The plant Dysosma versipellis is known for its antimicrobial and anticancer properties but is a rare and vulnerable perennial herb that is endemic to China. In this study, 224 isolates were isolated from various tissues of D. versipellis, and were classified into 53 different morphotypes according to culture characteristics and were identified by sequence analyses of the internal transcribed spacer (ITS) region of the rRNA gene. Although nine strains were not assignable at the phylum level, 44 belonged to at least 29 genera of 15 orders of Ascomycota (93%), Basidiomycota (6%), and Zygomycota (1%). Subsequent assays revealed antimicrobial activities of 19% of endophytic extracts against at least one pathogenic bacterium or fungus. Antimicrobial activity was also determined using the agar diffusion method and was most prominent in extracts from four isolates. Moreover, high performance liquid chromatography (HPLC) and ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry analyses (UPLC–QTOF MS) showed the presence of podophyllotoxin in two Fusarium strains, with the highest yield of 277 μg/g in Fusarium sp. (WB5121). Taken together, the present data suggest that various endophytic fungi of D. versipellis could be exploited as sources of novel natural antimicrobial or anticancer agents.
Identifying potentially vulnerable locations in a code base is critical as a pre-step for effective vulnerability assessment; i.e., it can greatly help security experts put their time and effort to where it is needed most. Metric-based and pattern-based methods have been presented for identifying vulnerable code. The former relies on machine learning and cannot work well due to the severe imbalance between non-vulnerable and vulnerable code or lack of features to characterize vulnerabilities. The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities.In this paper, we propose and implement a generic, lightweight and extensible framework, LEOPARD, to identify potentially vulnerable functions through program metrics. LEOPARD requires no prior knowledge about known vulnerabilities. It has two steps by combining two sets of systematically derived metrics. First, it uses complexity metrics to group the functions in a target application into a set of bins. Then, it uses vulnerability metrics to rank the functions in each bin and identifies the top ones as potentially vulnerable. Our experimental results on 11 real-world projects have demonstrated that, LEOPARD can cover 74.0% of vulnerable functions by identifying 20% of functions as vulnerable and outperform machine learning-based and static analysis-based techniques. We further propose three applications of LEOPARD for manual code review and fuzzing, through which we discovered 22 new bugs in real applications like PHP, radare2 and FFmpeg, and eight of them are new vulnerabilities.
We study throughput-optimum localized link scheduling in wireless networks. The majority of results on link scheduling assume binary interference models that simplify interference constraints in actual wireless communication. While the physical interference model reflects the physical reality more precisely, the problem becomes notoriously harder under the physical interference model. There have been just a few existing results on link scheduling under the physical interference model, and even fewer on more practical distributed or localized scheduling. In this paper, we tackle the challenges of localized link scheduling posed by the complex physical interference constraints. By integrating the partition and shifting strategies into the pick-and-compare scheme, we present a class of localized scheduling algorithms with provable throughput guarantee subject to physical interference constraints. The algorithm in the oblivious power setting is the first localized algorithm that achieves at least a constant fraction of the optimal capacity region subject to physical interference constraints. The algorithm in the uniform power setting is the first localized algorithm with a logarithmic approximation ratio to the optimal solution. Our extensive simulation results demonstrate performance efficiency of our algorithms.Index Terms-Localized link scheduling, physical interference model, maximum weighted independent set of links (MWISL), capacity region
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