Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique

NASA Astrophysics Data System (ADS)

Panda, Satyasen

2018-05-01

This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

PubMed

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

Improved artificial bee colony algorithm for wavefront sensor-less system in free space optical communication

NASA Astrophysics Data System (ADS)

Niu, Chaojun; Han, Xiang'e.

2015-10-01

Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.

A new improved artificial bee colony algorithm for ship hull form optimization

NASA Astrophysics Data System (ADS)

Huang, Fuxin; Wang, Lijue; Yang, Chi

2016-04-01

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

A novel artificial bee colony algorithm based on modified search equation and orthogonal learning.

PubMed

Gao, Wei-feng; Liu, San-yang; Huang, Ling-ling

2013-06-01

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system.

PubMed

Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M

2014-05-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

PubMed Central

Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.

2013-01-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt. PMID:25685507

A hybrid artificial bee colony algorithm and pattern search method for inversion of particle size distribution from spectral extinction data

NASA Astrophysics Data System (ADS)

Wang, Li; Li, Feng; Xing, Jian

2017-10-01

In this paper, a hybrid artificial bee colony (ABC) algorithm and pattern search (PS) method is proposed and applied for recovery of particle size distribution (PSD) from spectral extinction data. To be more useful and practical, size distribution function is modelled as the general Johnson's ? function that can overcome the difficulty of not knowing the exact type beforehand encountered in many real circumstances. The proposed hybrid algorithm is evaluated through simulated examples involving unimodal, bimodal and trimodal PSDs with different widths and mean particle diameters. For comparison, all examples are additionally validated by the single ABC algorithm. In addition, the performance of the proposed algorithm is further tested by actual extinction measurements with real standard polystyrene samples immersed in water. Simulation and experimental results illustrate that the hybrid algorithm can be used as an effective technique to retrieve the PSDs with high reliability and accuracy. Compared with the single ABC algorithm, our proposed algorithm can produce more accurate and robust inversion results while taking almost comparative CPU time over ABC algorithm alone. The superiority of ABC and PS hybridization strategy in terms of reaching a better balance of estimation accuracy and computation effort increases its potentials as an excellent inversion technique for reliable and efficient actual measurement of PSD.

A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking

NASA Astrophysics Data System (ADS)

Han, Yu-Yan; Gong, Dunwei; Sun, Xiaoyan

2015-07-01

A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.

Cooperative path planning for multi-USV based on improved artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Cao, Lu; Chen, Qiwei

2018-03-01

Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for multiple unmanned surface vehicle (multi-USV), an improved artificial bee colony (I-ABC) algorithm were proposed to solve the model of cooperative path planning for multi-USV. First the Voronoi diagram of battle field space is conceived to generate the optimal area of USVs paths. Then the chaotic searching algorithm is used to initialize the collection of paths, which is regard as foods of the ABC algorithm. With the limited data, the initial collection can search the optimal area of paths perfectly. Finally simulations of the multi-USV path planning under various threats have been carried out. Simulation results verify that the I-ABC algorithm can improve the diversity of nectar source and the convergence rate of algorithm. It can increase the adaptability of dynamic battlefield and unexpected threats for USV.

A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching

PubMed Central

Gong, Li-Gang

2014-01-01

Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC) algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do. PMID:24892107

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm.

PubMed

Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

2016-12-14

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

PubMed Central

Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

2016-01-01

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits. PMID:27983633

Lévy flight artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Sharma, Harish; Bansal, Jagdish Chand; Arya, K. V.; Yang, Xin-She

2016-08-01

Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments.

Object Detection Based on Template Matching through Use of Best-So-Far ABC

PubMed Central

2014-01-01

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution. PMID:24812556

Implementation and comparative analysis of the optimisations produced by evolutionary algorithms for the parameter extraction of PSP MOSFET model

NASA Astrophysics Data System (ADS)

Hadia, Sarman K.; Thakker, R. A.; Bhatt, Kirit R.

2016-05-01

The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (ABC), variant ABC and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that ABC algorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic ABC algorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variant ABC and basic ABC algorithm for the parameter extraction of the MOSFET model; also the implementation of the ABC algorithm is shown to be simpler than that of the PSO algorithm.

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.

PubMed

Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem

PubMed Central

Yu, Yi; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. PMID:29324743

Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

PubMed Central

2014-01-01

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences.

PubMed

Karaboga, D; Aslan, S

2016-04-27

The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.

Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm.

PubMed

Beloufa, Fayssal; Chikh, M A

2013-10-01

In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

PubMed Central

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. PMID:25961028

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

PubMed

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

ALGORITHM BASED ON ARTIFICIAL BEE COLONY FOR UNFOLDING OF NEUTRON SPECTRA OBTAINED WITH BONNER SPHERES.

PubMed

Silva, Everton R; Freitas, Bruno M; Santos, Denison S; Maurício, Cláudia L P

2018-04-13

Occupational neutron fields usually have energies from the thermal range to some MeV and the characterization of the spectra is essential for estimation of the radioprotection quantities. Thus, the spectrum must be unfolded based on a limited number of measurements. This study implemented an algorithm based on the bee colonies behavior, named Artificial Bee Colony (ABC), where the intelligent behavior of the bees in search of food is reproduced to perform the unfolding of neutron spectra. The experimental measurements used Bonner spheres and 6LiI (Eu) detector, with irradiations using a thermal neutron flux and three reference fields: 241Am-Be, 252Cf and 252Cf (D2O). The ABC obtained good estimation of the expected spectrum even without previous information and its results were closer to expected spectra than those obtained by the SPUNIT algorithm.

Artificial Bee Colony Optimization of Capping Potentials for Hybrid Quantum Mechanical/Molecular Mechanical Calculations.

PubMed

Schiffmann, Christoph; Sebastiani, Daniel

2011-05-10

We present an algorithmic extension of a numerical optimization scheme for analytic capping potentials for use in mixed quantum-classical (quantum mechanical/molecular mechanical, QM/MM) ab initio calculations. Our goal is to minimize bond-cleavage-induced perturbations in the electronic structure, measured by means of a suitable penalty functional. The optimization algorithm-a variant of the artificial bee colony (ABC) algorithm, which relies on swarm intelligence-couples deterministic (downhill gradient) and stochastic elements to avoid local minimum trapping. The ABC algorithm outperforms the conventional downhill gradient approach, if the penalty hypersurface exhibits wiggles that prevent a straight minimization pathway. We characterize the optimized capping potentials by computing NMR chemical shifts. This approach will increase the accuracy of QM/MM calculations of complex biomolecules.

A novel approach for dimension reduction of microarray.

PubMed

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

A Modified Artificial Bee Colony Algorithm for p-Center Problems

PubMed Central

Yurtkuran, Alkın

2014-01-01

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient. PMID:24616648

Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

PubMed

Zhu, Changxing; Zhao, Hongbo; Zhao, Ming

2014-01-01

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

Gene selection for cancer classification with the help of bees.

PubMed

Moosa, Johra Muhammad; Shakur, Rameen; Kaykobad, Mohammad; Rahman, Mohammad Sohel

2016-08-10

Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses. This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings. The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior. The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.

A Novel Extraction Approach of Extrinsic and Intrinsic Parameters of InGaAs/GaN pHEMTs

DTIC Science & Technology

2015-07-01

presented, for the first time, artificial bee colony algorithm is applied to the global-optimization based parameter extraction and a novel intrinsic...conservation of the gate charge is well satisfied which further validates this novel extraction method. Index Terms —InGaAs/GaN pHEMTs, artificial bee ...increase the uniqueness of the extraction. Artificial bee colony (ABC) algorithm is adopted as the optimizer due to its excellent ability to escape

Identification of cultivated land using remote sensing images based on object-oriented artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Li, Nan; Zhu, Xiufang

2017-04-01

Cultivated land resources is the key to ensure food security. Timely and accurate access to cultivated land information is conducive to a scientific planning of food production and management policies. The GaoFen 1 (GF-1) images have high spatial resolution and abundant texture information and thus can be used to identify fragmentized cultivated land. In this paper, an object-oriented artificial bee colony algorithm was proposed for extracting cultivated land from GF-1 images. Firstly, the GF-1 image was segmented by eCognition software and some samples from the segments were manually identified into 2 types (cultivated land and non-cultivated land). Secondly, the artificial bee colony (ABC) algorithm was used to search for classification rules based on the spectral and texture information extracted from the image objects. Finally, the extracted classification rules were used to identify the cultivated land area on the image. The experiment was carried out in Hongze area, Jiangsu Province using wide field-of-view sensor on the GF-1 satellite image. The total precision of classification result was 94.95%, and the precision of cultivated land was 92.85%. The results show that the object-oriented ABC algorithm can overcome the defect of insufficient spectral information in GF-1 images and obtain high precision in cultivated identification.

A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

PubMed Central

Xu, Gaochao; Hu, Liang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful. PMID:24385877

A novel artificial bee colony approach of live virtual machine migration policy using Bayes theorem.

PubMed

Xu, Gaochao; Ding, Yan; Zhao, Jia; Hu, Liang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

A new stochastic algorithm for inversion of dust aerosol size distribution

NASA Astrophysics Data System (ADS)

Wang, Li; Li, Feng; Yang, Ma-ying

2015-08-01

Dust aerosol size distribution is an important source of information about atmospheric aerosols, and it can be determined from multiwavelength extinction measurements. This paper describes a stochastic inverse technique based on artificial bee colony (ABC) algorithm to invert the dust aerosol size distribution by light extinction method. The direct problems for the size distribution of water drop and dust particle, which are the main elements of atmospheric aerosols, are solved by the Mie theory and the Lambert-Beer Law in multispectral region. And then, the parameters of three widely used functions, i.e. the log normal distribution (L-N), the Junge distribution (J-J), and the normal distribution (N-N), which can provide the most useful representation of aerosol size distributions, are inversed by the ABC algorithm in the dependent model. Numerical results show that the ABC algorithm can be successfully applied to recover the aerosol size distribution with high feasibility and reliability even in the presence of random noise.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

PubMed

Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

2015-10-01

Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

PubMed Central

Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution. PMID:29186166

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment.

PubMed

Li, Jianjun; Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

Development and applications of various optimization algorithms for diesel engine combustion and emissions optimization

NASA Astrophysics Data System (ADS)

Ogren, Ryan M.

For this work, Hybrid PSO-GA and Artificial Bee Colony Optimization (ABC) algorithms are applied to the optimization of experimental diesel engine performance, to meet Environmental Protection Agency, off-road, diesel engine standards. This work is the first to apply ABC optimization to experimental engine testing. All trials were conducted at partial load on a four-cylinder, turbocharged, John Deere engine using neat-Biodiesel for PSO-GA and regular pump diesel for ABC. Key variables were altered throughout the experiments, including, fuel pressure, intake gas temperature, exhaust gas recirculation flow, fuel injection quantity for two injections, pilot injection timing and main injection timing. Both forms of optimization proved effective for optimizing engine operation. The PSO-GA hybrid was able to find a superior solution to that of ABC within fewer engine runs. Both solutions call for high exhaust gas recirculation to reduce oxide of nitrogen (NOx) emissions while also moving pilot and main fuel injections to near top dead center for improved tradeoffs between NOx and particulate matter.

Image steganalysis using Artificial Bee Colony algorithm

NASA Astrophysics Data System (ADS)

Sajedi, Hedieh

2017-09-01

Steganography is the science of secure communication where the presence of the communication cannot be detected while steganalysis is the art of discovering the existence of the secret communication. Processing a huge amount of information takes extensive execution time and computational sources most of the time. As a result, it is needed to employ a phase of preprocessing, which can moderate the execution time and computational sources. In this paper, we propose a new feature-based blind steganalysis method for detecting stego images from the cover (clean) images with JPEG format. In this regard, we present a feature selection technique based on an improved Artificial Bee Colony (ABC). ABC algorithm is inspired by honeybees' social behaviour in their search for perfect food sources. In the proposed method, classifier performance and the dimension of the selected feature vector depend on using wrapper-based methods. The experiments are performed using two large data-sets of JPEG images. Experimental results demonstrate the effectiveness of the proposed steganalysis technique compared to the other existing techniques.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

NASA Astrophysics Data System (ADS)

Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo

2017-05-01

Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

Weighted Global Artificial Bee Colony Algorithm Makes Gas Sensor Deployment Efficient

PubMed Central

Jiang, Ye; He, Ziqing; Li, Yanhai; Xu, Zhengyi; Wei, Jianming

2016-01-01

This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the effect of global factors on the convergence speed in the search phase, but also provides the expression of global factor weights. Experiment on benchmark functions proved that the algorithm can improve the convergence speed greatly. We arrive at the gas diffusion concentration based on the theory of CFD and then simulate the gas diffusion model with the influence of buildings based on the algorithm. Simulation verified the effectiveness of the WGABC algorithm in improving the convergence speed in optimal deployment scheme of gas sensors. Finally, it is verified that the optimal deployment method based on WGABC algorithm can improve the monitoring efficiency of sensors greatly as compared with the conventional deployment methods. PMID:27322262

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

PubMed

Ju, Chunhua; Xu, Chonghuan

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

PubMed Central

Ju, Chunhua

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525

A novel cloning template designing method by using an artificial bee colony algorithm for edge detection of CNN based imaging sensors.

PubMed

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods.

A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors

PubMed Central

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods. PMID:22163903

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

PubMed

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-10-22

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

PubMed Central

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-01-01

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. PMID:23202042

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms.

PubMed

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Fu, Xiaolin

2018-05-08

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

Artificial bee colony in neuro - Symbolic integration

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Swarm intelligence is a research area that models the population of the swarm based on natural computation. Artificial bee colony (ABC) algorithm is a swarm based metaheuristic algorithm introduced by Karaboga to optimize numerical problem. Pattern-SAT is a pattern reconstruction paradigm that utilized 2SAT logical rule in representing the behavior of the desired pattern. The information of the desired pattern in terms of 2SAT logic is embedded to Hopfield neural network (HNN-P2SAT) and the desired pattern is reconstructed during the retrieval phase. Since the performance of HNN-P2SAT in Pattern-SAT deteriorates when the number of 2SAT clause increased, newly improved ABC is used to reduce the computation burden during the learning phase of HNN-P2SAT (HNN-P2SATABC). The aim of this study is to investigate the performance of Pattern-SAT produced by ABC incorporated with HNN-P2SAT and compare it with conventional standalone HNN. The comparison is examined by using Microsoft Visual Basic C++ 2013 software. The detailed comparison in doing Pattern-SAT is discussed based on global Pattern-SAT, ratio of activated clauses and computation time. The result obtained from computer simulation indicates the beneficial features of HNN-P2SATABC in doing Pattern-SAT. This finding is expected to result in a significant implication on the choice of searching method used to do Pattern-SAT.

An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

PubMed Central

Chen, Wei

2014-01-01

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm. PMID:25089292

An artificial bee colony algorithm for uncertain portfolio selection.

PubMed

Chen, Wei

2014-01-01

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Artificial bee colony algorithm with dynamic multi-population

NASA Astrophysics Data System (ADS)

Zhang, Ming; Ji, Zhicheng; Wang, Yan

2017-07-01

To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network

PubMed Central

Adak, M. Fatih; Yumusak, Nejat

2016-01-01

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data. PMID:26927124

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network.

PubMed

Adak, M Fatih; Yumusak, Nejat

2016-02-27

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

PubMed Central

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Hierarchical artificial bee colony algorithm for RFID network planning optimization.

PubMed

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

NASA Astrophysics Data System (ADS)

Ketabchi, Hamed; Ataie-Ashtiani, Behzad

2015-01-01

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision variables and more complexity. In terms of computational time, PSO and SIMPSA are the fastest. SCE needs the highest computational time, even up to four times in comparison to the fastest EAs. CACO and PSO can be recommended for application in CGMPs, in terms of both abovementioned criteria.

Modeling design iteration in product design and development and its solution by a novel artificial bee colony algorithm.

PubMed

Chen, Tinggui; Xiao, Renbin

2014-01-01

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

PubMed Central

Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di

2015-01-01

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy. PMID:26366164

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

PubMed Central

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Application of hybrid artificial fish swarm algorithm based on similar fragments in VRP

NASA Astrophysics Data System (ADS)

Che, Jinnuo; Zhou, Kang; Zhang, Xueyu; Tong, Xin; Hou, Lingyun; Jia, Shiyu; Zhen, Yiting

2018-03-01

Focused on the issue that the decrease of convergence speed and the precision of calculation at the end of the process in Artificial Fish Swarm Algorithm(AFSA) and instability of results, a hybrid AFSA based on similar fragments is proposed. Traditional AFSA enjoys a lot of obvious advantages in solving complex optimization problems like Vehicle Routing Problem(VRP). AFSA have a few limitations such as low convergence speed, low precision and instability of results. In this paper, two improvements are introduced. On the one hand, change the definition of the distance for artificial fish, as well as increase vision field of artificial fish, and the problem of speed and precision can be improved when solving VRP. On the other hand, mix artificial bee colony algorithm(ABC) into AFSA - initialize the population of artificial fish by the ABC, and it solves the problem of instability of results in some extend. The experiment results demonstrate that the optimal solution of the hybrid AFSA is easier to approach the optimal solution of the standard database than the other two algorithms. In conclusion, the hybrid algorithm can effectively solve the problem that instability of results and decrease of convergence speed and the precision of calculation at the end of the process.

Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation

PubMed Central

Vo, Brenda N.; Drovandi, Christopher C.; Pettitt, Anthony N.; Pettet, Graeme J.

2015-01-01

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ. PMID:26642072

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

On the Effectiveness of Nature-Inspired Metaheuristic Algorithms for Performing Phase Equilibrium Thermodynamic Calculations

PubMed Central

Fateen, Seif-Eddeen K.; Bonilla-Petriciolet, Adrian

2014-01-01

The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design. PMID:24967430

On the effectiveness of nature-inspired metaheuristic algorithms for performing phase equilibrium thermodynamic calculations.

PubMed

Fateen, Seif-Eddeen K; Bonilla-Petriciolet, Adrian

2014-01-01

The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design.

Modeling Design Iteration in Product Design and Development and Its Solution by a Novel Artificial Bee Colony Algorithm

PubMed Central

2014-01-01

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness. PMID:25431584

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

NASA Astrophysics Data System (ADS)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Array signal recovery algorithm for a single-RF-channel DBF array

NASA Astrophysics Data System (ADS)

Zhang, Duo; Wu, Wen; Fang, Da Gang

2016-12-01

An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna array in which signals are obtained from all antenna elements by only one microwave digital receiver. The spatially parallel array signals are converted into time-sequence signals, which are then sampled by the system. The proposed algorithm uses these time-sequence samples to recover the original parallel array signals by exploiting the second-order sparse structure of the array signals. Additionally, an optimization method based on the artificial bee colony (ABC) algorithm is proposed to improve the reconstruction performance. Using the proposed algorithm, the motion compensation problem for the single-RF-channel DBF array can be solved effectively, and the angle and Doppler information for the target can be simultaneously estimated. The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations.

Optimising the production of succinate and lactate in Escherichia coli using a hybrid of artificial bee colony algorithm and minimisation of metabolic adjustment.

PubMed

Tang, Phooi Wah; Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Napis, Suhaimi

2015-03-01

Metabolic engineering is a research field that focuses on the design of models for metabolism, and uses computational procedures to suggest genetic manipulation. It aims to improve the yield of particular chemical or biochemical products. Several traditional metabolic engineering methods are commonly used to increase the production of a desired target, but the products are always far below their theoretical maximums. Using numeral optimisation algorithms to identify gene knockouts may stall at a local minimum in a multivariable function. This paper proposes a hybrid of the artificial bee colony (ABC) algorithm and the minimisation of metabolic adjustment (MOMA) to predict an optimal set of solutions in order to optimise the production rate of succinate and lactate. The dataset used in this work was from the iJO1366 Escherichia coli metabolic network. The experimental results include the production rate, growth rate and a list of knockout genes. From the comparative analysis, ABCMOMA produced better results compared to previous works, showing potential for solving genetic engineering problems. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Damage identification of a TLP floating wind turbine by meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.

2015-12-01

Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring (SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP (Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms (GA), Artificial Immune System (AIS), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine (TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.

ABC Assay: Method Development and Application to Quantify the Role of Three DWV Master Variants in Overwinter Colony Losses of European Honey Bees.

PubMed

Kevill, Jessica L; Highfield, Andrea; Mordecai, Gideon J; Martin, Stephen J; Schroeder, Declan C

2017-10-27

Deformed wing virus (DWV) is one of the most prevalent honey bee viral pathogens in the world. Typical of many RNA viruses, DWV is a quasi-species, which is comprised of a large number of different variants, currently consisting of three master variants: Type A, B, and C. Little is known about the impact of each variant or combinations of variants upon the biology of individual hosts. Therefore, we have developed a new set of master variant-specific DWV primers and a set of standards that allow for the quantification of each of the master variants. Competitive reverse transcriptase polymerase chain reaction (RT-PCR) experimental design confirms that each new DWV primer set is specific to the retrospective master variant. The sensitivity of the ABC assay is dependent on whether DNA or RNA is used as the template and whether other master variants are present in the sample. Comparison of the overall proportions of each master variant within a sample of known diversity, as confirmed by next-generation sequence (NGS) data, validates the efficiency of the ABC assay. The ABC assay was used on archived material from a Devon overwintering colony loss (OCL) 2006-2007 study; further implicating DWV type A and, for the first time, possibly C in the untimely collapse of honey bee colonies. Moreover, in this study DWV type B was not associated with OCL. The use of the ABC assay will allow researchers to quickly and cost effectively pre-screen for the presence of DWV master variants in honey bees.

Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

NASA Astrophysics Data System (ADS)

Yang, Bin

2017-07-01

Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

ABC Assay: Method Development and Application to Quantify the Role of Three DWV Master Variants in Overwinter Colony Losses of European Honey Bees

PubMed Central

Kevill, Jessica L.; Highfield, Andrea; Mordecai, Gideon J.; Schroeder, Declan C.

2017-01-01

Deformed wing virus (DWV) is one of the most prevalent honey bee viral pathogens in the world. Typical of many RNA viruses, DWV is a quasi-species, which is comprised of a large number of different variants, currently consisting of three master variants: Type A, B, and C. Little is known about the impact of each variant or combinations of variants upon the biology of individual hosts. Therefore, we have developed a new set of master variant-specific DWV primers and a set of standards that allow for the quantification of each of the master variants. Competitive reverse transcriptase polymerase chain reaction (RT-PCR) experimental design confirms that each new DWV primer set is specific to the retrospective master variant. The sensitivity of the ABC assay is dependent on whether DNA or RNA is used as the template and whether other master variants are present in the sample. Comparison of the overall proportions of each master variant within a sample of known diversity, as confirmed by next-generation sequence (NGS) data, validates the efficiency of the ABC assay. The ABC assay was used on archived material from a Devon overwintering colony loss (OCL) 2006–2007 study; further implicating DWV type A and, for the first time, possibly C in the untimely collapse of honey bee colonies. Moreover, in this study DWV type B was not associated with OCL. The use of the ABC assay will allow researchers to quickly and cost effectively pre-screen for the presence of DWV master variants in honey bees. PMID:29077069

al3c: high-performance software for parameter inference using Approximate Bayesian Computation.

PubMed

Stram, Alexander H; Marjoram, Paul; Chen, Gary K

2015-11-01

The development of Approximate Bayesian Computation (ABC) algorithms for parameter inference which are both computationally efficient and scalable in parallel computing environments is an important area of research. Monte Carlo rejection sampling, a fundamental component of ABC algorithms, is trivial to distribute over multiple processors but is inherently inefficient. While development of algorithms such as ABC Sequential Monte Carlo (ABC-SMC) help address the inherent inefficiencies of rejection sampling, such approaches are not as easily scaled on multiple processors. As a result, current Bayesian inference software offerings that use ABC-SMC lack the ability to scale in parallel computing environments. We present al3c, a C++ framework for implementing ABC-SMC in parallel. By requiring only that users define essential functions such as the simulation model and prior distribution function, al3c abstracts the user from both the complexities of parallel programming and the details of the ABC-SMC algorithm. By using the al3c framework, the user is able to scale the ABC-SMC algorithm in parallel computing environments for his or her specific application, with minimal programming overhead. al3c is offered as a static binary for Linux and OS-X computing environments. The user completes an XML configuration file and C++ plug-in template for the specific application, which are used by al3c to obtain the desired results. Users can download the static binaries, source code, reference documentation and examples (including those in this article) by visiting https://github.com/ahstram/al3c. astram@usc.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Model selection and parameter estimation in structural dynamics using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Ben Abdessalem, Anis; Dervilis, Nikolaos; Wagg, David; Worden, Keith

2018-01-01

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours.

Global Discrete Artificial Boundary Conditions for Time-Dependent Wave Propagation

NASA Technical Reports Server (NTRS)

Ryabenkii, V. S.; Tsynkov, S. V.; Turchaninov, V. I.; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

We construct global artificial boundary conditions (ABCs) for the numerical simulation of wave processes on unbounded domains using a special non-deteriorating algorithm that has been developed previously for the long-term computation of wave-radiation solutions. The ABCs are obtained directly for the discrete formulation of the problem; in so doing, neither a rational approximation of 'non-reflecting kernels,' nor discretization of the continuous boundary conditions is required. The extent of temporal nonlocality of the new ABCs appears fixed and limited; in addition, the ABCs can handle artificial boundaries of irregular shape on regular grids with no fitting/adaptation needed and no accuracy loss induced. The non-deteriorating algorithm, which is the core of the new ABCs is inherently three-dimensional, it guarantees temporally uniform grid convergence of the solution driven by a continuously operating source on arbitrarily long time intervals, and provides unimprovable linear computational complexity with respect to the grid dimension. The algorithm is based on the presence of lacunae, i.e., aft fronts of the waves, in wave-type solutions in odd-dimension spaces, It can, in fact, be built as a modification on top of any consistent and stable finite-difference scheme, making its grid convergence uniform in time and at the same time keeping the rate of convergence the same as that of the non-modified scheme. In the paper, we delineate the construction of the global lacunae-based ABCs in the framework of a discretized wave equation. The ABCs are obtained for the most general formulation of the problem that involves radiation of waves by moving sources (e.g., radiation of acoustic waves by a maneuvering aircraft). We also present systematic numerical results that corroborate the theoretical design properties of the ABCs' algorithm.

Global Discrete Artificial Boundary Conditions for Time-Dependent Wave Propagation

NASA Astrophysics Data System (ADS)

Ryaben'kii, V. S.; Tsynkov, S. V.; Turchaninov, V. I.

2001-12-01

We construct global artificial boundary conditions (ABCs) for the numerical simulation of wave processes on unbounded domains using a special nondeteriorating algorithm that has been developed previously for the long-term computation of wave-radiation solutions. The ABCs are obtained directly for the discrete formulation of the problem; in so doing, neither a rational approximation of “nonreflecting kernels” nor discretization of the continuous boundary conditions is required. The extent of temporal nonlocality of the new ABCs appears fixed and limited; in addition, the ABCs can handle artificial boundaries of irregular shape on regular grids with no fitting/adaptation needed and no accuracy loss induced. The nondeteriorating algorithm, which is the core of the new ABCs, is inherently three-dimensional, it guarantees temporally uniform grid convergence of the solution driven by a continuously operating source on arbitrarily long time intervals and provides unimprovable linear computational complexity with respect to the grid dimension. The algorithm is based on the presence of lacunae, i.e., aft fronts of the waves, in wave-type solutions in odd-dimensional spaces. It can, in fact, be built as a modification on top of any consistent and stable finite-difference scheme, making its grid convergence uniform in time and at the same time keeping the rate of convergence the same as that of the unmodified scheme. In this paper, we delineate the construction of the global lacunae-based ABCs in the framework of a discretized wave equation. The ABCs are obtained for the most general formulation of the problem that involves radiation of waves by moving sources (e.g., radiation of acoustic waves by a maneuvering aircraft). We also present systematic numerical results that corroborate the theoretical design properties of the ABC algorithm.

Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models

NASA Astrophysics Data System (ADS)

Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas

2017-02-01

A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally, locally and un-identifiable model classes, and then to model updating of a two degree-of-freedom nonlinear structure with Duffing nonlinearities in its interstory force-deflection relationship.

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

PubMed Central

Sun, Lijuan; Guo, Jian; Xu, Bin; Li, Shujing

2017-01-01

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability. PMID:28127305

Laboratory Colonisation and Genetic Bottlenecks in the Tsetse Fly Glossina pallidipes

PubMed Central

Ciosi, Marc

2014-01-01

Background The IAEA colony is the only one available for mass rearing of Glossina pallidipes, a vector of human and animal African trypanosomiasis in eastern Africa. This colony is the source for Sterile Insect Technique (SIT) programs in East Africa. The source population of this colony is unclear and its genetic diversity has not previously been evaluated and compared to field populations. Methodology/Principal Findings We examined the genetic variation within and between the IAEA colony and its potential source populations in north Zimbabwe and the Kenya/Uganda border at 9 microsatellites loci to retrace the demographic history of the IAEA colony. We performed classical population genetics analyses and also combined historical and genetic data in a quantitative analysis using Approximate Bayesian Computation (ABC). There is no evidence of introgression from the north Zimbabwean population into the IAEA colony. Moreover, the ABC analyses revealed that the foundation and establishment of the colony was associated with a genetic bottleneck that has resulted in a loss of 35.7% of alleles and 54% of expected heterozygosity compared to its source population. Also, we show that tsetse control carried out in the 1990's is likely reduced the effective population size of the Kenya/Uganda border population. Conclusions/Significance All the analyses indicate that the area of origin of the IAEA colony is the Kenya/Uganda border and that a genetic bottleneck was associated with the foundation and establishment of the colony. Genetic diversity associated with traits that are important for SIT may potentially have been lost during this genetic bottleneck which could lead to a suboptimal competitiveness of the colony males in the field. The genetic diversity of the colony is lower than that of field populations and so, studies using colony flies should be interpreted with caution when drawing general conclusions about G. pallidipes biology. PMID:24551260

An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming

NASA Astrophysics Data System (ADS)

Hsu, Chih-Ming

2014-12-01

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.

Population-based metaheuristic optimization in neutron optics and shielding design

NASA Astrophysics Data System (ADS)

DiJulio, D. D.; Björgvinsdóttir, H.; Zendler, C.; Bentley, P. M.

2016-11-01

Population-based metaheuristic algorithms are powerful tools in the design of neutron scattering instruments and the use of these types of algorithms for this purpose is becoming more and more commonplace. Today there exists a wide range of algorithms to choose from when designing an instrument and it is not always initially clear which may provide the best performance. Furthermore, due to the nature of these types of algorithms, the final solution found for a specific design scenario cannot always be guaranteed to be the global optimum. Therefore, to explore the potential benefits and differences between the varieties of these algorithms available, when applied to such design scenarios, we have carried out a detailed study of some commonly used algorithms. For this purpose, we have developed a new general optimization software package which combines a number of common metaheuristic algorithms within a single user interface and is designed specifically with neutronic calculations in mind. The algorithms included in the software are implementations of Particle-Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and a Genetic Algorithm (GA). The software has been used to optimize the design of several problems in neutron optics and shielding, coupled with Monte-Carlo simulations, in order to evaluate the performance of the various algorithms. Generally, the performance of the algorithms depended on the specific scenarios, however it was found that DE provided the best average solutions in all scenarios investigated in this work.

Comparison of the algorithms classifying the ABC and GCB subtypes in diffuse large B-cell lymphoma.

PubMed

Boltežar, Lučka; Prevodnik, Veronika Kloboves; Perme, Maja Pohar; Gašljević, Gorana; Novaković, Barbara Jezeršek

2018-05-01

Different immunohistochemical algorithms for the classification of the activated B-cell (ABC) and germinal center B-cell (GCB) subtypes of diffuse large B-cell lymphoma (DLBCL) are applied in different laboratories. In the present study, 127 patients with DLCBL were investigated, all treated with rituximab and cyclophosphamide, hydroxydaunorubicin, oncovin and prednisone (CHOP) or CHOP-like regimens between April 2004 and December 2010. Multi-tumor tissue microarrays were prepared and were tested according to 4 algorithms: Hans; modified Hans; Choi; and modified Choi. For 39 patients, the flow cytometric quantification of CD19 and CD20 antigen expression was performed and the level of expression presented as molecules of equivalent soluble fluorochrome units. The Choi algorithm was demonstrated to be prognostic for OS and classified patients into the GCB subgroup with an HR of 0.91. No difference in the expression of the CD19 antigen between the ABC and GCB groups was observed, but the ABC subtype exhibited a decreased expression of the CD20 antigen compared with the GCB subtype.

A global optimization algorithm inspired in the behavior of selfish herds.

PubMed

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved Ant Colony Clustering Algorithm and Its Performance Study

PubMed Central

Gao, Wei

2016-01-01

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533

ABC versus CAB for cardiopulmonary resuscitation: a prospective, randomized simulator-based trial.

PubMed

Marsch, Stephan; Tschan, Franziska; Semmer, Norbert K; Zobrist, Roger; Hunziker, Patrick R; Hunziker, Sabina

2013-09-06

After years of advocating ABC (Airway-Breathing-Circulation), current guidelines of cardiopulmonary resuscitation (CPR) recommend CAB (Circulation-Airway-Breathing). This trial compared ABC with CAB as initial approach to CPR from the arrival of rescuers until the completion of the first resuscitation cycle. 108 teams, consisting of two physicians each, were randomized to receive a graphical display of either the ABC algorithm or the CAB algorithm. Subsequently teams had to treat a simulated cardiac arrest. Data analysis was performed using video recordings obtained during simulations. The primary endpoint was the time to completion of the first resuscitation cycle of 30 compressions and two ventilations. The time to execution of the first resuscitation measure was 32 ± 12 seconds in ABC teams and 25 ± 10 seconds in CAB teams (P = 0.002). 18/53 ABC teams (34%) and none of the 55 CAB teams (P = 0.006) applied more than the recommended two initial rescue breaths which caused a longer duration of the first cycle of 30 compressions and two ventilations in ABC teams (31 ± 13 vs.23 ± 6 sec; P = 0.001). Overall, the time to completion of the first resuscitation cycle was longer in ABC teams (63 ± 17 vs. 48 ± 10 sec; P <0.0001). This randomized controlled trial found CAB superior to ABC with an earlier start of CPR and a shorter time to completion of the first 30:2 resuscitation cycle. These findings endorse the change from ABC to CAB in international resuscitation guidelines.

Fundamentals and Recent Developments in Approximate Bayesian Computation

PubMed Central

Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka

2017-01-01

Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.] PMID:28175922

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

PubMed

Hashim, H A; Abido, M A

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study

PubMed Central

Hashim, H. A.; Abido, M. A.

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. PMID:25960738

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Deficiency of ATP-binding cassette transporters A1 and G1 in macrophages increases inflammation and accelerates atherosclerosis in mice.

PubMed

Westerterp, Marit; Murphy, Andrew J; Wang, Mi; Pagler, Tamara A; Vengrenyuk, Yuliya; Kappus, Mojdeh S; Gorman, Darren J; Nagareddy, Prabhakara R; Zhu, Xuewei; Abramowicz, Sandra; Parks, John S; Welch, Carrie; Fisher, Edward A; Wang, Nan; Yvan-Charvet, Laurent; Tall, Alan R

2013-05-24

Plasma high-density lipoprotein levels are inversely correlated with atherosclerosis. Although it is widely assumed that this is attributable to the ability of high-density lipoprotein to promote cholesterol efflux from macrophage foam cells, direct experimental support for this hypothesis is lacking. To assess the role of macrophage cholesterol efflux pathways in atherogenesis. We developed mice with efficient deletion of the ATP-binding cassette transporters A1 and G1 (ABCA1 and ABCG1) in macrophages (MAC-ABC(DKO) mice) but not in hematopoietic stem or progenitor populations. MAC-ABC(DKO) bone marrow (BM) was transplanted into Ldlr(-/-) recipients. On the chow diet, these mice had similar plasma cholesterol and blood monocyte levels but increased atherosclerosis compared with controls. On the Western-type diet, MAC-ABC(DKO) BM-transplanted Ldlr(-/-) mice had disproportionate atherosclerosis, considering they also had lower very low-density lipoprotein/low-density lipoprotein cholesterol levels than controls. ABCA1/G1-deficient macrophages in lesions showed increased inflammatory gene expression. Unexpectedly, Western-type diet-fed MAC-ABC(DKO) BM-transplanted Ldlr(-/-) mice displayed monocytosis and neutrophilia in the absence of hematopoietic stem and multipotential progenitor cells proliferation. Mechanistic studies revealed increased expressions of machrophage colony stimulating factor and granulocyte colony stimulating factor in splenic macrophage foam cells, driving BM monocyte and neutrophil production. These studies show that macrophage deficiency of ABCA1/G1 is proatherogenic likely by promoting plaque inflammation and uncover a novel positive feedback loop in which cholesterol-laden splenic macrophages signal BM progenitors to produce monocytes, with suppression by macrophage cholesterol efflux pathways.

CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

NASA Astrophysics Data System (ADS)

Mallick, S.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2016-07-01

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSA-DE-based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

On the problem of solving the optimization for continuous space based on information distribution function of ant colony algorithm

NASA Astrophysics Data System (ADS)

Min, Huang; Na, Cai

2017-06-01

These years, ant colony algorithm has been widely used in solving the domain of discrete space optimization, while the research on solving the continuous space optimization was relatively little. Based on the original optimization for continuous space, the article proposes the improved ant colony algorithm which is used to Solve the optimization for continuous space, so as to overcome the ant colony algorithm’s disadvantages of searching for a long time in continuous space. The article improves the solving way for the total amount of information of each interval and the due number of ants. The article also introduces a function of changes with the increase of the number of iterations in order to enhance the convergence rate of the improved ant colony algorithm. The simulation results show that compared with the result in literature[5], the suggested improved ant colony algorithm that based on the information distribution function has a better convergence performance. Thus, the article provides a new feasible and effective method for ant colony algorithm to solve this kind of problem.

Colony image acquisition and segmentation

NASA Astrophysics Data System (ADS)

Wang, W. X.

2007-12-01

For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems. The main problems are image acquisition and image segmentation. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. The developed colony image segmentation algorithm consists of the sub-algorithms: (1) image classification; (2) image processing; and (3) colony delineation. The colony delineation algorithm main contain: the procedures based on grey level similarity, on boundary tracing, on shape information and colony excluding. In addition, a number of algorithms are developed for colony analysis. The system has been tested and satisfactory.

Modulation-format-free and automatic bias control for optical IQ modulators based on dither-correlation detection.

PubMed

Li, Xiaolei; Deng, Lei; Chen, Xiaoman; Cheng, Mengfan; Fu, Songnian; Tang, Ming; Liu, Deming

2017-04-17

A novel automatic bias control (ABC) method for optical in-phase and quadrature (IQ) modulator is proposed and experimentally demonstrated. In the proposed method, two different low frequency sine wave dither signals are generated and added on to the I/Q bias signal respectively. Instead of power monitoring of the harmonics of the dither signal, dither-correlation detection is proposed and used to adjust the bias voltages of the optical IQ modulator. By this way, not only frequency spectral analysis isn't required but also the directional bias adjustment could be realized, resulting in the decrease of algorithm complexity and the growth of convergence rate of ABC algorithm. The results show that the sensitivity of the proposed ABC method outperforms that of the traditional dither frequency monitoring method. Moreover, the proposed ABC method is proved to be modulation-format-free, and the transmission penalty caused by this method for both 10 Gb/s optical QPSK and 17.9 Gb/s optical 16QAM-OFDM signal transmission are negligible in our experiment.

Production scheduling with ant colony optimization

NASA Astrophysics Data System (ADS)

Chernigovskiy, A. S.; Kapulin, D. V.; Noskova, E. E.; Yamskikh, T. N.; Tsarev, R. Yu

2017-10-01

The optimum solution of the production scheduling problem for manufacturing processes at an enterprise is crucial as it allows one to obtain the required amount of production within a specified time frame. Optimum production schedule can be found using a variety of optimization algorithms or scheduling algorithms. Ant colony optimization is one of well-known techniques to solve the global multi-objective optimization problem. In the article, the authors present a solution of the production scheduling problem by means of an ant colony optimization algorithm. A case study of the algorithm efficiency estimated against some others production scheduling algorithms is presented. Advantages of the ant colony optimization algorithm and its beneficial effect on the manufacturing process are provided.

Application of ant colony Algorithm and particle swarm optimization in architectural design

NASA Astrophysics Data System (ADS)

Song, Ziyi; Wu, Yunfa; Song, Jianhua

2018-02-01

By studying the development of ant colony algorithm and particle swarm algorithm, this paper expounds the core idea of the algorithm, explores the combination of algorithm and architectural design, sums up the application rules of intelligent algorithm in architectural design, and combines the characteristics of the two algorithms, obtains the research route and realization way of intelligent algorithm in architecture design. To establish algorithm rules to assist architectural design. Taking intelligent algorithm as the beginning of architectural design research, the authors provide the theory foundation of ant colony Algorithm and particle swarm algorithm in architectural design, popularize the application range of intelligent algorithm in architectural design, and provide a new idea for the architects.

The cabABC Operon Essential for Biofilm and Rugose Colony Development in Vibrio vulnificus

PubMed Central

Park, Jin Hwan; Jo, Youmi; Jang, Song Yee; Kwon, Haenaem; Irie, Yasuhiko; Parsek, Matthew R.; Kim, Myung Hee; Choi, Sang Ho

2015-01-01

A transcriptome analysis identified Vibrio vulnificus cabABC genes which were preferentially expressed in biofilms. The cabABC genes were transcribed as a single operon. The cabA gene was induced by elevated 3′,5′-cyclic diguanylic acid (c-di-GMP) and encoded a calcium-binding protein CabA. Comparison of the biofilms produced by the cabA mutant and its parent strain JN111 in microtiter plates using crystal-violet staining demonstrated that CabA contributed to biofilm formation in a calcium-dependent manner under elevated c-di-GMP conditions. Genetic and biochemical analyses revealed that CabA was secreted to the cell exterior through functional CabB and CabC, distributed throughout the biofilm matrix, and produced as the biofilm matured. These results, together with the observation that CabA also contributes to the development of rugose colony morphology, indicated that CabA is a matrix-associated protein required for maturation, rather than adhesion involved in the initial attachment, of biofilms. Microscopic comparison of the structure of biofilms produced by JN111 and the cabA mutant demonstrated that CabA is an extracellular matrix component essential for the development of the mature biofilm structures in flow cells and on oyster shells. Exogenously providing purified CabA restored the biofilm- and rugose colony-forming abilities of the cabA mutant when calcium was available. Circular dichroism and size exclusion analyses revealed that calcium binding induces CabA conformational changes which may lead to multimerization. Extracellular complementation experiments revealed that CabA can assemble a functional matrix only when exopolysaccharides coexist. Consequently, the combined results suggested that CabA is a structural protein of the extracellular matrix and multimerizes to a conformation functional in building robust biofilms, which may render V. vulnificus to survive in hostile environments and reach a concentrated infective dose. PMID:26406498

An intelligent identification algorithm for the monoclonal picking instrument

NASA Astrophysics Data System (ADS)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

PubMed Central

2013-01-01

Background The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored. Methods The present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A1 (the classification error) and A2 (the correlation factor). Otherwise, the B factor has four levels, specifically B1 (the Sequential Forward Selection, SFS), B2 (the Sequential Floating Forward Selection, SFFS), B3 (Artificial Bee Colony, ABC), and B4 (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS. Results A two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F0.01,3,72 = 4.0659 > f AB  = 0.09), (2) the levels of factor A have significative effects on the classification error (F0.02,1,72 = 5.0162 < f A  = 6.56), and (3) the levels of factor B over the classification error are not significative (F0.01,3,72 = 4.0659 > f B  = 0.08). Conclusions Considering the classification performance we found a superiority of using the factor A2 in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm. PMID:24369728

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Artificial Boundary Conditions Based on the Difference Potentials Method

NASA Technical Reports Server (NTRS)

Tsynkov, Semyon V.

1996-01-01

While numerically solving a problem initially formulated on an unbounded domain, one typically truncates this domain, which necessitates setting the artificial boundary conditions (ABC's) at the newly formed external boundary. The issue of setting the ABC's appears to be most significant in many areas of scientific computing, for example, in problems originating from acoustics, electrodynamics, solid mechanics, and fluid dynamics. In particular, in computational fluid dynamics (where external problems present a wide class of practically important formulations) the proper treatment of external boundaries may have a profound impact on the overall quality and performance of numerical algorithms. Most of the currently used techniques for setting the ABC's can basically be classified into two groups. The methods from the first group (global ABC's) usually provide high accuracy and robustness of the numerical procedure but often appear to be fairly cumbersome and (computationally) expensive. The methods from the second group (local ABC's) are, as a rule, algorithmically simple, numerically cheap, and geometrically universal; however, they usually lack accuracy of computations. In this paper we first present a survey and provide a comparative assessment of different existing methods for constructing the ABC's. Then, we describe a relatively new ABC's technique of ours and review the corresponding results. This new technique, in our opinion, is currently one of the most promising in the field. It enables one to construct such ABC's that combine the advantages relevant to the two aforementioned classes of existing methods. Our approach is based on application of the difference potentials method attributable to V. S. Ryaben'kii. This approach allows us to obtain highly accurate ABC's in the form of certain (nonlocal) boundary operator equations. The operators involved are analogous to the pseudodifferential boundary projections first introduced by A. P. Calderon and then also studied by R. T. Seeley. The apparatus of the boundary pseudodifferential equations, which has formerly been used mostly in the qualitative theory of integral equations and PDE'S, is now effectively employed for developing numerical methods in the different fields of scientific computing.

Application of the artificial bee colony algorithm for solving the set covering problem.

PubMed

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

PubMed Central

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

Model Specification Searches Using Ant Colony Optimization Algorithms

ERIC Educational Resources Information Center

Marcoulides, George A.; Drezner, Zvi

2003-01-01

Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.

Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach.

PubMed

Koutroumpas, Konstantinos; Ballarini, Paolo; Votsi, Irene; Cournède, Paul-Henry

2016-09-01

Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels. In this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC-SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC-SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data. Python scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software konstantinos.koutroumpas@ecp.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Anticipated Benefits of Care (ABC): psychometrics and predictive value in psychiatric disorders.

PubMed

Warden, D; Trivedi, M H; Carmody, T J; Gollan, J K; Kashner, T M; Lind, L; Crismon, M L; Rush, A J

2010-06-01

Attitudes and expectations about treatment have been associated with symptomatic outcomes, adherence and utilization in patients with psychiatric disorders. No measure of patients' anticipated benefits of treatment on domains of everyday functioning has previously been available. The Anticipated Benefits of Care (ABC) is a new, 10-item questionnaire used to measure patient expectations about the impact of treatment on domains of everyday functioning. The ABC was collected at baseline in adult out-patients with major depressive disorder (MDD) (n=528), bipolar disorder (n=395) and schizophrenia (n=447) in the Texas Medication Algorithm Project (TMAP). Psychometric properties of the ABC were assessed, and the association of ABC scores with treatment response at 3 months was evaluated. Evaluation of the ABC's internal consistency yielded Cronbach's alpha of 0.90-0.92 for patients across disorders. Factor analysis showed that the ABC was unidimensional for all patients and for patients with each disorder. For patients with MDD, lower anticipated benefits of treatment was associated with less symptom improvement and lower odds of treatment response [odds ratio (OR) 0.72, 95% confidence interval (CI) 0.57-0.87, p=0.0011]. There was no association between ABC and symptom improvement or treatment response for patients with bipolar disorder or schizophrenia, possibly because these patients had modest benefits with treatment. The ABC is the first self-report that measures patient expectations about the benefits of treatment on everyday functioning, filling an important gap in available assessments of attitudes and expectations about treatment. The ABC is simple, easy to use, and has acceptable psychometric properties for use in research or clinical settings.

A PML-FDTD ALGORITHM FOR SIMULATING PLASMA-COVERED CAVITY-BACKED SLOT ANTENNAS. (R825225)

EPA Science Inventory

A three-dimensional frequency-dependent finite-difference time-domain (FDTD) algorithm with perfectly matched layer (PML) absorbing boundary condition (ABC) and recursive convolution approaches is developed to model plasma-covered open-ended waveguide or cavity-backed slot antenn...

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

An Optimal Image-Based Method for Identification of Acoustic Emission (AE) Sources in Plate-Like Structures Using a Lead Zirconium Titanate (PZT) Sensor Array.

PubMed

Yan, Gang; Zhou, Li

2018-02-21

This paper proposes an innovative method for identifying the locations of multiple simultaneous acoustic emission (AE) events in plate-like structures from the view of image processing. By using a linear lead zirconium titanate (PZT) sensor array to record the AE wave signals, a reverse-time frequency-wavenumber (f-k) migration is employed to produce images displaying the locations of AE sources by back-propagating the AE waves. Lamb wave theory is included in the f-k migration to consider the dispersive property of the AE waves. Since the exact occurrence time of the AE events is usually unknown when recording the AE wave signals, a heuristic artificial bee colony (ABC) algorithm combined with an optimal criterion using minimum Shannon entropy is used to find the image with the identified AE source locations and occurrence time that mostly approximate the actual ones. Experimental studies on an aluminum plate with AE events simulated by PZT actuators are performed to validate the applicability and effectiveness of the proposed optimal image-based AE source identification method.

An Optimal Image-Based Method for Identification of Acoustic Emission (AE) Sources in Plate-Like Structures Using a Lead Zirconium Titanate (PZT) Sensor Array

PubMed Central

Zhou, Li

2018-01-01

This paper proposes an innovative method for identifying the locations of multiple simultaneous acoustic emission (AE) events in plate-like structures from the view of image processing. By using a linear lead zirconium titanate (PZT) sensor array to record the AE wave signals, a reverse-time frequency-wavenumber (f-k) migration is employed to produce images displaying the locations of AE sources by back-propagating the AE waves. Lamb wave theory is included in the f-k migration to consider the dispersive property of the AE waves. Since the exact occurrence time of the AE events is usually unknown when recording the AE wave signals, a heuristic artificial bee colony (ABC) algorithm combined with an optimal criterion using minimum Shannon entropy is used to find the image with the identified AE source locations and occurrence time that mostly approximate the actual ones. Experimental studies on an aluminum plate with AE events simulated by PZT actuators are performed to validate the applicability and effectiveness of the proposed optimal image-based AE source identification method. PMID:29466310

cosmoabc: Likelihood-free inference for cosmology

NASA Astrophysics Data System (ADS)

Ishida, Emille E. O.; Vitenti, Sandro D. P.; Penna-Lima, Mariana; Trindade, Arlindo M.; Cisewski, Jessi; M.; de Souza, Rafael; Cameron, Ewan; Busti, Vinicius C.

2015-05-01

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.

Torsion effect of swing frame on the measurement of horizontal two-plane balancing machine

NASA Astrophysics Data System (ADS)

Wang, Qiuxiao; Wang, Dequan; He, Bin; Jiang, Pan; Wu, Zhaofu; Fu, Xiaoyan

2017-03-01

In this paper, the vibration model of swing frame of two-plane balancing machine is established to calculate the vibration center position of swing frame first. The torsional stiffness formula of spring plate twisting around the vibration center is then deduced by using superposition principle. Finally, the dynamic balancing experiments prove the irrationality of A-B-C algorithm which ignores the torsion effect, and show that the torsional stiffness deduced by experiments is consistent with the torsional stiffness calculated by theory. The experimental datas show the influence of the torsion effect of swing frame on the separation ratio of sided balancing machines, which reveals the sources of measurement error and assesses the application scope of A-B-C algorithm.

Taking error into account when fitting models using Approximate Bayesian Computation.

PubMed

van der Vaart, Elske; Prangle, Dennis; Sibly, Richard M

2018-03-01

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error-calibrated ABC, to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models. © 2017 by the Ecological Society of America.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints.

PubMed

Kuldeep, B; Singh, V K; Kumar, A; Singh, G K

2015-01-01

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕp), stopband error (ϕs), transition band error (ϕt), peak reconstruction error (PRE), stopband attenuation (As) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Optimization of Stereo Matching in 3D Reconstruction Based on Binocular Vision

NASA Astrophysics Data System (ADS)

Gai, Qiyang

2018-01-01

Stereo matching is one of the key steps of 3D reconstruction based on binocular vision. In order to improve the convergence speed and accuracy in 3D reconstruction based on binocular vision, this paper adopts the combination method of polar constraint and ant colony algorithm. By using the line constraint to reduce the search range, an ant colony algorithm is used to optimize the stereo matching feature search function in the proposed search range. Through the establishment of the stereo matching optimization process analysis model of ant colony algorithm, the global optimization solution of stereo matching in 3D reconstruction based on binocular vision system is realized. The simulation results show that by the combining the advantage of polar constraint and ant colony algorithm, the stereo matching range of 3D reconstruction based on binocular vision is simplified, and the convergence speed and accuracy of this stereo matching process are improved.

Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

NASA Astrophysics Data System (ADS)

Saritha, R.; Vinod Chandra, S. S.

2017-10-01

In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

Finite-difference time-domain simulation of GPR data

NASA Astrophysics Data System (ADS)

Chen, How-Wei; Huang, Tai-Min

1998-10-01

Simulation of digital ground penetrating radar (GPR) wave propagation in two-dimensional (2-D) media is developed, tested, implemented, and applied using a time-domain staggered-grid finite-difference (FD) numerical method. Three types of numerical algorithms for constructing synthetic common-shot, constant-offset radar profiles based on an actual transmitter-to-receiver configuration and based on the exploding reflector concept are demonstrated to mimic different types of radar survey geometries. Frequency-dependent attenuation is also incorporated to account for amplitude decay and time shift in the recorded responses. The algorithms are based on an explicit FD solution to Maxwell's curl equations. In addition, the first-order TE mode responses of wave propagation phenomena are considered due to the operating frequency of current GPR instruments. The staggered-grid technique is used to sample the fields and approximate the spatial derivatives with fourth-order FDs. The temporal derivatives are approximated by an explicit second-order difference time-marching scheme. By combining paraxial approximation of the one-way wave equation ( A2) and the damping mechanisms (sponge filter), we propose a new composite absorbing boundary conditions (ABC) algorithm that effectively absorb both incoming and outgoing waves. To overcome the angle- and frequency-dependent characteristic of the absorbing behaviors, each ABC has two types of absorption mechanism. The first ABC uses a modified Clayton and Enquist's A2 condition. Moreover, a fixed and a floating A2 ABC that operates at one grid point is proposed. The second ABC uses a damping mechanism. By superimposing artificial damping and by alternating the physical attenuation properties and impedance contrast of the media within the absorbing region, those waves impinging on the boundary can be effectively attenuated and can prevent waves from reflecting back into the grid. The frequency-dependent characteristic of the damping mechanism can be used to adjust the width of the absorbing zone around the computational domain. By applying any combination of absorbing mechanism, non-physical reflections from the computation domain boundary can be effectively minimized. The algorithm enables us to use very thin absorbing boundaries. The model can be parameterized through velocity, relative electrical permittivity (dielectric constants), electrical conductivity, magnetic permeability, loss tangent, Q values, and attenuation. According to this scheme, widely varying electrical properties of near-surface earth materials can be modeled. The capability of simulating common-source, constant-offset and zero-offset gathers is also demonstrated through various synthetic examples. The synthetic cases for typical GPR applications include buried objects such as pipes of different materials, AVO analysis for ground water exploration, archaeological site investigation, and stratigraphy studies. The algorithms are also applied to iterative modeling of GPR data acquired over a gymnasium construction site on the NCCU campus.

Research on cutting path optimization of sheet metal parts based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Ling, H.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

In view of the disadvantages of the current cutting path optimization methods of sheet metal parts, a new method based on ant colony algorithm was proposed in this paper. The cutting path optimization problem of sheet metal parts was taken as the research object. The essence and optimization goal of the optimization problem were presented. The traditional serial cutting constraint rule was improved. The cutting constraint rule with cross cutting was proposed. The contour lines of parts were discretized and the mathematical model of cutting path optimization was established. Thus the problem was converted into the selection problem of contour lines of parts. Ant colony algorithm was used to solve the problem. The principle and steps of the algorithm were analyzed.

The Approximate Bayesian Computation methods in the localization of the atmospheric contamination source

NASA Astrophysics Data System (ADS)

Kopka, P.; Wawrzynczak, A.; Borysiewicz, M.

2015-09-01

In many areas of application, a central problem is a solution to the inverse problem, especially estimation of the unknown model parameters to model the underlying dynamics of a physical system precisely. In this situation, the Bayesian inference is a powerful tool to combine observed data with prior knowledge to gain the probability distribution of searched parameters. We have applied the modern methodology named Sequential Approximate Bayesian Computation (S-ABC) to the problem of tracing the atmospheric contaminant source. The ABC is technique commonly used in the Bayesian analysis of complex models and dynamic system. Sequential methods can significantly increase the efficiency of the ABC. In the presented algorithm, the input data are the on-line arriving concentrations of released substance registered by distributed sensor network from OVER-LAND ATMOSPHERIC DISPERSION (OLAD) experiment. The algorithm output are the probability distributions of a contamination source parameters i.e. its particular location, release rate, speed and direction of the movement, start time and duration. The stochastic approach presented in this paper is completely general and can be used in other fields where the parameters of the model bet fitted to the observable data should be found.

Bayesian experimental design for models with intractable likelihoods.

PubMed

Drovandi, Christopher C; Pettitt, Anthony N

2013-12-01

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables. © 2013, The International Biometric Society.

Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm

ERIC Educational Resources Information Center

Leite, Walter L.; Huang, I-Chan; Marcoulides, George A.

2008-01-01

This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…

Improved Ant Algorithms for Software Testing Cases Generation

PubMed Central

Yang, Shunkun; Xu, Jiaqi

2014-01-01

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations. PMID:24883391

Development of Fast Algorithms Using Recursion, Nesting and Iterations for Computational Electromagnetics

NASA Technical Reports Server (NTRS)

Chew, W. C.; Song, J. M.; Lu, C. C.; Weedon, W. H.

1995-01-01

In the first phase of our work, we have concentrated on laying the foundation to develop fast algorithms, including the use of recursive structure like the recursive aggregate interaction matrix algorithm (RAIMA), the nested equivalence principle algorithm (NEPAL), the ray-propagation fast multipole algorithm (RPFMA), and the multi-level fast multipole algorithm (MLFMA). We have also investigated the use of curvilinear patches to build a basic method of moments code where these acceleration techniques can be used later. In the second phase, which is mainly reported on here, we have concentrated on implementing three-dimensional NEPAL on a massively parallel machine, the Connection Machine CM-5, and have been able to obtain some 3D scattering results. In order to understand the parallelization of codes on the Connection Machine, we have also studied the parallelization of 3D finite-difference time-domain (FDTD) code with PML material absorbing boundary condition (ABC). We found that simple algorithms like the FDTD with material ABC can be parallelized very well allowing us to solve within a minute a problem of over a million nodes. In addition, we have studied the use of the fast multipole method and the ray-propagation fast multipole algorithm to expedite matrix-vector multiplication in a conjugate-gradient solution to integral equations of scattering. We find that these methods are faster than LU decomposition for one incident angle, but are slower than LU decomposition when many incident angles are needed as in the monostatic RCS calculations.

Artificial Bee Colony Optimization for Short-Term Hydrothermal Scheduling

NASA Astrophysics Data System (ADS)

Basu, M.

2014-12-01

Artificial bee colony optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The algorithm is tested on a multi-reservoir cascaded hydroelectric system having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. The algorithm is tested on two hydrothermal multi-reservoir cascaded hydroelectric test systems. The results of the proposed approach are compared with those of differential evolution, evolutionary programming and particle swarm optimization. From numerical results, it is found that the proposed artificial bee colony optimization based approach is able to provide better solution.

Routing and spectrum assignment based on ant colony optimization of minimum consecutiveness loss in elastic optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Xin, Xiangjun; Tian, Qinghua; Zhang, Qi; Rao, Lan; Tian, Feng; Luo, Biao; Liu, Yingjun; Tang, Bao

2016-10-01

Elastic Optical Networks are considered to be a promising technology for future high-speed network. In this paper, we propose a RSA algorithm based on the ant colony optimization of minimum consecutiveness loss (ACO-MCL). Based on the effect of the spectrum consecutiveness loss on the pheromone in the ant colony optimization, the path and spectrum of the minimal impact on the network are selected for the service request. When an ant arrives at the destination node from the source node along a path, we assume that this path is selected for the request. We calculate the consecutiveness loss of candidate-neighbor link pairs along this path after the routing and spectrum assignment. Then, the networks update the pheromone according to the value of the consecutiveness loss. We save the path with the smallest value. After multiple iterations of the ant colony optimization, the final selection of the path is assigned for the request. The algorithms are simulated in different networks. The results show that ACO-MCL algorithm performs better in blocking probability and spectrum efficiency than other algorithms. Moreover, the ACO-MCL algorithm can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness. Compared with other algorithms, the ACO-MCL algorithm can reduce the blocking rate by at least 5.9% in heavy load.

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin.

PubMed

Afanasyev, Pavel; Seer-Linnemayr, Charlotte; Ravelli, Raimond B G; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V; Pannu, Navraj S; Schatz, Michael; van Heel, Marin

2017-09-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the 'Einstein from random noise' problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous ('four-dimensional') cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, 'random-startup' three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external 'starting models'. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive 'ABC-4D' pipeline is based on the two-dimensional reference-free 'alignment by classification' (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure.

Development of the Assessment of Burden of COPD tool: an integrated tool to measure the burden of COPD.

PubMed

Slok, Annerika H M; in 't Veen, Johannes C C M; Chavannes, Niels H; van der Molen, Thys; Rutten-van Mölken, Maureen P M H; Kerstjens, Huib A M; Salomé, Philippe L; Holverda, Sebastiaan; Dekhuijzen, P N Richard; Schuiten, Denise; Asijee, Guus M; van Schayck, Onno C P

2014-07-10

In deciding on the treatment plan for patients with chronic obstructive pulmonary disease (COPD), the burden of COPD as experienced by patients should be the core focus. It is therefore important for daily practice to develop a tool that can both assess the burden of COPD and facilitate communication with patients in clinical practice. This paper describes the development of an integrated tool to assess the burden of COPD in daily practice. A definition of the burden of COPD was formulated by a Dutch expert team. Interviews showed that patients and health-care providers agreed on this definition. We found no existing instruments that fully measured burden of disease according to this definition. However, the Clinical COPD Questionnaire meets most requirements, and was therefore used and adapted. The adapted questionnaire is called the Assessment of Burden of COPD (ABC) scale. In addition, the ABC tool was developed, of which the ABC scale is the core part. The ABC tool is a computer program with an algorithm that visualises outcomes and provides treatment advice. The next step in the development of the tool is to test the validity and effectiveness of both the ABC scale and tool in daily practice.

Development of the Assessment of Burden of COPD tool: an integrated tool to measure the burden of COPD

PubMed Central

Slok, Annerika H M; in ’t Veen, Johannes C C M; Chavannes, Niels H; van der Molen, Thys; Rutten-van Mölken, Maureen P M H; Kerstjens, Huib A M; Salomé, Philippe L; Holverda, Sebastiaan; Dekhuijzen, PN Richard; Schuiten, Denise; Asijee, Guus M; van Schayck, Onno C P

2014-01-01

In deciding on the treatment plan for patients with chronic obstructive pulmonary disease (COPD), the burden of COPD as experienced by patients should be the core focus. It is therefore important for daily practice to develop a tool that can both assess the burden of COPD and facilitate communication with patients in clinical practice. This paper describes the development of an integrated tool to assess the burden of COPD in daily practice. A definition of the burden of COPD was formulated by a Dutch expert team. Interviews showed that patients and health-care providers agreed on this definition. We found no existing instruments that fully measured burden of disease according to this definition. However, the Clinical COPD Questionnaire meets most requirements, and was therefore used and adapted. The adapted questionnaire is called the Assessment of Burden of COPD (ABC) scale. In addition, the ABC tool was developed, of which the ABC scale is the core part. The ABC tool is a computer program with an algorithm that visualises outcomes and provides treatment advice. The next step in the development of the tool is to test the validity and effectiveness of both the ABC scale and tool in daily practice. PMID:25010353

Global Artificial Boundary Conditions for Computation of External Flow Problems with Propulsive Jets

NASA Technical Reports Server (NTRS)

Tsynkov, Semyon; Abarbanel, Saul; Nordstrom, Jan; Ryabenkii, Viktor; Vatsa, Veer

1998-01-01

We propose new global artificial boundary conditions (ABC's) for computation of flows with propulsive jets. The algorithm is based on application of the difference potentials method (DPM). Previously, similar boundary conditions have been implemented for calculation of external compressible viscous flows around finite bodies. The proposed modification substantially extends the applicability range of the DPM-based algorithm. In the paper, we present the general formulation of the problem, describe our numerical methodology, and discuss the corresponding computational results. The particular configuration that we analyze is a slender three-dimensional body with boat-tail geometry and supersonic jet exhaust in a subsonic external flow under zero angle of attack. Similarly to the results obtained earlier for the flows around airfoils and wings, current results for the jet flow case corroborate the superiority of the DPM-based ABC's over standard local methodologies from the standpoints of accuracy, overall numerical performance, and robustness.

Application of the Approximate Bayesian Computation methods in the stochastic estimation of atmospheric contamination parameters for mobile sources

NASA Astrophysics Data System (ADS)

Kopka, Piotr; Wawrzynczak, Anna; Borysiewicz, Mieczyslaw

2016-11-01

In this paper the Bayesian methodology, known as Approximate Bayesian Computation (ABC), is applied to the problem of the atmospheric contamination source identification. The algorithm input data are on-line arriving concentrations of the released substance registered by the distributed sensors network. This paper presents the Sequential ABC algorithm in detail and tests its efficiency in estimation of probabilistic distributions of atmospheric release parameters of a mobile contamination source. The developed algorithms are tested using the data from Over-Land Atmospheric Diffusion (OLAD) field tracer experiment. The paper demonstrates estimation of seven parameters characterizing the contamination source, i.e.: contamination source starting position (x,y), the direction of the motion of the source (d), its velocity (v), release rate (q), start time of release (ts) and its duration (td). The online-arriving new concentrations dynamically update the probability distributions of search parameters. The atmospheric dispersion Second-order Closure Integrated PUFF (SCIPUFF) Model is used as the forward model to predict the concentrations at the sensors locations.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

PubMed

Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

2018-04-10

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

Role for chondroitin sulfate glycosaminoglycan in NEDD9-mediated breast cancer cell growth.

PubMed

Iida, Joji; Dorchak, Jesse; Clancy, Rebecca; Slavik, Juliana; Ellsworth, Rachel; Katagiri, Yasuhiro; Pugacheva, Elena N; van Kuppevelt, Toin H; Mural, Richard J; Cutler, Mary Lou; Shriver, Craig D

2015-01-15

There are lines of evidence demonstrating that NEDD9 (Cas-L, HEF-1) plays a key role in the development, progression, and metastasis of breast cancer cells. We previously reported that NEDD9 plays a critical role for promoting migration and growth of MDA-MB-231. In order to further characterize the mechanisms of NEDD9-mediated cancer migration and growth, stable cells overexpressing NEDD9 were generated using HCC38 as a parental cell line which expresses low level of endogenous NEDD9. Microarray studies demonstrated that core proteins of CD44 and Serglycin were markedly upregulated in HCC38(NEDD9) cells compared to HCC38(Vector) cells, while those of Syndecan-1, Syndecan-2, and Versican were downregulated in HCC38(NEDD9). Importantly, enzymes generating chondroitin sulfate glycosaminoglycans (CS) such as CHST11, CHST15, and CSGALNACT1 were upregulated in HCC38(NEDD9) compared to HCC38(Vector). Immunofluorescence studies using specific antibody, GD3G7, confirmed the enhanced expression of CS-E subunit in HCC38(NEDD9). Immunoprecipitation and western blotting analysis demonstrated that CS-E was attached to CD44 core protein. We demonstrated that removing CS by chondroitinase ABC significantly inhibited anchorage-independent colony formation of HCC38(NEDD9) in methylcellulose. Importantly, the fact that GD3G7 significantly inhibited colony formation of HCC38(NEDD9) cells suggests that CS-E subunit plays a key role in this process. Furthermore, treatment of HCC38(NEDD9) cells with chondroitinase ABC or GD3G7 significantly inhibited mammosphere formation. Exogenous addition of CS-E enhanced colony formation and mammosphere formation of HCC38 parental and HCC38(Vector) cells. These results suggest that NEDD9 regulates the synthesis and expression of tumor associated glycocalyx structures including CS-E, which plays a key role in promoting and regulating breast cancer progression and metastasis and possibly stem cell phenotypes. Copyright © 2014 Elsevier Inc. All rights reserved.

Improved artificial bee colony algorithm for vehicle routing problem with time windows

PubMed Central

Yan, Qianqian; Zhang, Mengjie; Yang, Yunong

2017-01-01

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW. PMID:28961252

VNIR hyperspectral background characterization methods in adverse weather conditions

NASA Astrophysics Data System (ADS)

Romano, João M.; Rosario, Dalton; Roth, Luz

2009-05-01

Hyperspectral technology is currently being used by the military to detect regions of interest where potential targets may be located. Weather variability, however, may affect the ability for an algorithm to discriminate possible targets from background clutter. Nonetheless, different background characterization approaches may facilitate the ability for an algorithm to discriminate potential targets over a variety of weather conditions. In a previous paper, we introduced a new autonomous target size invariant background characterization process, the Autonomous Background Characterization (ABC) or also known as the Parallel Random Sampling (PRS) method, features a random sampling stage, a parallel process to mitigate the inclusion by chance of target samples into clutter background classes during random sampling; and a fusion of results at the end. In this paper, we will demonstrate how different background characterization approaches are able to improve performance of algorithms over a variety of challenging weather conditions. By using the Mahalanobis distance as the standard algorithm for this study, we compare the performance of different characterization methods such as: the global information, 2 stage global information, and our proposed method, ABC, using data that was collected under a variety of adverse weather conditions. For this study, we used ARDEC's Hyperspectral VNIR Adverse Weather data collection comprised of heavy, light, and transitional fog, light and heavy rain, and low light conditions.

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin

PubMed Central

Seer-Linnemayr, Charlotte; Ravelli, Raimond B. G.; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V.; Pannu, Navraj S.; Schatz, Michael; van Heel, Marin

2017-01-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the ‘Einstein from random noise’ problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous (‘four-dimensional’) cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, ‘random-startup’ three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external ‘starting models’. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive ‘ABC-4D’ pipeline is based on the two-dimensional reference-free ‘alignment by classification’ (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure. PMID:28989723

Ant-cuckoo colony optimization for feature selection in digital mammogram.

PubMed

Jona, J B; Nagaveni, N

2014-01-15

Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.

[Application of rational ant colony optimization to improve the reproducibility degree of laser three-dimensional copy].

PubMed

Cui, Xiao-Yan; Huo, Zhong-Gang; Xin, Zhong-Hua; Tian, Xiao; Zhang, Xiao-Dong

2013-07-01

Three-dimensional (3D) copying of artificial ears and pistol printing are pushing laser three-dimensional copying technique to a new page. Laser three-dimensional scanning is a fresh field in laser application, and plays an irreplaceable part in three-dimensional copying. Its accuracy is the highest among all present copying techniques. Reproducibility degree marks the agreement of copied object with the original object on geometry, being the most important index property in laser three-dimensional copying technique. In the present paper, the error of laser three-dimensional copying was analyzed. The conclusion is that the data processing to the point cloud of laser scanning is the key technique to reduce the error and increase the reproducibility degree. The main innovation of this paper is as follows. On the basis of traditional ant colony optimization, rational ant colony optimization algorithm proposed by the author was applied to the laser three-dimensional copying as a new algorithm, and was put into practice. Compared with customary algorithm, rational ant colony optimization algorithm shows distinct advantages in data processing of laser three-dimensional copying, reducing the error and increasing the reproducibility degree of the copy.

Fluconazole Resistance Associated with Drug Efflux and Increased Transcription of a Drug Transporter Gene, PDH1, in Candida glabrata

PubMed Central

Miyazaki, Haruko; Miyazaki, Yoshitsugu; Geber, Antonia; Parkinson, Tanya; Hitchcock, Christopher; Falconer, Derek J.; Ward, Douglas J.; Marsden, Katherine; Bennett, John E.

1998-01-01

Sequential Candida glabrata isolates were obtained from the mouth of a patient infected with human immunodeficiency virus type 1 who was receiving high doses of fluconazole for oropharyngeal thrush. Fluconazole-susceptible colonies were replaced by resistant colonies that exhibited both increased fluconazole efflux and increased transcripts of a gene which codes for a protein with 72.5% identity to Pdr5p, an ABC multidrug transporter in Saccharomyces cerevisiae. The deduced protein had a molecular mass of 175 kDa and was composed of two homologous halves, each with six putative transmembrane domains and highly conserved sequences of ATP-binding domains. When the earliest and most azole-susceptible isolate of C. glabrata from this patient was exposed to fluconazole, increased transcripts of the PDR5 homolog appeared, linking azole exposure to regulation of this gene. PMID:9661006

Ant algorithms for discrete optimization.

PubMed

Dorigo, M; Di Caro, G; Gambardella, L M

1999-01-01

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis

NASA Astrophysics Data System (ADS)

Kang, Fei; Li, Junjie; Ma, Zhenyue

2013-02-01

Determination of the critical slip surface with the minimum factor of safety of a slope is a difficult constrained global optimization problem. In this article, an artificial bee colony algorithm with a multi-slice adjustment method is proposed for locating the critical slip surfaces of soil slopes, and the Spencer method is employed to calculate the factor of safety. Six benchmark examples are presented to illustrate the reliability and efficiency of the proposed technique, and it is also compared with some well-known or recent algorithms for the problem. The results show that the new algorithm is promising in terms of accuracy and efficiency.

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

NASA Astrophysics Data System (ADS)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

PubMed Central

Liu, Wen

2014-01-01

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. PMID:24982924

Substrate Binding Protein DppA1 of ABC Transporter DppBCDF Increases Biofilm Formation in Pseudomonas aeruginosa by Inhibiting Pf5 Prophage Lysis

PubMed Central

Lee, Yunho; Song, Sooyeon; Sheng, Lili; Zhu, Lei; Kim, Jun-Seob; Wood, Thomas K.

2018-01-01

Filamentous phage impact biofilm development, stress tolerance, virulence, biofilm dispersal, and colony variants. Previously, we identified 137 Pseudomonas aeruginosa PA14 mutants with more than threefold enhanced and 88 mutants with more than 10-fold reduced biofilm formation by screening 5850 transposon mutants (PLoS Pathogens 5: e1000483, 2009). Here, we characterized the function of one of these 225 mutations, dppA1 (PA14_58350), in regard to biofilm formation. DppA1 is a substrate-binding protein (SBP) involved in peptide utilization via the DppBCDF ABC transporter system. We show that compared to the wild-type strain, inactivating dppA1 led to 68-fold less biofilm formation in a static model and abolished biofilm formation in flow cells. Moreover, the dppA1 mutant had a delay in swarming and produced 20-fold less small-colony variants, and both biofilm formation and swarming were complemented by producing DppA1. A whole-transcriptome analysis showed that only 10 bacteriophage Pf5 genes were significantly induced in the biofilm cells of the dppA1 mutant compared to the wild-type strain, and inactivation of dppA1 resulted in a 600-fold increase in Pf5 excision and a million-fold increase in phage production. As expected, inactivating Pf5 genes PA0720 and PA0723 increased biofilm formation substantially. Inactivation of DppA1 also reduced growth (due to cell lysis). Hence, DppA1 increases biofilm formation by repressing Pf5 prophage. PMID:29416528

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.

PubMed

Rajeswari, M; Amudhavel, J; Pothula, Sujatha; Dhavachelvan, P

2017-01-01

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

PubMed Central

Amudhavel, J.; Pothula, Sujatha; Dhavachelvan, P.

2017-01-01

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria. PMID:28473849

Automatic counting and classification of bacterial colonies using hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Detection and counting of bacterial colonies on agar plates is a routine microbiology practice to get a rough estimate of the number of viable cells in a sample. There have been a variety of different automatic colony counting systems and software algorithms mainly based on color or gray-scale pictu...

Artificial Boundary Conditions for Computation of Oscillating External Flows

NASA Technical Reports Server (NTRS)

Tsynkov, S. V.

1996-01-01

In this paper, we propose a new technique for the numerical treatment of external flow problems with oscillatory behavior of the solution in time. Specifically, we consider the case of unbounded compressible viscous plane flow past a finite body (airfoil). Oscillations of the flow in time may be caused by the time-periodic injection of fluid into the boundary layer, which in accordance with experimental data, may essentially increase the performance of the airfoil. To conduct the actual computations, we have to somehow restrict the original unbounded domain, that is, to introduce an artificial (external) boundary and to further consider only a finite computational domain. Consequently, we will need to formulate some artificial boundary conditions (ABC's) at the introduced external boundary. The ABC's we are aiming to obtain must meet a fundamental requirement. One should be able to uniquely complement the solution calculated inside the finite computational domain to its infinite exterior so that the original problem is solved within the desired accuracy. Our construction of such ABC's for oscillating flows is based on an essential assumption: the Navier-Stokes equations can be linearized in the far field against the free-stream back- ground. To actually compute the ABC's, we represent the far-field solution as a Fourier series in time and then apply the Difference Potentials Method (DPM) of V. S. Ryaben'kii. This paper contains a general theoretical description of the algorithm for setting the DPM-based ABC's for time-periodic external flows. Based on our experience in implementing analogous ABC's for steady-state problems (a simpler case), we expect that these boundary conditions will become an effective tool for constructing robust numerical methods to calculate oscillatory flows.

COSMOABC: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Ishida, E. E. O.; Vitenti, S. D. P.; Penna-Lima, M.; Cisewski, J.; de Souza, R. S.; Trindade, A. M. M.; Cameron, E.; Busti, V. C.; COIN Collaboration

2015-11-01

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present COSMOABC, a Python ABC sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled COSMOABC with the NUMCOSMO library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. COSMOABC is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX.

Approximate Bayesian computation for forward modeling in cosmology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akeret, Joël; Refregier, Alexandre; Amara, Adam

Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations. This approach relies on the ability to compute the likelihood of the data given a choice of model parameters. In many practical situations, the likelihood function may however be unavailable or intractable due to non-gaussian errors, non-linear measurements processes, or complex data formats such as catalogs and maps. In these cases, the simulation of mock data sets can often be made through forward modeling. We discuss how Approximate Bayesian Computation (ABC) can be used in these cases to derive an approximation to themore » posterior constraints using simulated data sets. This technique relies on the sampling of the parameter set, a distance metric to quantify the difference between the observation and the simulations and summary statistics to compress the information in the data. We first review the principles of ABC and discuss its implementation using a Population Monte-Carlo (PMC) algorithm and the Mahalanobis distance metric. We test the performance of the implementation using a Gaussian toy model. We then apply the ABC technique to the practical case of the calibration of image simulations for wide field cosmological surveys. We find that the ABC analysis is able to provide reliable parameter constraints for this problem and is therefore a promising technique for other applications in cosmology and astrophysics. Our implementation of the ABC PMC method is made available via a public code release.« less

Study on store-space assignment based on logistic AGV in e-commerce goods to person picking pattern

NASA Astrophysics Data System (ADS)

Xu, Lijuan; Zhu, Jie

2017-10-01

This paper studied on the store-space assignment based on logistic AGV in E-commerce goods to person picking pattern, and established the store-space assignment model based on the lowest picking cost, and design for store-space assignment algorithm after the cluster analysis based on similarity coefficient. And then through the example analysis, compared the picking cost between store-space assignment algorithm this paper design and according to item number and storage according to ABC classification allocation, and verified the effectiveness of the design of the store-space assignment algorithm.

An ant colony optimization based algorithm for identifying gene regulatory elements.

PubMed

Liu, Wei; Chen, Hanwu; Chen, Ling

2013-08-01

It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Approximate Bayesian Computation in the estimation of the parameters of the Forbush decrease model

NASA Astrophysics Data System (ADS)

Wawrzynczak, A.; Kopka, P.

2017-12-01

Realistic modeling of the complicated phenomena as Forbush decrease of the galactic cosmic ray intensity is a quite challenging task. One aspect is a numerical solution of the Fokker-Planck equation in five-dimensional space (three spatial variables, the time and particles energy). The second difficulty arises from a lack of detailed knowledge about the spatial and time profiles of the parameters responsible for the creation of the Forbush decrease. Among these parameters, the central role plays a diffusion coefficient. Assessment of the correctness of the proposed model can be done only by comparison of the model output with the experimental observations of the galactic cosmic ray intensity. We apply the Approximate Bayesian Computation (ABC) methodology to match the Forbush decrease model to experimental data. The ABC method is becoming increasing exploited for dynamic complex problems in which the likelihood function is costly to compute. The main idea of all ABC methods is to accept samples as an approximate posterior draw if its associated modeled data are close enough to the observed one. In this paper, we present application of the Sequential Monte Carlo Approximate Bayesian Computation algorithm scanning the space of the diffusion coefficient parameters. The proposed algorithm is adopted to create the model of the Forbush decrease observed by the neutron monitors at the Earth in March 2002. The model of the Forbush decrease is based on the stochastic approach to the solution of the Fokker-Planck equation.

A multilevel ant colony optimization algorithm for classical and isothermic DNA sequencing by hybridization with multiplicity information available.

PubMed

Kwarciak, Kamil; Radom, Marcin; Formanowicz, Piotr

2016-04-01

The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip. Copyright © 2016 Elsevier Ltd. All rights reserved.

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

NASA Astrophysics Data System (ADS)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

An Ant Colony Optimization and Hybrid Metaheuristics Algorithm to Solve the Split Delivery Vehicle Routing Problem

DTIC Science & Technology

2015-01-01

programming formulation of traveling salesman problems , Journal of the ACM, 7(4), 326-329. Montemanni, R., Gambardella, L. M., Rizzoli, A.E., Donati. A.V... salesman problem . BioSystem, 43(1), 73-81. Dror, M., Trudeau, P., 1989. Savings by split delivery routing. Transportation Science, 23, 141- 145. Dror, M...An Ant Colony Optimization and Hybrid Metaheuristics Algorithm to solve the Split Delivery Vehicle Routing Problem Authors: Gautham Rajappa

A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning.

PubMed

Kumar, Anupam; Kumar, Vijay

2017-05-01

In this paper, a novel concept of an interval type-2 fractional order fuzzy PID (IT2FO-FPID) controller, which requires fractional order integrator and fractional order differentiator, is proposed. The incorporation of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic controller (IT2FLC) with fractional controller of PID-type is investigated for time response measure due to both unit step response and unit load disturbance. The resulting IT2FO-FPID controller is examined on different delayed linear and nonlinear benchmark plants followed by robustness analysis. In order to design this controller, fractional order integrator-differentiator operators are considered as design variables including input-output scaling factors. A new hybridized algorithm named as artificial bee colony-genetic algorithm (ABC-GA) is used to optimize the parameters of the controller while minimizing weighted sum of integral of time absolute error (ITAE) and integral of square of control output (ISCO). To assess the comparative performance of the IT2FO-FPID, authors compared it against existing controllers, i.e., interval type-2 fuzzy PID (IT2-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), type-1 fuzzy PID (T1-FPID), and conventional PID controllers. Furthermore, to show the effectiveness of the proposed controller, the perturbed processes along with the larger dead time are tested. Moreover, the proposed controllers are also implemented on multi input multi output (MIMO), coupled, and highly complex nonlinear two-link robot manipulator system in presence of un-modeled dynamics. Finally, the simulation results explicitly indicate that the performance of the proposed IT2FO-FPID controller is superior to its conventional counterparts in most of the cases. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

NASA Astrophysics Data System (ADS)

Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

2016-10-01

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

NASA Astrophysics Data System (ADS)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

Travel in Time: Tradition to Technology. Proceedings of the 1985 ABC International Convention (Chicago, Illinois, October 1985).

ERIC Educational Resources Information Center

Bruno, Sam J., Ed.; Pettit, John D., Jr., Ed.

These conference proceedings contain the following 23 presentations: "Development of a Communication Skill Model Using Interpretive Structural Modeling" (Karen S. Nantz and Linda Gammill); "The Coincidence of Needs: An Inventional Model for Audience Analysis" (Gina Burchard); "A Computer Algorithm for Measuring Readability" (Terry D. Lundgren);…

Ant- and Ant-Colony-Inspired ALife Visual Art.

PubMed

Greenfield, Gary; Machado, Penousal

2015-01-01

Ant- and ant-colony-inspired ALife art is characterized by the artistic exploration of the emerging collective behavior of computational agents, developed using ants as a metaphor. We present a chronology that documents the emergence and history of such visual art, contextualize ant- and ant-colony-inspired art within generative art practices, and consider how it relates to other ALife art. We survey many of the algorithms that artists have used in this genre, address some of their aims, and explore the relationships between ant- and ant-colony-inspired art and research on ant and ant colony behavior.

Ant colony system algorithm for the optimization of beer fermentation control.

PubMed

Xiao, Jie; Zhou, Ze-Kui; Zhang, Guang-Xin

2004-12-01

Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.

PubMed

Jiang, Ailian; Zheng, Lihong

2018-03-29

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

PubMed Central

2018-01-01

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime. PMID:29596336

An element search ant colony technique for solving virtual machine placement problem

NASA Astrophysics Data System (ADS)

Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

2017-09-01

The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems.

PubMed

Mavrovouniotis, Michalis; Muller, Felipe M; Yang, Shengxiang

2016-06-13

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.

Architectures of Kepler Planet Systems with Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Morehead, Robert C.; Ford, Eric B.

2015-12-01

The distribution of period normalized transit duration ratios among Kepler’s multiple transiting planet systems constrains the distributions of mutual orbital inclinations and orbital eccentricities. However, degeneracies in these parameters tied to the underlying number of planets in these systems complicate their interpretation. To untangle the true architecture of planet systems, the mutual inclination, eccentricity, and underlying planet number distributions must be considered simultaneously. The complexities of target selection, transit probability, detection biases, vetting, and follow-up observations make it impractical to write an explicit likelihood function. Approximate Bayesian computation (ABC) offers an intriguing path forward. In its simplest form, ABC generates a sample of trial population parameters from a prior distribution to produce synthetic datasets via a physically-motivated forward model. Samples are then accepted or rejected based on how close they come to reproducing the actual observed dataset to some tolerance. The accepted samples form a robust and useful approximation of the true posterior distribution of the underlying population parameters. We build on the considerable progress from the field of statistics to develop sequential algorithms for performing ABC in an efficient and flexible manner. We demonstrate the utility of ABC in exoplanet populations and present new constraints on the distributions of mutual orbital inclinations, eccentricities, and the relative number of short-period planets per star. We conclude with a discussion of the implications for other planet occurrence rate calculations, such as eta-Earth.

Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun

2017-07-01

Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

PubMed Central

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments. PMID:22666056

Adapting an ant colony metaphor for multi-robot chemical plume tracing.

PubMed

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.

Joint optimization of maintenance, buffers and machines in manufacturing lines

NASA Astrophysics Data System (ADS)

Nahas, Nabil; Nourelfath, Mustapha

2018-01-01

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.

A Modified Artificial Bee Colony Algorithm Application for Economic Environmental Dispatch

NASA Astrophysics Data System (ADS)

Tarafdar Hagh, M.; Baghban Orandi, Omid

2018-03-01

In conventional fossil-fuel power systems, the economic environmental dispatch (EED) problem is a major problem that optimally determines the output power of generating units in a way that cost of total production and emission level be minimized simultaneously, and at the same time all the constraints of units and system are satisfied properly. To solve EED problem which is a non-convex optimization problem, a modified artificial bee colony (MABC) algorithm is proposed in this paper. This algorithm by implementing weighted sum method is applied on two test systems, and eventually, obtained results are compared with other reported results. Comparison of results confirms superiority and efficiency of proposed method clearly.

Ancient village fire escape path planning based on improved ant colony algorithm

NASA Astrophysics Data System (ADS)

Xia, Wei; Cao, Kang; Hu, QianChuan

2017-06-01

The roadways are narrow and perplexing in ancient villages, it brings challenges and difficulties for people to choose route to escape when a fire occurs. In this paper, a fire escape path planning method based on ant colony algorithm is presented according to the problem. The factors in the fire environment which influence the escape speed is introduced to improve the heuristic function of the algorithm, optimal transfer strategy, and adjustment pheromone volatile factor to improve pheromone update strategy adaptively, improve its dynamic search ability and search speed. Through simulation, the dynamic adjustment of the optimal escape path is obtained, and the method is proved to be feasible.

Poor concordance among nine immunohistochemistry classifiers of cell-of-origin for diffuse large B-cell lymphoma: implications for therapeutic strategies.

PubMed

Coutinho, Rita; Clear, Andrew James; Owen, Andrew; Wilson, Andrew; Matthews, Janet; Lee, Abigail; Alvarez, Rute; Gomes da Silva, Maria; Cabeçadas, José; Calaminici, Maria; Gribben, John G

2013-12-15

The opportunity to improve therapeutic choices on the basis of molecular features of the tumor cells is on the horizon in diffuse large B-cell lymphoma (DLBCL). Agents such as bortezomib exhibit selective activity against the poor outcome activated B-cell type (ABC) DLBCL. In order for targeted therapies to succeed in this disease, robust strategies that segregate patients into molecular groups with high reliability are needed. Although molecular studies are considered gold standard, several immunohistochemistry (IHC) algorithms have been published that claim to be able to stratify patients according to their cell-of-origin and to be relevant for patient outcome. However, results are poorly reproducible by independent groups. We investigated nine IHC algorithms for molecular classification in a dataset of DLBCL diagnostic biopsies, incorporating immunostaining for CD10, BCL6, BCL2, MUM1, FOXP1, GCET1, and LMO2. IHC profiles were assessed and agreed among three expert observers. A consensus matrix based on all scoring combinations and the number of subjects for each combination allowed us to assess reliability. The survival impact of individual markers and classifiers was evaluated using Kaplan-Meier curves and the log-rank test. The concordance in patient's classification across the different algorithms was low. Only 4% of the tumors have been classified as germinal center B-cell type (GCB) and 21% as ABC/non-GCB by all methods. None of the algorithms provided prognostic information in the R-CHOP (rituximab plus cyclophosphamide-adriamycin-vincristine-prednisone)-treated cohort. Further work is required to standardize IHC algorithms for DLBCL cell-of-origin classification for these to be considered reliable alternatives to molecular-based methods to be used for clinical decisions. ©2013 AACR.

Genetic relatedness among vaginal and anal isolates of Candida albicans from women with vulvovaginal candidiasis in north-east Brazil.

PubMed

Araújo Paulo de Medeiros, Mariana; Vieira de Melo, Ana Patrícia; Gonçalves, Sarah Santos; Milan, Eveline Pipolo; Chaves, Guilherme Maranhão

2014-11-01

Vulvovaginal candidiasis (VVC) is one of the most common causes of vaginitis and affects about 75% of women of reproductive age. In order to better understand the epidemiology and pathogenesis of this disease, we evaluated genetic relatedness among 62 clinical isolates of Candida albicans sequentially obtained from the anus and vagina of patients with sporadic and recurrent VVC. Evaluation of patients' demographic and clinical data, direct examination, and colony forming units (c.f.u.) counts of vaginal and anal samples were also performed. The genotypes of strains were determined with ABC genotyping and Randomly Amplified Polymorphic DNA (RAPD). Genotype A was the most prevalent (93.6%), followed by genotype C (6.4%), whereas genotype B was not found. We found the maintenance of the same ABC genotype, regardless of the body site of each patient. Most of the vaginal strains suffered microevolution, whereas most of the anal strains were replaced during the period of study. Vaginal and anal isolates of C. albicans obtained simultaneously from the same patient showed the same ABC genotype and high genetic similarity as determined by RAPD. Genotype A seemed to be dominant in both vaginal and anal isolates of patients with VVC. Our results corroborate the hypothesis that there are 'substrains' of the C. albicans vaginal clone successfully established, which dominate in an apparently random manner over the course of time. It is suggested that the anal reservoir constitutes a possible source for vaginal infection in most of the cases. © 2014 The Authors.

Application of ant colony algorithm in path planning of the data center room robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Ma, Jianming; Wang, Ying

2017-05-01

According to the Internet Data Center (IDC) room patrol robot as the background, the robot in the search path of autonomous obstacle avoidance and path planning ability, worked out in advance of the robot room patrol mission. The simulation experimental results show that the improved ant colony algorithm for IDC room patrol robot obstacle avoidance planning, makes the robot along an optimal or suboptimal and safe obstacle avoidance path to reach the target point to complete the task. To prove the feasibility of the method.

A modify ant colony optimization for the grid jobs scheduling problem with QoS requirements

NASA Astrophysics Data System (ADS)

Pu, Xun; Lu, XianLiang

2011-10-01

Job scheduling with customers' quality of service (QoS) requirement is challenging in grid environment. In this paper, we present a modify Ant colony optimization (MACO) for the Job scheduling problem in grid. Instead of using the conventional construction approach to construct feasible schedules, the proposed algorithm employs a decomposition method to satisfy the customer's deadline and cost requirements. Besides, a new mechanism of service instances state updating is embedded to improve the convergence of MACO. Experiments demonstrate the effectiveness of the proposed algorithm.

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

NASA Astrophysics Data System (ADS)

Zecchin, A. C.; Simpson, A. R.; Maier, H. R.; Marchi, A.; Nixon, J. B.

2012-09-01

Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm's searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm's searching behavior.

Finite grade pheromone ant colony optimization for image segmentation

NASA Astrophysics Data System (ADS)

Yuanjing, F.; Li, Y.; Liangjun, K.

2008-06-01

By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.

[Comparative evaluation of the sensitivity of Acinetobacter to colistin, using the prediffusion and minimum inhibitory concentration methods: detection of heteroresistant isolates].

PubMed

Herrera, Melina E; Mobilia, Liliana N; Posse, Graciela R

2011-01-01

The objective of this study is to perform a comparative evaluation of the prediffusion and minimum inhibitory concentration (MIC) methods for the detection of sensitivity to colistin, and to detect Acinetobacter baumanii-calcoaceticus complex (ABC) heteroresistant isolates to colistin. We studied 75 isolates of ABC recovered from clinically significant samples obtained from various centers. Sensitivity to colistin was determined by prediffusion as well as by MIC. All the isolates were sensitive to colistin, with MIC = 2µg/ml. The results were analyzed by dispersion graph and linear regression analysis, revealing that the prediffusion method did not correlate with the MIC values for isolates sensitive to colistin (r² = 0.2017). Detection of heteroresistance to colistin was determined by plaque efficiency of all the isolates with the same initial MICs of 2, 1, and 0.5 µg/ml, which resulted in 14 of them with a greater than 8-fold increase in the MIC in some cases. When the sensitivity of these resistant colonies was determined by prediffusion, the resulting dispersion graph and linear regression analysis yielded an r² = 0.604, which revealed a correlation between the methodologies used.

Colony image acquisition and genetic segmentation algorithm and colony analyses

NASA Astrophysics Data System (ADS)

Wang, W. X.

2012-01-01

Colony anaysis is used in a large number of engineerings such as food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing. In order to reduce laboring and increase analysis acuracy, many researchers and developers have made efforts for image analysis systems. The main problems in the systems are image acquisition, image segmentation and image analysis. In this paper, to acquire colony images with good quality, an illumination box was constructed. In the box, the distances between lights and dishe, camra lens and lights, and camera lens and dishe are adjusted optimally. In image segmentation, It is based on a genetic approach that allow one to consider the segmentation problem as a global optimization,. After image pre-processing and image segmentation, the colony analyses are perfomed. The colony image analysis consists of (1) basic colony parameter measurements; (2) colony size analysis; (3) colony shape analysis; and (4) colony surface measurements. All the above visual colony parameters can be selected and combined together, used to make a new engineeing parameters. The colony analysis can be applied into different applications.

Ant colony algorithm for clustering in portfolio optimization

NASA Astrophysics Data System (ADS)

Subekti, R.; Sari, E. R.; Kusumawati, R.

2018-03-01

This research aims to describe portfolio optimization using clustering methods with ant colony approach. Two stock portfolios of LQ45 Indonesia is proposed based on the cluster results obtained from ant colony optimization (ACO). The first portfolio consists of assets with ant colony displacement opportunities beyond the defined probability limits of the researcher, where the weight of each asset is determined by mean-variance method. The second portfolio consists of two assets with the assumption that each asset is a cluster formed from ACO. The first portfolio has a better performance compared to the second portfolio seen from the Sharpe index.

Solving NP-Hard Problems with Physarum-Based Ant Colony System.

PubMed

Liu, Yuxin; Gao, Chao; Zhang, Zili; Lu, Yuxiao; Chen, Shi; Liang, Mingxin; Tao, Li

2017-01-01

NP-hard problems exist in many real world applications. Ant colony optimization (ACO) algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a Physarum-based pheromone matrix optimization strategy in ant colony system (ACS) for solving NP-hard problems such as traveling salesman problem (TSP) and 0/1 knapsack problem (0/1 KP). In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.

At-Least Version of the Generalized Minimum Spanning Tree Problem: Optimization Through Ant Colony System and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Janich, Karl W.

2005-01-01

The At-Least version of the Generalized Minimum Spanning Tree Problem (L-GMST) is a problem in which the optimal solution connects all defined clusters of nodes in a given network at a minimum cost. The L-GMST is NPHard; therefore, metaheuristic algorithms have been used to find reasonable solutions to the problem as opposed to computationally feasible exact algorithms, which many believe do not exist for such a problem. One such metaheuristic uses a swarm-intelligent Ant Colony System (ACS) algorithm, in which agents converge on a solution through the weighing of local heuristics, such as the shortest available path and the number of agents that recently used a given path. However, in a network using a solution derived from the ACS algorithm, some nodes may move around to different clusters and cause small changes in the network makeup. Rerunning the algorithm from the start would be somewhat inefficient due to the significance of the changes, so a genetic algorithm based on the top few solutions found in the ACS algorithm is proposed to quickly and efficiently adapt the network to these small changes.

ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.

PubMed

Sagban, Rafid; Ku-Mahamud, Ku Ruhana; Abu Bakar, Muhamad Shahbani

2015-01-01

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

Real-time bacterial microcolony counting using on-chip microscopy

NASA Astrophysics Data System (ADS)

Jung, Jae Hee; Lee, Jung Eun

2016-02-01

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery.

Real-time bacterial microcolony counting using on-chip microscopy

PubMed Central

Jung, Jae Hee; Lee, Jung Eun

2016-01-01

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery. PMID:26902822

A novel global Harmony Search method based on Ant Colony Optimisation algorithm

NASA Astrophysics Data System (ADS)

Fouad, Allouani; Boukhetala, Djamel; Boudjema, Fares; Zenger, Kai; Gao, Xiao-Zhi

2016-03-01

The Global-best Harmony Search (GHS) is a stochastic optimisation algorithm recently developed, which hybridises the Harmony Search (HS) method with the concept of swarm intelligence in the particle swarm optimisation (PSO) to enhance its performance. In this article, a new optimisation algorithm called GHSACO is developed by incorporating the GHS with the Ant Colony Optimisation algorithm (ACO). Our method introduces a novel improvisation process, which is different from that of the GHS in the following aspects. (i) A modified harmony memory (HM) representation and conception. (ii) The use of a global random switching mechanism to monitor the choice between the ACO and GHS. (iii) An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The proposed GHSACO algorithm has been applied to various benchmark functions and constrained optimisation problems. Simulation results demonstrate that it can find significantly better solutions when compared with the original HS and some of its variants.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

PubMed

Mohsen, Abdulqader M

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

NASA Astrophysics Data System (ADS)

Hou, Huirang; Zheng, Dandan; Nie, Laixiao

2015-04-01

For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until -10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm

NASA Astrophysics Data System (ADS)

Vrugt, Jasper A.; Beven, Keith J.

2018-04-01

This essay illustrates some recent developments to the DiffeRential Evolution Adaptive Metropolis (DREAM) MATLAB toolbox of Vrugt (2016) to delineate and sample the behavioural solution space of set-theoretic likelihood functions used within the GLUE (Limits of Acceptability) framework (Beven and Binley, 1992, 2014; Beven and Freer, 2001; Beven, 2006). This work builds on the DREAM(ABC) algorithm of Sadegh and Vrugt (2014) and enhances significantly the accuracy and CPU-efficiency of Bayesian inference with GLUE. In particular it is shown how lack of adequate sampling in the model space might lead to unjustified model rejection.

Adaptive cockroach swarm algorithm

NASA Astrophysics Data System (ADS)

Obagbuwa, Ibidun C.; Abidoye, Ademola P.

2017-07-01

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

Application of ant colony optimization to optimal foragaing theory: comparison of simulation and field results

USDA-ARS?s Scientific Manuscript database

Ant Colony Optimization (ACO) refers to the family of algorithms inspired by the behavior of real ants and used to solve combinatorial problems such as the Traveling Salesman Problem (TSP).Optimal Foraging Theory (OFT) is an evolutionary principle wherein foraging organisms or insect parasites seek ...

A seismic fault recognition method based on ant colony optimization

NASA Astrophysics Data System (ADS)

Chen, Lei; Xiao, Chuangbai; Li, Xueliang; Wang, Zhenli; Huo, Shoudong

2018-05-01

Fault recognition is an important section in seismic interpretation and there are many methods for this technology, but no one can recognize fault exactly enough. For this problem, we proposed a new fault recognition method based on ant colony optimization which can locate fault precisely and extract fault from the seismic section. Firstly, seismic horizons are extracted by the connected component labeling algorithm; secondly, the fault location are decided according to the horizontal endpoints of each horizon; thirdly, the whole seismic section is divided into several rectangular blocks and the top and bottom endpoints of each rectangular block are considered as the nest and food respectively for the ant colony optimization algorithm. Besides that, the positive section is taken as an actual three dimensional terrain by using the seismic amplitude as a height. After that, the optimal route from nest to food calculated by the ant colony in each block is judged as a fault. Finally, extensive comparative tests were performed on the real seismic data. Availability and advancement of the proposed method were validated by the experimental results.

Performance advantages of CPML over UPML absorbing boundary conditions in FDTD algorithm

NASA Astrophysics Data System (ADS)

Gvozdic, Branko D.; Djurdjevic, Dusan Z.

2017-01-01

Implementation of absorbing boundary condition (ABC) has a very important role in simulation performance and accuracy in finite difference time domain (FDTD) method. The perfectly matched layer (PML) is the most efficient type of ABC. The aim of this paper is to give detailed insight in and discussion of boundary conditions and hence to simplify the choice of PML used for termination of computational domain in FDTD method. In particular, we demonstrate that using the convolutional PML (CPML) has significant advantages in terms of implementation in FDTD method and reducing computer resources than using uniaxial PML (UPML). An extensive number of numerical experiments has been performed and results have shown that CPML is more efficient in electromagnetic waves absorption. Numerical code is prepared, several problems are analyzed and relative error is calculated and presented.

Inverse estimation of the spheroidal particle size distribution using Ant Colony Optimization algorithms in multispectral extinction technique

NASA Astrophysics Data System (ADS)

He, Zhenzong; Qi, Hong; Wang, Yuqing; Ruan, Liming

2014-10-01

Four improved Ant Colony Optimization (ACO) algorithms, i.e. the probability density function based ACO (PDF-ACO) algorithm, the Region ACO (RACO) algorithm, Stochastic ACO (SACO) algorithm and Homogeneous ACO (HACO) algorithm, are employed to estimate the particle size distribution (PSD) of the spheroidal particles. The direct problems are solved by the extended Anomalous Diffraction Approximation (ADA) and the Lambert-Beer law. Three commonly used monomodal distribution functions i.e. the Rosin-Rammer (R-R) distribution function, the normal (N-N) distribution function, and the logarithmic normal (L-N) distribution function are estimated under dependent model. The influence of random measurement errors on the inverse results is also investigated. All the results reveal that the PDF-ACO algorithm is more accurate than the other three ACO algorithms and can be used as an effective technique to investigate the PSD of the spheroidal particles. Furthermore, the Johnson's SB (J-SB) function and the modified beta (M-β) function are employed as the general distribution functions to retrieve the PSD of spheroidal particles using PDF-ACO algorithm. The investigation shows a reasonable agreement between the original distribution function and the general distribution function when only considering the variety of the length of the rotational semi-axis.

A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray

NASA Astrophysics Data System (ADS)

Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu

2016-12-01

This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems

PubMed Central

Idris, Hajara; Junaidu, Sahalu B.; Adewumi, Aderemi O.

2017-01-01

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user’s Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user’s QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time. PMID:28545075

Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring

PubMed Central

Liu, Xiaoli; Chen, Hong-Xin; Kim, Jinxie

2014-01-01

A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline. PMID:25025089

The research of autonomous obstacle avoidance of mobile robot based on multi-sensor integration

NASA Astrophysics Data System (ADS)

Zhao, Ming; Han, Baoling

2016-11-01

The object of this study is the bionic quadruped mobile robot. The study has proposed a system design plan for mobile robot obstacle avoidance with the binocular stereo visual sensor and the self-control 3D Lidar integrated with modified ant colony optimization path planning to realize the reconstruction of the environmental map. Because the working condition of a mobile robot is complex, the result of the 3D reconstruction with a single binocular sensor is undesirable when feature points are few and the light condition is poor. Therefore, this system integrates the stereo vision sensor blumblebee2 and the Lidar sensor together to detect the cloud information of 3D points of environmental obstacles. This paper proposes the sensor information fusion technology to rebuild the environment map. Firstly, according to the Lidar data and visual data on obstacle detection respectively, and then consider two methods respectively to detect the distribution of obstacles. Finally fusing the data to get the more complete, more accurate distribution of obstacles in the scene. Then the thesis introduces ant colony algorithm. It has analyzed advantages and disadvantages of the ant colony optimization and its formation cause deeply, and then improved the system with the help of the ant colony optimization to increase the rate of convergence and precision of the algorithm in robot path planning. Such improvements and integrations overcome the shortcomings of the ant colony optimization like involving into the local optimal solution easily, slow search speed and poor search results. This experiment deals with images and programs the motor drive under the compiling environment of Matlab and Visual Studio and establishes the visual 2.5D grid map. Finally it plans a global path for the mobile robot according to the ant colony algorithm. The feasibility and effectiveness of the system are confirmed by ROS and simulation platform of Linux.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

PubMed Central

Ahmed, Gulnaz; Zou, Jianhua; Zhao, Xi; Sadiq Fareed, Mian Muhammad

2017-01-01

The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput. PMID:28241492

Design of healthy hearts in the heartland (H3): A practice-randomized, comparative effectiveness study.

PubMed

Ciolino, Jody D; Jackson, Kathryn L; Liss, David T; Brown, Tiffany; Walunas, Theresa L; Murakami, Linda; Chung, Isabel; Persell, Stephen D; Kho, Abel N

2018-06-02

The Healthy Hearts in the Heartland (H3) study is part of a nationwide effort, EvidenceNOW, seeking to better understand the ability of small primary care practices to improve "ABCS" clinical quality measures: appropriate Aspirin therapy, Blood pressure control, Cholesterol management, and Smoking cessation. H3 aimed to assess feasibility of implementing Point-of-Care (POC) or POC plus Population Management (POC + PM) quality improvement (QI) strategies to improve ABCS at practices in Illinois, Indiana, and Wisconsin. We describe the design and randomization of the H3 study. We conducted a two-arm (1:1, POC:POC + PM), practice-randomized, comparative effectiveness study in 226 primary care practices across four "waves" of randomization with a 12-month intervention period, followed by a six-month sustainability period. Randomization controlled imbalance in nine baseline variables through a modified constrained algorithm. Among others, we used initial, unverified estimates of baseline ABCS values. We randomized 112 and 114 practices to POC and POC + PM arms, respectively. Randomization ensured baseline comparability for all nine key variables, including the ABCS measures indicating proportion of patients at the practice level meeting each quality measure. Median(Inner Quartile Range) values were A: 0.78(0.66-0.86) in POC arm vs. 0.77(0.63-0.86) in POC + PM arm, B: 0.64(0.53-0.73) vs. 0.64(0.53-0.75), C: 0.78(0.63-0.86) vs. 0.75(0.64-0.81), S: 0.80(0.65-0.81) vs. 0.79(0.61-0.91). Surrogate estimates for the true ABCS at baseline coupled with the unique randomization logic achieved adequate baseline balance on these outcomes. Similar practice- or cluster-randomized trials may consider adaptations of this design. Final analyses on 12- and 18-month ABCS outcomes for the H3 study are forthcoming. This trial is registered on ClinicalTrials.gov (Initial post: 11/05/2015; identifier: NCT02598284; https://clinicaltrials.gov/ct2/show/NCT02598284?term=NCT02598284&rank=1). Copyright © 2018 Elsevier Inc. All rights reserved.

Scheduling in Sensor Grid Middleware for Telemedicine Using ABC Algorithm

PubMed Central

Vigneswari, T.; Mohamed, M. A. Maluk

2014-01-01

Advances in microelectromechanical systems (MEMS) and nanotechnology have enabled design of low power wireless sensor nodes capable of sensing different vital signs in our body. These nodes can communicate with each other to aggregate data and transmit vital parameters to a base station (BS). The data collected in the base station can be used to monitor health in real time. The patient wearing sensors may be mobile leading to aggregation of data from different BS for processing. Processing real time data is compute-intensive and telemedicine facilities may not have appropriate hardware to process the real time data effectively. To overcome this, sensor grid has been proposed in literature wherein sensor data is integrated to the grid for processing. This work proposes a scheduling algorithm to efficiently process telemedicine data in the grid. The proposed algorithm uses the popular swarm intelligence algorithm for scheduling to overcome the NP complete problem of grid scheduling. Results compared with other heuristic scheduling algorithms show the effectiveness of the proposed algorithm. PMID:25548557

Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows

PubMed Central

Jawarneh, Sana; Abdullah, Salwani

2015-01-01

This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Genrang; Lin, ZhengChun

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

A spatially informative optic flow model of bee colony with saccadic flight strategy for global optimization.

PubMed

Das, Swagatam; Biswas, Subhodip; Panigrahi, Bijaya K; Kundu, Souvik; Basu, Debabrota

2014-10-01

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.

Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli.

PubMed

Uehara, Shota; Fujimoto, Kazuhiro J; Tanaka, Shigenori

2015-07-07

Protein-ligand docking is an optimization problem, which aims to identify the binding pose of a ligand with the lowest energy in the active site of a target protein. In this study, we employed a novel optimization algorithm called fitness learning-based artificial bee colony with proximity stimuli (FlABCps) for docking. Simulation results revealed that FlABCps improved the success rate of docking, compared to four state-of-the-art algorithms. The present results also showed superior docking performance of FlABCps, in particular for dealing with highly flexible ligands and proteins with a wide and shallow binding pocket.

Efficient distribution of toy products using ant colony optimization algorithm

NASA Astrophysics Data System (ADS)

Hidayat, S.; Nurpraja, C. A.

2017-12-01

CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Combination of Ibrutinib and ABT-199 in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma.

PubMed

Kuo, Hsu-Ping; Ezell, Scott A; Schweighofer, Karl J; Cheung, Leo W K; Hsieh, Sidney; Apatira, Mutiah; Sirisawad, Mint; Eckert, Karl; Hsu, Ssucheng J; Chen, Chun-Te; Beaupre, Darrin M; Versele, Matthias; Chang, Betty Y

2017-07-01

Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma are the most prevalent B-lymphocyte neoplasms in which abnormal activation of the Bruton tyrosine kinase (BTK)-mediated B-cell receptor signaling pathway contributes to pathogenesis. Ibrutinib is an oral covalent BTK inhibitor that has shown some efficacy in both indications. To improve ibrutinib efficacy through combination therapy, we first investigated differential gene expression in parental and ibrutinib-resistant cell lines to better understand the mechanisms of resistance. Ibrutinib-resistant TMD8 cells had higher BCL2 gene expression and increased sensitivity to ABT-199, a BCL-2 inhibitor. Consistently, clinical samples from ABC-DLBCL patients who experienced poorer response to ibrutinib had higher BCL2 gene expression. We further demonstrated synergistic growth suppression by ibrutinib and ABT-199 in multiple ABC-DLBCL, GCB-DLBCL, and follicular lymphoma cell lines. The combination of both drugs also reduced colony formation, increased apoptosis, and inhibited tumor growth in a TMD8 xenograft model. A synergistic combination effect was also found in ibrutinib-resistant cells generated by either genetic mutation or drug treatment. Together, these findings suggest a potential clinical benefit from ibrutinib and ABT-199 combination therapy. Mol Cancer Ther; 16(7); 1246-56. ©2017 AACR . ©2017 American Association for Cancer Research.

Improving the Accuracy of Planet Occurrence Rates from Kepler Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Hsu, Danley C.; Ford, Eric B.; Ragozzine, Darin; Morehead, Robert C.

2018-05-01

We present a new framework to characterize the occurrence rates of planet candidates identified by Kepler based on hierarchical Bayesian modeling, approximate Bayesian computing (ABC), and sequential importance sampling. For this study, we adopt a simple 2D grid in planet radius and orbital period as our model and apply our algorithm to estimate occurrence rates for Q1–Q16 planet candidates orbiting solar-type stars. We arrive at significantly increased planet occurrence rates for small planet candidates (R p < 1.25 R ⊕) at larger orbital periods (P > 80 day) compared to the rates estimated by the more common inverse detection efficiency method (IDEM). Our improved methodology estimates that the occurrence rate density of small planet candidates in the habitable zone of solar-type stars is {1.6}-0.5+1.2 per factor of 2 in planet radius and orbital period. Additionally, we observe a local minimum in the occurrence rate for strong planet candidates marginalized over orbital period between 1.5 and 2 R ⊕ that is consistent with previous studies. For future improvements, the forward modeling approach of ABC is ideally suited to incorporating multiple populations, such as planets, astrophysical false positives, and pipeline false alarms, to provide accurate planet occurrence rates and uncertainties. Furthermore, ABC provides a practical statistical framework for answering complex questions (e.g., frequency of different planetary architectures) and providing sound uncertainties, even in the face of complex selection effects, observational biases, and follow-up strategies. In summary, ABC offers a powerful tool for accurately characterizing a wide variety of astrophysical populations.

Optic disc detection using ant colony optimization

NASA Astrophysics Data System (ADS)

Dias, Marcy A.; Monteiro, Fernando C.

2012-09-01

The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detection. Recently, the ACO was used in fundus images to detect edges, and therefore, to segment the OD and other anatomical retinal structures. We present an algorithm for the detection of OD in the retina which takes advantage of the Gabor wavelet transform, entropy and ACO algorithm. Forty images of the retina from DRIVE database were used to evaluate the performance of our method.

Refraction law and Fermat principle: a project using the ant colony optimization algorithm for undergraduate students in physics

NASA Astrophysics Data System (ADS)

Vuong, Q. L.; Rigaut, C.; Gossuin, Y.

2018-07-01

A programming project for undergraduate students in physics is proposed in this work. Its goal is to check the Snell–Descartes law of refraction using the Fermat principle and the ant colony optimization algorithm. The project involves basic mathematics and physics and is adapted to students with basic programming skills. More advanced tools can be used (but are not mandatory) as parallelization or object-oriented programming, which makes the project also suitable for more experienced students. We propose two tests to validate the program. Our algorithm is able to find solutions which are close to the theoretical predictions. Two quantities are defined to study its convergence and the quality of the solutions. It is also shown that the choice of the values of the simulation parameters is important to efficiently obtain precise results.

Application of cellular automatons and ant algorithms in avionics

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Selvesiuk, N. I.; Platoshin, G. A.; Semenova, E. V.

2018-03-01

The paper considers two algorithms for searching quasi-optimal solutions of discrete optimization problems with regard to the tasks of avionics placing. The first one solves the problem of optimal placement of devices by installation locations, the second one is for the problem of finding the shortest route between devices. Solutions are constructed using a cellular automaton and the ant colony algorithm.

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

PubMed Central

Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan

2017-01-01

With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service. PMID:28282894

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results.

PubMed

Otero, Fernando E B; Freitas, Alex A

2016-01-01

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

An ant colony based algorithm for overlapping community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

2015-06-01

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Wang, Geng; Zhou, Kexin; Zhang, Yeming

2018-04-01

The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

PubMed Central

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590

A tunable algorithm for collective decision-making.

PubMed

Pratt, Stephen C; Sumpter, David J T

2006-10-24

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

Clinical performance of three bolus calculators in subjects with type 1 diabetes mellitus: a head-to-head-to-head comparison.

PubMed

Zisser, Howard; Wagner, Robin; Pleus, Stefan; Haug, Cornelia; Jendrike, Nina; Parkin, Chris; Schweitzer, Matthias; Freckmann, Guido

2010-12-01

Insulin pump systems now provide automated bolus calculators (ABCs) that electronically calculate insulin boluses to address carbohydrate intake and out-of-range blood glucose (bG) levels. We compared the efficacy of three ABCs (Accu-Chek(®) Combo [Roche Insulin Delivery Systems (IDS), Inc., Fishers, IN, a member of the Roche Group], Animas(®) 2020 [Animas Corp., West Chester, PA, a Johnson and Johnson company], and MiniMed Paradigm Bolus Wizard(®) [Medtronic MiniMed, Northridge, CA]) to safely reduce postprandial hyperglycemia in type 1 diabetes mellitus (T1DM). T1DM subjects (n = 24) were recruited at a single center for a prospective, triple crossover study. ABCs with the programmed target range (80-140 mg/dL) were used in random order. Postprandial hyperglycemia was induced by reducing the calculated bolus by 25%. Two hours after test meals, the ABCs were allowed to determine whether a correction bolus was needed. Differences between 6-h bG values after test meals that achieved 2-h postprandial hyperglycemia and the mean of the target range (110 mg/dL) were determined. The mean difference between 6-h bG levels following test meals and the 110 mg/dL bG target with the MiniMed device (47.4 ± 31.8 mg/dL) was significantly higher than the Animas (17.3 ± 30.9 mg/dL) and Roche IDS (18.8 ± 33.8 mg/dL) devices (P = 0.0022 and P = 0.0049, respectively). The number of meals with 2-h postprandial hyperglycemia and bG levels at 2 h was similar. Roche IDS and Animas devices recommended correction boluses significantly (P = 0.0001 and P = 0.0002, respectively) more frequently than the MiniMed device. ABC use was not associated with severe hypoglycemia. There was no significant difference in the rate of mild hypoglycemia (bG <60 mg/dL not requiring assistance) among the three groups (Roche IDS and Animas, n = 2; MiniMed, n = 0). In this study, the Roche IDS and Animas devices were more efficacious in controlling postprandial hyperglycemia than the MiniMed device. This may be due, in part, to differences in ABC setup protocols and algorithms. Use of ABCs can assist in controlling postprandial glycemia without significant hypoglycemia.

Optimization research of railway passenger transfer scheme based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Ni, Xiang

2018-05-01

The optimization research of railway passenger transfer scheme can provide strong support for railway passenger transport system, and its essence is path search. This paper realized the calculation of passenger transfer scheme for high speed railway when giving the time and stations of departure and arrival. The specific method that used were generating a passenger transfer service network of high-speed railway, establishing optimization model and searching by Ant Colony Algorithm. Finally, making analysis on the scheme from LanZhouxi to BeiJingXi which were based on high-speed railway network of China in 2017. The results showed that the transfer network and model had relatively high practical value and operation efficiency.

An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem

NASA Astrophysics Data System (ADS)

Li, Dongni; Guo, Rongtao; Zhan, Rongxin; Yin, Yong

2018-06-01

In this article, an innovative artificial bee colony (IABC) algorithm is proposed, which incorporates two mechanisms. On the one hand, to provide the evolutionary process with a higher starting level, genetic programming (GP) is used to generate heuristic rules by exploiting the elements that constitute the problem. On the other hand, to achieve a better balance between exploration and exploitation, a leading mechanism is proposed to attract individuals towards a promising region. To evaluate the performance of IABC in solving practical and complex problems, it is applied to the intercell scheduling problem with limited transportation capacity. It is observed that the GP-generated rules incorporate the elements of the most competing human-designed rules, and they are more effective than the human-designed ones. Regarding the leading mechanism, the strategies of the ageing leader and multiple challengers make the algorithm less likely to be trapped in local optima.

Hybrid real-code ant colony optimisation for constrained mechanical design

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Bureerat, Sujin

2016-01-01

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

NASA Astrophysics Data System (ADS)

PourkamaliAnaraki, Maryam; Sadeghi, Mehdi

2016-03-01

Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/

The short version of the Activities-specific Balance Confidence (ABC) scale: its validity, reliability, and relationship to balance impairment and falls in older adults.

PubMed

Schepens, Stacey; Goldberg, Allon; Wallace, Melissa

2010-01-01

A shortened version of the ABC 16-item scale (ABC-16), the ABC-6, has been proposed as an alternative balance confidence measure. We investigated whether the ABC-6 is a valid and reliable measure of balance confidence and examined its relationship to balance impairment and falls in older adults. Thirty-five community-dwelling older adults completed the ABC-16, including the 6 questions of the ABC-6. They also completed the following clinical balance tests: unipedal stance time (UST), functional reach (FR), Timed Up and Go (TUG), and maximum step length (MSL). Participants reported 12-month falls history. Balance confidence on the ABC-6 was significantly lower than on the ABC-16, however scores were highly correlated. Fallers reported lower balance confidence than non-fallers as measured by the ABC-6 scale, but confidence did not differ between the groups with the ABC-16. The ABC-6 significantly correlated with all balance tests assessed and number of falls. The ABC-16 significantly correlated with all balance tests assessed, but not with number of falls. Test-retest reliability for the ABC-16 and ABC-6 was good to excellent. The ABC-6 is a valid and reliable measure of balance confidence in community-dwelling older adults, and shows stronger relationships to falls than does the ABC-16. The ABC-6 may be a more useful balance confidence assessment tool than the ABC-16. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

NASA Astrophysics Data System (ADS)

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

2018-04-01

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images.

An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas

PubMed Central

Shao, Jing; Yang, Lina; Peng, Ling; Chi, Tianhe; Wang, Xiaomeng

2015-01-01

China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining “replace” and “alter” operations, and the third is a “swap” strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures. PMID:26394148

Monitoring black-tailed prairie dog colonies with high-resolution satellite imagery

USGS Publications Warehouse

Sidle, John G.; Johnson, D.H.; Euliss, B.R.; Tooze, M.

2002-01-01

The United States Fish and Wildlife Service has determined that the black-tailed prairie dog (Cynomys ludovicianus) warrants listing as a threatened species under the Endangered Species Act. Central to any conservation planning for the black-tailed prairie dog is an appropriate detection and monitoring technique. Because coarse-resolution satellite imagery is not adequate to detect black-tailed prairie dog colonies, we examined the usefulness of recently available high-resolution (1-m) satellite imagery. In 6 purchased scenes of national grasslands, we were easily able to visually detect small and large colonies without using image-processing algorithms. The Ikonos (Space Imaging(tm)) satellite imagery was as adequate as large-scale aerial photography to delineate colonies. Based on the high quality of imagery, we discuss a possible monitoring program for black-tailed prairie dog colonies throughout the Great Plains, using the species' distribution in North Dakota as an example. Monitoring plots could be established and imagery acquired periodically to track the expansion and contraction of colonies.

Specific Pathogen Free Macaque Colonies: A Review of Principles and Recent Advances for Viral Testing and Colony Management

PubMed Central

Yee, JoAnn L.; Vandeford, Thomas H.; Didier, Elizabeth S.; Gray, Stanton; Lewis, Anne; Roberts, Jeffrey; Taylor, Kerry; Bohm, Rudolf P.

2016-01-01

Specific Pathogen Free (SPF) macaques provide valuable animal models for biomedical research. In 1989 the National Center for Research Resources (now Office of Research Infrastructure Programs ORIP) of the National Institutes of Health initiated experimental research contracts to establish and maintain SPF colonies. The derivation and maintenance of SPF macaque colonies is a complex undertaking requiring knowledge of the biology of the agents for exclusion and normal physiology and behavior of macaques, application of the latest diagnostic technology, facilities management, and animal husbandry. This review provides information on the biology of the four viral agents targeted for exclusion in ORIP SPF macaque colonies, describes current state-of-the-art viral diagnostic algorithms, presents data from proficiency testing of diagnostic assays between laboratories at institutions participating in the ORIP SPF program, and outlines management strategies for maintaining the integrity of SPF colonies using results of diagnostic testing as a guide to decision making. PMID:26932456

Genetic susceptibility, colony size, and water temperature drive white-pox disease on the coral Acropora palmata.

PubMed

Muller, Erinn M; van Woesik, Robert

2014-01-01

Outbreaks of coral diseases are one of the greatest threats to reef corals in the Caribbean, yet the mechanisms that lead to coral diseases are still largely unknown. Here we examined the spatial-temporal dynamics of white-pox disease on Acropora palmata coral colonies of known genotypes. We took a Bayesian approach, using Integrated Nested Laplace Approximation algorithms, to examine which covariates influenced the presence of white-pox disease over seven years. We showed that colony size, genetic susceptibility of the coral host, and high-water temperatures were the primary tested variables that were positively associated with the presence of white-pox disease on A. palmata colonies. Our study also showed that neither distance from previously diseased individuals, nor colony location, influenced the dynamics of white-pox disease. These results suggest that white-pox disease was most likely a consequence of anomalously high water temperatures that selectively compromised the oldest colonies and the most susceptible coral genotypes.

Genetic Susceptibility, Colony Size, and Water Temperature Drive White-Pox Disease on the Coral Acropora palmata

PubMed Central

Muller, Erinn M.; van Woesik, Robert

2014-01-01

Outbreaks of coral diseases are one of the greatest threats to reef corals in the Caribbean, yet the mechanisms that lead to coral diseases are still largely unknown. Here we examined the spatial-temporal dynamics of white-pox disease on Acropora palmata coral colonies of known genotypes. We took a Bayesian approach, using Integrated Nested Laplace Approximation algorithms, to examine which covariates influenced the presence of white-pox disease over seven years. We showed that colony size, genetic susceptibility of the coral host, and high-water temperatures were the primary tested variables that were positively associated with the presence of white-pox disease on A. palmata colonies. Our study also showed that neither distance from previously diseased individuals, nor colony location, influenced the dynamics of white-pox disease. These results suggest that white-pox disease was most likely a consequence of anomalously high water temperatures that selectively compromised the oldest colonies and the most susceptible coral genotypes. PMID:25372835

A hybrid monkey search algorithm for clustering analysis.

PubMed

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

Use of artificial bee colonies algorithm as numerical approximation of differential equations solution

NASA Astrophysics Data System (ADS)

Fikri, Fariz Fahmi; Nuraini, Nuning

2018-03-01

The differential equation is one of the branches in mathematics which is closely related to human life problems. Some problems that occur in our life can be modeled into differential equations as well as systems of differential equations such as the Lotka-Volterra model and SIR model. Therefore, solving a problem of differential equations is very important. Some differential equations are difficult to solve, so numerical methods are needed to solve that problems. Some numerical methods for solving differential equations that have been widely used are Euler Method, Heun Method, Runge-Kutta and others. However, some of these methods still have some restrictions that cause the method cannot be used to solve more complex problems such as an evaluation interval that we cannot change freely. New methods are needed to improve that problems. One of the method that can be used is the artificial bees colony algorithm. This algorithm is one of metaheuristic algorithm method, which can come out from local search space and do exploration in solution search space so that will get better solution than other method.

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Textural defect detect using a revised ant colony clustering algorithm

NASA Astrophysics Data System (ADS)

Zou, Chao; Xiao, Li; Wang, Bingwen

2007-11-01

We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.

Loss of plastoglobule kinases ABC1K1 and ABC1K3 causes conditional degreening, modified prenyl-lipids, and recruitment of the jasmonic acid pathway

USDA-ARS?s Scientific Manuscript database

Plastoglobules (PGs) are plastid lipid-protein particles. This study examines the function of PG-localized kinases ABC1K1 and ABC1K3 in Arabidopsis thaliana. Several lines of evidence suggested that ABC1K1 and ABC1K3 form a protein complex. Null mutants for both genes (abc1k1 and abc1k3) and the dou...

Dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization

NASA Astrophysics Data System (ADS)

Li, Li

2018-03-01

In order to extract target from complex background more quickly and accurately, and to further improve the detection effect of defects, a method of dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization was proposed. Firstly, the method of single-threshold selection based on Arimoto entropy was extended to dual-threshold selection in order to separate the target from the background more accurately. Then intermediate variables in formulae of Arimoto entropy dual-threshold selection was calculated by recursion to eliminate redundant computation effectively and to reduce the amount of calculation. Finally, the local search phase of artificial bee colony algorithm was improved by chaotic sequence based on tent mapping. The fast search for two optimal thresholds was achieved using the improved bee colony optimization algorithm, thus the search could be accelerated obviously. A large number of experimental results show that, compared with the existing segmentation methods such as multi-threshold segmentation method using maximum Shannon entropy, two-dimensional Shannon entropy segmentation method, two-dimensional Tsallis gray entropy segmentation method and multi-threshold segmentation method using reciprocal gray entropy, the proposed method can segment target more quickly and accurately with superior segmentation effect. It proves to be an instant and effective method for image segmentation.

Quantitation of Staphylococcus aureus in Seawater Using CHROMagar™ SA

PubMed Central

Pombo, David; Hui, Jennifer; Kurano, Michelle; Bankowski, Matthew J; Seifried, Steven E

2010-01-01

A microbiological algorithm has been developed to analyze beach water samples for the determination of viable colony forming units (CFU) of Staphylococcus aureus (S. aureus). Membrane filtration enumeration of S. aureus from recreational beach waters using the chromogenic media CHROMagar™SA alone yields a positive predictive value (PPV) of 70%. Presumptive CHROMagar™SA colonies were confirmed as S. aureus by 24-hour tube coagulase test. Combined, these two tests yield a PPV of 100%. This algorithm enables accurate quantitation of S. aureus in seawater in 72 hours and could support risk-prediction processes for recreational waters. A more rapid protocol, utilizing a 4-hour tube coagulase confirmatory test, enables a 48-hour turnaround time with a modest false negative rate of less than 10%. PMID:20222490

Effectiveness of the Assessment of Burden of Chronic Obstructive Pulmonary Disease (ABC) tool: study protocol of a cluster randomised trial in primary and secondary care

PubMed Central

2014-01-01

Background Chronic Obstructive Pulmonary Disease (COPD) is a growing worldwide problem that imposes a great burden on the daily life of patients. Since there is no cure, the goal of treating COPD is to maintain or improve quality of life. We have developed a new tool, the Assessment of Burden of COPD (ABC) tool, to assess and visualize the integrated health status of patients with COPD, and to provide patients and healthcare providers with a treatment algorithm. This tool may be used during consultations to monitor the burden of COPD and to adjust treatment if necessary. The aim of the current study is to analyse the effectiveness of the ABC tool compared with usual care on health related quality of life among COPD patients over a period of 18 months. Methods/Design A cluster randomised controlled trial will be conducted in COPD patients in both primary and secondary care throughout the Netherlands. An intervention group, receiving care based on the ABC tool, will be compared with a control group receiving usual care. The primary outcome will be the change in score on a disease-specific-quality-of-life questionnaire, the Saint George Respiratory Questionnaire. Secondary outcomes will be a different questionnaire (the COPD Assessment Test), lung function and number of exacerbations. During the 18 months follow-up, seven measurements will be conducted, including a baseline and final measurement. Patients will receive questionnaires to be completed at home. Additional data, such as number of exacerbations, will be recorded by the patients’ healthcare providers. A total of 360 patients will be recruited by 40 general practitioners and 20 pulmonologists. Additionally, a process evaluation will be performed among patients and healthcare providers. Discussion The new ABC tool complies with the 2014 Global Initiative for Chronic Obstructive Lung Disease guidelines, which describe the necessity to classify patients on both their airway obstruction and a comprehensive symptom assessment. It has been developed to classify patients, but also to provide visual insight into the burden of COPD and to provide treatment advice. Trial registration Netherlands Trial Register, NTR3788. PMID:25098313

[The ABC transporters of Saccharomyces cerevisiae].

PubMed

Wawrzycka, Donata

2011-01-01

The ABC transporters (ATP Binding Cassette) compose one of the bigest protein family with the great medical, industrial and economical impact. They are found in all organism from bacteria to man. ABC proteins are responsible for resistance of microorganism to antibiotics and fungicides and multidrug resistance of cancer cells. Mutations in ABC transporters genes cause seriuos deseases like cystic fibrosis, adrenoleucodystrophy or ataxia. Transport catalized by ABC proteins is charged with energy from the ATP hydrolysis. The ABC superfamily contains transporters, canals, receptors. Analysis of the Saccharomyces cerevisiae genome allowed to distinguish 30 potential ABC proteins which are classified into 6 subfamilies. The structural and functional similarity of the yeast and human ABC proteins allowes to use the S. cerevisiae as a model organism for ABC transporters characterisation. In this work the present state of knowleadge on yeast S. cerevisiae ABC proteins was summarised.

Identification of ABC Transporter Genes of Fusarium graminearum with Roles in Azole Tolerance and/or Virulence

PubMed Central

Döll, Katharina; Karlovsky, Petr; Deising, Holger B.; Wirsel, Stefan G. R.

2013-01-01

Fusarium graminearum is a plant pathogen infecting several important cereals, resulting in substantial yield losses and mycotoxin contamination of the grain. Triazole fungicides are used to control diseases caused by this fungus on a worldwide scale. Our previous microarray study indicated that 15 ABC transporter genes were transcriptionally upregulated in response to tebuconazole treatment. Here, we deleted four ABC transporter genes in two genetic backgrounds of F. graminearum representing the DON (deoxynivalenol) and the NIV (nivalenol) trichothecene chemotypes. Deletion of FgABC3 and FgABC4 belonging to group I of ABC-G and to group V of ABC-C subfamilies of ABC transporters, respectively, considerably increased the sensitivity to the class I sterol biosynthesis inhibitors triazoles and fenarimol. Such effects were specific since they did not occur with any other fungicide class tested. Assessing the contribution of the four ABC transporters to virulence of F. graminearum revealed that, irrespective of their chemotypes, deletion mutants of FgABC1 (ABC-C subfamily group V) and FgABC3 were impeded in virulence on wheat, barley and maize. Phylogenetic context and analyses of mycotoxin production suggests that FgABC3 may encode a transporter protecting the fungus from host-derived antifungal molecules. In contrast, FgABC1 may encode a transporter responsible for the secretion of fungal secondary metabolites alleviating defence of the host. Our results show that ABC transporters play important and diverse roles in both fungicide resistance and pathogenesis of F. graminearum. PMID:24244413

Using Approximate Bayesian Computation to infer sex ratios from acoustic data.

PubMed

Lehnen, Lisa; Schorcht, Wigbert; Karst, Inken; Biedermann, Martin; Kerth, Gerald; Puechmaille, Sebastien J

2018-01-01

Population sex ratios are of high ecological relevance, but are challenging to determine in species lacking conspicuous external cues indicating their sex. Acoustic sexing is an option if vocalizations differ between sexes, but is precluded by overlapping distributions of the values of male and female vocalizations in many species. A method allowing the inference of sex ratios despite such an overlap will therefore greatly increase the information extractable from acoustic data. To meet this demand, we developed a novel approach using Approximate Bayesian Computation (ABC) to infer the sex ratio of populations from acoustic data. Additionally, parameters characterizing the male and female distribution of acoustic values (mean and standard deviation) are inferred. This information is then used to probabilistically assign a sex to a single acoustic signal. We furthermore develop a simpler means of sex ratio estimation based on the exclusion of calls from the overlap zone. Applying our methods to simulated data demonstrates that sex ratio and acoustic parameter characteristics of males and females are reliably inferred by the ABC approach. Applying both the ABC and the exclusion method to empirical datasets (echolocation calls recorded in colonies of lesser horseshoe bats, Rhinolophus hipposideros) provides similar sex ratios as molecular sexing. Our methods aim to facilitate evidence-based conservation, and to benefit scientists investigating ecological or conservation questions related to sex- or group specific behaviour across a wide range of organisms emitting acoustic signals. The developed methodology is non-invasive, low-cost and time-efficient, thus allowing the study of many sites and individuals. We provide an R-script for the easy application of the method and discuss potential future extensions and fields of applications. The script can be easily adapted to account for numerous biological systems by adjusting the type and number of groups to be distinguished (e.g. age, social rank, cryptic species) and the acoustic parameters investigated.

ABCA Transporter Gene Expression and Poor Outcome in Epithelial Ovarian Cancer

PubMed Central

Hedditch, Ellen L.; Gao, Bo; Russell, Amanda J.; Lu, Yi; Emmanuel, Catherine; Beesley, Jonathan; Johnatty, Sharon E.; Chen, Xiaoqing; Harnett, Paul; George, Joshy; Williams, Rebekka T.; Flemming, Claudia; Lambrechts, Diether; Despierre, Evelyn; Lambrechts, Sandrina; Vergote, Ignace; Karlan, Beth; Lester, Jenny; Orsulic, Sandra; Walsh, Christine; Fasching, Peter; Beckmann, Matthias W.; Ekici, Arif B.; Hein, Alexander; Matsuo, Keitaro; Hosono, Satoyo; Nakanishi, Toru; Yatabe, Yasushi; Pejovic, Tanja; Bean, Yukie; Heitz, Florian; Harter, Philipp; du Bois, Andreas; Schwaab, Ira; Hogdall, Estrid; Kjaer, Susan K.; Jensen, Allan; Hogdall, Claus; Lundvall, Lene; Engelholm, Svend Aage; Brown, Bob; Flanagan, James; Metcalf, Michelle D; Siddiqui, Nadeem; Sellers, Thomas; Fridley, Brooke; Cunningham, Julie; Schildkraut, Joellen; Iversen, Ed; Weber, Rachel P.; Berchuck, Andrew; Goode, Ellen; Bowtell, David D.; Chenevix-Trench, Georgia; deFazio, Anna; Norris, Murray D.; MacGregor, Stuart; Haber, Michelle; Henderson, Michelle J.

2014-01-01

Background ATP-binding cassette (ABC) transporters play various roles in cancer biology and drug resistance, but their association with outcomes in serous epithelial ovarian cancer (EOC) is unknown. Methods The relationship between clinical outcomes and ABC transporter gene expression in two independent cohorts of high-grade serous EOC tumors was assessed with real-time quantitative polymerase chain reaction, analysis of expression microarray data, and immunohistochemistry. Associations between clinical outcomes and ABCA transporter gene single nucleotide polymorphisms were tested in a genome-wide association study. Impact of short interfering RNA–mediated gene suppression was determined by colony forming and migration assays. Association with survival was assessed with Kaplan–Meier analysis and log-rank tests. All statistical tests were two-sided. Results Associations with outcome were observed with ABC transporters of the “A” subfamily, but not with multidrug transporters. High-level expression of ABCA1, ABCA6, ABCA8, and ABCA9 in primary tumors was statistically significantly associated with reduced survival in serous ovarian cancer patients. Low levels of ABCA5 and the C-allele of rs536009 were associated with shorter overall survival (hazard ratio for death = 1.50; 95% confidence interval [CI] =1.26 to 1.79; P = 6.5e−6). The combined expression pattern of ABCA1, ABCA5, and either ABCA8 or ABCA9 was associated with particularly poor outcome (mean overall survival in group with adverse ABCA1, ABCA5 and ABCA9 gene expression = 33.2 months, 95% CI = 26.4 to 40.1; vs 55.3 months in the group with favorable ABCA gene expression, 95% CI = 49.8 to 60.8; P = .001), independently of tumor stage or surgical debulking status. Suppression of cholesterol transporter ABCA1 inhibited ovarian cancer cell growth and migration in vitro, and statin treatment reduced ovarian cancer cell migration. Conclusions Expression of ABCA transporters was associated with poor outcome in serous ovarian cancer, implicating lipid trafficking as a potentially important process in EOC. PMID:24957074

One-dimensional swarm algorithm packaging

NASA Astrophysics Data System (ADS)

Lebedev, Boris K.; Lebedev, Oleg B.; Lebedeva, Ekaterina O.

2018-05-01

The paper considers an algorithm for solving the problem of onedimensional packaging based on the adaptive behavior model of an ant colony. The key role in the development of the ant algorithm is the choice of representation (interpretation) of the solution. The structure of the solution search graph, the procedure for finding solutions on the graph, the methods of deposition and evaporation of pheromone are described. Unlike the canonical paradigm of an ant algorithm, an ant on the solution search graph generates sets of elements distributed across blocks. Experimental studies were conducted on IBM PC. Compared with the existing algorithms, the results are improved.

Dual ant colony operational modal analysis parameter estimation method

NASA Astrophysics Data System (ADS)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.

Regulation of Expression of abcA and Its Response to Environmental Conditions

PubMed Central

Villet, Regis A.; Truong-Bolduc, Que Chi; Wang, Yin; Estabrooks, Zoe; Medeiros, Heidi

2014-01-01

The ATP-dependent transporter gene abcA in Staphylococcus aureus confers resistance to hydrophobic β-lactams. In strain ISP794, abcA is regulated by the transcriptional regulators MgrA and NorG and shares a 420-nucleotide intercistronic region with the divergently transcribed pbp4 gene, which encodes the transpeptidase Pbp4. Exposure of exponentially growing cells to iron-limited media, oxidative stress, and acidic pH (5.5) for 0.5 to 2 h had no effect on abcA expression. In contrast, nutrient limitation produced a significant increase in abcA transcripts. We identified three additional regulators (SarA, SarZ, and Rot) that bind to the overlapping promoter region of abcA and pbp4 in strain MW2 and investigated their role in the regulation of abcA expression. Expression of abcA is decreased by 10.0-fold in vivo in a subcutaneous abscess model. In vitro, abcA expression depends on rot and sarZ regulators. Moenomycin A exposure of strain MW2 produced an increase in abcA transcripts. Relative to MW2, the MIC of moenomycin was decreased 8-fold for MW2ΔabcA and increased 10-fold for the MW2 abcA overexpresser, suggesting that moenomycin is a substrate of AbcA. PMID:24509312

How Can Bee Colony Algorithm Serve Medicine?

PubMed Central

Salehahmadi, Zeinab; Manafi, Amir

2014-01-01

Healthcare professionals usually should make complex decisions with far reaching consequences and associated risks in health care fields. As it was demonstrated in other industries, the ability to drill down into pertinent data to explore knowledge behind the data can greatly facilitate superior, informed decisions to ensue the facts. Nature has always inspired researchers to develop models of solving the problems. Bee colony algorithm (BCA), based on the self-organized behavior of social insects is one of the most popular member of the family of population oriented, nature inspired meta-heuristic swarm intelligence method which has been proved its superiority over some other nature inspired algorithms. The objective of this model was to identify valid novel, potentially useful, and understandable correlations and patterns in existing data. This review employs a thematic analysis of online series of academic papers to outline BCA in medical hive, reducing the response and computational time and optimizing the problems. To illustrate the benefits of this model, the cases of disease diagnose system are presented. PMID:25489530

How can bee colony algorithm serve medicine?

PubMed

Salehahmadi, Zeinab; Manafi, Amir

2014-07-01

Healthcare professionals usually should make complex decisions with far reaching consequences and associated risks in health care fields. As it was demonstrated in other industries, the ability to drill down into pertinent data to explore knowledge behind the data can greatly facilitate superior, informed decisions to ensue the facts. Nature has always inspired researchers to develop models of solving the problems. Bee colony algorithm (BCA), based on the self-organized behavior of social insects is one of the most popular member of the family of population oriented, nature inspired meta-heuristic swarm intelligence method which has been proved its superiority over some other nature inspired algorithms. The objective of this model was to identify valid novel, potentially useful, and understandable correlations and patterns in existing data. This review employs a thematic analysis of online series of academic papers to outline BCA in medical hive, reducing the response and computational time and optimizing the problems. To illustrate the benefits of this model, the cases of disease diagnose system are presented.

Evaluation of the influence of dominance rules for the assembly line design problem under consideration of product design alternatives

NASA Astrophysics Data System (ADS)

Oesterle, Jonathan; Lionel, Amodeo

2018-06-01

The current competitive situation increases the importance of realistically estimating product costs during the early phases of product and assembly line planning projects. In this article, several multi-objective algorithms using difference dominance rules are proposed to solve the problem associated with the selection of the most effective combination of product and assembly lines. The list of developed algorithms includes variants of ant colony algorithms, evolutionary algorithms and imperialist competitive algorithms. The performance of each algorithm and dominance rule is analysed by five multi-objective quality indicators and fifty problem instances. The algorithms and dominance rules are ranked using a non-parametric statistical test.

Analysis of image thresholding segmentation algorithms based on swarm intelligence

NASA Astrophysics Data System (ADS)

Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

2013-03-01

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction.

PubMed

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John Bo

2008-10-29

We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024-1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581-590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6 degrees C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, epsilon of 0.21) and an RMSE of 45.1 degrees C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3 degrees C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5 degrees C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.

Using Ant Colony Optimization for Routing in VLSI Chips

NASA Astrophysics Data System (ADS)

Arora, Tamanna; Moses, Melanie

2009-04-01

Rapid advances in VLSI technology have increased the number of transistors that fit on a single chip to about two billion. A frequent problem in the design of such high performance and high density VLSI layouts is that of routing wires that connect such large numbers of components. Most wire-routing problems are computationally hard. The quality of any routing algorithm is judged by the extent to which it satisfies routing constraints and design objectives. Some of the broader design objectives include minimizing total routed wire length, and minimizing total capacitance induced in the chip, both of which serve to minimize power consumed by the chip. Ant Colony Optimization algorithms (ACO) provide a multi-agent framework for combinatorial optimization by combining memory, stochastic decision and strategies of collective and distributed learning by ant-like agents. This paper applies ACO to the NP-hard problem of finding optimal routes for interconnect routing on VLSI chips. The constraints on interconnect routing are used by ants as heuristics which guide their search process. We found that ACO algorithms were able to successfully incorporate multiple constraints and route interconnects on suite of benchmark chips. On an average, the algorithm routed with total wire length 5.5% less than other established routing algorithms.

Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model

NASA Astrophysics Data System (ADS)

Deng, Guang-Feng; Lin, Woo-Tsong

This work presents Ant Colony Optimization (ACO), which was initially developed to be a meta-heuristic for combinatorial optimization, for solving the cardinality constraints Markowitz mean-variance portfolio model (nonlinear mixed quadratic programming problem). To our knowledge, an efficient algorithmic solution for this problem has not been proposed until now. Using heuristic algorithms in this case is imperative. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.

Genome-Wide Identification, Characterization and Phylogenetic Analysis of ATP-Binding Cassette (ABC) Transporter Genes in Common Carp (Cyprinus carpio).

PubMed

Liu, Xiang; Li, Shangqi; Peng, Wenzhu; Feng, Shuaisheng; Feng, Jianxin; Mahboob, Shahid; Al-Ghanim, Khalid A; Xu, Peng

2016-01-01

The ATP-binding cassette (ABC) gene family is considered to be one of the largest gene families in all forms of prokaryotic and eukaryotic life. Although the ABC transporter genes have been annotated in some species, detailed information about the ABC superfamily and the evolutionary characterization of ABC genes in common carp (Cyprinus carpio) are still unclear. In this research, we identified 61 ABC transporter genes in the common carp genome. Phylogenetic analysis revealed that they could be classified into seven subfamilies, namely 11 ABCAs, six ABCBs, 19 ABCCs, eight ABCDs, two ABCEs, four ABCFs, and 11 ABCGs. Comparative analysis of the ABC genes in seven vertebrate species including common carp, showed that at least 10 common carp genes were retained from the third round of whole genome duplication, while 12 duplicated ABC genes may have come from the fourth round of whole genome duplication. Gene losses were also observed for 14 ABC genes. Expression profiles of the 61 ABC genes in six common carp tissues (brain, heart, spleen, kidney, intestine, and gill) revealed extensive functional divergence among the ABC genes. Different copies of some genes had tissue-specific expression patterns, which may indicate some gene function specialization. This study provides essential genomic resources for future studies in common carp.

Genome-Wide Identification, Characterization and Phylogenetic Analysis of ATP-Binding Cassette (ABC) Transporter Genes in Common Carp (Cyprinus carpio)

PubMed Central

Peng, Wenzhu; Feng, Shuaisheng; Feng, Jianxin; Mahboob, Shahid; Al-Ghanim, Khalid A.

2016-01-01

The ATP-binding cassette (ABC) gene family is considered to be one of the largest gene families in all forms of prokaryotic and eukaryotic life. Although the ABC transporter genes have been annotated in some species, detailed information about the ABC superfamily and the evolutionary characterization of ABC genes in common carp (Cyprinus carpio) are still unclear. In this research, we identified 61 ABC transporter genes in the common carp genome. Phylogenetic analysis revealed that they could be classified into seven subfamilies, namely 11 ABCAs, six ABCBs, 19 ABCCs, eight ABCDs, two ABCEs, four ABCFs, and 11 ABCGs. Comparative analysis of the ABC genes in seven vertebrate species including common carp, showed that at least 10 common carp genes were retained from the third round of whole genome duplication, while 12 duplicated ABC genes may have come from the fourth round of whole genome duplication. Gene losses were also observed for 14 ABC genes. Expression profiles of the 61 ABC genes in six common carp tissues (brain, heart, spleen, kidney, intestine, and gill) revealed extensive functional divergence among the ABC genes. Different copies of some genes had tissue-specific expression patterns, which may indicate some gene function specialization. This study provides essential genomic resources for future studies in common carp. PMID:27058731

Aneurysmal Bone Cyst: An Analysis of 38 Cases and Report of Four Unusual Surface Ones

PubMed Central

Shooshtarizadeh, Tina; Movahedinia, Sajjadeh; Mostafavi, Hassan; Jamshidi, Khodamorad; Sami, Sam Hajialiloo

2016-01-01

Aneurysmal bone cyst (ABC) is a benign expansile bone tumor, most commonly involving the medulla of long bones. ABC rarely arises within the cortex or in the subperiosteal region, radiographically mimicking other conditions, in particular surface osteosarcomathat is low-grade in nature and may go secondary ABC changes, and telangiectatic osteosarcoma. Both of these are sometimes mistaken microscopically for primary ABC. We review the characteristics of ABC cases in our center and report four unusualsurface ABCs arising in the subperiosteal or cortical region of long bones, identified among 38 histologically proven ABCs during a four-year period in our center. The surface ABCs occurred at an older agewith a predilection for diaphysis of femur, tibia, and humerus. PMID:27200397

Independent activity of the homologous small regulatory RNAs AbcR1 and AbcR2 in the legume symbiont Sinorhizobium meliloti.

PubMed

Torres-Quesada, Omar; Millán, Vicenta; Nisa-Martínez, Rafael; Bardou, Florian; Crespi, Martín; Toro, Nicolás; Jiménez-Zurdo, José I

2013-01-01

The legume symbiont Sinorhizobium meliloti expresses a plethora of small noncoding RNAs (sRNAs) whose function is mostly unknown. Here, we have functionally characterized two tandemly encoded S. meliloti Rm1021 sRNAs that are similar in sequence and structure. Homologous sRNAs (designated AbcR1 and AbcR2) have been shown to regulate several ABC transporters in the related α-proteobacteria Agrobacterium tumefaciens and Brucella abortus. In Rm1021, AbcR1 and AbcR2 exhibit divergent unlinked regulation and are stabilized by the RNA chaperone Hfq. AbcR1 is transcribed in actively dividing bacteria, either in culture, rhizosphere or within the invasion zone of mature alfalfa nodules. Conversely, AbcR2 expression is induced upon entry into stationary phase and under abiotic stress. Only deletion of AbcR1 resulted into a discrete growth delay in rich medium, but both are dispensable for symbiosis. Periplasmic proteome profiling revealed down-regulation of the branched-chain amino acid binding protein LivK by AbcR1, but not by AbcR2. A double-plasmid reporter assay confirmed the predicted specific targeting of the 5'-untranslated region of the livK mRNA by AbcR1 in vivo. Our findings provide evidences of independent regulatory functions of these sRNAs, probably to fine-tune nutrient uptake in free-living and undifferentiated symbiotic rhizobia.

Independent Activity of the Homologous Small Regulatory RNAs AbcR1 and AbcR2 in the Legume Symbiont Sinorhizobium meliloti

PubMed Central

Torres-Quesada, Omar; Millán, Vicenta; Nisa-Martínez, Rafael; Bardou, Florian; Crespi, Martín; Toro, Nicolás; Jiménez-Zurdo, José I.

2013-01-01

The legume symbiont Sinorhizobium meliloti expresses a plethora of small noncoding RNAs (sRNAs) whose function is mostly unknown. Here, we have functionally characterized two tandemly encoded S. meliloti Rm1021 sRNAs that are similar in sequence and structure. Homologous sRNAs (designated AbcR1 and AbcR2) have been shown to regulate several ABC transporters in the related α-proteobacteria Agrobacterium tumefaciens and Brucella abortus. In Rm1021, AbcR1 and AbcR2 exhibit divergent unlinked regulation and are stabilized by the RNA chaperone Hfq. AbcR1 is transcribed in actively dividing bacteria, either in culture, rhizosphere or within the invasion zone of mature alfalfa nodules. Conversely, AbcR2 expression is induced upon entry into stationary phase and under abiotic stress. Only deletion of AbcR1 resulted into a discrete growth delay in rich medium, but both are dispensable for symbiosis. Periplasmic proteome profiling revealed down-regulation of the branched-chain amino acid binding protein LivK by AbcR1, but not by AbcR2. A double-plasmid reporter assay confirmed the predicted specific targeting of the 5′-untranslated region of the livK mRNA by AbcR1 in vivo. Our findings provide evidences of independent regulatory functions of these sRNAs, probably to fine-tune nutrient uptake in free-living and undifferentiated symbiotic rhizobia. PMID:23869210

Statistical Hypothesis Testing in Intraspecific Phylogeography: NCPA versus ABC

PubMed Central

Templeton, Alan R.

2009-01-01

Nested clade phylogeographic analysis (NCPA) and approximate Bayesian computation (ABC) have been used to test phylogeographic hypotheses. Multilocus NCPA tests null hypotheses, whereas ABC discriminates among a finite set of alternatives. The interpretive criteria of NCPA are explicit and allow complex models to be built from simple components. The interpretive criteria of ABC are ad hoc and require the specification of a complete phylogeographic model. The conclusions from ABC are often influenced by implicit assumptions arising from the many parameters needed to specify a complex model. These complex models confound many assumptions so that biological interpretations are difficult. Sampling error is accounted for in NCPA, but ABC ignores important sources of sampling error that creates pseudo-statistical power. NCPA generates the full sampling distribution of its statistics, but ABC only yields local probabilities, which in turn make it impossible to distinguish between a good fitting model, a non-informative model, and an over-determined model. Both NCPA and ABC use approximations, but convergences of the approximations used in NCPA are well defined whereas those in ABC are not. NCPA can analyze a large number of locations, but ABC cannot. Finally, the dimensionality of tested hypothesis is known in NCPA, but not for ABC. As a consequence, the “probabilities” generated by ABC are not true probabilities and are statistically non-interpretable. Accordingly, ABC should not be used for hypothesis testing, but simulation approaches are valuable when used in conjunction with NCPA or other methods that do not rely on highly parameterized models. PMID:19192182

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, J; Hill, G; Spiegel, J

Purpose: To investigate the clinical and dosimetric benefits of automatic gating of left breast mixed with breath-hold technique. Methods: Two Active Breathing Control systems, ABC2.0 and ABC3.0, were used during simulation and treatment delivery. The two systems are different such that ABC2.0 is a breath-hold system without beam control capability, while ABC3.0 has capability in both breath-hold and beam gating. At simulation, each patient was scanned twice: one with free breathing (FB) and one with breath hold through ABC. Treatment plan was generated on the CT with ABC. The same plan was also recalculated on the CT with FB. Thesemore » two plans were compared to assess plan quality. For treatments with ABC2.0, beams with MU > 55 were manually split into multiple subfields. All subfields were identical and shared the total MU. For treatment with ABC3.0, beam splitting was unnecessary. Instead, treatment was delivered in gating mode mixed with breath-hold technique. Treatment delivery efficiency using the two systems was compared. Results: The prescribed dose was 50.4Gy at 1.8Gy/fraction. The maximum heart dose averaged over 10 patients was 46.0±2.5Gy and 24.5±12.2Gy for treatments with FB and with ABC respectively. The corresponding heart V10 was 13.2±3.6% and 1.0±1.6% respectively. The averaged MUs were 99.8±7.5 for LMT, 99.2±9.4 for LLT. For treatment with ABC2.0, normally the original beam was split into 2 subfields. The averaged total time to delivery all beams was 4.3±0.4min for treatments with ABC2.0 and 3.3±0.6min for treatments with ABC3.0 in gating mode. Conclusion: Treatment with ABC tremendously reduced heart dose. Compared to treatments with ABC2.0, gating with ABC3.0 reduced the total treatment time by 23%. Use of ABC3.0 improved the delivery efficiency, and eliminated the possibility of mistreatments. The latter may happen with ABC2.0 where beam is not terminated when breath signal falls outside of the treatment window.« less

Ant colony optimisation-direct cover: a hybrid ant colony direct cover technique for multi-level synthesis of multiple-valued logic functions

NASA Astrophysics Data System (ADS)

Abd-El-Barr, Mostafa

2010-12-01

The use of non-binary (multiple-valued) logic in the synthesis of digital systems can lead to savings in chip area. Advances in very large scale integration (VLSI) technology have enabled the successful implementation of multiple-valued logic (MVL) circuits. A number of heuristic algorithms for the synthesis of (near) minimal sum-of products (two-level) realisation of MVL functions have been reported in the literature. The direct cover (DC) technique is one such algorithm. The ant colony optimisation (ACO) algorithm is a meta-heuristic that uses constructive greediness to explore a large solution space in finding (near) optimal solutions. The ACO algorithm mimics the ant's behaviour in the real world in using the shortest path to reach food sources. We have previously introduced an ACO-based heuristic for the synthesis of two-level MVL functions. In this article, we introduce the ACO-DC hybrid technique for the synthesis of multi-level MVL functions. The basic idea is to use an ant to decompose a given MVL function into a number of levels and then synthesise each sub-function using a DC-based technique. The results obtained using the proposed approach are compared to those obtained using existing techniques reported in the literature. A benchmark set consisting of 50,000 randomly generated 2-variable 4-valued functions is used in the comparison. The results obtained using the proposed ACO-DC technique are shown to produce efficient realisation in terms of the average number of gates (as a measure of chip area) needed for the synthesis of a given MVL function.

Catalytic and transport cycles of ABC exporters.

PubMed

Al-Shawi, Marwan K

2011-09-07

ABC (ATP-binding cassette) transporters are arguably the most important family of ATP-driven transporters in biology. Despite considerable effort and advances in determining the structures and physiology of these transporters, their fundamental molecular mechanisms remain elusive and highly controversial. How does ATP hydrolysis by ABC transporters drive their transport function? Part of the problem in answering this question appears to be a perceived need to formulate a universal mechanism. Although it has been generally hoped and assumed that the whole superfamily of ABC transporters would exhibit similar conserved mechanisms, this is proving not to be the case. Structural considerations alone suggest that there are three overall types of coupling mechanisms related to ABC exporters, small ABC importers and large ABC importers. Biochemical and biophysical characterization leads us to the conclusion that, even within these three classes, the catalytic and transport mechanisms are not fully conserved, but continue to evolve. ABC transporters also exhibit unusual characteristics not observed in other primary transporters, such as uncoupled basal ATPase activity, that severely complicate mechanistic studies by established methods. In this chapter, I review these issues as related to ABC exporters in particular. A consensus view has emerged that ABC exporters follow alternating-access switch transport mechanisms. However, some biochemical data suggest that alternating catalytic site transport mechanisms are more appropriate for fully symmetrical ABC exporters. Heterodimeric and asymmetrical ABC exporters appear to conform to simple alternating-access-type mechanisms.

An ATP-driven efflux pump is a novel pathogenicity factor in rice blast disease.

PubMed Central

Urban, M; Bhargava, T; Hamer, J E

1999-01-01

Cells tolerate exposure to cytotoxic compounds through the action of ATP-driven efflux pumps belonging to the ATP-binding cassette (ABC) superfamily of membrane transporters. Phytopathogenic fungi encounter toxic environments during plant invasion as a result of the plant defense response. Here we demonstrate the requirement for an ABC transporter during host infection by the fungal plant pathogen Magnaporthe grisea. The ABC1 gene was identified in an insertional mutagenesis screen for pathogenicity mutants. The ABC1 insertional mutant and a gene-replacement mutant arrest growth and die shortly after penetrating either rice or barley epidermal cells. The ABC1-encoded protein is similar to yeast ABC transporters implicated in multidrug resistance, and ABC1 gene transcripts are inducible by toxic drugs and a rice phytoalexin. However, abc1 mutants are not hypersensitive to antifungal compounds. The non-pathogenic, insertional mutation in ABC1 occurs in the promoter region and dramatically reduces transcript induction by metabolic poisons. These data strongly suggest that M.grisea requires the up-regulation of specific ABC transporters for pathogenesis; most likely to protect itself against plant defense mechanisms. PMID:9927411

Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.

PubMed

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization

PubMed Central

Zhu, Zhouquan

2013-01-01

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

PubMed Central

Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

2016-01-01

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

4th International Consensus Conference on Advanced Breast Cancer (ABC4), Lisbon, November 4, 2017 : ABC4 Consensus: Assessment by a Panel of German Experts.

PubMed

Untch, Michael; Würstlein, Rachel; Marschner, Norbert; Lüftner, Diana; Augustin, Doris; Briest, Susanne; Ettl, Johannes; Haidinger, Renate; Müller, Lothar; Müller, Volkmar; Ruckhäberle, Eugen; Harbeck, Nadia; Thomssen, Christoph

2018-05-01

The fourth international advanced breast cancer consensus conference (ABC4) on the diagnosis and treatment of advanced breast cancer (ABC) headed by Professor Fatima Cardoso was once again held in Lisbon on November 2 - 4, 2017. To simplify matters, the abbreviation ABC will be used hereinafter in the text. In clinical practice, the abbreviation corresponds to metastatic breast cancer or locally far-advanced disease. This year the focus was on new developments in the treatment of ABC. Topics discussed included the importance of CDK4/6 inhibition in hormone receptor (HR)-positive ABC, the use of dual antibody blockade to treat HER2-positive ABC, PARP inhibition in triple-negative ABC and the potential therapeutic outcomes. Another major area discussed at the conference was BRCA-associated breast cancer, the treatment of cerebral metastasis, and individualized treatment decisions based on molecular testing (so-called precision medicine). As in previous years, close cooperation with representatives from patient organizations from around the world is an important aspect of the ABC conference. This cooperation was reinforced and expanded at the ABC4 conference. A global alliance was founded at the conclusion of the consensus conference, which aims to promote and coordinate the measures considered necessary by patient advocates worldwide. Because the panel of experts was composed of specialists from all over the world, it was inevitable that the ABC consensus also reflected country-specific features. As in previous years, a team of German breast cancer specialists who closely followed the consensus voting of the ABC panelists in Lisbon and intensively discussed the votes has therefore commented on the consensus in the context of the current German guidelines on the diagnosis and treatment of breast cancer 1 ,  2 used in clinical practice in Germany. The ABC consensus is based on the votes of the ABC panelists in Lisbon.

Sustained delivery of thermostabilized chABC enhances axonal sprouting and functional recovery after spinal cord injury.

PubMed

Lee, Hyunjung; McKeon, Robert J; Bellamkonda, Ravi V

2010-02-23

Chondroitin sulfate proteoglycans (CSPGs) are a major class of axon growth inhibitors that are up-regulated after spinal cord injury (SCI) and contribute to regenerative failure. Chondroitinase ABC (chABC) digests glycosaminoglycan chains on CSPGs and can thereby overcome CSPG-mediated inhibition. But chABC loses its enzymatic activity rapidly at 37 degrees C, necessitating the use of repeated injections or local infusions for a period of days to weeks. These infusion systems are invasive, infection-prone, and clinically problematic. To overcome this limitation, we have thermostabilized chABC and developed a system for its sustained local delivery in vivo, obviating the need for chronically implanted catheters and pumps. Thermostabilized chABC remained active at 37 degrees C in vitro for up to 4 weeks. CSPG levels remained low in vivo up to 6 weeks post-SCI when thermostabilized chABC was delivered by a hydrogel-microtube scaffold system. Axonal growth and functional recovery following the sustained local release of thermostabilized chABC versus a single treatment of unstabilized chABC demonstrated significant differences in CSPG digestion. Animals treated with thermostabilized chABC in combination with sustained neurotrophin-3 delivery showed significant improvement in locomotor function and enhanced growth of cholera toxin B subunit-positive sensory axons and sprouting of serotonergic fibers. Therefore, improving chABC thermostability facilitates minimally invasive, sustained, local delivery of chABC that is potentially effective in overcoming CSPG-mediated regenerative failure. Combination therapy with thermostabilized chABC with neurotrophic factors enhances axonal regrowth, sprouting, and functional recovery after SCI.

The mitochondrial transporter ABC-me (ABCB10), a downstream target of GATA-1, is essential for erythropoiesis in vivo.

PubMed

Hyde, B B; Liesa, M; Elorza, A A; Qiu, W; Haigh, S E; Richey, L; Mikkola, H K; Schlaeger, T M; Shirihai, O S

2012-07-01

The mitochondrial transporter ATP binding cassette mitochondrial erythroid (ABC-me/ABCB10) is highly induced during erythroid differentiation by GATA-1 and its overexpression increases hemoglobin production rates in vitro. However, the role of ABC-me in erythropoiesis in vivo is unknown. Here we report for the first time that erythrocyte development in mice requires ABC-me. ABC-me-/- mice die at day 12.5 of gestation, showing nearly complete eradication of primitive erythropoiesis and lack of hemoglobinized cells at day 10.5. ABC-me-/- erythroid cells fail to differentiate because they exhibit a marked increase in apoptosis, both in vivo and ex vivo. Erythroid precursors are particularly sensitive to oxidative stress and ABC-me in the heart and its yeast ortholog multidrug resistance-like 1 have been shown to protect against oxidative stress. Thus, we hypothesized that increased apoptosis in ABC-me-/- erythroid precursors was caused by oxidative stress. Within this context, ABC-me deletion causes an increase in mitochondrial superoxide production and protein carbonylation in erythroid precursors. Furthermore, treatment of ABC-me-/- erythroid progenitors with the mitochondrial antioxidant MnTBAP (superoxide dismutase 2 mimetic) supports survival, ex vivo differentiation and increased hemoglobin production. Altogether, our findings demonstrate that ABC-me is essential for erythropoiesis in vivo.

An Application of the Difference Potentials Method to Solving External Problems in CFD

NASA Technical Reports Server (NTRS)

Ryaben 'Kii, Victor S.; Tsynkov, Semyon V.

1997-01-01

Numerical solution of infinite-domain boundary-value problems requires some special techniques that would make the problem available for treatment on the computer. Indeed, the problem must be discretized in a way that the computer operates with only finite amount of information. Therefore, the original infinite-domain formulation must be altered and/or augmented so that on one hand the solution is not changed (or changed slightly) and on the other hand the finite discrete formulation becomes available. One widely used approach to constructing such discretizations consists of truncating the unbounded original domain and then setting the artificial boundary conditions (ABC's) at the newly formed external boundary. The role of the ABC's is to close the truncated problem and at the same time to ensure that the solution found inside the finite computational domain would be maximally close to (in the ideal case, exactly the same as) the corresponding fragment of the original infinite-domain solution. Let us emphasize that the proper treatment of artificial boundaries may have a profound impact on the overall quality and performance of numerical algorithms. The latter statement is corroborated by the numerous computational experiments and especially concerns the area of CFD, in which external problems present a wide class of practically important formulations. In this paper, we review some work that has been done over the recent years on constructing highly accurate nonlocal ABC's for calculation of compressible external flows. The approach is based on implementation of the generalized potentials and pseudodifferential boundary projection operators analogous to those proposed first by Calderon. The difference potentials method (DPM) by Ryaben'kii is used for the effective computation of the generalized potentials and projections. The resulting ABC's clearly outperform the existing methods from the standpoints of accuracy and robustness, in many cases noticeably speed up the multigrid convergence, and at the same time are quite comparable to other methods from the standpoints of geometric universality and simplicity of implementation.

Predicting Flood in Perlis Using Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Nadia Sabri, Syaidatul; Saian, Rizauddin

2017-06-01

Flood forecasting is widely being studied in order to reduce the effect of flood such as loss of property, loss of life and contamination of water supply. Usually flood occurs due to continuous heavy rainfall. This study used a variant of Ant Colony Optimization (ACO) algorithm named the Ant-Miner to develop the classification prediction model to predict flood. However, since Ant-Miner only accept discrete data, while rainfall data is a time series data, a pre-processing steps is needed to discretize the rainfall data initially. This study used a technique called the Symbolic Aggregate Approximation (SAX) to convert the rainfall time series data into discrete data. As an addition, Simple K-Means algorithm was used to cluster the data produced by SAX. The findings show that the predictive accuracy of the classification prediction model is more than 80%.

75 FR 49549 - ABC & D Recycling, Inc.-Lease and Operation Exemption-a Line of Railroad in Ware, MA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-13

... DEPARTMENT OF TRANSPORTATION Surface Transportation Board [Docket No. FD 35397] ABC & D Recycling, Inc.--Lease and Operation Exemption--a Line of Railroad in Ware, MA ABC & D Recycling, Inc. (ABC & D..., ABC & D Recycling, Inc.--Lease and Operation Exemption--a Line of Railroad in Ware, Massachusetts (STB...

Use of a Short-Form Balance Confidence Scale to Predict Future Recurrent Falls in People With Parkinson Disease.

PubMed

Cole, Michael H; Rippey, Jodi; Naughton, Geraldine A; Silburn, Peter A

2016-01-01

To assess whether the 16-item Activities-specific Balance Confidence scale (ABC-16) and short-form 6-item Activities-specific Balance Confidence scale (ABC-6) could predict future recurrent falls in people with Parkinson disease (PD) and to validate the robustness of their predictive capacities. Twelve-month prospective cohort study. General community. People with idiopathic PD (N=79). Clinical tests were conducted to assess symptom severity, balance confidence, and medical history. Over the subsequent 12 months, participants recorded any falls on daily fall calendars, which they returned monthly by reply paid post. Logistic regression and receiver operating characteristic analyses estimated the sensitivities and specificities of the ABC-16 and ABC-6 for predicting future recurrent falls in this cohort, and "leave-one-out" validation was used to assess their robustness. Of the 79 patients who completed follow-up, 28 (35.4%) fell more than once during the 12-month period. Both the ABC-16 and ABC-6 were significant predictors of future recurrent falls, and moderate sensitivities (ABC-16: 75.0%; ABC-6: 71.4%) and specificities (ABC-16: 76.5%; ABC-6: 74.5%) were reported for each tool for a cutoff score of 77.5 and 65.8, respectively. The results have significant implications and demonstrate that the ABC-16 and ABC-6 independently identify patients with PD at risk of future recurrent falls. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

PubMed

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

Optimizing Robinson Operator with Ant Colony Optimization As a Digital Image Edge Detection Method

NASA Astrophysics Data System (ADS)

Yanti Nasution, Tarida; Zarlis, Muhammad; K. M Nasution, Mahyuddin

2017-12-01

Edge detection serves to identify the boundaries of an object against a background of mutual overlap. One of the classic method for edge detection is operator Robinson. Operator Robinson produces a thin, not assertive and grey line edge. To overcome these deficiencies, the proposed improvements to edge detection method with the approach graph with Ant Colony Optimization algorithm. The repairs may be performed are thicken the edge and connect the edges cut off. Edge detection research aims to do optimization of operator Robinson with Ant Colony Optimization then compare the output and generated the inferred extent of Ant Colony Optimization can improve result of edge detection that has not been optimized and improve the accuracy of the results of Robinson edge detection. The parameters used in performance measurement of edge detection are morphology of the resulting edge line, MSE and PSNR. The result showed that Robinson and Ant Colony Optimization method produces images with a more assertive and thick edge. Ant Colony Optimization method is able to be used as a method for optimizing operator Robinson by improving the image result of Robinson detection average 16.77 % than classic Robinson result.

Whole-Genome Survey of the Putative ATP-Binding Cassette Transporter Family Genes in Vitis vinifera

PubMed Central

Çakır, Birsen; Kılıçkaya, Ozan

2013-01-01

The ATP-binding cassette (ABC) protein superfamily constitutes one of the largest protein families known in plants. In this report, we performed a complete inventory of ABC protein genes in Vitis vinifera, the whole genome of which has been sequenced. By comparison with ABC protein members of Arabidopsis thaliana, we identified 135 putative ABC proteins with 1 or 2 NBDs in V. vinifera. Of these, 120 encode intrinsic membrane proteins, and 15 encode proteins missing TMDs. V. vinifera ABC proteins can be divided into 13 subfamilies with 79 “full-size,” 41 “half-size,” and 15 “soluble” putative ABC proteins. The main feature of the Vitis ABC superfamily is the presence of 2 large subfamilies, ABCG (pleiotropic drug resistance and white-brown complex homolog) and ABCC (multidrug resistance-associated protein). We identified orthologs of V. vinifera putative ABC transporters in different species. This work represents the first complete inventory of ABC transporters in V. vinifera. The identification of Vitis ABC transporters and their comparative analysis with the Arabidopsis counterparts revealed a strong conservation between the 2 species. This inventory could help elucidate the biological and physiological functions of these transporters in V. vinifera. PMID:24244377

PubMed Central

Untch, Michael; Würstlein, Rachel; Marschner, Norbert; Lüftner, Diana; Augustin, Doris; Briest, Susanne; Ettl, Johannes; Haidinger, Renate; Müller, Lothar; Müller, Volkmar; Ruckhäberle, Eugen; Harbeck, Nadia; Thomssen, Christoph

2018-01-01

The fourth international advanced breast cancer consensus conference (ABC4) on the diagnosis and treatment of advanced breast cancer (ABC) headed by Professor Fatima Cardoso was once again held in Lisbon on November 2 – 4, 2017. To simplify matters, the abbreviation ABC will be used hereinafter in the text. In clinical practice, the abbreviation corresponds to metastatic breast cancer or locally far-advanced disease. This year the focus was on new developments in the treatment of ABC. Topics discussed included the importance of CDK4/6 inhibition in hormone receptor (HR)-positive ABC, the use of dual antibody blockade to treat HER2-positive ABC, PARP inhibition in triple-negative ABC and the potential therapeutic outcomes. Another major area discussed at the conference was BRCA-associated breast cancer, the treatment of cerebral metastasis, and individualized treatment decisions based on molecular testing (so-called precision medicine). As in previous years, close cooperation with representatives from patient organizations from around the world is an important aspect of the ABC conference. This cooperation was reinforced and expanded at the ABC4 conference. A global alliance was founded at the conclusion of the consensus conference, which aims to promote and coordinate the measures considered necessary by patient advocates worldwide. Because the panel of experts was composed of specialists from all over the world, it was inevitable that the ABC consensus also reflected country-specific features. As in previous years, a team of German breast cancer specialists who closely followed the consensus voting of the ABC panelists in Lisbon and intensively discussed the votes has therefore commented on the consensus in the context of the current German guidelines on the diagnosis and treatment of breast cancer 1 ,  2 used in clinical practice in Germany. The ABC consensus is based on the votes of the ABC panelists in Lisbon. PMID:29880982

Genome-wide identification, phylogenetic analysis, and expression profiles of ATP-binding cassette transporter genes in the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae).

PubMed

Xiao, Lin-Fan; Zhang, Wei; Jing, Tian-Xing; Zhang, Meng-Yi; Miao, Ze-Qing; Wei, Dan-Dan; Yuan, Guo-Rui; Wang, Jin-Jun

2018-03-01

The ATP-binding cassette (ABC) is the largest transporter gene family and the genes play key roles in xenobiotic resistance, metabolism, and development of all phyla. However, the specific functions of ABC gene families in insects is unclear. We report a genome-wide identification, phylogenetic, and transcriptional analysis of the ABC genes in the oriental fruit fly, Bactrocera dorsalis (Hendel). We identified a total of 47 ABC genes (BdABCs) from the transcriptomic and genomic databases of B. dorsalis and classified these genes into eight subfamilies (A-H), including 7 ABCAs, 7 ABCBs, 9 ABCCs, 2 ABCDs, 1 ABCE, 3 ABCFs, 15 ABCGs, and 3 ABCHs. Comparative phylogenetic analysis of the ABCs suggests an orthologous relationship between B. dorsalis and other insect species in which these genes have been related to pesticide resistance and essential biological processes. Comparison of transcriptome and relative expression patterns of BdABCs indicated diverse multifunctions within different B. dorsalis tissues. The expression of 4, 10, and 14 BdABCs from 18 BdABCs was significantly upregulated after exposure to LD 50 s of malathion, avermectin, and beta-cypermethrin, respectively. The maximum expression level of most BdABCs (including BdABCFs, BdABCGs, and BdABCHs) occurred at 48h post exposures, whereas BdABCEs peaked at 24h after treatment. Furthermore, RNA interference-mediated suppression of BdABCB7 resulted in increased toxicity of malathion against B. dorsalis. These data suggest that ABC transporter genes might play key roles in xenobiotic metabolism and biosynthesis in B. dorsalis. Copyright © 2017 Elsevier Inc. All rights reserved.

Regulation of the ATPase activity of ABCE1 from Pyrococcus abyssi by Fe-S cluster status and Mg²⁺: implication for ribosomal function.

PubMed

Sims, Lynn M; Igarashi, Robert Y

2012-08-15

Ribosomal function is dependent on multiple proteins. The ABCE1 ATPase, a unique ABC superfamily member that bears two Fe₄S₄ clusters, is crucial for ribosomal biogenesis and recycling. Here, the ATPase activity of the Pyrococcus abyssi ABCE1 (PabABCE1) was studied using both apo- (without reconstituted Fe-S clusters) and holo- (with full complement of Fe-S clusters reconstituted post-purification) forms, and is shown to be jointly regulated by the status of Fe-S clusters and Mg²⁺. Typically ATPases require Mg²⁺, as is true for PabABCE1, but Mg²⁺ also acts as a negative allosteric effector that modulates ATP affinity of PabABCE1. Physiological [Mg²⁺] inhibits the PabABCE1 ATPase (K(i) of ∼1 μM) for both apo- and holo-PabABCE1. Comparative kinetic analysis of Mg²⁺ inhibition shows differences in degree of allosteric regulation between the apo- and holo-PabABCE1 where the apparent ATP K(m) of apo-PabABCE1 increases >30-fold from ∼30 μM to over 1 mM with M²⁺. This effect would significantly convert the ATPase activity of PabABCE1 from being independent of cellular energy charge (φ) to being dependent on φ with cellular [Mg²⁺]. These findings uncover intricate overlapping effects by both [Mg²⁺] and the status of Fe-S clusters that regulate ABCE1's ATPase activity with implications to ribosomal function. Copyright © 2012 Elsevier Inc. All rights reserved.

Dynamic vehicle routing with time windows in theory and practice.

PubMed

Yang, Zhiwei; van Osta, Jan-Paul; van Veen, Barry; van Krevelen, Rick; van Klaveren, Richard; Stam, Andries; Kok, Joost; Bäck, Thomas; Emmerich, Michael

2017-01-01

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon's benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment.

Research on global path planning based on ant colony optimization for AUV

NASA Astrophysics Data System (ADS)

Wang, Hong-Jian; Xiong, Wei

2009-03-01

Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.

Ant colony optimization for solving university facility layout problem

NASA Astrophysics Data System (ADS)

Mohd Jani, Nurul Hafiza; Mohd Radzi, Nor Haizan; Ngadiman, Mohd Salihin

2013-04-01

Quadratic Assignment Problems (QAP) is classified as the NP hard problem. It has been used to model a lot of problem in several areas such as operational research, combinatorial data analysis and also parallel and distributed computing, optimization problem such as graph portioning and Travel Salesman Problem (TSP). In the literature, researcher use exact algorithm, heuristics algorithm and metaheuristic approaches to solve QAP problem. QAP is largely applied in facility layout problem (FLP). In this paper we used QAP to model university facility layout problem. There are 8 facilities that need to be assigned to 8 locations. Hence we have modeled a QAP problem with n ≤ 10 and developed an Ant Colony Optimization (ACO) algorithm to solve the university facility layout problem. The objective is to assign n facilities to n locations such that the minimum product of flows and distances is obtained. Flow is the movement from one to another facility, whereas distance is the distance between one locations of a facility to other facilities locations. The objective of the QAP is to obtain minimum total walking (flow) of lecturers from one destination to another (distance).

Granulocyte-Colony Stimulating Factor Receptor, Tissue Factor, and VEGF-R Bound VEGF in Human Breast Cancer In Loco.

PubMed

Wojtukiewicz, Marek Z; Sierko, Ewa; Skalij, Piotr; Kamińska, Magda; Zimnoch, Lech; Brekken, Ralf A; Thorpe, Philip E

2016-01-01

Doxorubicin and docetaxel-based chemotherapy regimens used in breast cancer patients are associated with high risk of febrile neutropenia (FN). Granulocyte colony-stimulating factors (G-CSF) are recommended for both treating and preventing chemotherapy-induced neutropenia. Increased thrombosis incidence in G-CSF treated patients was reported; however, the underlying mechanisms remain unclear. The principal activator of blood coagulation in cancer is tissue factor (TF). It additionally contributes to cancer progression and stimulates angiogenesis. The main proangiogenic factor is vascular endothelial growth factor (VEGF). The aim of the study was to evaluate granulocyte-colony stimulating factor receptor (G-CSFR), tissue factor (TF) expression and vascular endothelial growth factor receptor (VEGF-R) bound VEGF in human breast cancer in loco. G-CSFR, TF and VEGFR bound VEGF (VEGF: VEGFR) were assessed in 28 breast cancer tissue samples. Immunohistochemical (IHC) methodologies according to ABC technique and double staining IHC procedure were employed utilizing antibodies against G-CSFR, TF and VEGF associated with VEGFR (VEGF: VEGFR). Expression of G-CSFR was demonstrated in 20 breast cancer tissue specimens (71%). In 6 cases (21%) the expression was strong (IRS 9-12). Strong expression of TF was observed in all investigated cases (100%). Moreover, expression of VEGF: VEGFR was visualized in cancer cells (IRS 5-8). No presence of G-CSFR, TF or VEGF: VEGFR was detected on healthy breast cells. Double staining IHC studies revealed co-localization of G-CSFR and TF, G-CSFR and VEGF: VEGFR, as well as TF and VEGF: VEGFR on breast cancer cells and ECs. The results of the study indicate that GCSFR, TF and VEGF: VEGFR expression as well as their co-expression might influence breast cancer biology, and may increase thromboembolic adverse events incidence.

The mitochondrial transporter ABC-me (ABCB10), a downstream target of GATA-1, is essential for erythropoiesis in vivo

PubMed Central

Hyde, B B; Liesa, M; Elorza, A A; Qiu, W; Haigh, S E; Richey, L; Mikkola, H K; Schlaeger, T M; Shirihai, O S

2012-01-01

The mitochondrial transporter ATP binding cassette mitochondrial erythroid (ABC-me/ABCB10) is highly induced during erythroid differentiation by GATA-1 and its overexpression increases hemoglobin production rates in vitro. However, the role of ABC-me in erythropoiesis in vivo is unknown. Here we report for the first time that erythrocyte development in mice requires ABC-me. ABC-me−/− mice die at day 12.5 of gestation, showing nearly complete eradication of primitive erythropoiesis and lack of hemoglobinized cells at day 10.5. ABC-me−/− erythroid cells fail to differentiate because they exhibit a marked increase in apoptosis, both in vivo and ex vivo. Erythroid precursors are particularly sensitive to oxidative stress and ABC-me in the heart and its yeast ortholog multidrug resistance-like 1 have been shown to protect against oxidative stress. Thus, we hypothesized that increased apoptosis in ABC-me−/− erythroid precursors was caused by oxidative stress. Within this context, ABC-me deletion causes an increase in mitochondrial superoxide production and protein carbonylation in erythroid precursors. Furthermore, treatment of ABC-me−/− erythroid progenitors with the mitochondrial antioxidant MnTBAP (superoxide dismutase 2 mimetic) supports survival, ex vivo differentiation and increased hemoglobin production. Altogether, our findings demonstrate that ABC-me is essential for erythropoiesis in vivo. PMID:22240895

On the Huygens absorbing boundary conditions for electromagnetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berenger, Jean-Pierre

A new absorbing boundary condition (ABC) is presented for the solution of Maxwell equations in unbounded spaces. Called the Huygens ABC, this condition is a generalization of two previously published ABCs, namely the multiple absorbing surfaces (MAS) and the re-radiating boundary condition (rRBC). The properties of the Huygens ABC are derived theoretically in continuous spaces and in the finite-difference (FDTD) discretized space. A solution is proposed to render the Huygens ABC effective for the absorption of evanescent waves. Numerical experiments with the FDTD method show that the effectiveness of the Huygens ABC is close to that of the PML ABCmore » in some realistic problems of numerical electromagnetics. It is also shown in the paper that a combination of the Huygens ABC with the PML ABC is very well suited to the solution of some particular problems.« less

Ant colony optimisation inversion of surface and borehole magnetic data under lithological constraints

NASA Astrophysics Data System (ADS)

Liu, Shuang; Hu, Xiangyun; Liu, Tianyou; Xi, Yufei; Cai, Jianchao; Zhang, Henglei

2015-01-01

The ant colony optimisation algorithm has successfully been used to invert for surface magnetic data. However, the resolution of the distributions of the recovered physical property for deeply buried magnetic sources is not generally very high because of geophysical ambiguities. We use three approaches to deal with this problem. First, the observed surface magnetic data are taken together with the three-component borehole magnetic anomalies to recover the distributions of the physical properties. This cooperative inversion strategy improves the resolution of the inversion results in the vertical direction. Additionally, as the ant colony tours the discrete nodes, we force it to visit the nodes with physical properties that agree with the drilled lithologies. These lithological constraints reduce the non-uniqueness of the inversion problem. Finally, we also implement a K-means cluster analysis for the distributions of the magnetic cells after each iteration, in order to separate the distributions of magnetisation intensity instead of concentrating the distribution in a single area. We tested our method using synthetic data and found that all tests returned favourable results. In the case study of the Mengku iron-ore deposit in northwest China, the recovered distributions of magnetisation are in good agreement with the locations and shapes of the magnetite orebodies as inferred by drillholes. Uncertainty analysis shows that the ant colony algorithm is robust in the presence of noise and that the proposed approaches significantly improve the quality of the inversion results.

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan

2016-11-01

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

PubMed Central

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John BO

2008-01-01

Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21) and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors. PMID:18959785

Development of a microbial high-throughput screening instrument based on elastic light scatter patterns

NASA Astrophysics Data System (ADS)

Bae, Euiwon; Patsekin, Valery; Rajwa, Bartek; Bhunia, Arun K.; Holdman, Cheryl; Davisson, V. Jo; Hirleman, E. Daniel; Robinson, J. Paul

2012-04-01

A microbial high-throughput screening (HTS) system was developed that enabled high-speed combinatorial studies directly on bacterial colonies. The system consists of a forward scatterometer for elastic light scatter (ELS) detection, a plate transporter for sample handling, and a robotic incubator for automatic incubation. To minimize the ELS pattern-capturing time, a new calibration plate and correction algorithms were both designed, which dramatically reduced correction steps during acquisition of the circularly symmetric ELS patterns. Integration of three different control software programs was implemented, and the performance of the system was demonstrated with single-species detection for library generation and with time-resolved measurement for understanding ELS colony growth correlation, using Escherichia coli and Listeria. An in-house colony-tracking module enabled researchers to easily understand the time-dependent variation of the ELS from identical colony, which enabled further analysis in other biochemical experiments. The microbial HTS system provided an average scan time of 4.9 s per colony and the capability of automatically collecting more than 4000 ELS patterns within a 7-h time span.

Investigating the compatibility of the biocontrol agent Clonostachys rosea IK726 with prodigiosin-producing Serratia rubidaea S55 and phenazine-producing Pseudomonas chlororaphis ToZa7.

PubMed

Kamou, Nathalie N; Dubey, Mukesh; Tzelepis, Georgios; Menexes, Georgios; Papadakis, Emmanouil N; Karlsson, Magnus; Lagopodi, Anastasia L; Jensen, Dan Funck

2016-05-01

This study was carried out to assess the compatibility of the biocontrol fungus Clonostachys rosea IK726 with the phenazine-producing Pseudomonas chlororaphis ToZa7 or with the prodigiosin-producing Serratia rubidaea S55 against Fusarium oxysporum f. sp. radicis-lycopersici. The pathogen was inhibited by both strains in vitro, whereas C. rosea displayed high tolerance to S. rubidaea but not to P. chlororaphis. We hypothesized that this could be attributed to the ATP-binding cassette (ABC) proteins. The results of the reverse transcription quantitative PCR showed an induction of seven genes (abcB1, abcB20, abcB26, abcC12, abcC12, abcG8 and abcG25) from subfamilies B, C and G. In planta experiments showed a significant reduction in foot and root rot on tomato plants inoculated with C. rosea and P. chlororaphis. This study demonstrates the potential for combining different biocontrol agents and suggests an involvement of ABC transporters in secondary metabolite tolerance in C. rosea.

Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

PubMed Central

Yin, Zhendong; Zhuang, Shufeng; Wu, Zhilu; Ma, Bo

2015-01-01

Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems. PMID:26426016

An Ant Colony Optimization algorithm for solving the fixed destination multi-depot multiple traveling salesman problem with non-random parameters

NASA Astrophysics Data System (ADS)

Ramadhani, T.; Hertono, G. F.; Handari, B. D.

2017-07-01

The Multiple Traveling Salesman Problem (MTSP) is the extension of the Traveling Salesman Problem (TSP) in which the shortest routes of m salesmen all of which start and finish in a single city (depot) will be determined. If there is more than one depot and salesmen start from and return to the same depot, then the problem is called Fixed Destination Multi-depot Multiple Traveling Salesman Problem (MMTSP). In this paper, MMTSP will be solved using the Ant Colony Optimization (ACO) algorithm. ACO is a metaheuristic optimization algorithm which is derived from the behavior of ants in finding the shortest route(s) from the anthill to a form of nourishment. In solving the MMTSP, the algorithm is observed with respect to different chosen cities as depots and non-randomly three parameters of MMTSP: m, K, L, those represents the number of salesmen, the fewest cities that must be visited by a salesman, and the most number of cities that can be visited by a salesman, respectively. The implementation is observed with four dataset from TSPLIB. The results show that the different chosen cities as depots and the three parameters of MMTSP, in which m is the most important parameter, affect the solution.

50 CFR 622.60 - Adjustment of management measures.

Code of Federal Regulations, 2013 CFR

2013-10-01

... markings and identification, allowable biological catch (ABC) and ABC control rules, rebuilding plans, sale... harvested shrimp (maintaining shrimp in whole condition, use as bait), target effort and fishing mortality... identification, vessel markings and identification, ABC and ABC control rules, rebuilding plans, sale and...

50 CFR 622.60 - Adjustment of management measures.

Code of Federal Regulations, 2014 CFR

2014-10-01

... markings and identification, allowable biological catch (ABC) and ABC control rules, rebuilding plans, sale... harvested shrimp (maintaining shrimp in whole condition, use as bait), target effort and fishing mortality... identification, vessel markings and identification, ABC and ABC control rules, rebuilding plans, sale and...

An ant colony optimization heuristic for an integrated production and distribution scheduling problem

NASA Astrophysics Data System (ADS)

Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju

2014-04-01

Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.

All-Optical Implementation of the Ant Colony Optimization Algorithm

PubMed Central

Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare

2016-01-01

We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems. PMID:27222098

Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest.

PubMed

Fraimout, Antoine; Debat, Vincent; Fellous, Simon; Hufbauer, Ruth A; Foucaud, Julien; Pudlo, Pierre; Marin, Jean-Michel; Price, Donald K; Cattel, Julien; Chen, Xiao; Deprá, Marindia; François Duyck, Pierre; Guedot, Christelle; Kenis, Marc; Kimura, Masahito T; Loeb, Gregory; Loiseau, Anne; Martinez-Sañudo, Isabel; Pascual, Marta; Polihronakis Richmond, Maxi; Shearer, Peter; Singh, Nadia; Tamura, Koichiro; Xuéreb, Anne; Zhang, Jinping; Estoup, Arnaud

2017-04-01

Deciphering invasion routes from molecular data is crucial to understanding biological invasions, including identifying bottlenecks in population size and admixture among distinct populations. Here, we unravel the invasion routes of the invasive pest Drosophila suzukii using a multi-locus microsatellite dataset (25 loci on 23 worldwide sampling locations). To do this, we use approximate Bayesian computation (ABC), which has improved the reconstruction of invasion routes, but can be computationally expensive. We use our study to illustrate the use of a new, more efficient, ABC method, ABC random forest (ABC-RF) and compare it to a standard ABC method (ABC-LDA). We find that Japan emerges as the most probable source of the earliest recorded invasion into Hawaii. Southeast China and Hawaii together are the most probable sources of populations in western North America, which then in turn served as sources for those in eastern North America. European populations are genetically more homogeneous than North American populations, and their most probable source is northeast China, with evidence of limited gene flow from the eastern US as well. All introduced populations passed through bottlenecks, and analyses reveal five distinct admixture events. These findings can inform hypotheses concerning how this species evolved between different and independent source and invasive populations. Methodological comparisons indicate that ABC-RF and ABC-LDA show concordant results if ABC-LDA is based on a large number of simulated datasets but that ABC-RF out-performs ABC-LDA when using a comparable and more manageable number of simulated datasets, especially when analyzing complex introduction scenarios. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Improved Bat Algorithm Applied to Multilevel Image Thresholding

PubMed Central

2014-01-01

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. PMID:25165733

Continental-Scale Mapping of Adelie Penguin Colonies from Landsat Imagery

NASA Technical Reports Server (NTRS)

Schwaller, Mathew R.; Southwell, Colin; Emmerson, Louise

2013-01-01

Breeding distribution of the Adlie penguin, Pygoscelis adeliae, was surveyed with Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data in an area covering approximately 330 of longitude along the coastline of Antarctica.An algorithm was designed to minimize radiometric noise and to retrieve Adlie penguin colony location and spatial extent from the ETM+data. In all, 9143 individual pixels were classified as belonging to an Adlie penguin colony class out of the entire dataset of 195 ETM+ scenes, where the dimension of each pixel is 30 m by 30 m,and each scene is approximately 180 km by 180 km. Pixel clustering identified a total of 187 individual Adlie penguin colonies, ranging in size from a single pixel (900 sq m) to a maximum of 875 pixels (0.788 sq km). Colony retrievals have a very low error of commission, on the order of 1% or less, and the error of omission was estimated to be 3% to 4% by population based on comparisons with direct observations from surveys across east Antarctica. Thus, the Landsat retrievals successfully located Adlie penguin colonies that accounted for 96 to 97% of the regional population used as ground truth. Geographic coordinates and the spatial extent of each colony retrieved from the Landsat data are available publically. Regional analysis found several areas where the Landsat retrievals suggest populations that are significantly larger than published estimates. Six Adlie penguin colonies were found that are believed to be previously unreported in the literature.

Development of a practical costing method for hospitals.

PubMed

Cao, Pengyu; Toyabe, Shin-Ichi; Akazawa, Kouhei

2006-03-01

To realize an effective cost control, a practical and accurate cost accounting system is indispensable in hospitals. In traditional cost accounting systems, the volume-based costing (VBC) is the most popular cost accounting method. In this method, the indirect costs are allocated to each cost object (services or units of a hospital) using a single indicator named a cost driver (e.g., Labor hours, revenues or the number of patients). However, this method often results in rough and inaccurate results. The activity based costing (ABC) method introduced in the mid 1990s can prove more accurate results. With the ABC method, all events or transactions that cause costs are recognized as "activities", and a specific cost driver is prepared for each activity. Finally, the costs of activities are allocated to cost objects by the corresponding cost driver. However, it is much more complex and costly than other traditional cost accounting methods because the data collection for cost drivers is not always easy. In this study, we developed a simplified ABC (S-ABC) costing method to reduce the workload of ABC costing by reducing the number of cost drivers used in the ABC method. Using the S-ABC method, we estimated the cost of the laboratory tests, and as a result, similarly accurate results were obtained with the ABC method (largest difference was 2.64%). Simultaneously, this new method reduces the seven cost drivers used in the ABC method to four. Moreover, we performed an evaluation using other sample data from physiological laboratory department to certify the effectiveness of this new method. In conclusion, the S-ABC method provides two advantages in comparison to the VBC and ABC methods: (1) it can obtain accurate results, and (2) it is simpler to perform. Once we reduce the number of cost drivers by applying the proposed S-ABC method to the data for the ABC method, we can easily perform the cost accounting using few cost drivers after the second round of costing.

Thermo-responsive magnetic liposomes for hyperthermia-triggered local drug delivery.

PubMed

Dai, Min; Wu, Cong; Fang, Hong-Ming; Li, Li; Yan, Jia-Bao; Zeng, Dan-Lin; Zou, Tao

2017-06-01

We prepared and characterised thermo-responsive magnetic liposomes, which were designed to combine features of magnetic targeting and thermo-responsive control release for hyperthermia-triggered local drug delivery. The particle size and zeta-potential of the thermo-responsive magnetic ammonium bicarbonate (MagABC) liposomes were about 210 nm and -14 mV, respectively. The MagABC liposomes showed encapsulation efficiencies of about 15% and 82% for magnetic nanoparticles (mean crystallite size 12 nm) and doxorubicin (DOX), respectively. The morphology of the MagABC liposomes was visualised using transmission electron microscope (TEM). The MagABC liposomes showed desired thermo-responsive release. The MagABC liposomes, when physically targeted to tumour cells in culture by a permanent magnetic field yielded a substantial increase in intracellular accumulation of DOX as compared to non-magnetic ammonium bicarbonate (ABC) liposomes. This resulted in a parallel increase in cytotoxicity for DOX loaded MagABC liposomes over DOX loaded ABC liposomes in tumour cells.

A Neisseria meningitidis fbpABC mutant is incapable of using nonheme iron for growth.

PubMed

Khun, H H; Kirby, S D; Lee, B C

1998-05-01

The neisserial fbpABC locus has been proposed to act as an iron-specific ABC transporter system. To confirm this assigned function, we constructed an fbpABC mutant in Neisseria meningitidis by insertional inactivation of fbpABC with a selectable antibiotic marker. The mutant was unable to use iron supplied from human transferrin, human lactoferrin, or iron chelates. However, the use of iron from heme and human hemoglobin was unimpaired. These results support the obligatory participation of fbpABC in neisserial periplasmic iron transport and do not indicate a role for this genetic locus in the heme iron pathway.

A Neisseria meningitidis fbpABC Mutant Is Incapable of Using Nonheme Iron for Growth

PubMed Central

Khun, Heng H.; Kirby, Shane D.; Lee, B. Craig

1998-01-01

The neisserial fbpABC locus has been proposed to act as an iron-specific ABC transporter system. To confirm this assigned function, we constructed an fbpABC mutant in Neisseria meningitidis by insertional inactivation of fbpABC with a selectable antibiotic marker. The mutant was unable to use iron supplied from human transferrin, human lactoferrin, or iron chelates. However, the use of iron from heme and human hemoglobin was unimpaired. These results support the obligatory participation of fbpABC in neisserial periplasmic iron transport and do not indicate a role for this genetic locus in the heme iron pathway. PMID:9573125

Simple heuristics and rules of thumb: where psychologists and behavioural biologists might meet.

PubMed

Hutchinson, John M C; Gigerenzer, Gerd

2005-05-31

The Centre for Adaptive Behaviour and Cognition (ABC) has hypothesised that much human decision-making can be described by simple algorithmic process models (heuristics). This paper explains this approach and relates it to research in biology on rules of thumb, which we also review. As an example of a simple heuristic, consider the lexicographic strategy of Take The Best for choosing between two alternatives: cues are searched in turn until one discriminates, then search stops and all other cues are ignored. Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics. Simulations show that the statistical structures of different environments affect which heuristics perform better, a relationship referred to as ecological rationality. We contrast ecological rationality with the stronger claim of adaptation. Rules of thumb from biology provide clearer examples of adaptation because animals can be studied in the environments in which they evolved. The range of examples is also much more diverse. To investigate them, biologists have sometimes used similar simulation techniques to ABC, but many examples depend on empirically driven approaches. ABC's theoretical framework can be useful in connecting some of these examples, particularly the scattered literature on how information from different cues is integrated. Optimality modelling is usually used to explain less detailed aspects of behaviour but might more often be redirected to investigate rules of thumb.

ABCA transporter gene expression and poor outcome in epithelial ovarian cancer.

PubMed

Hedditch, Ellen L; Gao, Bo; Russell, Amanda J; Lu, Yi; Emmanuel, Catherine; Beesley, Jonathan; Johnatty, Sharon E; Chen, Xiaoqing; Harnett, Paul; George, Joshy; Williams, Rebekka T; Flemming, Claudia; Lambrechts, Diether; Despierre, Evelyn; Lambrechts, Sandrina; Vergote, Ignace; Karlan, Beth; Lester, Jenny; Orsulic, Sandra; Walsh, Christine; Fasching, Peter; Beckmann, Matthias W; Ekici, Arif B; Hein, Alexander; Matsuo, Keitaro; Hosono, Satoyo; Nakanishi, Toru; Yatabe, Yasushi; Pejovic, Tanja; Bean, Yukie; Heitz, Florian; Harter, Philipp; du Bois, Andreas; Schwaab, Ira; Hogdall, Estrid; Kjaer, Susan K; Jensen, Allan; Hogdall, Claus; Lundvall, Lene; Engelholm, Svend Aage; Brown, Bob; Flanagan, James; Metcalf, Michelle D; Siddiqui, Nadeem; Sellers, Thomas; Fridley, Brooke; Cunningham, Julie; Schildkraut, Joellen; Iversen, Ed; Weber, Rachel P; Berchuck, Andrew; Goode, Ellen; Bowtell, David D; Chenevix-Trench, Georgia; deFazio, Anna; Norris, Murray D; MacGregor, Stuart; Haber, Michelle; Henderson, Michelle J

2014-07-01

ATP-binding cassette (ABC) transporters play various roles in cancer biology and drug resistance, but their association with outcomes in serous epithelial ovarian cancer (EOC) is unknown. The relationship between clinical outcomes and ABC transporter gene expression in two independent cohorts of high-grade serous EOC tumors was assessed with real-time quantitative polymerase chain reaction, analysis of expression microarray data, and immunohistochemistry. Associations between clinical outcomes and ABCA transporter gene single nucleotide polymorphisms were tested in a genome-wide association study. Impact of short interfering RNA-mediated gene suppression was determined by colony forming and migration assays. Association with survival was assessed with Kaplan-Meier analysis and log-rank tests. All statistical tests were two-sided. Associations with outcome were observed with ABC transporters of the "A" subfamily, but not with multidrug transporters. High-level expression of ABCA1, ABCA6, ABCA8, and ABCA9 in primary tumors was statistically significantly associated with reduced survival in serous ovarian cancer patients. Low levels of ABCA5 and the C-allele of rs536009 were associated with shorter overall survival (hazard ratio for death = 1.50; 95% confidence interval [CI] =1.26 to 1.79; P = 6.5e-6). The combined expression pattern of ABCA1, ABCA5, and either ABCA8 or ABCA9 was associated with particularly poor outcome (mean overall survival in group with adverse ABCA1, ABCA5 and ABCA9 gene expression = 33.2 months, 95% CI = 26.4 to 40.1; vs 55.3 months in the group with favorable ABCA gene expression, 95% CI = 49.8 to 60.8; P = .001), independently of tumor stage or surgical debulking status. Suppression of cholesterol transporter ABCA1 inhibited ovarian cancer cell growth and migration in vitro, and statin treatment reduced ovarian cancer cell migration. Expression of ABCA transporters was associated with poor outcome in serous ovarian cancer, implicating lipid trafficking as a potentially important process in EOC. © The Author 2014. Published by Oxford University Press. All rights reserved.

A comparison of non-parametric techniques to estimate incident photosynthetically active radiation from MODIS for monitoring primary production

NASA Astrophysics Data System (ADS)

Brown, M. G. L.; He, T.; Liang, S.

2016-12-01

Satellite-derived estimates of incident photosynthetically active radiation (PAR) can be used to monitor global change, are required by most terrestrial ecosystem models, and can be used to estimate primary production according to the theory of light use efficiency. Compared with parametric approaches, non-parametric techniques that include an artificial neural network (ANN), support vector machine regression (SVM), an artificial bee colony (ABC), and a look-up table (LUT) do not require many ancillary data as inputs for the estimation of PAR from satellite data. In this study, a selection of machine learning methods to estimate PAR from MODIS top of atmosphere (TOA) radiances are compared to a LUT approach to determine which techniques might best handle the nonlinear relationship between TOA radiance and incident PAR. Evaluation of these methods (ANN, SVM, and LUT) is performed with ground measurements at seven SURFRAD sites. Due to the design of the ANN, it can handle the nonlinear relationship between TOA radiance and PAR better than linearly interpolating between the values in the LUT; however, training the ANN has to be carried out on an angular-bin basis, which results in a LUT of ANNs. The SVM model may be better for incorporating multiple viewing angles than the ANN; however, both techniques require a large amount of training data, which may introduce a regional bias based on where the most training and validation data are available. Based on the literature, the ABC is a promising alternative to an ANN, SVM regression and a LUT, but further development for this application is required before concrete conclusions can be drawn. For now, the LUT method outperforms the machine-learning techniques, but future work should be directed at developing and testing the ABC method. A simple, robust method to estimate direct and diffuse incident PAR, with minimal inputs and a priori knowledge, would be very useful for monitoring global change of primary production, particularly of pastures and rangeland, which have implications for livestock and food security. Future work will delve deeper into the utility of satellite-derived PAR estimation for monitoring primary production in pasture and rangelands.

An improved harmony search algorithm for emergency inspection scheduling

NASA Astrophysics Data System (ADS)

Kallioras, Nikos A.; Lagaros, Nikos D.; Karlaftis, Matthew G.

2014-11-01

The ability of nature-inspired search algorithms to efficiently handle combinatorial problems, and their successful implementation in many fields of engineering and applied sciences, have led to the development of new, improved algorithms. In this work, an improved harmony search (IHS) algorithm is presented, while a holistic approach for solving the problem of post-disaster infrastructure management is also proposed. The efficiency of IHS is compared with that of the algorithms of particle swarm optimization, differential evolution, basic harmony search and the pure random search procedure, when solving the districting problem that is the first part of post-disaster infrastructure management. The ant colony optimization algorithm is employed for solving the associated routing problem that constitutes the second part. The comparison is based on the quality of the results obtained, the computational demands and the sensitivity on the algorithmic parameters.

50 CFR 648.55 - Framework adjustments to management measures.

Code of Federal Regulations, 2014 CFR

2014-10-01

... establish OFL, ABC, ACL, ACT, DAS allocations, rotational area management programs, percentage allocations... measures will be adjusted. (c) OFL, ABC, ACL, ACT, and AMs. The Council shall specify OFL, ABC, ACL, ACT... derive specifications for ABC, ACL, and ACT, as specified in paragraphs (c)(2) through (c)(5) of this...

50 CFR 648.55 - Framework adjustments to management measures.

Code of Federal Regulations, 2013 CFR

2013-10-01

... establish OFL, ABC, ACL, ACT, DAS allocations, rotational area management programs, percentage allocations... measures will be adjusted. (c) OFL, ABC, ACL, ACT, and AMs. The Council shall specify OFL, ABC, ACL, ACT... derive specifications for ABC, ACL, and ACT, as specified in paragraphs (c)(2) through (c)(5) of this...

50 CFR 648.55 - Framework adjustments to management measures.

Code of Federal Regulations, 2012 CFR

2012-10-01

... establish OFL, ABC, ACL, ACT, DAS allocations, rotational area management programs, percentage allocations... measures will be adjusted. (c) OFL, ABC, ACL, ACT, and AMs. The Council shall specify OFL, ABC, ACL, ACT... derive specifications for ABC, ACL, and ACT, as specified in paragraphs (c)(2) through (c)(5) of this...

50 CFR 648.55 - Framework adjustments to management measures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... establish OFL, ABC, ACL, ACT, DAS allocations, rotational area management programs, percentage allocations... measures will be adjusted. (c) OFL, ABC, ACL, ACT, and AMs. The Council shall specify OFL, ABC, ACL, ACT... derive specifications for ABC, ACL, and ACT, as specified in paragraphs (a)(2) through (5) of this...

Requirements for Information/Education Programs on Hypothermia.

DTIC Science & Technology

1979-10-23

NY (NBC) WEZF - Burlington, VT (ABC) Bangor, ME: 112,800 WLBZ - Bangor, ME (NBC) WABI - Bangor, ME (CBS) WVII - Bangor, ME (ABC) Presque Isle , ME...28,700 WAGM - Presque Isle , ME CBS(ABC-NBC) A-2 MID-ATLANTIC: 18,885,600 New York, NY: 6,375,500 Raleigh-Durham, NC: 451,800 WCBS - New York, NY (CBS...NY (ABC) WICZ - Binghamton, NY (NBC) WilingonNC: 135,300 WWAY -IWilmington, NC (ABC) WECT - Wilmington, NC NBC (CBS) Erie , PA: 132,600 WICU - Erie , PA

The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems.

PubMed

Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.

The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

PubMed Central

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

NASA Astrophysics Data System (ADS)

Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis F.

2016-04-01

BioMérieux is automating the microbiology laboratory in order to reduce cost (less manpower and consumables), to improve performance (increased sensitivity, machine algorithms) and to gain traceability through optimization of the clinical laboratory workflow. In this study, we evaluate the potential of Hyperspectral imaging (HSI) as a substitute to human visual observation when performing the task of microbiological culture interpretation. Microbial colonies from 19 strains subcategorized in 6 chromogenic classes were analyzed after a 24h-growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France). The HSI analysis was performed in the VNIR region (400-900 nm) using a linescan configuration. Using algorithms relying on Linear Spectral Unmixing, and using exclusively Diffuse Reflectance Spectra (DRS) as input data, we report interclass classification accuracies of 100% using a fully automatable approach and no use of morphological information. In order to eventually simplify the instrument, the performance of degraded DRS was also evaluated using only the most discriminant 14 spectral channels (a model for a multispectral approach) or 3 channels (model of a RGB image). The overall classification performance remains unchanged for our multispectral model but is degraded for the predicted RGB model, hints that a multispectral solution might bring the answer for an improved colony recognition.

Noninvasive forward-scattering system for rapid detection, characterization, and identification of Listeria colonies: image processing and data analysis

NASA Astrophysics Data System (ADS)

Rajwa, Bartek; Bayraktar, Bulent; Banada, Padmapriya P.; Huff, Karleigh; Bae, Euiwon; Hirleman, E. Daniel; Bhunia, Arun K.; Robinson, J. Paul

2006-10-01

Bacterial contamination by Listeria monocytogenes puts the public at risk and is also costly for the food-processing industry. Traditional methods for pathogen identification require complicated sample preparation for reliable results. Previously, we have reported development of a noninvasive optical forward-scattering system for rapid identification of Listeria colonies grown on solid surfaces. The presented system included application of computer-vision and patternrecognition techniques to classify scatter pattern formed by bacterial colonies irradiated with laser light. This report shows an extension of the proposed method. A new scatterometer equipped with a high-resolution CCD chip and application of two additional sets of image features for classification allow for higher accuracy and lower error rates. Features based on Zernike moments are supplemented by Tchebichef moments, and Haralick texture descriptors in the new version of the algorithm. Fisher's criterion has been used for feature selection to decrease the training time of machine learning systems. An algorithm based on support vector machines was used for classification of patterns. Low error rates determined by cross-validation, reproducibility of the measurements, and robustness of the system prove that the proposed technology can be implemented in automated devices for detection and classification of pathogenic bacteria.

Building Self-Esteem of Children and Adolescents through Adventure-Based Counseling.

ERIC Educational Resources Information Center

Nassar-McMillan, Sylvia C.; Cashwell, Craig S.

1997-01-01

Explores ways in which communities and school counselors can foster self-esteem in children and adolescents through adventure-based counseling (ABC). Discusses the importance of self-esteem, the philosophy and tenets of ABC, the effectiveness of ABC, and ways to integrate ABC concepts into groups. Focuses on prevention and intervention. (RJM)

ABCE1 is essential for S phase progression in human cells

PubMed Central

Toompuu, Marina; Kärblane, Kairi; Pata, Pille; Truve, Erkki; Sarmiento, Cecilia

2016-01-01

ABSTRACT ABCE1 is a highly conserved protein universally present in eukaryotes and archaea, which is crucial for the viability of different organisms. First identified as RNase L inhibitor, ABCE1 is currently recognized as an essential translation factor involved in several stages of eukaryotic translation and ribosome biogenesis. The nature of vital functions of ABCE1, however, remains unexplained. Here, we study the role of ABCE1 in human cell proliferation and its possible connection to translation. We show that ABCE1 depletion by siRNA results in a decreased rate of cell growth due to accumulation of cells in S phase, which is accompanied by inefficient DNA synthesis and reduced histone mRNA and protein levels. We infer that in addition to the role in general translation, ABCE1 is involved in histone biosynthesis and DNA replication and therefore is essential for normal S phase progression. In addition, we analyze whether ABCE1 is implicated in transcript-specific translation via its association with the eIF3 complex subunits known to control the synthesis of cell proliferation-related proteins. The expression levels of a few such targets regulated by eIF3A, however, were not consistently affected by ABCE1 depletion. PMID:26985706

Caenorhabditis elegans ABCRNAi transporters interact genetically with rde-2 and mut-7.

PubMed

Sundaram, Prema; Han, Wang; Cohen, Nancy; Echalier, Benjamin; Albin, John; Timmons, Lisa

2008-02-01

RNA interference (RNAi) mechanisms are conserved and consist of an interrelated network of activities that not only respond to exogenous dsRNA, but also perform endogenous functions required in the fine tuning of gene expression and in maintaining genome integrity. Not surprisingly, RNAi functions have widespread influences on cellular function and organismal development. Previously, we observed a reduced capacity to mount an RNAi response in nine Caenorhabditis elegans mutants that are defective in ABC transporter genes (ABC(RNAi) mutants). Here, we report an exhaustive study of mutants, collectively defective in 49 different ABC transporter genes, that allowed for the categorization of one additional transporter into the ABC(RNAi) gene class. Genetic complementation tests reveal functions for ABC(RNAi) transporters in the mut-7/rde-2 branch of the RNAi pathway. These second-site noncomplementation interactions suggest that ABC(RNAi) proteins and MUT-7/RDE-2 function together in parallel pathways and/or as multiprotein complexes. Like mut-7 and rde-2, some ABC(RNAi) mutants display transposon silencing defects. Finally, our analyses reveal a genetic interaction network of ABC(RNAi) gene function with respect to this part of the RNAi pathway. From our results, we speculate that the coordinated activities of ABC(RNAi) transporters, through their effects on endogenous RNAi-related mechanisms, ultimately affect chromosome function and integrity.

Antitumor effect of the novel sphingosine kinase 2 inhibitor ABC294640 is enhanced by inhibition of autophagy and by sorafenib in human cholangiocarcinoma cells.

PubMed

Ding, Xiwei; Chaiteerakij, Roongruedee; Moser, Catherine D; Shaleh, Hassan; Boakye, Jeffrey; Chen, Gang; Ndzengue, Albert; Li, Ying; Zhou, Yanling; Huang, Shengbing; Sinicrope, Frank A; Zou, Xiaoping; Thomas, Melanie B; Smith, Charles D; Roberts, Lewis R

2016-04-12

Sphingosine kinase 2 (Sphk2) has an oncogenic role in cancer. A recently developed first-in-class Sphk2 specific inhibitor ABC294640 displays antitumor activity in many cancer models. However, the role of Sphk2 and the antitumor activity of its inhibitor ABC294640 are not known in cholangiocarcinoma. We investigated the potential of targeting Sphk2 for the treatment of cholangiocarcinoma. We found that Sphk2 is overexpressed in five established human cholangiocarcinoma cell lines (WITT, HuCCT1, EGI-1, OZ and HuH28) and a new patient-derived cholangiocarcinoma cell line (LIV27) compared to H69 normal cholangiocytes. Inhibition of Sphk2 by ABC294640 inhibited proliferation and induced caspase-dependent apoptosis. Furthermore, we found that ABC294640 inhibited STAT3 phosphorylation, one of the key signaling pathways regulating cholangiocarcinoma cell proliferation and survival. ABC294640 also induced autophagy. Inhibition of autophagy by bafilomycin A1 or chloroquine potentiated ABC294640-induced cytotoxicity and apoptosis. In addition, ABC294640 in combination with sorafenib synergistically inhibited cell proliferation of cholangiocarcinoma cells. Strong decreases in STAT3 phosphorylation were observed in WITT and HuCCT1 cells exposed to the ABC294640 and sorafenib combination. These findings provide novel evidence that Sphk2 may be a rational therapeutic target in cholangiocarcinoma. Combinations of ABC294640 with sorafenib and/or autophagy inhibitors may provide novel strategies for the treatment of cholangiocarcinoma.

Expression, purification and characterization of GAPDH-ChSase ABC I from Proteus vulgaris in Escherichia coli.

PubMed

Li, Ye; Chen, Zhenya; Zhou, Zhao; Yuan, Qipeng

2016-12-01

Chondroitinases (ChSases) are a family of polysaccharide lyases that can depolymerize high molecular weight chondroitin sulfate (CS) and dermatan sulfate (DS). In this study, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which is stably expressed in different cells like normal cells and cancer cells and the expression is relatively insensitive to experimental conditions, was expressed as a fusion protein with ChSase ABC I. Results showed that the expression level and enzyme activity of GAPDH-ChSase ABC I were about 2.2 and 3.0 times higher than those of ChSase ABC I. By optimization of fermentation conditions, higher productivity of ChSase ABC I was achieved as 880 ± 61 IU/g wet cell weight compared with the reported ones. The optimal temperature and pH of GAPDH-ChSase ABC I were 40 °C and 7.5, respectively. GAPDH-ChSase ABC I had a kcat/Km of 131 ± 4.1 L/μmol s and the catalytic efficiency was decreased as compared to ChSase ABC I. The relative activity of GAPDH-ChSase ABC I remained 89% after being incubated at 30 °C for 180 min and the thermostability of ChSase ABC I was enhanced by GAPDH when it was incubated at 30, 35, 40 and 45 °C. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of activity-based costing (ABC) for a Peruvian NGO healthcare provider.

PubMed

Waters, H; Abdallah, H; Santillán, D

2001-01-01

This article describes the application of activity-based costing (ABC) to calculate the unit costs of the services for a health care provider in Peru. While traditional costing allocates overhead and indirect costs in proportion to production volume or to direct costs, ABC assigns costs through activities within an organization. ABC uses personnel interviews to determine principal activities and the distribution of individual's time among these activities. Indirect costs are linked to services through time allocation and other tracing methods, and the result is a more accurate estimate of unit costs. The study concludes that applying ABC in a developing country setting is feasible, yielding results that are directly applicable to pricing and management. ABC determines costs for individual clinics, departments and services according to the activities that originate these costs, showing where an organization spends its money. With this information, it is possible to identify services that are generating extra revenue and those operating at a loss, and to calculate cross subsidies across services. ABC also highlights areas in the health care process where efficiency improvements are possible. Conclusions about the ultimate impact of the methodology are not drawn here, since the study was not repeated and changes in utilization patterns and the addition of new clinics affected applicability of the results. A potential constraint to implementing ABC is the availability and organization of cost information. Applying ABC efficiently requires information to be readily available, by cost category and department, since the greatest benefits of ABC come from frequent, systematic application of the methodology in order to monitor efficiency and provide feedback for management. The article concludes with a discussion of the potential applications of ABC in the health sector in developing countries.

Abacavir induces platelet-endothelium interactions by interfering with purinergic signalling: A step from inflammation to thrombosis.

PubMed

Alvarez, Angeles; Rios-Navarro, Cesar; Blanch-Ruiz, Maria Amparo; Collado-Diaz, Victor; Andujar, Isabel; Martinez-Cuesta, Maria Angeles; Orden, Samuel; Esplugues, Juan V

2017-05-01

The controversy connecting Abacavir (ABC) with cardiovascular disease has been fuelled by the lack of a credible mechanism of action. ABC shares structural similarities with endogenous purines, signalling molecules capable of triggering prothrombotic/proinflammatory programmes. Platelets are leading actors in the process of thrombosis. Our study addresses the effects of ABC on interactions between platelets and other vascular cells, while exploring the adhesion molecules implicated and the potential interference with the purinergic signalling pathway. The effects of ABC on platelet aggregation and platelet-endothelium interactions were evaluated, respectively, with an aggregometer and a flow chamber system that reproduced conditions in vivo. The role of adhesion molecules and purinergic receptors in endothelial and platelet populations was assessed by selective pre-incubation with specific antagonists and antibodies. ABC and carbovir triphosphate (CBT) levels were evaluated by HPLC. The results showed that ABC promoted the adherence of platelets to endothelial cells, a crucial step for the formation of thrombi. This was not a consequence of a direct effect of ABC on platelets, but resulted from activation of the endothelium via purinergic ATP-P2X 7 receptors, which subsequently triggered an interplay between P-selectin and ICAM-1 on endothelial cells with constitutively expressed GPIIb/IIIa and GPIbα on platelets. ABC did not induce platelet activation (P-selectin expression or Ca 2+ mobilization) or aggregation, even at high concentrations. CBT levels in endothelial cells were lower than those required to induce platelet-endothelium interactions. Thus, ABC interference with endothelial purinergic signalling leads to platelet recruitment. This highlights the endothelium as the main cell target of ABC in this interaction, which is in line with previous experimental evidence that ABC induces manifestations of vascular inflammation. Copyright © 2017 Elsevier B.V. All rights reserved.

SU-E-T-401: Feasibility Study of Using ABC to Gate Lung SBRT Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, D; Xie, X; Shepard, D

2014-06-01

Purpose: The current SBRT treatment techniques include free breathing (FB) SBRT and gated FB SBRT. Gated FB SBRT has smaller target and less lung toxicity with longer treatment time. The recent development of direct connectivity between the ABC and linac allowing for automated beam gating. In this study, we have examined the feasibility of using ABC system to gate the lung SBRT treatment. Methods: A CIRS lung phantom with a 3cm sphere-insert and a moving chest plate was used in this study. Sinusoidal motion was used for the FB pattern. An ABC signal was imported to simulate breath holds. 4D-CTmore » was taken in FB mode and average-intensity-projection (AIP) was used to create FB and 50% gated FB SBRT planning CT. A manually gated 3D CT scan was acquired for ABC gated SBRT planning.An SBRT plan was created for each treatment option. A surface-mapping system was used for 50% gating and ABC system was used for ABC gating. A manually gated CBCT scan was also performed to verify setup. Results: Among three options, the ABC gated plan has the smallest PTV of 35.94cc, which is 35% smaller comparing to that of the FB plan. Consequently, the V20 of the left lung reduced by 15% and 23% comparing to the 50% gated FB and FB plans, respectively. The FB plan took 4.7 minutes to deliver, while the 50% gated FB plan took 18.5 minutes. The ABC gated plan delivery took only 10.6 minutes. A stationary target with 3cm diameter was also obtained from the manually gated CBCT scan. Conclusion: A strategy for ABC gated lung SBRT was developed. ABC gating can significantly reduce the lung toxicity while maintaining the target coverage. Comparing to the 50% gated FB SBRT, ABC gated treatment can also provide less lung toxicity as well as improved delivery efficiency. This research is funded by Elekta.« less

ABC transporters are involved in defense against permethrin insecticide in the malaria vector Anopheles stephensi.

PubMed

Epis, Sara; Porretta, Daniele; Mastrantonio, Valentina; Comandatore, Francesco; Sassera, Davide; Rossi, Paolo; Cafarchia, Claudia; Otranto, Domenico; Favia, Guido; Genchi, Claudio; Bandi, Claudio; Urbanelli, Sandra

2014-07-29

Proteins from the ABC family (ATP-binding cassette) represent the largest known group of efflux pumps, responsible for transporting specific molecules across lipid membranes in both prokaryotic and eukaryotic organisms. In arthropods they have been shown to play a role in insecticide defense/resistance. The presence of ABC transporters and their possible association with insecticide transport have not yet been investigated in the mosquito Anopheles stephensi, the major vector of human malaria in the Middle East and South Asian regions. Here we investigated the presence and role of ABCs in transport of permethrin insecticide in a susceptible strain of this mosquito species. To identify ABC transporter genes we obtained a transcriptome from untreated larvae of An. stephensi and then compared it with the annotated transcriptome of Anopheles gambiae. To analyse the association between ABC transporters and permethrin we conducted bioassays with permethrin alone and in combination with an ABC inhibitor, and then we investigated expression profiles of the identified genes in larvae exposed to permethrin. Bioassays showed an increased mortality of mosquitoes when permethrin was used in combination with the ABC-transporter inhibitor. Genes for ABC transporters were detected in the transcriptome, and five were selected (AnstABCB2, AnstABCB3, AnstABCB4, AnstABCmember6 and AnstABCG4). An increased expression in one of them (AnstABCG4) was observed in larvae exposed to the LD50 dose of permethrin. Contrary to what was found in other insect species, no up-regulation was observed in the AnstABCB genes. Our results show for the first time the involvement of ABC transporters in larval defense against permethrin in An. stephensi and, more in general, confirm the role of ABC transporters in insecticide defense. The differences observed with previous studies highlight the need of further research as, despite the growing number of studies on ABC transporters in insects, the heterogeneity of the results available at present does not allow us to infer general trends in ABC transporter-insecticide interactions.

Inhibition of sirtuins 1 and 2 impairs cell survival and migration and modulates the expression of P-glycoprotein and MRP3 in hepatocellular carcinoma cell lines.

PubMed

Ceballos, María Paula; Decándido, Giulia; Quiroga, Ariel Darío; Comanzo, Carla Gabriela; Livore, Verónica Inés; Lorenzetti, Florencia; Lambertucci, Flavia; Chazarreta-Cifre, Lorena; Banchio, Claudia; Alvarez, María de Luján; Mottino, Aldo Domingo; Carrillo, María Cristina

2018-06-01

Sirtuins (SIRTs) 1 and 2 deacetylases are overexpressed in hepatocellular carcinoma (HCC) and are associated with tumoral progression and multidrug resistance (MDR). In this study we analyzed whether SIRTs 1 and 2 activities blockage was able to affect cellular survival and migration and to modulate p53 and FoxO1 acetylation in HepG2 and Huh7 cells. Moreover, we analyzed ABC transporters P-glycoprotein (P-gp) and multidrug resistance-associated protein 3 (MRP3) expression. We used cambinol and EX-527 as SIRTs inhibitors. Both drugs reduced cellular viability, number of colonies and cellular migration and augmented apoptosis. In 3D cultures, SIRTs inhibitors diminished spheroid growth and viability. 3D culture was less sensitive to drugs than 2D culture. The levels of acetylated p53 and FoxO1 increased after treatments. Drugs induced a decrease in ABC transporters mRNA and protein levels in HepG2 cells; however, only EX-527 was able to reduce MRP3 mRNA and protein levels in Huh7 cells. This is the first work demonstrating the regulation of MRP3 by SIRTs. In conclusion, both drugs decreased HCC cells survival and migration, suggesting SIRTs 1 and 2 activities blockage could be beneficial during HCC therapy. Downregulation of the expression of P-gp and MRP3 supports the potential application of SIRTs 1 and 2 inhibitions in combination with conventional chemotherapy. Copyright © 2018 Elsevier B.V. All rights reserved.

Evaluation of the selection methods used in the exIWO algorithm based on the optimization of multidimensional functions

NASA Astrophysics Data System (ADS)

Kostrzewa, Daniel; Josiński, Henryk

2016-06-01

The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version inspired by dynamic growth of weeds colony. The authors of the present paper have modified the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals' selection. The goal of the project was to evaluate the modified exIWO by testing its usefulness for multidimensional numerical functions optimization. The optimized functions: Griewank, Rastrigin, and Rosenbrock are frequently used as benchmarks because of their characteristics.

Ant colony optimization algorithm for signal coordination of oversaturated traffic networks.

DOT National Transportation Integrated Search

2010-05-01

Traffic congestion is a daily and growing problem of the modern era in mostly all major cities in the world. : Increasing traffic demand strains the existing transportation system, leading to oversaturated network : conditions, especially at peak hou...

Concepts and applications of "natural computing" techniques in de novo drug and peptide design.

PubMed

Hiss, Jan A; Hartenfeller, Markus; Schneider, Gisbert

2010-05-01

Evolutionary algorithms, particle swarm optimization, and ant colony optimization have emerged as robust optimization methods for molecular modeling and peptide design. Such algorithms mimic combinatorial molecule assembly by using molecular fragments as building-blocks for compound construction, and relying on adaptation and emergence of desired pharmacological properties in a population of virtual molecules. Nature-inspired algorithms might be particularly suited for bioisosteric replacement or scaffold-hopping from complex natural products to synthetically more easily accessible compounds that are amenable to optimization by medicinal chemistry. The theory and applications of selected nature-inspired algorithms for drug design are reviewed, together with practical applications and a discussion of their advantages and limitations.

A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.

PubMed

Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao

2011-08-01

The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.

Ant Colony Optimization Algorithm for Centralized Dynamic Channel Allocation in Multi-Cell OFDMA Systems

NASA Astrophysics Data System (ADS)

Kim, Hyo-Su; Kim, Dong-Hoi

The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

Proposed algorithm to improve job shop production scheduling using ant colony optimization method

NASA Astrophysics Data System (ADS)

Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

2017-12-01

This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

Software Piracy Detection Model Using Ant Colony Optimization Algorithm

NASA Astrophysics Data System (ADS)

Astiqah Omar, Nor; Zakuan, Zeti Zuryani Mohd; Saian, Rizauddin

2017-06-01

Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be 62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.

Finite Element Electromagnetic Scattering: An Interactive Micro-Computer Algorithm

DTIC Science & Technology

1988-06-01

the array established to hold all of C the transformed Y values. C C xamax The maximum value of the X array (XARRAY) C yamax The maximum v.lue of the...PI, MAGI, NOOTS, NUMPTS REAL N;UMSETS, RAD,1 THETAI RL X-MAX, XFIIJ, XMAJOR, XMAX, XMIN, XORO, XST REAL YAMAX , YFIN, YMAJOR. YMAX, YMIIJ, YCRG...U) go to 1 b =selecti C C LOAD ARRAYS c =1 xamax =0.0 yamax = 0.0 gamax =0.0 tatle2 =title6(a,b,c) do 18 c =1,int(numpts) tarray~c) =thetaca,b,c

Volume Quantification of Acute Infratentorial Hemorrhage with Computed Tomography: Validation of the Formula 1/2ABC and 2/3SH

PubMed Central

Zhang, Yunyun; Yan, Jing; Fu, Yi; Chen, Shengdi

2013-01-01

Objective To compare the accuracy of formula 1/2ABC with 2/3SH on volume estimation for hypertensive infratentorial hematoma. Methods One hundred and forty-seven CT scans diagnosed as hypertensive infratentorial hemorrhage were reviewed. Based on the shape, hematomas were categorized as regular or irregular. Multilobular was defined as a special shape of irregular. Hematoma volume was calculated employing computer-assisted volumetric analysis (CAVA), 1/2ABC and 2/3SH, respectively. Results The correlation coefficients between 1/2ABC (or 2/3SH) and CAVA were greater than 0.900 in all subgroups. There were neither significant differences in absolute values of volume deviation nor percentage deviation between 1/2ABC and 2/3SH for regular hemorrhage (P>0.05). While for cerebellar, brainstem and irregular hemorrhages, the absolute values of volume deviation and percentage deviation by formula 1/2ABC were greater than 2/3SH (P<0.05). 1/2ABC and 2/3SH underestimated hematoma volume each by 10% and 5% for cerebellar hemorrhage, 14% and 9% for brainstem hemorrhage, 19% and 16% for regular hemorrhage, 9% and 3% for irregular hemorrhage, respectively. In addition, for the multilobular hemorrhage, 1/2ABC underestimated the volume by 9% while 2/3SH overestimated it by 2%. Conclusions For regular hemorrhage volume calculation, the accuracy of 2/3SH is similar to 1/2ABC. While for cerebellar, brainstem or irregular hemorrhages (including multilobular), 2/3SH is more accurate than 1/2ABC. PMID:23638025

Diagnosing and discriminating between primary and secondary aneurysmal bone cysts

PubMed Central

Sasaki, Hiromi; Nagano, Satoshi; Shimada, Hirofumi; Yokouchi, Masahiro; Setoguchi, Takao; Ishidou, Yasuhiro; Kunigou, Osamu; Maehara, Kosuke; Komiya, Setsuro

2017-01-01

Aneurysmal bone cysts (ABCs) are benign bony lesions frequently accompanied by multiple cystic lesions and aggressive bone destruction. They are relatively rare lesions, representing only 1% of bone tumors. The pathogenesis of ABCs has yet to be elucidated. In the present study, a series of 22 cases of primary and secondary ABC from patients treated in Department of Orthopedic Surgery, Kagoshima University Hospital (Kagoshima, Japan) from 2001–2015 were retrospectively analyzed. The average age at the time of diagnosis of primary ABC was 17.9 years. Intralesional curettage and artificial bone grafting were performed in the majority of the patients with primary ABC. The local recurrence rate following curettage for primary ABC was 18%, and the cause of local recurrence was considered to be insufficient curettage. Although no adjuvant therapy was administered during the surgeries, it may assist the prevention of local recurrence in certain cases. The cases of secondary ABC were preceded by benign bone tumors, including fibrous dysplasia, giant cell tumors, chondroblastoma and non-ossifying fibroma. The features of the secondary ABC typically reflected those of the preceding bone tumor. In the majority of cases, distinguishing the primary ABC from the secondary ABC was possible based on characteristic features, including age of the patient at diagnosis and the tumor location. In cases that exhibit ambiguous features, including a soft tissue mass or a thick septal enhancement on the preoperative magnetic resonance images, a biopsy must be obtained in order to exclude other types of aggressive bone tumors, including giant cell tumor, osteosarcoma and telangiectatic osteosarcoma. PMID:28454393

75 FR 11991 - ABC & D Recycling, Inc.-Lease and Operation Exemption-a Line of Railroad in Ware, MA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-12

... DEPARTMENT OF TRANSPORTATION Surface Transportation Board [STB Finance Docket No. 35356] ABC & D Recycling, Inc.--Lease and Operation Exemption--a Line of Railroad in Ware, MA ABC & D Recycling, Inc. (ABC & D), a noncarrier, has filed a verified notice of exemption under 49 CFR 1150.31 to lease from O...

Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

PubMed

Cui, Zhihua; Zhang, Yi

2014-02-01

As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.

Swarm Intelligence Optimization and Its Applications

NASA Astrophysics Data System (ADS)

Ding, Caichang; Lu, Lu; Liu, Yuanchao; Peng, Wenxiu

Swarm Intelligence is a computational and behavioral metaphor for solving distributed problems inspired from biological examples provided by social insects such as ants, termites, bees, and wasps and by swarm, herd, flock, and shoal phenomena in vertebrates such as fish shoals and bird flocks. An example of successful research direction in Swarm Intelligence is ant colony optimization (ACO), which focuses on combinatorial optimization problems. Ant algorithms can be viewed as multi-agent systems (ant colony), where agents (individual ants) solve required tasks through cooperation in the same way that ants create complex social behavior from the combined efforts of individuals.

Parameter Estimation for a Pulsating Turbulent Buoyant Jet Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Christopher, Jason; Wimer, Nicholas; Lapointe, Caelan; Hayden, Torrey; Grooms, Ian; Rieker, Greg; Hamlington, Peter

2017-11-01

Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other ``truth'' data to be used for the prediction of unknown parameters, such as flow properties and boundary conditions, in numerical simulations of real-world engineering systems. Here we introduce the ABC approach and then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, truth data are obtained from a direct numerical simulation (DNS) with known boundary conditions and problem parameters, while the ABC procedure utilizes lower fidelity large eddy simulations. Using spatially-sparse statistics from the 2D buoyant jet DNS, we show that the ABC method provides accurate predictions of true jet inflow parameters. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for predicting flow information, such as boundary conditions, that can be difficult to determine experimentally.

ABCE1 Is a Highly Conserved RNA Silencing Suppressor

PubMed Central

Kärblane, Kairi; Gerassimenko, Jelena; Nigul, Lenne; Piirsoo, Alla; Smialowska, Agata; Vinkel, Kadri; Kylsten, Per; Ekwall, Karl; Swoboda, Peter; Truve, Erkki; Sarmiento, Cecilia

2015-01-01

ATP-binding cassette sub-family E member 1 (ABCE1) is a highly conserved protein among eukaryotes and archaea. Recent studies have identified ABCE1 as a ribosome-recycling factor important for translation termination in mammalian cells, yeast and also archaea. Here we report another conserved function of ABCE1. We have previously described AtRLI2, the homolog of ABCE1 in the plant Arabidopsis thaliana, as an endogenous suppressor of RNA silencing. In this study we show that this function is conserved: human ABCE1 is able to suppress RNA silencing in Nicotiana benthamiana plants, in mammalian HEK293 cells and in the worm Caenorhabditis elegans. Using co-immunoprecipitation and mass spectrometry, we found a number of potential ABCE1-interacting proteins that might support its function as an endogenous suppressor of RNA interference. The interactor candidates are associated with epigenetic regulation, transcription, RNA processing and mRNA surveillance. In addition, one of the identified proteins is translin, which together with its binding partner TRAX supports RNA interference. PMID:25659154

First MRI application of an active breathing coordinator

NASA Astrophysics Data System (ADS)

Kaza, E.; Symonds-Tayler, R.; Collins, D. J.; McDonald, F.; McNair, H. A.; Scurr, E.; Koh, D.-M.; Leach, M. O.

2015-02-01

A commercial active breathing coordinator (ABC) device, employed to hold respiration at a specific level for a predefined duration, was successfully adapted for magnetic resonance imaging (MRI) use for the first time. Potential effects of the necessary modifications were assessed and taken into account. Automatic MR acquisition during ABC breath holding was achieved. The feasibility of MR-ABC thoracic and abdominal examinations together with the advantages of imaging in repeated ABC-controlled breath holds were demonstrated on healthy volunteers. Five lung cancer patients were imaged under MR-ABC, visually confirming the very good intra-session reproducibility of organ position in images acquired with the same patient positioning as used for computed tomography (CT). Using identical ABC settings, good MR-CT inter-modality registration was achieved. This demonstrates the value of ABC, since application of T1, T2 and diffusion weighted MR sequences provides a wider range of contrast mechanisms and additional diagnostic information compared to CT, thus improving radiotherapy treatment planning and assessment.

First MRI application of an active breathing coordinator.

PubMed

Kaza, E; Symonds-Tayler, R; Collins, D J; McDonald, F; McNair, H A; Scurr, E; Koh, D-M; Leach, M O

2015-02-21

A commercial active breathing coordinator (ABC) device, employed to hold respiration at a specific level for a predefined duration, was successfully adapted for magnetic resonance imaging (MRI) use for the first time. Potential effects of the necessary modifications were assessed and taken into account. Automatic MR acquisition during ABC breath holding was achieved. The feasibility of MR-ABC thoracic and abdominal examinations together with the advantages of imaging in repeated ABC-controlled breath holds were demonstrated on healthy volunteers. Five lung cancer patients were imaged under MR-ABC, visually confirming the very good intra-session reproducibility of organ position in images acquired with the same patient positioning as used for computed tomography (CT). Using identical ABC settings, good MR-CT inter-modality registration was achieved. This demonstrates the value of ABC, since application of T1, T2 and diffusion weighted MR sequences provides a wider range of contrast mechanisms and additional diagnostic information compared to CT, thus improving radiotherapy treatment planning and assessment.

First MRI application of an active breathing coordinator

PubMed Central

Kaza, E; Symonds-Tayler, R; Collins, D J; McDonald, F; McNair, H A; Scurr, E; Koh, D-M; Leach, M O

2015-01-01

Abstract A commercial active breathing coordinator (ABC) device, employed to hold respiration at a specific level for a predefined duration, was successfully adapted for magnetic resonance imaging (MRI) use for the first time. Potential effects of the necessary modifications were assessed and taken into account. Automatic MR acquisition during ABC breath holding was achieved. The feasibility of MR-ABC thoracic and abdominal examinations together with the advantages of imaging in repeated ABC-controlled breath holds were demonstrated on healthy volunteers. Five lung cancer patients were imaged under MR-ABC, visually confirming the very good intra-session reproducibility of organ position in images acquired with the same patient positioning as used for computed tomography (CT). Using identical ABC settings, good MR-CT inter-modality registration was achieved. This demonstrates the value of ABC, since application of T1, T2 and diffusion weighted MR sequences provides a wider range of contrast mechanisms and additional diagnostic information compared to CT, thus improving radiotherapy treatment planning and assessment. PMID:25633183

Effectiveness of the Assessment of Burden of COPD (ABC) tool on health-related quality of life in patients with COPD: a cluster randomised controlled trial in primary and hospital care

PubMed Central

Slok, Annerika H M; Kotz, Daniel; van Breukelen, Gerard; Chavannes, Niels H; Rutten-van Mölken, Maureen P M H; Kerstjens, Huib A M; van der Molen, Thys; Asijee, Guus M; Dekhuijzen, P N Richard; Holverda, Sebastiaan; Salomé, Philippe L; Goossens, Lucas M A; Twellaar, Mascha; in ‘t Veen, Johannes C C M; van Schayck, Onno C P

2016-01-01

Objective Assessing the effectiveness of the Assessment of Burden of COPD (ABC) tool on disease-specific quality of life in patients with chronic obstructive pulmonary disease (COPD) measured with the St. George's Respiratory Questionnaire (SGRQ), compared with usual care. Methods A pragmatic cluster randomised controlled trial, in 39 Dutch primary care practices and 17 hospitals, with 357 patients with COPD (postbronchodilator FEV1/FVC ratio <0.7) aged ≥40 years, who could understand and read the Dutch language. Healthcare providers were randomly assigned to the intervention or control group. The intervention group applied the ABC tool, which consists of a short validated questionnaire assessing the experienced burden of COPD, objective COPD parameter (eg, lung function) and a treatment algorithm including a visual display and treatment advice. The control group provided usual care. Researchers were blinded to group allocation during analyses. Primary outcome was the number of patients with a clinically relevant improvement in SGRQ score between baseline and 18-month follow-up. Secondary outcomes were the COPD Assessment Test (CAT) and the Patient Assessment of Chronic Illness Care (PACIC; a measurement of perceived quality of care). Results At 18-month follow-up, 34% of the 146 patients from 27 healthcare providers in the intervention group showed a clinically relevant improvement in the SGRQ, compared with 22% of the 148 patients from 29 healthcare providers in the control group (OR 1.85, 95% CI 1.08 to 3.16). No difference was found on the CAT (−0.26 points (scores ranging from 0 to 40); 95% CI −1.52 to 0.99). The PACIC showed a higher improvement in the intervention group (0.32 points (scores ranging from 1 to 5); 95% CI 0.14 to 0.50). Conclusions This study showed that use of the ABC tool may increase quality of life and perceived quality of care. Trial registration number NTR3788; Results. PMID:27401361

A multi-group firefly algorithm for numerical optimization

NASA Astrophysics Data System (ADS)

Tong, Nan; Fu, Qiang; Zhong, Caiming; Wang, Pengjun

2017-08-01

To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Application of Metaheuristic and Deterministic Algorithms for Aircraft Reference Trajectory Optimization =

NASA Astrophysics Data System (ADS)

Murrieta Mendoza, Alejandro

Aircraft reference trajectory is an alternative method to reduce fuel consumption, thus the pollution released to the atmosphere. Fuel consumption reduction is of special importance for two reasons: first, because the aeronautical industry is responsible of 2% of the CO2 released to the atmosphere, and second, because it will reduce the flight cost. The aircraft fuel model was obtained from a numerical performance database which was created and validated by our industrial partner from flight experimental test data. A new methodology using the numerical database was proposed in this thesis to compute the fuel burn for a given trajectory. Weather parameters such as wind and temperature were taken into account as they have an important effect in fuel burn. The open source model used to obtain the weather forecast was provided by Weather Canada. A combination of linear and bi-linear interpolations allowed finding the required weather data. The search space was modelled using different graphs: one graph was used for mapping the different flight phases such as climb, cruise and descent, and another graph was used for mapping the physical space in which the aircraft would perform its flight. The trajectory was optimized in its vertical reference trajectory using the Beam Search algorithm, and a combination of the Beam Search algorithm with a search space reduction technique. The trajectory was optimized simultaneously for the vertical and lateral reference navigation plans while fulfilling a Required Time of Arrival constraint using three different metaheuristic algorithms: the artificial bee's colony, and the ant colony optimization. Results were validated using the software FlightSIMRTM, a commercial Flight Management System, an exhaustive search algorithm, and as flown flights obtained from flightawareRTM. All algorithms were able to reduce the fuel burn, and the flight costs. None None None None None None None

A comparative study of an ABC and an artificial absorber for truncating finite element meshes

NASA Technical Reports Server (NTRS)

Oezdemir, T.; Volakis, John L.

1993-01-01

The type of mesh termination used in the context of finite element formulations plays a major role on the efficiency and accuracy of the field solution. The performance of an absorbing boundary condition (ABC) and an artificial absorber (a new concept) for terminating the finite element mesh was evaluated. This analysis is done in connection with the problem of scattering by a finite slot array in a thick ground plane. The two approximate mesh truncation schemes are compared with the exact finite element-boundary integral (FEM-BI) method in terms of accuracy and efficiency. It is demonstrated that both approximate truncation schemes yield reasonably accurate results even when the mesh is extended only 0.3 wavelengths away from the array aperture. However, the artificial absorber termination method leads to a substantially more efficient solution. Moreover, it is shown that the FEM-BI method remains quite competitive with the FEM-artificial absorber method when the FFT is used for computing the matrix-vector products in the iterative solution algorithm. These conclusions are indeed surprising and of major importance in electromagnetic simulations based on the finite element method.

Disease state fingerprint for fall risk assessment.

PubMed

Similä, Heidi; Immonen, Milla

2014-01-01

Fall prevention is an important and complex multifactorial challenge, since one third of people over 65 years old fall at least once every year. A novel application of Disease State Fingerprint (DSF) algorithm is presented for holistic visualization of fall risk factors and identifying persons with falls history or decreased level of physical functioning based on fall risk assessment data. The algorithm is tested with data from 42 older adults, that went through a comprehensive fall risk assessment. Within the study population the Activities-specific Balance Confidence (ABC) scale score, Berg Balance Scale (BBS) score and the number of drugs in use were the three most relevant variables, that differed between the fallers and non-fallers. This study showed that the DSF visualization is beneficial in inspection of an individual's significant fall risk factors, since people have problems in different areas and one single assessment scale is not enough to expose all the people at risk.

Diversity of ABC transporter genes across the plant kingdom and their potential utility in biotechnology.

PubMed

Lane, Thomas S; Rempe, Caroline S; Davitt, Jack; Staton, Margaret E; Peng, Yanhui; Soltis, Douglas Edward; Melkonian, Michael; Deyholos, Michael; Leebens-Mack, James H; Chase, Mark; Rothfels, Carl J; Stevenson, Dennis; Graham, Sean W; Yu, Jun; Liu, Tao; Pires, J Chris; Edger, Patrick P; Zhang, Yong; Xie, Yinlong; Zhu, Ying; Carpenter, Eric; Wong, Gane Ka-Shu; Stewart, C Neal

2016-05-31

The ATP-binding cassette (ABC) transporter gene superfamily is ubiquitous among extant organisms and prominently represented in plants. ABC transporters act to transport compounds across cellular membranes and are involved in a diverse range of biological processes. Thus, the applicability to biotechnology is vast, including cancer resistance in humans, drug resistance among vertebrates, and herbicide and other xenobiotic resistance in plants. In addition, plants appear to harbor the highest diversity of ABC transporter genes compared with any other group of organisms. This study applied transcriptome analysis to survey the kingdom-wide ABC transporter diversity in plants and suggest biotechnology applications of this diversity. We utilized sequence similarity-based informatics techniques to infer the identity of ABC transporter gene candidates from 1295 phylogenetically-diverse plant transcriptomes. A total of 97,149 putative (approximately 25 % were full-length) ABC transporter gene members were identified; each RNA-Seq library (plant sample) had 88 ± 30 gene members. As expected, simpler organisms, such as algae, had fewer unique members than vascular land plants. Differences were also noted in the richness of certain ABC transporter subfamilies. Land plants had more unique ABCB, ABCC, and ABCG transporter gene members on average (p < 0.005), and green algae, red algae, and bryophytes had significantly more ABCF transporter gene members (p < 0.005). Ferns had significantly fewer ABCA transporter gene members than all other plant groups (p < 0.005). We present a transcriptomic overview of ABC transporter gene members across all major plant groups. An increase in the number of gene family members present in the ABCB, ABCC, and ABCD transporter subfamilies may indicate an expansion of the ABC transporter superfamily among green land plants, which include all crop species. The striking difference between the number of ABCA subfamily transporter gene members between ferns and other plant taxa is surprising and merits further investigation. Discussed is the potential exploitation of ABC transporters in plant biotechnology, with an emphasis on crops.

ABC transporters affect the elimination and toxicity of CdTe quantum dots in liver and kidney cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Mingli; Yin, Huancai; Bai, Pengli

This paper aimed to investigate the role of adenosine triphosphate-binding cassette (ABC) transporters on the efflux and the toxicity of nanoparticles in liver and kidney cells. In this study, we synthesized CdTe quantum dots (QDs) that were monodispersed and emitted green fluorescence (maximum peak at 530 nm). Such QDs tended to accumulate in human hepatocellular carcinoma cells (HepG2), human kidney cells 2 (HK-2), and Madin-Darby canine kidney (MDCK) cells, and cause significant toxicity in all the three cell lines. Using specific inhibitors and inducers of P-glycoprotein (Pgp) and multidrug resistance associated proteins (Mrps), the cellular accumulation and subsequent toxicity ofmore » QDs in HepG2 and HK-2 cells were significantly affected, while only slight changes appeared in MDCK cells, corresponding well with the functional expressions of ABC transporters in cells. Moreover, treatment of QDs caused concentration- and time- dependent induction of ABC transporters in HepG2 and HK-2 cells, but such phenomenon was barely found in MDCK cells. Furthermore, the effects of CdTe QDs on ABC transporters were found to be greater than those of CdCl{sub 2} at equivalent concentrations of cadmium, indicating that the effects of QDs should be a combination of free Cd{sup 2+} and specific properties of QDs. Overall, these results indicated a strong dependence between the functional expressions of ABC transporters and the efflux of QDs, which could be an important reason for the modulation of QDs toxicity by ABC transporters. - Highlights: • ABC transporters contributed actively to the cellular efflux of CdTe quantum dots. • ABC transporters affected the cellular toxicity of CdTe quantum dots. • Treatment of CdTe quantum dots induced the gene expression of ABC transporters. • Free Cd{sup 2+} should be partially involved in the effects of QDs on ABC transporters. • Cellular efflux of quantum dots could be an important modulator for its toxicity.« less

Falls and confidence related quality of life outcome measures in an older British cohort

PubMed Central

Parry, S; Steen, N; Galloway, S; Kenny, R; Bond, J

2001-01-01

Falls are common in older subjects and result in loss of confidence and independence. The Falls Efficacy Scale (FES) and the Activities-specific Balance Confidence scale (ABC) were developed in North America to quantify these entities, but contain idiom unfamiliar to an older British population. Neither has been validated in the UK. The FES and the ABC were modified for use within British culture and the internal consistency and test-retest reliability of the modified scales (FES-UK and ABC-UK) assessed. A total of 193 consecutive, ambulant, new, and return patients (n=119; 62%) and their friends and relatives ("visitors", n=74; 38%) were tested on both scales, while the last 60 subjects were retested within one week. Internal reliability was excellent for both scales (Cronbach's alpha 0.97 (FES-UK), and 0.98 (ABC-UK)). Test-retest reliability was good for both scales, though superior for the ABC-UK (intraclass correlation coefficient 0.58 (FES-UK), 0.89 (ABC-UK)). There was evidence to suggest that the ABC-UK was better than the FES-UK at distinguishing between older patients and younger patients (|tABC| = 4.4; |tFES| = 2.3); and between fallers and non-fallers (|tABC| = 8.7; |tFES| = 5.0) where the t statistics are based on the comparison of two independent samples. The ABC-UK and FES-UK are both reliable and valid measures for the assessment of falls and balance related confidence in older adults. However, better test-retest reliability and more robust differentiation of subgroups in whom falls related quality of life would be expected to be different make the ABC-UK the current instrument of choice in assessing this entity in older British subjects.


Keywords: quality of life; falls; elderly; health status measurement PMID:11161077

Active Breathing Control for Hodgkin's Disease in Childhood and Adolescence: Feasibility, Advantages, and Limits

DOE Office of Scientific and Technical Information (OSTI.GOV)

Claude, Line; Malet, Claude Phys.; Pommier, Pascal

2007-04-01

Purpose: The challenge in early Hodgkin's disease (HD) in children is to maintain good survival rates while sparing organs at risk. This study assesses the feasibility of active breathing control (ABC) in children, and compares normal tissue irradiation with and without ABC. Methods and Materials: Between May 2003 and June 2004, seven children with HD with mediastinal involvement, median age 15, were treated by chemotherapy and involved-field radiation therapy. A free-breathing computed tomography simulation scan and one additional scan during deep inspiration using ABC were performed. A comparison between planning treatment with clinical target volume including supraclavicular regions, mediastinum, andmore » hila was performed, both in free breathing and using ABC. Results: For a prescription of 36 Gy, pulmonary dose-volume histograms revealed a mean reduction in lung volume irradiated at more than 20 Gy (V20) and 30 Gy (V30) of 25% and 26%, respectively, using ABC (p = 0.016). The mean volume of heart irradiated at 30 Gy or more decreased from 15% to 12% (nonsignificant). The mean dose delivered to breasts in girls was small in both situations (less than 2 Gy) and stable with or without ABC. Considering axillary irradiation, the mean dose delivered to breasts remained low (<9 Gy), without significant difference using ABC or not. The mean radiation dose delivered to thyroid was stable using ABC or not. Conclusions: Using ABC is feasible in childhood. The use of ABC decreases normal lung tissue irradiation. Concerning heart irradiation, a minimal gain is also shown. No significant change has been demonstrated concerning breast and thyroid irradiation.« less

Sensitive and Specific Fluorescent Probes for Functional Analysis of the Three Major Types of Mammalian ABC Transporters

PubMed Central

Lebedeva, Irina V.; Pande, Praveen; Patton, Wayne F.

2011-01-01

An underlying mechanism for multi drug resistance (MDR) is up-regulation of the transmembrane ATP-binding cassette (ABC) transporter proteins. ABC transporters also determine the general fate and effect of pharmaceutical agents in the body. The three major types of ABC transporters are MDR1 (P-gp, P-glycoprotein, ABCB1), MRP1/2 (ABCC1/2) and BCRP/MXR (ABCG2) proteins. Flow cytometry (FCM) allows determination of the functional expression levels of ABC transporters in live cells, but most dyes used as indicators (rhodamine 123, DiOC2(3), calcein-AM) have limited applicability as they do not detect all three major types of ABC transporters. Dyes with broad coverage (such as doxorubicin, daunorubicin and mitoxantrone) lack sensitivity due to overall dimness and thus may yield a significant percentage of false negative results. We describe two novel fluorescent probes that are substrates for all three common types of ABC transporters and can serve as indicators of MDR in flow cytometry assays using live cells. The probes exhibit fast internalization, favorable uptake/efflux kinetics and high sensitivity of MDR detection, as established by multidrug resistance activity factor (MAF) values and Kolmogorov-Smirnov statistical analysis. Used in combination with general or specific inhibitors of ABC transporters, both dyes readily identify functional efflux and are capable of detecting small levels of efflux as well as defining the type of multidrug resistance. The assay can be applied to the screening of putative modulators of ABC transporters, facilitating rapid, reproducible, specific and relatively simple functional detection of ABC transporter activity, and ready implementation on widely available instruments. PMID:21799851

A new model to predict weak-lensing peak counts. II. Parameter constraint strategies

NASA Astrophysics Data System (ADS)

Lin, Chieh-An; Kilbinger, Martin

2015-11-01

Context. Peak counts have been shown to be an excellent tool for extracting the non-Gaussian part of the weak lensing signal. Recently, we developed a fast stochastic forward model to predict weak-lensing peak counts. Our model is able to reconstruct the underlying distribution of observables for analysis. Aims: In this work, we explore and compare various strategies for constraining a parameter using our model, focusing on the matter density Ωm and the density fluctuation amplitude σ8. Methods: First, we examine the impact from the cosmological dependency of covariances (CDC). Second, we perform the analysis with the copula likelihood, a technique that makes a weaker assumption than does the Gaussian likelihood. Third, direct, non-analytic parameter estimations are applied using the full information of the distribution. Fourth, we obtain constraints with approximate Bayesian computation (ABC), an efficient, robust, and likelihood-free algorithm based on accept-reject sampling. Results: We find that neglecting the CDC effect enlarges parameter contours by 22% and that the covariance-varying copula likelihood is a very good approximation to the true likelihood. The direct techniques work well in spite of noisier contours. Concerning ABC, the iterative process converges quickly to a posterior distribution that is in excellent agreement with results from our other analyses. The time cost for ABC is reduced by two orders of magnitude. Conclusions: The stochastic nature of our weak-lensing peak count model allows us to use various techniques that approach the true underlying probability distribution of observables, without making simplifying assumptions. Our work can be generalized to other observables where forward simulations provide samples of the underlying distribution.

Team care of type 2 diabetes mellitus in Taiwan.

PubMed

Wang, Chih-Yuan; Yu, Neng-Chun; Sheu, Wayne Huey-Herng; Tsai, Shih-Tzer; Tai, Tong-Yuan

2014-12-01

Type 2 diabetes mellitus (T2DM) is a global health-care and national policy issue. As fluctuating glycemic control in diabetes often results in serious complications, we must encourage the diabetes educators' efforts at long-term follow-up among patients with T2DM. Therefore, certified diabetes educators (CDEs) play the most pivotal roles as life-long protectors for patients with T2DM. In the past 15 years, more than 4,000 CDEs have been trained and qualified, including health professionals such as physicians, nurses, dieticians, and pharmacists. The most important initiation of diabetes share care in Taiwan was originated in I-Lan County. Initiated to provide regional diabetes care, the name of this program is the Lan-Yang Diabetes Shared Care System. In 2006, the Taiwanese Association of Diabetes Educators (TADE) carried out a nationwide survey to evaluate the status of diabetes control in Taiwan, focusing on the "ABC" goal (A: HbA1c <7.0%, B: blood pressure <130/80 mmHg, C: LDL-cholesterol <100 mg/dl/total cholesterol <160 mg/dl). The results revealed that the percentage of patients with diabetes who fulfilled all ABC goals was only 4.1%. Five years later, in 2011, TADE compared two nationwide surveys and found total ABC attainment rates of 4.1% and 8.6%, respectively. The team-care approach to T2DM has been underway for over 20 years in Taiwan. Future interventions and treatment algorithms with team-based education should aim at preventing acute and chronic complications, which remains a long-term challenge in Taiwan. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Application of ant colony optimization in development of models for prediction of anti-HIV-1 activity of HEPT derivatives.

PubMed

Zare-Shahabadi, Vali; Abbasitabar, Fatemeh

2010-09-01

Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88). Copyright 2010 Wiley Periodicals, Inc.

Detection and Length Estimation of Linear Scratch on Solid Surfaces Using an Angle Constrained Ant Colony Technique

NASA Astrophysics Data System (ADS)

Pal, Siddharth; Basak, Aniruddha; Das, Swagatam

In many manufacturing areas the detection of surface defects is one of the most important processes in quality control. Currently in order to detect small scratches on solid surfaces most of the industries working on material manufacturing rely on visual inspection primarily. In this article we propose a hybrid computational intelligence technique to automatically detect a linear scratch from a solid surface and estimate its length (in pixel unit) simultaneously. The approach is based on a swarm intelligence algorithm called Ant Colony Optimization (ACO) and image preprocessing with Wiener and Sobel filters as well as the Canny edge detector. The ACO algorithm is mostly used to compensate for the broken parts of the scratch. Our experimental results confirm that the proposed technique can be used for detecting scratches from noisy and degraded images, even when it is very difficult for conventional image processing to distinguish the scratch area from its background.

Temporal dynamics of the ABC transporter response to insecticide treatment: insights from the malaria vector Anopheles stephensi

NASA Astrophysics Data System (ADS)

Epis, Sara; Porretta, Daniele; Mastrantonio, Valentina; Urbanelli, Sandra; Sassera, Davide; De Marco, Leone; Mereghetti, Valeria; Montagna, Matteo; Ricci, Irene; Favia, Guido; Bandi, Claudio

2014-12-01

In insects, ABC transporters have been shown to contribute to defence/resistance to insecticides by reducing toxic concentrations in cells/tissues. Despite the extensive studies about this detoxifying mechanism, the temporal patterns of ABC transporter activation have been poorly investigated. Using the malaria vector Anopheles stephensi as a study system, we investigated the expression profile of ABC genes belonging to different subfamilies in permethrin-treated larvae at different time points (30 min to 48 h). Our results showed that the expression of ABCB and ABCG subfamily genes was upregulated at 1 h after treatment, with the highest expression observed at 6 h. Therefore, future investigations on the temporal dynamics of ABC gene expression will allow a better implementation of insecticide treatment regimens, including the use of specific inhibitors of ABC efflux pumps.

Parameter Estimation for a Turbulent Buoyant Jet Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Christopher, Jason D.; Wimer, Nicholas T.; Hayden, Torrey R. S.; Lapointe, Caelan; Grooms, Ian; Rieker, Gregory B.; Hamlington, Peter E.

2016-11-01

Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other "truth" data to be used for the prediction of unknown model parameters in numerical simulations of real-world engineering systems. In this presentation, we introduce the ABC approach and then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, truth data are obtained from a simulation with known boundary conditions and problem parameters. Using spatially-sparse temperature statistics from the 2D buoyant jet truth simulation, we show that the ABC method provides accurate predictions of the true jet inflow temperature. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for engineering fluid dynamics research.

LhnR and upstream operon LhnABC in Agrobacterium vitis regulate the induction of tobacco hypersensitive responses, grape necrosis and swarming motility.

PubMed

Zheng, Desen; Hao, Guixia; Cursino, Luciana; Zhang, Hongsheng; Burr, Thomas J

2012-09-01

The characterization of Tn5 transposon insertional mutants of Agrobacterium vitis strain F2/5 revealed a gene encoding a predicted LysR-type transcriptional regulator, lhnR (for 'LysR-type regulator associated with HR and necrosis'), and an immediate upstream operon consisting of three open reading frames (lhnABC) required for swarming motility, surfactant production and the induction of a hypersensitive response (HR) on tobacco and necrosis on grape. The operon lhnABC is unique to A. vitis among the sequenced members in Rhizobiaceae. Mutagenesis of lhnR and lhnABC by gene disruption and complementation of ΔlhnR and ΔlhnABC confirmed their roles in the expression of these phenotypes. Mutation of lhnR resulted in complete loss of HR, swarming motility, surfactant production and reduced necrosis, whereas mutation of lhnABC resulted in loss of swarming motility, delayed and reduced HR development and reduced surfactant production and necrosis. The data from promoter-green fluorescent protein (gfp) fusions showed that lhnR suppresses the expression of lhnABC and negatively autoregulates its own expression. It was also shown that lhnABC negatively affects its own expression and positively affects the transcription of lhnR. lhnR and lhnABC constitute a regulatory circuit that coordinates the transcription level of lhnR, resulting in the expression of swarming, surfactant, HR and necrosis phenotypes. © 2012 THE AUTHORS. MOLECULAR PLANT PATHOLOGY © 2012 BSPP AND BLACKWELL PUBLISHING LTD.

Our bodies are our own: resistance to ABC-based HIV-prevention programmes in northern Tanzanian conservation organisations.

PubMed

Reid-Hresko, John

2014-01-01

ABC-based HIV-prevention programmes have been widely employed in northern Tanzanian wildlife conservation settings in an attempt to (re)shape the sexual behaviours of conservation actors. Utilising findings from 66 semi-structured interviews conducted in 2009-2010, this paper examines ABC prevention as a form of Foucauldian governmentality--circulating technologies of power that mobilise disciplinary technologies and attempt to transform such efforts into technologies of the self--and explores how individuals understand and respond to attempts to govern their behaviour. ABC regimes attempt to rework subjectivity, positioning HIV-related behaviours within a risk-based neoliberal rationality. However, efforts to use ABC as a technology to govern populations and individual bodies are largely incommensurate with existing Tanzanian sociocultural formations, including economic and gendered inequalities, and local understandings of sexuality. The language research participants used to talk about ABC and the justifications they offered for non-compliance illuminate this discrepancy. Data reveal that the recipients of ABC campaigns are active producers of understandings that work for them in their lives, but may not produce the behavioural shifts envisioned by programme goals. These findings corroborate previous research, which questions the continued plausibility of ABC as a stand-alone HIV- prevention framework.

Applying Activity Based Costing (ABC) Method to Calculate Cost Price in Hospital and Remedy Services

PubMed Central

Rajabi, A; Dabiri, A

2012-01-01

Background Activity Based Costing (ABC) is one of the new methods began appearing as a costing methodology in the 1990’s. It calculates cost price by determining the usage of resources. In this study, ABC method was used for calculating cost price of remedial services in hospitals. Methods: To apply ABC method, Shahid Faghihi Hospital was selected. First, hospital units were divided into three main departments: administrative, diagnostic, and hospitalized. Second, activity centers were defined by the activity analysis method. Third, costs of administrative activity centers were allocated into diagnostic and operational departments based on the cost driver. Finally, with regard to the usage of cost objectives from services of activity centers, the cost price of medical services was calculated. Results: The cost price from ABC method significantly differs from tariff method. In addition, high amount of indirect costs in the hospital indicates that capacities of resources are not used properly. Conclusion: Cost price of remedial services with tariff method is not properly calculated when compared with ABC method. ABC calculates cost price by applying suitable mechanisms but tariff method is based on the fixed price. In addition, ABC represents useful information about the amount and combination of cost price services. PMID:23113171

MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering.

PubMed

Su, Chien-Hao; Hsu, Ming-Tsung; Wang, Tse-Yi; Chiang, Sufeng; Cheng, Jen-Hao; Weng, Francis C; Kao, Cheng-Yan; Wang, Daryi; Tsai, Huai-Kuang

2011-08-15

MetaABC is a metagenomic platform that integrates several binning tools coupled with methods for removing artifacts, analyzing unassigned reads and controlling sampling biases. It allows users to arrive at a better interpretation via series of distinct combinations of analysis tools. After execution, MetaABC provides outputs in various visual formats such as tables, pie and bar charts as well as clustering result diagrams. MetaABC source code and documentation are available at http://bits2.iis.sinica.edu.tw/MetaABC/ CONTACT: dywang@gate.sinica.edu.tw; hktsai@iis.sinica.edu.tw Supplementary data are available at Bioinformatics online.

A performance evaluation of ACO and SA TSP in a supply chain network

NASA Astrophysics Data System (ADS)

Rao, T. Srinivas

2017-07-01

Supply Chain management and E commerce business solutions are one of the prominent areas of active research. In our paper we have modelled a supply chain model which aggregates all the manufacturers requirement and the products are supplied to all the manufacturer through a common vehicle routing algorithm. An appropriate tsp has been constructed for all the manufacturers which determines the shortest route thru which the aggregated material can be supplied in the shortest possible time. In this paper we have solved the shortest route through constructing a Simulated annealing algorithm and Ant colony algorithm and their performance is evaluated.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Characterisation of single domain ATP-binding cassette protien homologues of Theileria parva.

PubMed

Kibe, M K; Macklin, M; Gobright, E; Bishop, R; Urakawa, T; ole-MoiYoi, O K

2001-09-01

Two distinct genes encoding single domain, ATP-binding cassette transport protein homologues of Theileria parva were cloned and sequenced. Neither of the genes is tandemly duplicated. One gene, TpABC1, encodes a predicted protein of 593 amino acids with an N-terminal hydrophobic domain containing six potential membrane-spanning segments. A single discontinuous ATP-binding element was located in the C-terminal region of TpABC1. The second gene, TpABC2, also contains a single C-terminal ATP-binding motif. Copies of TpABC2 were present at four loci in the T. parva genome on three different chromosomes. TpABC1 exhibited allelic polymorphism between stocks of the parasite. Comparison of cDNA and genomic sequences revealed that TpABC1 contained seven short introns, between 29 and 84 bp in length. The full-length TpABC1 protein was expressed in insect cells using the baculovirus system. Application of antibodies raised against the recombinant antigen to western blots of T. parva piroplasm lysates detected an 85 kDa protein in this life-cycle stage.

Sulfadiazine resistance in Toxoplasma gondii: no involvement of overexpression or polymorphisms in genes of therapeutic targets and ABC transporters

PubMed Central

Doliwa, Christelle; Escotte-Binet, Sandie; Aubert, Dominique; Sauvage, Virginie; Velard, Frédéric; Schmid, Aline; Villena, Isabelle

2013-01-01

Several treatment failures have been reported for the treatment of toxoplasmic encephalitis, chorioretinitis, and congenital toxoplasmosis. Recently we found three Toxoplasma gondii strains naturally resistant to sulfadiazine and we developed in vitro two sulfadiazine resistant strains, RH-RSDZ and ME-49-RSDZ, by gradual pressure. In Plasmodium, common mechanisms of drug resistance involve, among others, mutations and/or amplification within genes encoding the therapeutic targets dhps and dhfr and/or the ABC transporter genes family. To identify genotypic and/or phenotypic markers of resistance in T. gondii, we sequenced and analyzed the expression levels of therapeutic targets dhps and dhfr, three ABC genes, two Pgp, TgABC.B1 and TgABC.B2, and one MRP, TgABC.C1, on sensitive strains compared to sulfadiazine resistant strains. Neither polymorphism nor overexpression was identified. Contrary to Plasmodium, in which mutations and/or overexpression within gene targets and ABC transporters are involved in antimalarial resistance, T. gondii sulfadiazine resistance is not related to these toxoplasmic genes studied. PMID:23707894

ABC1 Consensus Conference - a German Perspective: First International Consensus Conference on Advanced Breast Cancer (ABC1), Lisbon, November 5, 2011.

PubMed

Thomssen, Christoph; Marschner, Norbert; Untch, Michael; Decker, Thomas; Hegewisch-Becker, Susanna; Jackisch, Christian; Janni, Wolfgang; Hans-Joachim, Lück; von Minckwitz, Gunter; Scharl, Anton; Schneeweiss, Andreas; Tesch, Hans; Welt, Anja; Harbeck, Nadia

2012-02-01

A group of German breast cancer experts (medical oncologists and gynaecologists) reviewed and commented on the results of the first international 'Advanced Breast Cancer First Consensus Conference' (ABC1) for the diagnosis and treatment of advanced breast cancer. The ABC1 Conference is an initiative of the European School of Oncology (ESO) Metastatic Breast Cancer Task Force in cooperation with the EBCC (European Breast Cancer Conference), ESMO (European Society of Medical Oncology) and the American JNCI (Journal of the National Cancer Institute). The main focus of the ABC1 Conference was metastatic breast cancer (stage IV). The ABC1 consensus is based on the vote of 33 breast cancer experts from different countries and has been specified as a guideline for therapeutic practice by the German expert group. It is the objective of the ABC1 consensus as well as of the German comments to provide an internationally standardized and evidence-based foundation for qualified decision-making in the treatment of metastatic breast cancer.

Swarm Intelligence in Text Document Clustering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Xiaohui; Potok, Thomas E

2008-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior. The research field that attempts to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies is called Swarm Intelligence. Compared to the traditional algorithms, the swarm algorithms are usually flexible, robust, decentralized and self-organized. These characters make the swarm algorithms suitable for solving complex problems, such as document collection clustering. The major challenge of today's information society is being overwhelmed with information on any topic they are searching for. Fast and high-quality document clustering algorithms play an important role inmore » helping users to effectively navigate, summarize, and organize the overwhelmed information. In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. These clustering algorithms use stochastic and heuristic principles discovered from observing bird flocks, fish schools and ant food forage.« less

Reinforcement interval type-2 fuzzy controller design by online rule generation and q-value-aided ant colony optimization.

PubMed

Juang, Chia-Feng; Hsu, Chia-Hung

2009-12-01

This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q -values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS.

Iowa ABC connections : [tech transfer summary].

DOT National Transportation Integrated Search

2015-06-01

The Iowa Department of Transportation (DOT) and other organizations have : been developing accelerated bridge construction (ABC) concepts, details, and : processes, and Iowa has come to be viewed as a national leader in the area of : ABC. However, th...

Rhombohedral Multilayer Graphene: A Magneto-Raman Scattering Study.

PubMed

Henni, Younes; Ojeda Collado, Hector Pablo; Nogajewski, Karol; Molas, Maciej R; Usaj, Gonzalo; Balseiro, Carlos A; Orlita, Milan; Potemski, Marek; Faugeras, Clement

2016-06-08

Graphene layers are known to stack in two stable configurations, namely, ABA or ABC stacking, with drastically distinct electronic properties. Unlike the ABA stacking, little has been done to experimentally investigate the electronic properties of ABC graphene multilayers. Here, we report on the first magneto optical study of a large ABC domain in a graphene multilayer flake, with ABC sequences exceeding 17 graphene sheets. ABC-stacked multilayers can be fingerprinted with a characteristic electronic Raman scattering response, which persists even at room temperatures. Tracing the magnetic field evolution of the inter Landau level excitations from this domain gives strong evidence for the existence of a dispersionless electronic band near the Fermi level, characteristic of such stacking. Our findings present a simple yet powerful approach to probe ABC stacking in graphene multilayer flakes, where this highly degenerated band appears as an appealing candidate to host strongly correlated states.

Aggressive aneurysmal bone cyst of the maxilla confused with telangiectatic osteosarcoma.

PubMed

Lee, Hyun-Min; Cho, Kyu-Sup; Choi, Kyung-Un; Roh, Hwan-Jung

2012-06-01

Aneurysmal bone cyst (ABC) is a benign, expansile lesion typically affecting the long bones and vertebrae of patients younger than 20 years. Approximately 2% of ABCs occur in the head and neck region, most commonly affecting the mandible. Although the most common co-existing lesion associated with ABCs is the giant cell tumor, ABCs can be radiologically confused with telangiectatic osteosarcoma in cases of aggressive behavior and rapid growth. Here, we report a case of an aggressive ABC of the maxilla confused with telangiectatic osteosarcoma in a patient who underwent several operations for an osteoblastoma that was diagnosed histopathologically. This case highlights the need for a differential diagnosis both radiologically and histopathologically, because ABCs can easily be interpreted as a giant cell tumor or an osteoblastoma, and, on occasion, can be mistaken for osteogenic malignancies. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Improving Recreational Water Quality Assessments Through Novel Approaches to Quantifying Measurement Uncertainty

EPA Science Inventory

Bacteriological water quality in the Great Lakes is typically measured by the concentration of fecal indicator bacteria (FIB), and is reported via most probable number (MPN) or colony forming unit (CFU) values derived from algorithms relating \\raw data" in a FIB analysis procedu...

Framework for computationally efficient optimal irrigation scheduling using ant colony optimization

USDA-ARS?s Scientific Manuscript database

A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework achieves this goal by representing the problem in the form of a decisi...

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

PubMed

Khan, Arif Ul Maula; Torelli, Angelo; Wolf, Ivo; Gretz, Norbert

2018-05-08

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

Preclinical properties and human in vivo assessment of 123 I-ABC577 as a novel SPECT agent for imaging amyloid-β

PubMed Central

Okumura, Yuki; Kobayashi, Ryohei; Onishi, Takako; Shoyama, Yoshinari; Barret, Olivier; Alagille, David; Jennings, Danna; Marek, Kenneth; Seibyl, John; Tamagnan, Gilles; Tanaka, Akihiro; Shirakami, Yoshifumi

2016-01-01

Abstract Non-invasive imaging of amyloid-β in the brain, a hallmark of Alzheimer’s disease, may support earlier and more accurate diagnosis of the disease. In this study, we assessed the novel single photon emission computed tomography tracer 123 I-ABC577 as a potential imaging biomarker for amyloid-β in the brain. The radio-iodinated imidazopyridine derivative 123 I-ABC577 was designed as a candidate for a novel amyloid-β imaging agent. The binding affinity of 123 I-ABC577 for amyloid-β was evaluated by saturation binding assay and in vitro autoradiography using post-mortem Alzheimer’s disease brain tissue. Biodistribution experiments using normal rats were performed to evaluate the biokinetics of 123 I-ABC577. Furthermore, to validate 123 I-ABC577 as a biomarker for Alzheimer’s disease, we performed a clinical study to compare the brain uptake of 123 I-ABC577 in three patients with Alzheimer’s disease and three healthy control subjects. 123 I-ABC577 binding was quantified by use of the standardized uptake value ratio, which was calculated for the cortex using the cerebellum as a reference region. Standardized uptake value ratio images were visually scored as positive or negative. As a result, 123 I-ABC577 showed high binding affinity for amyloid-β and desirable pharmacokinetics in the preclinical studies. In the clinical study, 123 I-ABC577 was an effective marker for discriminating patients with Alzheimer’s disease from healthy control subjects based on visual images or the ratio of cortical-to-cerebellar binding. In patients with Alzheimer’s disease, 123 I-ABC577 demonstrated clear retention in cortical regions known to accumulate amyloid, such as the frontal cortex, temporal cortex, and posterior cingulate. In contrast, less, more diffuse, and non-specific uptake without localization to these key regions was observed in healthy controls. At 150 min after injection, the cortical standardized uptake value ratio increased by ∼60% in patients with Alzheimer’s disease relative to healthy control subjects. Both healthy control subjects and patients with Alzheimer’s disease showed minimal 123 I-ABC577 retention in the white matter. These observations indicate that 123 I-ABC577 may be a useful single photon emission computed tomography imaging maker to identify amyloid-β in the human brain. The availability of an amyloid-β tracer for single photon emission computed tomography might increase the accessibility of diagnostic imaging for Alzheimer’s disease. PMID:26490333

Expressing Constitutively Active Rheb in Adult Dorsal Root Ganglion Neurons Enhances the Integration of Sensory Axons that Regenerate Across a Chondroitinase-Treated Dorsal Root Entry Zone Following Dorsal Root Crush

PubMed Central

Wu, Di; Klaw, Michelle C.; Kholodilov, Nikolai; Burke, Robert E.; Detloff, Megan R.; Côté, Marie-Pascale; Tom, Veronica J.

2016-01-01

While the peripheral branch of dorsal root ganglion neurons (DRG) can successfully regenerate after injury, lesioned central branch axons fail to regrow across the dorsal root entry zone (DREZ), the interface between the dorsal root and the spinal cord. This lack of regeneration is due to the limited regenerative capacity of adult sensory axons and the growth-inhibitory environment at the DREZ, which is similar to that found in the glial scar after a central nervous system (CNS) injury. We hypothesized that transduction of adult DRG neurons using adeno-associated virus (AAV) to express a constitutively-active form of the GTPase Rheb (caRheb) will increase their intrinsic growth potential after a dorsal root crush. Additionally, we posited that if we combined that approach with digestion of upregulated chondroitin sulfate proteoglycans (CSPG) at the DREZ with chondroitinase ABC (ChABC), we would promote regeneration of sensory axons across the DREZ into the spinal cord. We first assessed if this strategy promotes neuritic growth in an in vitro model of the glial scar containing CSPG. ChABC allowed for some regeneration across the once potently inhibitory substrate. Combining ChABC treatment with expression of caRheb in DRG significantly improved this growth. We then determined if this combination strategy also enhanced regeneration through the DREZ after dorsal root crush in adult rats in vivo. After unilaterally crushing C4-T1 dorsal roots, we injected AAV5-caRheb or AAV5-GFP into the ipsilateral C5-C8 DRGs. ChABC or PBS was injected into the ipsilateral dorsal horn at C5-C8 to digest CSPG, for a total of four animal groups (caRheb + ChABC, caRheb + PBS, GFP + ChABC, GFP + PBS). Regeneration was rarely observed in PBS-treated animals, whereas short-distance regrowth across the DREZ was observed in ChABC-treated animals. No difference in axon number or length between the ChABC groups was observed, which may be related to intraganglionic inflammation induced by the injection. ChABC-mediated regeneration is functional, as stimulation of ipsilateral median and ulnar nerves induced neuronal c-Fos expression in deafferented dorsal horn in both ChABC groups. Interestingly, caRheb + ChABC animals had significantly more c-Fos+ nuclei indicating that caRheb expression in DRGs promoted functional synaptogenesis of their axons that regenerated beyond a ChABC-treated DREZ. PMID:27458339

ABC-B transporter genes in Dirofilaria immitis.

PubMed

Bourguinat, Catherine; Che, Hua; Mani, Thangadurai; Keller, Kathy; Prichard, Roger K

2016-08-01

Dirofilaria immitis is a filarial nematode causing infection and heartworm disease in dogs and other canids, cats, and occasionally in humans. Prevention with macrocyclic lactones (ML) is recommended during the mosquito transmission season. Recently, ML resistance has been reported. ABC-B transporter genes are thought to be involved in the mechanism of ML resistance in other nematodes. This study aimed to identify all the ABC-B transporter genes in D. immitis using as a reference the nDi.2.2 D. immitis whole genome, which is not completely annotated. Using bioinformatic tools and PCR amplification on pooled D. immitis genomic DNA and on pooled cDNA, nine ABC transporter genes including one pseudogene were characterized. Bioinformatic and phylogenetic analyses allowed identification of three P-glycoproteins (Pgps) (Dim-pgp-3 Dim-pgp-10, Dim-pgp-11), of two ABC-B half transporter genes (one ortholog of Cel-haf-4 and Cel-haf-9; and one ortholog of Cel-haf-1 and Cel-haf-3), of one ABC half transporter gene (ortholog of Cel-haf-5) that contained an ABC-C motif, and of one additional half transporter that would require functional study for characterization. The number of ABC-B transporter genes identified was lower than in Caenorhabditis elegans and Haemonchus contortus. Further studies are needed to understand their possible role in ML resistance in D. immitis. These ABC transporters constitute a base for ML resistance investigation in D. immitis and advance our understanding of the molecular biology of this parasite. Copyright © 2016. Published by Elsevier Ltd.

Downregulation of miR-29a/b/c in placenta accreta inhibits apoptosis of implantation site intermediate trophoblast cells by targeting MCL1.

PubMed

Gu, Yongzhong; Bian, Yuehong; Xu, Xiaofei; Wang, Xietong; Zuo, Changting; Meng, Jinlai; Li, Hongyan; Zhao, Shigang; Ning, Yunnan; Cao, Yongzhi; Huang, Tao; Yan, Junhao; Chen, Zi-Jiang

2016-12-01

Placenta accreta is defined as abnormal adhesion of placental villi to the uterine myometrium. Although this condition has become more common as a result of the increasing rate of cesarean sections, the underlying causative mechanism(s) remain elusive. Because microRNA-29a/b/c (miR-29a/b/c) have been shown to play important roles in placental development, this study evaluated the roles of these microRNAs in placenta accreta. Expression of miR-29a/b/c and myeloid cell leukemia-1 (MCL1) were quantified in patient tissues and HTR8/SVneo trophoblast cells using the real-time quantitative polymerase chain reaction. Western blotting was used to analyze expression of the MCL1 protein in HTR8/SVneo trophoblast cells with altered expression of miR-29a/b/c. To determine their role in apoptosis, miR-29a/b/c were overexpressed in HTR-8/SVneo cells, and levels of apoptosis were analyzed by flow cytometry. Luciferase activity assays were used to determine whether MCL1 is a target gene of miR-29a/b/c. Expression of miR-29a/b/c was significantly lower in creta sites compared to noncreta sites (p = 0.018, 0.041, and 0.022, respectively), but expression of MCL1 was upregulated in creta sites (p = 0.039). MCL1 expression was significantly downregulated in HTR-8/SVneo cells overexpressing miR-29a/b/c (p = 0.002, 0.008, and 0.013, respectively). Luciferase activity assays revealed that miR-29a/b/c directly target the 3' untranslated region of MCL1 in 293T cells. Over-expression of miR-29a/b/c induced apoptosis in the HTR-8/SVneo trophoblast cell line. Moreover, histopathological evaluation revealed that the number of implantation site intermediate trophoblast (ISIT) cells was increased in creta sites and that these cells were positive for MCL1. Our results demonstrate that in placenta accreta, miR-29a/b/c inhibits apoptosis of ISIT cells by targeting MCL1. These findings provide new insights into the pathogenesis of placenta accreta. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image Edge Tracking via Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Li, Ruowei; Wu, Hongkun; Liu, Shilong; Rahman, M. A.; Liu, Sanchi; Kwok, Ngai Ming

2018-04-01

A good edge plot should use continuous thin lines to describe the complete contour of the captured object. However, the detection of weak edges is a challenging task because of the associated low pixel intensities. Ant Colony Optimization (ACO) has been employed by many researchers to address this problem. The algorithm is a meta-heuristic method developed by mimicking the natural behaviour of ants. It uses iterative searches to find the optimal solution that cannot be found via traditional optimization approaches. In this work, ACO is employed to track and repair broken edges obtained via conventional Sobel edge detector to produced a result with more connected edges.

Approximate Bayesian computation in large-scale structure: constraining the galaxy-halo connection

NASA Astrophysics Data System (ADS)

Hahn, ChangHoon; Vakili, Mohammadjavad; Walsh, Kilian; Hearin, Andrew P.; Hogg, David W.; Campbell, Duncan

2017-08-01

Standard approaches to Bayesian parameter inference in large-scale structure assume a Gaussian functional form (chi-squared form) for the likelihood. This assumption, in detail, cannot be correct. Likelihood free inferences such as approximate Bayesian computation (ABC) relax these restrictions and make inference possible without making any assumptions on the likelihood. Instead ABC relies on a forward generative model of the data and a metric for measuring the distance between the model and data. In this work, we demonstrate that ABC is feasible for LSS parameter inference by using it to constrain parameters of the halo occupation distribution (HOD) model for populating dark matter haloes with galaxies. Using specific implementation of ABC supplemented with population Monte Carlo importance sampling, a generative forward model using HOD and a distance metric based on galaxy number density, two-point correlation function and galaxy group multiplicity function, we constrain the HOD parameters of mock observation generated from selected 'true' HOD parameters. The parameter constraints we obtain from ABC are consistent with the 'true' HOD parameters, demonstrating that ABC can be reliably used for parameter inference in LSS. Furthermore, we compare our ABC constraints to constraints we obtain using a pseudo-likelihood function of Gaussian form with MCMC and find consistent HOD parameter constraints. Ultimately, our results suggest that ABC can and should be applied in parameter inference for LSS analyses.

The minimal-ABC trees with B1-branches.

PubMed

Dimitrov, Darko; Du, Zhibin; Fonseca, Carlos M da

2018-01-01

The atom-bond connectivity index (or, for short, ABC index) is a molecular structure descriptor bridging chemistry to graph theory. It is probably the most studied topological index among all numerical parameters of a graph that characterize its topology. For a given graph G = (V, E), the ABC index of G is defined as [Formula: see text], where di denotes the degree of the vertex i, and ij is the edge incident to the vertices i and j. A combination of physicochemical and the ABC index properties are commonly used to foresee the bioactivity of different chemical composites. Additionally, the applicability of the ABC index in chemical thermodynamics and other areas of chemistry, such as in dendrimer nanostars, benzenoid systems, fluoranthene congeners, and phenylenes is well studied in the literature. While finding of the graphs with the greatest ABC-value is a straightforward assignment, the characterization of the tree(s) with minimal ABC index is a problem largely open and has recently given rise to numerous studies and conjectures. A B1-branch of a graph is a pendent path of order 2. In this paper, we provide an important step forward to the full characterization of these minimal trees. Namely, we show that a minimal-ABC tree contains neither 4 nor 3 B1-branches. The case when the number of B1-branches is 2 is also considered.

Use of an autologous bioengineered composite skin in extensive burns: Clinical and functional outcomes. A multicentric study.

PubMed

Gómez, C; Galán, J M; Torrero, V; Ferreiro, I; Pérez, D; Palao, R; Martínez, E; Llames, S; Meana, A; Holguín, P

2011-06-01

We report clinical and functional outcomes obtained after application of an autologous bioengineered composite skin (ABCS) produced in a single Spanish tissue-engineering unit. Twenty-five burned patients treated with ABCS from 1999 to 2007 in five burn centres were included in the study. Mean age was 29 years (SD 11), with mean total body surface area (TBSA) burned being 74% (SD 17) and mean full-thickness injury of 61% (SD 19) of TBSA. The mean area initially engrafted with ABCS was 24% (SD 13) of TBSA, with a final take of 49% (SD 30, range 0-100%). ABCS achieved permanent coverage of a mean of 11% (SD 8) of TBSA. In subset analyses, lack of pre- and post-application wound bed infection and lack of serious acute systemic complications at the time of engraftment were significantly associated with better ABCS take. Final take obtained with ABCS could be improved with the use of non-cytotoxic topical antibiotics following engraftment. The use of plasma to prepare ABCS reduces production costs: cost-effectiveness ratio is not a limitation for its use. In terms of patient satisfaction, cosmetic/functional outcomes (general appearance, texture, flexibility, sensitivity and colour) of ABCS and split-thickness autografts are not different statistically. Copyright © 2010 Elsevier Ltd and ISBI. All rights reserved.

Design, Synthesis and Biological Evaluation of Novel Phosphorylated Abacavir Derivatives as Antiviral Agents Against Newcastle Disease Virus Infection in Chicken.

PubMed

K A, Suresh; Venkata Subbaiah, Kadiam C; Lavanya, Rayapu; Chandrasekhar, Kuruva; Chamarti, Naga Raju; Kumar, M Suresh; Wudayagiri, Rajendra; Valluru, Lokanatha

2016-09-01

Newcastle disease virus is the most devastating virus in poultry industry. It can eradicate the entire poultry flocks once infected. This study is aimed to investigate the antiviral efficacy of novel phosphorylated analogues of the drug abacavir (ABC) against Newcastle disease virus (NDV). About 16 analogues of ABC were designed and docking was performed against fusion protein of NDV. Three compounds were identified and selected for synthesis and biological evaluation based on binding affinity and docking scores. The compounds were synthesized and characterized by IR, (1)H, (13)C, (31)P and CHN analysis and mass spectra. These compounds were tested for antiviral efficacy against NDV-infected DF-1 cells. Compound ABC-1 had shown potent antiviral activity as evidenced by significant reduction in plaque units and cytopathic effect. Therefore, ABC-1 was selected to test for NDV-infected chicken survival rate. Effective dose50 concentrations were determined for ABC-1. Antioxidant enzyme levels in brain, liver and lung tissues were estimated. Superoxide dismutase and catalase were significantly raised and lipid peroxidation and HA titer levels were decreased upon treatment with 2 mg/kg body weight ABC-1. Histopathological modifications were also restored in the ABC-1-treated group. These findings demonstrated ABC-1 as a potential antiviral agent against NDV in chicken.

The ABC and AUSSAT.

ERIC Educational Resources Information Center

McGarritty, Ian

1985-01-01

Discusses the Australian Broadcasting Corporation's (ABC) utilization of the AUSSAT telecommunications satellite to extend its television and radio transmission range to reach remote Australian audiences; the satellite's program gathering and interchange capabilities; and ABC's generation of other benefits to offset cost of satellite services.…

Anethole potentiates dodecanol's fungicidal activity by reducing PDR5 expression in budding yeast.

PubMed

Fujita, Ken-Ichi; Ishikura, Takayuki; Jono, Yui; Yamaguchi, Yoshihiro; Ogita, Akira; Kubo, Isao; Tanaka, Toshio

2017-02-01

trans-Anethole (anethole), a major component of anise oil, has a broad antimicrobial spectrum and a weaker antimicrobial potency than other available antibiotics. When combined with polygodial, nagilactone E, and n-dodecanol, anethole has been shown to exhibit synergistic antifungal activity against a budding yeast, Saccharomyces cerevisiae, and a human opportunistic pathogenic yeast, Candida albicans. However, the mechanism underlying this synergistic effect of anethole has not been characterized. We studied this mechanism using dodecanol-treated S. cerevisiae cells and focusing on genes related to multidrug efflux. Although dodecanol transiently reduced the number of colony forming units, this recovered to levels similar to those of untreated cells with continued incubation beyond 24h. Reverse transcription polymerase chain reaction analysis revealed overexpression of an ATP-binding cassette (ABC) transporter gene, PDR5, in addition to a slight increase in PDR11, PDR12, and PDR15 transcriptions in dodecanol-treated cells. In the presence of anethole, these effects were attenuated and the fungicidal activity of dodecanol was extended. Dodecanol showed longer lasting fungicidal activity against a Δpdr5. In addition, Δpdr3 and Δlge1, lack transcription factors of PDR5 and PDR3, were partly and completely susceptible to dodecanol, respectively. Furthermore, combination of anethole with fluconazole was also found to exhibit synergy on C. albicans. These results indicated that although anethole reduced the transcription of several transporters, PDR5 expression was particularly relevant to dodecanol efflux. Anethole is expected to be a promising candidate drug for the inhibition of efflux by reducing the transcription of several ABC transporters. Copyright © 2016 Elsevier B.V. All rights reserved.

Characterization of Two Metal Binding Lipoproteins as Vaccine Candidates for Enterococcal Infections

PubMed Central

Romero-Saavedra, Felipe; Laverde, Diana; Budin-Verneuil, Aurélie; Muller, Cécile; Bernay, Benoit; Benachour, Abdellah; Hartke, Axel; Huebner, Johannes

2015-01-01

Background Enterococcus faecium and faecalis are Gram-positive opportunistic pathogens that have become leading causes of nosocomial infections over the last decades. Especially multidrug resistant enterococci have become a challenging clinical problem worldwide. Therefore, new treatment options are needed and the identification of alternative targets for vaccine development has emerged as a feasible alternative to fight the infections caused by these pathogens. Results We extrapolate the transcriptomic data from a mice peritonitis infection model in E. faecalis to identify putative up-regulated surface proteins under infection conditions in E. faecium. After the bionformatic analyses two metal binding lipoproteins were identified to have a high homology (>72%) between the two species, the manganese ABC transporter substrate-binding lipoprotein (PsaAfm,) and the zinc ABC transporter substrate-binding lipoprotein (AdcAfm). These candidate lipoproteins were overexpressed in Escherichia coli and purified. The recombinant proteins were used to produce rabbit polyclonal antibodies that were able to induce specific opsonic antibodies that mediated killing of the homologous strain E. faecium E155 as well as clinical strains E. faecium E1162, Enterococcus faecalis 12030, type 2 and type 5. Mice were passively immunized with the antibodies raised against recombinant lipoproteins, showing significant reduction of colony counts in mice livers after the bacterial challenge and demonstrating the efficacy of these metal binding lipoproteins as promising vaccine candidates to treat infections caused by these enterococcal pathogens. Conclusion Overall, our results demonstrate that these two metal binding lipoproteins elicited specific, opsonic and protective antibodies, with an extensive cross-reactivity and serotype-independent coverage among these two important nocosomial pathogens. Pointing these two protein antigens as promising immunogens, that can be used as single components or as carrier proteins together with polysaccharide antigens in vaccine development against enterococcal infections. PMID:26322633

Activities-specific balance confidence scale for predicting future falls in Indian older adults.

PubMed

Moiz, Jamal Ali; Bansal, Vishal; Noohu, Majumi M; Gaur, Shailendra Nath; Hussain, Mohammad Ejaz; Anwer, Shahnawaz; Alghadir, Ahmad

2017-01-01

Activities-specific balance confidence (ABC) scale is a subjective measure of confidence in performing various ambulatory activities without falling or experiencing a sense of unsteadiness. This study aimed to examine the ability of the Hindi version of the ABC scale (ABC-H scale) to discriminate between fallers and non-fallers and to examine its predictive validity for prospective falls. This was a prospective cohort study. A total of 125 community-dwelling older adults (88 were men) completed the ABC-H scale. The occurrence of falls over the follow-up period of 12 months was recorded. Discriminative validity was analyzed by comparing the total ABC-H scale scores between the faller and non-faller groups. A receiver operating characteristic curve analysis and a logistic regression analysis were used to examine the predictive accuracy of the ABC-H scale. The mean ABC-H scale score of the faller group was significantly lower than that of the non-faller group (52.6±8.1 vs 73.1±12.2; P <0.001). The optimal cutoff value for distinguishing faller and non-faller adults was ≤58.13. The sensitivity, specificity, area under the curve, and positive and negative likelihood ratios of the cutoff score were 86.3%, 87.3%, 0.91 ( P <0.001), 6.84, and 0.16, respectively. The percentage test accuracy and false-positive and false-negative rates were 86.87%, 12.2%, and 13.6%, respectively. A dichotomized total ABC-H scale score of ≤58.13% (adjusted odds ratio =0.032, 95% confidence interval =0.004-0.25, P =0.001) was significantly related with future falls. The ABC-H scores were significantly and independently related with future falls in the community-dwelling Indian older adults. The ability of the ABC-H scale to predict future falls was adequate with high sensitivity and specificity values.

IMG-ABC: A Knowledge Base To Fuel Discovery of Biosynthetic Gene Clusters and Novel Secondary Metabolites.

PubMed

Hadjithomas, Michalis; Chen, I-Min Amy; Chu, Ken; Ratner, Anna; Palaniappan, Krishna; Szeto, Ernest; Huang, Jinghua; Reddy, T B K; Cimermančič, Peter; Fischbach, Michael A; Ivanova, Natalia N; Markowitz, Victor M; Kyrpides, Nikos C; Pati, Amrita

2015-07-14

In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of "big" genomic data for discovering small molecules. IMG-ABC relies on IMG's comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC's focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in Alphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG's extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world. Copyright © 2015 Hadjithomas et al.

High absolute basophil count is a powerful independent predictor of inferior overall survival in patients with primary myelofibrosis.

PubMed

Lucijanic, Marko; Livun, Ana; Stoos-Veic, Tajana; Pejsa, Vlatko; Jaksic, Ozren; Cicic, David; Lucijanic, Jelena; Romic, Zeljko; Orehovec, Biserka; Aralica, Gorana; Miletic, Marko; Kusec, Rajko

2018-05-01

To investigate the clinical and prognostic significance of absolute basophil count (ABC) in patients with primary myelofibrosis (PMF). We retrospectively investigated 58 patients with PMF treated in our institution in the period from 2006 to 2017. ABC was obtained in addition to other hematological and clinical parameters. Patients were separated into high and low ABC groups using the Receiver operating characteristic curve analysis. ABC was higher in PMF patients than in healthy controls (P < 0.001). Patients with high ABC had higher white blood cells (P < 0.001), higher red cell distribution width (P = 0.035), higher lactate dehydrogenase (P < 0.001), more frequently had circulatory blasts (P < 0.001), constitutional symptoms (P = 0.030) and massive splenomegaly (P = 0.014). ABC was also positively correlated with absolute monocyte count (AMC) (P < 0.001) and other components of differential blood count. There was no difference in ABC regarding driver mutations or degree of bone marrow fibrosis. Univariately, high ABC was significantly associated with inferior overall survival (hazard ratio (HR) 4.79, P < 0.001). This effect remained statistically significant (HR 4.27, P = 0.009) in a multivariate Cox regression model adjusted for age, gender, Dynamic International Prognostic Scoring System (HR 2.6, P = 0.001) and AMC (HR 8.45, P = 0.002). High ABC reflects higher disease activity and stronger proliferative potential of disease. ABC and AMC independently predict survival and therefore seem to reflect different underlying pathophysiologic processes. Hence, both have a potential for improvement of current prognostic scores. Basophils represent a part of malignant clone in PMF and are associated with unfavorable disease features and poor prognosis which is independent of currently established prognostic scoring system and monocytosis.

Aberrant immunoglobulin class switch recombination and switch translocations in activated B cell-like diffuse large B cell lymphoma.

PubMed

Lenz, Georg; Nagel, Inga; Siebert, Reiner; Roschke, Anna V; Sanger, Warren; Wright, George W; Dave, Sandeep S; Tan, Bruce; Zhao, Hong; Rosenwald, Andreas; Muller-Hermelink, Hans Konrad; Gascoyne, Randy D; Campo, Elias; Jaffe, Elaine S; Smeland, Erlend B; Fisher, Richard I; Kuehl, W Michael; Chan, Wing C; Staudt, Louis M

2007-03-19

To elucidate the mechanisms underlying chromosomal translocations in diffuse large B cell lymphoma (DLBCL), we investigated the nature and extent of immunoglobulin class switch recombination (CSR) in these tumors. We used Southern blotting to detect legitimate and illegitimate CSR events in tumor samples of the activated B cell-like (ABC), germinal center B cell-like (GCB), and primary mediastinal B cell lymphoma (PMBL) subgroups of DLBCL. The frequency of legitimate CSR was lower in ABC DLBCL than in GCB DLBCL and PMBL. In contrast, ABC DLBCL had a higher frequency of internal deletions within the switch mu (Smu) region compared with GCB DLBCL and PMBL. ABC DLBCLs also had frequent deletions within Sgamma and other illegitimate switch recombinations. Sequence analysis revealed ongoing Smu deletions within ABC DLBCL tumor clones, which were accompanied by ongoing duplications and activation-induced cytidine deaminase-dependent somatic mutations. Unexpectedly, short fragments derived from multiple chromosomes were interspersed within Smu in one case. These findings suggest that ABC DLBCLs have abnormalities in the regulation of CSR that could predispose to chromosomal translocations. Accordingly, aberrant switch recombination was responsible for translocations in ABC DLBCLs involving BCL6, MYC, and a novel translocation partner, SPIB.

Interaction of ABC multidrug transporters with anticancer protein kinase inhibitors: substrates and/or inhibitors?

PubMed

Hegedus, Csilla; Ozvegy-Laczka, Csilla; Szakács, Gergely; Sarkadi, Balázs

2009-05-01

Protein kinase inhibitors (PKI) are becoming key agents in modern cancer chemotherapy, and combination of PKIs with classical chemotherapeutic drugs may help to overcome currently untreatable metastatic cancers. Since chemotherapy resistance is a recurrent problem, mechanisms of resistance should be clarified in order to help further drug development. Here we suggest that in addition to PKI resistance based on altered target structures, the active removal of these therapeutic agents by the MDR-ABC transporters should also be considered as a major cause of clinical resistance. We discuss the occurring systemic and cellular mechanisms, which may hamper PKI efficiency, and document the role of selected MDR-ABC transporters in these phenomena through their interactions with these anticancer agents. Moreover, we suggest that PKI interactions with ABC transporters may modulate overall drug metabolism, including the fate of diverse, chemically or target-wise unrelated drugs. These effects are based on multiple forms of MDR-ABC transporter interaction with PKIs, as these compounds may be both substrates and/or inhibitors of an ABC transporter. We propose that these interactions should be carefully considered in clinical application, and a combined MDR-ABC transporter and PKI effect may bring a major advantage in future drug development.

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space.

PubMed

Kalathil, Shaeen; Elias, Elizabeth

2015-11-01

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB.

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space

PubMed Central

Kalathil, Shaeen; Elias, Elizabeth

2014-01-01

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB. PMID:26644921

Laboratory investigation of grouted coupler connection details for ABC bridge projects.

DOT National Transportation Integrated Search

2015-08-01

With an ever increasing desire to utilize accelerated bridge construction (ABC) techniques, it is becoming critical that bridge : designers and contractors have confidence in typical details. The Keg Creek Bridge on US 6 in Iowa was a recent ABC exam...

The ABC protein turned chloride channel whose failure causes cystic fibrosis

NASA Astrophysics Data System (ADS)

Gadsby, David C.; Vergani, Paola; Csanády, László

2006-03-01

CFTR chloride channels are encoded by the gene mutated in patients with cystic fibrosis. These channels belong to the superfamily of ABC transporter ATPases. ATP-driven conformational changes, which in other ABC proteins fuel uphill substrate transport across cellular membranes, in CFTR open and close a gate to allow transmembrane flow of anions down their electrochemical gradient. New structural and biochemical information from prokaryotic ABC proteins and functional information from CFTR channels has led to a unifying mechanism explaining those ATP-driven conformational changes.

CREATING AN IPHONE APPLICATION FOR COLLECTING CONTINUOUS ABC DATA

PubMed Central

Whiting, Seth W; Dixon, Mark R

2012-01-01

This paper provides an overview and task analysis for creating a continuous ABC data-collection application using Xcode on a Mac computer. Behavior analysts can program an ABC data collection system, complete with a customized list of target clients, antecedents, behaviors, and consequences to be recorded, and have the data automatically sent to an e-mail account after observations have concluded. Further suggestions are provided to customize the ABC data- collection system for individual preferences and clinical needs. PMID:23060682

Creating an iPhone application for collecting continuous ABC data.

PubMed

Whiting, Seth W; Dixon, Mark R

2012-01-01

This paper provides an overview and task analysis for creating a continuous ABC data-collection application using Xcode on a Mac computer. Behavior analysts can program an ABC data collection system, complete with a customized list of target clients, antecedents, behaviors, and consequences to be recorded, and have the data automatically sent to an e-mail account after observations have concluded. Further suggestions are provided to customize the ABC data- collection system for individual preferences and clinical needs.

ArgR is an essential local transcriptional regulator of the arcABC operon in Streptococcus suis and is crucial for biological fitness in an acidic environment.

PubMed

Fulde, Marcus; Willenborg, Joerg; de Greeff, Astrid; Benga, Laurentiu; Smith, Hilde E; Valentin-Weigand, Peter; Goethe, Ralph

2011-02-01

Streptococcus suis is one of the most important pathogens in pigs and can also cause severe infections in humans. Despite its clinical relevance, very little is known about the factors that contribute to its virulence. Recently, we identified a new putative virulence factor in S. suis, the arginine deiminase system (ADS), an arginine catabolic enzyme system encoded by the arcABC operon, which enables S. suis to survive in an acidic environment. In this study, we focused on ArgR, an ADS-associated regulator belonging to the ArgR/AhrC arginine repressor family. Using an argR knockout strain we were able to show that ArgR is essential for arcABC operon expression and necessary for the biological fitness of S. suis. By cDNA expression microarray analyses and quantitative real-time RT-PCR we found that the arcABC operon is the only gene cluster regulated by ArgR, which is in contrast to the situation in many other bacteria. Reporter gene analysis with gfp under the control of the arcABC promoter demonstrated that ArgR is able to activate the arcABC promoter. Electrophoretic mobility shift assays with fragments of the arcABC promoter and recombinant ArgR, and chromatin immunoprecipitation with antibodies directed against ArgR, revealed that ArgR interacts with the arcABC promoter in vitro and in vivo by binding to a region from -147 to -72 bp upstream of the transcriptional start point. Overall, our results show that in S. suis, ArgR is an essential, system-specific transcriptional regulator of the ADS that interacts directly with the arcABC promoter in vivo.

Transcriptome-Based Identification of ABC Transporters in the Western Tarnished Plant Bug Lygus hesperus

PubMed Central

Hull, J. Joe; Chaney, Kendrick; Geib, Scott M.; Fabrick, Jeffrey A.; Brent, Colin S.; Walsh, Douglas; Lavine, Laura Corley

2014-01-01

ATP-binding cassette (ABC) transporters are a large superfamily of proteins that mediate diverse physiological functions by coupling ATP hydrolysis with substrate transport across lipid membranes. In insects, these proteins play roles in metabolism, development, eye pigmentation, and xenobiotic clearance. While ABC transporters have been extensively studied in vertebrates, less is known concerning this superfamily in insects, particularly hemipteran pests. We used RNA-Seq transcriptome sequencing to identify 65 putative ABC transporter sequences (including 36 full-length sequences) from the eight ABC subfamilies in the western tarnished plant bug (Lygus hesperus), a polyphagous agricultural pest. Phylogenetic analyses revealed clear orthologous relationships with ABC transporters linked to insecticide/xenobiotic clearance and indicated lineage specific expansion of the L. hesperus ABCG and ABCH subfamilies. The transcriptional profile of 13 LhABCs representative of the ABCA, ABCB, ABCC, ABCG, and ABCH subfamilies was examined across L. hesperus development and within sex-specific adult tissues. All of the transcripts were amplified from both reproductively immature and mature adults and all but LhABCA8 were expressed to some degree in eggs. Expression of LhABCA8 was spatially localized to the testis and temporally timed with male reproductive development, suggesting a potential role in sexual maturation and/or spermatozoa protection. Elevated expression of LhABCC5 in Malpighian tubules suggests a possible role in xenobiotic clearance. Our results provide the first transcriptome-wide analysis of ABC transporters in an agriculturally important hemipteran pest and, because ABC transporters are known to be important mediators of insecticidal resistance, will provide the basis for future biochemical and toxicological studies on the role of this protein family in insecticide resistance in Lygus species. PMID:25401762

Targeting receptor-activator of nuclear kappaB ligand in aneurysmal bone cysts: verification of target and therapeutic response.

PubMed

Pelle, Dominic W; Ringler, Jonathan W; Peacock, Jacqueline D; Kampfschulte, Kevin; Scholten, Donald J; Davis, Mary M; Mitchell, Deanna S; Steensma, Matthew R

2014-08-01

Aneurysmal bone cyst (ABC) is a benign tumor of bone presenting as a cystic, expansile lesion in both the axial and appendicular skeleton. Axial lesions demand special consideration, because treatment-related morbidity can be devastating. In similar lesions, such as giant cell tumor of bone (GCTB), the receptor-activator of nuclear kappaB ligand (RANKL)-receptor-activator of nuclear kappaB (RANK) signaling axis is essential to tumor progression. Although ABC and GCTB are distinct entities, they both contain abundant multinucleated giant cells and are osteolytic characteristically. We hypothesize that ABCs express both RANKL and RANK similarly in a cell-type specific manner, and that targeted RANKL therapy will mitigate ABC tumor progression. Cellular expression of RANKL and RANK was determined in freshly harvested ABC samples using laser confocal microscopy. A consistent cell-type-specific pattern was observed: fibroblastlike stromal cells expressed RANKL strongly whereas monocyte/macrophage precursor and multinucleated giant cells expressed RANK. Relative RANKL expression was determined by quantitative real-time polymerase chain reaction in ABC and GCTB tissue samples; no difference in relative expression was observed (P > 0.05). In addition, we review the case of a 5-year-old boy with a large, aggressive sacral ABC. After 3 months of targeted RANKL inhibition with denosumab, magnetic resonance imaging demonstrated tumor shrinkage, bone reconstitution, and healing of a pathologic fracture. Ambulation, and bowel and bladder function were restored at 6 months. Denosumab treatment was well tolerated. Post hoc analysis demonstrated strong RANKL expression in the pretreatment tumor sample. These findings demonstrate that RANKL-RANK signal activation is essential to ABC tumor progression. RANKL-targeted therapy may be an effective alternative to surgery in select ABC presentations. Copyright © 2014 Mosby, Inc. All rights reserved.

SPECT Analysis of Cardiac Perfusion Changes After Whole-Breast/Chest Wall Radiation Therapy With or Without Active Breathing Coordinator: Results of a Randomized Phase 3 Trial

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zellars, Richard, E-mail: zellari@jhmi.edu; Bravo, Paco E.; Tryggestad, Erik

2014-03-15

Purpose: Cardiac muscle perfusion, as determined by single-photon emission computed tomography (SPECT), decreases after breast and/or chest wall (BCW) irradiation. The active breathing coordinator (ABC) enables radiation delivery when the BCW is farther from the heart, thereby decreasing cardiac exposure. We hypothesized that ABC would prevent radiation-induced cardiac toxicity and conducted a randomized controlled trial evaluating myocardial perfusion changes after radiation for left-sided breast cancer with or without ABC. Methods and Materials: Stages I to III left breast cancer patients requiring adjuvant radiation therapy (XRT) were randomized to ABC or No-ABC. Myocardial perfusion was evaluated by SPECT scans (before andmore » 6 months after BCW radiation) using 2 methods: (1) fully automated quantitative polar mapping; and (2) semiquantitative visual assessment. The left ventricle was divided into 20 segments for the polar map and 17 segments for the visual method. Segments were grouped by anatomical rings (apical, mid, basal) or by coronary artery distribution. For the visual method, 2 nuclear medicine physicians, blinded to treatment groups, scored each segment's perfusion. Scores were analyzed with nonparametric tests and linear regression. Results: Between 2006 and 2010, 57 patients were enrolled and 43 were available for analysis. The cohorts were well matched. The apical and left anterior descending coronary artery segments had significant decreases in perfusion on SPECT scans in both ABC and No-ABC cohorts. In unadjusted and adjusted analyses, controlling for pretreatment perfusion score, age, and chemotherapy, ABC was not significantly associated with prevention of perfusion deficits. Conclusions: In this randomized controlled trial, ABC does not appear to prevent radiation-induced cardiac perfusion deficits.« less

Yang-Mills theory and the ABC conjecture

NASA Astrophysics Data System (ADS)

He, Yang-Hui; Hu, Zhi; Probst, Malte; Read, James

2018-05-01

We establish a precise correspondence between the ABC Conjecture and 𝒩 = 4 super-Yang-Mills theory. This is achieved by combining three ingredients: (i) Elkies’ method of mapping ABC-triples to elliptic curves in his demonstration that ABC implies Mordell/Faltings; (ii) an explicit pair of elliptic curve and associated Belyi map given by Khadjavi-Scharaschkin; and (iii) the fact that the bipartite brane-tiling/dimer model for a gauge theory with toric moduli space is a particular dessin d’enfant in the sense of Grothendieck. We explore this correspondence for the highest quality ABC-triples as well as large samples of random triples. The conjecture itself is mapped to a statement about the fundamental domain of the toroidal compactification of the string realization of 𝒩 = 4 SYM.

Compact high order schemes with gradient-direction derivatives for absorbing boundary conditions

NASA Astrophysics Data System (ADS)

Gordon, Dan; Gordon, Rachel; Turkel, Eli

2015-09-01

We consider several compact high order absorbing boundary conditions (ABCs) for the Helmholtz equation in three dimensions. A technique called "the gradient method" (GM) for ABCs is also introduced and combined with the high order ABCs. GM is based on the principle of using directional derivatives in the direction of the wavefront propagation. The new ABCs are used together with the recently introduced compact sixth order finite difference scheme for variable wave numbers. Experiments on problems with known analytic solutions produced very accurate results, demonstrating the efficacy of the high order schemes, particularly when combined with GM. The new ABCs are then applied to the SEG/EAGE Salt model, showing the advantages of the new schemes.

Optimization on Paddy Crops in Central Java (with Solver, SVD on Least Square and ACO (Ant Colony Algorithm))

NASA Astrophysics Data System (ADS)

Parhusip, H. A.; Trihandaru, S.; Susanto, B.; Prasetyo, S. Y. J.; Agus, Y. H.; Simanjuntak, B. H.

2017-03-01

Several algorithms and objective functions on paddy crops have been studied to get optimal paddy crops in Central Java based on the data given from Surakarta and Boyolali. The algorithms are linear solver, least square and Ant Colony Algorithms (ACO) to develop optimization procedures on paddy crops modelled with Modified GSTAR (Generalized Space-Time Autoregressive) and nonlinear models where the nonlinear models are quadratic and power functions. The studied data contain paddy crops from Surakarta and Boyolali determining the best period of planting in the year 1992-2012 for Surakarta where 3 periods for planting are known and the optimal amount of paddy crops in Boyolali in the year 2008-2013. Having these analyses may guide the local agriculture government to give a decision on rice sustainability in its region. The best period for planting in Surakarta is observed, i.e. the best period is in September-December based on the data 1992-2012 by considering the planting area, the cropping area, and the paddy crops are the most important factors to be taken into account. As a result, we can refer the paddy crops in this best period (about 60.4 thousand tons per year) as the optimal results in 1992-2012 where the used objective function is quadratic. According to the research, the optimal paddy crops in Boyolali about 280 thousand tons per year where the studied factors are the amount of rainfalls, the harvested area and the paddy crops in 2008-2013. In this case, linear and power functions are studied to be the objective functions. Compared to all studied algorithms, the linear solver is still recommended to be an optimization tool for a local agriculture government to predict paddy crops in future.

The Cost of Library Services: Activity-Based Costing in an Australian Academic Library.

ERIC Educational Resources Information Center

Robinson, Peter; Ellis-Newman, Jennifer

1998-01-01

Explains activity-based costing (ABC), discusses the benefits of ABC to library managers, and describes the steps involved in implementing ABC in an Australian academic library. Discusses the budgeting process in universities, and considers benefits to the library. (Author/LRW)

Effect of SOS-induced levels of imuABC on spontaneous and damage-induced mutagenesis in Caulobacter crescentus.

PubMed

Alves, Ingrid R; Lima-Noronha, Marco A; Silva, Larissa G; Fernández-Silva, Frank S; Freitas, Aline Luiza D; Marques, Marilis V; Galhardo, Rodrigo S

2017-11-01

imuABC (imuAB dnaE2) genes are responsible for SOS-mutagenesis in Caulobacter crescentus and other bacterial species devoid of umuDC. In this work, we have constructed operator-constitutive mutants of the imuABC operon. We used this genetic tool to investigate the effect of SOS-induced levels of these genes upon both spontaneous and damage-induced mutagenesis. We showed that constitutive expression of imuABC does not increase spontaneous or damage-induced mutagenesis, nor increases cellular resistance to DNA-damaging agents. Nevertheless, the presence of the operator-constitutive mutation rescues mutagenesis in a recA background, indicating that imuABC are the only genes required at SOS-induced levels for translesion synthesis (TLS) in C. crescentus. Furthermore, these data also show that TLS mediated by ImuABC does not require RecA, unlike umuDC-dependent mutagenesis in E. coli. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of steam activation of biochar produced from a giant Miscanthus on copper sorption and toxicity.

PubMed

Shim, Taeyong; Yoo, Jisu; Ryu, Changkook; Park, Yong-Kwon; Jung, Jinho

2015-12-01

This study aims to evaluate the physiochemical properties, sorption characteristics, and toxicity effects of biochar (BC) produced from Miscanthus sacchariflorus via slow pyrolysis at 500°C and its steam activation product (ABC). Although BC has a much lower surface area than ABC (181 and 322m(2)g(-1), respectively), the Cu sorption capacities of BC and ABC are not significantly different (p>0.05). A two-compartment model successfully explains the sorption of BC and ABC as being dominated by fast and slow sorption processes, respectively. In addition, both BC and ABC efficiently eliminate the toxicity of Cu towards Daphnia magna. However, ABC itself induced acute toxicity to D. magna, which is possibly due to increased aromaticity upon steam activation. These findings suggest that activation of BC produced from M. sacchariflorus at a pyrolytic temperature of 500°C may not be appropriate in terms of Cu sorption and toxicity reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evolutionary Trajectories of Entomopathogenic Fungi ABC Transporters.

PubMed

Baral, Bikash

2017-01-01

The ABC protein superfamily-also called traffic ATPases-are energy-dependent ubiquitous proteins, representing one of the crucial and the largest family in the fungal genomes. The ATP-binding cassette endows a characteristic 200-250 amino acids and is omnipresent in all organisms ranging from prokaryotes to eukaryotes. Unlike in bacteria with nutrient import functions, ABC transporters in fungal entomopathogens serve as effective efflux pumps that are largely involved in the shuttle of metabolites across the biological membranes. Thus, the search for ABC proteins may prove of immense importance in elucidating the functional and molecular mechanism at the host-pathogen (insect-fungus) interface. Their sequence homology, domain topology, and functional traits led to the actual identification of nine different families in fungal entomopathogens. Evolutionary relationships within the ABC superfamily are discussed, concentrating on computational approaches for comparative identification of ABC transporters in insect-pathogenic fungi (entomopathogens) with those of animals, plants, and their bacterial orthologs. Ancestors of some fungal candidates have duplicated extensively in some phyla, while others were lost in one lineage or the other, and predictions for the cause of their duplications and/or loss in some phyla are made. ABC transporters of fungal insect-pathogens serve both defensive and offensive functions effective against land-dwelling and ground foraging voracious insects. This study may help to unravel the molecular cascades of ABC proteins to illuminate the means through which insects cope with fungal infection and fungal-related diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

Implication of RuvABC and RecG in homologous recombination in Streptomyces ambofaciens.

PubMed

Hoff, Grégory; Bertrand, Claire; Piotrowski, Emilie; Thibessard, Annabelle; Leblond, Pierre

2017-01-01

Most bacterial organisms rely on homologous recombination to repair DNA double-strand breaks and for the post-replicative repair of DNA single-strand gaps. Homologous recombination can be divided into three steps: (i) a pre-synaptic step in which the DNA 3'-OH ends are processed, (ii) a recA-dependent synaptic step allowing the invasion of an intact copy and the formation of Holliday junctions, and (iii) a post-synaptic step consisting of migration and resolution of these junctions. Currently, little is known about factors involved in homologous recombination, especially for the post-synaptic step. In Escherichia coli, branch migration and resolution are performed by the RuvABC complex, but could also rely on the RecG helicase in a redundant manner. In this study, we show that recG and ruvABC are well-conserved among Streptomyces. ΔruvABC, ΔrecG and ΔruvABC ΔrecG mutant strains were constructed. ΔruvABC ΔrecG is only slightly affected by exposure to DNA damage (UV). We also show that conjugational recombination decreases in the absence of RuvABC and RecG, but that intra-chromosomal recombination is not affected. These data suggest that RuvABC and RecG are indeed involved in homologous recombination in Streptomyces ambofaciens and that alternative factors are able to take over Holliday junction in Streptomyces. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms

PubMed Central

Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M.K.

2015-01-01

Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution. PMID:26110406

Linear antenna array optimization using flower pollination algorithm.

PubMed

Saxena, Prerna; Kothari, Ashwin

2016-01-01

Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.

An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.

Effect of β-elemene on the kinetics of intracellular transport of d-luciferin potassium salt (ABC substrate) in doxorubicin-resistant breast cancer cells and the associated molecular mechanism.

PubMed

Tang, Chao-Yuan; Zhu, Li-Xin; Yu, Jian-Dong; Chen, Zhi; Gu, Man-Cang; Mu, Chao-Feng; Liu, Qi; Xiong, Yang

2018-07-30

In order to explore the mechanism of the reversing multidrug resistance (MDR) phenotypes by β-elemene (β-ELE) in doxorubicin (DOX)-resistant breast cancer cells (MCF-7/DOX), both the functionality and quantity of the ABC transporters in MCF-7/DOX were studied. Bioluminescence imaging (BLI) was used to study the efflux of d-luciferin potassium salt, the substrate of ATP-binding cassette transporters (ABC transporters), in MCF-7/DOX cells treated by β-ELE. At the same time three major ABC transport proteins and genes-related MDR, P-glycoprotein (P-gp, ABCB1) and multidrug resistance-associated protein 1 (MRP, ABCC1) as well as breast cancer resistance protein (BCRP, ABCG2) were analyzed by q-PCR and Western blot. To investigate the efflux functionality of ABC transporters, MCF-7/DOX Fluc cell line with stably-overexpressed luciferase was established. BLI was then used to real-time monitor the efflux kinetics of d-luciferin potassium salt before and after MCF-7/DOX Fluc cells being treated with β-ELE or not. The results showed that the efflux of d-luciferin potassium salt from MCF-7/DOX Fluc was lessened when pretreated with β-ELE, which means that β-ELE may dampen the functionality of ABC transporters, thus decrease the efflux of d-fluorescein potassium or other chemotherapies which also serve as the substrates of ABC transporters. As the effect of β-ELE on the expression of ABC transporters, the results of q-PCR and Western blot showed that gene and protein expression of ABC transporters such as P-gp, MRP, and BCRP were down-regulated after the treatment of β-ELE. To verify the efficacy of β-ELE on reversing MDR, MCF-7/DOX cells were treated with the combination of DOX and β-ELE. MTT assay showed that β-ELE increased the inhibitory effect of DOX on the proliferation of MCF-7/DOX, and the IC 50 of the combination group was much lower than that of the single DOX or β-ELE treatment. In all, β-ELE may reverse MDR through the substrates of ABC transporters by two ways, to lessen the ABC protein efflux by weakening their functionality, or to reduce the quantity of ABC gene and protein expression. Copyright © 2018. Published by Elsevier B.V.

Is there continued evidence for an association between abacavir usage and myocardial infarction risk in individuals with HIV? A cohort collaboration.

PubMed

Sabin, Caroline A; Reiss, Peter; Ryom, Lene; Phillips, Andrew N; Weber, Rainer; Law, Matthew; Fontas, Eric; Mocroft, Amanda; de Wit, Stephane; Smith, Colette; Dabis, Francois; d'Arminio Monforte, Antonella; El-Sadr, Wafaa; Lundgren, Jens D

2016-03-31

In March 2008, the D:A:D study published results demonstrating an increased risk of myocardial infarction (MI) for patients on abacavir (ABC). We describe changes to the use of ABC since this date, and investigate changes to the association between ABC and MI with subsequent follow-up. A total of 49,717 D:A:D participants were followed from study entry until the first of an MI, death, 1 February 2013 or 6 months after last visit. Associations between a person's 10-year cardiovascular disease (CVD) risk and the likelihood of initiating or discontinuing ABC were assessed using multivariable logistic/Poisson regression. Poisson regression was used to assess the association between current ABC use and MI risk, adjusting for potential confounders, and a test of interaction was performed to assess whether the association had changed in the post-March 2008 period. Use of ABC increased from 10 % of the cohort in 2000 to 20 % in 2008, before stabilising at 18-19 %. Increases in use pre-March 2008, and subsequent decreases, were greatest in those at moderate and high CVD risk. Post-March 2008, those on ABC at moderate/high CVD risk were more likely to discontinue ABC than those at low/unknown CVD risk, regardless of viral load (≤1,000 copies/ml: relative rate 1.49 [95 % confidence interval 1.34-1.65]; >1,000 copies/ml: 1.23 [1.02-1.48]); no such associations were seen pre-March 2008. There was some evidence that antiretroviral therapy (ART)-naïve persons at moderate/high CVD risk post-March 2008 were less likely to initiate ABC than those at low/unknown CVD risk (odds ratio 0.74 [0.48-1.13]). By 1 February 2013, 941 MI events had occurred in 367,559 person-years. Current ABC use was associated with a 98 % increase in MI rate (RR 1.98 [1.72-2.29]) with no difference in the pre- (1.97 [1.68-2.33]) or post- (1.97 [1.43-2.72]) March 2008 periods (interaction P = 0.74). Despite a reduction in the channelling of ABC for patients at higher CVD risk since 2008, we continue to observe an association between ABC use and MI risk. Whilst confounding cannot be fully ruled out, this further diminishes channelling bias as an explanation for our findings.

Rapid detection of malignant bio-species using digital holographic pattern recognition and nano-photonics

NASA Astrophysics Data System (ADS)

Sarkisov, Sergey S.; Kukhtareva, Tatiana; Kukhtarev, Nickolai V.; Curley, Michael J.; Edwards, Vernessa; Creer, Marylyn

2013-03-01

There is a great need for rapid detection of bio-hazardous species particularly in applications to food safety and biodefense. It has been recently demonstrated that the colonies of various bio-species could be rapidly detected using culture-specific and reproducible patterns generated by scattered non-coherent light. However, the method heavily relies on a digital pattern recognition algorithm, which is rather complex, requires substantial computational power and is prone to ambiguities due to shift, scale, or orientation mismatch between the analyzed pattern and the reference from the library. The improvement could be made, if, in addition to the intensity of the scattered optical wave, its phase would be also simultaneously recorded and used for the digital holographic pattern recognition. In this feasibility study the research team recorded digital Gabor-type (in-line) holograms of colonies of micro-organisms, such as Salmonella with a laser diode as a low-coherence light source and a lensless high-resolution (2.0x2.0 micron pixel pitch) digital image sensor. The colonies were grown in conventional Petri dishes using standard methods. The digitally recorded holograms were used for computational reconstruction of the amplitude and phase information of the optical wave diffracted on the colonies. Besides, the pattern recognition of the colony fragments using the cross-correlation between the digital hologram was also implemented. The colonies of mold fungi Altenaria sp, Rhizophus, sp, and Aspergillus sp have been also generating nano-colloidal silver during their growth in specially prepared matrices. The silver-specific plasmonic optical extinction peak at 410-nm was also used for rapid detection and growth monitoring of the fungi colonies.

76 FR 42710 - Statement of Organization, Functions, and Delegations of Authority

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-19

..., Functions, and Delegations of Authority Part A, Office of the Secretary, Statement of Organization, Functions, and Delegations of Authority for the Department of Health and Human Services is being amended at... (ABC1). V. Under Chapter ABC, Section ABC.20 Functions, 2nd paragraph, replace ``State, tribal, and...

ABCs of Being Smart: S Is for Supporting

ERIC Educational Resources Information Center

Foster, Joanne

2014-01-01

Joanne Foster's article "R We There Yet?" was first published in "Parenting for High Potential" ("PHP") in 2006, which became the springboard for the "ABCs of Being Smart" series of columns. At that time, Foster invited "PHP" readers to think about their own versions of the "ABCs of Being…

75 FR 6706 - In the Matter of Certain Foldable Stools; Notice of Commission Decision Not To Review an Initial...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-10

... Distributing and Crate & Barrel with ``LTD Commodities, LLC, d/b/a abc Distributing'' and ``Euromarket Designs... of investigation named several respondents including the following: abc Distributing Inc. (``abc Distributing'') of Bannockburn, Illinois; Crate & Barrel, Inc. (``Crate & Barrel'') of Northbrook, Illinois...

ABC's of Construction. Final Report.

ERIC Educational Resources Information Center

Greater Baton Rouge Chamber of Commerce, LA.

The ABC's of Construction project was a demonstration project designed to integrate basic skills training with an industry-developed vocational-craft training program. The program was located at the central training facility of the Pelican Chapter of Associated Builders and Contractors (ABC), an organization made up of nearly 300 member companies…

The ABCs of Activity-Based Costing: A Cost Containment and Reallocation Tool.

ERIC Educational Resources Information Center

Turk, Frederick J.

1992-01-01

This article describes activity-based costing (ABC) and how this tool may help management understand the costs of major activities and identify possible alternatives. Also discussed are the traditional costing systems used by higher education and ways of applying ABC to higher education. (GLR)

Do You Know Your ABC?

ERIC Educational Resources Information Center

Neale, Claire

2013-01-01

Within primary schools, the core subjects of literacy and numeracy are highly regarded, and rightly so, as children need to learn to read, write and be numerically literate. This means that all children learn their ABCs at an early age, But, what about the "other ABC"--"Airway, Breathing and Circulation?" Accidents and medical…

Meta-Analysis of the Validation Studies of the Kaufman Assessment Battery for Children.

ERIC Educational Resources Information Center

Ochieng, Charles O.

2003-01-01

Conducted a meta-analysis of the Kaufman Assessment Battery for Children (K-ABC) to ascertain the numbers of factors in the mental processing subtest of the K-ABC. Analyses yielded sequential and simultaneous processing factors, suggesting that the original K-ABC theory was not supported. (SLD)

GPS-ABC radiated chamber testing overview and results : GPS-ABC Workshop VI : RTCA Washington, DC, March 30, 2017.

DOT National Transportation Integrated Search

2017-03-30

This presentation, which was given during the GPS-ABC Workshop VI in Washington, DC on March 30, 2017 details the authors' radiated testing protocols and results. GPS receiver testing was carried out April 25-29, 2016 at the Army : Research Laborator...

fbpABC gene cluster in Neisseria meningitidis is transcribed as an operon.

PubMed

Khun, H H; Deved, V; Wong, H; Lee, B C

2000-12-01

The neisserial fbpABC locus has been proposed to constitute a single transcriptional unit. To confirm this operonic arrangement, transcription assays using reverse transcriptase PCR amplification were conducted with Neisseria meningitidis. The presence of fbpAB and fbpBC transcripts obtained by priming cDNA synthesis with an fbpC-sequence-specific oligonucleotide indicates that fbpABC is organized as a single expression unit. The ratio of fbpA to fbpABC mRNA was approximately between 10- to 20-fold, as determined by real-time quantitative PCR.

fbpABC Gene Cluster in Neisseria meningitidis Is Transcribed as an Operon

PubMed Central

Khun, Heng H.; Deved, Vinay; Wong, Howard; Lee, B. Craig

2000-01-01

The neisserial fbpABC locus has been proposed to constitute a single transcriptional unit. To confirm this operonic arrangement, transcription assays using reverse transcriptase PCR amplification were conducted with Neisseria meningitidis. The presence of fbpAB and fbpBC transcripts obtained by priming cDNA synthesis with an fbpC-sequence-specific oligonucleotide indicates that fbpABC is organized as a single expression unit. The ratio of fbpA to fbpABC mRNA was approximately between 10- to 20-fold, as determined by real-time quantitative PCR. PMID:11083849

Placental ABC Transporters: Biological Impact and Pharmaceutical Significance.

PubMed

Joshi, Anand A; Vaidya, Soniya S; St-Pierre, Marie V; Mikheev, Andrei M; Desino, Kelly E; Nyandege, Abner N; Audus, Kenneth L; Unadkat, Jashvant D; Gerk, Phillip M

2016-12-01

The human placenta fulfills a variety of essential functions during prenatal life. Several ABC transporters are expressed in the human placenta, where they play a role in the transport of endogenous compounds and may protect the fetus from exogenous compounds such as therapeutic agents, drugs of abuse, and other xenobiotics. To date, considerable progress has been made toward understanding ABC transporters in the placenta. Recent studies on the expression and functional activities are discussed. This review discusses the placental expression and functional roles of several members of ABC transporter subfamilies B, C, and G including MDR1/P-glycoprotein, the MRPs, and BCRP, respectively. Since placental ABC transporters modulate fetal exposure to various compounds, an understanding of their functional and regulatory mechanisms will lead to more optimal medication use when necessary in pregnancy.

Placental ABC Transporters: Biological Impact and Pharmaceutical Significance

PubMed Central

Joshi, Anand A.; Vaidya, Soniya S.; St-Pierre, Marie V.; Mikheev, Andrei M.; Desino, Kelly E.; Nyandege, Abner N.; Audus, Kenneth L.; Unadkat, Jashvant D.; Gerk, Phillip M.

2017-01-01

The human placenta fulfills a variety of essential functions during prenatal life. Several ABC transporters are expressed in the human placenta, where they play a role in the transport of endogenous compounds and may protect the fetus from exogenous compounds such as therapeutic agents, drugs of abuse, and other xenobiotics. To date, considerable progress has been made toward understanding ABC transporters in the placenta. Recent studies on the expression and functional activities are discussed. This review discusses the placental expression and functional roles of several members of ABC transporter subfamilies B, C, and G including MDR1/P-glycoprotein, the MRPs, and BCRP, respectively. Since placental ABC transporters modulate fetal exposure to various compounds, an understanding of their functional and regulatory mechanisms will lead to more optimal medication use when necessary in pregnancy. PMID:27644937

Purification and characterization of chondroitinase ABC from Acinetobacter sp. C26.

PubMed

Zhu, Changliang; Zhang, Jingliang; Zhang, Jing; Jiang, Yanhui; Shen, Zhaopeng; Guan, Huashi; Jiang, Xiaolu

2017-02-01

An extracellular chondroitinase ABC (ChSase ABC, EC 4.2.2.4) produced by cultivating Acinetobacter sp. C26, was purified to homogeneity from the supernatant by ammonium sulfate fractionation, Q-Sepharose Fast Flow and Sephadex G-100 chromatography. The 76kDa enzyme was purified 48.09-fold to homogeneity with specific activity of 348.64U/mg, Using the chondroitin sulfate A (CS-A) as substrate, the maximal reaction rate (Vmax) and Michaelis-Menten constant (Km) of ChSase ABC were found to be 10.471μmol/min/ml and 0.105mg/ml, respectively. The enzyme showed the highest activity at the optimal conditions of pH 6.0 and 42 ∘C, respectively. This enzyme was stable at pH 5-10, 5-9 and 5-7 at 4°C, 37°C and 42°C, respectively. Investigation about thermal stability of ChSase ABC displayed that it was stable at 37°C. ChSase ABC activity was increased in presence of Na + , K + , Mn 2+ , 1,10-phenanthrolin and strongly inhibited by Cu 2+ , Hg 2+ , Al 3+ and SDS. These properties suggested that ChSase ABC from Acinetobacter sp. C26 bring promising prospects in medical and industry applications. Copyright © 2016 Elsevier B.V. All rights reserved.

The ABC transporter Rv1272c of Mycobacterium tuberculosis enhances the import of long-chain fatty acids in Escherichia coli.

PubMed

Martin, Audrey; Daniel, Jaiyanth

2018-02-05

Mycobacterium tuberculosis (Mtb), which causes tuberculosis, is capable of accumulating triacylglycerol (TAG) by utilizing fatty acids from host cells. ATP-binding cassette (ABC) transporters are involved in transport processes in all organisms. Among the classical ABC transporters in Mtb none have been implicated in fatty acid import. Since the transport of fatty acids from the host cell is important for dormancy-associated TAG synthesis in the pathogen, mycobacterial ABC transporter(s) could potentially be involved in this process. Based on sequence identities with a bacterial ABC transporter that mediates fatty acid import for TAG synthesis, we identified Rv1272c, a hitherto uncharacterized ABC-transporter in Mtb that also shows sequence identities with a plant ABC transporter involved in fatty acid transport. We expressed Rv1272c in E. coli and show that it enhances the import of radiolabeled fatty acids. We also show that Rv1272c causes a significant increase in the metabolic incorporation of radiolabeled long-chain fatty acids into cardiolipin, a tetra-acylated phospholipid, and phosphatidylglycerol in E. coli. This is the first report on the function of Rv1272c showing that it displays a long-chain fatty acid transport function. Copyright © 2018 Elsevier Inc. All rights reserved.

Abc Amino Acids: Design, Synthesis, and Properties of New Photoelastic Amino Acids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Standaert, Robert F; Park, Dr Seung Bum

2006-01-01

Photoisomerizable amino acids provide a direct avenue to the experimental manipulation of bioactive polypeptides, potentially allowing real-time, remote control of biological systems and enabling useful applications in nanobiotechnology. Herein, we report a new class of photoisomerizable amino acids intended to cause pronounced expansion and contraction in the polypeptide backbone, i.e., to be photoelastic. These compounds, termed Abc amino acids, employ a photoisomerizable azobiphenyl chromophore to control the relative disposition of aminomethyl and carboxyl substituents. Molecular modeling of nine Abc isomers led to the identification of one with particularly attractive properties, including the ability to induce contractions up to 13A inmore » the backbone upon transa?cis photoisomerization. This isomer, designated mpAbc, has substituents at meta and para positions on the inner (azo-linked) and outer rings, respectively. An efficient synthesis of Fmoc-protected mpAbc was executed in which the biaryl components were formed via Suzuki couplings and the azo linkage was formed via amine/nitroso condensation; protected forms of three other Abc isomers were prepared similarly. A decapeptide incorporating mpAbc was synthesized by conventional solid-phase methods and displayed characteristic azobenzene photochemical behavior with optimal conversion to the cis isomer at 360 nm and a thermal cisa?trans half life of 100 min. at 80 AoC.« less

Atomic-batched tensor decomposed two-electron repulsion integrals

NASA Astrophysics Data System (ADS)

Schmitz, Gunnar; Madsen, Niels Kristian; Christiansen, Ove

2017-04-01

We present a new integral format for 4-index electron repulsion integrals, in which several strategies like the Resolution-of-the-Identity (RI) approximation and other more general tensor-decomposition techniques are combined with an atomic batching scheme. The 3-index RI integral tensor is divided into sub-tensors defined by atom pairs on which we perform an accelerated decomposition to the canonical product (CP) format. In a first step, the RI integrals are decomposed to a high-rank CP-like format by repeated singular value decompositions followed by a rank reduction, which uses a Tucker decomposition as an intermediate step to lower the prefactor of the algorithm. After decomposing the RI sub-tensors (within the Coulomb metric), they can be reassembled to the full decomposed tensor (RC approach) or the atomic batched format can be maintained (ABC approach). In the first case, the integrals are very similar to the well-known tensor hypercontraction integral format, which gained some attraction in recent years since it allows for quartic scaling implementations of MP2 and some coupled cluster methods. On the MP2 level, the RC and ABC approaches are compared concerning efficiency and storage requirements. Furthermore, the overall accuracy of this approach is assessed. Initial test calculations show a good accuracy and that it is not limited to small systems.

Three-beam aerosol backscatter correlation lidar for wind profiling

NASA Astrophysics Data System (ADS)

Prasad, Narasimha S.; Radhakrishnan Mylapore, Anand

2017-03-01

The development of a three-beam aerosol backscatter correlation (ABC) light detection and ranging (lidar) to measure wind characteristics for wake vortex and plume tracking applications is discussed. This is a direct detection elastic lidar that uses three laser transceivers, operating at 1030-nm wavelength with ˜10-kHz pulse repetition frequency and nanosec class pulse widths, to directly obtain three components of wind velocities. By tracking the motion of aerosol structures along and between three near-parallel laser beams, three-component wind speed profiles along the field-of-view of laser beams are obtained. With three 8-in. transceiver modules, placed in a near-parallel configuration on a two-axis pan-tilt scanner, the lidar measures wind speeds up to 2 km away. Optical flow algorithms have been adapted to obtain the movement of aerosol structures between the beams. Aerosol density fluctuations are cross-correlated between successive scans to obtain the displacements of the aerosol features along the three axes. Using the range resolved elastic backscatter data from each laser beam, which is scanned over the volume of interest, a three-dimensional map of aerosol density can be generated in a short time span. The performance of the ABC wind lidar prototype, validated using sonic anemometer measurements, is discussed.

Atomic-batched tensor decomposed two-electron repulsion integrals.

PubMed

Schmitz, Gunnar; Madsen, Niels Kristian; Christiansen, Ove

2017-04-07

We present a new integral format for 4-index electron repulsion integrals, in which several strategies like the Resolution-of-the-Identity (RI) approximation and other more general tensor-decomposition techniques are combined with an atomic batching scheme. The 3-index RI integral tensor is divided into sub-tensors defined by atom pairs on which we perform an accelerated decomposition to the canonical product (CP) format. In a first step, the RI integrals are decomposed to a high-rank CP-like format by repeated singular value decompositions followed by a rank reduction, which uses a Tucker decomposition as an intermediate step to lower the prefactor of the algorithm. After decomposing the RI sub-tensors (within the Coulomb metric), they can be reassembled to the full decomposed tensor (RC approach) or the atomic batched format can be maintained (ABC approach). In the first case, the integrals are very similar to the well-known tensor hypercontraction integral format, which gained some attraction in recent years since it allows for quartic scaling implementations of MP2 and some coupled cluster methods. On the MP2 level, the RC and ABC approaches are compared concerning efficiency and storage requirements. Furthermore, the overall accuracy of this approach is assessed. Initial test calculations show a good accuracy and that it is not limited to small systems.

An ant colony optimization based feature selection for web page classification.

PubMed

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

The impact of active breathing control on internal mammary lymph node coverage and normal tissue exposure in breast cancer patients planned for left-sided postmastectomy radiation therapy.

PubMed

Barry, Aisling; Rock, Kathy; Sole, Claudio; Rahman, Mohammad; Pintilie, Melania; Lee, Grace; Fyles, Anthony; Koch, C Anne

The purpose of this study was to evaluate the impact of the active breathing control (ABC) technique on IMN coverage and organs at risk in patients planned for postmastectomy radiation therapy (PMRT), with the inclusion of the internal mammary lymph nodes (IMNs). The effect of body mass index (BMI) on recorded dosimetric parameters was examined in the same patient cohort. Fifty left-sided postmastectomy patients with breast cancer who underwent free-breathing (FB) and ABC-Elekta CT simulation scans were selected at random from an institutional breast cancer database between 2008 and 2014. The ABC plans were directly compared with FB plans from the same patient. The IMN planning target volume coverage met dosimetric criteria for coverage of receiving more than 90% of the prescribed dose (V90) >90%, although it decreased with ABC compared with FB (94.5% vs 98%, P < .001). Overall, ABC significantly reduced doses to all measured heart and left anterior descending coronary artery parameters, ipsilateral lung V20, and mean lung dose compared with FB (P < .001). There was no difference seen between the ABC and FB plans with respect to the dose to contralateral lung or contralateral breast. There was no correlation identified between BMI and any of the dosimetric parameters recorded from the ABC and FB plans. Our results suggest that ABC reduces IMN coverage in left-sided breast cancer patients planned for PMRT; however, dosimetric criteria for IMN coverage were still met, suggesting that this is not likely to be clinically significant. ABC led to significant sparing of organs at risk compared with FB conditions and was not affected by BMI. Collectively, the results support the use of ABC for breast cancer patients undergoing left-sided PMRT requiring regional nodal irradiation that includes the IMNs. Further prospective clinical studies are required to determine the impact of these results on late normal tissue effects. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

ATP-binding cassette transporters in reproduction: a new frontier

PubMed Central

Bloise, E.; Ortiga-Carvalho, T.M.; Reis, F.M.; Lye, S.J.; Gibb, W.; Matthews, S.G.

2016-01-01

BACKGROUND The transmembrane ATP-binding cassette (ABC) transporters actively efflux an array of clinically relevant compounds across biological barriers, and modulate biodistribution of many physiological and pharmacological factors. To date, over 48 ABC transporters have been identified and shown to be directly and indirectly involved in peri-implantation events and fetal/placental development. They efflux cholesterol, steroid hormones, vitamins, cytokines, chemokines, prostaglandins, diverse xenobiotics and environmental toxins, playing a critical role in regulating drug disposition, immunological responses and lipid trafficking, as well as preventing fetal accumulation of drugs and environmental toxins. METHODS This review examines ABC transporters as important mediators of placental barrier functions and key reproductive processes. Expression, localization and function of all identified ABC transporters were systematically reviewed using PubMed and Google Scholar websites to identify relevant studies examining ABC transporters in reproductive tissues in physiological and pathophysiological states. Only reports written in English were incorporated with no restriction on year of publication. While a major focus has been placed on the human, extensive evidence from animal studies is utilized to describe current understanding of the regulation and function of ABC transporters relevant to human reproduction. RESULTS ABC transporters are modulators of steroidogenesis, fertilization, implantation, nutrient transport and immunological responses, and function as ‘gatekeepers’ at various barrier sites (i.e. blood-testes barrier and placenta) against potentially harmful xenobiotic factors, including drugs and environmental toxins. These roles appear to be species dependent and change as a function of gestation and development. The best-described ABC transporters in reproductive tissues (primarily in the placenta) are the multidrug transporters p-glycoprotein and breast cancer-related protein, the multidrug resistance proteins 1 through 5 and the cholesterol transporters ABCA1 and ABCG1. CONCLUSIONS The ABC transporters have various roles across multiple reproductive tissues. Knowledge of efflux direction, tissue distribution, substrate specificity and regulation of the ABC transporters in the placenta and other reproductive tissues is rapidly expanding. This will allow better understanding of the disposition of specific substrates within reproductive tissues, and facilitate development of novel treatments for reproductive disorders as well as improved approaches to protecting the developing fetus. PMID:26545808

Long-term bleeding risk prediction in 'real world' patients with atrial fibrillation: Comparison of the HAS-BLED and ABC-Bleeding risk scores. The Murcia Atrial Fibrillation Project.

PubMed

Esteve-Pastor, María Asunción; Rivera-Caravaca, José Miguel; Roldan, Vanessa; Vicente, Vicente; Valdés, Mariano; Marín, Francisco; Lip, Gregory Y H

2017-10-05

Risk scores in patients with atrial fibrillation (AF) based on clinical factors alone generally have only modest predictive value for predicting high risk patients that sustain events. Biomarkers might be an attractive prognostic tool to improve bleeding risk prediction. The new ABC-Bleeding score performed better than HAS-BLED score in a clinical trial cohort but has not been externally validated. The aim of this study was to analyze the predictive performance of the ABC-Bleeding score compared to HAS-BLED score in an independent "real-world" anticoagulated AF patients with long-term follow-up. We enrolled 1,120 patients stable on vitamin K antagonist treatment. The HAS-BLED and ABC-Bleeding scores were quantified. Predictive values were compared by c-indexes, IDI, NRI, as well as decision curve analysis (DCA). Median HAS-BLED score was 2 (IQR 2-3) and median ABC-Bleeding was 16.5 (IQR 14.3-18.6). After 6.5 years of follow-up, 207 (2.84 %/year) patients had major bleeding events, of which 65 (0.89 %/year) had intracranial haemorrhage (ICH) and 85 (1.17 %/year) had gastrointestinal bleeding events (GIB). The c-index of HAS-BLED was significantly higher than ABC-Bleeding for major bleeding (0.583 vs 0.518; p=0.025), GIB (0.596 vs 0.519; p=0.017) and for the composite of ICH-GIB (0.593 vs 0.527; p=0.030). NRI showed a significant negative reclassification for major bleeding and for the composite of ICH-GIB with the ABC-Bleeding score compared to HAS-BLED. Using DCAs, the use of HAS-BLED score gave an approximate net benefit of 4 % over the ABC-Bleeding score. In conclusion, in the first "real-world" validation of the ABC-Bleeding score, HAS-BLED performed significantly better than the ABC-Bleeding score in predicting major bleeding, GIB and the composite of GIB and ICH.

Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

NASA Astrophysics Data System (ADS)

Ge, Junwei; Cai, Yu; Fang, Yiqiu

2018-05-01

This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

Metaheuristic optimization approaches to predict shear-wave velocity from conventional well logs in sandstone and carbonate case studies

NASA Astrophysics Data System (ADS)

Emami Niri, Mohammad; Amiri Kolajoobi, Rasool; Khodaiy Arbat, Mohammad; Shahbazi Raz, Mahdi

2018-06-01

Seismic wave velocities, along with petrophysical data, provide valuable information during the exploration and development stages of oil and gas fields. The compressional-wave velocity (VP ) is acquired using conventional acoustic logging tools in many drilled wells. But the shear-wave velocity (VS ) is recorded using advanced logging tools only in a limited number of wells, mainly because of the high operational costs. In addition, laboratory measurements of seismic velocities on core samples are expensive and time consuming. So, alternative methods are often used to estimate VS . Heretofore, several empirical correlations that predict VS by using well logging measurements and petrophysical data such as VP , porosity and density are proposed. However, these empirical relations can only be used in limited cases. The use of intelligent systems and optimization algorithms are inexpensive, fast and efficient approaches for predicting VS. In this study, in addition to the widely used Greenberg–Castagna empirical method, we implement three relatively recently developed metaheuristic algorithms to construct linear and nonlinear models for predicting VS : teaching–learning based optimization, imperialist competitive and artificial bee colony algorithms. We demonstrate the applicability and performance of these algorithms to predict Vs using conventional well logs in two field data examples, a sandstone formation from an offshore oil field and a carbonate formation from an onshore oil field. We compared the estimated VS using each of the employed metaheuristic approaches with observed VS and also with those predicted by Greenberg–Castagna relations. The results indicate that, for both sandstone and carbonate case studies, all three implemented metaheuristic algorithms are more efficient and reliable than the empirical correlation to predict VS . The results also demonstrate that in both sandstone and carbonate case studies, the performance of an artificial bee colony algorithm in VS prediction is slightly higher than two other alternative employed approaches.

A PhoPQ-Regulated ABC Transporter System Exports Tetracycline in Pseudomonas aeruginosa.

PubMed

Chen, Lin; Duan, Kangmin

2016-05-01

Pseudomonas aeruginosa is an important human pathogen whose infections are difficult to treat due to its high intrinsic resistance to many antibiotics. Here, we show that the disruption of PA4456, encoding the ATP binding component of a putative ATP-binding cassette (ABC) transporter, increased the bacterium's susceptible to tetracycline and other antibiotics or toxic chemicals. Fluorescence spectroscopy and antibiotic accumulation tests showed that the interruption of the ABC transporter caused increased intracellular accumulation of tetracycline, demonstrating a role of the ABC transporter in tetracycline expulsion. Site-directed mutagenesis proved that the conserved residues of E170 in the Walker B motif and H203 in the H-loop, which are important for ATP hydrolysis, were essential for the function of PA4456. Through a genome-wide search, the PhoPQ two-component system was identified as a regulator of the computationally predicted PA4456-4452 operon that encodes the ABC transporter system. A >5-fold increase of the expression of this operon was observed in the phoQ mutant. The results obtained also show that the expression of the phzA1B1C1D1E1 operon and the production of pyocyanin were significantly higher in the ABC transporter mutant, signifying a connection between the ABC transporter and pyocyanin production. These results indicated that the PhoPQ-regulated ABC transporter is associated with intrinsic resistance to antibiotics and other adverse compounds in P. aeruginosa, probably by extruding them out of the cell. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

TaABC1, a member of the activity of bc1 complex protein kinase family from common wheat, confers enhanced tolerance to abiotic stresses in Arabidopsis

PubMed Central

Wang, Caixiang; Jing, Ruilian; Mao, Xinguo; Chang, Xiaoping; Li, Ang

2011-01-01

Abiotic stresses such as drought, salinity, and low temperature have drastic effects on plant growth and development. However, the molecular mechanisms regulating biochemical and physiological changes in response to stresses are not well understood. Protein kinases are major signal transduction factors among the reported molecular mechanisms mediating acclimation to environmental changes. Protein kinase ABC1 (activity of bc1 complex) is involved in regulating coenzyme Q biosynthesis in mitochondria in yeast (Saccharomyces cersvisiae), and in balancing oxidative stress in chloroplasts in Arabidopsis thaliana. In the current study, TaABC1 (Triticum aestivum L. activity of bc1 complex) protein kinase was localized to the cell membrane, cytoplasm, and nucleus. The effects of overexpressing TaABC1 in transgenic Arabidopsis plants on responses to drought, salt, and cold stress were further investigated. Transgenic Arabidopsis overexpressing the TaABC1 protein showed lower water loss and higher osmotic potential, photochemistry efficiency, and chlorophyll content, while cell membrane stability and controlled reactive oxygen species homeostasis were maintained. In addition, overexpression of TaABC1 increased the expression of stress-responsive genes, such as DREB1A, DREB2A, RD29A, ABF3, KIN1, CBF1, LEA, and P5CS, detected by real-time PCR analysis. The results suggest that TaABC1 overexpression enhances drought, salt, and cold stress tolerance in Arabidopsis, and imply that TaABC1 may act as a regulatory factor involved in a multiple stress response pathways. PMID:21115661

Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC).

PubMed

Kwon, Deukwoo; Reis, Isildinha M

2015-08-12

When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed.

The feoABC Locus of Yersinia pestis Likely Has Two Promoters Causing Unique Iron Regulation

PubMed Central

O'Connor, Lauren; Fetherston, Jacqueline D.; Perry, Robert D.

2017-01-01

The FeoABC ferrous transporter is a wide-spread bacterial system. While the feoABC locus is regulated by a number of factors in the bacteria studied, we have previously found that regulation of feoABC in Yersinia pestis appears to be unique. None of the non-iron responsive transcriptional regulators that control expression of feoABC in other bacteria do so in Y. pestis. Another unique factor is the iron and Fur regulation of the Y. pestis feoABC locus occurs during microaerobic but not aerobic growth. Here we show that this unique iron-regulation is not due to a unique aspect of the Y. pestis Fur protein but to DNA sequences that regulate transcription. We have used truncations, alterations, and deletions of the feoA::lacZ reporter to assess the mechanism behind the failure of iron to repress transcription under aerobic conditions. These studies plus EMSAs and DNA sequence analysis have led to our proposal that the feoABC locus has two promoters: an upstream P1 promoter whose expression is relatively iron-independent but repressed under microaerobic conditions and the known downstream Fur-regulated P2 promoter. In addition, we have identified two regions that bind Y. pestis protein(s), although we have not identified these protein(s) or their function. Finally we used iron uptake assays to demonstrate that both FeoABC and YfeABCD transport ferrous iron in an energy-dependent manner and also use ferric iron as a substrate for uptake. PMID:28785546

A Manual for Implementation of ABC Video Duplication Projects.

ERIC Educational Resources Information Center

Hill, Joseph, Ed.

The ABC (Appalachian BOCES Consortium) consists of 10 BOCES (Boards of Cooperative Educational Services) which serve the 14 southern counties of New York State designated as Appalachia. Each year since 1974, the ABC has participated in regional video duplication projects, which have yielded a total of nearly 4,000 video titles. The complexity of…

Abrasive wear behavior of heat-treated ABC-silicon carbide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xiao Feng; Lee, Gun Y.; Chen, Da

2002-06-17

Hot-pressed silicon carbide, containing aluminum, boron, and carbon additives (ABC-SiC), was subjected to three-body and two-body wear testing using diamond abrasives over a range of sizes. In general, the wear resistance of ABC-SiC, with suitable heat treatment, was superior to that of commercial SiC.

Creating an iPhone Application for Collecting Continuous ABC Data

ERIC Educational Resources Information Center

Whiting, Seth W.; Dixon, Mark R.

2012-01-01

This paper provides an overview and task analysis for creating a continuous ABC data- collection application using Xcode on a Mac computer. Behavior analysts can program an ABC data collection system, complete with a customized list of target clients, antecedents, behaviors, and consequences to be recorded, and have the data automatically sent to…

75 FR 42069 - Fisheries of the South Atlantic and Gulf of Mexico; South Atlantic Fishery Management Council...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-20

...) will hold a meeting of its Scientific and Statistical Committee (SSC) to discuss Acceptable Biological Catch (ABC) Control Rules, and recommend ABC values for South Atlantic managed species. See... discuss ABC control rules for stocks which do not have peer reviewed quantitative stock assessments and...

Parents' Perspectives on Braille Literacy: Results from the ABC Braille Study

ERIC Educational Resources Information Center

Kamei-Hannan, Cheryl; Sacks, Sharon Zell

2012-01-01

Introduction: Parents who were the primary caretakers of children in the Alphabetic and Contracted Braille Study (ABC Braille Study) revealed their perspectives about braille literacy. Methods: A 30-item questionnaire was constructed by the ABC Braille research team, and researchers conducted telephone interviews with 31 parents who were the…

Association of Serotonin Concentration to Behavior and IQ in Autistic Children.

ERIC Educational Resources Information Center

Kuperman, Samuel; And Others

1987-01-01

The IQ and behavior patterns on the Autism Behavior Checklist (ABC) of 25 boys were compared to blood concentrations of platelet rich plasma (PRP) serotonin. Although no correlations were found between serotonin levels and IQ or ABC scales, four individual ABC items did correlate with serotonin concentrations. (Author/DB)

Confirmatory Factor Analysis of the Kaufman Assessment Battery for Children: A Reevaluation.

ERIC Educational Resources Information Center

Strommen, Erik

1988-01-01

Performed confirmatory factor analyses of Kaufman Assessment Battery for Children (K-ABC) using subtest correlations for standardization samples provided in manuals to test hypothesis that factors underlying K-ABC are substantially intercorrelated at all age levels for two- and three-factor models. Findings suggest K-ABC cannot distinguish between…

78 FR 48860 - New England Fishery Management Council; Public Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-12

... ABC Control Rule Working Group (ABC WG) and Electronic Monitoring Working Group (EM WG). DATES: The first meeting of the ABC Control Rule Working Group will be on Tuesday, September 3, 2013. The meeting..., times and places for the meetings will be posted on the Council's Web site, http://nefmc.org/ . FOR...

School-Based Management and Accountability Procedures Manual

ERIC Educational Resources Information Center

North Carolina Department of Public Instruction, 2004

2004-01-01

From the mission, several principles were developed to guide the School-Based Management and Accountability Program (the ABCs). (1) The ABCs sets standards for student performance and growth in the basics that are the foundation for further learning and achievement; (2) The accountability system in the ABCs plan is designed to result in improved…

Molecular analysis of imipenem-resistant Acinetobacter baumannii isolated from US service members wounded in Iraq, 2003–2008

USDA-ARS?s Scientific Manuscript database

Clonal spread and global dissemination of imipenem resistant (IR) A. baumannii-A. calcoaceticus complex (ABC) have been reported in recent years. However, the epidemiological features of the IR-ABCs in military treatment facilities (MTFs) have not been systematically studied. In this study, 298 ABC...

Enhanced High-Rate Shockwave Lithotripsy Stone Comminution in an In Vivo Porcine Model Using Acoustic Bubble Coalescence.

PubMed

Alavi Tamaddoni, Hedieh; Roberts, William W; Duryea, Alexander P; Cain, Charles A; Hall, Timothy L

2016-12-01

Cavitation plays a significant role in the efficacy of stone comminution during shockwave lithotripsy (SWL). Although cavitation on the surface of urinary stones helps to improve fragmentation, cavitation bubbles along the propagation path may shield or block subsequent shockwaves (SWs) and potentially induce collateral tissue damage. Previous in vitro work has shown that applying low-amplitude acoustic waves after each SW can force bubbles to consolidate and enhance SWL efficacy. In this study, the feasibility of applying acoustic bubble coalescence (ABC) in vivo was tested. Model stones were percutaneously implanted and treated with 2500 lithotripsy SWs at 120 SW/minute with or without ABC. Comparing the results of stone comminution, a significant improvement was observed in the stone fragmentation process when ABC was used. Without ABC, only 25% of the mass of the stone was fragmented to particles <2 mm in size. With ABC, 75% of the mass was fragmented to particles <2 mm in size. These results suggest that ABC can reduce the shielding effect of residual bubble nuclei, resulting in a more efficient SWL treatment.

ATP-binding cassette (ABC) proteins in aquatic invertebrates: Evolutionary significance and application in marine ecotoxicology.

PubMed

Jeong, Chang-Bum; Kim, Hui-Su; Kang, Hye-Min; Lee, Jae-Seong

2017-04-01

The ATP-binding cassette (ABC) protein superfamily is known to play a fundamental role in biological processes and is highly conserved across animal taxa. The ABC proteins function as active transporters for multiple substrates across the cellular membrane by ATP hydrolysis. As this superfamily is derived from a common ancestor, ABC genes have evolved via lineage-specific duplications through the process of adaptation. In this review, we summarized information about the ABC gene families in aquatic invertebrates, considering their evolution and putative functions in defense mechanisms. Phylogenetic analysis was conducted to examine the evolutionary significance of ABC gene families in aquatic invertebrates. Particularly, a massive expansion of multixenobiotic resistance (MXR)-mediated efflux transporters was identified in the absence of the ABCG2 (BCRP) gene in Ecdysozoa and Platyzoa, suggesting that a loss of Abcg2 gene occurred sporadically in these species during divergence of Protostome to Lophotrochozoa. Furthermore, in aquatic invertebrates, the ecotoxicological significance of MXR is discussed while considering the role of MXR-mediated efflux transporters in response to various environmental pollutants. Copyright © 2017 Elsevier B.V. All rights reserved.

Genome-wide identification of ATP-binding cassette (ABC) transporters and their roles in response to polycyclic aromatic hydrocarbons (PAHs) in the copepod Paracyclopina nana.

PubMed

Jeong, Chang-Bum; Kim, Duck-Hyun; Kang, Hye-Min; Lee, Young Hwan; Kim, Hui-Su; Kim, Il-Chan; Lee, Jae-Seong

2017-02-01

The ATP-binding cassette (ABC) protein superfamily is one of the largest gene families and is highly conserved in all domains. The ABC proteins play roles in several biological processes, including multi-xenobiotic resistance (MXR), by functioning as transporters in the cellular membrane. They also mediate the cellular efflux of a wide range of substrates against concentration gradients. In this study, 37 ABC genes belonging to eight distinct subfamilies were identified in the marine copepod Paracyclopina nana and annotated based on a phylogenetic analysis. Also, the functions of P-glycoproteins (P-gp) and multidrug resistance-associated proteins (MRPs), conferring MXR, were verified using fluorescent substrates and specific inhibitors. The activities of MXR-mediated ABC proteins and their transcriptional level were examined in response to polyaromatic hydrocarbons (PAHs), main components of the water-accommodated fraction. This study increases the understanding of the protective role of MXR in response to PAHs over the comparative evolution of ABC gene families. Copyright © 2016 Elsevier B.V. All rights reserved.