DEVELOPMENT AND APPLICATION OF AN EXPANDED STREPTOMYCES GENETIC CODE

Actinobacteria, especially those of genus Streptomyces, are a prominent source of bioactive natural products. The ability to site-specifically incorporate unnatural amino acids (UAAs) into natural product biosynthetic enzymes and ribosomally derived peptides in these organisms would constitute a valuable tool for drug discovery and development. The work described in this dissertation focuses on development and application of an expanded Streptomyces genetic code, including development of UAA incorporation systems based on amber suppression and sense codon reassignment, structural diversification of the model thiopeptide natural product thiostrepton using UAAs, and mapping protein-protein interactions in type II polyketide biosynthetic enzymes using photocrosslinking UAAs. First, we developed an amber suppression-based system of site-specific incorporation of p-iodo-L-phenylalanine (pIPhe) and p-azido-L-phenylalanine (pAzPhe) into superfolder GFP (sfGFP) in the model natural product producer Streptomyces venezuelae ATCC 15439. Next, the rare leucine codon TTA was reassigned to encode pIPhe and p-benzoyl-L-phenylalanine (pBpa) in S. coelicolor J1681 (ΔbldA), in which the unique tRNALeuUAA (bldA) that recognizes the TTA codon was deleted. In the S. venezuelae ΔbldA strain, we achieved 20-fold higher yields of UAA containing protein using the TTA reassignment system compared to the amber suppression-based system; and were able to incorporate up to 10 scattered or 5 tandem UAAs in a single protein using TTA reassignment. Finally, we have carried out preliminary work on two applications. In the first, we constructed and tested functionality of a system designed to incorporate pAzPhe into the actinorhodin ketosynthase β (KSβ) in S. coelicolor J1681 to interrogate protein-protein interactions in actinorhodin biosynthesis. In the second, we have begun developing a system for incorporation of UAAs into thiostrepton in the native producer Streptomyces laurentii ATCC 31255. Preliminary results confirm the functionality of amber suppression system in S. laurentii; and demonstrated development of a TipA-based fluorescent biosensor for detecting thiopeptide antibiotics in S. venezuelae. Work on these two applications has laid the foundation for development of tools to structurally diversify the ribosomally synthesized peptides and to address questions related to natural product biosynthesis and mechanism of action that are relevant to drug discovery and development

An Unsupervised Approach for Discovering Relevant Tutorial Fragments for APIs

Developers increasingly rely on API tutorials to facilitate software development. However, it remains a challenging task for them to discover relevant API tutorial fragments explaining unfamiliar APIs. Existing supervised approaches suffer from the heavy burden of manually preparing corpus-specific annotated data and features. In this study, we propose a novel unsupervised approach, namely Fragment Recommender for APIs with PageRank and Topic model (FRAPT). FRAPT can well address two main challenges lying in the task and effectively determine relevant tutorial fragments for APIs. In FRAPT, a Fragment Parser is proposed to identify APIs in tutorial fragments and replace ambiguous pronouns and variables with related ontologies and API names, so as to address the pronoun and variable resolution challenge. Then, a Fragment Filter employs a set of nonexplanatory detection rules to remove non-explanatory fragments, thus address the non-explanatory fragment identification challenge. Finally, two correlation scores are achieved and aggregated to determine relevant fragments for APIs, by applying both topic model and PageRank algorithm to the retained fragments. Extensive experiments over two publicly open tutorial corpora show that, FRAPT improves the state-of-the-art approach by 8.77% and 12.32% respectively in terms of F-Measure. The effectiveness of key components of FRAPT is also validated.Comment: 11 pages, 8 figures, In Proc. of 39rd IEEE International Conference on Software Engineering (ICSE'17

Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times

Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to emphasize semantic regions in weakly supervised semantic segmentation. MCC adroitly incorporates concepts from masked image modeling and contrastive learning to devise Transformer blocks that induce keys to contract towards semantically pertinent regions. Unlike prevalent techniques that directly eradicate patch regions in the input image when generating masks, we scrutinize the neighborhood relations of patch tokens by exploring masks considering keys on the affinity matrix. Moreover, we generate positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output. Elaborate experiments on commonly employed datasets evidences that the proposed MCC mechanism effectively aligns global and local perspectives within the image, attaining impressive performance. The source code is available at \url{https://github.com/fwu11/MCC}

An ultra-stable cryogenic sapphire cavity laser with an instability of 1.9×10−161.9\times10^{-16} based on a low vibration level cryostat

Cryogenic ultra-stable lasers have extremely low thermal noise limits and frequency drifts, but they are more seriously affected by vibration noise from cryostats. Main material candidates for cryogenic ultra-stable cavities include silicon and sapphire. Although sapphire has many excellent properties at low temperature, the development of sapphire-based cavities is less advanced than that of silicon-based. Using a homemade cryogenic sapphire cavity, we develop an ultra-stable laser source with a frequency instability of $1.9\times10^{-16}$. This is the best frequency instability level among similar systems using cryogenic sapphire cavities reported so far. Low vibration performance of the cryostat is demonstrated with a two-stage vibration isolation, and the vibration suppression is further improved by different mixing ratio of the gas-liquid helium. With this technique, vibrations at frequencies higher than tens of hertz are greatly suppressed.Comment: 4 pages, 4 figure

A more accurate model for finding tutorial segments explaining APIs

Developers prefer to utilize third-party libraries when they implement some functionalities and Application Programming Interfaces (APIs) are frequently used by them. Facing an unfamiliar API, developers tend to consult tutorials as learning resources. Unfortunately, the segments explaining a specific API scatter across tutorials. Hence, it remains a challenging issue to find the relevant segments. In this study, we propose a more accurate model to find the exact tutorial fragments explaining APIs. This new model consists of a text classifier with domain specific features. More specifically, we discover two important indicators to complement traditional text based features, namely co-occurrence APIs and knowledge based API extensions. In addition, we incorporate Word2Vec, a semantic similarity metric to enhance the new model. Extensive experiments over two publicly available tutorial datasets show that our new model could find up to 90% fragments explaining APIs and improve the state-of-the-art model by up to 30% in terms of F-measure.Comment: 11 pages, 11 figures, In Proc. of 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER'16), pp.157-16