Coordinates transformation and learning control for visually-guided voluntary movement with iteration: A Newton-like method in a function space

In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n+1)-th iteration is a sum of the motor command in then-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in then-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes.     

Demonstration of neutralizing autoantibodies against IL-1 alpha in sera from patients with rheumatoid arthritis

We have recently reported the presence of IgG which has a potent inhibitory activity against IL-1 alpha in some sera from patients with rheumatoid arthritis. The mechanism of this inhibition by IgG against IL-1 alpha is now elucidated. IgG with IL-1 alpha-inhibitory activity inhibited the binding of 125I-IL-1 alpha to receptors on rheumatoid synovial cells. In addition, preincubation of synovial cells with the inhibitory IgG did not block the binding of 125I-IL-1 alpha to receptors, suggesting a direct interaction between IgG and IL-1 alpha. To examine which region of the IgG, namely Fab or Fc region, has the inhibitory activity, the IgG was digested with papain, and Fab and Fc fragments were purified. Fab fragments, but not Fc fragments, inhibited both IL-1 alpha-induced thymocyte-proliferation and the binding of 125I-IL-1 alpha to receptors. We further demonstrated that the inhibitory IgG which was bound to protein A Sepharose could bind a significant amount of 125I-IL-1 alpha, whereas only a negligible binding of the radiolabeled ligand was detected when IgG without the inhibitory activity was used as control. Moreover, the binding of 125I-IL-1 alpha to IgG with the inhibitory activity was clearly blocked by Fab fragments of IgG having the inhibitory activity. Finally, affinity-purified IgG over an IL-alpha affinity column showed approximately 100-fold more potent inhibitory activity on IL-1 alpha-induced thymocyte proliferation compared with untreated IgG. From these results, we conclude that IgG molecules with IL-1-alpha-inhibitory activity are neutralizing autoantibodies against IL-1 alpha.     

Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.     

Analysis of common antigen of flagella in Bacillus cereus and Bacillus thuringiensis

Abstract Flagellar antigen of Bacillus cereus H.1 was purified and tested for serodiagnostic antigen by ELISA. The antibody against the flagellar antigen of B. cereus H.1 reacted not only with the homologous specific antigen but also reacted with the flagellar antigens of 23 strains of B. cereus . This common flagellar antigen of B. cereus was found to be due to 61-kDa protein by SDS-PAGE and immunoblot assay. Monoclonal antibody H15A5 against common antigenic epitope of B. cereus also reacted with flagellar antigens of 21 strains of Bacillus thuringiensis by ELISA. This monoclonal antibody reacted with the 61-kDa protein of the flagella of B. cereus H.1 and H.2 and B. thuringiensis Kurstaki HD1, Alesti and Aizawai juroi by immunoblot analysis. These results indicated that the common antigenic epitope of the 61-kDa protein existed in the flagella both of B. cereus and B. thuringiensis .