Zebrafish miR-214 modulates Hedgehog signaling to specify muscle cell fate

Numerous microRNAs (miRNAs) have been discovered in the genomes of higher eukaryotes, and functional studies indicate that they are important during development. However, little is known concerning the function of individual miRNAs. We approached this problem in zebrafish by combining identification of miRNA expression, functional analyses and experimental validation of potential targets. We show that miR-214 is expressed during early segmentation stages in somites and that varying its expression alters the expression of genes regulated by Hedgehog signaling. Inhibition of miR-214 results in a reduction or loss of slow-muscle cell types. We show that su(fu) mRNA, encoding a negative regulator of Hedgehog signaling, is targeted by miR-214. Through regulation of su(fu), miR-214 enables precise specification of muscle cell types by sharpening cellular responses to Hedgehog.     

The peptidoglycan recognition proteins LCa and LCx

Infection of bacteria triggers innate immune defense reactions in Drosophila. So far, the only bacterial component known to be recognized by the insect innate immune system is peptidoglycan, one of the most abundant constituents of the bacterial cell wall. Insects use peptidoglycan recognition proteins to detect peptidoglycan and to activate innate immune responses. Such specialized peptidoglycan receptors appear to have evolved from phage enzymes that hydrolyze bacterial cell walls. They are able to bind specific peptidoglycan molecules with distinct chemical moieties and activate innate immune pathways by interacting with other signaling proteins. Recent X-ray crystallographic studies of the peptidoglycan recognition proteins LCa, and LCx bound to peptidoglycan have provided structural insights into recognition of peptidoglycan and activation of innate immunity in insects. Received 28 December 2006; received after revision 2 February 2007; accepted 21 February 2007     

变时滞神经网络指数稳定性条件

基于时滞细胞神经网络(DCNNs)在图像处理等领域的广泛应用,有关它的研究引起了越来越多学者和专家的关注.早期DCNNs稳定性的结果大多由网络权矩阵的分量构成的代数不等式来表示.运用Lyapunov-Krasovskii泛函的方法,研究了DCNNs的指数稳定性,所得充分条件以矩阵的(半)正定形式出现,在实际应用中更加便于验证.与文献中的结果相比较,所得判据适用范围更广.