Application of In Vivo Induced Antigen Technology (IVIAT) to Bacillus anthracis

In vivo induced antigen technology (IVIAT) is an immuno-screening technique that identifies bacterial antigens expressed during infection and not during standard in vitro culturing conditions. We applied IVIAT to Bacillus anthracis and identified PagA, seven members of a N-acetylmuramoyl-L-alanine amidase autolysin family, three P60 family lipoproteins, two transporters, spore cortex lytic protein SleB, a penicillin binding protein, a putative prophage holin, respiratory nitrate reductase NarG, and three proteins of unknown function. Using quantitative real-time PCR comparing RNA isolated from in vitro cultured B. anthracis to RNA isolated from BALB/c mice infected with virulent Ames strain B. anthracis, we confirmed induced expression in vivo for a subset of B. anthracis genes identified by IVIAT, including L-alanine amidases BA3767, BA4073, and amiA (pXO2-42); the bacteriophage holin gene BA4074; and pagA (pXO1-110). The exogenous addition of two purified putative autolysins identified by IVIAT, N-acetylmuramoyl-L-alanine amidases BA0485 and BA2446, to vegetative B. anthracis cell suspensions induced a species-specific change in bacterial morphology and reduction in viable bacterial cells. Many of the proteins identified in our screen are predicted to affect peptidoglycan re-modeling, and our results support significant cell wall structural remodeling activity during B. anthracis infection. Identification of L-alanine amidases with B. anthracis specificity may suggest new potential therapeutic targets

A physarum-inspired approach to supply chain network design

A supply chain is a system which moves products from a supplier to customers, which plays a very important role in all economic activities. This paper proposes a novel algorithm for a supply chain network design inspired by biological principles of nutrients’ distribution in protoplasmic networks of slime mould Physarum polycephalum. The algorithm handles supply networks where capacity investments and product flows are decision variables, and the networks are required to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of flux during the iterative process, which is used in real-time updating of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Numerical examples are provided to illustrate the practicality and flexibility of the proposed method algorithm

Stakeholders, Green Manufacturing, and Practice Performance: Empirical Evidence from Chinese Fashion Businesses

This study explores the relationship among stakeholders, green manufacturing, and practice performance in the fashion business in China and focuses on assisting companies to enhance environmental awareness and green manufacturing practices. We collect research data by developing questionnaires for various Chinese enterprises. A five-point Likert scale is adopted to enable respondents to indicate the extent to which they agree with the items. Through tests and analyses, the questionnaire is validated as reliable, the structural equation model has a good fitting degree, and hypotheses are proved true. Specifically, corporate stakeholders have a significant positive impact on green manufacturing and practice performance, and green manufacturing has a significant positive impact on practice performance in the context of Chinese fashion businesses. Moreover, corporate stakeholders can have a positive impact on practice performance through green manufacturing. We also propose some policy implications, including implementing compulsive policies and regulations and encouraging and establishing preferential policies, such as tax concessions. Moreover, enterprises should actively strive to improve green manufacturing technology and management level to ensure the smooth implementation of green manufacturing practices. To retain sustained earnings and development, green manufacturing should be the bottom line of involved firms. We also emphasize that the importance of corporate stakeholders should be promoted in consideration of enterprises’ practice performance and future development

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research