Bo Wang

We have determined the complete chloroplast (cp) genome sequence of Cephalotaxus oliveri. The genome is 134,337 bp in length, encodes 113 genes, and lacks inverted repeat (IR) regions. Genome-wide mutational dynamics have been investigated through comparative analysis of the cp genomes of C. oliveri and C. wilsoniana. Gene order transformation analyses indicate that when distinct isomers are considered as alternative structures for the ancestral cp genome of cupressophyte and Pinaceae lineages, it is not possible to distinguish between hypotheses favoring retention of the same IR region in cupressophyte and Pinaceae cp genomes from a hypothesis proposing independent loss of IRA and IRB. Furthermore, in cupressophyte cp genomes, the highly reduced IRs are replaced by short repeats that have the potential to mediate homologous recombination, analogous to the situation in Pinaceae. The importance of repeats in the mutational dynamics of cupressophyte cp genomes is also illustrated by the accD reading frame, which has undergone extreme length expansion in cupressophytes. This has been caused by a large insertion comprising multiple repeat sequences. Overall, we find that the distribution of repeats, indels, and substitutions is significantly correlated in Cephalotaxus cp genomes, consistent with a hypothesis that repeats play a role in inducing substitutions and indels in conifer cp genomes.

The Kalman filter has been found to be useful in vast areas. However, it is well known that the successful use of the standard Kalman filter is greatly restricted by the strict requirements on a priori information of the model structure and statistics information of the process, and measurement noises. Generally speaking, the covariance matrix of process noise is harder to be determined than that of the measurement noise by routine experiments, since the statistical property of process noise cannot be obtained directly by collecting a large number of sensor data due to the intrinsic coupling of process noise and system dynamics. Considering such background of wide applications, this paper introduces one algorithm, recursive covariance estimation (RCE) algorithm, to estimate the unknown covariance matrix of noise from a sample of signals corrupted with the noise. Based on this idea, for a class of discrete-time linear-time-invariant systems where the covariance matrix of process noise is completely unknown, a new Kalman filtering algorithm named, Kalman filter with RCE, is presented to resolve this challenging problem of state estimation without the statistical information of process noise, and the rigorous stability analysis is given to show that this algorithm is optimal in the sense that the covariance matrix and state estimations are asymptotically consistent with the ideal Kalman filter when the exact covariance matrix of process noise is completely known a priori. Extensive simulation studies have also verified the theoretical results and the effectiveness of the proposed algorithm.

The Routledge handbook of systemic functional linguistics (hereafter the Handbook), the latest handbook on Systemic Functional Linguistics (SFL) edited by Tom Bartlett and Gerard O'Grady, provides ...