The regulation of host defences to infection by the microbiota

The skin and mucosal epithelia of humans and other mammals are permanently colonised by large microbial communities (the microbiota). Due to this life-long association with the microbiota, these microbes have an extensive influence over the physiology of their host organism. It is now becoming apparent that nearly all tissues and organ systems, whether in direct contact with the microbiota, or in deeper host sites, are under microbial influence. The immune system is perhaps the most profoundly affected, with the microbiota programming both its innate and adaptive arms. The regulation of immunity by the microbiota helps protect the host against intestinal and extra-intestinal infection by many classes of pathogen. In this review, we will discuss the experimental evidence supporting a role for the microbiota in regulating host defences to extra-intestinal infection, draw together common mechanistic themes, including the central role of pattern recognition receptors, and outline outstanding questions which need to be answered. This article is protected by copyright. All rights reserved

State anxiety modulates the return of fear

Current treatments for anxiety disorders are effective but limited by the high frequency of clinical relapse. Processes underlying relapse are thought to be experimentally modeled in fear conditioning experiments with return fear (ROF) inductions. Thereby reinstatement-induced ROF might be considered a model to study mechanisms underlying adversity-induced relapse. Previous studies have reported differential ROF (i.e. specific for the danger stimulus) but also generalized ROF (i.e. for safe and danger stimuli), but reasons for these divergent findings are not clear yet. Hence, the response pattern (i.e. differential or generalized) following reinstatement may be of importance for the prediction of risk or resilience for ROF. The aim of this study was to investigate state anxiety as a potential individual difference factor contributing to differentiability or generalization of return of fear. Thirty-six participants underwent instructed fear expression, extinction and ROF induction through reinstatement while physiological (skin conductance response, fear potentiated startle) and subjective measures of fear and US expectancy were acquired. Our data show that, as expected, high state anxious individuals show deficits in SCR discrimination between dangerous and safe cues after reinstatement induced ROF (i.e. generalization) as compared to low state anxious individuals. The ability to maintain discrimination under aversive circumstances is negatively associated with pathological anxiety and predictive of resilient responding while excessive generalization is a hallmark of anxiety disorders. Therefore, we suggest that experimentally induced ROF might prove useful in predicting relapse risk in clinical settings and might have implications for possible interventions for relapse prevention

A nanoindentation investigation of local strain rate sensitivity in dual-phase Ti alloys

Using nanoindentation we have investigated the local strain rate sensitivity in dual-phase Ti alloys, Ti-6Al-2Sn-4Zr-xMo (x=2 and 6), as strain rate sensitivity could be a potential factor causing cold dwell fatigue. Electron backscatter diffraction (EBSD) was used to select hard and soft grain orientations within each of the alloys. Nanoindentation based tests using the continuous stiffness measurement (CSM) method were performed with variable strain rates, on the order of 10−1 to 10−3s−1. Local strain rate sensitivity is determined using a power law linking equivalent flow stress and equivalent plastic strain rate. Analysis of residual impressions using both a scanning electron microscope (SEM) and a focused ion beam (FIB) reveals local deformation around the indents and shows that nanoindentation tested structures containing both α and β phases within individual colonies. This indicates that the indentation results are derived from averaged α/β properties. The results show that a trend of local rate sensitivity in Ti6242 and Ti6246 is strikingly different; as similar rate sensitivities are found in Ti6246 regardless of grain orientation, whilst a grain orientation dependence is observed in Ti6242. These findings are important for understanding dwell fatigue deformation modes, and the methodology demonstrated can be used for screening new alloy designs and microstructures

Understanding macroscale functionality of metal halide perovskites in terms of nanoscale heterogeneities

Hybrid metal halide perovskites have shown an unprecedented rise as semiconductor building blocks for solar energy conversion and light-emitting applications. Currently, the field moves empirically towards more and more complex chemical compositions, including mixed halide quadruple cation compounds that allow optical properties to be tuned and show promise for better stability. Despite tremendous progress in the field, there is a need for better understanding of mechanisms of efficiency loss and instabilities to facilitate rational optimization of composition. Starting from the device level and then diving into nanoscale properties, we highlight how structural and compositional heterogeneities affect macroscopic optoelectronic characteristics. Furthermore, we provide an overview of some of the advanced spectroscopy and imaging methods that are used to probe disorder and non-uniformities. A unique feature of hybrid halide perovskite compounds is the propensity for these heterogeneities to evolve in space and time under relatively mild illumination and applied electric fields, such as those found within active devices. This introduces an additional challenge for characterization and calls for application of complimentary probes that can aid in correlating the properties of local disorder with macroscopic function, with the ultimate goal of rationally tailoring synthesis towards optimal structures and compositions

MODULATION OF CALCIUM CHANNELS IN ARTERIAL SMOOTH-MUSCLE CELLS BY DIHYDROPYRIDINE ENANTIOMERS

The actions of the optical enantiomers of BAY K 8644 and Sandoz 202,791 were studied on barium inward currents recorded using the whole-cell configuration of the patch clamp technique from enzymatically isolated smooth muscle cells from the rabbit ear artery. The enantiomers were applied by bath perfusion or rapidly by a concentration jump technique, which enabled the study of drug action under equilibrium and nonequilibrium conditions. A larger effect of agonists was seen on peak inward current in 110 mM Ba when small rather than large depolarizations were applied. The midpoint voltage of the steady-state inactivation curve of IBa was -12.8 +/- 1.9 mV (n = 4) in the absence of drug, -16.4 +/- 2.5 mV (n = 4) in 1 microM (+)202,791, and -31.4 +/- 0.4 mV (n = 4) in 1 microM (-)202,791. The rate of onset of action of the agonist and antagonist enantiomers of BAY K 8644 and Sandoz 202,791 was studied by rapid application during 20-ms depolarizing steps from different holding potentials to +30 mV at 1 or 0.2 Hz. The drugs were applied as concentration jumps between two single pulses of a pulse train. The rates of onset of drug action on peak IBa during a 1-Hz pulse train were concentration dependent over the range of 100 nM-3 microM for both (+) and (-)202,791. The rate of onset of inhibition of peak current by antagonist enantiomers was not significantly influenced by the test pulse frequency. At a holding potential of -60 mV, the onset rate of the increase in peak IBa on application of 1 microM of agonist enantiomers (+)202,791 or (-)BAY K 8644 during a train of pulses occurred with mean time constants of 2.1 +/- 0.7 s (n = 7) and 2.3 +/- 0.2 s (n = 4), respectively. The onset of current increase on application of 1 microM (+)202,791 during a single voltage clamp step to 20 mV was faster, with a mean time constant of 380 +/- 80 ms (n = 3)

Data-efficient learning of feedback policies from image pixels using deep dynamical models

Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ( torques ) from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques