Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Identification and characterization of extracellular cyclic dipeptides as quorum-sensing signal molecules from Shewanella baltica, the specific spoilage organism of Pseudosciaena crocea during 4 °C storage.

Abstract: Quorum-sensing (QS) signaling molecules are able to mediate specific gene expression inside spoilage bacteria in response to population density and thus are implicated in food spoilage. In the present work, a total of 102 strains of spoilage bacteria were isolated from Pseudosciaena crocea at 4 °C storage, and of these, 60 strains were identified as Shewanella spp., and 48 strains (47.1%) were identified as S. baltica. In addition, the spoilage capabilities of three different S. baltica strains (00A, 00B, and 00C) were compared by total volatile base nitrogen (TVB-N) and sensory analysis (off-odors). Furthermore, four cyclic dipeptides (diketopiperazines, DKPs) that function as QS signal molecules were isolated and characterized from the extracellular metabolites of S. baltica 00C which had the strongest spoilage activity based on gas chromatography mass spectrometry (GC-MS). By supplementation of four synthesized DKPs, the spoilage capability of S. baltica could be significantly enhanced. So far, this was the first attempt to characterize DKPs as the signaling molecules in QS of S. baltica. Our study may provide some evidence of the role of DKPs involved in microbial spoilage.

Inoculation with Bacillus subtilis and Azospirillum brasilense produces abscisic acid that reduces irt1-mediated cadmium uptake of roots.

Abstract: Cadmium (Cd) contamination of agricultural soils represents a serious risk to crop safety. A new strategy using abscisic acid (ABA)-generating bacteria, Bacillus subtilis or Azospirillum brasilense, was developed to reduce the Cd accumulation in plants grown in Cd-contaminated soil. Inoculation with either bacterium resulted in a pronounced increase in the ABA level in wild-type Arabidopsis Col-0 plants, accompanied by a decrease in Cd levels in plant tissues, which mitigated the Cd toxicity. As a consequence, the growth of plants exposed to Cd was improved. Nevertheless, B. subtilis and A. brasilense inoculation had little effect on Cd levels and toxicity in the ABA-insensitive mutant snrk 2.2/2.3, indicating that the action of ABA is required for these bacteria to reduce Cd accumulation in plants. Furthermore, inoculation with either B. subtilis or A. brasilense downregulated the expression of IRT1 (iron-regulated transporter 1) in the roots of wild-type plants and had little effect on Cd levels in the ...

Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models

Abstract: In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks. However, the existing detection tools are far from satisfactory. In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs. Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies. Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods. In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area.