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PubMed2005

pSAT vectors: a modular series of plasmids for autofluorescent protein tagging and expression of multiple genes in plants.

Autofluorescent protein tags represent one of the major and, perhaps, most powerful tools in modern cell biology for visualization of various cellular processes in vivo. In addition, advances in confocal microscopy and the development of autofluorescent proteins with different excitation and emission spectra allowed their simultaneous use for detection of multiple events in the same cell. Nevertheless, while autofluorescent tags are widely used in plant research, the need for a versatile and comprehensive set of vectors specifically designed for fluorescent tagging and transient and stable expression of multiple proteins in plant cells from a single plasmid has not been met by either the industrial or the academic communities. Here, we describe a new modular satellite (SAT) vector system that supports N- and C-terminal fusions to five different autofluorescent tags, EGFP, EYFP, Citrine-YFP, ECFP, and DsRed2. These vectors carry an expanded multiple cloning site that allows easy exchange of the target genes between different autofluorescence tags, and expression of the tagged proteins is controlled by constitutive promoters, which can be easily replaced with virtually any other promoter of interest. In addition, a series of SAT vectors has been adapted for high throughput Gateway recombination cloning. Furthermore, individual expression cassettes can be assembled into Agrobacterium binary plasmids, allowing efficient transient and stable expression of multiple autofluorescently tagged proteins from a single vector following its biolistic delivery or Agrobacterium-mediated genetic transformation.

Base Sequence↗

PubMed2002

A novel single tetracycline-regulative adenoviral vector for tumor-specific Bax gene expression and cell killing in vitro and in vivo.

Using a binary adenoviral system, we recently showed that the human telomerase reverse transcriptase (hTERT) promoter induces tumor-specific Bax gene expression. However, the strong cytotoxicity of Bax and other pro-apoptotic genes to packaging 293 cells has so far hindered construction of the desired single adenoviral vectors expressing toxic genes. We report here the construction of a single bicistronic adenoviral vector for tumor-specific Bax expression. The vector (Ad/gBax) utilizes the Tet-Off system and expresses a GFP/Bax fusion protein for easy detection. The hTERT promoter drives the expression of tTA, a transactivator capable of binding to TRE (tetracycline-responsive element) in the absence of tetracycline, which in turn induces expression of the GFP-Bax gene. The addition of tetracycline in 293 cells blocks the binding of tTA to TRE and substantially inhibits GFP-Bax expression and toxicity, thus allowing the packaging and production of Ad/gBax. Our data show that Ad/gBax could drive the high expression of GFP-Bax in tumor cells but not in normal cells and mouse tissues. Furthermore, the expression of GFP-Bax fusion protein elicited tumor-specific apoptosis in a variety of human cancer cells in vitro and in vivo at a level comparable to that induced by the binary system. Thus, Ad/gBax may become a potent therapeutic agent for the treatment of cancers.

Adenoviridae↗

PubMed2006

Subcellular localization of interacting proteins by bimolecular fluorescence complementation in planta.

Bimolecular fluorescence complementation (BiFC) represents one of the most advanced and powerful tools for studying and visualizing protein-protein interactions in living cells. In this method, putative interacting protein partners are fused to complementary non-fluorescent fragments of an autofluorescent protein, such as the yellow spectral variant of the green fluorescent protein. Interaction of the test proteins may result in reconstruction of fluorescence if the two portions of yellow spectral variant of the green fluorescent protein are brought together in such a way that they can fold properly. BiFC provides an assay for detection of protein-protein interactions, and for the subcellular localization of the interacting protein partners. To facilitate the application of BiFC to plant research, we designed a series of vectors for easy construction of N-terminal and C-terminal fusions of the target protein to the yellow spectral variant of the green fluorescent protein fragments. These vectors carry constitutive expression cassettes with an expanded multi-cloning site. In addition, these vectors facilitate the assembly of BiFC expression cassettes into Agrobacterium multi-gene expression binary plasmids for co-expression of interacting partners and additional autofluorescent proteins that may serve as internal transformation controls and markers of subcellular compartments. We demonstrate the utility of these vectors for the analysis of specific protein-protein interactions in various cellular compartments, including the nucleus, plasmodesmata, and chloroplasts of different plant species and cell types.

Biological Assay↗

PubMed2004

Limits of recognition for simple vapor mixtures determined with a microsensor array.

The "limit of recognition" (LOR) has been defined as the minimum concentration at which reliable individual vapor recognition can be achieved with a multisensor array, and methodology for determining the LORs of individual vapors probabilistically on the basis of sensor array response patterns has been reported. This article explores the problems of defining and evaluating LORs for vapor mixtures in terms of the absolute and relative component vapor concentrations, where the mixture must be discriminated from those component vapors and from the subset of possible lower-order component mixtures. Monte Carlo simulations and principal components regression analyses of an extant database of calibrated responses to a set of 16 vapors from an array of 6 diverse polymer-coated surface acoustic wave sensors are used to illustrate the approach and to examine trends in LOR values among the 120 possible binary mixtures and 560 possible ternary mixtures in the data set. At concentrations exceeding the LOD, 89% of the binary mixtures could be reliably recognized (<5% error) over some composite concentration range, while only 3% of the ternary mixtures could be recognized. Most binary mixtures could be recognized only if the constituent vapor relative concentration ratio, defined in terms of multiples of the LOD for each vapor, was < or =20. Correlations with the Euclidean distance(s) separating the normalized constituent vapor response vectors allow reasonably accurate predictions of the limiting recognizable mixture composition ranges for binary and ternary cases. Results are considered in the context of using microsensor arrays for vapor detection and recognition in microanalytical systems.

Journal Article↗

PubMedDate not supplied

Application of the wavelet method for the simultaneous quantitative determination of benazepril and hydrochlorothiazide in their mixtures.

The discrete and continuous wavelet transforms were applied to the overlapping signal analysis of the ratio data signal for simultaneous quantitative determination of the title subject compounds in samples. The ratio spectra data of the binary mixtures containing benazepril (BE) and hydrochlorothiazide (HCT) were transferred as data vectors into the wavelet domain. Signal compression, followed by a 1-dimension continuous wavelet transform (CWT), was used to obtain coincident transformed signals for pure BE and HCT and their mixtures. The coincident transformed amplitudes corresponding to both maximum and minimum points allowed construction of calibration graphs for each compound in the binary mixture. The validity of CWT calibrations was tested by analyzing synthetic mixtures of the investigated compounds, and successful results were obtained. All calculations were performed within EXCEL, C++, and MATLAB6.5 softwares. The obtained results indicated that our approach was flexible and applicable for the binary mixture analysis.

Algorithms↗

PubMed2005

Protein structure and fold prediction using Tree-Augmented naïve Bayesian classifier.

Due to the large volume of protein sequence data, computational methods to determine the structure class and the fold class of a protein sequence have become essential. Several techniques based on sequence similarity, Neural Networks, Support Vector Machines (SVMs), etc. have been applied. Since most of these classifiers use binary classifiers for multi-classification, there may be (N) c2 classifiers required. This paper presents a framework using the Tree-Augmented Bayesian Networks (TAN) which performs multi-classification based on the theory of learning Bayesian Networks and using improved feature vector representation of (Ding et al., 2001). In order to enhance TAN's performance, pre-processing of data is done by feature discretization and post-processing is done by using Mean Probability Voting (MPV) scheme. The advantage of using Bayesian approach over other learning methods is that the network structure is intuitive. In addition, one can read off the TAN structure probabilities to determine the significance of each feature (say, hydrophobicity) for each class, which helps to further understand the complexity in protein structure. The experiments on the datasets used in three prominent recent works show that our approach is more accurate than other discriminative methods. The framework is implemented on the BAYESPROT web server and it is available at http://www-appn.comp.nus.edu.sg/~bioinfo/bayesprot/Default.htm. More detailed results are also available on the above website.

Algorithms↗

PubMed1996

Comparison of different neural network algorithms in the diagnosis of acute appendicitis.

Four different neural network algorithms, binary adaptive resonance theory (ART1), self-organizing map, learning vector quantization and back-propagation, were compared in the diagnosis of acute appendicitis with different parameter groups. The results show that supervised learning algorithms learning vector quantization and back-propagation were better than unsupervised algorithms in this medical decision making problem. The best results were obtained with the learning vector quantization. The self-organizing map algorithm showed good specificity, but this was in conjunction with lower sensitivity. The best parameter group was found to be the clinical signs. It seems beneficial to design a decision support system which uses these methods in the decision making process.

Adolescent↗

PubMedDate not supplied

Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition.

The concept of drug-likeness, an important characteristic for any compound in a screening library, is nevertheless difficult to pin down. Based on our belief that this concept is implicit within the collective experience of working chemists, we devised a data set to capture an intuitive human understanding of both this characteristic and ease of synthesis, a second key characteristic. Five chemists assigned a pair of scores to each of 3980 diverse compounds, with the component scores of each pair corresponding to drug-likeness and ease of synthesis, respectively. Using this data set, we devised binary classifiers with an artificial neural network and a support vector machine. These models were found to efficiently eliminate compounds that are not drug-like and/or hard-to-synthesize derivatives, demonstrating the suitability of these models for use as compound acquisition filters.

Combinatorial Chemistry Techniques↗

PubMed2001

Molecular classification of multiple tumor types.

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.

Algorithms↗

PubMed2005

PCP: a program for supervised classification of gene expression profiles.

UNLABELLED: PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms. AVAILABILITY: Free download at http://pcp.sourceforge.net

Algorithms↗

PubMed2004

Solid or gaseous circulating brain emboli: are they separable by transcranial ultrasound?

High-intensity transient signals (HITS) detected by transcranial Doppler (TCD) ultrasound may correspond to artifacts or to microembolic signals, the latter being either solid or gaseous emboli. The goal of this study was to assess what can be achieved with an automatic signal processing system for artifact/microembolic signals and solid/gas differentiation in different clinical situations. The authors studied 3,428 HITS in vivo in a multicenter study, i.e., 1,608 artifacts in healthy subjects, 649 solid emboli in stroke patients with a carotid stenosis, and 1,171 gaseous emboli in stroke patients with patent foramen ovale. They worked with the dual-gate TCD combined to three types of statistical classifiers: binary decision trees (BDT), artificial neural networks (ANN), and support vector machines (SVM). The sensitivity and specificity to separate artifacts from microembolic signals by BDT reached was 94% and 97%, respectively. For the discrimination between solid and gaseous emboli, the classifier achieved a sensitivity and specificity of 81% and 81% for BDT, 84% and 84% for ANN, and 86% and 86% for SVM, respectively. The current results for artifact elimination and solid/gas differentiation are already useful to extract data for future prospective clinical studies.

Algorithms↗

PubMed1999

Binary QSAR: a new method for the determination of quantitative structure activity relationships.

A new method (particularly suited to the analysis of High Throughput Screening data) is presented for the determination of quantitative structure activity relationships. The method, termed "Binary QSAR," accepts binary activity measurements (e.g., pass/fail or active/inactive) and molecular descriptor vectors as input. A Bayesian inference technique is used to predict whether or not a new compound will be active or inactive. Experiments were conducted on a data set of 1947 molecules. The results show that the method exhibits high accuracy and is robust to measurement errors.

Bayes Theorem↗

PubMed2001

Expression of mosquito active toxin genes by a Colombian native strain of the gram-negative bacterium Asticcacaulis excentricus.

Mosquito control with biological insecticides, such as Bacillus sp. toxins, has been used widely in many countries. However, rapid sedimentation away from the mosquito larvae feeding zone causes a low residual effect. In order to overcome this problem, it has been proposed to clone the Bacillus toxin genes in aquatic bacteria which are able to live in the upper part of the water column. Two strains of Asticcacaulis excentricus were chosen to introduce the B. sphaericus binary toxin gene and B. thuringiensis subsp. medellin cry11Bb gene cloned in suitable vectors. In feeding experiments with these aquatic bacteria, it was shown that Culex quinquefasciatus, Aedes aegypti, and Anopheles albimanus larvae were able to survive on a diet based on this wild bacterium. A. excentricus recombinant strains were able to express both genes, but the recombinant strain expressing the B. sphaericus binary toxin was toxic to mosquito larvae. Crude protease A. excentricus extracts did not degrade the Cry11Bb toxin. The flotability studies indicated that the recombinant A. excentricus strains remained in the upper part of the water column longer than the wild type Bacillus strains.

Animals↗

PubMedDate not supplied

Geometry-aware bases for shape approximation.

We introduce a new class of shape approximation techniques for irregular triangular meshes. Our method approximates the geometry of the mesh using a linear combination of a small number of basis vectors. The basis vectors are functions of the mesh connectivity and of the mesh indices of a number of anchor vertices. There is a fundamental difference between the bases generated by our method and those generated by geometry-oblivious methods, such as Laplacian-based spectral methods. In the latter methods, the basis vectors are functions of the connectivity alone. The basis vectors of our method, in contrast, are geometry-aware since they depend on both the connectivity and on a binary tagging of vertices that are "geometrically important" in the given mesh (e.g., extrema). We show that, by defining the basis vectors to be the solutions of certain least-squares problems, the reconstruction problem reduces to solving a single sparse linear least-squares problem. We also show that this problem can be solved quickly using a state-of-the-art sparse-matrix factorization algorithm. We show how to select the anchor vertices to define a compact effective basis from which an approximated shape can be reconstructed. Furthermore, we develop an incremental update of the factorization of the least-squares system. This allows a progressive scheme where an initial approximation is incrementally refined by a stream of anchor points. We show that the incremental update and solving the factored system are fast enough to allow an online refinement of the mesh geometry.

Algorithms↗

PubMedDate not supplied

A discrete fully recurrent network of max product units for associative memory and classification.

This paper defines the truncated normalized max product operation for the transformation of states of a network and provides a method for solving a set of equations based on this operation. The operation serves as the transformation for the set of fully connected units in a recurrent network that otherwise might consist of linear threshold units. Component values of the state vector and outputs of the units take on the values in the set [0, 0.1,..., 0.9, 1]. The result is a much larger state space given a particular number of units and size of connection matrix than for a network based on threshold units. Since the operation defined here can form the basis of transformations in a recurrent network with a finite number of states, fixed points or cycles are possible and the network based on this operation for transformations can be used as an associative memory or pattern classifier with fixed points taking on the role of prototypes. Discrete fully recurrent networks have proven themselves to be very useful as associative memories and as classifiers. However they are often based on units that have binary states. The effect of this is that the data to be processed consisting of vectors in R(n) have to be converted to vectors in [0, 1]m with m much larger than n since binary encoding based on positional notation is not feasible. This implies a large increase in the number of components. The effect can be lessened by allowing more states for each unit in our network. The network proposed demonstrates those properties that are desirable in an associative memory very well as the simulations show.

Algorithms↗

PubMed1990

Enhancer sequences from Arabidopsis thaliana obtained by library transformation of Nicotiana tabacum.

In this paper we report on the use of a bidirectional enhancer cloning vehicle to isolate and characterize new enhancer sequences from Arabidopsis thaliana. A library of A. thaliana genomic Sau3A segments was constructed in Escherichia coli in the binary plasmid enhancer cloning vehicle pROA97. The T-DNA based vector carries abbreviated TATA regions from the cauliflower mosaic virus 35S transcription unit upstream of two genes. The library was transferred via triparental mating into Agrobacterium tumefaciens. The neomycin phosphotransferase II gene was used for selection of kanamycin-resistant transformed tobacco callus cells. Approximately 1100 transgenic plants were regenerated and assayed for expression of the E. coli beta-glucuronidase (GUS) gene in leaves, stems, roots, or seeds. Plasmids carrying putative enhancer sequences were rescued from the genomes of transgenic plants and the cloned sequences were assayed for enhancer function in genetic selection experiments. Plants were regenerated from the kanamycin-resistant calli obtained in the secondary transformation experiments. Histochemical analysis of GUS activity in the leaf, stem, and root tissues of transgenic plants showed a variety of expression patterns. The DNA sequences are presented of five Arabidopsis segments which confer enhancer function.

Base Sequence↗

PubMed1980

Comparison of two unsupervised algorithms.

The binary decision element described by the decision rule depending upon weight vector w is a model of neuron examined in this paper. The environment of the element is described by some unknown, stationary distribution (p(kappa). The input signals kappa[n] of the element appear in each step n independently in accordance with the distribution p(kappa). During an unsupervised learning process the weight vector w[n] is changed on the base of the input vector kappa[n]. In the paper there are regarded two self-learning algorithms which are stochastic approximation type. For both algorithms the same rule of past experiences neglecting or the rule of weight decrease has been introduced. The first algorithm differs from the other one by a rule of weight increase. It has been proved that only one of these algorithms always leads to the same decision rule in a given environment p(kappa).

Decision Making↗

PubMed1989

A parallel implementation of the ALOPEX process.

Optimization techniques have found many applications in science, engineering, and industry. In all applications, the best value of a "cost function" is sought in a well-defined domain; this cost function in general depends on many parameters. An iterative optimization technique has been developed (ALOPEX) that uses feedback in order to optimize the response of a system. The cost function for this process is problem dependent and therefore quite flexible. The method has been applied successfully to different optimization problems such as pattern recognition, receptive field studies in the visual system of animals, curve fitting, etc. We present two special purpose hardware implementations for ALOPEX. The first method takes time O(logN + logm) and uses O(mN2) processing elements. The second method takes O(logN + m) time and uses O(N2) processing elements. Our basic architecture is a binary tree with N2 leaves (equal to the length of the vectors) and therefore had depth O(logN). Different implications of the two approaches will be discussed including similarities with the biological visual process.

Algorithms↗