The function of microRNAs (miRNAs) during alcoholic liver disease (ALD) has recently become of great interest in biological research. Studies have shown that ALD associated miRNAs play a crucial role in the regulation of liver-inflammatory agents such as tumour necrosis factor-alpha (TNF-α), one of the key inflammatory agents responsible for liver fibrosis (liver scarring) and the critical contributor of alcoholic liver disease. Lipopolysaccharide (LPS), a component of the cell wall of gram-negative bacteria, is responsible for TNF-α release by Kupffer cells. miRNAs are the critical mediators of LPS signalling in Kupffer cells, hepatocytes and hepatic stellate cells. Certain miRNAs, in particular miR-155 and miR-21, show a positive correlation in up-regulation of LPS signalling when they are exposed to ethanol. ALD is related to enhanced gut permeability that allows the levels of LPS to increase, leads to increased secretion of TNF-α by the Kupffer cells and subsequently promotes alcoholic liver injury through specific miRNAs. Meanwhile, two of the most frequently dysregulated miRNAs in steatohepatitis, miR-122 and miR-34a are the critical mediators in ethanol/LPS activated survival signalling during ALD. In this review, we summarize recent findings regarding the experimental and clinical aspects of functions of specific microRNAs, focusing mainly on inflammation and cell survival after ethanol/LPS treatment, and advances on the role of circulating miRNAs in human alcoholic disorders.

Robust periodic motion generation is developed for a class of mechanical systems with actuator dynamics. The virtual constraint (VC) approach is first refined under incomplete state measurements and it is then extended to the case where the actuator dynamics are brought into play for avoiding limitations in the system performance. The extended virtual constraint approach is subsequently coupled to the nonlinear H∞ synthesis to yield the robust output feedback periodic motion generation for mechanical systems of underactuation degree one, driven by electrical motors with their own dynamics. The effectiveness of the proposed synthesis is supported in the numerical study made for a cart-pendulum testbed.

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A methodology for asymptotic orbital synchronization of a set of homogeneous mechanical systems with one degree of underactuation is proposed. A reference model generating a reference orbit is first constructed under the virtual holonomic constraints approach. Then, the  ∞ synthesis is designed to, first, estimate the state vector of the homogeneous systems, provided that only position measurements are available, and second, to drive the state of the systems to that of the reference model. Due to the  ∞ synthesis properties, the homogeneous systems converge asymptotically to the reference orbit when there are no disturbances affecting the systems nor the measurements. When disturbances are present, robustness is guaranteed provided that the  2 gain of the closed-loop systems remains lower than an attenuation level 𝛾. Numerical results for a set of underactuated cart-pendulums corroborate the proposed methodology.

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The development of control laws for underactuated mechanical systems with pendulum-like behaviors is of paramount importance due to their use in the modeling of more complex systems and other challenging tasks. The underactuated feature describes constraints in the maneuverability and capabilities of a mechanical system with the advantage of offering less energy consumption. In this work, a novel methodology for solving the automation of evolved nonlinear controllers for the swing-up phase of switching control laws for underactuated inverted pendulums is proposed. Automatic synthesis of linear controllers with optimal performance applied to linear systems modeled as transfer functions is a forward leap proposed by Koza in 2003. Our proposed approach introduces the nonlinear nature within the automated construction of a set of swing-up controllers integrating an evolutionary process based on GP. The presented framework is based on an analytic behaviorist setup that merges Control Theory with Genetic Programming. Control Theory is applied to formulate the mathematical description of the problem and the design of the fitness function that guides the automated synthesis; Genetic Programming is implemented as an evolutionary engine for the construction of the solutions. The advantage is that the symbolic feature of Genetic Programming is exploited to develop large sets of nonlinear controllers that can be further studied with analytic tools from the Control Theory approach. The proposed framework is applied to an underactuated two-link inverted pendulum giving a set of 13590 evolved nonlinear swing-up controllers with the same and better fitness value than a state-of-the-art human-made design.