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This paper develops a decomposition-based coevolutionary algorithm for many-objective optimization, which evolves a number of subpopulations in parallel for approaching the set of Pareto optimal solutions. The many-objective problem is decomposed into a number of subproblems using a set of well-distributed weight vectors. Accordingly, each subpopulation of the algorithm is associated with a weight vector and is responsible for solving the corresponding subproblem. The exploration ability of the algorithm is improved by using a mating pool that collects elite individuals from the cooperative subpopulations for breeding the offspring. In the subsequent environmental selection, the top-ranked individuals in each subpopulation, which are appraised by aggregation functions, survive for the next iteration. Two new aggregation functions with distinct characteristics are designed in this paper to enhance the population diversity and accelerate the convergence speed. The proposed algorithm is compared with several state-of-the-art many-objective evolutionary algorithms on a large number of benchmark instances, as well as on a real-world design problem. Experimental results show that the proposed algorithm is very competitive.

Multimodal optimization (MMO) aims at finding multiple optimal (or close to optimal) solutions, which plays a crucial role in various fields. However, most of the efforts have been devoted to the continuous MMO domain, while little attention has been paid to discrete problems like the traveling salesman problem (TSP). This paper makes a proof of principle study on multimodal TSP. Particularly, we design a test suite for multimodal TSP and then develop an ant colony algorithm to accomplish the optimization task. The traditional ant algorithms such as the ant colony system are unable to maintain multiple solutions because of the global convergence. To deal with this problem, we propose a novel niching ant colony system (NACS). The algorithm employs a niching strategy and multiple pheromone matrices to preserve population diversity and keep the trace of multiple paths. The experimental results are presented to validate the good performance of the proposed algorithm.

Multimodal optimization problems which widely exist in the scientific research and engineering applications, has aroused a wide interest of researchers. For solving multimodal optimization problems, numerous niching algorithms have been proposed to locate and track multiple optima. However, most of these algorithms need a very strict choice of the population size parameter. This paper presents a new niching differential evolution algorithm which adaptively adjusts population size during the evolution. Particularly, we propose three techniques for performance enhancement: a heuristic clustering method, a population adaptation strategy, and an auxiliary movement strategy for the best individuals. The algorithm divides the population into several subpopulations and adaptively adjust the number of individuals and subpopulations according to the evolutionary state. In this way, the diversity of population is increased, while the computational cost is reduced. Experimental results verify that the proposed algorithm outperforms the other niching algorithms for multimodal optimization.

Bacillus bombyseptieus is a pathogen of Bombyx mori; it can cause bacterial septicemia in silkworm. One of the components of the parasporal crystal toxin of B. bombyseptieus, α-toxin, plays an important role in the process of infection in silkworm. In this study, we investigated the immune response of silkworm induced by α-toxin by using RNA-seq. We compared the changes in gene expression in the midgut, fatbody, and hemocytes of silkworm and in the B. mori embryonic cell line (BmE) after treatment with α-toxin and identified 952 differentially expressed genes and 353 differentially expressed long noncoding RNAs (lncRNAs). These regulated genes in different tissues were found to be enriched in different pathways. The upregulated genes in the midgut were mainly involved in peptidoglycan catabolic process and tyrosine kinase signaling pathway, whereas the downregulated genes were mainly involved in chitin metabolic pathways. The upregulated genes in fatbody were also involved in peptidoglycan catabolic process, but they were for a different peptidoglycan subtype. Further, genes encoding cecropins were enriched in the fatbody. The downregulated genes were mainly involved in the metabolic pathways of fundamental substances such as cellular protein metabolic process and nucleobase-containing compound metabolic process. These results suggest that α-toxin can induce various immune responses in silkworm, and further studies are warranted to understand the mechanism of α-toxin action in silkworm. Further, lncRNAs and differentially expressed genes were correlated using coexpression network analysis. Our findings revealed potential candidate genes and lncRNAs that might play important physiological functions in the immune response to α-toxins in silkworm.

Studies have reported that bone marrow mesenchymal stem cells (BMSCs) play an important role in immune regulation after organ transplantation. Some of the BMSC-mediated regulatory mechanisms are related to PD-L1 expression. However, the regulatory mechanism of PD-L1 expression is not fully understood. In this experiment, we confirmed the regulatory role of BMSCs in the rejection of liver transplantation and explored the possible mechanism by which PD-L1 expression is regulated in BMSCs. An orthotopic liver transplantation model in rats was established based on the "double-cuff technique". Third-generation BMSCs were injected into the rejection model rats via portal vein. At the same time, sera were obtained from rats in the allograft, isograft and sham-operated groups. The sera from these groups were separately added to BMSCs complete medium to partially mimic the in vivo environment in which BMSCs are exposed. After predicting and analysing microRNAs that regulate PD-L1 expression, we examined the microRNA and PD-L1 expression in BMSCs of the three groups cultured in the conditioned media. Moreover, a luciferase reporter assay was used to confirm the interaction between PD-L1 and microRNA. The study found that the liver function and liver graft histopathology of the BMSC-treated group were better than those of the allograft group. The Kaplan-Meier survival curve analysis showed that the median survival time (MST) of the BMSC-treated group was longer than that of the allograft group. Bioinformatics prediction and analysis showed that miR-17-5p is highly likely to regulate PD-L1. Compared with the other two groups of cells, BMSCs cultured with serum from allograft models showed higher PD-L1 expression, but lower miR-17-5p expression. Pearson correlation analysis showed that miR-17-5p and PD-L1 expression was negatively correlated, and further luciferase reporter assays confirmed that miR-17-5p interacted directly with the 3´-untranslated region (UTR) of PD-L1 mRNA. The results of this study demonstrate that BMSCs can attenuate liver allograft rejection and provide us with the idea that BMSCs may further upregulate PD-L1 expression by downregulating miR-17-5p expression, thereby attenuating liver allograft rejection.

Background. Platelet to white blood cell ratio (PWR) was an independent prognostic predictor for outcomes in some diseases. However, the prognostic role of PWR is still unclear in patients with hepatitis B related acute-on-chronic liver failure (ACLF). In this study, we evaluated the clinical performances of PWR in predicting prognosis in HBV-related ACLF. Methods. A total of 530 subjects were recruited, including 97 healthy controls and 433 with HBV-related ACLF. Liver function, prothrombin time activity (PTA), international normalized ratio (INR), HBV DNA measurement, and routine hematological testing were performed at admission. Results. At baseline, PWR in patients with HBV-related ACLF (14.03 ± 7.17) was significantly decreased compared to those in healthy controls (39.16 ± 9.80). Reduced PWR values were clinically associated with the severity of liver disease and the increased mortality rate. Furthermore, PWR may be an inexpensive, easily accessible, and significant independent prognostic index for mortality on multivariate analysis (HR = 0.660, 95% CI: 0.438–0.996, p=0.048) as well as model for end-stage liver disease (MELD) score. Conclusions. The PWR values were markedly decreased in ACLF patients compared with healthy controls and associated with severe liver disease. Moreover, PWR was an independent prognostic indicator for the mortality rate in patients with ACLF. This investigation highlights that PWR comprised a useful biomarker for prediction of liver severity.

Spodoptera litura is a polyphagous pest and can feed on more than 100 species of plants, causing great damage to agricultural production. The SNP results showed that there were gene exchanges between different regions. To explore the variations of larger segments in S. litura genome, we used genome resequencing samples from 14 regions of China, India, and Japan to study the copy number variations (CNVs). We identified 3976 CNV events and 1581 unique copy number variation regions (CNVRs) occupying the 108.5 Mb genome of S. litura. A total of 5527 genes that overlapped with CNVRs were detected. Selection signal analysis identified 19 shared CNVRs and 105 group-specific CNVRs, whose related genes were involved in various biological processes in S. litura. We constructed the first CNVs map in S. litura genome, and our findings will be valuable for understanding the genomic variations and population differences of S. litura.

Superpixel segmentation targets at grouping pixels in an image into atomic regions whose boundaries align well with the natural object boundaries. This paper first proposes a new feature representation for superpixel segmentation that holistically embraces color, contour, texture, and spatial features. Then, we introduce a clustering-based discriminability measure to iteratively evaluate the importance of different features. Integrating the feature representation and the discriminability measure, we propose a novel content-adaptive superpixel (CAS) segmentation algorithm. CAS is able to automatically and iteratively adjust the weights of different features to fit various properties of image instances. Experiments on several challenging datasets demonstrate that the proposed CAS outperforms the state-of-the-art methods and has a low computational cost.

Background Intrahepatic cholangiocarcinoma (ICC) is the second most common malignancy arising from the liver. Fibulin-1 has been demonstrated to be involved in various cancers, however, its role in ICC remains unclear. Methods To study the clinical value and potential molecular mechanism of Fibulin-1 in ICC, immunohistochemistry and bioinformatic analyses were performed using data in the Gene Expression Omnibus Datasets and The Cancer Genome Atlas database. Results Fibulin-1 expression was overexpressed in ICC tissues compared with adjacent non-cancerous tissues, and was significantly associated with unfavorable overall survival. Moreover, similar genes were identified by Gene Expression Profiling Interactive Analysis and microarray data set. Next, functional and pathway enrichment analysis demonstrated that Fibulin-1 was overrepresented in the pathways of extracellular matrix organization and angiogenesis, which are associated with tumor progression and potential for metastasis. Gene set enrichment analysis indicated that the gene sets of epithelial mesenchymal transition, TGF-beta signaling pathway and angiogenesis were enriched in tissues with high Fibulin-1 level. Furthermore, Fibulin-1 silencing suppressed the ability of ICC tumor cells to form colonies and siFibulin-1 repressed the endogenous protein level of p-AKT. Conclusion Collectively, this study suggests that Fibulin-1 overexpression may play key roles in the carcinogenesis and progression of ICC via regulation of tumor-related pathways.

Video server deployment problem (VSDP) is a crucial problem in enhancing video viewing experience. It is difficult for traditional deterministic optimization algorithm to solve VSDP because it is non-polynomial hard (NP-hard). Current server deployment patterns are challenged by the growing scale of networks, and it is therefore desirable to design appropriate optimization algorithm to cope with this issue. Owing to the promising performance in solving NP-hard problems, a genetic algorithm (GA)-based optimizer is developed in this paper. We propose a novel deterministic initialization strategy to deploy the GA population in promising initial positions for performance enhancement. In addition, by embedding the maximum cost minimum cost flow (MCMF) in the fitness evaluation, the algorithm optimizes the server deployment and the bandwidth allocation concurrently. This way, the proposed GA is comprehensive, which deals with different aspects involved in VSDP. Experimental results and the comparisons with other algorithms indicate that the proposed algorithm achieves superior performance in terms of solution quality and convergence rate.

Graphical abstractDisplay Omitted HighlightsProvide an updated and systematic review of distributed evolutionary algorithms.Classify the models into population and dimension-distributed groups semantically.Analyze the parallelism, search behaviors, communication costs, scalability, etc.Highlight recent research hotspots in this field.Discuss challenges and potential research directions in this field. The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

Yuan Yao,1 Shuting Feng,1 Mingming Xiao,2 Yan Li,1 Li Yang,1 Jiao Gong1 1Digestive System Department, 2Department of Pathology, ThePeople’s Hospital of Liaoning Province, Shenyang, Liaoning, People’sRepublic of China Abstract: Although metastasis-associated protein 1 (MTA1) has been widely li­nked to tumor metastasis, the relevant mechanisms remain to be elucidated, especially in gastric cancer. The aim of this study was to examine whether the MTA1 gene is associated with the process of proliferation and invasion by regulating several molecular targets in gastric cancer. MTA1 expression in 61 gastric cancer tissue and adjacent noncancerous tissues was analyzed by immunohistochemistry. The prognostic value of MTA1 for overall survival and disease-free survival was determined by Kaplan–Meier estimates, and the significance of differences between curves was evaluated by the log-rank test. Furthermore, overexpression of MTA1 in SGC7901 and BGC823 cells promoted cell cycle progression, cell adhesion, and cell invasion. Our study found that MTA1 is overexpressed in gastric cancers, which contributes to malignant cell growth by facilitating cell cycle progression through upregulation of cyclin D1 and accelerates the migration and invasion of human gastric cancer cells by regulating expression of fibronectin and MMP2/MMP9. Taken together, MTA1 was involved in the pathogenesis of gastric cancer and might be a candidate therapeutic target in gastric cancer. Keywords: cell cycle, cell adhesion, migration

Taxi-passenger matching plays a crucial role in modern taxi systems. However, currently, the greedy mechanisms are widely adopted, which may limit the quality of services provided by the systems. In this paper, we first formulate the taxi-passenger matching as a global optimization problem by considering the pickup rate and average waiting time of passengers. Then, we propose a parallel genetic algorithm to solve the problem. New operators, including initialization, crossover, and mutation, are designed specifically for the problem. In addition, we use a divide-and-conquer strategy for dimension reduction. The problem is divided into a number of sub-problems according to the geographical locations of passengers and taxis. Each sub-problem is then solved in a parallel way by a sub-component of our proposed algorithm. Experimental results validate the effectiveness and efficiency of the proposed algorithm. It is able to greatly enhance the quality of services provided by the taxi systems.

The cooperative co-evolution (CC) framework is one of the most efficient methods to solve large scale optimization problems. The traditional CC framework divides decision variables into several mutually-exclusive groups. In this paper, we propose the overlapped cooperative co-evolution (OCC) framework for large scale optimization problems. In OCC framework, the decision variables that have strong impacts on the optimization are overlapped by different groups. First, we devise the delta-disturbance strategy to detect the influential variables. Then the overlapped grouping strategy is proposed to overlap the influential variables. Finally, the OCC framework is proposed to allocate more computation resources to the influential decision variables. To compare the performance of CC and OCC, we combine two frameworks with the random grouping strategy and the differential grouping strategy, and the comparative experiments are conducted on the CEC2010 benchmark functions. The experimental results verify that the proposed OCC framework is promising through comparing with the CC framework.

Chronic severe hepatitis B (CSHB) is a critical clinical syndrome with a high mortality rate in China. The prognostic value of neutrophil to lymphocyte ratio (NLR) which is simple, low-cost, and useful inflammatory marker for hepatitis B virus (HBV)-infected patients is largely overlooked and without further exploration. This study assesses the association of NLR with prognosis of chronic hepatitis B (CHB) and CSHB patients. Two hundred and eighty subjects, including 79 with chronic hepatitis B (CHB) and 67 with chronic severe hepatitis B (CSHB), and 134 healthy individuals were retrospectively recruited into this study. Blood samples were collected to conduct liver function, prothrombin time activity (PTA), international normalized ratio (INR), HBV DNA measurement, and routine hematological testing. All patients were followed up for at least 3 months. NLR values in patients with CSHB (4.984 ± 3.608) and CHB (2.020 ± 1.182) were significantly higher than those in healthy control (1.638 ± 0.601) and patients with CSHB had the highest NLR values than CHB and healthy control. Increased NLR values were clinically associated with severe liver disease and higher mortality rate. NLR was found to be an independent predictor of mortality in multivariable Cox Regression models (HR = 3.912, 95%CI: 1.587-9.640, P = 0.003). NLR values are significantly increased in CHB and CSHB patients with the severity of liver disease. Moreover, NLR value is an independent predicting factor for the mortality rate in HBV-infected patients.

Virtual network embedding (VNE), which is a significant problem in cloud computing, has gained much attention among many researchers recently. Due to the NP-hardness of VNE, the existing solvers are either inefficient or inaccurate. This paper develops a novel algorithm based on the ant colony system (ACS). To solve the VNE problem, the algorithm structure concentrates on the link mapping from virtual network to substrate network. Particularly, for a specific virtual network request, we first sort the embedding sequence of virtual nodes according to their link resources. Then, ACS is used to embed the virtual nodes onto substrate nodes according the sorted sequence, while the virtual links are mapped via a shortest path strategy for the embedded nodes. For the first time, we propose a link resource heuristic information and incorporate it into the search process of ACS. The link resource heuristic information has two significant effects, one is to make virtual nodes tend to be embedded on the substrate nodes that cost less bandwidth, and the other is to confirm the connectivity of the substrate nodes that embed the virtual nodes. The proposed algorithm improves the optimization performance of VNE when compared with a few existing algorithms, while it substantially reduces the cost of time.

Taxi dispatch is a critical issue for taxi company to consider in modern life. This paper formulates the problem into a taxi-passenger matching model and proposes a parallel ant colony optimization algorithm to optimize the model. As the search space is large, we develop a region-dependent decomposition strategy to divide and conquer the problem. To keep the global performance, a critical region is defined to deal with the communications and interactions between the subregions. The experimental results verify that the proposed algorithm is effective, efficient, and extensible, which outperforms the traditional global perspective greedy algorithm in terms of both accuracy and efficiency.

Background/aims Growing evidence indicates that nonalcoholic fatty liver disease (NAFLD) and gallstone disease (GD) share the same risk factors, and that NAFLD may be associated with the occurrence of GD. However, overall results remain controversial. The aim of this study is to perform a meta-analysis to assess the relationship between GD and NAFLD. Materials and methods Five databases (PubMed, Medline, Embase, Web of Science, and Cochrane Library) were queried, and observational studies that assessed the association between GD and NAFLD were selected. We pooled the prevalence of GD in participants with NAFLD, and compared the prevalence of GD in NAFLD and non-NAFLD groups in four trials. Results Twelve studies met our inclusion criteria. The pooled prevalence of GD in cases with NAFLD was 17% (95% CI: 0.12-0.23). Compared with the non-NAFLD group, NAFLD was significantly correlated with GD (OR: 1.40, 95% CI: 1.23-1.59). Additional analyses reveal that participants in the GD group included more females (OR: 1.95, 95% CI: 1.36-2.79), were older (WMD: 6.61, 95% CI: 3.80-9.42), and had higher BMIs (WMD: 1.63, 95% CI: 0.62-2.65) in the population with NAFLD, compared to the non-GD group. Conclusion GD prevalence in NAFLD patients is higher than that in the general population. Furthermore, the occurrence of GD is significantly associated with the female sex, age and BMI in NAFLD patients.

The subgraph isomorphism problems, which aim to map subgraphs to a given graph, are widely seen in many applications and are usually nondeterministic polynomial-time complete (NP-complete). As a representative extension of the subgraph isomorphism problem, virtual network embedding (VNE) is a key problem in datacenter scheduling and network virtualization. Existing metaheuristic approaches to VNE problems tend to schedule networks as a whole. But when the problem scale grows, the performance of these approaches may degenerate due to the curse of dimensionality. In this article, we intend to propose a divide-and-conquer evolutionary algorithm with overlapping decomposition (ODEA) to solve large-scale VNE problems. First, realizing the fact that the decision variables in graph-based optimization problems like VNE are usually nonseparable, an overlapping decomposition method is introduced by investigating the characteristic of the network structure. In this method, the critical elements which have tight connections to many other nodes can belong to multiple subcomponents. As a result, the decision variables with tight connections can always be evolved together in multiple subcomponents. Second, to combine the subsolutions into a complete feasible solution, a competitive strategy is devised. Through the competition among critical elements, the optimizing information is shared among subcomponents, which can further improve the effectiveness of ODEA. The proposed ODEA can adopt different metaheuristics as the optimizer, and we conduct experiments on both the scenarios with a single virtual network and with a series of online networks. The experimental results verify that ODEA can significantly improve the performance of different metaheuristics in large-scale VNE problems.

Multi-solution problems extensively exist in practice. Particularly, the traveling salesman problem (TSP) may possess multiple shortest tours, from which travelers can choose one according to their specific requirements. However, very few efforts have been devoted to the multi-solution problems in the discrete domain. In order to fill this research gap and to effectively tackle the multi-solution TSP, we propose a niching memetic algorithm in this article. The proposed algorithm is characterized by a niche preservation technique to enable the parallel search of multiple optimal solutions; an adaptive neighborhood strategy to balance the exploration and exploitation; a critical edge-aware method to provide effective guidance to the reproduction; and a selective local search strategy to improve the search efficiency. To evaluate the performance of the proposed algorithm, we conduct comprehensive experiments on a recently published multi-solution optimization test suite. The experimental results show that our algorithm outperforms other compared algorithms. Furthermore, the proposed algorithm is adopted to tackle problems from the well-known TSPLIB library to obtain a set of distinct but good solutions.

The potentially bioactive 2-aryl-thiazolo[4,5- b ]pyridines were synthesized via palladium-catalyzed desulfitative cross-coupling reaction between multisubstituted thiazolo[4,5- b ]pyridine thioethers and boronic acids. Yields of 48%–94% were obtained with copper(I) thiophene-2-carboxylate in the system.

Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Operating mode scheduling is crucial for the lifetime of wireless sensor networks. However, the growing scale of networks has made such a scheduling problem more and more challenging, as existing set cover and evolutionary algorithms become unable to provide satisfactory efficiency due to the curse of dimensionality. In this paper, a Kuhn-Munkres parallel genetic algorithm is developed to solve the set cover problem and is applied to lifetime maximization of large-scale wireless sensor networks. The proposed algorithm schedules the sensors into a number of disjoint complete cover sets and activates them in batch for energy conservation. It uses a divide-and-conquer strategy of dimensionality reduction, and the polynomial Kuhn-Munkres algorithm are hence adopted to splice the feasible solutions obtained in each subarea to enhance the search efficiency substantially. To further improve global efficiency, a redundant-trend sensor schedule strategy is developed. Additionally, we meliorate the evaluation function through penalizing incomplete cover sets, which speeds up convergence. Eight types of experiments are conducted on a distributed platform to test and inform the effectiveness of the proposed algorithm. The results show that it offers promising performance in terms of the convergence rate, solution quality, and success rate.

This paper deals with the superpixel segmentation problem using a powerful global optimization technique: Differential Evolution. The algorithm mimics the process of nature evolution to realize efficient optimization, and it poses no restrictions on the form of objective functions. This way, we develop a novel and comprehensive objective function considering both local and global costs in the segmentation, including within-superpixel error, boundary gradient, a regularization term. The proposed method can produce superpixels in a computational time linear to the image size. Experimental results validate the competitive performance of our algorithm in terms of boundary adherence and segmentation capability.

Many-objective optimization has posed great challenges to existing evolutionary algorithms that are designed for solving two- or three-objective problems. Most of the algorithms do not scale well with the number of objectives due to the expansion of the objective space. In this paper, a hybrid evolutionary algorithm with dual populations (HEA-DP) is proposed to tackle many-objective problems. The algorithm combines the advantages of decomposition-based and indicator-based approaches by maintaining two populations. The fitness values of individuals in the first population are determined by an aggregation function, while individuals in the second population are evaluated according to an efficient performance indicator. The information about the objective space is shared by employing a reproduction strategy that chooses parents from both populations. In this way, the algorithm can explore the objective space more thoroughly and can have more stable performance. Several state-of-the-art many-objective algorithms are adopted as peer algorithms to validate the proposed algorithm. We test the algorithms on two commonly used many-objective problem suites using different numbers of objectives. Numerical results indicate that HEA-DP is highly competitive in most of the problem instances.

Rat calvarial osteoblasts were treated with lanthanum chloride (LaCl

The central role of multisubunit tethering complexes in intracellular trafficking has been established in yeast and mammalian systems. However, little is known about their roles in the stress responses and the early secretory pathway in Arabidopsis. In this study, Maigo2 (MAG2), which is equivalent to the yeast Tip20p and mammalian Rad50-interacting protein, is found to be required for the responses to salt stress, osmotic stress and abscisic acid in seed germination and vegetative growth, and MAG2-like (MAG2L) is partially redundant with MAG2 in response to environmental stresses. MAG2 strongly interacts with the central region of ZW10, and both proteins are important as plant endoplasmic reticulum (ER)-stress regulators. ER morphology and vacuolar protein trafficking are unaffected in the mag2, mag2l and zw10 mutants, and the secretory marker to the apoplast is correctly transported in mag2 plants, which indicate that MAG2 functions as a complex with ZW10, and is potentially involved in Golgi-to-ER retrograde trafficking. Therefore, a new role for ER-Golgi membrane trafficking in abiotic-stress and ER-stress responses is discovered.

The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.

Vehicle routing problem with time windows (VRPTW) is a well-known NP-hard combinatorial optimization problem that is crucial for transportation and logistics systems. Even though the particle swarm optimization (PSO) algorithm is originally designed to solve continuous optimization problems, in this paper, we propose a set-based PSO to solve the discrete combinatorial optimization problem VRPTW (S-PSO-VRPTW). The general method of the S-PSO-VRPTW is to select an optimal subset out of the universal set by the use of the PSO framework. As the VRPTW can be defined as selecting an optimal subgraph out of the complete graph, the problem can be naturally solved by the proposed algorithm. The proposed S-PSO-VRPTW treats the discrete search space as an arc set of the complete graph that is defined by the nodes in the VRPTW and regards the candidate solution as a subset of arcs. Accordingly, the operators in the algorithm are defined on the set instead of the arithmetic operators in the original PSO algorithm. Besides, the process of position updating in the algorithm is constructive, during which the constraints of the VRPTW are considered and a time-oriented, nearest neighbor heuristic is used. A normalization method is introduced to handle the primary and secondary objectives of the VRPTW. The proposed S-PSO-VRPTW is tested on Solomon's benchmarks. Simulation results and comparisons illustrate the effectiveness and efficiency of the algorithm.

Locating multiple optima of a problem is an important and challenging task for many real-world applications. In this paper, a random walk mutation strategy is proposed for differential evolution (DE) to handle multimodal optimization problems. The mutation strategy is able to find a balance between exploitation and exploration. First, the neighborhood and fitness information of individuals is incorporated into mutation to guide the formation of donor vectors. This facilitates the evolution of individuals toward their nearby optima. Second, the exploration ability of the mutation strategy is preserved by simulating a random walk process. Moreover, an archive technique is designed to detect converged subpopulations. The converged individuals are then reinitialized to search for other optima. This enhance the algorithm's exploration ability. Meanwhile, found optima can be maintained throughout the optimization process by using the archive technique. The random walk mutation strategy and the archive technique are integrated with DE to make a competitive multimodal algorithm. The resulting algorithm is tested on a recently proposed benchmark function set. Experimental results show that the proposed algorithm is able to provide better performance than a number of state-of-the-art multimodal algorithms.

Multimodal optimization amounts to finding multiple optima of a problem. In recent years, particle swarm optimization (PSO) algorithms have been widely used by the evolutionary computation community to tackle multimodal problems. However, the capability of using a suitable PSO communication topology to induce stable niching behavior has not been well explored. In this paper, we propose a minimum spanning tree (MST) topology for PSO to solve multimodal problems. In each iteration, a minimum spanning tree is built based on the configuration of particles. The neighbors of each particle are determined according to the MST. The MST topology is able to capture the distribution of particles using a small number of edges. Moreover, a number of max weighted edges in the MST are cut to avoid the genetic drift phenomenon and to enhance the niching performance. The proposed topology is integrated with a canonical PSO and a locally informed particle optimizer (LIPS) to tackle multimodal problems. Experiments have been conducted on the CEC2013 benchmark functions to test the performance the integrated algorithms. Experimental results show that PSOs with MST topology are very effective in solving multimodal problems.

Background & Aims microRNAs (miRNAs) are a class of small noncoding RNAs that can regulate gene expression by translation repression or mRNA degradation. Our aim was to evaluate the role of aberrantly expressed miRNAs in hepatocellular cancer (HCC). Methods miRNA expression in HCC tissues and cells was evaluated by qPCR array and Taqman miRNA assay. Cell proliferation, motility, invasion, and the angiogenesis index were quantitated using commercial assays. DNA methylation status, matrix metalloproteinases (MMPs) mRNA expression was quantitated by real-time PCR analysis. Results miRNA profiling identified a decrease in miR-125b expression in HCC tumor tissues and cell lines. The expression of miR-125b was significantly increased by the methylation inhibitor 5-aza-2′-deoxycytidine in HCC cells but not in normal controls, suggesting that the expression of miR-125b could be epigenetically modulated. Methylation-specific PCR revealed hypermethylation status of miR-125b in HCC cells compared to non-malignant controls. Cell proliferation, anchorage-independent growth, cell migration, invasion, and angiogenesis were significantly decreased by the introduction of miR-125b precursor in HCC cell lines. Placenta growth factor was identified as a target of miR-125b by bioinformatics analysis and experimentally verified using luciferase reporter constructs. Overexpression of miR-125b in HCC cells decreased PIGF expression, and altered the angiogenesis index. Furthermore, modulation of miR-125b also distorted expression of MMP-2 and -9, the mediators of enzymatic degradation of the extracellular matrix. Conclusions Our studies showing epigenetic silencing of miR-125b contributes to an invasive phenotype provide novel mechanistic insights and identify a potential target mechanism that could be manipulated for therapeutic benefit in HCC.

Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.

Previous studies have shown that the late embryogenesis abundant (LEA) group 3 proteins significantly respond to changes in environmental conditions. However, reports that demonstrate their biological role, especially in Arabidopsis, are notably limited. This study examines the functional roles of the Arabidopsis LEA group 3 proteins AtLEA3-3 and AtLEA3-4 in abiotic stress and ABA treatments. Expression of AtLEA3-3 and AtLEA3-4 is upregulated by ABA, high salinity, and osmotic stress. Results on the ectopic expression of AtLEA3-3 and AtLEA3-4 in E. coli suggest that both proteins play important roles in resistance to cold stress. Overexpression of AtLEA3-3 in Arabidopsis (AtLEA3-3-OE) confers salt and osmotic stress tolerance that is characterized during germination and early seedling establishment. However, AtLEA3-3-OE lines show sensitivity to ABA treatment during early seedling development. These results suggest that accumulation of AtLEA3-3 mRNA and/or proteins may help heterologous ABA reinitiate second dormancy during seedling establishment. Analysis of yellow fluorescent fusion proteins localization shows that AtLEA3-3 and AtLEA3-4 are mainly distributed in the ER and that AtLEA3-3 also localizes in the nucleus, and in response to salt, mannitol, cold, or BFA treatments, the localization of AtLEA3-3 and AtLEA3-4 is altered and becomes more condensed. Protein translocalization may be a positive and effective strategy for responding to abiotic stresses. Taken together, these results suggest that AtLEA3-3 has an important function during seed germination and seedling development of Arabidopsis under abiotic stress conditions.

The use of piezoelectric material as transducer is prevalent. Piezoelectric bending mode element can be used for vibration energy harvesting because it can convert mechanical energy into electrical energy. In this article, electrical impedance characterization and equivalent circuit of triple-layer piezoelectric bender are discussed. Triple-layer piezoelectric bending device is fabricated, measured and modeled. This paper is aimed to explore simple but practical equivalent circuit models, expressed using electrical parameters of triple-layer piezoelectric bender and investigate the applicability of the Van Dyke circuit model and the complex circuit model in modeling the specimen’s equivalent circuit. The models produced impedance curves that closely matched the impedance measured for piezoelectric sample. The impedance characterization can provide a good understanding on the electrical behaviors of the triple layer piezoelectric bender when analyzing the performance of piezoelectric device.

Multilayer actuators with twenty-six 0.7mm-layers (18mm×18mm×10mm) of lead zirconate titanate (PZT-4) were made and tested. These actuators were fabricated in the laboratory with simple techniques and cost low. The new fabrication method not only greatly reduced the cost of piezoelectric stack, but also made it possible to obtain any required size of piezoelectric stacks in the laboratory. A micro-displacement amplifier to match the fabricated multilayer actuators is also designed, fabricated and test. Experiment results show that the micro-displacement amplifier can obtain a peak to peak displacement of 160 um under the quasi-static condition.

Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed.

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.

In this work we focus on reconstruction of cross-cut shredded text documents (RCCSTD), which is of high interest in the fields of forensics and archeology. A novel genetic algorithm, with splicing-driven crossover, four mutation operators, and a row-oriented elitism strategy, is proposed to improve the capability of solving RCCSTD in complex space. We also design a novel and comprehensive objective function based on both edge and empty vector-based splicing error to guarantee that the correct reconstruction always has the lowest cost value. Experiments are conducted on six RCCSTD scenarios, with experimental results showing that the proposed algorithm significantly outperforms the previous best-known algorithms for this problem.

The aim of multimodal optimization is to locate multiple optima of a given problem. Evolutionary algorithms (EAs) are one of the most promising candidates for multimodal optimization. However, due to the use of greedy selection operators, the population of an EA will generally converge to one region of attraction. By incorporating a well-designed selection operator that can facilitate the formation of different species, EAs will be able to allow multiple convergence. Following this research avenue, we propose a novel selection operator, namely, queueing selection (QS) and integrate it with one of the most promising DE variants, called composite differential evolution (CoDE). The integrated algorithm (denoted by CoDE-QS) inherits the strong global search ability of CoDE and is capable of finding and maintaining multiple optima. It has been tested on the CEC2013 benchmark functions. Experimental results show that CoDE-QS is very competitive.

The variety of the contents of N, P, K and photo-biological characters in a bud and two leaves tea tree new shoots were determined to investigate the effects of different fertilization ways, i.e., single or combined N, P, K fertilizer. The regression equations of net photosynthetic rate (Pn) of tea tree in spring, summer and autumn were set up by stepwise regression analysis. The results showed that fertilization ways significantly affected the contents of N, P, K in new shoot. In the same fertilization ways, the contents of N, P in the new shoot was less in summer and autumn than in spring, but the content of K was in contrast. Combined application of N, P and K fertilizer improved Pn of the tea trees in spring. Single application of P fertilizer inhibited the Pn of the tea trees in summer and autumn, while single application of N fertilizer significantly improved it. In the same fertilization ways, the Pn of the tea trees in summer and autumn was higher than in spring. The stomatal conductance (g(s)) and transpiration rate (T(r)) had positive correlations to P, in summer and autumn. Regression and correlation analysis showed that the physiological factors affecting Pn of the tea trees varied with seasons, and the common factors were the Ci and the content of P. Ci had some subduction to Pn, and it was improved by the content of phosphorus.

Background Recently, the compatible solute 1, 4, 5, 6-tetrahydro-2-methyl-4-pyrimidinecarboxylic acid (ectoine) has attracted considerable interest due to its great potential as a protecting agent. To overcome the drawbacks of high salinity in the traditional bioprocess of ectoine using halophilic bacteria, various attempts have been made to engineer ectoine biosynthesis in nonhalophilic bacteria. Unfortunately, the yields of ectoine in these producers are still low and hardly meet the demands of large scale production. In this paper, the whole-cell biocatalytic process using aspartate and glycerol as substrates was tried for high production of ectoine in nonhalophilic bacteria. Results The ectoine genes ectABC from the halophilic bacterium Halomonas elongata were successfully introduced into Escherichia coli K-12 strain BW25113 under the arabinose-inducible promoter. To our delight, a large amount of ectoine was synthesized and excreted into the medium during the course of whole-cell biocatalysis, when using aspartate and glycerol as the direct substrates. At the low cell density of 5 OD/mL in flask, under the optimal conditions (100 mM sodium phosphate buffer (pH 7.0), 100 mM sodium aspartate, 100 mM KCl and 100 mM glycerol), the concentration of extracellular ectoine was increased to 2.67 mg/mL. At the high cell density of 20 OD/mL in fermentor, a maximum titre of 25.1 g/L ectoine was achieved in 24 h. Meanwhile, the biomass productivity of ectoine is as high as 4048 mg per gram dry cell weight (g DCW)−1, which is the highest value ever reported. Furthermore, it was demonstrated that the same batch of cells could be used for at least three rounds. Finally, a total yield of 63.4 g ectoine was obtained using one litre cells. Conclusion Using aspartate and glycerol as the direct substrates, high production of ectoine was achieved by the whole-cell biocatalysis in recombinant E. coli. Multiple rounds of whole-cell biocatalysis were established to further improve the production of ectoine. Our study herein provided a feasible biosynthesis process of ectoine with potential applications in large-scale industrial production.

In traditional differential evolution (DE) algorithms, the perturbation direction of mutation is not sophisticatedly designed, which performs ineffectively or inefficiently for optimizing some complex and large-scale problems. This paper designs an orthogonal predictive mutation scheme to solve this problem. The mutation investigates the landscape near the individuals by using orthogonal experimental design, and then applies factor analysis to predict a promising direction for the individuals to evolve. With a clear sense of search direction, the efficiency of DE is improved. Moreover, the step length of the proposed mutation is adaptively adjusted according to the effect of the prediction, which helps to balance the exploration and exploitation abilities of DE. By employing such a mutation scheme, a novel DE algorithm termed orthogonal predictive DE (OPDE) is proposed in this paper. As OPDE can adopt different kinds of classical mutation schemes for choosing the base vector and calculating the differential vector, we further develop an OPDE family including various OPDE variants. Experimental results demonstrate the effectiveness and high efficiency of the proposed algorithm.

This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a ``best improvement local search'' (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the nondeterministic polynomial (NP) complete multicast routing problem (MRP). The main contribution is the extension of particle swarm optimization (PSO) from the continuous domain to the binary or discrete domain. First, a novel bi-velocity strategy is developed to represent the possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP, where 1 stands for a node being selected to construct the multicast tree, whereas 0 stands for being otherwise. Second, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in the continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the Operation Research Library (OR-library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly since it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on genetic algorithms, ant colony optimization, and PSO.

The performance of a multimodal evolutionary algorithm is highly sensitive to the setting of population size. This paper introduces a generic archive technique to reduce the importance of properly setting the population size parameter. The proposed archive technique contains two components: subpopulation identification and convergence detection. The first component is used to identify subpopulations in a number of individuals while the second one is used to determine whether a subpopulation is converged. By using the two components, converged subpopulations are identified, and then, individuals in the converged subpopulations are stored in an external archive and re-initialized to search for other optima. We integrate the archive technique with several state-of-the-art PSO-based multimodal algorithms. Experiments are carried out on a recently proposed multimodal problem set to investigate the effect of the archive technique. The experimental results show that the proposed method can reduce the influence of the population size parameter and improve the performance of multimodal algorithms.