The toughness of adipose tissue: measurements and physical basis.

The measured toughness J(C) of adipose and dermal porcine tissues are 4.1 and 17 kJ m(-2), respectively, via a trouser tear test. An assessment is made of the contribution to overall toughness from the microstructural elements. The analysis suggests that the toughness of adipose tissue is determined by the collagen network that surrounds the adipocytes. The volume fraction of the interlobular septa is sufficiently low for it to make a negligible contribution to the macroscopic toughness.

DISCUSSION OF MECHANISMS OF ACCELERATED AND RETARDED FATIGUE CRACK GROWTH.

The relevance of the effective stress intensity range to crack growth is considered for constant and for variable amplitude loading. The accelerated and retarded growth associated with simple programmed loadings is reported for two steels and an aluminium alloy. The load interaction effects are due to several competing mechanisms, and not due to the single, popular mechanism of crack closure.

Differentiable neural substrates for learned and described value and risk.

Studies of human decision making emerge from two dominant traditions: learning theorists [1-3] study choices in which options are evaluated on the basis of experience, whereas behavioral economists and financial decision theorists study choices in which the key decision variables are explicitly stated. Growing behavioral evidence suggests that valuation based on these different classes of information involves separable mechanisms [4-8], but the relevant neuronal substrates are unknown. This is important for understanding the all-too-common situation in which choices must be made between alternatives that involve one or another kind of information. We studied behavior and brain activity while subjects made decisions between risky financial options, in which the associated utilities were either learned or explicitly described. We show a characteristic effect in subjects' behavior when comparing information acquired from experience with that acquired from description, suggesting that these kinds of information are treated differently. This behavioral effect was reflected neurally, and we show differential sensitivity to learned and described value and risk in brain regions commonly associated with reward processing. Our data indicate that, during decision making under risk, both behavior and the neural encoding of key decision variables are strongly influenced by the manner in which value information is presented.

Mechanics of biological networks: from the cell cytoskeleton to connective tissue.

From the cell cytoskeleton to connective tissues, fibrous networks are ubiquitous in metazoan life as the key promoters of mechanical strength, support and integrity. In recent decades, the application of physics to biological systems has made substantial strides in elucidating the striking mechanical phenomena observed in such networks, explaining strain stiffening, power law rheology and cytoskeletal fluidisation - all key to the biological function of individual cells and tissues. In this review we focus on the current progress in the field, with a primer into the basic physics of individual filaments and the networks they form. This is followed by a discussion of biological networks in the context of a broad spread of recent in vitro and in vivo experiments.

Behavior learning of a face robot based on the characteristics of human instruction

Computer simulation experiments were performed to examine the effectiveness of OR- and comparative-reinforcement learning algorithms. In the simulation, human rewards were given as +1 and -1. Two models of human instruction that determine which reward is to be given in every step of a human instruction were used. Results show that human instruction may have a possibility of including both model-A and model-B characteristics, and it can be expected that the comparative-reinforcement learning algorithm is more effective for learning by human instructions.