Exploration of yeast biodiversity and development of industrial applications / 生物工程学报

Yeast are comprised of diverse single-cell fungal species including budding yeast Saccharomyces cerevisiae and various nonconventional yeasts. Budding yeast is well known as an important industrial microorganism, which has been widely applied in various fields, such as biopharmaceutical and health industry, food, light industry and biofuels production. In the recent years, various yeast strains from different ecological environments have been isolated and characterized. Novel species have been continuously identified, and strains with diverse physiological characteristics such as stress resistance and production of bioactive compounds were selected, which proved abundant biodiversity of natural yeast resources. Genome mining of yeast strains, as well as multi-omics analyses (transcriptome, proteome and metabolome, etc.) can reveal diverse genetic diversity for strain engineering. The genetic resources including genes encoding various enzymes and regulatory proteins, promoters, and other elements, can be employed for development of robust strains. In addition to exploration of yeast natural diversity, phenotypes that are more suitable for industrial applications can be obtained by generation of a variety of genetic diversity through mutagenesis, laboratory adaptation, metabolic engineering, and synthetic biology design. The optimized genetic elements can be used to efficiently improve strain performance. Exploration of yeast biodiversity and genetic diversity can be employed to build efficient cell factories and produce biological enzymes, vaccines, various natural products as well as other valuable products. In this review, progress on yeast diversity is summarized, and the future prospects on efficient development and utilization of yeast biodiversity are proposed. The methods and schemes described in this review also provide a reference for exploration of diversity of other industrial microorganisms and development of efficient strains.

Wavelet entropy analysis for ictal electroencephalogram signals of child absence epilepsy / 生物医学工程学杂志

The integral and individual-scale wavelet entropy of electroencephalogram (EEG) were employed to investigate the information complexity in EEG and to explore the dynamic mechanism of child absence epilepsy (CAE). The digital EEG signals were collected from patients with CAE and normal controls. Time-frequency features were extracted by continuous wavelet transformation. Individual scale power spectrum characteristics were represented by wavelet-transform. The integral and individual-scale wavelet entropy of EEG were computed on the basis of individual scale power spectrum. The evolutions of wavelet entropy across ictal EEG of CAE were investigated and compared with normal controls. The integral wavelet entropy of ictal EEG is lower than inter-ictal EEG for CAE, and it also lower than normal controls. The individual-scale wavelet entropies of 12th scale (centered at 3 Hz) of ictal EEG in CAE was significantly higher than normal controls. The individual-scale wavelet entropies for α band (centered at 10 Hz) of ictal EEG in CAE were much lower than normal controls. The integral wavelet entropy of EEG can be considered as a quantitative parameter of complexity for EEG signals. The complexity of ictal EEG for CAE is obviously declined in CAE. The wavelet entropies declined could become quantitative electrophysiological parameters for epileptic seizures, and it also could provide a theoretical basis for the study of neuromodulation techniques in epileptic seizures.