A novel male-sterile mutant which lacks mature pollen, Brassisa campestris male sterile (bcms), was identified in Brassica campestris L. ssp. chinensis Makino (syn. B. rapa ssp. chinensis). Genetic analysis revealed that bcms was controlled by a single recessive mutation locus. Genome-wide transcriptional profiling was performed on the flower buds of both the bcms mutant and the wild-type from which it originated, and profiling analysis indicated that there were numerous changes in gene expression attributable to the gene mutation. This mutation resulted in down-regulation of a variety of genes and up-regulated expression of a few other genes. A total of 51 transcript-derived fragments (TDFs) were isolated: 32 specifically and 12 predominantly accumulated in wild-type flower buds, and two specifically and five predominantly accumulated in bcms flower buds. Sequence analysis showed that some of these TDFs share significant similarities with genes involved in different aspects of cellular development, such as signal transduction, cell wall biosynthesis and regulation. Most other TDFs showed no or very poor sequence similarities to entries in any database and might represent new candidate proteins involved in pollen development. Furthermore, spatial and temporal expression pattern analysis of 20 genes derived from cDNA-amplified fragment length polymorphism in different tissues of both the bcms and wild-type plants revealed their complex and dynamic expression patterns. The bcms mutant and the genes isolated in this paper provide excellent material for future studies on the molecular mechanism of male sterility.

In the past decade, the emerging machine vision-based measurement technology has gained great concerns among civil engineers due to its overwhelming merits of non-contact, long-distance, and high-resolution. A critical issue regarding to the measurement performance and accuracy of the vision-based system is how to identify and eliminate the systematic and unsystematic error sources. In this paper, a vision-based structural displacement measurement system integrated with a digital image processing approach is developed. The performance of the developed vision-based system is evaluated by comparing the results simultaneously obtained by the vision-based system and those measured by the magnetostrictive displacement sensor (MDS). A series of experiments are conducted on a shaking table to examine the influence factors which will affect the accuracy and stability of the vision-based system. It is demonstrated that illumination and vapor have a critical effect on the measurement results of the vision-based system.

Dipeptidyl peptidase 4 (DPP-4), a substrate-specific serine protease, has been validated as a promising drug target for the treatment of type 2 diabetes. DPP-4 inhibitors significantly lowered blood glucose levels in patients with type 2 diabetes without common body weight gain, hypoglycemia and gastrointestinal disturbance side effects. Therefore, DPP-4 inhibitors attracted more and more attention. In particular, non-peptidomimetic DPP-4 inhibitors have been a focus of research and development and made great progress in recent years, which resulted in the discovery of a wide variety of potent non-peptidomimetic DPP-4 inhibitors. Some of them, such as sitagliptin, alogliptin and linagliptin have already been used as marketed drugs, while others have been into clinical trials. Based on the core structural features of non-peptidomimetic DPP-4 inhibitors, seven types were classified in the article. For each type, we focused on the description of strategies for design and optimization, together with a discussion on concluded structure-activity relationships (SAR). In addition, the contribution of specific substituents to the inhibition of DPP-4 was summarized. Selectivity towards the inhibition of DPP-4 over dipeptidyl peptidase 8 (DPP-8) and dipeptidyl peptidase 9 (DPP-9) was also presented.

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The long-term performance of engineering structures in a corrosive environment will be significantly affected by the coupled action of corrosion and fatigue. In this article, a probabilistic corrosion fatigue analytical model is proposed by taking into account the effects of corrosion-induced reduction of the cross-sectional area and deterioration of the fatigue strength of structural components. The proposed model is exemplified to evaluate the probabilistic corrosion fatigue life of a typical welded joint in the suspension Tsing Ma Bridge instrumented with a long-term structural health monitoring system. A genetic algorithm–based mixture parameter estimation method is developed to facilitate the multimodal modeling of stress spectrum derived from the long-term monitoring data of dynamic strain. The achieved results demonstrate that with the increase in the service life, the reliability index of the investigated typical welded joint is dramatically reduced under the combined effect of corrosion and fatigue.