Journal articles on the topic 'DNA computing experiment'
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Nakajima, T., Y. Sakai, and A. Suyama. "Experiment of DNA computing with robot." Seibutsu Butsuri 40, supplement (2000): S152. http://dx.doi.org/10.2142/biophys.40.s152_3.
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Stoschek, E., M. Sturm, and T. Hinze. "DNA-Computing – ein funktionales Modell im laborpraktischen Experiment." Informatik Forschung und Entwicklung 16, no. 1 (February 2001): 35–52. http://dx.doi.org/10.1007/pl00009141.
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YAMAMOTO, MASAHITO, YUMI KAWAZOE, AZUMA OHUCHI, ATSUSHI KAMEDA, NOBUO MATSUURA, and TOSHIKAZU SHIBA. "LOCAL SEARCH BY CONCENTRATION-CONTROLLED DNA COMPUTING." International Journal of Computational Intelligence and Applications 02, no. 04 (December 2002): 447–55. http://dx.doi.org/10.1142/s1469026802000750.
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Concentration-controlled DNA computing is presented for accomplishing a local search for the solution of a shortest path problem. In this method, the concentrations of DNA representing edges are determined according to the costs on edges, and then the hybridization process is performed. Since the concentrations of hopeless candidate solutions tend to be small after the hybridization process, a local search by concentration-controlled DNA computing is a promising approach. In order to discuss about the relationship between given costs on edges in the graph and concentrations of generated DNA paths, a simulation model of the hybridization process is used and the results of a laboratory experiment are shown.
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Huang, You Rui, Jing Wang, and Xiao Min Tian. "DNA Addition Algorithm Based on Molecular Beacon." Advanced Materials Research 424-425 (January 2012): 1164–69. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.1164.
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A new DNA computing model to realize binary integer additions based on molecular beacon is described in this paper. The binary 0 and 1 are represented by two different fluorescent states of a molecular beacon since its two different structures. By designing sequences of molecular beacons skillfully, and using the relationship between each two corresponding bits and their result and carry bit of two operational numbers, the computing process to compute each two corresponding bits of the binary numbers is simulated in the test tube. Finally, the results can be read only by detecting whether the fluorescence in tube emits. The result of the experiment shows that the algorithm is simple and convenient, and the algorithm provides a new idea for DNA computing to realize arithmetic operations
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Tian, Xiang, Xiyu Liu, Hongyan Zhang, Minghe Sun, and Yuzhen Zhao. "A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model." PLOS ONE 15, no. 12 (December 2, 2020): e0242083. http://dx.doi.org/10.1371/journal.pone.0242083.
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A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.
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Huang, Chun, Ying-Jie Han, Jun-Wei Sun, Wei-Jun Zhu, Yan-Feng Wang, and Qing-Lei Zhou. "The Design and Application of Exclusive OR Logical Computation Based on DNA 3-Arm Sub-Tile Self-Assembly." Journal of Nanoelectronics and Optoelectronics 15, no. 11 (November 1, 2020): 1327–34. http://dx.doi.org/10.1166/jno.2020.2853.
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DNA algorithmic self-assembly plays a vital role in DNA computing, which is applied to create new DNA tiles and then guides these tiles into an algorithmic lattice. However, the larger the logical calculation scale is, the more tile sets will be needed, so that computing model design and experiment will be increasingly difficult. This paper presents a new DNA ‘3-arm sub-tile strategy’ that constructs XOR and half-adder logical circuits. The types of DNA tile corresponding to the logical values is unified in DNA XOR and half-adder logical circuits, which have only three kinds of DNA tiles: logic ‘0’, logic ‘1’ and fixation tile. Compared with the previous references, the amount of DNA tile types has been greatly reduced. Moreover, the half-adder molecular circuit has a distinctive feature, which is an application of the expansion of the XOR logic circuit. Meanwhile, a set of DNA 3-arm sub-tiles suitable for half-adder logical computation is designed on the NUPACK online server. The simulated experiments show that the correct rate of base pairing of the designed DNA encoding is high and the structures are stable. The DNA 3-arm sub-tile self-assembly methods provide a new way to form DNA logical circuits, and has a great potential in the expansion of the integrated circuits.
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Ma, Jingjing. "Molecular Logic Gate Based on DNA Strand Displacement Reaction." Journal of Nanoelectronics and Optoelectronics 16, no. 6 (June 1, 2021): 974–77. http://dx.doi.org/10.1166/jno.2021.3037.
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In this paper, I construct an XOR logic gate based on DNA strand displacement reaction, and verify our design through corresponding biochemical experiment. I designed several different DNA strands. Based on two basic DNA strand displacement reaction mechanisms, by adding different input strands and taking the signal of FAM fluorescent group as the output, the XOR logic gate is realized. The result shows that DNA strand displacement technology has important application value in DNA computing, especially in the construction of DNA molecular logic gates.
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Zhou, Shihua, Bin Wang, Xuedong Zheng, and Changjun Zhou. "An Image Encryption Scheme Based on DNA Computing and Cellular Automata." Discrete Dynamics in Nature and Society 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/5408529.
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Networks have developed very quickly, allowing the speedy transfer of image information through Internet. However, the openness of these networks poses a serious threat to the security of image information. The field of image encryption has drawn attention for this reason. In this paper, the concepts of 1-dimensional DNA cellular automata and T-DNA cellular automata are defined, and the concept of reversible T-DNA cellular automata is introduced. An efficient approach to encryption involving reversible T-DNA cellular automata as an encryption tool and natural DNA sequences as the main keys is here proposed. The results of a simulation experiment, performance analysis, and comparison to other encryption algorithms showed this algorithm to be capable of resisting brute force attacks, statistical attacks, and differential attacks. It also enlarged the key space enormously. It meets the criteria for one-time pad and resolves the problem that one-time pad is difficult to save.
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Zhang, Xinxin, Nan Zhao, and Jing Yang. "Operation of Queue and Stack by DNA Tiles." E3S Web of Conferences 218 (2020): 03051. http://dx.doi.org/10.1051/e3sconf/202021803051.
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DNA is used as self-nanomaterials to assemble into specific structures. DNA tile provides a new idea for the application of DNA tile in the field of computing. Recent years, Queue and Stack are important linear data structures which are used in various software systems widely. The implementation of DNA based queue and stack has been studied continuously for many years. In the traditional DNA computing, queue and stack are mostly realized by DNA strands displacement, restriction endonuclease and ligase were used. However, as an active material, it has a high requirement for enzyme experimental conditions. The purpose of this paper is to implement queue and stack structures using non-enzyme systems. The rule of Queue is characterized by FIFO (first in first out), which allows for insertion at one end of the list and deletion at the other. The rule of Stack is characterized by FILO(first in last out), which allows for insertion and deletion at one end of the list. We are aimed to implement Queue and Stack using self-assembly and disassembly via DNA Tiles. No enzymes are needed for the whole experiment. As an enzyme-free system, it provides a new method to implement stack and queue.
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Shan, Jing Yi, Zhi Xiang Yin, Xin Yu Tang, and Jing Jing Tang. "A DNA Computing Model for the AND Gate in Three-Valued Logical Circuit." Applied Mechanics and Materials 610 (August 2014): 764–68. http://dx.doi.org/10.4028/www.scientific.net/amm.610.764.
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Multiple-valued logic is an extended form of Boolean logic. In daily life, people often encounter the problem about the multiple-valued logic. With further study on Boolean logic, multiple-valued logic has been paid more and more attention by researchers. This paper achieves the operation of AND gate in three-valued logic by using the DNA hairpin structure. The experiment makes the DNA hairpin structure as the basic structure, and the molecular beacon as the input signal, and at last judges the logical results according to the intensity of fluorescence and gel electrophoresis. This method has the advantages that it has high sensitivity, good feasibility, and it is easy to observe. In addition, this method reduces the hybrid competition to a certain extent, and it is a new attempt to the research on multiple-valued logic.
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Jung, Jihye, and In-Chan Choi. "A multi-commodity network model for optimal quantum reversible circuit synthesis." PLOS ONE 16, no. 6 (June 22, 2021): e0253140. http://dx.doi.org/10.1371/journal.pone.0253140.
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Quantum computing is a newly emerging computing environment that has recently attracted intense research interest in improving the output fidelity, fully utilizing its high computing power from both hardware and software perspectives. In particular, several attempts have been made to reduce the errors in quantum computing algorithms through the efficient synthesis of quantum circuits. In this study, we present an application of an optimization model for synthesizing quantum circuits with minimum implementation costs to lower the error rates by forming a simpler circuit. Our model has a unique structure that combines the arc-subset selection problem with a conventional multi-commodity network flow model. The model targets the circuit synthesis with multiple control Toffoli gates to implement Boolean reversible functions that are often used as a key component in many quantum algorithms. Compared to previous studies, the proposed model has a unifying yet straightforward structure for exploiting the operational characteristics of quantum gates. Our computational experiment shows the potential of the proposed model, obtaining quantum circuits with significantly lower quantum costs compared to prior studies. The proposed model is also applicable to various other fields where reversible logic is utilized, such as low-power computing, fault-tolerant designs, and DNA computing. In addition, our model can be applied to network-based problems, such as logistics distribution and time-stage network problems.
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Chua, Kyle Matthew Chan, Janz Aeinstein Fauni Villamayor, Lorenzo Campos Bautista, and Roger Luis Uy. "Implementation of hyyrö’s bit-vector algorithm using advanced vector extensions 2." International Journal of Advances in Intelligent Informatics 5, no. 3 (October 29, 2019): 230. http://dx.doi.org/10.26555/ijain.v5i3.362.
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The Advanced Vector Extensions 2 (AVX2) instruction set architecture was introduced by Intel’s Haswell microarchitecture that features improved processing power, wider vector registers, and a rich instruction set. This study presents an implementation of the Hyyrö’s bit-vector algorithm for pairwise Deoxyribonucleic Acid (DNA) sequence alignment that takes advantage of Single-Instruction-Multiple-Data (SIMD) computing capabilities of AVX2 on modern processors. It investigated the effects of the length of the query and reference sequences to the I/O load time, computation time, and memory consumption. The result reveals that the experiment has achieved an I/O load time of ϴ(n), computation time of ϴ(n*⌈m/64⌉), and memory consumption of ϴ(n). The implementation computed more extended time complexity than the expected ϴ(n) due to instructional and architectural limitations. Nonetheless, it was par with other experiments, in terms of computation time complexity and memory consumption.
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K., Siddartha B., and Ravikumar G. K. "An efficient data masking for securing medical data using DNA encoding and chaotic system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6008. http://dx.doi.org/10.11591/ijece.v10i6.pp6008-6018.
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Data security is utmost important for ubiquitous computing of medical/diagnostic data or images. Along with must consider preserving privacy of patients. Recently, deoxyribose nucleic acid (DNA) sequences and chaotic sequence are jointly used for building efficient data masking model. However, the state-of-art model are not robust against noise and cropping attack (CA). Since in existing model most digits of each pixel are not altered. This work present efficient data masking (EDM) method using chaos and DNA based encryption method for securing health care data. For overcoming research challenges effective bit scrambling method is required. Firstly, this work present an efficient bit scrambling using logistic sine map and pseudorandom sequence using chaotic system. Then, DNA substitution is performed among them to resist against differential attack (DA), statistical attack (SA) and CA. Experiment are conducted on standard considering diverse images. The outcome achieved shows proposed model efficient when compared to existing models.
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ILIOPOULOS, COSTAS S., MIRKA MILLER, and SOLON P. PISSIS. "PARALLEL ALGORITHMS FOR MAPPING SHORT DEGENERATE AND WEIGHTED DNA SEQUENCES TO A REFERENCE GENOME." International Journal of Foundations of Computer Science 23, no. 02 (February 2012): 249–59. http://dx.doi.org/10.1142/s0129054112400114.
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One of the most ambitious trends in current biomedical research is the large-scale genomic sequencing of patients. Novel high-throughput (or next-generation) sequencing technologies have redefined the way genome sequencing is performed. They are able to produce millions of short sequences (reads) in a single experiment, and with a much lower cost than previously possible. Due to this massive amount of data, efficient algorithms for mapping these sequences to a reference genome are in great demand, and recently, there has been ample work for publishing such algorithms. One important feature of these algorithms is the support of multithreaded parallel computing in order to speedup the mapping process. In this paper, we design parallel algorithms, which make use of the message-passing parallelism model, to address this problem efficiently. The proposed algorithms also take into consideration the probability scores assigned to each base for occurring in a specific position of a sequence. In particular, we present parallel algorithms for mapping short degenerate and weighted DNA sequences to a reference genome.
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Benavides, Andres, Friman Sanchez, Juan F. Alzate, and Felipe Cabarcas. "DATMA: Distributed AuTomatic Metagenomic Assembly and annotation framework." PeerJ 8 (September 3, 2020): e9762. http://dx.doi.org/10.7717/peerj.9762.
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Background A prime objective in metagenomics is to classify DNA sequence fragments into taxonomic units. It usually requires several stages: read’s quality control, de novo assembly, contig annotation, gene prediction, etc. These stages need very efficient programs because of the number of reads from the projects. Furthermore, the complexity of metagenomes requires efficient and automatic tools that orchestrate the different stages. Method DATMA is a pipeline for fast metagenomic analysis that orchestrates the following: sequencing quality control, 16S rRNA-identification, reads binning, de novo assembly and evaluation, gene prediction, and taxonomic annotation. Its distributed computing model can use multiple computing resources to reduce the analysis time. Results We used a controlled experiment to show DATMA functionality. Two pre-annotated metagenomes to compare its accuracy and speed against other metagenomic frameworks. Then, with DATMA we recovered a draft genome of a novel Anaerolineaceae from a biosolid metagenome. Conclusions DATMA is a bioinformatics tool that automatically analyzes complex metagenomes. It is faster than similar tools and, in some cases, it can extract genomes that the other tools do not. DATMA is freely available at https://github.com/andvides/DATMA.
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Rajaee, N., K. Hong Ping, A. Lit, D. N. S. A. Salleh, and L. Y. Ng. "Comparison of Material Consumption, Experimental Protocols and Computation Time in DNA Computing." International Journal of Machine Learning and Computing 4, no. 4 (2014): 394–98. http://dx.doi.org/10.7763/ijmlc.2014.v4.443.
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Karakose, Mehmet, and Ugur Cigdem. "QPSO-Based Adaptive DNA Computing Algorithm." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/160687.
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DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.
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Tsuboi, Yusei, Zuwairie Ibrahim, and Osamu Ono. "Experimentally Constructing Semantic Models Based on DNA Computing." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (January 20, 2006): 77–83. http://dx.doi.org/10.20965/jaciii.2006.p0077.
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We propose a new DNA-based semantic model, constructed of DNA molecules, called asemantic model based on molecular computing(SMC). It is structured as a graph formed by the set of all (attribute, attribute value) pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to the terminal node, represents the object named on the tag. Inputting a set of input strands the forms object-representing dsDNAs via parallel self-assembly from encoded ssDNAs representing both attributes and attribute values (nodes), as directed by ssDNA splitting strands representing relations (edges) in the network. The success of experiments in constructing a small test model demonstrates that our proposed model suitably represents knowledge to storing vast amounts of information at high density.
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Santiago, Daniel. "A Partial Answer to the Question Posed by David A. Hughes, PhD, in the Article: “What is in the so-called COVID-19 ‘Vaccines’? Part 1: Evidence of a Global Crime Against Humanity”." International Journal of Vaccine Theory, Practice, and Research 2, no. 2 (September 7, 2022): 587–94. http://dx.doi.org/10.56098/ijvtpr.v2i2.56.
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In this comment, originally thought of as a “Letter to the Editor”, I want to address the opening question posed by David A. Hughes in the immediately preceding entry in this journal: “What is in the so-called COVID-19 ‘Vaccines’?” The views from under the microscope, ordinary light or electron scanning, all show undisclosed foreign objects that seem to activate themselves and aggregate into complexes that disrupt blood flow in all organ systems. With the spectral analysis using electron microscopy it is possible to determine the specific elements and relative quantities of the elements in those foreign entities. In this comment, I want to focus on the absence of certain elements that are universally present in the proteins of naturally occurring life forms from humans right down to bacteria and even the proteins formed from viruses. What is missing from the spectral analyses of the foreign elements in the main COVID-19 vaccines, Pfizer and Moderna for certain, and probably also missing from the other experimental products being widely distributed that are known to contain foreign aggregates of strange materials similar to those found in the Moderna and Pfizer injections, are the elements nitrogen and phosphorous. This is revealing because all natural DNA, RNA, and their protein products contain those missing elements. Nitrogen for protein synthesis and phosphorus for DNA, RNA, and energy transfer. Therefore, their absence from the foreign structures seen under many different microscopes in all of the COVID-19 so-called “vaccines” that have been examined, and also found in blood samples of persons injected with the Moderna and Pfizer concoctions, proves that these intentionally manufactured self-assembling components, built mainly from carbon-based materials used in computing and super-conductors, are connected with the avant-guard evolutionary theory and experimentation with what is known as XNA, Xeno (Greek for “foreign”), Nucleic Acid. Most of the relevant information is behind significant paywalls in esoteric journals specializing in this peculiar branch of highly theoretical and experimental chemistry. To leap to the bottom-line of my urgent comment on the Hughes’ paper, the edgy modified mRNA with N1-methylpseudouridine (Ψ) replacing the naturally occurring RNA nucleotide uridine (U) at least 728 times in each one of the 30 billion mRNA molecules in each of the Pfizer injections is an exmplary XNA. In this comment I want to explain why the inclusion of such an XNA may be the clue that leads to the unraveling of the already devastating and potentially exterminating impact of the ongoing COVID-19 experiment on the human race.
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Vidal, Pablo, and Ana Olivera. "Solving the DNA fragment assembly problem with a parallel discrete firefly algorithm implemented on GPU." Computer Science and Information Systems 15, no. 2 (2018): 273–93. http://dx.doi.org/10.2298/csis170510009v.
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The Deoxyribonucleic Acid Fragment Assembly Problem (DNA-FAP) consists in reconstructing a DNA chain from a set of fragments taken randomly. This problem represents an important step in the genome project. Several authors are proposed different approaches to solve the DNA-FAP. In particular, nature-inspired algorithms have been used for its resolution. Even they were obtaining good results; its computational time associated is high. The bio-inspired algorithms are iterative search processes that can explore and exploit efficiently the solution space. Firefly Algorithm is one of the recent evolutionary computing models which is inspired by the flashing light behaviour of fireflies. Recently, the Graphics Processing Units (GPUs) technology are emerge as a novel environment for a parallel implementation and execution of bio-inspired algorithms. Therefore, the use of GPU-based parallel computing it is possible as a complementary tool to speed-up the search. In this work, we design and implement a Discrete Firefly Algorithm (DFA) on a GPU architecture in order to speed-up the search process for solving the DNA Fragment Assembly Problem. Through several experiments, the efficiency of the algorithm and the quality of the results are demonstrated with the potential to applied for longer sequences or sequences of unknown length as well.
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Budiharto, Widodo. "Implementasi dan Evaluasi Penerapan Globus Toolkit untuk Aplikasi Grid Computing." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (June 1, 2012): 695. http://dx.doi.org/10.21512/comtech.v3i1.2469.
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Grid computing is a distributed computing technology that utilizes resources connected through a free computer network, yet coordinated with a specific mechanism. The development of grid computing infrastructure is not easy because it takes skill and experience in the installation and configuration of both Linux-based and open source program. In this study, the author built a grid computing infrastructure based on Debian 4, and used Globus Toolkit 4.1.2 on three computers. The WSRF technology was tried to run as an indication that the grid infrastructure has been successfully built. Based on some experiments in this study, grid computing can run well on the three computers with a user interface of web-based grid system using the UCLA Grid Portal. Overall the system runs fine, but it requires high experiences and comprehensions upon the Linux operating systems as well as computer networks in the installation process.
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Ding, Du Kun, Long Gen Li, Cun Xi Xie, and Tie Zhang. "A DNA-PID Controller for an Industry Robot." Advanced Materials Research 461 (February 2012): 109–12. http://dx.doi.org/10.4028/www.scientific.net/amr.461.109.
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In this paper, a DNA-PID controller is proposed for a 6-DOF robot. The experimental robot system has firstly been setup. Then the PID controllers of the robot joints are designed. Due to DNA algorithm’s excellent computing characteristics, it is researched and used to set the PID parameters on line, which are the proportional coefficient, the integral coefficient and the differential coefficient. To test the controllers, several experiments are performed. The computer simulation results show that the DNA-PID controllers have faster respond speed and less overshot, which can meet the need of robot control
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Liang, Xin, Wen Zhu, Zhibin Lv, and Quan Zou. "Molecular Computing and Bioinformatics." Molecules 24, no. 13 (June 26, 2019): 2358. http://dx.doi.org/10.3390/molecules24132358.
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Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.
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Listyawati, Ni Made Meliana, Adityas Widjajarto, and M. Teguh Kurniawan. "Implementasi dan Analisis Profil Sistem Pada Virtualisasi Paloalto Firewall Berdasarkan Metrik Sumber Daya Komputasi." Jurnal Sistem Komputer dan Informatika (JSON) 4, no. 1 (September 30, 2022): 112. http://dx.doi.org/10.30865/json.v4i1.4780.
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On the security aspect, it is necessary to know how effectively a firewall can protect network devices from DDoS attacks. The characteristics of a firewall have different functions in protecting the system from various external attacks that can attack and retrieve data. In this research, the implementation of Paloalto firewall virtualization aims to obtain the system profile function on the firewall based on the use of computing resources. Profiling of the firewall system of this experiment based on the consumption of computing resources in load testing. This experiment used a DDoS SYN flood attack on Kali Linux as an attacker and a virtualization Paloalto firewall that protects a web server on Ubuntu Server as an attack target. This research distinguished based on two test scenarios, namely based on testing the service HTTP allow and service HTTP block with Paloalto memory specifications at RAM 5.5 GB and RAM 8 GB specifications. Measurements were made based on computing resources on CPU, memory, and a session focused on before, during, and after DDoS SYN flood attacks. The pattern of usage of computing resources tends to be linear when a DDoS SYN flood attack occurs. The experimental results obtained on the highest use of computing resources during the attack were CPU usage with an average percentage of 95.8% and the second increase was in memory usage with an average percentage of 44%, and the session usage was 138682. For further research, it can use variations of DDoS attacks to get a wider profile.
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Sakowski, Sebastian, Tadeusz Krasiński, Joanna Sarnik, Janusz Blasiak, Jacek Waldmajer, and Tomasz Poplawski. "A detailed experimental study of a DNA computer with two endonucleases." Zeitschrift für Naturforschung C 72, no. 7-8 (July 14, 2017): 303–13. http://dx.doi.org/10.1515/znc-2016-0137.
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AbstractGreat advances in biotechnology have allowed the construction of a computer from DNA. One of the proposed solutions is a biomolecular finite automaton, a simple two-state DNA computer without memory, which was presented by Ehud Shapiro’s group at the Weizmann Institute of Science. The main problem with this computer, in which biomolecules carry out logical operations, is its complexity – increasing the number of states of biomolecular automata. In this study, we constructed (in laboratory conditions) a six-state DNA computer that uses two endonucleases (e.g.AcuI andBbvI) and a ligase. We have presented a detailed experimental verification of its feasibility. We described the effect of the number of states, the length of input data, and the nondeterminism on the computing process. We also tested different automata (with three, four, and six states) running on various accepted input words of different lengths such asab,aab,aaab,ababa, and of an unaccepted wordba. Moreover, this article presents the reaction optimization and the methods of eliminating certain biochemical problems occurring in the implementation of a biomolecular DNA automaton based on two endonucleases.
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Reme, Thierry X., Dirk Hose, John De Vos, Hartmut Goldschmidt, Friedrich Cremer, and Bernard Klein. "A New Method for Predictor Calculation Based on Signed-Rank Call Algorithms. Application to Microarray Gene Expression Analysis of Human Multiple Myeloma." Blood 104, no. 11 (November 16, 2004): 4876. http://dx.doi.org/10.1182/blood.v104.11.4876.4876.
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Abstract The simultaneous quantification of thousands of genes in gene expression profiling (GEP) on DNA chips is part of the whole-genome sequencing revolution. Affymetrix(R) chip technology provides both a quantitative fluorescence signal and a decision of absent or present gene expression (absent or present call) based on signed-rank algorithms applied to several hybridization repeats of each gene spread on a single chip. To avoid an empirical normalization between chips of the same experiment, we developed an analysis of GEP based on Affymetrix present or absent calls. Bone marrow aspirates from newly-diagnosed multiple myeloma (MM) patients were purified with CD138 automated magnetic cell sorting. Amplified RNA was run on U133A+B Affymetrix DNA microarrays for a first set of 65 patients, or U133Plus2 for a second cohort of 40 patients. Scan files were transferred to an Oracle(R) data base and analyzed with web-oriented scripts for both unsupervised and supervised non-parametric analysis on either the fluorescence signal or the Affymetrix call. To build a multiclass call-based predictor, the observed distribution of present call of each probeset was first compared between predetermined sample groups using a chi2 test and probesets were kept only if above a threshold compatible with further analysis and calculation time (usually 100 to 1000 genes). The power of a probeset list to classify the different groups (number of presence/number of probesets) was then evaluated for each sample of a group and compared to each sample of all other groups by calculating the reduced deviation (RD) in paired comparisons and evaluating the overall number of non significant comparisons (NS) with a chosen precision, the sum of the reduced deviations divided by the square root of the probeset number (f, independent of the list size), and the smallest RD (RDmin). The minimum predictive probeset list was obtained by deleting each probeset one after the other, and computing NS, f and RDmin from the remaining probeset list. If either NS is reduced, or both NS unchanged and f increased, or together NS and f unchanged and RDmin either increased or higher than precision, the probeset is left out and the process run again on the shortened list until no more leave-outs are possible. This method was successfully validated by determining a 22-gene sex predictor with the 65 patient series that made it possible to classify gender with no error in the 40 patient validation group. Partial loss of chromosome Y was confirmed in 3 male MM patients by short tandem repeat analysis. Significant predictors could not be generated with randomly selected patient groups. Validation was also successful with P <.001 in predicting the immunoglobulin light chain of the validation group after educating with the training group (lambda to kappa-type ratio between 1/4 and 1/3). Classification of the training set according to Salmon-Durie staging made it possible to generate a 97-gene predictor with a validation error of P <.01. This normalization-free method looks particularly promising for further applications like diagnostic classification (MGUS), prognostic grouping and prediction of response to treatment. In addition, it can be used as a powerful tool to mine generated or published data on all cancer types.
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Wang, Zhaocai, Xian Wu, and Tunhua Wu. "A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model." Computational Intelligence and Neuroscience 2022 (August 31, 2022): 1–16. http://dx.doi.org/10.1155/2022/1450756.
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The quota traveling salesman problem (QTSP) is a variant of the traveling salesman problem (TSP), which is a classical optimization problem. In the QTSP, the salesman visits some of the n cities to meet a given sales quota Q while having minimized travel costs. In this paper, we develop a DNA algorithm based on Adleman-Lipton model to solve the quota traveling salesman problem. Its time complexity is O n 2 + Q , which is a significant improvement over previous algorithms with exponential complexity. A coding scheme of element information is pointed out, and a reasonable biological algorithm is raised by using limited conditions, whose feasibility is verified by simulation experiments. The innovation of this study is to propose a polynomial time complexity algorithm to solve the QTSP. This advantage will become more obvious as the problem scale increases compared with the algorithm of exponential computational complexity. The proposed DNA algorithm also has the significant advantages of having a large storage capacity and consuming less energy during the operation. With the maturity of DNA manipulation technology, DNA computing, as one of the parallel biological computing methods, has the potential to solve more complex NP-hard problems.
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Dunn, K. E., M. C. Leake, A. J. M. Wollman, M. A. Trefzer, S. Johnson, and A. M. Tyrrell. "An experimental study of the putative mechanism of a synthetic autonomous rotary DNA nanomotor." Royal Society Open Science 4, no. 3 (March 2017): 160767. http://dx.doi.org/10.1098/rsos.160767.
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DNA has been used to construct a wide variety of nanoscale molecular devices. Inspiration for such synthetic molecular machines is frequently drawn from protein motors, which are naturally occurring and ubiquitous. However, despite the fact that rotary motors such as ATP synthase and the bacterial flagellar motor play extremely important roles in nature, very few rotary devices have been constructed using DNA. This paper describes an experimental study of the putative mechanism of a rotary DNA nanomotor, which is based on strand displacement, the phenomenon that powers many synthetic linear DNA motors. Unlike other examples of rotary DNA machines, the device described here is designed to be capable of autonomous operation after it is triggered. The experimental results are consistent with operation of the motor as expected, and future work on an enhanced motor design may allow rotation to be observed at the single-molecule level. The rotary motor concept presented here has potential applications in molecular processing, DNA computing, biosensing and photonics.
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Omidi, Mostafa, and Mohsen Alipour. "Review of DNA Computing And Its Application in Complex Problems Solvation, Adelman Problem Solution in Using of DNA Via Experimental Methods." IOSR Journal of Computer Engineering 18, no. 05 (May 2016): 110–15. http://dx.doi.org/10.9790/0661-180502110115.
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Wang, Zhaocai, Xiaoguang Bao, and Tunhua Wu. "A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing." Computational Intelligence and Neuroscience 2021 (January 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/8814947.
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The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.
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Barbi, Maria, Julien Mozziconacci, Jean-Marc Victor, Hua Wong, and Christophe Lavelle. "On the topology of chromatin fibres." Interface Focus 2, no. 5 (February 2012): 546–54. http://dx.doi.org/10.1098/rsfs.2011.0101.
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The ability of cells to pack, use and duplicate DNA remains one of the most fascinating questions in biology. To understand DNA organization and dynamics, it is important to consider the physical and topological constraints acting on it. In the eukaryotic cell nucleus, DNA is organized by proteins acting as spools on which DNA can be wrapped. These proteins can subsequently interact and form a structure called the chromatin fibre. Using a simple geometric model, we propose a general method for computing topological properties ( twist , writhe and linking number ) of the DNA embedded in those fibres. The relevance of the method is reviewed through the analysis of magnetic tweezers single molecule experiments that revealed unexpected properties of the chromatin fibre. Possible biological implications of these results are discussed.
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Rizal, Aminuddin. "Tahapan Desain dan Implementasi Model Machine Learning untuk Sistem Tertanam." Ultima Computing : Jurnal Sistem Komputer 12, no. 2 (November 19, 2020): 79–85. http://dx.doi.org/10.31937/sk.v12i2.1782.
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machine learning and edge computing currently becomes popular technology used in any discipline. Flexibility and adapt to the problem are the main advantages of its technology. In this paper, we explain step-by-step way to make a lightweight machine learning model especially intended for embedded system application. We use open source machine learning tool called as Weka to design the model. Moreover, we performed a simple stress recognition experiment to make our own dataset for evaluation. We evaluate algorithm complexity and accuracy for different well-known classifier such as support vector machine, simple logistic and hoeffding tree.
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Li, Taiyong, Jiayi Shi, Xinsheng Li, Jiang Wu, and Fan Pan. "Image Encryption Based on Pixel-Level Diffusion with Dynamic Filtering and DNA-Level Permutation with 3D Latin Cubes." Entropy 21, no. 3 (March 24, 2019): 319. http://dx.doi.org/10.3390/e21030319.
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Image encryption is one of the essential tasks in image security. In this paper, we propose a novel approach that integrates a hyperchaotic system, pixel-level Dynamic Filtering, DNA computing, and operations on 3D Latin Cubes, namely DFDLC, for image encryption. Specifically, the approach consists of five stages: (1) a newly proposed 5D hyperchaotic system with two positive Lyapunov exponents is applied to generate a pseudorandom sequence; (2) for each pixel in an image, a filtering operation with different templates called dynamic filtering is conducted to diffuse the image; (3) DNA encoding is applied to the diffused image and then the DNA-level image is transformed into several 3D DNA-level cubes; (4) Latin cube is operated on each DNA-level cube; and (5) all the DNA cubes are integrated and decoded to a 2D cipher image. Extensive experiments are conducted on public testing images, and the results show that the proposed DFDLC can achieve state-of-the-art results in terms of several evaluation criteria.
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Li, Yanni, Yuping Wang, and Liang Bao. "FACC: A Novel Finite Automaton Based on Cloud Computing for the Multiple Longest Common Subsequences Search." Mathematical Problems in Engineering 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/310328.
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Searching for the multiple longest common subsequences (MLCS) has significant applications in the areas of bioinformatics, information processing, and data mining, and so forth, Although a few parallel MLCS algorithms have been proposed, the efficiency and effectiveness of the algorithms are not satisfactory with the increasing complexity and size of biologic data. To overcome the shortcomings of the existing MLCS algorithms, and considering that MapReduce parallel framework of cloud computing being a promising technology for cost-effective high performance parallel computing, a novel finite automaton (FA) based on cloud computing called FACC is proposed under MapReduce parallel framework, so as to exploit a more efficient and effective general parallel MLCS algorithm. FACC adopts the ideas of matched pairs and finite automaton by preprocessing sequences, constructing successor tables, and common subsequences finite automaton to search for MLCS. Simulation experiments on a set of benchmarks from both real DNA and amino acid sequences have been conducted and the results show that the proposed FACC algorithm outperforms the current leading parallel MLCS algorithm FAST-MLCS.
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Cumbo, Fabio, Eleonora Cappelli, and Emanuel Weitschek. "A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer." Algorithms 13, no. 9 (September 17, 2020): 233. http://dx.doi.org/10.3390/a13090233.
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The recent advancements in cancer genomics have put under the spotlight DNA methylation, a genetic modification that regulates the functioning of the genome and whose modifications have an important role in tumorigenesis and tumor-suppression. Because of the high dimensionality and the enormous amount of genomic data that are produced through the last advancements in Next Generation Sequencing, it is very challenging to effectively make use of DNA methylation data in diagnostics applications, e.g., in the identification of healthy vs diseased samples. Additionally, state-of-the-art techniques are not fast enough to rapidly produce reliable results or efficient in managing those massive amounts of data. For this reason, we propose HD-classifier, an in-memory cognitive-based hyperdimensional (HD) supervised machine learning algorithm for the classification of tumor vs non tumor samples through the analysis of their DNA Methylation data. The approach takes inspiration from how the human brain is able to remember and distinguish simple and complex concepts by adopting hypervectors and no single numerical values. Exactly as the brain works, this allows for encoding complex patterns, which makes the whole architecture robust to failures and mistakes also with noisy data. We design and develop an algorithm and a software tool that is able to perform supervised classification with the HD approach. We conduct experiments on three DNA methylation datasets of different types of cancer in order to prove the validity of our algorithm, i.e., Breast Invasive Carcinoma (BRCA), Kidney renal papillary cell carcinoma (KIRP), and Thyroid carcinoma (THCA). We obtain outstanding results in terms of accuracy and computational time with a low amount of computational resources. Furthermore, we validate our approach by comparing it (i) to BIGBIOCL, a software based on Random Forest for classifying big omics datasets in distributed computing environments, (ii) to Support Vector Machine (SVM), and (iii) to Decision Tree state-of-the-art classification methods. Finally, we freely release both the datasets and the software on GitHub.
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Lee, Suk-Hwan. "Reversible Data Hiding for DNA Sequence Using Multilevel Histogram Shifting." Security and Communication Networks 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/3530969.
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A large number of studies have examined DNA storage to achieve information hiding in DNA sequences with DNA computing technology. However, most data hiding methods are irreversible in that the original DNA sequence cannot be recovered from the watermarked DNA sequence. This study presents reversible data hiding methods based on multilevel histogram shifting to prevent biological mutations, preserve sequence length, increase watermark capacity, and facilitate blind detection/recovery. The main features of our method are as follows. First, we encode a sequence of nucleotide bases with four-character symbols into integer values using the numeric order. Second, we embed multiple bits in each integer value by multilevel histogram shifting of noncircular type (NHS) and circular type (CHS). Third, we prevent the generation of false start/stop codons by verifying whether a start/stop codon is included in an integer value or between adjacent integer values. The results of our experiments confirmed that the NHS- and CHS-based methods have higher watermark capacities than conventional methods in terms of supplementary data used for decoding. Moreover, unlike conventional methods, our methods do not generate false start/stop codons.
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Zhang, Bo Wei, Guo Chang Gu, Xing Zhou Zhang, and Dong Liu. "High Speed Transaction Hardware Channel in Loosely-Coupled Reconfigurable Computing Emulation System." Advanced Materials Research 462 (February 2012): 456–63. http://dx.doi.org/10.4028/www.scientific.net/amr.462.456.
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The loosely-coupled reconfigurable computing model includes the host microprocessor in conjunction with an external stand-alone reconfigurable hardware which takes advantage of low cost in technology and development time. It can work as a fast emulation approach to study reconfigurable computing prototype system. One of the key features of such emulation system is the ability to perform the communication. In this paper, we proposed a high speed hardware channel with direct memory access(DMA) transaction method based on Xilinx ML555 development kit and PCI-express(peripheral component interconnection express) endpoint block IP. Experiments show that both read and write transaction speed in this design meet the theoretical maximum speed.
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Ibrahim, Zuwairie, John A. Rose, Akira Suyama, and Marzuki Khalid. "Experimental implementation and analysis of a DNA computing readout method based on real-time PCR with TaqMan probes." Natural Computing 7, no. 2 (May 15, 2007): 277–86. http://dx.doi.org/10.1007/s11047-007-9047-7.
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Liu, Chanjuan, Yuan Liu, Enqiang Zhu, Qiang Zhang, Xiaopeng Wei, and Bin Wang. "Cross-Inhibitor: a time-sensitive molecular circuit based on DNA strand displacement." Nucleic Acids Research 48, no. 19 (October 12, 2020): 10691–701. http://dx.doi.org/10.1093/nar/gkaa835.
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Abstract Designing biochemical systems that can be effectively used in diverse fields, including diagnostics, molecular computing and nanomachines, has long been recognized as an important goal of molecular programming and DNA nanotechnology. A key issue in the development of such practical devices on the nanoscale lies in the development of biochemical components with information-processing capacity. In this article, we propose a molecular device that utilizes DNA strand displacement networks and allows interactive inhibition between two input signals; thus, it is termed a cross-inhibitor. More specifically, the device supplies each input signal with a processor such that the processing of one input signal will interdict the signal of the other. Biochemical experiments are conducted to analyze the interdiction performance with regard to effectiveness, stability and controllability. To illustrate its feasibility, a biochemical framework grounded in this mechanism is presented to determine the winner of a tic-tac-toe game. Our results highlight the potential for DNA strand displacement cascades to act as signal controllers and event triggers to endow molecular systems with the capability of controlling and detecting events and signals.
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Zang, Wenke, Zhenni Jiang, and Liyan Ren. "Improved Spectral Clustering Based on Density Combining DNA Genetic Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (February 2, 2017): 1750010. http://dx.doi.org/10.1142/s0218001417500100.
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Spectral clustering has become very popular in recent years, due to the simplicity of its implementation as well as the performance of the method, in comparison with other popular ones. But many studies show that clustering results are sensitive to the selection of the similarity graph and its parameters, e.g. [Formula: see text] and [Formula: see text]. To address this issue, inspired by density sensitive similarity measure, we propose an improved spectral graph clustering method that utilizes the similarity measure based on data density combined with DNA genetic algorithms (ISC-DNA-GA), making it increase the distance of the pairs of data in the high density areas, which are located in different spaces. The method can reduce the similarity degree among the pairs of data in the same density region to find the spatial distribution characteristics of the complex data. After computing the Laplacian matrix, we apply DNA-GAs to obtain the clustering centroids and assign all of the points to the centroids, so as to achieve better clustering results. Experiments have been conducted on the artificial and real-world datasets with various multi-dimensions, using evaluation methods based on external clustering criteria. The results show that the proposed method improves the spectral clustering quality, and it is superior to those competing approaches.
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Ney, Peter, Lee Organick, Jeff Nivala, Luis Ceze, and Tadayoshi Kohno. "DNA Sequencing Flow Cells and the Security of the Molecular-Digital Interface." Proceedings on Privacy Enhancing Technologies 2021, no. 3 (April 27, 2021): 413–32. http://dx.doi.org/10.2478/popets-2021-0054.
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Abstract DNA sequencing is the molecular-to-digital conversion of DNA molecules, which are made up of a linear sequence of bases (A,C,G,T), into digital information. Central to this conversion are specialized fluidic devices, called sequencing flow cells, that distribute DNA onto a surface where the molecules can be read. As more computing becomes integrated with physical systems, we set out to explore how sequencing flow cell architecture can affect the security and privacy of the sequencing process and downstream data analysis. In the course of our investigation, we found that the unusual nature of molecular processing and flow cell design contributes to two security and privacy issues. First, DNA molecules are ‘sticky’ and stable for long periods of time. In a manner analogous to data recovery from discarded hard drives, we hypothesized that residual DNA attached to used flow cells could be collected and re-sequenced to recover a significant portion of the previously sequenced data. In experiments we were able to recover over 23.4% of a previously sequenced genome sample and perfectly decode image files encoded in DNA, suggesting that flow cells may be at risk of data recovery attacks. Second, we hypothesized that methods used to simultaneously sequence separate DNA samples together to increase sequencing throughput (multiplex sequencing), which incidentally leaks small amounts of data between samples, could cause data corruption and allow samples to adversarially manipulate sequencing data. We find that a maliciously crafted synthetic DNA sample can be used to alter targeted genetic variants in other samples using this vulnerability. Such a sample could be used to corrupt sequencing data or even be spiked into tissue samples, whenever untrusted samples are sequenced together. Taken together, these results suggest that, like many computing boundaries, the molecular-to-digital interface raises potential issues that should be considered in future sequencing and molecular sensing systems, especially as they become more ubiquitous.
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Xia, Hui. "Research on Asynchronous Parallel Finite Automaton in DPI Based on Cloud Computing." Applied Mechanics and Materials 427-429 (September 2013): 2774–77. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2774.
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We propose a new deep packet inspection mechanism: APFA(Asynchronous Parallel Finite Automaton) instead of the traditional one, which is developed based on detailed analysis over causes of status explosion and flaws of existing solutions. Applying Asynchronous Parallelism and Heuristically Forecast Mechanism, it can take advantage of multi-core platform, offering an effective method to avoid overlapping problem and semantic assaults. Experiments under environment with artificial flows show that the average space APFA occupies is 89.7% less than that of DFA and the average time is 24.4% less cost.
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Zhu, Shenli, Xiaoheng Deng, Wendong Zhang, and Congxu Zhu. "Image Encryption Scheme Based on Newly Designed Chaotic Map and Parallel DNA Coding." Mathematics 11, no. 1 (January 2, 2023): 231. http://dx.doi.org/10.3390/math11010231.
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In this paper, a new one-dimensional fractional chaotic map is proposed and an image encryption scheme based on parallel DNA coding is designed by using the chaotic map. The mathematical model of the new chaotic system combines a sine map and a fraction operation. Compared with some traditional one-dimensional chaotic systems, the new chaotic system has a larger range of chaotic parameters and better chaotic characteristics, which makes it more suitable for applications in information encryption. In addition, an image encryption algorithm based on parallel DNA coding is proposed, which overcomes the shortcoming of common DNA coding-based image encryption algorithms. Parallel computing significantly increases the speed of encryption and decryption algorithms. The initial key of the cryptosystem is designed to be related to the SHA-3 hash value of the plaintext image so that the algorithm can resist a chosen-plaintext attack. Simulation experiments and security analysis results show that the proposed image encryption scheme has good encryption performance and less time overhead, and has strong robustness to noise and data loss attacks, which indicates that the proposed image encryption scheme has good application potential in secure communication applications.
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Wille, Robert, and Rolf Drechsler. "BDD-Based Synthesis of Reversible Logic." International Journal of Applied Metaheuristic Computing 1, no. 4 (October 2010): 25–41. http://dx.doi.org/10.4018/jamc.2010100102.
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Reversible logic became a promising alternative to traditional circuits because of its applications in emerging technologies such as quantum computing, low-power design, DNA computing, or nanotechnologies. As a result, synthesis of the respective circuits is an intensely studied topic. However, most synthesis methods are limited, because they rely on a truth table representation of the function to be synthesized. In this paper, the authors present a synthesis approach that is based on Binary Decision Diagrams (BDDs). The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively. Boolean functions containing more than a hundred of variables can efficiently be synthesized. More precisely, a circuit can be obtained from a given BDD using an algorithm with linear worst case behavior regarding run-time and space requirements. Furthermore, using the proposed approach, theoretical results known from BDDs can be transferred to reversible circuits. Experiments show better results (with respect to the circuit cost) and a significantly better scalability in comparison to previous synthesis approaches.
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Galindo-Murillo, Rodrigo, and Thomas E. Cheatham. "Ethidium bromide interactions with DNA: an exploration of a classic DNA–ligand complex with unbiased molecular dynamics simulations." Nucleic Acids Research 49, no. 7 (March 25, 2021): 3735–47. http://dx.doi.org/10.1093/nar/gkab143.
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Abstract Visualization of double stranded DNA in gels with the binding of the fluorescent dye ethidium bromide has been a basic experimental technique in any molecular biology laboratory for >40 years. The interaction between ethidium and double stranded DNA has been observed to be an intercalation between base pairs with strong experimental evidence. This presents a unique opportunity for computational chemistry and biomolecular simulation techniques to benchmark and assess their models in order to see if the theory can reproduce experiments and ultimately provide new insights. We present molecular dynamics simulations of the interaction of ethidium with two different double stranded DNA models. The first model system is the classic sequence d(CGCGAATTCGCG)2 also known as the Drew–Dickerson dodecamer. We found that the ethidium ligand binds mainly stacked on, or intercalated between, the terminal base pairs of the DNA with little to no interaction with the inner base pairs. As the intercalation at the terminal CpG steps is relatively rapid, the resultant DNA unwinding, rigidification, and increased stability of the internal base pair steps inhibits further intercalation. In order to reduce these interactions and to provide a larger groove space, a second 18-mer DNA duplex system with the sequence d(GCATGAACGAACGAACGC) was tested. We computed molecular dynamics simulations for 20 independent replicas with this sequence, each with ∼27 μs of sampling time. Results show several spontaneous intercalation and base-pair eversion events that are consistent with experimental observations. The present work suggests that extended MD simulations with modern DNA force fields and optimized simulation codes are allowing the ability to reproduce unbiased intercalation events that we were not able to previously reach due to limits in computing power and the lack of extensively tested force fields and analysis tools.
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Ibrahim, Z., Y. Tsuboi, and O. Ono. "Hybridization-Ligation Versus Parallel Overlap Assembly: An Experimental Comparison of Initial Pool Generation for Direct-Proportional Length-Based DNA Computing." IEEE Transactions on Nanobioscience 5, no. 2 (June 2006): 103–9. http://dx.doi.org/10.1109/tnb.2006.875043.
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Raja, Sivakami, and Saravanan Ramaiah. "CCDEA: Consumer and Cloud – DEA Based Trust Assessment Model for the Adoption of Cloud Services." Cybernetics and Information Technologies 16, no. 3 (September 1, 2016): 52–69. http://dx.doi.org/10.1515/cait-2016-0034.
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Abstract Knowing the trust level of cloud service providers is a significant issue in the field of cloud computing for privacy and security reasons. The idea of this paper is to build up a Consumer and Cloud-Data Envelopment Analysis (CCDEA) trust assessment model for evaluating cloud services in two stages. In first stage, the believability index of each cloud Consumer (C) is calculated. The second stage incorporates Cloud-Data Envelopment Analysis (C-DEA) model for the trust assessment of cloud services from the viewpoint of cloud consumers. Several experiments were conducted and the results were analyzed to show the stability of our method in measuring the relative efficiency and effectiveness of cloud services through ranking mechanism.
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Ramasamy, Deepa, Arunagiri Kuha Deva Magendhra Rao, Thangarajan Rajkumar, and Samson Mani. "Experimental and Computational Approaches for Non-CpG Methylation Analysis." Epigenomes 6, no. 3 (August 16, 2022): 24. http://dx.doi.org/10.3390/epigenomes6030024.
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Cytosine methylation adjacent to adenine, thymine, and cytosine residues but not guanine of the DNA is distinctively known as non-CpG methylation. This CA/CT/CC methylation accounts for 15% of the total cytosine methylation and varies among different cell and tissue types. The abundance of CpG methylation has largely concealed the role of non-CpG methylation. Limitations in the early detection methods could not distinguish CpG methylation from non-CpG methylation. Recent advancements in enrichment strategies and high throughput sequencing technologies have enabled the detection of non-CpG methylation. This review discusses the advanced experimental and computational approaches to detect and describe the genomic distribution and function of non-CpG methylation. We present different approaches such as enzyme-based and antibody-based enrichment, which, when coupled, can also improve the sensitivity and specificity of non-CpG detection. We also describe the current bioinformatics pipelines and their specific application in computing and visualizing the imbalance of CpG and non-CpG methylation. Enrichment modes and the computational suites need to be further developed to ease the challenges of understanding the functional role of non-CpG methylation.
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Xie, Xin, and Yu-Chi Chen. "Decentralized Data Aggregation: A New Secure Framework Based on Lightweight Cryptographic Algorithms." Wireless Communications and Mobile Computing 2021 (April 14, 2021): 1–12. http://dx.doi.org/10.1155/2021/5565663.
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Blockchain has become very popular and suitable to the Internet of Things (IoT) field due to its nontamperability and decentralization properties. The number of IoT devices and leaders (who own IoT devices) is increased exponentially, and thus, data privacy and security are undoubtedly significant concerns. In this paper, we summarize some issues for the BeeKeeper system, a blockchain-based IoT system, proposed by Zhou et al., and then aim for presenting an improved solution for decentralized data aggregation (DDA) on IoT. Firstly, we formally state the security requirements of DDA. Secondly, we propose our basic DDA system by using secret sharing to improve its efficiency and smart contracts as the computing processors. Moreover, the proposed full-fledged system achieves data sharing (e.g., a leader to access data of others’ devices), which is realized by using local differential privacy and cryptographic primitives such as token-based encryption. Finally, to show the feasibility, we provide some implementations and experiments for the DDA systems.
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Bangert, John A., and James P. Cunningham. "Preliminary evaluation of Doppler-determined pole positions computed using World Geodetic System 1984." Symposium - International Astronomical Union 128 (1988): 131–40. http://dx.doi.org/10.1017/s0074180900119394.
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Since 1975, the Defense Mapping Agency (DMA) has been determining polar motion as a byproduct of computing the precise orbits of the Navy Navigation Satellite System (NNSS) satellites. The orbit determination process currently incorporates the NSWC 9Z2 terrestrial reference system and the NWL 10E-1 Earth Gravitational Model (EGM) to degree 28 and order 27. The World Geodetic System 1984 (WGS 84), developed by DMA, will replace the NSWC 9Z2/10E-1 system for NNSS orbit determination. The WGS 84 EGM to degree and order 41 will be utilized. This paper presents the results of two experiments which compared pole positions computed in the two systems. These comparisons indicate that use of WGS 84 improves agreement between pole position values resulting from the Nova-class satellite orbit solutions and the values determined by other modern techniques.