Structure and Activity of a Selective Antibiofilm Peptide SK-24 Derived from the NMR Structure of Human Cathelicidin LL-37

The deployment of the innate immune system in humans is essential to protect us from infection. Human cathelicidin LL-37 is a linear host defense peptide with both antimicrobial and immune modulatory properties. Despite years of studies of numerous peptides, SK-24, corresponding to the long hydrophobic domain (residues 9–32) in the anionic lipid-bound NMR structure of LL-37, has not been investigated. This study reports the structure and activity of SK-24. Interestingly, SK-24 is entirely helical (~100%) in phosphate buffer (PBS), more than LL-37 (84%), GI-20 (75%), and GF-17 (33%), while RI-10 and 17BIPHE2 are essentially randomly coiled (helix%: 7–10%). These results imply an important role for the additional N-terminal amino acids (likely E16) of SK-24 in stabilizing the helical conformation in PBS. It is proposed herein that SK-24 contains the minimal sequence for effective oligomerization of LL-37. Superior to LL-37 and RI-10, SK-24 shows an antimicrobial activity spectrum comparable to the major antimicrobial peptides GF-17 and GI-20 by targeting bacterial membranes and forming a helical conformation. Like the engineered peptide 17BIPHE2, SK-24 has a stronger antibiofilm activity than LL-37, GI-20, and GF-17. Nevertheless, SK-24 is least hemolytic at 200 µM compared with LL-37 and its other peptides investigated herein. Combined, these results enabled us to appreciate the elegance of the long amphipathic helix SK-24 nature deploys within LL-37 for human antimicrobial defense. SK-24 may be a useful template of therapeutic potentia

Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text

Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples. These approaches either train a language model from scratch or fine-tune a pre-trained language model using ID examples, and then take perplexity as output by the language model as OoD scores. In this paper, we analyse the complementary characteristics of both OoD detection methods and propose a multi-level knowledge distillation approach to integrate their strengths, while mitigating their limitations. Specifically, we use a fine-tuned model as the teacher to teach a randomly initialized student model on the ID examples. Besides the prediction layer distillation, we present a similarity-based intermediate layer distillation method to facilitate the student's awareness of the information flow inside the teacher's layers. In this way, the derived student model gains the teacher's rich knowledge about the ID data manifold due to pre-training, while benefiting from seeing only ID examples during parameter learning, which promotes more distinguishable features for OoD detection. We conduct extensive experiments over multiple benchmark datasets, i.e., CLINC150, SST, 20 NewsGroups, and AG News; showing that the proposed method yields new state-of-the-art performance.Comment: 11 page

Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. To further improve the model's generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over five target languages. The results show that our approach significantly outperforms existing state-of-the-art methods across the board.Comment: This paper is accepted by AAAI2020. Code is available at https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cros

The economic burden of cervical cancer from diagnosis to one year after final discharge in Henan Province, China: A retrospective case series study.

BACKGROUND: In China, the disease burden of cervical cancer remains substantial. Human papillomavirus (HPV) vaccines are expensive and not yet centrally funded. To inform immunization policy, understanding the economic burden of the disease is necessary. This study adopted a societal perspective and investigated costs and quality of life changes associated with cervical cancer from diagnosis to one year after final discharge in Henan province, China. METHODS: Inpatient records of cervical cancer patients admitted to the largest cancer hospital in Henan province between Jan. 2017 and Dec. 2018 were extracted. A telephone interview with four modules was conducted in Jun.-Jul. 2019 with a 40% random draw of patients to obtain direct non-medical costs and indirect costs associated with inpatients, costs associated with outpatient visits, and changes in quality of life status using the EQ-5D-5L instrument. Direct medical expenditures were converted to opportunity costs of care using cost-to-charge ratios obtained from hospital financial reports. For each clinical stage (IA-IV), total costs per case from diagnosis to one year after final discharge were extrapolated based on inpatient records, responses to the telephone interview, and recommendation on outpatient follow-ups by Chinese cervical cancer treatment guidelines. Loss in quality-adjusted life years was obtained using the 'under the curve' method and regression predictions. RESULTS: A total of 3,506 inpatient records from 1,323 patients were obtained. Among 541 randomly selected patients, 309 completed at least one module of the telephone interview. The average total costs per case associated with cervical cancer from diagnosis to one year after final discharge ranged from $8,066-$22,888 (in 2018 US Dollar) and the quality-adjusted life years loss varied from 0.05-0.26 for IA-IV patients. CONCLUSIONS: The economic burden associated with cervical cancer is substantial in Henan province. Our study provided important baseline information for cost-effectiveness analysis of HPV immunization program in China