Central representation of dynamics when manipulating handheld objects.

To explore the neural mechanisms related to representation of the manipulation dynamics of objects, we performed whole-brain fMRI while subjects balanced an object in stable and highly unstable states and while they balanced a rigid object and a flexible object in the same unstable state, in all cases without vision. In this way, we varied the extent to which an internal model of the manipulation dynamics was required in the moment-to-moment control of the object's orientation. We hypothesized that activity in primary motor cortex would reflect the amount of muscle activation under each condition. In contrast, we hypothesized that cerebellar activity would be more strongly related to the stability and complexity of the manipulation dynamics because the cerebellum has been implicated in internal model-based control. As hypothesized, the dynamics-related activation of the cerebellum was quite different from that of the primary motor cortex. Changes in cerebellar activity were much greater than would have been predicted from differences in muscle activation when the stability and complexity of the manipulation dynamics were contrasted. On the other hand, the activity of the primary motor cortex more closely resembled the mean motor output necessary to execute the task. We also discovered a small region near the anterior edge of the ipsilateral (right) inferior parietal lobule where activity was modulated with the complexity of the manipulation dynamics. We suggest that this is related to imagining the location and motion of an object with complex manipulation dynamics.

An experimental study on engine dynamics model based in-cylinder pressure estimation

The information provided by the in-cylinder pressure signal is of great importance for modern engine management systems. The obtained information is implemented to improve the control and diagnostics of the combustion process in order to meet the stringent emission regulations and to improve vehicle reliability and drivability. The work presented in this paper covers the experimental study and proposes a comprehensive and practical solution for the estimation of the in-cylinder pressure from the crankshaft speed fluctuation. Also, the paper emphasizes the feasibility and practicality aspects of the estimation techniques, for the real-time online application. In this study an engine dynamics model based estimation method is proposed. A discrete-time transformed form of a rigid-body crankshaft dynamics model is constructed based on the kinetic energy theorem, as the basis expression for total torque estimation. The major difficulties, including load torque estimation and separation of pressure profile from adjacent-firing cylinders, are addressed in this work and solutions to each problem are given respectively. The experimental results conducted on a multi-cylinder diesel engine have shown that the proposed method successfully estimate a more accurate cylinder pressure over a wider range of crankshaft angles. Copyright © 2012 SAE International.

Trajectory control based on discrete full-range dynamics

There has been an increasing interest in the use of mechanical dynamics, (e.g., assive, Elastic, And viscous dynamics) for energy efficient and agile control of robotic systems. Despite the impressive demonstrations of behavioural performance, The mechanical dynamics of this class of robotic systems is still very limited as compared to those of biological systems. For example, Passive dynamic walkers are not capable of generating joint torques to compensate for disturbances from complex environments. In order to tackle such a discrepancy between biological and artificial systems, We present the concept and design of an adaptive clutch mechanism that discretely covers the full-range of dynamics. As a result, The system is capable of a large variety of joint operations, including dynamic switching among passive, actuated and rigid modes. The main innovation of this paper is the framework and algorithm developed for controlling the trajectory of such joint. We present different control strategies that exploit passive dynamics. Simulation results demonstrate a significant improvement in motion control with respect to the speed of motion and energy efficiency. The actuator is implemented in a simple pendulum platform to quantitatively evaluate this novel approach.

Flexible representations of dynamics are used in object manipulation.

To manipulate an object skillfully, the brain must learn its dynamics, specifying the mapping between applied force and motion. A fundamental issue in sensorimotor control is whether such dynamics are represented in an extrinsic frame of reference tied to the object or an intrinsic frame of reference linked to the arm. Although previous studies have suggested that objects are represented in arm-centered coordinates [1-6], all of these studies have used objects with unusual and complex dynamics. Thus, it is not known how objects with natural dynamics are represented. Here we show that objects with simple (or familiar) dynamics and those with complex (or unfamiliar) dynamics are represented in object- and arm-centered coordinates, respectively. We also show that objects with simple dynamics are represented with an intermediate coordinate frame when vision of the object is removed. These results indicate that object dynamics can be flexibly represented in different coordinate frames by the brain. We suggest that with experience, the representation of the dynamics of a manipulated object may shift from a coordinate frame tied to the arm toward one that is linked to the object. The additional complexity required to represent dynamics in object-centered coordinates would be economical for familiar objects because such a representation allows object use regardless of the orientation of the object in hand.

Intracellular demography and the dynamics of Salmonella enterica infections.

An understanding of within-host dynamics of pathogen interactions with eukaryotic cells can shape the development of effective preventive measures and drug regimes. Such investigations have been hampered by the difficulty of identifying and observing directly, within live tissues, the multiple key variables that underlay infection processes. Fluorescence microscopy data on intracellular distributions of Salmonella enterica serovar Typhimurium (S. Typhimurium) show that, while the number of infected cells increases with time, the distribution of bacteria between cells is stationary (though highly skewed). Here, we report a simple model framework for the intensity of intracellular infection that links the quasi-stationary distribution of bacteria to bacterial and cellular demography. This enables us to reject the hypothesis that the skewed distribution is generated by intrinsic cellular heterogeneities, and to derive specific predictions on the within-cell dynamics of Salmonella division and host-cell lysis. For within-cell pathogens in general, we show that within-cell dynamics have implications across pathogen dynamics, evolution, and control, and we develop novel generic guidelines for the design of antibacterial combination therapies and the management of antibiotic resistance.

Modelling within-host spatiotemporal dynamics of invasive bacterial disease

Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics. © 2008 Grant et al.