Isolation and identification of Sclerotinia stem rot causal pathogen in Arabidopsis thaliana

A new stem rot disease is found to occur naturally on Arabidopsis plants in greenhouses of Fuzhou, China. In order to identify its pathogen, we conducted a series of fungal isolation and purification, plant reinoculation, and ascus and ascospore induction from the sclerotia. The isolate caused typical water-soaked lesions after reinoculation and produced sclerotia both on Arabidopsis plants and culture medium plates, and the sclerotia could be induced to produce discal apothecia and 8 binucleate ascospores per ascus. These disease symptom and fungal morphology data revealed that the fungus Sclerotinia sclerotiorum (Lib.) de Bary was the pathogen for Arabidopsis stem rot. To confirm this, we further amplified its large subunit ribosomal DNA (LSU rDNA) by polymerase chain reaction (PCR), and compared the sequence with the known LSU rDNA sequences in GenBank. The results show that the sequence shares the highest identities with the LSU rDNAs of different S. sclerotiorum strains. Taking all these data together, we concluded that the fungus that caused the Arabidopsis stem rot is S. sclerotiorum (Lib.) de Bary. This is the first report that Arabidopsis is naturally infected by S. sclerotiorum.

Nearly optimal stochastic approximation for online principal subspace estimation

Principal component analysis(PCA) has been widely used in analyzing high-dimensional data. It converts a set of observed data points of possibly correlated variables into a set of linearly uncorrelated variables via an orthogonal transformation. To handle streaming data and reduce the complexities of PCA,(subspace)online PCA iterations were proposed to iteratively update the orthogonal transformation by taking one observed data point at a time. Existing works on the convergence of(subspace) online PCA iterations mostly focus on the case where the samples are almost surely uniformly bounded. In this paper, we analyze the convergence of a subspace online PCA iteration under more practical assumptions and obtain a nearly optimal finite-sample error bound. Our convergence rate almost matches the minimax information lower bound. We prove that the convergence is nearly global in the sense that the subspace online PCA iteration is convergent with high probability for random initial guesses. This work also leads to a simpler proof of the recent work on analyzing online PCA for the first principal component only.

Intermittent Behaviors in Coupled Piecewise Expanding Map Lattices

In this paper,we propose a new method to study intermittent behaviors of coupled piecewise-expanding map lattices.We show that the successive transition between ordered and disordered phases occurs for almost every orbit when the coupling is small.That is,lim inf n→∞∑1≤i,j≤m|x_(i)(n)−x_(j)(n)|=0,lim sup n→∞∑1≤i,j≤m|x_(i)(n)−x_(j)(n)|≥c_(0)>0,where xi(n)correspond to the coordinates of m nodes at the iterative step n.Moreover,when the uncoupled system is generated by the tent map and the lattice consists of two nodes,we prove a phase transition occurs between synchronization and intermittent behaviors.That is,limn→∞|x_(1)(n)−x_(2)(n)|=0 for c−1/2<1/4 and intermittent behaviors occur for|c−1/2|>1/4,where 0≤c≤1 is the coupling.