Lignans, which are widely distributed in higher plants, represent a vast and rather diverse group of phenylpropane derivatives. They have attracted considerable attention due to their pharmacological activities. Some of the lignans have been developed approved therapeutics, and others are considered as lead structures for new drugs. This article is based on our previous review of lignans discovered in the period 2000-2004, and it provides a comprehensive compilation of the 354 new naturally occurring lignans obtained from 61 plant families between 2005 and 2011. We classified five main types according to their structural features, and provided the details of their sources, some typical structures, and diverse biological activities. A tabular compilation of the novel lignans by species is presented at the end. A total of 144 references were considered for this review.

Extremophilic fungi have been found to develop unique defences to survive extremes of pressure, temperature, salinity, desiccation, and pH, leading to the biosynthesis of novel natural products with diverse biological activities. The present review focuses on new extremophilic fungal natural products published from 2005 to 2017, highlighting the chemical structures and their biological potential.

Background Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network ( Epi-GTBN ). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm. Results We compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets. Conclusions The presented methodology ( Epi-GTBN ) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses. Electronic supplementary material The online version of this article (10.1186/s12859-019-3022-z) contains supplementary material, which is available to authorized users.

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