Ozone disrupts the communication between plants and insects in urban and suburban areas:an updated insight on plant volatiles

Plant-insect interactions are basic components of biodiversity conservation.To attain the international Sustainable Development Goals(SDGs),the interactions in urban and in suburban systems should be better understood to maintain the health of green infrastructure.The role of ground-level ozone(0_(3)) as an environmental stress disrupting interaction webs is presented.Ozone mixing ratios in suburbs are usually higher than in the center of cities and may reduce photosynthetic productivity at a relatively higher degree.Consequently,carbon-based defense capacities of plants may be suppressed by elevated 0_(3) more in the suburbs.However,contrary to this expectation,grazing damages by leaf beetles have been severe in some urban centers in comparison with the suburbs.To explain differences in grazing damages between urban areas and suburbs,the disruption of atmospheric communication signals by elevated 0_(3) via changes in plant-regulated biogenic volatile organic compounds and long-chain fatty acids are considered.The ecological roles of plant volatiles and the effects of 0_(3) from both a chemical and a biological perspective are presented.Ozone-disrupted plant volatiles should be considered to explain herbivory phenomena in urban and suburban systems.

Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

PBNA: An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.