Suspected Hepadnavirus Association with a Hepatocellular Carcinoma in a Black-Tailed Prairie Dog (Cynomys ludovicianus).

Hepatocellular carcinomas are the most commonly reported neoplasm of black-tailed prairie dogs (Cynomys ludovicianus). In several other closely related Sciuridae species, infection with species-specific hepadnaviruses is associated with the development of these tumours, but such a hepadnavirus has not yet been identified in any prairie dog species, although its presence has been hypothesized previously. An adult prairie dog was humanely destroyed due to progressive illness and the identification of a cranial abdominal mass that was determined on histopathology to be a hepatocellular carcinoma. Deep sequencing of the tumour tissue identified the presence of a hepadnavirus, similar in its genetic structure to woodchuck hepatitis virus. Electron microscopy showed the presence of viral particles similar in structure to other hepadnaviral particles. This report suggests that a hepadnavirus may be associated with the development of hepatocellular carcinomas in the prairie dog.

A cultured human neural network operates a robotic actuator.

The development of bio-electronic prostheses, hybrid human-electronics devices and bionic robots has been the aim of many researchers. Although neurophysiologic processes have been widely investigated and bio-electronics has developed rapidly, the dynamics of a biological neuronal network that receive sensory inputs, store and control information is not yet understood. Toward this end, we have taken an interdisciplinary approach to study the learning and response of biological neural networks to complex stimulation patterns. This paper describes the design, execution, and results of several experiments performed in order to investigate the behavior of complex interconnected structures found in biological neural networks. The experimental design consisted of biological human neurons stimulated by parallel signal patterns intended to simulate complex perceptions. The response patterns were analyzed with an innovative artificial neural network (ANN), called ITSOM (Inductive Tracing Self Organizing Map). This system allowed us to decode the complex neural responses from a mixture of different stimulations and learned memory patterns inherent in the cell colonies. In the experiment described in this work, neurons derived from human neural stem cells were connected to a robotic actuator through the ANN analyzer to demonstrate our ability to produce useful control from simulated perceptions stimulating the cells. Preliminary results showed that in vitro human neuron colonies can learn to reply selectively to different stimulation patterns and that response signals can effectively be decoded to operate a minirobot. Lastly the fascinating performance of the hybrid system is evaluated quantitatively and potential future work is discussed.

Learning in human neural networks on microelectrode arrays.

This paper describes experiments involving the growth of human neural networks of stem cells on a MEA (microelectrode array) support. The microelectrode arrays (MEAs) are constituted by a glass support in which a set of tungsten electrodes are inserted. The artificial neural network (ANN) paradigm was used by stimulating the neurons in parallel with digital patterns distributed on eight channels, then by analyzing a parallel multichannel output. In particular, the microelectrodes were connected following two different architectures, one inspired by the Kohonen's SOM, the other by the Hopfield network. The output signals have been analyzed in order to evaluate the possibility of organized reactions by the natural neurons.f The results show that the network of human neurons reacts selectively to the subministered digital signals, i.e., it produces similar output signals referred to identical or similar patterns, and clearly differentiates the outputs coming from different stimulations. Analyses performed with a special artificial neural network called ITSOM show the possibility to codify the neural responses to different patterns, thus to interpret the signals coming from the network of biological neurons, assigning a code to each output. It is straightforward to verify that identical codes are generated by the neural reactions to similar patterns. Further experiments are to be designed that improve the hybrid neural networks' capabilities and to test the possibility of utilizing the organized answers of the neurons in several ways.