In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available.(ABSTRACT TRUNCATED AT 400 WORDS)

Baker's yeast, Saccharomyces cerevisiae, was investigated for the combined influence of dissolved oxygen and glucose concentration in continuous culture. A reactor was operated at a range of dilution rates (0.1, 0.2, 0.25, 0.27, and 3.0 h(-1)), above and below the critical value that separates the oxidative and fermentation regions. For each dilution rate (D), steady states were established at each of five to ten different dissolved oxygen concentrations (DO) in the range of 0.01-5 mg/L. The use of on-line mass spectrometry facilitated the measurement of gaseous and dissolved O(2), CO(2), and ethanol. Intracellular carbohydrate, protein, RNA, DNA, lipid, and cytochrome concentrations were measured. Cell size measurements were reduced to specific surface areas. Cytochrome content showed up to 100% variation during a 20-day period of adaptation at D = 0.2 h(-1) to low DO. Eventually, the culture behaved the same at DO = 0.05 mg/L as it did initially at 3 mg/L. At D = 0.2, 0.25, and 0.27 h(-1), the transition between oxidation and fermentation was characterized by a critical DO which decreased with decreasing D. The X-D curves were shifted such that the critical D value was reduced with decreasing DO. Specific oxygen update rates varied with DO according to the saturation kinetics. Specific cell surface areas increased with decreasing DO. Cytochrome content generally decreased with decreasing DO, and Q(O(2) ) could be linearly related to the total cytochrome content, which exhibited a maximum at D = 0.27 h(-1).

In tissue engineering, precise control of cues in the microenvironment is essential to stimulate cells to undergo bioactivities such as proliferation, differentiation, and matrix production. However, current approaches are inefficient in providing nondepleting cues. In this study, we have developed a novel bioactive hydrogel (HAX-PolyP) capable of enhancing tissue growth by conjugating inorganic polyphosphate chains onto hyaluronic acid macromers. The immobilized polyphosphates provided constant osteoconductive stimulation to the embedded murine osteoblast precursor cells, resulting in up-regulation of osteogenic marker genes and enhanced levels of ALP activity. The osteoconductive activity was significantly higher when compared to those stimulated with free-floating polyphosphates. Even at very low concentrations, immobilization of polyphosphates onto the scaffold allowed sufficient signaling leading to more effective osteoconduction. These results demonstrate the potential of our novel material as an injectable bioactive scaffold, which can be clinically useful for developing bone grafts and bone regeneration applications.

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Coomassie brilliant blue staining developed by Pena (1980) was applied to cultured hepatocytes of adult rats with some modifications. Many of organelles in the cytoplasms were clearly visible as blue granules by this method. Various cytoskeletal elements were also visualized clearly. Because of its simplicity, Coomassie blue staining proved to be a very powerful tool for study of morphological changes of cell organelles and cytoskeletal systems of cultured hepatocytes.