Prostate enlargement in mice due to fetal exposure to low doses of estradiol or diethylstilbestrol and opposite effects at high doses

On the basis of results of studies using high doses of estrogens, exposure to estrogen during fetal life is known to inhibit prostate development. However, it is recognized in endocrinology that low concentrations of a hormone can stimulate a tissue, while high concentrations can have the opposite effect. We report here that a 50% increase in free-serum estradiol in male mouse fetuses (released by a maternal Silastic estradiol implant) induced a 40% increase in the number of developing prostatic glands during fetal life; subsequently, in adulthood, the number of prostatic androgen receptors per cell was permanently increased by 2-fold, and the prostate was enlarged by 30% (due to hyperplasia) relative to untreated males. However, as the free serum estradiol concentration in male fetuses was increased from 2- to 8-fold, adult prostate weight decreased relative to males exposed to the 50% increase in estradiol. As a model for fetal exposure to man-made estrogens, pregnant mice were fed diethylstilbestrol (DES) from gestation days 11 to 17. Relative to controls, DES doses of 0.02, 0.2, and 2.0 ng per g of body weight per day increased adult prostate weight, whereas a 200-ng-per-g dose decreased adult prostate weight in male offspring. Our findings suggest that a small increase in estrogen may modulate the action of androgen in regulating prostate differentiation, resulting in a permanent increase in prostatic androgen receptors and prostate size. For both estradiol and DES, prostate weight first increased then decreased with dose, resulting in an inverted-U dose-response relationship.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

An integrated modeling approach to predict flooding on urban basin.

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.