A universal primer for isolation of fragments of a gene encoding phytoene desaturase for use in virus-induced gene silencing (VIGS) studies

We have been using Virus-Induced Gene Silencing (VIGS) to test the function of genes that are candidates for involvement in floral senescence. Although VIGS is a powerful tool for assaying the effects of gene silencing in plants, relatively few taxa have been studied using this approach, and most that have are in the Solanaceae. We typically use silencing of phytoene desaturase (PDS) in preliminary tests of the feasibility of using VIGS. Silencing this gene, whose product is involved in carotene biosynthesis, results in a characteristic photobleaching phenotype in the leaves. We have found that efficient silencing requires the use of fragments that are more than 90% homologous to the target gene. To simplify testing the effectiveness of VIGS in a range of species, we designed a set of universal primers to a region of the PDS gene that is highly conserved among species, and that therefore allows an investigator to isolate a fragment of the homologous PDS gene from the species of interest. We report the sequences of these primers and the results of VIGS experiments in horticultural species from the Asteraceae, Leguminosae, Balsaminaceae and Solanaceae

RA2: predicting simulation execution time for cloud-based design space explorations

Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds

Characterization of surface EMG with cumulative residual entropy

The cumulative residual entropy (CREn) is an alternative measure of uncertainty in a random variable. In this paper, we applied CREn as a feature extraction method to characterize six hand and wrist motions from four-channel surface electromyography (SEMG) signals. For comparison, fuzzy entropy, sample entropy and approximate entropy were also used to characterize the SEMG signals. The support vector machine (SVM) and linear discriminant analysis (LDA) were used to discriminate six hand and wrist motions in order to evaluate the performance of different entropies. The experimental results indicate that the CREn-based classification outperforms other entropy based methods with the best classification accuracy of is 97.17±1.97% by SVM and 93.56±4.13 by LDA. Furthermore, the computational complexity of CREn is lower than those of other entropies. It suggests that CREn has the potential to be applied as an effective feature extraction method in the control of SEMG-based multifunctional prosthesis. © 2012 IEEE.published_or_final_versio

Guide them through: an automatic crowd control framework using multi-objective genetic programming

We propose an automatic crowd control framework based on multi-objective optimisa- tion of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for opti- mal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front al- lows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quanti- tatively measured better, but also well aligned with domain experts’ recommendations on effective crowd control such as “slower is faster” and “asymmetric control”. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20%) congestion along critical segments of the path

Critical Dimension for Stable Self-gravitating Stars in AdS

We study the self-gravitating stars with a linear equation of state, $P=a \rho$, in AdS space, where $a$ is a constant parameter. There exists a critical dimension, beyond which the stars are always stable with any central energy density; below which there exists a maximal mass configuration for a certain central energy density and when the central energy density continues to increase, the configuration becomes unstable. We find that the critical dimension depends on the parameter $a$, it runs from $d=11.1429$ to 10.1291 as $a$ varies from $a=0$ to 1. The lowest integer dimension for a dynamically stable self-gravitating configuration should be $d=12$ for any $a \in [0,1]$ rather than $d=11$, the latter is the case of self-gravitating radiation configurations in AdS space.Comment: Revtex, 11 pages with 7 eps figure

Comments on Drinfeld Realization of Quantum Affine Superalgebra Uq[gl(m∣n)(1)]U_q[gl(m|n)^{(1)}] and its Hopf Algebra Structure

By generalizing the Reshetikhin and Semenov-Tian-Shansky construction to supersymmetric cases, we obtain Drinfeld current realization for quantum affine superalgebra $U_q[gl(m|n)^{(1)}]$. We find a simple coproduct for the quantum current generators and establish the Hopf algebra structure of this super current algebra.Comment: Some errors and misprints corrected and a remark in section 4 removed. 12 pages, Latex fil