Natural Language Processing Using Neighbour Entropy-based Segmentation

In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018

The Activity of Small Urea‐γ‐AApeptides Toward Gram‐Positive Bacteria

Host Defense Peptides (HDPs) have gained considerable interest due to the omnipresent threat of bacterial infection as a serious public health concern. However, development of HDPs is impeded by several drawbacks, such as poor selectivity, susceptibility to proteolytic degradation, low‐to‐moderate activity and requiring complex syntheses. Herein we report a class of lipo‐linear α/urea‐γ‐AApeptides with a hybrid backbone and low molecular weight. The heterogeneous backbone not only enhances chemodiversity, but also shows effective antimicrobial activity against Gram‐positive bacteria and is capable of disrupting bacterial membranes and killing bacteria rapidly. Given their low molecular weight and ease of access via facile synthesis, they could be practical antibiotic agents.Double‐AA peptides: We investigated a new class of small linear molecules as potential antibiotic agents against Gram‐positive bacteria. Our studies suggest that these compounds can disrupt bacterial membranes and kill bacteria rapidly. Given their low molecular weight and ease of accessibility through a facile synthesis approach, they are good candidates for development into antibiotic agents.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/1/cmdc201900520-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/2/cmdc201900520.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/3/cmdc201900520_am.pd