Rice Transformation as a Means to Study Gene Expression

An exceptionally effective transformation procedure has been established by using class I embryo-derived rice callus. Every treated callus clump yielded multiple independently transformed plants (average 40 plantlets). Analysis of genomic DNA blots and pollen expressing green fluorescent protein (GFP) from T0 plants revealed that 64% bore a single locus T-DNA insertion in which half had one T-DNA copy. Additive transgene expression was observed fromT0 plants with GFP driven by mUbi1 promoter. Transgenic plants could be rapidly characterized by analyzing GFP pollen from T0 plants without the need for further generations or genomic DNA blot analysis. Agrobacterium tumefaciens-mediated transformation of microspore-derived callus for generating large numbers of T-DNA haploid and doubled haploid(DH) plants has also been investigated. The established transformation procedure resulted in 100% transformation frequency for class I microspore-derived rice callus. Each callus typically yields multiple independent transgenic plants. Genomic DNA blot analysis suggested 98% of the transgenic plants are independent events. About half of the transgenic plants were identified as haploid plants, whereas half are DH hemizygous or homozygous transgenic plants. DH homozygous transgenic plants were obtained from T0plants and confirmed by pollen GFP expression and genomic blot analysis in T0transgenic DH plants. In this study, about 60% ofT0transgenic DH plants had a single locus T-DNA insertion of which 45% bore one T-DNA copy. Furthermore, in a population of over 2,000 haploid and doubled haploid T-DNA plants , about 25% showed phenotypic differences from non-transformed haploid plants. Approximately 5% were seriously phenotypically abnormal including lethal or semi-lethal mutants. This highly efficient transformation procedure using microspore-derived callus could be valuable in speeding up plant breeding and in new gene discovery. Diversification of the mUbi1 promoter led to a minimal promoter that has a similar function as the original mUbi1. Transient and stable transformation as measured from gene expression driven by the minimal promoter suggested that it has a similar function as the original wild type promoter

Causal Inference under Network Interference Using a Mixture of Randomized Experiments

In randomized experiments, the classic stable unit treatment value assumption (SUTVA) states that the outcome for one experimental unit does not depend on the treatment assigned to other units. However, the SUTVA assumption is often violated in applications such as online marketplaces and social networks where units interfere with each other. We consider the estimation of the average treatment effect in a network interference model using a mixed randomization design that combines two commonly used experimental methods: Bernoulli randomized design, where treatment is independently assigned for each individual unit, and cluster-based design, where treatment is assigned at an aggregate level. Essentially, a mixed randomization experiment runs these two designs simultaneously, allowing it to better measure the effect of network interference. We propose an unbiased estimator for the average treatment effect under the mixed design and show the variance of the estimator is bounded by $O({d^2}n^{-1}p^{-1})$ where $d$ is the maximum degree of the network, $n$ is the network size, and $p$ is the probability of treatment. We also establish a lower bound of $\Omega(d^{1.5}n^{-1}p^{-1})$ for the variance of any mixed design. For a family of sparse networks characterized by a growth constant $\kappa \leq d$, we improve the upper bound to $O({\kappa^7 d}n^{-1}p^{-1})$. Furthermore, when interference weights on the edges of the network are unknown, we propose a weight-invariant design that achieves a variance bound of $O({d^3}n^{-1}p^{-1})$

Distributed Detection Over Blockchain-Aided Internet Of Things In The Presence Of Attacks

Distributed detection over a blockchain-aided Internet of Things (BIoT) network in the presence of attacks is considered, where the integrated blockchain is employed to secure data exchanges over the BIoT as well as data storage at the agents of the BIoT. We consider a general adversary model where attackers jointly exploit the vulnerability of IoT devices and that of the blockchain employed in the BIoT. The optimal attacking strategy which minimizes the Kullback-Leibler divergence is pursued. It can be shown that this optimization problem is nonconvex, and hence it is generally intractable to find the globally optimal solution to such a problem. To overcome this issue, we first propose a relaxation method that can convert the original nonconvex optimization problem into a convex optimization problem, and then the analytic expression for the optimal solution to the relaxed convex optimization problem is derived. The optimal value of the relaxed convex optimization problem provides a detection performance guarantee for the BIoT in the presence of attacks. In addition, we develop a coordinate descent algorithm which is based on a capped water-filling method to solve the relaxed convex optimization problem, and moreover, we show that the convergence of the proposed coordinate descent algorithm can be guaranteed

Stochastic partial differential equations driven by space-time fractional noises

International audienceIn this paper, we study a class of stochastic partial differential equations (SPDEs) driven by space-time fractional noises. Our method consists in studying first the nonlocal SPDEs and showing then the convergence of the family of these equations and the limit gives the solution to the SPDE