Pituitary gonadotropins regulate spermatogonial differentiation and proliferation in the rat

The relative regulatory roles of the pituitary gonadotropins, luteinizing hormone and follicle stimulating hormone in the spermatogonial proliferation has been studied using specific antibodies against these hormones in the immature rats. Immunoneutralization of luteinizing hormone for 7 days resulted in significant reduction in tetraploid cells and total absence of haploid cells, while there was a relative increase in the diploid population. This was also accomopanied by a decrease in spermatogonial proliferation as indicated by a decrease in [H-3] thymidine incorporation into DNA by purified spermatogonia. Administration bf follicle stimulating hormone als for 7 days also caused a significant decrease in the rate of spermatogonial proliferation. Withdrawal of follicle stimulating hormone led to a significant reduction in tetraploid and haploid cells However interestingly, it failed to totally abolish the appearance of these cells. Administration of testosterone (3mg/day/rat) for 2 days along with the gonadotropin a/s could partially reverse the effect on spermatogonial proliferation. It is concluded that (i) both luteinizing hormone and follicle stimulating hormone are involved in spermatogonial proliferation, (ii) lack of testosterone consequent of the neutralization of luteinizing hormone prevented the entry of spermatogonial cells into meiosis, (iii) testosterone may be involved in spermatogonial proliferation providing a mitotic signal and (v) both follicle stimulating hormone and testosterone act synergistically and lack of any one of the hormones results in impairment of spermatogonial proliferation.

Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. Using the aeroelastic analysis, it is found that the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power ale obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

Mathematical modelling of neurons and neural networks

The basic concepts and techniques involved in the development and analysis of mathematical models for individual neurons and networks of neurons are reviewed. Some of the interesting results obtained from recent work in this field are described. The current status of research in this field in India is discussed

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

On the problem of spurious patterns in neural associative memory models

The problem of spurious patterns in neural associative memory models is discussed, Some suggestions to solve this problem from the literature are reviewed and their inadequacies are pointed out, A solution based on the notion of neural self-interaction with a suitably chosen magnitude is presented for the Hebb learning rule. For an optimal learning rule based on linear programming, asymmetric dilution of synaptic connections is presented as another solution to the problem of spurious patterns, With varying percentages of asymmetric dilution it is demonstrated numerically that this optimal learning rule leads to near total suppression of spurious patterns. For practical usage of neural associative memory networks a combination of the two solutions with the optimal learning rule is recommended to be the best proposition.

Transitional intermittency detection by neural network

A neural network has been used to predict the flow intermittency from velocity signals in the transition zone in a boundary layer. Unlike many of the available intermittency detection methods requiring a proper threshold choice in order to distinguish between the turbulent and non-turbulent parts of a signal, a trained neural network does not involve any threshold decision. The intermittency prediction based on the neural network has been found to be very satisfactory.

Glassy behavior in neural network models of associative memory

Neural network models of associative memory exhibit a large number of spurious attractors of the network dynamics which are not correlated with any memory state. These spurious attractors, analogous to "glassy" local minima of the energy or free energy of a system of particles, degrade the performance of the network by trapping trajectories starting from states that are not close to one of the memory states. Different methods for reducing the adverse effects of spurious attractors are examined with emphasis on the role of synaptic asymmetry. (C) 2002 Elsevier Science B.V. All rights reserved.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved.

VIP blockade leads to microcephaly in mice via disruption of Mcph1-Chk1 signaling

Autosomal recessive primary microcephaly (MCPH) is a genetic disorder that causes a reduction of cortical outgrowth without severe interference with cortical patterning. It is associated with mutations in a number of genes encoding protein involved in mitotic spindle formation and centrosomal activities or cell cycle control. We have shown previously that blocking vasoactive intestinal peptide (VIP) during gestation in mice by using a VIP antagonist (VA) results in microcephaly. Here, we have shown that the cortical abnormalities caused by prenatal VA administration mimic the phenotype described in MCPH patients and that VIP blockade during neurogenesis specifically disrupts Mcph1 signaling. VA administration reduced neuroepithelial progenitor proliferation by increasing cell cycle length and promoting cell cycle exit and premature neuronal differentiation. Quantitative RT-PCR and Western blot showed that VA downregulated Mcph1. Inhibition of Mcph1 expression led to downregulation of Chk1 and reduction of Chk1 kinase activity. The inhibition of Mcph1 and Chk1 affected the expression of a specific subset of cell cycle-controlling genes and turned off neural stem cell proliferation in neurospheres. Furthermore, in vitro silencing of either Mcph1 or Chk1 in neurospheres mimicked VA-induced inhibition of cell proliferation. These results demonstrate that VIP blockade induces microcephaly through Mcph1 signaling and suggest that VIP/Mcph1/Chk1 signaling is key for normal cortical development.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.