Transplantable neural progenitor populations derived from Rhesus monkey embryonic stem cells

Cell-based therapies using embryonic stem cells (ESCs) in the treatment of neural disease will require the generation of homogenous donor neural progenitor (NP) populations. Here we describe an efficient culture system containing hepatocyte growth factor (HGF) and G5 supplement for the production of highly enriched (88.3% +/- 8.1%)populations of NPs from rhesus monkey ESCs. Additional purification resulted in NP preparations that were 98% nestin positive. Moreover, NPs, as monolayers or neurospheres, could be maintained for prolonged periods of time in media containing HGF+G5 or G5 alone. In vitro differentiation and in vivo transplantation assays showed that NPs could differentiate into neurons, astrocytes, and oligodendrocytes. The kinds and quantities of differentiated cells derived from NPs were closely correlated with their niches in vivo. Glial differentiation was predominant in periventricular areas, whereas cells migrating into the cortex were mostly neurons. Cell counts showed that 2 months after transplantation, approximately 25% of transplanted NPs survived and 65% - 80% of the surviving transplanted cells migrated along the ventricular wall or in a radial fashion. Subcloning demonstrated that several clonal lines derived from NPs expressed nestin and differentiated into three neural lineages in vitro and in rat brains in vivo. In contrast, some subcloned lines showed restricted differentiation both in vitro and in vivo in rat brains. These observations set the stage for obtaining highly enriched NPs and evaluating the efficacy of NP-based transplantation therapy in the nonhuman primate and will provide a platform for probing the molecular mechanisms that control neural induction.

Determining of the delay time for a heating ventilating and air-conditioning plant using two weighted neural network approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

Circuit design and simulation of a CMOS-based preamplifier for brain neural signals

A novel CMOS-based preamplifier for amplifying brain neural signal obtained by scalp electrodes in brain-computer interface (BCI) is presented in this paper. By means of constructing effective equivalent input circuit structure of the preamplifier, two capacitors of 5 pF are included to realize the DC suppression compared to conventional preamplifiers. Then this preamplifier is designed and simulated using the standard 0.6 mu m MOS process technology model parameters with a supply voltage of 5 volts. With differential input structures adopted, simulation results of the preamplifier show that the input impedance amounts to more than 2 Gohm with brain neural signal frequency of 0.5 Hz-100 Hz. The equivalent input noise voltage is 18 nV/Hz(1/2). The common mode rejection ratio (CMRR) of 112 dB and the open-loop differential gain of 90 dB are achieved.