The proresolution therapeutic tanshinone IIA drives inflammation resolution by reverse migration.

Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles.

At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, self-localising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve multi-robot SLAM in the pipe environment.

Following neutralization of infectious threats, neutrophils must be removed from inflammatory sites for normal tissue function to be restored. Recently, a new paradigm has emerged, in which viable neutrophils migrate away from inflammatory sites by a process best described as reverse migration. It has generally been assumed that this process is the mirror image of chemotaxis, where neutrophils are drawn into the areas of infection or tissue damage by gradients of chemotactic cues. Indeed, efforts are underway to identify cues that drive neutrophils away by the reverse process, fugetaxis. By using photoconvertible pigments expressed in neutrophils in transparent zebrafish larvae, we were able to image the position of each neutrophil during inflammation resolution in vivo. These neutrophil coordinates were analysed within a dynamic modelling framework, using different forms of the drift–diffusion equation with model selection and parameter estimation based on approximate Bayesian computation. This analysis revealed the experimental data were best fitted by a model incorporating a diffusion term but no drift term—where the presence of drift would indicate fugetaxis. This result, for the first time, provides rigorous data-driven evidence that reverse migration of neutrophils in vivo is not a form of fugetaxis, but rather a stochastic redistribution.

Accurate physical modeling of vehicle dynamics requires extensive a priori knowledge of the studied vehicle. In contrast, data-driven modeling approaches require only a set of data that are a good account of the vehicle's driving envelope. In this brief, we compare, for the first time, the prediction capabilities of both approaches applied to a large-scale real-world driving data set. The data set contains several cornering maneuvers, acceleration, and deceleration stages and was collected over public roads. Linear and nonlinear physical models were identified through nonlinear optimization of their unknown parameters. Closed-form subspace identification methods were used to initialize the estimate of a linear state-space model, and the initialization was then refined through nonlinear optimization. The optimized models were validated against 59 km of independent driving data. The model fits, in the longitudinal velocity, were 68.9% versus 80.2% for the nonlinear physical model and linear data-driven (second-order) model, respectively, and, in the yaw rate, 43.0% versus 63.5%. These results show that, for this vehicle, a simple linear data-driven model outperformed both linear and nonlinear physical models under real-world driving conditions. This has important implications for control design approaches in autonomous vehicles.