Background: Floral scent is one of the important strategies for ensuring fertilization and for determining seed or fruit set. Research on plant scents has hampered mainly by the invisibility of this character, its dynamic nature, and complex mixtures of components that are present in very small quantities. Most progress in scent research, as in other areas of plant biology, has come from the use of molecular and biochemical techniques. Although volatile components have been identified in several orchid species, the biosynthetic pathways of orchid flower fragrance are far from understood. We investigated how flower fragrance was generated in certainPhalaenopsisorchids by determining the chemical components of the floral scent, identifying floral expressed-sequence-tags (ESTs), and deducing the pathways of floral scent biosynthesis inPhalaneopsis bellinaby bioinformatics analysis.Results: The main chemical components in theP. bellinaflower were shown by gas chromatography-mass spectrometry to be monoterpenoids, benzenoids and phenylpropanoids. The set of floral scent producing enzymes in the biosynthetic pathway from glyceraldehyde-3-phosphate (G3P) to geraniol and linalool were recognized through data mining of theP. bellinafloral EST database (dbEST). Transcripts preferentially expressed inP. bellinawere distinguished by comparing the scent floral dbEST to that of a scentless species,P. equestris, and included those encoding lipoxygenase, epimerase, diacylglycerol kinase and geranyl diphosphate synthase. In addition, EST filtering results showed that transcripts encoding signal transduction and Myb transcription factors and methyltransferase, in addition to those for scent biosynthesis, were detected byin silicohybridization of theP. bellinaunigene database against those of the scentless species, rice andArabidopsis. Altogether, we pinpointed 66% of the biosynthetic steps from G3P to geraniol, linalool and their derivatives.Conclusion: This systems biology program combined chemical analysis, genomics and bioinformatics to elucidate the scent biosynthesis pathway and identify the relevant genes. It integrates the forward and reverse genetic approaches to knowledge discovery by which researchers can study non-model plants.

This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.

In recent years, the pervasive use of computers and the Internet has created an unprecedented environment for e-learning. However, the rapid expansion in the number of disparate information sources and variety of data available affects e-learning significantly. Nonetheless, there has been a growing awareness that courseware should automatically adjust to the profiles of individual learners. Over the past few years, much effort has been expended to enable personalization for e-learning by semantic web techniques. Although the semantic web offers a theoretical framework for flexibility and interoperability in e-learning resources, there is no consensus ontology that can be used to describe learning profiles directly for personal e-learning environments. This means that their actual applications are as yet unknown. Positing that ontologies actually provide viable solutions for knowledge management, in this article, we present a three-module architecture for a personalized e-learning environment for bioinformatics. The architecture facilitates a personalized e-material recommender that does item-based collaborative filtering (CF) + adapted vector space model (VSM), explicit and implicit scoring, and a concept of tasks focused on rating literature for the e-learner. Meanwhile, the knowledge discovery process can be tailored to acquiring knowledge for professional requirements. Validation for our architecture is provided by a case study for biological institutions. The experimental results show that our architecture is helpful for professional requirements, improving recommendation quality, and satisfying users.

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.