Alternaria in food: Ecophysiology, mycotoxin production and toxicology

Alternaria species are common saprophytes or pathogens of a wide range of plants pre- and post-harvest. This review considers the relative importance of Alternaria species, their ecology, competitiveness, production of mycotoxins and the prevalence of the predominant mycotoxins in different food products. The available toxicity data on these toxins and the potential future impacts of Alternaria species and their toxicity in food products pre- and post-harvest are discussed. The growth of Alternaria species is influenced by interacting abiotic factors, especially water activity (a w ), temperature and pH. The boundary conditions which allow growth and toxin production have been identified in relation to different matrices including cereal grain, sorghum, cottonseed, tomato, and soya beans. The competitiveness of Alternaria species is related to their water stress tolerance, hydrolytic enzyme production and ability to produce mycotoxins. The relationship between A. tenuissima and other phyllosphere fungi has been examined and the relative competitiveness determined using both an Index of Dominance (I D ) and the Niche Overlap Index (NOI) based on carbon-utilisation patterns. The toxicology of some of the Alternaria mycotoxins have been studied; however, some data are still lacking. The isolation of Alternaria toxins in different food products including processed products is reviewed. The future implications of Alternaria colonization/infection and the role of their mycotoxins in food production chains pre- and post-harvest are discussed

Comparing trotting and turning strategies on the quadrupedal Oncilla Robot

In this paper, we compare three different trotting techniques and five different turning strategies on a small, compliant, biologically inspired quadrupedal robot, the Oncilla. The locomotion techniques were optimized on the actual hardware using a treadmill setup, without relying on models. We found that using half ellipses as foot trajectories resulted in the fastest gaits, as well as the highest robustness against parameter changes. Furthermore, we analyzed the importance of using the scapulae for turning, from which we observed that although not necessary, they are needed for turning with a higher speed

Animal models to study hepatitis C virus infection

With more than 71 million chronically infected people, the hepatitis C virus (HCV) is a major global health concern. Although new direct acting antivirals have significantly improved the rate of HCV cure, high therapy cost, potential emergence of drug-resistant viral variants, and unavailability of a protective vaccine represent challenges for complete HCV eradication. Relevant animal models are required, and additional development remains necessary, to effectively study HCV biology, virus-host interactions and for the evaluation of new antiviral approaches and prophylactic vaccines. The chimpanzee, the only non-human primate susceptible to experimental HCV infection, has been used extensively to study HCV infection, particularly to analyze the innate and adaptive immune response upon infection. However, financial, practical, and especially ethical constraints have urged the exploration of alternative small animal models. These include different types of transgenic mice, immunodeficient mice of which the liver is engrafted with human hepatocytes (humanized mice) and, more recently, immunocompetent rodents that are susceptible to infection with viruses that are closely related to HCV. In this review, we provide an overview of the currently available animal models that have proven valuable for the study of HCV, and discuss their main benefits and weaknesses

Informed microarchitecture design space exploration using workload dynamics

Program runtime characteristics exhibit significant variation. As microprocessor architectures become more complex, their efficiency depends on the capability of adapting with workload dynamics. Moreover, with the approaching billion-transistor microprocessor era, it is not always economical or feasible to design processors with thermal cooling and reliability redundancy capabilities that target an application’s worst case scenario. Therefore, analyzing complex workload dynamics early, at the microarchitecture design stage, is crucial to forecast workload runtime behavior across architecture design alternatives and evaluate the efficiency of workload scenariobased architecture optimizations. Existing methods focus exclusively on predicting aggregated workload behavior. In this paper, we propose accurate and efficient techniques and models to reason about workload dynamics across the microarchitecture design space without using detailed cyclelevel simulations. Our proposed techniques employ waveletbased multiresolution decomposition and neural network based non-linear regression modeling. We extensively evaluate the efficiency of our predictive models in forecasting performance, power and reliability domain workload dynamics that the SPEC CPU 2000 benchmarks manifest on high-performance microprocessors with a microarchitecture design space that consists of 9 key parameters. Our results show that the models achieve high accuracy in revealing workload dynamic behavior across a large microarchitecture design space. We also demonstrate that the proposed techniques can be used to efficiently explore workload scenario-driven architecture optimizations. 1

Single-photon detection timing jitter in a visible light photon counter

Visible light photon counters (VLPCs) offer many attractive features as photon detectors, such as high quantum efficiency and photon number resolution. We report measurements of the single-photon timing jitter in a VLPC, a critical performance factor in a time-correlated single-photon counting measurement, in a fiber-coupled closed-cycle cryocooler. The measured timing jitter is 240 ps full-width-at-half-maximum at a wavelength of 550 nm, with a dark count rate of 25 000 counts per second. The timing jitter increases modestly at longer wavelengths to 300 ps at 1000 nm, and increases substantially at lower bias voltages as the quantum efficiency is reduced

Transfer learning of gaits on a quadrupedal robot

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Learning new gaits for compliant robots is a challenging multi-dimensional optimization task. Furthermore, to ensure optimal performance, the optimization process must be repeated for every variation in the environment, for example for every change in inclination of the terrain. This is unfortunately not possible using current approaches, since the time required for the optimization is simply too high. Hence, a sub-optimal gait is often used. The goal in this manuscript is to reduce the learning time of a particle swarm algorithm, such that the robot's gaits can be optimized over a wide variety of terrains. To facilitate this, we use transfer learning by sharing knowledge about gaits between the different environments. Our findings indicate that using transfer learning new robust gaits can be discovered faster compared to traditional methods that learn a gait for each environment independently.EC/FP7/248311/EU/Adaptive Modular Architecture for Rich Motor Skills/AMARS

Trainable hardware for dynamical computing using error backpropagation through physical media

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers

Turbulent Stresses in Local Simulations of Radiation-Dominated Accretion Disks, and the Possibility of the LIghtman-Eardley Instability

We present the results of a series of radiation-MHD simulations of a local patch of an accretion disk, with fixed vertical gravity profile but with different surface mass densities and a broad range of radiation to gas pressure ratios. Each simulation achieves a thermal equilibrium that lasts for many cooling times. After averaging over times long compared to a cooling time, we find that the vertically integrated stress is approximately proportional to the vertically-averaged total thermal (gas plus radiation) pressure. We map out--for the first time on the basis of explicit physics--the thermal equilibrium relation between stress and surface density: the stress decreases (increases) with increasing surface mass density when the simulation is radiation (gas) pressure dominated. The dependence of stress on surface mass density in the radiation pressure dominated regime suggests the possibility of a Lightman-Eardley inflow instability, but global simulations or shearing box simulations with much wider radial boxes will be necessary to confirm this and determine its nonlinear behavior.Comment: accepted for publication in The Astrophysical Journa