Summary Retrocyclin-101 (RC101) and Protegrin-1 (PG1) are two important antimicrobial peptides that can be used as therapeutic agents against bacterial and/or viral infections, especially those caused by the HIV-1 or sexually transmitted bacteria. Because of their antimicrobial activity and complex secondary structures, they have not yet been produced in microbial systems and their chemical synthesis is prohibitively expensive. Therefore, we created chloroplast transformation vectors with the RC101 or PG1 coding sequence, fused with GFP to confer stability, furin or Factor Xa cleavage site to liberate the mature peptide from their fusion proteins and a His-tag to aid in their purification. Stable integration of RC101 into the tobacco chloroplast genome and homoplasmy were confirmed by Southern blots. RC101 and PG1 accumulated up to 32%–38% and 17%~26% of the total soluble protein. Both RC101 and PG1 were cleaved from GFP by corresponding proteases in vitro, and Factor Xa–like protease activity was observed within chloroplasts. Confocal microscopy studies showed location of GFP fluorescence within chloroplasts. Organic extraction resulted in 10.6-fold higher yield of RC101 than purification by affinity chromatography using His-tag. In planta bioassays with Erwinia carotovora confirmed the antibacterial activity of RC101 and PG1 expressed in chloroplasts. RC101 transplastomic plants were resistant to tobacco mosaic virus infections, confirming antiviral activity. Because RC101 and PG1 have not yet been produced in other cell culture or microbial systems, chloroplasts can be used as bioreactors for producing these proteins. Adequate yield of purified antimicrobial peptides from transplastomic plants should facilitate further preclinical studies.

Community Question Answering (CQA) service provides a platform for increasing number of users to ask and answer for their own needs but unanswered questions still exist within a fixed period. To address this, the paper aims to route questions to the right answerers who have a top rank in accordance of their previous answering performance. In order to rank the answerers, we propose a framework called Question Routing (QR) which consists of four phases: (1) performance profiling, (2) expertise estimation, (3) availability estimation, and (4) answerer ranking. Applying the framework, we conduct experiments with Yahoo! Answers 1 dataset and the results demonstrate that on average each of 1,713 testing questions obtains at least one answer if it is routed to the top 20 ranked answerers.

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In this paper, we investigate the novel problem of automatic question identification in the microblog environment. It contains two steps: detecting tweets that contain questions (we call them "interrogative tweets") and extracting the tweets which really seek information or ask for help (so called "qweets") from interrogative tweets. To detect interrogative tweets, both traditional rule-based approach and state-of-the-art learning-based method are employed. To extract qweets, context features like short urls and Tweetspecific features like Retweets are elaborately selected for classification. We conduct an empirical study with sampled one hour's English tweets and report our experimental results for question identification on Twitter.