Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction.

BackgroundMost of earlier studies in the field of literature-based discovery have adopted Swanson's ABC model that links pieces of knowledge entailed in disjoint literatures. However, the issue concerning their practicability remains to be solved since most of them did not deal with the context surrounding the discovered associations and usually not accompanied with clinical confirmation. In this study, we aim to propose a method that expands and elaborates the existing hypothesis by advanced text mining techniques for capturing contexts. We extend ABC model to allow for multiple B terms with various biological types.ResultsWe were able to concretize a specific, metabolite-related hypothesis with abundant contextual information by using the proposed method. Starting from explaining the relationship between lactosylceramide and arterial stiffness, the hypothesis was extended to suggest a potential pathway consisting of lactosylceramide, nitric oxide, malondialdehyde, and arterial stiffness. The experiment by domain experts showed that it is clinically valid.ConclusionsThe proposed method is designed to provide plausible candidates of the concretized hypothesis, which are based on extracted heterogeneous entities and detailed relation information, along with a reliable ranking criterion. Statistical tests collaboratively conducted with biomedical experts provide the validity and practical usefulness of the method unlike previous studies. Applying the proposed method to other cases, it would be helpful for biologists to support the existing hypothesis and easily expect the logical process within it.

Polymeric magnetic particles (PMPs) have become a powerful tool for the separation and concentration of microorganisms from a heterogeneous liquid matrix. The functionalization of PMPs with polycationic polymers, such as chitosan, provides an effective means of capturing a broad spectrum of pathogenic bacteria through the intrinsic nature of chitosan interacting with the surface components of bacteria. Here, we report a fairly simple approach for the preparation of starch magnetic microparticles (SMMPs) through molecular rearrangement of short-chain glucans (SCGs) produced by enzymatic debranching of waxy maize starch. The surfaces of SMMPs were readily functionalized with chitosan through electrostatic interaction and hydrogen bonding. The chitosan-functionalized SMMPs (CS@SMMPs) showed high capture efficiency (>90%) for both Gram-positive and Gram-negative bacteria. To further investigate the mechanisms of chitosan–bacteria interaction, we employed model bacteria with different surface compositions. The outer-core lipopolysaccharides as well as the surface charge of bacteria were found to be important for the specific interactions of chitosan to bacteria. The biocompatible paramagnetic materials developed in this study would be promising in removing or separating bacteria from contaminated water for hygienic purposes or subsequent biochemical analysis of certain pathogenic bacteria present in the sample.

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