Cellular UDP-glucose deficiency caused by a single point mutation in the UDP-glucose pyrophosphorylase gene

We previously isolated a mutant cell that is the only mammalian cell reported to have a persistently low level of UDP-glucose. In this work we obtained a spontaneous revertant whose UDP-glucose level lies between those found in the wild type and the mutant cell. The activity of UDP-glucose pyrophosphorylase (UDPG:PP), the enzyme that catalyzes the formation of UDP-glucose, was in the mutant 4% and in the revertant 56% of the activity found in the wild type cell. Sequence analysis of UDPG: PP cDNAs from the mutant cell showed one missense mutation, which changes amino acid residue 115 from glycine to aspartic acid. The substituted glycine is located within the largest stretch of strictly conserved residues among eukaryotic UDPG:PPs. The analysis of the cDNAs from the revertant cell indicated the presence of an equimolar mixture of the wild type and the mutated mRNAs, suggesting that the mutation has reverted in only one of the alleles. In summary, we demonstrate that the G115D substitution in the Chinese hamster UDPG:PP dramatically impairs its enzymatic activity, thereby causing cellular UDP-glucose deficiency.     

Observer-based predictive controller design with network-enhanced time-delay compensation

State feedback control is very attractive due to the precise computation of the gain matrix, but the implementation of a state feedback controller is possible only when all state variables are directly measurable. This condition is almost impossible to accomplish due to the excess number of required sensors or unavailability of states for measurement in most of the practical situations. Hence, the need for an estimator or observer is obvious to estimate all the state variables by observing the input and the output of the controlled system. As such, the goal of this paper is to provide a control design methodology based on a Luenberger observer design that can assure the closed-loop performances of a vehicle drivetrain with backlash, while compensating the network-enhanced time-varying delays. This goal is achieved in a sequential manner: firstly, a piecewise linear model of two inertias drivetrain, which takes into consideration the backlash nonlinearity and the network-enhanced time-varying delay effects is derived; then, a Luenberger observer which estimates the state variables is synthesized and the robust full state-feedback predictive controller based on flexible control Lyapunov functions is designed to explicitly take into account the bounds of the disturbances caused by time-varying delays and to guarantee also the input-to-state stability of the system in a non-conservative way. The full state-feedback predictive control strategy based on the Luenberger observer design was experimentally tested on a vehicle drivetrain emulator controlled through controller area network, with the aim of minimizing the backlash effects while compensating the network-enhanced delays.     

An elitist non-dominated sorting genetic algorithm enhanced with a neural network applied to the multi-objective optimization of a polysiloxane synthesis process

This paper presents an original software implementation of the elitist non-dominated sorting genetic algorithm (NSGA-II) applied and adapted to the multi-objective optimization of a polysiloxane synthesis process. An optimized feed-forward neural network, modeling the variation in time of the main parameters of the process, was used to calculate the vectorial objective function of NSGA-II, as an enhancement to the multi-objective optimization procedure. An original technique was utilized in order to find the most appropriate parameters for maximizing the performance of NSGA-II. The algorithm provided the optimum reaction conditions (reaction temperature, reaction time, amount of catalyst, and amount of co-catalyst), which maximize the reaction conversion and minimize the difference between the obtained viscometric molecular weight and the desired molecular weight. The algorithm has proven to be able to find the entire non-dominated Pareto front and to quickly evolve optimal solutions as an acceptable compromise between objectives competing with each other. The use of the neural network makes it also suitable to the multi-objective optimization of processes for which the amount of knowledge is limited.     

The EuQoS system: a solution for QoS routing in heterogeneous networks [Quality of Service based Routing Algorithms for Heterogeneous Networks]

EuQoS is the acronym for "end-to-end quality of service support over heterogeneous networks", which is a European research project aimed at building an entire QoS framework, addressing all the relevant network layers, protocols, and technologies. This framework, which includes the most common access networks (xDSL, UMTS, WiFi, and LAN) is being prototyped and tested in a multidomain scenario throughout Europe, composing what we call the EuQoS system. In this article we present the novel QoS routing mechanisms that are being developed and evaluated in the framework of this project. The preliminary performance results validate the design choices of the EuQoS system, and confirm the potential impact this project is likely to have in the near future     

Symbolic Analysis of Linear Non-Reciprocal Circuits

A symbolic expression of a transfer function in a linear non-reciprocal circuit is proved; the symbolic parameters may be proper immittances and controlled source parameters. the effectiveness of computation of symbol product coefficients is improved by using a generation-and-test algorithm of symbol combinations. A comparison between the most efficient symbolic analysis methods and the proposed method is performed. It is concluded that the proposed method is competitive with these methods.     

The Role of IL-22 in Wound Healing. Potential Implications in Clinical Practice

Wound healing is a complex process that is mediated and influenced by several cytokines, chemokines, and growth factors. Interleukin-22 (IL-22) is a cytokine that plays a critical role in tissue regeneration. Our study is a systematic review that addressed the implications of IL-22 in the healing of wounds caused by external factors. Thirteen studies were included in our review, most of them being experimental studies. Three clinical studies underlined the potential role of IL-22 in day-to-day clinical practice. IL-22 plays a central role in wound healing, stimulating the proliferation, migration, and differentiation of the cells involved in tissue repair. However, overexpression of IL-22 can cause negative effects, such as keloid scars or peritoneal adhesions. The results of the presented studies are promising, but further research that validates the roles of IL-22 in clinical practice and analyzes its potential implication in surgical healing is welcomed.