Using radial basis function networks and significance testing to select effective siRNA sequences

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. We propose a method for selecting effective siRNA target sequences by using a radial basis function (RBF) network and statistical significance analysis for a large number of known effective and ineffective siRNAs. The siRNA classification is first carried out by using the RBF network and then the preferred and unpreferred nucleotides for effective siRNAs at individual positions are chosen by significance testing. The gene degradation measure is defined as a score based on the preferred and unpreferred nucleotides. The effectiveness for the proposed method was confirmed by evaluating effective and ineffective siRNAs for the recently reported genes (15 genes, 196 sequences) and comparing the scores thus obtained with those obtained using other scoring methods. Since the score is closely correlated with the degree of gene degradation, it can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed method may be applicable for many other genes. It will therefore be useful for selecting siRNA sequences in mammalian genes.     

Using radial basis function networks and significance testing to select effective siRNA sequences

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. We propose a method for selecting effective siRNA target sequences by using a radial basis function (RBF) network and statistical significance analysis for a large number of known effective and ineffective siRNAs. The siRNA classification is first carried out by using the RBF network and then the preferred and unpreferred nucleotides for effective siRNAs at individual positions are chosen by significance testing. The gene degradation measure is defined as a score based on the preferred and unpreferred nucleotides. The effectiveness for the proposed method was confirmed by evaluating effective and ineffective siRNAs for the recently reported genes (15 genes, 196 sequences) and comparing the scores thus obtained with those obtained using other scoring methods. Since the score is closely correlated with the degree of gene degradation, it can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed method may be applicable for many other genes. It will therefore be useful for selecting siRNA sequences in mammalian genes.     

Discriminant analysis of promoter regions in Escherichia coli sequences

We have previously developed a general method based on the statistical technique of discriminant analysis to predict splice junctions in eukaryotic mRNA sequences [Nakata, K., Kanehisa, M. and DeLisi, C. (1985) Nucleic Acids Res., 13, 5327-5340]. In order to evaluate further applicability of this method, we now analyze the promoter region of Escherichia coli sequences. The attributes used for discrimination include the accuracy of consensus sequence patterns measured by the perceptron algorithm, the thermal stability map, the base composition and the Calladine-Dickerson rules for helical twist angle, roll angle, torsion angle and propeller twist angle. When applied to selected E. coli sequences in the GenBank database, the method correctly identifies 75% of the true promoter regions.     

Batch RNAi selector: a standalone program to predict specific siRNA candidates in batches with enhanced sensitivity

RNA interference (RNAi) is a popular and effective method for silencing gene expression. siRNAs should be gene-specific and effective to achieve specific and potent gene silencing. However, most currently available siRNA design programs are web-based programs that either require each sequence be submitted individually, making large-batch analyses difficult to conduct, or only provide limited options for searching off-target candidates (e.g. NCBI-BLAST). We have developed a stand-alone, enhanced RNAi design program that overcomes these shortcomings. We have implemented WU-BLAST, FASTA and SSEARCH homology searches for siRNA candidates to improve gene specific siRNA selection and to identity siRNA candidates that could lead to off-target gene silencing. We also included many new features such as siRNA score calculation and calculation of siRNA internal stability to help select highly potent siRNAs. This program is freely available for academic and commercial use (), and can be installed and run on any Linux machine. Our program automates the search for siRNAs and the resulting data files including a list of siRNA primers with scores and database search results for each siRNA candidate are stored locally for easy retrieval and inspection when needed.     

www.rnaworkbench.com: A new program for analyzing RNA interference

RNA interference (RNAi) has become an important tool to study and utilize gene silencing by introducing short interfering RNA (siRNA). In order to predict the most efficient siRNAs, a new software tool, RNA Workbench (RNAWB), has been designed and is freely available (after registration) on http://www.rnaworkbench.com. In addition to the standard selection rules, RNAWB includes the possibility of statistical analyses of the applied selection rules (criteria). The role of RNA secondary structures in the RNA interference process as well as the application of sequence rules are discussed to show the applicability of the software.     

New shape-based auroral oval segmentation driven by LLS-RHT

A new method that exploits shape to localize the auroral oval in satellite imagery is introduced. The core of the method is driven by the linear least-squares (LLS) randomized Hough transform (RHT). The LLS-RHT is a new fast variant of the RHT suitable when not all necessary conditions of the RHT can be satisfied. The method is also compared with the three existing methods for aurora localization, namely the histogram-based k-means [C.C. Hung, G. Germany, K-means and iterative selection algorithms in image segmentation, IEEE Southeastcon 2003 (Session 1: Software Development)], adaptive thresholding [X. Li, R. Ramachandran, M. He, S. Movva, J.A. Rushing, S.J. Graves, W. Lyatsky, A. Tan, G.A. Germany, Comparing different thresholding algorithms for segmenting auroras, in: Proceedings of the International Conference on Information Technology: Coding and Computing, vol. 6, 2004, pp. 594-601], and pulse-coupled neural network-based [G.A. Germany, G.K. Parks, H. Ranganath, R. Elsen, P.G. Richards, W. Swift, J.F. Spann, M. Brittnacher, Analysis of auroral morphology: substorm precursor and onset on January 10, 1997, Geophys. Res. Lett. 25 (15) (1998) 3042-3046] methods. The methodologies and their performance on real image data are both considered in the comparison. These images include complications such as random noise, low contrast, and moderate levels of key obscuring phenomena.     

Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques

Intelligent techniques have been applied in a range of industrial environments [Meziane F, Vadera S, Kobbacy K, Proudlove N. Intelligent systems in manufacturing: current developments and future prospects. Integrated Manuf Syst 2000;11(4):218–38; Stephanopoulos G, Han C. Intelligent systems in process engineering: a review. Comput Chem Eng, 1996;20 (6–7):743–91; Johnston AB, Maguire LP, McGinnity TM. Using business improvement techniques to inform the optimisation of production cycle time: an industrial case study. Proceedings of the IEEE SMC UK-RI Chapter conference 2004 on intelligent cybernetic systems. September 7–8, 2004 ISSN:1744–9189; Proudlove NC, Vadera S, Kobbacy KAH. Intelligent management systems in operations: A review. J Oper Res Soc, 1998;49(7):682–99] although their implementation is not the first choice of many process engineers. In contrast process engineers in a diverse range of manufacturing environments regularly deploy business improvement techniques, such as the six-sigma methodology. Such techniques aim to control and subsequently identify the relationship between the process inputs and outputs so that a process engineer can more accurately predict how the process output shall perform based on the system inputs. Factors such as cost reduction, automatic process control or simply process prediction may be the defining factors in establishing prediction models.     

KTBoost预测模型的改进及应用

针对目前KTBoost预测模型中存在的精度低、拟合效果较差的问题,给出一种改进的KTBoost预测模型.首先提出了OGWO算法,使用反正切函数对传统灰狼优化算法(GWO)中的收敛因子进行优化,以解决算法中的无效迭代问题,然后运用OGWO算法对KTBoost模型中的超参数进行优化,从而提高模型预测的精度;最后,为了验证模...     

OptiRNAi, an RNAi design tool

RNA interference (RNAi), a recently developed reverse genetics tool, has many advantages compared to traditional gene knockout methods. Appropriate selection of double stranded RNAs identical to a specific region(s) of the target gene is critical for the successful implementation of this technology. Recently, Elbashir et al. [Methods 26 (2002) 199] has established empirical criteria for siRNA sequence selection that significantly improved the success rate for RNAi attempts. We have developed OptiRNAi, a computational tool, which uses the Elbashir et al. criteria to predict appropriate target sequences for siRNA production. Specificity of these siRNAs for the target of interest can then be assessed by the investigator using the embedded Blast search engine optimized for RNAi design. Thus, OptiRNAi is an efficient and user friendly tool for RNAi design based on criteria that are more stringent than other available tools.     

基于DP-RFR的多模型软测量建模

实际工业过程往往是一个多工况、非线性的大规模复杂系统,使得单一模型软测量建模方法难以充分挖掘数据信息。针对这一问题提出了一种基于密度峰(Density Peak,DP)聚类和随机森林回归(Random Forest Regression,RFR)的多模型软测量建模方法,从而对主导变量进行估计。首先,利用DP聚类算法对训练数据进行划分;其次,采用RFR方法建立各子类的回归子模型;最后采用开关切换的方法进行多模型融合。将提出方法应用于TE过程和丁烷蒸馏过程的软测量建模中,分别对产物G含量和丙烷含量进行估计。仿真结果表明估计精度得到提高,证明该方法是有效的。     

Sync your data: update propagation for heterogeneous protein databases

The traditional model of bench (wet) chemistry in many life sciences domain is today actively complimented by computer-based discoveries utilizing the growing number of online data sources. A typical computer-based discovery scenario for many life scientists includes the creation of local caches of pertinent information from multiple online resources such as Swissprot [Nucleic Acid Res. 1(28), 45–48 (2000)], PIR [Nucleic Acids Res. 28(1), 41–44 (2000)], PDB [The Protein DataBank. Wiley, New York (2003)], to enable efficient data analysis. This local caching of data, however, exposes their research and eventual results to the problems of data staleness, that is, cached data may quickly be obsolete or incorrect, dependent on the updates that are made to the source data. This represents a significant challenge to the scientific community, forcing scientists to be continuously aware of the frequent changes made to public data sources, and more importantly aware of the potential effects on their own derived data sets during the course of their research. To address this significant challenge, in this paper we present an approach for handling update propagation between heterogeneous databases, guaranteeing data freshness for scientists irrespective of their choice of data source and its underlying data model or interface. We propose a middle-layer–based solution wherein first the change in the online data source is translated to a sequence of changes in the middle-layer; next each change in the middle-layer is propagated through an algebraic representation of the translation between the source and the target; and finally the net-change is translated to a set of changes that are then applied to the local cache. In this paper, we present our algebraic model that represents the mapping of the online resource to the local cache, as well as our adaptive propagation algorithm that can incrementally propagate both schema and data changes from the source to the cache in a data model independent manner. We present a case study based on a joint ongoing project with our collaborators in the Chemistry Department at UMass-Lowell to explicate our approach.     

Prediction of carbon-13 NMR chemical shift of alkanes with rooted path vector

Systematic studies were further made on graph theory in quantitative structure-spectrum relationships (QSSR) for various areas of spectroscopies. Chemical shifts (CS) in alkanes for carbon-13 nuclear magnetic resonance (13C NMR) were well correlated with a set of novel molecular graph indices, called the rooted path vector of various lengths, as several multivariate regression equations as following:CS=3.022+5.336P1+7.356P2-1.648P3+0.83859P4+0.210P5-0.138P6-0.506P7+2.486P8-1.669P9; n=402, m=9, R=0.944, RCV=0.9413, S.D.=3.333, F=358.343, U=35833.211, Q=4355.422 for all types (primary, secondly, tertiary, quaternary as well as methane) of carbon atoms CS=0.983+6.811P1+7.584P2-2.029P3+0.809P4+0.106P5+0.043P6-0.124P7+1.715P8-1.101P9; n=374, m=9, R=0.975, RCV=0.9737, S.D.=2.303, F=773.372, U=36912.109, Q=1930.363 for primary, secondly, tertiary (including methane) carbon atoms; and CS=27.819+2.351P2+0.549P3-0.440P4+0.170P5-0.050P6; n=27, m=5, R=0.992, RCV=0.9674, S.D.=0.324, F=265.418, U=138.891, Q=2.198 for quaternary carbon atoms, respectively. Quite good estimation and prediction results were obtained from the quantitative molecular modeling and the performance of multiple linear regression (MLR) equations were tested to work well through cross-validation (CV) with the leave-one-out (LOO) procedure.     

Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China)

Infectious diarrhea is an important public health problem around the world. Meteorological factors have been strongly linked to the incidence of infectious diarrhea. Therefore, accurately forecast the number of infectious diarrhea under the effect of meteorological factors is critical to control efforts. In recent decades, development of artificial neural network (ANN) models, as predictors for infectious diseases, have created a great change in infectious disease predictions. In this paper, a three layered feed-forward back-propagation ANN (BPNN) model trained by Levenberg–Marquardt algorithm was developed to predict the weekly number of infectious diarrhea by using meteorological factors as input variable. The meteorological factors were chosen based on the strongly relativity with infectious diarrhea. Also, as a comparison study, the support vector regression (SVR), random forests regression (RFR) and multivariate linear regression (MLR) also were applied as prediction models using the same dataset in addition to BPNN model. The 5-fold cross validation technique was used to avoid the problem of overfitting in models training period. Further, since one of the drawbacks of ANN models is the interpretation of the final model in terms of the relative importance of input variables, a sensitivity analysis is performed to determine the parametric influence on the model outputs. The simulation results obtained from the BPNN confirms the feasibility of this model in terms of applicability and shows better agreement with the actual data, compared to those from the SVR, RFR and MLR models. The BPNN model, described in this paper, is an efficient quantitative tool to evaluate and predict the infectious diarrhea using meteorological factors.     

On-the-fly Workload Prediction and Redistribution in the Distributed Timed Model Checker Zeus

In this work we present the on-the-fly workload prediction and redistribution techniques used in Zeus [Braberman, V., A. Olivero and F. Schapachnik, Zeus: A distributed timed model checker based on kronos, in: Workshop on Parallel and Distributed Model Checking, affiliated to CONCUR 2002 (13^th International Conference on Concurrency Theory), ENTCS 68 (2002), Braberman, V., A. Olivero and F. Schapachnik, Issues in Distributed Model-Checking of Timed Automata: building zeus, to appear in International Journal of Software Tools for Technology Transfer (2004)], a Distributed Model Checker that evolves from the tool Kronos [Daws, C., A. Olivero, S. Tripakis and S. Yovine, The Tool KRONOS, in: Proceedings of Hybrid Systems III, LNCS 1066 (1996), pp. 208–219].After reviewing why it is so hard to have good speedups in distributed timed model checking, we present the methods used to get promising results when verifying reachability properties over timed automata [Alur, R. and D. L. Dill, A theory of timed automata, Theoretical Computer Science 126 (1994) 183–235].     

Multi-modal estimation of collinearity and parallelism in natural image sequences

In this paper we address the statistics of second-order relations of feature vectors derived from image sequences. We compute the individual vector components corresponding to the visual modalities orientation, contrast transition, optic flow, and colour by conventional low-level early vision algorithms. As a main result, we observe that collinear (or parallel) line pairs are, with very great likelihood, also associated with other identical features, for example sharing the same flow pattern, or colour or even sharing multiple feature combinations. It is known that low level processes, such as edge detection, optic flow estimation and stereo are ambiguous. Our results provide support for the assumption that the ambiguity of low level processes can be substantially reduced by integrating information across visual modalities. Furthermore, the attempt to model the application of Gestalt laws in computer vision systems based on statistical measurements, as suggested recently by some researchers (Krüger N 1998 Neural Process. Lett. 8 117-29; Elder H and Goldberg R M 1998 Perception Suppl. 27 11; Sigman M, Cecchi G A, Gilbert C D and Magnasco M O 200.1 Proc. Natl Acad. Sci. USA 98 1935-49; Geisler W S, Perry J S, Super B J and Gallogly D P 2002 Vis. Res. 41 711-24), gets further support and the results in this paper suggest formulation of Gestalt principles in artificial vision systems in a multi-modal way.     

Predicting intrusion goal using dynamic Bayesian network with transfer probability estimation

Predicting the intentions of an observed agent and taking corresponding countermeasures is the essential part for the future proactive intrusion detection systems (IDS) as well as intrusion prevention systems (IPS). In this paper, an approach of dynamic Bayesian network with transfer probability estimation was developed to predict whether the goal of system call sequences is normal or not, with early-warnings being launched, so as to ensure that some appropriate countermeasures could be taken in advance. Since complete set of system call state transfer can hardly be built in real environments, the empirical results show that the newly emerging system call transfer would have great impact on the prediction performance if we straightly use dynamic Bayesian network without transfer probability estimation. Therefore, we estimate the probability of new state transfer to predict the goals of system call sequences together with those in conditional probability table (CPT). It surmounts the difficulties of manually selecting compensating parameters with dynamic Bayesian network approach [Feng L, Guan X, Guo S, Gao Y, Liu P. Predicting the intrusion intentions by observing system call sequences. Computers & Security 2004; 23/3: 241–252] and obviously makes our prediction model more applicable. The University of New Mexico (UNM) and KLINNS data sets were analyzed and the experimental results show that it performs very well for predicting the goals of system call sequences with high accuracy and furthermore dispenses with much more manual work for selecting compensating parameters.     

A note on variable threshold concept lattices: Threshold-based operators are reducible to classical concept-forming operators

The present paper deals with formal concept analysis of data with fuzzy attributes. We clarify several points of a new approach of [S.Q. Fan, W.X. Zhang, Variable threshold concept lattice, Inf. Sci., accepted for publication] which is based on using thresholds in concept-forming operators. We show that the extent- and intent-forming operators from [S.Q. Fan, W.X. Zhang, Inf. Sci., accepted for publication] can be defined in terms of basic fuzzy set operations and the original operators as introduced and studied e.g. in [R. Belohlavek, Fuzzy Galois connections, Math. Logic Quarterly 45 (4) (1999) 497-504; R. Belohlavek, Concept lattices and order in fuzzy logic, Ann. Pure Appl. Logic 128 (2004) 277-298; S. Pollandt, Fuzzy Begriffe, Springer-Verlag, Berlin/Heidelberg, 1997]. As a consequence, main properties of the new operators from [S.Q. Fan, W.X. Zhang, Inf. Sci., accepted for publication], including the properties studied in [S.Q. Fan, W.X. Zhang, Inf. Sci., accepted for publication], can be obtained as consequences of the original operators from [R. Belohlavek, 1999; R. Belohlavek, 2004; S. Pollandt, 1997].     

Properties of a Variational Model for Video Inpainting

We consider a variational model analyzed in March and Riey (Inverse Probl Imag 11(6): 997–1025, 2017) for simultaneous video inpainting and motion estimation. The model has applications in the field of recovery of missing data in archive film materials. A gray-value video content is reconstructed in a spatiotemporal region where the video data is lost. A variational method for motion compensated video inpainting is used, which is based on the simultaneous estimation of apparent motion in the video data. Apparent motion is mathematically described by a vector field of velocity, denoted optical flow, which is estimated through gray-value variations of the video data. The functional to be minimized is defined on a space of vector valued functions of bounded variation and the relaxation method of the Calculus of Variations is used. We introduce in the functional analyzed in March and Riey(Inverse Probl Imag 11(6): 997–1025, 2017) a suitable positive weight, and we show that diagonal minimizing sequences of the functional converge, up to subsequences as the weight tends to infinity, to minimizers of an appropriate limit functional. Such a limit functional is the relaxed version of a functional, modified with suitable improvements, proposed by Lauze and Nielsen (2004) and which permits an accurate joint reconstruction both of the optical flow and of the video content.