Key Selection Strategies for a Novel Service Index Model to Expedite Service Discovery and Composition

The rapid growth of Internet applications and the widespread use of cloud computing platforms have accelerated the development of Service Computing. As a result, the number of services on cloud computing platforms is growing exponentially, and these services are often provided by different providers for building more complex applications to meet the needs of users. However, due to the diversity of services, a single service is often unable to meet the needs of users, and they need to be used in combination. This poses a major challenge for the management and discovery of Service Computing. To address this challenge, the multilevel index model has become the state-of-the-art approach for managing and retrieving services from service repositories in the Service Computing domain. However, adding and retrieving services to the model in a timely and accurate manner remains a persistent problem. These problems affect the efficiency of discovering and composing services in Service Computing, and thus, new methods and techniques need to be investigated to optimise the management and discovery of Service Computing. The existing key selection methods for solving this problem do not effectively solve the problem of service addition and are based on the assumption that the distribution probability of service parameters is equal, which is not always the practice case. Certain services have the same input or output parameters, and some are frequently invoked by users, resulting in an unequal invocation of retrieval request parameters for each service. Furthermore, the invocation frequency of popular services changes over time. Existing key selection methods cannot handle such changes, thereby cannot guarantee service retrieval efficiency. In light of these challenges, this thesis aims to optimise and enhance the multilevel index model. Suitable key selection methods have been proposed to address issues including the inefficient service addition operation, equal appearing probability of service parameters, and service hotspot drift respectively. By reducing the time incurred during the service addition and retrieval process in the index model, the proposed approaches can efficiently improve the efficiency of service discovery and composition.</p

Upregulation of SQSTM1/p62 contributes to nickel-induced malignant transformation of human bronchial epithelial cells

<p>Chronic lung inflammation is accepted as being associated with the development of lung cancer caused by nickel exposure. Therefore, identifying the molecular mechanisms that lead to a nickel-induced sustained inflammatory microenvironment that causes transformation of human bronchial epithelial cells is of high significance. In the current studies, we identified SQSTM1/p62 as a novel nickel-upregulated protein that is important for nickel-induced inflammatory TNF expression, subsequently resulting in transformation of human bronchial epithelial cells. We found that nickel exposure induced SQSTM1 protein upregulation in human lung epithelial cells in vitro and in mouse lung tissues <i>in vivo</i>. The SQSTM1 upregulation was also observed in human lung squamous cell carcinoma. Further studies revealed that the knockdown of <i>SQSTM1</i> expression dramatically inhibited transformation of human lung epithelial cells upon chronic nickel exposure, whereas ectopic expression of SQSTM1 promoted such transformation. Mechanistic studies showed that the SQSTM1 upregulation by nickel was the compromised result of upregulating <i>SQSTM1</i> mRNA transcription and promoting SQSTM1 protein degradation. We demonstrated that nickel-initiated SQSTM1 protein degradation is mediated by macroautophagy/autophagy <i>via</i> an MTOR-ULK1-BECN1 axis, whereas RELA is important for <i>SQSTM1</i> transcriptional upregulation following nickel exposure. Furthermore, SQSTM1 upregulation exhibited its promotion of nickel-induced cell transformation through exerting an impetus for nickel-induced inflammatory <i>TNF</i> mRNA stability. Consistently, the MTOR-ULK1-BECN1 autophagic cascade acted as an inhibitory effect on nickel-induced TNF expression and cell transformation. Collectively, our results demonstrate a novel SQSTM1 regulatory network that promotes a nickel-induced tumorigenic effect in human bronchial epithelial cells, which is negatively controlled by an autophagic cascade following nickel exposure.</p