Continuous desulfurization and bacterial community structure of an integrated bioreactor developed to treat SO_2 from a gas stream

Sulfide dioxide（SO2） is often released during the combustion processes of fossil fuels. An integrated bioreactor with two sections, namely, a suspended zone（SZ） and immobilized zone（IZ）, was applied to treat SO2 for 6 months. Sampling ports were set in both sections to investigate the performance and microbial characteristics of the integrated bioreactor. SO2 was effectively removed by the synergistic effect of the SZ and IZ, and more than 85%removal efficiency was achieved at steady state. The average elimination capacity of SO2 in the bioreactor was 2.80 g/（m3·hr） for the SZ and 1.50 g/（m3· hr） for the IZ. Most SO2 was eliminated in the SZ. The liquid level of the SZ and the water content ratio of the packing material in the IZ affected SO2 removal efficiency. The SZ served a key function not only in SO2 elimination, but also in moisture maintenance for the IZ. The desired water content in IZ could be feasibly maintained without any additional pre-humidification facilities. Clone libraries of 16 S r DNA directly amplified from the DNA of each sample were constructed and sequenced to analyze the community composition and diversity in the individual zones.The desulfurization bacteria dominated both zones. Paenibacillus sp. was present in both zones, whereas Ralstonia sp. existed only in the SZ. The transfer of SO2 to the SZ involved dissolution in the nutrient solution and biodegradation by the sulfur-oxidizing bacteria.This work presents a potential biological treatment method for waste gases containing hydrophilic compounds.

String kernels construction and fusion:a survey with bioinformatics application

The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of sequences.Therefore,the kernel method is significant in the solution of bioinformatics problems.Various kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish tasks.Mass biological sequence data occur in practical applications.Research of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in bioinformatics.The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction.This review focuses on the requirements of classification tasks of biological sequence data.It studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.