Zika Virus Growth in Human Kidney Cells Is Restricted by an Elevated Glucose Level

Mosquito-borne Zika virus (ZIKV) became a real threat to human health due to the lack of vaccine and effective antiviral treatment. The virus has recently been responsible for a global outbreak leading to millions of infected cases. ZIKV complications were highlighted in adults with Guillain–Barré syndrome and in newborns with increasing numbers of congenital disorders ranging from mild developmental delays to fatal conditions. The ability of ZIKV to establish a long-term infection in diverse organs including the kidneys has been recently documented but the consequences of such a viral infection are still debated. Our study aimed to determine whether the efficiency of ZIKV growth in kidney cells relates to glucose concentration. Human kidney HK-2 cells were infected with different ZIKV strains in presence of normal and high glucose concentrations. Virological assays showed a decrease in viral replication without modifying entry steps (viral binding, internalization, fusion) under high glucose conditions. This decrease replication was associated with a lower virus progeny and increased cell viability when compared to ZIKV-infected HK-2 cells in normal glucose concentration. In conclusion, we showed for the first time that an elevated glucose level influences ZIKV replication level with an effect on kidney cell survival.     

A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system...     

Transient-state Analytical Solution for Arbitrarily-Located Multiwells in Triangular-Shaped Unconfined Aquifer

Water Resources Management - This work presents an analytical solution for the linearized Boussinesq equation describing the nature of well hydraulics in equilateral triangular-shaped unconfined...     

Output feedback controller for polytopic systems exploiting the direct searching of the design space

This paper deals with the problem of designing a robust static output feedback controller for polytopic systems. The current research that tackled this problem is mainly based on LMI method, which is conservative by nature. In this paper, a novel approach is proposed, which considers the design space of the controller parameters and iteratively partitions the space to small simplexes. Then, by assessing the stability in each simplex, the solution space for design parameters is directly determined. It has been theoretically proved that, if there exists a feasible solution in the design space, the algorithm can find it. To validate the result of the proposed approach, comparative simulation examples are given to illustrate the performance of the design methodology as compared to those of previous approaches.     

Adaptive robust boundary control of shaft vibrations under perturbations with unknown upper bounds

This paper presents robust and adaptive boundary control designs to stabilize the two‐dimensional vibration of hybrid shaft model. The hybrid shaft is mathematically represented by a set of partial differential equations, governing the shaft vibrations, coupled to ordinary differential equations, describing rigid body spinning and dynamic boundary conditions. The control objective is to stabilize the transverse vibrations of the perturbed shaft while regulating the spinning rate. To achieve this, the paper first establishes robust boundary control laws that fulfil the control objective in the presence of modeling uncertainties and external disturbances operating over the shaft domain and boundary. Lyapunov‐based analyses show that the proposed robust control exponentially stabilizes the shaft with vanishing distributive perturbations, while assuring ultimately bounded vibrations in the case of nonvanishing perturbations. Then, adaptive control philosophy is utilized to achieve redesigned robust controllers that only use online adaptation of control gains without acquiring the knowledge of bounds on perturbations, as well as dynamic parameters. An advantage of this design is avoiding an overconservative robust control law, which may induce poor stability and chattering in tackling system perturbations with unknown upper bounds. Simulations through finite element method illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.