and spike angles (widths) increase linearly with distance from the cellular source to the recording electrodes. Starting with the recorded spike amplitudes and angles for each cell, a numerical algorithm is iterated to estimate the true value of the amplitude and angle minus these passive electrical distance effects. The resolved amplitude is a direct, consistent estimate of the size of each recorded neuron. The results indicate that a dichotomy of small and large cells is recorded in roughly a 2:1 ratio. The dichotomy of cell sizes is consistent with the available histological data, although a larger ratio of small to large cells (approximately 10:1) would be expected. Thus, a sampling bias for large cells is apparent, which may be reflective of the larger soma/proximal geometries of such cells. As the technique determines the size of each individual neuron, such biases are eliminated from population studies of the neural tissue. Furthermore, knowledge about the size of each individual neuron permits more detailed analyses of the interactions and contributions of single cells within a network of cells based upon size.

Real-time prediction of unsteady aerodynamics: Application for aircraft control and manoeuvrability enhancement.

The capability to control unsteady separated flow fields could dramatically enhance aircraft agility. To enable control, however, real-time prediction of these flow fields over a broad parameter range must be realized. The present work describes real-time predictions of three-dimensional unsteady separated flow fields and aerodynamic coefficients using neural networks. Unsteady surface-pressure readings were obtained from an airfoil pitched at a constant rate through the static stall angle. All data sets were comprised of 15 simultaneously acquired pressure records and one pitch angle record. Five such records and the associated pitch angle histories were used to train the neural network using a time-series algorithm. Post-training, the input to the network was the pitch angle (alpha), the angular velocity (dalpha/dt), and the initial 15 recorded surface pressures at time (t (0)). Subsequently, the time (t+Deltat) network predictions, for each of the surface pressures, were fed back as the input to the network throughout the pitch history. The results indicated that the neural network accurately predicted the unsteady separated flow fields as well as the aerodynamic coefficients to within 5% of the experimental data. Consistent results were obtained both for the training set as well as for generalization to both other constant pitch rates and to sinusoidal pitch motions. The results clearly indicated that the neural-network model could predict the unsteady surface-pressure distributions and aerodynamic coefficients based solely on angle of attack information. The capability for real-time prediction of both unsteady separated flow fields and aerodynamic coefficients across a wide range of parameters in turn provides a critical step towards the development of control systems targeted at exploiting unsteady aerodynamics for aircraft manoeuvrability enhancement.

Characterization of fluid distribution through a porous substrate under dynamic g conditions.

Dedicated electronic hardware has been constructed to monitor fluid distributions inside a plant rooting/nutrient substrate (Rockwool). With this hardware the effect of dynamically varying gravity states, from enhanced 2g to reduced 0.01g, on solution distributions inside a cube of substrate was monitored aboard the NASA KC-135 reduced gravity research aircraft. The 8 vertices and the center of the cube were used to place sinusoidal voltage sources (electrodes), emitting different fixed frequencies, inside the substrate. Using another set of 9 electrodes the voltage fields were detected across all frequencies. Since the substrate cannot conduct, those frequencies which appeared on any detector (sensor) were indicative of the conductive liquid pathways inside the substrate. An analysis algorithm was developed to visualize the fluid distributions under g-level conditions. Even though the duration of the experiment was short, gravity induced changes in fluid position were readily and reliably detected. Since fluids carry the nutrients necessary for plant growth these data and techniques can lead to the development of a uniform nutrient supply system supportive of optimal plant growth in space.

Recording of simultaneous single-unit activity in the dragonfly ganglia.

A technique for discriminating simultaneously active single units from multiple-unit data records has been developed. Multiple-unit records were obtained extracellularly from the dragonfly mesothoracic ganglion using two paired-electrode sets. The multiple-unit records were post processed based on the unique physical characteristics imparted to each spike via the tissue medium and spatial geometry of cells. It was assumed that the action potential amplitude falls off roughly as the inverse of distance squared from the recording electrode. Further, it was assumed that the tissue RC characteristics coupled with action potential amplitudes and neuron dipole characteristics impart a spike waveform unique to each cell. Accordingly, spikes were sorted by amplitude ratio as well as by matching of spike waveforms. Additional waveform characterization was derived from the spike angle (width) within grouped spikes. Decomposition of the multiple-unit records based on these parameters yielded clustered spike records from defined cellular sources. The defined clusters were combined to provide the cumulative record for a large number of simultaneously active single units.

Neural network analyses of stochastic information: application to neurobiological data.

Simultaneous recordings from over 50 neural cells were obtained from the dragonfly ganglia. To explore the biological information processing strategies reflected therein, data analysis methods were designed for use with artificial neural networks (ANN). Most methods are degraded by different cell spike trains that vary in mean firing frequencies by well over an order of magnitude. Based on underlying cell physiology, the occurrence of each spike is likely to be a stochastic function. To overcome such degradation problems in ANN use, a gaussian spike train representation was synthesized for each cell using raw data. This representation retained the exact spiking times and provided a biologically plausible probabilistic value for the time of occurrence for each spike. A 3-layer, feed-forward, ANN was trained on these data using a gradient descent learning algorithm. The task was to predict the neural activity at time (t + 1) given the neural activity at time (t). Following training, the network sum-squared prediction error was less than 0.01. Further, the temporal reproduction of the neural firing patterns was corroborated. The results indicated that the ANN could accurately reproduce the neural firing patterns in both the spatial and temporal domain using the stochastic spike train data. Encoding parameters for the spike trains using synthesized gaussian representations were optimized. The "lesion" studies were performed to determine the contribution of each cell to ANN predictions. The capability to "fine tune" both the information representation of spike trains and the ANN architecture provides significant advantages in the analysis of biological information processing by neural cells.(ABSTRACT TRUNCATED AT 250 WORDS)

A neural network simulation of simultaneous single-unit activity recorded from the dragonfly ganglia.

Techniques are described that allow the use of multiple neuron spike data in a computational neural network architecture. The network architecture was devised to match the number of actual neurons from which data were obtained. The network was successfully trained to accurately predict the multiple neuron spike trains. Simultaneous spike histories of 44 neurons were modeled by a network architecture consisting of 44 input units, 88 hidden units with recurrent connections and 44 output units. The activation function of each unit was determined by data unique to a single neuron. These data were coupled with an analog gradient that preserved both the exact spiking times and the relative spiking tendency of each neuron. The input activation values were compared to network output target values calculated to occur 5 msec forward in the composite spiking records of all neurons. Following 2000 training cycles with the gradient data, the average error of each unit in the network was 0.0016. Discrete output values for each network unit were correlated with those of all other units. These correlations were comparable to those done using the actual neuron data. Both correlations reveal a functional connectivity pattern among the units and neurons. These connectivity patterns indicate that the networks may synthesize patterns of activity needed for biological function; in this case, flight patterns carried out in the mesothoracic ganglion of the dragonfly. This model represents, to the best of our knowledge, the first computer based network simulation using actual experimental neural data obtained from a large number of spontaneously active cells in a small intact ganglion.

Plasmid pAO1 of Arthrobacter oxidans encodes 6-hydroxy-D-nicotine oxidase: cloning and expression of the gene in Escherichia coli.

The 160 kb plasmid pAO1 from Arthrobacter oxidans (Brandsch and Decker 1984) was subcloned in Escherichia coli with the aid of the plasmid vectors pUR222 and pBR322. Screening of the recombinant clones for enzyme activity revealed that the flavoenzyme 6-hydroxy-D-nicotine oxidase (6-HDNO), one of the enzymes of the nicotine-degradative pathway in A. oxidans, is encoded on pAO1. Immunoprecipitation of 35S-methionine-labelled E. coli cells with 6-HDNO-specific antiserum and expression of recombinant plasmid DNA in E. coli "maxicells" revealed that 6-HDNO is made as a 52,000 dalton protein, approximately 4,500 daltons larger than 6-HDNO from A. oxidans. The 6-HDNO activity was constitutively expressed in E. coli cells, possibly from an A. oxidans promoter, as shown by subcloning of the 6-HDNO gene in pBR322, using the expression vector pKK223-3 and the promoter probe vector pCB192.