Quantitative Analysis of Candida Cell Wall Components by Flow Cytometrywith Triple-Fluorescence Staining

This work was supported by the European Commission within the FP7 Framework Programme [Fungitect-Grant No 602125]. We also thank Thomas Sauer, Vienna Biocenter Campus (VBC), Austria, for technical support at the FACS facility of the MFPL, Karl Kuchler, MFPL-Department of Medical Biochemistry, Medical University of Vienna, Max F. Perutz Laboratories, Campus Vienna Biocenter, Vienna, Austria and Ernst Thuer, Centre for Genomic Regulation, Barcelona, Spain, for advice on statistical approaches. Neil Gow acknowledges the support of the Wellcome Trust and the MRC Centre for Medical MycologyPeer reviewedPublisher PD

Co-transport-induced instability of membrane voltage in tip-growing cells

A salient feature of stationary patterns in tip-growing cells is the key role played by the symports and antiports, membrane proteins that translocate two ionic species at the same time. It is shown that these co-transporters destabilize generically the membrane voltage if the two translocated ions diffuse differently and carry a charge of opposite (same) sign for symports (antiports). Orders of magnitude obtained for the time and lengthscale are in agreement with experiments. A weakly nonlinear analysis characterizes the bifurcation

Studying charge-trapping defects within the silicon lattice of a p-channel CCD using a single-trap “pumping” technique.

The goals of future space missions such as Euclid require unprecedented positional accuracy from the responsible detector. Charge coupled devices (CCDs) can be manufactured with exceptional charge transfer properties; however the harsh radiation environment of space leads to damage within the silicon lattice, predominantly through proton collisions. The resulting lattice defects can trap charge, degrading the positional accuracy and reducing the useful operating time of a detector. Mitigation of such effects requires precise knowledge of defects and their effects on charge transfer within a CCD. We have used the technique of single-trap ``pumping'' to study two such charge trapping defects; the silicon divacancy and the carbon interstitial, in a p-channel CCD. We show this technique can be used to give accurate information about trap parameters required for radiation damage models and correction algorithms. We also discuss some unexpected results from studying defects in this way

Pixel-level modelling and verification for the EUCLID VIS CCD

Euclid is an M-class mission selected for the next phase of ESA’s long-term Cosmic Vision programme. The mission’s primary aim is to provide insight into the physical cause of the accelerating universe which will be achieved by investigating the nature of dark energy, dark matter and gravity. The investigation will involve the measurement of the effects of intervening gravitational potentials on visible light from distant galaxies, a process known as weak gravitational lensing. The CCD273 was designed by e2v for the Euclid mission, and is based on an older design for the CCD204, with changes designed and implemented to improve operation under irradiation. These changes include a narrower buried channel in the serial register to reduce trap interactions during readout. Pixel level models have been developed to improve knowledge of charge packet distribution within the CCD273 and CCD204 using Silvaco TCAD, enabling more effective predictions to be made about device functionality before and after irradiation which can be immediately tested on both devices. This paper is focussed on latest results, with particular reference to the on-going model verification, designed to build confidence in the device models and provide feedback to further improve the modelling effort

Expansion of Foxp3+ T-cell populations by Candida albicans enhances both Th17-cell responses and fungal dissemination after intravenous challenge

Research Funding Wellcome Trust, UK. Grant Numbers: 086827, 080088 NIH. Grant Number: DE022550Peer reviewedPublisher PD

A comparative study of proton radiation damage in p- and n-channel

It has been demonstrated that p-channel charge coupled devices (CCDs) are more radiation hard than conventional n-channel devices as they are not affected by the dominant electron trapping caused by the displacement damage defect the E-centre (phosphorus-vacancy). This paper presents a summary of the results from a comparative study of n-channel and p-channel CCDs each type operated under the same conditions. The CCD tested is the e2v technologies plc CCD47-20, a 1024 ? 1024 frame transfer device with a split output register, fabricated using the same mask to form n-channel and p-channel devices. The p-channel devices were irradiated to a 10 MeV equivalent proton fluence of 4.07?1010 protons.cm-2 and 1.35?1011 protons.cm-2, an n-channel CCD was irradiated to a 10 MeV equivalent proton fluence of 1.68?109 protons.cm-2, however due to time constraints the n-channel device was not characterised, n-channel comparisons are instead made using a CCD02. As expected the p-channel CCD demonstrated improved radiation tolerance when compared to the n-channel CCD, at -90 ?C there is an approximate ×7 and ×15 improvement in tolerance to radiation induced parallel and serial CTI respectively for equivalent pixel geometries

Proton irradiation of a swept charge device at cryogenic temperature and the subsequent annealing

A number of studies have demonstrated that a room temperature proton irradiation may not be sufficient to provide an accurate estimation of the impact of the space radiation environment on detector performance. This is a result of the relationship between defect mobility and temperature, causing the performance to vary subject to the temperature history of the device from the point at which it was irradiated. Results measured using Charge Coupled Devices (CCD) irradiated at room temperature therefore tend to differ from those taken when the device was irradiated at a cryogenic temperature, more appropriate considering the operating conditions in space, impacting the prediction of in-flight performance. This paper describes the cryogenic irradiation, and subsequent annealing of an e2v technologies Swept Charge Device (SCD) CCD236 irradiated at −35.4°C with a 10 MeV equivalent proton fluence of 5.0 × 108 protons centerdot cm−2. The CCD236 is a large area (4.4 cm2) X-ray detector that will be flown on-board the Chandrayaan-2 and Hard X-ray Modulation Telescope spacecraft, in the Chandrayaan-2 Large Area Soft X-ray Spectrometer and the Soft X-ray Detector respectively. The SCD is readout continually in order to benefit from intrinsic dither mode clocking, leading to suppression of the surface component of the dark current and allowing the detector to be operated at warmer temperatures than a conventional CCD. The SCD is therefore an excellent choice to test and demonstrate the variation in the impact of irradiation at cryogenic temperatures in comparison to a more typical room temperature irradiation

Optimized Artificial Neural Network Using Differential Evolution for Prediction of RF Power in VHF/UHF TV and GSM 900 Bands for Cognitive Radio Networks

Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approac

Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study

Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance. This will enable the selection of channel with less noise among idle (free) channels. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. The models used, implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary and swarm intelligence algorithms. This was found to enhance the accuracy and generalization of the ANN model. For this problem, DE/best/1/bin was found to yield a better performance as compared with the other algorithms implemented