This paper presents a novel approach to building an interval dynamic model for an industrial plant with uncertainty by an interval neural network (INN). A new type of randomized learner model, named interval random vector functional-link network (IRVFLN), is proposed to take advantages of the inherent RVFLN in rapid modeling. The IRVFLN model is equipped with interval hidden input weights (and biases), which are randomly assigned from certain distribution/range and remain fixed, and the interval output weights can be evaluated by solving a couple of least squares problems. The comparative numerical experiments have verified the good potential of the proposed IRVFLN with the interval learner models produced by the error back-propagation algorithm. In the following modeling application, some measures for building IRVFLN with unknown but bounded (UBB) errors requirements are discussed in depth, in order to modeling an uncertain dynamic plant with IRVFLN by bounded-error data in either known or unknown error bounds. Finally, as a case study, the IRVFLN is applied to modeling a chemical interval dynamic plant with recycling, where the simulation results and generalization ability analysis demonstrate that the proposed method is suitable and effective. INDEX TERMS Interval neural network (INN), random vector functional-link network (RVFLN), unknown but bounded (UBB) errors, uncertain dynamic system modeling.

No abstract

No abstract

Affibodies targeting intracellular proteins have a great potential to function as ideal therapeutic agents. However, little is known about how the affibodies enter target cells to interact with intracellular target proteins. We have previously developed the HPV16E7 affibody (ZHPV16E7384) for HPV16 positive cervical cancer treatment. Here, we explored the underlying mechanisms of ZHPV16E7384 and found that ZHPV16E7384 significantly inhibited the proliferation of target cells and induced a G1/S phase cell cycle arrest. Furthermore, ZHPV16E7384 treatment resulted in the upregulation of retinoblastoma protein (Rb) and downregulation of phosphorylated Rb (pRb), E2F1, cyclin D1, and CDK4 in the target cells. Moreover, treatment with dynamin or the caveolin-1 inhibitor not only significantly suppressed the internalization of ZHPV16E7384 into target cells but also reversed the regulation of cell cycle factors by ZHPV16E7384. Overall, these results indicate that ZHPV16E7384 was likely internalized specifically into target cells through dynamin- and caveolin-1 mediated endocytosis. ZHPV16E7384 induced the cell cycle arrest in the G1/S phase at least partially by interrupting HPV16E7 binding to and degrading Rb, subsequently leading to the downregulation of E2F1, cyclin D1, CDK4, and pRb, which ultimately inhibited target cell proliferation. These findings provide a rationale of using ZHPV16E7384 to conduct a clinical trial for target therapy in cervical cancer.