Zhengyi Li

Application of probabilistic neural network in bacterial identification by biochemical profiles

An algorithm of Probabilistic Neural Network (PNN) for bacterial identification based on the probability matrix of API 20E system as a case study is reported. The PNN shows the correct identification of all the taxa belonging to the training and test sets and possesses merits over the conventional methods.

Proteomic analysis of Vibrio metschnikovii under cold stress using a quadrupole Orbitrap mass spectrometer.

Vibrio metschnikovii is a food-borne pathogen found in seafood worldwide. We studied the global proteome responses of V. metschnikovii under cold stress by nano-flow ultra-high-performance liquid chromatography coupled to a quadrupole Orbitrap mass spectrometer. A total of 2066 proteins were identified, among which 288 were significantly upregulated and 572 were downregulated. Functional categorization of these proteins revealed distinct differences between cold-stressed and control cells. Quantitative reverse transcription polymerase chain reaction analysis was also performed to determine the mRNA expression levels of seventeen cold stress-related genes. The results of this study should improve our understanding of the metabolic activities of cold-adapted bacteria and will facilitate a better systems-based understanding of V. metschnikovii.