Background: The beauty and power of the genome editing mechanism, CRISPR Cas9 endonuclease system, lies in the fact that it is RNA-programmable such that Cas9 can be guided to any genomic loci complementary to a 20-nt...
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Background: The beauty and power of the genome editing mechanism, CRISPR Cas9 endonuclease system, lies in the fact that it is RNA-programmable such that Cas9 can be guided to any genomic loci complementary to a 20-nt RNA, single guide RNA (sgRNA), to cleave double stranded DNA, allowing the introduction of wanted mutations. Unfortunately, it has been reported repeatedly that the sgRNA can also guide Cas9 to off-target sites where the DNA sequence is homologous to sgRNA. Results: Using human genome and Streptococcus pyogenes Cas9 (SpCas9) as an example, this article mathematically analyzed the probabilities of off-target homologies of sgRNAs and discovered that for large genome size such as human genome, potential off-target homologies are inevitable for sgRNA selection. A highly efficient computationl algorithm was developed for whole genome sgRNA design and off-target homology searches. By means of a dynamically constructed sequence-indexed database and a simplified sequence alignment method, this algorithm achieves very high efficiency while guaranteeing the identification of all existing potential off-target homologies. Via this algorithm, 1,876,775 sgRNAs were designed for the 19,153 human mRNA genes and only two sgRNAs were found to be free of off-target homology. Conclusions: By means of the novel and efficient sgRNA homology search algorithm introduced in this article, genome wide sgRNA design and off-target analysis were conducted and the results confirmed the mathematical analysis that for a sgRNA sequence, it is almost impossible to escape potential off-target homologies. Future innovations on the CRISPR Cas9 gene editing technology need to focus on how to eliminate the Cas9 off-target activity.
Numerical algorithm for solving dynamic problems of the theory of viscoelastic medium of Kelvin-Voigt is worked out on the basis of Ivanov's method of constructing finite difference schemes with prescribed dissipa...
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ISBN:
(纸本)9783319202396;9783319202389
Numerical algorithm for solving dynamic problems of the theory of viscoelastic medium of Kelvin-Voigt is worked out on the basis of Ivanov's method of constructing finite difference schemes with prescribed dissipative properties. In one-dimensional problem the results of computations are compared with the exact solution, describing the propagation of plane monochromatic waves. When solving two-dimensional problems, the total approximation method based on the splitting of the system with respect to the spatial variables is applied. The algorithm is tested on solving the problem of traveling surface waves. For illustration of the method, the numerical solution of Lamb's problem about instantaneous action of concentrated force on the boundary of a half-plane is represented in viscoelastic formulation.
This article explores the computational intricacies of H. Rutishauser's Quotient-Difference (Q-D) algorithm and C programming code, a revolutionary advancement in polynomial analysis. Our specific focus is on cubi...
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This article explores the computational intricacies of H. Rutishauser's Quotient-Difference (Q-D) algorithm and C programming code, a revolutionary advancement in polynomial analysis. Our specific focus is on cubic polynomials featuring absolute, distinct non-zero real roots. Emphasizing the algorithm's distinctive capability to simultaneously approximate all zeros independently of external data. Notably, it proves invaluable in diverse domains, such as determining continuous fraction representations for meromorphic functions and serving as a powerful tool in complex analysis for the direct localization of poles and zeros. To bring this innovation into practice, the article introduces a meticulously crafted C language program, complete with a comprehensive algorithm and flowchart. Supported by illustrative examples, this implementation underscores the algorithm's robustness and effectiveness across various real-world scenarios.
This paper presents a new computational algorithm for reliability inference with dynamic hybrid Bayesian network. It features a component-based algorithm and structure to represent complex engineering systems characte...
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ISBN:
(纸本)9781479949434
This paper presents a new computational algorithm for reliability inference with dynamic hybrid Bayesian network. It features a component-based algorithm and structure to represent complex engineering systems characterized by discrete functional states (including degraded states), and models of underlying physics of failure, with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using pre-computation and dynamic programming for real-time monitoring of system health. The scope of this research includes new modeling approach, computation algorithm, and an example application for on-line System Health Management.
Techniques for detecting leakage in water pipe networks have been developed worldwide in order to reduce unaccounted-for water quantity and enhance the reliability of the pipe networks. In this paper computational alg...
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Techniques for detecting leakage in water pipe networks have been developed worldwide in order to reduce unaccounted-for water quantity and enhance the reliability of the pipe networks. In this paper computational algorithms utilizing principal component analysis (PCA) were developed so that the algorithms can be used in a realistic water pipe network management situation in which the daily flow data of a district metered area (DMA) are needed to be verified for a possible relation with a water leak incident. For the improvement of the algorithms, it was assumed that a manager of a water pipe network uses these algorithms every day to test if yesterday's inflow data to a DMA were an outlier according to the PCA computational algorithm. The flow data used in this study were analyzed to determine the best flow data size for the field use of the developed PCA algorithm. For various flow data sets, which were defined as the smaller sizes of the flow measured in days than the whole data set available, a reference modeling for the PCA was applied to calculate the model outliers by moving the flow data sets day by day. For each DMA the effective outlier detection rates (EODRs) were calculated for the whole range of the defined time windows. The maximum effective outlier detection rate for a DMA was obtained as the maximum of the calculated EODRs. The process and results of the sensitivity analyses of the model parameters were used to suggest guidance on how to determine model parameters for a given flow data.
Recently, hesitant fuzzy sets (HFSs) have been used extensively in time series forecasting. HFSs have inherent characteristics of addressing problem of non-stochastic hesitancy that is developed as a result of the ava...
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Recently, hesitant fuzzy sets (HFSs) have been used extensively in time series forecasting. HFSs have inherent characteristics of addressing problem of non-stochastic hesitancy that is developed as a result of the availability of numerous techniques for fuzzification of time series data. In the present study, we have developed an HFS-based computational method for weighted fuzzy time series (WFTS) forecasting. The proposed method addresses the three main issues of appropriate partitioning of the universe of discourse (UOD) into unequal-length intervals, inclusion of hesitancy during fuzzification of time series data, recurrence, and weighting of fuzzy logical relation (FLR) in fuzzy time series forecasting. The proposed method uses a non-parametric clustering approach of adaptive radius clustering for accurate partitioning of UOD and HFS for inclusion of hesitancy in time series during process of fuzzification. The recurrence and weighting of FLRs are handled using Markov weights, which are then subsequently optimized by utilizing the popular swarm intelligence technique of grey wolf optimization. A simple computational method is provided that incorporates the optimized weights, thus simplifying the forecasting process. The proposed WFTS forecasting method is implemented in the Python programming language to forecast benchmark time series data of the University of Alabama and financial time series data of Taiwan stock exchange (TAIEX), market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. The model's performance is measured by means of root-mean-square error (RMSE), and its reduced amount demonstrates the model's outperformance in forecasting of three diversified time series data taken in the study.
A mean-field social control problem for uncertain nonlinear stochastic systems is investigated by using a robust static output feedback (SOF) strategy. First, the problem in the single decision maker case is investiga...
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A mean-field social control problem for uncertain nonlinear stochastic systems is investigated by using a robust static output feedback (SOF) strategy. First, the problem in the single decision maker case is investigated in terms of guaranteed cost control approaches to derive suboptimal conditions at the supremum of the cost function. The Karush-Kuhn-Tucker (KKT) condition is used to derive the necessary conditions which are expressed as a large stochastic combined matrix equation (SCME). Second, the preliminary results in the single decision maker case are used to study the Pareto optimal strategy in a cooperative game. As our main contribution, we derive the high-order centralised strategies and the low-order decentralised strategies, respectively, for the cooperative game. In order to avoid the difficulty of higher-order dimensional problem related to SCMEs, a new reduced-order decomposition numerical scheme by means of Newton's method is developed. The computation for designing the proposed strategy set can be performed in low dimension, even when the number of decision makers approachs to infinity. Moreover, the degradation of the cost function is rigorously evaluated by comparing the centralised strategy set with the proposed strategy set. Finally, several numerical experiments are conducted to demonstrate the usefulness and effectiveness of the proposed strategy set.
Finding the history of a groundwater contaminant plume from final measurements is an ill-posed problem and, consequently, its solution is extremely sensitive to errors in the input data. In this paper, we study this p...
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Finding the history of a groundwater contaminant plume from final measurements is an ill-posed problem and, consequently, its solution is extremely sensitive to errors in the input data. In this paper, we study this problem mathematically. So, firstly, existence and uniqueness theorems of a quasi-solution in an appropriate class of admissible initial data are given. Secondly, in order to overcome the ill-posedness of the problem and also approximate the quasi-solution, two approaches (computational and iterative algorithms) are provided. In the computational algorithm, the finite element method and TSVD regularization are applied. This method is tested by two numerical examples. The results reveal the efficiency and applicability of the proposed method. Also, in order to construct the iterative methods, an explicit formula for the gradient of the cost functional J is given. This result helps us to construct two iterative methods, i.e., the conjugate gradient algorithm and Landweber iteration algorithm. We prove the Lipschitz continuity of the gradient of the cost functional, monotonicity and convergence of the iterative methods. At the end of the paper, a numerical example is given to show the validation of the iterative algorithms.
Backward erosion piping (BEP) is a complex degradation mechanism in geotechnical flood protection infrastructure (GFPI) that is still relatively less understood, particularly when considering its time -dependent featu...
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Backward erosion piping (BEP) is a complex degradation mechanism in geotechnical flood protection infrastructure (GFPI) that is still relatively less understood, particularly when considering its time -dependent features. This manuscript presents a novel dual random lattice modeling approach for three-dimensional simulation of BEP, with a focus on its evolution over time. The key novelty of this presented framework is twofold: (1) we propose and incorporate a novel constitutive relationship for computation of time -dependent soil erosion based on the theory of rate processes, and (2) we devise an algorithm for calculation of coupled degradation of the dual lattices for accurate computation of 3-D hydraulic gradients. The constitutive relationship was developed from fundamental granular physics, and brings the potential to provide deeper fundamental physical understanding of the phenomenon. The capabilities of the modeling framework are investigated by comparison with available laboratory experiments which illustrates good agreement in the spatial advancement of piping erosion, pipe progression speeds, as well as the evolution of local gradients. To the best knowledge of the authors, the presented model is the first to be able to capture all of the aforementioned features when simulating BEP.
Chronic heart failure (CHF) is the primary cause of death among patients with cardiovascular diseases, representing the advanced stage in the development of several cardiovascular conditions. Zhenwu decoction (ZWD) ha...
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Chronic heart failure (CHF) is the primary cause of death among patients with cardiovascular diseases, representing the advanced stage in the development of several cardiovascular conditions. Zhenwu decoction (ZWD) has gained widespread recognition as an efficacious remedy for CHF due to its potent therapeutic properties and absence of adverse effects. Nevertheless, the precise molecular mechanisms underlying its actions remain elusive. This study endeavors to unravel the intricate pharmacological underpinnings of five herbs within ZWD concerning CHF through an integrated approach. Initially, pertinent data regarding ZWD and CHF were compiled from established databases, forming the foundation for constructing an intricate network of active component-target interactions. Subsequently, a pioneering method for evaluating node significance was formulated, culminating in the creation of core functional association space (CFAS). To discern vital components, a novel dynamic programming algorithm was devised and used to determine the core component group (CCG) within the CFAS. Enrichment analysis of the CCG targets unveiled the potential coordinated molecular mechanisms of ZWD, illuminating its capacity to ameliorate CHF by modulating genes and related signaling pathways involved in pathological remodeling. Notable pathways encompass PI3K-Akt, diabetic cardiomyopathy, cAMP and MAPK signaling. Concluding the computational analyses, in vitro experiments were executed to assess the effects of vanillic acid, paradol, 10-gingerol and methyl cinnamate. Remarkably, these compounds demonstrated efficacy in reducing the production of ANP and BNP within isoprenaline-induced AC 16 cells, further validating their potential therapeutic utility. This investigation underscores the efficacy of the proposed model in enhancing the precision and reliability of CCG selection within ZWD, thereby presenting a novel avenue for mechanistic inquiries, compound refinement and the secondary developme
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