Balancing Thymocyte Adhesion and Motility: A Functional Linkage Between β1 Lntegrins and The Motility Receptor RHAMM

Thymocyte differentiation involves several processes that occur in different anatomic sites within the thymus. Therefore, thymocytes must have the ability to respond to signals received from stromal cells and adopt either adhesive or motile behavior. We will discuss our data indicating human thymocytes use α4β1 integrin, α5β1 integrin and RHAMM to mediate these activities. Immature multinegative (MN; CD3–4–8–19-) thymocytes use α4β1 and α5β1 integrins to mediate weak and strong adhesion. This subset also uses α4β1 integrin to mediate motility. As thymocytes differentiate, they begin to express and use RHAMM to mediate motility in conjunction with α4β1 and α5β1 integrins. Motile thymocytes use β1 integrins to maintain weakly adhesive contacts with substrate to provide traction for locomoting cells, thus weak adhesion is a requirement of motile behavior. Hyaluronan (HA) is also required by thymocytes to mediate motility. HA binding to cell surface RHAMM redistributes intracellular RHAMM to the cell surface where it functions to mediate motility. We propose that the decision to maintain adhesive or motile behavior is based on the balance between low and high avidity binding conformations of β1 integrins on thymocytes and that RHAMM:HA interactions decrease high avidity binding conformations of integrins pushing the balance toward motile behavior

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Generation of guided waves in hollow cylinders by wedge and comb type transducers

It was shown by Denos C. Gazis in 19591 that in linearly elastic hollow circular cylinders there exists an infinite number of “normal modes”, each of which has its own propagation characteristics such as phase and group velocity as well as their own displacement and stress distributions throughout the cross section of the cylinder. It was also shown that, even for a given mode, these characteristics changed with changing frequency. In general, when such a cylinder is loaded by external forces, all of the modes of the structure will be excited in varying strengths determined by the characteristics of the applied loading. From a nondestructive evaluation (NDE) point of view, however, there are some modes which, due to their unique characteristics, are more sensitive to the quantities being measured or the defects being sought. It would be advantageous, therefore, to modify the applied loading so as to excite with appreciable amplitude only those modes which are found to be sensitive to the quantity being measured. In order to do this however, the relationship between the applied loading and the amplitudes of the generated modes must be understood. In this paper, the general problem of determining the amplitudes with which each propagating mode is generated due to the application of specific types of separable, time harmonic loading is investigated. (The more general problem of non-separable loading can be found in a recent paper2). The general results for separable loading are then specialized to two types of transducers commonly used in NDE to determine how the parameters of these two types of sources affect the amplitudes of the generated modes

Lamb Wave Mode Selection for Increased Sensitivity ot Interfacial Weaknesses of Adhesive Bonds

Interface quality between layers in a layered structure is critical in fracture and fatigue analysis. A theoretical and quantitative solution to the problem from a NDE point of view would be desirable in both manufacturing and for in-service investigation of a variety of different structures. For example a great need exists to develop a reliable and efficient inspection program of adhesive bond delamination and interfacial weakness detection in aging aircraft noting that the bond degradation generally preceeds cracking in the aluminum skin, starting at the rivet holes