How Cells Communicate with Each Other in the Tumor Microenvironment: Suggestions to Design Novel Therapeutic Strategies in Cancer Disease

Connexin- and pannexin (Panx)-formed hemichannels (HCs) and gap junctions (GJs) operate an interaction with the extracellular matrix and GJ intercellular communication (GJIC), and on account of this they are involved in cancer onset and progression towards invasiveness and metastatization. When we deal with cancer, it is not correct to omit the immune system, as well as neglecting its role in resisting or succumbing to formation and progression of incipient neoplasia until the formation of micrometastasis, nevertheless what really occurs in the tumor microenvironment (TME), which are the main players and which are the tumor or body allies, is still unclear. The goal of this article is to discuss how the pivotal players act, which can enhance or contrast cancer progression during two important process: “Activating Invasion and Metastasis” and the “Avoiding Immune Destruction”, with a particular emphasis on the interplay among GJIC, Panx-HCs, and the purinergic system in the TME without disregarding the inflammasome and cytokines thereof derived. In particular, the complex and contrasting roles of Panx1/P2X7R signalosome in tumor facilitation and/or inhibition is discussed in regard to the early/late phases of the carcinogenesis. Finally, considering this complex interplay in the TME between cancer cells, stromal cells, immune cells, and focusing on their means of communication, we should be capable of revealing harmful messages that help the cancer growth and transform them in body allies, thus designing novel therapeutic strategies to fight cancer in a personalized manner.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

A semicontinuous approach for heterogeneous azeotropic distillation processes

The separation of azeotropes has substantial energy and investment costs, and the available methods require high capital costs for reconstruction of process plants. As an alternative, a semicontinuous configuration that utilizes an existing plant with minor modifications has been explored. In this paper, a semicontinuous, heterogeneous azeotropic distillation process is proposed and acetic acid dehydration process is used as a case study. To carry out the simulation work, Aspen HYSYS^® simulation software is used along with MATLAB^® and an interface program to handle the mode-transition of the semicontinuous process. Sensitivity analyses on operating parameters are performed to identify the process limits. Comparisons are made to conventional heterogeneous azeotropic distillation, and dividing-wall distillation column on the annual cost. The results proved that the semicontinuous system is the best setup in terms of total annual costs and energy requirements.     

Landau-Zener模型中纠缠演化及转移的研究

研究了受外场驱动的两个二能级系统分别与两个单模量子化光场相互作用模型中纠缠演化及转移问题。该工作主要是对外场驱动的Landau-Zener模型进行了研究，采用旋转波近似的方法，通过数值计算详细分析了二能级系统初始状态、能级间的耦合常数以及驱动外场的参数对子系统间纠缠和转移特性的影响。结果表明，适当调节模型中的参数，可以使系统初始纠缠完全转移为光腔场间的纠缠，实现二能级系统与光场间的最大纠缠转移。     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Resistive switching characteristics of ZnO–graphene quantum dots and their use as an active component of an organic memory cell with one diode-one resistor architecture

We investigated the resistive switching characteristics of a polystyrene:ZnO–graphene quantum dots system and its potential application in a one diode-one resistor architecture of an organic memory cell. The log–log I–V plot and the temperature-variable I–V measurements revealed that the switching mechanism in a low-current state is closely related to thermally activated transport. The turn-on process was induced by a space-charge-limited current mechanism resulted from the ZnO–graphene quantum dots acting as charge trap sites, and charge transfer through filamentary path. The memory device with a diode presented a ∼10³ I_ON/I_OFF ratio, stable endurance cycles (10² cycles) and retention times (10⁴ s), and uniform cell-to-cell switching. The one diode-one resistor architecture can effectively reduce cross-talk issue and realize a cross bar array as large as ∼3 kbit in the readout margin estimation. Furthermore, a specific word was encoded using the standard ASCII character code.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.