Background:Botrytis cinerea, a haploid Euascomycete fungus that infects numerous crops, has been used as a model system for studying molecular phytopathology. Botrytis cinerea adopts various modes of infection, which are mediated by a number of pathogenicity and virulence-related genes. Many of these genes have not been reported previously.Objectives:This study aimed to investigate development and pathogenicity-related genes between a novel nonpathogenic mutant and the Wild Type (WT) in B. cinerea.Materials and Methods:Digital Gene Expression (DGE) tag profiling can reveal novel genes that may be involved in development and pathogenicity of plant pathogen. A large volume of B. cinerea tag-seq was generated to identify differential expressed genes by the Illumina DGE tag profiling technology.Results:A total of 4,182,944 and 4,182,021 clean tags were obtained from the WT and a nonpathogenic mutant stain (BCt89), respectively, and 10,410 differentially expressed genes were identified. In addition, 84 genes were expressed in the WT only while 34 genes were expressed in the mutant only. A total of 664 differentially expressed genes were involved in 91 Kyoto Encyclopedia of Genes and Genome pathways, including signaling and metabolic pathways.Conclusions:Expression levels of 1,426 genes were significantly up-regulated in the mutant compared to WT. Furthermore, 301 genes were down-regulated with False Discovery Rates (FDR) of < 0.001 and absolute value of log2 Ratio of ≥ 1.
For the deep-hole drilling is in closed or half-closed condition and the cutting situation can’t be controlled directly, it brings a big challenge to attain the machining precision. Deep-hole honing is an effective process method that can ensure the precision of size, geometry shape and surface quality. A new machining technologies of finishing honing on precision deep-hole are discussed. Two typical machining examples were illustrated, and the machining effection were also discussed. The key technology and chief feature of strong honing were analyzed through honing experiment of titanium alloy and PH stainless steel. The experiments indicate that strong honing is one effective method to solve the problem of precision deep-hole machining of difficult-to-cut materials and the honing tools which was used in the experiments have the feature of good rigidity, high cutting efficiency.
To solve the problem of drilling small-diameter deep-hole on difficult-to-cut materials, a kind of DF system combined with low frequency axial vibratory drilling was used. Its vibratory frequency was controlled by a frequency converter and could be adjusted continuously on-line. An inner chip removal deep-hole bit with dimension Φ8.95×500mm was developed and the material of the cutting tip was carbide alloy. Then, the drilling experiment of small-diameter deep-hole on the difficult-to-cut materials 1Cr11Ni4NMo was done. It can be found in the experiment that the chip remove was smooth, the cutting force was little, the cutting tool wear was light, the process of drilling was stable, and many factors of interfering roughness could be canceled or lightened effectively. The results show that DF vibratory drilling system can improve the machining precision and increase efficiency.
For the deep-hole drilling bit works in closed or half-closed condition and the cutting situation can not be observed and controlled directly, it brings a big challenge to monitor the cutting tools and the cutting process. To solve the problem, improved testing methods and data processing techniques were developed. A new condition monitoring method of deep-hole drilling based on multi-sensor data fusion was discussed in the paper. The signals of vibration and cutting force were collected when the condition of deep-hole drilling on stainless steel was normal and abnormal. Four eigenvectors were extracted on time-domain analysis and frequency-domain analysis of the signals. Then the combined four eigenvectors were sent to BP neural networks data fusioning center. The fusioning results indicate that cutting force signal can reflect the condition of drill bit better than vibration signal and multi-sensor data fusion is superior to single-sensor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.