Human immunodeficiency virus type 1 (HIV-1) replicates efficiently in nonproliferating monocytes and macrophages but not in resting primary T lymphocytes. To determine the contribution of cell division to the HIV-1 replicative cycle in T cells, we evaluated HIV-1 expression, integration of proviral DNA, and production of infectious progeny virus in C8166 T-lymphoid cells blocked in cell division by treatment with either mitomycin, a DNA cross-linker, or aphidicolin, a DNA polymerase alpha inhibitor. The arrest of cell division was confirmed by assay of [3H]thymidine uptake; the nondividing cells remained viable for at least 3 days after treatment. HIV-1 was expressed and replicated equally well in nondividing and dividing C8166 cells, as judged by the comparison of the levels of p24 core antigens in culture supernatants, the proportion of cells expressing HIV-1 specific antigens, the pattern and quantity of HIV-1 DNA present in the extrachromosomal and total cellular DNA fractions, and the biological activity of progeny viruses. A polymerase chain reaction-based viral DNA integration assay indicated that HIV-1 provirus was integrated in C8166 cells treated with either of the two inhibitors of cell division. Similar results were obtained by using growth-arrested Jurkat T-lymphoid cells. We conclude that cell division and cellular DNA synthesis are not required for efficient HIV-1 expression in T cells.

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This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

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