Statistical physics of hard combinatorial optimization: Vertex cover problem
NASA Astrophysics Data System (ADS)
Zhao, Jin-Hua; Zhou, Hai-Jun
2014-07-01
Typical-case computation complexity is a research topic at the boundary of computer science, applied mathematics, and statistical physics. In the last twenty years, the replica-symmetry-breaking mean field theory of spin glasses and the associated message-passing algorithms have greatly deepened our understanding of typical-case computation complexity. In this paper, we use the vertex cover problem, a basic nondeterministic-polynomial (NP)-complete combinatorial optimization problem of wide application, as an example to introduce the statistical physical methods and algorithms. We do not go into the technical details but emphasize mainly the intuitive physical meanings of the message-passing equations. A nonfamiliar reader shall be able to understand to a large extent the physics behind the mean field approaches and to adjust the mean field methods in solving other optimization problems.
Neural Meta-Memes Framework for Combinatorial Optimization
NASA Astrophysics Data System (ADS)
Song, Li Qin; Lim, Meng Hiot; Ong, Yew Soon
In this paper, we present a Neural Meta-Memes Framework (NMMF) for combinatorial optimization. NMMF is a framework which models basic optimization algorithms as memes and manages them dynamically when solving combinatorial problems. NMMF encompasses neural networks which serve as the overall planner/coordinator to balance the workload between memes. We show the efficacy of the proposed NMMF through empirical study on a class of combinatorial problem, the quadratic assignment problem (QAP).
Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication
ERIC Educational Resources Information Center
Wolf, Michael Maclean
2009-01-01
Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrix-vector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific…
Coelho, V N; Coelho, I M; Souza, M J F; Oliveira, T A; Cota, L P; Haddad, M N; Mladenovic, N; Silva, R C P; Guimarães, F G
2016-01-01
This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.
Smooth Constrained Heuristic Optimization of a Combinatorial Chemical Space
2015-05-01
ARL-TR-7294•MAY 2015 US Army Research Laboratory Smooth ConstrainedHeuristic Optimization of a Combinatorial Chemical Space by Berend Christopher...7294•MAY 2015 US Army Research Laboratory Smooth ConstrainedHeuristic Optimization of a Combinatorial Chemical Space by Berend Christopher...
Statistical Mechanics of Combinatorial Auctions
NASA Astrophysics Data System (ADS)
Galla, Tobias; Leone, Michele; Marsili, Matteo; Sellitto, Mauro; Weigt, Martin; Zecchina, Riccardo
2006-09-01
Combinatorial auctions are formulated as frustrated lattice gases on sparse random graphs, allowing the determination of the optimal revenue by methods of statistical physics. Transitions between computationally easy and hard regimes are found and interpreted in terms of the geometric structure of the space of solutions. We introduce an iterative algorithm to solve intermediate and large instances, and discuss competing states of optimal revenue and maximal number of satisfied bidders. The algorithm can be generalized to the hard phase and to more sophisticated auction protocols.
Bifurcation-based approach reveals synergism and optimal combinatorial perturbation.
Liu, Yanwei; Li, Shanshan; Liu, Zengrong; Wang, Ruiqi
2016-06-01
Cells accomplish the process of fate decisions and form terminal lineages through a series of binary choices in which cells switch stable states from one branch to another as the interacting strengths of regulatory factors continuously vary. Various combinatorial effects may occur because almost all regulatory processes are managed in a combinatorial fashion. Combinatorial regulation is crucial for cell fate decisions because it may effectively integrate many different signaling pathways to meet the higher regulation demand during cell development. However, whether the contribution of combinatorial regulation to the state transition is better than that of a single one and if so, what the optimal combination strategy is, seem to be significant issue from the point of view of both biology and mathematics. Using the approaches of combinatorial perturbations and bifurcation analysis, we provide a general framework for the quantitative analysis of synergism in molecular networks. Different from the known methods, the bifurcation-based approach depends only on stable state responses to stimuli because the state transition induced by combinatorial perturbations occurs between stable states. More importantly, an optimal combinatorial perturbation strategy can be determined by investigating the relationship between the bifurcation curve of a synergistic perturbation pair and the level set of a specific objective function. The approach is applied to two models, i.e., a theoretical multistable decision model and a biologically realistic CREB model, to show its validity, although the approach holds for a general class of biological systems.
Quantum Resonance Approach to Combinatorial Optimization
NASA Technical Reports Server (NTRS)
Zak, Michail
1997-01-01
It is shown that quantum resonance can be used for combinatorial optimization. The advantage of the approach is in independence of the computing time upon the dimensionality of the problem. As an example, the solution to a constraint satisfaction problem of exponential complexity is demonstrated.
Multiobjective optimization of combinatorial libraries.
Agrafiotis, D K
2002-01-01
Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.
Combinatorial Optimization in Project Selection Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Dewi, Sari; Sawaluddin
2018-01-01
This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.
Wieberger, Florian; Kolb, Tristan; Neuber, Christian; Ober, Christopher K; Schmidt, Hans-Werner
2013-04-08
In this article we present several developed and improved combinatorial techniques to optimize processing conditions and material properties of organic thin films. The combinatorial approach allows investigations of multi-variable dependencies and is the perfect tool to investigate organic thin films regarding their high performance purposes. In this context we develop and establish the reliable preparation of gradients of material composition, temperature, exposure, and immersion time. Furthermore we demonstrate the smart application of combinations of composition and processing gradients to create combinatorial libraries. First a binary combinatorial library is created by applying two gradients perpendicular to each other. A third gradient is carried out in very small areas and arranged matrix-like over the entire binary combinatorial library resulting in a ternary combinatorial library. Ternary combinatorial libraries allow identifying precise trends for the optimization of multi-variable dependent processes which is demonstrated on the lithographic patterning process. Here we verify conclusively the strong interaction and thus the interdependency of variables in the preparation and properties of complex organic thin film systems. The established gradient preparation techniques are not limited to lithographic patterning. It is possible to utilize and transfer the reported combinatorial techniques to other multi-variable dependent processes and to investigate and optimize thin film layers and devices for optical, electro-optical, and electronic applications.
Combinatorial optimization games
DOE Office of Scientific and Technical Information (OSTI.GOV)
Deng, X.; Ibaraki, Toshihide; Nagamochi, Hiroshi
1997-06-01
We introduce a general integer programming formulation for a class of combinatorial optimization games, which immediately allows us to improve the algorithmic result for finding amputations in the core (an important solution concept in cooperative game theory) of the network flow game on simple networks by Kalai and Zemel. An interesting result is a general theorem that the core for this class of games is nonempty if and only if a related linear program has an integer optimal solution. We study the properties for this mathematical condition to hold for several interesting problems, and apply them to resolve algorithmic andmore » complexity issues for their cores along the line as put forward in: decide whether the core is empty; if the core is empty, find an imputation in the core; given an imputation x, test whether x is in the core. We also explore the properties of totally balanced games in this succinct formulation of cooperative games.« less
Aerospace applications of integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in solving combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on a large space structure and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
Aerospace Applications of Integer and Combinatorial Optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Aerospace applications on integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem. for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
NASA Astrophysics Data System (ADS)
Doerr, Timothy; Alves, Gelio; Yu, Yi-Kuo
2006-03-01
Typical combinatorial optimizations are NP-hard; however, for a particular class of cost functions the corresponding combinatorial optimizations can be solved in polynomial time. This suggests a way to efficiently find approximate solutions - - find a transformation that makes the cost function as similar as possible to that of the solvable class. After keeping many high-ranking solutions using the approximate cost function, one may then re-assess these solutions with the full cost function to find the best approximate solution. Under this approach, it is important to be able to assess the quality of the solutions obtained, e.g., by finding the true ranking of kth best approximate solution when all possible solutions are considered exhaustively. To tackle this statistical issue, we provide a systematic method starting with a scaling function generated from the fininte number of high- ranking solutions followed by a convergent iterative mapping. This method, useful in a variant of the directed paths in random media problem proposed here, can also provide a statistical significance assessment for one of the most important proteomic tasks - - peptide sequencing using tandem mass spectrometry data.
A methodology to find the elementary landscape decomposition of combinatorial optimization problems.
Chicano, Francisco; Whitley, L Darrell; Alba, Enrique
2011-01-01
A small number of combinatorial optimization problems have search spaces that correspond to elementary landscapes, where the objective function f is an eigenfunction of the Laplacian that describes the neighborhood structure of the search space. Many problems are not elementary; however, the objective function of a combinatorial optimization problem can always be expressed as a superposition of multiple elementary landscapes if the underlying neighborhood used is symmetric. This paper presents theoretical results that provide the foundation for algebraic methods that can be used to decompose the objective function of an arbitrary combinatorial optimization problem into a sum of subfunctions, where each subfunction is an elementary landscape. Many steps of this process can be automated, and indeed a software tool could be developed that assists the researcher in finding a landscape decomposition. This methodology is then used to show that the subset sum problem is a superposition of two elementary landscapes, and to show that the quadratic assignment problem is a superposition of three elementary landscapes.
Hernando, Leticia; Mendiburu, Alexander; Lozano, Jose A
2013-01-01
The solution of many combinatorial optimization problems is carried out by metaheuristics, which generally make use of local search algorithms. These algorithms use some kind of neighborhood structure over the search space. The performance of the algorithms strongly depends on the properties that the neighborhood imposes on the search space. One of these properties is the number of local optima. Given an instance of a combinatorial optimization problem and a neighborhood, the estimation of the number of local optima can help not only to measure the complexity of the instance, but also to choose the most convenient neighborhood to solve it. In this paper we review and evaluate several methods to estimate the number of local optima in combinatorial optimization problems. The methods reviewed not only come from the combinatorial optimization literature, but also from the statistical literature. A thorough evaluation in synthetic as well as real problems is given. We conclude by providing recommendations of methods for several scenarios.
TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization
2016-11-28
objective 9 4.6 On The Recoverable Robust Traveling Salesman Problem . . . . . 11 4.7 A Bicriteria Approach to Robust Optimization...be found. 4.6 On The Recoverable Robust Traveling Salesman Problem The traveling salesman problem (TSP) is a well-known combinatorial optimiza- tion...procedure for the robust traveling salesman problem . While this iterative algorithms results in an optimal solution to the robust TSP, computation
NASA Astrophysics Data System (ADS)
Hartmann, Alexander K.; Weigt, Martin
2005-10-01
A concise, comprehensive introduction to the topic of statistical physics of combinatorial optimization, bringing together theoretical concepts and algorithms from computer science with analytical methods from physics. The result bridges the gap between statistical physics and combinatorial optimization, investigating problems taken from theoretical computing, such as the vertex-cover problem, with the concepts and methods of theoretical physics. The authors cover rapid developments and analytical methods that are both extremely complex and spread by word-of-mouth, providing all the necessary basics in required detail. Throughout, the algorithms are shown with examples and calculations, while the proofs are given in a way suitable for graduate students, post-docs, and researchers. Ideal for newcomers to this young, multidisciplinary field.
Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Li, Baoqing; Yuan, Xiaobing
2017-01-01
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms. PMID:28753962
Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing
2017-07-19
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.
Liu, Zhi-Hua; Xie, Shangxian; Lin, Furong; Jin, Mingjie; Yuan, Joshua S
2018-01-01
Lignin valorization has recently been considered to be an essential process for sustainable and cost-effective biorefineries. Lignin represents a potential new feedstock for value-added products. Oleaginous bacteria such as Rhodococcus opacus can produce intracellular lipids from biodegradation of aromatic substrates. These lipids can be used for biofuel production, which can potentially replace petroleum-derived chemicals. However, the low reactivity of lignin produced from pretreatment and the underdeveloped fermentation technology hindered lignin bioconversion to lipids. In this study, combinatorial pretreatment with an optimized fermentation strategy was evaluated to improve lignin valorization into lipids using R. opacus PD630. As opposed to single pretreatment, combinatorial pretreatment produced a 12.8-75.6% higher lipid concentration in fermentation using lignin as the carbon source. Gas chromatography-mass spectrometry analysis showed that combinatorial pretreatment released more aromatic monomers, which could be more readily utilized by lignin-degrading strains. Three detoxification strategies were used to remove potential inhibitors produced from pretreatment. After heating detoxification of the lignin stream, the lipid concentration further increased by 2.9-9.7%. Different fermentation strategies were evaluated in scale-up lipid fermentation using a 2.0-l fermenter. With laccase treatment of the lignin stream produced from combinatorial pretreatment, the highest cell dry weight and lipid concentration were 10.1 and 1.83 g/l, respectively, in fed-batch fermentation, with a total soluble substrate concentration of 40 g/l. The improvement of the lipid fermentation performance may have resulted from lignin depolymerization by the combinatorial pretreatment and laccase treatment, reduced inhibition effects by fed-batch fermentation, adequate oxygen supply, and an accurate pH control in the fermenter. Overall, these results demonstrate that combinatorial
It looks easy! Heuristics for combinatorial optimization problems.
Chronicle, Edward P; MacGregor, James N; Ormerod, Thomas C; Burr, Alistair
2006-04-01
Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks.
Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.
Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen
2016-07-01
This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well.
A Combinatorial Platform for the Optimization of Peptidomimetic Methyl-Lysine Reader Antagonists
NASA Astrophysics Data System (ADS)
Barnash, Kimberly D.
Post-translational modification of histone N-terminal tails mediates chromatin compaction and, consequently, DNA replication, transcription, and repair. While numerous post-translational modifications decorate histone tails, lysine methylation is an abundant mark important for both gene activation and repression. Methyl-lysine (Kme) readers function through binding mono-, di-, or trimethyl-lysine. Chemical intervention of Kme readers faces numerous challenges due to the broad surface-groove interactions between readers and their cognate histone peptides; yet, the increasing interest in understanding chromatin-modifying complexes suggests tractable lead compounds for Kme readers are critical for elucidating the mechanisms of chromatin dysregulation in disease states and validating the druggability of these domains and complexes. The successful discovery of a peptide-derived chemical probe, UNC3866, for the Polycomb repressive complex 1 (PRC1) chromodomain Kme readers has proven the potential for selective peptidomimetic inhibition of reader function. Unfortunately, the systematic modification of peptides-to-peptidomimetics is a costly and inefficient strategy for target-class hit discovery against Kme readers. Through the exploration of biased chemical space via combinatorial on-bead libraries, we have developed two concurrent methodologies for Kme reader chemical probe discovery. We employ biased peptide combinatorial libraries as a hit discovery strategy with subsequent optimization via iterative targeted libraries. Peptide-to-peptidomimetic optimization through targeted library design was applied based on structure-guided library design around the interaction of the endogenous peptide ligand with three target Kme readers. Efforts targeting the WD40 reader EED led to the discovery of the 3-mer peptidomimetic ligand UNC5115 while combinatorial repurposing of UNC3866 for off-target chromodomains resulted in the discovery of UNC4991, a CDYL/2-selective ligand, and
Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju
2004-10-01
Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.
Distributed Combinatorial Optimization Using Privacy on Mobile Phones
NASA Astrophysics Data System (ADS)
Ono, Satoshi; Katayama, Kimihiro; Nakayama, Shigeru
This paper proposes a method for distributed combinatorial optimization which uses mobile phones as computers. In the proposed method, an ordinary computer generates solution candidates and mobile phones evaluates them by referring privacy — private information and preferences. Users therefore does not have to send their privacy to any other computers and does not have to refrain from inputting their preferences. They therefore can obtain satisfactory solution. Experimental results have showed the proposed method solved room assignment problems without sending users' privacy to a server.
Automatic Summarization as a Combinatorial Optimization Problem
NASA Astrophysics Data System (ADS)
Hirao, Tsutomu; Suzuki, Jun; Isozaki, Hideki
We derived the oracle summary with the highest ROUGE score that can be achieved by integrating sentence extraction with sentence compression from the reference abstract. The analysis results of the oracle revealed that summarization systems have to assign an appropriate compression rate for each sentence in the document. In accordance with this observation, this paper proposes a summarization method as a combinatorial optimization: selecting the set of sentences that maximize the sum of the sentence scores from the pool which consists of the sentences with various compression rates, subject to length constrains. The score of the sentence is defined by its compression rate, content words and positional information. The parameters for the compression rates and positional information are optimized by minimizing the loss between score of oracles and that of candidates. The results obtained from TSC-2 corpus showed that our method outperformed the previous systems with statistical significance.
Stochastic dynamics and combinatorial optimization
NASA Astrophysics Data System (ADS)
Ovchinnikov, Igor V.; Wang, Kang L.
2017-11-01
Natural dynamics is often dominated by sudden nonlinear processes such as neuroavalanches, gamma-ray bursts, solar flares, etc., that exhibit scale-free statistics much in the spirit of the logarithmic Ritcher scale for earthquake magnitudes. On phase diagrams, stochastic dynamical systems (DSs) exhibiting this type of dynamics belong to the finite-width phase (N-phase for brevity) that precedes ordinary chaotic behavior and that is known under such names as noise-induced chaos, self-organized criticality, dynamical complexity, etc. Within the recently proposed supersymmetric theory of stochastic dynamics, the N-phase can be roughly interpreted as the noise-induced “overlap” between integrable and chaotic deterministic dynamics. As a result, the N-phase dynamics inherits the properties of the both. Here, we analyze this unique set of properties and conclude that the N-phase DSs must naturally be the most efficient optimizers: on one hand, N-phase DSs have integrable flows with well-defined attractors that can be associated with candidate solutions and, on the other hand, the noise-induced attractor-to-attractor dynamics in the N-phase is effectively chaotic or aperiodic so that a DS must avoid revisiting solutions/attractors thus accelerating the search for the best solution. Based on this understanding, we propose a method for stochastic dynamical optimization using the N-phase DSs. This method can be viewed as a hybrid of the simulated and chaotic annealing methods. Our proposition can result in a new generation of hardware devices for efficient solution of various search and/or combinatorial optimization problems.
NASA Astrophysics Data System (ADS)
Chandra, Rishabh
Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.
Osaba, E; Carballedo, R; Diaz, F; Onieva, E; de la Iglesia, I; Perallos, A
2014-01-01
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.
Osaba, E.; Carballedo, R.; Diaz, F.; Onieva, E.; de la Iglesia, I.; Perallos, A.
2014-01-01
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test. PMID:25165731
A Discriminative Sentence Compression Method as Combinatorial Optimization Problem
NASA Astrophysics Data System (ADS)
Hirao, Tsutomu; Suzuki, Jun; Isozaki, Hideki
In the study of automatic summarization, the main research topic was `important sentence extraction' but nowadays `sentence compression' is a hot research topic. Conventional sentence compression methods usually transform a given sentence into a parse tree or a dependency tree, and modify them to get a shorter sentence. However, this method is sometimes too rigid. In this paper, we regard sentence compression as an combinatorial optimization problem that extracts an optimal subsequence of words. Hori et al. also proposed a similar method, but they used only a small number of features and their weights were tuned by hand. We introduce a large number of features such as part-of-speech bigrams and word position in the sentence. Furthermore, we train the system by discriminative learning. According to our experiments, our method obtained better score than other methods with statistical significance.
Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization.
Hartenfeller, Markus; Proschak, Ewgenij; Schüller, Andreas; Schneider, Gisbert
2008-07-01
We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.
Global gene expression analysis by combinatorial optimization.
Ameur, Adam; Aurell, Erik; Carlsson, Mats; Westholm, Jakub Orzechowski
2004-01-01
Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.
ERIC Educational Resources Information Center
Kolata, Gina
1985-01-01
To determine how hard it is for computers to solve problems, researchers have classified groups of problems (polynomial hierarchy) according to how much time they seem to require for their solutions. A difficult and complex proof is offered which shows that a combinatorial approach (using Boolean circuits) may resolve the problem. (JN)
Combinatorial effects of arginine and fluoride on oral bacteria.
Zheng, X; Cheng, X; Wang, L; Qiu, W; Wang, S; Zhou, Y; Li, M; Li, Y; Cheng, L; Li, J; Zhou, X; Xu, X
2015-02-01
Dental caries is closely associated with the microbial disequilibrium between acidogenic/aciduric pathogens and alkali-generating commensal residents within the dental plaque. Fluoride is a widely used anticaries agent, which promotes tooth hard-tissue remineralization and suppresses bacterial activities. Recent clinical trials have shown that oral hygiene products containing both fluoride and arginine possess a greater anticaries effect compared with those containing fluoride alone, indicating synergy between fluoride and arginine in caries management. Here, we hypothesize that arginine may augment the ecological benefit of fluoride by enriching alkali-generating bacteria in the plaque biofilm and thus synergizes with fluoride in controlling dental caries. Specifically, we assessed the combinatory effects of NaF/arginine on planktonic and biofilm cultures of Streptococcus mutans, Streptococcus sanguinis, and Porphyromonas gingivalis with checkerboard microdilution assays. The optimal NaF/arginine combinations were selected, and their combinatory effects on microbial composition were further examined in single-, dual-, and 3-species biofilm using bacterial species-specific fluorescence in situ hybridization and quantitative polymerase chain reaction. We found that arginine synergized with fluoride in suppressing acidogenic S. mutans in both planktonic and biofilm cultures. In addition, the NaF/arginine combination synergistically reduced S. mutans but enriched S. sanguinis within the multispecies biofilms. More importantly, the optimal combination of NaF/arginine maintained a "streptococcal pressure" against the potential growth of oral anaerobe P. gingivalis within the alkalized biofilm. Taken together, we conclude that the combinatory application of fluoride and arginine has a potential synergistic effect in maintaining a healthy oral microbial equilibrium and thus represents a promising ecological approach to caries management. © International & American
Combinatorial Effects of Arginine and Fluoride on Oral Bacteria
Zheng, X.; Cheng, X.; Wang, L.; Qiu, W.; Wang, S.; Zhou, Y.; Li, M.; Li, Y.; Cheng, L.; Li, J.; Zhou, X.
2015-01-01
Dental caries is closely associated with the microbial disequilibrium between acidogenic/aciduric pathogens and alkali-generating commensal residents within the dental plaque. Fluoride is a widely used anticaries agent, which promotes tooth hard-tissue remineralization and suppresses bacterial activities. Recent clinical trials have shown that oral hygiene products containing both fluoride and arginine possess a greater anticaries effect compared with those containing fluoride alone, indicating synergy between fluoride and arginine in caries management. Here, we hypothesize that arginine may augment the ecological benefit of fluoride by enriching alkali-generating bacteria in the plaque biofilm and thus synergizes with fluoride in controlling dental caries. Specifically, we assessed the combinatory effects of NaF/arginine on planktonic and biofilm cultures of Streptococcus mutans, Streptococcus sanguinis, and Porphyromonas gingivalis with checkerboard microdilution assays. The optimal NaF/arginine combinations were selected, and their combinatory effects on microbial composition were further examined in single-, dual-, and 3-species biofilm using bacterial species–specific fluorescence in situ hybridization and quantitative polymerase chain reaction. We found that arginine synergized with fluoride in suppressing acidogenic S. mutans in both planktonic and biofilm cultures. In addition, the NaF/arginine combination synergistically reduced S. mutans but enriched S. sanguinis within the multispecies biofilms. More importantly, the optimal combination of NaF/arginine maintained a “streptococcal pressure” against the potential growth of oral anaerobe P. gingivalis within the alkalized biofilm. Taken together, we conclude that the combinatory application of fluoride and arginine has a potential synergistic effect in maintaining a healthy oral microbial equilibrium and thus represents a promising ecological approach to caries management. PMID:25477312
Microbatteries for Combinatorial Studies of Conventional Lithium-Ion Batteries
NASA Technical Reports Server (NTRS)
West, William; Whitacre, Jay; Bugga, Ratnakumar
2003-01-01
Integrated arrays of microscopic solid-state batteries have been demonstrated in a continuing effort to develop microscopic sources of power and of voltage reference circuits to be incorporated into low-power integrated circuits. Perhaps even more importantly, arrays of microscopic batteries can be fabricated and tested in combinatorial experiments directed toward optimization and discovery of battery materials. The value of the combinatorial approach to optimization and discovery has been proven in the optoelectronic, pharmaceutical, and bioengineering industries. Depending on the specific application, the combinatorial approach can involve the investigation of hundreds or even thousands of different combinations; hence, it is time-consuming and expensive to attempt to implement the combinatorial approach by building and testing full-size, discrete cells and batteries. The conception of microbattery arrays makes it practical to bring the advantages of the combinatorial approach to the development of batteries.
Structure-based design of combinatorial mutagenesis libraries
Verma, Deeptak; Grigoryan, Gevorg; Bailey-Kellogg, Chris
2015-01-01
The development of protein variants with improved properties (thermostability, binding affinity, catalytic activity, etc.) has greatly benefited from the application of high-throughput screens evaluating large, diverse combinatorial libraries. At the same time, since only a very limited portion of sequence space can be experimentally constructed and tested, an attractive possibility is to use computational protein design to focus libraries on a productive portion of the space. We present a general-purpose method, called “Structure-based Optimization of Combinatorial Mutagenesis” (SOCoM), which can optimize arbitrarily large combinatorial mutagenesis libraries directly based on structural energies of their constituents. SOCoM chooses both positions and substitutions, employing a combinatorial optimization framework based on library-averaged energy potentials in order to avoid explicitly modeling every variant in every possible library. In case study applications to green fluorescent protein, β-lactamase, and lipase A, SOCoM optimizes relatively small, focused libraries whose variants achieve energies comparable to or better than previous library design efforts, as well as larger libraries (previously not designable by structure-based methods) whose variants cover greater diversity while still maintaining substantially better energies than would be achieved by representative random library approaches. By allowing the creation of large-scale combinatorial libraries based on structural calculations, SOCoM promises to increase the scope of applicability of computational protein design and improve the hit rate of discovering beneficial variants. While designs presented here focus on variant stability (predicted by total energy), SOCoM can readily incorporate other structure-based assessments, such as the energy gap between alternative conformational or bound states. PMID:25611189
Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization.
Bauer, Markus; Klau, Gunnar W; Reinert, Knut
2007-07-27
The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account. We present a graph-based representation for sequence-structure alignments, which we model as an integer linear program (ILP). We sketch how we compute an optimal or near-optimal solution to the ILP using methods from combinatorial optimization, and present results on a recently published benchmark set for RNA alignments. The implementation of our algorithm yields better alignments in terms of two published scores than the other programs that we tested: This is especially the case with an increasing number of input sequences. Our program LARA is freely available for academic purposes from http://www.planet-lisa.net.
Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort.
Jeschek, Markus; Gerngross, Daniel; Panke, Sven
2016-03-31
Rational flux design in metabolic engineering approaches remains difficult since important pathway information is frequently not available. Therefore empirical methods are applied that randomly change absolute and relative pathway enzyme levels and subsequently screen for variants with improved performance. However, screening is often limited on the analytical side, generating a strong incentive to construct small but smart libraries. Here we introduce RedLibs (Reduced Libraries), an algorithm that allows for the rational design of smart combinatorial libraries for pathway optimization thereby minimizing the use of experimental resources. We demonstrate the utility of RedLibs for the design of ribosome-binding site libraries by in silico and in vivo screening with fluorescent proteins and perform a simple two-step optimization of the product selectivity in the branched multistep pathway for violacein biosynthesis, indicating a general applicability for the algorithm and the proposed heuristics. We expect that RedLibs will substantially simplify the refactoring of synthetic metabolic pathways.
Structure-based design of combinatorial mutagenesis libraries.
Verma, Deeptak; Grigoryan, Gevorg; Bailey-Kellogg, Chris
2015-05-01
The development of protein variants with improved properties (thermostability, binding affinity, catalytic activity, etc.) has greatly benefited from the application of high-throughput screens evaluating large, diverse combinatorial libraries. At the same time, since only a very limited portion of sequence space can be experimentally constructed and tested, an attractive possibility is to use computational protein design to focus libraries on a productive portion of the space. We present a general-purpose method, called "Structure-based Optimization of Combinatorial Mutagenesis" (SOCoM), which can optimize arbitrarily large combinatorial mutagenesis libraries directly based on structural energies of their constituents. SOCoM chooses both positions and substitutions, employing a combinatorial optimization framework based on library-averaged energy potentials in order to avoid explicitly modeling every variant in every possible library. In case study applications to green fluorescent protein, β-lactamase, and lipase A, SOCoM optimizes relatively small, focused libraries whose variants achieve energies comparable to or better than previous library design efforts, as well as larger libraries (previously not designable by structure-based methods) whose variants cover greater diversity while still maintaining substantially better energies than would be achieved by representative random library approaches. By allowing the creation of large-scale combinatorial libraries based on structural calculations, SOCoM promises to increase the scope of applicability of computational protein design and improve the hit rate of discovering beneficial variants. While designs presented here focus on variant stability (predicted by total energy), SOCoM can readily incorporate other structure-based assessments, such as the energy gap between alternative conformational or bound states. © 2015 The Protein Society.
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.
Chen, Xin; Wu, Qiong; Sun, Ruimin; Zhang, Louxin
2012-01-01
The discovery of single-nucleotide polymorphisms (SNPs) has important implications in a variety of genetic studies on human diseases and biological functions. One valuable approach proposed for SNP discovery is based on base-specific cleavage and mass spectrometry. However, it is still very challenging to achieve the full potential of this SNP discovery approach. In this study, we formulate two new combinatorial optimization problems. While both problems are aimed at reconstructing the sample sequence that would attain the minimum number of SNPs, they search over different candidate sequence spaces. The first problem, denoted as SNP - MSP, limits its search to sequences whose in silico predicted mass spectra have all their signals contained in the measured mass spectra. In contrast, the second problem, denoted as SNP - MSQ, limits its search to sequences whose in silico predicted mass spectra instead contain all the signals of the measured mass spectra. We present an exact dynamic programming algorithm for solving the SNP - MSP problem and also show that the SNP - MSQ problem is NP-hard by a reduction from a restricted variation of the 3-partition problem. We believe that an efficient solution to either problem above could offer a seamless integration of information in four complementary base-specific cleavage reactions, thereby improving the capability of the underlying biotechnology for sensitive and accurate SNP discovery.
Combinatorial optimization problem solution based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Peng
2017-08-01
Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.
Global Optimal Trajectory in Chaos and NP-Hardness
NASA Astrophysics Data System (ADS)
Latorre, Vittorio; Gao, David Yang
This paper presents an unconventional theory and method for solving general nonlinear dynamical systems. Instead of the direct iterative methods, the discretized nonlinear system is first formulated as a global optimization problem via the least squares method. A newly developed canonical duality theory shows that this nonconvex minimization problem can be solved deterministically in polynomial time if a global optimality condition is satisfied. The so-called pseudo-chaos produced by linear iterative methods are mainly due to the intrinsic numerical error accumulations. Otherwise, the global optimization problem could be NP-hard and the nonlinear system can be really chaotic. A conjecture is proposed, which reveals the connection between chaos in nonlinear dynamics and NP-hardness in computer science. The methodology and the conjecture are verified by applications to the well-known logistic equation, a forced memristive circuit and the Lorenz system. Computational results show that the canonical duality theory can be used to identify chaotic systems and to obtain realistic global optimal solutions in nonlinear dynamical systems. The method and results presented in this paper should bring some new insights into nonlinear dynamical systems and NP-hardness in computational complexity theory.
Landscape Encodings Enhance Optimization
Klemm, Konstantin; Mehta, Anita; Stadler, Peter F.
2012-01-01
Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states) of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space. Here we show how redundant non-invertible encodings enhance optimization by enriching the density of low-energy states. In addition, smooth landscapes may be established on encoded state spaces to guide local search dynamics towards the ground state. PMID:22496860
Combinatorial Methods for Exploring Complex Materials
NASA Astrophysics Data System (ADS)
Amis, Eric J.
2004-03-01
Combinatorial and high-throughput methods have changed the paradigm of pharmaceutical synthesis and have begun to have a similar impact on materials science research. Already there are examples of combinatorial methods used for inorganic materials, catalysts, and polymer synthesis. For many investigations the primary goal has been discovery of new material compositions that optimize properties such as phosphorescence or catalytic activity. In the midst of the excitement generated to "make things", another opportunity arises for materials science to "understand things" by using the efficiency of combinatorial methods. We have shown that combinatorial methods hold potential for rapid and systematic generation of experimental data over the multi-parameter space typical of investigations in polymer physics. We have applied the combinatorial approach to studies of polymer thin films, biomaterials, polymer blends, filled polymers, and semicrystalline polymers. By combining library fabrication, high-throughput measurements, informatics, and modeling we can demonstrate validation of the methodology, new observations, and developments toward predictive models. This talk will present some of our latest work with applications to coating stability, multi-component formulations, and nanostructure assembly.
OPTIMIZING THROUGH CO-EVOLUTIONARY AVALANCHES
DOE Office of Scientific and Technical Information (OSTI.GOV)
S. BOETTCHER; A. PERCUS
2000-08-01
We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by ''self-organized critically,'' a concept introduced to describe emergent complexity in many physical systems. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, extremal optimization successively replaces extremely undesirable elements of a sub-optimal solution with new, random ones. Large fluctuations, called ''avalanches,'' ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Those phase transitions are found in the parameter space of most optimization problems, and have recently been conjectured to be the origin of some of the hardest instances in computational complexity. We will demonstrate how extremal optimization can be implemented for a variety of combinatorial optimization problems. We believe that extremal optimization will be a useful tool in the investigation of phase transitions in combinatorial optimization problems, hence valuable in elucidating the origin of computational complexity.« less
Puthiyedth, Nisha; Riveros, Carlos; Berretta, Regina; Moscato, Pablo
2015-01-01
Background The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. Methods We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,β)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. Results Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease. PMID:26106884
Causal gene identification using combinatorial V-structure search.
Cai, Ruichu; Zhang, Zhenjie; Hao, Zhifeng
2013-07-01
With the advances of biomedical techniques in the last decade, the costs of human genomic sequencing and genomic activity monitoring are coming down rapidly. To support the huge genome-based business in the near future, researchers are eager to find killer applications based on human genome information. Causal gene identification is one of the most promising applications, which may help the potential patients to estimate the risk of certain genetic diseases and locate the target gene for further genetic therapy. Unfortunately, existing pattern recognition techniques, such as Bayesian networks, cannot be directly applied to find the accurate causal relationship between genes and diseases. This is mainly due to the insufficient number of samples and the extremely high dimensionality of the gene space. In this paper, we present the first practical solution to causal gene identification, utilizing a new combinatorial formulation over V-Structures commonly used in conventional Bayesian networks, by exploring the combinations of significant V-Structures. We prove the NP-hardness of the combinatorial search problem under a general settings on the significance measure on the V-Structures, and present a greedy algorithm to find sub-optimal results. Extensive experiments show that our proposal is both scalable and effective, particularly with interesting findings on the causal genes over real human genome data. Copyright © 2013 Elsevier Ltd. All rights reserved.
Fuel management optimization using genetic algorithms and code independence
DOE Office of Scientific and Technical Information (OSTI.GOV)
DeChaine, M.D.; Feltus, M.A.
1994-12-31
Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs) address these problems and, therefore, appear ideal for fuel management optimization. They do not require derivative information and work well with combinatorial. functions. The GAs are a stochastic method based on concepts from biological genetics. They take a group of candidate solutions, called the population, and use selection, crossover, and mutation operators to create the next generation of bettermore » solutions. The selection operator is a {open_quotes}survival-of-the-fittest{close_quotes} operation and chooses the solutions for the next generation. The crossover operator is analogous to biological mating, where children inherit a mixture of traits from their parents, and the mutation operator makes small random changes to the solutions.« less
Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; ...
2014-10-23
Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)
Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres
Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less
Podlewska, Sabina; Czarnecki, Wojciech M; Kafel, Rafał; Bojarski, Andrzej J
2017-02-27
The growing computational abilities of various tools that are applied in the broadly understood field of computer-aided drug design have led to the extreme popularity of virtual screening in the search for new biologically active compounds. Most often, the source of such molecules consists of commercially available compound databases, but they can also be searched for within the libraries of structures generated in silico from existing ligands. Various computational combinatorial approaches are based solely on the chemical structure of compounds, using different types of substitutions for new molecules formation. In this study, the starting point for combinatorial library generation was the fingerprint referring to the optimal substructural composition in terms of the activity toward a considered target, which was obtained using a machine learning-based optimization procedure. The systematic enumeration of all possible connections between preferred substructures resulted in the formation of target-focused libraries of new potential ligands. The compounds were initially assessed by machine learning methods using a hashed fingerprint to represent molecules; the distribution of their physicochemical properties was also investigated, as well as their synthetic accessibility. The examination of various fingerprints and machine learning algorithms indicated that the Klekota-Roth fingerprint and support vector machine were an optimal combination for such experiments. This study was performed for 8 protein targets, and the obtained compound sets and their characterization are publically available at http://skandal.if-pan.krakow.pl/comb_lib/ .
Kwok, T; Smith, K A
2000-09-01
The aim of this paper is to study both the theoretical and experimental properties of chaotic neural network (CNN) models for solving combinatorial optimization problems. Previously we have proposed a unifying framework which encompasses the three main model types, namely, Chen and Aihara's chaotic simulated annealing (CSA) with decaying self-coupling, Wang and Smith's CSA with decaying timestep, and the Hopfield network with chaotic noise. Each of these models can be represented as a special case under the framework for certain conditions. This paper combines the framework with experimental results to provide new insights into the effect of the chaotic neurodynamics of each model. By solving the N-queen problem of various sizes with computer simulations, the CNN models are compared in different parameter spaces, with optimization performance measured in terms of feasibility, efficiency, robustness and scalability. Furthermore, characteristic chaotic neurodynamics crucial to effective optimization are identified, together with a guide to choosing the corresponding model parameters.
Dynamical analysis of continuous higher-order hopfield networks for combinatorial optimization.
Atencia, Miguel; Joya, Gonzalo; Sandoval, Francisco
2005-08-01
In this letter, the ability of higher-order Hopfield networks to solve combinatorial optimization problems is assessed by means of a rigorous analysis of their properties. The stability of the continuous network is almost completely clarified: (1) hyperbolic interior equilibria, which are unfeasible, are unstable; (2) the state cannot escape from the unitary hypercube; and (3) a Lyapunov function exists. Numerical methods used to implement the continuous equation on a computer should be designed with the aim of preserving these favorable properties. The case of nonhyperbolic fixed points, which occur when the Hessian of the target function is the null matrix, requires further study. We prove that these nonhyperbolic interior fixed points are unstable in networks with three neurons and order two. The conjecture that interior equilibria are unstable in the general case is left open.
Hard decoding algorithm for optimizing thresholds under general Markovian noise
NASA Astrophysics Data System (ADS)
Chamberland, Christopher; Wallman, Joel; Beale, Stefanie; Laflamme, Raymond
2017-04-01
Quantum error correction is instrumental in protecting quantum systems from noise in quantum computing and communication settings. Pauli channels can be efficiently simulated and threshold values for Pauli error rates under a variety of error-correcting codes have been obtained. However, realistic quantum systems can undergo noise processes that differ significantly from Pauli noise. In this paper, we present an efficient hard decoding algorithm for optimizing thresholds and lowering failure rates of an error-correcting code under general completely positive and trace-preserving (i.e., Markovian) noise. We use our hard decoding algorithm to study the performance of several error-correcting codes under various non-Pauli noise models by computing threshold values and failure rates for these codes. We compare the performance of our hard decoding algorithm to decoders optimized for depolarizing noise and show improvements in thresholds and reductions in failure rates by several orders of magnitude. Our hard decoding algorithm can also be adapted to take advantage of a code's non-Pauli transversal gates to further suppress noise. For example, we show that using the transversal gates of the 5-qubit code allows arbitrary rotations around certain axes to be perfectly corrected. Furthermore, we show that Pauli twirling can increase or decrease the threshold depending upon the code properties. Lastly, we show that even if the physical noise model differs slightly from the hypothesized noise model used to determine an optimized decoder, failure rates can still be reduced by applying our hard decoding algorithm.
Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun
2018-07-01
This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.
Hard Constraints in Optimization Under Uncertainty
NASA Technical Reports Server (NTRS)
Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.
2008-01-01
This paper proposes a methodology for the analysis and design of systems subject to parametric uncertainty where design requirements are specified via hard inequality constraints. Hard constraints are those that must be satisfied for all parameter realizations within a given uncertainty model. Uncertainty models given by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles, are the focus of this paper. These models, which are also quite practical, allow for a rigorous mathematical treatment within the proposed framework. Hard constraint feasibility is determined by sizing the largest uncertainty set for which the design requirements are satisfied. Analytically verifiable assessments of robustness are attained by comparing this set with the actual uncertainty model. Strategies that enable the comparison of the robustness characteristics of competing design alternatives, the description and approximation of the robust design space, and the systematic search for designs with improved robustness are also proposed. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, this methodology is applicable to a broad range of engineering problems.
Charleston, M A
1995-01-01
This article introduces a coherent language base for describing and working with characteristics of combinatorial optimization problems, which is at once general enough to be used in all such problems and precise enough to allow subtle concepts in this field to be discussed unambiguously. An example is provided of how this nomenclature is applied to an instance of the phylogeny problem. Also noted is the beneficial effect, on the landscape of the solution space, of transforming the observed data to account for multiple changes of character state.
Combinatorial Algorithms for Portfolio Optimization Problems - Case of Risk Moderate Investor
NASA Astrophysics Data System (ADS)
Juarna, A.
2017-03-01
Portfolio optimization problem is a problem of finding optimal combination of n stocks from N ≥ n available stocks that gives maximal aggregate return and minimal aggregate risk. In this paper given N = 43 from the IDX (Indonesia Stock Exchange) group of the 45 most-traded stocks, known as the LQ45, with p = 24 data of monthly returns for each stock, spanned over interval 2013-2014. This problem actually is a combinatorial one where its algorithm is constructed based on two considerations: risk moderate type of investor and maximum allowed correlation coefficient between every two eligible stocks. The main outputs resulted from implementation of the algorithms is a multiple curve of three portfolio’s attributes, e.g. the size, the ratio of return to risk, and the percentage of negative correlation coefficient for every two chosen stocks, as function of maximum allowed correlation coefficient between each two stocks. The output curve shows that the portfolio contains three stocks with ratio of return to risk at 14.57 if the maximum allowed correlation coefficient between every two eligible stocks is negative and contains 19 stocks with maximum allowed correlation coefficient 0.17 to get maximum ratio of return to risk at 25.48.
Lexicographic goal programming and assessment tools for a combinatorial production problem.
DOT National Transportation Integrated Search
2008-01-01
NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including : heuristics and metaheuristics. The addition of multiple optimization criteria can further complicate : comparison of these solution technique...
Optimized Reaction Conditions for Amide Bond Formation in DNA-Encoded Combinatorial Libraries.
Li, Yizhou; Gabriele, Elena; Samain, Florent; Favalli, Nicholas; Sladojevich, Filippo; Scheuermann, Jörg; Neri, Dario
2016-08-08
DNA-encoded combinatorial libraries are increasingly being used as tools for the discovery of small organic binding molecules to proteins of biological or pharmaceutical interest. In the majority of cases, synthetic procedures for the formation of DNA-encoded combinatorial libraries incorporate at least one step of amide bond formation between amino-modified DNA and a carboxylic acid. We investigated reaction conditions and established a methodology by using 1-ethyl-3-(3-(dimethylamino)propyl)carbodiimide, 1-hydroxy-7-azabenzotriazole and N,N'-diisopropylethylamine (EDC/HOAt/DIPEA) in combination, which provided conversions greater than 75% for 423/543 (78%) of the carboxylic acids tested. These reaction conditions were efficient with a variety of primary and secondary amines, as well as with various types of amino-modified oligonucleotides. The reaction conditions, which also worked efficiently over a broad range of DNA concentrations and reaction scales, should facilitate the synthesis of novel DNA-encoded combinatorial libraries.
Zhou, Yikang; Li, Gang; Dong, Junkai; Xing, Xin-Hui; Dai, Junbiao; Zhang, Chong
2018-05-01
Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric plate-based, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
Effect of the Implicit Combinatorial Model on Combinatorial Reasoning in Secondary School Pupils.
ERIC Educational Resources Information Center
Batanero, Carmen; And Others
1997-01-01
Elementary combinatorial problems may be classified into three different combinatorial models: (1) selection; (2) partition; and (3) distribution. The main goal of this research was to determine the effect of the implicit combinatorial model on pupils' combinatorial reasoning before and after instruction. Gives an analysis of variance of the…
Optimization of Error-Bounded Lossy Compression for Hard-to-Compress HPC Data
DOE Office of Scientific and Technical Information (OSTI.GOV)
Di, Sheng; Cappello, Franck
Since today’s scientific applications are producing vast amounts of data, compressing them before storage/transmission is critical. Results of existing compressors show two types of HPC data sets: highly compressible and hard to compress. In this work, we carefully design and optimize the error-bounded lossy compression for hard-tocompress scientific data. We propose an optimized algorithm that can adaptively partition the HPC data into best-fit consecutive segments each having mutually close data values, such that the compression condition can be optimized. Another significant contribution is the optimization of shifting offset such that the XOR-leading-zero length between two consecutive unpredictable data points canmore » be maximized. We finally devise an adaptive method to select the best-fit compressor at runtime for maximizing the compression factor. We evaluate our solution using 13 benchmarks based on real-world scientific problems, and we compare it with 9 other state-of-the-art compressors. Experiments show that our compressor can always guarantee the compression errors within the user-specified error bounds. Most importantly, our optimization can improve the compression factor effectively, by up to 49% for hard-tocompress data sets with similar compression/decompression time cost.« less
Exploiting Quantum Resonance to Solve Combinatorial Problems
NASA Technical Reports Server (NTRS)
Zak, Michail; Fijany, Amir
2006-01-01
Quantum resonance would be exploited in a proposed quantum-computing approach to the solution of combinatorial optimization problems. In quantum computing in general, one takes advantage of the fact that an algorithm cannot be decoupled from the physical effects available to implement it. Prior approaches to quantum computing have involved exploitation of only a subset of known quantum physical effects, notably including parallelism and entanglement, but not including resonance. In the proposed approach, one would utilize the combinatorial properties of tensor-product decomposability of unitary evolution of many-particle quantum systems for physically simulating solutions to NP-complete problems (a class of problems that are intractable with respect to classical methods of computation). In this approach, reinforcement and selection of a desired solution would be executed by means of quantum resonance. Classes of NP-complete problems that are important in practice and could be solved by the proposed approach include planning, scheduling, search, and optimal design.
Combinatorial chemical bath deposition of CdS contacts for chalcogenide photovoltaics
Mokurala, Krishnaiah; Baranowski, Lauryn L.; de Souza Lucas, Francisco W.; ...
2016-08-01
Contact layers play an important role in thin film solar cells, but new material development and optimization of its thickness is usually a long and tedious process. A high-throughput experimental approach has been used to accelerate the rate of research in photovoltaic (PV) light absorbers and transparent conductive electrodes, however the combinatorial research on contact layers is less common. Here, we report on the chemical bath deposition (CBD) of CdS thin films by combinatorial dip coating technique and apply these contact layers to Cu(In,Ga)Se 2 (CIGSe) and Cu 2ZnSnSe 4 (CZTSe) light absorbers in PV devices. Combinatorial thickness steps ofmore » CdS thin films were achieved by removal of the substrate from the chemical bath, at regular intervals of time, and in equal distance increments. The trends in the photoconversion efficiency and in the spectral response of the PV devices as a function of thickness of CdS contacts were explained with the help of optical and morphological characterization of the CdS thin films. The maximum PV efficiency achieved for the combinatorial dip-coating CBD was similar to that for the PV devices processed using conventional CBD. Finally, the results of this study lead to the conclusion that combinatorial dip-coating can be used to accelerate the optimization of PV device performance of CdS and other candidate contact layers for a wide range of emerging absorbers.« less
Gobin, Oliver C; Schüth, Ferdi
2008-01-01
Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.
Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.
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
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
Combinatorial Optimization of Heterogeneous Catalysts Used in the Growth of Carbon Nanotubes
NASA Technical Reports Server (NTRS)
Cassell, Alan M.; Verma, Sunita; Delzeit, Lance; Meyyappan, M.; Han, Jie
2000-01-01
Libraries of liquid-phase catalyst precursor solutions were printed onto iridium-coated silicon substrates and evaluated for their effectiveness in catalyzing the growth of multi-walled carbon nanotubes (MWNTs) by chemical vapor deposition (CVD). The catalyst precursor solutions were composed of inorganic salts and a removable tri-block copolymer (EO)20(PO)70(EO)20 (EO = ethylene oxide, PO = propylene oxide) structure-directing agent (SDA), dissolved in ethanol/methanol mixtures. Sample libraries were quickly assayed using scanning electron microscopy after CVD growth to identify active catalysts and CVD conditions. Composition libraries and focus libraries were then constructed around the active spots identified in the discovery libraries to understand how catalyst precursor composition affects the yield, density, and quality of the nanotubes. Successful implementation of combinatorial optimization methods in the development of highly active, carbon nanotube catalysts is demonstrated, as well as the identification of catalyst formulations that lead to varying densities and shapes of aligned nanotube towers.
Optimization of lattice surgery is NP-hard
NASA Astrophysics Data System (ADS)
Herr, Daniel; Nori, Franco; Devitt, Simon J.
2017-09-01
The traditional method for computation in either the surface code or in the Raussendorf model is the creation of holes or "defects" within the encoded lattice of qubits that are manipulated via topological braiding to enact logic gates. However, this is not the only way to achieve universal, fault-tolerant computation. In this work, we focus on the lattice surgery representation, which realizes transversal logic operations without destroying the intrinsic 2D nearest-neighbor properties of the braid-based surface code and achieves universality without defects and braid-based logic. For both techniques there are open questions regarding the compilation and resource optimization of quantum circuits. Optimization in braid-based logic is proving to be difficult and the classical complexity associated with this problem has yet to be determined. In the context of lattice-surgery-based logic, we can introduce an optimality condition, which corresponds to a circuit with the lowest resource requirements in terms of physical qubits and computational time, and prove that the complexity of optimizing a quantum circuit in the lattice surgery model is NP-hard.
Exact model reduction of combinatorial reaction networks
Conzelmann, Holger; Fey, Dirk; Gilles, Ernst D
2008-01-01
Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs) to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks. PMID:18755034
Identification of combinatorial drug regimens for treatment of Huntington's disease using Drosophila
NASA Astrophysics Data System (ADS)
Agrawal, Namita; Pallos, Judit; Slepko, Natalia; Apostol, Barbara L.; Bodai, Laszlo; Chang, Ling-Wen; Chiang, Ann-Shyn; Michels Thompson, Leslie; Marsh, J. Lawrence
2005-03-01
We explore the hypothesis that pathology of Huntington's disease involves multiple cellular mechanisms whose contributions to disease are incrementally additive or synergistic. We provide evidence that the photoreceptor neuron degeneration seen in flies expressing mutant human huntingtin correlates with widespread degenerative events in the Drosophila CNS. We use a Drosophila Huntington's disease model to establish dose regimens and protocols to assess the effectiveness of drug combinations used at low threshold concentrations. These proof of principle studies identify at least two potential combinatorial treatment options and illustrate a rapid and cost-effective paradigm for testing and optimizing combinatorial drug therapies while reducing side effects for patients with neurodegenerative disease. The potential for using prescreening in Drosophila to inform combinatorial therapies that are most likely to be effective for testing in mammals is discussed. combinatorial treatments | neurodegeneration
The disadvantage of combinatorial communication.
Lachmann, Michael; Bergstrom, Carl T
2004-11-22
Combinatorial communication allows rapid and efficient transfer of detailed information, yet combinatorial communication is used by few, if any, non-human species. To complement recent studies illustrating the advantages of combinatorial communication, we highlight a critical disadvantage. We use the concept of information value to show that deception poses a greater and qualitatively different threat to combinatorial signalling than to non-combinatorial systems. This additional potential for deception may represent a strategic barrier that has prevented widespread evolution of combinatorial communication. Our approach has the additional benefit of drawing clear distinctions among several types of deception that can occur in communication systems.
The disadvantage of combinatorial communication.
Lachmann, Michael; Bergstrom, Carl T.
2004-01-01
Combinatorial communication allows rapid and efficient transfer of detailed information, yet combinatorial communication is used by few, if any, non-human species. To complement recent studies illustrating the advantages of combinatorial communication, we highlight a critical disadvantage. We use the concept of information value to show that deception poses a greater and qualitatively different threat to combinatorial signalling than to non-combinatorial systems. This additional potential for deception may represent a strategic barrier that has prevented widespread evolution of combinatorial communication. Our approach has the additional benefit of drawing clear distinctions among several types of deception that can occur in communication systems. PMID:15556886
FOREWORD: Focus on Combinatorial Materials Science Focus on Combinatorial Materials Science
NASA Astrophysics Data System (ADS)
Chikyo, Toyohiro
2011-10-01
About 15 years have passed since the introduction of modern combinatorial synthesis and high-throughput techniques for the development of novel inorganic materials; however, similar methods existed before. The most famous was reported in 1970 by Hanak who prepared composition-spread films of metal alloys by sputtering mixed-material targets. Although this method was innovative, it was rarely used because of the large amount of data to be processed. This problem is solved in the modern combinatorial material research, which is strongly related to computer data analysis and robotics. This field is still at the developing stage and may be enriched by new methods. Nevertheless, given the progress in measurement equipment and procedures, we believe the combinatorial approach will become a major and standard tool of materials screening and development. The first article of this journal, published in 2000, was titled 'Combinatorial solid state materials science and technology', and this focus issue aims to reintroduce this topic to the Science and Technology of Advanced Materials audience. It covers recent progress in combinatorial materials research describing new results in catalysis, phosphors, polymers and metal alloys for shape memory materials. Sophisticated high-throughput characterization schemes and innovative synthesis tools are also presented, such as spray deposition using nanoparticles or ion plating. On a technical note, data handling systems are introduced to familiarize researchers with the combinatorial methodology. We hope that through this focus issue a wide audience of materials scientists can learn about recent and future trends in combinatorial materials science and high-throughput experimentation.
Antolini, Ermete
2017-02-13
Combinatorial chemistry and high-throughput screening represent an innovative and rapid tool to prepare and evaluate a large number of new materials, saving time and expense for research and development. Considering that the activity and selectivity of catalysts depend on complex kinetic phenomena, making their development largely empirical in practice, they are prime candidates for combinatorial discovery and optimization. This review presents an overview of recent results of combinatorial screening of low-temperature fuel cell electrocatalysts for methanol oxidation. Optimum catalyst compositions obtained by combinatorial screening were compared with those of bulk catalysts, and the effect of the library geometry on the screening of catalyst composition is highlighted.
Combinatorial Nano-Bio Interfaces.
Cai, Pingqiang; Zhang, Xiaoqian; Wang, Ming; Wu, Yun-Long; Chen, Xiaodong
2018-06-08
Nano-bio interfaces are emerging from the convergence of engineered nanomaterials and biological entities. Despite rapid growth, clinical translation of biomedical nanomaterials is heavily compromised by the lack of comprehensive understanding of biophysicochemical interactions at nano-bio interfaces. In the past decade, a few investigations have adopted a combinatorial approach toward decoding nano-bio interfaces. Combinatorial nano-bio interfaces comprise the design of nanocombinatorial libraries and high-throughput bioevaluation. In this Perspective, we address challenges in combinatorial nano-bio interfaces and call for multiparametric nanocombinatorics (composition, morphology, mechanics, surface chemistry), multiscale bioevaluation (biomolecules, organelles, cells, tissues/organs), and the recruitment of computational modeling and artificial intelligence. Leveraging combinatorial nano-bio interfaces will shed light on precision nanomedicine and its potential applications.
Quadratic constrained mixed discrete optimization with an adiabatic quantum optimizer
NASA Astrophysics Data System (ADS)
Chandra, Rishabh; Jacobson, N. Tobias; Moussa, Jonathan E.; Frankel, Steven H.; Kais, Sabre
2014-07-01
We extend the family of problems that may be implemented on an adiabatic quantum optimizer (AQO). When a quadratic optimization problem has at least one set of discrete controls and the constraints are linear, we call this a quadratic constrained mixed discrete optimization (QCMDO) problem. QCMDO problems are NP-hard, and no efficient classical algorithm for their solution is known. Included in the class of QCMDO problems are combinatorial optimization problems constrained by a linear partial differential equation (PDE) or system of linear PDEs. An essential complication commonly encountered in solving this type of problem is that the linear constraint may introduce many intermediate continuous variables into the optimization while the computational cost grows exponentially with problem size. We resolve this difficulty by developing a constructive mapping from QCMDO to quadratic unconstrained binary optimization (QUBO) such that the size of the QUBO problem depends only on the number of discrete control variables. With a suitable embedding, taking into account the physical constraints of the realizable coupling graph, the resulting QUBO problem can be implemented on an existing AQO. The mapping itself is efficient, scaling cubically with the number of continuous variables in the general case and linearly in the PDE case if an efficient preconditioner is available.
ERIC Educational Resources Information Center
Brusco, Michael J.; Kohn, Hans-Friedrich; Stahl, Stephanie
2008-01-01
Dynamic programming methods for matrix permutation problems in combinatorial data analysis can produce globally-optimal solutions for matrices up to size 30x30, but are computationally infeasible for larger matrices because of enormous computer memory requirements. Branch-and-bound methods also guarantee globally-optimal solutions, but computation…
Two is better than one; toward a rational design of combinatorial therapy.
Chen, Sheng-Hong; Lahav, Galit
2016-12-01
Drug combination is an appealing strategy for combating the heterogeneity of tumors and evolution of drug resistance. However, the rationale underlying combinatorial therapy is often not well established due to lack of understandings of the specific pathways responding to the drugs, and their temporal dynamics following each treatment. Here we present several emerging trends in harnessing properties of biological systems for the optimal design of drug combinations, including the type of drugs, specific concentration, sequence of addition and the temporal schedule of treatments. We highlight recent studies showing different approaches for efficient design of drug combinations including single-cell signaling dynamics, adaption and pathway crosstalk. Finally, we discuss novel and feasible approaches that can facilitate the optimal design of combinatorial therapy. Copyright © 2016 Elsevier Ltd. All rights reserved.
Saldaña, Erick; Siche, Raúl; da Silva Pinto, Jair Sebastião; de Almeida, Marcio Aurélio; Selani, Miriam Mabel; Rios-Mera, Juan; Contreras-Castillo, Carmen J
2018-02-01
This study aims to optimize simultaneously the lipid profile and instrumental hardness of low-fat mortadella. For lipid mixture optimization, the overlapping of surface boundaries was used to select the quantities of canola, olive, and fish oils, in order to maximize PUFAs, specifically the long-chain n-3 fatty acids (eicosapentaenoic-EPA, docosahexaenoic acids-DHA) using the minimum content of fish oil. Increased quantities of canola oil were associated with higher PUFA/SFA ratios. The presence of fish oil, even in small amounts, was effective in improving the nutritional quality of the mixture, showing lower n-6/n-3 ratios and significant levels of EPA and DHA. Thus, the optimal lipid mixture comprised of 20, 30 and 50% fish, olive and canola oils, respectively, which present PUFA/SFA (2.28) and n-6/n-3 (2.30) ratios within the recommendations of a healthy diet. Once the lipid mixture was optimized, components of the pre-emulsion used as fat replacer in the mortadella, such as lipid mixture (LM), sodium alginate (SA), and milk protein concentrate (PC), were studied to optimize hardness and springiness to target ranges of 13-16 N and 0.86-0.87, respectively. Results showed that springiness was not significantly affected by these variables. However, as the concentration of the three components increased, hardness decreased. Through the desirability function, the optimal proportions were 30% LM, 0.5% SA, and 0.5% PC. This study showed that the pre-emulsion decreases hardness of mortadella. In addition, response surface methodology was efficient to model lipid mixture and hardness, resulting in a product with improved texture and lipid quality.
Combinatorial therapy discovery using mixed integer linear programming.
Pang, Kaifang; Wan, Ying-Wooi; Choi, William T; Donehower, Lawrence A; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong
2014-05-15
Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. zhandong.liu@bcm.edu Supplementary data are available at Bioinformatics online.
Nonparametric Combinatorial Sequence Models
NASA Astrophysics Data System (ADS)
Wauthier, Fabian L.; Jordan, Michael I.; Jojic, Nebojsa
This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This paper presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three sequence datasets which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution induced by the prior. By integrating out the posterior our method compares favorably to leading binding predictors.
NASA Astrophysics Data System (ADS)
Rahman, P. A.
2018-05-01
This scientific paper deals with the model of the knapsack optimization problem and method of its solving based on directed combinatorial search in the boolean space. The offered by the author specialized mathematical model of decomposition of the search-zone to the separate search-spheres and the algorithm of distribution of the search-spheres to the different cores of the multi-core processor are also discussed. The paper also provides an example of decomposition of the search-zone to the several search-spheres and distribution of the search-spheres to the different cores of the quad-core processor. Finally, an offered by the author formula for estimation of the theoretical maximum of the computational acceleration, which can be achieved due to the parallelization of the search-zone to the search-spheres on the unlimited number of the processor cores, is also given.
Latimer, Luke N; Dueber, John E
2017-06-01
A common challenge in metabolic engineering is rapidly identifying rate-controlling enzymes in heterologous pathways for subsequent production improvement. We demonstrate a workflow to address this challenge and apply it to improving xylose utilization in Saccharomyces cerevisiae. For eight reactions required for conversion of xylose to ethanol, we screened enzymes for functional expression in S. cerevisiae, followed by a combinatorial expression analysis to achieve pathway flux balancing and identification of limiting enzymatic activities. In the next round of strain engineering, we increased the copy number of these limiting enzymes and again tested the eight-enzyme combinatorial expression library in this new background. This workflow yielded a strain that has a ∼70% increase in biomass yield and ∼240% increase in xylose utilization. Finally, we chromosomally integrated the expression library. This library enriched for strains with multiple integrations of the pathway, which likely were the result of tandem integrations mediated by promoter homology. Biotechnol. Bioeng. 2017;114: 1301-1309. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Combinatorial optimization in foundry practice
NASA Astrophysics Data System (ADS)
Antamoshkin, A. N.; Masich, I. S.
2016-04-01
The multicriteria mathematical model of foundry production capacity planning is suggested in the paper. The model is produced in terms of pseudo-Boolean optimization theory. Different search optimization methods were used to solve the obtained problem.
Optimization of Smart Structure for Improving Servo Performance of Hard Disk Drive
NASA Astrophysics Data System (ADS)
Kajiwara, Itsuro; Takahashi, Masafumi; Arisaka, Toshihiro
Head positioning accuracy of the hard disk drive should be improved to meet today's increasing performance demands. Vibration suppression of the arm in the hard disk drive is very important to enhance the servo bandwidth of the head positioning system. In this study, smart structure technology is introduced into the hard disk drive to suppress the vibration of the head actuator. It has been expected that the smart structure technology will contribute to the development of small and light-weight mechatronics devices with the required performance. First, modeling of the system is conducted with finite element method and modal analysis. Next, the actuator location and the control system are simultaneously optimized using genetic algorithm. Vibration control effect with the proposed vibration control mechanisms has been evaluated by some simulations.
NASA Astrophysics Data System (ADS)
Doerr, Timothy P.; Alves, Gelio; Yu, Yi-Kuo
2005-08-01
Typical combinatorial optimizations are NP-hard; however, for a particular class of cost functions the corresponding combinatorial optimizations can be solved in polynomial time using the transfer matrix technique or, equivalently, the dynamic programming approach. This suggests a way to efficiently find approximate solutions-find a transformation that makes the cost function as similar as possible to that of the solvable class. After keeping many high-ranking solutions using the approximate cost function, one may then re-assess these solutions with the full cost function to find the best approximate solution. Under this approach, it is important to be able to assess the quality of the solutions obtained, e.g., by finding the true ranking of the kth best approximate solution when all possible solutions are considered exhaustively. To tackle this statistical issue, we provide a systematic method starting with a scaling function generated from the finite number of high-ranking solutions followed by a convergent iterative mapping. This method, useful in a variant of the directed paths in random media problem proposed here, can also provide a statistical significance assessment for one of the most important proteomic tasks-peptide sequencing using tandem mass spectrometry data. For directed paths in random media, the scaling function depends on the particular realization of randomness; in the mass spectrometry case, the scaling function is spectrum-specific.
Xiang, X D
Combinatorial materials synthesis methods and high-throughput evaluation techniques have been developed to accelerate the process of materials discovery and optimization and phase-diagram mapping. Analogous to integrated circuit chips, integrated materials chips containing thousands of discrete different compositions or continuous phase diagrams, often in the form of high-quality epitaxial thin films, can be fabricated and screened for interesting properties. Microspot x-ray method, various optical measurement techniques, and a novel evanescent microwave microscope have been used to characterize the structural, optical, magnetic, and electrical properties of samples on the materials chips. These techniques are routinely used to discover/optimize and map phase diagrams of ferroelectric, dielectric, optical, magnetic, and superconducting materials.
Zalesak, J; Todt, J; Pitonak, R; Köpf, A; Weißenbacher, R; Sartory, B; Burghammer, M; Daniel, R; Keckes, J
2016-12-01
Because of the tremendous variability of crystallite sizes and shapes in nano-materials, it is challenging to assess the corresponding size-property relationships and to identify microstructures with particular physical properties or even optimized functions. This task is especially difficult for nanomaterials formed by self-organization, where the spontaneous evolution of microstructure and properties is coupled. In this work, two compositionally graded TiAlN films were (i) grown using chemical vapour deposition by applying a varying ratio of reacting gases and (ii) subsequently analysed using cross-sectional synchrotron X-ray nanodiffraction, electron microscopy and nanoindentation in order to evaluate the microstructure and hardness depth gradients. The results indicate the formation of self-organized hexagonal-cubic and cubic-cubic nanolamellae with varying compositions and thicknesses in the range of ∼3-15 nm across the film thicknesses, depending on the actual composition of the reactive gas mixtures. On the basis of the occurrence of the nanolamellae and their correlation with the local film hardness, progressively narrower ranges of the composition and hardness were refined in three steps. The third film was produced using an AlCl 3 /TiCl 4 precursor ratio of ∼1.9, resulting in the formation of an optimized lamellar microstructure with ∼1.3 nm thick cubic Ti(Al)N and ∼12 nm thick cubic Al(Ti)N nanolamellae which exhibits a maximal hardness of ∼36 GPa and an indentation modulus of ∼522 GPa. The presented approach of an iterative nanoscale search based on the application of cross-sectional synchrotron X-ray nanodiffraction and cross-sectional nanoindentation allows one to refine the relationship between (i) varying deposition conditions, (ii) gradients of microstructure and (iii) gradients of mechanical properties in nanostructured materials prepared as thin films. This is done in a combinatorial way in order to screen a wide range of
Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem
NASA Astrophysics Data System (ADS)
Skakov, E. S.; Malysh, V. N.
2018-03-01
The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.
Jiménez-Moreno, Ester; Montalvillo-Jiménez, Laura; Santana, Andrés G; Gómez, Ana M; Jiménez-Osés, Gonzalo; Corzana, Francisco; Bastida, Agatha; Jiménez-Barbero, Jesús; Cañada, Francisco Javier; Gómez-Pinto, Irene; González, Carlos; Asensio, Juan Luis
2016-05-25
Development of strong and selective binders from promiscuous lead compounds represents one of the most expensive and time-consuming tasks in drug discovery. We herein present a novel fragment-based combinatorial strategy for the optimization of multivalent polyamine scaffolds as DNA/RNA ligands. Our protocol provides a quick access to a large variety of regioisomer libraries that can be tested for selective recognition by combining microdialysis assays with simple isotope labeling and NMR experiments. To illustrate our approach, 20 small libraries comprising 100 novel kanamycin-B derivatives have been prepared and evaluated for selective binding to the ribosomal decoding A-Site sequence. Contrary to the common view of NMR as a low-throughput technique, we demonstrate that our NMR methodology represents a valuable alternative for the detection and quantification of complex mixtures, even integrated by highly similar or structurally related derivatives, a common situation in the context of a lead optimization process. Furthermore, this study provides valuable clues about the structural requirements for selective A-site recognition.
Combinatorial investigation of Fe–B thin-film nanocomposites
Brunken, Hayo; Grochla, Dario; Savan, Alan; Kieschnick, Michael; Meijer, Jan D; Ludwig, Alfred
2011-01-01
Combinatorial magnetron sputter deposition from elemental targets was used to create Fe–B composition spread type thin film materials libraries on thermally oxidized 4-in. Si wafers. The materials libraries consisting of wedge-type multilayer thin films were annealed at 500 or 700 °C to transform the multilayers into multiphase alloys. The libraries were characterized by nuclear reaction analysis, Rutherford backscattering, nanoindentation, vibrating sample magnetometry, x-ray diffraction (XRD) and transmission electron microscopy (TEM). Young's modulus and hardness values were related to the annealing parameters, structure and composition of the films. The magnetic properties of the films were improved by annealing in a H2 atmosphere, showing a more than tenfold decrease in the coercive field values in comparison to those of the vacuum-annealed films. The hardness values increased from 8 to 18 GPa when the annealing temperature was increased from 500 to 700 °C. The appearance of Fe2B phases, as revealed by XRD and TEM, had a significant effect on the mechanical properties of the films. PMID:27877435
ERIC Educational Resources Information Center
Kittredge, Kevin W.; Marine, Susan S.; Taylor, Richard T.
2004-01-01
A molecule possessing other functional groups that could be hydrogenerated is examined, where a variety of metal catalysts are evaluated under similar reaction conditions. Optimizing organic reactions is both time and labor intensive, and the use of a combinatorial parallel synthesis reactor was great time saving device, as per summary.
NASA Astrophysics Data System (ADS)
Green, Martin L.; Takeuchi, Ichiro; Hattrick-Simpers, Jason R.
2013-06-01
High throughput (combinatorial) materials science methodology is a relatively new research paradigm that offers the promise of rapid and efficient materials screening, optimization, and discovery. The paradigm started in the pharmaceutical industry but was rapidly adopted to accelerate materials research in a wide variety of areas. High throughput experiments are characterized by synthesis of a "library" sample that contains the materials variation of interest (typically composition), and rapid and localized measurement schemes that result in massive data sets. Because the data are collected at the same time on the same "library" sample, they can be highly uniform with respect to fixed processing parameters. This article critically reviews the literature pertaining to applications of combinatorial materials science for electronic, magnetic, optical, and energy-related materials. It is expected that high throughput methodologies will facilitate commercialization of novel materials for these critically important applications. Despite the overwhelming evidence presented in this paper that high throughput studies can effectively inform commercial practice, in our perception, it remains an underutilized research and development tool. Part of this perception may be due to the inaccessibility of proprietary industrial research and development practices, but clearly the initial cost and availability of high throughput laboratory equipment plays a role. Combinatorial materials science has traditionally been focused on materials discovery, screening, and optimization to combat the extremely high cost and long development times for new materials and their introduction into commerce. Going forward, combinatorial materials science will also be driven by other needs such as materials substitution and experimental verification of materials properties predicted by modeling and simulation, which have recently received much attention with the advent of the Materials Genome
García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César
2006-05-01
In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.
Optimization of topological quantum algorithms using Lattice Surgery is hard
NASA Astrophysics Data System (ADS)
Herr, Daniel; Nori, Franco; Devitt, Simon
The traditional method for computation in the surface code or the Raussendorf model is the creation of holes or ''defects'' within the encoded lattice of qubits which are manipulated via topological braiding to enact logic gates. However, this is not the only way to achieve universal, fault-tolerant computation. In this work we turn attention to the Lattice Surgery representation, which realizes encoded logic operations without destroying the intrinsic 2D nearest-neighbor interactions sufficient for braided based logic and achieves universality without using defects for encoding information. In both braided and lattice surgery logic there are open questions regarding the compilation and resource optimization of quantum circuits. Optimization in braid-based logic is proving to be difficult to define and the classical complexity associated with this problem has yet to be determined. In the context of lattice surgery based logic, we can introduce an optimality condition, which corresponds to a circuit with lowest amount of physical qubit requirements, and prove that the complexity of optimizing the geometric (lattice surgery) representation of a quantum circuit is NP-hard.
Solving the Container Stowage Problem (CSP) using Particle Swarm Optimization (PSO)
NASA Astrophysics Data System (ADS)
Matsaini; Santosa, Budi
2018-04-01
Container Stowage Problem (CSP) is a problem of containers arrangement into ships by considering rules such as: total weight, weight of one stack, destination, equilibrium, and placement of containers on vessel. Container stowage problem is combinatorial problem and hard to solve with enumeration technique. It is an NP-Hard Problem. Therefore, to find a solution, metaheuristics is preferred. The objective of solving the problem is to minimize the amount of shifting such that the unloading time is minimized. Particle Swarm Optimization (PSO) is proposed to solve the problem. The implementation of PSO is combined with some steps which are stack position change rules, stack changes based on destination, and stack changes based on the weight type of the stacks (light, medium, and heavy). The proposed method was applied on five different cases. The results were compared to Bee Swarm Optimization (BSO) and heuristics method. PSO provided mean of 0.87% gap and time gap of 60 second. While BSO provided mean of 2,98% gap and 459,6 second to the heuristcs.
cDREM: inferring dynamic combinatorial gene regulation.
Wise, Aaron; Bar-Joseph, Ziv
2015-04-01
Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.
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.
Cryptographic Combinatorial Securities Exchanges
NASA Astrophysics Data System (ADS)
Thorpe, Christopher; Parkes, David C.
We present a useful new mechanism that facilitates the atomic exchange of many large baskets of securities in a combinatorial exchange. Cryptography prevents information about the securities in the baskets from being exploited, enhancing trust. Our exchange offers institutions who wish to trade large positions a new alternative to existing methods of block trading: they can reduce transaction costs by taking advantage of other institutions’ available liquidity, while third party liquidity providers guarantee execution—preserving their desired portfolio composition at all times. In our exchange, institutions submit encrypted orders which are crossed, leaving a “remainder”. The exchange proves facts about the portfolio risk of this remainder to third party liquidity providers without revealing the securities in the remainder, the knowledge of which could also be exploited. The third parties learn either (depending on the setting) the portfolio risk parameters of the remainder itself, or how their own portfolio risk would change if they were to incorporate the remainder into a portfolio they submit. In one setting, these third parties submit bids on the commission, and the winner supplies necessary liquidity for the entire exchange to clear. This guaranteed clearing, coupled with external price discovery from the primary markets for the securities, sidesteps difficult combinatorial optimization problems. This latter method of proving how taking on the remainder would change risk parameters of one’s own portfolio, without revealing the remainder’s contents or its own risk parameters, is a useful protocol of independent interest.
Mondal, Milon; Radeva, Nedyalka; Fanlo‐Virgós, Hugo; Otto, Sijbren; Klebe, Gerhard
2016-01-01
Abstract Fragment‐based drug design (FBDD) affords active compounds for biological targets. While there are numerous reports on FBDD by fragment growing/optimization, fragment linking has rarely been reported. Dynamic combinatorial chemistry (DCC) has become a powerful hit‐identification strategy for biological targets. We report the synergistic combination of fragment linking and DCC to identify inhibitors of the aspartic protease endothiapepsin. Based on X‐ray crystal structures of endothiapepsin in complex with fragments, we designed a library of bis‐acylhydrazones and used DCC to identify potent inhibitors. The most potent inhibitor exhibits an IC50 value of 54 nm, which represents a 240‐fold improvement in potency compared to the parent hits. Subsequent X‐ray crystallography validated the predicted binding mode, thus demonstrating the efficiency of the combination of fragment linking and DCC as a hit‐identification strategy. This approach could be applied to a range of biological targets, and holds the potential to facilitate hit‐to‐lead optimization. PMID:27400756
Hard and Soft Constraints in Reliability-Based Design Optimization
NASA Technical Reports Server (NTRS)
Crespo, L.uis G.; Giesy, Daniel P.; Kenny, Sean P.
2006-01-01
This paper proposes a framework for the analysis and design optimization of models subject to parametric uncertainty where design requirements in the form of inequality constraints are present. Emphasis is given to uncertainty models prescribed by norm bounded perturbations from a nominal parameter value and by sets of componentwise bounded uncertain variables. These models, which often arise in engineering problems, allow for a sharp mathematical manipulation. Constraints can be implemented in the hard sense, i.e., constraints must be satisfied for all parameter realizations in the uncertainty model, and in the soft sense, i.e., constraints can be violated by some realizations of the uncertain parameter. In regard to hard constraints, this methodology allows (i) to determine if a hard constraint can be satisfied for a given uncertainty model and constraint structure, (ii) to generate conclusive, formally verifiable reliability assessments that allow for unprejudiced comparisons of competing design alternatives and (iii) to identify the critical combination of uncertain parameters leading to constraint violations. In regard to soft constraints, the methodology allows the designer (i) to use probabilistic uncertainty models, (ii) to calculate upper bounds to the probability of constraint violation, and (iii) to efficiently estimate failure probabilities via a hybrid method. This method integrates the upper bounds, for which closed form expressions are derived, along with conditional sampling. In addition, an l(sub infinity) formulation for the efficient manipulation of hyper-rectangular sets is also proposed.
Decorated Heegaard Diagrams and Combinatorial Heegaard Floer Homology
NASA Astrophysics Data System (ADS)
Hammarsten, Carl
Heegaard Floer homology is a collection of invariants for closed oriented three-manifolds, introduced by Ozsvath and Szabo in 2001. The simplest version is defined as the homology of a chain complex coming from a Heegaard diagram of the three manifold. In the original definition, the differentials count the number of points in certain moduli spaces of holomorphic disks, which are hard to compute in general. More recently, Sarkar and Wang (2006) and Ozsvath, Stipsicz and Szabo, (2009) have determined combinatorial methods for computing this homology with Z2 coefficients. Both methods rely on the construction of very specific Heegaard diagrams for the manifold, which are generally very complicated. Given a decorated Heegaard diagram H for a closed oriented 3-manifold Y, that is a Heegaard diagram together with a collection of embedded paths satisfying certain criteria, we describe a combinatorial recipe for a chain complex CF'[special character omitted]( H). If H satisfies some technical constraints we show that this chain complex is homotopically equivalent to the Heegaard Floer chain complex CF[special character omitted](H) and hence has the Heegaard Floer homology HF[special character omitted](Y) as its homology groups. Using branched spines we give an algorithm to construct a decorated Heegaard diagram which satisfies the necessary technical constraints for every closed oriented Y. We present this diagram graphically in the form of a strip diagram.
A combinatorial approach to the design of vaccines.
Martínez, Luis; Milanič, Martin; Legarreta, Leire; Medvedev, Paul; Malaina, Iker; de la Fuente, Ildefonso M
2015-05-01
We present two new problems of combinatorial optimization and discuss their applications to the computational design of vaccines. In the shortest λ-superstring problem, given a family S1,...,S(k) of strings over a finite alphabet, a set Τ of "target" strings over that alphabet, and an integer λ, the task is to find a string of minimum length containing, for each i, at least λ target strings as substrings of S(i). In the shortest λ-cover superstring problem, given a collection X1,...,X(n) of finite sets of strings over a finite alphabet and an integer λ, the task is to find a string of minimum length containing, for each i, at least λ elements of X(i) as substrings. The two problems are polynomially equivalent, and the shortest λ-cover superstring problem is a common generalization of two well known combinatorial optimization problems, the shortest common superstring problem and the set cover problem. We present two approaches to obtain exact or approximate solutions to the shortest λ-superstring and λ-cover superstring problems: one based on integer programming, and a hill-climbing algorithm. An application is given to the computational design of vaccines and the algorithms are applied to experimental data taken from patients infected by H5N1 and HIV-1.
Quantum algorithm for energy matching in hard optimization problems
NASA Astrophysics Data System (ADS)
Baldwin, C. L.; Laumann, C. R.
2018-06-01
We consider the ability of local quantum dynamics to solve the "energy-matching" problem: given an instance of a classical optimization problem and a low-energy state, find another macroscopically distinct low-energy state. Energy matching is difficult in rugged optimization landscapes, as the given state provides little information about the distant topography. Here, we show that the introduction of quantum dynamics can provide a speedup over classical algorithms in a large class of hard optimization problems. Tunneling allows the system to explore the optimization landscape while approximately conserving the classical energy, even in the presence of large barriers. Specifically, we study energy matching in the random p -spin model of spin-glass theory. Using perturbation theory and exact diagonalization, we show that introducing a transverse field leads to three sharp dynamical phases, only one of which solves the matching problem: (1) a small-field "trapped" phase, in which tunneling is too weak for the system to escape the vicinity of the initial state; (2) a large-field "excited" phase, in which the field excites the system into high-energy states, effectively forgetting the initial energy; and (3) the intermediate "tunneling" phase, in which the system succeeds at energy matching. The rate at which distant states are found in the tunneling phase, although exponentially slow in system size, is exponentially faster than classical search algorithms.
Combinatorial structures to modeling simple games and applications
NASA Astrophysics Data System (ADS)
Molinero, Xavier
2017-09-01
We connect three different topics: combinatorial structures, game theory and chemistry. In particular, we establish the bases to represent some simple games, defined as influence games, and molecules, defined from atoms, by using combinatorial structures. First, we characterize simple games as influence games using influence graphs. It let us to modeling simple games as combinatorial structures (from the viewpoint of structures or graphs). Second, we formally define molecules as combinations of atoms. It let us to modeling molecules as combinatorial structures (from the viewpoint of combinations). It is open to generate such combinatorial structures using some specific techniques as genetic algorithms, (meta-)heuristics algorithms and parallel programming, among others.
NASA Astrophysics Data System (ADS)
Youl Jung, Kyeong
2010-08-01
Conventional solution-based combinatorial chemistry was combined with spray pyrolysis and applied to optimize the luminescence properties of (Y x, Gd y, Al z)BO 3:Eu 3+ red phosphor under vacuum ultraviolet (VUV) excitation. For the Y-Gd-Al ternary system, a compositional library was established to seek the optimal composition at which the highest luminescence under VUV (147 nm) excitation could be achieved. The Al content was found to mainly control the relative peak ratio (R/O) of red and orange colors due to the 5D 0→ 7F 2 to 5D 0→ 7F 1 transitions of Eu 3+. The substitution of Gd atoms in the place of Y sites did not contribute to change the R/O ratio, but was helpful to enhance the emission intensity. As a result, the 613 nm emission peak due to the 5D 0→ 7F 2 transitions of Eu 3+ was intensified by increasing the Al/Gd ratio at a fixed Y content, resulting in the improvement of the color coordinate. Finally, the optimized host composition was (Y 0.11, Gd 0.10, Al 0.79)BO 3 in terms of the emission intensity at 613 nm and the color coordinate.
Solving optimization problems by the public goods game
NASA Astrophysics Data System (ADS)
Javarone, Marco Alberto
2017-09-01
We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.
Construction of a scFv Library with Synthetic, Non-combinatorial CDR Diversity.
Bai, Xuelian; Shim, Hyunbo
2017-01-01
Many large synthetic antibody libraries have been designed, constructed, and successfully generated high-quality antibodies suitable for various demanding applications. While synthetic antibody libraries have many advantages such as optimized framework sequences and a broader sequence landscape than natural antibodies, their sequence diversities typically are generated by random combinatorial synthetic processes which cause the incorporation of many undesired CDR sequences. Here, we describe the construction of a synthetic scFv library using oligonucleotide mixtures that contain predefined, non-combinatorially synthesized CDR sequences. Each CDR is first inserted to a master scFv framework sequence and the resulting single-CDR libraries are subjected to a round of proofread panning. The proofread CDR sequences are assembled to produce the final scFv library with six diversified CDRs.
Silva, Aleidy; Lee, Bai-Yu; Clemens, Daniel L; Kee, Theodore; Ding, Xianting; Ho, Chih-Ming; Horwitz, Marcus A
2016-04-12
Tuberculosis (TB) remains a major global public health problem, and improved treatments are needed to shorten duration of therapy, decrease disease burden, improve compliance, and combat emergence of drug resistance. Ideally, the most effective regimen would be identified by a systematic and comprehensive combinatorial search of large numbers of TB drugs. However, optimization of regimens by standard methods is challenging, especially as the number of drugs increases, because of the extremely large number of drug-dose combinations requiring testing. Herein, we used an optimization platform, feedback system control (FSC) methodology, to identify improved drug-dose combinations for TB treatment using a fluorescence-based human macrophage cell culture model of TB, in which macrophages are infected with isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible green fluorescent protein (GFP)-expressing Mycobacterium tuberculosis (Mtb). On the basis of only a single screening test and three iterations, we identified highly efficacious three- and four-drug combinations. To verify the efficacy of these combinations, we further evaluated them using a methodologically independent assay for intramacrophage killing of Mtb; the optimized combinations showed greater efficacy than the current standard TB drug regimen. Surprisingly, all top three- and four-drug optimized regimens included the third-line drug clofazimine, and none included the first-line drugs isoniazid and rifampin, which had insignificant or antagonistic impacts on efficacy. Because top regimens also did not include a fluoroquinolone or aminoglycoside, they are potentially of use for treating many cases of multidrug- and extensively drug-resistant TB. Our study shows the power of an FSC platform to identify promising previously unidentified drug-dose combinations for treatment of TB.
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
NASA Astrophysics Data System (ADS)
Venkata Subbaiah, K.; Raju, Ch.; Suresh, Ch.
2017-08-01
The present study aims to compare the conventional cutting inserts with wiper cutting inserts during the hard turning of AISI 4340 steel at different workpiece hardness. Type of insert, hardness, cutting speed, feed, and depth of cut are taken as process parameters. Taguchi’s L18 orthogonal array was used to conduct the experimental tests. Parametric analysis carried in order to know the influence of each process parameter on the three important Surface Roughness Characteristics (Ra, Rz, and Rt) and Material Removal Rate. Taguchi based Grey Relational Analysis (GRA) used to optimize the process parameters for individual response and multi-response outputs. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor.
Dynamic combinatorial libraries: new opportunities in systems chemistry.
Hunt, Rosemary A R; Otto, Sijbren
2011-01-21
Combinatorial chemistry is a tool for selecting molecules with special properties. Dynamic combinatorial chemistry started off aiming to be just that. However, unlike ordinary combinatorial chemistry, the interconnectedness of dynamic libraries gives them an extra dimension. An understanding of these molecular networks at systems level is essential for their use as a selection tool and creates exciting new opportunities in systems chemistry. In this feature article we discuss selected examples and considerations related to the advanced exploitation of dynamic combinatorial libraries for their originally conceived purpose of identifying strong binding interactions. Also reviewed are examples illustrating a trend towards increasing complexity in terms of network behaviour and reversible chemistry. Finally, new applications of dynamic combinatorial chemistry in self-assembly, transport and self-replication are discussed.
Tumor-targeting peptides from combinatorial libraries*
Liu, Ruiwu; Li, Xiaocen; Xiao, Wenwu; Lam, Kit S.
2018-01-01
Cancer is one of the major and leading causes of death worldwide. Two of the greatest challenges infighting cancer are early detection and effective treatments with no or minimum side effects. Widespread use of targeted therapies and molecular imaging in clinics requires high affinity, tumor-specific agents as effective targeting vehicles to deliver therapeutics and imaging probes to the primary or metastatic tumor sites. Combinatorial libraries such as phage-display and one-bead one-compound (OBOC) peptide libraries are powerful approaches in discovering tumor-targeting peptides. This review gives an overview of different combinatorial library technologies that have been used for the discovery of tumor-targeting peptides. Examples of tumor-targeting peptides identified from each combinatorial library method will be discussed. Published tumor-targeting peptide ligands and their applications will also be summarized by the combinatorial library methods and their corresponding binding receptors. PMID:27210583
Mondal, Milon; Radeva, Nedyalka; Fanlo-Virgós, Hugo; Otto, Sijbren; Klebe, Gerhard; Hirsch, Anna K H
2016-08-01
Fragment-based drug design (FBDD) affords active compounds for biological targets. While there are numerous reports on FBDD by fragment growing/optimization, fragment linking has rarely been reported. Dynamic combinatorial chemistry (DCC) has become a powerful hit-identification strategy for biological targets. We report the synergistic combination of fragment linking and DCC to identify inhibitors of the aspartic protease endothiapepsin. Based on X-ray crystal structures of endothiapepsin in complex with fragments, we designed a library of bis-acylhydrazones and used DCC to identify potent inhibitors. The most potent inhibitor exhibits an IC50 value of 54 nm, which represents a 240-fold improvement in potency compared to the parent hits. Subsequent X-ray crystallography validated the predicted binding mode, thus demonstrating the efficiency of the combination of fragment linking and DCC as a hit-identification strategy. This approach could be applied to a range of biological targets, and holds the potential to facilitate hit-to-lead optimization. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
Turkett, Jeremy A; Bicker, Kevin L
2017-04-10
Growing prevalence of antibiotic resistant bacterial infections necessitates novel antimicrobials, which could be rapidly identified from combinatorial libraries. We report the use of the peptoid library agar diffusion (PLAD) assay to screen peptoid libraries against the ESKAPE pathogens, including the optimization of assay conditions for each pathogen. Work presented here focuses on the tailoring of combinatorial peptoid library design through a detailed study of how peptoid lipophilicity relates to antibacterial potency and mammalian cell toxicity. The information gleaned from this optimization was then applied using the aforementioned screening method to examine the relative potency of peptoid libraries against Staphylococcus aureus, Acinetobacter baumannii, and Enterococcus faecalis prior to and following functionalization with long alkyl tails. The data indicate that overall peptoid hydrophobicity and not simply alkyl tail length is strongly correlated with mammalian cell toxicity. Furthermore, this work demonstrates the utility of the PLAD assay in rapidly evaluating the effect of molecular property changes in similar libraries.
Azimi, Sayyed M; Sheridan, Steven D; Ghannad-Rezaie, Mostafa; Eimon, Peter M; Yanik, Mehmet Fatih
2018-05-01
Identification of optimal transcription-factor expression patterns to direct cellular differentiation along a desired pathway presents significant challenges. We demonstrate massively combinatorial screening of temporally-varying mRNA transcription factors to direct differentiation of neural progenitor cells using a dynamically-reconfigurable magnetically-guided spotting technology for localizing mRNA, enabling experiments on millimetre size spots. In addition, we present a time-interleaved delivery method that dramatically reduces fluctuations in the delivered transcription-factor copy-numbers per cell. We screened combinatorial and temporal delivery of a pool of midbrain-specific transcription factors to augment the generation of dopaminergic neurons. We show that the combinatorial delivery of LMX1A, FOXA2 and PITX3 is highly effective in generating dopaminergic neurons from midbrain progenitors. We show that LMX1A significantly increases TH -expression levels when delivered to neural progenitor cells either during proliferation or after induction of neural differentiation, while FOXA2 and PITX3 increase expression only when delivered prior to induction, demonstrating temporal dependence of factor addition. © 2018, Azimi et al.
Tumor-targeting peptides from combinatorial libraries.
Liu, Ruiwu; Li, Xiaocen; Xiao, Wenwu; Lam, Kit S
2017-02-01
Cancer is one of the major and leading causes of death worldwide. Two of the greatest challenges in fighting cancer are early detection and effective treatments with no or minimum side effects. Widespread use of targeted therapies and molecular imaging in clinics requires high affinity, tumor-specific agents as effective targeting vehicles to deliver therapeutics and imaging probes to the primary or metastatic tumor sites. Combinatorial libraries such as phage-display and one-bead one-compound (OBOC) peptide libraries are powerful approaches in discovering tumor-targeting peptides. This review gives an overview of different combinatorial library technologies that have been used for the discovery of tumor-targeting peptides. Examples of tumor-targeting peptides identified from each combinatorial library method will be discussed. Published tumor-targeting peptide ligands and their applications will also be summarized by the combinatorial library methods and their corresponding binding receptors. Copyright © 2017. Published by Elsevier B.V.
Use of combinatorial chemistry to speed drug discovery.
Rádl, S
1998-10-01
IBC's International Conference on Integrating Combinatorial Chemistry into the Discovery Pipeline was held September 14-15, 1998. The program started with a pre-conference workshop on High-Throughput Compound Characterization and Purification. The agenda of the main conference was divided into sessions of Synthesis, Automation and Unique Chemistries; Integrating Combinatorial Chemistry, Medicinal Chemistry and Screening; Combinatorial Chemistry Applications for Drug Discovery; and Information and Data Management. This meeting was an excellent opportunity to see how big pharma, biotech and service companies are addressing the current bottlenecks in combinatorial chemistry to speed drug discovery. (c) 1998 Prous Science. All rights reserved.
1989-12-01
to construct because the mechanism is a dispatching procedure. Since all nonpreemptive schedules are contained in the set of all preemptive schedules...the optimal value of T’.. in the preemptive case is at least a lower bound on the optimal T., for the nonpreemptive schedules. This principle is the...adapt to changes in the enviro.nment. In hard real-time systems, tasks are also distinguished as preemptable and nonpreemptable . A task is preemptable
Asessing for Structural Understanding in Childrens' Combinatorial Problem Solving.
ERIC Educational Resources Information Center
English, Lyn
1999-01-01
Assesses children's structural understanding of combinatorial problems when presented in a variety of task situations. Provides an explanatory model of students' combinatorial understandings that informs teaching and assessment. Addresses several components of children's structural understanding of elementary combinatorial problems. (Contains 50…
Combinatorial Strategies for the Development of Bulk Metallic Glasses
NASA Astrophysics Data System (ADS)
Ding, Shiyan
The systematic identification of multi-component alloys out of the vast composition space is still a daunting task, especially in the development of bulk metallic glasses that are typically based on three or more elements. In order to address this challenge, combinatorial approaches have been proposed. However, previous attempts have not successfully coupled the synthesis of combinatorial libraries with high-throughput characterization methods. The goal of my dissertation is to develop efficient high-throughput characterization methods, optimized to identify glass formers systematically. Here, two innovative approaches have been invented. One is to measure the nucleation temperature in parallel for up-to 800 compositions. The composition with the lowest nucleation temperature has a reasonable agreement with the best-known glass forming composition. In addition, the thermoplastic formability of a metallic glass forming system is determined through blow molding a compositional library. Our results reveal that the composition with the largest thermoplastic deformation correlates well with the best-known formability composition. I have demonstrated both methods as powerful tools to develop new bulk metallic glasses.
Nonlinear Multidimensional Assignment Problems Efficient Conic Optimization Methods and Applications
2015-06-24
WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Arizona State University School of Mathematical & Statistical Sciences 901 S...SUPPLEMENTARY NOTES 14. ABSTRACT The major goals of this project were completed: the exact solution of previously unsolved challenging combinatorial optimization... combinatorial optimization problem, the Directional Sensor Problem, was solved in two ways. First, heuristically in an engineering fashion and second, exactly
Development of New Sensing Materials Using Combinatorial and High-Throughput Experimentation
NASA Astrophysics Data System (ADS)
Potyrailo, Radislav A.; Mirsky, Vladimir M.
New sensors with improved performance characteristics are needed for applications as diverse as bedside continuous monitoring, tracking of environmental pollutants, monitoring of food and water quality, monitoring of chemical processes, and safety in industrial, consumer, and automotive settings. Typical requirements in sensor improvement are selectivity, long-term stability, sensitivity, response time, reversibility, and reproducibility. Design of new sensing materials is the important cornerstone in the effort to develop new sensors. Often, sensing materials are too complex to predict their performance quantitatively in the design stage. Thus, combinatorial and high-throughput experimentation methodologies provide an opportunity to generate new required data to discover new sensing materials and/or to optimize existing material compositions. The goal of this chapter is to provide an overview of the key concepts of experimental development of sensing materials using combinatorial and high-throughput experimentation tools, and to promote additional fruitful interactions between computational scientists and experimentalists.
MIFT: GIFT Combinatorial Geometry Input to VCS Code
1977-03-01
r-w w-^ H ^ß0318is CQ BRL °RCUMr REPORT NO. 1967 —-S: ... MIFT: GIFT COMBINATORIAL GEOMETRY INPUT TO VCS CODE Albert E...TITLE (and Subtitle) MIFT: GIFT Combinatorial Geometry Input to VCS Code S. TYPE OF REPORT & PERIOD COVERED FINAL 6. PERFORMING ORG. REPORT NUMBER...Vehicle Code System (VCS) called MORSE was modified to accept the GIFT combinatorial geometry package. GIFT , as opposed to the geometry package
Combinatorial stresses kill pathogenic Candida species
Kaloriti, Despoina; Tillmann, Anna; Cook, Emily; Jacobsen, Mette; You, Tao; Lenardon, Megan; Ames, Lauren; Barahona, Mauricio; Chandrasekaran, Komelapriya; Coghill, George; Goodman, Daniel; Gow, Neil A. R.; Grebogi, Celso; Ho, Hsueh-Lui; Ingram, Piers; McDonagh, Andrew; De Moura, Alessandro P. S.; Pang, Wei; Puttnam, Melanie; Radmaneshfar, Elahe; Romano, Maria Carmen; Silk, Daniel; Stark, Jaroslav; Stumpf, Michael; Thiel, Marco; Thorne, Thomas; Usher, Jane; Yin, Zhikang; Haynes, Ken; Brown, Alistair J. P.
2012-01-01
Pathogenic microbes exist in dynamic niches and have evolved robust adaptive responses to promote survival in their hosts. The major fungal pathogens of humans, Candida albicans and Candida glabrata, are exposed to a range of environmental stresses in their hosts including osmotic, oxidative and nitrosative stresses. Significant efforts have been devoted to the characterization of the adaptive responses to each of these stresses. In the wild, cells are frequently exposed simultaneously to combinations of these stresses and yet the effects of such combinatorial stresses have not been explored. We have developed a common experimental platform to facilitate the comparison of combinatorial stress responses in C. glabrata and C. albicans. This platform is based on the growth of cells in buffered rich medium at 30°C, and was used to define relatively low, medium and high doses of osmotic (NaCl), oxidative (H 2O2) and nitrosative stresses (e.g., dipropylenetriamine (DPTA)-NONOate). The effects of combinatorial stresses were compared with the corresponding individual stresses under these growth conditions. We show for the first time that certain combinations of combinatorial stress are especially potent in terms of their ability to kill C. albicans and C. glabrata and/or inhibit their growth. This was the case for combinations of osmotic plus oxidative stress and for oxidative plus nitrosative stress. We predict that combinatorial stresses may be highly signif cant in host defences against these pathogenic yeasts. PMID:22463109
Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints
NASA Astrophysics Data System (ADS)
Juliandri, Dedy; Mawengkang, Herman; Bu'ulolo, F.
2018-01-01
Vehicle Routing Problem (VRP) is an important element of many logistic systems which involve routing and scheduling of vehicles from a depot to a set of customers node. This is a hard combinatorial optimization problem with the objective to find an optimal set of routes used by a fleet of vehicles to serve the demands a set of customers It is required that these vehicles return to the depot after serving customers’ demand. The problem incorporates time windows, fleet and driver scheduling, pick-up and delivery in the planning horizon. The goal is to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the overall costs of all routes over the planning horizon. We model the problem as a linear mixed integer program. We develop a combination of heuristics and exact method for solving the model.
Lin, Jingjing; Jing, Honglei
2016-01-01
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662
A New Approach for Proving or Generating Combinatorial Identities
ERIC Educational Resources Information Center
Gonzalez, Luis
2010-01-01
A new method for proving, in an immediate way, many combinatorial identities is presented. The method is based on a simple recursive combinatorial formula involving n + 1 arbitrary real parameters. Moreover, this formula enables one not only to prove, but also generate many different combinatorial identities (not being required to know them "a…
Sensorless optimal sinusoidal brushless direct current for hard disk drives
NASA Astrophysics Data System (ADS)
Soh, C. S.; Bi, C.
2009-04-01
Initiated by the availability of digital signal processors and emergence of new applications, market demands for permanent magnet synchronous motors have been surging. As its back-emf is sinusoidal, the drive current should also be sinusoidal for reducing the torque ripple. However, in applications like hard disk drives, brushless direct current (BLDC) drive is adopted instead of sinusoidal drive for simplification. The adoption, however, comes at the expense of increased harmonics, losses, torque pulsations, and acoustics. In this paper, we propose a sensorless optimal sinusoidal BLDC drive. First and foremost, the derivation for an optimal sinusoidal drive is presented, and a power angle control scheme is proposed to achieve an optimal sinusoidal BLDC. The scheme maintains linear relationship between the motor speed and drive voltage. In an attempt to execute the sensorless drive, an innovative power angle measurement scheme is devised, which takes advantage of the freewheeling diodes and measures the power angle through the detection of diode voltage drops. The objectives as laid out will be presented and discussed in this paper, supported by derivations, simulations, and experimental results. The proposed scheme is straightforward, brings about the benefits of sensorless sinusoidal drive, negates the need for current sensors by utilizing the freewheeling diodes, and does not incur additional cost.
Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chavarría-Miranda, Daniel; Panyala, Ajay R.; Halappanavar, Mahantesh
Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) {more » on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.« less
Masmoudi, Fatma; Ben Khedher, Saoussen; Kamoun, Amel; Zouari, Nabil; Tounsi, Slim; Trigui, Mohamed
2017-04-01
This work is directed towards Bacillus amyloliquefaciens strain BLB371 metabolite production for biocontrol of fungal phytopathogens. In order to maximise antifungal metabolite production by this strain, two approaches were combined: random mutagenesis and medium component optimization. After three rounds of mutagenesis, a hyper active mutant, named M3-7, was obtained. It produces 7 fold more antifungal metabolites (1800AU/mL) than the wild strain in MC medium. A hybrid design was applied to optimise a new medium to enhance antifungal metabolite production by M3-7. The new optimized medium (35g/L of peptone, 32.5g/L of sucrose, 10.5g/L of yeast extract, 2.4g/L of KH 2 PO 4 , 1.3g/L of MgSO 4 and 23mg/L of MnSO 4 ) achieved 1.62 fold enhancement in antifungal compound production (3000AU/mL) by this mutant, compared to that achieved in MC medium. Therefore, combinatory effect of these two approaches (mutagenesis and medium component optimization) allowed 12 fold improvement in antifungal activity (from 250UA/mL to 3000UA/mL). This improvement was confirmed against several phytopathogenic fungi with an increase of MIC and MFC over than 50%. More interestingly, a total eradication of gray mold was obtained on tomato fruits infected by Botrytis cinerea and treated by M3-7, compared to those treated by BLB371. From the practical point of view, combining random mutagenesis and medium optimization could be considered as an excellent tool for obtaining promising biological products useful against phytopathogenic fungi. Copyright © 2017 Elsevier GmbH. All rights reserved.
One step DNA assembly for combinatorial metabolic engineering.
Coussement, Pieter; Maertens, Jo; Beauprez, Joeri; Van Bellegem, Wouter; De Mey, Marjan
2014-05-01
The rapid and efficient assembly of multi-step metabolic pathways for generating microbial strains with desirable phenotypes is a critical procedure for metabolic engineering, and remains a significant challenge in synthetic biology. Although several DNA assembly methods have been developed and applied for metabolic pathway engineering, many of them are limited by their suitability for combinatorial pathway assembly. The introduction of transcriptional (promoters), translational (ribosome binding site (RBS)) and enzyme (mutant genes) variability to modulate pathway expression levels is essential for generating balanced metabolic pathways and maximizing the productivity of a strain. We report a novel, highly reliable and rapid single strand assembly (SSA) method for pathway engineering. The method was successfully optimized and applied to create constructs containing promoter, RBS and/or mutant enzyme libraries. To demonstrate its efficiency and reliability, the method was applied to fine-tune multi-gene pathways. Two promoter libraries were simultaneously introduced in front of two target genes, enabling orthogonal expression as demonstrated by principal component analysis. This shows that SSA will increase our ability to tune multi-gene pathways at all control levels for the biotechnological production of complex metabolites, achievable through the combinatorial modulation of transcription, translation and enzyme activity. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
NASA Astrophysics Data System (ADS)
Gen, Mitsuo; Lin, Lin
Many combinatorial optimization problems from industrial engineering and operations research in real-world are very complex in nature and quite hard to solve them by conventional techniques. Since the 1960s, there has been an increasing interest in imitating living beings to solve such kinds of hard combinatorial optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs), which can often outperform conventional optimization methods when applied to difficult real-world problems. In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following engineering design problems: transportation planning models, layout design models and two-stage logistics models in logistics systems; job-shop scheduling, resource constrained project scheduling in manufacturing system.
Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity.
Gilmer, Jesse I; Person, Abigail L
2017-12-13
Combinatorial expansion by the cerebellar granule cell layer (GCL) is fundamental to theories of cerebellar contributions to motor control and learning. Granule cells (GrCs) sample approximately four mossy fiber inputs and are thought to form a combinatorial code useful for pattern separation and learning. We constructed a spatially realistic model of the cerebellar GCL and examined how GCL architecture contributes to GrC combinatorial diversity. We found that GrC combinatorial diversity saturates quickly as mossy fiber input diversity increases, and that this saturation is in part a consequence of short dendrites, which limit access to diverse inputs and favor dense sampling of local inputs. This local sampling also produced GrCs that were combinatorially redundant, even when input diversity was extremely high. In addition, we found that mossy fiber clustering, which is a common anatomical pattern, also led to increased redundancy of GrC input combinations. We related this redundancy to hypothesized roles of temporal expansion of GrC information encoding in service of learned timing, and we show that GCL architecture produces GrC populations that support both temporal and combinatorial expansion. Finally, we used novel anatomical measurements from mice of either sex to inform modeling of sparse and filopodia-bearing mossy fibers, finding that these circuit features uniquely contribute to enhancing GrC diversification and redundancy. Our results complement information theoretic studies of granule layer structure and provide insight into the contributions of granule layer anatomical features to afferent mixing. SIGNIFICANCE STATEMENT Cerebellar granule cells are among the simplest neurons, with tiny somata and, on average, just four dendrites. These characteristics, along with their dense organization, inspired influential theoretical work on the granule cell layer as a combinatorial expander, where each granule cell represents a unique combination of inputs
Signal dimensionality and the emergence of combinatorial structure.
Little, Hannah; Eryılmaz, Kerem; de Boer, Bart
2017-11-01
In language, a small number of meaningless building blocks can be combined into an unlimited set of meaningful utterances. This is known as combinatorial structure. One hypothesis for the initial emergence of combinatorial structure in language is that recombining elements of signals solves the problem of overcrowding in a signal space. Another hypothesis is that iconicity may impede the emergence of combinatorial structure. However, how these two hypotheses relate to each other is not often discussed. In this paper, we explore how signal space dimensionality relates to both overcrowding in the signal space and iconicity. We use an artificial signalling experiment to test whether a signal space and a meaning space having similar topologies will generate an iconic system and whether, when the topologies differ, the emergence of combinatorially structured signals is facilitated. In our experiments, signals are created from participants' hand movements, which are measured using an infrared sensor. We found that participants take advantage of iconic signal-meaning mappings where possible. Further, we use trajectory predictability, measures of variance, and Hidden Markov Models to measure the use of structure within the signals produced and found that when topologies do not match, then there is more evidence of combinatorial structure. The results from these experiments are interpreted in the context of the differences between the emergence of combinatorial structure in different linguistic modalities (speech and sign). Copyright © 2017 Elsevier B.V. All rights reserved.
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 ...
NASA Astrophysics Data System (ADS)
Dao, Son Duy; Abhary, Kazem; Marian, Romeo
2017-06-01
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to "learn" from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.
Sabar, Nasser R; Ayob, Masri; Kendall, Graham; Qu, Rong
2015-02-01
Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite.
Jézéquel, Laetitia; Loeper, Jacqueline; Pompon, Denis
2008-11-01
Combinatorial libraries coding for mosaic enzymes with predefined crossover points constitute useful tools to address and model structure-function relationships and for functional optimization of enzymes based on multivariate statistics. The presented method, called sequence-independent generation of a chimera-ordered library (SIGNAL), allows easy shuffling of any predefined amino acid segment between two or more proteins. This method is particularly well adapted to the exchange of protein structural modules. The procedure could also be well suited to generate ordered combinatorial libraries independent of sequence similarities in a robotized manner. Sequence segments to be recombined are first extracted by PCR from a single-stranded template coding for an enzyme of interest using a biotin-avidin-based method. This technique allows the reduction of parental template contamination in the final library. Specific PCR primers allow amplification of two complementary mosaic DNA fragments, overlapping in the region to be exchanged. Fragments are finally reassembled using a fusion PCR. The process is illustrated via the construction of a set of mosaic CYP2B enzymes using this highly modular approach.
Manipulating Combinatorial Structures.
ERIC Educational Resources Information Center
Labelle, Gilbert
This set of transparencies shows how the manipulation of combinatorial structures in the context of modern combinatorics can easily lead to interesting teaching and learning activities at every level of education from elementary school to university. The transparencies describe: (1) the importance and relations of combinatorics to science and…
Li, Jin; Tran, Maggie; Siwabessy, Justy
2016-01-01
Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and
Li, Jin; Tran, Maggie; Siwabessy, Justy
2016-01-01
Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and
Combinatorial Interdependence in Lottery
ERIC Educational Resources Information Center
Helman, Danny
2005-01-01
This paper examines a real life question of gamble facing lottery players. Combinatorial dependence plays a central role in shaping the game probabilistic structure, but might not carry the merited weight in punters' considerations.
Polynomial functors and combinatorial Dyson-Schwinger equations
NASA Astrophysics Data System (ADS)
Kock, Joachim
2017-04-01
We present a general abstract framework for combinatorial Dyson-Schwinger equations, in which combinatorial identities are lifted to explicit bijections of sets, and more generally equivalences of groupoids. Key features of combinatorial Dyson-Schwinger equations are revealed to follow from general categorical constructions and universal properties. Rather than beginning with an equation inside a given Hopf algebra and referring to given Hochschild 1-cocycles, our starting point is an abstract fixpoint equation in groupoids, shown canonically to generate all the algebraic structures. Precisely, for any finitary polynomial endofunctor P defined over groupoids, the system of combinatorial Dyson-Schwinger equations X = 1 + P(X) has a universal solution, namely the groupoid of P-trees. The isoclasses of P-trees generate naturally a Connes-Kreimer-like bialgebra, in which the abstract Dyson-Schwinger equation can be internalised in terms of canonical B+-operators. The solution to this equation is a series (the Green function), which always enjoys a Faà di Bruno formula, and hence generates a sub-bialgebra isomorphic to the Faà di Bruno bialgebra. Varying P yields different bialgebras, and cartesian natural transformations between various P yield bialgebra homomorphisms and sub-bialgebras, corresponding for example to truncation of Dyson-Schwinger equations. Finally, all constructions can be pushed inside the classical Connes-Kreimer Hopf algebra of trees by the operation of taking core of P-trees. A byproduct of the theory is an interpretation of combinatorial Green functions as inductive data types in the sense of Martin-Löf type theory (expounded elsewhere).
Combinatorial effects on clumped isotopes and their significance in biogeochemistry
NASA Astrophysics Data System (ADS)
Yeung, Laurence Y.
2016-01-01
The arrangement of isotopes within a collection of molecules records their physical and chemical histories. Clumped-isotope analysis interrogates these arrangements, i.e., how often rare isotopes are bound together, which in many cases can be explained by equilibrium and/or kinetic isotope fractionation. However, purely combinatorial effects, rooted in the statistics of pairing atoms in a closed system, are also relevant, and not well understood. Here, I show that combinatorial isotope effects are most important when two identical atoms are neighbors on the same molecule (e.g., O2, N2, and D-D clumping in CH4). When the two halves of an atom pair are either assembled with different isotopic preferences or drawn from different reservoirs, combinatorial effects cause depletions in clumped-isotope abundance that are most likely between zero and -1‰, although they could potentially be -10‰ or larger for D-D pairs. These depletions are of similar magnitude, but of opposite sign, to low-temperature equilibrium clumped-isotope effects for many small molecules. Enzymatic isotope-pairing reactions, which can have site-specific isotopic fractionation factors and atom reservoirs, should express this class of combinatorial isotope effect, although it is not limited to biological reactions. Chemical-kinetic isotope effects, which are related to a bond-forming transition state, arise independently and express second-order combinatorial effects related to the abundance of the rare isotope. Heteronuclear moeties (e.g., Csbnd O and Csbnd H), are insensitive to direct combinatorial influences, but secondary combinatorial influences are evident. In general, both combinatorial and chemical-kinetic factors are important for calculating and interpreting clumped-isotope signatures of kinetically controlled reactions. I apply this analytical framework to isotope-pairing reactions relevant to geochemical oxygen, carbon, and nitrogen cycling that may be influenced by combinatorial
Preparation of cherry-picked combinatorial libraries by string synthesis.
Furka, Arpád; Dibó, Gábor; Gombosuren, Naran
2005-03-01
String synthesis [1-3] is an efficient and cheap manual method for preparation of combinatorial libraries by using macroscopic solid support units. Sorting the units between two synthetic steps is an important operation of the procedure. The software developed to guide sorting can be used only when complete combinatorial libraries are prepared. Since very often only selected components of the full libraries are needed, new software was constructed that guides sorting in preparation of non-complete combinatorial libraries. Application of the software is described in details.
ERIC Educational Resources Information Center
Duarte, Robert; Nielson, Janne T.; Dragojlovic, Veljko
2004-01-01
A group of techniques aimed at synthesizing a large number of structurally diverse compounds is called combinatorial synthesis. Synthesis of chemiluminescence esters using parallel combinatorial synthesis and mix-and-split combinatorial synthesis is experimented.
NASA Astrophysics Data System (ADS)
Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.
2012-05-01
The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.
NASA Astrophysics Data System (ADS)
Xue, Wei; Wang, Qi; Wang, Tianyu
2018-04-01
This paper presents an improved parallel combinatory spread spectrum (PC/SS) communication system with the method of double information matching (DIM). Compared with conventional PC/SS system, the new model inherits the advantage of high transmission speed, large information capacity and high security. Besides, the problem traditional system will face is the high bit error rate (BER) and since its data-sequence mapping algorithm. Hence the new model presented shows lower BER and higher efficiency by its optimization of mapping algorithm.
Invention as a combinatorial process: evidence from US patents
Youn, Hyejin; Strumsky, Deborah; Bettencourt, Luis M. A.; Lobo, José
2015-01-01
Invention has been commonly conceptualized as a search over a space of combinatorial possibilities. Despite the existence of a rich literature, spanning a variety of disciplines, elaborating on the recombinant nature of invention, we lack a formal and quantitative characterization of the combinatorial process underpinning inventive activity. Here, we use US patent records dating from 1790 to 2010 to formally characterize invention as a combinatorial process. To do this, we treat patented inventions as carriers of technologies and avail ourselves of the elaborate system of technology codes used by the United States Patent and Trademark Office to classify the technologies responsible for an invention's novelty. We find that the combinatorial inventive process exhibits an invariant rate of ‘exploitation’ (refinements of existing combinations of technologies) and ‘exploration’ (the development of new technological combinations). This combinatorial dynamic contrasts sharply with the creation of new technological capabilities—the building blocks to be combined—that has significantly slowed down. We also find that, notwithstanding the very reduced rate at which new technologies are introduced, the generation of novel technological combinations engenders a practically infinite space of technological configurations. PMID:25904530
Combining local search with co-evolution in a remarkably simple way
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boettcher, S.; Percus, A.
2000-05-01
The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problem. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. In contrast to genetic algorithms, which operate on an entire gene-pool of possible solutions, extremal optimization successively replaces extremely undesirable elements of a single sub-optimal solution with new, random ones. Large fluctuations, or avalanches, ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements heuristics inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Phase transitions are found in many combinatorial optimization problems, and have been conjectured to occur in the region of parameter space containing the hardest instances. We demonstrate how extremal optimization can be implemented for a variety of hard optimization problems. We believe that this will be a useful tool in the investigation of phase transitions in combinatorial optimization, thereby helping to elucidate the origin of computational complexity.« less
Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M Natália D S; Cagide Fajin, J Luis; Morell, Carlos; Ruiz, Reinaldo Molina; Cañizares-Carmenate, Yudith; Dominguez, Elena Rosa
2008-01-01
Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.
Cooperative combinatorial optimization: evolutionary computation case study.
Burgin, Mark; Eberbach, Eugene
2008-01-01
This paper presents a formalization of the notion of cooperation and competition of multiple systems that work toward a common optimization goal of the population using evolutionary computation techniques. It is proved that evolutionary algorithms are more expressive than conventional recursive algorithms, such as Turing machines. Three classes of evolutionary computations are introduced and studied: bounded finite, unbounded finite, and infinite computations. Universal evolutionary algorithms are constructed. Such properties of evolutionary algorithms as completeness, optimality, and search decidability are examined. A natural extension of evolutionary Turing machine (ETM) model is proposed to properly reflect phenomena of cooperation and competition in the whole population.
Martín H., José Antonio
2013-01-01
Many practical problems in almost all scientific and technological disciplines have been classified as computationally hard (NP-hard or even NP-complete). In life sciences, combinatorial optimization problems frequently arise in molecular biology, e.g., genome sequencing; global alignment of multiple genomes; identifying siblings or discovery of dysregulated pathways. In almost all of these problems, there is the need for proving a hypothesis about certain property of an object that can be present if and only if it adopts some particular admissible structure (an NP-certificate) or be absent (no admissible structure), however, none of the standard approaches can discard the hypothesis when no solution can be found, since none can provide a proof that there is no admissible structure. This article presents an algorithm that introduces a novel type of solution method to “efficiently” solve the graph 3-coloring problem; an NP-complete problem. The proposed method provides certificates (proofs) in both cases: present or absent, so it is possible to accept or reject the hypothesis on the basis of a rigorous proof. It provides exact solutions and is polynomial-time (i.e., efficient) however parametric. The only requirement is sufficient computational power, which is controlled by the parameter . Nevertheless, here it is proved that the probability of requiring a value of to obtain a solution for a random graph decreases exponentially: , making tractable almost all problem instances. Thorough experimental analyses were performed. The algorithm was tested on random graphs, planar graphs and 4-regular planar graphs. The obtained experimental results are in accordance with the theoretical expected results. PMID:23349711
Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems
Van Benthem, Mark H.; Keenan, Michael R.
2008-11-11
A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.
Dynamic combinatorial libraries: from exploring molecular recognition to systems chemistry.
Li, Jianwei; Nowak, Piotr; Otto, Sijbren
2013-06-26
Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.
Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors
Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang
2015-01-01
The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. PMID:26110383
Combinatorial influence of environmental parameters on transcription factor activity.
Knijnenburg, T A; Wessels, L F A; Reinders, M J T
2008-07-01
Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. The Matlab code is available from the authors upon request. Supplementary data are available at Bioinformatics online.
Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem.
Nallaperuma, Samadhi; Neumann, Frank; Sudholt, Dirk
2017-01-01
Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to our theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed-time budget. We follow this approach and present a fixed-budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson Problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed-time budget. In particular, we analyze Manhattan and Euclidean TSP instances and Randomized Local Search (RLS), (1+1) EA and (1+[Formula: see text]) EA algorithms for the TSP in a smoothed complexity setting, and derive the lower bounds of the expected fitness gain for a specified number of generations.
NASA Astrophysics Data System (ADS)
Yan, Zongkai; Zhang, Xiaokun; Li, Guang; Cui, Yuxing; Jiang, Zhaolian; Liu, Wen; Peng, Zhi; Xiang, Yong
2018-01-01
The conventional methods for designing and preparing thin film based on wet process remain a challenge due to disadvantages such as time-consuming and ineffective, which hinders the development of novel materials. Herein, we present a high-throughput combinatorial technique for continuous thin film preparation relied on chemical bath deposition (CBD). The method is ideally used to prepare high-throughput combinatorial material library with low decomposition temperatures and high water- or oxygen-sensitivity at relatively high-temperature. To check this system, a Cu(In, Ga)Se (CIGS) thin films library doped with 0-19.04 at.% of antimony (Sb) was taken as an example to evaluate the regulation of varying Sb doping concentration on the grain growth, structure, morphology and electrical properties of CIGS thin film systemically. Combined with the Energy Dispersive Spectrometer (EDS), X-ray Photoelectron Spectroscopy (XPS), automated X-ray Diffraction (XRD) for rapid screening and Localized Electrochemical Impedance Spectroscopy (LEIS), it was confirmed that this combinatorial high-throughput system could be used to identify the composition with the optimal grain orientation growth, microstructure and electrical properties systematically, through accurately monitoring the doping content and material composition. According to the characterization results, a Sb2Se3 quasi-liquid phase promoted CIGS film-growth model has been put forward. In addition to CIGS thin film reported here, the combinatorial CBD also could be applied to the high-throughput screening of other sulfide thin film material systems.
Tuning the physical properties of amorphous In–Zn–Sn–O thin films using combinatorial sputtering
Ndione, Paul F.; Zakutayev, A.; Kumar, M.; ...
2016-12-05
Transparent conductive oxides and amorphous oxide semiconductors are important materials for many modern technologies. Here, we explore the ternary indium zinc tin oxide (IZTO) using combinatorial synthesis and spatially resolved characterization. The electrical conductivity, work function, absorption onset, mechanical hardness, and elastic modulus of the optically transparent (>85%) amorphous IZTO thin films were found to be in the range of 10–2415 S/cm, 4.6–5.3 eV, 3.20–3.34 eV, 9.0–10.8 GPa, and 111–132 GPa, respectively, depending on the cation composition and the deposition conditions. Furthermore, this study enables control of IZTO performance over a broad range of cation compositions.
A preliminary study to metaheuristic approach in multilayer radiation shielding optimization
NASA Astrophysics Data System (ADS)
Arif Sazali, Muhammad; Rashid, Nahrul Khair Alang Md; Hamzah, Khaidzir
2018-01-01
Metaheuristics are high-level algorithmic concepts that can be used to develop heuristic optimization algorithms. One of their applications is to find optimal or near optimal solutions to combinatorial optimization problems (COPs) such as scheduling, vehicle routing, and timetabling. Combinatorial optimization deals with finding optimal combinations or permutations in a given set of problem components when exhaustive search is not feasible. A radiation shield made of several layers of different materials can be regarded as a COP. The time taken to optimize the shield may be too high when several parameters are involved such as the number of materials, the thickness of layers, and the arrangement of materials. Metaheuristics can be applied to reduce the optimization time, trading guaranteed optimal solutions for near-optimal solutions in comparably short amount of time. The application of metaheuristics for radiation shield optimization is lacking. In this paper, we present a review on the suitability of using metaheuristics in multilayer shielding design, specifically the genetic algorithm and ant colony optimization algorithm (ACO). We would also like to propose an optimization model based on the ACO method.
Investigations of quantum heuristics for optimization
NASA Astrophysics Data System (ADS)
Rieffel, Eleanor; Hadfield, Stuart; Jiang, Zhang; Mandra, Salvatore; Venturelli, Davide; Wang, Zhihui
We explore the design of quantum heuristics for optimization, focusing on the quantum approximate optimization algorithm, a metaheuristic developed by Farhi, Goldstone, and Gutmann. We develop specific instantiations of the of quantum approximate optimization algorithm for a variety of challenging combinatorial optimization problems. Through theoretical analyses and numeric investigations of select problems, we provide insight into parameter setting and Hamiltonian design for quantum approximate optimization algorithms and related quantum heuristics, and into their implementation on hardware realizable in the near term.
Combinatorial Mathematics: Research into Practice
ERIC Educational Resources Information Center
Sriraman, Bharath; English, Lyn D.
2004-01-01
Implications and suggestions for using combinatorial mathematics in the classroom through a survey and synthesis of numerous research studies are presented. The implications revolve around five major themes that emerge from analysis of these studies.
Lv, Xiaomei; Gu, Jiali; Wang, Fan; Xie, Wenping; Liu, Min; Ye, Lidan; Yu, Hongwei
2016-12-01
Metabolic engineering of microorganisms for heterologous biosynthesis is a promising route to sustainable chemical production which attracts increasing research and industrial interest. However, the efficiency of microbial biosynthesis is often restricted by insufficient activity of pathway enzymes and unbalanced utilization of metabolic intermediates. This work presents a combinatorial strategy integrating modification of multiple rate-limiting enzymes and modular pathway engineering to simultaneously improve intra- and inter-pathway balance, which might be applicable for a range of products, using isoprene as an example product. For intra-module engineering within the methylerythritol-phosphate (MEP) pathway, directed co-evolution of DXS/DXR/IDI was performed adopting a lycopene-indicated high-throughput screening method developed herein, leading to 60% improvement of isoprene production. In addition, inter-module engineering between the upstream MEP pathway and the downstream isoprene-forming pathway was conducted via promoter manipulation, which further increased isoprene production by 2.94-fold compared to the recombinant strain with solely protein engineering and 4.7-fold compared to the control strain containing wild-type enzymes. These results demonstrated the potential of pathway optimization in isoprene overproduction as well as the effectiveness of combining metabolic regulation and protein engineering in improvement of microbial biosynthesis. Biotechnol. Bioeng. 2016;113: 2661-2669. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Kajiwara, Shota; Yamada, Ryosuke; Ogino, Hiroyasu
2018-04-10
Simple and cost-effective lipase expression host microorganisms are highly desirable. A combinatorial library strategy is used to improve the secretory expression of lipase from Bacillus thermocatenulatus (BTL2) in the culture supernatant of Saccharomyces cerevisiae. A plasmid library including expression cassettes composed of sequences encoding one of each 15 promoters, 15 secretion signals, and 15 terminators derived from yeast species, S. cerevisiae, Pichia pastoris, and Hansenula polymorpha, is constructed. The S. cerevisiae transformant YPH499/D4, comprising H. polymorpha GAP promoter, S. cerevisiae SAG1 secretion signal, and P. pastoris AOX1 terminator, is selected by high-throughput screening. This transformant expresses BTL2 extra-cellularly with a 130-fold higher than the control strain, comprising S. cerevisiae PGK1 promoter, S. cerevisiae α-factor secretion signal, and S. cerevisiae PGK1 terminator, after cultivation for 72 h. This combinatorial library strategy holds promising potential for application in the optimization of the secretory expression of proteins in yeast. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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).
Combinatorial theory of Macdonald polynomials I: proof of Haglund's formula.
Haglund, J; Haiman, M; Loehr, N
2005-02-22
Haglund recently proposed a combinatorial interpretation of the modified Macdonald polynomials H(mu). We give a combinatorial proof of this conjecture, which establishes the existence and integrality of H(mu). As corollaries, we obtain the cocharge formula of Lascoux and Schutzenberger for Hall-Littlewood polynomials, a formula of Sahi and Knop for Jack's symmetric functions, a generalization of this result to the integral Macdonald polynomials J(mu), a formula for H(mu) in terms of Lascoux-Leclerc-Thibon polynomials, and combinatorial expressions for the Kostka-Macdonald coefficients K(lambda,mu) when mu is a two-column shape.
Solforosi, Laura; Mancini, Nicasio; Canducci, Filippo; Clementi, Nicola; Sautto, Giuseppe Andrea; Diotti, Roberta Antonia; Clementi, Massimo; Burioni, Roberto
2012-07-01
A novel phagemid vector, named pCM, was optimized for the cloning and display of antibody fragment (Fab) libraries on the surface of filamentous phage. This vector contains two long DNA "stuffer" fragments for easier differentiation of the correctly cut forms of the vector. Moreover, in pCM the fragment at the heavy-chain cloning site contains an acid phosphatase-encoding gene allowing an easy distinction of the Escherichia coli cells containing the unmodified form of the phagemid versus the heavy-chain fragment coding cDNA. In pCM transcription of heavy-chain Fd/gene III and light chain is driven by a single lacZ promoter. The light chain is directed to the periplasm by the ompA signal peptide, whereas the heavy-chain Fd/coat protein III is trafficked by the pelB signal peptide. The phagemid pCM was used to generate a human combinatorial phage display antibody library that allowed the selection of a monoclonal Fab fragment antibody directed against the nucleoprotein (NP) of Influenza A virus.
Combinatorial Dyson-Schwinger equations and inductive data types
NASA Astrophysics Data System (ADS)
Kock, Joachim
2016-06-01
The goal of this contribution is to explain the analogy between combinatorial Dyson-Schwinger equations and inductive data types to a readership of mathematical physicists. The connection relies on an interpretation of combinatorial Dyson-Schwinger equations as fixpoint equations for polynomial functors (established elsewhere by the author, and summarised here), combined with the now-classical fact that polynomial functors provide semantics for inductive types. The paper is expository, and comprises also a brief introduction to type theory.
A Simple Combinatorial Codon Mutagenesis Method for Targeted Protein Engineering.
Belsare, Ketaki D; Andorfer, Mary C; Cardenas, Frida S; Chael, Julia R; Park, Hyun June; Lewis, Jared C
2017-03-17
Directed evolution is a powerful tool for optimizing enzymes, and mutagenesis methods that improve enzyme library quality can significantly expedite the evolution process. Here, we report a simple method for targeted combinatorial codon mutagenesis (CCM). To demonstrate the utility of this method for protein engineering, CCM libraries were constructed for cytochrome P450 BM3 , pfu prolyl oligopeptidase, and the flavin-dependent halogenase RebH; 10-26 sites were targeted for codon mutagenesis in each of these enzymes, and libraries with a tunable average of 1-7 codon mutations per gene were generated. Each of these libraries provided improved enzymes for their respective transformations, which highlights the generality, simplicity, and tunability of CCM for targeted protein engineering.
Lin-Gibson, Sheng; Sung, Lipiin; Forster, Aaron M; Hu, Haiqing; Cheng, Yajun; Lin, Nancy J
2009-07-01
Multicomponent formulations coupled with complex processing conditions govern the final properties of photopolymerizable dental composites. In this study, a single test substrate was fabricated to support multiple formulations with a gradient in degree of conversion (DC), allowing the evaluation of multiple processing conditions and formulations on one specimen. Mechanical properties and damage response were evaluated as a function of filler type/content and irradiation. DC, surface roughness, modulus, hardness, scratch deformation and cytotoxicity were quantified using techniques including near-infrared spectroscopy, laser confocal scanning microscopy, depth-sensing indentation, scratch testing and cell viability. Scratch parameters (depth, width, percent recovery) were correlated to composite modulus and hardness. Total filler content, nanofiller and irradiation time/intensity all affected the final properties, with the dominant factor for improved properties being a higher DC. This combinatorial platform accelerates the screening of dental composites through the direct comparison of properties and processing conditions across the same sample.
Combinatorial influence of environmental parameters on transcription factor activity
Knijnenburg, T.A.; Wessels, L.F.A.; Reinders, M.J.T.
2008-01-01
Motivation: Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. Results: We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. Availability: The Matlab code is available from the authors upon request. Contact: t.a.knijnenburg@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18586711
Sahib, Mouayad A.; Gambardella, Luca M.; Afzal, Wasif; Zamli, Kamal Z.
2016-01-01
Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. PMID:27829025
Luo, Li; Luo, Le; Zhang, Xinli; He, Xiaoli
2017-07-10
Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.
Rapid NMR Assignments of Proteins by Using Optimized Combinatorial Selective Unlabeling.
Dubey, Abhinav; Kadumuri, Rajashekar Varma; Jaipuria, Garima; Vadrevu, Ramakrishna; Atreya, Hanudatta S
2016-02-15
A new approach for rapid resonance assignments in proteins based on amino acid selective unlabeling is presented. The method involves choosing a set of multiple amino acid types for selective unlabeling and identifying specific tripeptides surrounding the labeled residues from specific 2D NMR spectra in a combinatorial manner. The methodology directly yields sequence specific assignments, without requiring a contiguously stretch of amino acid residues to be linked, and is applicable to deuterated proteins. We show that a 2D [(15) N,(1) H] HSQC spectrum with two 2D spectra can result in ∼50 % assignments. The methodology was applied to two proteins: an intrinsically disordered protein (12 kDa) and the 29 kDa (268 residue) α-subunit of Escherichia coli tryptophan synthase, which presents a challenging case with spectral overlaps and missing peaks. The method can augment existing approaches and will be useful for applications such as identifying active-site residues involved in ligand binding, phosphorylation, or protein-protein interactions, even prior to complete resonance assignments. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Mardirossian, Narbe; Head-Gordon, Martin
2016-06-07
A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation is presented in this paper. The final 12-parameter functional form is selected from approximately 10 × 10 9 candidate fits that are trained on a training set of 870 data points and tested on a primary test set of 2964 data points. The resulting density functional, ωB97M-V, is further tested for transferability on a secondary test set of 1152 data points. For comparison, ωB97M-V is benchmarked against 11 leading density functionals including M06-2X, ωB97X-D, M08-HX, M11, ωM05-D, ωB97X-V, and MN15. Encouragingly, the overall performance of ωB97M-V on nearlymore » 5000 data points clearly surpasses that of all of the tested density functionals. Finally, in order to facilitate the use of ωB97M-V, its basis set dependence and integration grid sensitivity are thoroughly assessed, and recommendations that take into account both efficiency and accuracy are provided.« less
The construction of combinatorial manifolds with prescribed sets of links of vertices
NASA Astrophysics Data System (ADS)
Gaifullin, A. A.
2008-10-01
To every oriented closed combinatorial manifold we assign the set (with repetitions) of isomorphism classes of links of its vertices. The resulting transformation \\mathcal{L} is the main object of study in this paper. We pose an inversion problem for \\mathcal{L} and show that this problem is closely related to Steenrod's problem on the realization of cycles and to the Rokhlin-Schwartz-Thom construction of combinatorial Pontryagin classes. We obtain a necessary condition for a set of isomorphism classes of combinatorial spheres to belong to the image of \\mathcal{L}. (Sets satisfying this condition are said to be balanced.) We give an explicit construction showing that every balanced set of isomorphism classes of combinatorial spheres falls into the image of \\mathcal{L} after passing to a multiple set and adding several pairs of the form (Z,-Z), where -Z is the sphere Z with the orientation reversed. Given any singular simplicial cycle \\xi of a space X, this construction enables us to find explicitly a combinatorial manifold M and a map \\varphi\\colon M\\to X such that \\varphi_* \\lbrack M \\rbrack =r[\\xi] for some positive integer r. The construction is based on resolving singularities of \\xi. We give applications of the main construction to cobordisms of manifolds with singularities and cobordisms of simple cells. In particular, we prove that every rational additive invariant of cobordisms of manifolds with singularities admits a local formula. Another application is the construction of explicit (though inefficient) local combinatorial formulae for polynomials in the rational Pontryagin classes of combinatorial manifolds.
Directed differentiation of embryonic stem cells using a bead-based combinatorial screening method.
Tarunina, Marina; Hernandez, Diana; Johnson, Christopher J; Rybtsov, Stanislav; Ramathas, Vidya; Jeyakumar, Mylvaganam; Watson, Thomas; Hook, Lilian; Medvinsky, Alexander; Mason, Chris; Choo, Yen
2014-01-01
We have developed a rapid, bead-based combinatorial screening method to determine optimal combinations of variables that direct stem cell differentiation to produce known or novel cell types having pre-determined characteristics. Here we describe three experiments comprising stepwise exposure of mouse or human embryonic cells to 10,000 combinations of serum-free differentiation media, through which we discovered multiple novel, efficient and robust protocols to generate a number of specific hematopoietic and neural lineages. We further demonstrate that the technology can be used to optimize existing protocols in order to substitute costly growth factors with bioactive small molecules and/or increase cell yield, and to identify in vitro conditions for the production of rare developmental intermediates such as an embryonic lymphoid progenitor cell that has not previously been reported.
Directed Differentiation of Embryonic Stem Cells Using a Bead-Based Combinatorial Screening Method
Tarunina, Marina; Hernandez, Diana; Johnson, Christopher J.; Rybtsov, Stanislav; Ramathas, Vidya; Jeyakumar, Mylvaganam; Watson, Thomas; Hook, Lilian; Medvinsky, Alexander; Mason, Chris; Choo, Yen
2014-01-01
We have developed a rapid, bead-based combinatorial screening method to determine optimal combinations of variables that direct stem cell differentiation to produce known or novel cell types having pre-determined characteristics. Here we describe three experiments comprising stepwise exposure of mouse or human embryonic cells to 10,000 combinations of serum-free differentiation media, through which we discovered multiple novel, efficient and robust protocols to generate a number of specific hematopoietic and neural lineages. We further demonstrate that the technology can be used to optimize existing protocols in order to substitute costly growth factors with bioactive small molecules and/or increase cell yield, and to identify in vitro conditions for the production of rare developmental intermediates such as an embryonic lymphoid progenitor cell that has not previously been reported. PMID:25251366
Ito, Yoichiro; Yamanishi, Mamoru; Ikeuchi, Akinori; Imamura, Chie; Matsuyama, Takashi
2015-01-01
Combinatorial screening used together with a broad library of gene expression cassettes is expected to produce a powerful tool for the optimization of the simultaneous expression of multiple enzymes. Recently, we proposed a highly tunable protein expression system that utilized multiple genome-integrated target genes to fine-tune enzyme expression in yeast cells. This tunable system included a library of expression cassettes each composed of three gene-expression control elements that in different combinations produced a wide range of protein expression levels. In this study, four gene expression cassettes with graded protein expression levels were applied to the expression of three cellulases: cellobiohydrolase 1, cellobiohydrolase 2, and endoglucanase 2. After combinatorial screening for transgenic yeasts simultaneously secreting these three cellulases, we obtained strains with higher cellulase expressions than a strain harboring three cellulase-expression constructs within one high-performance gene expression cassette. These results show that our method will be of broad use throughout the field of metabolic engineering. PMID:26692026
Running Clubs--A Combinatorial Investigation.
ERIC Educational Resources Information Center
Nissen, Phillip; Taylor, John
1991-01-01
Presented is a combinatorial problem based on the Hash House Harriers rule which states that the route of the run should not have previously been traversed by the club. Discovered is how many weeks the club can meet before the rule has to be broken. (KR)
Combinatorial invariants and covariants as tools for conical intersections.
Ryb, Itai; Baer, Roi
2004-12-01
The combinatorial invariant and covariant are introduced as practical tools for analysis of conical intersections in molecules. The combinatorial invariant is a quantity depending on adiabatic electronic states taken at discrete nuclear configuration points. It is invariant to the phase choice (gauge) of these states. In the limit that the points trace a loop in nuclear configuration space, the value of the invariant approaches the corresponding Berry phase factor. The Berry phase indicates the presence of an odd or even number of conical intersections on surfaces bounded by these loops. Based on the combinatorial invariant, we develop a computationally simple and efficient method for locating conical intersections. The method is robust due to its use of gauge invariant nature. It does not rely on the landscape of intersecting potential energy surfaces nor does it require the computation of nonadiabatic couplings. We generalize the concept to open paths and combinatorial covariants for higher dimensions obtaining a technique for the construction of the gauge-covariant adiabatic-diabatic transformation matrix. This too does not make use of nonadiabatic couplings. The importance of using gauge-covariant expressions is underlined throughout. These techniques can be readily implemented by standard quantum chemistry codes. (c) 2004 American Institute of Physics.
Learning to Predict Combinatorial Structures
NASA Astrophysics Data System (ADS)
Vembu, Shankar
2009-12-01
The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A; Akram, M Usman; Jamal, Habib Ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows.
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A.; Akram, M. Usman; Jamal, Habib ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016
Measuring and Specifying Combinatorial Coverage of Test Input Configurations
Kuhn, D. Richard; Kacker, Raghu N.; Lei, Yu
2015-01-01
A key issue in testing is how many tests are needed for a required level of coverage or fault detection. Estimates are often based on error rates in initial testing, or on code coverage. For example, tests may be run until a desired level of statement or branch coverage is achieved. Combinatorial methods present an opportunity for a different approach to estimating required test set size, using characteristics of the test set. This paper describes methods for estimating the coverage of, and ability to detect, t-way interaction faults of a test set based on a covering array. We also develop a connection between (static) combinatorial coverage and (dynamic) code coverage, such that if a specific condition is satisfied, 100% branch coverage is assured. Using these results, we propose practical recommendations for using combinatorial coverage in specifying test requirements. PMID:28133442
An Investigation into Post-Secondary Students' Understanding of Combinatorial Questions
ERIC Educational Resources Information Center
Bulone, Vincent William
2017-01-01
The purpose of this dissertation was to study aspects of how post-secondary students understand combinatorial problems. Within this dissertation, I considered understanding through two different lenses: i) student connections to previous problems; and ii) common combinatorial distinctions such as ordered versus unordered and repetitive versus…
NASA Astrophysics Data System (ADS)
Tong, Wei
2017-04-01
Combinatorial material research offers fast and efficient solutions to identify promising and advanced materials. It has revolutionized the pharmaceutical industry and now is being applied to accelerate the discovery of other new compounds, e.g. superconductors, luminescent materials, catalysts etc. Differing from the traditional trial-and-error process, this approach allows for the synthesis of a large number of compositionally diverse compounds by varying the combinations of the components and adjusting the ratios. It largely reduces the cost of single-sample synthesis/characterization, along with the turnaround time in the material discovery process, therefore, could dramatically change the existing paradigm for discovering and commercializing new materials. This talk outlines the use of combinatorial materials approach in the material discovery in transportation sector. It covers the general introduction to the combinatorial material concept, state of art for its application in energy-related research. At the end, LBNL capabilities in combinatorial materials synthesis and high throughput characterization that are applicable for material discovery research will be highlighted.
Social interaction as a heuristic for combinatorial optimization problems
NASA Astrophysics Data System (ADS)
Fontanari, José F.
2010-11-01
We investigate the performance of a variant of Axelrod’s model for dissemination of culture—the Adaptive Culture Heuristic (ACH)—on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents’ strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N1/4 so that the number of agents must increase with the fourth power of the problem size, N∝F4 , to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F6 which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.
Human Performance on Hard Non-Euclidean Graph Problems: Vertex Cover
ERIC Educational Resources Information Center
Carruthers, Sarah; Masson, Michael E. J.; Stege, Ulrike
2012-01-01
Recent studies on a computationally hard visual optimization problem, the Traveling Salesperson Problem (TSP), indicate that humans are capable of finding close to optimal solutions in near-linear time. The current study is a preliminary step in investigating human performance on another hard problem, the Minimum Vertex Cover Problem, in which…
Kim, Hyo Jin; Turner, Timothy Lee; Jin, Yong-Su
2013-11-01
Recent advances in metabolic engineering have enabled microbial factories to compete with conventional processes for producing fuels and chemicals. Both rational and combinatorial approaches coupled with synthetic and systematic tools play central roles in metabolic engineering to create and improve a selected microbial phenotype. Compared to knowledge-based rational approaches, combinatorial approaches exploiting biological diversity and high-throughput screening have been demonstrated as more effective tools for improving various phenotypes of interest. In particular, identification of unprecedented targets to rewire metabolic circuits for maximizing yield and productivity of a target chemical has been made possible. This review highlights general principles and the features of the combinatorial approaches using various libraries to implement desired phenotypes for strain improvement. In addition, recent applications that harnessed the combinatorial approaches to produce biofuels and biochemicals will be discussed. Copyright © 2013 Elsevier Inc. All rights reserved.
Programming gene expression with combinatorial promoters
Cox, Robert Sidney; Surette, Michael G; Elowitz, Michael B
2007-01-01
Promoters control the expression of genes in response to one or more transcription factors (TFs). The architecture of a promoter is the arrangement and type of binding sites within it. To understand natural genetic circuits and to design promoters for synthetic biology, it is essential to understand the relationship between promoter function and architecture. We constructed a combinatorial library of random promoter architectures. We characterized 288 promoters in Escherichia coli, each containing up to three inputs from four different TFs. The library design allowed for multiple −10 and −35 boxes, and we observed varied promoter strength over five decades. To further analyze the functional repertoire, we defined a representation of promoter function in terms of regulatory range, logic type, and symmetry. Using these results, we identified heuristic rules for programming gene expression with combinatorial promoters. PMID:18004278
Scott-Phillips, Thomas C; Blythe, Richard A
2013-11-06
In a combinatorial communication system, some signals consist of the combinations of other signals. Such systems are more efficient than equivalent, non-combinatorial systems, yet despite this they are rare in nature. Why? Previous explanations have focused on the adaptive limits of combinatorial communication, or on its purported cognitive difficulties, but neither of these explains the full distribution of combinatorial communication in the natural world. Here, we present a nonlinear dynamical model of the emergence of combinatorial communication that, unlike previous models, considers how initially non-communicative behaviour evolves to take on a communicative function. We derive three basic principles about the emergence of combinatorial communication. We hence show that the interdependence of signals and responses places significant constraints on the historical pathways by which combinatorial signals might emerge, to the extent that anything other than the most simple form of combinatorial communication is extremely unlikely. We also argue that these constraints can be bypassed if individuals have the socio-cognitive capacity to engage in ostensive communication. Humans, but probably no other species, have this ability. This may explain why language, which is massively combinatorial, is such an extreme exception to nature's general trend for non-combinatorial communication.
Combinatorial vector fields and the valley structure of fitness landscapes.
Stadler, Bärbel M R; Stadler, Peter F
2010-12-01
Adaptive (downhill) walks are a computationally convenient way of analyzing the geometric structure of fitness landscapes. Their inherently stochastic nature has limited their mathematical analysis, however. Here we develop a framework that interprets adaptive walks as deterministic trajectories in combinatorial vector fields and in return associate these combinatorial vector fields with weights that measure their steepness across the landscape. We show that the combinatorial vector fields and their weights have a product structure that is governed by the neutrality of the landscape. This product structure makes practical computations feasible. The framework presented here also provides an alternative, and mathematically more convenient, way of defining notions of valleys, saddle points, and barriers in landscape. As an application, we propose a refined approximation for transition rates between macrostates that are associated with the valleys of the landscape.
Balancing focused combinatorial libraries based on multiple GPCR ligands
NASA Astrophysics Data System (ADS)
Soltanshahi, Farhad; Mansley, Tamsin E.; Choi, Sun; Clark, Robert D.
2006-08-01
G-Protein coupled receptors (GPCRs) are important targets for drug discovery, and combinatorial chemistry is an important tool for pharmaceutical development. The absence of detailed structural information, however, limits the kinds of combinatorial design techniques that can be applied to GPCR targets. This is particularly problematic given the current emphasis on focused combinatorial libraries. By linking an incremental construction method (OptDesign) to the very fast shape-matching capability of ChemSpace, we have created an efficient method for designing targeted sublibraries that are topomerically similar to known actives. Multi-objective scoring allows consideration of multiple queries (actives) simultaneously. This can lead to a distribution of products skewed towards one particular query structure, however, particularly when the ligands of interest are quite dissimilar to one another. A novel pivoting technique is described which makes it possible to generate promising designs even under those circumstances. The approach is illustrated by application to some serotonergic agonists and chemokine antagonists.
Combinatorial games with a pass: a dynamical systems approach.
Morrison, Rebecca E; Friedman, Eric J; Landsberg, Adam S
2011-12-01
By treating combinatorial games as dynamical systems, we are able to address a longstanding open question in combinatorial game theory, namely, how the introduction of a "pass" move into a game affects its behavior. We consider two well known combinatorial games, 3-pile Nim and 3-row Chomp. In the case of Nim, we observe that the introduction of the pass dramatically alters the game's underlying structure, rendering it considerably more complex, while for Chomp, the pass move is found to have relatively minimal impact. We show how these results can be understood by recasting these games as dynamical systems describable by dynamical recursion relations. From these recursion relations, we are able to identify underlying structural connections between these "games with passes" and a recently introduced class of "generic (perturbed) games." This connection, together with a (non-rigorous) numerical stability analysis, allows one to understand and predict the effect of a pass on a game.
Singh, Narender; Guha, Rajarshi; Giulianotti, Marc; Pinilla, Clemencia; Houghten, Richard; Medina-Franco, Jose L.
2009-01-01
A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR and natural products collections. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different to current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries are located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space. PMID:19301827
Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines
Tabchy, Adel; Eltonsy, Nevine; Housman, David E.; Mills, Gordon B.
2013-01-01
There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance. PMID:23577104
Systematic identification of combinatorial drivers and targets in cancer cell lines.
Tabchy, Adel; Eltonsy, Nevine; Housman, David E; Mills, Gordon B
2013-01-01
There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance.
Cascaded Optimization for a Persistent Data Ferrying Unmanned Aircraft
NASA Astrophysics Data System (ADS)
Carfang, Anthony
This dissertation develops and assesses a cascaded method for designing optimal periodic trajectories and link schedules for an unmanned aircraft to ferry data between stationary ground nodes. This results in a fast solution method without the need to artificially constrain system dynamics. Focusing on a fundamental ferrying problem that involves one source and one destination, but includes complex vehicle and Radio-Frequency (RF) dynamics, a cascaded structure to the system dynamics is uncovered. This structure is exploited by reformulating the nonlinear optimization problem into one that reduces the independent control to the vehicle's motion, while the link scheduling control is folded into the objective function and implemented as an optimal policy that depends on candidate motion control. This formulation is proven to maintain optimality while reducing computation time in comparison to traditional ferry optimization methods. The discrete link scheduling problem takes the form of a combinatorial optimization problem that is known to be NP-Hard. A derived necessary condition for optimality guides the development of several heuristic algorithms, specifically the Most-Data-First Algorithm and the Knapsack Adaptation. These heuristics are extended to larger ferrying scenarios, and assessed analytically and through Monte Carlo simulation, showing better throughput performance in the same order of magnitude of computation time in comparison to other common link scheduling policies. The cascaded optimization method is implemented with a novel embedded software system on a small, unmanned aircraft to validate the simulation results with field experiments. To address the sensitivity of results on trajectory tracking performance, a system that combines motion and link control with waypoint-based navigation is developed and assessed through field experiments. The data ferrying algorithms are further extended by incorporating a Gaussian process to opportunistically learn
Combinatorial enzyme technology for the conversion of agricultural fibers to functional properties
USDA-ARS?s Scientific Manuscript database
The concept of combinatorial chemistry has received little attention in agriculture and food research, although its applications in this area were described more than fifteen years ago (1, 2). More recently, interest in the use of combinatorial chemistry in agrochemical discovery has been revitalize...
Optimization of Highway Work Zone Decisions Considering Short-Term and Long-Term Impacts
2010-01-01
strategies which can minimize the one-time work zone cost. Considering the complex and combinatorial nature of this optimization problem, a heuristic...combination of lane closure and traffic control strategies which can minimize the one-time work zone cost. Considering the complex and combinatorial nature ...zone) NV # the number of vehicle classes NPV $ Net Present Value p’(t) % Adjusted traffic diversion rate at time t p(t) % Natural diversion rate
DNA-Encoded Dynamic Combinatorial Chemical Libraries.
Reddavide, Francesco V; Lin, Weilin; Lehnert, Sarah; Zhang, Yixin
2015-06-26
Dynamic combinatorial chemistry (DCC) explores the thermodynamic equilibrium of reversible reactions. Its application in the discovery of protein binders is largely limited by difficulties in the analysis of complex reaction mixtures. DNA-encoded chemical library (DECL) technology allows the selection of binders from a mixture of up to billions of different compounds; however, experimental results often show low a signal-to-noise ratio and poor correlation between enrichment factor and binding affinity. Herein we describe the design and application of DNA-encoded dynamic combinatorial chemical libraries (EDCCLs). Our experiments have shown that the EDCCL approach can be used not only to convert monovalent binders into high-affinity bivalent binders, but also to cause remarkably enhanced enrichment of potent bivalent binders by driving their in situ synthesis. We also demonstrate the application of EDCCLs in DNA-templated chemical reactions. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Genetic algorithms for the vehicle routing problem
NASA Astrophysics Data System (ADS)
Volna, Eva
2016-06-01
The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.
Combinatorial synthesis of ceramic materials
Lauf, Robert J [Oak Ridge, TN; Walls, Claudia A [Oak Ridge, TN; Boatner, Lynn A [Oak Ridge, TN
2010-02-23
A combinatorial library includes a gelcast substrate defining a plurality of cavities in at least one surface thereof; and a plurality of gelcast test materials in the cavities, at least two of the test materials differing from the substrate in at least one compositional characteristic, the two test materials differing from each other in at least one compositional characteristic.
Combinatorial synthesis of ceramic materials
Lauf, Robert J.; Walls, Claudia A.; Boatner, Lynn A.
2006-11-14
A combinatorial library includes a gelcast substrate defining a plurality of cavities in at least one surface thereof; and a plurality of gelcast test materials in the cavities, at least two of the test materials differing from the substrate in at least one compositional characteristic, the two test materials differing from each other in at least one compositional characteristic.
Experimental Design for Combinatorial and High Throughput Materials Development
NASA Astrophysics Data System (ADS)
Cawse, James N.
2002-12-01
In the past decade, combinatorial and high throughput experimental methods have revolutionized the pharmaceutical industry, allowing researchers to conduct more experiments in a week than was previously possible in a year. Now high throughput experimentation is rapidly spreading from its origins in the pharmaceutical world to larger industrial research establishments such as GE and DuPont, and even to smaller companies and universities. Consequently, researchers need to know the kinds of problems, desired outcomes, and appropriate patterns for these new strategies. Editor James Cawse's far-reaching study identifies and applies, with specific examples, these important new principles and techniques. Experimental Design for Combinatorial and High Throughput Materials Development progresses from methods that are now standard, such as gradient arrays, to mathematical developments that are breaking new ground. The former will be particularly useful to researchers entering the field, while the latter should inspire and challenge advanced practitioners. The book's contents are contributed by leading researchers in their respective fields. Chapters include: -High Throughput Synthetic Approaches for the Investigation of Inorganic Phase Space -Combinatorial Mapping of Polymer Blends Phase Behavior -Split-Plot Designs -Artificial Neural Networks in Catalyst Development -The Monte Carlo Approach to Library Design and Redesign This book also contains over 200 useful charts and drawings. Industrial chemists, chemical engineers, materials scientists, and physicists working in combinatorial and high throughput chemistry will find James Cawse's study to be an invaluable resource.
GALAXY: A new hybrid MOEA for the optimal design of Water Distribution Systems
NASA Astrophysics Data System (ADS)
Wang, Q.; Savić, D. A.; Kapelan, Z.
2017-03-01
A new hybrid optimizer, called genetically adaptive leaping algorithm for approximation and diversity (GALAXY), is proposed for dealing with the discrete, combinatorial, multiobjective design of Water Distribution Systems (WDSs), which is NP-hard and computationally intensive. The merit of GALAXY is its ability to alleviate to a great extent the parameterization issue and the high computational overhead. It follows the generational framework of Multiobjective Evolutionary Algorithms (MOEAs) and includes six search operators and several important strategies. These operators are selected based on their leaping ability in the objective space from the global and local search perspectives. These strategies steer the optimization and balance the exploration and exploitation aspects simultaneously. A highlighted feature of GALAXY lies in the fact that it eliminates majority of parameters, thus being robust and easy-to-use. The comparative studies between GALAXY and three representative MOEAs on five benchmark WDS design problems confirm its competitiveness. GALAXY can identify better converged and distributed boundary solutions efficiently and consistently, indicating a much more balanced capability between the global and local search. Moreover, its advantages over other MOEAs become more substantial as the complexity of the design problem increases.
A Systematic Study of Simple Combinatorial Configurations.
ERIC Educational Resources Information Center
Dubois, Jean-Guy
1984-01-01
A classification of the simple combinatorial configurations which correspond to various cases of distribution and ordering of objects into boxes is given (in French). Concrete descriptions, structured relations, translations, and formalizations are discussed. (MNS)
NASA Astrophysics Data System (ADS)
Burello, E.; Bologa, C.; Frecer, V.; Miertus, S.
Combinatorial chemistry and technologies have been developed to a stage where synthetic schemes are available for generation of a large variety of organic molecules. The innovative concept of combinatorial design assumes that screening of a large and diverse library of compounds will increase the probability of finding an active analogue among the compounds tested. Since the rate at which libraries are screened for activity currently constitutes a limitation to the use of combinatorial technologies, it is important to be selective about the number of compounds to be synthesized. Early experience with combinatorial chemistry indicated that chemical diversity alone did not result in a significant increase in the number of generated lead compounds. Emphasis has therefore been increasingly put on the use of computer assisted combinatorial chemical techniques. Computational methods are valuable in the design of virtual libraries of molecular models. Selection strategies based on computed physicochemical properties of the models or of a target compound are introduced to reduce the time and costs of library synthesis and screening. In addition, computational structure-based library focusing methods can be used to perform in silico screening of the activity of compounds against a target receptor by docking the ligands into the receptor model. Three case studies are discussed dealing with the design of targeted combinatorial libraries of inhibitors of HIV-1 protease, P. falciparum plasmepsin and human urokinase as potential antivirial, antimalarial and anticancer drugs. These illustrate library focusing strategies.
Gian-Carlos Rota and Combinatorial Math.
ERIC Educational Resources Information Center
Kolata, Gina Bari
1979-01-01
Presents the first of a series of occasional articles about mathematics as seen through the eyes of its prominent scholars. In an interview with Gian-Carlos Rota of the Massachusetts Institute of Technology he discusses how combinatorial mathematics began as a field and its future. (HM)
A Model of Students' Combinatorial Thinking
ERIC Educational Resources Information Center
Lockwood, Elise
2013-01-01
Combinatorial topics have become increasingly prevalent in K-12 and undergraduate curricula, yet research on combinatorics education indicates that students face difficulties when solving counting problems. The research community has not yet addressed students' ways of thinking at a level that facilitates deeper understanding of how students…
Combinatorial fabrication and screening of organic light-emitting device arrays
NASA Astrophysics Data System (ADS)
Shinar, Joseph; Shinar, Ruth; Zhou, Zhaoqun
2007-11-01
The combinatorial fabrication and screening of 2-dimensional (2-d) small molecular UV-violet organic light-emitting device (OLED) arrays, 1-d blue-to-red arrays, 1-d intense white OLED libraries, 1-d arrays to study Förster energy transfer in guest-host OLEDs, and 2-d arrays to study exciplex emission from OLEDs is described. The results demonstrate the power of combinatorial approaches for screening OLED materials and configurations, and for studying their basic properties.
Validation of an Instrument and Testing Protocol for Measuring the Combinatorial Analysis Schema.
ERIC Educational Resources Information Center
Staver, John R.; Harty, Harold
1979-01-01
Designs a testing situation to examine the presence of combinatorial analysis, to establish construct validity in the use of an instrument, Combinatorial Analysis Behavior Observation Scheme (CABOS), and to investigate the presence of the schema in young adolescents. (Author/GA)
Su, Zhangli
2016-01-01
Combinatorial patterns of histone modifications are key indicators of different chromatin states. Most of the current approaches rely on the usage of antibodies to analyze combinatorial histone modifications. Here we detail an antibody-free method named MARCC (Matrix-Assisted Reader Chromatin Capture) to enrich combinatorial histone modifications. The combinatorial patterns are enriched on native nucleosomes extracted from cultured mammalian cells and prepared by micrococcal nuclease digestion. Such enrichment is achieved by recombinant chromatin-interacting protein modules, or so-called reader domains, which can bind in a combinatorial modification-dependent manner. The enriched chromatin can be quantified by western blotting or mass spectrometry for the co-existence of histone modifications, while the associated DNA content can be analyzed by qPCR or next-generation sequencing. Altogether, MARCC provides a reproducible, efficient and customizable solution to enrich and analyze combinatorial histone modifications. PMID:26131849
Solving NP-Hard Problems with Physarum-Based Ant Colony System.
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.
Seo, Hyung-Min; Jeon, Jong-Min; Lee, Ju Hee; Song, Hun-Suk; Joo, Han-Byul; Park, Sung-Hee; Choi, Kwon-Young; Kim, Yong Hyun; Park, Kyungmoon; Ahn, Jungoh; Lee, Hongweon; Yang, Yung-Hun
2016-01-01
Furfural is a toxic by-product formulated from pretreatment processes of lignocellulosic biomass. In order to utilize the lignocellulosic biomass on isobutanol production, inhibitory effect of the furfural on isobutanol production was investigated and combinatorial application of two oxidoreductases, FucO and YqhD, was suggested as an alternative strategy. Furfural decreased cell growth and isobutanol production when only YqhD or FucO was employed as an isobutyraldehyde oxidoreductase. However, combinatorial overexpression of FucO and YqhD could overcome the inhibitory effect of furfural giving higher isobutanol production by 110% compared with overexpression of YqhD. The combinatorial oxidoreductases increased furfural detoxification rate 2.1-fold and also accelerated glucose consumption 1.4-fold. When it compares to another known system increasing furfural tolerance, membrane-bound transhydrogenase (pntAB), the combinatorial aldehyde oxidoreductases were better on cell growth and production. Thus, to control oxidoreductases is important to produce isobutanol using furfural-containing biomass and the combinatorial overexpression of FucO and YqhD can be an alternative strategy.
Synthesis and characterization of catalysts and electrocatalysts using combinatorial methods
NASA Astrophysics Data System (ADS)
Ramanathan, Ramnarayanan
This thesis documents attempts at solving three problems. Bead-based parallel synthetic and screening methods based on matrix algorithms were developed. The method was applied to search for new heterogeneous catalysts for dehydrogenation of methylcyclohexane. The most powerful use of the method to date was to optimize metal adsorption and evaluate catalysts as a function of incident energy, likely to be important in the future, should availability of energy be an optimization parameter. This work also highlighted the importance of order of addition of metal salts on catalytic activity and a portion of this work resulted in a patent with UOP LLC, Desplaines, Illinois. Combinatorial methods were also investigated as a tool to search for carbon-monoxide tolerant anode electrocatalysts and methanol tolerant cathode electrocatalysts, resulting in discovery of no new electrocatalysts. A physically intuitive scaling criterion was developed to analyze all experiments on electrocatalysts, providing insight for future experiments. We attempted to solve the CO poisoning problem in polymer electrolyte fuel cells using carbon molecular sieves as a separator. This approach was unsuccessful in solving the CO poisoning problem, possibly due to the tendency of the carbon molecular sieves to concentrate CO and CO 2 in pore walls.
Simulating the component counts of combinatorial structures.
Arratia, Richard; Barbour, A D; Ewens, W J; Tavaré, Simon
2018-02-09
This article describes and compares methods for simulating the component counts of random logarithmic combinatorial structures such as permutations and mappings. We exploit the Feller coupling for simulating permutations to provide a very fast method for simulating logarithmic assemblies more generally. For logarithmic multisets and selections, this approach is replaced by an acceptance/rejection method based on a particular conditioning relationship that represents the distribution of the combinatorial structure as that of independent random variables conditioned on a weighted sum. We show how to improve its acceptance rate. We illustrate the method by estimating the probability that a random mapping has no repeated component sizes, and establish the asymptotic distribution of the difference between the number of components and the number of distinct component sizes for a very general class of logarithmic structures. Copyright © 2018. Published by Elsevier Inc.
Students' Verification Strategies for Combinatorial Problems
ERIC Educational Resources Information Center
Mashiach Eizenberg, Michal; Zaslavsky, Orit
2004-01-01
We focus on a major difficulty in solving combinatorial problems, namely, on the verification of a solution. Our study aimed at identifying undergraduate students' tendencies to verify their solutions, and the verification strategies that they employ when solving these problems. In addition, an attempt was made to evaluate the level of efficiency…
New Hardness Results for Diophantine Approximation
NASA Astrophysics Data System (ADS)
Eisenbrand, Friedrich; Rothvoß, Thomas
We revisit simultaneous Diophantine approximation, a classical problem from the geometry of numbers which has many applications in algorithms and complexity. The input to the decision version of this problem consists of a rational vector α ∈ ℚ n , an error bound ɛ and a denominator bound N ∈ ℕ + . One has to decide whether there exists an integer, called the denominator Q with 1 ≤ Q ≤ N such that the distance of each number Q ·α i to its nearest integer is bounded by ɛ. Lagarias has shown that this problem is NP-complete and optimization versions have been shown to be hard to approximate within a factor n c/ loglogn for some constant c > 0. We strengthen the existing hardness results and show that the optimization problem of finding the smallest denominator Q ∈ ℕ + such that the distances of Q·α i to the nearest integer are bounded by ɛ is hard to approximate within a factor 2 n unless {textrm{P}} = NP.
Counting Pizza Pieces and Other Combinatorial Problems.
ERIC Educational Resources Information Center
Maier, Eugene
1988-01-01
The general combinatorial problem of counting the number of regions into which the interior of a circle is divided by a family of lines is considered. A general formula is developed and its use is illustrated in two situations. (PK)
Ant algorithms for discrete optimization.
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.
Combinatorial complexity of pathway analysis in metabolic networks.
Klamt, Steffen; Stelling, Jörg
2002-01-01
Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.
An Indexed Combinatorial Library: The Synthesis and Testing of Insect Repellents
NASA Astrophysics Data System (ADS)
Miles, William H.; Gelato, Kathy A.; Pompizzi, Kristen M.; Scarbinsky, Aislinn M.; Albrecht, Brian K.; Reynolds, Elaine R.
2001-04-01
An indexed combinatorial library of amides was prepared by the reaction of amines and acid chlorides. A simple test for insect repellency using fruit flies (Drosophila melanogaster) allowed the determination of the most repellent sublibraries. The student-generated data were collected and analyzed to determine the most active amide(s) in the library. This experiment illustrates the fundamentals of combinatorial chemistry, a field that has undergone explosive growth in the last decade.
More Combinatorial Proofs via Flagpole Arrangements
ERIC Educational Resources Information Center
DeTemple, Duane; Reynolds, H. David, II
2006-01-01
Combinatorial identities are proved by counting the number of arrangements of a flagpole and guy wires on a row of blocks that satisfy a set of conditions. An identity is proved by first deriving and then equating two expressions that each count the number of permissible arrangements. Identities for binomial coefficients and recursion relations…
NASA Astrophysics Data System (ADS)
Kabnure, Bahubali Bhupal; Shinde, Vasudev Dhondiram; Kolhapure, Rakesh Ramchandra
2018-05-01
Ductile irons are important engineering materials because of its high strength to weight ratio and castability. The ductile iron castings are used widely for automobile applications due to their wide spectrum of property range. Weight reduction is important in automobile to improve its fuel efficiency which can be achieved by thinning down the casting sections without altering its functionality. Generally, automobile castings are having varying section thickness. Varying thickness castings offers different cooling rates while solidification of the casting. The solidification cooling rate decides the final microstructure of the cast components. Cooling rate was found to affect directly the amount of pearlite and ultimately the as cast properties in varying thickness ductile iron castings. In view of this, the automobile impeller casting is selected for study in the present work as it consists of varying section thickness in which small sections are connected to central hub. The casting solidification simulations were performed and analyzed. The solidification cooling rates were analyzed further to correlate the experimental processing parameters. The samples from poured castings were analyzed for microstructure and hardness at different section thickness. Multiple response optimization of microstructure and hardness was carried out by combined Taguchi and Grey Relational Analysis (GRA). Contribution of input variables on the output variables is attained using ANOVA.
Parry-Romberg reconstruction: optimal timing for hard and soft tissue procedures.
Slack, Ginger C; Tabit, Christina J; Allam, Karam A; Kawamoto, Henry K; Bradley, James P
2012-11-01
For the treatment of Parry-Romberg syndrome or progressive hemifacial atrophy, we studied 3 controversial issues: (1) optimal timing, (2) need for skeletal reconstruction, and (3) need for soft tissue (medial canthus/lacrimal duct) reconstruction. Patients with Parry-Romberg syndrome (>5 y follow-up) were divided into 2 groups: (1) younger than 14 years and (2) 14 years or older (n = 43). Sex, age, severity of deformity, number of procedures, operative times, and augmentation fat volumes were recorded. Physician and patient satisfaction surveys (5-point scale) were obtained, preoperative and postoperative three-dimensional computed tomographic scans were reviewed, and a digital three-dimensional photogrammetry system was used to determine volume retention. Our results indicate that the younger patient group required more procedures compared with the older patient group (4.3 versus 2.8); however, the younger group had higher patient/family satisfaction scores (3.8 versus 3.0). Skeletal and soft tissue reconstruction resulted in improved symmetry score (60% preoperatively to 93% final) and satisfaction scores (3.4 preoperatively to 3.8 final). Patients with Parry-Romberg syndrome required multiple corrective surgeries but showed improvements even when beginning before puberty. Soft and hard tissue reconstruction was beneficial.
A combinatorial perspective of the protein inference problem.
Yang, Chao; He, Zengyou; Yu, Weichuan
2013-01-01
In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from peptide identification results. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we devote ourselves to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound, and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain an analytical expression for protein inference. Our method achieves comparable results with ProteinProphet in a more efficient manner in experiments on two data sets of standard protein mixtures and two data sets of real samples. Based on our model, we study the impact of unique peptides and degenerate peptides (degenerate peptides are peptides shared by at least two proteins) on protein probabilities. Meanwhile, we also study the relationship between our model and ProteinProphet. We name our program ProteinInfer. Its Java source code, our supplementary document and experimental results are available at: >http://bioinformatics.ust.hk/proteininfer.
Lessel, Uta; Wellenzohn, Bernd; Fischer, J Robert; Rarey, Matthias
2012-02-27
A case study is presented illustrating the design of a focused CDK2 library. The scaffold of the library was detected by a feature trees search in a fragment space based on reactions from combinatorial chemistry. For the design the software LoFT (Library optimizer using Feature Trees) was used. The special feature called FTMatch was applied to restrict the parts of the queries where the reagents are permitted to match. This way a 3D scoring function could be simulated. Results were compared with alternative designs by GOLD docking and ROCS 3D alignments.
Synthesizing optimal waste blends
DOE Office of Scientific and Technical Information (OSTI.GOV)
Narayan, V.; Diwekar, W.M.; Hoza, M.
Vitrification of tank wastes to form glass is a technique that will be used for the disposal of high-level waste at Hanford. Process and storage economics show that minimizing the total number of glass logs produced is the key to keeping cost as low as possible. The amount of glass produced can be reduced by blending of the wastes. The optimal way to combine the tanks to minimize the vole of glass can be determined from a discrete blend calculation. However, this problem results in a combinatorial explosion as the number of tanks increases. Moreover, the property constraints make thismore » problem highly nonconvex where many algorithms get trapped in local minima. In this paper the authors examine the use of different combinatorial optimization approaches to solve this problem. A two-stage approach using a combination of simulated annealing and nonlinear programming (NLP) is developed. The results of different methods such as the heuristics approach based on human knowledge and judgment, the mixed integer nonlinear programming (MINLP) approach with GAMS, and branch and bound with lower bound derived from the structure of the given blending problem are compared with this coupled simulated annealing and NLP approach.« less
NASA Astrophysics Data System (ADS)
Yeung, L.
2015-12-01
I present a mode of isotopic ordering that has purely combinatorial origins. It can be important when identical rare isotopes are paired by coincidence (e.g., they are neighbors on the same molecule), or when extrinsic factors govern the isotopic composition of the two atoms that share a chemical bond. By itself, combinatorial isotope pairing yields products with isotopes either randomly distributed or with a deficit relative to a random distribution of isotopes. These systematics arise because of an unconventional coupling between the formation of singly- and multiply-substituted isotopic moieties. In a random distribution, rare isotopes are symmetrically distributed: Single isotopic substitutions (e.g., H‒D and D‒H in H2) occur with equal probability, and double isotopic substitutions (e.g., D2) occur according to random chance. The absence of symmetry in a bond-making complex can yield unequal numbers of singly-substituted molecules (e.g., more H‒D than D‒H in H2), which is recorded in the product molecule as a deficit in doubly-substituted moieties and an "anticlumped" isotope distribution (i.e., Δn < 0). Enzymatic isotope pairing reactions, which can have site-specific isotopic fractionation factors and atom reservoirs, should express this class of combinatorial isotope effect. Chemical-kinetic isotope effects, which are related to the bond-forming transition state, arise independently and express second-order combinatorial effects. In general, both combinatorial and chemical factors are important for calculating and interpreting clumped-isotope signatures of individual reactions. In many reactions relevant to geochemical oxygen, carbon, and nitrogen cycling, combinatorial isotope pairing likely plays a strong role in the clumped isotope distribution of the products. These isotopic signatures, manifest as either directly bound isotope clumps or as features of a molecule's isotopic anatomy, could be exploited as tracers of biogeochemistry that can
NASA Astrophysics Data System (ADS)
Potyrailo, Radislav A.; Chisholm, Bret J.; Olson, Daniel R.; Brennan, Michael J.; Molaison, Chris A.
2002-02-01
Design, validation, and implementation of an optical spectroscopic system for high-throughput analysis of combinatorially developed protective organic coatings are reported. Our approach replaces labor-intensive coating evaluation steps with an automated system that rapidly analyzes 8x6 arrays of coating elements that are deposited on a plastic substrate. Each coating element of the library is 10 mm in diameter and 2 to 5 micrometers thick. Performance of coatings is evaluated with respect to their resistance to wear abrasion because this parameter is one of the primary considerations in end-use applications. Upon testing, the organic coatings undergo changes that are impossible to quantitatively predict using existing knowledge. Coatings are abraded using industry-accepted abrasion test methods at single-or multiple-abrasion conditions, followed by high- throughput analysis of abrasion-induced light scatter. The developed automated system is optimized for the analysis of diffusively scattered light that corresponds to 0 to 30% haze. System precision of 0.1 to 2.5% relative standard deviation provides capability for the reliable ranking of coatings performance. While the system was implemented for high-throughput screening of combinatorially developed organic protective coatings for automotive applications, it can be applied to a variety of other applications where materials ranking can be achieved using optical spectroscopic tools.
DNA Assembly Techniques for Next Generation Combinatorial Biosynthesis of Natural Products
Cobb, Ryan E.; Ning, Jonathan C.; Zhao, Huimin
2013-01-01
Natural product scaffolds remain important leads for pharmaceutical development. However, transforming a natural product into a drug entity often requires derivatization to enhance the compound’s therapeutic properties. A powerful method by which to perform this derivatization is combinatorial biosynthesis, the manipulation of the genes in the corresponding pathway to divert synthesis towards novel derivatives. While these manipulations have traditionally been carried out via restriction digestion/ligation-based cloning, the shortcomings of such techniques limit their throughput and thus the scope of corresponding combinatorial biosynthesis experiments. In the burgeoning field of synthetic biology, the demand for facile DNA assembly techniques has promoted the development of a host of novel DNA assembly strategies. Here we describe the advantages of these recently-developed tools for rapid, efficient synthesis of large DNA constructs. We also discuss their potential to facilitate the simultaneous assembly of complete libraries of natural product biosynthetic pathways, ushering in the next generation of combinatorial biosynthesis. PMID:24127070
Kasperkiewicz, Paulina; Poreba, Marcin; Snipas, Scott J.; Parker, Heather; Winterbourn, Christine C.; Salvesen, Guy S.; Drag, Marcin
2014-01-01
The exploration of protease substrate specificity is generally restricted to naturally occurring amino acids, limiting the degree of conformational space that can be surveyed. We substantially enhanced this by incorporating 102 unnatural amino acids to explore the S1–S4 pockets of human neutrophil elastase. This approach provides hybrid natural and unnatural amino acid sequences, and thus we termed it the Hybrid Combinatorial Substrate Library. Library results were validated by the synthesis of individual tetrapeptide substrates, with the optimal substrate demonstrating more than three orders of magnitude higher catalytic efficiency than commonly used substrates of elastase. This optimal substrate was converted to an activity-based probe that demonstrated high selectivity and revealed the specific presence of active elastase during the process of neutrophil extracellular trap formation. We propose that this approach can be successfully used for any type of endopeptidase to deliver high activity and selectivity in substrates and probes. PMID:24550277
Combinatorial chemistry on solid support in the search for central nervous system agents.
Zajdel, Paweł; Pawłowski, Maciej; Martinez, Jean; Subra, Gilles
2009-08-01
The advent of combinatorial chemistry was one of the most important developments, that has significantly contributed to the drug discovery process. Within just a few years, its initial concept aimed at production of libraries containing huge number of compounds (thousands to millions), so called screening libraries, has shifted towards preparation of small and medium-sized rationally designed libraries. When applicable, the use of solid supports for the generation of libraries has been a real breakthrough in enhancing productivity. With a limited amount of resin and simple manual workups, the split/mix procedure may generate thousands of bead-tethered compounds. Beads can be chemically or physically encoded to facilitate the identification of a hit after the biological assay. Compartmentalization of solid supports using small reactors like teabags, kans or pellicular discrete supports like Lanterns resulted in powerful sort and combine technologies, relying on codes 'written' on the reactor, and thus reducing the need for automation and improving the number of compounds synthesized. These methods of solid-phase combinatorial chemistry have been recently supported by introduction of solid-supported reagents and scavenger resins. The first part of this review discusses the general premises of combinatorial chemistry and some methods used in the design of primary and focused combinatorial libraries. The aim of the second part is to present combinatorial chemistry methodologies aimed at discovering bioactive compounds acting on diverse GPCR involved in central nervous system disorders.
Optimizing Perioperative Decision Making: Improved Information for Clinical Workflow Planning
Doebbeling, Bradley N.; Burton, Matthew M.; Wiebke, Eric A.; Miller, Spencer; Baxter, Laurence; Miller, Donald; Alvarez, Jorge; Pekny, Joseph
2012-01-01
Perioperative care is complex and involves multiple interconnected subsystems. Delayed starts, prolonged cases and overtime are common. Surgical procedures account for 40–70% of hospital revenues and 30–40% of total costs. Most planning and scheduling in healthcare is done without modern planning tools, which have potential for improving access by assisting in operations planning support. We identified key planning scenarios of interest to perioperative leaders, in order to examine the feasibility of applying combinatorial optimization software solving some of those planning issues in the operative setting. Perioperative leaders desire a broad range of tools for planning and assessing alternate solutions. Our modeled solutions generated feasible solutions that varied as expected, based on resource and policy assumptions and found better utilization of scarce resources. Combinatorial optimization modeling can effectively evaluate alternatives to support key decisions for planning clinical workflow and improving care efficiency and satisfaction. PMID:23304284
Optimizing perioperative decision making: improved information for clinical workflow planning.
Doebbeling, Bradley N; Burton, Matthew M; Wiebke, Eric A; Miller, Spencer; Baxter, Laurence; Miller, Donald; Alvarez, Jorge; Pekny, Joseph
2012-01-01
Perioperative care is complex and involves multiple interconnected subsystems. Delayed starts, prolonged cases and overtime are common. Surgical procedures account for 40-70% of hospital revenues and 30-40% of total costs. Most planning and scheduling in healthcare is done without modern planning tools, which have potential for improving access by assisting in operations planning support. We identified key planning scenarios of interest to perioperative leaders, in order to examine the feasibility of applying combinatorial optimization software solving some of those planning issues in the operative setting. Perioperative leaders desire a broad range of tools for planning and assessing alternate solutions. Our modeled solutions generated feasible solutions that varied as expected, based on resource and policy assumptions and found better utilization of scarce resources. Combinatorial optimization modeling can effectively evaluate alternatives to support key decisions for planning clinical workflow and improving care efficiency and satisfaction.
Criticism of EFSA's scientific opinion on combinatorial effects of 'stacked' GM plants.
Bøhn, Thomas
2018-01-01
Recent genetically modified plants tend to include both insect resistance and herbicide tolerance traits. Some of these 'stacked' GM plants have multiple Cry-toxins expressed as well as tolerance to several herbicides. This means that non-target organisms in the environment (biodiversity) will be co-exposed to multiple stressors simultaneously. A similar co-exposure may happen to consumers through chemical residues in the food chain. EFSA, the responsible unit for minimizing risk of harm in European food chains, has expressed its scientific interest in combinatorial effects. However, when new data showed how two Cry-toxins acted in combination (added toxicity), and that the same Cry-toxins showed combinatorial effects when co-exposed with Roundup (Bøhn et al., 2016), EFSA dismissed these new peer-reviewed results. In effect, EFSA claimed that combinatorial effects are not relevant for itself. EFSA was justifying this by referring to a policy question, and by making invalid assumptions, which could have been checked directly with the lead-author. With such approach, EFSA may miss the opportunity to improve its environmental and health risk assessment of toxins and pesticides in the food chain. Failure to follow its own published requests for combinatorial effects research, may also risk jeopardizing EFSA's scientific and public reputation. Copyright © 2017. Published by Elsevier Ltd.
Combinatorial treatments enhance recovery following facial nerve crush.
Sharma, Nijee; Moeller, Carl W; Marzo, Sam J; Jones, Kathryn J; Foecking, Eileen M
2010-08-01
To investigate the effects of various combinatorial treatments, consisting of a tapering dose of prednisone (P), a brief period of nerve electrical stimulation (ES), and systemic testosterone propionate (TP) on improving functional recovery following an intratemporal facial nerve crush injury. Prospective, controlled animal study. After a right intratemporal facial nerve crush, adult male Sprague-Dawley rats were divided into the following eight treatment groups: 1) no treatment, 2) P only, 3) ES only, 4) ES + P, 5) TP only, 6) TP + P, 7) ES + TP, and 8) ES + TP + P. For each group n = 4-8. Recovery of the eyeblink reflex and vibrissae orientation and movement were assessed. Changes in peak amplitude and latency of evoked response, in response to facial nerve stimulation, was also recorded weekly. : Brief ES of the proximal nerve stump most effectively accelerated the initiation of functional recovery. Also, ES or TP treatments enhanced recovery of some functional parameters more than P treatment. When administered alone, none of the three treatments improved recovery of complete facial function. Only the combinatorial treatment of ES + TP, regardless of the presence of P, accelerated complete functional recovery and return of normal motor nerve conduction. Our findings suggest that a combinatorial treatment strategy of using brief ES and TP together promises to be an effective therapeutic intervention for promoting regeneration following facial nerve injury. Administration of P neither augments nor hinders recovery.
Combinatorial Color Space Models for Skin Detection in Sub-continental Human Images
NASA Astrophysics Data System (ADS)
Khaled, Shah Mostafa; Saiful Islam, Md.; Rabbani, Md. Golam; Tabassum, Mirza Rehenuma; Gias, Alim Ul; Kamal, Md. Mostafa; Muctadir, Hossain Muhammad; Shakir, Asif Khan; Imran, Asif; Islam, Saiful
Among different color models HSV, HLS, YIQ, YCbCr, YUV, etc. have been most popular for skin detection. Most of the research done in the field of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins, skin colors of Indian sub-continentals have not been focused separately. Combinatorial algorithms, without affecting asymptotic complexity can be developed using the skin detection concepts of these color models for boosting detection performance. In this paper a comparative study of different combinatorial skin detection algorithms have been made. For training and testing 200 images (skin and non skin) containing pictures of sub-continental male and females have been used to measure the performance of the combinatorial approaches, and considerable development in success rate with True Positive of 99.5% and True Negative of 93.3% have been observed.
The Combinatorial Trace Method in Action
ERIC Educational Resources Information Center
Krebs, Mike; Martinez, Natalie C.
2013-01-01
On any finite graph, the number of closed walks of length k is equal to the sum of the kth powers of the eigenvalues of any adjacency matrix. This simple observation is the basis for the combinatorial trace method, wherein we attempt to count (or bound) the number of closed walks of a given length so as to obtain information about the graph's…
Natural products and combinatorial chemistry: back to the future.
Ortholand, Jean-Yves; Ganesan, A
2004-06-01
The introduction of high-throughput synthesis and combinatorial chemistry has precipitated a global decline in the screening of natural products by the pharmaceutical industry. Some companies terminated their natural products program, despite the unproven success of the new technologies. This was a premature decision, as natural products have a long history of providing important medicinal agents. Furthermore, they occupy a complementary region of chemical space compared with the typical synthetic compound library. For these reasons, the interest in natural products has been rekindled. Various approaches have evolved that combine the power of natural products and organic chemistry, ranging from the combinatorial total synthesis of analogues to the exploration of natural product scaffolds and the design of completely unnatural molecules that resemble natural products in their molecular characteristics.
Generation of Dynamic Combinatorial Libraries Using Hydrazone‐Functionalized Surface Mimetics
Hewitt, Sarah H.
2018-01-01
Dynamic combinatorial chemistry (DCC) represents an approach, whereby traditional supramolecular scaffolds used for protein surface recognition might be exploited to achieve selective high affinity target recognition. Synthesis, in situ screening and amplification under selection pressure allows the generation of ligands, which bear different moieties capable of making multivalent non‐covalent interactions with target proteins. Generic tetracarboxyphenyl porphyrin scaffolds bearing four hydrazide moieties have been used to form dynamic combinatorial libraries (DCLs) using aniline‐catalyzed reversible hydrazone exchange reactions, in 10 % DMSO, 5 mm NH4OAc, at pH 6.75. High resolution mass spectrometry (HRMS) was used to monitor library composition and establish conditions under which equilibria were established.
NASA Astrophysics Data System (ADS)
Masuda, Kazuaki; Aiyoshi, Eitaro
We propose a method for solving optimal price decision problems for simultaneous multi-article auctions. An auction problem, originally formulated as a combinatorial problem, determines both every seller's whether or not to sell his/her article and every buyer's which article(s) to buy, so that the total utility of buyers and sellers will be maximized. Due to the duality theory, we transform it equivalently into a dual problem in which Lagrange multipliers are interpreted as articles' transaction price. As the dual problem is a continuous optimization problem with respect to the multipliers (i.e., the transaction prices), we propose a numerical method to solve it by applying heuristic global search methods. In this paper, Particle Swarm Optimization (PSO) is used to solve the dual problem, and experimental results are presented to show the validity of the proposed method.
Comprehensive human transcription factor binding site map for combinatory binding motifs discovery.
Müller-Molina, Arnoldo J; Schöler, Hans R; Araúzo-Bravo, Marcos J
2012-01-01
To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.
Comprehensive Human Transcription Factor Binding Site Map for Combinatory Binding Motifs Discovery
Müller-Molina, Arnoldo J.; Schöler, Hans R.; Araúzo-Bravo, Marcos J.
2012-01-01
To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%–20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory “DNA words.” From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%—far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of “DNA words,” newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters. PMID:23209563
An evolutionary strategy based on partial imitation for solving optimization problems
NASA Astrophysics Data System (ADS)
Javarone, Marco Alberto
2016-12-01
In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem whose search space grows exponentially, increasing the number of cities, up to becoming NP-hard. The solutions of the TSP can be codified by arrays of cities, and can be evaluated by fitness, computed according to a cost function (e.g. the length of a path). Our method is based on the evolution of an agent population by means of an imitative mechanism, we define 'partial imitation'. In particular, agents receive a random solution and then, interacting among themselves, may imitate the solutions of agents with a higher fitness. Since the imitation mechanism is only partial, agents copy only one entry (randomly chosen) of another array (i.e. solution). In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. driven towards an ordered phase. We highlight that the adopted 'partial imitation' mechanism allows the population to generate solutions over time, before reaching the final equilibrium. Results of numerical simulations show that our method is able to find, in a finite time, both optimal and suboptimal solutions, depending on the size of the considered search space.
Learning With Mixed Hard/Soft Pointwise Constraints.
Gnecco, Giorgio; Gori, Marco; Melacci, Stefano; Sanguineti, Marcello
2015-09-01
A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples of supervised learning, which can be violated at the cost of some penalization (quantified by the choice of a suitable loss function) play the role of soft pointwise constraints. Constrained variational calculus is exploited to derive a representer theorem that provides a description of the functional structure of the optimal solution to the proposed learning paradigm. It is shown that such an optimal solution can be represented in terms of a set of support constraints, which generalize the concept of support vectors and open the doors to a novel learning paradigm, called support constraint machines. The general theory is applied to derive the representation of the optimal solution to the problem of learning from hard linear pointwise constraints combined with soft pointwise constraints induced by supervised examples. In some cases, closed-form optimal solutions are obtained.
Neural correlates of implicit and explicit combinatorial semantic processing
Graves, William W.; Binder, Jeffrey R.; Desai, Rutvik H.; Conant, Lisa L.; Seidenberg, Mark S.
2010-01-01
Language consists of sequences of words, but comprehending phrases involves more than concatenating meanings: A boat house is a shelter for boats, whereas a summer house is a house used during summer, and a ghost house is typically uninhabited. Little is known about the brain bases of combinatorial semantic processes. We performed two fMRI experiments using familiar, highly meaningful phrases (LAKE HOUSE) and unfamiliar phrases with minimal meaning created by reversing the word order of the familiar items (HOUSE LAKE). The first experiment used a 1-back matching task to assess implicit semantic processing, and the second used a classification task to engage explicit semantic processing. These conditions required processing of the same words, but with more effective combinatorial processing in the meaningful condition. The contrast of meaningful versus reversed phrases revealed activation primarily during the classification task, to a greater extent in the right hemisphere, including right angular gyrus, dorsomedial prefrontal cortex, and bilateral posterior cingulate/precuneus, areas previously implicated in semantic processing. Positive correlations of fMRI signal with lexical (word-level) frequency occurred exclusively with the 1-back task and to a greater spatial extent on the left, including left posterior middle temporal gyrus and bilateral parahippocampus. These results reveal strong effects of task demands on engagement of lexical versus combinatorial processing and suggest a hemispheric dissociation between these levels of semantic representation. PMID:20600969
Iconicity and the Emergence of Combinatorial Structure in Language.
Verhoef, Tessa; Kirby, Simon; de Boer, Bart
2016-11-01
In language, recombination of a discrete set of meaningless building blocks forms an unlimited set of possible utterances. How such combinatorial structure emerged in the evolution of human language is increasingly being studied. It has been shown that it can emerge when languages culturally evolve and adapt to human cognitive biases. How the emergence of combinatorial structure interacts with the existence of holistic iconic form-meaning mappings in a language is still unknown. The experiment presented in this paper studies the role of iconicity and human cognitive learning biases in the emergence of combinatorial structure in artificial whistled languages. Participants learned and reproduced whistled words for novel objects with the use of a slide whistle. Their reproductions were used as input for the next participant, to create transmission chains and simulate cultural transmission. Two conditions were studied: one in which the persistence of iconic form-meaning mappings was possible and one in which this was experimentally made impossible. In both conditions, cultural transmission caused the whistled languages to become more learnable and more structured, but this process was slightly delayed in the first condition. Our findings help to gain insight into when and how words may lose their iconic origins when they become part of an organized linguistic system. Copyright © 2015 Cognitive Science Society, Inc.
The combinatorial control of alternative splicing in C. elegans
2017-01-01
Normal development requires the right splice variants to be made in the right tissues at the right time. The core splicing machinery is engaged in all splicing events, but which precise splice variant is made requires the choice between alternative splice sites—for this to occur, a set of splicing factors (SFs) must recognize and bind to short RNA motifs in the pre-mRNA. In C. elegans, there is known to be extensive variation in splicing patterns across development, but little is known about the targets of each SF or how multiple SFs combine to regulate splicing. Here we combine RNA-seq with in vitro binding assays to study how 4 different C. elegans SFs, ASD-1, FOX-1, MEC-8, and EXC-7, regulate splicing. The 4 SFs chosen all have well-characterised biology and well-studied loss-of-function genetic alleles, and all contain RRM domains. Intriguingly, while the SFs we examined have varied roles in C. elegans development, they show an unexpectedly high overlap in their targets. We also find that binding sites for these SFs occur on the same pre-mRNAs more frequently than expected suggesting extensive combinatorial control of splicing. We confirm that regulation of splicing by multiple SFs is often combinatorial and show that this is functionally significant. We also find that SFs appear to combine to affect splicing in two modes—they either bind in close proximity within the same intron or they appear to bind to separate regions of the intron in a conserved order. Finally, we find that the genes whose splicing are regulated by multiple SFs are highly enriched for genes involved in the cytoskeleton and in ion channels that are key for neurotransmission. Together, this shows that specific classes of genes have complex combinatorial regulation of splicing and that this combinatorial regulation is critical for normal development to occur. PMID:29121637
Optimization Strategies for Sensor and Actuator Placement
NASA Technical Reports Server (NTRS)
Padula, Sharon L.; Kincaid, Rex K.
1999-01-01
This paper provides a survey of actuator and sensor placement problems from a wide range of engineering disciplines and a variety of applications. Combinatorial optimization methods are recommended as a means for identifying sets of actuators and sensors that maximize performance. Several sample applications from NASA Langley Research Center, such as active structural acoustic control, are covered in detail. Laboratory and flight tests of these applications indicate that actuator and sensor placement methods are effective and important. Lessons learned in solving these optimization problems can guide future research.
NASA Astrophysics Data System (ADS)
Vallet, B.; Soheilian, B.; Brédif, M.
2014-08-01
The 3D reconstruction of similar 3D objects detected in 2D faces a major issue when it comes to grouping the 2D detections into clusters to be used to reconstruct the individual 3D objects. Simple clustering heuristics fail as soon as similar objects are close. This paper formulates a framework to use the geometric quality of the reconstruction as a hint to do a proper clustering. We present a methodology to solve the resulting combinatorial optimization problem with some simplifications and approximations in order to make it tractable. The proposed method is applied to the reconstruction of 3D traffic signs from their 2D detections to demonstrate its capacity to solve ambiguities.
ERIC Educational Resources Information Center
Barratt, Barnaby B.
1975-01-01
This study investigated the emergence of combinatorial competence in early adolescence and the effectiveness of a programmed discovery training procedure. Significant increases in combinatorial skill with age were shown; it was found that the expression of this skill was significantly facilitated if problems involved concrete material of low…
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.
Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network
NASA Technical Reports Server (NTRS)
Kuhn, D. Richard; Kacker, Raghu; Lei, Yu
2010-01-01
This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.
Sample size determination in combinatorial chemistry.
Zhao, P L; Zambias, R; Bolognese, J A; Boulton, D; Chapman, K
1995-01-01
Combinatorial chemistry is gaining wide appeal as a technique for generating molecular diversity. Among the many combinatorial protocols, the split/recombine method is quite popular and particularly efficient at generating large libraries of compounds. In this process, polymer beads are equally divided into a series of pools and each pool is treated with a unique fragment; then the beads are recombined, mixed to uniformity, and redivided equally into a new series of pools for the subsequent couplings. The deviation from the ideal equimolar distribution of the final products is assessed by a special overall relative error, which is shown to be related to the Pearson statistic. Although the split/recombine sampling scheme is quite different from those used in analysis of categorical data, the Pearson statistic is shown to still follow a chi2 distribution. This result allows us to derive the required number of beads such that, with 99% confidence, the overall relative error is controlled to be less than a pregiven tolerable limit L1. In this paper, we also discuss another criterion, which determines the required number of beads so that, with 99% confidence, all individual relative errors are controlled to be less than a pregiven tolerable limit L2 (0 < L2 < 1). PMID:11607586
A combinatorial morphospace for angiosperm pollen
NASA Astrophysics Data System (ADS)
Mander, Luke
2016-04-01
The morphology of angiosperm (flowering plant) pollen is extraordinarily diverse. This diversity results from variations in the morphology of discrete anatomical components. These components include the overall shape of a pollen grain, the stratification of the exine, the number and form of any apertures, the type of dispersal unit, and the nature of any surface ornamentation. Different angiosperm pollen morphotypes reflect different combinations of these discrete components. In this talk, I ask the following question: given the anatomical components of angiosperm pollen that are known to exist in the plant kingdom, how many unique biologically plausible combinations of these components are there? I explore this question from the perspective of enumerative combinatorics using an algorithm I have written in the Python programming language. This algorithm (1) calculates the number of combinations of these components; (2) enumerates those combinations; and (3) graphically displays those combinations. The result is a combinatorial morphospace that reflects an underlying notion that the process of morphogenesis in angiosperm pollen can be thought of as an n choose k counting problem. I compare the morphology of extant and fossil angiosperm pollen grains to this morphospace, and suggest that from a combinatorial point of view angiosperm pollen is not as diverse as it could be, which may be a result of developmental constraints.
NASA Astrophysics Data System (ADS)
Kodali, Anuradha
In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a
NASA Astrophysics Data System (ADS)
Simonton, Dean Keith
2010-06-01
Campbell (1960) proposed that creative thought should be conceived as a blind-variation and selective-retention process (BVSR). This article reviews the developments that have taken place in the half century that has elapsed since his proposal, with special focus on the use of combinatorial models as formal representations of the general theory. After defining the key concepts of blind variants, creative thought, and disciplinary context, the combinatorial models are specified in terms of individual domain samples, variable field size, ideational combination, and disciplinary communication. Empirical implications are then derived with respect to individual, domain, and field systems. These abstract combinatorial models are next provided substantive reinforcement with respect to findings concerning the cognitive processes, personality traits, developmental factors, and social contexts that contribute to creativity. The review concludes with some suggestions regarding future efforts to explicate creativity according to BVSR theory.
ERIC Educational Resources Information Center
Stevens, Victoria
2014-01-01
The author considers combinatory play as an intersection between creativity, play, and neuroaesthetics. She discusses combinatory play as vital to the creative process in art and science, particularly with regard to the incubation of new ideas. She reviews findings from current neurobiological research and outlines the way that the brain activates…
NASA Astrophysics Data System (ADS)
Sun, Zhiyuan; Guo, Bing; Rao, Zhimin; Zhao, Qingliang
2014-08-01
In consideration of the excellent property of SiC, the ground micro-structured surface quality is hard to meet the requirement - consequently the ultrasonic vibration assisted polishing (UVAP) of micro-structures of molds is proposed in this paper. Through the orthogonal experiment, the parameters of UVAP of micro-structures were optimized. The experimental results show that, abrasive polishing process, the effect of the workpiece feed rate on the surface roughness (Ra), groove tip radius (R) and material removal rate (MRR) of micro-structures is significant. While, the UVAP, the most significant effect factor for Ra, R and MRR is the ultrasonic amplitude of the ultrasonic vibration. In addition, within the scope of the polishing process parameters selected by preliminary experiments, ultrasonic amplitude of 2.5μm, polishing force of 0.5N, workpiece feed rate of 5 mm·min-1, polishing wheel rotational speed of 50rpm, polishing time of 35min, abrasive size of 100nm and the polishing liquid concentration of 15% is the best technology of UVAP of micro-structures. Under the optimal parameters, the ground traces on the micro-structured surface were removed efficiently and the integrity of the edges of the micro-structure after grinding was maintained efficiently.
Device for preparing combinatorial libraries in powder metallurgy.
Yang, Shoufeng; Evans, Julian R G
2004-01-01
This paper describes a powder-metering, -mixing, and -dispensing mechanism that can be used as a method for producing large numbers of samples for metallurgical evaluation or electrical or mechanical testing from multicomponent metal and cermet powder systems. It is designed to make use of the same commercial powders that are used in powder metallurgy and, therefore, to produce samples that are faithful to the microstructure of finished products. The particle assemblies produced by the device could be consolidated by die pressing, isostatic pressing, laser sintering, or direct melting. The powder metering valve provides both on/off and flow rate control of dry powders in open capillaries using acoustic vibration. The valve is simple and involves no relative movement, avoiding seizure with fine powders. An orchestra of such valves can be arranged on a building platform to prepare multicomponent combinatorial libraries. As with many combinatorial devices, identification and evaluation of sources of mixing error as a function of sample size is mandatory. Such an analysis is presented.
Optimizing Sensor and Actuator Arrays for ASAC Noise Control
NASA Technical Reports Server (NTRS)
Palumbo, Dan; Cabell, Ran
2000-01-01
This paper summarizes the development of an approach to optimizing the locations for arrays of sensors and actuators in active noise control systems. A type of directed combinatorial search, called Tabu Search, is used to select an optimal configuration from a much larger set of candidate locations. The benefit of using an optimized set is demonstrated. The importance of limiting actuator forces to realistic levels when evaluating the cost function is discussed. Results of flight testing an optimized system are presented. Although the technique has been applied primarily to Active Structural Acoustic Control systems, it can be adapted for use in other active noise control implementations.
Chen, Hong-Zhang; Liu, Zhi-Hua
2015-06-01
Pretreatment is a key unit operation affecting the refinery efficiency of plant biomass. However, the poor efficiency of pretreatment and the lack of basic theory are the main challenges to the industrial implementation of the plant biomass refinery. The purpose of this work is to review steam explosion and its combinatorial pretreatment as a means of overcoming the intrinsic characteristics of plant biomass, including recalcitrance, heterogeneity, multi-composition, and diversity. The main advantages of the selective use of steam explosion and other combinatorial pretreatments across the diversity of raw materials are introduced. Combinatorial pretreatment integrated with other unit operations is proposed as a means to exploit the high-efficiency production of bio-based products from plant biomass. Finally, several pilot- and demonstration-scale operations of the plant biomass refinery are described. Based on the principle of selective function and structure fractionation, and multi-level and directional composition conversion, an integrated process with the combinatorial pretreatments of steam explosion and other pretreatments as the core should be feasible and conform to the plant biomass refinery concept. Combinatorial pretreatments of steam explosion and other pretreatments should be further exploited based on the type and intrinsic characteristics of the plant biomass used, the bio-based products to be made, and the complementarity of the processes. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Potyrailo, Radislav A; Chisholm, Bret J; Morris, William G; Cawse, James N; Flanagan, William P; Hassib, Lamyaa; Molaison, Chris A; Ezbiansky, Karin; Medford, George; Reitz, Hariklia
2003-01-01
Coupling of combinatorial chemistry methods with high-throughput (HT) performance testing and measurements of resulting properties has provided a powerful set of tools for the 10-fold accelerated discovery of new high-performance coating materials for automotive applications. Our approach replaces labor-intensive steps with automated systems for evaluation of adhesion of 8 x 6 arrays of coating elements that are discretely deposited on a single 9 x 12 cm plastic substrate. Performance of coatings is evaluated with respect to their resistance to adhesion loss, because this parameter is one of the primary considerations in end-use automotive applications. Our HT adhesion evaluation provides previously unavailable capabilities of high speed and reproducibility of testing by using a robotic automation, an expanded range of types of tested coatings by using the coating tagging strategy, and an improved quantitation by using high signal-to-noise automatic imaging. Upon testing, the coatings undergo changes that are impossible to quantitatively predict using existing knowledge. Using our HT methodology, we have developed several coatings leads. These HT screening results for the best coating compositions have been validated on the traditional scales of coating formulation and adhesion loss testing. These validation results have confirmed the superb performance of combinatorially developed coatings over conventional coatings on the traditional scale.
Chatterjee, Kaushik; Lin-Gibson, Sheng; Wallace, William E.; Parekh, Sapun H.; Lee, Young J.; Cicerone, Marcus T.; Young, Marian F.; Simon, Carl G.
2011-01-01
Cells are known to sense and respond to the physical properties of their environment and those of tissue scaffolds. Optimizing these cell-material interactions is critical in tissue engineering. In this work, a simple and inexpensive combinatorial platform was developed to rapidly screen three-dimensional (3D) tissue scaffolds and was applied to screen the effect of scaffold properties for tissue engineering of bone. Differentiation of osteoblasts was examined in poly(ethylene glycol) hydrogel gradients spanning a 30-fold range in compressive modulus (≈ 10 kPa to ≈ 300 kPa). Results demonstrate that material properties (gel stiffness) of scaffolds can be leveraged to induce cell differentiation in 3D culture as an alternative to biochemical cues such as soluble supplements, immobilized biomolecules and vectors, which are often expensive, labile and potentially carcinogenic. Gel moduli of ≈ 225 kPa and higher enhanced osteogenesis. Furthermore, it is proposed that material-induced cell differentiation can be modulated to engineer seamless tissue interfaces between mineralized bone tissue and softer tissues such as ligaments and tendons. This work presents a combinatorial method to screen biological response to 3D hydrogel scaffolds that more closely mimics the 3D environment experienced by cells in vivo. PMID:20378163
Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria
Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M
2014-01-01
Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hunt, H.B. III; Rosenkrantz, D.J.; Stearns, R.E.
We study both the complexity and approximability of various graph and combinatorial problems specified using two dimensional narrow periodic specifications (see [CM93, HW92, KMW67, KO91, Or84b, Wa93]). The following two general kinds of results are presented. (1) We prove that a number of natural graph and combinatorial problems are NEXPTIME- or EXPSPACE-complete when instances are so specified; (2) In contrast, we prove that the optimization versions of several of these NEXPTIME-, EXPSPACE-complete problems have polynomial time approximation algorithms with constant performance guarantees. Moreover, some of these problems even have polynomial time approximation schemes. We also sketch how our NEXPTIME-hardness resultsmore » can be used to prove analogous NEXPTIME-hardness results for problems specified using other kinds of succinct specification languages. Our results provide the first natural problems for which there is a proven exponential (and possibly doubly exponential) gap between the complexities of finding exact and approximate solutions.« less
Lee, M L; Schneider, G
2001-01-01
Natural products were analyzed to determine whether they contain appealing novel scaffold architectures for potential use in combinatorial chemistry. Ring systems were extracted and clustered on the basis of structural similarity. Several such potential scaffolds for combinatorial chemistry were identified that are not present in current trade drugs. For one of these scaffolds a virtual combinatorial library was generated. Pharmacophoric properties of natural products, trade drugs, and the virtual combinatorial library were assessed using a self-organizing map. Obviously, current trade drugs and natural products have several topological pharmacophore patterns in common. These features can be systematically explored with selected combinatorial libraries based on a combination of natural product-derived and synthetic molecular building blocks.
Combinatorial Characterization of TiO2 Chemical Vapor Deposition Utilizing Titanium Isopropoxide.
Reinke, Michael; Ponomarev, Evgeniy; Kuzminykh, Yury; Hoffmann, Patrik
2015-07-13
The combinatorial characterization of the growth kinetics in chemical vapor deposition processes is challenging because precise information about the local precursor flow is usually difficult to access. In consequence, combinatorial chemical vapor deposition techniques are utilized more to study functional properties of thin films as a function of chemical composition, growth rate or crystallinity than to study the growth process itself. We present an experimental procedure which allows the combinatorial study of precursor surface kinetics during the film growth using high vacuum chemical vapor deposition. As consequence of the high vacuum environment, the precursor transport takes place in the molecular flow regime, which allows predicting and modifying precursor impinging rates on the substrate with comparatively little experimental effort. In this contribution, we study the surface kinetics of titanium dioxide formation using titanium tetraisopropoxide as precursor molecule over a large parameter range. We discuss precursor flux and temperature dependent morphology, crystallinity, growth rates, and precursor deposition efficiency. We conclude that the surface reaction of the adsorbed precursor molecules comprises a higher order reaction component with respect to precursor surface coverage.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Van Duren, Jeroen K; Koch, Carl; Luo, Alan
, based on Al-Cr-Fe-Ni, shows compressive strain >10% and specific compressive yield strength of 229 MPa x cc/g, yet does not show ductility in tensile tests due to cleavage. When replacing Cr in Al-Cr-Fe-based 4- and 5-element LDHEA with Mn, hardness drops 2x. Combined with compression test results, including those on the ternaries Al-Cr-Fe and Al-Mn-Fe suggest that Al-Mn-Fe-based LDHEA are still worth pursuing. These initial results only represent one compressive stress-strain curve per composition without any property optimization. As such, reproducibility needs to be followed by optimization to show their full potential. When including Li, Mg, and Zn, single-phase Li-Mg-Al-Ti-Zn LDHEA has been found with a specific ultimate compressive strength of 289MPa x cc/g. Al-Ti-Mn-Zn showed a specific ultimate compressive strength of 73MPa x cc/g. These initial results after hot isostatic pressing (HIP) of the ball-milled powders represent the lower end of what is possible, since no secondary processing (e.g. extrusion) has been performed to optimize strength and ductility. Compositions for multi-phase (e.g. dual-phase) LDHEA were identified largely by automated searches through CALPHAD databases, while screening for large face-centered-cubic (FCC) volume fractions, followed by experimental verification. This resulted in several new alloys. Li-Mg-Al-Mn-Fe and Mg-Mn-Fe-Co ball-milled powders upon HIP show specific ultimate compressive strengths of 198MPa x cc/g and 45MPa x cc/g, respectively. Several malleable quarternary Al-Zn-based alloys have been found upon arc/induction melting, yet with limited specific compressive yield strength (<75 MPa x cc/g). These initial results are all without any optimization for strength and/or ductility. High-throughput experimentation allowed us to triple the existing experimental HEA database as published in the past 10 years in less than 2 years which happened at a rate 10x higher than previous methods. Furthermore, we showed that high
Combinatorial Approaches for the Identification of Brain Drug Delivery Targets
Stutz, Charles C.; Zhang, Xiaobin; Shusta, Eric V.
2018-01-01
The blood-brain barrier (BBB) represents a large obstacle for the treatment of central nervous system diseases. Targeting endogenous nutrient transporters that transcytose the BBB is one promising approach to selectively and noninvasively deliver a drug payload to the brain. The main limitations of the currently employed transcytosing receptors are their ubiquitous expression in the peripheral vasculature and the inherent low levels of transcytosis mediated by such systems. In this review, approaches designed to increase the repertoire of transcytosing receptors which can be targeted for the purpose of drug delivery are discussed. In particular, combinatorial protein libraries can be screened on BBB cells in vitro or in vivo to isolate targeting peptides or antibodies that can trigger transcytosis. Once these targeting reagents are discovered, the cognate BBB transcytosis system can be identified using techniques such as expression cloning or immunoprecipitation coupled with mass spectrometry. Continued technological advances in BBB genomics and proteomics, membrane protein manipulation, and in vitro BBB technology promise to further advance the capability to identify and optimize peptides and antibodies capable of mediating drug transport across the BBB. PMID:23789958
Identities for Generalized Fibonacci Numbers: A Combinatorial Approach
ERIC Educational Resources Information Center
Plaza, A.; Falcon, S.
2008-01-01
This note shows a combinatorial approach to some identities for generalized Fibonacci numbers. While it is a straightforward task to prove these identities with induction, and also by arithmetical manipulations such as rearrangements, the approach used here is quite simple to follow and eventually reduces the proof to a counting problem. (Contains…
Optimal placement of tuning masses on truss structures by genetic algorithms
NASA Technical Reports Server (NTRS)
Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.
1993-01-01
Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.
Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.
Hüffner, Falk; Komusiewicz, Christian; Niedermeier, Rolf; Wernicke, Sebastian
2017-01-01
Fixed-parameter algorithms are designed to efficiently find optimal solutions to some computationally hard (NP-hard) problems by identifying and exploiting "small" problem-specific parameters. We survey practical techniques to develop such algorithms. Each technique is introduced and supported by case studies of applications to biological problems, with additional pointers to experimental results.
Liao, Chenzhong; Liu, Bing; Shi, Leming; Zhou, Jiaju; Lu, Xian-Ping
2005-07-01
Based on the structural characters of PPAR modulators, a virtual combinatorial library containing 1226,625 compounds was constructed using SMILES strings. Selected ADME filters were employed to compel compounds having poor drug-like properties from this library. This library was converted to sdf and mol2 files by CONCORD 4.0, and was then docked to PPARgamma by DOCK 4.0 to identify new chemical entities that may be potential drug leads against type 2 diabetes and other metabolic diseases. The method to construct virtual combinatorial library using SMILES strings was further visualized by Visual Basic.net that can facilitate the needs of generating other type virtual combinatorial libraries.
Park, Je Won; Nam, Sang-Jip; Yoon, Yeo Joon
2017-06-15
Nature has a talent for inventing a vast number of natural products, including hybrids generated by blending different scaffolds, resulting in a myriad of bioactive chemical entities. Herein, we review the highlights and recent trends (2010-2016) in the combinatorial biosynthesis of sugar-containing antibiotics where nature's structural diversification capabilities are exploited to enable the creation of new anti-infective and anti-proliferative drugs. In this review, we describe the modern combinatorial biosynthetic approaches for polyketide synthase-derived complex and aromatic polyketides, non-ribosomal peptide synthetase-directed lipo-/glycopeptides, aminoglycosides, nucleoside antibiotics, and alkaloids, along with their therapeutic potential. Finally, we present the feasible nexus between combinatorial biosynthesis, systems biology, and synthetic biology as a toolbox to provide new antibiotics that will be indispensable in the post-antibiotic era. Copyright © 2016 Elsevier Inc. All rights reserved.
Optimal Control Surface Layout for an Aeroservoelastic Wingbox
NASA Technical Reports Server (NTRS)
Stanford, Bret K.
2017-01-01
This paper demonstrates a technique for locating the optimal control surface layout of an aeroservoelastic Common Research Model wingbox, in the context of maneuver load alleviation and active utter suppression. The combinatorial actuator layout design is solved using ideas borrowed from topology optimization, where the effectiveness of a given control surface is tied to a layout design variable, which varies from zero (the actuator is removed) to one (the actuator is retained). These layout design variables are optimized concurrently with a large number of structural wingbox sizing variables and control surface actuation variables, in order to minimize the sum of structural weight and actuator weight. Results are presented that demonstrate interdependencies between structural sizing patterns and optimal control surface layouts, for both static and dynamic aeroelastic physics.
Combinatorial development of antibacterial Zr-Cu-Al-Ag thin film metallic glasses.
Liu, Yanhui; Padmanabhan, Jagannath; Cheung, Bettina; Liu, Jingbei; Chen, Zheng; Scanley, B Ellen; Wesolowski, Donna; Pressley, Mariyah; Broadbridge, Christine C; Altman, Sidney; Schwarz, Udo D; Kyriakides, Themis R; Schroers, Jan
2016-05-27
Metallic alloys are normally composed of multiple constituent elements in order to achieve integration of a plurality of properties required in technological applications. However, conventional alloy development paradigm, by sequential trial-and-error approach, requires completely unrelated strategies to optimize compositions out of a vast phase space, making alloy development time consuming and labor intensive. Here, we challenge the conventional paradigm by proposing a combinatorial strategy that enables parallel screening of a multitude of alloys. Utilizing a typical metallic glass forming alloy system Zr-Cu-Al-Ag as an example, we demonstrate how glass formation and antibacterial activity, two unrelated properties, can be simultaneously characterized and the optimal composition can be efficiently identified. We found that in the Zr-Cu-Al-Ag alloy system fully glassy phase can be obtained in a wide compositional range by co-sputtering, and antibacterial activity is strongly dependent on alloy compositions. Our results indicate that antibacterial activity is sensitive to Cu and Ag while essentially remains unchanged within a wide range of Zr and Al. The proposed strategy not only facilitates development of high-performing alloys, but also provides a tool to unveil the composition dependence of properties in a highly parallel fashion, which helps the development of new materials by design.
Combinatorial development of antibacterial Zr-Cu-Al-Ag thin film metallic glasses
NASA Astrophysics Data System (ADS)
Liu, Yanhui; Padmanabhan, Jagannath; Cheung, Bettina; Liu, Jingbei; Chen, Zheng; Scanley, B. Ellen; Wesolowski, Donna; Pressley, Mariyah; Broadbridge, Christine C.; Altman, Sidney; Schwarz, Udo D.; Kyriakides, Themis R.; Schroers, Jan
2016-05-01
Metallic alloys are normally composed of multiple constituent elements in order to achieve integration of a plurality of properties required in technological applications. However, conventional alloy development paradigm, by sequential trial-and-error approach, requires completely unrelated strategies to optimize compositions out of a vast phase space, making alloy development time consuming and labor intensive. Here, we challenge the conventional paradigm by proposing a combinatorial strategy that enables parallel screening of a multitude of alloys. Utilizing a typical metallic glass forming alloy system Zr-Cu-Al-Ag as an example, we demonstrate how glass formation and antibacterial activity, two unrelated properties, can be simultaneously characterized and the optimal composition can be efficiently identified. We found that in the Zr-Cu-Al-Ag alloy system fully glassy phase can be obtained in a wide compositional range by co-sputtering, and antibacterial activity is strongly dependent on alloy compositions. Our results indicate that antibacterial activity is sensitive to Cu and Ag while essentially remains unchanged within a wide range of Zr and Al. The proposed strategy not only facilitates development of high-performing alloys, but also provides a tool to unveil the composition dependence of properties in a highly parallel fashion, which helps the development of new materials by design.
Combinatorial development of antibacterial Zr-Cu-Al-Ag thin film metallic glasses
Liu, Yanhui; Padmanabhan, Jagannath; Cheung, Bettina; Liu, Jingbei; Chen, Zheng; Scanley, B. Ellen; Wesolowski, Donna; Pressley, Mariyah; Broadbridge, Christine C.; Altman, Sidney; Schwarz, Udo D.; Kyriakides, Themis R.; Schroers, Jan
2016-01-01
Metallic alloys are normally composed of multiple constituent elements in order to achieve integration of a plurality of properties required in technological applications. However, conventional alloy development paradigm, by sequential trial-and-error approach, requires completely unrelated strategies to optimize compositions out of a vast phase space, making alloy development time consuming and labor intensive. Here, we challenge the conventional paradigm by proposing a combinatorial strategy that enables parallel screening of a multitude of alloys. Utilizing a typical metallic glass forming alloy system Zr-Cu-Al-Ag as an example, we demonstrate how glass formation and antibacterial activity, two unrelated properties, can be simultaneously characterized and the optimal composition can be efficiently identified. We found that in the Zr-Cu-Al-Ag alloy system fully glassy phase can be obtained in a wide compositional range by co-sputtering, and antibacterial activity is strongly dependent on alloy compositions. Our results indicate that antibacterial activity is sensitive to Cu and Ag while essentially remains unchanged within a wide range of Zr and Al. The proposed strategy not only facilitates development of high-performing alloys, but also provides a tool to unveil the composition dependence of properties in a highly parallel fashion, which helps the development of new materials by design. PMID:27230692
Napolitano, Roberta; Soesbe, Todd C; De León-Rodríguez, Luis M; Sherry, A Dean; Udugamasooriya, D Gomika
2011-08-24
The sensitivity of magnetic resonance imaging (MRI) contrast agents is highly dependent on the rate of water exchange between the inner sphere of a paramagnetic ion and bulk water. Normally, identifying a paramagnetic complex that has optimal water exchange kinetics is done by synthesizing and testing one compound at a time. We report here a rapid, economical on-bead combinatorial synthesis of a library of imaging agents. Eighty different 1,4,7,10-tetraazacyclododecan-1,4,7,10-tetraacetic acid (DOTA)-tetraamide peptoid derivatives were prepared on beads using a variety of charged, uncharged but polar, hydrophobic, and variably sized primary amines. A single chemical exchange saturation transfer image of the on-bead library easily distinguished those compounds having the most favorable water exchange kinetics. This combinatorial approach will allow rapid screening of libraries of imaging agents to identify the chemical characteristics of a ligand that yield the most sensitive imaging agents. This technique could be automated and readily adapted to other types of MRI or magnetic resonance/positron emission tomography agents as well.
Robust quantum optimizer with full connectivity
Nigg, Simon E.; Lörch, Niels; Tiwari, Rakesh P.
2017-01-01
Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example, quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size than classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. By leveraging the phenomenon of flux quantization, all-to-all connectivity with sufficient tunability to implement many relevant optimization problems is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the nondeterministic polynomial-time hard (NP-hard) and fully connected number partitioning problem in the presence of dissipation. PMID:28435880
Robust quantum optimizer with full connectivity.
Nigg, Simon E; Lörch, Niels; Tiwari, Rakesh P
2017-04-01
Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example, quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size than classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. By leveraging the phenomenon of flux quantization, all-to-all connectivity with sufficient tunability to implement many relevant optimization problems is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the nondeterministic polynomial-time hard (NP-hard) and fully connected number partitioning problem in the presence of dissipation.
Limpoco, F Ted; Bailey, Ryan C
2011-09-28
We directly monitor in parallel and in real time the temporal profiles of polymer brushes simultaneously grown via multiple ATRP reaction conditions on a single substrate using arrays of silicon photonic microring resonators. In addition to probing relative polymerization rates, we show the ability to evaluate the dynamic properties of the in situ grown polymers. This presents a powerful new platform for studying modified interfaces that may allow for the combinatorial optimization of surface-initiated polymerization conditions.
Combinatorial study of degree assortativity in networks.
Estrada, Ernesto
2011-10-01
Why are some networks degree-degree correlated (assortative), while most of the real-world ones are anticorrelated (disassortative)? Here, we prove, by combinatorial methods, that the assortativity of a network depends only on three structural factors: transitivity (clustering coefficient), intermodular connectivity, and branching. Then, a network is assortative if the contributions of the first two factors are larger than that of the third. Highly branched networks are likely to be disassortative.
Siol, Sebastian; Dhakal, Tara P; Gudavalli, Ganesh S; Rajbhandari, Pravakar P; DeHart, Clay; Baranowski, Lauryn L; Zakutayev, Andriy
2016-06-08
High-throughput computational and experimental techniques have been used in the past to accelerate the discovery of new promising solar cell materials. An important part of the development of novel thin film solar cell technologies, that is still considered a bottleneck for both theory and experiment, is the search for alternative interfacial contact (buffer) layers. The research and development of contact materials is difficult due to the inherent complexity that arises from its interactions at the interface with the absorber. A promising alternative to the commonly used CdS buffer layer in thin film solar cells that contain absorbers with lower electron affinity can be found in β-In2S3. However, the synthesis conditions for the sputter deposition of this material are not well-established. Here, In2S3 is investigated as a solar cell contact material utilizing a high-throughput combinatorial screening of the temperature-flux parameter space, followed by a number of spatially resolved characterization techniques. It is demonstrated that, by tuning the sulfur partial pressure, phase pure β-In2S3 could be deposited using a broad range of substrate temperatures between 500 °C and ambient temperature. Combinatorial photovoltaic device libraries with Al/ZnO/In2S3/Cu2ZnSnS4/Mo/SiO2 structure were built at optimal processing conditions to investigate the feasibility of the sputtered In2S3 buffer layers and of an accelerated optimization of the device structure. The performance of the resulting In2S3/Cu2ZnSnS4 photovoltaic devices is on par with CdS/Cu2ZnSnS4 reference solar cells with similar values for short circuit currents and open circuit voltages, despite the overall quite low efficiency of the devices (∼2%). Overall, these results demonstrate how a high-throughput experimental approach can be used to accelerate the development of contact materials and facilitate the optimization of thin film solar cell devices.
A combinatorial code for pattern formation in Drosophila oogenesis.
Yakoby, Nir; Bristow, Christopher A; Gong, Danielle; Schafer, Xenia; Lembong, Jessica; Zartman, Jeremiah J; Halfon, Marc S; Schüpbach, Trudi; Shvartsman, Stanislav Y
2008-11-01
Two-dimensional patterning of the follicular epithelium in Drosophila oogenesis is required for the formation of three-dimensional eggshell structures. Our analysis of a large number of published gene expression patterns in the follicle cells suggests that they follow a simple combinatorial code based on six spatial building blocks and the operations of union, difference, intersection, and addition. The building blocks are related to the distribution of inductive signals, provided by the highly conserved epidermal growth factor receptor and bone morphogenetic protein signaling pathways. We demonstrate the validity of the code by testing it against a set of patterns obtained in a large-scale transcriptional profiling experiment. Using the proposed code, we distinguish 36 distinct patterns for 81 genes expressed in the follicular epithelium and characterize their joint dynamics over four stages of oogenesis. The proposed combinatorial framework allows systematic analysis of the diversity and dynamics of two-dimensional transcriptional patterns and guides future studies of gene regulation.
Herrgård, Markus J.
2014-01-01
High-cell-density fermentation for industrial production of chemicals can impose numerous stresses on cells due to high substrate, product, and by-product concentrations; high osmolarity; reactive oxygen species; and elevated temperatures. There is a need to develop platform strains of industrial microorganisms that are more tolerant toward these typical processing conditions. In this study, the growth of six industrially relevant strains of Escherichia coli was characterized under eight stress conditions representative of fed-batch fermentation, and strains W and BL21(DE3) were selected as platforms for transposon (Tn) mutagenesis due to favorable resistance characteristics. Selection experiments, followed by either targeted or genome-wide next-generation-sequencing-based Tn insertion site determination, were performed to identify mutants with improved growth properties under a subset of three stress conditions and two combinations of individual stresses. A subset of the identified loss-of-function mutants were selected for a combinatorial approach, where strains with combinations of two and three gene deletions were systematically constructed and tested for single and multistress resistance. These approaches allowed identification of (i) strain-background-specific stress resistance phenotypes, (ii) novel gene deletion mutants in E. coli that confer single and multistress resistance in a strain-background-dependent manner, and (iii) synergistic effects of multiple gene deletions that confer improved resistance over single deletions. The results of this study underscore the suboptimality and strain-specific variability of the genetic network regulating growth under stressful conditions and suggest that further exploration of the combinatorial gene deletion space in multiple strain backgrounds is needed for optimizing strains for microbial bioprocessing applications. PMID:25085490
On the existence of binary simplex codes. [using combinatorial construction
NASA Technical Reports Server (NTRS)
Taylor, H.
1977-01-01
Using a simple combinatorial construction, the existence of a binary simplex code with m codewords for all m is greater than or equal to 1 is proved. The problem of the shortest possible length is left open.
Wafer-scale growth of VO2 thin films using a combinatorial approach
Zhang, Hai-Tian; Zhang, Lei; Mukherjee, Debangshu; Zheng, Yuan-Xia; Haislmaier, Ryan C.; Alem, Nasim; Engel-Herbert, Roman
2015-01-01
Transition metal oxides offer functional properties beyond conventional semiconductors. Bridging the gap between the fundamental research frontier in oxide electronics and their realization in commercial devices demands a wafer-scale growth approach for high-quality transition metal oxide thin films. Such a method requires excellent control over the transition metal valence state to avoid performance deterioration, which has been proved challenging. Here we present a scalable growth approach that enables a precise valence state control. By creating an oxygen activity gradient across the wafer, a continuous valence state library is established to directly identify the optimal growth condition. Single-crystalline VO2 thin films have been grown on wafer scale, exhibiting more than four orders of magnitude change in resistivity across the metal-to-insulator transition. It is demonstrated that ‘electronic grade' transition metal oxide films can be realized on a large scale using a combinatorial growth approach, which can be extended to other multivalent oxide systems. PMID:26450653
Hard particle effect on surface generation in nano-cutting
NASA Astrophysics Data System (ADS)
Xu, Feifei; Fang, Fengzhou; Zhang, Xiaodong
2017-12-01
The influence of the hard particle on the surface generation, plastic deformation and processing forces in nano-cutting of aluminum is investigated by means of molecular dynamics simulations. In this investigation, a hard particle which is simplified as a diamond ball is embedded under the free surface of workpiece with different depths. The influence of the position of the hard ball on the surface generation and other material removal mechanism, such as the movement of the ball under the action of cutting tool edge, is revealed. The results show that when the hard particle is removed, only a small shallow pit is left on the machined surface. Otherwise, it is pressed down to the subsurface of the workpiece left larger and deeper pit on the generated surface. Besides that, the hard particle in the workpiece would increase the processing force when the cutting tool edge or the plastic carriers interact with the hard particle. It is helpful to optimize the cutting parameters and material properties for obtaining better surface quality in nano-cutting of composites or other materials with micro/nanoscale hard particles in it.
Kell, Douglas B
2012-03-01
A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. Copyright © 2012 WILEY Periodicals, Inc.
Effects of hard mask etch on final topography of advanced phase shift masks
NASA Astrophysics Data System (ADS)
Hortenbach, Olga; Rolff, Haiko; Lajn, Alexander; Baessler, Martin
2017-07-01
Continuous shrinking of the semiconductor device dimensions demands steady improvements of the lithographic resolution on wafer level. These requirements challenge the photomask industry to further improve the mask quality in all relevant printing characteristics. In this paper topography of the Phase Shift Masks (PSM) was investigated. Effects of hard mask etch on phase shift uniformity and mask absorber profile were studied. Design of experiments method (DoE) was used for the process optimization, whereas gas composition, bias power of the hard mask main etch and bias power of the over-etch were varied. In addition, influence of the over-etch time was examined at the end of the experiment. Absorber depth uniformity, sidewall angle (SWA), reactive ion etch lag (RIE lag) and through pitch (TP) dependence were analyzed. Measurements were performed by means of Atomic-force microscopy (AFM) using critical dimension (CD) mode with a boot-shaped tip. Scanning electron microscope (SEM) cross-section images were prepared to verify the profile quality. Finally CD analysis was performed to confirm the optimal etch conditions. Significant dependence of the absorber SWA on hard mask (HM) etch conditions was observed revealing an improvement potential for the mask absorber profile. It was found that hard mask etch can leave a depth footprint in the absorber layer. Thus, the etch depth uniformity of hard mask etch is crucial for achieving a uniform phase shift over the active mask area. The optimized hard mask etch process results in significantly improved mask topography without deterioration of tight CD specifications.
Hybrid Nested Partitions and Math Programming Framework for Large-scale Combinatorial Optimization
2010-03-31
optimization problems: 1) exact algorithms and 2) metaheuristic algorithms . This project will integrate concepts from these two technologies to develop...optimal solutions within an acceptable amount of computation time, and 2) metaheuristic algorithms such as genetic algorithms , tabu search, and the...integer programming decomposition approaches, such as Dantzig Wolfe decomposition and Lagrangian relaxation, and metaheuristics such as the Nested
Combinatorial Histone Acetylation Patterns Are Generated by Motif-Specific Reactions.
Blasi, Thomas; Feller, Christian; Feigelman, Justin; Hasenauer, Jan; Imhof, Axel; Theis, Fabian J; Becker, Peter B; Marr, Carsten
2016-01-27
Post-translational modifications (PTMs) are pivotal to cellular information processing, but how combinatorial PTM patterns ("motifs") are set remains elusive. We develop a computational framework, which we provide as open source code, to investigate the design principles generating the combinatorial acetylation patterns on histone H4 in Drosophila melanogaster. We find that models assuming purely unspecific or lysine site-specific acetylation rates were insufficient to explain the experimentally determined motif abundances. Rather, these abundances were best described by an ensemble of models with acetylation rates that were specific to motifs. The model ensemble converged upon four acetylation pathways; we validated three of these using independent data from a systematic enzyme depletion study. Our findings suggest that histone acetylation patterns originate through specific pathways involving motif-specific acetylation activity. Copyright © 2016 Elsevier Inc. All rights reserved.
Building synthetic gene circuits from combinatorial libraries: screening and selection strategies.
Schaerli, Yolanda; Isalan, Mark
2013-07-01
The promise of wide-ranging biotechnology applications inspires synthetic biologists to design novel genetic circuits. However, building such circuits rationally is still not straightforward and often involves painstaking trial-and-error. Mimicking the process of natural selection can help us to bridge the gap between our incomplete understanding of nature's design rules and our desire to build functional networks. By adopting the powerful method of directed evolution, which is usually applied to protein engineering, functional networks can be obtained through screening or selecting from randomised combinatorial libraries. This review first highlights the practical options to introduce combinatorial diversity into gene circuits and then examines strategies for identifying the potentially rare library members with desired functions, either by screening or selection.
High-throughput screening for combinatorial thin-film library of thermoelectric materials.
Watanabe, Masaki; Kita, Takuji; Fukumura, Tomoteru; Ohtomo, Akira; Ueno, Kazunori; Kawasaki, Masashi
2008-01-01
A high-throughput method has been developed to evaluate the Seebeck coefficient and electrical resistivity of combinatorial thin-film libraries of thermoelectric materials from room temperature to 673 K. Thin-film samples several millimeters in size were deposited on an integrated Al2O3 substrate with embedded lead wires and local heaters for measurement of the thermopower under a controlled temperature gradient. An infrared camera was used for real-time observation of the temperature difference Delta T between two electrical contacts on the sample to obtain the Seebeck coefficient. The Seebeck coefficient and electrical resistivity of constantan thin films were shown to be almost identical to standard data for bulk constantan. High-throughput screening was demonstrated for a thermoelectric Mg-Si-Ge combinatorial library.
A combinatorial filtering method for magnetotelluric time-series based on Hilbert-Huang transform
NASA Astrophysics Data System (ADS)
Cai, Jianhua
2014-11-01
Magnetotelluric (MT) time-series are often contaminated with noise from natural or man-made processes. A substantial improvement is possible when the time-series are presented as clean as possible for further processing. A combinatorial method is described for filtering of MT time-series based on the Hilbert-Huang transform that requires a minimum of human intervention and leaves good data sections unchanged. Good data sections are preserved because after empirical mode decomposition the data are analysed through hierarchies, morphological filtering, adaptive threshold and multi-point smoothing, allowing separation of noise from signals. The combinatorial method can be carried out without any assumption about the data distribution. Simulated data and the real measured MT time-series from three different regions, with noise caused by baseline drift, high frequency noise and power-line contribution, are processed to demonstrate the application of the proposed method. Results highlight the ability of the combinatorial method to pick out useful signals, and the noise is suppressed greatly so that their deleterious influence is eliminated for the MT transfer function estimation.
The Role of the Goal in Solving Hard Computational Problems: Do People Really Optimize?
ERIC Educational Resources Information Center
Carruthers, Sarah; Stege, Ulrike; Masson, Michael E. J.
2018-01-01
The role that the mental, or internal, representation plays when people are solving hard computational problems has largely been overlooked to date, despite the reality that this internal representation drives problem solving. In this work we investigate how performance on versions of two hard computational problems differs based on what internal…
Automated Combinatorial Chemistry in the Organic Chemistry Majors Laboratory
ERIC Educational Resources Information Center
Nichols, Christopher J.; Hanne, Larry F.
2010-01-01
A multidisciplinary experiment has been developed in which students each synthesize a combinatorial library of 48 hydrazones with the aid of a liquid-handling robot. Each product is then subjected to a Kirby-Bauer disk diffusion assay to assess its antibacterial activity. Students gain experience working with automation and at the…
Pruning-Based, Energy-Optimal, Deterministic I/O Device Scheduling for Hard Real-Time Systems
2005-02-01
However, DPM via I/O device scheduling for hard real - time systems has received relatively little attention. In this paper,we present an offline I/O...polynomial time. We present experimental results to show that EDS and MDO reduce the energy consumption of I/O devices significantly for hard real - time systems .
A combinatorial approach towards the design of nanofibrous scaffolds for chondrogenesis
NASA Astrophysics Data System (ADS)
Ahmed, Maqsood; Ramos, Tiago André Da Silva; Damanik, Febriyani; Quang Le, Bach; Wieringa, Paul; Bennink, Martin; van Blitterswijk, Clemens; de Boer, Jan; Moroni, Lorenzo
2015-10-01
The extracellular matrix (ECM) is a three-dimensional (3D) structure composed of proteinaceous fibres that provide physical and biological cues to direct cell behaviour. Here, we build a library of hybrid collagen-polymer fibrous scaffolds with nanoscale dimensions and screen them for their ability to grow chondrocytes for cartilage repair. Poly(lactic acid) and poly (lactic-co-glycolic acid) at two different monomer ratios (85:15 and 50:50) were incrementally blended with collagen. Physical properties (wettability and stiffness) of the scaffolds were characterized and related to biological performance (proliferation, ECM production, and gene expression) and structure-function relationships were developed. We found that soft scaffolds with an intermediate wettability composed of the highly biodegradable PLGA50:50 and collagen, in two ratios (40:60 and 60:40), were optimal for chondrogenic differentiation of ATDC5 cells as determined by increased ECM production and enhanced cartilage specific gene expression. Long-term cultures indicated a stable phenotype with minimal de-differentiation or hypertrophy. The combinatorial methodology applied herein is a promising approach for the design and development of scaffolds for regenerative medicine.
A combinatorial approach towards the design of nanofibrous scaffolds for chondrogenesis.
Ahmed, Maqsood; Ramos, Tiago André da Silva; Damanik, Febriyani; Quang Le, Bach; Wieringa, Paul; Bennink, Martin; van Blitterswijk, Clemens; de Boer, Jan; Moroni, Lorenzo
2015-10-07
The extracellular matrix (ECM) is a three-dimensional (3D) structure composed of proteinaceous fibres that provide physical and biological cues to direct cell behaviour. Here, we build a library of hybrid collagen-polymer fibrous scaffolds with nanoscale dimensions and screen them for their ability to grow chondrocytes for cartilage repair. Poly(lactic acid) and poly (lactic-co-glycolic acid) at two different monomer ratios (85:15 and 50:50) were incrementally blended with collagen. Physical properties (wettability and stiffness) of the scaffolds were characterized and related to biological performance (proliferation, ECM production, and gene expression) and structure-function relationships were developed. We found that soft scaffolds with an intermediate wettability composed of the highly biodegradable PLGA50:50 and collagen, in two ratios (40:60 and 60:40), were optimal for chondrogenic differentiation of ATDC5 cells as determined by increased ECM production and enhanced cartilage specific gene expression. Long-term cultures indicated a stable phenotype with minimal de-differentiation or hypertrophy. The combinatorial methodology applied herein is a promising approach for the design and development of scaffolds for regenerative medicine.
High hardness in the biocompatible intermetallic compound β-Ti3Au.
Svanidze, Eteri; Besara, Tiglet; Ozaydin, M Fevsi; Tiwary, Chandra Sekhar; Wang, Jiakui K; Radhakrishnan, Sruthi; Mani, Sendurai; Xin, Yan; Han, Ke; Liang, Hong; Siegrist, Theo; Ajayan, Pulickel M; Morosan, E
2016-07-01
The search for new hard materials is often challenging, but strongly motivated by the vast application potential such materials hold. Ti3Au exhibits high hardness values (about four times those of pure Ti and most steel alloys), reduced coefficient of friction and wear rates, and biocompatibility, all of which are optimal traits for orthopedic, dental, and prosthetic applications. In addition, the ability of this compound to adhere to ceramic parts can reduce both the weight and the cost of medical components. The fourfold increase in the hardness of Ti3Au compared to other Ti-Au alloys and compounds can be attributed to the elevated valence electron density, the reduced bond length, and the pseudogap formation. Understanding the origin of hardness in this intermetallic compound provides an avenue toward designing superior biocompatible, hard materials.
NASA Astrophysics Data System (ADS)
Jakubczyk, Dorota; Jakubczyk, Paweł
2018-02-01
We propose combinatorial approach to the representation of Schur-Weyl duality in physical systems on the example of one-dimensional spin chains. Exploiting the Robinson-Schensted-Knuth algorithm, we perform decomposition of the dual group representations into irreducible representations in a fully combinatorial way. As representation space, we choose the Hilbert space of the spin chains, but this approach can be easily generalized to an arbitrary physical system where the Schur-Weyl duality works.
Guturu, Parthasarathy; Dantu, Ram
2008-06-01
Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.
Integrated microfluidic devices for combinatorial cell-based assays.
Yu, Zeta Tak For; Kamei, Ken-ichiro; Takahashi, Hiroko; Shu, Chengyi Jenny; Wang, Xiaopu; He, George Wenfu; Silverman, Robert; Radu, Caius G; Witte, Owen N; Lee, Ki-Bum; Tseng, Hsian-Rong
2009-06-01
The development of miniaturized cell culture platforms for performing parallel cultures and combinatorial assays is important in cell biology from the single-cell level to the system level. In this paper we developed an integrated microfluidic cell-culture platform, Cell-microChip (Cell-microChip), for parallel analyses of the effects of microenvironmental cues (i.e., culture scaffolds) on different mammalian cells and their cellular responses to external stimuli. As a model study, we demonstrated the ability of culturing and assaying several mammalian cells, such as NIH 3T3 fibroblast, B16 melanoma and HeLa cell lines, in a parallel way. For functional assays, first we tested drug-induced apoptotic responses from different cell lines. As a second functional assay, we performed "on-chip" transfection of a reporter gene encoding an enhanced green fluorescent protein (EGFP) followed by live-cell imaging of transcriptional activation of cyclooxygenase 2 (Cox-2) expression. Collectively, our Cell-microChip approach demonstrated the capability to carry out parallel operations and the potential to further integrate advanced functions and applications in the broader space of combinatorial chemistry and biology.
Integrated microfluidic devices for combinatorial cell-based assays
Yu, Zeta Tak For; Kamei, Ken-ichiro; Takahashi, Hiroko; Shu, Chengyi Jenny; Wang, Xiaopu; He, George Wenfu; Silverman, Robert
2010-01-01
The development of miniaturized cell culture platforms for performing parallel cultures and combinatorial assays is important in cell biology from the single-cell level to the system level. In this paper we developed an integrated microfluidic cell-culture platform, Cell-microChip (Cell-μChip), for parallel analyses of the effects of microenvir-onmental cues (i.e., culture scaffolds) on different mammalian cells and their cellular responses to external stimuli. As a model study, we demonstrated the ability of culturing and assaying several mammalian cells, such as NIH 3T3 fibro-blast, B16 melanoma and HeLa cell lines, in a parallel way. For functional assays, first we tested drug-induced apoptotic responses from different cell lines. As a second functional assay, we performed "on-chip" transfection of a reporter gene encoding an enhanced green fluorescent protein (EGFP) followed by live-cell imaging of transcriptional activation of cyclooxygenase 2 (Cox-2) expression. Collectively, our Cell-μChip approach demonstrated the capability to carry out parallel operations and the potential to further integrate advanced functions and applications in the broader space of combinatorial chemistry and biology. PMID:19130244
Switched Systems and Motion Coordination: Combinatorial Challenges
NASA Technical Reports Server (NTRS)
Sadovsky, Alexander V.
2016-01-01
Problems of routing commercial air traffic in a terminal airspace encounter different constraints: separation assurance, aircraft performance limitations, regulations. The general setting of these problems is that of a switched control system. Such a system combines the differentiable motion of the aircraft with the combinatorial choices of choosing precedence when traffic routes merge and choosing branches when the routes diverge. This presentation gives an overview of the problem, the ATM context, related literature, and directions for future research.
"One-sample concept" micro-combinatory for high throughput TEM of binary films.
Sáfrán, György
2018-04-01
Phases of thin films may remarkably differ from that of bulk. Unlike to the comprehensive data files of Binary Phase Diagrams [1] available for bulk, complete phase maps for thin binary layers do not exist. This is due to both the diverse metastable, non-equilibrium or instable phases feasible in thin films and the required volume of characterization work with analytical techniques like TEM, SAED and EDS. The aim of the present work was to develop a method that remarkably facilitates the TEM study of the diverse binary phases of thin films, or the creation of phase maps. A micro-combinatorial method was worked out that enables both preparation and study of a gradient two-component film within a single TEM specimen. For a demonstration of the technique thin Mn x Al 1- x binary samples with evolving concentration from x = 0 to x = 1 have been prepared so that the transition from pure Mn to pure Al covers a 1.5 mm long track within the 3 mm diameter TEM grid. The proposed method enables the preparation and study of thin combinatorial samples including all feasible phases as a function of composition or other deposition parameters. Contrary to known "combinatorial chemistry", in which a series of different samples are deposited in one run, and investigated, one at a time, the present micro-combinatorial method produces a single specimen condensing a complete library of a binary system that can be studied, efficiently, within a single TEM session. That provides extremely high throughput for TEM characterization of composition-dependent phases, exploration of new materials, or the construction of phase diagrams of binary films. Copyright © 2018 Elsevier B.V. All rights reserved.
Iconicity and the Emergence of Combinatorial Structure in Language
ERIC Educational Resources Information Center
Verhoef, Tessa; Kirby, Simon; de Boer, Bart
2016-01-01
In language, recombination of a discrete set of meaningless building blocks forms an unlimited set of possible utterances. How such combinatorial structure emerged in the evolution of human language is increasingly being studied. It has been shown that it can emerge when languages culturally evolve and adapt to human cognitive biases. How the…
A quantum annealing approach for fault detection and diagnosis of graph-based systems
NASA Astrophysics Data System (ADS)
Perdomo-Ortiz, A.; Fluegemann, J.; Narasimhan, S.; Biswas, R.; Smelyanskiy, V. N.
2015-02-01
Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.
Koyama, Michihisa; Tsuboi, Hideyuki; Endou, Akira; Takaba, Hiromitsu; Kubo, Momoji; Del Carpio, Carlos A; Miyamoto, Akira
2007-02-01
Computational chemistry can provide fundamental knowledge regarding various aspects of materials. While its impact in scientific research is greatly increasing, its contributions to industrially important issues are far from satisfactory. In order to realize industrial innovation by computational chemistry, a new concept "combinatorial computational chemistry" has been proposed by introducing the concept of combinatorial chemistry to computational chemistry. This combinatorial computational chemistry approach enables theoretical high-throughput screening for materials design. In this manuscript, we review the successful applications of combinatorial computational chemistry to deNO(x) catalysts, Fischer-Tropsch catalysts, lanthanoid complex catalysts, and cathodes of the lithium ion secondary battery.
A combinatorial approach to protein docking with flexible side chains.
Althaus, Ernst; Kohlbacher, Oliver; Lenhof, Hans-Peter; Müller, Peter
2002-01-01
Rigid-body docking approaches are not sufficient to predict the structure of a protein complex from the unbound (native) structures of the two proteins. Accounting for side chain flexibility is an important step towards fully flexible protein docking. This work describes an approach that allows conformational flexibility for the side chains while keeping the protein backbone rigid. Starting from candidates created by a rigid-docking algorithm, we demangle the side chains of the docking site, thus creating reasonable approximations of the true complex structure. These structures are ranked with respect to the binding free energy. We present two new techniques for side chain demangling. Both approaches are based on a discrete representation of the side chain conformational space by the use of a rotamer library. This leads to a combinatorial optimization problem. For the solution of this problem, we propose a fast heuristic approach and an exact, albeit slower, method that uses branch-and-cut techniques. As a test set, we use the unbound structures of three proteases and the corresponding protein inhibitors. For each of the examples, the highest-ranking conformation produced was a good approximation of the true complex structure.
Single cell systems biology by super-resolution imaging and combinatorial labeling
Lubeck, Eric; Cai, Long
2012-01-01
Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral separability of fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using Fluorescence in situ Hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured the mRNA levels of 32 genes simultaneously in single S. cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells provides a natural approach to bring systems biology into single cells. PMID:22660740
Application of combinatorial biocatalysis for a unique ring expansion of dihydroxymethylzearalenone
USDA-ARS?s Scientific Manuscript database
Combinatorial biocatalysis was applied to generate a diverse set of dihydroxymethylzearalenone derivatives with modified ring structure. In one chemoenzymatic reaction sequence, dihydroxymethylzearalenone was first subjected to a unique enzyme-catalyzed oxidative ring opening reaction that creates ...
Harańczyk, Maciej; Gutowski, Maciej
2007-01-01
We describe a procedure of finding low-energy tautomers of a molecule. The procedure consists of (i) combinatorial generation of a library of tautomers, (ii) screening based on the results of geometry optimization of initial structures performed at the density functional level of theory, and (iii) final refinement of geometry for the top hits at the second-order Möller-Plesset level of theory followed by single-point energy calculations at the coupled cluster level of theory with single, double, and perturbative triple excitations. The library of initial structures of various tautomers is generated with TauTGen, a tautomer generator program. The procedure proved to be successful for these molecular systems for which common chemical knowledge had not been sufficient to predict the most stable structures.
High hardness in the biocompatible intermetallic compound β-Ti3Au
Svanidze, Eteri; Besara, Tiglet; Ozaydin, M. Fevsi; Tiwary, Chandra Sekhar; Wang, Jiakui K.; Radhakrishnan, Sruthi; Mani, Sendurai; Xin, Yan; Han, Ke; Liang, Hong; Siegrist, Theo; Ajayan, Pulickel M.; Morosan, E.
2016-01-01
The search for new hard materials is often challenging, but strongly motivated by the vast application potential such materials hold. Ti3Au exhibits high hardness values (about four times those of pure Ti and most steel alloys), reduced coefficient of friction and wear rates, and biocompatibility, all of which are optimal traits for orthopedic, dental, and prosthetic applications. In addition, the ability of this compound to adhere to ceramic parts can reduce both the weight and the cost of medical components. The fourfold increase in the hardness of Ti3Au compared to other Ti–Au alloys and compounds can be attributed to the elevated valence electron density, the reduced bond length, and the pseudogap formation. Understanding the origin of hardness in this intermetallic compound provides an avenue toward designing superior biocompatible, hard materials. PMID:27453942
Amosa, Mutiu K
2016-08-01
Sorption optimization and mechanism of hardness and alkalinity on bifunctional empty fruit bunch-based powdered activation carbon (PAC) were studied. The PAC possessed both high surface area and ion-exchange properties, and it was utilized in the treatment of biotreated palm oil mill effluent. Batch adsorption experiments designed with Design Expert(®) were conducted in correlating the singular and interactive effects of the three adsorption parameters: PAC dosage, agitation speed and contact time. The sorption trends of the two contaminants were sequentially assessed through a full factorial design with three factor interaction models and a central composite design with polynomial models of quadratic order. Analysis of variance revealed the significant factors on each design response with very high R(2) values indicating good agreement between model and experimental values. The optimum operating conditions of the two contaminants differed due to their different regions of operating interests, thus necessitating the utility of desirability factor to get consolidated optimum operation conditions. The equilibrium data for alkalinity and hardness sorption were better represented by the Langmuir isotherm, while the pseudo-second-order kinetic model described the adsorption rates and behavior better. It was concluded that chemisorption contributed majorly to the adsorption process.
Combinatorial structure of genome rearrangements scenarios.
Ouangraoua, Aïda; Bergeron, Anne
2010-09-01
In genome rearrangement theory, one of the elusive questions raised in recent years is the enumeration of rearrangement scenarios between two genomes. This problem is related to the uniform generation of rearrangement scenarios and the derivation of tests of statistical significance of the properties of these scenarios. Here we give an exact formula for the number of double-cut-and-join (DCJ) rearrangement scenarios between two genomes. We also construct effective bijections between the set of scenarios that sort a component as well studied combinatorial objects such as parking functions, labeled trees, and prüfer codes.
Lin, En-Chiang; Cole, Jesse J; Jacobs, Heiko O
2010-11-10
This article reports and applies a recently discovered programmable multimaterial deposition process to the formation and combinatorial improvement of 3D nanostructured devices. The gas-phase deposition process produces charged <5 nm particles of silver, tungsten, and platinum and uses externally biased electrodes to control the material flux and to turn deposition ON/OFF in selected domains. Domains host nanostructured dielectrics to define arrays of electrodynamic 10 × nanolenses to further control the flux to form <100 nm resolution deposits. The unique feature of the process is that material type, amount, and sequence can be altered from one domain to the next leading to different types of nanostructures including multimaterial bridges, interconnects, or nanowire arrays with 20 nm positional accuracy. These features enable combinatorial nanostructured materials and device discovery. As a first demonstration, we produce and identify in a combinatorial way 3D nanostructured electrode designs that improve light scattering, absorption, and minority carrier extraction of bulk heterojunction photovoltaic cells. Photovoltaic cells from domains with long and dense nanowire arrays improve the relative power conversion efficiency by 47% when compared to flat domains on the same substrate.
Combinatorial and high-throughput screening of materials libraries: review of state of the art.
Potyrailo, Radislav; Rajan, Krishna; Stoewe, Klaus; Takeuchi, Ichiro; Chisholm, Bret; Lam, Hubert
2011-11-14
Rational materials design based on prior knowledge is attractive because it promises to avoid time-consuming synthesis and testing of numerous materials candidates. However with the increase of complexity of materials, the scientific ability for the rational materials design becomes progressively limited. As a result of this complexity, combinatorial and high-throughput (CHT) experimentation in materials science has been recognized as a new scientific approach to generate new knowledge. This review demonstrates the broad applicability of CHT experimentation technologies in discovery and optimization of new materials. We discuss general principles of CHT materials screening, followed by the detailed discussion of high-throughput materials characterization approaches, advances in data analysis/mining, and new materials developments facilitated by CHT experimentation. We critically analyze results of materials development in the areas most impacted by the CHT approaches, such as catalysis, electronic and functional materials, polymer-based industrial coatings, sensing materials, and biomaterials.
Evaluation of the Current Status of the Combinatorial Approach for the Study of Phase Diagrams
Wong-Ng, W.
2012-01-01
This paper provides an evaluation of the effectiveness of using the high throughput combinatorial approach for preparing phase diagrams of thin film and bulk materials. Our evaluation is based primarily on examples of combinatorial phase diagrams that have been reported in the literature as well as based on our own laboratory experiments. Various factors that affect the construction of these phase diagrams are examined. Instrumentation and analytical approaches needed to improve data acquisition and data analysis are summarized. PMID:26900530
A path-oriented knowledge representation system: Defusing the combinatorial system
NASA Technical Reports Server (NTRS)
Karamouzis, Stamos T.; Barry, John S.; Smith, Steven L.; Feyock, Stefan
1995-01-01
LIMAP is a programming system oriented toward efficient information manipulation over fixed finite domains, and quantification over paths and predicates. A generalization of Warshall's Algorithm to precompute paths in a sparse matrix representation of semantic nets is employed to allow questions involving paths between components to be posed and answered easily. LIMAP's ability to cache all paths between two components in a matrix cell proved to be a computational obstacle, however, when the semantic net grew to realistic size. The present paper describes a means of mitigating this combinatorial explosion to an extent that makes the use of the LIMAP representation feasible for problems of significant size. The technique we describe radically reduces the size of the search space in which LIMAP must operate; semantic nets of more than 500 nodes have been attacked successfully. Furthermore, it appears that the procedure described is applicable not only to LIMAP, but to a number of other combinatorially explosive search space problems found in AI as well.
Breast cancer prognosis by combinatorial analysis of gene expression data.
Alexe, Gabriela; Alexe, Sorin; Axelrod, David E; Bonates, Tibérius O; Lozina, Irina I; Reiss, Michael; Hammer, Peter L
2006-01-01
The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors. Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines. LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics. The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized
Automated Lead Optimization of MMP-12 Inhibitors Using a Genetic Algorithm.
Pickett, Stephen D; Green, Darren V S; Hunt, David L; Pardoe, David A; Hughes, Ian
2011-01-13
Traditional lead optimization projects involve long synthesis and testing cycles, favoring extensive structure-activity relationship (SAR) analysis and molecular design steps, in an attempt to limit the number of cycles that a project must run to optimize a development candidate. Microfluidic-based chemistry and biology platforms, with cycle times of minutes rather than weeks, lend themselves to unattended autonomous operation. The bottleneck in the lead optimization process is therefore shifted from synthesis or test to SAR analysis and design. As such, the way is open to an algorithm-directed process, without the need for detailed user data analysis. Here, we present results of two synthesis and screening experiments, undertaken using traditional methodology, to validate a genetic algorithm optimization process for future application to a microfluidic system. The algorithm has several novel features that are important for the intended application. For example, it is robust to missing data and can suggest compounds for retest to ensure reliability of optimization. The algorithm is first validated on a retrospective analysis of an in-house library embedded in a larger virtual array of presumed inactive compounds. In a second, prospective experiment with MMP-12 as the target protein, 140 compounds are submitted for synthesis over 10 cycles of optimization. Comparison is made to the results from the full combinatorial library that was synthesized manually and tested independently. The results show that compounds selected by the algorithm are heavily biased toward the more active regions of the library, while the algorithm is robust to both missing data (compounds where synthesis failed) and inactive compounds. This publication places the full combinatorial library and biological data into the public domain with the intention of advancing research into algorithm-directed lead optimization methods.
Automated Lead Optimization of MMP-12 Inhibitors Using a Genetic Algorithm
2010-01-01
Traditional lead optimization projects involve long synthesis and testing cycles, favoring extensive structure−activity relationship (SAR) analysis and molecular design steps, in an attempt to limit the number of cycles that a project must run to optimize a development candidate. Microfluidic-based chemistry and biology platforms, with cycle times of minutes rather than weeks, lend themselves to unattended autonomous operation. The bottleneck in the lead optimization process is therefore shifted from synthesis or test to SAR analysis and design. As such, the way is open to an algorithm-directed process, without the need for detailed user data analysis. Here, we present results of two synthesis and screening experiments, undertaken using traditional methodology, to validate a genetic algorithm optimization process for future application to a microfluidic system. The algorithm has several novel features that are important for the intended application. For example, it is robust to missing data and can suggest compounds for retest to ensure reliability of optimization. The algorithm is first validated on a retrospective analysis of an in-house library embedded in a larger virtual array of presumed inactive compounds. In a second, prospective experiment with MMP-12 as the target protein, 140 compounds are submitted for synthesis over 10 cycles of optimization. Comparison is made to the results from the full combinatorial library that was synthesized manually and tested independently. The results show that compounds selected by the algorithm are heavily biased toward the more active regions of the library, while the algorithm is robust to both missing data (compounds where synthesis failed) and inactive compounds. This publication places the full combinatorial library and biological data into the public domain with the intention of advancing research into algorithm-directed lead optimization methods. PMID:24900251
NASA Astrophysics Data System (ADS)
Lu, Hai-Bo; Liu, Wei-Qiang
2014-04-01
Validated by the correlated experiments, a nose-tip with forward-facing cavity/opposing jet/the combinatorial configuration of forward-facing cavity and opposing jet thermal protection system (TPS) are investigated numerically. The physical mechanism of these TPS is discussed, and the cooling efficiency of them is compared. The combinatorial system is more suitable to be the TPS for the high speed vehicles which need fly under various flow conditions with long-range and long time.
Optimal placement of excitations and sensors for verification of large dynamical systems
NASA Technical Reports Server (NTRS)
Salama, M.; Rose, T.; Garba, J.
1987-01-01
The computationally difficult problem of the optimal placement of excitations and sensors to maximize the observed measurements is studied within the framework of combinatorial optimization, and is solved numerically using a variation of the simulated annealing heuristic algorithm. Results of numerical experiments including a square plate and a 960 degrees-of-freedom Control of Flexible Structure (COFS) truss structure, are presented. Though the algorithm produces suboptimal solutions, its generality and simplicity allow the treatment of complex dynamical systems which would otherwise be difficult to handle.
Nash Social Welfare in Multiagent Resource Allocation
NASA Astrophysics Data System (ADS)
Ramezani, Sara; Endriss, Ulle
We study different aspects of the multiagent resource allocation problem when the objective is to find an allocation that maximizes Nash social welfare, the product of the utilities of the individual agents. The Nash solution is an important welfare criterion that combines efficiency and fairness considerations. We show that the problem of finding an optimal outcome is NP-hard for a number of different languages for representing agent preferences; we establish new results regarding convergence to Nash-optimal outcomes in a distributed negotiation framework; and we design and test algorithms similar to those applied in combinatorial auctions for computing such an outcome directly.
NASA Technical Reports Server (NTRS)
Rash, James L.
2010-01-01
NASA's space data-communications infrastructure, the Space Network and the Ground Network, provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft via orbiting relay satellites and ground stations. An implementation of the methods and algorithms disclosed herein will be a system that produces globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary search, a class of probabilistic strategies for searching large solution spaces, constitutes the essential technology in this disclosure. Also disclosed are methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithm itself. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally, with applicability to a very broad class of combinatorial optimization problems.
Children's Strategies for Solving Two- and Three-Dimensional Combinatorial Problems.
ERIC Educational Resources Information Center
English, Lyn D.
1993-01-01
Investigated strategies that 7- to 12-year-old children (n=96) spontaneously applied in solving novel combinatorial problems. With experience in solving two-dimensional problems, children were able to refine their strategies and adapt them to three dimensions. Results on some problems indicated significant effects of age. (Contains 32 references.)…
Xu, Huayong; Yu, Hui; Tu, Kang; Shi, Qianqian; Wei, Chaochun; Li, Yuan-Yuan; Li, Yi-Xue
2013-01-01
We are witnessing rapid progress in the development of methodologies for building the combinatorial gene regulatory networks involving both TFs (Transcription Factors) and miRNAs (microRNAs). There are a few tools available to do these jobs but most of them are not easy to use and not accessible online. A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts. In this work, we compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. We streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. As a result, we released the cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module. In this article we propose cGRNB, a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets
Method and apparatus for combinatorial chemistry
Foote, Robert S.
2007-02-20
A method and apparatus are provided for performing light-directed reactions in spatially addressable channels within a plurality of channels. One aspect of the invention employs photoactivatable reagents in solutions disposed into spatially addressable flow streams to control the parallel synthesis of molecules immobilized within the channels. The reagents may be photoactivated within a subset of channels at the site of immobilized substrate molecules or at a light-addressable site upstream from the substrate molecules. The method and apparatus of the invention find particularly utility in the synthesis of biopolymer arrays, e.g., oligonucleotides, peptides and carbohydrates, and in the combinatorial synthesis of small molecule arrays for drug discovery.
Method and apparatus for combinatorial chemistry
Foote, Robert S [Oak Ridge, TN
2012-06-05
A method and apparatus are provided for performing light-directed reactions in spatially addressable channels within a plurality of channels. One aspect of the invention employs photoactivatable reagents in solutions disposed into spatially addressable flow streams to control the parallel synthesis of molecules immobilized within the channels. The reagents may be photoactivated within a subset of channels at the site of immobilized substrate molecules or at a light-addressable site upstream from the substrate molecules. The method and apparatus of the invention find particularly utility in the synthesis of biopolymer arrays, e.g., oligonucleotides, peptides and carbohydrates, and in the combinatorial synthesis of small molecule arrays for drug discovery.
Discovery of Cationic Polymers for Non-viral Gene Delivery using Combinatorial Approaches
Barua, Sutapa; Ramos, James; Potta, Thrimoorthy; Taylor, David; Huang, Huang-Chiao; Montanez, Gabriela; Rege, Kaushal
2015-01-01
Gene therapy is an attractive treatment option for diseases of genetic origin, including several cancers and cardiovascular diseases. While viruses are effective vectors for delivering exogenous genes to cells, concerns related to insertional mutagenesis, immunogenicity, lack of tropism, decay and high production costs necessitate the discovery of non-viral methods. Significant efforts have been focused on cationic polymers as non-viral alternatives for gene delivery. Recent studies have employed combinatorial syntheses and parallel screening methods for enhancing the efficacy of gene delivery, biocompatibility of the delivery vehicle, and overcoming cellular level barriers as they relate to polymer-mediated transgene uptake, transport, transcription, and expression. This review summarizes and discusses recent advances in combinatorial syntheses and parallel screening of cationic polymer libraries for the discovery of efficient and safe gene delivery systems. PMID:21843141
Ye, Yusen; Gao, Lin; Zhang, Shihua
2017-01-01
Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions. PMID:29033978
Ye, Yusen; Gao, Lin; Zhang, Shihua
2017-01-01
Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions.
Optimizing Variational Quantum Algorithms Using Pontryagin’s Minimum Principle
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yang, Zhi -Cheng; Rahmani, Armin; Shabani, Alireza
We use Pontryagin’s minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed quantum approximate optimization algorithm. Focusing on the Sherrington-Kirkpatrick spin glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. The optimality of the bang-bang protocols and the characteristic time scale ofmore » the pulses provide an efficient parametrization of the protocol and inform the search for effective hybrid (classical and quantum) schemes for tackling combinatorial optimization problems. Moreover, we find that the success rates of our optimal bang-bang protocols remain high even in the presence of weak external noise and coupling to a thermal bath.« less
Optimizing Variational Quantum Algorithms Using Pontryagin’s Minimum Principle
Yang, Zhi -Cheng; Rahmani, Armin; Shabani, Alireza; ...
2017-05-18
We use Pontryagin’s minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed quantum approximate optimization algorithm. Focusing on the Sherrington-Kirkpatrick spin glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. The optimality of the bang-bang protocols and the characteristic time scale ofmore » the pulses provide an efficient parametrization of the protocol and inform the search for effective hybrid (classical and quantum) schemes for tackling combinatorial optimization problems. Moreover, we find that the success rates of our optimal bang-bang protocols remain high even in the presence of weak external noise and coupling to a thermal bath.« less
ERIC Educational Resources Information Center
Harrison, Don K.; Brown, Dorothy R.
The hard-to-employ, both urban and rural, share common characteristics of inadequate income, slum housing, inferior education, no medical attention, and lack of real job opportunities. The deficiencies dove-tail, and families are often afflicted with all. The picture may seem bleak, but there is optimism in reclamation of the so-called…
Sentence Processing in an Artificial Language: Learning and Using Combinatorial Constraints
ERIC Educational Resources Information Center
Amato, Michael S.; MacDonald, Maryellen C.
2010-01-01
A study combining artificial grammar and sentence comprehension methods investigated the learning and online use of probabilistic, nonadjacent combinatorial constraints. Participants learned a small artificial language describing cartoon monsters acting on objects. Self-paced reading of sentences in the artificial language revealed comprehenders'…
Obfuscation Framework Based on Functionally Equivalent Combinatorial Logic Families
2008-03-01
of Defense, or the United States Government . AFIT/GCS/ENG/08-12 Obfuscation Framework Based on Functionally Equivalent Combinatorial Logic Families...time, United States policy strongly encourages the sale and transfer of some military equipment to foreign governments and makes it easier for...Proceedings of the International Conference on Availability, Reliability and Security, 2007. 14. McDonald, J. Todd and Alec Yasinsac. “Of unicorns and random
Statistical molecular design of building blocks for combinatorial chemistry.
Linusson, A; Gottfries, J; Lindgren, F; Wold, S
2000-04-06
The reduction of the size of a combinatorial library can be made in two ways, either base the selection on the building blocks (BB's) or base it on the full set of virtually constructed products. In this paper we have investigated the effects of applying statistical designs to BB sets compared to selections based on the final products. The two sets of BB's and the virtually constructed library were described by structural parameters, and the correlation between the two characterizations was investigated. Three different selection approaches were used both for the BB sets and for the products. In the first two the selection algorithms were applied directly to the data sets (D-optimal design and space-filling design), while for the third a cluster analysis preceded the selection (cluster-based design). The selections were compared using visual inspection, the Tanimoto coefficient, the Euclidean distance, the condition number, and the determinant of the resulting data matrix. No difference in efficiency was found between selections made in the BB space and in the product space. However, it is of critical importance to investigate the BB space carefully and to select an appropriate number of BB's to result in an adequate diversity. An example from the pharmaceutical industry is then presented, where selection via BB's was made using a cluster-based design.
Wang, Yen-Ling
2014-01-01
Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the BesttrainBesttest and FasttrainFasttest prediction results. The potential inhibitors were selected from NCI database by screening according to BesttrainBesttest + FasttrainFasttest prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study. PMID:24864236
NASA Astrophysics Data System (ADS)
Takabe, Satoshi; Hukushima, Koji
2016-05-01
Typical behavior of the linear programming (LP) problem is studied as a relaxation of the minimum vertex cover (min-VC), a type of integer programming (IP) problem. A lattice-gas model on the Erdös-Rényi random graphs of α -uniform hyperedges is proposed to express both the LP and IP problems of the min-VC in the common statistical mechanical model with a one-parameter family. Statistical mechanical analyses reveal for α =2 that the LP optimal solution is typically equal to that given by the IP below the critical average degree c =e in the thermodynamic limit. The critical threshold for good accuracy of the relaxation extends the mathematical result c =1 and coincides with the replica symmetry-breaking threshold of the IP. The LP relaxation for the minimum hitting sets with α ≥3 , minimum vertex covers on α -uniform random graphs, is also studied. Analytic and numerical results strongly suggest that the LP relaxation fails to estimate optimal values above the critical average degree c =e /(α -1 ) where the replica symmetry is broken.
Takabe, Satoshi; Hukushima, Koji
2016-05-01
Typical behavior of the linear programming (LP) problem is studied as a relaxation of the minimum vertex cover (min-VC), a type of integer programming (IP) problem. A lattice-gas model on the Erdös-Rényi random graphs of α-uniform hyperedges is proposed to express both the LP and IP problems of the min-VC in the common statistical mechanical model with a one-parameter family. Statistical mechanical analyses reveal for α=2 that the LP optimal solution is typically equal to that given by the IP below the critical average degree c=e in the thermodynamic limit. The critical threshold for good accuracy of the relaxation extends the mathematical result c=1 and coincides with the replica symmetry-breaking threshold of the IP. The LP relaxation for the minimum hitting sets with α≥3, minimum vertex covers on α-uniform random graphs, is also studied. Analytic and numerical results strongly suggest that the LP relaxation fails to estimate optimal values above the critical average degree c=e/(α-1) where the replica symmetry is broken.
NASA Astrophysics Data System (ADS)
Potyrailo, Radislav A.; Hassib, Lamyaa
2005-06-01
Multicomponent polymer-based formulations of optical sensor materials are difficult and time consuming to optimize using conventional approaches. To address these challenges, our long-term goal is to determine relationships between sensor formulation and sensor response parameters using new scientific methodologies. As the first step, we have designed and implemented an automated analytical instrumentation infrastructure for combinatorial and high-throughput development of polymeric sensor materials for optical sensors. Our approach is based on the fabrication and performance screening of discrete and gradient sensor arrays. Simultaneous formation of multiple sensor coatings into discrete 4×6, 6×8, and 8×12 element arrays (3-15μL volume per element) and their screening provides not only a well-recognized acceleration in the screening rate, but also considerably reduces or even eliminates sources of variability, which are randomly affecting sensors response during a conventional one-at-a-time sensor coating evaluation. The application of gradient sensor arrays provides additional capabilities for rapid finding of the optimal formulation parameters.
Extremal Optimization: Methods Derived from Co-Evolution
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boettcher, S.; Percus, A.G.
1999-07-13
We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance provesmore » competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.« less
Legrand, Yves-Marie; van der Lee, Arie; Barboiu, Mihail
2007-11-12
In this paper we report an extended series of 2,6-(iminoarene)pyridine-type ZnII complexes [(Lii)2Zn]II, which were surveyed for their ability to self-exchange both their ligands and their aromatic arms and to form different homoduplex and heteroduplex complexes in solution. The self-sorting of heteroduplex complexes is likely to be the result of geometric constraints. Whereas the imine-exchange process occurs quantitatively in 1:1 mixtures of [(Lii)2Zn]II complexes, the octahedral coordination process around the metal ion defines spatial-frustrated exchanges that involve the selective formation of heterocomplexes of two, by two different substituents; the bulkiest ones (pyrene in principle) specifically interact with the pseudoterpyridine core, sterically hindering the least bulky ones, which are intermolecularly stacked with similar ligands of neighboring molecules. Such a self-sorting process defined by the specific self-constitution of the ligands exchanging their aromatic substituents is self-optimized by a specific control over their spatial orientation around a metal center within the complex. They ultimately show an improved charge-transfer energy function by virtue of the dynamic amplification of self-optimized heteroduplex architectures. These systems therefore illustrate the convergence of the combinatorial self-sorting of the dynamic combinatorial libraries (DCLs) strategy and the constitutional self-optimized function.
Forghani, Fereidoun; Park, Joong-Hyun; Oh, Deog-Hwan
2015-06-01
Slightly acidic electrolyzed water (SAEW) has been proved as an effective sanitizer against microorganisms attached to foods. However, its physical properties and inactivation efficacy are affected by several factors such as water hardness. Therefore, in this study the effect of water hardness on SAEW properties were studied. Pure cultures of foodborne bacteria were used in vitro and in vivo to evaluate the inactivation efficacy of the SAEWs produced. Results obtained showed water hardness to be an important factor in the production of SAEW. Low water hardness may result in the necessity of further optimization of production process. In this study the addition of 5% HCl and 2 M NaCl at 1.5 mL/min flow rate was found to be the best electrolyte concentration for the optimization of SAEW production from low hardness water (34 ± 2 mg/L). Furthermore, the results showed that pre-heating was a better approach compared to post-production heating of SAEW, resulting in higher ACC values and therefor better sanitization efficacy. Copyright © 2014 Elsevier Ltd. All rights reserved.
Micromagnetic simulations with periodic boundary conditions: Hard-soft nanocomposites
Wysocki, Aleksander L.; Antropov, Vladimir P.
2016-12-01
Here, we developed a micromagnetic method for modeling magnetic systems with periodic boundary conditions along an arbitrary number of dimensions. The main feature is an adaptation of the Ewald summation technique for evaluation of long-range dipolar interactions. The method was applied to investigate the hysteresis process in hard-soft magnetic nanocomposites with various geometries. The dependence of the results on different micromagnetic parameters was studied. We found that for layered structures with an out-of-plane hard phase easy axis the hysteretic properties are very sensitive to the strength of the interlayer exchange coupling, as long as the spontaneous magnetization for the hardmore » phase is significantly smaller than for the soft phase. The origin of this behavior was discussed. Additionally, we investigated the soft phase size optimizing the energy product of hard-soft nanocomposites.« less
Solving multi-objective optimization problems in conservation with the reference point method
Dujardin, Yann; Chadès, Iadine
2018-01-01
Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems. PMID:29293650
Boehm, Markus; Wu, Tong-Ying; Claussen, Holger; Lemmen, Christian
2008-04-24
Large collections of combinatorial libraries are an integral element in today's pharmaceutical industry. It is of great interest to perform similarity searches against all virtual compounds that are synthetically accessible by any such library. Here we describe the successful application of a new software tool CoLibri on 358 combinatorial libraries based on validated reaction protocols to create a single chemistry space containing over 10 (12) possible products. Similarity searching with FTrees-FS allows the systematic exploration of this space without the need to enumerate all product structures. The search result is a set of virtual hits which are synthetically accessible by one or more of the existing reaction protocols. Grouping these virtual hits by their synthetic protocols allows the rapid design and synthesis of multiple follow-up libraries. Such library ideas support hit-to-lead design efforts for tasks like follow-up from high-throughput screening hits or scaffold hopping from one hit to another attractive series.
Mongoose: Creation of a Rad-Hard MIPS R3000
NASA Technical Reports Server (NTRS)
Lincoln, Dan; Smith, Brian
1993-01-01
This paper describes the development of a 32 Bit, full MIPS R3000 code-compatible Rad-Hard CPU, code named Mongoose. Mongoose progressed from contract award, through the design cycle, to operational silicon in 12 months to meet a space mission for NASA. The goal was the creation of a fully static device capable of operation to the maximum Mil-883 derated speed, worst-case post-rad exposure with full operational integrity. This included consideration of features for functional enhancements relating to mission compatibility and removal of commercial practices not supported by Rad-Hard technology. 'Mongoose' developed from an evolution of LSI Logic's MIPS-I embedded processor, LR33000, code named Cobra, to its Rad-Hard 'equivalent', Mongoose. The term 'equivalent' is used to infer that the core of the processor is functionally identical, allowing the same use and optimizations of the MIPS-I Instruction Set software tool suite for compilation, software program trace, etc. This activity was started in September of 1991 under a contract from NASA-Goddard Space Flight Center (GSFC)-Flight Data Systems. The approach affected a teaming of NASA-GSFC for program development, LSI Logic for system and ASIC design coupled with the Rad-Hard process technology, and Harris (GASD) for Rad-Hard microprocessor design expertise. The program culminated with the generation of Rad-Hard Mongoose prototypes one year later.
Combinatorial interpretation of Haldane-Wu fractional exclusion statistics.
Aringazin, A K; Mazhitov, M I
2002-08-01
Assuming that the maximal allowed number of identical particles in a state is an integer parameter, q, we derive the statistical weight and analyze the associated equation that defines the statistical distribution. The derived distribution covers Fermi-Dirac and Bose-Einstein ones in the particular cases q=1 and q--> infinity (n(i)/q-->1), respectively. We show that the derived statistical weight provides a natural combinatorial interpretation of Haldane-Wu fractional exclusion statistics, and present exact solutions of the distribution equation.
Chang, Yi-Pin; Chu, Yen-Ho
2014-05-16
The design, synthesis and screening of diversity-oriented peptide libraries using a "libraries from libraries" strategy for the development of inhibitors of α1-antitrypsin deficiency are described. The major buttress of the biochemical approach presented here is the use of well-established solid-phase split-and-mix method for the generation of mixture-based libraries. The combinatorial technique iterative deconvolution was employed for library screening. While molecular diversity is the general consideration of combinatorial libraries, exquisite design through systematic screening of small individual libraries is a prerequisite for effective library screening and can avoid potential problems in some cases. This review will also illustrate how large peptide libraries were designed, as well as how a conformation-sensitive assay was developed based on the mechanism of the conformational disease. Finally, the combinatorially selected peptide inhibitor capable of blocking abnormal protein aggregation will be characterized by biophysical, cellular and computational methods.
Chuah, Yon Jin; Zhang, Ying; Wu, Yingnan; Menon, Nishanth V; Goh, Ghim Hian; Lee, Ann Charlene; Chan, Vincent; Zhang, Yilei; Kang, Yuejun
2015-09-01
Cell sheet engineering has been exploited as an alternative approach in tissue regeneration and the use of stem cells to generate cell sheets has further showed its potential in stem cell-mediated tissue regeneration. There exist vast interests in developing strategies to enhance the formation of stem cell sheets for downstream applications. It has been proved that stem cells are sensitive to the biophysical cues of the microenvironment. Therefore we hypothesized that the combinatorial substratum properties could be tailored to modulate the development of cell sheet formation and further influence its multipotency. For validation, polydimethylsiloxane (PDMS) of different combinatorial substratum properties (including stiffness, roughness and wettability) were created, on which the human bone marrow derived mesenchymal stem cells (BMSCs) were cultured to form cell sheets with their multipotency evaluated after induced differentiation. The results showed that different combinatorial effects of these substratum properties were able to influence BMSC behavior such as adhesion, spreading and proliferation during cell sheet development. Collagen formation within the cell sheet was enhanced on substrates with lower stiffness, higher hydrophobicity and roughness, which further assisted the induced chondrogenesis and osteogenesis, respectively. These findings suggested that combinatorial substratum properties had profound effects on BMSC cell sheet integrity and multipotency, which had significant implications for future biomaterials and scaffold designs in the field of BMSC-mediated tissue regeneration. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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.
Zhou, Qian-Mei; Chen, Qi-Long; Du, Jia; Wang, Xiu-Feng; Lu, Yi-Yu; Zhang, Hui; Su, Shi-Bing
2014-01-01
In order to explore the synergistic mechanisms of combinatorial treatment using curcumin and mitomycin C (MMC) for breast cancer, MCF-7 breast cancer xenografts were conducted to observe the synergistic effect of combinatorial treatment using curcumin and MMC at various dosages. The synergistic mechanisms of combinatorial treatment using curcumin and MMC on the inhibition of tumor growth were explored by differential gene expression profile, gene ontology (GO), ingenuity pathway analysis (IPA) and Signal–Net network analysis. The expression levels of selected genes identified by cDNA microarray expression profiling were validated by quantitative RT-PCR (qRT-PCR) and Western blot analysis. Effect of combinatorial treatment on the inhibition of cell growth was observed by MTT assay. Apoptosis was detected by flow cytometric analysis and Hoechst 33258 staining. The combinatorial treatment of 100 mg/kg curcumin and 1.5 mg/kg MMC revealed synergistic inhibition on tumor growth. Among 1501 differentially expressed genes, the expression of 25 genes exhibited an obvious change and a significant difference in 27 signal pathways was observed (p < 0.05). In addition, Mapk1 (ERK) and Mapk14 (MAPK p38) had more cross-interactions with other genes and revealed an increase in expression by 8.14- and 11.84-fold, respectively during the combinatorial treatment by curcumin and MMC when compared with the control. Moreover, curcumin can synergistically improve tumoricidal effect of MMC in another human breast cancer MDA-MB-231 cells. Apoptosis was significantly induced by the combinatorial treatment (p < 0.05) and significantly inhibited by ERK inhibitor (PD98059) in MCF-7 cells (p < 0.05). The synergistic effect of combinatorial treatment by curcumin and MMC on the induction of apoptosis in breast cancer cells may be via the ERK pathway. PMID:25226537
Smolensky, Paul; Goldrick, Matthew; Mathis, Donald
2014-08-01
Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization-Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. Copyright © 2013 Cognitive Science Society, Inc.
On Maximal Hard-Core Thinnings of Stationary Particle Processes
NASA Astrophysics Data System (ADS)
Hirsch, Christian; Last, Günter
2018-02-01
The present paper studies existence and distributional uniqueness of subclasses of stationary hard-core particle systems arising as thinnings of stationary particle processes. These subclasses are defined by natural maximality criteria. We investigate two specific criteria, one related to the intensity of the hard-core particle process, the other one being a local optimality criterion on the level of realizations. In fact, the criteria are equivalent under suitable moment conditions. We show that stationary hard-core thinnings satisfying such criteria exist and are frequently distributionally unique. More precisely, distributional uniqueness holds in subcritical and barely supercritical regimes of continuum percolation. Additionally, based on the analysis of a specific example, we argue that fluctuations in grain sizes can play an important role for establishing distributional uniqueness at high intensities. Finally, we provide a family of algorithmically constructible approximations whose volume fractions are arbitrarily close to the maximum.
Optimal placement of actuators and sensors in control augmented structural optimization
NASA Technical Reports Server (NTRS)
Sepulveda, A. E.; Schmit, L. A., Jr.
1990-01-01
A control-augmented structural synthesis methodology is presented in which actuator and sensor placement is treated in terms of (0,1) variables. Structural member sizes and control variables are treated simultaneously as design variables. A multiobjective utopian approach is used to obtain a compromise solution for inherently conflicting objective functions such as strucutal mass control effort and number of actuators. Constraints are imposed on transient displacements, natural frequencies, actuator forces and dynamic stability as well as controllability and observability of the system. The combinatorial aspects of the mixed - (0,1) continuous variable design optimization problem are made tractable by combining approximation concepts with branch and bound techniques. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure set forth.
Bagheri, Neda; Shiina, Marisa; Lauffenburger, Douglas A; Korn, W Michael
2011-02-01
Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced
Thermal analysis of combinatorial solid geometry models using SINDA
NASA Technical Reports Server (NTRS)
Gerencser, Diane; Radke, George; Introne, Rob; Klosterman, John; Miklosovic, Dave
1993-01-01
Algorithms have been developed using Monte Carlo techniques to determine the thermal network parameters necessary to perform a finite difference analysis on Combinatorial Solid Geometry (CSG) models. Orbital and laser fluxes as well as internal heat generation are modeled to facilitate satellite modeling. The results of the thermal calculations are used to model the infrared (IR) images of targets and assess target vulnerability. Sample analyses and validation are presented which demonstrate code products.
Learning optimal quantum models is NP-hard
NASA Astrophysics Data System (ADS)
Stark, Cyril J.
2018-02-01
Physical modeling translates measured data into a physical model. Physical modeling is a major objective in physics and is generally regarded as a creative process. How good are computers at solving this task? Here, we show that in the absence of physical heuristics, the inference of optimal quantum models cannot be computed efficiently (unless P=NP ). This result illuminates rigorous limits to the extent to which computers can be used to further our understanding of nature.
Lin, Chun-Yuan; Wang, Yen-Ling
2014-01-01
Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the Best(train)Best(test) and Fast(train)Fast(test) prediction results. The potential inhibitors were selected from NCI database by screening according to Best(train)Best(test) + Fast(train)Fast(test) prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.
Yu, Jong-Sung; Kim, Min-Sik; Kim, Jung Ho
2010-12-14
Combinatorial synthesis and screening were used to identify methanol-tolerant non-platinum cathode electrocatalysts for use in direct methanol fuel cells (DMFCs). Oxygen reduction consumes protons at the surface of DMFC cathode catalysts. In combinatorial screening, this pH change allows one to differentiate active catalysts using fluorescent acid-base indicators. Combinatorial libraries of carbon-supported catalyst compositions containing Ru, Mo, W, Sn, and Se were screened. Ternary and quaternary compositions containing Ru, Sn, Mo, Se were more active than the "standard" Alonso-Vante catalyst, Ru(3)Mo(0.08)Se(2), when tested in liquid-feed DMFCs. Physical characterization of the most active catalysts by powder X-ray diffraction, gas adsorption, and X-ray photoelectron spectroscopy revealed that the predominant crystalline phase was hexagonal close-packed (hcp) ruthenium, and showed a surface mostly covered with oxide. The best new catalyst, Ru(7.0)Sn(1.0)Se(1.0), was significantly more active than Ru(3)Se(2)Mo(0.08), even though the latter contained smaller particles.
Chattaraj, Pratim K; Ayers, Paul W; Melin, Junia
2007-08-07
Ayers, Parr, and Pearson recently showed that insight into the hard/soft acid/base (HSAB) principle could be obtained by analyzing the energy of reactions in hard/soft exchange reactions, i.e., reactions in which a soft acid replaces a hard acid or a soft base replaces a hard base [J. Chem. Phys., 2006, 124, 194107]. We show, in accord with the maximum hardness principle, that the hardness increases for favorable hard/soft exchange reactions and decreases when the HSAB principle indicates that hard/soft exchange reactions are unfavorable. This extends the previous work of the authors, which treated only the "double hard/soft exchange" reaction [P. K. Chattaraj and P. W. Ayers, J. Chem. Phys., 2005, 123, 086101]. We also discuss two different approaches to computing the hardness of molecules from the hardness of the composing fragments, and explain how the results differ. In the present context, it seems that the arithmetic mean of fragment softnesses is the preferable definition.
Magnetic hyperthermia with hard-magnetic nanoparticles
NASA Astrophysics Data System (ADS)
Kashevsky, Bronislav E.; Kashevsky, Sergey B.; Korenkov, Victor S.; Istomin, Yuri P.; Terpinskaya, Tatyana I.; Ulashchik, Vladimir S.
2015-04-01
Recent clinical trials of magnetic hyperthermia have proved, and even hardened, the Ankinson-Brezovich restriction as upon magnetic field conditions applicable to any site of human body. Subject to this restriction, which is harshly violated in numerous laboratory and small animal studies, magnetic hyperthermia can relay on rather moderate heat source, so that optimization of the whole hyperthermia system remains, after all, the basic problem predetermining its clinical perspectives. We present short account of our complex (theoretical, laboratory and small animal) studies to demonstrate that such perspectives should be related with the hyperthermia system based on hard-magnetic (Stoner-Wohlfarth type) nanoparticles and strong low-frequency fields rather than with superparamagnetic (Brownian or Neél) nanoparticles and weak high-frequency fields. This conclusion is backed by an analytical evaluation of the maximum absorption rates possible under the field restriction in the ideal hard-magnetic (Stoner-Wohlarth) and the ideal superparamagnetic (single relaxation time) systems, by theoretical and experimental studies of the dynamic magnetic hysteresis in suspensions of movable hard-magnetic particles, by producing nanoparticles with adjusted coercivity and suspensions of such particles capable of effective energy absorption and intratumoral penetration, and finally, by successful treatment of a mice model tumor under field conditions acceptable for whole human body.
Template-based combinatorial enumeration of virtual compound libraries for lipids
2012-01-01
A variety of software packages are available for the combinatorial enumeration of virtual libraries for small molecules, starting from specifications of core scaffolds with attachments points and lists of R-groups as SMILES or SD files. Although SD files include atomic coordinates for core scaffolds and R-groups, it is not possible to control 2-dimensional (2D) layout of the enumerated structures generated for virtual compound libraries because different packages generate different 2D representations for the same structure. We have developed a software package called LipidMapsTools for the template-based combinatorial enumeration of virtual compound libraries for lipids. Virtual libraries are enumerated for the specified lipid abbreviations using matching lists of pre-defined templates and chain abbreviations, instead of core scaffolds and lists of R-groups provided by the user. 2D structures of the enumerated lipids are drawn in a specific and consistent fashion adhering to the framework for representing lipid structures proposed by the LIPID MAPS consortium. LipidMapsTools is lightweight, relatively fast and contains no external dependencies. It is an open source package and freely available under the terms of the modified BSD license. PMID:23006594
Template-based combinatorial enumeration of virtual compound libraries for lipids.
Sud, Manish; Fahy, Eoin; Subramaniam, Shankar
2012-09-25
A variety of software packages are available for the combinatorial enumeration of virtual libraries for small molecules, starting from specifications of core scaffolds with attachments points and lists of R-groups as SMILES or SD files. Although SD files include atomic coordinates for core scaffolds and R-groups, it is not possible to control 2-dimensional (2D) layout of the enumerated structures generated for virtual compound libraries because different packages generate different 2D representations for the same structure. We have developed a software package called LipidMapsTools for the template-based combinatorial enumeration of virtual compound libraries for lipids. Virtual libraries are enumerated for the specified lipid abbreviations using matching lists of pre-defined templates and chain abbreviations, instead of core scaffolds and lists of R-groups provided by the user. 2D structures of the enumerated lipids are drawn in a specific and consistent fashion adhering to the framework for representing lipid structures proposed by the LIPID MAPS consortium. LipidMapsTools is lightweight, relatively fast and contains no external dependencies. It is an open source package and freely available under the terms of the modified BSD license.
Galaxy Redshifts from Discrete Optimization of Correlation Functions
NASA Astrophysics Data System (ADS)
Lee, Benjamin C. G.; Budavári, Tamás; Basu, Amitabh; Rahman, Mubdi
2016-12-01
We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming (ILP) are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of ILP to astronomical analysis.
Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4
NASA Astrophysics Data System (ADS)
Evensen, Erik; Joseph-McCarthy, Diane; Weiss, Gregory A.; Schreiber, Stuart L.; Karplus, Martin
2007-07-01
Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.
Hydrogel design of experiments methodology to optimize hydrogel for iPSC-NPC culture.
Lam, Jonathan; Carmichael, S Thomas; Lowry, William E; Segura, Tatiana
2015-03-11
Bioactive signals can be incorporated in hydrogels to direct encapsulated cell behavior. Design of experiments methodology methodically varies the signals systematically to determine the individual and combinatorial effects of each factor on cell activity. Using this approach enables the optimization of three ligands concentrations (RGD, YIGSR, IKVAV) for the survival and differentiation of neural progenitor cells. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Extremal problems for topological indices in combinatorial chemistry.
Tichy, Robert F; Wagner, Stephan
2005-09-01
Topological indices of molecular graphs are related to several physicochemical characteristics; recently, the inverse problem for some of these indices has been studied, and it has some applications in the design of combinatorial libraries for drug discovery. It is thus very natural to study also extremal problems for these indices, i.e., finding graphs having minimal or maximal index. In this paper, these questions will be discussed for three different indices, namely the sigma-index, the c-index and the Z-index, with emphasis on the sigma-index.
2012-01-01
us.army.mil ABSTRACT Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper , we enumerate...through a narrative, mission, quest, or scenario. In this paper we argue for a combinatorial optimization search approach to selecting and ordering...the role of an expert for the purposes of practicing skills and knowledge in realistic situations in a learning-by-doing approach to performance
Optimal mapping of irregular finite element domains to parallel processors
NASA Technical Reports Server (NTRS)
Flower, J.; Otto, S.; Salama, M.
1987-01-01
Mapping the solution domain of n-finite elements into N-subdomains that may be processed in parallel by N-processors is an optimal one if the subdomain decomposition results in a well-balanced workload distribution among the processors. The problem is discussed in the context of irregular finite element domains as an important aspect of the efficient utilization of the capabilities of emerging multiprocessor computers. Finding the optimal mapping is an intractable combinatorial optimization problem, for which a satisfactory approximate solution is obtained here by analogy to a method used in statistical mechanics for simulating the annealing process in solids. The simulated annealing analogy and algorithm are described, and numerical results are given for mapping an irregular two-dimensional finite element domain containing a singularity onto the Hypercube computer.
ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.
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.
Apparatus for combinatorial screening of electrochemical materials
Kepler, Keith Douglas [Belmont, CA; Wang, Yu [Foster City, CA
2009-12-15
A high throughput combinatorial screening method and apparatus for the evaluation of electrochemical materials using a single voltage source (2) is disclosed wherein temperature changes arising from the application of an electrical load to a cell array (1) are used to evaluate the relative electrochemical efficiency of the materials comprising the array. The apparatus may include an array of electrochemical cells (1) that are connected to each other in parallel or in series, an electronic load (2) for applying a voltage or current to the electrochemical cells (1), and a device (3), external to the cells, for monitoring the relative temperature of each cell when the load is applied.
Combinatorial quantisation of the Euclidean torus universe
NASA Astrophysics Data System (ADS)
Meusburger, C.; Noui, K.
2010-12-01
We quantise the Euclidean torus universe via a combinatorial quantisation formalism based on its formulation as a Chern-Simons gauge theory and on the representation theory of the Drinfel'd double DSU(2). The resulting quantum algebra of observables is given by two commuting copies of the Heisenberg algebra, and the associated Hilbert space can be identified with the space of square integrable functions on the torus. We show that this Hilbert space carries a unitary representation of the modular group and discuss the role of modular invariance in the theory. We derive the classical limit of the theory and relate the quantum observables to the geometry of the torus universe.
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.
Smith, J E
2012-01-01
Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes
Unraveling Quantum Annealers using Classical Hardness
Martin-Mayor, Victor; Hen, Itay
2015-01-01
Recent advances in quantum technology have led to the development and manufacturing of experimental programmable quantum annealing optimizers that contain hundreds of quantum bits. These optimizers, commonly referred to as ‘D-Wave’ chips, promise to solve practical optimization problems potentially faster than conventional ‘classical’ computers. Attempts to quantify the quantum nature of these chips have been met with both excitement and skepticism but have also brought up numerous fundamental questions pertaining to the distinguishability of experimental quantum annealers from their classical thermal counterparts. Inspired by recent results in spin-glass theory that recognize ‘temperature chaos’ as the underlying mechanism responsible for the computational intractability of hard optimization problems, we devise a general method to quantify the performance of quantum annealers on optimization problems suffering from varying degrees of temperature chaos: A superior performance of quantum annealers over classical algorithms on these may allude to the role that quantum effects play in providing speedup. We utilize our method to experimentally study the D-Wave Two chip on different temperature-chaotic problems and find, surprisingly, that its performance scales unfavorably as compared to several analogous classical algorithms. We detect, quantify and discuss several purely classical effects that possibly mask the quantum behavior of the chip. PMID:26483257
Improved mine blast algorithm for optimal cost design of water distribution systems
NASA Astrophysics Data System (ADS)
Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon
2015-12-01
The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.
Optimization of personalized therapies for anticancer treatment.
Vazquez, Alexei
2013-04-12
As today, there are hundreds of targeted therapies for the treatment of cancer, many of which have companion biomarkers that are in use to inform treatment decisions. If we would consider this whole arsenal of targeted therapies as a treatment option for every patient, very soon we will reach a scenario where each patient is positive for several markers suggesting their treatment with several targeted therapies. Given the documented side effects of anticancer drugs, it is clear that such a strategy is unfeasible. Here, we propose a strategy that optimizes the design of combinatorial therapies to achieve the best response rates with the minimal toxicity. In this methodology markers are assigned to drugs such that we achieve a high overall response rate while using personalized combinations of minimal size. We tested this methodology in an in silico cancer patient cohort, constructed from in vitro data for 714 cell lines and 138 drugs reported by the Sanger Institute. Our analysis indicates that, even in the context of personalized medicine, combinations of three or more drugs are required to achieve high response rates. Furthermore, patient-to-patient variations in pharmacokinetics have a significant impact in the overall response rate. A 10 fold increase in the pharmacokinetics variations resulted in a significant drop the overall response rate. The design of optimal combinatorial therapy for anticancer treatment requires a transition from the one-drug/one-biomarker approach to global strategies that simultaneously assign makers to a catalog of drugs. The methodology reported here provides a framework to achieve this transition.
Intrinsic information carriers in combinatorial dynamical systems
NASA Astrophysics Data System (ADS)
Harmer, Russ; Danos, Vincent; Feret, Jérôme; Krivine, Jean; Fontana, Walter
2010-09-01
Many proteins are composed of structural and chemical features—"sites" for short—characterized by definite interaction capabilities, such as noncovalent binding or covalent modification of other proteins. This modularity allows for varying degrees of independence, as the behavior of a site might be controlled by the state of some but not all sites of the ambient protein. Independence quickly generates a startling combinatorial complexity that shapes most biological networks, such as mammalian signaling systems, and effectively prevents their study in terms of kinetic equations—unless the complexity is radically trimmed. Yet, if combinatorial complexity is key to the system's behavior, eliminating it will prevent, not facilitate, understanding. A more adequate representation of a combinatorial system is provided by a graph-based framework of rewrite rules where each rule specifies only the information that an interaction mechanism depends on. Unlike reactions, which deal with molecular species, rules deal with patterns, i.e., multisets of molecular species. Although the stochastic dynamics induced by a collection of rules on a mixture of molecules can be simulated, it appears useful to capture the system's average or deterministic behavior by means of differential equations. However, expansion of the rules into kinetic equations at the level of molecular species is not only impractical, but conceptually indefensible. If rules describe bona fide patterns of interaction, molecular species are unlikely to constitute appropriate units of dynamics. Rather, we must seek aggregate variables reflective of the causal structure laid down by the rules. We call these variables "fragments" and the process of identifying them "fragmentation." Ideally, fragments are aspects of the system's microscopic population that the set of rules can actually distinguish on average; in practice, it may only be feasible to identify an approximation to this. Most importantly, fragments are
Intrinsic information carriers in combinatorial dynamical systems.
Harmer, Russ; Danos, Vincent; Feret, Jérôme; Krivine, Jean; Fontana, Walter
2010-09-01
Many proteins are composed of structural and chemical features--"sites" for short--characterized by definite interaction capabilities, such as noncovalent binding or covalent modification of other proteins. This modularity allows for varying degrees of independence, as the behavior of a site might be controlled by the state of some but not all sites of the ambient protein. Independence quickly generates a startling combinatorial complexity that shapes most biological networks, such as mammalian signaling systems, and effectively prevents their study in terms of kinetic equations-unless the complexity is radically trimmed. Yet, if combinatorial complexity is key to the system's behavior, eliminating it will prevent, not facilitate, understanding. A more adequate representation of a combinatorial system is provided by a graph-based framework of rewrite rules where each rule specifies only the information that an interaction mechanism depends on. Unlike reactions, which deal with molecular species, rules deal with patterns, i.e., multisets of molecular species. Although the stochastic dynamics induced by a collection of rules on a mixture of molecules can be simulated, it appears useful to capture the system's average or deterministic behavior by means of differential equations. However, expansion of the rules into kinetic equations at the level of molecular species is not only impractical, but conceptually indefensible. If rules describe bona fide patterns of interaction, molecular species are unlikely to constitute appropriate units of dynamics. Rather, we must seek aggregate variables reflective of the causal structure laid down by the rules. We call these variables "fragments" and the process of identifying them "fragmentation." Ideally, fragments are aspects of the system's microscopic population that the set of rules can actually distinguish on average; in practice, it may only be feasible to identify an approximation to this. Most importantly, fragments are
Phthalocyanine-loaded graphene nanoplatform for imaging-guided combinatorial phototherapy
Taratula, Olena; Patel, Mehulkumar; Schumann, Canan; Naleway, Michael A; Pang, Addison J; He, Huixin; Taratula, Oleh
2015-01-01
We report a novel cancer-targeted nanomedicine platform for imaging and prospect for future treatment of unresected ovarian cancer tumors by intraoperative multimodal phototherapy. To develop the required theranostic system, novel low-oxygen graphene nanosheets were chemically modified with polypropylenimine dendrimers loaded with phthalocyanine (Pc) as a photosensitizer. Such a molecular design prevents fluorescence quenching of the Pc by graphene nanosheets, providing the possibility of fluorescence imaging. Furthermore, the developed nanoplatform was conjugated with poly(ethylene glycol), to improve biocompatibility, and with luteinizing hormone-releasing hormone (LHRH) peptide, for tumor-targeted delivery. Notably, a low-power near-infrared (NIR) irradiation of single wavelength was used for both heat generation by the graphene nanosheets (photothermal therapy [PTT]) and for reactive oxygen species (ROS)-production by Pc (photodynamic therapy [PDT]). The combinatorial phototherapy resulted in an enhanced destruction of ovarian cancer cells, with a killing efficacy of 90%–95% at low Pc and low-oxygen graphene dosages, presumably conferring cytotoxicity to the synergistic effects of generated ROS and mild hyperthermia. An animal study confirmed that Pc loaded into the nanoplatform can be employed as a NIR fluorescence agent for imaging-guided drug delivery. Hence, the newly developed Pc-graphene nanoplatform has the significant potential as an effective NIR theranostic probe for imaging and combinatorial phototherapy. PMID:25848255
Optimization design of urban expressway ramp control
NASA Astrophysics Data System (ADS)
Xu, Hongke; Li, Peiqi; Zheng, Jinnan; Sun, Xiuzhen; Lin, Shan
2017-05-01
In this paper, various types of expressway systems are analyzed, and a variety of signal combinations are proposed to mitigate traffic congestion. And various signal combinations are used to verify the effectiveness of the multi-signal combinatorial control strategy. The simulation software VISSIM was used to simulate the system. Based on the network model of 25 kinds of road length combinations and the simulation results, an optimization scheme suitable for the practical road model is summarized. The simulation results show that the controller can reduce the travel time by 25% under the large traffic flow and improve the road capacity by about 20%.
NASA Astrophysics Data System (ADS)
Strom, C. S.; Bennema, P.
1997-03-01
This work (Part II) explores the relation between units and morphology. It shows the equivalence in behaviour between the attachment energies and the results of Monte Carlo growth kinetics simulations. The energetically optimal combination of the F slices in 1 1 0, 0 1 1 and 1 1 1 in a monomolecular interpretation leads to unsatisfactory agreement with experimentally observed morphology. In a tetrameric (or octameric) interpretation, the unit cell must be subdivided self-consistently in terms of stable molecular clusters. Thus, the presence or absence of the 1 1 1 form functions as a direct experimental criterion for distinguishing between monomolecular growth layers, and tetrameric (or octameric) growth layers of the same composition, but subjected to the condition of combinatorial compatibility, as the F slices combine to produce the growth habit. When that condition is taken into account, the tetrameric (or octameric) theoretical morphology in the broken bond model is in good agreement with experiment over a wide range. Subjectmatter for future study is summarized.
Xu, Yuquan; Zhou, Tong; Zhang, Shuwei; Espinosa-Artiles, Patricia; Wang, Luoyi; Zhang, Wei; Lin, Min; Gunatilaka, A A Leslie; Zhan, Jixun; Molnár, István
2014-08-26
Combinatorial biosynthesis aspires to exploit the promiscuity of microbial anabolic pathways to engineer the synthesis of new chemical entities. Fungal benzenediol lactone (BDL) polyketides are important pharmacophores with wide-ranging bioactivities, including heat shock response and immune system modulatory effects. Their biosynthesis on a pair of sequentially acting iterative polyketide synthases (iPKSs) offers a test case for the modularization of secondary metabolic pathways into "build-couple-pair" combinatorial synthetic schemes. Expression of random pairs of iPKS subunits from four BDL model systems in a yeast heterologous host created a diverse library of BDL congeners, including a polyketide with an unnatural skeleton and heat shock response-inducing activity. Pairwise heterocombinations of the iPKS subunits also helped to illuminate the innate, idiosyncratic programming of these enzymes. Even in combinatorial contexts, these biosynthetic programs remained largely unchanged, so that the iPKSs built their cognate biosynthons, coupled these building blocks into chimeric polyketide intermediates, and catalyzed intramolecular pairing to release macrocycles or α-pyrones. However, some heterocombinations also provoked stuttering, i.e., the relaxation of iPKSs chain length control to assemble larger homologous products. The success of such a plug and play approach to biosynthesize novel chemical diversity bodes well for bioprospecting unnatural polyketides for drug discovery.
Hard X-ray Imaging for Measuring Laser Absorption Spatial Profiles on the National Ignition Facility
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dewald, E L; Jones, O S; Landen, O L
2006-04-25
Hard x-ray (''Thin wall'') imaging will be employed on the National Ignition Facility (NIF) to spatially locate laser beam energy deposition regions on the hohlraum walls in indirect drive Inertial Confinement Fusion (ICF) experiments, relevant for ICF symmetry tuning. Based on time resolved imaging of the hard x-ray emission of the laser spots, this method will be used to infer hohlraum wall motion due to x-ray and laser ablation and any beam refraction caused by plasma density gradients. In optimizing this measurement, issues that have to be addressed are hard x-ray visibility during the entire ignition laser pulse with intensitiesmore » ranging from 10{sup 13} to 10{sup 15} W/cm{sup 2}, as well as simultaneous visibility of the inner and the outer laser drive cones. In this work we will compare the hard x-ray emission calculated by LASNEX and analytical modeling with thin wall imaging data recorded previously on Omega and during the first hohlraum experiments on NIF. Based on these calculations and comparisons the thin wall imaging will be optimized for ICF/NIF experiments.« less
Combinatorial FSK modulation for power-efficient high-rate communications
NASA Technical Reports Server (NTRS)
Wagner, Paul K.; Budinger, James M.; Vanderaar, Mark J.
1991-01-01
Deep-space and satellite communications systems must be capable of conveying high-rate data accurately with low transmitter power, often through dispersive channels. A class of noncoherent Combinatorial Frequency Shift Keying (CFSK) modulation schemes is investigated which address these needs. The bit error rate performance of this class of modulation formats is analyzed and compared to the more traditional modulation types. Candidate modulator, demodulator, and digital signal processing (DSP) hardware structures are examined in detail. System-level issues are also discussed.
Optimization of Coil Element Configurations for a Matrix Gradient Coil.
Kroboth, Stefan; Layton, Kelvin J; Jia, Feng; Littin, Sebastian; Yu, Huijun; Hennig, Jurgen; Zaitsev, Maxim
2018-01-01
Recently, matrix gradient coils (also termed multi-coils or multi-coil arrays) were introduced for imaging and B 0 shimming with 24, 48, and even 84 coil elements. However, in imaging applications, providing one amplifier per coil element is not always feasible due to high cost and technical complexity. In this simulation study, we show that an 84-channel matrix gradient coil (head insert for brain imaging) is able to create a wide variety of field shapes even if the number of amplifiers is reduced. An optimization algorithm was implemented that obtains groups of coil elements, such that a desired target field can be created by driving each group with an amplifier. This limits the number of amplifiers to the number of coil element groups. Simulated annealing is used due to the NP-hard combinatorial nature of the given problem. A spherical harmonic basis set up to the full third order within a sphere of 20-cm diameter in the center of the coil was investigated as target fields. We show that the median normalized least squares error for all target fields is below approximately 5% for 12 or more amplifiers. At the same time, the dissipated power stays within reasonable limits. With a relatively small set of amplifiers, switches can be used to sequentially generate spherical harmonics up to third order. The costs associated with a matrix gradient coil can be lowered, which increases the practical utility of matrix gradient coils.
Combinatorial nanomedicines for colon cancer therapy.
Anitha, A; Maya, S; Sivaram, Amal J; Mony, U; Jayakumar, R
2016-01-01
Colon cancer is one of the major causes of cancer deaths worldwide. Even after surgical resection and aggressive chemotherapy, 50% of colorectal carcinoma patients develop recurrent disease. Thus, the rationale of developing new therapeutic approaches to improve the current chemotherapeutic regimen would be highly recommended. There are reports on the effectiveness of combination chemotherapy in colon cancer and it has been practiced in clinics for long time. These approaches are associated with toxic side effects. Later, the drug delivery research had shown the potential of nanoencapsulation techniques and active targeting as an effective method to improve the effectiveness of chemotherapy with less toxicity. This current focus article provides a brief analysis of the ongoing research in the colon cancer area using the combinatorial nanomedicines and its outcome. © 2015 Wiley Periodicals, Inc.
Statistical significance of combinatorial regulations
Terada, Aika; Okada-Hatakeyama, Mariko; Tsuda, Koji; Sese, Jun
2013-01-01
More than three transcription factors often work together to enable cells to respond to various signals. The detection of combinatorial regulation by multiple transcription factors, however, is not only computationally nontrivial but also extremely unlikely because of multiple testing correction. The exponential growth in the number of tests forces us to set a strict limit on the maximum arity. Here, we propose an efficient branch-and-bound algorithm called the “limitless arity multiple-testing procedure” (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP lists significant combinations without any limit, whereas the family-wise error rate is rigorously controlled under the threshold. In the human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and find hidden associations in heterogeneous data. PMID:23882073
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boman, Erik G.; Catalyurek, Umit V.; Chevalier, Cedric
2015-01-16
This final progress report summarizes the work accomplished at the Combinatorial Scientific Computing and Petascale Simulations Institute. We developed Zoltan, a parallel mesh partitioning library that made use of accurate hypergraph models to provide load balancing in mesh-based computations. We developed several graph coloring algorithms for computing Jacobian and Hessian matrices and organized them into a software package called ColPack. We developed parallel algorithms for graph coloring and graph matching problems, and also designed multi-scale graph algorithms. Three PhD students graduated, six more are continuing their PhD studies, and four postdoctoral scholars were advised. Six of these students and Fellowsmore » have joined DOE Labs (Sandia, Berkeley), as staff scientists or as postdoctoral scientists. We also organized the SIAM Workshop on Combinatorial Scientific Computing (CSC) in 2007, 2009, and 2011 to continue to foster the CSC community.« less
Three-dimensional unstructured grid generation via incremental insertion and local optimization
NASA Technical Reports Server (NTRS)
Barth, Timothy J.; Wiltberger, N. Lyn; Gandhi, Amar S.
1992-01-01
Algorithms for the generation of 3D unstructured surface and volume grids are discussed. These algorithms are based on incremental insertion and local optimization. The present algorithms are very general and permit local grid optimization based on various measures of grid quality. This is very important; unlike the 2D Delaunay triangulation, the 3D Delaunay triangulation appears not to have a lexicographic characterization of angularity. (The Delaunay triangulation is known to minimize that maximum containment sphere, but unfortunately this is not true lexicographically). Consequently, Delaunay triangulations in three-space can result in poorly shaped tetrahedral elements. Using the present algorithms, 3D meshes can be constructed which optimize a certain angle measure, albeit locally. We also discuss the combinatorial aspects of the algorithm as well as implementational details.
Statistical Modeling for Radiation Hardness Assurance: Toward Bigger Data
NASA Technical Reports Server (NTRS)
Ladbury, R.; Campola, M. J.
2015-01-01
New approaches to statistical modeling in radiation hardness assurance are discussed. These approaches yield quantitative bounds on flight-part radiation performance even in the absence of conventional data sources. This allows the analyst to bound radiation risk at all stages and for all decisions in the RHA process. It also allows optimization of RHA procedures for the project's risk tolerance.
ERIC Educational Resources Information Center
Hubert, Lawrence J.; Baker, Frank B.
1978-01-01
The "Traveling Salesman" and similar combinatorial programming tasks encountered in operations research are discussed as possible data analysis models in psychology, for example, in developmental scaling, Guttman scaling, profile smoothing, and data array clustering. A short overview of various computational approaches from this area of…
2014-01-01
All-oxide-based photovoltaics (PVs) encompass the potential for extremely low cost solar cells, provided they can obtain an order of magnitude improvement in their power conversion efficiencies. To achieve this goal, we perform a combinatorial materials study of metal oxide based light absorbers, charge transporters, junctions between them, and PV devices. Here we report the development of a combinatorial internal quantum efficiency (IQE) method. IQE measures the efficiency associated with the charge separation and collection processes, and thus is a proxy for PV activity of materials once placed into devices, discarding optical properties that cause uncontrolled light harvesting. The IQE is supported by high-throughput techniques for bandgap fitting, composition analysis, and thickness mapping, which are also crucial parameters for the combinatorial investigation cycle of photovoltaics. As a model system we use a library of 169 solar cells with a varying thickness of sprayed titanium dioxide (TiO2) as the window layer, and covarying thickness and composition of binary compounds of copper oxides (Cu–O) as the light absorber, fabricated by Pulsed Laser Deposition (PLD). The analysis on the combinatorial devices shows the correlation between compositions and bandgap, and their effect on PV activity within several device configurations. The analysis suggests that the presence of Cu4O3 plays a significant role in the PV activity of binary Cu–O compounds. PMID:24410367
Edge connectivity and the spectral gap of combinatorial and quantum graphs
NASA Astrophysics Data System (ADS)
Berkolaiko, Gregory; Kennedy, James B.; Kurasov, Pavel; Mugnolo, Delio
2017-09-01
We derive a number of upper and lower bounds for the first nontrivial eigenvalue of Laplacians on combinatorial and quantum graph in terms of the edge connectivity, i.e. the minimal number of edges which need to be removed to make the graph disconnected. On combinatorial graphs, one of the bounds corresponds to a well-known inequality of Fiedler, of which we give a new variational proof. On quantum graphs, the corresponding bound generalizes a recent result of Band and Lévy. All proofs are general enough to yield corresponding estimates for the p-Laplacian and allow us to identify the minimizers. Based on the Betti number of the graph, we also derive upper and lower bounds on all eigenvalues which are ‘asymptotically correct’, i.e. agree with the Weyl asymptotics for the eigenvalues of the quantum graph. In particular, the lower bounds improve the bounds of Friedlander on any given graph for all but finitely many eigenvalues, while the upper bounds improve recent results of Ariturk. Our estimates are also used to derive bounds on the eigenvalues of the normalized Laplacian matrix that improve known bounds of spectral graph theory.
When do combinatorial mechanisms apply in the production of inflected words?
Cholin, Joana; Rapp, Brenda; Miozzo, Michele
2010-01-01
A central question for theories of inflected word processing is to determine under what circumstances compositional procedures apply. Some accounts (e.g., the dual-mechanism model; Clahsen, 1999 ) propose that compositional processes only apply to verbs that take productive affixes. For all other verbs, inflected forms are assumed to be stored in the lexicon in a nondecomposed manner. This account makes clear predictions about the consequences of disruption to the lexical access mechanisms involved in the spoken production of inflected forms. Briefly, it predicts that nonproductive forms (which require lexical access) should be more affected than productive forms (which, depending on the language task, may not). We tested these predictions through the detailed analysis of the spoken production of a German-speaking individual with an acquired lexical impairment resulting from a stroke. Analyses of response accuracy, error types, and frequency effects revealed that combinatorial processes are not restricted to verbs that take productive inflections. On this basis, we propose an alternative account, the stem-based assembly model (SAM), which posits that combinatorial processes may be available to all stems and not only to those that combine with productive affixes.
When do combinatorial mechanisms apply in the production of inflected words?
Cholin, Joana; Rapp, Brenda; Miozzo, Michele
2010-01-01
A central question for theories of inflected word processing is to determine under what circumstances compositional procedures apply. Some accounts (e.g., the Dual Mechanism Model; Clahsen, 1999) propose that compositional processes only apply to verbs that take productive affixes. For all other verbs, inflected forms are assumed to be stored in the lexicon in a non-decomposed manner. This account makes clear predictions about the consequences of disruption to the lexical access mechanisms involved in the spoken production of inflected forms. Briefly, it predicts that non-productive forms (which require lexical access) should be more affected than productive forms (which, depending on the language task, may not). We tested these predictions through the detailed analysis of the spoken production of a German-speaking individual with an acquired lexical impairment resulting from a stroke. Analyses of response accuracy, error types, and frequency effects revealed that combinatorial processes are not restricted to verbs that take productive inflections. On this basis, we propose an alternative account, the Stem-based Assembly Model (SAM) that posits that combinatorial processes may be available to all stems, and not only those that combine with productive affixes. PMID:21104479
Pourhassan, Mojgan; Neumann, Frank
2018-06-22
The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which meta-heuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a Cluster-Based approach and a Node-Based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this paper, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the Node-Based approach solves the hard instance of the Cluster-Based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the Node-Based approach for a class of Euclidean instances.
Roberts, Gareth; Lewandowski, Jirka; Galantucci, Bruno
2015-08-01
Communication systems are exposed to two different pressures: a pressure for transmission efficiency, such that messages are simple to produce and perceive, and a pressure for referential efficiency, such that messages are easy to understand with their intended meaning. A solution to the first pressure is combinatoriality--the recombination of a few basic meaningless forms to express an infinite number of meanings. A solution to the second is iconicity--the use of forms that resemble what they refer to. These two solutions appear to be incompatible with each other, as iconic forms are ill-suited for use as meaningless combinatorial units. Furthermore, in the early stages of a communication system, when basic referential forms are in the process of being established, the pressure for referential efficiency is likely to be particularly strong, which may lead it to trump the pressure for transmission efficiency. This means that, where iconicity is available as a strategy, it is likely to impede the emergence of combinatoriality. Although this hypothesis seems consistent with some observations of natural language, it was unclear until recently how it could be soundly tested. This has changed thanks to the development of a line of research, known as Experimental Semiotics, in which participants construct novel communication systems in the laboratory using an unfamiliar medium. We conducted an Experimental Semiotic study in which we manipulated the opportunity for iconicity by varying the kind of referents to be communicated, while keeping the communication medium constant. We then measured the combinatoriality and transmission efficiency of the communication systems. We found that, where iconicity was available, it provided scaffolding for the construction of communication systems and was overwhelmingly adopted. Where it was not available, however, the resulting communication systems were more combinatorial and their forms more efficient to produce. This study enriches
Anitha, A; Deepa, N; Chennazhi, K P; Lakshmanan, Vinoth-Kumar; Jayakumar, R
2014-09-01
Evaluation of the combinatorial anticancer effects of curcumin/5-fluorouracil loaded thiolated chitosan nanoparticles (CRC-TCS-NPs/5-FU-TCS-NPs) on colon cancer cells and the analysis of pharmacokinetics and biodistribution of CRC-TCS-NPs/5-FU-TCS-NPs in a mouse model. CRC-TCS-NPs/5-FU-TCS-NPs were developed by ionic cross-linking. The in vitro combinatorial anticancer effect of the nanomedicine was proven by different assays. Further the pharmacokinetics and biodistribution analyses were performed in Swiss Albino mouse using HPLC. The 5-FU-TCS-NPs (size: 150±40nm, zeta potential: +48.2±5mV) and CRC-TCS-NPs (size: 150±20nm, zeta potential: +35.7±3mV) were proven to be compatible with blood. The in vitro drug release studies at pH4.5 and 7.4 showed a sustained release profile over a period of 4 days, where both the systems exhibited a higher release in acidic pH. The in vitro combinatorial anticancer effects in colon cancer (HT29) cells using MTT, live/dead, mitochondrial membrane potential and cell cycle analysis measurements confirmed the enhanced anticancer effects (2.5 to 3 fold). The pharmacokinetic studies confirmed the improved plasma concentrations of 5-FU and CRC up to 72h, unlike bare CRC and 5-FU. To conclude, the combination of 5-FU-TCS-NPs and CRC-TCS-NPs showed enhanced anticancer effects on colon cancer cells in vitro and improved the bioavailability of the drugs in vivo. The enhanced anticancer effects of combinatorial nanomedicine are advantageous in terms of reduction in the dosage of 5-FU, thereby improving the chemotherapeutic efficacy and patient compliance of colorectal cancer cases. Copyright © 2014 Elsevier B.V. All rights reserved.
Number Partitioning via Quantum Adiabatic Computation
NASA Technical Reports Server (NTRS)
Smelyanskiy, Vadim N.; Toussaint, Udo
2002-01-01
We study both analytically and numerically the complexity of the adiabatic quantum evolution algorithm applied to random instances of combinatorial optimization problems. We use as an example the NP-complete set partition problem and obtain an asymptotic expression for the minimal gap separating the ground and exited states of a system during the execution of the algorithm. We show that for computationally hard problem instances the size of the minimal gap scales exponentially with the problem size. This result is in qualitative agreement with the direct numerical simulation of the algorithm for small instances of the set partition problem. We describe the statistical properties of the optimization problem that are responsible for the exponential behavior of the algorithm.
Transport of calcium ions through a bulk membrane by use of a dynamic combinatorial library.
Saggiomo, Vittorio; Lüning, Ulrich
2009-07-07
In a bulk membrane transport experiment, a dynamic combinatorial library (DCL) has been used to transport calcium ions; the calcium ions amplify the formation of a macrocyclic carrier which results in transport.
Mechanics of an Asymmetric Hard-Soft Lamellar Nanomaterial
DOE Office of Scientific and Technical Information (OSTI.GOV)
Shi, Weichao; Fredrickson, Glenn H.; Kramer, Edward J.
2016-03-24
Nanolayered lamellae are common structures in nanoscience and nanotechnology, but most are nearly symmetric in layer thickness. Here, we report on the structure and mechanics of highly asymmetric and thermodynamically stable soft–hard lamellar structures self-assembled from optimally designed PS 1-(PI-b-PS 2) 3 miktoarm star block copolymers. The remarkable mechanical properties of these strong and ductile PS (polystyrene)-based nanomaterials can be tuned over a broad range by varying the hard layer thickness while maintaining the soft layer thickness constant at 13 nm. Upon deformation, thin PS lamellae (<100 nm) exhibited kinks and predamaged/damaged grains, as well as cavitation in the softmore » layers. In contrast, deformation of thick lamellae (>100 nm) manifests cavitation in both soft and hard nanolayers. In situ tensile-SAXS experiments revealed the evolution of cavities during deformation and confirmed that the damage in such systems reflects both plastic deformation by shear and residual cavities. The aspects of the mechanics should point to universal deformation behavior in broader classes of asymmetric hard–soft lamellar materials, whose properties are just being revealed for versatile applications.« less
NASA Astrophysics Data System (ADS)
Zadeh, S. M.; Powers, D. M. W.; Sammut, K.; Yazdani, A. M.
2016-12-01
Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle's mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.
BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts.
Yang, Kun; Stracquadanio, Giovanni; Luo, Jingchuan; Boeke, Jef D; Bader, Joel S
2016-03-15
Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment. BioPartsBuilder is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder joel.bader@jhu.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.
Helping Students Who Are Deaf or Hard of Hearing Succeed
ERIC Educational Resources Information Center
Luckner, John L.; Slike, Samuel B.; Johnson, Harold
2012-01-01
A hearing loss of any type has the potential to adversely impact development leading to language, literacy, social, and academic delays. Currently, approximately 87% of students who are deaf or hard of hearing spend at least part of each day in a general education classroom. In order to optimally benefit from receiving educational services in the…
Combinatorial synthesis of bimetallic complexes with three halogeno bridges.
Gauthier, Sébastien; Quebatte, Laurent; Scopelliti, Rosario; Severin, Kay
2004-06-07
Methods for the synthesis of bimetallic complexes in which two different metal fragments are connected by three chloro or bromo bridges are reported. The reactions are general, fast, and give rise to structurally defined products in quantitative yields. Therefore, they are ideally suited for generating a library of homo- and heterobimetallic complexes in a combinatorial fashion. This is of special interest for applications in homogeneous catalysis. Selected members of this library were synthesized and comprehensively characterized; single-crystal X-ray analyses were performed for 15 new bimetallic compounds.
Minovski, Nikola; Perdih, Andrej; Solmajer, Tom
2012-05-01
The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.
Evaluation of HardSys/HardDraw, An Expert System for Electromagnetic Interactions Modelling
1993-05-01
interactions ir complex systems. This report gives a description of HardSys/HardDraw and reviews the main concepts used in its design. Various aspects of its ...HardDraw, an expert system for the modelling of electromagnetic interactions in complex systems. It consists of two main components: HardSys and HardDraw...HardSys is the advisor part of the expert system. It is knowledge-based, that is it contains a database of models and properties for various types of
Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density
Coronnello, Claudia; Hartmaier, Ryan; Arora, Arshi; Huleihel, Luai; Pandit, Kusum V.; Bais, Abha S.; Butterworth, Michael; Kaminski, Naftali; Stormo, Gary D.; Oesterreich, Steffi; Benos, Panayiotis V.
2012-01-01
MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for mi
A stochastic discrete optimization model for designing container terminal facilities
NASA Astrophysics Data System (ADS)
Zukhruf, Febri; Frazila, Russ Bona; Burhani, Jzolanda Tsavalista
2017-11-01
As uncertainty essentially affect the total transportation cost, it remains important in the container terminal that incorporates several modes and transshipments process. This paper then presents a stochastic discrete optimization model for designing the container terminal, which involves the decision of facilities improvement action. The container terminal operation model is constructed by accounting the variation of demand and facilities performance. In addition, for illustrating the conflicting issue that practically raises in the terminal operation, the model also takes into account the possible increment delay of facilities due to the increasing number of equipment, especially the container truck. Those variations expectantly reflect the uncertainty issue in the container terminal operation. A Monte Carlo simulation is invoked to propagate the variations by following the observed distribution. The problem is constructed within the framework of the combinatorial optimization problem for investigating the optimal decision of facilities improvement. A new variant of glow-worm swarm optimization (GSO) is thus proposed for solving the optimization, which is rarely explored in the transportation field. The model applicability is tested by considering the actual characteristics of the container terminal.
Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Schneider, Gisbert
2014-01-07
Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Multifunctional combinatorial-designed nanoparticles for nucleic acid therapy
NASA Astrophysics Data System (ADS)
Amiji, Mansoor M.
2016-05-01
Recent advances in biomedical sciences, especially in the field of human genetics, is increasingly considered to facilitate a new frontier in development of novel disease-modifying therapeutics. One of major challenges in the development of nucleic acid therapeutics is efficient and specific delivery of the molecules to the target tissue and cell upon systemic administration. In this report, I discuss our strategy to develop combinatorial-designed multifunctional nanoparticle assemblies based on natural biocompatible and biodegradable polymers for nucleic acid delivery in: (1) overcoming tumor drug resistance and (2) genetic modulation of macrophage functional phenotype from M1 to M2 in treatment of inflammatory diseases.
A coherent Ising machine for 2000-node optimization problems
NASA Astrophysics Data System (ADS)
Inagaki, Takahiro; Haribara, Yoshitaka; Igarashi, Koji; Sonobe, Tomohiro; Tamate, Shuhei; Honjo, Toshimori; Marandi, Alireza; McMahon, Peter L.; Umeki, Takeshi; Enbutsu, Koji; Tadanaga, Osamu; Takenouchi, Hirokazu; Aihara, Kazuyuki; Kawarabayashi, Ken-ichi; Inoue, Kyo; Utsunomiya, Shoko; Takesue, Hiroki
2016-11-01
The analysis and optimization of complex systems can be reduced to mathematical problems collectively known as combinatorial optimization. Many such problems can be mapped onto ground-state search problems of the Ising model, and various artificial spin systems are now emerging as promising approaches. However, physical Ising machines have suffered from limited numbers of spin-spin couplings because of implementations based on localized spins, resulting in severe scalability problems. We report a 2000-spin network with all-to-all spin-spin couplings. Using a measurement and feedback scheme, we coupled time-multiplexed degenerate optical parametric oscillators to implement maximum cut problems on arbitrary graph topologies with up to 2000 nodes. Our coherent Ising machine outperformed simulated annealing in terms of accuracy and computation time for a 2000-node complete graph.
Dissection of combinatorial control by the Met4 transcriptional complex.
Lee, Traci A; Jorgensen, Paul; Bognar, Andrew L; Peyraud, Caroline; Thomas, Dominique; Tyers, Mike
2010-02-01
Met4 is the transcriptional activator of the sulfur metabolic network in Saccharomyces cerevisiae. Lacking DNA-binding ability, Met4 must interact with proteins called Met4 cofactors to target promoters for transcription. Two types of DNA-binding cofactors (Cbf1 and Met31/Met32) recruit Met4 to promoters and one cofactor (Met28) stabilizes the DNA-bound Met4 complexes. To dissect this combinatorial system, we systematically deleted each category of cofactor(s) and analyzed Met4-activated transcription on a genome-wide scale. We defined a core regulon for Met4, consisting of 45 target genes. Deletion of both Met31 and Met32 eliminated activation of the core regulon, whereas loss of Met28 or Cbf1 interfered with only a subset of targets that map to distinct sectors of the sulfur metabolic network. These transcriptional dependencies roughly correlated with the presence of Cbf1 promoter motifs. Quantitative analysis of in vivo promoter binding properties indicated varying levels of cooperativity and interdependency exists between members of this combinatorial system. Cbf1 was the only cofactor to remain fully bound to target promoters under all conditions, whereas other factors exhibited different degrees of regulated binding in a promoter-specific fashion. Taken together, Met4 cofactors use a variety of mechanisms to allow differential transcription of target genes in response to various cues.
NASA Astrophysics Data System (ADS)
Orito, Yukiko; Yamamoto, Hisashi; Tsujimura, Yasuhiro; Kambayashi, Yasushi
The portfolio optimizations are to determine the proportion-weighted combination in the portfolio in order to achieve investment targets. This optimization is one of the multi-dimensional combinatorial optimizations and it is difficult for the portfolio constructed in the past period to keep its performance in the future period. In order to keep the good performances of portfolios, we propose the extended information ratio as an objective function, using the information ratio, beta, prime beta, or correlation coefficient in this paper. We apply the simulated annealing (SA) to optimize the portfolio employing the proposed ratio. For the SA, we make the neighbor by the operation that changes the structure of the weights in the portfolio. In the numerical experiments, we show that our portfolios keep the good performances when the market trend of the future period becomes different from that of the past period.
Potta, Thrimoorthy; Zhen, Zhuo; Grandhi, Taraka Sai Pavan; Christensen, Matthew D.; Ramos, James; Breneman, Curt M.; Rege, Kaushal
2014-01-01
We describe the combinatorial synthesis and cheminformatics modeling of aminoglycoside antibiotics-derived polymers for transgene delivery and expression. Fifty-six polymers were synthesized by polymerizing aminoglycosides with diglycidyl ether cross-linkers. Parallel screening resulted in identification of several lead polymers that resulted in high transgene expression levels in cells. The role of polymer physicochemical properties in determining efficacy of transgene expression was investigated using Quantitative Structure-Activity Relationship (QSAR) cheminformatics models based on Support Vector Regression (SVR) and ‘building block’ polymer structures. The QSAR model exhibited high predictive ability, and investigation of descriptors in the model, using molecular visualization and correlation plots, indicated that physicochemical attributes related to both, aminoglycosides and diglycidyl ethers facilitated transgene expression. This work synergistically combines combinatorial synthesis and parallel screening with cheminformatics-based QSAR models for discovery and physicochemical elucidation of effective antibiotics-derived polymers for transgene delivery in medicine and biotechnology. PMID:24331709
Dibó, Gábor
2012-02-01
Combinatorial chemistry was introduced in the 1980s. It provided the possibility to produce new compounds in practically unlimited number. New strategies and technologies have also been developed that made it possible to screen very large number of compounds and to identify useful components in mixtures containing millions of different substances. This dramatically changed the drug discovery process and the way of thinking of synthetic chemists. In addition, combinatorial strategies became useful in areas such as pharmaceutical research, agrochemistry, catalyst design, and materials research. Prof. Árpád Furka is one of the pioneers of combinatorial chemistry.
Wang, Zongjie; Calpe, Blaise; Zerdani, Jalil; Lee, Youngsang; Oh, Jonghyun; Bae, Hojae; Khademhosseini, Ali; Kim, Keekyoung
2016-07-01
In the developing heart, a specific subset of endocardium undergoes an endothelial-to-mesenchymal transformation (EndMT) thus forming nascent valve leaflets. Extracellular matrix (ECM) proteins and growth factors (GFs) play important roles in regulating EndMT but the combinatorial effect of GFs with ECM proteins is less well understood. Here we use microscale engineering techniques to create single, binary, and tertiary component microenvironments to investigate the combinatorial effects of ECM proteins and GFs on the attachment and transformation of adult ovine mitral valve endothelial cells to a mesenchymal phenotype. With the combinatorial microenvironment microarrays, we utilized 60 different combinations of ECM proteins (Fibronectin, Collagen I, II, IV, Laminin) and GFs (TGF-β1, bFGF, VEGF) and were able to identify new microenvironmental conditions capable of modulating EndMT in MVECs. Experimental results indicated that TGF-β1 significantly upregulated the EndMT while either bFGF or VEGF downregulated EndMT process markedly. Also, ECM proteins could influence both the attachment of MVECs and the response of MVECs to GFs. In terms of attachment, fibronectin is significantly better for the adhesion of MVECs among the five tested proteins. Overall collagen IV and fibronectin appeared to play important roles in promoting EndMT process. Great consistency between macroscale and microarrayed experiments and present studies demonstrates that high-throughput cellular microarrays are a promising approach to study the regulation of EndMT in valvular endothelium. Biotechnol. Bioeng. 2016;113: 1403-1412. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
Bond-orientational analysis of hard-disk and hard-sphere structures.
Senthil Kumar, V; Kumaran, V
2006-05-28
We report the bond-orientational analysis results for the thermodynamic, random, and homogeneously sheared inelastic structures of hard-disks and hard-spheres. The thermodynamic structures show a sharp rise in the order across the freezing transition. The random structures show the absence of crystallization. The homogeneously sheared structures get ordered at a packing fraction higher than the thermodynamic freezing packing fraction, due to the suppression of crystal nucleation. On shear ordering, strings of close-packed hard-disks in two dimensions and close-packed layers of hard-spheres in three dimensions, oriented along the velocity direction, slide past each other. Such a flow creates a considerable amount of fourfold order in two dimensions and body-centered-tetragonal (bct) structure in three dimensions. These transitions are the flow analogs of the martensitic transformations occurring in metals due to the stresses induced by a rapid quench. In hard-disk structures, using the bond-orientational analysis we show the presence of fourfold order. In sheared inelastic hard-sphere structures, even though the global bond-orientational analysis shows that the system is highly ordered, a third-order rotational invariant analysis shows that only about 40% of the spheres have face-centered-cubic (fcc) order, even in the dense and near-elastic limits, clearly indicating the coexistence of multiple crystalline orders. When layers of close-packed spheres slide past each other, in addition to the bct structure, the hexagonal-close-packed (hcp) structure is formed due to the random stacking faults. Using the Honeycutt-Andersen pair analysis and an analysis based on the 14-faceted polyhedra having six quadrilateral and eight hexagonal faces, we show the presence of bct and hcp signatures in shear ordered inelastic hard-spheres. Thus, our analysis shows that the dense sheared inelastic hard-spheres have a mixture of fcc, bct, and hcp structures.
Energetic electrons, hard x-ray emission and MHD activity studies in the IR-T1 tokamak.
Agah, K Mikaili; Ghoranneviss, M; Elahi, A Salar
2015-01-01
Determinations of plasma parameters as well as the Magnetohydrodynamics (MHD) activity, energetic electrons energy and energy confinement time are essential for future fusion reactors experiments and optimized operation. Also some of the plasma information can be deduced from these parameters, such as plasma equilibrium, stability, and MHD instabilities. In this contribution we investigated the relation between energetic electrons, hard x-ray emission and MHD activity in the IR-T1 Tokamak. For this purpose we used the magnetic diagnostics and a hard x-ray spectroscopy in IR-T1 tokamak. A hard x-ray emission is produced by collision of the runaway electrons with the plasma particles or limiters. The mean energy was calculated from the slope of the energy spectrum of hard x-ray photons.
An approach for addressing hard-to-detect hot spots.
Abelquist, Eric W; King, David A; Miller, Laurence F; Viars, James A
2013-05-01
The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) survey approach is comprised of systematic random sampling coupled with radiation scanning to assess acceptability of potential hot spots. Hot spot identification for some radionuclides may not be possible due to the very weak gamma or x-ray radiation they emit-these hard-to-detect nuclides are unlikely to be identified by field scans. Similarly, scanning technology is not yet available for chemical contamination. For both hard-to-detect nuclides and chemical contamination, hot spots are only identified via volumetric sampling. The remedial investigation and cleanup of sites under the Comprehensive Environmental Response, Compensation, and Liability Act typically includes the collection of samples over relatively large exposure units, and concentration limits are applied assuming the contamination is more or less uniformly distributed. However, data collected from contaminated sites demonstrate contamination is often highly localized. These highly localized areas, or hot spots, will only be identified if sample densities are high or if the environmental characterization program happens to sample directly from the hot spot footprint. This paper describes a Bayesian approach for addressing hard-to-detect nuclides and chemical hot spots. The approach begins using available data (e.g., as collected using the standard approach) to predict the probability that an unacceptable hot spot is present somewhere in the exposure unit. This Bayesian approach may even be coupled with the graded sampling approach to optimize hot spot characterization. Once the investigator concludes that the presence of hot spots is likely, then the surveyor should use the data quality objectives process to generate an appropriate sample campaign that optimizes the identification of risk-relevant hot spots.
Identification of Osteoconductive and Biodegradable Polymers from a Combinatorial Polymer Library
Brey, Darren M.; Chung, Cindy; Hankenson, Kurt D.; Garino, Jonathon P.; Burdick, Jason A.
2012-01-01
Combinatorial polymer syntheses are now being utilized to create libraries of materials with potential utility for a wide variety of biomedical applications. We recently developed a library of photopolymerizable and biodegradable poly(β-amino ester)s (PBAEs) that possessed a range of tunable properties. In this work, the PBAE library was assessed for candidate materials that met design criteria (e.g., physical properties such as degradation and mechanical strength and in vitro cell viability and osteoconductive behavior) for scaffolding in mineralized tissue repair. The most promising candidate, A6, was then processed into 3-dimensional porous scaffolds and implanted subcutaneously and only presented a mild inflammatory response. The scaffolds were then implanted intramuscularly and into a critically-sized cranial defect either alone or loaded with bone morphogenetic protein-2 (BMP-2). The samples in both locations displayed mineralized tissue formation in the presence of BMP-2, as evident through radiographs, micro-computed tomography, and histology, while samples without BMP-2 showed minimal or no mineralized tissue. These results illustrate a process to identify a candidate scaffolding material from a combinatorial polymer library, and specifically for the identification of an osteoconductive scaffold with osteoinductive properties via the inclusion of a growth factor. PMID:20198696
Jensen, Erik C.; Stockton, Amanda M.; Chiesl, Thomas N.; Kim, Jungkyu; Bera, Abhisek; Mathies, Richard A.
2013-01-01
A digitally programmable microfluidic Automaton consisting of a 2-dimensional array of pneumatically actuated microvalves is programmed to perform new multiscale mixing and sample processing operations. Large (µL-scale) volume processing operations are enabled by precise metering of multiple reagents within individual nL-scale valves followed by serial repetitive transfer to programmed locations in the array. A novel process exploiting new combining valve concepts is developed for continuous rapid and complete mixing of reagents in less than 800 ms. Mixing, transfer, storage, and rinsing operations are implemented combinatorially to achieve complex assay automation protocols. The practical utility of this technology is demonstrated by performing automated serial dilution for quantitative analysis as well as the first demonstration of on-chip fluorescent derivatization of biomarker targets (carboxylic acids) for microchip capillary electrophoresis on the Mars Organic Analyzer. A language is developed to describe how unit operations are combined to form a microfluidic program. Finally, this technology is used to develop a novel microfluidic 6-sample processor for combinatorial mixing of large sets (>26 unique combinations) of reagents. The digitally programmable microfluidic Automaton is a versatile programmable sample processor for a wide range of process volumes, for multiple samples, and for different types of analyses. PMID:23172232
LAS0811: from combinatorial chemistry to activation of antioxidant response element.
Zhu, Ming; Baek, Hyounggee; Liu, Ruiwu; Song, Aimin; Lam, Kit; Lau, Derick
2009-01-01
The antioxidant response element (ARE) and its transcription factor, nuclear factor-erythroid 2 p45-related factor 2 (Nrf2), are potential targets for cancer chemoprevention. We sought to screen small molecules synthesized with combinatorial chemistry for activation of ARE. By high-throughput screening of 9400 small molecules from 10 combinatorial chemical libraries using HepG2 cells with an ARE-driven reporter, we have identified a novel small molecule, 1,2-dimethoxy-4,5-dinitrobenzene (LAS0811), as an activator of the ARE. LAS0811 upregulated the activity of NAD(P)H:quinone oxidoreductase 1 (NQO1), a representative antioxidative enzyme regulated by ARE. It enhanced production of an endogenous reducing agent, glutathione (GSH). In addition, LAS0811 induced expression of heme oxygenase 1 (HO1), which is an ARE-regulated enzyme with anti-inflammatory activity. Furthermore, LAS0811 reduced cell death due to the cytotoxic stress of a strong oxidant, t-butyl hydroperoxide (t-BOOH). Mechanistically, LAS0811 upregulated the expression of Nrf2 and promoted its translocation into the nuclei leading to subsequent ARE activation. Taken together, LAS0811 is a novel activator of the ARE and its associated detoxifying genes and, thus, a potential agent for cancer chemoprevention.
LAS0811: From Combinatorial Chemistry to Activation of Antioxidant Response Element
Zhu, Ming; Baek, Hyounggee; Liu, Ruiwu; Song, Aimin; Lam, Kit; Lau, Derick
2009-01-01
The antioxidant response element (ARE) and its transcription factor, nuclear factor-erythroid 2 p45-related factor 2 (Nrf2), are potential targets for cancer chemoprevention. We sought to screen small molecules synthesized with combinatorial chemistry for activation of ARE. By high-throughput screening of 9400 small molecules from 10 combinatorial chemical libraries using HepG2 cells with an ARE-driven reporter, we have identified a novel small molecule, 1,2-dimethoxy-4,5-dinitrobenzene (LAS0811), as an activator of the ARE. LAS0811 upregulated the activity of NAD(P)H:quinone oxidoreductase 1 (NQO1), a representative antioxidative enzyme regulated by ARE. It enhanced production of an endogenous reducing agent, glutathione (GSH). In addition, LAS0811 induced expression of heme oxygenase 1 (HO1), which is an ARE-regulated enzyme with anti-inflammatory activity. Furthermore, LAS0811 reduced cell death due to the cytotoxic stress of a strong oxidant, t-butyl hydroperoxide (t-BOOH). Mechanistically, LAS0811 upregulated the expression of Nrf2 and promoted its translocation into the nuclei leading to subsequent ARE activation. Taken together, LAS0811 is a novel activator of the ARE and its associated detoxifying genes and, thus, a potential agent for cancer chemoprevention. PMID:19794825
A Combinatorial Grassmannian Representation of the Magic Three-Qubit Veldkamp Line
NASA Astrophysics Data System (ADS)
Saniga, Metod
2017-10-01
It is demonstrated that the magic three-qubit Veldkamp line occurs naturally within the Veldkamp space of combinatorial Grassmannian of type $G_2(7)$, $\\mathcal{V}(G_2(7))$. The lines of the ambient symplectic polar space are those lines of $\\mathcal{V}(G_2(7))$ whose cores feature an odd number of points of $G_2(7)$. After introducing basic properties of three different types of points and six distinct types of lines of $\\mathcal{V}(G_2(7))$, we explicitly show the combinatorial Grassmannian composition of the magic Veldkamp line; we first give representatives of points and lines of its core generalized quadrangle GQ$(2,2)$, and then additional points and lines of a specific elliptic quadric $\\mathcal{Q}^{-}$(5,2), a hyperbolic quadric $\\mathcal{Q}^{+}$(5,2) and a quadratic cone $\\widehat{\\mathcal{Q}}$(4,2) that are centered on the GQ$(2,2)$. In particular, each point of $\\mathcal{Q}^{+}$(5,2) is represented by a Pasch configuration and its complementary line, the (Schl\\"afli) double-six of points in $\\mathcal{Q}^{-}$(5,2) comprise six Cayley-Salmon configurations and six Desargues configurations with their complementary points, and the remaining Cayley-Salmon configuration stands for the vertex of $\\widehat{\\mathcal{Q}}$(4,2).
A binary plasmid system for shuffling combinatorial antibody libraries.
Collet, T A; Roben, P; O'Kennedy, R; Barbas, C F; Burton, D R; Lerner, R A
1992-11-01
We have used a binary system of replicon-compatible plasmids to test the potential for promiscuous recombination of heavy and light chains within sets of human Fab fragments isolated from combinatorial antibody libraries. Antibody molecules showed a surprising amount of promiscuity in that a particular heavy chain could recombine with multiple light chains with retention of binding to a protein antigen. The degree to which a given heavy chain productively paired with any light chain to bind antigen varied from 43% to 100% and depended strongly on the heavy-chain sequence. Such productive crosses resulted in a set of Fab fragments of similar apparent binding constants, which seemed to differ mainly in the amount of active Fab fragment produced in the bacterial cell. The dominance of the heavy chain in the antibody-antigen interaction was further explored in a set of directed crosses, in which heavy and light chains derived from antigen-specific clones were crossed with nonrelated heavy and light chains. In these crosses, an Fab fragment retained antigen binding only if it contained a heavy chain from an antigen-specific clone. In no case did the light chain confer detectable affinity when paired with indifferent heavy chains. The surprising promiscuity of heavy chains has ramifications for the evaluation of the diversity of combinatorial libraries made against protein antigens and should allow the combination of one such promiscuous heavy chain with an engineered light chain to form an Fab fragment carrying synthetic cofactors to assist in antibody catalysis.
A binary plasmid system for shuffling combinatorial antibody libraries.
Collet, T A; Roben, P; O'Kennedy, R; Barbas, C F; Burton, D R; Lerner, R A
1992-01-01
We have used a binary system of replicon-compatible plasmids to test the potential for promiscuous recombination of heavy and light chains within sets of human Fab fragments isolated from combinatorial antibody libraries. Antibody molecules showed a surprising amount of promiscuity in that a particular heavy chain could recombine with multiple light chains with retention of binding to a protein antigen. The degree to which a given heavy chain productively paired with any light chain to bind antigen varied from 43% to 100% and depended strongly on the heavy-chain sequence. Such productive crosses resulted in a set of Fab fragments of similar apparent binding constants, which seemed to differ mainly in the amount of active Fab fragment produced in the bacterial cell. The dominance of the heavy chain in the antibody-antigen interaction was further explored in a set of directed crosses, in which heavy and light chains derived from antigen-specific clones were crossed with nonrelated heavy and light chains. In these crosses, an Fab fragment retained antigen binding only if it contained a heavy chain from an antigen-specific clone. In no case did the light chain confer detectable affinity when paired with indifferent heavy chains. The surprising promiscuity of heavy chains has ramifications for the evaluation of the diversity of combinatorial libraries made against protein antigens and should allow the combination of one such promiscuous heavy chain with an engineered light chain to form an Fab fragment carrying synthetic cofactors to assist in antibody catalysis. Images PMID:1438192
Gurevich-Messina, Juan M; Giudicessi, Silvana L; Martínez-Ceron, María C; Acosta, Gerardo; Erra-Balsells, Rosa; Cascone, Osvaldo; Albericio, Fernando; Camperi, Silvia A
2015-01-01
Short cyclic peptides have a great interest in therapeutic, diagnostic and affinity chromatography applications. The screening of 'one-bead-one-peptide' combinatorial libraries combined with mass spectrometry (MS) is an excellent tool to find peptides with affinity for any target protein. The fragmentation patterns of cyclic peptides are quite more complex than those of their linear counterparts, and the elucidation of the resulting tandem mass spectra is rather more difficult. Here, we propose a simple protocol for combinatorial cyclic libraries synthesis and ring opening before MS analysis. In this strategy, 4-hydroxymethylbenzoic acid, which forms a benzyl ester with the first amino acid, was used as the linker. A glycolamidic ester group was incorporated after the combinatorial positions by adding glycolic acid. The library synthesis protocol consisted in the following: (i) incorporation of Fmoc-Asp[2-phenylisopropyl (OPp)]-OH to Ala-Gly-oxymethylbenzamide-ChemMatrix, (ii) synthesis of the combinatorial library, (iii) assembly of a glycolic acid, (iv) couple of an Ala residue in the N-terminal, (v) removal of OPp, (vi) peptide cyclisation through side chain Asp and N-Ala amino terminus and (vii) removal of side chain protecting groups. In order to simultaneously open the ring and release each peptide, benzyl and glycolamidic esters were cleaved with ammonia. Peptide sequences could be deduced from the tandem mass spectra of each single bead evaluated. The strategy herein proposed is suitable for the preparation of one-bead-one-cyclic depsipeptide libraries that can be easily open for its sequencing by matrix-assisted laser desorption/ionisation MS. It employs techniques and reagents frequently used in a broad range of laboratories without special expertise in organic synthesis. Copyright © 2014 European Peptide Society and John Wiley & Sons, Ltd.
Surface Morphology and Hardness Analysis of TiCN Coated AA7075 Aluminium Alloy
NASA Astrophysics Data System (ADS)
Srinath, M. K.; Ganesha Prasad, M. S.
2017-12-01
Successful titanium carbonitride (TiCN) coating on AA7075 plates using the PVD technique depends upon many variables, including temperature, pressure, incident angle and energy of the reactive ions. Coated specimens have shown an increase in their surface hardness of 2.566 GPa. In this work, an attempt to further augment the surface hardness and understand its effects on the surface morphology was performed through heat treatments at 500°C for different duration of times. Specimen's heat treated at 500°C for 1 h exhibited a maximum surface hardness of 6.433 GPa, corresponding to an increase of 92.07%. The XRD results showed the presence of Al2Ti and AlTi3N and indicate the bond created between them. Unit cell lattice parameters in the XRD data are calculated using Bragg's law. The SEM images exhibit increasing crack sizes as the heat treatment time is increased. From the studies, the heat treatment duration can be optimized to 1 h, which exhibited an augmented surface hardness, as further increases in durations caused a drop in the surface hardness. The heat treatment effectively modified the surface hardness. Equations providing the relationships that temperature and time have with the reaction parameters are presented.
Feng, Qiang; Chen, Yiran; Sun, Bo; Li, Songjie
2014-01-01
An optimization method for condition based maintenance (CBM) of aircraft fleet considering prognostics uncertainty is proposed. The CBM and dispatch process of aircraft fleet is analyzed first, and the alternative strategy sets for single aircraft are given. Then, the optimization problem of fleet CBM with lower maintenance cost and dispatch risk is translated to the combinatorial optimization problem of single aircraft strategy. Remain useful life (RUL) distribution of the key line replaceable Module (LRM) has been transformed into the failure probability of the aircraft and the fleet health status matrix is established. And the calculation method of the costs and risks for mission based on health status matrix and maintenance matrix is given. Further, an optimization method for fleet dispatch and CBM under acceptable risk is proposed based on an improved genetic algorithm. Finally, a fleet of 10 aircrafts is studied to verify the proposed method. The results shows that it could realize optimization and control of the aircraft fleet oriented to mission success.
Chen, Yiran; Sun, Bo; Li, Songjie
2014-01-01
An optimization method for condition based maintenance (CBM) of aircraft fleet considering prognostics uncertainty is proposed. The CBM and dispatch process of aircraft fleet is analyzed first, and the alternative strategy sets for single aircraft are given. Then, the optimization problem of fleet CBM with lower maintenance cost and dispatch risk is translated to the combinatorial optimization problem of single aircraft strategy. Remain useful life (RUL) distribution of the key line replaceable Module (LRM) has been transformed into the failure probability of the aircraft and the fleet health status matrix is established. And the calculation method of the costs and risks for mission based on health status matrix and maintenance matrix is given. Further, an optimization method for fleet dispatch and CBM under acceptable risk is proposed based on an improved genetic algorithm. Finally, a fleet of 10 aircrafts is studied to verify the proposed method. The results shows that it could realize optimization and control of the aircraft fleet oriented to mission success. PMID:24892046
Abeylath, Sampath C.; Amiji, Mansoor
2011-01-01
With the non-specific toxicity of anticancer drugs to healthy tissues upon systemic administration, formulations capable of enhanced selectivity in delivery to the tumor mass and cells are highly desirable. Based on the diversity of the drug payloads, we have investigated a combinatorial-designed strategy where the nano-sized formulations are tailored based on the physicochemical properties of the drug and the delivery needs. Individually functionalized C2 to C12 lipid-, thiol-, and poly(ethylene glycol) (PEG)-modified dextran derivatives were synthesized via “click” chemistry from O-pentynyl dextran and relevant azides. These functionalized dextrans in combination with anticancer drugs form nanoparticles by self-assembling in aqueous medium having PEG surface functionalization and intermolecular disulfide bonds. Using anticancer drugs with logP values ranging from −0.5 to 3.0, the optimized nanoparticles formulations were evaluated for preliminary cellular delivery and cytotoxic effects in SKOV3 human ovarian adenocarcinoma cells. The results show that with the appropriate selection of lipid-modified dextran, one can effectively tailor the self-assembled nano-formulation for intended therapeutic payload. PMID:21978947
Making it stick: chasing the optimal stem cells for cardiac regeneration
Quijada, Pearl; Sussman, Mark A
2014-01-01
Despite the increasing use of stem cells for regenerative-based cardiac therapy, the optimal stem cell population(s) remains in a cloud of uncertainty. In the past decade, the field has witnessed a surge of researchers discovering stem cell populations reported to directly and/or indirectly contribute to cardiac regeneration through processes of cardiomyogenic commitment and/or release of cardioprotective paracrine factors. This review centers upon defining basic biological characteristics of stem cells used for sustaining cardiac integrity during disease and maintenance of communication between the cardiac environment and stem cells. Given the limited successes achieved so far in regenerative therapy, the future requires development of unprecedented concepts involving combinatorial approaches to create and deliver the optimal stem cell(s) that will enhance myocardial healing. PMID:25340282
Combinatorial control of messenger RNAs by Pumilio, Nanos and Brain Tumor Proteins
Arvola, René M.
2017-01-01
ABSTRACT Eukaryotes possess a vast array of RNA-binding proteins (RBPs) that affect mRNAs in diverse ways to control protein expression. Combinatorial regulation of mRNAs by RBPs is emerging as the rule. No example illustrates this as vividly as the partnership of 3 Drosophila RBPs, Pumilio, Nanos and Brain Tumor, which have overlapping functions in development, stem cell maintenance and differentiation, fertility and neurologic processes. Here we synthesize 30 y of research with new insights into their molecular functions and mechanisms of action. First, we provide an overview of the key properties of each RBP. Next, we present a detailed analysis of their collaborative regulatory mechanism using a classic example of the developmental morphogen, hunchback, which is spatially and temporally regulated by the trio during embryogenesis. New biochemical, structural and functional analyses provide insights into RNA recognition, cooperativity, and regulatory mechanisms. We integrate these data into a model of combinatorial RNA binding and regulation of translation and mRNA decay. We then use this information, transcriptome wide analyses and bioinformatics predictions to assess the global impact of Pumilio, Nanos and Brain Tumor on gene regulation. Together, the results support pervasive, dynamic post-transcriptional control. PMID:28318367
Combinatorial control of messenger RNAs by Pumilio, Nanos and Brain Tumor Proteins.
Arvola, René M; Weidmann, Chase A; Tanaka Hall, Traci M; Goldstrohm, Aaron C
2017-11-02
Eukaryotes possess a vast array of RNA-binding proteins (RBPs) that affect mRNAs in diverse ways to control protein expression. Combinatorial regulation of mRNAs by RBPs is emerging as the rule. No example illustrates this as vividly as the partnership of 3 Drosophila RBPs, Pumilio, Nanos and Brain Tumor, which have overlapping functions in development, stem cell maintenance and differentiation, fertility and neurologic processes. Here we synthesize 30 y of research with new insights into their molecular functions and mechanisms of action. First, we provide an overview of the key properties of each RBP. Next, we present a detailed analysis of their collaborative regulatory mechanism using a classic example of the developmental morphogen, hunchback, which is spatially and temporally regulated by the trio during embryogenesis. New biochemical, structural and functional analyses provide insights into RNA recognition, cooperativity, and regulatory mechanisms. We integrate these data into a model of combinatorial RNA binding and regulation of translation and mRNA decay. We then use this information, transcriptome wide analyses and bioinformatics predictions to assess the global impact of Pumilio, Nanos and Brain Tumor on gene regulation. Together, the results support pervasive, dynamic post-transcriptional control.
Multifunctionalized biocatalytic P22 nanoreactor for combinatory treatment of ER+ breast cancer.
Chauhan, Kanchan; Hernandez-Meza, Juan M; Rodríguez-Hernández, Ana G; Juarez-Moreno, Karla; Sengar, Prakhar; Vazquez-Duhalt, Rafael
2018-02-20
Tamoxifen is the standard endocrine therapy for breast cancers, which require metabolic activation by cytochrome P450 enzymes (CYP). However, the lower and variable concentrations of CYP activity at the tumor remain major bottlenecks for the efficient treatment, causing severe side-effects. Combination nanotherapy has gained much recent attention for cancer treatment as it reduces the drug-associated toxicity without affecting the therapeutic response. Here we show the modular design of P22 bacteriophage virus-like particles for nanoscale integration of virus-driven enzyme prodrug therapy and photodynamic therapy. These virus capsids carrying CYP activity at the core are decorated with photosensitizer and targeting moiety at the surface for effective combinatory treatment. The estradiol-functionalized nanoparticles are recognized and internalized into ER+ breast tumor cells increasing the intracellular CYP activity and showing the ability to produce reactive oxygen species (ROS) upon UV 365 nm irradiation. The generated ROS in synergy with enzymatic activity drastically enhanced the tamoxifen sensitivity in vitro, strongly inhibiting tumor cells. This work clearly demonstrated that the targeted combinatory treatment using multifunctional biocatalytic P22 represents the effective nanotherapeutics for ER+ breast cancer.
ERIC Educational Resources Information Center
Tsai, Yu-Ling; Chang, Ching-Kuch
2009-01-01
This article reports an alternative approach, called the combinatorial model, to learning multiplicative identities, and investigates the effects of implementing results for this alternative approach. Based on realistic mathematics education theory, the new instructional materials or modules of the new approach were developed by the authors. From…
COPD: A stepwise or a hit hard approach?
Ferreira, A J; Reis, A; Marçal, N; Pinto, P; Bárbara, C
2016-01-01
Current guidelines differ slightly on the recommendations for treatment of Chronic Obstructive Pulmonary Disease (COPD) patients, and although there are some undisputed recommendations, there is still debate regarding the management of COPD. One of the hindrances to deciding which therapeutic approach to choose is late diagnosis or misdiagnosis of COPD. After a proper diagnosis is achieved and severity assessed, the choice between a stepwise or "hit hard" approach has to be made. For GOLD A patients the stepwise approach is recommended, whilst for B, C and D patients this remains debatable. Moreover, in patients for whom inhaled corticosteroids (ICS) are recommended, a step-up or "hit hard" approach with triple therapy will depend on the patient's characteristics and, for patients who are being over-treated with ICS, ICS withdrawal should be performed, in order to optimize therapy and reduce excessive medications. This paper discusses and proposes stepwise, "hit hard", step-up and ICS withdrawal therapeutic approaches for COPD patients based on their GOLD group. We conclude that all approaches have benefits, and only a careful patient selection will determine which approach is better, and which patients will benefit the most from each approach. Copyright © 2016 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.
Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Kay, B. K.; Castagnoli, L.; Biosciences Division
This unit describes the process and analysis of affinity selecting bacteriophage M13 from libraries displaying combinatorial peptides fused to either a minor or major capsid protein. Direct affinity selection uses target protein bound to a microtiter plate followed by purification of selected phage by ELISA. Alternatively, there is a bead-based affinity selection method. These methods allow one to readily isolate peptide ligands that bind to a protein target of interest and use the consensus sequence to search proteomic databases for putative interacting proteins.
Song, Suk-yoon; Hur, Byung-ung; Lee, Kyung-woo; Choi, Hyo-jung; Kim, Sung-soo; Kang, Goo; Cha, Sang-hoon
2009-03-31
The dual-vector system-II (DVS-II), which allows efficient display of Fab antibodies on phage, has been reported previously, but its practical applicability in a phage-displayed antibody library has not been verified. To resolve this issue, we created two small combinatorial human Fab antibody libraries using the DVS-II, and isolation of target-specific antibodies was attempted. Biopanning of one antibody library, termed DVFAB-1L library, which has a 1.3 x 10(7) combinatorial antibody complexity, against fluorescein-BSA resulted in successful isolation of human Fab clones specific for the antigen despite the presence of only a single light chain in the library. By using the unique feature of the DVS-II, an antibody library of a larger size, named DVFAB-131L, which has a 1.5 x 10(9) combinatorial antibody complexity, was also generated in a rapid manner by combining 1.3 x 10(7) heavy chains and 131 light chains and more diverse anti-fluorescein-BSA Fab antibody clones were successfully obtained. Our results demonstrate that the DVS-II can be applied readily in creating phage-displayed antibody libraries with much less effort, and target-specific antibody clones can be isolated reliably via light chain promiscuity of antibody molecule.
Castanotto, Daniela; Sakurai, Kumi; Lingeman, Robert; Li, Haitang; Shively, Louise; Aagaard, Lars; Soifer, Harris; Gatignol, Anne; Riggs, Arthur; Rossi, John J.
2007-01-01
Despite the great potential of RNAi, ectopic expression of shRNA or siRNAs holds the inherent risk of competition for critical RNAi components, thus altering the regulatory functions of some cellular microRNAs. In addition, specific siRNA sequences can potentially hinder incorporation of other siRNAs when used in a combinatorial approach. We show that both synthetic siRNAs and expressed shRNAs compete against each other and with the endogenous microRNAs for transport and for incorporation into the RNA induced silencing complex (RISC). The same siRNA sequences do not display competition when expressed from a microRNA backbone. We also show that TAR RNA binding protein (TRBP) is one of the sensors for selection and incorporation of the guide sequence of interfering RNAs. These findings reveal that combinatorial siRNA approaches can be problematic and have important implications for the methodology of expression and use of therapeutic interfering RNAs. PMID:17660190
Ashbaugh, Alyssa G.; Jiang, Xuesong; Zheng, Jesse; Tsai, Andrew S.; Kim, Woo-Shin; Thompson, John M.; Miller, Robert J.; Shahbazian, Jonathan H.; Wang, Yu; Dillen, Carly A.; Ordonez, Alvaro A.; Chang, Yong S.; Jain, Sanjay K.; Jones, Lynne C.; Sterling, Robert S.; Mao, Hai-Quan; Miller, Lloyd S.
2016-01-01
Bacterial biofilm formation is a major complication of implantable medical devices that results in therapeutically challenging chronic infections, especially in cases involving antibiotic-resistant bacteria. As an approach to prevent these infections, an electrospun composite coating comprised of poly(lactic-coglycolic acid) (PLGA) nanofibers embedded in a poly(ε-caprolactone) (PCL) film was developed to locally codeliver combinatorial antibiotics from the implant surface. The release of each antibiotic could be adjusted by loading each drug into the different polymers or by varying PLGA:PCL polymer ratios. In a mouse model of biofilm-associated orthopedic-implant infection, three different combinations of antibiotic-loaded coatings were highly effective in preventing infection of the bone/joint tissue and implant biofilm formation and were biocompatible with enhanced osseointegration. This nanofiber composite-coating technology could be used to tailor the delivery of combinatorial antimicrobial agents from various metallic implantable devices or prostheses to effectively decrease biofilm-associated infections in patients. PMID:27791154
Combinatorial and high-throughput approaches in polymer science
NASA Astrophysics Data System (ADS)
Zhang, Huiqi; Hoogenboom, Richard; Meier, Michael A. R.; Schubert, Ulrich S.
2005-01-01
Combinatorial and high-throughput approaches have become topics of great interest in the last decade due to their potential ability to significantly increase research productivity. Recent years have witnessed a rapid extension of these approaches in many areas of the discovery of new materials including pharmaceuticals, inorganic materials, catalysts and polymers. This paper mainly highlights our progress in polymer research by using an automated parallel synthesizer, microwave synthesizer and ink-jet printer. The equipment and methodologies in our experiments, the high-throughput experimentation of different polymerizations (such as atom transfer radical polymerization, cationic ring-opening polymerization and emulsion polymerization) and the automated matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF MS) sample preparation are described.
Optimization of Materials and Interfaces for Spintronic Devices
NASA Astrophysics Data System (ADS)
Clark, Billy
In recent years' Spintronic devices have drawn a significant amount of research attention. This interest comes in large part from their ability to enable interesting and new technology such as Spin Torque Transfer Random Access Memory or improve existing technology such as High Signal Read Heads for Hard Disk Drives. For the former we worked on optimizing and improving magnetic tunnel junctions by optimizing their thermal stability by using Ta insertion layers in the free layer. We further tried to simplify the design of the MTJ stack by attempting to replace the Co/Pd multilayer with CoPd alloy. In this dissertation, we detail its development and examine the switching characteristics. Lastly we look at a highly spin polarized material, Fe2MnGe, for optimizing Hard Drive Disk read heads.
Fast globally optimal segmentation of 3D prostate MRI with axial symmetry prior.
Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron
2013-01-01
We propose a novel global optimization approach to segmenting a given 3D prostate T2w magnetic resonance (MR) image, which enforces the inherent axial symmetry of the prostate shape and simultaneously performs a sequence of 2D axial slice-wise segmentations with a global 3D coherence prior. We show that the proposed challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. With this regard, we introduce a novel coupled continuous max-flow model, which is dual to the studied convex relaxed optimization formulation and leads to an efficient multiplier augmented algorithm based on the modern convex optimization theory. Moreover, the new continuous max-flow based algorithm was implemented on GPUs to achieve a substantial improvement in computation. Experimental results using public and in-house datasets demonstrate great advantages of the proposed method in terms of both accuracy and efficiency.
Finding Minimal Addition Chains with a Particle Swarm Optimization Algorithm
NASA Astrophysics Data System (ADS)
León-Javier, Alejandro; Cruz-Cortés, Nareli; Moreno-Armendáriz, Marco A.; Orantes-Jiménez, Sandra
The addition chains with minimal length are the basic block to the optimal computation of finite field exponentiations. It has very important applications in the areas of error-correcting codes and cryptography. However, obtaining the shortest addition chains for a given exponent is a NP-hard problem. In this work we propose the adaptation of a Particle Swarm Optimization algorithm to deal with this problem. Our proposal is tested on several exponents whose addition chains are considered hard to find. We obtained very promising results.
Residual stress control and design of next-generation ultra-hard gear steels
NASA Astrophysics Data System (ADS)
Qian, Yana
In high power density transmission systems, Ni-Co secondary hardening steels have shown great potential for next-generation gear applications due to their excellent strength, toughness and superior fatigue performance. Study of residual stress generation and evolution in Ferrium C61 and C67 gear steels revealed that shot peening and laser peening processes effectively produce desired beneficial residual stress in the steels for enhanced fatigue performance. Surface residual stress levels of -1.4GPa and -1.5GPa were achieved in shot peened C61 and laser peened C67, respectively, without introducing large surface roughness or defects. Higher compressive residual stress is expected in C67 according to a demonstrated correlation between attainable residual stress and material hardness. Due to the lack of appropriate shot media, dual laser peening is proposed for future peening optimization in C67. A novel non-destructive synchrotron radiation technique was implemented and applied for the first time for residual stress distribution analysis in gear steels with large composition and property gradients. Observed substantial residual stress redistribution and material microstructure change during the rolling contact fatigue screening test with extremely high 5.4GPa load indicates the unsuitability of the test as a fatigue life predictor. To exploit benefits of higher case hardness and associated residual stress, a new material and process (CryoForm70) aiming at 70Rc surface hardness was designed utilizing the systems approach based on thermodynamics and secondary hardening mechanisms. The composition design was first validated by the excellent agreement between experimental and theoretical core martensite start temperature in the prototype. A novel cryogenic deformation process was concurrently designed to increase the case martensite volume fraction from 76% to 92% for enhanced strengthening efficiency and surface hardness. High temperature vacuum carburizing was
DOE Office of Scientific and Technical Information (OSTI.GOV)
Shu, Deming, E-mail: shu@aps.anl.gov; Shvyd’ko, Yuri V.; Stoupin, Stanislav
2016-07-27
A new thin-film diamond crystal mounting apparatus has been designed at the Advanced Photon Source (APS) for coherence preservation hard x-ray optics with optimized thermal contact and minimized crystal strain. This novel mechanical design can be applied to new development in the field of: x-ray optics cavities for hard x-ray free-electron laser oscillators (XFELOs), self-seeding monochromators for hard x-ray free-electron laser (XFEL) with high average thermal loading, high heat load diamond crystal monochromators and beam-sharing/beam-split-and-delay devices for XFEL facilities and future upgraded high-brightness coherent x-ray source in the MBA lattice configuration at the APS.
Parameterized Complexity of k-Anonymity: Hardness and Tractability
NASA Astrophysics Data System (ADS)
Bonizzoni, Paola; Della Vedova, Gianluca; Dondi, Riccardo; Pirola, Yuri
The problem of publishing personal data without giving up privacy is becoming increasingly important. A precise formalization that has been recently proposed is the k-anonymity, where the rows of a table are partitioned in clusters of size at least k and all rows in a cluster become the same tuple after the suppression of some entries. The natural optimization problem, where the goal is to minimize the number of suppressed entries, is hard even when the stored values are over a binary alphabet or the table consists of a bounded number of columns. In this paper we study how the complexity of the problem is influenced by different parameters. First we show that the problem is W[1]-hard when parameterized by the value of the solution (and k). Then we exhibit a fixed-parameter algorithm when the problem is parameterized by the number of columns and the number of different values in any column.
Graphical models for optimal power flow
Dvijotham, Krishnamurthy; Chertkov, Michael; Van Hentenryck, Pascal; ...
2016-09-13
Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithmmore » for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. In conclusion, numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.« less
USDA-ARS?s Scientific Manuscript database
Plant cell wall polysaccharides, which consist of polymeric backbones with various types of substitution, were studied using the concept of combinatorial enzyme technology for conversion of agricultural fibers to functional products. Using citrus pectin as the starting substrate, an active oligo spe...
Multiple-variable neighbourhood search for the single-machine total weighted tardiness problem
NASA Astrophysics Data System (ADS)
Chung, Tsui-Ping; Fu, Qunjie; Liao, Ching-Jong; Liu, Yi-Ting
2017-07-01
The single-machine total weighted tardiness (SMTWT) problem is a typical discrete combinatorial optimization problem in the scheduling literature. This problem has been proved to be NP hard and thus provides a challenging area for metaheuristics, especially the variable neighbourhood search algorithm. In this article, a multiple variable neighbourhood search (m-VNS) algorithm with multiple neighbourhood structures is proposed to solve the problem. Special mechanisms named matching and strengthening operations are employed in the algorithm, which has an auto-revising local search procedure to explore the solution space beyond local optimality. Two aspects, searching direction and searching depth, are considered, and neighbourhood structures are systematically exchanged. Experimental results show that the proposed m-VNS algorithm outperforms all the compared algorithms in solving the SMTWT problem.
Design of diversity and focused combinatorial libraries in drug discovery.
Young, S Stanley; Ge, Nanxiang
2004-05-01
Using well-characterized chemical reactions and readily available monomers, chemists are able to create sets of compounds, termed libraries, which are useful in drug discovery processes. The design of combinatorial chemical libraries can be complex and there has been much information recently published offering suggestions on how the design process can be carried out. This review focuses on literature with the goal of organizing current thinking. At this point in time, it is clear that benchmarking of current suggested methods is required as opposed to further new methods.
Block clustering based on difference of convex functions (DC) programming and DC algorithms.
Le, Hoai Minh; Le Thi, Hoai An; Dinh, Tao Pham; Huynh, Van Ngai
2013-10-01
We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.
2008-12-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS INTEGRATING MONETARY AND NON-MONETARY REENLISTMENT INCENTIVES UTILIZING THE...Monetary and Non- monetary Reenlistment Incentives Utilizing the Combinatorial Retention Auction Mechanism (CRAM) 6. AUTHOR(S) Brooke Zimmerman 5...iii Approved for public release; distribution is unlimited INTEGRATING MONETARY AND NON-MONETARY REENLISTMENT INCENTIVES UTILIZING THE
Haplotyping for disease association: a combinatorial approach.
Lancia, Giuseppe; Ravi, R; Rizzi, Romeo
2008-01-01
We consider a combinatorial problem derived from haplotyping a population with respect to a genetic disease, either recessive or dominant. Given a set of individuals, partitioned into healthy and diseased, and the corresponding sets of genotypes, we want to infer "bad'' and "good'' haplotypes to account for these genotypes and for the disease. Assume e.g. the disease is recessive. Then, the resolving haplotypes must consist of bad and good haplotypes, so that (i) each genotype belonging to a diseased individual is explained by a pair of bad haplotypes and (ii) each genotype belonging to a healthy individual is explained by a pair of haplotypes of which at least one is good. We prove that the associated decision problem is NP-complete. However, we also prove that there is a simple solution, provided the data satisfy a very weak requirement.
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.
Haverkamp, Alexander; Hansson, Bill S.; Knaden, Markus
2018-01-01
Insects, including those which provide vital ecosystems services as well as those which are devastating pests or disease vectors, locate their resources mainly based on olfaction. Understanding insect olfaction not only from a neurobiological but also from an ecological perspective is therefore crucial to balance insect control and conservation. However, among all sensory stimuli olfaction is particularly hard to grasp. Our chemical environment is made up of thousands of different compounds, which might again be detected by our nose in multiple ways. Due to this complexity, researchers have only recently begun to explore the chemosensory ecology of model organisms such as Drosophila, linking the tools of chemical ecology to those of neurogenetics. This cross-disciplinary approach has enabled several studies that range from single odors and their ecological relevance, via olfactory receptor genes and neuronal processing, up to the insects' behavior. We learned that the insect olfactory system employs strategies of combinatorial coding to process general odors as well as labeled lines for specific compounds that call for an immediate response. These studies opened new doors to the olfactory world in which insects feed, oviposit, and mate. PMID:29449815
A Rapid Python-Based Methodology for Target-Focused Combinatorial Library Design.
Li, Shiliang; Song, Yuwei; Liu, Xiaofeng; Li, Honglin
2016-01-01
The chemical space is so vast that only a small portion of it has been examined. As a complementary approach to systematically probe the chemical space, virtual combinatorial library design has extended enormous impacts on generating novel and diverse structures for drug discovery. Despite the favorable contributions, high attrition rates in drug development that mainly resulted from lack of efficacy and side effects make it increasingly challenging to discover good chemical starting points. In most cases, focused libraries, which are restricted to particular regions of the chemical space, are deftly exploited to maximize hit rate and improve efficiency at the beginning of the drug discovery and drug development pipeline. This paper presented a valid methodology for fast target-focused combinatorial library design in both reaction-based and production-based ways with the library creating rates of approximately 70,000 molecules per second. Simple, quick and convenient operating procedures are the specific features of the method. SHAFTS, a hybrid 3D similarity calculation software, was embedded to help refine the size of the libraries and improve hit rates. Two target-focused (p38-focused and COX2-focused) libraries were constructed efficiently in this study. This rapid library enumeration method is portable and applicable to any other targets for good chemical starting points identification collaborated with either structure-based or ligand-based virtual screening.
Novel In Vivo Model for Combinatorial Fluorescence Labeling in Mouse Prostate
Fang, Xiaolan; Gyabaah, Kenneth; Nickkholgh, Bita; Cline, J. Mark; Balaji, K.C.
2015-01-01
BACKGROUND The epithelial layer of prostate glands contains several types of cells, including luminal and basal cells. Yet there is paucity of animal models to study the cellular origin of normal or neoplastic development in the prostate to facilitate the treatment of heterogenous prostate diseases by targeting individual cell lineages. METHODS We developed a mouse model that expresses different types of fluorescent proteins (XFPs) specifically in prostatic cells. Using an in vivo stochastic fluorescent protein combinatorial strategy, XFP signals were expressed specifically in prostate of Protein Kinase D1 (PKD1) knock-out, K-RasG12D knock-in, and Phosphatase and tensin homolog (PTEN) and PKD1 double knock-out mice under the control of PB-Cre promoter. RESULTS In vivo XFP signals were observed in prostate of PKD1 knock-out, K-RasG12D knock-in, and PTEN PKD1 double knock-out mice, which developed normal, hyperplastic, and neoplastic prostate, respectively. The patchy expression pattern of XFPs in neoplasia tissue indicated the clonal origin of cancer cells in the prostate. CONCLUSIONS The transgenic mouse models demonstrate combinatorial fluorescent protein expression in normal and cancerous prostatic tissues. This novel prostate-specific fluorescent labeled mouse model, which we named Prorainbow, could be useful in studying benign and malignant pathology of prostate. PMID:25753731
Novel In Vivo model for combinatorial fluorescence labeling in mouse prostate.
Fang, Xiaolan; Gyabaah, Kenneth; Nickkholgh, Bita; Cline, J Mark; Balaji, K C
2015-06-15
The epithelial layer of prostate glands contains several types of cells, including luminal and basal cells. Yet there is paucity of animal models to study the cellular origin of normal or neoplastic development in the prostate to facilitate the treatment of heterogenous prostate diseases by targeting individual cell lineages. We developed a mouse model that expresses different types of fluorescent proteins (XFPs) specifically in prostatic cells. Using an in vivo stochastic fluorescent protein combinatorial strategy, XFP signals were expressed specifically in prostate of Protein Kinase D1 (PKD1) knock-out, K-Ras(G) (12) (D) knock-in, and Phosphatase and tensin homolog (PTEN) and PKD1 double knock-out mice under the control of PB-Cre promoter. In vivo XFP signals were observed in prostate of PKD1 knock-out, K-Ras(G) (12) (D) knock-in, and PTEN PKD1 double knock-out mice, which developed normal, hyperplastic, and neoplastic prostate, respectively. The patchy expression pattern of XFPs in neoplasia tissue indicated the clonal origin of cancer cells in the prostate. The transgenic mouse models demonstrate combinatorial fluorescent protein expression in normal and cancerous prostatic tissues. This novel prostate-specific fluorescent labeled mouse model, which we named Prorainbow, could be useful in studying benign and malignant pathology of prostate. © 2015 Wiley Periodicals, Inc.
Remember Hard But Think Softly: Metaphorical Effects of Hardness/Softness on Cognitive Functions.
Xie, Jiushu; Lu, Zhi; Wang, Ruiming; Cai, Zhenguang G
2016-01-01
Previous studies have found that bodily stimulation, such as hardness biases social judgment and evaluation via metaphorical association; however, it remains unclear whether bodily stimulation also affects cognitive functions, such as memory and creativity. The current study used metaphorical associations between "hard" and "rigid" and between "soft" and "flexible" in Chinese, to investigate whether the experience of hardness affects cognitive functions whose performance depends prospectively on rigidity (memory) and flexibility (creativity). In Experiment 1, we found that Chinese-speaking participants performed better at recalling previously memorized words while sitting on a hard-surface stool (the hard condition) than a cushioned one (the soft condition). In Experiment 2, participants sitting on a cushioned stool outperformed those sitting on a hard-surface stool on a Chinese riddle task, which required creative/flexible thinking, but not on an analogical reasoning task, which required both rigid and flexible thinking. The results suggest the hardness experience affects cognitive functions that are metaphorically associated with rigidity or flexibility. They support the embodiment proposition that cognitive functions and representations can be grounded in bodily states via metaphorical associations.
Optimizing an Actuator Array for the Control of Multi-Frequency Noise in Aircraft Interiors
NASA Technical Reports Server (NTRS)
Palumbo, D. L.; Padula, S. L.
1997-01-01
Techniques developed for selecting an optimized actuator array for interior noise reduction at a single frequency are extended to the multi-frequency case. Transfer functions for 64 actuators were obtained at 5 frequencies from ground testing the rear section of a fully trimmed DC-9 fuselage. A single loudspeaker facing the left side of the aircraft was the primary source. A combinatorial search procedure (tabu search) was employed to find optimum actuator subsets of from 2 to 16 actuators. Noise reduction predictions derived from the transfer functions were used as a basis for evaluating actuator subsets during optimization. Results indicate that it is necessary to constrain actuator forces during optimization. Unconstrained optimizations selected actuators which require unrealistically large forces. Two methods of constraint are evaluated. It is shown that a fast, but approximate, method yields results equivalent to an accurate, but computationally expensive, method.
Effect of raw materials and hardening process on hardness of manually forged knife
NASA Astrophysics Data System (ADS)
Balkhaya, Suwarno
2017-06-01
Knives are normally made by forging process either using a machine or traditional method by means of hammering process. This present work was conducted to study the effects of steel raw materials and hardening process on the hardness of manually forged knives. The knife samples were made by traditional hammering (forging) process done by local blacksmith. Afterward, the samples were heat treated with two different hardening procedures, the first was based on the blacksmith procedure and the second was systematically done at the laboratory. The forging was done in the temperature ranged between 900-950°C, while the final temperature ranged between 650-675°C. The results showed that knives made of spring steel and heat treated in simulated condition at the laboratory obtained higher level of hardness, i.e. 62 HRC. In general, knives heat treated by local blacksmith had lower level of hardness that those obtained from simulated condition. Therefore, we concluded that the traditional knife quality in term of hardness can be improved by optimizing the heat treatment schedule.
NASA Astrophysics Data System (ADS)
Goto, Masahiro; Sasaki, Michiko; Xu, Yibin; Zhan, Tianzhuo; Isoda, Yukihiro; Shinohara, Yoshikazu
2017-06-01
p- and n-type bismuth telluride thin films have been synthesized by using a combinatorial sputter coating system (COSCOS). The crystal structure and crystal preferred orientation of the thin films were changed by controlling the coating condition of the radio frequency (RF) power during the sputter coating. As a result, the p- and n-type films and their dimensionless figure of merit (ZT) were optimized by the technique. The properties of the thin films such as the crystal structure, crystal preferred orientation, material composition and surface morphology were analyzed by X-ray diffraction, energy-dispersive X-ray spectroscopy and atomic force microscopy. Also, the thermoelectric properties of the Seebeck coefficient, electrical conductivity and thermal conductivity were measured. ZT for n- and p-type bismuth telluride thin films was found to be 0.27 and 0.40 at RF powers of 90 and 120 W, respectively. The proposed technology can be used to fabricate thermoelectric p-n modules of bismuth telluride without any doping process.
What Diagrams Argue in Late Imperial Chinese Combinatorial Texts.
Bréard, Andrea
2015-01-01
Attitudes towards diagrammatic reasoning and visualization in mathematics were seldom spelled out in texts from pre-modern China, although illustrations figure prominently in mathematical literature since the eleventh century. Taking the sums of finite series and their combinatorial interpretation as a case study, this article investigates the epistemological function of illustrations from the eleventh to the nineteenth century that encode either the mathematical objects themselves or represent their related algorithms. It particularly focuses on the two illustrations given in Wang Lai's (1768-1813) Mathematical Principles of Sequential Combinations, arguing that they reflect a specific mode of nineteenth-century mathematical argumentative practice and served as a heuristic model for later authors.
Fabrication and characterization of thin-film phosphor combinatorial libraries
NASA Astrophysics Data System (ADS)
Mordkovich, V. Z.; Jin, Zhengwu; Yamada, Y.; Fukumura, T.; Kawasaki, M.; Koinuma, H.
2002-05-01
The laser molecular beam epitaxy method was employed to fabricate thin-film combinatorial libraries of ZnO-based phosphors on different substrates. Fabrication of both pixel libraries, on the example of Fe-doped ZnO, and spread libraries, on the example of Eu-doped ZnO, has been demonstrated. Screening of the Fe-doped ZnO libraries led to the discovery of weak green cathodoluminescence with the maximum efficiency at the Fe content of 0.58 mol %. Screening of the Eu-doped ZnO libraries led to the discovery of unusual reddish-violet cathodoluminescence which is observed in a broad range of Eu concentration. No photoluminescence was registered in either system.
Hegde, Mahesh; Mantelingu, Kempegowda; Pandey, Monica; Pavankumar, Chottanahalli S; Rangappa, Kanchugarakoppal S; Raghavan, Sathees C
2016-10-01
Cancer is a multifactorial disease, which makes it difficult to cure. Since more than one defective cellular component is often involved during oncogenesis, combination therapy is gaining prominence in the field of cancer therapeutics. The purpose of this study was to investigate the combinatorial effects of a novel PARP inhibitor, P10, and HDAC inhibitor, SAHA, in leukemic cells. Combinatorial effects of P10 and SAHA were tested using propidium iodide staining in different leukemic cells. Further, flowcytometry-based assays such as calcein-AM/ethidium homodimer staining, annexin-FITC/PI staining, and JC-1 staining were carried out to elucidate the mechanism of cell death. In addition, cell-cycle analysis, immunocytochemistry studies, and western blotting analysis were conducted to check the combinatorial effect in Nalm6 cells. Propidium iodide staining showed that P10 in combination with SAHA induced cell death in Nalm6 cells, in which PARP expression and activity is high with a combination index of <0.2. Annexin-FITC/PI staining, JC-1 staining, and other biochemical assays revealed that P10 in combination with SAHA induced apoptosis by causing a change in mitochondrial membrane potential in >65 % cells. Importantly, combinatorial treatment induced S phase arrest in 40-45 % cells due to DNA damage and plausible replicative stress. Finally, we demonstrated that treatment with P10 led to DNA strand breaks, which were further potentiated by SAHA (p < 0.01), leading to activation of apoptosis and increased cell death in PARP-positive leukemic cells. Our study reveals that coadministration of PARP inhibitor with SAHA could be used as a combination therapy against leukemic cells that possess high levels of intrinsic PARP activity.
An Onto-Semiotic Analysis of Combinatorial Problems and the Solving Processes by University Students
ERIC Educational Resources Information Center
Godino, Juan D.; Batanero, Carmen; Roa, Rafael
2005-01-01
In this paper we describe an ontological and semiotic model for mathematical knowledge, using elementary combinatorics as an example. We then apply this model to analyze the solving process of some combinatorial problems by students with high mathematical training, and show its utility in providing a semiotic explanation for the difficulty of…
Hybridization of decomposition and local search for multiobjective optimization.
Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto
2014-10-01
Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.
Loeffler, Felix F; Foertsch, Tobias C; Popov, Roman; Mattes, Daniela S; Schlageter, Martin; Sedlmayr, Martyna; Ridder, Barbara; Dang, Florian-Xuan; von Bojničić-Kninski, Clemens; Weber, Laura K; Fischer, Andrea; Greifenstein, Juliane; Bykovskaya, Valentina; Buliev, Ivan; Bischoff, F Ralf; Hahn, Lothar; Meier, Michael A R; Bräse, Stefan; Powell, Annie K; Balaban, Teodor Silviu; Breitling, Frank; Nesterov-Mueller, Alexander
2016-06-14
Laser writing is used to structure surfaces in many different ways in materials and life sciences. However, combinatorial patterning applications are still limited. Here we present a method for cost-efficient combinatorial synthesis of very-high-density peptide arrays with natural and synthetic monomers. A laser automatically transfers nanometre-thin solid material spots from different donor slides to an acceptor. Each donor bears a thin polymer film, embedding one type of monomer. Coupling occurs in a separate heating step, where the matrix becomes viscous and building blocks diffuse and couple to the acceptor surface. Furthermore, we can consecutively deposit two material layers of activation reagents and amino acids. Subsequent heat-induced mixing facilitates an in situ activation and coupling of the monomers. This allows us to incorporate building blocks with click chemistry compatibility or a large variety of commercially available non-activated, for example, posttranslationally modified building blocks into the array's peptides with >17,000 spots per cm(2).
Combinatorial sputtering of Ga-doped (Zn,Mg)O for contact applications in solar cells
Rajbhandari, Pravakar P.; Bikowski, Andre; Perkins, John D.; ...
2016-09-20
In this study, the development of tunable contact materials based on environmentally friendly chemical elements using scalable deposition approaches is necessary for existing and emerging solar energy conversion technologies. In this paper, the properties of ZnO alloyed with magnesium (Mg), and doped with gallium (Ga) are studied using combinatorial thin film experiments. As a result of these studies, the optical band gap of the sputtered Zn 1-xMg xO thin films was determined to vary from 3.3 to 3.6 eV for a compositional spread of Mg content in the 0.04 < x < 0.17 range. Depending on whether or not Gamore » dopants were added, the electron concentrations were on the order of 10 17 cm -3 or 10 20 cm -3, respectively. Based on these results and on the Kelvin Probe work function measurements, a band diagram was derived using basic semiconductor physics equations. The quantitative determination of how the energy levels of Ga-doped (Zn, Mg)O thin films change as a function of Mg composition presented here, will facilitate their use as optimized contact layers for both Cu 2ZnSnS 4 (CZTS), Cu(In, Ga)Se 2 (CIGS) and other solar cell absorbers.« less
Fuzzy multiobjective models for optimal operation of a hydropower system
NASA Astrophysics Data System (ADS)
Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.
2013-06-01
Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.
Shende, Pravin; Patil, Sampada; Gaud, R S
2017-07-01
The aim of the present study was to use a combinatorial approach of inclusion complexation and dendrimer synthesization of gefitinib using solvent-free technique for targeting EGFR-TK to treat Non-Small-Cell Lung Cancer (NSCLC). The inclusion complex of gefitinib with β-cyclodextrin was prepared by trituration method. This complex encapsulated G4 PAMAM dendrimers were synthesized by Michael addition and amidation reactions using green chemistry and then PEGylated by conjugation reaction. FTIR and DSC confirmed the formation of inclusion complex of gefitinib and β-cyclodextrin and PEGylation of G4 PAMAM dendrimers. Gefitinib showed higher solubility, encapsulation efficiency and controlled release profile from PEGylated dendrimers compared to inclusion complex. The PEGylated dendrimers of inclusion complex of gefitinib were found to reduce hemolytic toxicity and lesser GI 50 value on Human lung cancer cell line A-549 by effective targeting EGFR-TK. A combinatorial approach of inclusion complexation and dendrimer synthesization is one of the alternative advanced approaches to treat NSCLC. Copyright © 2017 Elsevier B.V. All rights reserved.
A versatile modular vector system for rapid combinatorial mammalian genetics.
Albers, Joachim; Danzer, Claudia; Rechsteiner, Markus; Lehmann, Holger; Brandt, Laura P; Hejhal, Tomas; Catalano, Antonella; Busenhart, Philipp; Gonçalves, Ana Filipa; Brandt, Simone; Bode, Peter K; Bode-Lesniewska, Beata; Wild, Peter J; Frew, Ian J
2015-04-01
Here, we describe the multiple lentiviral expression (MuLE) system that allows multiple genetic alterations to be introduced simultaneously into mammalian cells. We created a toolbox of MuLE vectors that constitute a flexible, modular system for the rapid engineering of complex polycistronic lentiviruses, allowing combinatorial gene overexpression, gene knockdown, Cre-mediated gene deletion, or CRISPR/Cas9-mediated (where CRISPR indicates clustered regularly interspaced short palindromic repeats) gene mutation, together with expression of fluorescent or enzymatic reporters for cellular assays and animal imaging. Examples of tumor engineering were used to illustrate the speed and versatility of performing combinatorial genetics using the MuLE system. By transducing cultured primary mouse cells with single MuLE lentiviruses, we engineered tumors containing up to 5 different genetic alterations, identified genetic dependencies of molecularly defined tumors, conducted genetic interaction screens, and induced the simultaneous CRISPR/Cas9-mediated knockout of 3 tumor-suppressor genes. Intramuscular injection of MuLE viruses expressing oncogenic H-RasG12V together with combinations of knockdowns of the tumor suppressors cyclin-dependent kinase inhibitor 2A (Cdkn2a), transformation-related protein 53 (Trp53), and phosphatase and tensin homolog (Pten) allowed the generation of 3 murine sarcoma models, demonstrating that genetically defined autochthonous tumors can be rapidly generated and quantitatively monitored via direct injection of polycistronic MuLE lentiviruses into mouse tissues. Together, our results demonstrate that the MuLE system provides genetic power for the systematic investigation of the molecular mechanisms that underlie human diseases.
Code of Federal Regulations, 2010 CFR
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Distinctively colored hard hats, or hard caps... STANDARDS-UNDERGROUND COAL MINES Miscellaneous § 75.1720-1 Distinctively colored hard hats, or hard caps; identification for newly employed, inexperienced miners. Hard hats or hard caps distinctively different in color...
Code of Federal Regulations, 2014 CFR
2014-07-01
... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Distinctively colored hard hats, or hard caps... STANDARDS-UNDERGROUND COAL MINES Miscellaneous § 75.1720-1 Distinctively colored hard hats, or hard caps; identification for newly employed, inexperienced miners. Hard hats or hard caps distinctively different in color...